Diffusion probabilistic models (DPMs) have shown remarkable performance in
high-resolution image synthesis, but their sampling efficiency is still to be
desired due to the typically large number of sampling steps. Recent
advancements in high-order numerical ODE solvers for DPMs have enabled the
generation of high-quality images with much fewer sampling steps. While this is
a significant development, most sampling methods still employ uniform time
steps, which is not optimal when using a small number of steps. To address this
issue, we propose a general framework for designing an optimization problem
that seeks more appropriate time steps for a specific numerical ODE solver for
DPMs. This optimization problem aims to minimize the distance between the
ground-truth solution to the ODE and an approximate solution corresponding to
the numerical solver. It can be efficiently solved using the constrained trust
region method, taking less than $15$ seconds. Our extensive experiments on both
unconditional and conditional sampling using pixel- and latent-space DPMs
demonstrate that, when combined with the state-of-the-art sampling method
UniPC, our optimized time steps significantly improve image generation
performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet,
compared to using uniform time steps.

通过设计一个优化问题，并使用约束信任区域方法，我们提出了一种针对特定数值 ODE 解算器的扩散概率模型 (DPMs) 寻找更合适的时间步长的通用框架，该框架能显著改善图像生成性能。

优化时间步长加速扩散采样

Accelerating Diffusion Sampling with Optimized Time Steps

Existing text-to-image diffusion models struggle to synthesize realistic
images given dense captions, where each text prompt provides a detailed
description for a specific image region. To address this, we propose
DenseDiffusion, a training-free method that adapts a pre-trained text-to-image
model to handle such dense captions while offering control over the scene
layout. We first analyze the relationship between generated images' layouts and
the pre-trained model's intermediate attention maps. Next, we develop an
attention modulation method that guides objects to appear in specific regions
according to layout guidance. Without requiring additional fine-tuning or
datasets, we improve image generation performance given dense captions
regarding both automatic and human evaluation scores. In addition, we achieve
similar-quality visual results with models specifically trained with layout
conditions.

通过 DenseDiffusion 方法，我们能够在不需额外微调或数据集的情况下，有效改善给定密集描述的图像生成性能，并且达到与专门训练有场景布局条件的模型相似的视觉效果。