标题： 约束扩散隐式模型 显示英文标题

标题： Constrained Diffusion Implicit Models

摘要： 本文描述了一种使用预训练扩散模型求解噪声线性逆问题的高效算法。 扩展去噪扩散隐式模型（DDIM）的范式，我们提出了约束扩散隐式模型（CDIM），通过修改扩散更新以在最终输出上施加约束。 对于无噪声的逆问题，CDIM精确满足约束；在噪声情况下，我们将CDIM推广以在噪声残差分布上满足精确约束。 在各种任务和指标上的实验表明，CDIM表现出强大的性能，其推理加速效果与无约束的DDIM类似：比之前的条件扩散方法快10到50倍。 我们在许多问题上展示了我们方法的通用性，包括超分辨率、去噪、修复、去模糊和3D点云重建。 显示英文摘要

摘要： 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 constrained 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 unconstrained 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.