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

377 evaluation prompts covering diverse scenarios
Bilingual support: Both Chinese and English prompts
Multi-dimensional annotations: 9 different evaluation dimensions
Comprehensive coverage: Aesthetic design and artistic photography scenarios

📊 Dataset Structure

The dataset includes the following fields:

Field	Description
`prompt_text_cn`	Chinese version of the prompt
`prompt_text_en`	English version of the prompt
`Application Scenario`	The application context
`Expression Form`	Form of expression annotations
`Element Composition`	Element combination patterns
`Entity Description`	Entity description types

🏷️ Annotation Dimensions

1. Application Scenario

Aesthetic design: Logo design, character design, product design, etc.
Art : Photography, artistic creation, etc.
Entertainment : Entertainment and personalized content
Film : Film and storytelling scenarios
Functional design : Efficiency and functional design

2. Expression Form

Pronoun Reference: Contains pronoun references
Negation: Contains negative expressions
Consistency: Requires consistent elements

3. Element Combination

Anti-Realism: Anti-realistic combinations
Multi-Entity Feature Matching: Complex multi-entity combinations
Layout & Typography: Specific layout requirements

4. Entity Description

Attribute : Attribute descriptions
Relation : Relationship descriptions
Action/State : Action or state descriptions
Quantity : Quantity specifications

📁 Files

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

🚀 Usage

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

Model Evaluation: Comprehensive evaluation of text-to-image models
Benchmark Comparison: Compare different models across various dimensions
Research: Study model capabilities in different scenarios
Fine-tuning: Use as training or validation data for model improvement

📄 Citation

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

@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.

🤝 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

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