  The Diffusion Model (DM) has emerged as the SOTA approach for image
synthesis. However, the existing DM cannot perform well on some image-to-image
translation (I2I) tasks. Different from image synthesis, some I2I tasks, such
as super-resolution, require generating results in accordance with GT images.
Traditional DMs for image synthesis require extensive iterations and large
denoising models to estimate entire images, which gives their strong generative
ability but also leads to artifacts and inefficiency for I2I. To tackle this
challenge, we propose a simple, efficient, and powerful DM framework for I2I,
called DiffI2I. Specifically, DiffI2I comprises three key components: a compact
I2I prior extraction network (CPEN), a dynamic I2I transformer (DI2Iformer),
and a denoising network. We train DiffI2I in two stages: pretraining and DM
training. For pretraining, GT and input images are fed into CPEN$_{S1}$ to
capture a compact I2I prior representation (IPR) guiding DI2Iformer. In the
second stage, the DM is trained to only use the input images to estimate the
same IRP as CPEN$_{S1}$. Compared to traditional DMs, the compact IPR enables
DiffI2I to obtain more accurate outcomes and employ a lighter denoising network
and fewer iterations. Through extensive experiments on various I2I tasks, we
demonstrate that DiffI2I achieves SOTA performance while significantly reducing
computational burdens.
