Random slope and random intercept correlation at every level of X

Question

Lets say individuals are nested within each ID and I am trying to a predict level 1 outcome Y from a level 1 predictor X1 or X2 with random slopes and intercepts. X1 and X2 are equivalent to each other except that they have a different mean. Using the nlme package in R, I ran the following:

> library(nlme)
> set.seed(123)
> Y=rnorm(100)+seq(.01,1,.01)
> X1=rep(1:10,10)
> X2=X1-10
> ID=sort(rep(1:10,10))
> Model1=lme(method="ML",Y~X1,random=~X1|ID)
> Model2=lme(method="ML",Y~X2,random=~X2|ID)
> Model1
Linear mixed-effects model fit by maximum likelihood
  Data: NULL 
  Log-likelihood: -136.9366
  Fixed: Y ~ X1 
(Intercept)          X1 
 0.45724997  0.02511926 

Random effects:
 Formula: ~X1 | ID
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev     Corr  
(Intercept) 0.42405364 (Intr)
X1          0.01936927 -0.87 
Residual    0.91060089       

Number of Observations: 100
Number of Groups: 10 
> Model2
Linear mixed-effects model fit by maximum likelihood
  Data: NULL 
  Log-likelihood: -136.9366
  Fixed: Y ~ X2 
(Intercept)          X2 
 0.70844259  0.02511926 

Random effects:
 Formula: ~X2 | ID
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev     Corr  
(Intercept) 0.27279076 (Intr)
X2          0.01932177 -0.645
Residual    0.91060680       

Number of Observations: 100
Number of Groups: 10 

As shown, the fixed effects slope for X1 and X2 are the same, but the fixed effect intercepts are different. The random slope and intercept correlation differs depending on the mean of X. The correlations are being calculated at the point were X=0.

Question: Is there a way to calculate the correlation at every point of X without running the model multiple times? Seems like it would be a useful technique.