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.

提出了一种名为 DiffI2I 的简单、高效和强大的 I2I 扩散模型，通过引入紧凑的 I2I 先验提取网络（CPEN），动态 I2I 转换器（DI2Iformer）和去噪网络来解决了传统 DM 在某些 I2I 任务中的效率和生成能力方面的问题。通过在各种 I2I 任务上进行广泛实验证明，DiffI2I 在显著减少计算负担的同时达到了 SOTA 水平。