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- W4378718323 abstract "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." @default.
- W4378718323 created "2023-05-30" @default.
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- W4378718323 date "2023-05-26" @default.
- W4378718323 modified "2023-09-27" @default.
- W4378718323 title "Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling" @default.
- W4378718323 doi "https://doi.org/10.48550/arxiv.2305.16965" @default.
- W4378718323 hasPublicationYear "2023" @default.
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