We empirically study the effect of noise scheduling strategies for denoising diffusion generative models. There are three findings: (1) the noise scheduling is crucial for the performance, and the optimal one depends on the task (e.g., image sizes), (2) when increasing the image size, the optimal noise scheduling shifts towards a noisier one (due to increased redundancy in pixels), and (3) simply scaling the input data by a factor of $b$ while keeping the noise schedule function fixed (equivalent to shifting the logSNR by $\log b$) is a good strategy across image sizes. This simple recipe, when combined with recently proposed Recurrent Interface Network (RIN), yields state-of-the-art pixel-based diffusion models for high-resolution images on ImageNet, enabling single-stage, end-to-end generation of diverse and high-fidelity images at 1024$\times$1024 resolution (without upsampling/cascades).

翻译：我们通过实验研究了噪声调度策略对去噪扩散生成模型的影响。主要发现有三点：(1) 噪声调度对模型性能至关重要，且最优调度策略取决于具体任务（如图像尺寸）；(2) 随着图像尺寸增大，最优噪声调度会向更嘈杂的方向偏移（这是由于像素冗余增加所致）；(3) 将输入数据简单地缩放$b$倍，同时保持噪声调度函数固定（相当于将logSNR平移$\log b$），是一种适用于不同图像尺寸的良好策略。这一简单方法结合近期提出的循环接口网络（RIN），在ImageNet上实现了高分辨率图像像素级扩散模型的最优性能，能够以单阶段端到端方式生成1024$\times$1024分辨率（无需上采样/级联）的多样且高保真图像。