Recently, diffusion models have demonstrated a remarkable ability to solve inverse problems in an unsupervised manner. Existing methods mainly focus on modifying the posterior sampling process while neglecting the potential of the forward process. In this work, we propose Shortcut Sampling for Diffusion (SSD), a novel pipeline for solving inverse problems. Instead of initiating from random noise, the key concept of SSD is to find the "Embryo", a transitional state that bridges the measurement image y and the restored image x. By utilizing the "shortcut" path of "input-Embryo-output", SSD can achieve precise and fast restoration. To obtain the Embryo in the forward process, We propose Distortion Adaptive Inversion (DA Inversion). Moreover, we apply back projection and attention injection as additional consistency constraints during the generation process. Experimentally, we demonstrate the effectiveness of SSD on several representative tasks, including super-resolution, deblurring, and colorization. Compared to state-of-the-art zero-shot methods, our method achieves competitive results with only 30 NFEs. Moreover, SSD with 100 NFEs can outperform state-of-the-art zero-shot methods in certain tasks.

翻译：最近，扩散模型在无监督方式下解决逆问题方面展现出卓越能力。现有方法主要集中于修改后验采样过程，而忽略了前向过程的潜力。本文提出一种新颖的逆问题求解框架——扩散捷径采样（SSD）。SSD的核心思想并非从随机噪声出发，而是寻找"胚胎"（Embryo）——一种连接测量图像y与恢复图像x的过渡状态。通过利用"输入-胚胎-输出"的捷径路径，SSD能够实现精确且快速的恢复。为在前向过程中获取胚胎，我们提出了畸变自适应反演（DA Inversion）。此外，我们在生成过程中应用反向投影和注意力注入作为额外的连续性约束。实验结果表明，SSD在超分辨率、去模糊和着色等代表性任务中具有有效性。与最先进的零样本方法相比，我们的方法仅需30次神经函数评估（NFEs）即可取得竞争性结果。值得注意的是，在特定任务中，采用100次NFEs的SSD更能超越最先进的零样本方法。