ByteDance Seed Proposes Data-Space Iteration, a New Few-Step Generation Paradigm Beating Distillation Baselines
_akhaliq · x · 2026-10-17
ByteDance Seed researchers introduce data-space iteration, a few-step generation framework that removes flow discretization entirely. Instead of iterating along the probability flow with fixed, manually chosen timestep schedules, a shared generator directly refines predictions in data space each step. Requiring minimal changes to distribution matching distillation (DMD) and no schedule-specific training, it out-of-the-box surpasses standard distillation baselines on class-conditional ImageNet 256×256 and matches or beats schedule-search-selected variants.
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