A comprehensive collection of 92,256 carefully curated samples from GPT-4o for training advanced multimodal AI systems capable of understanding and generating images from text descriptions.
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ShareGPT 4O represents a significant advancement in multimodal AI training data, providing researchers and developers with access to high-quality image generation samples across diverse categories and complexity levels.
The ShareGPT 4O dataset has been meticulously constructed to address the growing need for high-quality multimodal training data in artificial intelligence research. Each sample within the dataset has been generated using GPT-4o's advanced image generation capabilities, ensuring consistent quality and adherence to text prompts across the entire collection.
The dataset encompasses two primary categories of image generation tasks. The first category includes 45,717 text-to-image samples, where detailed text descriptions are paired with corresponding generated images. These samples cover a wide range of subjects, including natural scenes, artistic compositions, architectural structures, character portraits, and abstract concepts. The second category contains 46,539 text-and-image-to-image samples, which demonstrate the model's ability to modify, enhance, or transform existing images based on textual instructions.
The curation process involved careful selection and validation to ensure each sample meets strict quality standards. Images are provided at a consistent 1024×1024 pixel resolution, offering sufficient detail for training while maintaining computational efficiency. The accompanying text descriptions range from simple object descriptions to complex scene compositions, providing training data for models across different complexity levels.
Designed with researchers and AI developers in mind, ShareGPT 4O offers essential features for effective multimodal model training.
Diverse range of prompts spanning artistic styles, objects, scenes, and concepts with detailed annotations.
Each sample has been carefully selected and validated for training effectiveness and visual quality.
Built on peer-reviewed research with detailed methodology and reproducible results.
Structured format optimized for training multimodal AI models with standardized annotations.
ShareGPT 4O is built upon rigorous academic research documented in our peer-reviewed paper. The methodology combines advanced data collection techniques with systematic quality validation to create a dataset that advances the state of multimodal AI research.
Our research demonstrates significant improvements in image generation quality when models are trained on this curated dataset compared to traditional approaches. The systematic approach to data collection and annotation provides researchers with a reliable foundation for developing next-generation multimodal AI systems.
Chen, J., Cai, Z., Chen, P., et al. (2025). ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation. arXiv:2506.18095
Experience the capabilities of Janus-4o, a multimodal large language model trained on ShareGPT 4O data, capable of both text-to-image and text-and-image-to-image generation.
Access comprehensive documentation, code examples, and tutorials to begin working with ShareGPT 4O in your research projects.
Complete guides for dataset usage, model training, and evaluation procedures.
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