CSGO is a text-to-image generation model based on content style synthesis. It generates and automatically cleans stylized data triplets through a data-building pipeline and has constructed the first large-scale style transfer dataset, IMAGStyle, consisting of 210,000 image triplets. The CSGO model employs end-to-end training and clearly decouples content and style features through independent feature injection. It supports image-driven style transfer, text-driven style synthesis, and text-editing-driven style synthesis, offering benefits such as inference without the need for fine-tuning, retaining the generative capabilities of the original text-to-image models, and unifying style transfer and style synthesis.