  Diffusion models have emerged as the de facto choice for generating visual
signals. However, training a single model to predict noise across various
levels poses significant challenges, necessitating numerous iterations and
incurring significant computational costs. Various approaches, such as loss
weighting strategy design and architectural refinements, have been introduced
to expedite convergence. In this study, we propose a novel approach to design
the noise schedule for enhancing the training of diffusion models. Our key
insight is that the importance sampling of the logarithm of the Signal-to-Noise
ratio (logSNR), theoretically equivalent to a modified noise schedule, is
particularly beneficial for training efficiency when increasing the sample
frequency around $\log \text{SNR}=0$. We empirically demonstrate the
superiority of our noise schedule over the standard cosine schedule.
Furthermore, we highlight the advantages of our noise schedule design on the
ImageNet benchmark, showing that the designed schedule consistently benefits
different prediction targets.
