Spatially Adaptive Noise Injection

2026-09-16

AI Summary

该研究针对图像生成中噪声注入均匀性问题，提出空间自适应噪声注入(SANI)方法，动态调整每个像素的噪声应用，提升生成图像质量。

Abstract

Diffusion samplers reverse a learned noising process using either stochastic (DDPM) or deterministic (DDIM) updates, which represent endpoints of a single family controlled by a scalar noise-injection variance that is applied identically at every spatial location. This uniform approach neglects the geometry of natural images: high-curvature regions such as edges and textures, where the denoiser is uncertain, benefit from stochastic correction, whereas smooth regions, where the score is precise, are degraded by injected noise. This work investigates whether each pixel requires stochastic correction at a given timestep and introduces Spatially Adaptive Noise Injection (SANI), a novel sampling framework that dynamically adjusts noise application on a per-pixel basis. SANI integrates a probabilistic gating mechanism with a derived spatially adaptive variance, ensuring that noise is injected precisely where needed to refine complex features while preserving well-formed structures. Experimental results and decoupling ablations demonstrate that SANI consistently improves Fréchet Inception Distance (FID) over the vanilla DDPM and DDIM endpoint samplers across diverse sampling timesteps, while remaining competitive with variance-learning baselines, highlighting the importance of spatial adaptivity in diffusion sampling.

Problem

• Spatially Adaptive Noise Injection
• diffusion samplers
• stochastic correction
• natural images
• per-pixel basis

Innovation

• Spatially Adaptive Noise Injection
• probabilistic gating mechanism
• spatially adaptive variance