diff --git a/src/diffusers/utils/dynamic_modules_utils.py b/src/diffusers/utils/dynamic_modules_utils.py
index 2a1cea10e14d..856966dd29b5 100644
--- a/src/diffusers/utils/dynamic_modules_utils.py
+++ b/src/diffusers/utils/dynamic_modules_utils.py
@@ -299,7 +299,10 @@ def get_cached_module_file(
     # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
     pretrained_model_name_or_path = str(pretrained_model_name_or_path)
 
-    module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
+    if subfolder is not None:
+        module_file_or_url = os.path.join(pretrained_model_name_or_path, subfolder, module_file)
+    else:
+        module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
 
     if os.path.isfile(module_file_or_url):
         resolved_module_file = module_file_or_url
@@ -384,7 +387,11 @@ def get_cached_module_file(
                 if not os.path.exists(submodule_path / module_folder):
                     os.makedirs(submodule_path / module_folder)
             module_needed = f"{module_needed}.py"
-            shutil.copyfile(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
+            if subfolder is not None:
+                source_path = os.path.join(pretrained_model_name_or_path, subfolder, module_needed)
+            else:
+                source_path = os.path.join(pretrained_model_name_or_path, module_needed)
+            shutil.copyfile(source_path, submodule_path / module_needed)
     else:
         # Get the commit hash
         # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
diff --git a/tests/models/test_models_auto.py b/tests/models/test_models_auto.py
index 3506f8fa0d17..57a7760ea841 100644
--- a/tests/models/test_models_auto.py
+++ b/tests/models/test_models_auto.py
@@ -1,6 +1,10 @@
+import json
+import os
+import tempfile
 import unittest
 from unittest.mock import MagicMock, patch
 
+import torch
 from transformers import CLIPTextModel, LongformerModel
 
 from diffusers.models import AutoModel, UNet2DConditionModel
@@ -35,6 +39,45 @@ def test_load_from_model_index(self):
         )
         assert isinstance(model, CLIPTextModel)
 
+    def test_load_dynamic_module_from_local_path_with_subfolder(self):
+        CUSTOM_MODEL_CODE = (
+            "import torch\n"
+            "from diffusers import ModelMixin, ConfigMixin\n"
+            "from diffusers.configuration_utils import register_to_config\n"
+            "\n"
+            "class CustomModel(ModelMixin, ConfigMixin):\n"
+            "    @register_to_config\n"
+            "    def __init__(self, hidden_size=8):\n"
+            "        super().__init__()\n"
+            "        self.linear = torch.nn.Linear(hidden_size, hidden_size)\n"
+            "\n"
+            "    def forward(self, x):\n"
+            "        return self.linear(x)\n"
+        )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            subfolder = "custom_model"
+            model_dir = os.path.join(tmpdir, subfolder)
+            os.makedirs(model_dir)
+
+            with open(os.path.join(model_dir, "modeling.py"), "w") as f:
+                f.write(CUSTOM_MODEL_CODE)
+
+            config = {
+                "_class_name": "CustomModel",
+                "_diffusers_version": "0.0.0",
+                "auto_map": {"AutoModel": "modeling.CustomModel"},
+                "hidden_size": 8,
+            }
+            with open(os.path.join(model_dir, "config.json"), "w") as f:
+                json.dump(config, f)
+
+            torch.save({}, os.path.join(model_dir, "diffusion_pytorch_model.bin"))
+
+            model = AutoModel.from_pretrained(tmpdir, subfolder=subfolder, trust_remote_code=True)
+            assert model.__class__.__name__ == "CustomModel"
+            assert model.config["hidden_size"] == 8
+
 
 class TestAutoModelFromConfig(unittest.TestCase):
     @patch(