Conditional Diffusion Distillation
Authors: name: Kangfu Mei, name: Mauricio Delbracio, name: Hossein Talebi, name: Zhengzhong Tu, name: Vishal M. Patel, name: Peyman Milanfar

Generative diffusion models provide strong priors for text-to-image
generation and thereby serve as a foundation for conditional generation tasks
such as image editing, restoration, and super-resolution. However, one major
limitation of diffusion models is their slow sampling time. To address this
challenge, we present a novel conditional distillation method designed to
supplement the diffusion priors with the help of image conditions, allowing for
conditional sampling with very few steps. We directly distill the unconditional
pre-training in a single stage through joint-learning, largely simplifying the
previous two-stage procedures that involve both distillation and conditional
finetuning separately. Furthermore, our method enables a new
parameter-efficient distillation mechanism that distills each task with only a
small number of additional parameters combined with the shared frozen
unconditional backbone. Experiments across multiple tasks including
super-resolution, image editing, and depth-to-image generation demonstrate that
our method outperforms existing distillation techniques for the same sampling
time. Notably, our method is the first distillation strategy that can match the
performance of the much slower fine-tuned conditional diffusion models.