  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).
