Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models
Authors: Parham Rezaei
Arash Marioriyad
Mahdieh Soleymani Baghshah
Mohammad Hossein Rohban
Venue: Accepted by TMLR ()

Abstract: Despite the ability of text-to-image models to generate high-quality, realistic, and diverse images, they face challenges in compositional generation, often struggling to accurately represent details specified in the input prompt. A prevalent issue in compositional generation is the misalignment of spatial relationships, as models often fail to faithfully generate images that reflect the spatial configurations specified between objects in the input prompts.
To address this challenge, we propose a novel probabilistic framework for modeling the relative spatial positioning of objects in a scene, leveraging the concept of Probability of Superiority (PoS). Building on this insight, we make two key contributions. First, we introduce a novel evaluation metric, PoS-based Evaluation (PSE), designed to assess the alignment of 2D and 3D spatial relationships between text and image, with improved adherence to human judgment. Second, we propose PoS-based Generation (PSG), an inference-time method that improves the alignment of 2D and 3D spatial relationships in T2I models without requiring fine-tuning. PSG employs a PoS-based reward function that can be utilized in two distinct ways: (1) as a gradient-based guidance mechanism applied to the cross-attention maps during the denoising steps, or (2) as a search-based strategy that evaluates a set of initial noise vectors to select the best one. Extensive experiments demonstrate that the PSE metric exhibits stronger alignment with human judgment compared to traditional center-based metrics, providing a more nuanced and reliable measure of complex spatial relationship accuracy in text-image alignment. Furthermore, PSG significantly enhances the ability of text-to-image models to generate images with specified spatial configurations, outperforming state-of-the-art methods across multiple evaluation metrics and benchmarks.

Published in Transactions on Machine Learning Research (12/2025)
Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models
Parham Rezaei
parhamin@gmail.com com
Deportment of Computer Engineering
Sharif University of Technology
Arash Marioriyad
amshmarriorigpll@@maaaccom
Department of Computer Engineering
Sharif University of Technology
Mahdieh Soleymani Baghshah Department of Computer Engineering Sharif University of Technology
soleymani@@hariij..d edu
Mohammad Hossein Rohban
rohban@sharige edu
Department of Computer Engineering Sharif University of Technology
Reviewed on OpenReview: https: // openreview. net/ forum? id= mF mFlanJKVFD
Abstract
Despite the ability of text-to-image models to generate high-quality, realistic, and diverse images, they lace challenges in compositional generation, often struggling to accurately represent details specified in the input prompt. A prevalent issue compositional generation the misaligument of spatial relationships, as models often fail to faithfully generate images that reflect the spatial configurations specified between objects in the input prompts. To address this challenge, we propose a novel probabilistic framework for modeling the relative spatial positioning of objects in scene, leveraging the concept of Probubility of Superiority (PoS). Building on this insight, we make two key contributions. First, we introduce novel evaluation metric, PoS- based Evaluation (PSE), designed to assess the alignment of 2D and 3D spatial relationships between text and image, with improved adherence to human judgment. Second, we propose PoS-based Generation (PSG), an inference- time method that improves the alignment of 2D and 3D spatial relationships T2I models without requiring fine-tuning. PSG employs a PoS-based reward function that can be utilized in two distinct ways: (1)
pradicnt-based guidance mochanism applied to the cross-attention maps during the denoising steps, (2) search-bused strategy that evaluates set of initial noise vectors to select the best one. Extensive experiments demonstrate that the PSE motric cxhibits stronger alignment with human judgment compared to traditional center- based metrics, providing a more muanced and reliable mcasure of complex spatial relationship accuracy in text-image alignment. Furthermore, PSG significantly enhances the ability of text-to-image models to generate images with specified spatial configurations, outperfrmiing state-of-the-art methods across multiple evaluation metrics and benchmarks
1
Introduction
Recent advancements in computational power and large multimodal datasets have led to the development of powerful text-to-image (T2I) models, such as Stable Diffusson (2] and DALL-E B 4 which generate high-
The codebase available http::///ttttha..cceeaaaaaaaaaar httpp//aaaaraaaiaiiaeeeaaaaaaaaa

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SDXI
SDXI+ PSG
SDXI
SIXLE+ PSG
SDXL
SDXI + PSG
panda is sitting on box. There is green lamp to the right of the panda and a red lamp the left of the panda. dark anime style.
A princess is walking with tiara on her head, an angel to her left and demon to her right.
A black and white painting of house. It is cold day. There is a dry tree to the left of the house. There horse the left of the dry tree.
SDXL
SDXL + PSG
SDXI.
SDXL + PSG
SDXL
SDXL PSG
sheep behind plate.
A bird top left of girl.
A computer in front of
Figure 1: Qualitative comparison of our proposed method, PSG, using SDXL ( as the backbone with the baseline on variety of complex prompt with one or more 2D and 3D spatial relationships. Our method generates images with accurate spatial relations even for prompts defining several relations between objects.
quality and diverse images from text descriptions. However, concerns have arisen regarding their reliability in generating images that align with the specific details in input prompts, leading to well-known failures referred to as compositional generation issues.
These failures can categorized into four main types (5:: object missing, where the generated image 9); incorrect attribute binding, where 12); incorrect spatial relationships, where the
omits one or more objects explicitly mentioned in the prompt 6
attributes are not correctly associated with entities 100 spatial arrangement of objects in the image does not match the description (13) 14); and cannemccy-relatedd issues, where the model fails to represent the exact specified number of entities in the prompt (15) (6).
This work addresses the challenge of 2D and 3D spatial relationships in T21 models, focusing on overcoming two key hurdles: generating all specified objects without omission and demonstrating a robust geometric understanding to accurately interpret relative positions and orientations.
Improving compositional generation T21 models can be achieved through fine-tuning on large multimodal datasets 17 14 18), process that is costly, timo-consuming and infeasible in resource-limited settings. have gained popularity for adjusting latent embeddings during inference without modifying model parameters, thereby preserving model integrity and preventing overfitting, Another approach involves incorporating supplementary inputs, such as layouts and However, these methods can be impractical, as they often require detailed supplementary inputs that place a burden on users, may involve the costly integration of large language models, and can introduce undesirable biascs in generation.
Alternatively, training-free methods [19 12
10
20 21
scene graphs, alongside text prompts 20
22
23
24.
Hence, training-free free method that does not require additional inputs could be ideal for addressing compo- sitional generation challenges. However, such solution currently exists for managing spatial relationships in T2I models, despite spatial alignment being well-cxplored area. Previous studics have primarily used a center-based approach to model spatial relationships, which assesses image-Lext alignment by focusing on the centers of detected bounding boxes (3 all shape and size of objects, lcading to inaccuracics. Additionally, center-basod methods rely on discrete spatial inference, which is incompatible with the continuous and differentiable reward functions required for training- free guided generation.
25 26 27). This approach ignores factors such as the over-
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dog sitting on chair left
A truck bottom of
An orange top of hat
cat
SDXL
PSG
SDXL
Getting it
Right
Figure 2: Qualitative comparison with the state-of-the-an SDXL model (1 and the fine-tuning based model Getting it Right (14, on prompts with spatial instructions.
This study introduces a novel probabilistic framework for analyzing spatial relationships between objects using the Probability of Superiority (PoS) concept (28), enabling a more human-like interpretation of spa- tial relationships by accounting for the overall characteristics of objects. Hence, this work presents two main contributions: (1) PoS-based Evaluation (PSE), a novel evaluation metric that reliably asscsses 2D and 3D spatial relationship alignment between image and text, and (2) PoS-based Generation (PSG), an inference-time method that enhances T21 models' ability to generate images with specified 2D and 3D spatial relationships by incorporating Paseb reward function, without modifying model parameters. Following established paradigms in the field, PSG can be implemented in both gradienl-based and search-based forms.
Comprehensive experiments demonstrate that PSE aligns more closely with human judgment than commonly used evaluation motrics such as VISOR 13) T2I-CompBench (25), MRS--Benchmak (26). CLIP Score (29)
XVLM (0)) and Image Reward 31 as measured by Spearman, Kendall, and Pearson correlations. Notably, PSE achieves improvement of approximately 0.2 in Spearman correlation over VTSOR, highlighting that probabilistie perspective spatial relationships provides more reliable assessment than traditional center- based and object detection- based methods.
Additionally, PSG outperforms state-of-the-art methods, including fine-tuning-based approaches, across mul- tiple benchmarks and model backbones (fig. [ and fig. 2 Using the VISOR metric 13) standard for evaluating spatial relationships T2I models, PSG achieves a significant 32% performance improvement over the leading competitor, while introducing minimal bias, as assessed by quality and diversity evaluations.
Finally, major challenge in text-image alignment rescarch is the high computational cost of model eval- uation, as most composit ional metrics require numerous samples for reliable results. This poses difficulties for large-scale models like Stable Diffusion XL (I) and DALL-E 3 4 underscoring the need for an efficient evaluation metric that reduces sample requirements. Inspired by the approach introduced by (32) we pro- pose Online PSE (OPSE), an ouline version of the PSE metric to reliably detect the superior model using only a limited number of samples.
2 Related Work
Spatial Relationship Generation. In recent years, various methods have been introduced to address the problem of incorrect spatial relationships in T2I models. The fine-tuning busong-bbnood approach Getting it Right (14 highlights the underrepreeentation of spatial relationships in standard vision-language datasets. To address
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this, the authors fine-tuned ciatasioonbaseed T2I models dataset they created by re-captioning six million images from CC-12M [33, Segment Anything (SA) (34,, COCO (5), and LATON-Aesthelic (36). Moreover, several fine-tuning methods enhance text-imen image alignment in T21 models by incorporating additional inputs, such as layouts 37
Since fine-tuning approaches are computationally intensive and prone to overlitting, & line of research has introduced training-fres methods that use inference-time approaches enhance model performance on spatial relationships, often incorporating supplementary inputs (55)
22 (0)30) BD). These approaches typically adjust intermediate latent embeddings during the denoising process without modifying the model's parameters. To the best of our knowledge, no training- free method currently addresses the spatial relationship problem in T2T models without requiring additional inputs.
[6 A7
38
49:
20
Spatial Relationships Evaluation. Most evaluation metrics specialized spatial relationship alignment are center-basee meaning they infer spatial relationships based on the bounding box centers of objects. For example, (B) propose meric called VISOR which employs the open vocabulary object detector OWL-VIT (5T) to assess the spatial relationship alignment by comparing the center points of object bounding boxes identified by the object detector. Moreover, 121-CompBeuch (25) and HIRS-Benchmark (26), two well- known benchmarks for compositional generation, assess spatial relationships using object centers obtained by UniDet (52) However, center-based approaches have critical limitations as they do not account for the overall characteristics of objects. Additionally, there are also embedding based and VQA-based evaluation metrics, such as (53) and (54) that address the spatial relationship problem in T2I models.
3 Probabilistic Viewpoint to Spatial Relationship
Detection
Mask Extraction
Projection
Cat
Grounded SAM
PoBiCar, Right
Relation
0.9927
PSE
Depth Anything
If 3D Relation
0.9855
0.0072
PoBiCar, Bond))
Relation Left
Bowl
Grounded SAM
Projection
Detection
Mask Extraction
Figure 3: Process of PSE metric for the prompt "A cat to the right of bowl". The depth detection is only applied for 3D relations.
To quantify spatial relationships from probabilistic perspective, start with simple case. Consider two distributions, A and B, which, with probability 1, take single point values æ and y, respectively. In this scenario, determining whether A is to the right of B reduces to evaluating the condition > This can be formally expressed using the indicator function T(x > y), where returns 1 if the condition holds. This indicator function can be reformulated as shown in eq.
T(x. v) = EX Y))
(1)
=Xx-Ay=n(X>>
With this intuition, for more general case, let and B represent two probability density distributions defined over one-dimensional space. The concept of distribution A being positioned to the right of distri-
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bution B can be quantified by eq. (1) as the likelihood that randomly selected sample from distribution A is greater than randomly selected sample from distribution B. The formal definition which is known Probability of Superiority (PoS) (28 is expressed in eq. (2)
PAS(A, B) = Px>AAYB(XX>>> Y)
= -
(2)
Px(x)Pn(y)dzdy
In bounded discrete space, given that the range of A is [sA,tAl and the range of B is [sB,tb], we derive eq. (3)
to LA Σ Σ PA(1)PB(j).
(oS
(3)
j=
Thus, A is considered to be positioned the right of when Po5 B) R 1, indicating that the probability of a point in A being to the right of point in B nearly Similarly, to the left of B if PoS A) 1.
To extend beyond one-dimensional distributions, A and B. These one-dimensional distributions capture spatial relationships along v, such as left-right bbove-belowy positioning. Thus, we define PoS on these projections to assess spatial alignment. For example, in 2D, with 5 = (0, -1), is above B if POS (B), [T)()) = and below if PoS(TT,(A), (B)) TT) x 1. To simplify notation, we define the Projected-PP Pos for vector I as described in eq. 4
normalized projections onto vector v, denoted
PAS,(A, B) (O)
(4)
To quantify common directions such as left, top, or in front, we use the standard XYZ axes. The reverse of each relation is modeled by swapping A and or equivalently using - More complex directions, like top-right or between, can be captured by combining the Projected- of cach component.
Including Distance. As shown in appendix B.4 eq. (2 can be extended to account for distance, enabling the representation of relations such as close and far.
Building on the proposed PoS-based framework, we introduce alignment and training-free method to improve spatial relationship modeling in T21 models.
reliable evaluation metric for text-image
3.1 Evaluating Spatial Relationships Alignment using PoS
Using the concept of Projected-PoS (eq. A).. we introduce a novel evaluation metric, PSE (PoS-basod Evaluation), to assess the spatial relationship alignment. between text and image. Given pair of text and image, first extract the object names and their spatial relationships from the text. The image, along with the extracted object names, is then input into Grounded SAM (55), which integrates the Sogment Anything Model (SAM) Ha with Grounding DINO (56) to detect and segment the regions related to the objects. For 2D relationships, the Projected-PoS is directly applied between the segmentation outputs. For 3D relationships, a monocular depth map detector is first used to estimate the relative depth of objects. The depth values for each segment are then quantized to determine the ž-related values. Thus, for both 2D and 3D cases, the PSE evaluation metric for a text-image pair containing objects A and B and their spatial relationship is defined eq. (
(5) Note that the negative projection vector gives us the Projected-Poe PoS for the inverse relation, such as "left" for "right". For example, if object is entirely to the right of object B, meaning all of its points are positioned to the right of B's points, the PoS (A, B) would be close to 1, while PoSm... (A, B) would approach As result, the assigned score, PSE(A, B; Right), would be nearly 1, indicating true alignment. For a visual representation, refer to fig.
PSE(A, = [PoS,, (P,S,. (A, PoS (A,
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The modification in PSE to the absolute PoS value provides benefits in ambiguous scenarios, where object is positioned near the middle of another. In such cases, the values for PoSe and PoSeja are nonzero but close, leading to a PSE score near zero, which aligns more closely with human evaluation. In contrast, conter-based evaluation metrics like VISOR (3)) focus solely on the positions of object centers, leading to rigid scoring of or 0, which favors one object being definitively to the right or left of the other.
PSE also offers greater reliability than center-based methods in scenarios where the relative center positions of objects contradict the spatial arrangement that humans intuitively interpret as correct, particularly for objects that can span across the scene, such as trees, demonstrated in fig.
A dog the right of
Center Coordinates
Soore
(511.,
511.3
Visor:
(604.3, 864.6)
Right PoS - LLfft PoS =- 0.1188
PSE:
Figure Inconsistency of center-based evaluation with human perception of spatial relationships for the prompt "a dog the right of tree". Even though the dog is not positioned to the right of the tree, the tree's horizontal expansion causes the VISOR metric to inaccurately label the relationship between the dog and the tree true.
Hence, PSE provides human-like measure of confidence in spatial relation evaluation. If all points of one object lie to the right of all points of another, PSE confidently assigns score of PSE 1, indicating that one object is to the right of the other. Moreover, this confidence does not increase further by moving the object farther to the right.
Notably, PSE assigns a continuous score between and 1, allowing for the application of threshold to determine the presence of a specified relationship. Based on our experiments in appendix[A.]] we found that threshold of 0.5 closely aligns with the original PSE score, offering an effective binary evaluation method.
3.2 Improving Spatial Relationships Alignment using PoS
Building upon the PSE metric introduced in section 3.1 now leverage this metric as reward function to guide image generation. Specifically, we propose PSG (PoS-based Generation), inference-tine alignment enhancement method that steers the generation process to produce images that conform to the spatial configurations described in the input prompt. PSG operates without requiring any training or fine-tuning of the underlying generalive model. Consistent. with established practices in the field, the PoS-based guidance can be implemented through either gradient-based optimization or search- strategies.
Gradient-Based PSG (Generative Semantic Nursing). Gradient-based PSG can be viewed continuous optimization over the latent representations, where the core objective is defined by PoS- based reward (or loss) function that encourages spatial alignment. This reward can be computed either on the final generated image or intermediate cross-attention maps extracted during inference; in this work, we primarily focus on the latter. Notably, in T2I models such as U-Net-based architectures (e.g.. Stable Diffusion) or biT-based models, 2D cross-attention maps offer approximate object segmentations before image synthesis is complete. These normalized cross-attention maps can be interpreted as probability distributions over spatial locations, indicating the likelihood that a given object associated with each region of the image. Given this probabilistie interpretation, the PoS-based formulation (eq. (3) can be directly

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Published in Transactions on Machine Learning Research (12/2025)
15
30
Denoising Step
50
Dog
SD 1.4
SD 1.4 + PSG
0.73
0.70
0.62
0.60
0.54
PoS Value
0.73
0.90
0.92
0,95
0.93
Figure Results of applying gradient-based PSG to Stable Diffusion 1.4 on cross-attention maps of dog and cat for the prompt "A dog to the left of a Cat". The distribution of attention of each object is gradually shifted to its correct relative position.
applied to cross-attention maps, enhancing the alignment of 2D spatial relationships during the generation process.
More precisley, consider one of the extracted relations named r (like "being to the left of*) between tokens and j (object associated with token i is to the left of object associated to token j). We then use the Projected- PoS of the attention maps A, and A, on the vector of relation r namely 57. as described in the previous subscction. This term now defines a loss for how much the relation is established. We employ this loss function to use the concept of generalive semantic nursing (8) and update the latent zę at the denoising step & using eq. ( where L is equal to the -PoSp Aj). The square is due to practical performance.
2 24 a: V.
(6)
Notably, fig. depicts the changes in the value of the PoS,, (Ai, Aj) with and without applying eq. (6) to the latent, along with the projected cross-attention maps of entities onto the x-axis. It is apparent that as long as the entities are an incorrect spatial layout, the value of the proposed PoS-based metric remains low. Combined and more complex form of relationships can be created by merging these losses. For example, by shifting the latent once using the leftward loss and once using the top-ward loss, one can guide towards the top-left relation presented in fig. More exemplary instances are provided in appendix B.3
Search-Based PSG (Inferenco-time Scaling). In this context, scarch-based refers to discrete scarch over the initial noise vectors of diffusion model, serving models inherently incorporate stochasticity, and prior works (57 58 59) have demonstrated that generation quality can be improved by selecting more favorable initial noises. One widely adopted technique for such inference-time euhancement is the Best-of-N stralegy.
inference-tine scaling method. Diffusion
We adopt this approach by using the PSE score as a reward function guide the selection process. Specif- ically, we generate N = 32 samples per prompt and select the image that achieves the highest PSE score, computed by the evaluation pipeline described in section 3.1 While 2D cross-attention maps offer useful spatial cues, they do not inherently capture 3D spatial relationships. In contrast, the final generated images contain sufficient structural and depth information to support 3D-aware evaluation. Consequently, search- based PSG is particularly well-snited for enhancing 3D spatial alignment, and it offers a model-agnostic solution that does not require access the internal architecture or training process of the diffusion model.
4 Experiments
We begin by conducting thorough evaluation of our method's effectiveness in assessing spatial accuracy in images, followed by an analysis of how PSG improves alignment with spatial instructions across various benchmarks.
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4.1 Experiments of Posebased Evaluation (PSE)
We evaluate the performance of our PoS-based evaluation metric, PSE, against other established embedding- based and detection-based metrics.
We use images generated by models: SDXL (1, Kandinsky 3 (O), PixArt-a 61, and SDXL-PSG to compare the effectiveness of the evaluation metrics on generated images.
Table 1: Correlations the 2K images of the human evaluation between the evaluators and the human ground truth remonstrates that PSE is better correlated with human judgments compared to other metries. PSE (0.5) represents a binary version of PSE with threshold of 0.5.
Model
Spearman (t)
Kendall (t)
Pearson (t)
VISOR
0.551
0.551
0.551
T21- T21-CompBench
24
0.437
0.423
0.439
HRS-Bench (26
0.383
0.364
0.384
Clip Score 29
0.269
0.220
0.268
XVLM 300
0,113
0.092
0.134
Image Reward 31
0.326
0.266
0.349
PSE
0.726
0.642
0.778
PSE (0.5)
0.755
0.755
0.755
Alignment to Human Judgment. Using the four aforementioned models, we randomly selected 500 prompls from a 5,000-prompt. sub-dataset, yielding a total of 2,000 generaled images. Three human eval- uators, randomly selected from a pool of volunteers, assessed these images. A custom user interface (UI) was developed to present image-prompt pairs random order, and evaluators were instructed assign one of the following labels each pair: (1) object missing, (2) object present but relationally incorrect, or (3) correctly aligned. To mitigate potential bias, we intentionally refrained from providing strict definitions for relational correctness, allowing evaluators to apply their own judgment. The aggregated annotations were subsequently used to compute correlation metrics and analyze the optimism of cach scoring method. As presented in lable [ rank correlation analysis indicates that the PSE metric achieves the highest. alignment with human judgments.
Morcover, PSE specifically designed to handle cases where objects are not well-soparatod. To evaluate its effectiveness in such scenarios, selected subset of images generated by the SDXL. model that exhibited higher levels of object occlusion, as identified in human study. On this challenging subset, PSE achieved substantial improvement in Pearson correlation with human judgments, rising from 0.241 (VISOR metric) to 0.582, demonstrating its superior alignment with human perception compared to center- center-based metrics, even on difficult samples.
An effective evaluation metric should precisely discern whether relationship is present or absent in an image. Inaceuracies in the detection of incorrect relationships can lead to overly optimistic assessments. To assess this issue, we evaluated the performance of four metrics -VISOR, T2I-CompBench, HRS-Benchmark, and PSE using Precision, Recall, Accuracy, Specificity, and the F1 Score, with human asscssments serving as the ground truth.
Reliability Test.
In this comparison, used binary outcomes to score individual image-prompt pairs. VISOR inherently provides binary scores, whereas PSE requires thresholding; classified relationships correct when the PSE score was 0.5 or higher. Similarly, for T2I-CompBench, a threshold of 0.1 was applied to produce binary outputs. As shown in table 2 PSE outperforms other metrics in Accuracy, Recall, and F1 Score, indicating it provides more reliable assessments of true relationships. VISOR's low Recall suggests that tends to overestimate correctness, scoring images too optimistically. In contrast, T2I-CompBench and BeRs-Benchmark are overly restrictive, while PSE achieves a better balance in this trade-off. The threshold of 0.5 for PSE was chosen based on analysis of the distance between the thresholded and original PSE
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scores real datasets introduced by (62) Detailed plots for the hyperparameter selection provided in Appendix appendix B.1
Table 2: Comparison of the reliability of binary predictions for spatial relationships. The table shows that VISOR tends to misclassify images with incorrect relationships, while HRS and T2I-ComBeench tend to misclassify images with correct relationships. In contrast, PSE provides a more balanced evaluation.
Model
Precision (1)
Recall (t)
Accuracy (t)
Specificity (t)
F1 Score (t)
VISOR (3
86.53
62.26
76.5
90.49
72.42
HRS (6
40.67
72.14
73.25
73.53
52.02
T21-Compbench 25
33.23
88.43
74.65
72.52
48.32
PSE (0.5)
78.82
88.78
88.9
88.95
83.51
Online PoS-based Evaluation (OPSE). Both VISOR and PSE metrics rely datasets with large number of generated images to provide robust assessments of generative models, using 30k and 5k prompts, respectively. However, when selecting the model with the highest PSE value among multiple candidates, acquiring the necessary number of prompts can be costly, particularly due to the API expenses of large-scale models. To reduce this cost, rather than performing batch sampling- -whth suboptimal models- we propose leveraging the Multi-Armed Bandit approach (63) as suggested by (32
which risks allocating resources to
for model selection. As shown in fig. employing the UCB algorithm (65) 66 effectively converges sampling from the optimal model based on the ranking in table provided in Appendix appendix A.6
Further details on the algorithm are
OPSE Choice of Models
Models
40
Kandinsky
SDXL
Steps Chosen
SDXL + PSG
30
20
20
40
60
80
Steps
Figure 6: Number of times each model is chosen by the OPSE algorithm for 100 rounds. The models are SDXL, Kandinsky, and SDXL + PSG. We observe that OPSE can accurately find the best model and sample from it.
4.2 Experiments of PoS-based Generation (PSG)
Experimental Setup. We evaluated PSG on two backbone models: Stable Diffusion 1.4 (2), lightweight model with limited capacity, and Stable Diffusion XL (II) more advanced and computationally powerful model. For Stable Diflusion 1.4, PSG was applied during the first 25 steps of the denoising process, using an initial step factor 20. In contrast, for Stable Diffusion XL, the loss function was applied over the first steps, with larger initial step factor of 1000. The impact of these hyperparameter choices further analyzod in appendix B.I We used an NVIDIA A100 GPU for the experiments. Three know- compositional generation benchmarks adopted for comparisons.

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Table 3: Comparison PSE 2D and 3D scores models used for our human experiment, (This is not a performance validation for PSG as it uses the same measure).
Model
PSF22 (%) t
PSEsD (%)
PixArt-a
17.20
27.21
Kandinsky 3
26.07
28.52
SDXL
20.34
28.60
SDXL+PSG
65.89
78.10
Table 4: Quantitative comparison of our gradient-based PSG, compared to baselines using VISOR metrics VISOR dataset.
Model
OA (%)
VISOR (%)
VISORtand (%) 1
VISOR, (56 1
VISOR2 (%)
VISOR; (36)
VISOR; (56) 1
GLIDE 677
3.4
1.9
55.96
6:36
1.06
0.16
0.02
GLIDE
CDM
6.33
63.47
19.7
DALLE-mini 08
27.24
16.21
59.53
39
17.5
6.42
1.94
CogView2 (8
18.88
12.31
65.26
33.78
11.6
3.4
0.44
DALLE-v2 (70)
37.42
72.78
7.66
SD 1.4 + CDM
23.21
15.02
64.63
38 98
11.62
5.22
1.26
SD 1.4 2
20.6
18.84
63.65
46.10
20.12
7.26
1.82
2.1 2
30.9
65.2
36.5
17.1
Getting Right
60.55
44.05
72.75
71.14
52.66
33.92
18.48
SD XL 1
65.74
45.01
68. 46
78.52
$5.38
32.98
13.16
SD 1.1 + PSG (1)
35.29
30.0
85.0
60.48
34.74
18.32
6.46
SD 14 PSG
44.92
39.98
89.0
71.58
47.72
28.56
12.06
SDXL. PSG (1)
76.8
74.8
97.4
93.52
84.72
70.88
50.08
SDXL PSG (2)
80.02
77.35
96.6
94.98
87.02
74.36
53.04
VISOR Benchmark. VISOR 13) is a widely used benchmark framework for assessing spatial relation- ships in T21 models. It comprises using 30,000 prompts based on the objects of COCO (35) to test object positioning in four key directions: left, right, top, and bottom. 5,000 prompts with four random seeds to where used compare PSG with re-evaluated bascline models. The detailed explanation of the benchmark is postponed to appendix A.4 Notably, table shooss that PSG not only enhances spatial relationship accuracy but also improves Object Accuracy (OA) by mitigating object overlap issues. Based on VISOR metric, our method achieves a 30% increase over prior methods and models. Moreover, VISORcond, it attains impressive 97%, highlighting consistent spatial alignment. Additionally, it improves VISOR, 1/233/4 by 20%, surpassing baseline methods by achieving a higher probability of spatial accuracy across different random seeds. We further tested a prompt-simplifted PSG variant ("(2) versions in table ! that replaces explicit spatial terms with "and". This modification boosted OA by 9% for SD 1.4 and 3% for SDXL. Notably, PSG on SD 1.4 outperformed SD 2.1, despite SD 2.1's overall advantage on VISOR metrics.
T2l-Compeench and MRRS-Benchmark. We further evaluated PSG using T21-CompBench PRRS-Benchmark (26), focusing on their spatial relationship assessment. As shown in table 5 PSG improves SDXL's 's spatial alignment on T2I-CompBench by 15% over standard SDXL and 8% over DALLE-3. Results for SD 1.4 are deferred to appendix B.2 Similarly, table Table@showw shows that PSG enhances spatial accuracy on HRS- Benchmark, outperforming layout-guided generative models. T21-CompBBnnch evaluates 3D spatial relations using detection scores and IoU constraints in addition to comparing the depth center of objects. However, focus on evaluating the generation of correct relative 3D positions, in addition to T21-CompBench, we used specified experiments for this matter. We used Grounded SAM and Depth Anything as more recent detectors to extract depth center of objects. We then evaluate the spatial accuracy by comparing the center points. In another experiment, we also included an loU constraint similar to 121-CompBench. We used a threshold of 0.5 for the object detection and N 32 (scction 3.2) for applying PSG. As shown table 7 our method not only improves SDXL's performance on Tee-ChmpBench but also achieves gains in depth-focusee 3D spatial assessments.
(25 and
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Table 5: Quantitative comparison of baselines using 122l-ComBennch spatial benchmark for gradient- based
PSG on SDXL backbone.
Model
T2T Spatial Score (%) t
GORS
25
0.1815
Getting it Right
0.2133
PixArt-a 66
0.2064
Kandinsky v2.2
0.1912
SD-XL 11
0.2133
DALLE-3
0.2865
SDXL + PSG
0.3601
Table 6: Quantitative comparison of gradient-based PSG, compared to baselines with Stable Diffusion back- bone using HRS spatial benchmark (scores of other models are adopted from GrounDiT (72)). This bench- mark consists of prompts that establish relationships among multiple objects within each prompt.
Model
Spatial (%) f
Stable Diffusion
(2)
8.48
PixArt-o (1
17.86
Layout-Guidance Guidance
20
16.47
Attention- CeenttionRefoousingg
40
24.45
Biff (48
16.31
R&B 50
30.14
SDXL PSG (ours)
34.33
Table 7: Quantitative comparison of maacch-assed PSG method with the SDXL baseline in generating 3D
spatial relationships.
Model
T21-CB 3D (%) 1
Depth w/ loU (0.2) (%) t
Depth w/o loU (%)
SDXL
35.66
28.46
43.10
SDXT. + PSG
37.13
33.15
63.67
Embeddiin-baseed Alignment Analysis. Beyond object detector-based metrics, which primarily assess object presence in generated images, we also evaluated text-image alignment using the embedding- CLIP Score. This metric measures the similarity between a generated image and its corresponding prompt within the CLIP model's embedding space (73). Applying the PSG to SDXL on the COCO dataset captions 35) improves the CLIP Score from 0.313 0.316, underscoring PSG's effectiveness in enhancing alignment.
Quality and Diversity Results. Controlling generation diffusion models by modifying latent em- beddings during denoising often degrades image quality (74). To evaluate these side effects, we selected 20,000 image-caption pairs from MS-COCO 2017, ensuring that each pair contained least one distinct spatial relationship between two objects, identified via bounding box annotations. The captions were then augmented to explicitly specify object positioning, We generated images using SDXL alone and SDXL with PSG, comparing their quality and diversity metrics against COCO images as the reference (table 8 For feature extraction, we used DINOv2-ViT-LL/
(75). Additionally, FID scores were reported using Inception- V3. Results indicate a slight quality reduction FID (76) and CMMD (77 scores, aligning with PSG's emphasis on spatial alignment rather than pure image quality. Given the known concerns associated with the FID metric ( we also evaluated Precision and Recall (78) metrics, both of which demonstrated perfor- mance levels close to the baseline. Additionally, the RKE metric (79), reference-free free measure of diversity,
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indicated higher mode count with PSG compared to SDXL alone, confirming that PSG enhances mode diversity.
Table 8: Quality and diversity comparison of our proposed evaluation method, PSG, with baseline SDXL COCO dataset.
Quality FID (1)
Diversity
Model
CMMD (4)
Precision (t)
Recall (t)
RKE(1)
SDXL
0.77
221.07/20.22
0.85
0.59
401.63
SDXL + PSG
0.79
238.05/21.52
0.83
0.60
412.73
Diversity of Bounding Boxes. Layout-guides generative models, while capable of placing objects in specific positions, rely on manually defined layouts, introducing significant bias into the generated images. To examine this phenomenon, we analyzed the template of RealCompo (80) for bounding box generation in appendix B.5 The experiments demonstrate that methods relying on LLM-generated layouts exhibit limited spatial diversity in object position and size. In contrast, our proposed approach (PSG) produces significantly more diverse and natural layouts in the final images, better reflecting real-world variability.
Gencralization to Other Backboncs. We also applicd our gradicnt-based PSG method on top of trans- former architecture and search-based PSG, iee, Best-of-N PSG, various state-of-the-arl models which demonstrated that the improvements are not limited to the choice of the model. The results are poned to appendix B.2
5 Conclusion
This work introduces novel probabilistic framework for modeling spatial relationships between objects, inspired by the concept. of Probability of Superiorily (PoS). The main contributions are as follows: (1) PSE (PoS-based based Evaluation), which provides smoother and more human-aligned assessments 2D and 3D spatial relations than conventional nanter-based motrics by accounting for the holistic characteristics of objects; (2) PSG (PoS-based Generation). training-free free method that utilizes baseb reward functionn, applied either through continuous gradient-based approach or discrete search-based strategy, resulting in superior spatial relation aligument in both 2D and 3D compared to state-of-the-art methods; and (3) OPSE, an online variant of PSE, which enables efficient and reliable evaluation of T2I models using minimal number of samples. The limitations of both contributions are discussed in appendix A.5 and appendix B.8
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References
[1] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.
[2] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IBEE/CVF conference computer vision and pattern recognition, pages 10684-10695, 2022.
[3] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Nya. Sutskever. Zero-shot text-to-image generation. In International conference on machine learning, pages 8821-8831. Pmir, 2021.
[4] James Betker, Gabriel Goh, Li Jing, i TimBrooks, Jianfeng Wang, Tinjie Li, f LongOuyang, Jun- tangZhuang, t JoyceLee, i YufeiGuo, t WesamManassra, t PrafullaDharriwal, CaseyChu, f YunxinJiao, and Aditya Ramesh. Improving image generation with better captions.
[5] Arash Marioriyad, Parham Rezaci, Mahdich Soleymani Baghshah, and Mohammad Hossein Rohban. Diffusion beats autoregressive: An evaluation of compositional generation in text-to-image models. ar Xiv preprint 24Xiv 10.22775, 2024.
[6] Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. Autend-and-excite: Attention- based semantic guidance for text-to-image diffusion models. ACM Transactions Graphics (TOG), 42:1 - 10, 2023.
[7] Aishwarya Agarwal, Srikrishna Karanam, K Joseph, Apoorv Saxena, Koustava Goswami, and Bal- aji Vasan Srinivasan. A-star: Test-time attention segregation and retention for text-to-image synthesis. In Proceedings of the IEEE/CVF International Conference on Compuler Vision (ICCV), pages 2283
2293, October 2023.
[8] Yang Zhang, Teoh Tze Tzun, Lim Wei Hern, Tiviatis Sim, and Kenji Kawaguchi. Enhancing se- mantic fidelity in text-to-image synthesis: Attention regulation in diffusion models. ar preprint arXiv:240S.063: 2024.
[9] Kota Sucyoshi and Takashi Matsubara. Predicated diffusion: Predicate logic-based attention guidance for text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Compuler Vision and Pattern Recognition, pages 8651-8660, 2024.
[10] Royi Rassin, Eran Hirsch, Daniel Glickman, Shauli Ravfogel, Yoav Goldberg, and Gal Chechik. Linguis- tic binding in diffusion models: Enhancing attribute correspondence through attention map alignment. In Thirth-seventh Conference on Neural Information Processing Systems, 2023.
111] Yumeng Li, Margret Keuper, Dan Zhang, and Anna Khoreva. Divide & bind your attention for improved generative semantic nursing. In 34th British Machine Vision Conference 2023, BMVC 2023, 2023.
[1] Weixi Feng, Xuchai He, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang. Training-fres free structured diffusson guidance for composi- tional text-to-image synthesis. In The Eleventh International Conference on Learning Representations, 2023.
[13] Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitla Baral, and Yezhou Yang. Benchmarking spatial relationships in text-to-image generation. arx preprint ar Xrx:::2212 10015, 2022.
[14] Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hannanch Hajishirzi, Vasudev Lal, Chitta Baral, et al. Getting it right: Improving spatial consistency in texi-to-image models. arXiv preprint axXiv:244.001199 011997, 2024.
[15] Oz Zafar, Lior Wolf, and Idan Schwartz. Iterative object count optimization for text-to-image diffusion models. arXiv preprint arXiv:2408. 1721, 2024.
13

---

Published in Transactions on Machine Learning Research (12/2025)
[16] Lital Binyamin, Yoad Tewel, Hilit Segev, Eran Hirsch, Royi Rassin, and Gal Chechik. Make it count: Text-to-inage generation with an accurate number of objects. ai preprint or arXiv:246.. 10210, 2024.
[17] Arman Zarei, Keivan Rezaei, Samyadeep Basu, Mehrdad Saberi, Mazda Moayeri, Priyatham Kat- takinda, and Soheil Feizi. Understanding and mitigating compositional issues in text-to-image generative models. orXiv preprint urxiv:2406. 0.84447744444 2024.
[18] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the FEEE/CVF conference computer vision and pattern recognition, pages 22500-22510, 2023.
[19] Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B. Tenenbaum. Compositional visual generation with composable diffusion models, 2023.
[20] Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention guid- ance. In Proceedings of the IEEE/CVE /CFF Winter Conference on Applications of Computer Vision, pages 5343-5353, 2024.
[21] Taihang Hu, Linxuan Li, Joost van de Weijer, Hongcheng Gao, Fahad Shahbaz Khan, Jian Yang, Ming-Ming Cheng, Kai Wang, and Yaxing Wang. Token merging for training-free semantic binding in text-to-image synthesis. Advances in Neural Information Processing Systems, 37:137646- 131166666-37772, 2024.
[22] Yunji Kim, Jiyoung Lee, Jin-Hwa Kim, Jung- Woo Ha, and Jun-Yan Zhu. Dense text-to-image generation with attention modulation. In Proceedings of the IEEE/CVE International Conference on Computer Vision, pages 7701-7711, 2023.
[23] Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, et al. Reco: Region-controlle text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14246-14255, 2023.
[24] Sherwin Bahmmani, Jeong Joon Park, Despoina Paschalidou, Xingguang Yan, Gordon Wetzstein, Leonidas Guibas, and Andrea Tagliasacchi. Cc3d: Layout-conditioned generation of compositional 3d scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7171-
7181, 2023.
[25] Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A comprehensive benchmark for open-world compositional texl-Lo-image generation. In Thirly-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023.
[26] Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen, Faizan Farooq Khan, Li Erran Li, and Mohamed Elhoseiny. Hrs-bench: Holistie, reliable and scalable benchmark for text-to-image models, 2023.
[27] Dhruba Ghosh, Hanna Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-in text-to-image alignment, 2023.
[28] Douglas A. Wolfe and Robert V. Hogg. On constructing statistics and reporting data. The American Statistician, 25(4):27-30 -0, 1971
[29] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXi::2104.08713 2021.
[30] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. In International Conference on Machine Learning, 2021.
[31] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, editors, Aduances in Neural Informa- Curran Associates, Inc., 2023.
tion Processing Systems, volume 36, pages 15903-159333
14

---

Published in Transactions on Machine Learning Research (12/2025)
[32] Xiaoyan Hu, Ho fung Leung, and Farzan Farnia. An aptimism-based approach to online evaluation of generative models, 2024.
[33] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In CVPR, 2021.
[34] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitchead, Alexander C. Berg, Wan- Yen Lo, Piotr Dollár, and Ross Girshick. Segment anything, 2023.
[35] Tsung-Y Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft coco: Common objects in context, 2015.
[36] LAION. Laion-coco 600m. http:://latoo.aaabloo//aaoooo 2022.
[37] Lvmin Zhang, Anyi Rao, and Mancesh Agrawala. Adding conditional control to text-to-image diffusion models, 2023.
(38) Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation, 2023.
[39]
Guangeong Zheng, Xianpan Zhou, Xuewei Li, Zhongang Qi, Ying Shan, and Xi Li. Layoutdiffusion: Controllable diffusion model for layout-to-inage generation, 2024.
[40] Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischin- ski, Ohad Fried, and Xi Yin. Spatext: Spatio-textual representation for controllable image generation. In 2023 IEFE/CVE /CF Conference on Computer Vision and Pattern Recognition (CVPR), page 18370-18380. IEEE, June 2023.
[41] Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. Instancediflu- sion: Instance-level control for image generation, 2024.
[42] Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman. Make-a- scene: Scene-basod text-to-image generation with human priors, 2022.
[43] Jiaxin Cheng, Xiao Liang, Xingjian Shi, Tong He, Tianjun Xiao, and Mu Li. Layoutdiffuse: Adapting foundational diffusion models for layout-to-image generation, 2023.
[44] Gwanghyun Kim, Hayeon Kim, Hoigi Seo, Dong Un Kang, and Se Young Chun. Beyondscene: Higher- resolution human-centric scene generation with pretrained diffusion, 2024.
[45] Omer Bar-Tal Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation, 2023.
[46] Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, and Bin Cui. Mastering text-to- image diffusion: Recaptioning, planning, and generating with multimodal llms, 2024.
[47] Long Lian, Boyi Li, Adam Yala, and Trevor Darrell. Lim-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models, 2024.
[88] Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion, 2023.
[49] Quynh Phung, Songwei Ge, and Jia-Bin Huang. Grounded text-to-image synthesis with attention refocusing, 2023.
50] Jiayu Xiao, Henglei Lv, Liang Li, Shuhui Wang, and Qingming Huang. R&b: Region and boundary aware zero-shot grounded text-to-image generation, 2023.
15

---

Published in Transactions on Machine Learning Research (12/2025)
[51] Neil Houlsby Matthias Minderer, Alexey Gritsenko. Scaling open-vocabulary object detection. NeurIPS, 2023.
[2]
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühh. Simple multi- detection. In CVPR, 2022.
[33 Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Danny Driess, Pete Florence, Dorsa Sadigh, Leonidas Guibas, and Fei Xia. Spatialvhm: Endowing vision-language models with spatial reasoning capabilities, 2024.
[44]
Zhiqiu Lin, Deepak Pathak, Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, and Deva Ramanan. Evaluating text-to-visual generation with image-to-text generation, 2024.
[55] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling woendworld models for diverse visual tasks, 2024.
[6] Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European Conference on Compuler Vision, pages 38 55. Springer, 2024.
[57] Yujian Liu, Yang Zhang, Tommi Jaakkola, and Shiyu Chang. Correcting diffusion generation through resampling. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages 8713 8723, 2024.
[58] Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini, and Zeynop Akata. If at first you don't succeed, try, try again: Faithful diflusion-based Lext-lo-image generation by selection. arXie preprinl arXio:2305.1338 2023.
[99 Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, et al. Inference-tine time scaling for diffusion models beyond scaling denoising steps. orXiv preprinl ar arXiv:2501.099 097722, 2025.
600] Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev, Anastasia Maltseva, Said Azizov, Igor Pavlov, Julia Agafonova, Andrey Kuznetsov, and Denis Dimitrov. Kandinsky 3.0 technical report, 2024.
[61] Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, al. Pixart-\a: Fast training of diffusion transformer for photorcalistic text-lo-image synthesis. ai preprint ar arXio:2310.00430 2023.
[62] Amita Kamath, Jack Hessel, and Kai-Wei Chang. What's "up" with vision-language models? investi- gating their struggle with spatial reasoning. In EMNLP, 2023.
[63] Herbert Robbins. Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 58(5):527-533) 1952.
[64] Parham Rezaei, Farzan Farnia, and Cheuk Ting Li. Be more diverse than the most diverse: Optimal mixtures of generative models via mixture-UCG UCB bandit algorithms. In The Thirteenth International Conference on Learning Representations, 2025.
(65] Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2/3):235 256, 2002.
(66) Wassily Hoeflding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(30)::13-3) 1963.
[67] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Tlya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models, 2022.
16

---

Published in Transactions on Machine Learning Research (12/2025)
[68] Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phuc Le Khac, Luke Melas, and Ritobrata Ghosh. Dall e mini, 7 2021.
[69] Ming Ding, Wendi Zheng, Wenyi Hong, and Jie Tang. Cogview2: Faster and better text-to-image generation via hierarchical transformers, 2022.
[70] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. ArXiv, abs/2204.06125,, 2022.
[71 Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva, V.Ya. Arkhipkin, Igor Pavlov, Tlya Ryabov, Angelina Kuts, Alexander Panchenko, Andrey Kuznetsov, and Denis Dimitrov. Kandinsky: an improved lext-to-image synthesis with image prior and latent diffusion. In Conference on Empirical Methods in Natural Language Processing, 2023.
72] Phillip Y. Lee, Taehoon Yoon, and Minhyuk Sung. Groundit: Grounding diffusion transformers via noisy patch transplantation, 2024.
[33 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Πlya Sutskever. Learning transferable visual models from natural language supervision. In International Conference Machine Learning, 2021.
[74] Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, and Mengdi Wang. Gradient guidance for diffusion models: An optimization perspective, 2024. URL https://orri.. org/ hts/bb//24404 14743, 10.
[75] Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeddin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2334.07199, 2023.
[66] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018.
[77] Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, and Sanjiv Kumar. Rethinking fid: Towards better evaluation metric for image generation, 2024.
[78] Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila, Improved precision and recall metric for assessing generative models, 2019.
[79 Mohammad Jalali, Cheuk Ting Li, and Farzan Farnia. An information--heorrtic evaluation of generative models in learning multi-modal distributions. In Thirty-sevente Conference on Neural Information Processing Systems, 2023.
[80] Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Kaini Wang, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, and Bin Cui. Realeompo: Balancing realism and compositionalle improves text-to-image diffusion models. Advances in Neural Information Processing Systems, 2024.
[81] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Doso- vitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby, Simple open-vocabulary object detection with vision transform- ers, 2022.
[82] Wassily Hoelfding, Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13-33 30, March 1963.
[83] OpenAl. Gpt-4 technical report, 2024.
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A
PSE
A.1 PSE Threshold Hyper-parameter
What'sUp 62) introduces several dataset of images labeled according to the spatial relationships between objects depicted in the images. To compare PSE with other evaluators using metrics such as Precision, Rocall, Accuracy, Specificity, and F1 Score, we need to determine an appropriate threshold. For this purpose, we analyze the performance of both the default PSE and its thresholded version on two real-world datasets from What's The first dataset is What's Section A, which consists of 408 images with 2D spatial relationship captions. The second dataset is COCO-Spatial, containing 2,687 images with spatial relationship captions. As shown in appendix A.2. in the first experiment, thresholds of 0.4 and 0.5 yield results closest to the non- oon-thresholded version of PSE, while in the sccond experiment, thresholds of 0.5 and 0.6 provide the closest results. Based on these observations, we select 0.5 as the threshold for the thresholded version of PSE.
COCO-Spatial Dataset
What'sUp Dataset
1.00
Threshold Scores
1.0
Theshhold Senres
Main PSE Seare. 0.728
Main PSE Score: 0,707
0.90
Score
Score
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0.75
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0.70
0.0
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Threshold
Threshold
(a) The results of thresholded version of PSE for dif- ferent threshold values on the COCO-Spatial dataset.
(b) The results of thresholded version of PSE for dif- ferent threshold values on the What'sUp datasel.
Figure 7: Thresholded version of PSE for different threshold values on the COCO-Spatial and What's Upp datasets.
A.2 Visual Clarification of Ineffectiveness of Using Centers
fig. 4 illustrates that relying on the center of bounding boxes, as done by other detection-based evaluators, can lead to approving incorrect relationships. Even using the mask centers of each object does not resolve this issue. For example, the x-coordinates of the mask centers for the dog and tree are 612.0 and 599.8, respectively, which again results in the dog being incorrectly identified as to the right of the tree.
Another challenge arises when an object is neither to the right nor the left from a human perspective. For instance, in fig. [ we observe that the chair neither to the right nor the left of the TV. The centers of the bounding boxes and masks for the TV and chair are detailed in table g The x-coordinate of the chair's bounding box center is less than that of the TV's bounding box center. Consequently, metrics based baster-based directional relationships, such as those used by VISOR and HRS-Benchmark, would incorrectly classify the chair as being to the left of the TV. Similarly, segrenntalion-basel mask centers fail to address this issue, as the chair's mask center is also positioned to the left of the TV's center.
In contrast, PSE evaluates this scenario more accurately. The PSE values for both left and right relationships are notably low for the image, indicating that it does not consider either relationship a valid description of the scene.

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Table 9: Centers of bounding boxes and masks for the TV and chair. The PSE Left score also reported.
Figure 8: A failure case of VI- SOR metric. VISOR incorrectly determines the chair to the left of the TV, while the chair is nci- ther to the right nor left of the TV.
Objecs
Center
Center
Center
Mask Center
LEFT
Chuir TV
507.68 512.71
729.54 451.53
507.86 510.91
693.94 116.20
0.01
A.3 Robustness of Detector
Grounded-SAM was selected for its reported superior performance over commonly used models (55). How- ever, to make sure that our score robust enough use on segmentations extracted from models like Groundol-SAM, we used 100 randomly selected VISOR prompts and generated images using SDXL back- bone. We (1) apply three mask corruptions and observe minimal impact on PSE (table 10 (2) perform PSG (inference time scaling) with N = 4 and PSE across multiple detectors, noting consistent scores (table 11 These results show that first, PSE is robust to common corruptions of the mask and also the type of detector used for mask extraction.
Table 10: Dropout removes x% of the mask, Jitter randomly moves the mask x pixels, and Morph crodes and dilates with 3x3 kernel.
Dropout
Jitter
Morph (Opening)
Corruption 10%
30%
50%
80%
5px
20px
50px
100px
1 it
2 it
it
10 it
PSE Score
29.40
29.39
29.40
29.37
29.75
29.83
29.77
31.32
29.40
29.41
29.50
30.44
Table 11: PSE and PSG best-of-N (N = 4) with combination of various detectors. over-optimistie metric.
indicates non- PSE,
Model
SDXL
+Grounde--SAM (PSG)
+ClipSeg (PSG)
+Mask2Former( PPSG)
Grounded-SAN (PSE)
29.39
53.71
48.86
45.07
CLIPSeg (PSE)
30.68
44.78
52.04
42.31
Fask2Former (PSE)
26.43
41.23
40.45
46.79
*Gounding-Diee Dino (Box Center)
43.00
77.50
63.00
62.75
In addition, we note that the baseline spatial detection metric (VISOR) uses OWL-VIT. As our goal is lo detect objects generated images, we compared how OWL-VIT performs in comparison to Grounded-SAr on generated images on 500 SDXL generatod images. We used the prompts from our human cvaluation experiment. We compared how many times each detector missed an object which was labeled as presented by the human evaluators. Grounded-SAN. showed 5.8% detection miss rate, which was significantly lower
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than OIL at 11% Therefore, Grouded-SAAN is reasonable choice for detector. Even though, by advancements in object segmentation, our method can be applied on top of any other detector too.
A.4 Center-based Evaluation Metrics
Center-based evaluation metrics assess the aligmment of spatial relationships between the generated image and the imput prompt by analyzing the bounding box centers of objects identified by an object detector. For instance, if the center of one object's bounding box is positioned to the right of another, these metrics infer that the first object is located to the right of the second. However, unlike our proposed PoS-based evaluation metric (PSE), center-based metrics fail to account for critical factors such object shape and size. This limitation often leads to inaccuracies, particularly in complex scenes with intricate spatial relationships.
Through this work, we evaluated the performance of the PSE in comparison to three center-based evaluation metrics as follows.
VISOR metrics. The VISOR benchmark introduces six evaluation metrics to assess spatial relationship alignment in generated images. Among these, the VISOR metric evaluates spatial accuracy by deter- mining whether both objects are accurately generated and positioned as specified in the prompt. In contrast, the VISORcond metric measures spatial accuracy conditional on the correct generation of both entities. An- other key metric, VISORa, calculates the success rate of generating out of 4 images (where ranges from to 4) that adhere to the spatial relationships described in the prompt. To score spatial relationship align- ment, the VISOR benchmark utilizes the open vocabulary object detector OWL-ViT (81), which identifies objects in the image and compares the center points of their bounding boxes to assess compliance with the spatial relationships specified in the textual input.
T21-CompBench and HRS-Bench Metrics. T2I-CompBench and HRSS-enchmark are two prominent benchmarks designed to evaluate compositional generation tasks in text-to-image (T2I) models. In order to assess the spatial relationship aligument between the generated image and the input prompt, both bench- marks utilize center-based evaluation metrics by employing UniDet (52) as the object detector extract the bounding boxes of objects. Spatial relationship accuracy is then determined by comparing the relative positions of the bounding box depth conters, providing mcasure of how well the generated images adhere to the specified spatial relationships in the input prompts.
A.5 Limitations
Our proposed PoS-based evaluation metric (PSE) offers a smoother and more muanced assessment of spatial relationships compared center-based approaches by adopting probabilistic perspective that accounts for the overall shape and size of objects. However, similar to center-based methods, PSE is inherently influenced by the performance of the depth estimation or segmentation model it relies on. Specifically, PSF determines object's mask in the generated image using segmentation outputs provided by models such as SAM. Consequently, any limitations or inaccuracies in these tools can propagate through the evaluation process, potentially affecting the reliability of the overall assessment.
A.6 Online PoS-based Evaluation Details
As discussed in the main paper, one drawback of using VISOR or our sub-dataset for PSE evaluation the need to generate thousands of images for each model conduct reliable comparison. For users seeking to identify the best-performing model among several candidates, this can be prohibitively expensive due to the current API costs associated with image generation. To address this issue, we suggest technique for evaluating models in an online manner, which avoids the need for generating large batches of images. This approach reduces the computational burden of sampling from suboptimal models.
We adopt framework similar to that introduced in 122 Let the set of generative T2I models be denoted as g : [G], where each generator g e 9 produces images distributed according to Py over a dataset D. The evaluation process is carried out over T steps, where, each step, the algorithm selects generator ge from
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G and collects set of samples 2ạ Pg generated by the chosen model. The goal is to design algorithm that effectively balances exploration and exploitation to identify the optimal model (i.e., the one with the highest PSE score).
scores for individual samples are independent and additive. This property enables the use of established methods for optimizing the selection process. Specifically, the soore of generator g can be expressed as Eg~p, [<)],, allowing the application of tools like Hoeffding's inequality (22) to compute optimistic bounds. In this context, (SE(x) is the PSE score for individual samples, and empirical estimate of the generator's true score. Using these
A notable feature of PSE is its Sccres
the mean of the IID samples serves propertios, we employ the Upper Confidence Bound (UCB) algorithm, a well-establiseed approach in the multi-armed bandit framework, to dynamically identify the best-performing model. The IID property of the samples arises from the use of random seeds and prompts for image generation.
During each step of sampling, the model with the highest upper confidence bound for empirical PSE score is selected. Let ne(g)) denote the number of times model i has been chosen up to step The upper confidence bound for the i-th model at step t is defined
PSEucB(gat)- EMEDI( PSE(x) (g(
In(() (e(
(7)
where controls the probability of failure (selecting suboptimal model with lower true score). Based empirical observations, we set a = 2 in our experiment. At each step i, the generative model with the highest PSEven (9; t) value is selected. This strategy minimizes the risk of excessive sampling from suboptimal models, ensuring a more efficient and accurate evaluation process.

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B PSG
B.1
Our Generative Semantic Nursing method involves two key hyperparameters: the number of denoising steps during which noise optimization is applied and the scaling factor of the loss function. To determine optimal values for these hyperparameters and ensure fairness our main experiments, we employed a set of ten objects not included in the COCO dataset. From this set, 100 pairs were randomly selected and assigned random spatial relationships. Images were then generated using the PSG method, implemented on both the Stable Diffusion 1.4 and Stable Diffusion XL backbones.
For PSG applied to Stable Diffusion XT, we experimented with scaling factors ranging from 100 to 2500 in increments of 900 and optimization step counts ranging from 4 to 22 in increments of 6. The VISOR metric was used to evaluate spatial accuracy. Our results indicate that increasing the scale factor and step count generally improves adherence to the specified spatial relationships. However, beyond certain thresholds, these increases lead to dogradation the quality of the generated images. For instance, a low scale factor provides insufficient guidance, resulting in outputs resemblingg SD-XL's default behavior, as shown in fig. g Through experimentation, we determined that configuration of (10 steps, 1000 scale factor) offered optimal balance, achieving better spatial relationship adherence without compromising image quality. Illustrative examples of the effects of high and low step counts and scale factors are provided in fig. 9 fig.
and fig. 12
For PSG applied to Stable Diffusson 1.4, optimal performance was achieved with approximately 20 steps and considerably smaller scale factor. After experimenting with scale factors and step counts ranging from 10 to 25, we identified the best configuration as 20 steps and a scale factor of 25. In contrast, applying PSG to Stable Diffusion XL required significantly fewer steps (at most 10) keeping the time overhcad of PSG relatively low. On an NVIDIA A100 GPU, applying PSG during the first 10 steps resulted in less than a 20% increase in processing time of SD1.4. Further reducing the number of steps can decrease this overhead even more, maintaining the method's efficiency. Figure 10 illustrates the scalability of our methods based on PoS when additional computation is available. It further shows that gradicnt- based latent optimization is generally more effective, while search-based methods provide way to exploit. unbounded computation for continued performance gains.
Furthermore, apply Gradient-Basse PSG using the 32 x 32 cross-attention maps, based the analysis in table 12 (on 500 sample prompts), which demonstrates their superiority.
Table 12: Comparison of different U-Net layers for Sradient-Baseee PSG shows the superiority of the 32 x 32 cross-attention maps of SDXL.
Metric
Without PSG
32x32 Maps PSG
64x64 Maps PSG
VISOR
44.0 21.0
74.4
48.2
PSE
62.0
32.0
B.2 Further Quantitative Results
The overall spatial score, as evaluated using the HRS-Benchnark 26), is presented in table G Additionally, the score, segmented by prompt difficulty, is shown in table 13 Scores for other models are taken from the supplementary materials of the HRS-Benchmark. The T21-CompBench 2D spatial score for SD 1.4 is also
reported in table [4
Generalization Other Backbones. For inference time scaling PSG, we additionally used 100 ran- domly selected prompts from the VISOR prompts and up 1.6K images to analyze the behavior when apllied on different models. Table table 16 shows that models with various architectures can method
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Table 13: Quantitative assessment of our proposed generative semantic nursing method PSG on top of SDXL, based on the HRS-Spatial benchmark split on the prompt difficulty.
Model
Easy
Medium
Hard
SDV1
21.75
0
SDV2
1.19
0
Glide
2.49
0
CogView 2
8.88
0
0
DALL-E V2
28.34
0
0
Paella
8.78
0
0
minDALL-E
4.29
0
0
DALL-EMini
15.17
0
PSG
68.56
28.44
5.98
to improve their alignment to spatial constraint defined the prompt. Furthermore, table 16 also suggests that by using more compute (increasing N), we can consistently achieve better performance.
Generative Semantic Nursing On Transformer Architecture. We applied PSG on the average cross-altention maps of PixArt-a a transformer-based model. We used scale 10 and applied guidance
Few Steps
Many Steps
Low Scale
High Scale
Figure Qualitative analysis of the effect of the scale factor of PSG's loss function and the number of denoising steps it is applied, for the prompt "a dog to the left of cat".

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0.7
gradient-ban based based
Score
gradienc
0.60
0.55
0.3
0.50
16
32
64
00
16
32
64
80
Time (5)
Time (s)
(a) Scaling VISOR Score
(b) Scaling PSE Score
Figure 10: Scaling behavior of search-based and gradient-based PSG by scaling the number of images in Best-of-N and increasing the number of time-steps during which the latent optimization is applied.
(a) Scale 10
(b) Scale 16
(c) Scale 4
(d) Scale 10
Figure 11: The effect of the scale factor in the PSG method. As the scale increases, the generated images can become more distorted. Using a medium scale instead a very small one can prevent incomplete images.
(a) 1000 steps
(b) 1900 steps
Figure 12: The effect of optimization step counts in the PSG method. A higher number of steps distorts the images more, resulting in lower quality.
for 20 steps. Using the same prompts as the previous experiment, PSE improved from 28.50 to 57.87, and center-direction accuracy increased from 40% to 68%. This indicates that our method is not limited by the U-Net architecture and can also applied on transformer-bast generative models.
Additional Complex Prompts. Beyond table which includes prompts with multiple relationships, we evaluate PSG two sets of 100 prompts. The first set contains three objects with spatial relationships

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Table 14: Quantitative comparison baselines using 121-CompBench spatial benchmark for PSG on SD 1.4 backbone.
Model
T2T Spatial Score(%) t
SD 1.4
0.1246
SD 2 2
0.1342
Composable v2
0.0800
Structured v2
0.1386
Attn-Exct v2 0
0.1455
DALLE-2 (70)
0.1283
SD + PSG
0.2349
Table 15: Quantitative comparison of SDXL and SDXL+PSC on PSE and VISOR metries (average score of the relationships) for 100 prompts with three and four objects.
# Objects
Model
PSE
VISOR
SDXL
30.8
57.0
SDXL+PSS
63.8
72.5
SDXL
31.5
54.0
4
SDXL+ PSG
68.4
58.0
Table 16: PSE/ (center position accuracy) of PSG inference time scaling. This table demonstrates the effoctiveness our method for different models. Incrcasing N, shows that by using more compute we can get consistently better alignment. We observe that applying PSG, even with small N, has a noticeable effect on the accuracy of position of objects.
Model
w/o PSG f
PSG N=21
PSG N
PSG N
PSG N=16 1
FLUX
45.52 / 60
56.85 / 73
72.05 / 82
83.60 / 91
93.59
96
SANAL.5
72.62 /
80.31
89
87.81 /
90.58 / 97
93.71
999
SD3
47.41 /
64.29 / 70
75.36 / 86
82.58 / 91
88.13
94
SDXL
29.39
/ 43
40.07
64
55.56
/ 76
67.14 / 86
78.23
93
SD2.1
16.07
32
29.25
50
43.70
75
60.94
89
77.52
95
(o.g. "a balloon the bottom of chicken and the left of lamp'), following the VISOR multi-object template. The second set contains four objects with three relationships (e.g. "a cup the right of a dog and a bicycle on the left of a man"). Table 15 demonstrates the effectiveness of PSG on these more complex prompts.
Search-Based VISOR. Since the search-based variant of PSG can also be applied with alternative center- based verifiers such as VISOR, conducted direct comparison between the two. Specifically, used Best-of-16 SDXL-generated images
subset of 100 prompts from table In table L7 report both the PSE and VISOR scores for these images. Because using the same verifier for both generation and evaluation risks reward hacking (i.e., each method achieves its highest score under ils own verifier), we additionally performed human evaluation. Annotators assigned a score of if all objects were present and their spatial relationships correct, and otherwise. Table 17 shows that PSG consistently outperforms VISOR as a search- bearch-based method. This result aligns with our carlier finding that PSE correlates more strongly with human judgments.
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Table 17: Quantitative comparison search- based methods on VISOR, PSE, and Human evaluation.
Method
VISOR
PSE
Human
Search- Cearch-Based--e BassedVISSO
92.0
33.8
22.0
72.0
77.5
61.0
B.3 Further Qualitative Results
Figures 15 and 16 provide visualization of the effectiveness of the PSG method compared to the baseline model (Stable Diffusion XL) and the fine-tuning method ("Getting Right"). The similarity in the overall appearance of the images for each prompt between the baseline model and the PSG-applied version can be attributed to the of the same seed for generating images across all models. fig, [7 further illustrates the accuracy of spatial relationships demonstrating that PSG maintains correct spatial relationships while adhering to the stylistic features specified in the text prompt.
Additionally, fig. 18 presents qualitative assessment PSG's performance on combined spatial relation- ships. The results demonstrate that PSG accurately infers spatial relationships, even in complex scenarios involving combinations of primary directions. For prompts that focus providing 3D instructions, please refer to fig. 19
B.4 Incorporating Distance
Distance can be incorporated into PoS to model proximity (e.g., close or surrounding) and separation (e.g.. lar). eq. 2 can be extended by introducing a threshold e, which defines distance constraint, resulting in the following PoS variation:
PoS = PX>AY>B(XX +
(8)
where e controls the degree of separation, either as a fixed constant or a function of object sizes. Summing projected Posd across the four principal directions yields:
disttnnance (;, = =PoS right (A, B;c)+
PoSt (A, B;c)+
(9)
Post (A, B; c)+
Posd bottom (A, B;c)
This formulation rewards or penalizes deviations beyond e pixels (by inverting the loss sign), effectively incorporating distance modeling. Notably, summing over all four directions dissipates directional effects, making it roughly equivalent to the IoU loss used for attention binding 77) To evaluate this modification, conducted experiments on 100 multi-object prompts with fixed random seeds and = 5 (corresponding to an attention-man map resolution of 16 by 16). The far loss increased the average L2 norm distance between object mask centers from 102.5 pixcls to 130.8, while the close loss reduced it to 90.7 pixels, demonstrating that this approach effectively improves distance-based 2D relations.
B.5 Diversity of Bounding Boxes.
The dependency of Layout-guided generative models, not only undermines the model's autonomy but also fails to address the core goal of understanding and correctly applying relational positions directly from the prompt without external input. As the text is informative enough about the spatial relationships. In contrast, our model uses the prompt itself to guide the diffusion process, ensuring spatial relationships are applied naturally during generation.
We explored an automated alternative where Large Language Models (LLMs) suggest layouts based on prompts 47 80 However, in experiment generating 50 images from prompt sets (e.g., "an object to

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the right of another"), LLM-generated bounding boxes exhibited low diversity, with bounding box centers and sizes frequently clustering. This lack of variation reduces the natural diversity expected in real-world scenarios. In contrast, our approach with SDXL + PSG, driven purely by the prompt, produces significantly more diverse bounding boxes compared to RealCompo's template (80) for bounding box generation using GPT-40 as confirmed by lable 18
Table 18: Variance of center and size of boxes for SDXL + PSG (Generative Semantric Nursing) and RealCompo (8]) shows that our method's effect has better diversity of size and position of objects compared to using LLM guided bounding box for objects.
Model
Center A (1)
Center B (1)
Size A (t)
Size B (t)
SDXL + PSG
0.127
0.095
0.109
0.063
RealCompo
0.036
0.07
0.041
0.025
SDXL + PSG
0.092
0.111
0,169
0.291
RealCompo
0.036
0.042
0.037
0.059
SDXL + PSG
0.088
0.056
0.125
0.161
RealCompo
0.05
0.051
0.058
0.174
SDXL + PSG
0.081
0.159
0.129
0.281
RealCompo
0.043
0.038
0.068
0.09
B.6 Ablation Studies
Super-category Pairs Split. Score. In fig. 14 we visualize the performance of our method, PSG, along- side the baseline backbones, based on the super-category pairs objects. The super-categories are derived from the COCO datasot. is evident that our models consistently improve the VISOR score. The poor performance on the "person-person' pair is due to the fact. that, in the VISOR datasel, there only one "persoonrellaiion-person pair. Additionally, observe that the performance of method is also depen- dent on the initial performance of the backbone. When the backbone performs better initially, our method approaches optimal accuracy.
Order Bias. fig. 13 suggests that the accuracy of backbones for the first and second objects is not consis- tent. In our experiment, both Stable Diffusion 1.4 and XL demonstrate higher accuracy for the first object. While our method prinarily focuses enhancing spatial relationships, we observe that it consistently im- proves the overall accuracy of object generation. Notably, in Stable Diffusion XL, which already exhibits good accuracy in generation, our method reduces the number of missing objects for cach generated image.
Relation Split Scores. We have presented the Object Accuracy and VISOR score for each relation type in Table table 19 Our method has improved both object accuracy and VISOR score across all relationships. Additionally, it is worth noting that the performance of our model on Left and Right relationships is somewhat better than on Top and Bottom relationships. We hypothesize that this difference is due to the relative rarity of Top and Bottom relationships in the VISOR dataset. This effect of rarity further illustrated in fig, [4] where we observe lower performance on pairs of outdoor objects when compared to indoor objects and appliances, these pairs less likely to appear together in real image datasets.
B.7 Using LLMs for Spatial Relationship Extraction
We suggest using the following prompt, which specifically engineered to output the object and relationship pairs for our loss function. Additionally, code provided to identify the tokens associated with the objects using Stable Diffusion's 's tokenizer.
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Object Generation Bias: Order of Objectt in Text
Object
91.58
Both
ObjectA
Objject B
2141
10.34
62-
21.66
25.5
24.6
20
SD14.180
SD 14 PSO (and)
AZ
40 PN0
ML. PSO
Model
Figure 13: Order bias in generated images with PSG variants applied on SD 1.4 and SDXL backbones. all experiments, Object A refers to the first object and Object B refers to the second object.
Example LLM Instruction
Analyze the provided input text to extract spatial relationships between objects, then output these relationships in a structured format. Follow these steps preciscly:
1. Identify Spatial Relationships: Locale any spatial relationships, either explicitly stated or implicitly conveyed. Possible relationships are "left," " "right," " "top," " "bottom," " "behind," or "in front." " Note relationships that may be implied by context well. Erample:
Input: "A horse is rumning to the left of car."
Relationship: left
2. Identify Related Objects: For each spatial relationship, determine the two objects involved.
Exomple:
Object horse
Object car
3. Format the Output: Present each object pair and their spatial relationship in the format (object1, object2, relationship), with each pair new line.
Erample:
Input: "The man is reading a book while sitting. dog is sitting to the right of his chair."
Output:
(man, chair, top)
(dog, chair, right)
Task:
Apply this process to the following text and provide only the final output in the specified format. Use step-by-step reasoning but keep the format of the final response.
Imput Tezt:
B.8 Limitations
Our proposed training-free free method provides a computationally efficient, on-the-fly solution for addressing spatial relationship challenges T2I models. However, important acknowledge certain limitations that can be explored and addressed in future research.
Limitations of the training datasct T2I models. Spatial relationship-rclated examples are often underrepresented in large-scale multi-modal datasets. Additionally, these datasets may contain various spatial relationship-based biases. Such issues likely key contributors to the poor performance of T2I
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Table 19: Comparison Visor and Object Accuracy scores of our models and the backbones split by relationship type shows that our method consistently improve the accuracy of prompts with each direction.
VISOR (%) (t)
Object Accuracy (%) (t)
Models
Left
Right
Top
Bottom
Left
Right
Тop
Bottom
SD 1.4 SD 1.4 + PSG
18.71
18.23
22.01
16.26
29.32
28.9
33.31
26.68
30.64
31.4
34.36
23.54
31.25
34.86
41.08
30.71
SD 1.4 + PSG (and)
41.52
39.7
40.31
38.4
44.89
44.06
45.1
45.59
SD XL
43.14
45.53
46.74
44.55
62.56
64.7
67.43
68.06
SD XL. + PSG
77.29
77.18
72.75
72.25
78.29
78.15
75.5
75.44
SD XL PSG (and)
82.18
80.83
74.16
72.67
83.46
82.09
77.42
77.41
models on spatial relationship benchmarks. Consequently, training-free generation methods that guide the diffusion model during inference may not offer comprehensive solution to the spatial relationship problem, particularly for complex text prompts involving numerous entities. In such cases, extensive fine-tuning of T21 models carefully curated datasets tailored to specific domains may be necessary achieve optimal performance. However, this fine-tuning process typically demands substantial computational resources and time, highlighting the need for future research to strike a balance between training-free methods and fine- tuning-bast based approaches.
Limitations of the CLIP text encoder. The limited capability of the CLIP text encoder to accurately understand and represent objects and their spatial relationships may significantly affect the performance of T2I models in generating spatially coherent images. As most T2I models rely the CLIP text encoder the backbone for processing textual inputs, any deficiencies in the encoder's ability to capture and encode spatial relationships can propagate through the denoiser, resulting in suboptimal outputs or outright failures in the generated images. Surprisingly, much of the existing research has overlooked this potential root cause, often attributing these failures to other factors without adequately considering the inherent limitations of the CL.IP text encoder. To address this gap, our future work will focus on enhancing the text encoding capabilitics of T2I models, loveraging the PoS-based perspective proposed in this study.
Memory Usage and Computation Time. The amount of available information for aspects such spatial relationships depends the generative model used. For instance, standard 2D text-to-image diffusion models, such as Stable Diffusion, do not provide information about the third axis dess-epthh-- in an image, We proposed a method for generative semantic mursing, which introduces minimal overhead to the model. However, in cases where the model does not inherently provide 3D spatial relationships, we employ initial noise search using PoS. This approach requires segmentation and/or depth estimation models, introducing some computational and memory overhead. Our experiments show that the time overhead is negligible, but as models scale, higher GPU memory will be required. However, the significant improvement compositional generation achieved by PSG justifies this trade-off. Importantly, PSG's reliance external estimators is not intrinsi;; for instance, in 2D spatial relationships, it can directly leverage the architecture of T2T models. eliminating the need for additional estimators.
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Performance by Supercategory Pair
Performance by Supercategory Pair
whicle
vebicle
animal
animal
accesiry-
30
40
kichea
kitche-
farniture
Simihue
20
dastonic-
appliance
appliance
indoor
indour
petron
vabede
matdeat
amiud
Borenkory
Seart
kinchep
fird
fanatrer
desttonle
appliauro
Andey
Pestin
veluele
catdeou
amama
Bucentory
sporti
kitulen
fanofiur
electeale
apoliare
Indua
SD 1.4
(b) SD XL
Performance by Supercategory Pair
Performance by Suporcategory Pair
param-
perem-
ousder
60
oundour-
80
anina
animal
accewary-
accessrry
60
sports
sports
kiccha
kitches
40
foo3
Soud
farainre
Simirre
appliance
10
appliaace
indoor
indor
portan
vabide
curdoor
keicko
fivd
fanature
distvaie
appliacc
Inskier
pessel
vediete
cuntbur
anauul
spozts
kikdben
Rial
farutiur
dratuale
applincc
(c) SD 1.4 + PSG
(d) SD XL + PSG
Porformance by Supercatogory Pait
Performance by Supercatogory Pair
paoon
pine--
vehicle
vehicle
ouideor
oandou
80
sports-
sports-
fad
Sodd
40
farnitue
Saminure
docionie
20
20
lance
appliance
indoar
indour
martal
valetee
casdoor
antiond
Rervary
peoto
kitchon
find
fanditrre
desstaiic
apilisee
lasker
pessse
vedlete
antnne
kilubre
Auol
Benttur
destouie
spiliaec
Indore
(e)
PSG (and)
(f) SD 1.4 + PSG (and)
Figure 14: VISOR unconditional scores our models and baselines split by super-category pairs. We observe that our models consistently achieve better VISOR unconditional scores.
13

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Published in Transactions on Machine Learning Research (12/2025)
SDXL + PSG
SDXL
Getting it Right
strawberry on top of a donut
table bottom of bievele
a car on top of hamburger
Figure 15: Qualitative comparison of our proposed method, PSG, versus Stable Diffusion XL and Getting it Right on spatial relttee prompts.
SDXL. + PSG
SDXL
Getting it Right
man
riding a
horse to
the right of tree
a bird to
the right of
a wine glass
a dog the left of car
Figure 16: Qualitative comparison of our proposed method, PSG, versus Stable Diffusion XL. and Getting it Right on spatial relationshipp-elltted prompts.
14

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Published in Transactions on Machine Learning Research (12/2025)
top of bus, in pink colored anime style
SDXL
PSG
SD XL
green the left of bottle, detailed
SDXL
PSG
XL
standing the right of woman, anime goth style
SDXL
PSG
SD XL
somber painting a car bottom of an elephant
SDXL
PSG
SD XL
Figure 17: Qualitative comparison of our proposed method, PSG, versus Stable Diffusion XL on artistic prompts.
15

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Published in Transactions on Machine Learning Research (12/2025)
SDXL PSG
SDXL
Getting it Right
bird top right of donut
seagull top left of umbrella
potted plant bottom left of ghair
Figure 18: Qualitative comparison of our proposed method, PSG, versus Stable Diffusion XL and Getting it Right prompts with combined spatial relationships,
SDXL
SDXL PSG
SDXL
SDXL PSG
SDXL
SDXL
chicken in front of bowl.
chair behind desk.
dog hidden by a frog.
Figure 19: Qualitative comparison of our proposed method, PSG, versus Stable Diffusion XL 3D instruc- tions. Similar to Conppehnn,nn,, "hidden' treated equivalent to "behind".
Without Guidance
Far Guidance
Close Guidance
Without Guidance
Far Guidance
Close Guidance
A black and white photo of horse and bam
A photo of dog and ball
Without Guidance
Far Guidance (9)
Far Guidance (15)
zoomed out photo a car and plane
Figure 20: Illustaation close vs. far guidance using PoS with the distance loss described in appendix B.4 The hyperparameter (e.g., 9 or the bottom figure) specifies the threshold for what. considered far.
16


Reviews:
[{"review_id": "Aci5e4P4Y0", "reviewer": "Reviewer_Qw6y", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\nThe paper is generally well-written and easy-to-understand. The authors systematically define the limitations of existing evaluation metrics like VISOR, and builds upon a simple solution to better model the spatial relationship between objects. The proposed metric (PSE) also correlates to human judgement well (better than VISOR, and other existing metrics) which shows the effectiveness of PSE. For PSG, both the gradient-based and search-based methods make sense, and overall the experimental results supports the claims. The qualitative samples are adequate, and depicts the usefulness of the proposed metric.\n\n**Major Weaknesses**\n\n(1) As the authors say in A.5., the success of the proposed metric entirely depends on the accuracy of the segmentation and depth estimation model. For densely populated scenes, Grounding DINO may fail in locating complex referring text prompt (such as \"object in the far right which in not in the bottom\"), and the metric will inherently incur noise. I would suggest the authors in reporting such failure case and consider providing an ablation with different segmentation and depth estimation modules. \n\n(2) Though the authors claim in B.4. that the concept of relative distance ('close' vs 'far') can be modeled by PoS by incorporating a constant 'c', the value for 'c' can be different based on the context. For example, 'far' in a zoomed out image would require a lower value of 'c' compared to a zoomed in view. Though I agree that the same issue is present in previous metrics like VISOR, PSE does not solve it. I would also suggest the authors to provide ablation on the value of 'c', and provide some visualization for them.\n\n(3) The search-based PSG method requires generating N samples per prompt which can be extremely expensive for large T2I models. Moreover, choosing a higher value of N is important to achieve a substantial gain. The paper does not provide any concrete analysis of latency for PSG. \n\n(4) I would also be interested to know the effect of PSD for T2V models (though out of the scope for this particular paper, but is interesting).\n\n **Minor Weakness**\n\n(1) The abstract mentions 'PSG employs a Part-of-Speech PoS-based reward function ...'; what is Part-of-Speech?\n\n\nOverall, the paper provides a good contribution with adequate experiments and supports most of its claims effectively by pointing out limitations in previous evaluation metrics.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a probabilistic framework to model the relative spatial positioning of different objects in a scene. The authors argues that the current text-to-image generation models struggles to accurately follow input prompts to reflect the specified spatial configuration between different objects, and the existing evaluation metrics (VISOR, T2I-CompBench etc) can not effectively penalize such deficiencies in the generated images. In this work, the authors first proposes a PoS (Probability of Superiority) - based evaluation metric, PSE, which is designed to access the alignment of objects in 2D and 3D scenes generated by T2I models. Next, based on PSE, the authors propose PoS-based Generation (PSG), an inference-time method that improves spatial alignment without requiring extra fine-tuning or additional input. Lastly, this paper also defines online PSE (OPSE), an online version of the PSE metric to reliably detect the superior model using only a limited number of samples. The experiments show that PSG improves the ability of T2I models to preserve stronger spatial relationship, and PSE metric exhibits stronger alignment with human judgment compared to traditional center-based metrics.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please refer to the major weaknesses section.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "35RF8v56Ab", "reviewer": "Reviewer_fbcu", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and easy to follow.\n2. Modelling the spatial relationships between objects in generated image using probability is intuitively more robust than using center points. The human study also shows higher correlation for PSE than other metrics, such as VISOR, T2I-CompBench, etc.\n3. The proposed PoS-based generation is a simple and straightforward approach to enhance generation with specified spatial configurations.\n\nWeaknesses:\n1. What is the inter-rater agreement for the human study and how are the human rater results aggregated?\n2. Why only a specific PSG method is chosen for each benchmark in Section 4?  E.g. only gradient-based PSG is evaluated in Table 4-6. Is it because search-based PSG cannot bring significant benefits on these benchmarks?\n3. For the evaluations for PoS-based generation,  is it possible to also employ gradient-based or search-based method for image generation using other metrics, such as VISOR? Comparing the generation results with PSE-guided generation would clearly show the advantage of PSE.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a probabilistic formulation of spatial relations between objects in text-to-image (T2I) generation using the Probability of Superiority (PoS). Specifically, the authors introduce (i) PSE, an evaluation metric that compares projected distributions (from masks and optional depth) to score 2D/3D relations, and (ii) PSG, an inference-time guidance method that improves spatial alignment either by gradient nudging on cross-attention maps or by Best-of-N selection using PSE as a reward. They further present OPSE, a UCB bandit procedure to identify the best model with fewer samples. Empirically, PSE correlates better with human judgments than center-based metrics, and PSG improves spatial benchmarks (VISOR, T2I-CompBench, HRS) with modest quality trade-offs.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "There is a typo in the last figure in Figure 1 \"A computer in fornt of a cow.\"", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "9pFOYLg7R9", "reviewer": "Reviewer_4mMA", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n1. A PoS based technique to determine the spatial orientation of objects is proposed. The motivation and formulation of the framework is sound and meaningful.\n2. The PoS based metric outperforms traditional metrics such as VISOR and CLIP score.\n\n**Weaknesses**\n \n1. It is unclear from the text how the probabilities and the projections are computed for computing the evaluation metric in Section 4.1. Which layer of the U-Net diffusion model is used to compute the cross-attention map? Does the selection of the layer affect the performance? \n2. How generalizable is the proposed method when generating multiple objects with multiple relationships among themselves? Also, how robust are the metrics when evaluating spatial relationships with multiple objects? \n3. Can the formulation be extended to include relations such as \"covered\" in the 2-D case and \"behind\" in the 3-D case?\n4. Can this formulation be used for editing images? For example, can it be used for swapping different objects in the image? The paper should discuss more in-depth implications about their proposed method for future applications.\n5. Can the PoS based generation be used as an additional loss when training a diffusion model from scratch? Currently, it seems it can only be applied on pre-trained diffusion models.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a framework based on Probability of Superiority (PoS) to evaluate the spatial relationships of objects as specified in text prompts in diffusion models. The proposed method utilizes the cross-attention maps generated by the diffusion model for each object in the text prompt. Using these cross-attention maps, a PoS metric is computed to determine the 2-D spatial location of the probability densities relative to each other. The PoS metric is also extended to 3-D object generation by incorporating depth maps generated using Depth Anything. PoS is further used as a training free technique to improve generation performance of pretrained diffusion models.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. The paper needs more in-depth explanation on how the probabilities and projections are computed.\n2. The paper needs to show an ablation study that determines the best layer to be used for computing the cross-attention map for PoS.\n3. The papers needs to include the performance and robustness of the model with respect to the number of objects in the text prompt. Ideally, show the performance of the method with 2, 3, 4, .. etc objects in the text prompt.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)