This is not surprising. When you establish identification by fixing the factor variance, the loadings are indeterminate with respect to sign. Remember, to create the model-implied covariance matrix, pairs of loadings will be multiplied together--if both are positive or both are negative, the result is a positive covariance either way. If one is positive an one is negative, the result is negative, and it does not matter which one was positive and which one was negative. By contrast, when you establish identification by fixing a factor loading, the sign of that loading provides an orientation for the signs of the other loadings.
If you prefer one set of signs or the other, and you want to fix factor variances, use starting values for some loadings. Once you get the optimization process started on one side of the mountain or the other, it will very likely stay there.