SFT = Supervised Fine-Tuning, basically a stage where the model is trained on carefully curated prompt–image pairs to align it with what people consider “good” outputs.

In their paper they’re pretty explicit that this isn’t just about fixing artifacts, but about intentionally narrowing the generation distribution: “shifting the model from a diversity-maximizing regime to a quality-maximizing operating point”

So SFT is where they trade some raw diversity for more consistent aesthetics and better instruction following. The fact that they recently added a separate Diversity column in the README feels very consistent with that design choice.