Difference between LDA and Naive Bayes LDA: linear discriminant analysis

Suppose we have a classification problem. I understand that the data can be such that the features may have discrete values or continuous values.

Suppose our data contains continuous feature values. Then we can apply Naive Bayes using a distribution. Lets assume the data to be normally distributed and so use Naive Bayes with normal distribution. We can also apply LDA which also uses Normal distribution.

Using Naive Bayes we assume the features to be independent and by using LDA we assume the covariance to be same for all the classes.

How does these assumptions make these 2 models perform differently and which is a better model and in which conditions?
As far as I know, Gaussian LDA and Gaussian Naive Bayes both assume the features follow normal distributions. However, GNB places a stronger assumption on feature independence, while Gaussian LDA does not require features to be independent.
See this.