# Code for the Paper titled ["Perpetual Misogyny: How Gendered Tropes Shape Text-To-Image AI Personalization"](http://arxiv.org/pdf/2505.04600)




### Repository Structure

```
CIVITAI_VISUALIZATIONS/
├── .virtual_documents/                   # Temporary files from Jupyter
├── data/                                 # Final curated datasets
│   ├── subset1/                          # Specific data splits or versions
│   ├── subset2/
│   └── ...
├── misc/
│   └── credentials/                      # API keys and sensitive config (excluded from versioning)
├── plots/                                # Output plots and figures used in the paper
├── ├── jupyter_notebooks/                          # Main analysis notebooks
│   ├── 0_Scraping_image_metadata.ipynb         # Scrapes CivitAI metadata via API
│   ├── Section_1_Figure_1_image_grid.ipynb     # Grid of sample images
│   ├── Section_3-2-1_Figure_3_histogram.ipynb  # Histogram of upload trends
│   ├── Section_3-2-1_Figure_4_Mivolo.ipynb     # Model activity plot (Mivolo-focused)
│   ├── Section_3-3-1_Figure_5_tags.ipynb       # Tag frequency and usage visualizations
│   ├── Section_3-3-3_download_popular_models.ipynb # Download models for analysis
│   ├── Section_3-3-3_Figure_6.ipynb            # Promotional tag usage patterns
│   ├── Section_3-3-4_Figure_8a.ipynb           # Ranking of popular models
│   ├── Section_3-3-4_Figure_8b.ipynb           # Continuation of model rankings
│   ├── Section_3-3-4_Figure_9_Sankey.ipynb     # Sankey diagram: user-model contributions
│   ├── Section_3-3-4_LLM_annotation.ipynb      # Annotations using large language models
│   ├── Section_3-4_extract_LoRA_metadata.ipynb # LoRA metadata extraction
│   ├── SuppM_Figure_12_Danbooru_Taxonomy.ipynb # Danbooru tag taxonomy: visualization
│   ├── SuppM_Figure_13_Danbooru_taxonomy.ipynb # Tag grouping and structure
│   └── SuppM_Figure_13.ipynb                   # Supplementary figure generation
├── misc/                                       # Utility scripts and API credentials (excluded)
├── plots/                                      # Output plots and visualizations
├── public/                                     # Optional public-facing files
├── .gitignore
├── .gitmodules
├── README.md                                   # This file
└── requirements.txt                            # Python dependencies
```

# Project Setup

## Requirements
This project requires Python 3.8 or higher. Ensure you have it installed before proceeding.

## Installation

1. **Clone the Repository**  
   ```sh
   git clone https://gitlab.uzh.ch/latent-canon/pm-paper.git
   cd pm-paper
   ```

2. **Create a Virtual Environment**  
   (Recommended to avoid dependency conflicts)
   ```sh
   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   venv\Scripts\activate     # On Windows
   ```

3. **Install Dependencies**  
   ```sh
   pip install -r requirements.txt
   ```

4. **Jupyter Notebook Setup (Optional)**  
   If running Jupyter notebooks, ensure the environment is linked:
   ```sh
   python -m ipykernel install --user --name=venv --display-name "Python (venv)"
   ```

## Notes
- Ensure you have necessary system dependencies installed (e.g., `opencv` may require additional system libraries).
- If you encounter any issues, ensure you're using the correct Python environment (`venv` activated).