Guidance is a crucial technique for extracting the best performance out of
image-generating diffusion models. Traditionally, a constant guidance weight
has been applied throughout the sampling chain of an image. We show that
guidance is clearly harmful toward the beginning of the chain (high noise
levels), largely unnecessary toward the end (low noise levels), and only
beneficial in the middle. We thus restrict it to a specific range of noise
levels, improving both the inference speed and result quality. This limited
guidance interval improves the record FID in ImageNet-512 significantly, from
1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial
across different sampler parameters, network architectures, and datasets,
including the large-scale setting of Stable Diffusion XL. We thus suggest
exposing the guidance interval as a hyperparameter in all diffusion models that
use guidance.

通过限制特定范围的噪声水平，改进了图像生成扩散模型中的引导技术，从而在各个参数、网络结构和数据集上显著提高推理速度和结果质量，建议将引导间隔作为扩散模型的超参数暴露出来。

在有限区间内应用引导改善扩散模型的样本和分布质量

Applying Guidance in a Limited Interval Improves Sample and Distribution  Quality in Diffusion Models

A popular tool for unsupervised modelling and mining multi-aspect data is
tensor decomposition. In an exploratory setting, where and no labels or ground
truth are available how can we automatically decide how many components to
extract? How can we assess the quality of our results, so that a domain expert
can factor this quality measure in the interpretation of our results? In this
paper, we introduce AutoTen, a novel automatic unsupervised tensor mining
algorithm with minimal user intervention, which leverages and improves upon
heuristics that assess the result quality. We extensively evaluate AutoTen's
performance on synthetic data, outperforming existing baselines on this very
hard problem. Finally, we apply AutoTen on a variety of real datasets,
providing insights and discoveries. We view this work as a step towards a fully
automated, unsupervised tensor mining tool that can be easily adopted by
practitioners in academia and industry.

该论文介绍了一种名为 AutoTen 的自动无监督张量挖掘算法，可实现对多方面数据无标签或基准真实性的探索。它可以自动决定提取多少成分以及如何评估结果质量，并在合成数据和真实数据集上得到了良好性能表现。