It depends on what you're going to do. If you're going to get prediction intervals for y, you can use the ln(y) model, then just take the anti-log of the lower and upper interval numbers (only works for individual y values, not for mean of Y). You would also need to understand how the coefficients can be interpreted (1 unit change in x for a beta %change in y). If you want prediction intervals for both Y and mean of Y I would probably just use the robust SE model. Just be aware the dependent variable is different, so you can't directly compare the R-squared or other model based statistics. You would need a "pseudo-rsquared" from anti-log y-hat values from the ln(y) model to calculate a comparable r-squared between the two. I don't know too much about the pros and cons comparing the two model types, though (may be some literature on it).