$\text{I}^2\text{AM}$: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps
Authors: Junseo Park
Hyeryung Jang
Venue: ICLR 2025 Poster (2025)
Keywords: Image-to-Image Diffusion Model
Interpretable AI
Explainable AI
Image Attribution
Primary area: interpretability and explainable AI

Abstract: Large-scale diffusion models have made significant advances in image generation, particularly through cross-attention mechanisms. While cross-attention has been well-studied in text-to-image tasks, their interpretability in image-to-image (I2I) diffusion models remains underexplored. This paper introduces Image-to-Image Attribution Maps $(\textbf{I}^2\textbf{AM})$, a method that enhances the interpretability of I2I models by visualizing bidirectional attribution maps, from the reference image to the generated image and vice versa. $\text{I}^2\text{AM}$ aggregates cross-attention scores across time steps, attention heads, and layers, offering insights into how critical features are transferred between images. We demonstrate the effectiveness of $\text{I}^2\text{AM}$ across object detection, inpainting, and super-resolution tasks. Our results demonstrate that $\text{I}^2\text{AM}$ successfully identifies key regions responsible for generating the output, even in complex scenes. Additionally, we introduce the Inpainting Mask Attention Consistency Score (IMACS) as a novel evaluation metric to assess the alignment between attribution maps and inpainting masks, which correlates strongly with existing performance metrics. Through extensive experiments, we show that $\text{I}^2\text{AM}$ enables model debugging and refinement, providing practical tools for improving I2I model's performance and interpretability.

Published as a conference paper at ICLR 2025
AM: INTERPRETING IMAGE-TO-IMAGE LATENT DIFFUSION MODELS VIA BI- BI-ATTRIBUTION MAPS
Junseo Park and Hyeryung Jang Department of Computer Science & Artificial Intelligence, Dongguk University
ABSTRACT
Large-scale diffusion models have made significant advances in image genera- tion, particularly through cross-attention mechanisms. While cross-attention has been well-studied in text-to-image tasks, their interprctability in image-to-image (I2I) diffusion models remains underexplored. This paper introduces Image-to- Image Attribution Maps (I' AM), a method that enhances the interpretability of 121 models by visualizing bidirectional attribution maps, from the reference image to the generated image and vice versa. AaM aggregates cross-attention scores across time steps, attention heads, and layers, offering insights into how criti- cal features are transferred between images. We demonstrate the effectiveness of 1 AM across object detection, inpainting, and super-resolution tasks. Our results demonstrate that 1 AM successfully identifies key regions responsible for gener- ating the output, even in complex scenes. Additionally, we introduce the Inpaint- ing Mask Attention Consistency Score (LMACS) as a novel evaluation metric to assess the alignment between attribution maps and inpainting masks, which corre- lates strongly with existing performance metrics. Through extensive experiments, we show that AM enables model debugging and refinement, providing practical tools for improving 121 model's performance and interpretability.
INTRODUCTION
Latent diffusion models (LDMs) have recently gained popularity as powerful methods for generat- ing images from random noise with textual description (text-to-image, T2I) (Ramesh et al. 2022
Saharia et 2022b Esser al., 2024). or image (image-to-image. 12I) (Song et al.) al.] [024]] Morelli
et al.) 2023 Koley et al. 2024). Despite their prevalent adoption, these models have often been developed without& a comprehensive investigation into their reliability and interpretability. As these methods are increasingly applied in various fields, ensuring their trustworthy and understandable operation becomes critical. Explainable Artificial Intelligence (XAI) plays a crucial role in address- ing this need by providing insights into how and why Al models produce their outputs. For LDMs, XAI methods offer the opportunity to interpret complex internal processes, enabling researchers to identify and mitigate potential issues, enhance model performance, and build user trust. Recent XAI efforts Hertz et al. (202);; Tang et al. (2022); Tumanyan et al. (2023) have largely focused on T21 models, where text inputs segmented into tokens, allowing for straightforward analysis via cross-attention maps, i.c., how individual tokens influence different parts of the generated output. However, applying similar interpretability methods 12I models, while promising, presents signif- icant challenges. The spatial and contextual continuity between the input (which we call reference) image and the output (which we call generated) image complicates this token- wwse approach 12I models. Unlike T2I models, where tokens have discrete and non-spatial relationships, 121 models must account for the complex correlations within and between the reference and generated images. This continuity reveals a considerable gap in our understanding and ability to interpret 12I models from both perspectives: from reference to generated and vice versa.
Despite the challenges of applying token-based methods to 121 models, the shared image domain between the reference and generated images opens new possibilities for bi-directional attribution mapping. Such an approach provides deeper insights into how 121 models capture and transfer vi- sual information between input and output domains, offering a more comprehensive understanding
"Correspondence Hyeryung Jang

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of the model's internal processes. In this paper, we address two key research questions that guide our approach to bi-directional attribution. The first question (Q1) asks, "Which regions of the gen- erated image are influenced most by the reference image?" This focuses the generated image's perspective, exploring how the model utilizes the reference image during generation. The second question (Q2) asks, "Which parts of the reference image contribute most the generated image?" This shifts the focus to the reference image, assessing whether the model captures and effectively transfers critical specifies from the input for generating the output. To answer these questions, we introduce the Image-to- to-ggge Attribution maps (I AM) method. 12 AM consolidates cross- -ross-attention maps across various axes such as time steps, attention heads, and layers to provide detailed un- derstanding of how the LDM operates. This approach allows us to visualize attribution maps from two perspectives: (i) from the reference to the generated image; and (ii) from the generated image back to the reference. The distinction between these two maps is crucial: while the former focuses on how the reference image affects specific parts of the generated image, the latter examines how the generated image relates back to the reference. This dual approach enhances our ability to interpret the intricate behavior of I2I diffusion models various tasks, highlighting the key contributions from each perspective.
To validate the effectiveness of A'AM method, we conducted extensive experiments across various tasks and models, including object detection, inpainting, and super-resolution (Cheng) et al. 2022; Yang et al., 2023a b). Our results demonstrate that FAA successfully cap- tures critical attribution patterns in each task, offering valuable insights into the underly- ing generation process. Fig. I provides a clear example of an inpainting task, specifi- cally in virtual try-on, illustrating both directions of influence. The top attribution map shows how different areas of the generated image are influenced by the reference image (Q1). Conversely, the bottom map demonstrates how different regions of the reference image con- tribute to the generation process (Q2). While the bottom map seems to indicate that key in- formation (i.c., logo) is not extracted, the patch- level analysis in the right map illustrates which reference information was extracted during the generation of that grid cell. We also introduced a new evaluation metric for reference-based im- age inpainting tasks, measuring the consistency between the two attribution maps. This met- ric offers a reliable way assess how well the model captures key details from the reference image, showing strong consistency with down- stream performance. Finally, we explored the utility of 12 AM for debugging and improving 12I models. By examining bi-directional attri- bution maps, we identified instances where the model failed capture essential details from the reference or misrepresented certain features in the generated image, adjusting the 121 model in targeted manner.
Diffusion Model
Refveence image
Figure 1: Cross-attention maps using I AM. The top map shows how the generated image is influenced by the reference image (Q1), while the bottom map illus- trates how the reference image contributes to the gen- erated image (Q2). The right map highlights specific reference-to-owee ou-put patch contributions.
2 RELATED WORK
Perturbation- based aftribution methods. Perturbation based methods explain model predictions by altering input features and observing changes in the output, making them suitable for black-box models. Occlusion Sensitivity Zeiler & Fergus| (2014b) occludes parts of the input to evaluate their influence on predictions. LIME Ribeiro et al. (2016) perturbs inputs and trains interpretable local model to approximate the model's predictions, while RISE Petsiuk (2018) generates pixel- wise saliency maps using randomized input masking. Although these methods computationally intensive, they are valuable for analyzing models without requiring access to internal parameters.
Gradient-based attribution methods. Gradient-based attribution methods determine input feature importance by analyzing the gradients of the model's 's output. Early work (Zeiler & Fergus] 2014a] Simonyann 2013) highlighted the significance of individual input pixels in image-based models.
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Class activation maps (Zhou et al. 2016: Selvaraju al. 2016) further improved visualization by combining gradient and activation maps. Techniques such as SmoothGrad Smilkov et al. (2017) and FullGrad Srimivas & Fleuret 2219) enhanced these visualizations through gradient smoothing and dual importance scoring, while Score-CAM CAM Wang et al. (2020) emphasized global feature contribu- tions. Despite their utility, these methods face challenges like gradient noise and handling negative contributions, which can obscure accurate interpretation.
Attention-based attribution methods. With the rise of transformers, attention-based attribution maps have become a key focus for understanding model behavior, particularly in text-to-image tasks. These methods intuitively capture the relative importance of tokens, leading to practical applications in areas like image editing (Hertz et al.) [0022] Epstein et al. 2023), where object attributes (c.g., style, location, shape) are adjusted by manipulating attention maps. Layout guidance methods (Cao et at. 2023 Kim et al. 2023b Tuuanyya et al.) 2023) use structural information encoded in attention maps refine image generation layouts, while other approaches (Shi et al.) 2024) Tewel et al.) 2024 ensure subject consistency across multiple images by sharing transferring attention maps.
Semantic correspondence techniques (Zhang et al., 2024 Hedlin et al. 2024) further align cross- attention maps with image content. DAAM Tang et al. (2022) has been instrumental in interpreting cross-attention in text-to-image (T2I) models, revealing the insights of contiioondt interactions. However, these approaches primarily focus single-directional token attribution, limiting their applicability to image-to-image (12I) tasks.
3 PRELIMINARIES
Diffusion models. Diffusion models Sohl-Dickstenn et al. (2015); Ho al. (2020); Song et al. (202)) are probabilistic generative models that progressively denoise a Gaussian noise sample e ~ NO, 1) to generate data. Starting with a sample Xu from unknown distribution q(xo), the goal is to train a parametric model (xo) to approximate ((x).. These models can be viewed as a sequence of equally weighted denoising autoencoders (x(x,, l). which operate over series of time steps / = 1... T. Each step involves predicting a cleaner version of the noisy input x,, derived from the original input Xp- The training objective is expressed as:
LDM ()) = Ex,e, ||ee Ex(x,, 0)
Latent Diffusion Models (LDMs) Rombach et al. (2022) function similarly but operate in a com- pressed latent space rather than in the data space. The encoder & maps the input to latent code z {x), where noise is added in the latent space to produce z, := axE(x) + o,e. image-to- image tasks, the LDM model e ( f, ci) is trained to denoise z, using modified objective:
LLDM (, Φ) Ez,er.e. [||e co 11
where c₁ = a is a conditioning vector derived from a so-called reference image I, i.e., input image, via the image encoder o. During training, both eg and & are jointly optimized minimize the LDM loss in (2). yielding a reverse process that gradually denoises the noise to generate the final output x, referred to as the generated image.
METHODOLOGY: I'AM
In this work, we introduce Image-to-Image Attribution Maps, which we call A'A a method aimed interpreting latent diffusion models using cross-attention maps. Attribution mapping is powerful tool for analyzing the relationship between specific parts of an image or text (e.g.. patches tokens) and output features. Unlike text-to-image models, which rely on token-based representa- tions, amage-to-image generation allows for bidirectional analysis visualizing two distinct attribu- tion maps corresponding two research questions Q1 and Q2. where embeddings of all patches from both the reference and the generated images are required. This level of detailed attribution is challenging for T2I models due to the abstract nature of text tokens. In 12I LDMs, reference and generated images act as both queries and keys in the cross- attennttnn mechanism, allowing us to map the flow of information between the reference and generated images.
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SLableVITON
DCIVTONN
PASD
Impon& Ourppt
Lager
Layer
Loyer
Layers
Lagerr
Layer
Layer
(a) Inpainting task
(b) Super-resolution task
Figure 2: Visualization of layer-level attribution maps (LLAM) for each task. (a) LLAMs for StableVITON and DCI-VTON models at layers and 8 demonstrate how clothing features are progressively incorporated during inpainting process. (b) LLAMS for PASD model show the contribution reference data refining image resolution at different layers.
Overview A'M The AM method en- ables the visualization of bidirectional attribu- tion maps by dividing reference and generated images into smaller patches and amalyzing their interactions across three key axes: diffusion time steps, attention heads, and layers. This ap- proach can be applied to various tasks such as segmentation, style transfer, colorization, and depth estimation. By examining each compo- nent time, head, and layer- individually, we gain deeper insights into the model's behavior and its task-specific details. For example, Fig. demonstrates how layer-level analysis aggre- gates information across time steps and atten- tion heads, revealing broader patterns and atten- tion flows across layers different tasks. In Fig. 2a attention scores gradually transit from coarse to fine-grained features as layers deepen, starting with basic color information and even- tually capturing intricate details like logos and buttons. Conversely, in Fig. 2h) since LQ (low- quality) input signals provided with the initial noise, the early layers seem to interpret the input as already containing basic information, leading them to pay less attention to coarse features. Further details are provided in the Appendix A.7 This analysis applies generated and reference images, where maps for generated images are denoted by the subscript g. and those for reference images by
Image embbedding
c///11
Residual Block
Self Attention
Cross Attention
(a) U-Net layer at time and layer
Encoder
(b) U-Net encoder
(c) Image encoder
Figure (a) U-Net layer time f and layer where the image embeddings are supplied to the cross- attention. (b) U-Net encoder providing multi-scale im- embeddings (1), and (c) image encoder supplying fixed-size embeddings сı the cross attenttttnnionn module.
4.1 BASIC FUNCTIONS
In this subsection, we describe the core operations involved in our A2AM method.
Attention score. At the core of our approach is the computation of attention score, which quantifies the interaction between different regions of the reference and generated images. The attention score computed as follows:
QK ddk
Attn Attn_Score Q, K) softmax
where Q is the query matrix, K is the key matrix, and de is the dimension of the key vectors. In our model, both the query and key matrices operate across several axes: time steps attention heads n,
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and layers 1. To capture how the model processes information at different levels within the cross- attention module, we define the attribution map at a specific time step t. attention head n, and layer (1) I i, which encapsulates the relationships between the reference and generated images.
Summation operation. aggregate information across these axes, we introduce a summation operation allowing aggregation over any subset of the axes (l, (]. The summation is defined as:
(1) Sum a
Σ A
( 2
where S. (t. n,1} represents the axes over which the summation is performed. This operation enables us to analyze the attention behavior across time steps, heads, and layers, providing a com- prehensive view of how different patches of reference or generated images contribute to one another.
Threshold-based transformation. After the summation, apply threshold-based transforma- tion to refine the attention maps. The attention map m normalized the range [0, 1]. and a threshold 5 is applied to filter out lower values. The final attention map m is computed as:
T( 8),
(5)
where II(-) is an indicator function that retains values greater than the threshold. This operation ensures that the most relevant attention regions are highlighted while filtering out insignificant areas. The threshold 8 is typically set to 0.4 to balance visibility and relevance.
4.2 UNIFIED-LEVER ATTRIBUTION MAPS
Bidirectional attention scores. In approach, bidirectional attention scores across time steps, attention heads, and layers form the basis of our operations. As shown in Fig. Bal the LDM with an
L-layered cross-attention module derives the pre-cross-attention vectors { JJ 1 at each time step t and layer I. Image embeddings of the reference image I are obtained from various image encoder
T which can be multi-scale feature vectors (!) from U-Net encoders (as in Fig. 3b) or fixed-size embeddings 0₁ from CLIP or DINOv2 (as Fig. [ For simplicity, we use notations of fixed- size embedding Cr. We adopt multi-head cross-attention mechanism with = 1, .,..., N heads. Bidirectional attention scores quantify the interactions between the reference and generated images
in two directions. The vectors (f(")) and с₁ are conditioned to each other through multi-head cross-attention mechanism.
Refereeneetoo-Generatee (R2G) attention score M Mg... This score captures the influence of the query Q (reference patch сτ) on the key K (generated patch r!") in the key vectors (gen- erated image). This corresponds to Q1, measuring how each reference patch influences the generated image.
-nteraated-t--RRfeeeeee Reference (G2R) attention score M, This score shows how the query (gen- erated patch) corresponds to the key (reference patch) in the key vectors (reference image). This corresponds Q2, measuring how the generated image relates back to the reference image.
Generated-
The attention scores are computed as: (1) g.A.n Attn.Score (W k.n Cr, ((() (())) and T, Attn SionSeereWll (() (C g W. CT), (6)
where (-(9) 9.n and (!) k.n are projection matrices for queries and keys, respectively. While the size of the attention scores may vary across layers, for consistency in aggregation, we resize attention scores to a common size of (HW, HW), where H/ and W denote the height and width of the original image Xy. These bidirectional attention maps allow us to analyze the transfer of information between the reference and generated images from both perspectives.
Unified-level attribution map (ULAM). To obtain a holistic representation of the attention patterns, introduce the unified-level attribution map (ULAM), which aggregates the attention scores in (6) across (t, n, l}. Before applying the summation, we first perform a column-wiss averaging of the
attention scores (!)
g and (!)
1. reducing their dimensions from (HW, HW) to (HW). This
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step ensures that the attention maps more compact and interpretable while retaining the key relationships. The ULAM for and G2R, denoted as Mg and M,, are computed as:
Mg (t..). (M g.t.n):
M, (u.,r, (M (t,n):
where Mg...n M.. are the column-wise averaged scores, and the summation is applied over all time steps. heads, and layers. This aggregated ULAM captures the overall contribution of different regions of the reference image to the generated image, and vice versa, measuring relevance loss by examining the alignment of concentrated attention score distributions. After summation, these maps are reshaped to the spatial dimensions (H, W) for visualization, and threshold-basee transforma- tion (5) is applied to filter out lower values, ensuring that the final maps focus on the most critical attention regions.
4.3 LAVER/HEAD/TIME-BEE
ATTRIBUTION MAPS
To fully understand the behavior of diffusion models, it is important to break down the model's core components time steps, attention heads, and layers. To this end, we introduce three types of attribution maps that offer insights into the role of each of these components in the image generation process. For detailed formulas, please see Appendix A.3
Time-level attribution map (TLAM). The time-level attribution map (TLAM) visualizes the gener- ation process over time. The TLAM for a given time group T for R2G and G2R directions, denoted as Mg.r and M,r, is obtained by summing (t) g.t.n and M f, over heads and layers for a specific time window [r, (T+1)) 1)A(]. This results in T group = T/A/ time-level maps. Leveraging the final
TLAMs, being applied reshaping and threshold-baat based transformation, {Mg.r. M Teme we can visualize the gradual formation of the generated image over time.
Head-level attribution map (HLAM). The head-level attribution map (HLAM) reveals the con- tributions of different attention heads. For R2G and G2R directions, the HLAM is computed by summing (1) g.t.r. and M across time steps and layers, denoted as (Mg. M.,. m==1- After sum- mation and reshaping, we apply threshold-basee transformation to obtain the final HLAM. Each head captures different information, enhancing contextual understanding. The model generates se- mantically aligned outcomes, emphasizing this effect remains unaffected even when integrating the time and layer axes.
Layer-level attribution map (LLAM). The layer-level attribution map (LLAM) highlights how each layer processes and transforms the input features as they propagate through the network. The LLAMs for R2G direction, denoted as {M,) )/E,, with L being the number of layers, obtained via summation of (() (1) g. and f.t.n over time steps and heads. The layer-level maps offer better indicator of model performance across different layers and help uncover areas for potential im- provement. On the other hand, amalyzing the LLAMs for G2R direction M,") often proves less informative. This is because tends to distribute attention more uniformly across the reference image in most layers, mixing irrelevant components (e.g., background and unmasked regions) with relevant information.
Specific-reference attribution map (SRAM). Given the challenges associated with interpret- ing the G2R layer-level attribution map, we propose a more targeted solution: the specific- reference attribution map (SRAM). SRAM shows the regions of the reference image that contribute to the generated patch. While the layer-level map M captures the broad inter- actions from the column-wise averaged scores, SRAM refines this by selecting the relevant i- th row from, the fundamental attention score MD f. in (6) that corresponds to a specific patch in the generated image. By summing across time steps and attention heads, we con- struct the SRAM (M 1L which highlights
RW
image
imnge
Falieancc
Falirence
Sum
Figure 4: Overview of SRAM, showing how attention scores from patch embeddings of the reference im- age (clothing) are calculated to analyze correlation with a specific generated patch i. The red point on the cloth- ing indicates the reference patch with the highest influ- ence on the generated image.

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how each layer of the model reflects information from the reference image during the generation of a particular (i-th) patch in the output image. See Fig. 1 and for illustrative examples.
SRAM provides more concrete positional mapping details between the generated and reference images, making it a valuable feedback signal for model training. For instance, in object inpainting tasks, SRAM guides the model in focusing on the relevant object parts without considering irrelevant background information from the reference image. In super-resolution tasks, it helps improve the sharpness and structural consistency of the generated image by leveraging spatial correspondences between the reference and generated images. Leveraging insights from SRAM can guide the model's learning process by enhancing the roles of specific layers. For example, in layers responsible for capturing important features, training can be improved by strengthening attention mechanisms on patches that are crucial for accurate generation. This focus on critical layers and patches can be used to introduce useful priors into the model, as further explored in Sec. 5.4
4.4 INPAINTING MASK ATTENTION CONSISTENCY SCORE
We introduce the Inpainting Mask Attention Consistency Score (IMACS), a metric for evaluating reference-based inpainting tasks in image-to-image latent diffusion models. IMACS measures the alignment between attention maps of generated and reference images (Mg and M,) with their re- spective masks (xg and x,). For example, in virtual try-on test, the inpainting masks for generated or reference images represent, for example, clothing- mask and cloth mask, respectively. This metric quantifies the consistency between the bidirectional attention maps and the respective painting/reference masks, evaluating the effectiveness of information transfer between two images. The score for the R2G attention map, denoted as IMACSg. is computed as:
IMACS ΣnWw (Mg O xg)
X EnW (Mg O (1-xg))
ΣH.w Xg
Σn.w Xg)
where À is a penalty factor (default 3), penalizing misaligned attention. The score for the G2R atten- tion map, denoted as IMACS,, is defined similarly. The higher values of IMACS indicate better alignment between the attention maps and the corresponding masks, and thus, superior performance as XAI metric. Increasing À imposes stronger penalties attention deviating from mask regions, helping to detect issues such as overfocusing on irrelevant areas or neglecting critical regions.
5 EXPERIMENTAL RESULTS
We conducted extensive experiments using AM across multiple tasks, including object detection, inpainting. and super-resolution to evaluate its effectiveness in interpreting mage-to-imaage LDMs. These experiments demonstrate the ability of 2 AM to enhance interpretubility and reveal underlying model behavior across tasks. Experiment details are in Appendix A.4 and A..5 The ablation study of 8 and X for clear map visualization and IMACS consistency is provided in Appendix A.6
5.1 ОВJЕCТ DETECTION
In this experiment, we evaluate AM's capability in object detection using images generated by the Paint-by-Example (PBE) model the COCO Lin al. (2014) dataset. The goal is to assess how effectively 12 A'M captures and visualizes critical object features in both reference and generated im- ages, even in unseen scenarios. We utilize the unified-level R2G attribution map Mg to analyze how I AM retrieves key object regions responsible for object formation and object boundaries obtained by PBE, particularly in complex scenes with multiple objects (Fig. 5).
The comparison with baseline object detection models is shown in Tab. The metric mloU>0 computes the mean Intersection over Union for COCO images where the baseline category score exceeds 0.5, evaluating the baseline's object detection ability on real images. For PBE-generated images, we use mloU 20.5 to assess detection performance. While there is a performance drop for generated images compared to ground truth, the PBE- 18E-generated images still capture key object fea- tures from the reference image, and furthermore, the baselines such YOLOv3, trained on COCO data, successfully reveal regions critical for object formation. To assess performance in unseen sce- narios, we use three baselines: DAAM Tang al. 2002)) 'overall', and 'randomjo~300" DAAM
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Method
Supervised manner Seen
pizza 140, 81, 271,
Haster-RCNN Ren al. 6
0.3225
0.2658
Mask-RCNN He et al 2017]
0.3294
0.2706
YOLOv3 Redmton 2018
0,2448
0.1978
MaskHormar Cheng
2022
0,3568
0.2932
RTMDet Lyd ef al. 2014
0.3228
0.2516
Lnsupervised munner & Unseen datuset
DAAM Tang ct 2022
0.1507
Overall
0.1807
Randompo-907
0.2028
Ours
0.2416
Object detection pipeline using PBE and A'M.. ULAM for R2G direction highlights key regions for object detection in the generated image.
Figure 5:
Table 1: Performance comparison of object detec- tion methods, with significant results highlighted in yellow
is an attention- based T2I method that applies softmax differently from AAM, producing 'overall' results in PBE. See the Appendix A.7 for details. The 'overall' assumes bounding boxes cover the entire image, while the 1o-dommo-30%% compares results with randomly generated bounding boxes covering 10 ~ 30% of the image. Although COCO a seen dataset for existing baseline methods and unseen for PBE and our method, I2 AM achieves competitive mIOU of 0.2416 and even out- performs YOLOv3 in certain cases. This highlights 12 AM'' ability to extract relevant knowledge from reference images, even in challenging environments with generated content.
5.2
IMAGE INPAINTING
We evaluate 12 PA''s interpretability in image inpainting tasks using models like PBE Yang et al.
(2023a). DCI-VTON Gou et al. (2023). and StableVITON Kim et al. (2023a). Image inpainting involves filling masked regions of an image by referencing another image, making cross-attention maps essential for understanding how the reference image influences the inpainting process.
DENNTON
DENTON
Generoted Image
Unihed--evee
Head-LLevel Attribution Stap
(a) Tive-Level Attribution Map Mg.
(b) Head- Attribution Map Mg...
Figure 6: Autrbuttoo maps for R2G. (a) TLAM Mg.- visualizes changes in attention patterns over time from t = I' to / 1, highlighting where the model focuses each step. (b) HLAM Mg.. shows the contributions of the attention heads, comparing how each head focuses on specific regions of the image.
To address Q1, we first analyze the R2G map, focusing the areas where the reference image contributes most to the filled-in regions in the generated output. The time-level attribution map (TLAM) Mg.., illustrated in Fig. 6al, shows how the model progressively refines object structure over time, assigning higher attention to crucial details like facial features and clothing logos. As the inpainting process unfolds, the model transitions from low-frequency to high-frequency features. The head-level map (HLAM) Mg.., displayed in Fig. ob reveals variations across attention heads, with some attention extending beyond the masked area. Integrating information beyond the masked region may result in the loss of certain features but also improves contextual understanding, enabling semantically consistent outputs. Since HI.AM was expected to capture diverse information, this positive outcome. Fig, 6 demonstrates the capacity of the LDM to recognize and integrate key patterns throughout the inpainting process.
behavior is
Next, we extend this analysis in Fig. for StableVITON model, using bidirectional attribution maps. The time-level G2R maps M,,. shown on the left, indicate minimal changes over time, while the head-level G2R maps M,,, highlight diversity across heads, with common focus on logos. In contrast, the R2G maps in the middle panel show how details such as clothing patterns and textures progressively transferred from the reference to the generated image, as reflected in Mg. Finally,
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Clothing
Lovel Level Actribucion Map
Agnostic map
Head-Level Attribution Map
Generated image
SRAM
Timeel mndd AST Buation Ming
7
Iy Level Aww.ratttioo Hop
Q2:G2R
QRR2G 1..
Figure 7: Generation flow of VtabIeViTON through F'A..
The traits derived from G2R maps are shown
(left), and the usage is displayed in R2G maps (middle). (right) We visualize M(5) sr,3 capturing key blending features that link basic properties with finer details.
ir shown on the right, provides positional mapping at layer 5, indicating how clothing colors and pattems from the reference image are integrated into the final output. Additional results are provided in the Appendix for further details.
the SRAM ((
5.3 REFERRRRSOLUIONN
In this section, we evaluate the PASD Yang et al. (2023b) model for super-resolution tasks, where the goal is to enhance the resolution of low-resolution image by progressively refining it. Unlike inpainting, which reconstructs specific masked regions, super-resolution aims to improve the overall image resolution while preserving key details from the input. First, Fig. provides insights into both
Q1 and Q2 using bidirectional (R2G and G2R) maps. The HLAMS (Ma.n, Mr,.) reveal that dif- ferent heads handle distinct regions of text descriptions from the reference image but work together to enhance fine details in the final output. The TLAMs (Mg.r, Mr,r) show that the PASD model consistently focuses critical image features, e.g., text descriptions, throughout the process, main- taining stable attention across time steps. The consistent attention distribution across time and heads highlights the model's reliance on detailed information from the low-resolution input to improve the high-resolution image.
Fig. 9 exxends this analysis to a layer-level view using the SRAMs (M!)). illustrating how patches at similar positions across different layers contribute to super-resolution. Layers 2 to 7 show precise attention alignment, indicating the model's effectiivnees in preserving critical image features from the low-resolution input at appropriate locations. However, attention scores in higher layers (e.g.. layers 9 and 11) display slight misalignments. This suggests that while the lower layers focus on refining key structural features and capturing accurate positional information, the higher layers may struggle with processing more abstract and challenging patterns. As also shown in Fig. 2 each layer performs distinct function, with some layers prioritizing fine details while others focus on higher- level semantics. We note that similar results are observed in another super-resolutin model, SeeSR (see Appendix(A.7
Q2-02k
Heod BIE Art butionttinon Map
Heae Levt Abribattonn Map
Figure 8: Improvement process of super-resolution, showing how content from the G2R map is meaningfully incorporated into the R2G map. Progressive retinement of details from low to high resolution is visualized through head- and time-level maps.
Figure 9: Gradual change in attention within SRAM M.) at corresponding positions in multiple layers. Lower layers achieve intuitive position alignment, while higher layers manage more complex contexts. Zoom in to check for position alignment.

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Method
FID L
KID 1
LPIPS 1
SSIM
IMACS, 1
IMACS, 1
DCI-VTON Gou et al. 2023
13.0953
0.0334
0.0824
0.8612
0.0785
StableVITON et al. 2023a
10.6755
0.0064
0.0817
0.8634
0.3083
0.3388
Custom
11.6572
0.0042
0,1020
0.8396
0.0833
0.0403
Refined custom
11.5420
0.0022
0.0964
0.8644
0.2215
0.0948
Quantitative comparison on VITON-HD datasct. Evaluation metries include FID, KID, I.PPS, and SSIM for downstream tasks, alongside our proposed XAI evaluation metric, IMACS (with = 0.4, 3). Bold and underline indicate the best and second-best result, respectively.
Table 2:
5.4 APPLICATIONS: IMACS AND MODEL DEBUGGING
This section explores the utility of our proposed metrics g for model debugging and per- formance improvement. IMACS, alongside traditional metries like FID, KID, LPIPS, and SSIM, provides deeper insights into attention alignment between reference and generated images.
Our analysis highlights variations in attention dispersion within LDMs, particularly in ULAMS (Mg.MA M,) during inpainting tasks, where mask alignment is critical. For example, DCI-VTON exhibited scattered attention scores, leading to color discrepancies, information loss from the ref- erence, and unnecessary patterns (see Fig. 66. degrading the model's consistency with the refer- ence image. In contrast, StableVITON showed better alignment, producing more accurate and consistent results. This improved alignment was reflected in higher IMACS scores (Tab. D. which directly correlated with other metrics. StableVITON achieved the highest aligmment,
followed by the refined custom model, the original custom model, which will be discussed below, and DCI-VTON. These results demonstrate clear re- lationship between higher IMACS scores and bet- ter task performance, confirming the metric's ef- fectiveness in evaluating model accuracy.
Clothing
Custom model
Refined Custiom model
We also used IMACS for model debugging. Our experiments confirmed that each layer plays dis- tinct roles in inpainting (Fig- [. As illustrated in Fig. 7 specific layers in well-trained models like StableVITON effectively map spatial information. In our custom model, detailed in Appendix A.5 layer 2 exhibited similar capabilities but suffered from score dispersion and geometric misalignment
SRAM M To address this, we applied loss functions aimed at i) densifying attention distribu- tions and ii) improving semantic alignment. As shown in Fig. [T0] the refined custom model cor- rected color inconsistencies and enhanced atten- tion accuracy, resulting in better overall perfor- mance, as also observed Tab. 2
Specitic Reference Attiiuutonn Mup
Figure 10: Comparative analysis of the custom model before and after improvements based SRAM dehugging. The orange hox highlights color confusion the initial model, while the black box shows improved color consistency in the refined model. The SRAM illustrates how debugging im- proved performance.
CONCLUSION
We propose the Image-to-Image Attribution Maps (12 AM), a method for bi-directional analysis of how 12I models transfer visual information between input and output domains. By aggregating cross-attention maps across time steps, attention heads, and layers, i'A generates two attribution maps: capturing the influence of the reference image the generated image, and another showing how the generated image relates back to the reference. Our experiments across object detection, inpainting, and super-resolution tasks demonstrate that I AM effectively enhances model interpretability and captures critical attribution patterns. Moreover, I2AM provides valuable insights for model debugging and refinement. While primarily tested in paired settings with reference-basee tasks, preliminary results indicate its potential for unpaired scenarios. Future work aims to extend 1 AM to broader applications, such as colorization and style transfer, further expand its utility.
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ACKNOWLEDGMENTS
This research was supported by the MiIttMinistry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(I1PP-2225-2000 00005-202000-018899 and Artificial Intelligence Convergence Innovation Human Resources Development(ITP-2225444 2023-00254592) supervised by the IITP(Instiuuee for Information & Communications Technology Planning & Evaluation).
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A
APPENDIX
A.1
RELATED WORK
Image inpainting with text-to-image diffusion models. Beyond traditional image inpainting tech- niques that employ noisy backgrounds outside mask regions, text-to-image diffusion models such as GLIDE Nichol et al.) (202I) integrate masked images with text prompts. This innovation enhances the diffusion process, using information from outside the mask to improve both contextual relevance and visual consistency. Similarly, Blended diffusion Avrahami et al. (2022) utilizes CLIP scores to align generated images with text prompts, and SmartBrush Xie al. (2023) is proposed to predict object shapes within a masked area, thus preserving the integrity of the surrounding background.
Image-to-image diffusion-basee image inpainting. Image-to-image diffusion models for inpaint- ing are less common than text-to-iman -iage models. Notable examples include Palette (aharria et al. (222a)) and Seint-by-Example (PBE) Yang et al. (2023a). Palette serves general framework for image-to-image translation, yielding accurate results for inpainting but cannot be visualized with our proposed method due its concatenated image input. To reduce self-referencing in image genera- tion. PBE uses strong augmentation and only the CLS token from the CLIP image encoder Radford al. (2021) to focus on relevant objects while ignoring background noise. Since PBE provides im- age embeddings with rross-attention, our method can be applied. Additionally, StableVrTON Kim jet al. (2023a) and DCI-VTON Gou al. (2023) excel the specialized task of virtually dressing clothes and can also utilize our methodology by integrating ControlNet/Zhang et al. (2023) structures and warping networks Ge al. 20011
A.2 PRELIMINARIES
Image inpainting. This task involves controlling image editing using semantic masks. While tra- ditional image inpainting Lugmayr et al. (2022) focused solely on filling masked areas, recent ap- proaches, like
image inpainting Xie et al. 2003); Nichol al. (202T); Avrahami et al. (0022;; Couairon et al.] (2022); Yu et al. (203)) use guidance such as text or segmentation maps to fill masked regions. The main focus this paper, VITON, is a type image inpainting where clothes are virtually worn on person. The unique aspect is that while maintaining the pose, body shape, and identity of the person, the clothing product must seamlessly deform to the desired cloth- ing area. Additionally, preserving the details of the clothing product is requirement.
Classfier-free guidance. Classifier-free guidance Ho Salimans (022) (CFG) is a method for trading off the quality and diversity of samples generated by diffusion models. It is commonly used in text, class, and image-conditioned image generation to enhance the visual quality of generated images and create sampled images that better match the conditions. CFG effectively shifts probabil- ity towards data achieving high likelihood for the condition с₁. Training for unconditional denoising involves setting the condition is set to s 2 1, extrapolating the modified score estimate towards the conditional output E, while moving away from the unconditional output eve direction.
null value at regular intervals. At inference time, the guide scale
è Euc s(c: Cuc),
(9)
WFCFG
WPO CFG
CFC
W/D CFG
Agnosiicmap Tnyut Imagee
Uniied-LLvel Aitribution Map
Unifedkteval Altribttion Map
Figure 11: R2G visualization results with and without CFG, using PBE. The dispersion of attention scores exceeded the inpainting mask's 's range when CFG was not used.
The diffuusion model relies on Classifier free Guidance (CFG) technology, as shown in Fig. II depicting the time-and-head integrated attribution map based the presence of CFG. CPG assigns
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higher likelihood the reference image, shifting the output accordingly. This shifted output is repeatedly utilized as imput for the model, resulting in shift in the distribution of the attribution map. With the application of CFG, the model better reflects the reference image, facilitating the synthesis of images in appropriate regions.
A.3 LAVER/HEAD/TIME- LEVEL ATTRIBUTION MAPS
This subsection presents the derivation the omitted equations and additional samples.
Time-level attribution map. TLAM for each group {Mg. M - E R(H,W) provide how the model gradually constructs the image by monitoring its generation process over time.
HW Σ
HW
! g.t.m
M
M
Σ
M [..
(10)
HW
HW
Mr, (n, (M
Mg. -am
(11)
(++1)
(r+1)-Ar
Mg.
Σ
Mg
Mr.
Σ
M
(12)
Head-level attribution map. HLAM (Mg. M each attention head to different regions of the generated image. varied distribution of heads suggests effective detection and emphasis on multiple features, with the primary object consistently scoring high.
1.V
R((W)) reveals the contributions of
M Mg.n (r.)) (M g.tn),
M,n (um (M (t.
(13)
Layer-level attribution map. LLAM (M,) (H,W) provides insights into how each layer captures, transforms, and interacts with features, highlighting their impact the final generated image while revealing potential areas for improvement.
M i1 (um (M g...).)
(14)
Specific- Reference Attribution Map. SRAM (M 1L
sr. E (H, identifies which areas of the reference image specific patch of the generated image examines, utilizing (1()) matrix with dimensions (HW, HW). Sum (M()
fft. represented as
M Sr,
(15)
Lee
Lee
Lee
Generated Image & Condition
Specifii-Reference Attribution Map
Figure 12: Specific-Reference Reference Attribution Map (() visualization. The information from the refer- ence image that the small red box during synthesis referenced is indicated by the red arrows.
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Low-resolution High-resolution
Unified-Level
t=
TimeLovel Attibcaioon Map
Head-Level Attribution Map
Figure 13: Attribution maps for generated image (R2G) in super-resolution.
A.4 IMPLEMENTATION DETAILS
Datasets. Paint-by-Example (PBE) was trained on the OpenImages Kuznetsova et al. 20220) con- sists of 16 million bounding boxes for 600 object classes across 1.9 million images.
and DCI-VTON were trained on VITON-HD Choi et al. (2021). It is dataset for high-resolution (i.e., 1024 x 768) virtual try-on of clothing items. Specifically, consists of 13, 679 frontal-view woman and top clothing image pairs are further split into 11, 62,/2, 032 training/testing pairs.
Evaluation. We evaluated DCI-VTON, StableVITON, and custom model using IMACS. To demonstrate the consistency of IMACS with downstream tasks, we additionally employ evaluation metries from the VITON task: FID, KID, SSIM, and LPIPS. Specifically, we use paired settings, where person images wearing reference clothing are available, and unpaired settings, where person images wearing reference clothing are not available. In paired settings, we apply SSIM and LPIPS to measure the similarity between the two images, while in unpaired settings, we use FID and KID to measure the statistical similarity between real and generated images. Lower scores for FID, KID, and LPIPS, and higher scores for SSIM and IMACS, indicate better performance. We conduct all evaluations on images of size 512 x 384 and all figures are visualized using A2AM introduced in Section 4
Existing models. We studied the three inpainting models: Paint-by-Exampee (PBE) Yang et al. 2023a), StableVITON Kim et al. (2023a). and DCI-VTON Gou al. (023). While PBE and
DCI-VTON follow the architecture in Fig. 3el StabOe follows Fig. 3b Unlike PBE and
DCI-VTON, which rely on the CLS token, StableVITON utilizes all patch embeddings from the reference image. allowing for bidirectional visualization. Similarly, the super-resolution models PASD Yang al. (2023b) and SeeSR Wu et al. (2024) adopt the structure Fig. 3b and enable bidirectional visualization through attennttt on all patch embeddings. The number of cross- attention layers (L) is determined by the decoder blocks, as interactions between input and reference data are primarily learned during the decoding stage. PBE, VtableVITON, DCI-VTON, SeeSR, and our custom model have cross-attention layers, while PASD has 15. Each model used T 50 and T Trroup
5 with DDIM Song et al. (2022) sampler, except for PASD, which had T 20 and T Tgroup = 4. Most models used 8 attention heads, while SeeSR had varying attention heads: 20 for resolution 16, 10 for resolution 32, and for resolution 64. CFG scales of 5, 5, 1.5, 5 and 9, respectively. We visualized the reverse SRAM (( for layer in StableVITON. Pre-trained models were obtained from their respective GitHub repositories. However, due to limited samples for IMACS, measurement, demonstrating its consistency with downstream task performance was challenging. This led to the development of custom model utilizing all patch embeddings from reference images, extending the approach of StableVITON and PASD to other image-to-image latent diffusion models.
A.5 CUSTOM MODEL
The custom model, based on Stable Diffusion v1.5, replaces the CLIP text encoder with a large image encoder that uses all patch embeddings. It follows the architecture Fig, 3e and was fine- tuned on the VITON-HD dataset. The model takes person image E (..).)) a clothing- agnostic person representation x, ((H,W,)) a dense pose Xa (H,,3), and clothing image I E TR WW3), filling the agnostic map x, with the clothing image (reference image) I. The input to
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Tharet Moki
2
C2
5D Encoder R
Decoder
Na.
Figure 14: An overview of custom model. For VITON task, the model takes three inputs: the noisy image, agnostic map, and dense pose. Image embeddings serve as the key and value for the cross-attention.
U-Net expands the initial convolution layer to 12 channels 44+4+4= = 12), initialized with zero weights. All components except and I pass through the encoder E. The custom model overview is shown in Fig. [4 The model was trained for 90 epochs with a batch size of 64. DDIM was used for 50 steps with T group 5, and CFG scale (s) of 5. The custom model has 8 attention heads (N 8) and 9 cosss-attention layers (L = 9), with SRAM visualizations focusing on layer 2.
We observed that similar to StableVITON in layer 2, spatial mapping occurs between reference patches and generated patches. However, we identified misalignment in the SRAM's attention scores for each patch and incorporated this into the loss function for training. The goal is improve color consistency by accurately matching the semantic correspondence of each patch. The proposed loss function consists of three components: LDCML Lrv. and Lewg. Given the standard attention map w²) (where the query is the generated image and the key the reference image),
A R 2D coordinate map TR(N w' 22), and ground truth warped clothing mask W E {0, 1) the loss functions are computed as follows. Note that these loss functions are applied exclusively to the warped clothing region.
Weighted Center Coordinate Map: Calculate the weighted center coordinates C
(H' W' :2) by combining the attention map and coordinate map at each position:
Cija
(Aijut Dkin);
(16)
Pistance-Centeriy Maximization Loss (Cocwn.): C, adjusts the coordinate x by pushing the centers the right within the same row, while Ly adjusts y by pushing the centers below within the same column. This aligns the relative positions of patch centers, promoting semantic correspondence and spatial consistency:
H' W'-1
w
C.
Σ
Σ
Σ
max(O, Cijio Ci,,,)):
(17)
==1
#'-1
Egg
Σ
Σ
ma C.j. Ck.j.))
(18)
LOCMI L. + byy
(19)
Total Variation Loss (Cyv): Mitigates abrupt changes caused by LUCML, ensuring smooth transitions and maintaining appropriate spacing between patches:
Crv |
(20)
Center-weighted Gaussian Loss (Lowa): Encourage the attention scores for each gener- ated patch to focus on the center coordinates using Gaussian-based distance calculation.
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The standard deviation a is set 1:
A.j.a= = V (CCiso - -)2² + (Ci,,1 - 1)3,
Aesua E WRH..WWW W.
(21)
H' W
W w
A Liiht 20
LewG H'W'Ww'
exp
+ Aijab
(22)
These loss functions help learn the precise correspondence between patches and improve the consis- tency and quality of the generated image. LDCMI. aligns semantic correspondence between patches, LTv enhances spatial consistency, and LewG ensures the attention map focuses on the center coor- dinates, ultimately yielding more accurate and consistent results. Finally, the overall loss function combines these components along with the baseline latent diffusion model loss LLDM to optimize all aspects of the model's 's performance jointly:
L = LLDM + ADCML CDCML. + XTV + AewaLowa,
(23)
Where ÂDCMI. 0.01, ÀTv 0.0001, and Aewa 2, these values represent the strength of each loss term during training.
Overlaid Ma
Overlaid M,
Clothing
Genersted image
0,3412
0.1931
Figure 15: Example of refined custom model with the IMACS metric. Each attribution map shows high attention scores aligned with its mask region. The scores below the figures are IMACS values measured with À set to 3.
A.6 ABLATION STUDY
An ablation study was conducted to find the optimal threshold for filtering small values to improve 12 AM''s clarity and accuracy. Tab. shows the results of adjusting the threshold values while per- forming object detection with A' on PBE. A threshold 8 0.2 sets values below 0.2 zero in a [0, 1]-normalized map. with the highest mlou>0.5 gen achieved at the values of 0.4 (default) and 0.5. Additionally, we examined the effect of changing the IMACS penalty factor on the corre- lation with downstream task metrics, as shown Tab. 4 A larger resulted in a greater decrease in IMACS for models with significant information loss from the reference image, observed in DCI-VTON. Furthermore, we confirmed that the correlation remained stable even with changes in the penalty factor X.
A.7 ADDITIONAL RESULTS
Comparison with DAAM.
The primary difference between 12AM and T2I attention map visualization methods like
DAAM Dang et al. (2022) and prompt-to-prompl Hertz et al. (202)) lies in the direction of softmax
m50
Method
0.2
0.3
0.4
0.5
0.6
Ours
0.2413
0,2413
0.2416
0.2416
0.24
Table 3: Ablation study the threshold 8 the attention maps. The default 6 is 0.4
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3 (default)
5
10
Method
1MACS, T
IMACS,
IMACS,
IMACS,
IMACS,
IMACS,
IMACS
IMACS,
DCI-VTON
0.4291
0.0785
-0.3051
-1.2155
StableVITON
0,5376
0.3534
0.3083
0.3388
0.07
0.3211
-0.1219
0.301
Custom
0.2352
0.0677
0.0833
0.0403
0.1901
0.0091
-0.4579
-0.0527
Refined custom 0.4675
0.1249
0.2215
0.0948
0.029
0.0513
00.3105
0.0112
Table 4: Ablation study of the penalty factor À on the IMACS metric. As À increases, the punishment for attention scores outside the masked region intensifies. A larger À indicates that the model loses more informa- tion, the value drop becomes more significant. Bold and underline indicate the best and second-hest result, respectively.
application. T21 models allow visualization only of the R2G attribution map, which we compare with M g.t.n from equation 3 DAAM uses standard approach to calculate attention scores, treat- ing the generated image as the Query and the prompt as the Key. This approach separates tokens, highlighting those with the most influence on the generated patches, while automatically reducing the values of less important tokens.
contrast, the contextual continuity of the reference image makes it impossible to separate, so we focus on how it influences the generated patches whole. We treat the reference image as a single token and reverse the softmax direction, using it as the Query in equation B This allows to understand its influence the generated patches holistically. However, this approach does not identify which specific reference patches contribute information.
To assess the relevance of the reference information, visualize the G2R attribution map by treat- ing the generated image as a single token. G2R provides a similar analysis to T21 by comparing reference patches and complements R2G. However, if irrelevant areas dominate the generated im-
age, distortions may arise in G2R. To address this, we propose SRAM M() gr. which identifies which generated patches extract meaningful information from the reference. SRAM can be computed for all generated patches and aids in interpreting their significance.
Applying softmax in DAAM's direction 12I models cannot produce interpretable maps. Using DAAM's softmax on the generated image results in uniform attention values across pixels, as the row sums of the softmax are (see Fig. [6). Fig. [6a shows ULAM Mg computed with Sta- bleVITON using all patch embeddings. While attention scores should theoretically be identical, cumulative errors from resizing, normalization, and floating-point inaccuracies introduce discrep- ancies, resulting in a meaningless map. Fig. T6b shows ULAM Mg computed with only the CLS embedding, which eliminates these errors and assigns identical scores across all regions. This result matches the 'overall' row in Tab. [ for object detection tasks. In contrast, by reversing the softmax direction, our approach can visualize the R2G attribution map using only the CLS embedding (e.g.. PBE and DCI-VTON)
StableVITON
DCI-VTON
Input& Output
Cloching Agroodiee mup
FILA
(a) All patch embeddings
(b) Only CLSS embedding
Figure 16: Unified- Level Attribution Map Mg results using DAAM. (a) In the process of aggregating all patch embeddings from the reference image, emror accumulates, resulting in meaningless attention map. (b) Visualization using reference image's 's CLS embeddings. In the error-free case, all attention values are the same.
Comparison of PASD and SeeSR. This paper investigates whether the attribution map trends ob- served in the resolutio model PASD Yang et al. (2023b) are also present in the SR model SeeSR Wu et al. (2024). The SR models discussed here provide low-resolution signals during infer- ence. As mentioned in previous works (Choi et al., 2022 Lin et al., 2024). the noise schedule used in the representative latent diffusion model, Stable Diffusion, does not guarantee SNR (signal-
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to-noise ratio) of zero the final time step during training. leading to residual signals and train-test discrepancies. SeeSR and PASD combine low-quality (LQ) input with pure noise during inference to address the mismatch. Specifically, when independent Gaussian noises Zr and z are present, conventional inference starts the denoising process from zy, whereas SeeSR and PASD proceed as follows:
ZT = VOTZLR + VI - CT2T
SEeST
(24)
VāgăTZIR + vI - a, T
pasd
(25)
where T is the terminal time step, ZLR is the LQ latent, âT is the noise magnitude, and c, controls the residual signal ZLR- In PASD, ag is set to 0.1189. Therefore, both PASD and SeeSR, as ex- perimented with in this paper, receive low- owwffreuenccy information along with the input, allowing the initial layers to remain less influenced by coarse features, as shown in
While PASD introduces &a to reduce excessive interference from ZLO. SeeSR relies on higher volume of L. data. As shown in Fig. 17 our visualization method highlights the differences in gen- eration processes based on initial noise. Fig. 17 presents SeeSR results for the prompt "c ose-up, electronic, clean, high- high-reso resoluuioon ent numbers of attention heads depending on resolution (16 resolution: 20 heads, 32 resolution: 10 heads, 64 resolution: 5 heads), totaling 35 attention heads, with 8, 4, and 2 heads extracted from 16, 32, and 64 resolutions, respectively, for visualization. Additionally, SeeSR uses 50 time steps (Tgroup = 5) compared to PASD's 20 (Tgrop = 4), leading to longer generation times. The nar- rower attention score range in SeeSR likely reflects either distortion due to strong LQ signals or sufficient information being provided. HLAMS (Mg.n. M.,.) show that each attention head learns different information tailored to diverse parts of the input image. similar to PASD, and this trend is also observed in TLAM Mg.r. In TLAM Mr.,. differences in LQ input levels during inference impact visualization and lead to less consistent attention scores. This indicates that SeeSR's longer time steps and stronger LQ signals offer more information early in the process, with consistency decreasing over time. Similarly, as shown in Fig. [8] we observe that low involvement in coarse features of the early layers and positional alignment weakens from low-resolution to high-resolution layers through SRAM M()) s. following the same trend as PASD.
8k" on the same sample. SeeSR uses differ-
Head Airinutton
Figure 17: Visualization of SeeSR super-resolution results across 35 attention heads: 8 heads at 16 resolution, 4 heads at 32 resolution, and 2 heads at 64 resolution. The prompt used for this visualization is "close-up electronic, clean, high- regh-resolutt uin, 8k". HLAMS (Mg.., M.,) show each head learning and adapting to different parts of the input image. Mg.r aligns with PASD patterss, while M., displays less consistency in attention scores over time, likely due to SeeSR's longer time steps and stronger low-quality
signals. The prominent low low-quality signal also results in a narrower distribution of attention scores.
Insights into SRAM. SRAM provides mapping information between each patch of the generated image and the reference image, addressing distortions caused by unnecessary patches. In Fig. I ULAM Mg effectively uses reference image data, while ULAM M, struggles to extract critical de- tails, such as clothing logos, leading to potential detail loss. This distortion arises from the G2R attribution map, which treats the generated image as single token. SRAM, however, provides attribution maps for each patch (e.g.. the red box around the logo), confirming that semantic infor- mation is accurately extracted from the reference patches. Notably, the first row, the second column of the grid highlights the retrieval of logo details from the reference image, ensuring precise logo generation.
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Layer 2
Layer
Layer
Layer
Layer
LLAM
SRAM
Layer
Layer
Layer 7
Figure 18: LLAM M.): 1 Strong low-quality signal in initial noise results in lower attention score on coarse
features; SRAM M Lower layers exhibit geometric correspondence, but this trend diminishes as the reso-
88.5"
lution increases.
Heat Map
SRAM
Figure 19: SRAM M! shows the alignment hetween the specific generated patch and reference image, enabling verification of effective information transfer. This figure. enlarged view of Fig [ illustrates the concept. While the bottom map in the first column appears to miss critical details (e.g.. clothing logo), SRAM reveals that the generated patches in the box, including logo area, are correctly matched to regions of the reference image.
A.8 LIMITATION
We calculate the attention map by considering the characteristies of the diffusion model, segmenting it by time step f, attention heads n, and layer l. Based on this, we propose ULAM, which combines all axes, well as TLAM (segmented by time step), HLAM (segmented by attention head), and LLAM (segmented by layer). However, it is also possible to calculate the map by considering two simultancously, rather than just one. This can be achieved by modifying the aggregation in equations [1 [3] and [4] For example, as shown in Fig. 2 we can examine the roles of layers at different resolutions in the inpainting model, considering both the layer and other axes. The results are shown in Fig. 20
Fig. 20al shows the TLAM for each layer at different resolutions (16. 32, 64), where we observe minor changes in attention scores, but the result is similar to the original TLAM Mg. Fig. 20b presents the HIAMM for each layer different resolutions, highlighting the varying distribution of attention scores across heads for each layer. However, since we expected the HLAM to capture the extraction of information from diverse regions by each head, this characteristic is sufficiently confirmed through the original HIAM Mg.n. which integrates all layers.
We conducted experiments in a paired setting. Previous study Jia et al. (202T) has shown that in an unpaired setting, image-to-image translation leads to semantic flip to maintain the distribution of transformed images, often reversing the meaning of the input despite visually plausible outputs. Although we do not provide a deep analysis of the unpaired setting, the results of StableVITON in the unpaired setting, shown in Fig. 21 maintain similar consistency to the paired setting. This suggests the potential to extend 12 AM to unpaired setups. Finally, while intended to apply our method to tasks like colorization, depth estimation, and style transfer. due to the lack of models that cross moss-attention with reference images, we only present results for object detection, image
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inpainting, and super-resolution, Visualization of attribution maps for more tasks remains direction for future work.
StableViToN
TLAM
HLAM
Generated image
Lnftted
layer-TAM
16 layerr-
32 layer-TAM
32 layer-HHAAM
64 layer-TAM
64 Layer-HLAM
(a) TLAM Mg. by layers
(b) HLAM Mg., by layers
Figure 20: The R2G attribution map aggregated by layer resolution is shown. It consists of layers grouped into three sets with 16, 32, and resolutions. (a) Compared to TLAM, the attention scores differ detail but maintain the same overall pattern over time. (b) The HLAM shows of the 8 heads. Each layer has a different attention distribution, and even the same head index does not focus on the same perspective. This observation indicates that each head attends different regions, allowing the model to process diverse types of information effectively.
StableVITON
Generated image
Unified.Level
Head Level Attraution Map
T
Time-Level Attribution
Opecific-Reerencce Attribution Map
Figure 21: Visualization results of AAM in unpaired setting. The attribution map shows similar trends the paired setting, suggesting the model's robustness in the unpaired environment. ULAM Mg: A map reveals overall trend, scores evenly distributed within the masked region. HILAM Mg Each head exhibits rich expressiveness and considers global relationships, even in the unpaired setting. TLAM Mg.r: Detailed attention scores are consistently maintained. SRAM M Mapping information for generated patches works correctly in the unpaired setting.
23


Reviews:
[{"review_id": "g1sp3vv8PU", "reviewer": "Reviewer_HMWz", "summary": "This paper introduces a method called Image-to-Image Attribution Maps (I2AM) that enhances the interpretability of image-to-image (I2I) diffusion models by visualizing bidirectional attribution maps between the reference and generated images. I2AM aggregates cross-attention scores across time steps, attention heads, and layers, providing insights into feature transfers between images. It has been demonstrated to be effective in tasks like object detection, inpainting, and super-resolution, successfully identifying key regions for output generation even in complex scenes. Additionally, the paper introduces the Inpainting Mask Attention Consistency Score (IMACS), a new metric that assesses the alignment between attribution maps and inpainting masks, correlating strongly with existing performance metrics. Extensive experiments show that I2AM aids in model debugging and refinement, offering practical tools to improve the performance and interpretability of I2I models.", "questions": "1.The explanation in Figure 3 (b) is somewhat puzzling, why say \"low-resolution signals result in lower involvement from early layers in controlling coarse features. \" does not seem common sense, and why there is only one type of visualization in (a) and two in (b)?\n2.Why did you choose PASD as the SR model? In fact, there are many influential generative SR models in the recent top conferences, like SUPIR, SeeSR. Will your approach come to the same conclusion on these models?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper introduces Image-to-Image Attribution Maps (I2AM), a novel method for enhancing the interpretability of image-to-image (I2I) diffusion models. I2AM visualizes bidirectional attribution maps, providing a clear view of feature transfers between reference and generated images.\n2.Task Effectiveness: Demonstrates I2AM's effectiveness in object detection, inpainting, and super-resolution tasks.  Successfully identifies critical regions for output generation, even in complex scenes.\n3.IMACS Metric: Introduces the Inpainting Mask Attention Consistency Score (IMACS), a new evaluation metric correlating with existing performance metrics.", "weaknesses": "1.The IMACS metric does not seem to be very relevant to the topic of this article, I2AM, which seems to be designed specifically for Inpainting and has no general use for other T2I tasks.\n2.Some of the tasks selected in this article do not seem to be the most representative of I2I tasks, such as generative Inpating, image editing, image super-resolution, etc., so I have some concerns about the reliability and robustness of the conclusions in this article.", "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": "cg3cRd64sc", "reviewer": "Reviewer_4XJm", "summary": "This paper describes  a way to aggregate and visualize cross-attention similarity maps in image-to-image latent diffusion models. Those visualizations help to understand which regions of the generated image were influenced most by the reference image, and which parts of the reference image contributed most to the generated image. The paper also proposes a new metric for evaluating reference-based image inpainting through estimating how well the inpainting model captures details from the reference image. They perform experiments on tasks like image inpainting and image super-resolution in order to show the visualization capabilities of the proposed method.", "questions": "1. The \"right map\" in Figure 1 is difficult to interpret—could the authors clarify what it represents? Additionally, there appears to be an artifact in the first row, second column of the right 3x3 grid; what caused this?\n\n2. In the experiment described in Section 5.4, what loss functions were used to \"densify attention distributions\" and \"improve semantic alignment\"? A description of these would be helpful.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The proposed method for cross-attention similarity map visualizations is clearly explained.", "weaknesses": "1. The paper’s contribution appears incremental, focusing on the adaptation of existing methods to a new context. It mostly describes a way to aggregate and visualize cross-attention similarity maps of a pre-trained image-to-image diffusion denoiser network by averaging across different dimensions. Similar approaches have already been employed by other papers for visualizing the cross-attention layers, as also discussed in the related work of this paper. So the main novelty of this work is the application of the same principles of cross-attention map visualizations to the image-to-image diffusion models. Therefore, the significance of this work to the field is quite limited.\n\n2. The head-level attribution map introduced in Section 4.3 may not be fully meaningful, as it averages across layers. It’s unclear why a given head index should play a consistent role across all layers or how its visualization provides useful information. The authors could consider clarifying this assumption.\n\n3. Inpainting Mask Attention Similarity Score (IMACS) in Section 4.4, Equation 8, appears to yield a matrix, but it’s unclear how this is transformed into a scalar score. Additional clarification on this process would improve understanding.\n\n4. Figure 2 needs more descriptive labeling. Terms such as c_I^{(l)}/c_I​ should be clearly defined within the figure caption or the main text, as their meaning is not immediately obvious.\n\n5. Lines 304-305 state, “the layer-level maps offer a better indicator of model performance across different layers and help uncover areas for potential improvement.” It’s not clear how a layer-level attribution map can serve as a performance indicator. Additional explanation or evidence supporting this claim would be helpful.", "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": "i5oSkNUjUb", "reviewer": "Reviewer_zNnp", "summary": "The interpretability of cross-attention mechanisms in image-to-image (I2I) diffusion models remains underexplored, while cross-attention maps in text-to-image (T2I) models have been extensively studied. This paper advances interpretability in I2I models by visualizing attributions from two perspectives: from the reference image to the generated image and from the generated image back to the reference. The authors propose a novel method for interpreting latent diffusion models using cross-attention maps from these dual perspectives. This approach enables researchers to debug and enhance I2I models in various tasks, such as object detection, inpainting, and super-resolution. Additionally, they introduce an inpainting mask attention consistency score as an evaluation metric.", "questions": "My main concern is that it remains unclear whether using a fixed CLIP image embedding alone is sufficient for effective visualization, as demonstrated in Figure 1. I would appreciate if the authors could provide further clarification on this point, as it may influence my overall rating.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This method visualizes bidirectional attribution maps across multiple axes, including diffusion time steps, attention heads, and layers, offering a comprehensive analysis. It also reveals insights into the coarse-to-fine process as layers deepen, enhancing interpretability. \n\n- Additionally, the paper introduces a specific-reference attribution map tailored for inpainting tasks, helping guide the model to focus on relevant object parts while disregarding irrelevant background information from the reference image.\n\n- The authors conduct comprehensive experiments across various tasks to validate the improvements enabled by these visualization techniques.", "weaknesses": "- In ULAM, the column-wise averaging limits the ability to visualize specific region-to-region mappings. For instance, in virtual try-on applications, it’s challenging to verify whether a logo in the generated image aligns with the logo in the reference image. According to the authors' visualization in Figure 1, the reference image’s logo appears to contribute less to the generated image, making detailed correspondence difficult to interpret.\n\n- Additionally, a question arises regarding the visualization approach: given a CLIP image embedding, how can each token be accurately mapped back to the original reference image? It remains unclear whether using a fixed CLIP image embedding alone is sufficient for effective visualization.\n\n- Lastly, some formatting issues were noted, such as Figure 3 appearing before Figure 2.", "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": "iKWaBEpJwG", "reviewer": "Reviewer_N4o3", "summary": "This paper presents a method to improve the interpretability of Image-to-Image (I2I) models by visualizing bidirectional attribution maps and proposing a new evaluation metric. The introduced Image-to-Image Attribution Maps ($I^2AM$) aggregate cross-attention scores across time steps, attention heads, and layers, providing insights into how key features transfer between images. Experimental results indicate that this method, enables model debugging and refinement, offering practical tools to enhance I2I model performance and interpretability.", "questions": "Please refer to the Weaknesses for more details.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces a well-designed method that improves interpretability in I2I models.\n\n- The analysis of Image-to-Image Attribution Maps is interesting, demonstrating the utility of $I^2AM$ across tasks like object detection, inpainting, and super-resolution.\n\n- The writing and presentation are generally clear and accessible, making it easier for readers to follow the proposed method.", "weaknesses": "- In the \"Unsupervised manner & Unseen dataset\" section of Table 1, a baseline comparison is missing. Applying [1]'s attribute map to identify key regions would provide a fair comparison and strengthen the analysis.\n\n- Additional comparative experiments and visualizations would benefit the paper. For instance, more qualitative examples in tasks such as object detection, image inpainting, and super-resolution would enhance understanding of the method's effectiveness.\n\n- The Specific-reference Attribution Map (SRAM) lacks formal definitions. It remains unclear how reference patch selection and attention aggregation are conducted, even with the explanations in Figure 4.\n\n-  The choices for parameters δ and λ are not adequately justified through experiments. Additional ablation studies or experiments exploring the effect of varying δ and λ values would strengthen the argument for their selection, making the methodology more transparent and grounded.\n\n[1] Tang, Raphael, et al. “What the DAAM: Interpreting Stable Diffusion Using Cross Attention.” Annual Meeting of the Association for Computational Linguistics (2022).", "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": "f8CiAkEuxG", "reviewer": "Reviewer_bmkU", "summary": "The paper proposes a method called Image-to-Image Attention Mapping (I2AM) for improving the explainability of diffusion models. I2AM visualizes bidirectional attention mapping between reference and generated images to understand how the model processes image data. This method is particularly useful for image-to-image (I2I) tasks since it can capture complex relationships between reference and generated images. Furthermore, the authors introduce a new metric called Image Repair Attention Consistency Score (IMACS) to evaluate model performance and attention distribution consistency", "questions": "See Weaknesses.  I will change my rating based on the responses.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method offers a novel perspective on interpreting LDMs by focusing on the relationship between reference and generated images. \n\n2. The authors also introduce a new metric, Image Repair Attention Consistency Score (IMACS), to evaluate model performance and attention distribution consistency, which adds value to the research.\n\n3. The experimental results demonstrate the effectiveness of I2AM in identifying key feature regions for tasks such as object detection, image repair, and super-resolution.\n\n4. The paper is written in a clear and concise manner.", "weaknesses": "1. The paper does not include an ablation study to demonstrate the effectiveness of the core components of the proposed method. \n\n2. The authors compare their method with several baseline models, but they do not mention any other attention-based methods or explain why they chose those specific models. Adding comparisons with other attention-based methods would help to understand the relative strengths and weaknesses of the proposed approach.\n\n3. The authors do not discuss the limitations of their method.  Does the method work well for any diffusion models?", "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)