Why SiLU Matters: Smooth Activations for Advanced AI Models

When I teach activation functions, we cover the usual suspects—Sigmoid, Tanh, ReLU, etc.—but I also introduce activations like ELU, Swish, and SiLU. Students often ask, “Where are these even used?”

A great example is SD3.5, where SiLU (Sigmoid-weighted Linear Unit) play crucial roles. Here, SiLU is commonly paired with normalization layers like AdaLayerNorm and SD35AdaLayerNormZeroX. Large diffusion models like these require smooth gradient flows to ensure stable and high-quality image generation. The use of smoother activations like SiLU, in contrast to sharper ones like ReLU, enhances model stability and the synthesis of fine details, making them indispensable for advanced applications.

The SiLU function is defined as silu(x)=x∗σ(x), where σ(x) is the logistic sigmoid function. Notice how it resembles ReLU but is smoother at the origin, which facilitates better gradient flow.