Linearly Constrained Diffusion Implicit Models
Vivek Jayaram, Ira Kemelmacher-Shlizerman, Steve Seitz, John Thickstun
We introduce linearly constrained diffusion implicit models (CDIM), a method for solving noisy linear inverse problems 10-50x faster than existing diffusion-based methods. CDIM accelerates conditional inference through a novel projection algorithm that dynamically updates the number of projection steps and step size based on the expected denoising trajectory. This leads to far more efficient conditional update steps that match the efficiency of the unconditional steps from denoising diffusion implicit models (DDIM). Furthermore, for linear inverse problems without measurement noise, CDIM exactly satisfies the constraints, even when existing methods fail to do so. We demonstrate CDIM’s effectiveness across super-resolution, denoising, inpainting, deblurring, and 3D point cloud reprojection.