We present an extension of the positive real and bounded real balanced truncation model reduction methods to large-scale descriptor systems. These methods are based on balancing the solutions of the projected Lur'e matrix equations. Important properties of these methods are that, respectively, passivity and contractivity are preserved in the reduced-order models and that there exist approximation error bounds. We also discuss the numerical solution of the projected Lur'e equations. Numerical examples are given.