Call .compute() in all plot methods?

I noticed what I think might be a performance bug: should .compute() be called on the input data in all the plotting methods (e.g. plot.pcolormesh()) like it is in .plot() here https://github.com/pydata/xarray/blob/bf27e2c1fd81f5e0afe1ef91c13f651db54b44bb/xarray/plot/plot.py#L166 See also discussion in https://github.com/pydata/xarray/issues/2390.

I was making plots from a large dataset of a quantity that is the output of quite a bit of computation. A script which made an animation of the full time-series (a couple of thousand time points) actually ran significantly faster than a script that made pcolormesh plots of just 3 time points (~2hrs compared to ~5hrs). The difference I can think of is that the animation script called .values before the animation function, but the plotting script called xarray.plot.pcolormesh() without calling .values/.load()/.compute() first. A modified version of the script that calls .load() before any plot calls reduced the run time to 30 mins even though I plotted 18 time points, not just 3.

2d plots might all be covered by adding a darray = darray.compute() call in newplotfunc()?https://github.com/pydata/xarray/blob/bf27e2c1fd81f5e0afe1ef91c13f651db54b44bb/xarray/plot/plot.py#L609
I guess the 1d plot functions would all need to be modified individually.

Start in xarray/plot/plot.py, comparing the existing .plot() call near line 166 with newplotfunc() near line 609 and the individual 1D plotting methods. Investigate whether lazy inputs are recomputed across pcolormesh() and related methods, using the reported large-dataset plotting behavior as a benchmark. Done means the appropriate plotting methods handle computation consistently without the reported repeated-work slowdown.