  We propose to use pretraining to boost general image-to-image translation.
Prior image-to-image translation methods usually need dedicated architectural
design and train individual translation models from scratch, struggling for
high-quality generation of complex scenes, especially when paired training data
are not abundant. In this paper, we regard each image-to-image translation
problem as a downstream task and introduce a simple and generic framework that
adapts a pretrained diffusion model to accommodate various kinds of
image-to-image translation. We also propose adversarial training to enhance the
texture synthesis in the diffusion model training, in conjunction with
normalized guidance sampling to improve the generation quality. We present
extensive empirical comparison across various tasks on challenging benchmarks
such as ADE20K, COCO-Stuff, and DIODE, showing the proposed pretraining-based
image-to-image translation (PITI) is capable of synthesizing images of
unprecedented realism and faithfulness.
