Generalized Diff-in-Diff to capture treatment effect on relation between Y and X

I use a diff-in-diff design to capture the effect of a treatment on the coefficient b1 of the following model: Y = b0 + b1*X + u.
The full model therefore is: Y = b0 + b1*X + b2*POST+b3*TREAT+b4*(POST*TREAT)+b5*(POST*X)+b6*(T REAT*X)+b7*(POST*TREAT*X)+u with the coefficient of interest being b7.

Now I wanted to increase the precision of my DiD estimates by adding other explanatory variables for Y (i.e., decrease standard errors). To do so, I first use a "generalized" diff-in-diff model, that is, I add a full set of firm-fixed and time-fixed effects to soak up variation. Therefore, I use the following Stata regression:

xi: areg Y X 1.POST 1.TREAT 1.POST#1.TREAT 1.POST#c.X 1.TREAT#c.X 1.POST#1.TREAT#c.X i.year, cl(firmid) absorb(firmid)

1.POST 1.TREAT and 1.POST#1.TREAT should drop out (because they are perfectly colinear with the fixed effects), but the rest should survive.

However - my results on b7 seem to suffer a lot and get insignificant when i use firm- and year-fixed effect to soak up variation - in my opinion these fixed effect should not hurt the DiD triple-interaction; am I doing something wrong here?

Furthermore, I thought of adding further controls that vary in time, such as firmsize (i skipped the variables that will drop out anyway here):

xi: areg Y X 1.POST#c.X 1.TREAT#c.X 1.POST#1.TREAT#c.X c.FIRMSIZE i.year, cl(firmid) absorb(firmid)

Is that recommendable or am I getting into new econometrical problems here?