diff --git a/scripts/demo/streamlit_helpers.py b/scripts/demo/streamlit_helpers.py
index ddc9c6b..dfb0056 100644
--- a/scripts/demo/streamlit_helpers.py
+++ b/scripts/demo/streamlit_helpers.py
@@ -325,10 +325,8 @@ def init_sampling(
 
 def get_discretization(discretization, key=1):
     if discretization == "LegacyDDPMDiscretization":
-        use_new_range = st.checkbox(f"Start from highest noise level? #{key}", False)
         discretization_config = {
             "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
-            "params": {"legacy_range": not use_new_range},
         }
     elif discretization == "EDMDiscretization":
         sigma_min = st.number_input(f"sigma_min #{key}", value=0.03)  # 0.0292
diff --git a/sgm/modules/diffusionmodules/discretizer.py b/sgm/modules/diffusionmodules/discretizer.py
index f632186..f7bedc5 100644
--- a/sgm/modules/diffusionmodules/discretizer.py
+++ b/sgm/modules/diffusionmodules/discretizer.py
@@ -6,6 +6,26 @@ from ...util import append_zero
 from ...modules.diffusionmodules.util import make_beta_schedule
 
 
+def generate_roughly_equally_spaced_steps(n, m):
+    # 0, ..., m - 1
+    m -= 1
+
+    # We are getting rid of leading 0 later, so increase steps
+    n += 1
+
+    # Calculate the step size
+    step = m / (n - 1)
+
+    # Generate the list
+    steps_reversed = [int(m - i * step) for i in range(n)]
+    steps = steps_reversed[::-1]
+
+    # Get rid of leading 0
+    steps = steps[1:]
+
+    return np.array(steps)
+
+
 class Discretization:
     def __call__(self, n, do_append_zero=True, device="cuda", flip=False):
         sigmas = self.get_sigmas(n, device)
@@ -33,7 +53,6 @@ class LegacyDDPMDiscretization(Discretization):
         linear_start=0.00085,
         linear_end=0.0120,
         num_timesteps=1000,
-        legacy_range=True,
     ):
         self.num_timesteps = num_timesteps
         betas = make_beta_schedule(
@@ -42,23 +61,15 @@ class LegacyDDPMDiscretization(Discretization):
         alphas = 1.0 - betas
         self.alphas_cumprod = np.cumprod(alphas, axis=0)
         self.to_torch = partial(torch.tensor, dtype=torch.float32)
-        self.legacy_range = legacy_range
 
     def get_sigmas(self, n, device):
         if n < self.num_timesteps:
-            c = self.num_timesteps // n
-
-            if self.legacy_range:
-                timesteps = np.asarray(list(range(0, self.num_timesteps, c)))
-                timesteps += 1  # Legacy LDM Hack
-            else:
-                timesteps = np.asarray(list(range(0, self.num_timesteps + 1, c)))
-                timesteps -= 1
-                timesteps = timesteps[1:]
-
+            timesteps = generate_roughly_equally_spaced_steps(n, self.num_timesteps)
             alphas_cumprod = self.alphas_cumprod[timesteps]
-        else:
+        elif n == self.num_timesteps:
             alphas_cumprod = self.alphas_cumprod
+        else:
+            raise ValueError
 
         to_torch = partial(torch.tensor, dtype=torch.float32, device=device)
         sigmas = to_torch((1 - alphas_cumprod) / alphas_cumprod) ** 0.5