Gravitational waves (GWs) detected by the LIGO and Virgo observatories encode descriptions of their astrophysical progenitors. To characterize these systems, physical GW signal models are inverted using Bayesian inference coupled with stochastic samplers---a task that can take O(day) for a typical binary black hole. Several recent efforts have attempted to speed this up by using normalizing flows to estimate the posterior distribution conditioned on the observed data. In this study, we further develop these techniques to achieve results nearly indistinguishable from standard samplers when evaluated on real GW data, with inference times of one minute per event. This is enabled by (i) incorporating detector nonstationarity from event to event by conditioning on a summary of the noise characteristics, (ii) using an embedding network adapted to GW signals to compress data, and (iii) adopting a new inference algorithm that makes use of underlying physical equivariances.