Scale-wise Distillation of Diffusion Models
Authors: name: Nikita Starodubcev, name: Denis Kuznedelev, name: Artem Babenko, name: Dmitry Baranchuk

We present SwD, a scale-wise distillation framework for diffusion models
(DMs), which effectively employs next-scale prediction ideas for
diffusion-based few-step generators. In more detail, SwD is inspired by the
recent insights relating diffusion processes to the implicit spectral
autoregression. We suppose that DMs can initiate generation at lower data
resolutions and gradually upscale the samples at each denoising step without
loss in performance while significantly reducing computational costs. SwD
naturally integrates this idea into existing diffusion distillation methods
based on distribution matching. Also, we enrich the family of distribution
matching approaches by introducing a novel patch loss enforcing finer-grained
similarity to the target distribution. When applied to state-of-the-art
text-to-image diffusion models, SwD approaches the inference times of two full
resolution steps and significantly outperforms the counterparts under the same
computation budget, as evidenced by automated metrics and human preference
studies.