arXiv:2603.07860v2 Announce Type: replace Abstract: 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
The continuous development in diffusion models and the increasing computational demands of their application to inverse problems necessitate more efficient sampling methods.
This development proposes a more computationally efficient method for applying diffusion models to inverse problems, which could accelerate research and application in various AI fields requiring high-fidelity image or data generation.
The proposed 'Sparse Scheduled Diffusion Guidance' (Spin) method reduces the computational cost of posterior sampling in diffusion models, potentially making these powerful AI tools more accessible and faster to deploy.
- · AI researchers
- · Companies using diffusion models
- · Fields relying on inverse problem solutions
- · Computational infrastructure providers
- · Inefficient inverse problem solving methods
Faster and cheaper application of diffusion models to complex data generation and inverse problems.
Broadened adoption of diffusion models in sectors like medical imaging, material science, and personalized design due to reduced computational barriers.
New research directions in AI focusing on sparse guidance techniques and their theoretical implications for generative models.
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Read at arXiv cs.LG