Efficient Score Pre-computation for Diffusion Models via Cross-Matrix Krylov Projection

• URL: http://arxiv.org/abs/2511.17634v1
• Date: Wed, 19 Nov 2025 07:21:49 GMT
• Title: Efficient Score Pre-computation for Diffusion Models via Cross-Matrix Krylov Projection
• Authors: Kaikwan Lau, Andrew S. Na, Justin W. L. Wan,
• Abstract summary: This paper presents a novel framework to accelerate score-based diffusion models.<n>It first converts the standard stable diffusion model into the Fokker-Planck formulation which results in solving large linear systems for each image.<n>The core innovation is a cross-matrix Krylov projection method that exploits mathematical similarities between matrices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel framework to accelerate score-based diffusion models. It first converts the standard stable diffusion model into the Fokker-Planck formulation which results in solving large linear systems for each image. For training involving many images, it can lead to a high computational cost. The core innovation is a cross-matrix Krylov projection method that exploits mathematical similarities between matrices, using a shared subspace built from ``seed" matrices to rapidly solve for subsequent ``target" matrices. Our experiments show that this technique achieves a 15.8\% to 43.7\% time reduction over standard sparse solvers. Additionally, we compare our method against DDPM baselines in denoising tasks, showing a speedup of up to 115$\times$. Furthermore, under a fixed computational budget, our model is able to produce high-quality images while DDPM fails to generate recognizable content, illustrating our approach is a practical method for efficient generation in resource-limited settings.

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