Pretrained diffusion models are effective priors for Bayesian inverse problems, but posterior sampling with these priors is often costly because data-consistency guidance is applied throughout the full reverse trajectory. Existing methods have shown that vector-Jacobian products through the denoiser can sometimes be avoided, yet they typically still rely on dense guidance through the full trajectory or expensive inner solves. We introduce Sparse Scheduled Diffusion Guidance for Inverse Problems (Spin), a solver that avoids starting posterior sampling from pure noise. Spin first samples from a posterior time-marginal at an intermediate timestep $t_*$, and then uses that state as a warm start for a guided reverse diffusion process. At guidance time, instead of enforcing the measurement constraint at every denoising step, Spin applies lightweight corrections only at scheduled timesteps where the denoiser can still clean up artifacts. The resulting procedure decouples prior refinement from data consistency: the prior supplies denoising, while lightweight pixel-space optimization enforces the measurement constraint without backpropagation through the denoiser or decoder. Across linear and nonlinear inverse problems on FFHQ and ImageNet, Spin achieves competitive reconstruction quality with a substantially better runtime--memory profile, running 2x faster on pixel-space models and up to 50x faster on latent diffusion models, with lower memory costs.

翻译：预训练扩散模型是贝叶斯逆问题的有效先验，但使用这些先验进行后验采样通常成本高昂，因为数据一致性引导贯穿整个反向轨迹。现有方法已表明，有时可避免通过降噪器计算向量-雅可比乘积，但它们通常仍依赖贯穿整个轨迹的密集引导或代价高昂的内部求解。我们提出面向逆问题的稀疏调度扩散引导（Spin），这是一种无需从纯噪声启动后验采样的求解器。Spin首先在中间时间步$t_*$处对后验时间边际分布进行采样，然后将该状态作为引导反向扩散过程的暖启动。在引导时刻，Spin不在每个降噪步骤强制执行测量约束，而是仅在调度的时间步执行轻量级修正，此时降噪器仍能消除伪影。由此产生的过程将先验精炼与数据一致性解耦：先验提供降噪功能，而轻量级像素空间优化在不通过降噪器或解码器反向传播的情况下强制执行测量约束。在FFHQ和ImageNet上的线性和非线性逆问题中，Spin以显著更优的运行时间-内存配置文件实现了具有竞争力的重建质量，在像素空间模型上运行速度提升2倍，在潜在扩散模型上提升高达50倍，且内存成本更低。