Anyone who’s used to the mainstream open-source models knows that Chinese prompts often have to be written in a roundabout way—they just don’t get a lot of local concepts. Now, a few domestic models are all about nailing Chinese understanding and local aesthetics, but this isn’t just a simple matter of swapping out the training data.
The quality of Chinese annotation data, coverage of local cultural elements, and alignment with abstract descriptions like chengyu imagery all need dedicated effort. From my own experience, when you’re generating images with a classical Chinese vibe, national trend style, or specific seasonal atmosphere, domestic models definitely cut through the hassle—just plain, straightforward language gets you the right look.
Technically, how well they align Chinese semantics with visuals is what really sets these models apart.
The quality of Chinese annotations is the real bottleneck—if the corpus is dirty, everything downstream is screwed. There’s really no shortcut for this kind of work.
I also try to avoid writing about Chinese-specific concepts like solar terms or idioms—overseas models basically have no clue about those. Domestic ones definitely get the vibe better.
Totally agree on the labeling part—if the training data is dirty, everything downstream is a waste of time. Honestly, the domestic players aren’t that clean either; cleaning it manually costs a fortune.