Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models
Authors: Jungwon Park
Jungmin Ko
Dongnam Byun
Jangwon Suh
Wonjong Rhee
Venue: ICLR 2025 Poster (2025)
Keywords: text-to-image diffusion model
diffusion model
text-to-image generative model
cross-attention
Primary area: generative models

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.

Published as a conference paper at ICLR 2025
CROSS-ATTENTION HEAD POSITION PATTERNS CAN ALIGN WITH HUMAN VISUAL CONCEPTS IN TEXT-TO- IMAGE GENERATIVE MODELS
Jungwon Park Jungmin Ko", Dongnam " Suh', Wonjong Rheel.2+
DDpartment of Intelligence and Information, Seoul National University
#Interdisciplinary Program in Artificial Intelligence, Scoul National University fquoded97, jungminko, east928, rxwe5607, wrhee]@snu.co ac.
ABSTRACT
Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance range of visual generative tasks. However, our understanding of cross-attention layers remains somewhat limited. this study, introduce mechanistic interpretability approach for diffusion models by constructing Head Relevance Vectors (HRVs) that align with human-spectin sppeaaiee visual concepts. An HRV for given visual concept has length equal the total number of tross-attentin heads, with each element representing the importance of the corresponding head for the given visual concept. To validate HRVs as in- terpretable features, we develop an ordered weakening analysis that demonstrates propose concept sirengthening and concept
their effectiveness. Furthermore, adjusting methods and apply them to enhance three visual generative tasks. Our results show that HRVs can reduce misinterpretations age generation, successfully modify five challenging attributes in image editing, and mitigate catastrophic neglect in multi-concept generation. Overall, our work provides advancement in understanding cross-attention layers and introduces new approaches for fine-controlling these layers at the head leve
polysemous words in im-
INTRODUCTION
Recent advancements in Text-to-Imase Image (T2I) models have demonstrated an unprecedented ability to generate high-quality images with strong image-text alignment. These models often leverage power- ful pre-trained text encoders; for instance, Stable Diffusion (Rombach et al. 2022) uses CLIP (Rad-) ford et al. )02T), while Imagen (Saharia al. 2022) uses T5 (Raffel al., 2020). Given that language is more expressive than previously used supervision signals (Gandelsman et al., (023), text representations have empowered visual generative tasks, allowing for high degree of con- trol over the generation process. However, our understanding of the inner workings of T2I models remains limited, and even the latest models continue to struggle with certain failure cases.
Significant progress has been made in understanding the inner workings of deep neural networks. Olah al. (2018) demonstrated numerous examples showing interpretable features at various levels in neural networks; Olah et al. (2020) expanded this analysis by exploring connections between units in the networks. Notably, Templeton et (2024) identified interpretable features in the cutting-edge large language model (LLM) Claude 3 Sonnet-and used these features guide the model's ''s genera- tion towards safer outcomes. Building on this line of work, we analyze T2I generative models with focus their cross-attention (CA) layers. We introduce novel method construct head relevance vectors (HRV') that align with human--peecfied visual concepts. An HRV for a given visual concept a vector whose length equals the total number of CA heads, with each element representing the importance of the corresponding head for that concept. We demonstrate that these vectors reflect
"Corresponding Author.
'Our code is available at wtthhht/ttgghhhaaeaa// http://iithhb..cm/m//--D///////

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weakened head positions (Most to least relevant)
II
31
41
51
62 71 128
Weakening of
Color
blue sofa"
Furniture
Ordered weakening analysis of CA heads from high low relevance for visual concepts.
3. Reducing neglect multi-concept generation
Correcting misinterppotation
Improving image editing
Ours
"An dlive-salored plate on table"
Teapot made of glose granite"
"a deer and horse"
(h) Enhancing three visual generative tasks with head relevance vectors,
Figure 1: We develop method for constructing head relevance vectors (HRVs) that align with useful visual concepts. For a specified visual concept, an HRV assigns relevance score to individual cross-attention heads, revealing their importance for the visual concept. Our analysis shows that the constructed HRVs can serve as interpretable features. We also demonstrate that HRV can be effectively integrated for improving three visual generative tasks.
human- inntaniitterpettble features by using ordered weakening analysis, where sequentially weaken the activation of CA heads and examine the resulting generated images (Figure Ia).
To demonstrate the utility of HRVs, we propose concept strengthening and concept adjusting meth- ods to enhance three visual generative tasks (Figure lb). In image generation, our approach sig- nificantly reduces the misinterpretation of polysemous words. When generating images from text prompts containing such words, our method decreased the misinterpretation rate from 63.0% to 15.9%. In image editing, our method enhances the widely used Prompt-to-Promps (P2P) (Hertz et al. 2022) algorithm in modifying object attributes (color, material, geometric patterns) and image attributes (image style, weather conditions), achieving 2.32% 11.79% higher image-text align- ment scores compared to state-of-the methods. Additionally, in human evaluations, it received more than twice the preference scores compared existing methods. In multi-concept generation, our method improves upon the state-of-the-an art Attend-and-Hi By reducing catastrophic neglect- the omission of objects or attributes generated images our approach enhances performance by 2.3% 6.3% across two benchmark types.
(Chefer et al. (023)) algorithm.
We use Stable Diffusion v1.4 (Rombach et al., 2022 as the primary model for our analysis and experiments. To show that our findings are not limited to a single model, we also show that they are consistent in Stable Diffusion XL (Podell et al., 2024). Furthermore, we show that our approach allows for flexible adjustment of human-specified concepts. These results indicate that method of constructing HRVs can be also useful for different model architectures and target concept.
2 RELATED WORK
Early works on interpretable neurons and interpretable features: Early works on visual gener- ative models have trained variational autoencoders (VAEs) using specifically designed loss functions on datasets with distinct attributes (Kulkarni et al. 2015 Higgins et al.) 2017; Chen et al. 2018 Klys et al. 2018). While these methods successfully controlled few attributes present
train- ing dataset, they were limited by a lack of fine-grained control, strong dependence on training data, attributes. Another approach to identify meaningful
and possible need for manual supervision features in intermediate layers of neural networks (e.g., generative adversarial networks (GANs)), and modify those features to control the generation outputs (Plumerault et al. 2020 Shen & Zhou 2021). Also, seminal work by Olah et al. (018) demonstrated that interpretable features in neural networks can be identified at various levels: single neurons, spatial positions, channels, or groups of neurons different positions and channels. They presented numerous examples showing how
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these interpretable features emerge at various levels within neural network's architecture. While these efforts successfully revealed meaningful features, they were limited by an inability to cap- ture user-specified human-specified concepts. Moreover, the identified features were not always directly usable for controlling attributes in generative tasks.
Recent works with multi-modal and generative models: Since the development of powerful multi-modal models that can map images and text to a joint embedding space, few studies have used text to interpret intermediate representations in vision models (Goh et al., 2021 Hernandez et al.) 2022 Yuksekgonul et al., 2023b). Most recently, Gandelsman et al. (2023) examined self-attention heads from the last layers of CLIP-ViT (Dosovitskiy et al. 2021), identifying meaningful correla- tions with several visual concepts. While these studies primarily focused on non-generative models, study shifts focus to the interpretable features within visual generative models. Regarding the recently proposed large language model, Claude Sonnet, [emmlleton et al. (2024) demonstrated that identifying meaningful concepts from model activations provides valuable issihtts into understand- ing model behavior. Among the various features identified, their use of safety-related features to guide text generation towards safer outcomes is particularly relevant for large-scale text-generative models. Our study also explores large-scale generative models, showing that human-specitnn visual concepts can be captured and applied in three distinct visual generative tasks.
Text-to-image diffusion models with cross-attention layers: Building on the large- T2I
diffusion models, researchers have developed methods to tackle various visual generative tasks, such as image editing (Hertz et al. 2022) and multi-concept generation (Chefer et al. (023). Many of these studies utilize the publicly available Stable Diffusion (Rombach et al.) 2022). which efficiently generates images through a diffusion denoising process in latent space. This model incorporates an autoencoder and a U-Net (Ronneberger al. 2015) with multi-head cross-attention (CA) layers that integrate CLIP (Radford et al. 2021) text embeddings. Researchers have explored these CA layers enhance control over text-conditioned image generation (Feng al. 2022, Parmar et al.
2023
Chefer et al., 2023 Tumanyan et al., 2023] Wu et al.] 2023). For example, P2P (Hertz et al. 2022)
manipulates image layout and structure by swapping CA maps between source and target prompts. However, these methods typically update entire CA layers without offering fine-grained control over individual attention heads. We introduce head relevance vectors and develop two techniques for head-level control of CA layers, enabling precise steering of human-specified visual concepts.
METHOD FOR CONSTRUCTING HEAD RELEVANCE VECTORS
The core idea involves selecting a set of visual concepts of interest, using large language model (LLM) to pre-select 10 associated words for each concept, and utilizing random image gener- ations for updating head relevance vectors (HRVs). The key to a successful update lies in identifying the visual concept that best matches a particular head and increasing the corresponding element of the HRVs. Figure Fillustrates the process of a single update. We begin by providing background on the cross- cross-attention (CA) layer, followed by detailed descriptions of our methodology for construct- ing HRVs that correspond to a set of human-specified visual concepts.
Cross-attention in T21 diffusion models: Let P be a generation prompt, Z, be a noisy image generation timestep f, and vi()) be a CLIP text-encoder. In each CA head, the spatial features of the noisy image ((z)
projected to a query matrix Q = lo (ő(Z)), while the CLIP text embedding (P)) is projected to a key matrix K = Iı (P)) and a value matrix V = ly (x)()) using learned linear layers lo, Jx, and lv. Then, the CA map is calculated by measuring the correlations between Q and K as
QKT Vd
M = softmax
(I)
where d is the projection dimension of the keys and queries. The CA output MV is a weighted aver- age of the value V. with the weights determined by the CA map M. This operation is performed in parallel across multi-head in the CA layer, and their outputs concatenated and linearly projected using a learned linear layer to produce the final CA output.
Image generation prompts, visual concepts, and concept-words: To generate 2,100 random images, we used 2,100 generation prompts. Of these, 1,000 prompts were constructed using 1,000 ImageNet classes (Deng et al. 2009). formatted as 'A photo of (class name)." The remaining 1,100
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Cuncatenation of the token embedddings
Concatcanic
"syple"
Ki
Sample concepe- word "wilte" concepts
"sar"
Text Encoddor
Projectoon
K Concatenated
0% +)
tnkes embodddins
component Relenance
Uidnniing of the head relesance vecters
Avsrage pooling
Argmax
Image query
M mops
CA for concept. (C2)
Update the A-ih compunent
Emnge
"A photo af Shih
Head Relevance Fector for s-th cancapt (C.)
P
Figure 2: Overview of a single HRV update for a cross-attention (CA) head position h. While generating random image, the most relevant visual concept is identified. Then the concept's head relevance vector (HRV) is updated to have an increased value in position h. For illustration purpose, we are showing only visual concepts (N = 5) and CA heads (H = 6). our main experiments, we adopt N 34 and H 128. This update is repeated over all the head positions h = l,.... and all timesteps = =1,1, ....... T for sufficiently large number random image generations.
prompts were adopted from PromptHero (PromptHero) to enhance diversity. The visual concepts arbitrary set. In our study, we have interacted with GPT-4o (OpenAl| 2024) to list 34 commonly used visual concepts including object categories and image properties. While study mainly focuses the 34 visual concepts, demonstrate in Appendix Itha that new visual concept can be flexibly and easily added. For each of the selected visual concepts, we have generated 10 representative concept-words using GPT-4o. The full list of 34 visual concepts, along with 10 corresponding concept-words for each, can be found in Table of Appendix
can be specified
T2I models and head relevance vectors: We focus on Stable Diffusion v1, which contains 128 CA heads. Let Ci...., CN represent the specified visual concepts, where N = 34 in our main experiments. For each visual concept Cn. we define a head relevance vector (HRV) as an H -ddimensional vector that expresses how each CA head is activated by the corresponding visual concept. Initially, all head relevance vectors are set to zero and are iteratively updated following the process illustrated in Figure 2 The full iteration involved 2,100 generated images, with each image used to iterate over all H 128 heads and T = 50 timesteps. Extension to a larger diffusion model, Stable Diffusion XL, is discussed in Section 6.1
Concatenation of the N token embeddings: In each HRV update shown in Figure 2 we gener- ate a concatenation of token embeddings for the N visual concepts and use to identify the best matching visual concept for the h-th head. To enhance diversity in the process, we randomly sample one concept-wol word for each visual concept from the list provided in Table 3 of Appendix A The sampled N concept-words are individually embedded using the CLIP text encoder (b(-), followed by the learned linear key projection layer 1(h) at the h-th CA head position. This produces N key matrices K,, , KN E 7 each corresponding to concept, with each key matrix con- taining 77 token embeddings. where F is feature dimension. For instance, if the sampled word for the third concept C3 is 'white,' the corresponding key matrix K3 would be [<SOT>> <white>> <EOT>,- <EOT>], where <SOT> and <EOT> key-projected embeddings of special to- kens. We only extract the embedding of the semantic token <white> from K3, denoted K3 E RF where = 1 since there is only one semantic token, <white>. Similarly, we extract the embeddings for the other concepts, obtaining K K2.... KN These N embeddings are then concatenated to form K Kg. ... Kv N where N' represents the total number of semantic tokens across all N concepts (N' = N each concept-word consists of a single token).
Updating of the head relevance vectors: We calculate the cross-attention (CA) maps M x²''' where R is the width height of the image latent, by applying Eq. using the con-
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catenated token embeddings K RN'' in place of K, and the image query Q(h) RR from h-th head position. This measures the correlation between the visual information at h-th head posi- tion and the textual information of the N visual concepts, resulting in N groups of CA maps. If word consists of multiple tokens, we average the CA maps across the token dimension so that each word corresponds to a single CA map. This process produces a matrix of shape I R² XN This matrix then averaged across spatial dimension (i.e., R²) to yield single strength value for each visual concept Cn. n 1, ..... N. We use the argmax operation on these N strength values to identify the visual concept with the largest value, which eliminates the problem of different representation scales across H CA heads (Appendix [.2)). Then, we update that visual concept's 's HRV by increasing its h-th component by one. This update process is repeated over all head positions h = L..... ................................ 128 and all timesteps t .,. 50 for 2100 random image generations. At the end, each HRV is normalized to have its L1 norm equivalent to = 128. Pseudo-code is provided in Appendix B.I
4 ORDERED WEAKENING ANALYSIS OF HEAD RELEVANCE VECTORS
In this section, we investigate whether the constructed HRVs can effectively and reliably serve as interpretable features. As our primary analysis tool, we introduce ordered weakening across the H cross-attention heads by multiplying -2 to a target head's CA maps. This weakening is applied consistently across all timesteps during image generation, but only to the CA maps of all semantic tokens, leaving special tokens unchanged. Using this approach, we compare images generated under two different head weakening orders. In the most relevant head positions first (MoRHF), we weaken H heads starting from the strongest to the weakest, where the strength of head h is determined by the value of the h-th element in the HRV. In the least relevant head positions first (LeRHF), the order is reversed, from the weakest to the strongest. If an HRV reliably represents a visual concept, expect MoRHF to impact the corresponding concept in the generated images more quickly than LeRHF. The head weakening is inspired by a rescaling technique in P2P (Hertz et al. 2022) and the ordering was inspired by the metrics defined in Saaek al. (2016) and "Tomsett al. (2020).
Analysis results for three visual concepts are shown in Figure 3 Comparison of generated images are shown in Figure 3a] In the top case, where the visual concept of Material is weakened, the characteristics of copper in the generated image already disappeared when the most relevant 11 heads are weakened. In contrast, the copper remains visible until the least relevant 71 heads are weakened. A similar observation can be made for the visual concepts of Animals and Geometric Patterns. An additional 33 examples can be found in Figures 11 and 13 15 of Appendix C is noted that a caution is required when analyzing the results, especially for LeRHF. For the HRV of a given visual concept, the least relevant heads have little effect on the concept of interest but could be significantly relevant to other visual concepts. Therefore, interpreting the changes observed in the Lenere-geerrated images requires careful consideration of other visual concepts.
We have also plotted the trends of CLIP image-text similarity in Figure 3b) CLIP similarity was measured between the generated images and the concept-words used in the prompts. For each data point in the CLIP score plots, we used between 30 and 150 images, depending on the specific visual concept. The prompt templates and concept-words used for each visual concept are detailed in Ap- pendix C.I] These plots clearly show that relevant concepts MoRHF weakening, while they are preserved for a longer duration in LeRHF weakening, Additional similarity plots for six more visual concepts can be found in Figure of of Appendix C.2 Addition- ally, we compare HRV-based ordered weakening with random order weakening in Appendix further supporting HRV's concept-awarenee
removed significantly faster during
5 STEERING VISUAL CONCEPTS IN THREE VISUAL GENERATIVE TASKS
Head relevance vectors are not only valuable as interpretable features but can also be used to steer visual concepts in generative tasks. In this section, we demonstrate that polysemous word challenges can be addressed and that leading methods, such as P2P (Hertz et al. 2022) for image editing and Attend-and- -xccite (A&EE) (Chefer et al.) (023) for multi-concept generation, can be significantly enhanced. All of our experiments are conducted using Stable Diffusion v1.4, 50 timesteps with PNDM sampling (Liu et al. 2022). and classifier-free free guidance at a scale of 7.5. All CLiP-based metrics are calculated using the OpenCLIP ViT-H/14 model (Iharco al. 202T
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of weakened head positions
I1
21
31
61
71
128
Weakening of
Material
"o teapot made of copper"
Most Relevant Head positions First
Animats
"photo of fox"
Lesst Relevant Head positions first
Most Relevant positions First
Geometric Patterns
pillow with leopard pattern"
Relevan! (a) Generated images as weakening progresses in either MoRHF or LeRHF order.
Material
Animals
0.34
222
6.22
6790
630
611
234
6:38
The Number Weakend Head Rosibors
The Mumber of Weakerd Head Postionss
Number Weskend Hled
20
10 125
(b) Change in CLIP image-text similarity score as weakening progresses in either MoRHF or LeRHF order.
Figure Ordered weakening analysis for three visual concepts: The visual concept of interest disappears significantly faster with MoRHF, where the most relevant heads in the corresponding HRV are weakened first. Note that 128 corresponds to the weakening of all heads.
<Rescaling vectors steering visual concepts>
<Rescaling CA maps at /t-th head position
Target token(s)
Concept
Strengthening
Rescaling
HRV of desired
Concupt
2- Fill
Adjusting
Reseling Vector
HRV destred concept
HRV undesired concept
maps
Rescaled CA maps
Figure 4: Two rescaling vectors for visual concept steering. Left: of desired visual concept the rescaling vector. Concept adjusting combines HRVs of desired and an undesired visual concepts to define the rescaling vector: (HRV of desired concept) - 1 (HRV of undesired concept). Here, H 128 denotes the number of CA heads. Right: For both concept steering methods, the h-th CA map of a target token is rescaled using The the h-th element of the rescaling vector, where h 1.... H. Here, L 77 denotes the token length.
Concept strengthening uses HRV
Two rescaling vectors for visual concept steering concept strengthening and concept ad- justing: We define two rescaling vectors as illustrated in Figure both utilizing pre-constructed HRVs. In Section 3 we have constructed 34 HRVs for the 34 visual concepts listed in Table Appendix[AI A We utilize the pre-constructed HRVs to steer the corresponding concepts. For some vi- sual generative tasks, only desired concept can be identified, and apply concept strengthening. In other tasks, both a desired and an undesired concept can be identified, typically in cases where the generative model fails to meet the user's intention. In such cases, we apply concept adjusting. The rescaling vector of concept adjusting is designed to be closely related to that of concept strengthen- ing: the vectors become equivalent when desired and undesired concepts are identical.

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5.1 IMAGE GENERATION REDUCING MISINTERPRETATION OF POLYSEMOUS WORDS
The same word can have different meanings de- pending the context. Stable Diffusion (SD) models are known for misinterpreting such pol- ysemous words, often generating images that do not comply with the user's intended mean- ing. Two examples are shown in Figure [ SD fails to recognize that 'lavender' clearly refers to a color and 'Apple' to an electronic device. This misinterpretation by SD may arise from limitations in the CLIP text encoder, which has been reported to exhibit bag-of- words is- sues (Yuksckgonul et al.] 2023a). The problem can be resolved by adopting concept adjusting to SD as shown in Figure 5 where we name our method SD-HRV. For the lavender case, SD-HRV resolves this issue by applying con- cept adjusting to the token 'lavender,' using Color as the desired visual concept and Plants as the undesired visual concept. For the Apple case, concept adjusting is applied to the token 'Apple,' using Brand Logos as the desired and Fruits as the undesired. Examples of 10 cases, including the 2 cases shown in Figure 5 can be found in Figures 18 of Appendix [D.]] There, we provide 10 images generated with 10 random seeds for each case. Additionally, we investigated the performance of concept strengthening compared to concept adjusting and found, as expected, that concept adjusting performs better. The details can be found Appendix D.3
SD
SD-HRV (Ours)
"A vase in lovender color"
"An Apple device on table"
Figure Examples of image generations from Stable Diffusion (SD) and SD-HRV (ours) using prompts frequently misinterpreted by T2I mod- els. SD-HRV effectively reduces misinterpreta- tion compared to SD.
We have also performed human evaluation over the 10 cases, each with 10 random seeds, and the details can be found in Appendix D.2 Based on the human evaluation, the human perceived misinterpretation rate dropped significantly from 63.0% 15.9% when SD-HRV was adopted.
5.2 IMAGE EDITING SUCCESSFUL EDITING FOR FIVE CHALLENGING VISUAL CONCEPTS
Image editing involves generating an image that aligns with the target prompt while minimizing structural changes from the source image. Although recently developed methods excel at image editing, certain visual concepts remain challenging to edit. For example, concepts related to mate- rials, geometric patterns, image styles, and weather conditions are known to be particularly difficult to edit. To address this problem, we propose applying concept strengthening to P2P (Hertz et al.) 2022). We refer to our method as P2P-HRV. The key idea to apply concept strengthening to the edited token, the token that describes how the attribute of the source image should be changed, thereby strengthening the concept related to the editing target. The detailed explanations of P2P- HRV method are provided in Appendix E.
Experimental settings: We focus on five challenging visual concepts editing targets, includ- ing three object attributes (Color, Material, and Geometric Patterns) and two image attributes (Im- age Styles and Weather Conditions). We compare P2P-HRV with SDEdit (Meng al. 2021
P2P (Hertz et al., 2022), PnP (Tumanyan et al. 2023 , MasaCtrl (Cao et al. 2023). and FPE (Liu
et al. 2024). For each method, we generate 500 edited images for each editing target (250 for Weather Conditions) using the prompts described in Appendix E.2 For object attributes (Color, Ma- terial, and Geometric Patterns), we evaluated performance using both CLIP (Radford et al., 2021 and BG-DINO scores. The CLIP score evaluates CLIP image-text similarity between the edited image and the target prompt. The BG-DINO score assesses structure preservation using Grounded- SAM-2 (Ravi et al. 2024 Ren et al. 2024) for extracting non-object parts from the source and edited images and then comparing them with DINOv2 (Oquab et al., 2023) embeddings. The BG- DINO score is inspired by the segment ceimeent-nndennen and-embed metrics used in prior research (Parmar et al.) 2023 Kim et al. 2024). For image attributes (Image Styles and Weather Conditions), we conducted human evaluation to assess human preference (HP) scores, BG-DINO is not well-suited for these edits, which involve modifying the entire image rather than preserving non-object parts. In the human evaluation, we compared PRP-HRV with the other methods by asking 'Which edited image better matches the target description, while maintaining essential details of the source image?' We normalized the Hcose setting P2P-HRV's preference score 100.
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Source
Our
SDEdit (0.5)
SoEdit (0.7)
P2P
PnP
MasaCtri
FPE
Color
Prompt a blue car"
Material
Prompt "a bowl made of weed glass"
Geometric Patferss
Prompt: "a pillow with palka-dot leopard pattern"
Irouge Style
Prompt "a pap-art impressionist style painting a farmland
Weather
Prompt "a cat on Surnty showy day"
Figure 6: Examples of image editing for the five challenging visual concepts. In these examples, all comparison methods frequently fail to make the desired edits, whereas our P2P-HRV successfully achieves the intended modifications.
Color
Material
Geometric Patterns
0.93
0.93
0.05
DINO
BG DINO
0.91
0.90
0.03
0.89
0.81
0.7
0.85
0.8
CLIP Image-Text Similarity (T)
CLIP Image-Text Similarity (?)
CUP Image-Tiet Similarity
SDEdit (0.5)
SDEdit (0.7)
P2P
PnPP
MassaCtri
FPE
P2P-HRV (Ours)
Figure 7: Quantitative comparison of image editing methods for three object attributes using CLIP and BG-DINO scores.
Table 1: CLIP scores and human evaluation scores for two image attributes. The best and second-ber best results highlighted in bold and underlined, spectively.
Experimental results: Figure presents five exemplary cases of image editing across the five challenging visual concepts. The figure demonstrates that approach significantly improves image-text alignment compared to the previously known methods. While only five cases are shown in this figure, an extensive list of additional examples can be found in Fig- ures 24 of Appendix E.S For the edit- ing three object attributes. CLIP similar- ity and BG-DINO scores are presented in Fig- ure P2P-HRV achieves Pareto-optimal per- formance compared to previous SOTA methods across all three object attributes. For the edit- ing of two image attributes, CLIP similarity and human preference performance are presented in Our P2P-HRV improves CLIP perfor-
Image Attribute
Method
Image Style
Weather Conditions
CLIP
HP-score
CLIP
HP-score
SDEdit (0.5)
0,2938
0.2817
SDEdit (0.7)
0.3217
15.8
0.2908
39.5
P2P
0.3120
30.6
0.2788
33.9
PnP
0.3286
41.9
0.3046
35.0
MassaCtrl
0,2722
0.2524
FPE
0.3236
25.7
0.2962
P2P (Ours)
0.3424
100
0.3348
100
Table
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mance by 4.20% on Image Style and 9.91% Weather Conditions compared to the previous SOTA, PnP. In the human evaluation, we have found that P2P-HRV received 2.39 and 2.53 times more votes in HP- for Image Style and Weather Conditions, respectively, compared to the second-best methods.
5.3 MULTI-CONCEPT GENERATION - REDUCING CATASTROPHIC NEGLECT
T21 generative models often struggle with multi-concept generation, failing capture all the speci- fied subjects or attributes in prompt. To address this issue, known as catastrophic neglect, Attend- and-Excite (A&E) (Chefer et al. 2023) iteratively updates the image latent using gradients derived from the CAA maps of selected tokens during image generation. To further improve A&E, we propose incorporating our concept strengthening approach, which we refer to as A&E-HRV.
Experimental settings: We investigated A&E-HRY using two types of prompts: (i) 'a {Animal A} and a (Animal B)" (Type I) and (ii) Color }} (Animal A} and a (Color B} (Animal B) (Type 2), originally examined in the A&E work (Chefer et al. 2023). Type evaluates multi-object gen- cration, while Type 2 adds the challenge of binding color attributes to each animal. In these ex- periments, we used 12 animals and 10 colors, detailed in Appendix [F] We assessed 66 prompts for Type and 150 for Type 2, with 30 random seeds applied across all methods. Following A&E, adopted three evaluation metrics. Full prompt similarity measures the CLIP similarity between the generated image and the full prompt. Minimum object similarity is calculated by measuring the CLIP image-text similarity between the image and sub-prompts, which are created by split- ting the original prompt at 'and,' The lower similarity score between the two sub-prompts is then reported. BLIP-score measures the CLIP text-text similarity between the prompt and the image caption generated with BLIP-2 (Li al. (203)).
Experimental results: Table 2 presents the quantitative comparisons, while Figure shows qual- itative results. As shown in Table 2 A&E-HRV outperforms other methods across all metrics and prompt types. In Figure the top row shows results for Type 1 prompt, while the bottom row shows results for Type 2 prompt. The existing methods either neglect key visual concepts or fail to generate realistic images. In contrast, our approach captures all concepts and generates realistic images for both prompt types. Additional comparisons provided in Figure 36 of Appendix F.2
Table 2: Quantitative results of multi-concept generation. The best and second-best results are highlighted in bold and underlined, respectively. The percentage in parentheses indicates the im- provement over the second best result, A&E.
Typel: a [Animal A) and a [Animaa B)
Typel: a (Color (Animal A] and a [Color B) [Animal B)
Method
Full Promptt
Min. Object
BLIP-score
Full Prompt
Min. Object
BLiP-scere
Stuble Diffusion
0,3000
0,1611
0,5934
0.3420
0.1458
0.5633
Strnctured Diffusion
0.2831
0.1545
0.5626
0.3307
0.1424
0.5508
Excite (A&E)
0.3544
0,2017
0.7049
0.3883
0.1972
0.6373
A&É HRV (Ours)
0.3702 (+4.5%)
0,2078 (+3.0%)
0.7491 (+6.3% (
0.3971 (+2.3%
0.2073 (+5.1 (%)
0.6590 (+3,2%)
A&E-HIV (Ours)
Stable Diffusion
Structured Diffusion
A&E
Type
Prompt: "a frog and beat"
Type
R
Prompt: "a blus sat. and a brown robbit"
Figure 8: Examples of multi-concepp generation for Type and Type 2 prompts. We compare Stable Diffusion (Rombach et al.) (2022). Structured Diffusion (Feng et al. 2022), and Attend-an andd Excite Chefer et al. 2023) with ours.

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DISCUSSION
6.1 EXTENSIONS TO A LARGER ARCHITECTURE
The recently introduced Stable Diffusion XI. (SDXL) (Podell et al. 2024 adopts U-Net backbone that three times larger than its predecessors, scaling up to 1300 cross- (CA) heads. To investigate the generalization capability of HRV, we have performed an extended study with SDXL. Using SDXL, we conducted the ordered weakening analysis to evaluate whether HRVs can serve as interpretable features. An exemplary result for the visual concept Furniture shown in Figure | The sofa disappeared when the most relevant 211 heads were weakened. In contrast, the sofa re- mained visible until the least relevant 711 heads were weakened. Additional 45 examples and similarity plots for nine visual concepts can be found in Figures 37 of Appendix G.1 For SDXI. experiments, we utilize the SD-XL 1.0-base model.
# of weakened head positions
111
211
311
411
511
611
711
1300
Weakening of
Most Relevant
Furniture
"photo a sofa"
Least Relevan? Head position Hions First
Figure 9: Ordered weakening analysis for SDXL: generated images are shown as weakening pro- gresses in either MoRHF or LeRHF order.
6.2 DO GENERATION TIMESTEPS AFFECT HOW HEADS RELATE TO VISUAL CONCEPT?
Diffusion models generate images by iteratively process- ing an image latent through the same U-Net network. A natural question is whether the patterns of head relevance vectors change across different timesteps during genera- tion. To explore this, we calculated head relevance vec- tors for each visual concept every timestep, resulting in 1700 vectors (34 visual concepts x 50 timesteps). Fig- ure pressents a t-SNE (Van der Maaten & Hinton (0088) plot of these 1700 vectors. In the t-SNE plot, visual con- cepts are clearly separated, while timesteps are not. This indicates that generation timesteps do not significantly af- feet the head relevance patterns of each visual concept. Further analysis, including cosine similarity plots, is pro- vided in Appendix
Fadal Expressione
Arimals
People
Countre
She
40
Generation Timestop
Lightin Conditinss
Time
Musical Insrumerts
Pook
Naturo Scenes
Vegelabics
Taclal
Vehidla
Imigee
Elcchoiica
Booss and
Tools
Wrimtn Chanacter
Geometric Pattern
Epont Ecudpment
Figure 10: t-SNE plot of 1700 head relevance vectors across 34 visual con- cepts and 50 generation timesteps.
6.3 LIMITATION
We examined the 34 concepts listed in Table 3 of Appendix A and identified two types of failure cases. The first type arises from limitations in the underlying T21 model, while the second is related our proposed algorithm the concept-words used to represent the concept. Appendix H provides a detailed discussion of these failure cases, along with examples for each, shown in Figures 45 46
7 CONCLUSION
In this work, we present findings from our exploration of cross-attention heads in T2I models. We demonstrate that head relevance vectors (HRVs) can be effectively and reliably constructed for human-specified visual concepts without requiring any modifications or fine-tuning the T2I model. Furthermore, show that HRVs be successfully applied improve performance of three visual generative tasks. Our work provides an advancement in understanding cross-attention layers and introduces novel approaches for exploiting these layers at the head-level.
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8 REPRODUCIBILITY STATEMENT
We provide our core codebase in our code repository. which includes the methodology implementa- tion, settings, generation prompts, and benchmarks for image editing and multi-concept generation.
ETHIC STATEMENT
Our work presents new techniques for controlling and refining text-to-image diffusion models, hancing both image generation and editing capabilities. While these advancements hold significant potential for creative and practical applications, they also raise ethical concerns about the possible misuse of generative models, such as creating manipulated media for disinformation. It is important recognize that any image editing or generation tool be used for both positive and negative pur- poses, making responsible use essential. Fortunately, various research in detecting harmful content and preventing malicious editing are making significant progress. We believe our detailed analysis of cross-attention heads will contribute to these efforts by providing deeper understanding of the mechanisms behind the tex--to-image generative models.
ACKNOWLEDGEMENTS
This work was partly supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R C0020R1A2C220 1222001A222071332 and by Institute Information & communications Technology Planning & Evaluation (IIP)) grant funded by the Korea government (MSIT) (NNO.SS-2021-1211 1133, Artificial Intelligence Graduate School Program (Seoul National University)), [No. RS-2023-0023522 Development of autonomous driving big data processing. management, search, and sharing interface technology to provide autonomous driving data accord- ing the purpose of usage]).
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CONTENTS
34 visual concepts and full list of concept-words
16
Details of HRV construction
17
B. Pseudo-code for HRV construction
17
B.2 Role of the argmax operation in HRV construction
18
C Details and additional results ordered weakening analysis
19
Prompts used for ordered weakening analysis
19
Additional results on ordered weakening analysis
20
Comparison with random order weakening
20
Ordered rescaling with varied rescaling factors
20
Details on reducing misinterpretation of polysemous words
27
D.I Prompts and selected concepts for reducing misinterpretation
27
D.2 Human evaluation)
30
D.3 Comparison of concept strengthening and concept adjusting
30
Details and additional results on image editing
31
Detailed explanations on P2P-HRV
Prompts for image editing
31
Metrics for image editing and human evaluation two image attributes
32
Trade-off effect of self- oeff-attention replacement in P2P and P2P-HRV
33
E.5
Additional results on image editing
36
Details and additional experiments on multi-concept generation
46
Comparison with attribute- binding method
46
F2
Additional results on multi-concept generation
47
Additional results using SDXL
48
G.1
Additional results on ordered weakening analysis
48
G.2
Ordered weakening analysis with more complex images
................................
54
G.3
Reducing misinterpretation in SDXL
55
Limitation
56
Additional analysis: the effect of timesteps on head relevance vectors)
59
Extending human visual concepts
59
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A 34 VISUAL CONCEPTS AND FULL LIST OF CONCEPT-WORDS
In this paper, we use 34 visual concepts, each paired with 10 concept-words, as shown in Table 3
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 dog, cat, elephant, lion, bird, fish, butterfly, bear, horse,
Animals
Plants
tree, flower, grass, bush, cactus, vine, oak tree, moss, tulip, rose
Fruits and Vegetables
apple, bonanu, carrot, tomato, broccoli, strawberry, potato, orange, lettuce, grapes
People
child, woman, man, elderly person, teenager, athlete, farmer, doctor, teacher, tourist
Vehicles
cur, bicycle, airplune, train, bout, motorcycle, bus, truck, scooter, helicopier
Buildings
skyseraper, house, church, library, museum, school, hospital, warehouse, apartment, custle
Furniture
chair, table, sofa, bed, bookshelf, desk, wardrobe, dresser, cabinet, coffee table
smariphone, laptop, television, cameru, tublet, headphones, microwave, refrigerator, printer, smartwatch
Electronics
Clothing
t-shirt, jeans, jacks. dress. skirt, sweater, shorts. coat, blouse, pants
Accessories
hat, scarf. sunglasses, watch, belt, necklace, carrings. bracelet, gloves. handbag
Tools
hammer. screwdriver. wrench. drill, saw, pliers, tape measure, chisel, level, shovel
Toys
doll, teddy bear, puzzle, action figure, toy car. lego, ball, kite. yo-yo, board game
Food and Beverages
pizza, salad, burger, pasta. soup, coffee, tea, juice, cake, sandwich
Books and Documents
novel. dictionary, textbook. magazine, newspaper, journal. notebook, manual, report, brochure
soccer ball, tennis racket, basketball, baseball glove, hockey stick, golf club, surfboard. ski poles, boxing gloves, cricket bat
Sports Equipment
Musical Instruments
guitar, piano, violin, drum, flute, suxophone, trumpet, harp. cello, clarinet
latin alphabet, greek alphabet, english alphabet, punctuation marks, mathematical symbols. uppercuse letters, lowercase letters, digits, consonants, vowels
Written Characters
Shape
circle, square, triangle, rectangle, oval, hexagon, octagon, stur, heart, diamond
Texture
smooth, rough, fuzzy. slippery, sticky, soft, hard, bumpy, grainy, glossy
Size
small, medium, large, tiny, huge, miniature, giguntic, petite, bulky, massive one, two, three, four, five, six, seven, eight, nine, ten
Counting
Image style
abstract, realistic, minimalist, curtoon, vintage, modern, black and white, sepia, surreul, pop art
Material
wood, metal, plastic, glass, fabric, paper, stone, rubber. leather, ceramic
Lighting Conditions
bright, dim, durk, natural light, artificial light, backlit, soft light, harsh light, spotlight, twilight
Facial Expressions
happy, sad, angry. surprised, confused, laughing, crying. smiling, frowning. disgusted
Facial Features
eyes, nose, mouth, cars, eyebrows, lips, cheeks, chin, forehead, teeth
Poses and Gestures
standing, sitting. running. jumping, waving, clapping. pointing, hugging, dancing, kneeling
Nature Scenes
forest, beuch, mountain, river, desert, meadow, waterfall, jungle, lake, canyon
Weather Conditions
windy, sunny, cloudy, stormy, foggy, rainy, snowy, hail, thunderstorm, drizzle
Time of Day
momning, noon, afternoon, evening, night, sunrise, sunset, midnight, dusk, dawn
Geo-locations
New York, Beijing. Paris, Tokyo, London, Sydney, Cairo, Rome, Moscow, Rio de Janeiro
Brand Logos
Nike, McDonald's s, Coca-Cola, Google, Microsoft, Amazon, Adidas, Sumsung. Starbucks, BMW stripes, polka dots, chevron, plaid, argyle, houndstooth, lattice, hexagons, spiruls, waves
Geometric Patterns
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B DETAILS OF HRV CONSTRUCTION
B.1 PSEUDO-CODE FOR HRV CONSTRUCTION
Algorithm HRV construction
Require: N: Number of human-speciffin visual concepts
Require: T: Total number of generation timesteps
Require: H: Total number of CA heads
Require: Set of prompts for random image generation
Require: S: Set of concept-words covering N visual concepts
Require: ei: CLIP text-encoder
Require: p(h) Key projection layer for the h-th cross-attention (CA) head
K
Require: {: Function to extract for semantic token embeddings
Require: Q(t.);; Image query matrix timestep I and the /ithh CA head
1: Initialize HRV matrix V as zero matrix RN Nxx
2: for each prompt P E IP do
3: while generating random image with prompt P do
4: for all 1, ..,,...77 do do
5: for all h .......
6:
for all = .,2 do Sample concept-word W, for visual concept C, from $
7:
8:
Compute key-projected embedding of W.. K,, = xx (w(W,)) 1T7
9:
Extract semantic token embeddings: K,, {(K,)
end for Concatenate K, K K n along the token dimension:
11:
K K K 2..... KN] V
(2)
Calculate the CA map M using K = K and Q Q((h) E RR
12:
RT
M = softmax
E R
(3)
vd
13:
Average M along the token dimension for multi-token concept-words, resulting in matrix of shape R xNN
14:
Average the resulting matrix over the spatial dimension (R²), producing M R
15:
Apply an argmax operation over the token dimension (N):
M argmax (M)
(4)
16:
Update the h-th column of the HRV matrix by adding M:
Vir + Vl. + M
(5)
17:
end for
end for
19: end while
20:
end for
21:
Return: HRV matrix V e N
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B.2 ROLE OF THE ARGMAX OPERATION IN HRV CONSTRUCTION
During HRV construction, we apply the argmax operation to the averaged CA maps before using them to update the HRV matrix (see Eq. 4. This step addresses the varying representation scales across H CA heads. To demonstrate these scale differences, we compute the averaged L1-norm of the CA maps before applying the softmax operation (refer to Eq.
B for notations):
L
1 R
Σ R
(1.N) KT Vd
N
In Table we show the mean and standard deviation of ((eA) across 2100 generation prompts and 50 timesteps for each CA head in Stable Diffusion v1.4. The CA heads exhibit variation in their representation scales, with the head having the largest scale showing a mean value 8.1 times higher than that of the smallest scale. Since the softmax operation maps large-scale values closer to a Dirac-delta distribution and small-scale values closer to a uniform distribution, it is necessary to align the scales between CA heads before accumulating the information into the HRV matrix. We achieve this by simply applying the argmax operation, as shown in Eq. 4 which resolves the issue of differing representation scales across the CA heads.
As explained the previous paragraph, the softmax operation introduces an imbalance when sum- ming without the argmax operation, where CA heads with larger scales produce vectors closer to Dirac-delta distribution, while those with smaller scales produce vectors closer to a uniform distribu- tion. This imbalance favors CA heads with larger representation scales, leading to overemphasis on the largest concept chosen by these larger-scale heads compared to the largest concept chosen by smaller-scale CA heads. For example, CA head at [Layer 15- 4] would be overemphasized compared to the largest concept chosen by the CA head at [Layer 16-Head 1]. A similar issue arises when using the max operation instead of argmax, as the maximum values from larger-scale CA heads are much larger than those from smaller-scall CA heads. To address this, we apply the argmax operation before summation, ensur- ing that the largest concept from each CA head contributes a value of 1 to the HRV matrix, regardless of its scale. This straightforward approach eliminates the bias toward larger-scale scale CA heads.
shown Table the largest concept chosen by the
Table 4: Mean and standard deviation of the averaged Ll-norm (tth), of CA maps before applying the softmax operation. The statistics are calculated over 2100 generation prompts and 50 timesteps. The largest and smallest mean values are highlighted in bold.
Head
Head 2
Head
Head 4
Head
Head 6
Head 7
Head 8
Layer
1.16
0.16
0.26
1.45
0.36
1,75
+
0.92 0.22
Layer
1.24 0.21
1.19 4: 0.29
1,30 0.24
1.43 +: 0.20
0.99 à: 0.34
1.08 4 0.24
0.89 4: 0.12
1.07 0.28
Layer
1.48 0.12
2.42
1.33 0.14
1.95
2.25
0.47
1.69
0.20
1.69 0.38
Layer
1.79 0,25
2.10 1 0.32
1,10 1.101000 0.28
1.13 1 0.14
1.39 1 0,16
2.18 1 0.70
1.57 1 0.24
1.14 1 0.16
L
1.38 0.16
1.55
0.36
1.44
0.21
1.46
0.48
1,30 0.28
Layer 6
1.65 0,20
1.87 + 0.56
1.41 + 0.27
1,50 + 0.17
2.30 0.29
2.05 + 0.63
1.65 + 0:21
1.84 + 0.62
Layer? 7
1.64 0.52
1.79 + 0.70
1.30 0.19
1.34 + 0.22
2.37 + 0.36
2.37 3.7700..0 0.30
+
1.90 0.54
Layer
1.24 0,14
2.06 + 0.26
1,53 +: 0.20
1.64 + 0.19
1.28 + 0.21
1.66 0.22
1.82 + 0.20
2.14 + 0.31
Layer 9
2.10 0.26
2.19 +
0.22
2.10 0.25
0.21
1.32+
+ 0.19
2.00 0.28
Layer 10
1.89 0.23
1.14 + 0.12
1.47 0 0.18
2,17 + 0.21
1.39 + 0,16
1,50 0.19
2.12 + 0.21
1.95 0.29
Layer
2.19 0.20
1.64 +
2.38 0.25
2.15
0.26
2.37
0.21
2.15 0.29
Layer 12
1.26 0.13
2,36 + 0.62
1.49 + 0.21
1.49 + 0.15
1.44 + 0.18
1,78 + 0.33
1.26 + 0.14
2,10 0.28
Layer
1.46 0.21
1.40 4:
0.22
1.44
0.47
1.51
0.22
1.27 0.16
Layer 14
1.17 0.30
1.19 1. 0.34
1.59 0.49
2,10 1. 1.05
0.98 1. 0.29
1.51 1 0.34
1.02 1. 0.47
2,35 0.55
Layer
1.20 0.38
2.16
0.40
0.39
0.76
1.48 0.37
Layer 16
0.51 0.04
1.80 1 0.33
1.14 1
1.84 1 0.33
0.91 L. 0.38
1.15 1 0.18
1.17 1 0.11
1.06 0.21
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C DETAILS AND ADDITIONAL RESULTS ON ORDERED WEAKENING ANALYSIS
In this section, we present the generation prompts used for the ordered weakening analysis intro- duced in Section[4] [ Additionally, we provide MoRHF and LeRHF plots for 6 more visual concepts, along with more detailed qualitative results.
C.I PROMPTS USED FOR ORDERED WEAKENING ANALYSIS
We conducted the ordered weakening analysis across visual concepts: Animals, Color, Fruits and Vegetables, Furniture, Geometric Patterns, Image Style, Material, Nature Scenes, and Weather conditions. The prompt templates and words for each concept are listed in Ta For each prompt, use random seeds generate images. For instance, the concept Color used the prompt template "a {Color) (Objects A) J with 3 random seeds, covering 10 colors and 5 objects. This results 150 generated images per data point in the line plot shown in Figure 3b of the manuscript.
Table 5: Prompt and word list for ordered weakening analysis. Visual concepts marked with an asterisk(") use words that do not overlap with the concept-word list in Table 3
Prompt Template
Visual Concept
Words
photo a (Animals)
Animals"
[Animals): rabbit, frog, sheep. pig. chicken, dolphin, goat, duck, deer. fox
{Color) (Objects)
Color
|Color]: coral, beige, violet. eyan, magenta, indigo, orange, turquoise, teal, khaki
[Objects]: car, bench, bowl, balloon, ball
photo of (Fruits and Vegetables)
Fruits and Vegetables'
[Fruits and Vegetables): :: lemons. blueberries, onions, raspberries, pincapples. cherries, cucumbers, bell peppers, cauliflowers, mangoes
photo of (Furniture)
Furniture
[Farniture): j: bed, table, chair, sofa, recliner. bookshelf, dresser, wardrobe, coffee table,
TV stand
[Objects] with a (Geometric Patterns | pattern
Geometric Patterss
Objject):: T-shrt, pillow, wallpaper. umbrella, blanket
(Geametric Patterns]: I: polka-dot, leopard, stripe, greck-key, plaid
(Image Style) style painting of (Landscapes}
Image Style
[mmage Style): cubist, pop art, steampunk, impressionist, black-and-whiid watercolor, cartoon, minimalist, sepia, sketch
(Landseapes): castle, mountain, cityscape, farmland, forest
(Ohjects) made of (Material)
Material*
(Objects): bowl, cup, table, ball, teapot
(Materiaf): copper, marhle, jade, gold. basalt, silver, clay, steel, tin, bronze
photo af (Nature Scener)
Nature Scenes*
(Nature Scenes); glacier, coral reef, swamp, pond, fjord, rainforest, grassland, marsh, creek, island
{Animats) [Weather Conditions)
Weather Conditions
(Animals): cat, dog, rabbitt, frog, bird
{ Weather Conditions]: ) snowy, rainy, finggy, stormy
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C.2 ADDITIONAL RESULTS ON ORDERED WEAKENING ANALYSIS
Figure [1 presents the randomly selected examples of the ordered weakening analysis for 6 addi- tional visual concepts: Animals, Fruity and Vegetables, Furniture, Material, Nature Scenes, and Weather Conditioos. The MoRHF weakening rapidly removes concept-relevant content, whereas the LeRHF weakening either preserves the original image longer or removes irrelevant content first. Figure 12 shows changes in CLIP image-text similarity scores for MoRHF and LcRHF weakening across these six visual concepts, showing consistent trends. Overall, these results demonstrate how the head relevance vector (HRV) effectively prioritizes heads based on their relevance to each visual concept. Additional qualitative examples are provided in Figure 13 15
C.3 COMPARISON WITH RANDOM ORDER WEAKENING
As additional analysis, compare HRV-based ordered weakening with random order weaken- ing by calculating the area between the LeRHF and MoRHF line plots. The definitions of MoRHF and LeRHF for HRV-based ordered weakening are provided in Section 4 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-firss order based on this random ordering. larger (LeRHF - MoRHF) area indicates that the ordering of CA heads better reflects the relevance of the corresponding concept. Table [ compares HRV-based ordered weakening and three random weak- ening approaches across six visual concepts. The results show that HRV-based ordered weakening achieves a higher (LeRHF MoRHF) area, demonstrating its effectiveness in ordering heads based on their relevance to the given concept.
Table 6: Comparison of (LeRHF MoRHF) areas between HRV-based ordered weakening and three random weakening cases across six visual concepts. Larger values indicate better alignment of CA head ordering with the relevance of the corresponding concept. Random Order Mean rep- resents the average value across the three random order cases. The highest value for each concept is highlighted in bold.
Material
Geometric Patterns
Furniture
Imuge Style
Color
Animals
Average
Ours (HRV)
6.63
14.75
14.42
9.46
7.33
8.13
10.12
Random Order Case
-1.94
4.29
-5.48
-1.81
-1.8
3.02
-0.63
Random Order Case
0.14
8.38
-1.89
-3.39
0.19
Rundom Order Case 1.68
-3.61
2.99
2.20
5.33
-2.91
0.95
Random Order Mean 1.86
0.27
1.96
-0.50
0.04
0.10
0.62
C.4 ORDERED RESCALING WITH VARIED RESCALING FACTORS
In the ordered weakening analysis, selected - the rescaling factor. This choice is inspired by P2P-rescaling (Hertz et al. 2022), which uses factors in the range of [-2, 2] to adjust the CA maps of the U-Net for image editing. To explore the impact of different rescaling factors, we present two examples in Figures 17 A rescaling factor of 1 leaves the original image generation process unchanged, while factors greater than strengthen the concept and factors smaller than weaken it. Strengthening produces minimal changes, likely because the concept is already present in the image. Weakening works effectively with factors below 0, with stronger effects observed as the factor decreases further.
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wackened positions
11
21
31
41
51
61
7L
128
Weakening of
Most Relevont Head positions First
Lesst Relevant
a violet bench"
Most Relevant
Fruits and Vegetabias
"photo of bell peppers"
Leas! Releven! Head positions First
Most Relevant Head positions First
Furniture
"photo of coffee table"
positionns
Relevont positions
Nature Scenes
"photo a fjord"
Lesst Rel Relevent Head
Most Relevont Head positions First
Image Style
style pointing of cityscaps"
positions First
Head positions First
Weather Conditions
a bird on day"
Least Relevant
Figure 11: Ordered weakening analysis of six additional concepts: qualitative results using Stable Diffusion
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Vegetables
0.24
Modnl
0.26
McR
0.24
0.22
Score
0.20
0.22
0.18
0.26
0.12
The Number of Weakend Head Positions
The Mumber Winakend Head Posit oms
The Number of Whalkand Head Rositions
Nature
Image Style
Conditions
McR
0.29
0.22
0.26
0.24
0.20
0.32
0.20
0.18
0.11
0.28
0.20
The Number of Weakend Head Positions
The Number Weeskend Head Posit CITS
The Number of WWeskerdd Head Posiltons
Figure 12: Ordered weakening analysis of six additional concepts: quantitative results using Stable Diffusion vl.
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# of weakened head positions
Weakening of
Most positions
Materiil
"a bowl made of marble"
Ledst Relevent Head posi fions First
Most Head posittors First
photo
Least Relevant Head
a deer*
Most Relevent Head positions First
Geometric Patterns
umbrella with polka- pattern"
Least Relevant Head posi positions First
Most Head positions First
Least Relevent
"a coral cor"
Most Releevnn Head hions First
Fruits and Vegefables
of cucumbers"
Head positions
Head posi Fions First
Furniture
"photo of wardrobe"
Least Relevant Head
Mast Relevent Head positions First
Nature Scenes
"photo of lake'
Least Relevant Head posi hions First
Head posi Fions First
Image Style
black-and- white style painting of mounioon""
Least Relevant Head positions First
Most Relevent Head posi tions First
Weather Conditions
bird foggy day"
Least Head positions First
Figure 13: Ordered weakening analysis of nine concepts with additional examples: qualitative sults using Stable Diffusion v1 (Part of 3).
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# of weakened head positions
Weakening of
Most positions
Materiill
"a ball made of jade"
Ledst Relevent Head posi fions First
Most
Head posittors First
Least Relevent Head
photo of
Most Relevant Head positions First
Geometric Patterns
"a blanket
Least Relevant Head pasi First
with plaid pattern"
Most Head positions First
yellow balloon"
Least Relevent
Most Reievvnn Head hions First
Fruits and Vegefables
"hoto of oranges"
Head positions
Head posi Fions First
Furniture
photo of bedd
Least Relevant Head
Mast Relevent Head positions First
Nature Scenes
Least Relevant Head posi hions First
"photo of desert"
Head posi Fions First
Image Style
"a impressionist style painting of castle"
Least Relevant Head positions First
Most Relevent Head posi tions First
Weather Conditions
bird rainy day"
Least Head positions First
Figure 14: Ordered weakening analysis of nine concepts with additional examples: qualitative sults using Stable Diffusion v1 (Part of 3).
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# of weakened head positions
Weakening of
Most positions
Materiil
"a table made of silver"
Ledst Relevent Head posi fions First
Most Head posittons First
Least Relevant Head lions
photo of rabbit"
Most Relevant Head positions First
Geometric Patterns
"a pillow with stripe pattern""
Least Relevant Head positions First
Most Head positions First
Least Relevent
"a pink ball"
Most eeieennn Head hions First
Fruits and Vegefables
"hoto of lemons"
Head positions First
MILL
Head posi Fions First
Furniture
JINGE
photo of bookshel! P
Least Relevant Head positions
Mast Relevent Head positions First
Nature Scenes
"photo of beach"
Least Relevant Head posi hions First
Head posi Fions First
Image Style
style painting of forest
Least Relevant Head positions First
Most Relevent Head posi tions First
Weather Conditions
"a oot snowy day"
Least Head positions First
Figure 15: Ordered weakening analysis of nine concepts with additional examples: qualitative sults using Stable Diffusion v1 (Part of 3).
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05
Concept
Color
Prompt "an orange rose'
Figure 16: Ordered rescaling with varying rescaling factors, using HRV for the Color concept and the generation prompt "an orange rose.' As the rescaling factor decreases, the weakening effect becomes more pronounced.
4.5
-05
rescalig
0.5
Concupt Fruits and Vegelsbles
Prampt Aphoto of grapes"
Figure 17: Ordered rescaling with varying rescaling factors, using HRV for the Fruits and Vegeta- bles concept and the generation prompt 'photo of grapes.' As the rescaling factor decreases, the weakening effect becomes pronounced.
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D DETAILS ON REDUCING MISINTERPRETATION OF POLYSEMOUS WORDS
D.1 PROMPTS AND SELECTED CONCEPTS FOR REDUCING MISINTER
We identified 10 prompts that the text-to-image (T2I) generative model frequently misinterprets and carefully selected desired and undesired concepts from our 34 visual concepts to help reduce these misinterpretations. Table lists these 10 prompts, along with the desired and undesired concepts for each polysemous word. For both Stable Diffusion (SD) and SD-HRV, we generated 100 images us- ing these prompts with 10 random seeds. The full set generated images shown in Figures [8] and 19 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 misinterpreted as Food and Beverages instead of Tools, SD-HRV only partially resolved the issue by removing 'nut' as Food and Beverages but failed to generate it Tools. This suggests that SD-HRV is not perfect, and there is still room for improvement in addressing such misinterpretations. Our current implementation for SD-HRV requires manual settings for the target token, well as the desired and undesired concepts. However, our tests with an LLM show that it effectively identifies the inputs needed for SD-HRV, suggesting that constructing an automatic pipeline using LLMs feasible.
Table 7: The list of prompts often misinterpreted, with polysemous words underlined.
Prompt
Desired Concept
Undesired Concept
in lavender color
Color
Plants
Apple device on table
Brand Logos
Fruits and Vegetables
A rose-colored vase
Color
Plants
A single orange-colored plate
Color
Fruits and Vegetables
A crane flying over a grass field
Animals
Tools
A bowl in mint color a table
Color
Fruits and Vegetables
An olive-colored plate table
Color
Fruits and Vegetables
plum-colores bowl on table
Color
Fruits and Vegetables
An upricot-colored bowl on a table
Color
Fruits and Vegetables
A single rusted nut
Tools
Food and Beverages
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Desired Concept: Color Undesired Concept: Plants
SD-HRV (Ours)
Prompt: "A
lavender color"
Desired Concept: Brand Logas Undesired Concept: Fruits Vegetables
(Ours)
Prompt: "An Apple device table"
Desired Concept: Undesired Concept: Plants
SD
SD-HRV (Ours)
Prompt: rose-colored vase"
Concept: Color Undesired Concapt: Fruits and Vegetables
SD
SD-HRV (Ours)
Prompt: single cronge-colored plate"
Dasired Concept: Animals
Undesired Concept: Tools
SD
SD-HRV (Ours)
Prompt: crane flying over grass field"
Figure 18: Complete set of generated images used for the human evaluation (Part of 2). Images showing misinterpretations are marked with red boxes.
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Desired Concept: Color Undesired Concept: and Vegetables
SD-HRV (Ours)
Prompt: "A bowl mint table"
Desired Concept: Color Undesired Concept: Fruits and Vegetables
SD
SD-HRV (Ours)
Prompt: "An olive-colored plate on table"
Desired Concept: Golor Undesired Concept: Fruits and Vegetables
SD-HRV (Durs)
Prompt: plum-colored plate on
Concept: Color
Undesired Concept: Fruits and Vegetables
5D
SD-HRV (Ours)
Prompt: "An apricot-colored bowl on table"
Desired Concept t: Tools Undesired Concept: Food and Severages
SD-HRV (Ours)
Prompt: "A single rusted nut"
Figure 19: Complete set of generated images used for the human evaluation (Part 2 of 2). Images showing misinterpretations are marked with red boxes.
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D.2 HUMAN EVALUATION
We evaluate the human perceived misinterpretation rate using Amazon Mechanical Turk (AMT), requiring participants to have 500 HIT approvals, an approval rate above 98%, and live in the US. The survey begins with a sample question accompanied by its correct answer, which is repeated the end without the answer. Participants who missed the sample question are excluded, leaving 36 valid responses. The misinterpretation rate measures how often polysemous words are misinterpreted in the generated images. We use prompts from Table and 10 random seeds to generate 100 images for each T21 model. This results in 200 total images for comparison between Stable Diffusion (SD) and SD-HRV (Ours). These images are organized into 10 problem sets, each containing 20 images generated with the same prompt using either SD or SD-HRV. Each problem set consists of 4 questions, with each question presenting images generated using the same T21 model but with different random seeds. Each participant receives randomly selected problem sets, containing 12 questions and 60 images. Details of the human evaluation setup are summarized shown 5 images and asked 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 to determine the count of images with misinterpretations.. After applying concept adjusting with our head relevance vectors on Stable Diffusion, the misinterpretation rate drops from 63.0% to 15.9%.
in Table 8 For each question, participants
Table 8: Overview of human evaluation details for assessing misinterpretation.
Number of valid responses
Number of questions participant
Type of questions
Number of questions
Filtering process
Counting images that
12 questions with
36
40
satisfy the given condition
60 generated images
D.3 COMPARISON OF CONCEPT STRENGTHENING AND CONCEPT ADJUSTING
We can also apply concept strengthening, instead of concept adjusting, on Stable Diffusion to reduce misinterpretations. While this approach resolves misinterppetations some prompts, is fully effective in others. Figure 20 shows two cases: the left column shows where concept strengthening fails, and the right column shows where succeeds. In contrast, concept adjusting succeeds in both cases. This is likely because, in some instances, the undesired concepts are relatively strong, requiring explicit redirection of the T2I model away from those undesired concepts.
Stable Diffusion (SD)
Concept
Strengthening
SD + Concept
Adjusting
Prompt: vase in lavender color"
Prompt: "An apricot-colored bowl on table"
Figure 20: Comparison of concept strengthening and concept adjusting. Concept strengthhnnng fails in the left case, while concept adjusting succeeds both cases.
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E DETAILS AND ADDITIONAL RESULTS ON IMAGE EDITING
E.1 DETAILED EXPLANATIONS ON P2P-HRV
Brief overview of P2P replacement. P2P generates target images using the CA maps calculated during the source image generation. Given a source prompt P and target prompt P", P2P simulta- neously generates images for both prompts, starting from the same Gaussian noise Z and the same random seed s. The diffusion denoising process unfolds over timesteps = 1, ......,T. T, where t represents pure noise and t = T the fully denoised image. Let denote the CA replacement steps in P2P. During the first T. timesteps (1 S Te), P2P injects structural information from the source image into the target by replacing the CA maps of the target prompt with those from the source prompt. For the remaining timesteps (t > Te), target image is generated using its own CA maps without replacement. At each timestep if M, and M; represent CA maps from the source and target prompts, respectively, and M, denotes the modified CA maps used for generating the target image, then M, is calculated according to the following equation:
[M,, if Te M,
M, M)
otherwise
In short, P2P uses these modified CA maps M. and the target prompt P to generate target images.
P2P-HRV. We enhance P2P by applying concept strengthening on the edited token. Consider the source prompt P 'a blue car' and the target prompt pa 'a red car'. During the first To timesteps, P2P replaces the CA maps of p with those of P while generating the target image. However, this can lead to a mismatch with the target prompt, as the CA maps for 'blue' may interfere with properly changing the color from 'blue' to 'red.' To address this, we leave the CA maps for the edited token ('red' in this case) unchanged during the first тe timesteps, while replacing the CA maps for the other tokens. This ensures that the CA maps for 'blue' are excluded from the target image generation. The calculation of the modified CA maps M, is therefore adjusted as
Ch (M)) (M))
if Tor j + if S Tes j otherwise,
(M,MM, MT, M,
(8)
(M:)
where i represents a pixel value, j a source text token, j target text token, h a CA head position index, and e = for all h 1,... ..... 128. The term (M,). denotes the "..m"'--commnnntt of the modified h-th CA map R. To further steer the model to focus on the concept being edited, we apply concept strengthening by setting C₁ = The where I' the h-th component of the rescale vector defined in Figure 4 of Section 5 This is applied across all head positions h 1,... 128. This final method is referred to as P2P-HRV.
E.2 PROMPTS FOR IMAGE EDITING
We compare P2P-HRV with several state-of-the-art image editing methods across five editing tar- gets, including three object attributes-Color Material, and Geometric Putterns-and two image attributes-Imen Style and Weather Conditions. The prompt template and concept-words for each visual concept are listed in Table D For each prompt, we generate images using 10 random seeds. For example, in the Color editing task, 10 random seeds are used with the prompt template (Color A} {Objects}' (source prompt) + 'a {Color B} {Objects)' (target prompt), covering 10 color pairs (Color A, Color B) and 5 objects (Objects), resulting in 500 generated images for each T2I model. The words for Color A and Color B are sampled from the concept-word set of the visual concept Color in the first row of Table 9 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 attribute pairs for all five editing tasks be found in core codebase.
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Table 9: Prompt and word list for image editing
Prompt Template
Visual Concept
Words
|Color } (Objects)
Color
+ a (Color B) {Objects)
{Color): blue, brown, red, purple, pink, yellow, green, white, gray, black
(Objects]: car, bench, bowl, balloon, ball
{Objects} made of [Material A} [Objects) made of (Material B)
Material
[Objects]: howl, cup, tahle, ball, teapot
(Material); wood, glass, steel, copper, silver, marble, paper. jade, gold, basalt,
granite, clay, leather
{Objects} with (Geometric Patterns A} pattern
Geometric putterns
{Objects) with a (Geometric Patterns B) pattern
[Object]: T-shirt, pillow, wallpaper, umbrella, blanket
(Goometric Patterus): polka-dot, loopard, stripe, greck-key. plaid
[Image Style A} style painting of (Landscapes)
Image Style
(Image Style B) style painting of (Landscapes)
(Image Style): cubist, pop art, steampunk, impressionist, black-and watercolor, cartoon, minimalist, sepia, sketch
(Landscupes): ): castle, mountain, cityscape, farmland, forest
[Animals} on /Weather Conditions A} day
Weather Conditions
+ [Animals) on (Weather Conditions B} day
[nimalss:: cat, dog. rabbit, frog, bird
Weather Conditions): snowy. rainy, foggy, stormy
E.3 METRICS FOR IMAGE EDITING AND HUMAN EVALUATION ON TWO IMAGE ATTRIBUTES
For object attributes (Color, Material, and Geometric Patterns). we evaluated performance using CLIP (Radford et al.) 202T) and BG-DINO scores. The CLIP score measures the CL.PP image-text similarity between the edited image and the target prompt, assessing how well the edited image aligns with the target prompt. Mcanwhile, the BG-DINO score assesses structure preservation, focusing only on the non-object parts of the image, as the editing targets are restricted to the objects themselves. To compute the BG-DINO score, et al. 2024) to extract non-object parts from the source and edited images, process these segmented images with the DINOv2 (Oquab al., 2023) model to obtain embeddings, and calculate cosine similarity between these two embeddings. While prior works (Parmar et al. 2023 Kim et al. 2024) have employed regment-and-emberr metrics using LPIPS (Zhang al. 2018) or CLIP embeddings to focus on specific image regions, we adopt DINOv2 embeddings because the DINOv2 model is trained with self-sun self-supervised objective that enables to capture unique image characteristics.
use Grounded-SAM--2 (Ravi et al.) 2024 Ren
For image attributes (Image Styles and Weather Conditions), we conducted a human evaluation to assess human preference (HP) scores, rather than using the BG-DINO score, as BG-DINO is not suitable for evaluating structure preservation in these cases, which involve edits across the entire image. Using Amazon Mechanical Turk (AMT), we measured HP scores while ensuring quality by requiring participants to have over 500 HIT approvals, approval rate above 98%, and live in the US. Each survey begins with a sample question that includes the correct answer, which repeated at the end without the answer provided. After filtering out raters who missed the sample question, we collect 28 valid responses for Image Style and 35 for Weather Conditions.
We use 50 prompt pairs for Image Style and 25 for Weather Conditions, presented in Table For human evaluation, we randomly select a seed previously used to measure CLIP image-text similari- ties. Images then generated for each prompt pair using P2P-HRV and four other nigh-performing
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methods, resulting in 250 images for Image Style and 125 for Weather Conditions. This creates 200 binary choice questions for Image Style and 100 for Weather Conditions, with each participant an- swering 20 randomly selected questions. Details of the human evaluation setup summarized in Table In each question, we present participants with two images-one generated using our ap- proach and the other by a different method-and ask, 'Which edited image better matches the target description, while maintaining essential details of the source image?' If participants cannot decide, they can select the option 'Cannot Determine/ / Both Equally." The results are shown in Table [ of Section 5.2] The HC-score in Table is calculated by dividing the number of selections for the other method by the number of selections for ours and multiplying by 100. For example, an HP score of 35.0 for PnP (Tumanyan et al. 2023) in Weather Conditions editing indicates that our method
received 2.86 (= 100/35.0) times more votes than PnP in this editing task.
Table 10: Overview of human evaluation details for two image attribute editing.
Number valid responses
Number questions
Editing Target
Type of questions
Number of questions
Filtering process
per participant
Binary choice
Image Style
28
200
20
question
Binary choice
Weather Conditions
35
100
20
0
question
E.4 TRADE-OFF EFFECT OF SELF-ATTENTION REPLACEMENT IN P2P AND P2P-HRV
While P2P primarily focuses on cross-attention (CA) map replacement, it also shows that adjusting the self-attention (SA) replacement rates can enhance structure preservation. The SA replacement rate determines the initial timesteps during which the SA maps of the edited images are replaced with those of the source images. Higher replacement rates enhance structure preservation by incor- porating more structural information from the source images but can reduce image-text alignment due to the increased reliance on source image data. This trade-off effect is illustrated in Figure 21 where both P2P and P2P-HRV are evaluated in Color editing benchmark with varying SA replace- ment rates. In this figure, BG-DINO measures structure preservation, while the CLIP image-text similarity measures image-text alignment. Notably, P2P-HRV consistently achieves significantly higher image-text alignment across all SA replacement rates compared to P2P. This result shows clear Pareto-optimal improvements of P2P-HRV over P2P. For all editing benchmarks in this paper, we use an SA replacement rate of 0.4 for P2P and 0.9 for P2P-HRV, as both provide a balanced trade-off between the two metrics. Examples of images generated with varying SA replacement rates are shown in Figures 22 and
0.94
0.6
0.5
1.0
0.8
0.4
0.6
0.5
0.3
8G-DINO
0.4
0.96
0.2
0.3
0.2
0.82
0.1
0.1
0.78
P2P
0.0
P2P-HRV (Ours)
0.0
0.74
0.300
0.305
0.310
0,315
0.320
0.325
0.330
0.335
0.340
CLIP Image-Text Similarity
Figure 21: Trade-off effect of self- replacement in 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 balanced trade-off between the CLIP and BG-DINO scores. These values are used for P2P and P2P-HRV in all experiments presented in this paper.
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1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Source
Structure Preservation
Self-Attention Replacement Rate
Image-Text Alignment
P2P
Prompt: "a pink yellow car"
P2P
Ours
Prompt: green white bench"
Ours
Prompt: "a blue balloon"
P2P
Ours
Prompt: purple
Ours
Prompt:
blue ball"
Figure 22: Qualitative results of image editing comparing P2P (Hertz et al. 2022 and ours, based on the variation of self-attention replacement rate (Part of 2).
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1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.2
Source
Structure Preservation
Self-Attention Replacement Rate
Image-Text Alignment
Ours
Prompt: a blue brown car"
P2P
Prompt:
P2P
Ours
Prompt: "a blue red balloon"
P2P
Prompt: "a pink
P2P
Ours
Prompt:
white black ball"
Figure 23: Qualitative results of image editing comparing P2P (Hertz et al. 2022 and ours, based on the variation of self-attention replacement rate (Part of 2).
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E.5
ADDITIONAL RESULTS ON IMAGE EDITING
Figures 34 34 two image attributes.
present additional qualitative results of image editing for three object attributes and
Source
Ours
SDEdit (0.5)
SOEdit (0.7)
PnP
MasaCrri
Color
Prompt yellow
Prompt :a teapot made of giase granite
Geometric Aatterns
Prompt "a umbrella with stripe pathern"
Prompt oubist sketch style painting cityscape"
Weather Conditions
Prompt :aa dog foggy rainy day"
Figure 24: Qualitative results of image editing for three object attributes and two image at- tributes (Part of 2).
Source
Ours
(0.5)
SDEdit (0.7)
Color
Prompt :
Prompt aa cup made of gold basalt"
Geometric
Aatherns
Prompt "a blanket with plaid polka-dot patter"
Prompt minimalist pop-art style painting
Weather Conditione
Prompt :a frog on foggy snowy day"
Figure 25: Qualitative results of image editing for three object attributes and two image at- tributes (Part 2 of 2).
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a pink yellow bench"
Figure 26: Qualitative results of image editing for the visual concept Color (Part 1 of 2). The results were generated using random seeds were used the quantitative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a blue red car"
Figure 27: Qualitative results of image editing for the visual concept Color (Part 2 of 2). The results were generated using random seeds were used the quantitative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a bowl made of paper jade"
Figure 28: Qualitative results of image editing for the visual concept Material (Part of 2). The results were generated using random seeds and were used the quantitative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a teapot made of glass granite"
Figure 29: Qualitative results of image editing for the visual concept Material (Part 2 of 2). The results were generated using random seeds and were used the quantitative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a pillow with pelka-det leopard pattern"
Figure 30: Qualitative results of image editing for the visual concept Geometric Pat- terns (Part of 2). The results were generated using 10 random seeds and were used in the quanti- tative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a blanket with plaid polka-dot pattern"
Figure 31: Qualitative results of image editing for the visual concept Geometric Pat- terns (Part of 2). The results were generated using 10 random seeds and were used in the quanti- tative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a pop-art impressionist style painting of farmland"
Figure 32: Qualitative results of image editing for the visual concept Image Style (Part of 2). The results were generated using random seeds and were used the quantitative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a watercoler sepia style painting of a mountain"
Figure 33: Qualitative results of image editing for the visual concept Image Style (Part 2 of 2). The results were generated using random seeds and were used the quantitative evaluation.
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Source
Ours
SDEdit (0.5)
SDEdit (0.7)
P2P
PnP
MasaCtri
FPE
Prompt "a frog on sunny snowy day"
Figure 34: Qualitative results of image editing for the visual concept Weather Conditions. The results were generated using random seeds and were used the quantitative evaluation.
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F DETAILS AND ADDITIONAL EXPERIMENTS ON MULTI-CONCEPT GENERATION
Brend-and--xcitee (A&E) excites signals for subjects using averaged cross-attention maps, which are averaged across different CA layers. A&E-HRV extends this by using HRVs to re-weight each cross-attention map before averaging. Both A&E and A&E-HRV are applied only to noun tokens, as in the original Attend-and-Excite In our multi-concept generation experiments, we used two types of prompts: (i) 'a [Animal A} and (Animal BY (Type 1) and (ii) 'a {Color A} (Animal A} and [Color }} {Animal BY (Type 2). Table 11 lists the 12 animals and 10 colors used to generate these prompts, with the full prompt list available 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, fish blue, brown, red, purple, pink, yellow, green, white, gray, black
Color
F.1 COMPARISON WITH ENTRIBBUEEBBNNDIAA METHOD
In this section, we compare A&E-HRV with the recently proposed attribute-binding method, Syn- Gen (Rassin al. 2024). Since SynGen requires attribute words to be included in the prompt, focus our comparison on Type 2 prompts. The quantitative results, shown Table [2] show that our approach consistently outperforms SynGen across all three mntriics-full prompt similarity, minimum object similarity, and BLIP-score-B by margins 2.8% to 5.8%. The qualitative results in Figure 35 show that SynGen often fails generate both objects, while our approach generates object concepts more reliably.
Table 12:
Type 2 results: Multi-concept generation using SynGen and our method. The percentage parentheses indicates the improvement over the result of SynGen.
Type2: (Color 4} (Animal A} and (Color B} (Animal B}
Method
Full Prompt
Min. Object
BLIP-seore
SynGen
0.3820
0.1960
0.6398
A&B-HRV (Ours)
0.3971 (+4.0%)
0.2073 (45.8%) )
0.6580 (+2.8%) %)
Prompt: blue cat and brown rabbit"
Prompt: yellow dog and pink bird"
A&E-HRV (Ours)
SynGen
Figure 35: Qualitative comparison of the results for Type prompts between SynGen (Rassin al. 2024) and ours.
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F.2
ADDITIONAL RESULTS ON MULTI-CONCEPP GENERATION
Figure 36 presents additional qualitative results of multi-concept generation for both Type and Type prompts.
A&E HRV (Ours)
Stable Diffusion
Structured Diffusion
A&E
Prompt: "a dog and frog"
Type
Prompt: "a panguin and herse"
Prompt: "a and deer
Prompt: penguin
Prompt: "a black frog and red bind"
Type
Prompt: "a brown bear and red fish"
Prompt: "a gnay and frog"
Prompt: "a green bird and bear"
Figure 36: Qualitative comparison of the results for Type and Type 2 prompts. We compare Stable Diffusion (Rombach et al. (022)). Structured Diffusion (Feng et al. 2022), and Attend-and -nddd-and- Excite (Chefer et al. 2023) with ours.
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G
ADDITIONAL RESULTS USING SDXL
G.1 ADDITIONAL RESULTS ON ORDERED WEAKENING ANALYSIS
Figures 42
present additional results from the ordered weakening analysis on Stable Diffusion
XL (SDXL) (Podell et al. 2024).
Material
Animals
Geometrric Patterns
HoRuE Leter
NotHF Lette
0.28
0.66
LotHe
0.210
0.22
0.225
0.20
0.200
0.36
0.38
0.0
1330
The Sumber of Weekend Heed Posttions
The Number Weakend Head Posttions
The Number Weakend Head Pos bions
Color
Fruits and Vegetables
Furniture
0.26
HERVE Lobit
0.30
MoRi lettl
0.275
MoRir
lett
0.22
0.20
CUP Score
0.25
0.200
0.225
0.18
2.00
0.200
8.18
0.175
012
1210
The Number of Weakend Iiead Positions
The Number of Weatand Head Posttions
The Number of Weatend Head Positions
Sceness
Image Style
Weather Conditions
0.300
Humr Latit
0.24
Mutte Latir
Matee
0.25
0.33
0.20
0.22
CUP Score
0.25
0.22
0.15
0.20
0.14
0.18
0.12
0.36
400 The Number of Weakend Head Positions
1980 The Number of Weakend Head Positions
225
The Number Weakend Head bons
Figure 37: Ordered weakening analysis using SDXL: Change CLIP image-text similarity score as weakening progresses in either MoRHF LeRHF order.
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# of weakened head positions
Weakening of
Most positions
Material
ball copper"
Ledst Relevent Head posi fions First
Most Head posittons First
"photo of dolphin"
Least Relevent Head First
Most Relevent Head positions First
Geometric Patterns
Least Relevant Head posi tisnt First
polka-dot pattern"
Most Head positions First
Least Relevent
"a orange car"
Most Relevant Head hions First
Fruits and Vegefables
photo poppers"
Head positions
Head posi Fions First
"photo of bed"
Least Relevant Head
Mast Relevent Head positions First
Nature Scenes
"photo of pond"
Least Relevant Head posi hions First
Head posi Fions First
Image Style
pop style painting castie"
Least Relevant Head positions First
Most Relevent Head posi tions First
Weather Conditions
bird stormy day"
Least Head positions First
Figure 38: Ordered weakening analysis using SDXL: Generated images weakening progresses in either MoRHF or LeRHF order (Part of 5).
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# of weakened head positions
11
111
211
411
511
611
1300
Weakening of
Most Relevont
Head positions First
Material
teapot of basalt
Head poùl lions
positions
Animals
"photo sheap"
Least Relevant Head lions First
Most Relevan! Head positions First
Patterns
"a bianket
stripe pattern"
Head poùl lions
positione
Color
Least Relevent
beige
Head positions First
Most Reievvnt Head posi hions First
Fruits and Vegelables
"photo of blueberries'
Least Relevant
Most Relevant posi
Furniture
Least Relevant Head positions First
"photo of stand"
Most Relevant Head positions First
Scenes
"photo of jungle"
Head posi lions
Most Relevant posi
Image Style
sketch style painting cityscape"
Least Relevant Head positions First
Most Relevent Head posi tions First
Weather Conditions
Least Relevant
rainy day"
Figure 39: Ordered weakening analysis using SDXL: Generated images as weakening progresses in either MoRHF LeRHF order (Part of 5).
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of weakened head positions
Weakening
Most Relevont Head First
Material
"a bowl made gold"
Head positions
Mast positions
Animals
ppoto cat"
Least Relevant Head positions First
Head positions First
Geometric
pillow plaid pattern"
Least Relevan! Head positiins First
Most Relevont Head positions First
Color
yellow bowl
Head positions
Most Relevon! Head positions First
Fruits and Vegellabes
"photo cucumbers'
Head positions First
Furniture
"photo of chair*
Leest Relevont Head positions First
Most Relevant Head positions First
Nature Scenes
"photo mountain"
Least Relevant Head
Most Relevant Head tlansionss First
Image Style
a black-ond-wnit and-whire style painting of castle"
Least Relevant
Most Relevant Head positions First
Weather Conditions
rabbit snowy day"
Lesst Relevant Head positions First
Figure 40: Ordered weakening analysis using SDXL: Generated images weakening progresses in either MORHF or LeRHF order (Part of 5).
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# of weakened head positions
Weakening of
Most positions
Materiil
Ledst Relevent Head posi fions First
cup of jade
Most Head posittors First
photto of frog"
Least Relevent Head
Most Relevant Head positions First
Geometric Patterns
Least Relevant Head positions First
greak-key pattern"
Most Head positions First
PLACE
Least Relevent
pink
Most eeveennn Head hions First
Fruits and Vegefables
photo cauliflowers"
Head positions First
Head posi Fions First
photo of dresser"
Least Relevant Head
Mast Relevent Head positions First
Nature Scenes
"photo of forest*
Least Relevant Head posi hions First
Head posi Fions First
Image Style
sepia painting formland"
Least Relevant Head positions First
Most Relevent Head posi tions First
Weather Conditions
dog stormy day"
Least Head positions First
Figure 41: Ordered weakening analysis using SDXL: Generated images weakening progresses in either MoRHF or LeRHF order (Part of 5).
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of weakened head positions 111
211
311
411
S11
611
711
1300
Weakening of
Most Relevan! Head positions First
table made
Least Relexont
Head positions First
Most Relevant Head posi Hooes First
Animats
"photo
a fax"
Head positions First
Relevant positions
Geometric Patterns
Least Relevant Head positions First
with leapard pattern"
Most Relevant Head posi liors First
Color
brown bench"
positions First
Most Reievent Head First
Fruits and Vegetables
"photo pineopples'
positiors
Relevent positions
Furniture
"photo wardrobe'
Least Relevant Head posiliurs First
Most Relevant Head posi Hoors First
Scenes
"photo desert"
positions
Most Reievant First
Image Style
"a minimolist style painting a mountain'
positiors
positions
Weather
frog on rainy day"
Least Relevant Head positiors First
Figure 42: Ordered weakening analysis using SDXL: Generated images as weakening progresses in either MoRHF LeRHF order (Part of 5).
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G.2 ORDERED WEAKENING ANALYSIS WITH MORE COMPLEX IMAGES
In this section, we present two examples of ordered weakening analysis applied to more complex images using the prompts 'a plastic car melting' and 'a metal chair rusting. Before starting the analysis, we introduce new concept, Physical and Chemical Processes, into our set of 34 concepts, adding five corresponding concept-words: melting, rusting, boiling, freezing, and burning. We then re-compute HRV vectors. In the top example of Figure 43 we perform ordered weakening analysis with the generation prompt 'a plastic car melting' by weakening either the Physical and Chemical Processes or the Vehicles concept. When weakening Physical and Chemical Processes MoRHF order, the concept of 'melting' is eliminated first, while the 'car' persists for longer period. In contrast, when weakening Vehicles, the concept of 'car' is eliminated first, and 'melting' preserved longer. Notably, the entangled property 'plastic' is initially affected when weakening 'melting,'' but it removed more slowly from the image. This is even more apparent in the bottom example of Figure [43] where weakening Physical and Chemical Processes eliminates 'rusting' first, while the concept of 'metal' retained longer. These examples demonstrate that HRV and ordered weakening analysis work well with more complex images.
weakened head positions
11
111
411
511
611
1300
Weakening
Most Head positions First
Physical and Chemical Processes
Least Relevant Head positions First
Most
"a plastic cor melting"
Head positions
Vehicles
Least Relevant
Head posilions First
Most Relevent Head positions First
Physical
Chemicol
Processes
positions
Mast Relevant Head positions First
chair rusting"
Least Relevant Head positions First
Figure 43: Ordered weakening analysis with more complex images: Generated images as weakening progresses in either MoRHF or LeRHF order using SDXL.
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G.3 REDUCING MISINTERPRETATION SDXL
SDXL significantly improves image generation performance compared to SD vI models, thanks to its three times larger U-Net backbone and CLIP text encoders. It also reduces misinterpretation issues with prompts from 7 but the problem is not entirely resolved, as undesired concepts still appear in generated images. For instance, among 100 images generated using these prompts. nearly all included the desired concepts, but about 35 also contained undesired concepts. Figure 44 illus- trates this with two prompts: 'An Apple device on a table' and 'A rose-colored vase.' With SDXL, desired concepts (Apple device or rose-colored vase) are consistently generated; however, one image the first prompt included the undesired concept fruit apple, and nine images for the second prompt included the undesired concept of flower rose. In both cases, SDXL-HRV (SDXL with concept adjusting) effectively reduces misinterpretation by preventing the generation undesired concepts.
In Figure 44 SDXL-HRV images for 'A rose-colored vase' tend to exhibit rose coloring across most parts of the images. We suspect this issue is related the normalization of HRV vectors, which are currently normalized to have an L1 norm equal to their length, H. This approach is based on the fact that vector with all elements set to also has L1 norm of H. and using this vector rescaling factor does not alter the image generation process. For SD v1.4, = 128, but for SDXL, H 1300, which may cause some HRV vector elements become too large when rescaling CA maps. One possible solution is to clamp each HRV element to upper bound b, replacing any value greater than with b. Future work will explore this and other normalization strategies to identify approaches better suited to high H value.
Desired Concept: Loges Undesired Concept: Fruits and Vegetables
SDXL
SDXL-HRV (Ours)
Prompt: "An Apple device on table"
Desired Concept: Colar
Undesired Concept: Plants
SDXL
SDKL-HRV (Ours)
Prompt: "A rose-colored vase"
Figure 44: Two examples misinterpretaatin reduction in SDXL. Images showing misinterpreta- tions are marked with red boxes.
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H LIMITATION
We examined 34 concepts listed in Table 3] and identified two types of failure cases. The first type stems from limitations in the underlying T2I model, where struggles to correctly understand certain concepts. For example, in Counting and Lighting Conditions, the model fails to generate accurate outputs, shown Figure 45] The second type related our HRV or concept-words. Here, the T2I model correctly understands the concept, but MoRHF and LeRHF weakening fail to produce meaningful differences. An example of this Facial Expression, with related failure cases shown in Figure 46
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 'four' in the Counting examples or 'natural light,' 'spotlight," and 'dark light' in the Lighting Conditions exam- ples. These issues make it difficult to assess whether HRVs identify appropriate CA head orderings relevant to these concepts, the base model, SDXL, does not reliably generate the intended out- puts. We believe such failures could be addressed in the future with more advanced T2I models. For the second type of failure, shown in Figure (46] SDXL correctly generates facial expressions that match the prompts. However, HRV fails to find meaningful CA head orderings, preventing it from distinguishing between MoRHF and LeRHF weakening. This may be due to the concept-words used for Facial Expression being too broad to represent the concept effectively. The concept-words for Facial Expression include Happy, Sad, Angry. Surprised, Confused, Laughing, Crying, Smiling, Frowning, Disgusted. We will further explore this limitation in future work.
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#
weakened head positions
11
111
1300
Weakening of
Most Relevant Head positions
balls"
Head pool tions
Most
positions
Counting
Least Relevant Head positions First
"oour ballss
Most Relevant Head positions First
"five balls"
Head posi hons
Mast Relevent Head positions First
flower in natural light"
posl
Most positions
Lighting
flower in spotlight
Lesat Relevent Head positions First
Most Relevant Head positions First
flwae in dark light"
Head positions
Figure 45; First type of failure cases: The baseline T2I model, SDXL, struggles to correctly derstand the concepts, making it difficult to assess whether HRVs identify appropriate CA head orderings relevant the corresponding concepts. The images generated with SDXL.
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of weakened head positions
211
311
411
511
1300
Weakening
Most Relevent Head First
"a face with happy expression"
Least positions
Most Relevant posi
Focial Expression
"a face with
Least Relevant Head positions First
sad expression"
Most Head posi tions First
"a face with ongry expression"
Least Relevont Head positions First
Figure 46: Second type of failure cases: Our HRV fails to identify the relevant CA head order- ing for the corresponding concept, preventing from distinguishing between MoRHF and LeRHI weakening. The images are generated with SDXL.
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I ADDITIONAL ANALYSIS: THE EFFECT OF TIMESTEPS ON HEAD RELEVANCE VECTORS
In this section, further analyze 1700 vectors (34 visual conceptsx:00 timesteps) obtained from Section 6.2 We start by reshaping these vectors into a tensor with dimensions 34x50x 128, where 34 represents visual concepts. 50 represents timesteps, and 128 represents the number of CA head positions in the T2I model. We then average this tensor over the timestep dimension to obtain 34 vectors, each with a size of 128, corresponding to the head relevance vectors (HRVs). Similarly, averaging over the visual concept dimension yields 50 vectors, also of size 128, which we refer to as timestep vectors. To examine directional variations the 34 HRVs, we compute and visualize their cosine similarities in Figure 47a We also visualize the cosine similarities between the 50 timestep vectors in Figure 47b. Compared to Figure 47a Figure (4bb shows almost no directional variation between the 50 timestep vectors (note the colorbar scale). This supports the conclusion of Section 6.2, where we suggested that the generation timesteps do not significantly affect the head relevance patterns of each visual concept.
10
0.8
Visual Concept Index
10
Cosine Similarity
Generation Timestep
0.99
Cosine unallrriy
15
20
0.98
20
0
30
25
0.97
0.2
40
30
0.96
0.0
10
30
Visual Concept Index
Generation Timestep
(a) Cosine similarities of 34 head relevance vec- tors (HRVs).
(b) Cosine similarities of 50 timestep vectors.
Figure 47: Cosine similarity plots of (a) 34 head relevance vectors and (b) timestep vectors. Visual concepts are clearly separated, while timesteps not.
J EXTENDING HUMAN VISUAL CONCEPTS
In this paper, we use 34 visual concepts to construct head relevance vectors (HRVs), but users can flexibly add remove visual concepts as needed. this section, we explore the effect of adding a new visual concept to the existing set of 34. To demonstrate this, we add the concept Tableware, creating set of 35 extended visual concepts. We then construct HRVs individually for both the 34-concept and 35-concept sets and compare them through visualization. Stable Diffusion vI has 16 multi-head CA layers, each containing 8 CA heads, for a total of 128 heads, making HRV visualization straightforward In Figure 49 visualize each set of HRVs, with darker colors representing higher values. The two sets of HRVs for the original 34 visual concepts (Figure 48a and Figure 48b) are highly similar. This indicates that adding the new concept Tableware does not significantly alter the patterns of HRVs for each concept. Additionally, Figure 49 shows examples of ordered weakening analysis with the added concept Tableware, demonstrating that the HRV for the new concept Tableware is effective.
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Published as a conference paper at ICLR 2025
(a) Visualization HRVs for visual concepts
(b) Visualization of HRVs 35 visual concepts
Figure 48: Visualization of head relevance vectors (HRVs) for (a) 34 visual concepts used in this paper, and (b) 35 extended visual concepts (the original 34 visual concepts plus the Tableware con- cept). HRVs for (a) and (b) are constructed individually (Best viewed with zoom).
of weskened head positions
21
51
Weakening of
Most Relevont First
Tobleware
Lesst Relevent Head positions First
"photo solad bow!"
Most Relevont Head tosts First
"photo of pot*
Least Relevant positionns
Figure 49; Ordered weakening analysis for Tableware concept: Generated images as weakening progresses in either MoRHF or LeRHF order using Stable Diffusion v1.4.
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Reviews:
[{"review_id": "SFnL1P08kn", "reviewer": "Reviewer_miju", "summary": "This work proposes Head Relevance Vectors (HRVs). HRs are an extension of the findings from previous works such as Hertz et al.'s P2P where cross attention maps were used to better understand t2i models and to edit images via prompts. HRV proposes using multiple concept words and concatenating them into a concept embedding matrix K which can then be applied to different heads of the cross-attention and by doing so, disentangle the different heads based on the concept they seem to be focusing on. The authors show this disentanglement of heads based on the concepts learned improved editing of images.", "questions": "There have been recent works that show the <SOT> and <EOT> CAs capture different concepts. I would be interested to see if the authors found anything interesting regarding HRV and these tokens. I am also curious as to how the weakening and strengthening would work on more complex images that share entangled objects and concepts. For instance, what would weakening of \"melting\" look like for \"a plastic car melting\". I think this would be an interesting experiment since adjective and verb concepts are entangled with an object in a given image and HRV might to better in these cases than the counterparts.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The motivation of the paper is clear and build on a well studied problem of understanding the role of cross-attention and what they learn in editing T2I models. The experiments are visually appealing and tell the story of the paper well, especially the weakening of HRVs that shows weakening based on the most and least relevant concepts / heads. The authors show that using HRVs to edit images works better than SDEdit,P2P, etc. They also show improvement over Attend and Excite for the problem of catastrophic forgetting in T2I models.\n\n While in the weaknesses, I do mention my thoughts on the originality of this work, I believe using previous findings around CAs and targeting different heads and their roles in generating different concepts would be interesting to the community.", "weaknesses": "I would argue that the work, while interesting, does not have new insight compared to what previous works such as P2P and Diffusion Self-Guidance have already already shown in regards to the role of cross-attentions. However, this work does take a step towards using those findings to narrow down on head-level manipulation of concept vectors. It goes without saying that T2I models could benefit from more comprehensive evaluation on larger set of generated images / human evaluation. However, I do understand the challenges this poses as well.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "epjID2ib6q", "reviewer": "Reviewer_d4iN", "summary": "The paper proposes a method of constructing so called \"HRV\" vectors, which align with visual concepts. The authors leverage cross-attention layers of Stable Diffusion model to learn those vectors for predefined concepts. The proposed method helps to solve three known issues of the image synthesis task.", "questions": "1. Could you give more details about why there are some irrelevant concepts after a certain point of ordered weakening (Fig 9)?\n2. Could you give more details about how the \"h\" is chosen/computed in the method of HRV updates?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Good motivation and a clear idea\n2. Comprehensive quantitative and qualitative comparisons with many other solutions\n3. The experiments, settings, and other details are mainly clearly explained", "weaknesses": "1. It requires fixing a set of concepts beforehand for every HRV construction. Does not have a study of how the HRV matrix will be changed when some concepts are changed or replaced after the construction.\n2. Manual settings, choice, and configuration are required for every concept (case) during inference (Sec 5.1, Fig 5). \n3. Lack of failed cases, there are no details about the limitations of this method.\n4. Even though there is a section for bigger / novel models (SDXL), all experiments, studies, and comparisons are based on SD v1. New models might eliminate many issues the proposed method tries to solve.", "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": "2WYUXS774s", "reviewer": "Reviewer_yNut", "summary": "This paper tries to understand cross-attention (CA) layers *regarding attention heads*.\n* The authors introduce N head relevance vectors (HRV) for N visual concepts.\n* The strength of an attention head to the HRVs represent the relevance of the head to the concept.\n\nAbove properties are interpreted by *ordered weakening analysis*.\n* Sequentially weaken the activations of CA heads to observe weakened visual concepts.\n\nBoosting and reducing the strength of different heads control the strength of visual concepts. It helps three applications: 1) correcting mis-interpretation of words in t2i generation, 2) boosting prompt-to-prompt, 3) reducing neglect in multi-object generation.", "questions": "I wonder the interpolation between different strengths of a head. For example, interpolating  material=[-2, 2]?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper provides a new perspective in understanding the features in text-to-image generation: different heads.\n2. Qualitative examples (Figure 3a) and CLIP similarities (Figure3b) along weakening MoRHF and LeRHF clearly show the effect of weakening different heads.\n3. The appendix provides extensive qualitative results to remove doubt for cherry-picked results.\n4. The proposed method is useful for three applications: 1) correcting mis-interpretation of words in t2i generation, 2) boosting prompt-to-prompt, 3) reducing neglect in multi-object generation.\n5. Discussions resolve natural questions: extension to SDXL and effect across different timesteps.", "weaknesses": "1. The paper should provide principles of the proposed approaches.\n    * L224 Why should we count each visual concept having the largest value to update the HRVs?\n    * This is the most critical weakness for not giving a higher rating. I think the perspective is worth noticing but a solid paper should provide why/how it works.\n    * Answering this question with theoretical justifications or intuition would strengthen the paper.\n2. HRV should be described more clearly.\n    * L205 a concatenation of token embeddings // concat along which axis? I guess the result of concatenation is $N \\times (d + H)$. Then the query Q does not match the cross-attention operation because $Q\\in R^d$. Am I missing something?\n    * L210 K1, ..., KN should be denoted in Figure 2.\n    * Adding equations and proper notations would help readers to understand the operation.\n3. Human evaluation should be explained in more detail.  Appendix C.2 is not enough. Adding a table with Number of participants, Number and types of questions, Number of questions per participant, and Any quality control measures used would strengthen the user study.\n\nMisc.: Related works -> Related work", "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": "1fyGwTygxE", "reviewer": "Reviewer_Efqm", "summary": "This paper mainly focuses on the explainability of text-to-image diffusion model. The authors propose a new metric based on the cross-attention heads in the diffusion UNet to illustrate the correlation between each attention head and visual concepts. Based on the proposed Head Relevance Vectors, the authors further propose several applications including solving polysemous words problems and image editing.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The idea of correlating visual concepts with diffusion models is interesting.", "weaknesses": "1. I suggest the authors add textual description of the proposed HRV instead of directly showing Fig. 2 and Fig. 4 for better understanding.\n2. I wonder why <SOT> and many <EOT> are required during update of HRV?\n3. It would be better to used SDXL or some more recent models such as SD3 as primary model, given that SD1.5 is kind of outdated.\n4. It would be better to add a random weakening baseline in Fig.3.\n5. In Sec.5.1 the authors show that by utilizing HRV the SD can generate more proper concepts. I wonder if this method can be compared with using classifier guidance, where the model is encouraged to align the generated image with wanted concepts in terms of CLIP score.", "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)