This repository contains the code and data for our paper:

When Cultures Meet: Multicultural Text-to-Image Generation Parth Bhalerao, Oana Ignat, Brian Trinh, Mounika Yalamarty ACL Findings 2026

We introduce multicultural text-to-image generation as a new task, where a person from one cultural background is depicted in the context of a landmark from a different culture. We present MosAIG, a Multi-Agent framework for Image Generation that leverages LLMs with distinct cultural personas to generate richer, more culturally grounded image captions.

Our benchmark contains 9,000 images spanning:

• 5 countries (United States, Germany, India, Spain, Vietnam)
• 5 languages (English, Hindi, German, Spanish, Vietnamese)
• 3 age groups (Child, Adult, Elder)
• 2 genders (Male, Female)
• 25 historical landmarks

MosAIG/
├── Alt/                            # AltDiffusion image generation pipeline
│   ├── AltDiffusion-m18-Running-Instructions.pdf
│   ├── BatchImageGenerationAltDiffusion.py
│   ├── PromptTranslation.py
│   ├── RunAltExtended.py
│   ├── main-2.py
│   └── main-3.py
├── Flux/                           # FLUX image generation pipeline
│   ├── BatchImageGenerationFlux.py
│   └── FluxNotebook.zip
├── Metrics-Code/                   # Evaluation metrics
│   ├── Metrics-1.py
│   ├── Metrics-2.py
│   ├── Metrics-3.ipynb
│   └── Metrics-4.ipynb
├── Multi-Agent-Setup/              # MosAIG multi-agent framework
│   ├── Final-Multi-V2.py
│   └── Simple-Crew-Setup.py
├── Single Agent Hardcoded Prompts/ # Simple baseline
│   └── SingleHardcoded-Prompts.py
├── data/                           # Metadata and example data
│   ├── example_data/
│   ├── Alt_MultiAgent.xlsx
│   ├── Alt_Simple.xlsx
│   ├── Flux_Multiagent.xlsx
│   └── Flux_Simple.xlsx
└── images/                         # Sample generated images

The full dataset of 9,000 images along with metadata is available on HuggingFace:

🤗 AIM-SCU/Multi-Cultural-Single-Multi-Agent-Images

The data/ folder in this repository contains the metadata spreadsheets with image captions and demographic keywords for all four model settings:

• Alt_MultiAgent.xlsx — AltDiffusion multi-agent captions
• Alt_Simple.xlsx — AltDiffusion simple captions
• Flux_Multiagent.xlsx — FLUX multi-agent captions
• Flux_Simple.xlsx — FLUX simple captions

MosAIG is a model-agnostic multi-agent prompting framework that decomposes cultural and demographic reasoning across three specialized agents:

• Country Agent — describes culturally appropriate attire and accessories
• Landmark Agent — describes architectural and environmental details
• Age-Gender Agent — describes demographic-specific physical traits

These agents interact over two rounds of conversation, supervised by a Moderator Agent, and the outputs are summarized by a Summarizer Agent into a final image caption (≤77 tokens).

The framework is implemented using CrewAI and LLaMA 3.1-8B.

We evaluate four model configurations:

• AltDiffusion (BAAI/AltDiffusion-m18): multilingual open-source T2I model supporting 18 languages
• FLUX (Freepik/flux.1-lite-8B-alpha): state-of-the-art English-language T2I model

We evaluate across five dimensions:

Install the required packages:

pip install crewai
pip install diffusers transformers accelerate
pip install torch torchvision
pip install openai-clip
pip install pandas openpyxl

If you use this work, please cite:

@inproceedings{bhalerao2026mosaig,
  title     = {When Cultures Meet: Multicultural Text-to-Image Generation},
  author    = {Bhalerao, Parth and Ignat, Oana and Trinh, Brian and Yalamarty, Mounika},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year      = {2026}
}

• 📄 Paper: ACL Findings 2026
• 🤗 Dataset: HuggingFace
• 💻 Code: GitHub