wangruoyu/MagicBench

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

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:

@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

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