diff --git a/skrample/common.py b/skrample/common.py
index 2df6746..4f9adaa 100644
--- a/skrample/common.py
+++ b/skrample/common.py
@@ -113,7 +113,8 @@ def exp[T: Sample](x: T) -> T:
 
 
 def sigmoid[T: Sample](array: T) -> T:
-    return 1 / (1 + exp(-array))  # type: ignore
+    arrexp: T = exp(array)
+    return arrexp / (1 + arrexp)  # type: ignore
 
 
 def softmax[T: tuple[Sample, ...]](elems: T) -> T:
diff --git a/skrample/pytorch/noise.py b/skrample/pytorch/noise.py
index 7da69e3..9e81e47 100644
--- a/skrample/pytorch/noise.py
+++ b/skrample/pytorch/noise.py
@@ -9,7 +9,7 @@
 
 
 def schedule_to_ramp(schedule: NDArray[np.float64]) -> NDArray[np.float64]:
-    return np.concatenate([[0], np.flip(schedule[:, 1])])
+    return np.concatenate([schedule[:, 1], [0]])
 
 
 @dataclass(frozen=True)
@@ -212,13 +212,8 @@ def generate(self) -> torch.Tensor:
         return noise / noise.std()  # Scaled back to roughly unit variance
 
 
-@dataclass(frozen=True)
-class BrownianProps(TensorNoiseProps):
-    reverse: bool = False
-
-
 @dataclass
-class Brownian(TensorNoiseCommon[BrownianProps]):
+class Brownian(TensorNoiseCommon[None]):
     """Uses torchsde.BrownianInterval to generate noise along a fixed timestep.
     generate() will raise StopIteration at the end of the ramp."""
 
@@ -241,23 +236,17 @@ def __post_init__(self) -> None:
         self._step: int = 0
 
         # Basic sanitization to normalize 0->1
+        if self.ramp[0] > self.ramp[-1]:
+            self.ramp = -self.ramp
         self.ramp -= self.ramp.min()
         self.ramp /= self.ramp.max()
-        if self.ramp[0] > self.ramp[-1]:
-            self.ramp = np.flip(self.ramp)
 
     def generate(self) -> torch.Tensor:
         if self._step + 1 >= len(self.ramp):
             raise StopIteration
 
-        if self.props.reverse:
-            # - 2 because you still get the next sequentiall
-            step = len(self.ramp) - self._step - 2
-        else:
-            step = self._step
-
-        sigma = self.ramp[step]
-        sigma_next = self.ramp[step + 1]
+        sigma = self.ramp[self._step]
+        sigma_next = self.ramp[self._step + 1]
         self._step += 1
 
         return self._tree(sigma, sigma_next) / abs(sigma_next - sigma) ** 0.5
@@ -267,7 +256,7 @@ def from_inputs(
         cls,
         shape: tuple[int, ...],
         seed: torch.Generator,
-        props: BrownianProps = BrownianProps(),
+        props: None = None,
         dtype: torch.dtype = torch.float32,
         ramp: NDArray[np.float64] = np.linspace(0, 1, 1000, dtype=np.float64),
     ) -> Self:
diff --git a/tests/miscellaneous.py b/tests/miscellaneous.py
index 14ce907..dc6d4ac 100644
--- a/tests/miscellaneous.py
+++ b/tests/miscellaneous.py
@@ -185,7 +185,10 @@ def test_bashforth() -> None:
 
 
 def test_sigmoid() -> None:
-    assert abs(torch.sigmoid(torch.tensor(1.5, dtype=torch.float64)).item() - sigmoid(1.5)) < 1e-12
+    items = spowf(torch.linspace(-2, 2, 9, dtype=torch.float64), 2)
+    a = torch.sigmoid(items)
+    b = sigmoid(items)
+    assert torch.allclose(a, b, rtol=0, atol=1e-12), (a.tolist(), b.tolist())
 
 
 def test_softmax() -> None: