Torch-Advent-Civilization-Evolution: Accelerating Diffusion Model for Image Restoration

Recently, diffusion models as a hot paradigm have shown considerable superiority in image restoration with an unsupervised manner. However, they require iterative refinement from isotropic Gaussian through thousands of steps to produce a sample with exceptional quality. Most existing methods are devoted to designing fast solvers for the reverse stochastic differential equation (SDE) to accelerate sampling, while neglecting the potential of forward SDE. To better stimulate this potential, we propose the Torch-Advent-Civilization-Evolution (TACE), a novel diffusion model-based zero-shot framework for image restoration. Specifically, we propose the “Torch”, a latent vector that explicitly contains content information from the measurement image. By utilizing the Torch instead of isotropic Gaussian as initialization, our TACE significantly accelerates image restoration with better consistency and realness. To acquire the Torch from the forward process, we propose Prometheus SDEs, a cluster of equivalent SDEs. Furthermore, we construct a Conditional Guidance Projection (CGP) for the reverse SDE to strengthen the consistency of restored images. Finally, we design a Civilization Shuttle Strategy (CSS) for the generation process to enhance the realness of restored images. Extensive experiments validate that our TACE achieves state-of-the-art performance with fewer sampling steps in various typical tasks, such as super-resolution, deblurring, and colorization.