Few-Step Generation via Data-Space Iteration
Data-space iteration is a few-step generation framework that bypasses fixed flow discretization by having a generator directly refine its data predictions in data space during each step. This method integrates easily with Distribution Matching Distillation (DMD) and outperforms existing discretized flow methods on ImageNet tasks, offering a simple and effective alternative for fast sample generation.