diff --git a/sgm/modules/diffusionmodules/discretizer.py b/sgm/modules/diffusionmodules/discretizer.py
index 33d716c..0e4e1a7 100644
--- a/sgm/modules/diffusionmodules/discretizer.py
+++ b/sgm/modules/diffusionmodules/discretizer.py
@@ -1,6 +1,7 @@
 import torch
 import numpy as np
 from functools import partial
+from abc import abstractmethod
 
 from ...util import append_zero
 from ...modules.diffusionmodules.util import make_beta_schedule
@@ -13,11 +14,15 @@ def generate_roughly_equally_spaced_steps(
 
 
 class Discretization:
-    def __call__(self, n, do_append_zero=True, device="cuda", flip=False):
-        sigmas = self.get_sigmas(n, device)
+    def __call__(self, n, do_append_zero=True, device="cpu", flip=False):
+        sigmas = self.get_sigmas(n, device=device)
         sigmas = append_zero(sigmas) if do_append_zero else sigmas
         return sigmas if not flip else torch.flip(sigmas, (0,))
 
+    @abstractmethod
+    def get_sigmas(self, n, device):
+        raise NotImplementedError("abstract class should not be called")
+
 
 class EDMDiscretization(Discretization):
     def __init__(self, sigma_min=0.02, sigma_max=80.0, rho=7.0):
@@ -25,7 +30,7 @@ class EDMDiscretization(Discretization):
         self.sigma_max = sigma_max
         self.rho = rho
 
-    def get_sigmas(self, n, device):
+    def get_sigmas(self, n, device="cpu"):
         ramp = torch.linspace(0, 1, n, device=device)
         min_inv_rho = self.sigma_min ** (1 / self.rho)
         max_inv_rho = self.sigma_max ** (1 / self.rho)
@@ -40,6 +45,7 @@ class LegacyDDPMDiscretization(Discretization):
         linear_end=0.0120,
         num_timesteps=1000,
     ):
+        super().__init__()
         self.num_timesteps = num_timesteps
         betas = make_beta_schedule(
             "linear", num_timesteps, linear_start=linear_start, linear_end=linear_end
@@ -48,7 +54,7 @@ class LegacyDDPMDiscretization(Discretization):
         self.alphas_cumprod = np.cumprod(alphas, axis=0)
         self.to_torch = partial(torch.tensor, dtype=torch.float32)
 
-    def get_sigmas(self, n, device):
+    def get_sigmas(self, n, device="cpu"):
         if n < self.num_timesteps:
             timesteps = generate_roughly_equally_spaced_steps(n, self.num_timesteps)
             alphas_cumprod = self.alphas_cumprod[timesteps]