BriefGPT.xyz
Jun, 2024
非混合扩散:噪声分配加速扩散训练
Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
HTML
PDF
Yiheng Li, Heyang Jiang, Akio Kodaira, Masayoshi Tomizuka, Kurt Keutzer...
TL;DR
通过提出不可混合扩散方法,该研究旨在改进扩散模型的训练速度和准确性。该方法通过为图像数据分配目标噪声,限定了图像的扩散区域,同时保持噪声的高斯分布,从而实现更快的训练速度和更好的还原质量。
Abstract
In this paper, we point out suboptimal noise-data mapping leads to slow training of
diffusion models
. During
diffusion training
, current methods diffuse each image across the entire noise space, resulting in a mi
→