diff --git a/GPy/inference/latent_function_inference/inferenceX.py b/GPy/inference/latent_function_inference/inferenceX.py
index f68f17cb..a8ed2d09 100644
--- a/GPy/inference/latent_function_inference/inferenceX.py
+++ b/GPy/inference/latent_function_inference/inferenceX.py
@@ -45,17 +45,23 @@ class InferenceX(Model):
         super(InferenceX, self).__init__(name)
         self.likelihood = model.likelihood.copy()
         self.kern = model.kern.copy()
-        if model.kern.useGPU:
-            from ...models import SSGPLVM
-            if isinstance(model, SSGPLVM):
-                self.kern.GPU_SSRBF(True)
-            else:
-                self.kern.GPU(True)
+#         if model.kern.useGPU:
+#             from ...models import SSGPLVM
+#             if isinstance(model, SSGPLVM):
+#                 self.kern.GPU_SSRBF(True)
+#             else:
+#                 self.kern.GPU(True)
         from copy import deepcopy
         self.posterior = deepcopy(model.posterior)
         if hasattr(model, 'variational_prior'):
             self.uncertain_input = True
-            self.variational_prior = model.variational_prior.copy()
+            from ...models.ss_gplvm import IBPPrior
+            from ...models.ss_mrd import IBPPrior_SSMRD
+            if isinstance(model.variational_prior, IBPPrior) or isinstance(model.variational_prior, IBPPrior_SSMRD):
+                from ...core.parameterization.variational import SpikeAndSlabPrior
+                self.variational_prior = SpikeAndSlabPrior(pi=05,learnPi=False, group_spike=False)
+            else:
+                self.variational_prior = model.variational_prior.copy()
         else:
             self.uncertain_input = False
         if hasattr(model, 'inducing_inputs'):
@@ -147,9 +153,9 @@ class InferenceX(Model):
             from ...core.parameterization.variational import SpikeAndSlabPrior
             if isinstance(self.variational_prior, SpikeAndSlabPrior):
                 # Update Log-likelihood
-                KL_div = self.variational_prior.KL_divergence(self.X, N=self.Y.shape[0])
+                KL_div = self.variational_prior.KL_divergence(self.X)
                 # update for the KL divergence
-                self.variational_prior.update_gradients_KL(self.X, N=self.Y.shape[0])
+                self.variational_prior.update_gradients_KL(self.X)
             else:
                 # Update Log-likelihood
                 KL_div = self.variational_prior.KL_divergence(self.X)
diff --git a/GPy/kern/_src/rbf.py b/GPy/kern/_src/rbf.py
index c6998370..73f2d0a4 100644
--- a/GPy/kern/_src/rbf.py
+++ b/GPy/kern/_src/rbf.py
@@ -20,7 +20,6 @@ class RBF(Stationary):
     _support_GPU = True
     def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
         super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
-        self.psicomp = PSICOMP_RBF()
         if self.useGPU:
             self.psicomp = PSICOMP_RBF_GPU()
         else:
@@ -36,6 +35,7 @@ class RBF(Stationary):
         dc = super(RBF, self).__getstate__()
         if self.useGPU:
             dc['psicomp'] = PSICOMP_RBF()
+            dc['useGPU'] = False
         return dc
 
     def __setstate__(self, state):
diff --git a/GPy/models/bayesian_gplvm_minibatch.py b/GPy/models/bayesian_gplvm_minibatch.py
index 71f69eb2..7a7bb0e8 100644
--- a/GPy/models/bayesian_gplvm_minibatch.py
+++ b/GPy/models/bayesian_gplvm_minibatch.py
@@ -64,9 +64,6 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
             self.logger.debug("creating inference_method var_dtc")
             inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
 
-        if kernel.useGPU and isinstance(inference_method, VarDTC_GPU):
-            kernel.psicomp.GPU_direct = True
-
         super(BayesianGPLVMMiniBatch,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
                                            name=name, inference_method=inference_method,
                                            normalizer=normalizer,
diff --git a/GPy/plotting/matplot_dep/visualize.py b/GPy/plotting/matplot_dep/visualize.py
index 6903c2d2..b17900db 100644
--- a/GPy/plotting/matplot_dep/visualize.py
+++ b/GPy/plotting/matplot_dep/visualize.py
@@ -451,7 +451,7 @@ class mocap_data_show(matplotlib_show):
         self.initialize_axes_modify()
         self.draw_vertices()
         self.initialize_axes()
-        self.finalize_axes_modify()
+        #self.finalize_axes_modify()
         self.draw_edges()
         self.axes.figure.canvas.draw()
 
@@ -470,12 +470,20 @@ class mocap_data_show(matplotlib_show):
         self.line_handle[0].remove()
 
     def finalize_axes(self):
-        self.axes.set_xlim(self.x_lim)
-        self.axes.set_ylim(self.y_lim)
-        self.axes.set_zlim(self.z_lim)
-        self.axes.auto_scale_xyz([-1., 1.], [-1., 1.], [-1., 1.])
+#         self.axes.set_xlim(self.x_lim)
+#         self.axes.set_ylim(self.y_lim)
+#         self.axes.set_zlim(self.z_lim)
+#         self.axes.auto_scale_xyz([-1., 1.], [-1., 1.], [-1., 1.])
 
-#        self.axes.set_aspect('equal')
+        extents = np.array([getattr(self.axes, 'get_{}lim'.format(dim))() for dim in 'xyz'])
+        sz = extents[:,1] - extents[:,0]
+        centers = np.mean(extents, axis=1)
+        maxsize = max(abs(sz))
+        r = maxsize/2
+        for ctr, dim in zip(centers, 'xyz'):
+            getattr(self.axes, 'set_{}lim'.format(dim))(ctr - r, ctr + r)
+        
+#         self.axes.set_aspect('equal')
 #         self.axes.autoscale(enable=False)
 
     def finalize_axes_modify(self):