Few-step text-to-image gains a feature-moment constraint: Z-Image-Turbo hits 94.25% on Chinese LongText-Bench at two evaluations
Few-step text-to-image generation must render prompt details with very few sampling steps, and distribution matching distillation (DMD) relies on learned score differences whose statistics need not align. Intrinsic Distribution Matching keeps the full DMD correction and adds a moment constraint on the means and per-coordinate standard deviations of the generator's exponential-moving-average features, with no external feature encoder. On Z-Image-Turbo 6B at two network evaluations it reaches 94.25% and 90.20% on Chinese and English LongText-Bench, the highest scores among the compared methods at that budget.