scikit-learn - Should I fit model with TF or TF-IDF?
In the word sampling steps in LDA the word count is used as weights for the multinomial dist. Re-weighting the TF's by its IDF's would dispropotionally increase the chance of rare words being sampled, making them have a stronger influence in topic assignment. But rare words have equally low probabilities of showing up in all documents and topics, hence flattening out the topic distribution of each document.

That is a similar reason why it is recommended to remove stop-words before running LDA.