diff --git a/diffrax/step_size_controller/adaptive.py b/diffrax/step_size_controller/adaptive.py
index 5297050d..0cc0afc0 100644
--- a/diffrax/step_size_controller/adaptive.py
+++ b/diffrax/step_size_controller/adaptive.py
@@ -496,11 +496,7 @@ def _scale(_y0, _y1_candidate, _y_error):
         factor2 = 1 if _zero_coeff(coeff2) else prev_inv_scaled_error ** coeff2
         factor3 = 1 if _zero_coeff(coeff3) else prev_prev_inv_scaled_error ** coeff3
         factormin = jnp.where(keep_step, 1, self.factormin)
-        factor = jnp.clip(
-            self.safety * factor1 * factor2 * factor3,
-            a_min=factormin,
-            a_max=self.factormax,
-        )
+        factor = jnp.nanmin(jnp.array([self.factormax, jnp.nanmax(jnp.array([self.safety * factor1 * factor2 * factor3, factormin]))]))
         # Once again, see above. In case we have gradients on {i,p,d}coeff.
         # (Probably quite common for them to have zero tangents if passed across
         # a grad API boundary as part of a larger model.)