diff --git a/src/diffusers/schedulers/scheduling_ald.py b/src/diffusers/schedulers/scheduling_ald.py
index bb3633e3ee..82d3b8230e 100644
--- a/src/diffusers/schedulers/scheduling_ald.py
+++ b/src/diffusers/schedulers/scheduling_ald.py
@@ -132,7 +132,7 @@ class ALDScheduler(SchedulerMixin, ConfigMixin):
             self.set_timesteps(num_inference_steps)
 
         self.sigmas = torch.tensor(
-            torch.exp(torch.linspace(torch.log(sigma_min), torch.log(sigma_max), num_inference_steps)),
+            np.exp(np.linspace(np.log(sigma_min), np.log(sigma_max), num_inference_steps)),
             dtype=torch.float32,
         )
 
diff --git a/tests/test_scheduler.py b/tests/test_scheduler.py
index 194fb66f66..0e1ddd9e59 100755
--- a/tests/test_scheduler.py
+++ b/tests/test_scheduler.py
@@ -20,6 +20,7 @@ import numpy as np
 import torch
 
 from diffusers import (
+    ALDScheduler,
     DDIMScheduler,
     DDPMScheduler,
     IPNDMScheduler,
@@ -875,6 +876,112 @@ class ScoreSdeVeSchedulerTest(unittest.TestCase):
             self.assertEqual(output_0.shape, sample.shape)
             self.assertEqual(output_0.shape, output_1.shape)
 
+class ALDSchedulerTest(unittest.TestCase):
+    # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration), similar to SDE VE
+    scheduler_classes = (ALDScheduler,)
+    forward_default_kwargs = ()
+
+    @property
+    def dummy_sample(self):
+        batch_size = 4
+        num_channels = 3
+        height = 8
+        width = 8
+
+        sample = torch.rand((batch_size, num_channels, height, width))
+
+        return sample
+
+    @property
+    def dummy_sample_deter(self):
+        batch_size = 4
+        num_channels = 3
+        height = 8
+        width = 8
+
+        num_elems = batch_size * num_channels * height * width
+        sample = torch.arange(num_elems)
+        sample = sample.reshape(num_channels, height, width, batch_size)
+        sample = sample / num_elems
+        sample = sample.permute(3, 0, 1, 2)
+
+        return sample
+
+    def dummy_model(self):
+        def model(sample, t, *args):
+            return sample * t / (t + 1)
+
+        return model
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 100,
+            "sigma_min": 0.01,
+            "sigma_max": 1.0,
+            "step_lr": 0.00002,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+
+        for scheduler_class in self.scheduler_classes:
+            sample = self.dummy_sample
+            residual = 0.1 * sample
+
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_config(tmpdirname)
+
+            output = scheduler.step(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+            new_output = new_scheduler.step(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        kwargs.update(forward_kwargs)
+
+        for scheduler_class in self.scheduler_classes:
+            sample = self.dummy_sample
+            residual = 0.1 * sample
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_config(tmpdirname)
+
+            output = scheduler.step(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+            new_output = new_scheduler.step(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_timesteps(self):
+        for timesteps in [10, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_sigmas(self):
+        for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01], [1, 1, 1]):
+            self.check_over_configs(sigma_min=sigma_min, sigma_max=sigma_max)
+
+    def test_time_indices(self):
+        for t in [0.1, 0.5, 0.75]:
+            self.check_over_forward(time_step=t)
 
 class LMSDiscreteSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (LMSDiscreteScheduler,)