diff --git a/metastable_baselines/distributions/distributions.py b/metastable_baselines/distributions/distributions.py
index 22dc522..b400c64 100644
--- a/metastable_baselines/distributions/distributions.py
+++ b/metastable_baselines/distributions/distributions.py
@@ -159,9 +159,8 @@ class UniversalGaussianDistribution(SB3_Distribution):
         self.learn_features = sde_learn_features
         self.sde_latent_softmax = sde_latent_softmax
 
-        if self.use_hybrid and not self.use_sde:
-            print('[!] use_hybrid forces use_sde to be true')
-            self.use_sde = True
+        if self.use_hybrid:
+            assert self.use_sde, 'use_sde has to be set to use use_hybrid'
 
         assert (self.par_type != ParametrizationType.NONE) == (
             self.cov_strength == Strength.FULL), 'You should set an ParameterizationType iff the cov-strength is full'
@@ -318,13 +317,14 @@ class UniversalGaussianDistribution(SB3_Distribution):
         return self.prob_squashing_type.apply(sample)
 
     def _sample_sde(self) -> th.Tensor:
+        # More Reparametrization trick to pass gradients
         noise = self.get_noise(self._latent_sde)
         actions = self.distribution.mean + noise
         self.gaussian_actions = actions
         return self.prob_squashing_type.apply(actions)
 
     def _sample_hybrid(self) -> th.Tensor:
-        f = self.hybrid_rex_factor
+        f = self.hybrid_rex_fac
         actions = self._sample_normal()*f + self._sample_sde()*(1-f)
         return self.prob_squashing_type.apply(actions)