diff --git a/priorConditionedAnnealing/pca.py b/priorConditionedAnnealing/pca.py
index dd0a775..40f6d1c 100644
--- a/priorConditionedAnnealing/pca.py
+++ b/priorConditionedAnnealing/pca.py
@@ -79,7 +79,8 @@ class PCA_Distribution(SB3_Distribution):
         init_std: int = 1,
         window: int = 64,
         epsilon: float = 1e-6,
-        Base_Noise=noise.White_Noise
+        skip_conditioning: bool = False,
+        Base_Noise=noise.White_Noise,
     ):
         super().__init__()
 
@@ -89,12 +90,13 @@ class PCA_Distribution(SB3_Distribution):
         self.init_std = init_std
         self.window = window
         self.epsilon = epsilon
+        self.skip_conditioning = skip_conditioning
 
-        if Base_Noise.__class__ != noise.White_Noise:
+        self.base_noise = Base_Noise((1, action_dim))
+
+        if not isinstance(self.base_noise, noise.White_Noise):
             print('[!] Non-White Noise was not yet tested!')
 
-        self.base_noise = Base_Noise((1, )+action_dim)
-
         # Premature optimization is the root of all evil
         self._build_conditioner()
         # *Optimizes it anyways*
@@ -118,9 +120,10 @@ class PCA_Distribution(SB3_Distribution):
     def entropy(self) -> th.Tensor:
         return sum_independent_dims(self.distribution.entropy())
 
-    def sample(self, traj: th.Tensor, epsilon=None) -> th.Tensor:
+    def sample(self, traj: th.Tensor, f_sigma: int = 1, epsilon=None) -> th.Tensor:
         pi_mean, pi_std = self.distribution.mean, self.distribution.scale
         rho_mean, rho_std = self._conditioning_engine(traj, pi_mean, pi_std)
+        rho_std *= f_sigma
         eta = self._get_rigged(pi_mean, pi_std,
                                rho_mean, rho_std,
                                epsilon)
@@ -137,6 +140,9 @@ class PCA_Distribution(SB3_Distribution):
             if epsilon == None:
                 epsilon = self.base_noise(pi_mean.shape)
 
+            if self.skip_conditioning:
+                return epsilon.detach()
+
             Delta = rho_mean - pi_mean
             Pi_mu = 1 / pi_std
             Pi_sigma = rho_std / pi_std