diff --git a/src/diffusers/models/auto_model.py b/src/diffusers/models/auto_model.py
index bfe386f1f619..ada0d54e5419 100644
--- a/src/diffusers/models/auto_model.py
+++ b/src/diffusers/models/auto_model.py
@@ -19,6 +19,7 @@
 
 from ..configuration_utils import ConfigMixin
 from ..utils import logging
+from ..utils.dynamic_modules_utils import get_class_from_dynamic_module, resolve_trust_remote_code
 
 
 logger = logging.get_logger(__name__)
@@ -114,6 +115,8 @@ def from_pretrained(cls, pretrained_model_or_path: Optional[Union[str, os.PathLi
             disable_mmap ('bool', *optional*, defaults to 'False'):
                 Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
                 is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
+            trust_remote_cocde (`bool`, *optional*, defaults to `False`):
+                Whether to trust remote code
 
         <Tip>
 
@@ -140,22 +143,22 @@ def from_pretrained(cls, pretrained_model_or_path: Optional[Union[str, os.PathLi
         You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
         ```
         """
-        cache_dir = kwargs.pop("cache_dir", None)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        token = kwargs.pop("token", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        revision = kwargs.pop("revision", None)
         subfolder = kwargs.pop("subfolder", None)
-
-        load_config_kwargs = {
-            "cache_dir": cache_dir,
-            "force_download": force_download,
-            "proxies": proxies,
-            "token": token,
-            "local_files_only": local_files_only,
-            "revision": revision,
-        }
+        trust_remote_code = kwargs.pop("trust_remote_code", False)
+
+        hub_kwargs_names = [
+            "cache_dir",
+            "force_download",
+            "local_files_only",
+            "proxies",
+            "resume_download",
+            "revision",
+            "token",
+        ]
+        hub_kwargs = {name: kwargs.pop(name, None) for name in hub_kwargs_names}
+
+        # load_config_kwargs uses the same hub kwargs minus subfolder and resume_download
+        load_config_kwargs = {k: v for k, v in hub_kwargs.items() if k not in ["subfolder", "resume_download"]}
 
         library = None
         orig_class_name = None
@@ -189,15 +192,35 @@ def from_pretrained(cls, pretrained_model_or_path: Optional[Union[str, os.PathLi
             else:
                 raise ValueError(f"Couldn't find model associated with the config file at {pretrained_model_or_path}.")
 
-        from ..pipelines.pipeline_loading_utils import ALL_IMPORTABLE_CLASSES, get_class_obj_and_candidates
-
-        model_cls, _ = get_class_obj_and_candidates(
-            library_name=library,
-            class_name=orig_class_name,
-            importable_classes=ALL_IMPORTABLE_CLASSES,
-            pipelines=None,
-            is_pipeline_module=False,
-        )
+        has_remote_code = "auto_map" in config and cls.__name__ in config["auto_map"]
+        trust_remote_code = resolve_trust_remote_code(trust_remote_code, pretrained_model_or_path, has_remote_code)
+        if not (has_remote_code and trust_remote_code):
+            raise ValueError(
+                "Selected model repository does not happear to have any custom code or does not have a valid `config.json` file."
+            )
+
+        if has_remote_code and trust_remote_code:
+            class_ref = config["auto_map"][cls.__name__]
+            module_file, class_name = class_ref.split(".")
+            module_file = module_file + ".py"
+            model_cls = get_class_from_dynamic_module(
+                pretrained_model_or_path,
+                subfolder=subfolder,
+                module_file=module_file,
+                class_name=class_name,
+                **hub_kwargs,
+                **kwargs,
+            )
+        else:
+            from ..pipelines.pipeline_loading_utils import ALL_IMPORTABLE_CLASSES, get_class_obj_and_candidates
+
+            model_cls, _ = get_class_obj_and_candidates(
+                library_name=library,
+                class_name=orig_class_name,
+                importable_classes=ALL_IMPORTABLE_CLASSES,
+                pipelines=None,
+                is_pipeline_module=False,
+            )
 
         if model_cls is None:
             raise ValueError(f"AutoModel can't find a model linked to {orig_class_name}.")
diff --git a/src/diffusers/utils/dynamic_modules_utils.py b/src/diffusers/utils/dynamic_modules_utils.py
index 674eb65773f0..de947a12e200 100644
--- a/src/diffusers/utils/dynamic_modules_utils.py
+++ b/src/diffusers/utils/dynamic_modules_utils.py
@@ -247,6 +247,7 @@ def find_pipeline_class(loaded_module):
 def get_cached_module_file(
     pretrained_model_name_or_path: Union[str, os.PathLike],
     module_file: str,
+    subfolder: Optional[str] = None,
     cache_dir: Optional[Union[str, os.PathLike]] = None,
     force_download: bool = False,
     proxies: Optional[Dict[str, str]] = None,
@@ -353,6 +354,7 @@ def get_cached_module_file(
             resolved_module_file = hf_hub_download(
                 pretrained_model_name_or_path,
                 module_file,
+                subfolder=subfolder,
                 cache_dir=cache_dir,
                 force_download=force_download,
                 proxies=proxies,
@@ -410,6 +412,7 @@ def get_cached_module_file(
                 get_cached_module_file(
                     pretrained_model_name_or_path,
                     f"{module_needed}.py",
+                    subfolder=subfolder,
                     cache_dir=cache_dir,
                     force_download=force_download,
                     proxies=proxies,
@@ -424,6 +427,7 @@ def get_cached_module_file(
 def get_class_from_dynamic_module(
     pretrained_model_name_or_path: Union[str, os.PathLike],
     module_file: str,
+    subfolder: Optional[str] = None,
     class_name: Optional[str] = None,
     cache_dir: Optional[Union[str, os.PathLike]] = None,
     force_download: bool = False,
@@ -497,6 +501,7 @@ def get_class_from_dynamic_module(
     final_module = get_cached_module_file(
         pretrained_model_name_or_path,
         module_file,
+        subfolder=subfolder,
         cache_dir=cache_dir,
         force_download=force_download,
         proxies=proxies,