| # 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={[email protected]}, | |
| 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: [email protected] | |
| --- | |
| **Keywords**: text-to-image, evaluation, benchmark, dataset, computer vision, AI, machine learning |