diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py
index aab4d16172d9..c56fbf588bf0 100644
--- a/src/diffusers/models/modeling_utils.py
+++ b/src/diffusers/models/modeling_utils.py
@@ -517,6 +517,18 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
 
+        if torch_dtype is not None:
+            deprecate("torch_dtype", "0.30.0", "Using `torch_dtype` is depcrecated. Use `dtype`, instead.")
+
+        dtype_kwarg = kwargs.pop("dtype", None)
+
+        if torch_dtype is not None and dtype_kwarg is not None:
+            raise ValueError(
+                "You have passed both `torch_dtype` and `dtype` as a keyword argument. Please make sure to only pass `dtype`."
+            )
+
+        dtype = torch_dtype or dtype_kwarg
+
         allow_pickle = False
         if use_safetensors is None:
             use_safetensors = True
@@ -670,7 +682,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                         model,
                         state_dict,
                         device=param_device,
-                        dtype=torch_dtype,
+                        dtype=dtype,
                         model_name_or_path=pretrained_model_name_or_path,
                     )
 
@@ -755,12 +767,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     "error_msgs": error_msgs,
                 }
 
-        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
+        if dtype is not None and not isinstance(dtype, torch.dtype):
             raise ValueError(
-                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
+                f"{dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
             )
-        elif torch_dtype is not None:
-            model = model.to(torch_dtype)
+        elif dtype is not None:
+            model = model.to(dtype)
 
         model.register_to_config(_name_or_path=pretrained_model_name_or_path)
 
diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py
index 769fcd2e832a..d51019e2ea84 100644
--- a/src/diffusers/pipelines/pipeline_utils.py
+++ b/src/diffusers/pipelines/pipeline_utils.py
@@ -1078,6 +1078,18 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
+        if torch_dtype is not None:
+            deprecate("torch_dtype", "0.30.0", "Using `torch_dtype` is depcrecated. Use `dtype`, instead.")
+
+        dtype_kwarg = kwargs.pop("dtype", None)
+
+        if torch_dtype is not None and dtype_kwarg is not None:
+            raise ValueError(
+                "You have passed both `torch_dtype` and `dtype` as a keyword argument. Please make sure to only pass `dtype`."
+            )
+
+        dtype = torch_dtype or dtype_kwarg
+
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
         if not os.path.isdir(pretrained_model_name_or_path):
@@ -1268,7 +1280,7 @@ def load_module(name, value):
                     pipelines=pipelines,
                     is_pipeline_module=is_pipeline_module,
                     pipeline_class=pipeline_class,
-                    torch_dtype=torch_dtype,
+                    torch_dtype=dtype,
                     provider=provider,
                     sess_options=sess_options,
                     device_map=device_map,
@@ -1300,7 +1312,7 @@ def load_module(name, value):
                 "local_files_only": local_files_only,
                 "token": token,
                 "revision": revision,
-                "torch_dtype": torch_dtype,
+                "torch_dtype": dtype,
                 "custom_pipeline": custom_pipeline,
                 "custom_revision": custom_revision,
                 "provider": provider,