Constrained Diffusion Implicit Models
Keywords: Diffusion, Inverse Problems, DDIM, Inpainting
TL;DR: We solve linear inverse problems with diffusion models 10-50x faster than existing methods with comparable quality.
Abstract: This paper describes an efficient algorithm for solving noisy linear inverse problems using pretrained diffusion models. Extending the paradigm of denoising diffusion implicit models (DDIM), we propose conditional diffusion implicit models (CDIM) that modify the diffusion updates to enforce a constraint upon the final output. For noiseless inverse problems, CDIM exactly satisfies the constraints; in the noisy case, we generalize CDIM to satisfy an exact constraint on the residual distribution of the noise. Experiments across a variety of tasks and metrics show strong performance of CDIM, with analogous inference acceleration to unconditional DDIM: $10$ to $50$ times faster than previous conditional diffusion methods. We demonstrate the versatility of our approach on many problems including super-resolution, denoising, inpainting, deblurring, and 3D point cloud reconstruction.
Primary Area: generative models
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Submission Number: 9323
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