  The recent release of 220+ million BP/RP spectra in $\textit{Gaia}$ DR3
presents an opportunity to apply deep learning models to an unprecedented
number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so
massive that no previous spectroscopic survey can provide enough stellar labels
to cover the BP/RP parameter space. We present an unsupervised, deep,
generative model for BP/RP spectra: a $\textit{scatter}$ variational
auto-encoder. We design a non-traditional variational auto-encoder which is
capable of modeling both $(i)$ BP/RP coefficients and $(ii)$ intrinsic scatter.
Our model learns a latent space from which to generate BP/RP spectra (scatter)
directly from the data itself without requiring any stellar labels. We
demonstrate that our model accurately reproduces BP/RP spectra in regions of
parameter space where supervised learning fails or cannot be implemented.
