How is the noise in the forward process in Denoising Diffusion Probabilistic Models computed?

The inputs are decayed towards the origin using this formula within Denoising Diffusion Probabilistic Models (DDPMs): $$q\left(\mathbf{x}_{1: T} \mid \mathbf{x}_0\right):=\prod_{t=1}^T q\left(\mathbf{x}_t \mid \mathbf{x}_{t-1}\right), \quad q\left(\mathbf{x}_t \mid \mathbf{x}_{t-1}\right):=\mathcal{N}\left(\mathbf{x}_t ; \sqrt{1-\beta_t} \mathbf{x}_{t-1}, \beta_t \mathbf{I}\right)$$ I however do not understand how the origin is determined. How is the noise in the forward process or diffusion process computed? In the original DDPM paper it is only stated that: the forward process variances $β_t$ can be learned by reparameterization [33] or held constant as hyperparameters.