SEED-X is a unified and versatile foundation model designed to bridge the gap between multimodal AI capabilities and real-world applicability. It integrates two key enhanced features: (1) understanding images of arbitrary sizes and ratios, and (2) enabling multi-granularity image generation. These features allow SEED-X to effectively respond to various user instructions and interact with diverse visual data, making it suitable for a wide range of real-world applications. The model is pre-trained on extensive multimodal data, including image-caption pairs, grounded image-text data, interleaved image-text data, OCR data, and pure texts. It is further fine-tuned using instruction tuning to align with human instructions across different domains, such as image editing, text-rich QA, grounded and referencing QA, and slide generation tasks. Evaluations on public benchmarks and real-world applications demonstrate that SEED-X achieves competitive performance in multimodal comprehension and state-of-the-art results in image generation. SEED-X's capabilities include acting as an interactive designer, generating images with creative intent and offering modification suggestions, and functioning as a knowledgeable personal assistant, comprehending images of various sizes and providing relevant suggestions. The model's effectiveness is validated through qualitative examples in text-to-image generation, image manipulation, and multimodal comprehension, showcasing its versatility and potential in real-world scenarios.SEED-X is a unified and versatile foundation model designed to bridge the gap between multimodal AI capabilities and real-world applicability. It integrates two key enhanced features: (1) understanding images of arbitrary sizes and ratios, and (2) enabling multi-granularity image generation. These features allow SEED-X to effectively respond to various user instructions and interact with diverse visual data, making it suitable for a wide range of real-world applications. The model is pre-trained on extensive multimodal data, including image-caption pairs, grounded image-text data, interleaved image-text data, OCR data, and pure texts. It is further fine-tuned using instruction tuning to align with human instructions across different domains, such as image editing, text-rich QA, grounded and referencing QA, and slide generation tasks. Evaluations on public benchmarks and real-world applications demonstrate that SEED-X achieves competitive performance in multimodal comprehension and state-of-the-art results in image generation. SEED-X's capabilities include acting as an interactive designer, generating images with creative intent and offering modification suggestions, and functioning as a knowledgeable personal assistant, comprehending images of various sizes and providing relevant suggestions. The model's effectiveness is validated through qualitative examples in text-to-image generation, image manipulation, and multimodal comprehension, showcasing its versatility and potential in real-world scenarios.