README.md

# Magic Bench: A Comprehensive Text-to-Image Generation Evaluation Dataset

## 📖 Overview

Magic Bench is a comprehensive evaluation dataset designed for text-to-image generation models. It contains 377 carefully curated prompts with detailed annotations across multiple dimensions, providing both Chinese and English versions for cross-lingual evaluation.

## 🎯 Dataset Features
- **Systematic and Comprehensive Categorization**: We develop a taxonomy that systematically captures the core capabilities and application scenarios of T2I models.
- **Multiple Test Points per Prompt**: To better reflect the user perspective, Magic-Bench-377 embeds multiple capabilities within a single prompt.
- **Clarity and Visualizability**: Prompts should be concise, explicit and easy to visualize, while avoiding vague or non-visualizable descriptions
- **Neutrality and Fairness.**: Descriptions involving regional specificity, references to celebrities, or copyrighted characters must be avoided to ensure fair evaluation across all models.
 

## 📊 Dataset Structure
| Field | Description |
|-------|-------------|
| `Prompt_text_cn`| Chinese version of the prompt |
| `Prompt_text_en`| English version of the prompt |
| `Application Scenario`| To account for the diversity of real world, magic bench 377 is divided  into five categories |
| `Expression Form`| Refers to semantic units not directly pointing to visual elements but testing model’s understanding and reasoning over special forms of expressio |
| `Element Composition`| Refers to visual elements or information arising from the combination of multiple element |
| `Element`| Refers to visual elements or information that can be expressed by a single semantic unit, typically a word |

## 🏷️ Taxonomy introduction

### 1. Application Scenario 
- **Aesthetic design**: Focuses on model use as a visual tool in professional design contexts, such as poster design, logo design, product design, etc. Models are expected to provide visually appealing outputs with high aesthetic quality.
- **Art** : Focuses on user needs for high-level artistic creation, requiring models to generate outputs aligned with artistic styles, aesthetics, and visual imagination, such as oil painting, watercolor, sketching, or abstract expression.
- **Entertainment** : Focuses on user needs for casual, creative and entertaining content, often reflecting internet culture (e.g., memes, emojis, or playful illustrations). The goal is to stimulate fun, amusement, or humor.
- **Film** : Focuses on user needs for story-driven content creation, such as storyboards, cinematic scenes, or animated sequences. Models are expected to understand narrative details and generate scenes with coherent environments and character interactions.
- **Functional design** : Focuses on user needs for practical work and learning materials, such as teaching slides, product manuals, or office diagrams. Outputs emphasize clarity, conciseness and informativeness.

### 2. Expression Form 
- **Pronoun Reference**: Pronouns (he, she, it, they) referring back to entities mentioned earlier in the text, requiring the model to resolve co-reference.
- **Negation**: Negative expressions such as "no", "without" or "does not".
- **Consistency**: Multiple entities of the same type sharing the same attribute.

### 3. Element Composition 
- **Anti-Realism**: Combinations that contradict real-world cognition or physical laws.
- **Multi-Entity Feature Matching**: Multiple entities of the same type with distinct attribute values.
- **Layout & Typography**: Descriptions of spatial or positional relationships among images, text, or symbols.

### 4. Element 
- **Entity**: Semantic units referring to entities such as people, animals, scenes, costumes, and decorations, including real-world and virtual entities, man-made objects, and natural elements.
- **Entity Description**: Semantic units describing the quantity, attributes, forms, states, or relationships of entities.
- **Image Description**: Semantic units that describe visual elements of a scene, including style, aesthetics, and artistic knowledge.
  
## 📁 Files

- `magic_bench_dataset.csv`: Complete dataset
- `magic_bench_chinese.csv`: Chinese prompts with labels
- `magic_bench_english.csv`: English prompts with labels

## 🚀 Usage

```python
import pandas as pd

# Load the complete dataset
df = pd.read_csv('magic_bench_dataset.csv')

# Load Chinese version
df_cn = pd.read_csv('magic_bench_chinese.csv')

# Load English version
df_en = pd.read_csv('magic_bench_english.csv')


```

## 📈 Statistics

- **Total prompts**: 377
- **Aesthetic design prompts**: 95 (25.2%)
- **Art prompts**: 80 (21.2%)
- **Prompts with style specifications**: 241 (63.9%)
- **Prompts requiring aesthetic knowledge**: 131 (34.7%)
- **Prompts with atmospheric elements**: 22 (5.8%)

## 🎯 Use Cases

1. **Model Evaluation**: Comprehensive evaluation of text-to-image models
2. **Research**: Study model capabilities in different scenarios
3. **Fine-tuning**: Use as training or validation data for model improvement

## 📄 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{magic_bench_377,
  title={Magic Bench: A Comprehensive Text-to-Image Generation Evaluation Dataset},
  author={outongtong},
  year={2025},
  email={outongtong.ott@bytedance.com},
  url={https://huggingface.co/datasets/ByteDance-Seed/MagicBench}
}
```

## 📜 License

This dataset is released under the [cc-by-nc-4.0](LICENSE).

## 🤝 Contributing

We welcome contributions to improve the dataset. Please feel free to:
- Report issues or suggest improvements
- Submit pull requests with enhancements
- Share your evaluation results using this dataset

## 📞 Contact

For questions or collaborations, please contact: outongtong.ott@bytedance.com

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**Keywords**: text-to-image, evaluation, benchmark, dataset, computer vision, AI, machine learning