diff --git a/src/diffusers/training_utils.py b/src/diffusers/training_utils.py
index 0e0d0ce5b568..d2bf3fe07185 100644
--- a/src/diffusers/training_utils.py
+++ b/src/diffusers/training_utils.py
@@ -379,7 +379,7 @@ def __init__(
 
     @classmethod
     def from_pretrained(cls, path, model_cls, foreach=False) -> "EMAModel":
-        _, ema_kwargs = model_cls.load_config(path, return_unused_kwargs=True)
+        _, ema_kwargs = model_cls.from_config(path, return_unused_kwargs=True)
         model = model_cls.from_pretrained(path)
 
         ema_model = cls(model.parameters(), model_cls=model_cls, model_config=model.config, foreach=foreach)
diff --git a/tests/others/test_ema.py b/tests/others/test_ema.py
index 5bed42b8488f..3443e6366f01 100644
--- a/tests/others/test_ema.py
+++ b/tests/others/test_ema.py
@@ -59,6 +59,25 @@ def simulate_backprop(self, unet):
         unet.load_state_dict(updated_state_dict)
         return unet
 
+    def test_from_pretrained(self):
+        # Save the model parameters to a temporary directory
+        unet, ema_unet = self.get_models()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            ema_unet.save_pretrained(tmpdir)
+
+            # Load the EMA model from the saved directory
+            loaded_ema_unet = EMAModel.from_pretrained(tmpdir, model_cls=UNet2DConditionModel, foreach=False)
+
+        # Check that the shadow parameters of the loaded model match the original EMA model
+        for original_param, loaded_param in zip(ema_unet.shadow_params, loaded_ema_unet.shadow_params):
+            assert torch.allclose(original_param, loaded_param, atol=1e-4)
+
+        # Verify that the optimization step is also preserved
+        assert loaded_ema_unet.optimization_step == ema_unet.optimization_step
+
+        # Check the decay value
+        assert loaded_ema_unet.decay == ema_unet.decay
+
     def test_optimization_steps_updated(self):
         unet, ema_unet = self.get_models()
         # Take the first (hypothetical) EMA step.
@@ -194,6 +213,25 @@ def simulate_backprop(self, unet):
         unet.load_state_dict(updated_state_dict)
         return unet
 
+    def test_from_pretrained(self):
+        # Save the model parameters to a temporary directory
+        unet, ema_unet = self.get_models()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            ema_unet.save_pretrained(tmpdir)
+
+            # Load the EMA model from the saved directory
+            loaded_ema_unet = EMAModel.from_pretrained(tmpdir, model_cls=UNet2DConditionModel, foreach=True)
+
+        # Check that the shadow parameters of the loaded model match the original EMA model
+        for original_param, loaded_param in zip(ema_unet.shadow_params, loaded_ema_unet.shadow_params):
+            assert torch.allclose(original_param, loaded_param, atol=1e-4)
+
+        # Verify that the optimization step is also preserved
+        assert loaded_ema_unet.optimization_step == ema_unet.optimization_step
+
+        # Check the decay value
+        assert loaded_ema_unet.decay == ema_unet.decay
+
     def test_optimization_steps_updated(self):
         unet, ema_unet = self.get_models()
         # Take the first (hypothetical) EMA step.