This is not a valid approach. This affine-invariant sampler is a Metropolis-Hastings variant, which means the proposal distribution appears in two different spots in the algorithm. It appears in generating proposals, of course, and you've altered that spot. But the PDF of the proposal also is used when deciding whether to accept or reject the current proposal. If you change it in one place and not the other, there's no guarantee what will happen. 

There's another issue here: the "stretch move" proposal used by this affine-invariant sampler uses a discrete proposal distribution! Each walker selects another walker and moves a certain percentage towards or away from that other walker. The percentage is fixed; it's a tuning parameter of the algorithm. I can't say for a fact that this is your problem, but it's easy to imagine a scenario where the step size is small, and your rounding drags it back to the current value no matter what. Could this be causing your problem? Yes. 

You may be wondering what to do next, but that's a matter for another question:

https://stats.stackexchange.com/questions/8572/what-mcmc-algorithms-techniques-are-used-for-discrete-parameters