Abstract: We present CosmicMan, a text-to-image foundation model specialized for generating high-fidelity human images. Unlike current general-purpose foundation models that are stuck in the dilemma of inferior quality and text-image misalignment for humans, CosmicMan enables generating photo-realistic human images with meticulous appearance, reasonable structure, and precise text-image alignment with detailed dense descriptions.At the heart of CosmicMan's success are the new reflections and perspectives on data and model:$(1)$ We found that data quality and a scalable data production flow are essential for the final results from trained models. Hence, we propose a new data production paradigm \textbf{Annotate Anyone}, which serves as a perpetual data flywheel to produce high-quality data with accurate yet cost-effective annotations over time. Based on this, we constructed a large-scale dataset CosmicMan-HQ 1.0, with $6$ Million high-quality real-world human images in a mean resolution of $1488\times 1255$, and attached with precise text annotations deriving from $115$ Million attributes in diverse granularities.$(2)$ We argue that a text-to-image foundation model specialized for humans must be pragmatic -- easy to integrate into down-streaming tasks while effective in producing high-quality human images. Hence, we propose to model the relationship between dense text descriptions and image pixels in a decomposed manner, and present Decomposed-Attention-Refocusing (Daring). Daring is a training framework that seamlessly decomposes the cross-attention features in the existing text-to-image diffusion model, and enforces attention refocusing without adding extra modules. Through Daring, we show that explicitly discretizing continuous text space into several basic groups that align with human body structure is the key to tackling the misalignment problem in a breeze.