Initialization-Aware Score-Based Diffusion Sampling

2025-12-29 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Generative AI · Depth: Expert, extended

Summary

This research introduces an "initialization-aware" sampling strategy for Score-Based Generative Models (SGMs) to address the high computational cost associated with traditional Gaussian-initialized samplers. The authors present a Kullback-Leibler (KL) convergence analysis for Variance Exploding (VE) diffusion samplers, highlighting the critical role of the backward process initialization. Based on this, they propose a theoretically grounded method that learns the reverse-time initialization, directly minimizing the initialization error. This procedure is independent of the specific score training, network architecture, and discretization scheme. Experiments on toy distributions (Gaussian Mixture Models, heavy-tailed distributions) and benchmark image datasets (FFHQ-64, ImageNet-512 subsets) demonstrate competitive or improved generative quality with significantly fewer sampling steps, reducing computational cost and energy use.

Key takeaway

For Computer Vision Engineers and Research Scientists working with Score-Based Generative Models, adopting an initialization-aware sampling strategy can drastically cut computational costs without sacrificing output quality. You should consider learning an optimal intermediate initialization for your diffusion models, particularly for heavy-tailed data or conditional generation tasks, to enable faster sampling with fewer steps and potentially lighter model architectures.

Key insights

Optimizing the backward process initialization in SGMs significantly reduces computational cost while maintaining generative quality.

Principles

• KL divergence decomposes into initialization, training, and discretization errors.
• Shorter diffusion horizons ease score model burden and improve stability.
• Intermediate distributions simplify as noise increases, enabling efficient approximation.

Method

The proposed method learns the reverse-time initialization by minimizing the KL divergence between the target intermediate distribution $\vec{p}_{T}$ and a parametric model $p_{0}^{\theta}$, enabling short-horizon diffusion sampling.

In practice

• Use Normalizing Flows (e.g., TarFlow) to model intermediate distributions.
• Initialize samplers from $\sigma_{T}=7$ instead of $\sigma_{T}=80$ for efficiency.
• Apply a "Training Factor" to normalize noised data during training.

Topics

• Score-based Generative Models
• Diffusion Sampling
• Initialization Strategies
• Kullback-Leibler Convergence
• Computational Efficiency

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.