GeoDiv: Framework for Measuring Geographical Diversity in Text-to-Image Models
Authors: Abhipsa Basu
Mohana Singh
Shashank Agnihotri
Margret Keuper
Venkatesh Babu Radhakrishnan
Venue: ICLR 2026 Poster (2026)
Keywords: geographical diversity
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We propose GeoDiv, a framework that formally defines and quantifies geographical diversity in generative models.

Abstract: Text-to-image (T2I) models are rapidly gaining popularity, yet their outputs often lack geographical diversity, reinforce stereotypes, and misrepresent regions. Given their broad reach, it is critical to rigorously evaluate how these models portray the world. Existing diversity metrics either rely on curated datasets or focus on surface-level visual similarity, limiting interpretability. We introduce *GeoDiv*, a framework leveraging large language and vision-language models to assess geographical diversity along two complementary axes: the Socio-Economic Visual Index (SEVI), capturing economic and condition-related cues, and the Visual Diversity Index (VDI), measuring variation in primary entities and backgrounds. Applied to images generated by models such as Stable Diffusion and FLUX.1-dev across $10$ entities and $16$ countries, *GeoDiv* reveals a consistent lack of diversity and identifies fine-grained attributes where models default to biased portrayals. Strikingly, depictions of countries like India, Nigeria, and Colombia are disproportionately impoverished and worn, reflecting underlying socio-economic biases. These results highlight the need for greater geographical nuance in generative models. *GeoDiv* provides the first systematic, interpretable framework for measuring such biases, marking a step toward fairer and more inclusive generative systems. Project page: https://abhipsabasu.github.io/geodiv

Published as a conference paper at ICLR 2026
GEODIV: FRAMEWORK FOR MEASURING GEOGRAPHI- CAL DIVERSITY IN TEXT-TO-IMAGE MODELS
Abhipsa Basu'
Mohana Singh
Shashank Agnihotri
Margret 2 R. Venkatesh Babu'
Vision and Al Lab, Indian Institute of Science, Bangalore, India
Chair for Machine Learning, University of Mannheim, Mannheim, Germany
3 Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
ABSTRACT
Text-to-image (T2I) models are rapidly gaining popularity, yet their outputs often lack geographical diversity, reinforce stereotypes, and misrepresent regions. Given their broad reach, it is critical to rigorously evaluate how these models portray the world. Existing diversity metrics either rely on curated datasets or focus on surface- level visual similarity, limiting interpretability. We introduce GeoDiv, framework leveraging large language and vision- laiinnl models to assess geographical diversity along two complementary axes: the Socio-Fconomie Visual Index (SEVI), capturing economic and condition-related cues, and the Visual Diversity Index (VDI), measuring variation in primary entities and backgrounds. Applied to images generated by models such as Stable Diffusion and FLUX. across 10 entities and 16 countries, GeoDiv reveals consistent lack of diversity and identifies fine- grained attributes where models default to biased portrayals. Strikingly, depictions of countries like India, Nigeria, and Colombia are disproportionately impoverished and worn, reflecting underlying socio-economic biases. These results highlight the need for greater geographical nuance in generative models. GeoDiv provides the first systematic, interpretable framework for measuring such biases, marking step toward fairer and more inclusive generative systems. Project page: https faabhissbbauu.. github..hh..... wttbbooo/geoii.
INTRODUCTION
As Text-to-Image (T2I) models gain traction in public and commercial applications, a central question arises: whose world are they representing? Trained on internet-scale data, these models often misrepresent regions and reinforce harmful socio-economic and regional biases (Basu et al. 2023). For instance, prompting Stable Diffusion (Rombach et al.) (222)) with 'photo of a car in Africa' often yields scenes with dusty, worn-out surroundings and damaged vehicles, overlooking the continent's 's visual and economic diversity. Recent studies confirm that these images frequently lack geographical diversity (Hall et al. 2023; 2024 Askari Hemmat et al., 2024). Moreover, early evidence also points to socio-cconomic skew (Turk) 2023): images from some countries like India overwhelmingly depict poverty or dilapidation, while others appear consistently polished or affluent (e.g., Japan). Such disparities challenge the aspiration of these models function faithful world models (Pouget et al. 2024 Astolfi et al. 2024).
With growing evidence that T2I models exhibit visual and socio-economic disparities across regions (see Figure [ there is a need for an automated framework to capture fine-grained geo-diversity. Existing approaches, whether based narrowly curated datasets (Hall et al. 2024 Ramaswamy et al.)
2023 Gaviria Rojas et al. (022) or low-level visual dissimilarity metrics (Friedman & Dieng 2023). struggle reveal such deeper, country-speeiff patterns. Although recent works use Large Language Models (LLMs) and Vision-Language Models (VLMs) to assess realism (Li et al. (025), prompt
consistency (Hu et al.) 2023 Cho et al. 2023), , or concept diversity (Rassin et al. [0224] Teotia et
2025), these formulations remain insufficient for geo-diversity, which spans economic, environmental,
Equal contribution
Work done while interning the Vision and AT Lab, Indian Institute of Science, Bangalore

---

Published as a conference paper at ICLR 2026
"A photo of a house in Nigeria"
Real World (GeoDE)
FLUX.1
SD3m
SD2.1
low diversity in landecaping around house
low diversity In house material
low diversiiy in socio ecanomic lovels
high landscaping
(a)
sodio economic
GeoDiv
Entity Appearance
Background Appearance
Afficncc
Maintenance
USA House, $D3.5
Egypt
UAE
Nigeria
Japan
Colombia
UK
India
Car, FLUX.1
Stove, SD3m
Backyard, SD2.1
(b)
Figure 1: Lack of Geographical Diversity observed in T21 Generations and the Need for GeoDiv. (a) text-to-image models produce systematically low visual diversity for the same prompt across countries (example: 'a photo of house in Nigeria'), failing reflect the rich variation seen in real-world images (Ramaswamy et al. (023). (b) GeoDiv provides an automated, reference-free framework that quantify such fine-grained geographical differences by evaluating images along four interpretable axes: Appea--ppeee (sloped/flat roof),
-agerooundApperanncce (paved/unpaved road), Affluence (luxury/modest settings), and Maintenance (manicured/unkempp) Examples show how the same entity type varies dramatically across countries and generative models.
and contextual variation. A single diversity metric cannot capture such multidimensional aspects, limiting interpretability and masking eegion-specife biases.
In this work, we propose GeoDiv, a framework for quantifying geo-diversity along two complemen- tary axes. The Socio-Economic Visual Index (SEVT) captures socio-economic cues through two interpretable dimensions: (a) Affluence, ranging from impoverished to affluent depictions, and (b) Maintenance, measuring physical condition from worn to pristine. Both are rated on 1-5 scale using VLM judgments and are closely tied to societal well-being (Awaworyi Churchill et al. (025). The Visual Diversity Index (VDI) measures variation in (a) Entily Appeurance, reflecting attributes such shape, material, color of the primary entity, and (b) Background Appearance, capturing contextual variability (c.g.. type of roads visible). Fig. [1 [illusstates how these dimensions differ across geographies and generative models. VDI employs L.LMs extract entity and background attributes, while VLMs aid in estimating their distributions with respect to images across countries. For each SEVI and VDI dimension, diversity is quantified using the interpretable Hill Number, defined the exponential of the entropy of attribute value distributions (Leinster] (021). While geo-diversity also encompasses cultural, historical, and aesthetic dimensions that remain difficult measure scale, GeoDiv is modular and can incorporate new axes as methods advance. Since our approach relies on the implicit world knowledge embedded LLMs and VLMs, we validate both SEVI and VDI extensively through human studies.
Applied to 160, 000 images generated by Stable Diffusion v2.1 (SD2.1), v3 (SD3m), v3.5 (SD3.5) Rombach et al. (2022). and FLUX. (black-forest-lbbs 2024,, across 10 common entities (e.g., house, car, etc) and countries, GeoDiv reveals several key insights. Images from countries like India, Nigeria, and Colombia are consistently found to be impoverished and worn out than those from USA, UK, or Japan, highlighting systemic socio-economic bias. Interesting leuetry-level biases also observed in case of Entity and Background appearance. For instance, SD3.5 shows 99% Egyptian houses to be made of stones, while 88% UK houses to be built of bricks. Across models, backgrounds of 77% of car images from Nigeria show dirt/gravel road, compared
2

---

Published as a conference paper at ICLR 2026
to US which generates paved roads 85% of the time. Interestingly, FLUX.1 images score highly on SEVI but low on VDI, suggesting a trade-off between image polish and diversity. Thus, GeoDiv captures nuanced geographical biases and gaps in generative models, providing systematic and interpretable framework for auditing geographical representation. Our key contributions are:
We introduce GeoDiv, an interpretable evaluation framework for measuring geo-diversity in generative models along two complementary axes: Socio-Economic Visual Index (SEVI) and Visual Diversity Index (VDI), quantifying socio-economic and visual diversity by leveraging the world knowledge of large language models (LLMs) and vision-language models (VLMs).
We obtain and release structured attribute-value sets using L.Mss, for evaluating the geo-diversity of 10 common entities (e.g.. house) across both SEVI and VDI. We also provide the full prompts and filtering mechanisms needed generate comparable evaluations for new entities.
We curate a dataset of 160,000 synthetic images generated with four open- open-source diffusion models, covering 16 countries and 10 entities. For a subset, we collect VDI attribute annotations and country-level SEVI ratings from crowdworkers via crowdsourcing platforms. These human- annotated datasets are then used to evaluate multiple LLM-VLM combinations for implementing GeoDiv. All annotations and the codebase are released to support benchmarking of future models.
GeoDiv uncovers regional biases and key limitations in current generative models, demonstrating its utility
effective and interpretable diagnostic tool for assessing geographical diversity, compared to existing diversity measurement baselines. We release diversity scores across all GeoDiv dimensions for the curated synthetic dataset, enabling practitioners to systematically improve the geo-diversity of diffusion models.
2 RELATED WORK
Metrics Measuring Image Diversity: Image diversity metrics are typically categorized into two types. The first compares given image set to a reference set, c.g., FID (Heusel et al. (017)) which compares feature distributions using a pre-trained Inception network (Szegedy et al. (017). We exclude such metrics due to the absence of large-scale geo-diverse reference datasets (Gaviria Rojas) iet al. 2022: Ramaswamy et al., (023). The second type assesses variation within the given set. Pairwise Distance Metries (Fan al. 2024 Boutin et al. (023) compute average distances between image embeddings (e.g., Inception or CLIP (Radford et al.) (00TT)) while Vendi-Score (Friedman & Dieng 2023) measures entropy over the eigenvalues of the feature kernel matrix. However, these approaches capture only visual variation. Because of their uninterpretable nature, the extent to which such metrics can capture the nuances of geo-diversity is unclear. On the contrary, our proposed framework GeoDiv measures the multiple dimensions of geo-diversity in an interpretable manner.
Leveraging the World Knowledge of Large-Scale Models: Trained on internet-scale data, LLMs and VLMs encode rich knowledge about global cultures and demographics, which many recent works have utilized to measure stereotypes, consistency, realism and diversity images. OASIS (De- Idashtiin et al. 2025) quantifies stereotypes in text-to-image generation by comparing real- world attribute distributions for different nationalities with those inferred from generated images via a VQA model. TIFA (Hu et al. 2023) and DSG (Cho al. 2023) evaluate mmage-prompt consistency by generating questions from the L.M and finding corresponding answers for each image through a VLM, where the latter adopts a Davidsonian Scene Graph to avoid hallucinations, duplications, and omissions in the generated questions. REAL (Li et al. 2025) employs a VQA model to measure the realism of images from text-to-image models. The LLM-VLM paradigm has also been used by a few prior works to identify and measure biases in given set of images (Chinchure et al. 2024 Mandal et 2024). whereas Basu et al. (2025) utilize diffusion models augment existing biased training sets Baasu et al., 2024). GRADE (Rassin et al.) 2024 is the first method that employs the L.MM-V.M paradigm to assess visual diversity in everyday objects. However, geo-diversity being
more complex, first segregate it into multiple axes, and then propose metrics to measure each of them by leveraging the LLM-VLM approach in different ways.
Over the recent years, multiple works have uncov- ered harmful geographical biases in real and synthetic datasets. Such studies can be divided into two broad categories. The first category investigates the representation of countries within both real image datasets (De Vries et al., 2019; (Shankar et al., 2017 Naggita et al. 2023 Waag et al., 2022 Faisal et al. 2022) and synthetic ones (Basu et al.
Geographical Biases in Text-to- Text-to-Image Models:
2023 The second category studies the the extent of
3

---

Published as a conference paper at ICLR 2026
variations within country in the images (Hal et al. 2023 2024 Askari Hemmat et al. 2024 which show that existing metrics fail to capture geographical variations within country. While our paper focuses on the second category. most of the previous works rely existing geo-diverse datasets like GeoDE (Ramaswamy et al., 2023) to measure geo-diversity and similar aspects, constraining such metrics concepts and countries covered in those datasets. Our paper attempts mitigate this limitation, and introduces a framework that measures geo-diversity in a reference-independen and interpretable manner, extendable any number of entities and countries.
PROPOSED FRAMEWORK: GEODIV
Background
Cars in Nigeria (SD2.1)
"Generate questions and answers about Car"
"Generate questions and answers about background"
Entity-Scom 0.81
Diverstt!!
Background Scoor 0.60
"What type of road temain is visible?"
"What the color the car?"
Dilversity
VOA
'pink: 'gray', 'white', 'yellow' "black, 'brown', 'blue'...
'paved road', 'drr/gravel road', 'natural ground 'tlld"
Entity Attributes
Affluence 1.81 High Impoverishment
Background Attributes
Affluence
Maintenance
"Impoverished or Affluent?
"Wom out or well maintained?"
Mainteenance 2.06
Maintenance
Low
GeoDiv Questions and Answers
GeoDiv Score Computation
Socio-Economic Visuall Index (SEVI)
Diversity (VDI)
Figure GeoDiv Pipeline. Given an entity e and country c, LLMs generate attribute-based questions specific to e, and a fixed set of background-related questions applicable across entities. A VQA model predicts answer distributions over an image set for both question types, from which GeoDiv computes the Visual Diversity Index (VDI) via normalized Hill number. The VQA model also rates each image on Affluence and Maintenance compute the Sacio-Economic Visual Index (SEVI).
Motivated by clear human-identified disparities in how T2I models depict different regions, we aim develop a principled method to quantify such geographical variation. We introduce GeoDiv, systematic and interpretable framework for measuring the geo-diversity of images generated for given entity and country. Given collection of images D, we extract subset DE corresponding to entity e E È and country e E C. These images are synthetically generated using text-to-image models with prompts of the form 'a photo of {e} in {c}'. In this section, first introduce the two core axes along which GeoDiv assesses geo-diversity, and then describe how it is quantitatively computed for each dimension.
3.1 VISUAL DIVERSITY INDEX (VDI)
To assess the visual variation images across geographies, we define the Visual Diversity Index (VDI) along two axes: Entity-Appearance and Background-Appearaat
Entity-Appearance examines the visual attributes of entities (e.g., houses, cars) within a country. Manually defining comprehensive set of attributes for each entity is infeasible, multiple LLMs to generate candidate question-answer annwerr (Q&A) sets, and consolidate them into a unified list using aggregator LLM. The same Q&A sets applied across countries for comparability. Finally, a VQA model answers these questions for each image the set De and the resulting distribution of answers across the images is used to compute per-question entity diversity.
leverage
Background-Appearaan assesses the scene context (c.g., presence of modern infrastructure, type of roads, etc). We divide background into indoor and outdoor categories. An L.LM first generates
4

---

Published as a conference paper at ICLR 2026
fixed set of contextual questions and answer choices for each category (an example outdoor- category question: 'What rype of road or terrain is visible?"). Each image is first classified by a VQA model as indoor or outdoor. Based on the prediction, specciff questions and answers are input to the VQA model. The resulting answer distributions are then utilized to calculate background diversity.
3.2 SOCIO-ECONOMIC VISUAL INDEX (SEVI)
To capture economic status and visual cues of physical upkeep across geographies, we introduce the Socio-Economic Visual Index (SEVI) with two dimensions: Affluence and Maintenance. attentive reader may enquire about the difference between the two. Affluence reflects the overall wealth depicted an image, while Maintenance evaluates the physical condition of the primary entity, both crucial to understand societal well-being (Awaworyi Churchill et al., 2025). For each image, a Vision- SaiionLanguage Model (VLM) predicts scores for these dimensions on 1-5 scale:
Affluence (1-5): Impoverished + Low + Moderate - High - Luxury. Maintenance (1-5): Severely Damaged - Poor Moderate Well-Maintained - Excellent.
The VLM is prompted with detailed descriptions of these scales and scores each image individually to provide interpretable socio-economic visual signals. Finally, the distribution the Affluence and Maintenance scores for an image set DE is studied to assess socio-economic diversity.
GeoDiv integrates both SEVI and VDI dimensions for comprehensive diversity assessment. All questions, and answers used are included in Appendix $ H and prompts are included in the github
repo
htt//ggtttuaa / wtw../u...oooo///////////aaa
3.3 DIVERSITY COMPUTATION
Using the distributions obtained from the VDI and SEVI questions, we quantify the uniformity of answer distributions by computing the Hill Number. This is biodiversity-inse inspiree metric that represents the effective number of distinct categories (or "species") in community and is calculated by exponentiating Shannon's 's entropy, which captures the uniformity of the distribution. Consider question dJ (related either SEVI or VDI attributes), having set of answers denoted by As. Given that the values of an attribute can too large to enumerate exhaustively, we generate an approximate set of answers per question by leveraging the world knowledge of the LLMs, denoting the same as As (see codebase for prompt details). Hill numbers represent the "effective number of answers" represented in the distribution and range from 1 (when a single answer class is over-represented, yielding zero entropy) to [ (when all provided answers are equally well-represented, yielding maximum diversity). Since the number of plausible answers can vary across different questions, we compute a Normalized Hill Number (ranging between 0 and 1) to enable fair comparison between questions with varying answer-set sizes, as defined below:
Diversity-Score = exp(HI(P.) -1
[ -
where Ps is the answer distribution for gk. and H-)) denotes Shannon entropy. Diversity for Affluence and Maintenance are computed directly using Diversity-Score. The Entity-Appearance and Ankkground-Appparamne Diversity are calculated by averaging Diversity-Seon over all related questions for the individual dimensions.
On Computing Gocio-Economic Diversity, When evaluating socio-economie diversity in synthetic images, key question arises: should the ideal scenario emphasize affluence and high physical upkeep, or represent the full spectrum of vocio-economic conditions? We adopt the latter to promote inclusivity, additionally reporting the mean Affluence and Maintenance ratings (on 1-5 scale, subsection 3.2) per country or dataset. This reveals systematic biases, with models disproportionately generating affluent impoverished images depending on the country prompted.
5

---

Published as a conference paper at ICLR 2026
Table 1: Performance of various VQA models in identifying VDI answers and SEVI scores compared against human annotations. Gemini- 5- flash achieves the highest accuracy on entity and background questions, as well as the strongest correlation with human ratings on the SEVI metrics. Qwen2 5-VL is competitive, while LLaVA underperforms substantially.
VDI Answers (Aceuracy)
SEVI Scores (Spearman's
Models
Entity
Background
Overall
Affluence
Maintenance
Gemi n niniiii2..5-- ni-225-flash gpt-4o
0.87
0.85
0.86
0.76
0.69
0.85
0.81
0.83
0.76
0.76
Qwen2. 11 ava-vv 6-mistral hb
0.85
0.77
0.81
0.69
0.71
0.70
0.66
0.68
0.65
0.68
EXPERIMENTAL SETUP AND VALIDATION FOR GEODIV
4.1 DATASET DETAILS
Entities. We evaluate geo-diversity of images belonging to 10 entities commonly studied in prior works (Hall et al. 2024), well represented in well-known geo-diverse datasets (Ramaswamy et al. 2023): backyard, bag, car, chair, cooking pot, dog, house, plate of food, shopfroni and stove.
Countries. Our analysis spans 16 countries across diverse regions: the United States (USA), Mexico, Colombia, the United Kingdom (UK), Italy, Spain, Japan, South Korea, Indonesia, China, India, the UAE, Turkey, Philippines. Egypt, and Nigeria.
Generative Models. We measure the geo-diversity of images generated by models such as SDv2.1, v3m, v3.5 (Rombach et al. 2022). and FLUX. (black-forest-lab)) 2024). For each entity-country pair, we generate 250 images per model, resulting in 40, 000 images per model. Thus, our synthetic
dataset comprises of 160, 000 images overall. Further dataset details can be found in Appendix M
and samples can be observed in Appendix Fig.
23
24
25
and 26
4.2 VALIDATING GEODIV COMPONENTS
VQA Accuracy for Entity and Background Diversity. The VDI dimensions depend on the VQA model's 's ability to correctly recognize visual attributes. We evaluate this by sampling 12 images per entity (randomly chosen from the four T2I models), each paired with one entity- and one background- based question, yielding 240 image-question pairs. Each pair is annotated by three (Prolific 2024) crowd-workers using A.M--genertee answer choices, with majority voting for the final label. The questions are deliberately generic, requiring minimal region-specifie knowledge to avoid bias. Table reports the accuracy of the VQA model's predictions when compared against human annotations during the validation study. Among the four VLMs tested, gemini mimini-a -- performs best with 86% overall accuracy (87% for entity, 85% for background), while Qwen2. 5-VL and gpt-4o achieve comparable results but slightly lag on background questions.
Validating the SEVI Metrics. The Affluence and Maintenance dimensions of SEVI capture nuanced aspects of wealth and physical condition. To evaluate alignment with human judgment, we conduct a country-wise study: for each country, 4 images per concept (40 total) are sampled across all T2I models, yielding 80 image-question pairs. Owing to participant unavailability, Nigeria and Turkey are excluded. Native annotators (via (Prolific 2024)) rate each image on the SEVI scale, with three ratings per image, producing 1120 ratings overall. On this benchmark, Gemi n ni-2. 5- fl lash achieves high Spearman correlations with human scores (p 0.76 for Affluence, p 0.69 for Maintenance), with similar performances by the other models. Overall, the open-source Qwen2. 5-VL can be seamlessly used for implementing GeoDiv (see Appendix Section I.3) though we adopt Gemini-2 5- for its slightly superior performance on VDI.
Further details the human studies (remuneration, instructions, etc), country-wise correlation coefficients for the SEVI dimensions, and a robustness analysis of the metric across all axes are shared in the Appendix |

---

Published as a conference paper at ICLR 2026
Affluence Analysis
Maintemance Analysis
Diversity-Score
Mean Score
Diversity-Score
Mean Score
FLUX 1 0.5 0.0. 033 0.3
0.3
3.5 3.3 4.0
0.3
0.2
0.3
0.1
0.1
6.1
4.4
4.4
4.9
0.8
08
502.1 0.2 0.2 0.3
0.4
0.6
15
1.5
2.0
3.
0:4
0.3
0.5
0.6
2.3
2.5
2.9
3.800
5D3m 0.2 0.2 0.2
0.3 0.3
2.0 2.1 3.3
0-1 0.3
0.3 0.3
4.344
SD3.5 0.3
0.4400
0.3
2.0 2.1
3.7
0.3 0.3 0.3
8.0
3:1
4.4
NG IN CO US UK
0.0
NG IN C0 US UK 1P
NG IN CO US UK
0.0
NG IN CO US UK
Figure 3: SEVI Diversity and Mean Ratings across Datasets and Countries. India (IN), Nigeria (NG), and Colombia (CO) are seen to receive lower SEVI ratings, while the US, UK, and Japan (JP) rank highest-revealing strong socio-cconomic biases in country-level image representations.
Strikingly, none of the models generate images spanning diverse socio-economic strata.
4.3 IMPLEMENTATION STEPS
We the Gemini- 5- model for all experiments due to its superior performance ($4.2). The hyperparameter details are provided in Appendix AA.4 SEVI scores are obtained by directly prompting the VLM to rate images Affluence and Maintenance. The VDI analysis involves several steps, detailed below:
Question and Answer Generation. For Entity-Appearance, diverse attribute-related questions generated by an ensemble of five LLMs, and consolidated using a separate aggregator LLM (see Appendix A full model versions). This ensures comprehensive attribute coverage for entities whose characteristics may vary widely. In contrast, background questions (e.g., crowded vs. quiet) are generally applicable across scenes and do not require per-entity customization. Therefore, fixed set of background questions is generated using Gemini. Answers for all questions are obtained from Gemin and further cleaned by the same to remove redundant or problematic responses (see our codebase for prompts and $ H for the resulting questions and answers).
To reduce the effects of the intrinsic biases of the VLM, we perform the following control steps:
Visibility Step for Undetectable Attributes. After generating question-anae answer pairs for background and entities, VLM filters out images where the questioned attribute is not visually detectable (Cho al. 2023 to reduce hallucinations in the VQA step (Appendix B. shows the rejection percentages).
Multi-Select Responses. This allows selecting multiple valid answers and avoids distortions from forced single-choice formats.
None The Above (NOTA). To account for any missing answer in those generated by the LLM, we append special NOTA option before querying the VQA model. Only 2.6% image-question pairs obtained NOTA as the answer. This lets the model abstain when no option fits, reducing hallucinations due to forced guessing instead of acknowledging uncertainty (Kalai et al. 2025). See Appendix B.2 for finer-grained analysis.
5 WHAT DOES GEODIV REVEAL ABOUT GEO-DIVERSITY?
The GeoDiv framework is applied to images from four T2I models, spanning 10 entities and 16 countries (see Section[4). Overall SEVI and VDI trends are shown in Figures 3aaurss3ami and with detailed analyses across datasets ($ 5.1 and countries ($ 5.2 below.
5.1 DIVERSITY COMPARISON ACROSS DATASETS
FLUX.1 Images Appear the Richest, Yet No Dataset Offers Balanced SEVI Coverage. The average Affluence Diversity Score is similar across the T2I models (0.35 + 0.01). While the average Maintenance Diversity-Seon is 0.34 + 0.12, FLUX.1 images show severe lack of variation in the physical conditions of the entities depicted, with a low score 0.15. This indicates that no model provides balanced coverage across all socio-economie strata. FLUX.1 tends to generate polished,
7

---

Published as a conference paper at ICLR 2026
Dateset-based
Country-based VDI
502.1
Japen
Spain
United States of America
Turkey
FLUX
United Kingdom
SD3.5
Mexico
1.0
0.0
0.4
Diversity-Scores
Entity
Background
Diversity-Scores
Figure 4: VDI Scores across (a) Datasets, (b) Countries. Model-wise VDI diversities are similar, with SD2.1 achieving higher scores than the others. Mexico and the UK show low entity and background diversity, while Japan scores highest.
aesthetically pleasing images, achieving mean Affluence and Maintenance ratings of 3.82 and 4.73 respectively (on the - 5 scale defined in Section 3 In contrast, the remaining models show similar, lower scores, with S scoring the lowest: mean Affluence of 2.41 and Maintenance of 3 23. These observations are demonstrated for a selected group of countries Fig. Aggregating across all entities and countries, Affluence and Maintenance show a moderate positive correlation (p 0.5): more affluent items tend to appear better maintained. Yet this pattern varies by entity, sometimes even reversing. GeoDiv highlights such cases; for instance, Nigerian clay pot on muddy ground scores low on affluence (score: 1) but high on maintenance (score: 4), while an Egyptian luxury sports car scores high on affluence (score: 5) but low on maintenance (score: 2) due to visible dust on the hood.
Synthetic Images Lack Visual Diversity. The Entity-Appearance Diversity-Score is highest for S (0.51), followed by SD3m (0.45), FLUX.1 (0.42), and SDv3.5 (0.40) (see Fig. 4. While these scores indicate general lack of diversity in entity appearances, the issue is more pronounced for background appearance, where all datasets score low (0.31 on average). Overall, the limited variation in both dimensions highlights clear opportunity for improvement by data curators and model developers. In particular, FLUX.1 I exhibits very low VDI diversity while achieving the highest SEVI ratings, suggesting it produces consistently polished, yet overly similar-looking images.
Overall Geo-Diversity Tends to Decrease Newer Diffusion Model Versions. Averaged across SEVI and VDI, FLUX.11 shows the lowest scores, while SID2. 1 ranks highest among T21 models, consistent with prior findings (Rassin et al., 2024 Hall et al., 2023) (Appendix Table (6) Though differences are modest, they underscore the need improve both visual and socio-cconomic diversity in synthetic image generation and demonstrate GeoDiv's utility in assessing geo-diversity.
5.2 COUNTRY-BASED GEO-DIVERSITY
India, Nigeria, and Colombia Portrayed as Poorest; Japan, UAE, and UK as Wealthiest. Across datasets, the mean Affluence and Maintenance diversity scores per country are low, 0.36 and 0.38, highlighting a severe lack of nocio-economic inclusivity, with India and Japan exhibiting the least diversity. Strong biases emerge (see Fig. 3): India, Nigeria, and Colombia are consistently portrayed as the poorest (average Affluence: 2.31, Maintenance: 3.34), while Japan, UAE, and UK appear as the wealthiest (Affluence: 3.53, Maintenance: 4.30). This trend is less apparent in FLUX. images, as it generates polished images uniformly. These results expose a pronounced socio-econome -coonomcc bias in synthetic image generation, entrenching narrow and stereotypical socio-economic portrayals.
Entity-Apeearacee Diversity Low Across Countries; But Are The Distributions Similar? The mean Diversity across countries only 0.47, indicating limited variation in entity attributes and exposing both global and country country-specific biases (see Fig. D.. For example, models consistently fail to generate chairs without backrests irrespective of countries. On the other hand, country-specific biases emerge, for example, SD3m shows very few cushioned chairs for Nigeria and the Philippines, whereas the UK and USA samples rarely depict hard-seated chairs (see Appendix D.2 for more examples). Beyond absolute diversity, compute AmsennShhanon Distance (JSD) to capture distributional differences between countries. Fig. rrrportss the maximum JSD averaged across questions for each model and entity, showing sharp divergences in some cases. For instance, Egyptian houses generated
8

---

Published as a conference paper at ICLR 2026
Entity Analysis
Background Analysis
0.8
15 Distance
0.6
0.4
0.2
0.0
backsyrd
car
chair
cooking pat
deg
House
plate of food
starefront
Stove
backyard
bag
car
chair
cooking post
dog
house
plata of food
storefront
stove
Rud Imean)
Rod (maxl
5023 Imean)
5021 (mox)
5035 (meen)
5035 [meo]
5m3m (mean)
5d5m Imax]
Figure 5: Country-wise maximum and mean JS Divergence across Entities and Models. High maximum values for both Entity and Background indicate high cross-country variations in the respective attribute value distributions.
by SD3.5 differ markedly from others (Appendix Fig. 9), caused by distinct exterior materials and adjoining ground cover. Thus, GeoDiv reveals both global biases and substantial cross-country variation, while offering framework readily extendable to new entities, countries, and models. To enhance interpretability, we release full per-question distributions in the supplementary, enabling practitioners to prompt underrepresented attributes explicitly (see Appendix $ G for an example).
Background Diversity Strikingly Lower than Entity Diversity. The average Background Diversity- Score across countries is 0.33, significantly lower than that of matity-Appearang indicating severe lack of variations in the generated backgrounds. Irrespective of countries and models, most back- grounds tend to be quiet and empty, without significant crowd presence, even in case entities like cars and houses. Similarly, mountains and hills are depicted only 12% times on average across countries-least depicted in Nigeria (1.1%), and most depicted in Turkey (24%), indicating underrep- resentation of crucial natural feature. Waterbodies are depicted even lesser, in only 3.4% images. We plot the maximum JSD averaged across questions for each model and entity across distributions of country- -pirs Fig. 55| The high values are caused by cross-country variations: for instance, across models, backgrounds of 77% of car images from Nigeria show dirt/gravel road, compared to US which generates paved roads 85% of the time. Similarly, 57% of Indian images show dense buildings in the background, compared to only 17% for the UAE.
Egypt Most Geo-Diverse Country, India The Least. Averaging across all four GeoDiv scores, we find Egypt, Colombia, Turkey, and Spain to be among the most geo-diverse countries, whereas Japan, the UK, the US, and India rank among the least. The mean GeoDiv score per country is 0.39, a predictably low value that underscores the need to improve the diversity of generative models across all analyzed dimensions. Country-level scores are reported in Appendix Table [5] Interestingly, we also observe weak negative correlation between GeoDiv scores and both GDP nominal and per capita (p- = -0.27 and -0.28, respectively), suggesting that generative models tend to produce less diverse imagery for wealthier countries.
Detailed visualizations of the SEVI and VDI scores across models, entities and countries, along with crucial examples of observed biases presented in Appendix E $
D and $ respectively. The
variation in SEVI and VDI scores per entity is further discussed Appendix C.T
6 DISCUSSION
Comparison with Existing Baselines. Vendi-Score (Friedman & Dieng 2023) measures visual diver- sity within image sets, but overlooks key aspects of geo-diversity that GeoDiv measures. For example, GeoDiv's SEVI axis on Affluence and Maintenance reflects socio-economic context that Vendi-Score cannot detect. To assess the relationship between Vendi-Score and GeoDiv (combined across all axes), we compute their correlations. Only Entity Diversity has high correlation (Pearson's 's p 0.56) while the others are lower (e.g., 0.06 for maintenance). This shows that although entity specific diversity be measured by Vendi score, it lags behind in multidimensional diversity computations. Detailed results are in Appendix Table [5 in Appendix K
We discuss another method DIMCIM (Teotia et al. 2025)

---

Published as a conference paper at ICLR 2026
Geo-Diversity of Real-World Dataset. To benchmark synthetic images against geographically representative real-world dataset, we evaluate GeoDE (Ramaswamy et al. 2023) using GeoDiv. Clear differences emerge: GeoDE achieves substantially higher Enpety-Appearance Diversity (0.60 vs. 0.44 for synthetic images). Baakkrouud-Appearaann 0.31). On the SEVI axis, GeoDE exhibits markedly greater diversity in Maintenance (0.61), while its Affluence diversity, though the highest among all datasets, remains comparable to others. These findings highlight GeoDE consistently more geo-diverse than synthetic datasets, particularly in Entit-Appearran and Maintenance, likely because it is crowd-collectes and thus, it is expected to reflect real- world variations better. Detailed entity- and country- scores are provided in Appendix Fig. 13 and
Diversity is closer but still higher in GeoDE (0.42 vs.
7 CHALLENGES AND LIMITATIONS
We analyze the geo-diversity of four T2I models across 16 countries and 10 entities, but extending this evaluation to broader set of regions and entity types may uncover additional patterns and biases. To support such extensions, we publicly release the question and answer distributions for every country-entity-modee combination used this study. We also provide all prompts in our codebase, enabling researchers to easily adapt our framework to new entities, countries, and generative models.
For the VDI axis, the questions and their corresponding answer sets are generated using the world knowledge of LLMs, since exhaustively enumerating all possible entity background attributes and their values infeasible. For the SEVI axis, we explicitly define the levels affluence and maintenance due to the absence of any established or standardized scales for these socio-economic cues. Furthermore, our diversity score estimates also rely on LLMs and VLMs, which may carry inherent biases. To mitigate this, we restrict questions to generic entity and background attributes, avoiding region-specific knowledge. The goal is to reveal how model generations vary even on basic attribute distributions across entities and countries. Large-scale human studies (including country-wise studies for the SEVI metrics) reinforce GeoDiv's 's reliability, while the visibility and NOTA checks further reduce hallucinations.
An important aspect of geo-diversity is cultural representation; whether generative models capture local cultural contexts or default to globalized visuals. We quantify this using a Cultural Localization score via our VQA- pipeline, analogous to the Affluence and Maintenance scores. We observe higher disagreement between the VQA model and human annotators for countries like the USA and UK, while Japan and Colombia show better alignment, reflecting regional variations in model-human agreement. Full results are in Appendix L
Another limitation is reliance on Gemini-2 1al.iii-.55F1aah 5-Flash, closed-source model; despite strong quality and alignment with human judgments, budget constraints limit large-scale evaluations across entities and countries. As noted in Section 4.2 open-source Qwen2. . 5 VL is practical alternative. showing high agreement with Gemini on all four diversity axes (average correlation p = 0.83) across two datasets and six entities (Appendix L.3. Continued progress in open-source VLMs will enable broader, richer, and more cost-effective assessments of global diversity.
CONCLUSION
In this work, we introduced GeoDiv, a multidimensional framework that leverages the world knowl- edge of LLMS and VLMs to quantify geographical diversity in image datasets. To capture disparities in socio-economic status, physical upkeep, and variations in entities (e.g.. houses, cars) and their contexts, we proposed two axes: (a) the Docio-Economic Visual Index (SEVI). which uses a VLM to assess affluence and maintenance, and (b) the Visual Diversity Index (VDI), which evaluates entity and background diversity with L.MM VIM guidance. Applying GeoDiv to images from four T21 models across 16 countries and 10 entities, we found systematic gaps: diversity in entities and backgrounds declines in newer models, while SEVI scores consistently mark India, Nigeria, and Colombia as impoverished and poorly maintained. By contrast, FLUX.1 generates more affluent depictions but with low visual diversity, revealing a trade-off between sophistication and inclusivity. GeoDiv provides first step toward interpretable audits of T2I geographical inclusivity with minimal human oversight, and we hope it inspires efforts to build generative systems that are not only visually appealing but also globally representative.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENTS
This work is supported by grant from Google Research and the Kotak IISc AI-ML Centre (KIAC). The authors are grateful Soumya Dutta (LEAP Lab, IISc) for their valuable feedback. Shashank Agnihotri and Margret Keuper acknowledge support by the DFG Research Unit 5336 Learning to Sense (L22S.. The authors further acknowledge support by the state of Baden- Württemberg through bwHPC and the German Research Foundation (DFG) through grant INST 35/1597-1 FUGG. The authors are grateful for the computing time provided on the begh-perffommncce computer HoreKa by the National High- Performcrf Computing Center KIT (NHR @ KIT). This center is jointly supported by the Federal Ministry of Education and Research and the Ministry of Science, Research, part of the National High-Performance Computing (NHR)
and the Arts of joint funding program (hitps://www) https://www.nnhrg ww.nhr-verein.no www.bb--ercin.dee////ourpppaaee partterrs-...sss HoreKa is partly funded by the German Research Foundation (DFG).
REFERENCES
Reyhane Askari Hemmat, Melissa Hall, Alicia Sun, Candace Ross, Michal Drozdzal, and Adriana Romero-Soriano. Improving geo-diversity of generated images with contextualized vendi score guidance. In European Conference on Computer Vision, pp. 213-229. Springer, 2024.
Pietro Astolfi, Marlene Careil, Melissa Hall, Oscar Mañas, Matthew Muckley, Jakob Verbeck, realism pareto fronts of
Adriana Romero Soriano, and Michal Drozdzal. Consistency-diveriie reeli conditional image generative models. arXiv preprint arXiv:2406. 10429, 2024.
Sefa Awaworyi Churchill, Vidal Paton-Cole, and Senam Acquah. The wellbeing implications of household home repair and renovation expenditure. Journal of Housing and the Built Environment, pp. 1-23, 2025.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint 13X1, 2025.
Abhipsa Basu, R. Venkatesh Babu, and Danish Pruthi. Inspecting the geographical representativeness of images from text-to-image models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5136-5147, October 2023.
Abhipsa Basu, Saswat Subhajyoti Mallick, et Mitigating biases in blackbox feature extractors for image classification tasks. Advances in Neural Information Processing Systems, 37:10641 06411- 1- 106439, 2024.
Abhipsa Basu, Aviral Gupta, Abhijnya Bhat, and R Venkatesh Babu. Harnessing diffusion-generrated synthetic images for fair image classification. arXiv preprint arXiv:25/1.0877 2025.
black-forest-labs.
htttss//hoggiiffesscccoo-dd--- FLUX. 1 dev. 2024. Accessed: 2025-05-16.
FLUX.I-dev.
labs/
Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, and Thomas Serre. Diffusion models as artists: Are closing the gap between humans and machines? arXiv preprint arXiv:2301.11722, 2023.
Aditya Chinchure, Pushkar Shukla, Gaurav Bhatt, Kiri Salij, Kartik Hosanagar, Leonid Sigal, and Matthew Turk. Tibet: Identifying and evaluating biases in text-to-image generative models. In European Conference on Computer Vision, pp. 429-446. Springer, 2024.
Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, and Su Wang. Davidsonian scene graph: Improving reliability fine-grained evaluation for text-to-image generation. arXiv preprinl arXiv:2310.118255 2023.
Google Cloud. Vertex AI REST API Documentation.
Vertex-ai
restt 2024. Accessed: 2025-05-19.
11

---

Published as a conference paper at ICLR 2026
Terrance De Vries, Ishan Misra, Changhan Wang, and Laurens Van der Maaten. Does object recognition work for everyone? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 52-59, 2019.
Sepehr Dehdashtian, Gautam Sreekumar, and Vishnu Naresh Boddeti. Oasis uncovers: High- qualltyu (2i models, same old stereotypes. In International Conference on Learning Representations, 2025.
Fahim Faisal, Yinkai Wang, and Antonios Anastasopoulos. Dataset geography: Mapping language data to language users. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 1381-3411, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi: 10. 1122.acl-long 239
URL https::/aclanttolllgggooo
Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian. Scaling laws of synthetic images for model training... for now. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 7382-7392, 2024.
Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning. Transactions on Machine Learning Research, 2023. ISSN 2835-8856.
William Gaviria Rojas, Sudnya Diamos, Keertan Kini, David Kanter, Vijay Janapa Reddi, and Cody Coleman. The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world. Advances Neural Information Processing Systems, 35:12999-12900, 2022.
Google. Gemini API Documentation.
google dev/ http//mminnniii http:o/ee.dd//emmmiaaaaa/////// 5-pro 2024. Accessed: 2025-05-16.
Melissa Hall, Candace Ross, Adina Williams, Nicolas Carion, Michal Drozdzal, and Adriana Romero Soriano. Dig in: Evaluating disparities in image generations with indicators for geographic diversity. arXiv preprint arXiv:23008.00 06198, 2023.
Melissa Hall, Samuel J Bell, Candace Ross, Adina Williams, Michal Drozdzal, and Adriana Romero Soriano. Towards geographic inclusion in the evaluation of text-to-image models. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 585-601, 2024.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 20406-201 20417, 2023.
Adam Tauman Kalai, Ofir Nachum, Santosh S Vempala, and Edwin Zhang. Why language models hallucinate. arXiv preprint arXiv:2509. 2444044500000664, 2025.
Tom Leinster. Entropy and diversity: the axiomatic approach. Cambridge university press, 2021.
Ran Li, Xiaomeng Jin, al. Real: Realism evaluation of text-to-image generation models for effective data augmentation. arXiv preprint arXiv:2502. 10663, 2025.
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. In European Conference on Computer Vision, pp. 366-384. Springer, 2024.
Abhishek Mandal, Susan Leavy, and Suzanne Little. Generated bias: Auditing internal bias dynamics of text-to-image generative models. In European Conference on Computer Vision, pp. 96-1 11. Springer, 2024.
12

---

Published as a conference paper at ICLR 2026
Keziah Naggita, Julienne LaChance, and Alice Xiang. Flickr africa: Examining geo-diversity in large-scale, human-centric visual data. In Proceedings of the 2023 AAAI/ACM Conference Al, Ethics, and Society, AIES '23. pp. 520-530, New York, NY, USA, 2023. Association for Computing Machinery. ISBN 9798400700210. doi: 10.1145/360011 .3604659. URL
httpps://bi.org// 01w....11113311111535 360454
Angéline Pouget. Lucas Beyer, Emanuele Bugliarello, Xiao Wang, Andreas Steiner, Xiaohua Zhai, and Ibrahim M Alabdulmohsin. No filter: Cultural and socioeconomic diversity in contrastive vision-language models. Advances Neural Information Processing Systems, 37:106474-106496
2024.
Prolific. Prolific: Participant Recruitment Platform. Accessed: 2025-05-16.
2024.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learming, Pp. 8748-8763. PmLR, 2021.
Vikram Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron Adcock, Laurens van der Maaten, Deepti Ghadiyaram, and Olga Russakovsky. Geode: a geographically diverse evaluation dataset for object recogmition. Advances in Neural Information Processing Systems, 36:66127-661311 2023.
Royi Rassin, Aviv Slobodkin, Shauli Ravfogel, Yanai Elazar, and Yoav Goldberg. Grade: Quantifying sample diversity in text-to-image models. arXiv preprint arXiv:2410.22599. 2024.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10695, 2022.
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and Scullcy. No classification without representation: Assessing geodiversity issues in open data sets for the developing world. arXiv preprint arXiv: 1.1000836, 2017.
Christian Szegedy, Sergey loffe, Vincent Vanhoucke, and Alexander Alemi. Inception-vé. v4, inception- resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, volume 31, 2017.
Revant Teotia, Candace Ross, Karen Ulirich, Sumit Chopra, Adriana Romero-Soriano, Melissa Hall, and Matthew Muckley. Dimcim: quantitative evaluation framework for default-mode diversity and generalization in text-to-image generative models. arXiv preprint arX6v:2506.0511 2025.
Victoria Turk. Generative ai like midjoumny creates images full of stereotypes. //restofworrld.or//2022.
https:
wwwaaaaeesseeeeteeeeeeeeeee Oct 2023.
Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, and Olga Russakovsky. Revise: A tool for measuring and mitigating bias in visual datasets. International Journal of Computer Vision, 130(7):1790-111) 2022.
13

---

Published as a conference paper at ICLR 2026
GeoDiv: A Multidimensional Framework for Measuring Geographical Diversity in Images
Supplementary Material
GeoDiv, introduced in the main paper, is framework for assessing dataset geo-diversity across multiple dimensions. This supplementary material provides extended details in support of the main results. The following sections outline the details and additional analyses.
A
Implementation Details
15
A.1
Implementation Details For Text-to- text-to-Image Generative Models
15
A.2
Compute Resources
15
A.3
LLMs used for Enpity-pppearacce Attribute- Mtarue Generations
16
A.4
Hyperparameter Details
16
A.5
Indoor-Outdoor Distribution of Images
16
Visibility Failures and NOTA Statistics
16
Percentage of Images Failing the Visibility Check
16
B.2
Percentage of Images with (None of the Above) NOTA Options
17
C GeoDiv Diversity Extended Analysis
18
GeoDiv Diversity Comparison Across Entities
18
C.2 Analysis Overall GeoDiv Scores
19
Entity and Background Diversity Scores
20
D.I
Entity Diversity Scores
20
D.2 Bias Patterns Entity Attributes Revealed by GeoDiv
D.3 Background Diversity Scores
23
E SEVI Scores Country and Entity- Details
24
Affluence Scores
24
2
Maintenance Scores
25
GeoDl:; Observations
Dataset
27
Data Distribution
27
F2
Entity-Appearance Diversity
27
Background-Appearance Diversity
27
Improving Geo-Diversity Using GeoDiv: An Application
27
H
Question-Answer (QA) set for VDI Scores
28
QA set for Entity Diversity part of VDI scores,
28
H.2 QA set for Background Diversity part of VDI scores.
34
14

---

Published as a conference paper at ICLR 2026
Validating GeoDiv Extended Details
35
Survey Details
35
I.2
Country-wise Correlation Analysis for SEVI Scores
38
1.3
Comparison Between Closed And Open Source Models
................................
38
4
Statistical Robustness of GeoDiv
................................
38
1.5
Inter-Annotator Agreement Across SEVI and VDI axes
Qualitative Examples
42
Comparison of GeoDiv with Existing Baselines Extended Discussion
48
K.1
Vendi-Score vs GeoDiv Scores
48
K.2 Comparison with DIMCIM
48
Cultural Localization
48
M Dataset Details Extended Discussions
49
Broad Societal Impact of GeoDiv
51
A IMPLEMENTATION DETAILS
A.1 IMPLEMENTATION DETAILS FOR TEXT-TO-IMAGE GENERATIVE MODELS
All synthetic datasets were generated using publicly available models from the Hugging Face Hub. Default generation settings provided by the respective model repositories used unless otherwise specified. Image generation was performed via the di fusers library, using standard inference pipelines. Prompts were constructed per entity-country pair using the template: "A photo a/an <entity> in <countrys Each model was queried to generate 250 images per entity- country pair, totaling 40,000 images per model.
The models used are:
Stable Diffusion 2.1 (SD2.1
Stable Diffusion (SD3m
Stable Diffusion 3.5 (SD3.5
FLUX.11dev (FLUX.1
SD2.1 images were generated at resolution of 768 x 768, while SD3m, SD3.5, and FLUX.1 used 1024 x 1024. For reproducibility, generation was performed with fixed seeds each batch. No further post-processing was applied to the generated images.
A.2 COMPUTE RESOURCES
Image generation experiments were conducted on an NVIDIA RTX 6000 GPU (48GB VRAM). For all LLM and VLM-based tasks. including question question-answer answer generation and VQA, we use Gemini-2.5-1 5-Flash (Google) 2024) accessed via the Vertex AI API (Cloud) 2024) with dy- namic thinking enabled for optimal token efficiency as well as batch processing for cost and time
15

---

Published as a conference paper at ICLR 2026
efficiency. The estimated cost for computing the VDI component of our diversity score, including visibility checks and VQA for both entity and background analysis, is approximately $58.64 per entity -uuesttt--uuuonnnn combination (across 250 images per set). The SEVI score computation for the same combination costs approximately $9.46, resulting in a total cost of $68.10 10 per complete diversity assessment. On the other hand, experiments using formed locally using an NVIDIA RTX A5000 (24GB VRAM), incur no additional computational costs but require significantly longer processing times.
---VV--32B-nnstrucee
per-
A.3 LLMS USED FOR ENTITY-APPEARAN ATTRIBUTE- VALUE GENERATIONS
As each entity may have its own distinct features, we generate questions and answers inquiring about its various attributes using an ensemble of 5 LLMs, later consolidating them using a neutral one (cla cclaude-opus- - -4-1 020222220000 D. Here, we specify the names and model versions of each such LLM for reproducibility ease.
Googge 2024
gpt-40-2024-08-065
1wenn2.5-VL-32B-IIsttu (Bai et al. 2025
Mistrallshma11-3322222---------1
1.ama-3.2-11-vv4
The prompts used for these models can be found in the publicly released codebase.
A.4 HYPERPARAMETER DETAILS
We use the Gemini 1.iinii-2555 2. flash model for all our experiments due to its strong empirical per- formance ($4.2). Across all stages, the LLM and VLM are configured with a temperature of 0.0, top-p value of 0.01, and top-k value of enforce deterministic generations. The maximum number of output tokens is set to 4000, while thinking budget is set to dynamic mode. All experiments are executed using batch-processing mode for computational efficiency.
A.5 OUUDORR DISTRIBUTION OF IMAGES
For calculating background diversity, we classify whether each image depicts an indoor outdoor scene (see subsection B the main paper). Table 2 details the indoor-outdoor distribution achieved from this step before conducting the remaining VQA steps of the pipeline. Since chosen entities are inspired by those analyzed in the GeoDE dataset (Ramaswamy et al. 2023), we further mention the groups (indoor common, indoor rare, outdoor common, outdoor rare) which each of the chosen entities belong to, as assigned by the authors. Notably, while most of the GcoDE images adhere their assigned indoor/outdoor groups, synthetic datasets display major deviations in depiction of typically indoor entities like bags, chairs, stoves, and cooking pots, frequently generating them in outdoor settings.
B VISIBILITY FAILURES AND NOTA STATISTICS
B. PERCENTAGE OF IMAGES FAILING THE VISIBILITY CHECK
Most entity-question pairs fail the visibility check for fewer than 5% of images. few of those with higher failure rates. All findings qualitatively verified through image inspection to confirm the reasons for non- -onaaneerabillty. Below, we list our observations for each entity.
Stove images that are traditional wood-fired or charcoal-fired fail for questions inquiring about the type of stove, and those with hidden/distorted cooktops fail for questions querying about the cooktop
-mmalll-mmmmmmmm 2 1 wnst 2506
16

---

Published as a conference paper at ICLR 2026
Table Indoor-Outdoor Distribution.
Group
Object
Indoor
Outdoor
GeoDB
SDv2
SDv3
SDv3.5
FLUXX1
Avg
GeoDE
SDv2
SDv3
SDv3.5
FLUX.1
Avg.
Indoor
bag
95.71
3.78
10.07
13.34
35.16
31.61
4.29
96.22
89.93
86.66
64.34
68.39
communi
chuir
84.09
39.37
11.89
98.59
87.62
60.63
Indoor nore
plate food
cooking
95.96
0.87
26.61
10.57
57.29
98.95
38.26
4,04
99.13
5.00
73.39
89.43
42.71
61.74
Xove
93.14
16.37
67.18
$7.64
87.74
64.41
6.86
83.63
32.82
42.36
12:26
35.59
Qutdoor
backyard
0.06
0.00
0.00
0.00
0.022
0.02
99.94
100.00
100.00
100.00
99.98
99.98
car
2.27
0.49
99.51
common
house
0.00
0.00
0.02
0.00
0.00
0.00
100.00
100.00
99.98
100.00
100.00
100.00
Outdoor
dog
28.87
0.23
0.69
2.38
2.69
6.97
71.13
99.77
99.31
97.62
97.31
93.03
vare
storefront
8.59
0.23
0.93
2.04
100.00
99.77
99.07
99.54
97.96
type. The latter is higher for SD2.1, which shows depictions of distorted renderings of traditional or repurposed stoves with no discernable cooktop, and FLUX.1 which has similar depictions of wood-burning compartments with visible cooktops.
House images fail for questions about doors when the door features are obscured. Car images where the roof is not clearly visible fails for the question roof types. Daylight images of cars often fail for the question on whether the lights are on or off due to difficulty in observing the head and tail lights. Chairs in which the back is fully covered with fabric obscured by cushions tend to fail on the question about the type of backrest (solid, slatted, or woven). Interestingly, this failure rate lower for SD2.1 and SD3.5, suggesting lower proportion of cushioned chairs in these datasets, a pattern corroborated by the responses to the question on cushioned versus hard seats. Storefront images with only display window visible or shutters fail for the question on type of entrance.
We define question as "low-coverage" if the visibility checks retain fewer than 50% of the original image set. Such questions are excluded from further processing. Among the 111 unique questions considered for entity diversity, we identify two that fall into this category: "What kind of controls are visible on the stove: knobs, buttons, or touchscreen display?" and "Does the bag have zipper, buckle, or flap closure?". The first is inherently difficult to answer using synthetic images, while in the second case, bag images often do not clearly reveal the type closure.
Table 3: Visibility Check Failure Rates for Selected Entity-Question Pairs Across Datasets.
Entity
Question
SD2.1
SD3m
SD3.5
FLUX.1
GeoDE
Bag
Is the bag's closure type visible or identifiable in the image?
44.5
50.5
43.05
28.95
25.22
Car
Is it visible detectable from the image if the car's lights are turned on or off?
22.8
8.55
11.48
1.42
18.22
Is the car's roof type visible or identifiable in the 14.92
30.47
20.8
22.32
11.12
image?
Chair
Is the construction style of the chair's backrest visible or identifiable in the image?
2.28
22.7
1.95
15.67
23.25
House
Is visible or detectable from the image whether 17.15
9.72
9.9
2.03
30.11
door on the house is open or closed?
Storefront
Is the type of the storefiront entrance visible or identifiable in the image?
27.28
16.25
7.53
2.5
19.26
Stove
Is the stove's cooktop type visible identifiable in the image?
36.05
10.2
12.0
26.25
0.92
B.2 PERCENTAGE OF IMAGES WITH (NONE OF THE ABOVE) NOTA OPTIONS
During the VQA stage (i.e., the stage of obtaining answers to the questions from images before calculating the VDI scores) we add a 'None of the Above' option to the answer list for each question, as discussed in Subsection 4 (main paper). Table details the NOTA percentages across datasets for all questions per entity. We qualitatively verify these cases by visually inspecting the images and the VQA model's reasoning for selecting NOTA.
17

---

Published as a conference paper at ICLR 2026
Stove has the highest NoTA percentage at 5.51%. The first question with high NOTA is "What is the primary material of the stove's body: stainless steel or enamel/painted metal?" It is comparatively higher for SD3.5, with a lot of rustic representations of stove with iron / stone corrugated metal bodies, except for the UK, USA, and Japan. The other question with high NOTA "What lype of cooktop does the stove have: gas burners, electric coils, a flat glass/ceramic top?" which again fails for images with representations of traditional stoves.
For storefront, all datasets show similar NOTA (avg. 4.32%), mostly due to two questions: "Is the facade primarily made of brick, wood, or glass?" and "Is the storefront entrance a single door, double doors, or a revolving door?". For the first, option Concrete/Stone may be missing. For the second question, open entrance (like in malls) and accordion-style metal gates are absent. In SD3m, both questions show stronger geographical disparities with lower NOTA for UK, USA, and Italy.
For bag, the higher NOTA rate is observed to be result of question on "Does the bag have zipper, buckle, or flap closure?" which examines an attribute that is inherently open-ended. Thus, bags with drawstrings, open-topped totes, plastic bags with tied handles are not represented by this question.
The slightly high NOTA rate for car results from "Is the car a sedan or SUV?" which does not cover all types of cars, missing options like hatchbacks.
For Cooking pot. backyard, chair, house, plate of food, and dog, NOTA rate is consistently < 3% across all datasets.
For questions where more than 30% of images result in NOTA, we include an 'Others' as an option in the distribution.
Table 4: NOTA percentages per entity across datasets, with per-entity average.
Entity
SD2.1
SD3m
SD3.5
FLUX.1
GeoDE
Entity Avg.
Bag
6.05
3.84
4.63
0.99
2.73
3.65
Backyard
0.22
0.48
0.19
0.52
0.33
0.35
Car
2.02
2.34
4.41
3.63
5.17
3.51
Chair
0.95
1.25
1.00
1.45
1.66
1.26
Cooking Pot
1.24
0.79
0.27
0.06
3.65
1.20
Dog
0.12
0.03
0.07
0.68
0.06
0.19
House
3.55
2.35
1.76
1,53
2.04
2.25
Plate of Food
2.12
0.74
1.29
0.98
3.07
1.64
Storefront
4.16
5,48
6.90
1,08
3.96
4.32
Stove
6.15
3.20
9.31
3.54
5.34
5.51
Dataset Avg.
2.66
2.98
2.05
2.80
1.44
C GEODIV DIVERSITY EXTENDED ANALYSIS
C.1 GEODIV DIVERSITY COMPARISON ACROSS ENTITIES
In section of the main paper, we discuss the SEVI and VDI diversities across datasets and countries. In this section, we perform similar analyses, but based on the entities we chose for this paper. Our observations are noted below:
SEVI Diversity Analysis. The overall SEVI diversity is predictably low across entities, with average scores of 0.36 for Affluence and 0.39 for Maintenance. Among the entities. stove and chair images exhibit the highest diversity across both SEVI dimensions, while plate of food images are the least diverse. In terms of Affluence ratings (on a receive the highest average scores (3.34), whereas cooking pot and stove images are rated more impoverished (average rating: 2.50). The trends for Maintenance ratings differ slightly: plate food and dog images receive the highest ratings (average 4.66), while cooking pot and stove images are rated lowest, mirroring the pattern observed for Affluence (average 3.20). Overall, we observe not only lack of diversity in the SEVI dimensions at the entity level but also significant differences in SEVI ratings, suggesting that models generate images reflecting varying vocio-economic conditions depending the entity prompted. These trends are demonstrated in Fig.
scale), backyard and house images
18

---

Published as a conference paper at ICLR 2026
Analysis
Analysis
Diversity-Sene
Mean Score
Diversity-Scom
Mean Score
oloee
storefront
plate of food
house
Endiles
dog
cooking pot
chair
car
beg
backyard
0.0
0.0
0.2
0.4
Scarms
Figure 6: Affluence and Maintenance (SEVI) Scores across Entities. Chair and Stove images show the highest variance Affluence, whereas Cooking Pot and Stove images appear the least affluent. For Maintenance, Stove, Cooking Pot and Chair turn out to be the most diverse, though the mean ratings are low for each of them.
Entity-based
beg
stove
Enitles
beckyard
piate of food
Entay
chair
Beckground
Diversity
Figure 7: Entity and Background Appearance (VDI) Scores across Entities. While Bag and Stove images demonstrate considerably higher entity diversity, Chair and Plate of Food are the least diverse. The Background-Diverse the Entity Diversity-Scoree them are closeups of the entity itself, whereas dogs, cars and houses demonstrate variation in the background some extent.
for these Entities vary considerably, and are distinctly lower than Plate of Food images understandably the least diverse, as of
VDI Diversity Analysis. The Entity Appearance diversity, while low in general for most entities (with average Diversity-Score of 0.44), varies significantly among the same. For instance, Chair, Storefront and Plate of Food are the least diverse, owing to similar answers getting generated across countries (mean score of 0.36). On the other hand, Bags and Stoves vary the most in their attribute values (with a mean score of 0.55). The Background Diversity-Scores are considerably lower than of 0.29 across entities). While these
those for the entities (mean the 10 studied entities, the images belonging to Plate of Food, Storefront and Stove have strikingly low background variation, with mean score of only 0.18. While Plate of Food images are primarily closeups, Storefront and Stove images are also mostly placed in country-win wise similar backgrounds. These trends are shown Fig.
similar for out of
C.2 ANALYSIS ON OVERALL GEODIV SCORES
GeoDiv Scores Across Countries. GeoDiv comprises of four dimensions Affluence and Main- tenance (SEVI), with Entity and Background Diversity (VDI). We combine the Diversity-Scone -Scores obtained under each dimension by averaging, to compute final geo-diversity score per country. The
19

---

Published as a conference paper at ICLR 2026
scores be seen in T where find countries like the UK, US, Japan and India to have lower scores, in comparison with those like Egypt and Colombia.
Table 5: Average GeoDiv scores across countries.
Country
GeoDiv Score
Egypt
0,4106
Colombia
0,4079
Turkey
0,4049
Spain
0,4046
Indonesia
0.3999
China
0.3967
Italy
0.3942
South Korea
0,3932
Philippines
0,3915
United Arab Emirates
0.3878
Nigeria
0,3877
Mexico
0.3817
United States
0,3681
United Kingdom
0.3645
Japan
0,3623
India
0.3372
GeoDiv Scores Across Datasets. We further combine the SEVI and VDI scores by averaging. and report the final dataset-wise geo-diversity values, estimated by GeoDiv in Table While all datasets appear similarly diverse, SD2.1 images dominate the overall scores, whereas FLUX.] images achieve the least scores. Overall, all datasets have low values, indicating the urgent need to enhance the geographical nuances in the generative models.
Table 6: Average GeoDiv Scores across models.
GeoDiv Score Model
SD2.1
0.4251
SD3m
0.3655
SD3.5
0.3455
FLUX.1
0.3153
D ENTITY AND BACKGROUND DIVERSITY SCORES
D.1 ENTITY DIVERSITY SCORES
Figure presents heatmaps of entity-diversity scores across entities and countries.
Dataset Level. SD2.1 achieves the highest dataset-level average (0.51) and SD3.5 the lowest (0.40), is evident in Figure 8 The variance across countries per dataset generally v 0.01 across all T2I models, and across entities is in range [0.001, 0.008). Variance is relatively small, reflecting homogeneous generations.
Entity Level. The average diversity across all datasets and countries varies notably by entity type. Bags show the highest average diversity at about 0.58, followed by stoves (0.52) and dogs (0.50). Chairs have the lowest average diversity at around 0.32, and plate of food also scores low at about 0.36. House exhibits the highest variance across dataset (0.004). Chair and dog show the lowest dataset variances (~z 0.0007), indicating consistent diversity levels across datasets for these entities. Variance across countries within an entity is generally higher than variance across datasets, with cooking pots showing the highest geographic variance (== 0.02), followed by stoves (* 0.02) and dogs (r= 0.01). Plate of food and cars have the lowest country-level variances (Rs 0.002 and 0.004, respectively). suggesting consistent diversity worldwide for these categories.
20

---

Published as conference paper at ICLR 2026
SD2.1
SD3m
0.8
Country
Country
9 h
0.7
abcdes f
abcder f
0066
F
G
E
G
Sugiang Score
Entity
Entity
-0.5
SD3.5
FLUX.1
m O P
0.4
h K
Country
Country
0.3
9
abcues 1
b f
0.2
A
Entity
Entity
Figure 8: Entity diversity scores across generative models. Countries (a-p): a) USA b) UK c) UAE
d) Turkey e) Spain South Korea g) Philippines Nigeria i) Mexico j) Japan k) Italy 1) Indonesia m) India n) Egypt o) Colombia
China. Entities (A-J): A) Backyard B) Bag C) Car D) Chair E) Cooking pot F) Dog G) house H) Plate of food I) Storefront J) Stove. The dataset with highest average diversity is SD2.1 and lowest is FLUX.1.
Country Level spread is narrow, from 0.43 (Mexico) to 0.47 (Japan), indicating that the country- level differences are subtle compared to dataset and entity-level differences. This is evident from Figure which shows higher variation horizontally (along Entity) than vertically (along Country). Cross- stability (coefficient of variation across country means) indicate SD3.5 is the most polarized by country (SD2.1 (=s 0.04). SD3m (+s 0.04), FLUX.1 (+s 0.05), and SD3.5 (ss 0.07)).
D.2 BIAS PATTERNS IN ENTITY ATTRIBUTES REVEALED BY GEODIV
As discussed in $ i in the main paper, we observe both global and cross-country biases within model generations. The below observations relate to most countries, making the biases in global in nature.
1. Chairs without backrests are absent in SD3.5 and FL.UX.1, 1, chairs with single central bases never appear; exclusively multi-legged designs are visible in all the synthetic datasets. SD3.5 and FLUX.1 have bias towards brown coloured, cushioned, and solid-backed chairs, while SD2.1 defaults to Hatted-backed wooden chairs.
2. Backyard images in FLUX.1 are almost always grass-only, with distinct pathways and plants and shrubs. Interestingly, while all datasets hardly show any grass cover for Nigeria, FLUX.1 I images for Nigeria are largely biased towards the same.
3. SD2.1 images of Bag default to non--geomerriccusstruttum shaped bags, FLUX.1 defaults brown-coloured, leather bags.
4. While majority of SD2.1 I car images do not have logos brand badges, SD3m and FLUX.1 almost always do.
5. Single-handles Cooking pots or those without handles are hardly generated, defaulting to only multiple-handled variations across all the datasets.
21

---

Published as a conference paper at ICLR 2026
6. SD3m images only show dogs with folded ears unlike the other datasets which show higher diversity.
7. Plate of food displays one of the lowest diversities across datasets, always depicting vegeta- bles, dense with multiple types of food in the plate, almost always with some garnish, and white, round plates.
8. The cooktop type of Stove images in SD3.5 and FLUX.1 are only gas burners.
9. FLUX.1 1 always depicts multi-storeyed houses with chimneys, porches, grass and paving ground-cover (except Egypt), trees.
Such biases vary in severity across datasets, but others reveal alarming geographic variations. Here we note some examples of such biases across countries:
1. Chair: SD3m shows very few cushioned chairs for Nigeria and the Philippines, and images for Egypt show an over-representation of chairs with woven backrests, whereas the UK and USA samples rarely depict hard-seated chairs.
2. Backyard: SD2.1 and SD3m images for Nigeria show no patio / deck, while for Spain is always present. There is a striking bias in depiction of primary ground cover in most datasets, for Nigeria (only dirt/gravel), India and Egypt (no grass), USA (only grass). UK and USA images are always depicted with outdoor furniture, while it is biased towards absence for Nigeria.
3. Bag images show country-specife biases for material: SD2.1 1 and SD3.5 bags are biased towards fabric in general, but Nigeria has higher proportion of plastic, while the UK, USA, Italy and Japan are the only countries showing leather; SD3m shows only fabric bags for India; Mexico shows higher proportion of patterned and fabric bags, even FLUX.1 which is otherwise biased towards leather. SD3.5 images for Egypt, India, Mexico and Turkey do not have any visible brand logo or label.
4. Car images show a consistent bias towards unpaved surfaces for Nigeria and Egypt across most datasets, including in FLUXX11 which otherwise defaults to paved surfaces. SD3.5 images for Mexico do show logos or brand badges, while defaulting always showing for most other countries.
5. Storefront images in FLUX.1 1 always have lights on except in Nigeria. SD3m shows higher diversity for presence of sidewalk only for Nigeria, leaning towards 'no', whereas it defaults to 'yes' for other countries.
6. Stove shows high disparity in representation across countries, especially in SD3.5. In SD3.5, UK and USA only have multiple burner stoves while India, Nigeria, and Egypt only show single burner ones. In fact, SD3.5 has disproportionately chooses cooktop type as others for almost all countries, especially Egypt (> 93%), while UK and USA are equally biased towards gas burners. SD2. doesn't 't show ovens along with the stoves for most countries, except in USA where it exclusively shows those with ovens.
7. House images for Egypt and UAE show bias towards being depicted solely as flat-roofed. Ground cover for Egypt, Nigeria, India never show grass, while USA always shows only grass. SD3m doesn't I even show paving as ground cover for Egypt, Nigeria, India, only dirt/gravel. For SD3.5, house images of Egypt share distinct features compared to the other countries, owing to its overrepresentation of stones as the primary construction material, and dirt/gravel as the ground cover (see Fig.
There are also some country-specific patterns that seem to be consistent across datasets and entities. For example, there is an apparent correlation between China and the colour red. While FLUX.1 bag images are biased towards brown colour, in case of China it is biased towards red. Some other entities and datasets that show red-colour bias for China include Chairs and Bags in SD3m, and Storefront in SD2.1, 1, SD3.5, and FLUX.1.
22

---

Published as a conference paper at ICLR 2026
Average Js Distance Between Countries
sd35 house
0.5
Colambia
3.165
0.000
0.285
0.758
0.179
0.229
0.165
0.219
0.744
0.370
Egyot-O.368
0.470
0.000
0.237
0.399
0.484
0.375
0.407
0.406
0,459
0.366
0.436
0.318
0.241
0.492
0.518
India-
3.248
0:205
0.237
0.000
0:271
0.317
0.290
0.393
0:303
0,270
0.299
0.206
0.216
0.200
0.408
0.437
0.4
Indonesia
3.203
0.213
0.399
0.271
0.000
0.366
0,202
0.309
0.205
0.150
0.213
0.333
0.291
0.403
0.360
0.334
3.247
0.258
0.317
0.395
0.292
0.238
0.256
0.326
Japan
3.229
0.288
0.372
0.180
0.244
0.327
0.267
0.3
Country
Mexico
3.274
0.179
0.407
0.395
0.309
0.395
0.390
0.000
340
0,322
0.31/
0.331
0.313
0.429
0.410
0.438
Is
Migeria
3.249
0.229
0.406
0.505
0.205
0.341
0.246
0.348
0.000
0.170
0.238
0.315
0.241
0.345
0.345
0.390
Philippines
0.186
0.105
0.459
0.278
0.158
0.292
0.256
0.322
0.170
0.000
0.242
0.273
0.294
0.374
0.376
0.380
0.2
South
3.162
0.269
0.178
0.247
Spain
Turkey
3.25
0.318
0.216
0.238
0.313
0.000
0.1
United Arab Emirates 3.352
0.395
0,241
0.280
0.403
0.392
0.377
0.429
0.245
0.374
0.330
0.322
0.247
0.000
0.420
0.472
United Kingdom
3.276
0.320
0,482
0.106
0. 0.300
2.256 0.327
0.110
0.345
0.376
0.247
2
0.245
0.420
0.000
0.251
United States of America
0.369
0.393
0.518
0.437
0.384
0.326
0.267
0.458
0.390
0,380 0.342
0.295
0.353 0.472
0:251
0.000
0.0
China
Calomibia
Egyat
Indla
interesia
Rilly
Japan
Moxice
Higerts
Phllippines
South Korea
Spain
Unitd Arab Entirates
Tarkey
United Kingstom
United States of America
Country
Figure 9: Jensen Shannon Distances (JSD) Entity Attribute Distributions Across Countries for SD3.5 images of House. We note the higher JSD values for countries like Egypt, Mexico and USA, signalling that they possess distinct features compared the other studied countries.
D.3 BACKGROUND DIVERSITY SCORES
Figure illustraass the entity- and country-wise background diversity score heatmaps. Compared Entity Diversity Scores, the Background Diversity Scores are lower.
Dataset Level. The overall average background diversity across all synthetic datasets and entities is 0.31. As with entity diversity, SD2.1 scores the highest at 0.35, followed by FLUX.1 (0.32) and SD3m (0.31). SD3.5 records the lowest at 0.28, The variance across countries per dataset is approximately 0.02, and across entities is in range [0.001, 0.009].
Entity Level. Highest background diversity is for dogs (0.42) and cars (0.40), reflecting naturally varied scenes. Lowest are plate of food (0.10) and storefront (0.21), both entities with limited background depictions across generated images. As Figure shows, plate of food images with no background context were dropped from the VQA pipeline through the visibility checks. Largest lataset-to-datasee disagreement occurs for bags and cars (0.01), suggesting models differ most in how they situate these objects. Chairs (0.03) and dogs (0.02) show the highest cross-country variability, implying strong geographic differences in their backgrounds.
Country Level. Highest background diversity is seen in Indonesia (0.37), Nigeria (0.36), and Colombia (0.35). Lowest diversity appears in Italy (0.25). Spain (0.27), and the UK (0.27). Overall, developing regions (Nigeria. Indonesia, Philippines) tend to show richer background variation, while European countries (Italy, Spain, UK) exhibit more uniform contexts. China (0.08) and Italy (0.08) show the lowest variance, suggesting more consistent backgrounds across different objects.
23

---

Published as conference paper at ICLR 2026
SD2.1
SD3m
0.7
m
K
Country
abcderf Country
0.6
h
9
abcue f
0.5
Ausaano S score
F
E
G
Entity
Entity
0.4
SD3.5
FLUX.1
m O P
K
Country
f Country
abcues 9 h
0.1
Entity
Entity
Figure 10: Background diversity scores across generative models. Countries (a-p): a) USA b)
UK c) UAE Turkey Spain f) South Korea g) Philippines h) Nigeria Mexico j) Japan k) Italy
Indonesia m) India n) Egypt o) Colombia p) China. Entities (A-J): A) Backyard B) Bag C) Car D) Chair E) Cooking pot Dog G) house H) Plate of food I) Storefront J) Stove.
Table Comparison of entity and background diversity across datasets. SD21 ranks first for entity and background diversity but with notable variance. FLUX.1 and SD3m are more consistent but less diverse. Dataset rank based on number of entities for which had highest diversity.
Entity Diversity
Background Diversity
Dataset
Rank
Mean
Std
Ranki
Mean
Std
SD21
(7/10)
0,508
0.114
1 (7/10)
0.354
0.149
SD3m
(2/10)
0.448
0.104
3 (0/10)
0.306
0.137
FLUX.1
(0/10)
0.424
0.117
2 (3/10)
0.317
0.141
SD35
(1/10)
0.397
0.114
4 (0/10)
0.283
0.143
Summary, We evaluate both entity- and background-level diversity across datasets by comparing average diversity scores, entity-wise rankings, and per-country variation (see Table
E SEVI SCORES COUNTRY AND ENTITY-WISH DETAILS
E.1 AFFLURNCE SCORES
Figure II details the country-entity wise affluence entities and T2I models, which countries show least variance in Affluence level, whereas overall, the diversity across T2I models appears similar.
It clearly shows which for which
24

---

Published as a conference paper at ICLR 2026
abc a 9 k mm 0
Country p
E
H
Entity
Entity
SD3.5
FLUX.1
1 k mn 0 2
Country
Country
9 h
9 h
1 0
E F Entity
H
F
Entity
Figure 11: Affluence diversity scores across generative models. Countries (a-p): a) USA b) UK UAE d) Turkey e) Spain f) South Korea g) Philippines h) Nigeria i) Mexico j) Japan k) Italy Indonesia m) India n) Egypt o) Colombia p) China. Entities (A-J): A) Backyard B) Bag C) Car D) Chair E) Cooking pot Dog G) house H) Plate of food I) Storefront J) Stove.
E.2 MAINTENANCE SCORES
Figure details the country-entity wise maintenance Diversity-scores FLUX.1 has remarkably low diversity in terms of its maintenance, across countries and entities.
25

---

Published as a conference paper at ICLR 2026
SD2.1
K mm P
P
Country h
b 9 ht
10.
E
H
Entity
Entity
SD3.5
FLUX.1
9 h 1 k mm 0 P
00.
Country
Country
9
H
Entity
Entity
Maintenance diversity scores across generative models. Countries (a-p): a) USA b) UK UAE d) Turkey e) Spain f) South Korea g) Philippines h) Nigeria i) Mexico j) Japan k) Italy Indonesia m) India n) Egypt o) Colombia p) China. Entities (A-J): A) Backyard B) Bag C) Car D)
Figure 12:
Chair E) Cooking pot Dog G) house H) Plate of food I) Storefront J) Stove.
26

---

Published as a conference paper at ICLR 2026
F GEODE: OBSERVATIONS ON A REAL-WOOT WORLD DATASET
F.1 DATA DISTRIBUTION
Table 8 provides the entity-country wise counts of images in the GeoDE dataset used in this work.
Table 8: GeoDE entity-country distribution. In table below, we show the entity counts by
country.
Entity
UK
Nig
Tur
Indo
Col
.lap
Ind
Chi
USA
Mex
UAE
SKor
Spa
Ita
Egy
Phil
backyard
60
352
192
125
64
1.3
13
63
33
74
13
103
176
178
312
126
212
73
87
26
77
124
154
75
208
car
92
203
161
136
97
139
80
0
58
35
51
106
137
54
45
chair
175
cooking pot
75
116
162
87
110
177
25
0
56
23
35
95
97
54
59
dog
24
161
house
63
307
150
117
108
168
12
0
74
20
32
58
53
20
40
plate
154
216
95
storefront
38
143
161
133
86
116
40
66
21
35
70
60
71
45
stove
46
137
140
90
161
15
66
25
68
128
73
Total
623
2018
1547
278
476
953
F.2 ENTITY-PPPERRNCE DIVERSITY
The GeoDE real-world dataset has average diversity score of 0.60, noticeably higher than that of the synthetic datasets analyzed (which range roughly between 0.40 to 0.51). Despite this higher diversity, GeoDE still exhibits inherent biases. For example, while generated images of cars display reasonable variability in viewing angles, GeoDE car images tend to be biased towards side views. This highlights how even carefully curated real datasets have distributional skew.
F.3 BACKGROUND-APPARRANE DIVERSITY
GeoDE shows the strongest background variation (0.41), higher than the T2I models. However, background diversity is still significantly lower than entity diversity-score (0.60). Figure shows the heatmaps for GeoDE across all four axes of diversity.
G IMPROVING GEO-DIVERSITY USING GEODIV: AN APPLICATION
Based on our discussion $ GeoDiv assesses the geo-diversity of set of images belonging to certain entity and country. Applied to images from multiple diffusion-based models, the proposed framework uncovers significant lack visual and socio-economic diversitics. In this section, we demonstrate how the insights it provides be directly applied improve inclusivity in practice. As the GeoDiv framework produces detailed distributions over answer categories and socio-economic traits, enables identification and correction of geographical imbalances for data curators. Similarly, model creators can use these metrics to uncover and mitigate model biases-something we illustrate with concrete example.
Building on findings from prior work (Basu et al. 2023 Askari Hemmat et al. 2024). which suggest that prompt design can reduce generative model biases, we apply simple mitigation strategy using our Affluence scores. We observe that the Affluence ratings for India were among the lowest across countries when using default prompt (e.g., "photo of a house"".. To counter this, we design new prompts that explicitly specify different affluence levels, and generated images accordingly. The number images generated per affluence level was inversely proportional to the distribution predicted by the VQA model the original image set. To assess the impact of this intervention, we ask human annotators from India to label both the original and the balanced image sets, and computed the diversity-sce score of the resulting distributions. We found that this prompt-based balancing strategy leads to an increase in diversity for every model evaluated, with an average increase 0.33, indicating improved diversity in the generated outputs (see Table
27

---

Published as conference paper at ICLR 2026
Entity Diversity
Background Diversity
K
0.8
Country
Country
9 h
a bbcue 1
Entity
Entity
Affluence Diversity
Maintenance Diversity
abcues 9 h 1 mn O P
Country
Country
Entity
Entity
Figure 13: Diversity scores across the four axes for GeoDE. Countries (a-p): a) USA b) UK c) UAE d) Turkey e) Spain South Korea g) Philippines Nigeria i) Mexico j) Japan k) Italy 1) Indonesia m) India n) Egypt o) Colombia p) China. Entities (A-J): A) Backyard B) Bag C) Car D) Chair E) Cooking pot F) Dog G) house H) Plate of food Storefront J) Stove.
Table 9: Improvement in Affluence Diversity achieved by utilizing GeoDiv's Affluence Scores
Model
Original
Balanced
Difference
SD2.1
0.56
0.94
+0.38
SD3m
0.62
0.87
+0.25
FLUX.1
0.52
0.88
+0.36
While our mitigation strategy is simple, it demonstrates that once our metrics reveal underlying biases, they can be used to guide actionable interventions that enhance fairness and representation generated content.
H QUESTION-ANSWER (QA) SET FOR VDI SCORES
H.1 QA SET FOR ENTITY DIVERSITY PART OF VDI SCORES.
Table 10 provides the entity-wise question and answer-lists used for calculating entity diversity.
28

---

Published as a conference paper at ICLR 2026
Table 10: Entity-wise questions and their corresponding answer lists.
Entity
Question
Answer List
Are there any animals or pets in the back- yard?
Yes, No
Are there any distinct pathways or walk- ways visible in the backyard?
Yes, No
Backyard
A.e there any plants, trees, or shrubs in the backyard?
Yes, No
4. Are there any structures (e.g., a shed, playhouse) in the backyard?
Yes, No
Are there any visible recreational items (e.g., swing set, trampoline, basketball hoop) in the backyard?
Yes, No
6. Is body of water (e.g., pool, pond, or fountain) visible in the backyard?
Yes, No
there a garden or vegetable patch in the backyard?
Yes, No
there patio or deck attached to the house in the backyard?
Yes, No
9. Is there a visible grill or outdoor kitchen area in the backyard?
Yes, No
10. Is there any outdoor furniture (e.g., a table, chairs) in the backyard?
Yes, No
11. What is the primary ground cover in the backyard: grass, paving (concrete/tiles/- stone), or dirt/gravel?
Grass, Paving, Dirt/Gravel
1. Is brand logo or label visible on the bag?
Yes, No
2. Does the bag have any visible external pockets or compartments?
Yes, No
Bag
3. Does the bag have zipper, buckle, or flap closure??
Zipper, Buckle, Flap
4. Does the bag have handles, shoulder strap, or both?
Handles, Both, Shoulder strap
5. Is the bag backpack, handbag, or tote bag?
Backpack, Tote bag. Handbag
Is the bag being carried by a person, placed on surface, or hanging? Is the bag's overall shape best described rectangular, circular, trapezoidal, or non- geometric/unsttuutureed??
Carried by a person, Placed on a sur- face, Hanging Circular, Unstructured, Rectangular, Trapezoidal
8. Is the bag made of fabric, leather, or plastic?
Plastic, Fabric, Leather
9. Is the bag's surface solid color pat- terned?
Solid color, Patterned
10. What is the main color of the bag?
White, Black, Purple, Blue, Green, Orange, Red, Yellow, Brown, Pink, Gray
Are any wheels visible on the car?
Yes, No Yes, No
2. Are there any logos brand badges the car?
Car
3. Are any of the following modifications visible on the car: a spoiler, a roof rack, or custom rims?
Yes, No
4. Is there a license plate on the car?
Yes, No
(continued on next page)
29

---

Published as a conference paper at ICLR 2026
(continued from previous page)
Entity
Question
Answer List
5. Is the convertible does it have fixed roof?
Convertible, Fixed roof
6. Is the car viewed from the front, side, or rear?
Front, Rear, Side
Does the car appear modern or vintage?
Modern, Vintage On, Off
8. Are the car's lights turned on or off? Is the car a sedan or SUV?
Sedan, Suv Stationary, Moving
10. Is the car moving or stationary?
11. the car on paved surface (like
Unpaved surface, Paved surface
street driveway) an unpaved one (like grass or dirt)?
12. What is the primary color of the car?
White, Black, Blue, Orange, Brown, Red, Yellow, Green, Beige, Gray
1. Does the chair have backrest?
Yes, No
2. Does the chair have armrests?
Yes, No
3. Does the chair have wheels?
Yes, No
Chair
the seat of the chair cushioned or hard?
Hard, Cushioned
5. Does the chair have multiple distinet legs
Multiple distinct legs, A single cen-
or single central base?
tral base
6. Is the chair designed for single person or multiple people?
Multiple people, Single person
7. Is the chair's seat primarily square round?
Round, Square
8. Is the backrest of the chair solid, slatted, or woven?
Slatted, Woven, Solid
9. What the primary material of the chair (c.g., wood, metal, plastic, fabric, woven)? curved?
Stone, Metal, Leather, Wood, Plas- tic, Fabric Straight, Curved
10. Is the chair's backrest straight or
11. What is the primary color the chair?
White, Black, Purple, Blue, Orange, Brown, Red, Yellow, Green, Gray
1. Are there any visible markings, patterns,
Yes, No
or logos on the cooking pot?
2. Does the pot have lid it?
Yes, No Yes, No
Cooking Pot
3. Is the pot placed on cooking surface
(e.g., stove, burner, or fire)?
4. Is the pot taller than it is wide? any food liquid visible inside the pot?
Yes, No Yes, No
Does the pot have single handle or multiple handles?
No handles, Single handle, Multiple handles
7. What material does the pot appear to made of?
Copper, Cast iron, Stainless steel, Ceramic, Enamel Blue, Red, Copper, Silver, Green, Brown, Orange, White, Black
8. What is the primary color of the cooking pot?
Are there any objects like toys, leash, or food near the dog?
Yes, No
2. Is the dog wearing an accessory (e.g.. collar, harness)?
Yes, No
Dog
the dog alone or with other animals or people?
With other animals, Alone, With people, With other animals and peo- ple
(continued on next page)
30

---

Published as a conference paper at ICLR 2026
(continued from previous page)
Entity
Question
Answer List
4. What is the dog's primary activity posture (e.g., standing, sitting, lying down, motion/playing, eating, slceping)?
Walking, Eating, Running, Playing, Standing, Lying down, Sitting
5. Is the dog in an indoor or outdoor set- ting?
Outdoor, Indoor
6. Does the dog's fur appear predomi- nantly as single solid color, or does have multiple distinct colors/patterns (e.g.. spots, patches)?
Multiple colors/patterns, Single solid color
7. Does the dog have short, medium, or long fur?
Medium, Long, Short
floppy (whole ear droops down), erect, or folded (ear starts upright but bends partway)?
8. Are the dog's
Erect, Folded (ear starts upright but bends partway), Floppy (whole ear droops down)
9. Is the dog's mouth open or closed?
Closed, Open
1. Are there any trees visible near the house?
Yes, No
2. Do the windows on the house have shut- ters?
Yes, No
House
3. Does the house have porch bal- cony?
Yes, No
4. Is there chimney the house?
Yes, No
5. Is there a fence on the property?
Yes, No
6. Is there garage visible, attached to the house?
Yes, No
7. What is the main color of the house's exterior?
White, Yellow, Brown, Beige, Gray
8. Is the house single-storey or multi- storey?
Multi-storey, Single-storey
9. What is the primary ground cover around the house: grass, paving (concrete/tiles/s- tone), or dirt/gravel?
Grass, Paving, Dirt/gravel
10. Is the roof of the house flat sloped?
Flat, Sloped Concrete, Stone, Metal, Wood, Glass, Brick Closed, Open
11. What is the primary exterior material of the house?
12. a door on the house open or closed?
1. Are any vegetables visible on the plate? Is there any food item on the plate that visually resembles meat, fish, or eggs?
Yes, No Yes, No
Plate of Food
3. Is a sauce or liquid topping visible on the food?
Yes, No
4. Is any cutlery (c.g., fork, knife, spoon) visible next to the plate?
Yes, No
5. Is more than half of the plate's surface covered by food?
Yes, No
6. Is the food on the plate topped with any garnish, like fresh herbs or seeds?
Yes, No
7. Is the plate a single solid color?
Yes, No Yes, No
8. there any food item on the plate that vi- sually resembles rice, bread, pasta, or pota- toes?
9. Are there smaller dishes or bowls visible along with the main plate of food?
Yes, No
10. Is the plate primarily white or black?
White, Black
(continued next page)
31

---

Published as a conference paper at ICLR 2026
(continued from previous page)
Entity
Question
Answer List
11. Is the plate round square?
Square, Round Single, Multiple
12. Is the plate made up of a single kind of food (e.g., only cookies) or multiple differ- ent types (e.g., rice, curry, and vegetables)?
13. Is the plate of food on table, placemat, or countertop?
Placemat, Table, Countertop
14. the food on the plate solid, liquid, or a mix of both?
A mix of both, Solid, Liquid
1. Are there any items placed outside the storefront, such as displays, furniture, or plants?
Yes, No
Storefront
2. Are there any lights on inside or on the exterior of the storefront?
Yes, No
3. Are there any signs or logos identifying the store visible on the storefront?
Yes, No
4. Are there products or displays visible in the storefront window?
Yes, No
5. Does the storefront have awning canopy?
Yes, No
6. Is there sidewalk in front of the store- front?
Yes, No
7. Is there 'Open' or 'Closed' sign the storefront?
Yes, No
8. Is the storefront entrance single door, double doors, or a revolving door?
Single door, Double doors, Revoly- ing door Part of a larger building, Standalone structure Glass, Wood, Brick
9. Is the storefront part of larger building or standalone structure?
10. Is the facade primarily made of brick, wood, or glass?
11. Is the main entrance door to the store- front open or closed?
Closed, Partially open, Open
12. What is the primary color of the store- front's facade?
Blue, Red, Pink, Purple, Gray, Green, Yellow, Orange, Brown, White, Beige
1. Are there multiple burners or heating zones visible on the cooktop?
Yes, No
2. Does the stove have backguard or splash guard?
Yes, No
Stove
3. Does the stove's oven door have a glass window?
Yes, No
4. Is there digital clock or timer display on the stove?
Yes, No
5. Is there range hood or vent above the stove?
Yes, No
6. Is there
integrated below the
Yes, No
cooktop?
7. Is there any cookware, such pot or pan, on the stove?
Yes, No
What kind of controls are visible on the stove: knobs, buttons, or touchscreen display?
Touchscreen display, Knobs
Buttons,
9. What is the primary material of the
Stainless metal
steel, Enamel/painted
stove's body: stainless steel or enamel/- painted metal?
(continued next page)
32

---

Published as a conference paper at ICLR 2026
(continued from previous page)
Entity
Question
Answer List
10. What is the primary color of the stove?
Red, Blue, Cream, Gray, Silver, Green, White, Black Gas burners, Electric coils, Flat glass/ceramic top
11. What type of cooktop does the stove have: gas burners, electric coils, or a flat glass/ceramic top?
12. Is the stove freestanding or built into the surrounding counter?
Built-in, in, Freestanding
33

---

Published as a conference paper at ICLR 2026
H.2 QA SET FOR BACKGROUND DIVERSITY PART OF VDI SCORES.
Table provides the question-answer list set (common across all entities) for calculating background diversity.
Table 11: Questions and their corresponding answer lists for Background Diversity Scores.
Scene
Question
Answer List
1. Which main elements are visible in the background?
Walls, Windows, Furniture, Ap- pliances (e.g., fridge, microwave, washing machine), Electronic equip- ment (c.g., tvs, computers, speak- ers), Plain / solid color background Tiled floor, Wooden floor, Carpeted floor, Concrete floor
Indoor
2. What type of floor or ground is visible?
3. What type of environment is visible?
Residential, Commercial / public, Plain solid color background Organized (several elements present, but neat, intentional arrangement), Cluttered (many elements, visually noisy, no clear order), Minimalist (very few no elements at all, mostly empty or plain)
4. What best describes the visual order in this image?
1. What natural features, if any, are visible in the background of the image?
Trees forest plants, Mountains hills, Waterbody, Open ground fields
Outdoor
2. What type modern infrastructure is visible in the background?
Transport-related (paved roads, ve- hicles, bridges, rail tracks), Utility- related (electric poles, wires, wa- ter tanks, pipelines), High-rise / in- dustrial (skyscrapers, factories, con- struction sites, large machinery) Sparse / open (fields, wide spaces, buildings). Moder- ate (some houses/buildings, not crowded). Dense / crowded (clus- tered buildings, narrow streets, crowded interiors)
3. How dense is the built environment the background?
few
4. What type of road or terrain is visible?
Paved road, Dirt / gravel road (man- made), Natural ground / grass (wild, non-constructed), Tiled courtyard- style surface
5. What type of background elements are most visible?
Natural (trees, sky, soil, water, mountains), Built structures (walls, windows, houses, buildings. fences), Mixed (both natural and built ele- ments visible)
6. How busy does the background appear, crowded (many people, vehicles, signs of activity), moderately busy (some human ac- tivity), or quiet / empty (few or no people or vehicles)?
Crowded, Moderately busy, Quiet / empty
34

---

Published as a conference paper at ICLR 2026
Study on Image-based Question Answering Japan
You will be shown a number of images, and each such image will be accompanied by THREE questions. Each image will primarily portray an entity. The questions will enquire about three things:
Affluence: Whether the overall image reflects impoverished or affluent conditions.
General Condition:
The physical state the depicted entity (e.g., worn, damaged,
pristine).
The extent to which culturally specific symbols (c.g.. reli- gious motifs, traditional architecture) of your country are present versus globalized visual cues in the entity.
Cultural Localization:
Answer ALL questions.
Total time: 30 minutes
Instructions:
1. See the image very carefully before answering question.
2. Each question can be answered scale of I to 5.
3. We will define the scores within each scale for each question. READ them carefully.
Figure 14: Instructions for the SEVI-based Human Annotation Task
I VALIDATING GEODIV EXTENDED DETAILS
I.1 SURVEY DETAILS
We validate the SEVI and VDI components of GeoDiv by conducting rigorous human studies, as shown in subsection 4.2 (main paper). For studies conducted on each axes, we utilize the Prolific platform (Prolific 2024]. For the SEVI component, we enquire the crowdworkers (hired from 14 different countries, excluding Nigeria and Turkey) about the Affluence and Maintenance in the images shown, the same scale of 1 to 5 as defined for these For the VDI scores, we hire crowdworkers per country. totaling 42 participants, and report the Spearman's ''s rank correlation coefficient P between the LLM-predicted Affluence and Maintenance scores with the corresponding average human scores. Each annotator is allowed a span of 30 minutes complete the survey. The instructions for the study, specific to Japan as example, including question to assess the Cultural Localization of images (discussed in Appendix L are shown in Figure 14 A screenshot of instructions can be seen in Figure T5]
dimensions in Section (main paper).
For VDI, instead of directly asking for diversity scores, we validate the performance of the VQA model by obtaining answers for subset of image-question pairs from the crowdworkers, where equal number of questions enquire about the entity and the background respectively. Three crowdworkers are randomly hired for this task, and the overall annotation requires around minutes to complete. In addition to the VQA questions, we ask every user to rate the images on a) their realism (on Likert-scale of to 5), where high score denotes high realism, and b) the confidence of the user in answering the question (on Likert-scall of to 5), where a high score denotes high confidence. The exact instructions for annotation are described Figure [6] A screenshot of an image and the questions asked for it can be seen in Figure 17
35

---

Published as a conference paper at ICLR 2026
image entity: options are scale of to 5, where each score is defined within the options.
image
depicted image.
1 Impoverished: Severe visible deeaay; disrepair, dirt, broken infrastructure, minimal
economic activity.
2- Low Affluence: Basic but aging structures; modest upkeep; informal or patchy development visible.
3 Moderate Affluence: Clean and functional spaces; organized but simple environments;
middle-incon mnddle-income indicators.
4 High Affluence: Well-maintained, vibrant areas; professional storefronts; signs of prosperity and civic care.
High Affluence (Luxury): Sleek, modern, designer elements; upscale brands;
spotless, elite environments.
of entity: chair. image the general condition the entity. The options scale defined options.
1- Severely Damaged: Major disrepair, heavy rust, breakage, or abandonment visible.
O 2 Poor Condition: Noticeable wear, aging. dirt, minor missing parts, but still recognizable.
3 Moderately Maintained: Functional, intact, but with small flaws like scuffs fading.
Well Maintained: Clean, organized, minor cosmetic only, functional damage.
5 Excellent Condition: Polished, pristine, flawless; appears new or recently serviced.
This of entity: chair. Rate
the cultural locolization the entity with each defined the options.
country, options are scale
Highly globalized: subject displays
markers appears
universally generic global in design.
2 Slightly localized: The subject shows minor cultural hints, but these are subtle and easily
overshadowed global aesthetics.
3 Moderately localized:
The subject blends global and cultural elements, suggestinga
recognizable yet dominant cultural idencity.
4 Strongly localized: The subject prominentyy features distinctivv cultural elements that
clearly to
context.
5 Deeply rooted in culture: The subject embodies cultural uniqueness through highly characteristic and tradition-rich visual cues.
Next
Figure 15: Sample questions for the SEVI dimensions, including a question on measuring Cultural Localization for a given image. For each image-question pair, the scales for each of these dimensions are defined.
Study on Image-based Question Answering
You will be shown number of images, and each such image will be accompanied by FOUR questions. Answer ALL questions. Total time: 45 minutes Instructions:
1. See the image very carefully before answering question.
2. Each question will be associated with options.
3. Multiple options can be correct for the first two questions.
4. If you do not feel any of the options is correct, select None of the above.
5. You can refer to the internet in case you want to know more about certain options.
6. The bottom two questions are single-options only.
Figure 16: Instructions for the Image-based Question Answering Task
36

---

Published as a conference paper at ICLR 2026
Image
What type roof does the house have (e.g., gabled, flat, tiled)?
type road terrain visible?
Choose option
Choose an option
Rate confidence in answering the question.
Rate the image its realism, scale of where means not realistic all, means highly realistic.
High confidence
Medium confidence
Low confidence
2
Sample questions for the VDI-based VQA model Validation. Along with the VDI questions (one entity and background question for each image), we also ask the users about the Realism of the given image, as well as their confidence in answering the question.
Figure 17:
Each crowdworker paid at of $8 per hour.
37

---

Published as a conference paper at ICLR 2026
Table 12: Country-wise Spearman's Correlation Coeflicient between human and model ratings for SEVI dimensions. Gemini- Deminii2... ... outperforms the open- variants.
Qwen2. 5-VL
1lava 1lava-v1.6
Gemi ni-2.5
Country
Affluence
Maintenance
Affluence
Maintenance
Affluence
Maintenance
India
0.87
0.72
0.84
0.81
0.89
0.80
China
0.76
0.78
0.76
0.75
0.88
0.80
USA
0.72
0.67
0.53
0.79
0.69
0.62
Colombia
0.84
0.85
0.84
0.74
0.88
0.81
Egypt
0.66
0.86
0.73
0.76
0.62
0.58
UAE
0.82
0.89
0.81
0.67
0.69
0.83
UK
0.44
0.53
0,45
0.61
0.67
0.37
South Korea
0.54
0.70
0.75
0.71
0.66
0.62
Mexico
0.76
0.75
0.82
0.74
0.90
0.86
Japan
0.59
0.56
0.50
0.55
0.71
0.68
Philippines
0.64
0.72
0.69
0.63
0.70
0.64
Indonesia
0.56
0.49
0.35
0.62
0.75
0.72
Italy
0.74
0.70
0.67
0.73
0.72
0.60
Spain
0.67
0.68
0.64
0.68
0.70
0.68
Average
0.69
0.71
0.65
0.68
0.76
0.69
1.2 COUNTRY-WISE CORRELATION ANALYSIS FOR SEVI SCORES
Expanding on Table 1 (main paper), which shows the SEVI correlations with human ratings for Qwen2.5- 1lava aavavv..... mistral-7b hf and Gemi ni 5, we show the country-wise Spearman's 's correlation coefficient p for each model in Table 12 The Affluence and Maintenance rating correlations for Gemini- 1iiini-2... remains similar to the other models for most countries.
1.3 COMPARISON BETWERN CLOSED AND OPEN SOURCE MODELS
While our VQA pipeline employs a closed-source model (Gemini 2.5 Flash), it can be substituted with any efficient open-source alternative. In this section, we examine the correlation between the diversity scores produced by Gemini 2.5 Flash and those obtained from Qwen2.5-VL-32B VL---B--Isstrutt-AV- across the four diversity axes. The analysis conducted on one synthetic dataset (FLUX. 1) for six entities (Bag, Chair, Cooking Pot, House, Plate of Food, and Storefront) spanning all countries considered in the main study. We additionally report the correlation for real-world dataset (GeoDE) as well (see Table 13
Both the closed and open model shows high agreement across all four diversity axes on both the synthetic (FLUX.1) and real (GeoDE) datasets, indicating broadly consistent scoring behavior. The average correlation across entities and diversity axes is 0.831 for FLUX.1 and 0.826 for GeoDE, respectively.
I.4 STATISTICAL ROBUSTNESS OF GEODIV
The previous section shows the robustness of GeoDiv to varying models. We further analyse the statistical behaviour GeoDiv scores across prompt and seed variations.
Robustness to Varying Image Generation Prompts. In this work, we evaluate the geo-diversity with respect to 'default (minimal) prompts' to analyse what attribute values the T2I model associates to certain geographies without explicit mention. For this analysis, try the following prompt variations which have minimal semantic changes to generate 100 images for the USA, Colombia, India and Egypt across the 3 entities (house, chair, stove) using models (SD2.1 and FLUX.I).
38

---

Published as a conference paper at ICLR 2026
Table 13: Correlation between Flash and Qwen across all four axes of GeoDiv scores.
Dataset
Entity Name
Affluence
Maintenance
Background
Entity
Bag
0.924
0.316
0.412
0.921
Chair
0.926
0.979
0.978
0,986
Cooking pot
0.923
0.982
0,889
0.985
FLUX.1
House
0.796
0.819
0.885
0.875
Plate of food
0.813
0.532
0.727
0.697
Storefront
0.955
0.836
0.910
0.887
Bag
0.931
0,874
0.379
0.811
Chair
0.962
0.894
0.387
0.870
Cooking pot
0.976
0.949
0.555
0.924
GeoDE
House
0.981
0.971
0.839
0.803
Plate of food
0.953
0.896
0.673
0.800
Storefront
0.945
0.921
0.708
0.812
Original Prompt
photo of <entity>> in <country>
Prompt Variants
an image of a <entity>> in <country>>
2. <entity> in <country>
3. a <entity>> located <country>
Table 14: Effect of VIM prompt perturbations SEVI scores.
Affluence (Orig)
Affluence (Pert)
Maintenance (Orig)
Maintenance
Entity
Dataset
(Pert)
SD2.1
0.36
0.36
0.41
0.39
house
FLUX.1
0.22
0.22
0.05
0.07
SD2.1
0.41
0.42
0.60
0.57
chair
FLUX.1
0.63
0.62
0.20
0.23
As our original prompt, Variant and Variant 2 are very neutral, with the only difference to our original prompt being that the generated image does not have be photo but could also be drawing or cartoon. Variant 3 additionally uses more sophisticated wording, "located in" instead of "in", potentially preconditioning the models in specific way. We discuss our observations below:
SD2.1 exhibits high rank-consisstnncy among the prompt variations across all four indicating that its country-level diversity scores are largely insensitive to them. The diversity scores obtained from every prompt variant achieves strong agreement with the scores from the original images, with high overall Spearman correlations at p = 0.80 (variant 1), p 0.85 (variant 2) and p 0.80 (variant 3). This shows that the underlying diversity patterns learned by SD2. remain stable even when prompt phrasing is slightly altered.
FLUX.1 more sensitive to prompt changes than SD2.1. The correlations are p = 0.65 (variant 1), p 0.80 (variant 2), and 0.45 (variant 3), which still significantly high. This observation crucially indicates that different mnnge-generative models exhibit differing levels of sensitivity to prompts.
We observe that the correlation scores for FLUX are most affected by changes along the background axis. Even small modifications to the prompt induce different semantic directions the diffusion model. For example, the phrase "A photo of" pushes the model toward more realistic and commonly
39

---

Published as a conference paper at ICLR 2026
photographed environments, whereas "an image of" broadens the modality include bock--maage-liee compositions, studio setups, or cleaner, more curated scenes.
These shifts visible in empirical distributions. For instance, variant 1 images of stove show a marked increase in clean, organized kitchen layouts, consistent with a stock-photo bias triggered by the more generic "image" phrasing. Similarly, for chair, variant 3 increases the frequency of courtyard or tiled surfaces while reducing natural ground textures, suggesting that the word "located" pushes the model to place objects within more explicitly constructed or architectural contexts.
Taken together, these examples illustrate that different variations of the prompt introduce distinct semantic steering behaviours that subtly shift the generated distributions. To avoid introducing unintended stylistic biases and to remain grounded in realistic depictions, we therefore adopt the most neutral form of the prompt as our standard.
Robustness to Variation of VQA Prompts. We perturb the SEVI prompts to the VLM for both affluence and maintenance via GPT. The results Table 14 show that the scores change negligeably from the original (orig) with the VLM prompt perturbation (pert).
Robustness Varying Image Budgets. To assess the statistical stability of our diversity metric, we evaluated how diversity scores change as function of the number of generated images. For each image-budget n E 10, 50, 100, 150, 200, 250, we generated three independent samples using different random seeds. For each axis of our metric (affluence, maintenance, entity, and background), we calculate the normalized hill numbers for each country-entit-ddatsee triplet and take the average across the three seeds. We list our observations below:
Entity
Background
Maintenance
Affluence
Diversity Score
0.60
0.43
0.38
0.50
040
0.4
0.30
0.29
0.20
0.32
120
200
222
30
320
200
30
333
230
No. of Images
FLUX.1
GeoDE
SD2.1
SD3.5
S03m
Figure 18: Effect of image budget on GeoDiv estimates. All axes show rapid convergence of diversity scores and stable model ranking, indicating statistical robustness of the metrics.
A budget of 100-150 images per concept-country pair is sufficient for stable and reproducible metric estimation. Across all axes, diversity scores converge smoothly as the number of images increases. Large fluctuations are visible at < 50 images, which diminish substantially, and finally become negligible after 100 images. For 1500250 images, confidence intervals are extremely narrow, indicating high reliability (see Figure 18).
Consistent Model and Country Ranking Across Image Budgets. The real-word dataset GeoDE still exhibits the highest diversity scores, and Flux the lowest. This pattern persists across all 4 diversity axes and all values of n > 50 (see Figure 18). and holds true for the ranking of the studied countries as well (see Figure 19p.
These results suggest that the metric is statistically well-behaved and convergent, suitable for large-scale quantitative evaluation. The width of 95% confidence intervals decreases mono- tonically with the number of images. This indicates that seed-induced randomness vanishes with larger sample sizes, and the metric's 's uncertainty is rell-behaved and predictable.
Robustness to Re-runs and Different Seed Image Sets We rerun the full pipeline three times on the same set of 250 3D3m-generated Indian house images and observe most 0.01 standard deviation in the resulting scores (Entity: 0.009, Background: 0.001, Affluence: 0.013, Maintenance: 0.006, overall: 0.007). We additionally generate three independent sets of SD3m images for the same
40

---

Published as a conference paper at ICLR 2026
Stability Image Budgets
Country Rankings Image Budgets
1.00
6073
Country Highesi Diversity
0100
Spearman
0.72
6300
10
20
160
250
200
233
12
200
:20
265
No. of Images
No. of Images
Country
Arts
Coomba
Olra
Eaty
Indoresla
mapp nes
Migers
Turkey
Unrted Arab iivietas
Enftly
Jepn
South Koroa
United Kingsom
Barlgmm.nd
Mraim
Spars
Uastral Ghens Amoenrra
Figure 19: Left: Spearman rank correlation between country rankings obtained at each image budget and the 250-image baseline. Across all diversity axes the rankings converge quickly and remain stable once 2 100 images are used. Right: Country diversity rankings for the overall GeoDiv score across different image budgets (1 = highest diversity).
entity-country pair using different seeds and find maximum standard deviation of only 0.05 across all GeoDiv axes (Entity: 0.018, Background: 0.044, Affluence: 0.023, Maintenance: 0.008, overall: 0.023).
1.5 INTER-ANNOTATOR AGREEMENT ACROSS SEVI AND VDI AXES
Our human validation exhibits strong inter- annotator agreement across both axes, demonstrating the reliability of the collected scores. For the SEVI axis, majority consensus is reached in 85% of the 1, 120 annotated images, and the ordinal consistency is robust: Kendall's 5 = 0.54 for 0.61 for both, levels comparable exceeding agreement reported in prior work (e.g., Cho et al. [1])- For the VDI axis, annotators show high pairwise agreement, with 87% agreement on entity-diversity and 80% on background- diversity questions. These results indicate substantial annotator consensus and confirm that the human annotations provide stable and reliable foundation for validating GeoDiv's axes.
Affluence and 0.53 for Maintenance, with Spearman's
41

---

Published as a conference paper at ICLR 2026
J QUALITATIVE EXAMPLES
We show examples of house images of Nigeria, sampled from each dataset in Figure 20 While GeoDE shows a variety houses, of different architectures and levels of affluence and maintenance. can notice striking lack of diversity in all levels in the generated images. While FLUX.1 images look highly affluent and polished (affluence score: 4.15, maintenance score: 4.99), SD2.1 represents ruralized images of impoverished, ill-maintained houses (affluence score: 1.74, maintenance score: 2.02), and SD3m depict well-maintained houses with consistent bare-earth landscapes (affluence score: 2.18, maintenance score: 3.81). These examples further motivate the need for frameworks that can quantify this lack of diversity within images. GeoDiv can quantify geo-diversity on multiple dimensions like affluence, maintenance, background and entity-diversity separately, making it useful tool that can distinguish among images from datasets, and even entities and countries.
Figure 21 presents a cross-dataset visual comparison of car images for Indonesia. an antity-counity pair exhibiting the highest cross-country variance in entity diversity scores. GeoDE shows a relatively low entity diversity score (0.49), with real-world images capturing mid-range, commonly used vehicles in typical Indonesian urban contexts. SDv2 yields the highest diversity score for cars (0.850), showcasing a wide range of types, colors, and settings. However, it records the lowest maintenance (2.04) and affluence (1.9) scores for cars in Indonesia, well below the dataset average, frequently depicting rustic, vintage, and even deteriorated vehicles. SD3m exhibits moderate entity diversity (0.714) but very low background diversity (0.303), capturing mostly street-level scenes (urban, paved roads, moderately busy backgrounds) and low contextual variance. FLUX.1 scores lower in entity diversity (0.540), heavily skewed toward polished, high-end SUVs and sedans in modern, afflueen-llokinn neighborhoods, reflecting a synthetic bias toward suburban affluence. The comparative visualization illustrates how real and synthetic datasets differ not only in realism but in the socio-cultural and contextual representation of common entities.
While the UK and USA rank among the lowest on the VDI (Visual Diversity Index), and India Visual Indicators), FLUX.1
and Nigeria score among the lowest on the SEVI consistently assigns high scores to all four countries, exceeding on the affluence axis and close to on the maintenance axis. Figure 22 displays FLUX.1's generation of 'houses" across these countries. FLUX.1 consistently generates upscale, multi-storey houses with manicured lawns, porches, and lush green surroundings across all countries. This uniform aesthetic, often resembling Western suburban affluence, reflects a bias toward idcalized, high-end housing. As result, while the images are visually appealing, they lack cultural and structural diversity, demonstrating high affluence but low geo-specific realism.
House in Nigeria
GeoDE
$D2.1
SD3m
FLUX.1
Figure 20: Qualitative examples of house images from Nigeria across datasets. GeoDE shows balanced rural, suburban and urban scenes, while S and SD3m show strong rural bias and FLUX.I shows suburban bias. Each column shares the same generation seed across synthetic models for controlled comparison.
42

---

Published as a conference paper at ICLR 2026
Car in Indonesia
GeoDE
SD2.1
SD3m
FLUX.1
Figure 21: Comparison of car images for Indonesia across datasets. Rows: GeoDE (Entity diversity 0.49), SD2.1 (0.85). SD3m (0.71), FLUX.1 (0.54). SD2.1 shows highest entity diversity with varied car types and contexts; FLUX.1 skews toward affluent suburban scenes. Indonesia shows the highest cross-country variance (0.03) for the car entity.
Nigeria
UAE
India
UK
Figure 22: Comparison of house images generated by FLUX.1 across countries.
In Fig. and 26 we provide samples from each of the chosen T2I models, from each of the 10 entities, and selected countries (due space constraint).
43

---

Published as a conference paper at ICLR 2026
FLUX.1
USA
Japan
UAE
India
Egypt
Philippines
Backyard
Bag
Car
Chair
Cooking Pot
Dog
House
Plate Of Food
Storefront
Stove
Figure 23: Dataset Samples from the FLUX.1 model. across 6 countries and 10 entities. We note distinct country-wise features for each image.
44

---

Published as a conference paper at ICLR 2026
SD2.1
USA
Japan
UAE
India
Egypt
Philippines
Backyard
Bag
Car
Chair
Cooking Pot
Dog
House
Plate Of Food
Storefront
Stove
Figure 24: Dataset Samples from the SD2.1 model. across 6 countries and 10 entities. We note distinct country-wise features for each image.
45

---

Published as a conference paper at ICLR 2026
SD3m
USA
Japan
UAE
India
Egypt
Philippines
Backyard
Bag
Car
Chair
Cooking Pot
Dog
House
Plate Of Food
Storefront
Stove
Figure 25: Dataset Samples from the SD3m model. across 6 countries and 10 entities. We note distinct country-wise features for each image.
46

---

Published as a conference paper at ICLR 2026
SD3.5
USA
Japan
UAE
India
Egypt
Philippines
Backyard
Bag
Car
Chair
Cooking Pot
Dog
House
Plate
Of Food
Storefront
Stove
Figure 26: Dataset Samples from the SD3.5 model. across 6 countries and 10 entities. We note distinct country-wise features for each image.
47

---

Published as a conference paper at ICLR 2026
K COMPARISON OF GEODIV WITH EXISTING BASELINES EXTENDED DISCUSSION
K.1 VENDI-SCORE VS GEODIV SCORES
In the main paper, we analyze the relationship between the proposed VDI metrics and the Vendi- Score (Friedman & Dieng 2023), a measure visual diversity within image sets. Specifically, we compute the Pearson correlation between the Vendi-Score and the four aspects of GeoDiv: (a) Entity Diversity, (b) Background Diversity, and (c) Affluence Diversity, (d) Maintenance Diversity, The country-wise correlations, averaged across datasets and entities vary (p = 0.56, 0.23, 0.37 and 0.06 respectively, as shown in (1). We find a moderate correlation for to very weak correlation for Affluence, Background-Appearaaee and Maintenance, showing that Vendi-Score focuses mostly on the primary entity, and that our metrics capture aspects of image diversity that go beyond general visual dissimilarity.
and weak
Importantly, while Vendi-Score offers quantitative estimate of diversity, it is non-interpretable, making it difficult to explain why a particular image group receives high or low score. contrast, the SEVI and VDI metrics are inherently interpretable: they are grounded in entropy computed from VQA-derived answers to specific semantic questions, allowing for more transparent understanding of what drives diversity score.
Table 15: Pearson's Correlation Coefficient (p) between Vendi-Score and a) Entity Diversity (Entity- Div), b) Background Diversity (Background-Divv, c) Affluence Diversity (Affluence-Div) and d) Maintenance Diversity (Maintenance-DDig Divi.. Correlations across datasets is very weak, showing that the VDI scores capture features beyond visual diversity,
Model_name Entity-Div Background-Div
Affluence-Div
Maintenance-Div
FLUX.1
0.59
0.03
0.11
0.20
SD2.1
0.63
0.42
0.41
0.14
SD3m
0.61
0.31
0.45
-0.01
SD3.5
0.43
0.18
0.51
-0.09
K.2 COMPARISON WITH DIMCIM
Teotia et al. 2025) measure image diversity by querying reliable VQA models on entity attributes and use VQA-Scome (Lin et al. 2024) to estimate diversity. However, there are key differences from our GeoDiv approach. First, it ignores geo-diversity, focusing solely on entity-appearance variation. Second, unlike GeoDiv, which collects separate attribute-value sets per entity, DIMCIM uses fixed set of attributes shared across all entities, making it highly restrictive. Its scores are also less interpretable, often appearing uniform across T2I generations, for example, for the question "What material is the bag made of?", DIMCIM assigns a high score (0.8) even all bags are leather, whereas GeoDiv gives a more realistic score (0. 18). Overall, GeoDiv provides a more comprehensive framework for evaluating geo-diversity.
L CULTURAL LOCALIZATION
The dimensions we measure within the SEVI axis of GeoDiv are: a) Afflueece, and b) Maintenance. One other important aspect of Docio-Economic Visual Index is Cultural Localization, i.e., the extent of representation of the cultures prevalent in a country in the images generated from the same. We define it on scale of 1 to 5 in the following way: Highly Globalized, 2 Slightly Localized, 3 Moderately Localized, = Strongly Localized, 5 = Deeply Rooted in Culture. With such definitions conduct a country-wise human study (see Appendix [ on the same,
of Cultural Localization, where the models are asked to output a score on scale of 1 to 5 using the same definitions. The prompt used for computing Cultural Localization is shown below:
48

---

Published as a conference paper at ICLR 2026
You are a visual-lanquage model tasked with evaluating how culturally aligned generated image of object is within the context of specific region. You will be provided with the object, the regien, and an image depicting the object situated in that region. Your task is to assess visual elements, including both the object and its background features. Definitions:
Cultural Aligament:
visible cues that reflect traditional, recognizable features specific to the region's s or country's s identity.
Globalization: ques that are generic, globally common, or lack strong ties to local culture.
Your decision must be based only visible present in the image. Use the following continuous 5-point scale to rate the cultural alignment:
1 Highly globalized: No distinct cultural markers; universally generic global in appearance.
2 slightly lccalized: Minor cultural hints are present but overshadowed by glcbal aesthetics.
3 Moderately localized: A mix of global and local cues; suggesting a recognizable yet not dominant cultural identity.
4 Strongly Iccalized: Clear and preminent cultural elements tied to the local or regional identity.
5 Deeply rooted in culture: Embodiment of the cuitural uniqueness through highly characteristic and tradition-rich visual cues.
Provids your answer in JSON format:
reasoning_stepe: ['Step 1', 'step 2', ...l, answer: (1, ....
What is the cultural alignment of the generated inage based visual cues alenc? Respond only with a single integer between (highly globalized) and 5 (deeply rosted in culture), and provide the reasoning,
Object: (entity) Regien:
(country) Selection:
The average Spearman's rank correlation coefficient & across countries (0.41 for Gemini-a 2. 0.40 and Qwen2. 5-VL turns out to be much lesser than those of Affluence and Maintenance, We hypothesize that this happens as the aspect of "Cultural Localization" demands specific knowledge for people residing in each country, and it is often not trivial to rate images on the same due to subjectivity. The only countries for which Gemini-2.
has a moderate-to- tigh positive correlation (i.e, p 2 0.4) with the human scores are: India, UK, South Korea, Mexico, Japan and Italy. Across all datasets studied in this paper, we thus assess the Cultural Localization scores of these countries, and find that suprisingly, GeoDE images have much lower average score (1.87), while SD2.1 images have the highest average score (3.28). SD3m and FLUX.1 images score similarly (2.79 and 2.66). This shows that GeoDE images relatively more globalized, with less references country-wise cultures, while the trend opposite for SD2.1 (as shown in Figure] 27
M DATASET DETAILS- EXTENDED DISCUSSIONS
Choice of Countries. The countries chosen (USA, UK, India, Japan, Spain, Italy, Mexico, Philippines, Egypt, Nigeria, Colombia, South Korea, China, Indonesia. Turkey and UAE) represent multiple con- tinents like North and South America, Europe, Asia and Africa. They were chosen understand how differently generative models depict large spectrum of countries including the US as well Nigeria, and they have been inspired by previous works that have studied similar countries (Ramaswamy et al.
2023
Basu et al. 2023; Gaviria Rojas al. 2022 Hall et al. 2023).
Choice of Entities. Our selection of entities follows the protocol established in prior studies
examining geographical disparities in image datasets (Basu al. 2023 Hall et al. 2023 2024).
49

---

Published as a conference paper at ICLR 2026
Cultural Localization Scores
India
South Korea
Mexico
Japan
Italy
2
3
Scores
GeoDE
SDv3
SDv2
FLUX.1
Figure 27: Assessing Cultural Localization. In general, we find India, Mexico and Japan to have more culturally localized images per model (including the real-world GeoDE dataset), with SD2.1 (SDv2) achieving higher scores than SD3m (SDv3) and FLUX.1.
Specifically, adopt all six entities used by Hall al. 2024) bag, car, cooking pot, dog, plate of food, and storefront-ant supplement these with four additional entities commonly studied in the literature: chair, stove, backyard, and house (Ramaswamy et al. 2023 Gaviria Rojas al. 2022 Hall
et al. 2023 2024). These ten entities represent everyday objects with wide socio-cultura relevance. Furthermore, GeoDE provides loose grouping of entities into four categories: Indoor common, Indoor rare, Outdoor common, and Outdoor rare. As shown in Table 2 in the Appendix, our selected entities collectively provide good coverage of all these categories.
50

---

Published as a conference paper at ICLR 2026
N BROAD SOCIETAL IMPACT OF GEODIV
Our proposed framework, GeoDiv, measures geographic diversity in image datasets by evaluating images of a given entity from different countries. We believe this can positively impact the community by highlighting over- or under-represeentaioon of visual attributes across regions. A potential limitation lies in the fixed answer lists generated by the LLM for measuring background and entity diversity as these may not capture the full global spectrum, potentially reinforcing existing biases. To mitigate this, we incorporate 'None of the Above' option during the VQA stage, allowing the model flag missing answers specific to certain countries and entities.
51


Reviews:
[{"review_id": "XxVJvVHu89", "reviewer": "Reviewer_oi9r", "summary": "The paper presents GeoDiv to measure geographical diversity in text-to-image (T2I). It is driven by LLM/VLM and focusses on (a) SEVI (Socio-Economic Visual Index), and (b) VDI (Visual Diversity Index). The paper reports (1) systematic geographic/socioeconomic biases (e.g., India, Nigeria, Colombia show as poor), (2) reduced diversity in entities and backgrounds for newer models. These conclusions are arrived from 160k images from four diffusion models across 16 countries and 10 entities.\n\nOverall, while the paper seeks to address an important problem, concerns with what exactly is being measured, and lack of statistical rigor significantly reduces the reliability of the paper's findings.", "questions": "- There have been studies into geographical stereotypes in generative models with similar conclusions (e.g., see [a] below). Could the authors clarify the contribution of the paper beyond this?\n\n[a] https://restofworld.org/2023/ai-image-stereotypes", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Auditing geo-diversity of generative models is important, but has not received sufficient attention, apart from a few research publications and media articles. \n\n- Analysis was performed on a large-scale set of generated images. The dataset involving 160k synthetic images across 16 countries × 10 entities, prompts, QA sets, and annotations could be valuable to the community.\n\n- Human validation with reasonable Spearman correlation (about 0.7) for the evaluation.", "weaknesses": "- The paper mentions bias and stereotypes interchangeably. However, these two concepts are different from each other as noted by OASIS. And, in the context of generative models, we are largely interested in stereotypes. Furthermore, measuring stereotypes depends on the baseline diversity of concepts in the real world, and deviations from this baseline. However, the current paper does not take this baseline into account, so it is unclear what exactly is being measured, and if the conclusions are meaningful.\n\n- There is heavy use of LLM/VLM to both define and measure attributes. The model's predictions themselves are potentially biased. These biases are not evaluated or accounted for. \n\n- There is heavy use of LLM/VLM to both define and measure attributes. The paper provides point estimates of quantified metrics, and it not clear how robust or reliable these are to slight variations of the proposed approach. For example, different models (partially addressed), different prompts, number of images per attribute used for analysis, etc.\n\n- While the proposed SEVI and VDI are interpretable metrics, they lack statistical grounding.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZuF9b45BSR", "reviewer": "Reviewer_WAhk", "summary": "The paper investigates biases that arise from geographic diversity in text-to-image generative models. The authors propose GeoDiv, a framework designed to assess geographic representation through two complementary metrics: the Socio-Economic Visual Index (SEVI) and the Visual Diversity Index (VDI). Experiments are conducted on multiple diffusion models, including Stable Diffusion (v2.1, v3, v3.5) and FLUX.1-dev.", "questions": "- L160: \" Manually defining a comprehensive set of attributes for each entity is infeasible, so we leverage multiple LLMs to generate candidate question-answer sets, and consolidate them into a unified list using a neutral LLM.\" What impact does using multiple LLMs have compared to a single model, and what is the associated computational overhead?\n\n- L216: \"Consider a question qk (related to either SEVI or VDI attributes), having a set of possible answers denoted by Ak.\"How is the answer set Ak defined and quantified for each question? Since the full space of possible answers is likely intractable, are you instead working with a sampled or limited subset of Ak? If so, how is the quality or representativeness of this subset ensured?\n\n- L27: \"where Pk is the answer distribution for qk, and H(·) denotes Shannon entropy.\" How exactly is Pk defined in this context? Does it represent a probability distribution over all possible answers or over token-level probabilities produced by the model? If it is over answers, how is this distribution estimated or normalized, given the discrete and potentially open-ended nature of responses?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper addresses an important issue of geographical diversity and socio-economic bias in text-to-image models, advancing beyond traditional demographic or visual diversity metrics toward a more region-aware fairness perspective.\n- The study builds a large-scale benchmark comprising 160,000 generated images across 16 countries, 10 object categories, and 4 text-to-image models, enabling a comprehensive analysis of model-, country-, and attribute-level biases, further validated through strong correlations with human ratings (ρ≈0.7–0.8 for SEVI).", "weaknesses": "- Novelty: \n   - The framework’s core methodology lacks novelty. The idea heavily relies on prompt-based probing and LLM/VLM-assisted scoring, which have been explored in prior works such as [1] and [2], making the contribution more of an application to a new bias dimension rather than a methodological advance.\n   - The proposed indices (SEVI and VDI) add interpretability but do not introduce fundamentally new techniques beyond existing entropy- or attribute-based bias quantification methods. Probing variations using LLM and VQA have been extensively explored in works like [1] and [2].\n\n- The framework’s reliance on closed-source evaluators such as Gemini-2.5 and Qwen2.5-VL introduces potential evaluator bias and limits reproducibility, while its high computational and monetary cost (~$68 per entity–country pair) further constrains accessibility and scalability for large-scale or continuous audits.\n\n\n[1] TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models\n\n[2] Generated Bias: Auditing Internal Bias Dynamics of Text-to-Image Generative Models", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c1Wl1Z9bSX", "reviewer": "Reviewer_oxKU", "summary": "This paper introduces GeoDiv, an interpretable framework using LLMs and VLMs to measure geographical diversity in text-to-image (T2I) models, addressing limitations of existing metrics. It assesses images along two axes: the Socio-Economic Visual Index (SEVI) for affluence/maintenance, and the Visual Diversity Index (VDI) for entity/background variety. The authors validate GeoDiv against human judgments, confirming its reliability. Applying GeoDiv reveals significant geographical biases in T2I models, such as stereotypical portrayals of certain countries.", "questions": "I have a few questions, primarily related to the calculation and presentation of the Visual Diversity Index (VDI), which seem connected to the paper's organizational structure:\n- Placement of VDI Calculation: Section 3.1 introduces VDI and mentions using VQA, but the actual calculation method (Normalized Hill Number, Eq. 1) appears later, after Section 3.2. Could the authors confirm this placement is intentional and perhaps clarify the VDI pipeline within Section 3.1 itself for better flow?\n- Role of Q&A Pairs: If the final VDI score is based on the diversity of the VQA model's output distribution (measured by the Hill Number), what is the specific role of generating detailed Question-Answer pairs beforehand? How do these pre-defined pairs contribute to the final diversity score if the score reflects the diversity of the VQA outputs themselves?\n- VDI Metric in Table 1: Table 1 lists \"Accuracy\" under the VDI column. Given VDI is ultimately a 0-1 diversity score, could the authors clarify what this \"Accuracy\" refers to? Is it measuring the accuracy of the VQA model's intermediate categorical answers against human annotations (as part of the framework validation), rather than the final VDI score itself? Clarifying this distinction would be helpful.\n\n\nMoreover, one high-level question. Due to GDPR, the open-sourced generative models might not be trained on EU countries' images. Do you think this can be a potential reason why \"European countries (Italy, Spain, UK) exhibit more uniform contexts\"?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. The paper addresses the important and novel problem of quantifying geographical bias in T2I models by decoupling diversity into interpretable socio-economic and visual axes.\n2. The proposed GeoDiv framework is rigorously validated against human judgments, building trust in its reliability.\n3. The framework produces strong empirical results that highlight specific biases and demonstrate its advantages over existing diversity metrics.\n4. The paper is clearly written, well-organized, and effectively presented with helpful figures.", "weaknesses": "The paper's main weakness lies in its narrative structure, which could more clearly delineate the problem from the proposed solution. While the paper successfully demonstrates (1) that T2I models exhibit geographical bias and (2) that the GeoDiv framework is a valid VLM-based method to measure this, the presentation intertwines these two major points.\n\nA potentially stronger narrative might be:\n1. First, establish the core problem: Demonstrate unequivocally that significant geographical bias exists in T2I model outputs. This could potentially leverage some of the human-annotated scores upfront to ground the problem in human perception, making the need for an automated metric clear.\n2. Then, introduce GeoDiv as the solution: Present the framework as the proposed method to systematically and interpretably measure the established bias. The strong correlation between VLM and human scores (Sec 4.2) then serves as the crucial validation for this specific methodological contribution.\n\nThis revised structure would create a clearer separation between motivating the problem and validating the proposed tool, potentially strengthening the paper's overall argument by first concluding \"T2I image generation is biased (as perceived by humans)\" before concluding \"and our VLM-based GeoDiv is a reliable way to measure this.\" The current organization mixes the validation of the tool (correlation) with the findings from the tool (bias results) without this clear sequential flow.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KKJQY4tPAP", "reviewer": "Reviewer_UsfU", "summary": "This paper introduces GeoDiv, a framework for measuring geographical diversity in Text-to-Image (T2I) models . The authors argue that existing diversity metrics are often uninterpretable or rely on curated datasets, failing to capture nuanced geographical and socio-economic biases. GeoDiv leverages LLMs and VLMs to assess diversity along two primary axes: the Socio-Economic Visual Index (SEVI), which quantifies cues like 'Affluence' and 'Maintenance', and the Visual Diversity Index (VDI), which measures variation in entity and background appearance. Applying this framework to 160,000 images generated by four T2I models across 10 entities and 16 countries, the authors found a consistent lack of diversity and significant socio-economic biases. Notably, depictions of countries such as India, Nigeria, and Colombia were found to be disproportionately impoverished and worn compared to others, demonstrating the framework's utility in identifying specific, interpretable biases.", "questions": "**1.** Could the authors discuss the limitations of this VDI approach, particularly the risk that the LLM's own biases and limited knowledge may prevent it from generating a truly comprehensive and culturally aware set of visual attributes?\n\n**2.** The high failure rate of the visibility check for certain questions (e.g., over 50%) is a concern. Could the authors comment on how this aggressive filtering might affect the validity of the final VDI scores? Does removing a large portion of the images for a given question introduce a form of selection bias into the measurement?\n\n**3.** For the human studies used to validate the framework, could the authors please report the IAA scores?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "**1. A Novel Evaluation Framework**\n\nThe paper introduces GeoDiv, a novel and well-structured framework for the critical task of measuring geographical diversity in text-to-image models. By decomposing the complex concept of \"geo-diversity\" into two clear and complementary axes, the framework provides a highly interpretable and systematic way to diagnose model biases. This multi-dimensional approach is a significant contribution over existing monolithic or uninterpretable diversity metrics.\n\n**2. A Methodologically Rigorous and Nuanced Definition of Diversity**\n\nA standout strength of this work is its thoughtful and rigorous approach to defining socio-economic diversity. The authors adeptly navigate the paradox of whether to evaluate against real-world inequality or an idealized \"fair\" world. Their solution, which separately measure the *breadth* of representation (Diversity Score) and the *central tendency* of that representation (Mean Score), is an elegant and powerful way to identify stereotyping without demanding unrealistic portrayals.\n\n**3. Comprehensive Validation and Demonstrated Practical Application**\n\nThe paper's claims are supported by a large-scale empirical study and rigorous validation. The authors not only generated a massive dataset of 160,000 images but also conducted extensive human studies to validate that their VLM-based metrics correlate well with human judgments. Crucially, the paper includes a proof-of-concept experiment, demonstrating how the diagnostic insights from GeoDiv can be directly used to guide a simple prompt-based mitigation strategy and measurably improve diversity.\n\n**4. Rich, Granular, and Actionable Insights from the Analysis**\n\nThe paper does not just present high-level scores; it provides a rich and granular analysis of the results that yields specific, actionable insights. For example, it uncovers concrete biases like the overrepresentation of \"stone\" for Egyptian houses and \"dirt roads\" for Nigeria, and identifies an interesting trade-off in the FLUX.1 model between aesthetic polish and visual diversity. This level of detailed analysis demonstrates the diagnostic power of the GeoDiv framework and provides concrete targets for model improvement.\n\n**5. Clear Writing and High-Quality Presentation**\n\nThe paper is well-written and clearly structured, presenting a complex, multi-component framework in an intuitive and easy-to-follow manner. The arguments are logically laid out, and the claims are well-supported by the evidence presented. Furthermore, the figures and tables are of high quality, effectively visualizing the data and making the key findings—such as the country-level SEVI disparities—easy to understand.", "weaknesses": "**1. Limited Scope of Evaluated Entities and Countries**\n\nWhile the study is large in scale, its conclusions are based on a curated set of 10 common entities and 16 countries. These entities are mostly globally common objects, and the countries, while diverse, still represent a fraction of global diversity. The framework's effectiveness and the specific biases it uncovers might not generalize to more culturally-specific entities (e.g., traditional clothing, regional architecture) or to the many countries not included in the analysis.\n\n**2. Attribute Definitions are Constrained by LLM Capabilities**\n\nThe Visual Diversity Index (VDI) relies on an ensemble of LLMs to generate the question-answer sets that define the visual attributes for each entity. This approach is fundamentally limited by the collective \"imagination,\" knowledge, and inherent biases of the LLMs used. The framework may fail to generate questions about important, culturally-nuanced visual features that are outside the LLMs' training data distribution, potentially leading to an incomplete or skewed measurement of diversity.\n\n**3. Lack of Reported Inter-Annotator Agreement for Human Validation**\n\nThe paper's validation of its core SEVI and VDI metrics relies on human studies, but it fails to report standard Inter-Annotator Agreement (IAA) metrics. While the authors mention using three annotators and resolving disagreements via majority vote or averaging, they do not quantify the level of agreement among these human annotators. This omission makes it difficult to fully assess the consistency and reliability of the human-generated ground truth data used to validate the framework's components.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)