---
title: "Diffusion Models Through a Global Lens: Are They Culturally"
description: "Text-to-image diffusion models produce impressive pictures, but it is unclear whether they depict… 61 references, cited by 7 papers here."
authors: ["Zahra Bayramli", "Ayhan Suleymanzade", "Na Min An", "Huzama Ahmad", "Eunsu Kim", "Junyeong Park", "James Thorne", "Alice Oh"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.1503/"
doi: "10.18653/v1/2025.acl-long.1503"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/bayramli-2025-diffusion-models-through-global-lens/"
---
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# Diffusion Models Through a Global Lens: Are They Culturally Inclusive?

**Authors:** Zahra Bayramli, Ayhan Suleymanzade, Na Min An, Huzama Ahmad, [Eunsu Kim](https://md.research.elamur.ai/free/en/authors/eunsu-kim/), [Junyeong Park](https://md.research.elamur.ai/free/en/authors/junyeong-park/), [James Thorne](https://md.research.elamur.ai/free/en/authors/james-thorne/) and [Alice Oh](https://md.research.elamur.ai/free/en/authors/alice-oh/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.1503/) · [PDF](https://aclanthology.org/2025.acl-long.1503.pdf) · [DOI 10.18653/v1/2025.acl-long.1503](https://doi.org/10.18653/v1/2025.acl-long.1503) · [OpenAlex](https://openalex.org/W4412889715)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [7](https://md.research.elamur.ai/free/en/papers/bayramli-2025-diffusion-models-through-global-lens/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Text-to-image diffusion models produce impressive pictures, but it is unclear whether they depict cultural artifacts accurately, especially for less-represented countries. The authors built CultDiff, a benchmark covering ten countries. The set mixes high-resource cultures, such as WEIRD nations, China and South Korea, with low-resource ones, defined by how well their main languages are resourced (e.g., Ethiopia, Azerbaijan). For each country they assembled artifact lists for architecture/landmarks, clothing and food, and turned them into templated prompts. They gathered real reference photos from the web and generated matching images with three diffusion models. Native annotators then rated the generated images on three aspects: closeness to the real photos, agreement with the description, and realism. Models were noticeably weaker for low-resource countries across these aspects. The authors also used the ratings to train CultDiff-S, a learned image-image similarity metric. It tracks human judgments better than standard metrics. The work matters because it documents cultural bias in generative models and offers a cheaper automatic substitute for costly human evaluation.

## Key points
- **Benchmark scope:** CultDiff spans 10 countries and 3 categories (architecture, clothing, food), with 50 artifacts per category per country. Each artifact has five real reference images, collected with Bulk Bing Image Downloader to reduce bias from viewpoint, lighting or weather.
- **Models evaluated:** SD-XL, SD-3-Medium and FLUX.1-dev.
- **Human evaluation:** Three annotators per country, recruited via Prolific or directly for low-resource countries. They judged image similarity, description fidelity and realism. Ethiopia, Azerbaijan and Indonesia stood out as poorly served.
- **CultDiff-S metric:** A Vision Transformer trained contrastively on positive and negative image pairs derived from human feedback. It correlated with human scores better than existing metrics such as FID-style and CLIP-based measures.
- **Limitations:** The small annotator pool (three per country) limits robustness. Culture is equated with country borders, which ignores within-country variation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Korea Advanced Institute of Science and Technology](https://md.research.elamur.ai/free/en/universities/korea-advanced-institute-of-science-and-technology/) — University, Daejeon, South Korea · 51 works · 3,167 citations

Also on the byline: [State Oil Company of Azerbaijan Republic](https://ror.org/00tcejj11).

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## Cite

Cite as: Zahra Bayramli, Ayhan Suleymanzade, Na Min An, Huzama Ahmad, Eunsu Kim, Junyeong Park, James Thorne and Alice Oh. 2025. Diffusion Models Through a Global Lens: Are They Culturally Inclusive?. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{bayramli2025diffusion,
  title = {{Diffusion Models Through a Global Lens: Are They Culturally Inclusive?}},
  author = {Zahra Bayramli, Ayhan Suleymanzade, Na Min An, Huzama Ahmad, Eunsu Kim, Junyeong Park, James Thorne and Alice Oh},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1503},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1503}
}
```

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*Updated 30 October 2026*