As the computing power of modern hardware is increasing strongly, pre-traineddeep learning models (e.g., BERT, GPT-3) learned on large-scale datasets haveshown their effectiveness over conventional methods. The big progress is mainlycontributed to the representation ability of transformer and its variantarchitectures. In this paper, we study the low-level computer vision task(e.g., denoising, super-resolution and deraining) and develop a new pre-trainedmodel, namely, image processing transformer (IPT). To maximally excavate thecapability of transformer, we present to utilize the well-known ImageNetbenchmark for generating a large amount of corrupted image pairs. The IPT modelis trained on these images with multi-heads and multi-tails. In addition, thecontrastive learning is introduced for well adapting to different imageprocessing tasks. The pre-trained model can therefore efficiently employed ondesired task after fine-tuning. With only one pre-trained model, IPToutperforms the current state-of-the-art methods on various low-levelbenchmarks. Code is available at https://github.com/huawei-noah/Pretrained-IPTand https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/IPT