Lux models astronomical data with noisy labels using a multi-output, latent-variable framework. It simultaneously infers latent parameters and predicts multiple observed properties while accounting for measurement uncertainties and label noise. The code generates synthetic observations for model validation and supports probabilistic analyses of stellar and galactic datasets. Lux enables flexible model training and evaluation and handles heterogeneous datasets efficiently.