diff --git a/GPy/plotting/matplot_dep/dim_reduction_plots.py b/GPy/plotting/matplot_dep/dim_reduction_plots.py
index f8413671..0ba082df 100644
--- a/GPy/plotting/matplot_dep/dim_reduction_plots.py
+++ b/GPy/plotting/matplot_dep/dim_reduction_plots.py
@@ -31,7 +31,7 @@ def plot_latent(model, labels=None, which_indices=None,
                 resolution=50, ax=None, marker='o', s=40,
                 fignum=None, plot_inducing=False, legend=True,
                 plot_limits=None, 
-                aspect='auto', updates=False, **kwargs):
+                aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
     """
     :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
     :param resolution: the resolution of the grid on which to evaluate the predictive variance
@@ -60,7 +60,7 @@ def plot_latent(model, labels=None, which_indices=None,
     def plot_function(x):
         Xtest_full = np.zeros((x.shape[0], model.X.shape[1]))
         Xtest_full[:, [input_1, input_2]] = x
-        _, var = model.predict(Xtest_full)
+        _, var = model.predict(Xtest_full, **predict_kwargs)
         var = var[:, :1]
         return np.log(var)
 
@@ -81,7 +81,7 @@ def plot_latent(model, labels=None, which_indices=None,
     view = ImshowController(ax, plot_function,
                             (xmin, ymin, xmax, ymax),
                             resolution, aspect=aspect, interpolation='bilinear',
-                            cmap=pb.cm.binary, **kwargs)
+                            cmap=pb.cm.binary, **imshow_kwargs)
 
     # make sure labels are in order of input:
     ulabels = []
diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py
index 84747d05..8f3e55b0 100644
--- a/GPy/plotting/matplot_dep/models_plots.py
+++ b/GPy/plotting/matplot_dep/models_plots.py
@@ -97,7 +97,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
         for d in which_data_ycols:
             plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
-            plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
+            if not plot_raw: plots['dataplot'] = ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], data_symbol, mew=1.5)
 
         #optionally plot some samples
         if samples: #NOTE not tested with fixed_inputs
@@ -151,7 +151,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         for d in which_data_ycols:
             m_d = m[:,d].reshape(resolution, resolution).T
             plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
-            plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
+            if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
 
         #set the limits of the plot to some sensible values
         ax.set_xlim(xmin[0], xmax[0])