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.

提出一种名为 “SSD” 的新型反演流程，使用 “Embryo” 的概念进行前向过程，采用快捷路径，包括 “输入 - Embryo - 输出”，在图像超分辨率、去模糊和上色等任务中实验有效性，探讨 DA 反演和反投影等一系列额外的生成一致性约束条件。与现有的 zero-shot 方法相比，只需 30 个 NFEs 就可取得竞争性的结果。