Cross-Resolution Distribution Matching for Diffusion Distillation

Diffusion distillation is central to accelerating image andvideo generation, yet existing methods are fundamentally limited by thedenoising process, where step reduction has largely saturated. Partial-timestep, low-resolution generation can further accelerate inference, butit suffers from noticeable quality degradation due to cross-resolutiondistribution gaps. We propose Cross-Resolution Distribution MatchingDistillation (RMD), a novel distillation framework that bridges cross-resolution distribution gaps for high-fidelity, few-step multi-resolutioncascaded inference. Specifically, RMD divides the timestep intervals foreach resolution using logarithmic signal-to-noise ratio (logSNR) curves,and introduces logSNR-based mapping to compensate for resolution-induced shifts. Distribution matching is conducted along resolution tra-jectories to reduce the gap between low-resolution generator distributionsand the teacher’s high-resolution distribution. In addition, a predicted-noise re-injection mechanism is incorporated during upsampling to stabi-lize training and improve synthesis quality. Quantitative and qualitativeresults show that RMD preserves high-fidelity generation while acceler-ating inference across various backbones. Notably, RMD achieves up to33.4× speedup on SDXL and 25.6× on Wan2.1-14B, while preservinghigh visual fidelity.