This paper introduces a novel simulation-based inference framework to constrain cosmological and astrophysical parameters from the Lyman-alpha forest 1D power spectrum using CAMELS hydrodynamic simulations. It demonstrates excellent performance when trained and tested on the same galaxy formation model, and proposes multi-domain training as an effective solution to overcome biases when dealing with different models.