allow models to run with a user-provided dtype map instead of a single dtype

src/diffusers/models/modeling_utils.py

@@ -714,7 +714,10 @@ def save_pretrained(
             if safe_serialization:
                 # At some point we will need to deal better with save_function (used for TPU and other distributed
                 # joyfulness), but for now this enough.
-                safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
+                try:
+                    safetensors.torch.save_file(shard, filepath, metadata={"format": "pt"})
+                except RuntimeError:
+                    safetensors.torch.save_model(model_to_save, filepath, metadata={"format": "pt"})
             else:
                 torch.save(shard, filepath)
 

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -592,6 +592,11 @@ def _get_final_device_map(device_map, pipeline_class, passed_class_obj, init_dic
                 loaded_sub_model = passed_class_obj[name]
 
         else:
+            sub_model_dtype = (
+                torch_dtype.get(name, torch_dtype.get("default", torch.float32))
+                if isinstance(torch_dtype, dict)
+                else torch_dtype
+            )
             loaded_sub_model = _load_empty_model(
                 library_name=library_name,
                 class_name=class_name,
@@ -600,7 +605,7 @@ def _get_final_device_map(device_map, pipeline_class, passed_class_obj, init_dic
                 is_pipeline_module=is_pipeline_module,
                 pipeline_class=pipeline_class,
                 name=name,
-                torch_dtype=torch_dtype,
+                torch_dtype=sub_model_dtype,
                 cached_folder=kwargs.get("cached_folder", None),
                 force_download=kwargs.get("force_download", None),
                 proxies=kwargs.get("proxies", None),
@@ -616,7 +621,12 @@ def _get_final_device_map(device_map, pipeline_class, passed_class_obj, init_dic
     # Obtain a sorted dictionary for mapping the model-level components
     # to their sizes.
     module_sizes = {
-        module_name: compute_module_sizes(module, dtype=torch_dtype)[""]
+        module_name: compute_module_sizes(
+            module,
+            dtype=torch_dtype.get(module_name, torch_dtype.get("default", torch.float32))
+            if isinstance(torch_dtype, dict)
+            else torch_dtype,
+        )[""]
         for module_name, module in init_empty_modules.items()
         if isinstance(module, torch.nn.Module)
     }

src/diffusers/pipelines/pipeline_utils.py

@@ -552,9 +552,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing a dduf file
-            torch_dtype (`str` or `torch.dtype`, *optional*):
+            torch_dtype (`str` or `torch.dtype` or `dict[str, Union[str, torch.dtype]]`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
+                dtype is automatically derived from the model's weights. To load submodels with different dtype pass a
+                `dict` (for example `{'transformer': torch.bfloat16, 'vae': torch.float16}`). Set the default dtype for
+                unspecified components with `default` (for example `{'transformer': torch.bfloat16, 'default':
+                torch.float16}`). If a component is not specified and no default is set, `torch.float32` is used.
             custom_pipeline (`str`, *optional*):
 
                 <Tip warning={true}>
@@ -703,7 +706,7 @@ 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 and not isinstance(torch_dtype, torch.dtype):
+        if torch_dtype is not None and not isinstance(torch_dtype, dict) and not isinstance(torch_dtype, torch.dtype):
             torch_dtype = torch.float32
             logger.warning(
                 f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
@@ -950,14 +953,19 @@ def load_module(name, value):
                 loaded_sub_model = passed_class_obj[name]
             else:
                 # load sub model
+                sub_model_dtype = (
+                    torch_dtype.get(name, torch_dtype.get("default", torch.float32))
+                    if isinstance(torch_dtype, dict)
+                    else torch_dtype
+                )
                 loaded_sub_model = load_sub_model(
                     library_name=library_name,
                     class_name=class_name,
                     importable_classes=importable_classes,
                     pipelines=pipelines,
                     is_pipeline_module=is_pipeline_module,
                     pipeline_class=pipeline_class,
-                    torch_dtype=torch_dtype,
+                    torch_dtype=sub_model_dtype,
                     provider=provider,
                     sess_options=sess_options,
                     device_map=current_device_map,

tests/pipelines/test_pipelines_common.py

@@ -2283,6 +2283,29 @@ def run_forward(pipe):
         self.assertTrue(np.allclose(output_without_group_offloading, output_with_group_offloading1, atol=1e-4))
         self.assertTrue(np.allclose(output_without_group_offloading, output_with_group_offloading2, atol=1e-4))
 
+    def test_torch_dtype_dict(self):
+        components = self.get_dummy_components()
+        if not components:
+            self.skipTest("No dummy components defined.")
+
+        pipe = self.pipeline_class(**components)
+
+        specified_key = next(iter(components.keys()))
+
+        with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdirname:
+            pipe.save_pretrained(tmpdirname)
+            torch_dtype_dict = {specified_key: torch.bfloat16, "default": torch.float16}
+            loaded_pipe = self.pipeline_class.from_pretrained(tmpdirname, torch_dtype=torch_dtype_dict)
+
+        for name, component in loaded_pipe.components.items():
+            if isinstance(component, torch.nn.Module) and hasattr(component, "dtype"):
+                expected_dtype = torch_dtype_dict.get(name, torch_dtype_dict.get("default", torch.float32))
+                self.assertEqual(
+                    component.dtype,
+                    expected_dtype,
+                    f"Component '{name}' has dtype {component.dtype} but expected {expected_dtype}",
+                )
+
 
 @is_staging_test
 class PipelinePushToHubTester(unittest.TestCase):