feat: cuda device_map for pipelines.

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -613,6 +613,9 @@ def _assign_components_to_devices(
 
 
 def _get_final_device_map(device_map, pipeline_class, passed_class_obj, init_dict, library, max_memory, **kwargs):
+    # TODO: seperate out different device_map methods when it gets to it.
+    if device_map != "balanced":
+        return device_map
     # To avoid circular import problem.
     from diffusers import pipelines
 

src/diffusers/pipelines/pipeline_utils.py

@@ -67,6 +67,7 @@
     numpy_to_pil,
 )
 from ..utils.hub_utils import _check_legacy_sharding_variant_format, load_or_create_model_card, populate_model_card
+from ..utils.testing_utils import torch_device
 from ..utils.torch_utils import empty_device_cache, get_device, is_compiled_module
 
 
@@ -108,7 +109,8 @@
 for library in LOADABLE_CLASSES:
     LIBRARIES.append(library)
 
-SUPPORTED_DEVICE_MAP = ["balanced"]
+# TODO: support single-device namings
+SUPPORTED_DEVICE_MAP = ["balanced"] + [torch_device]
 
 logger = logging.get_logger(__name__)
 
@@ -988,12 +990,15 @@ def load_module(name, value):
         _maybe_warn_for_wrong_component_in_quant_config(init_dict, quantization_config)
         for name, (library_name, class_name) in logging.tqdm(init_dict.items(), desc="Loading pipeline components..."):
             # 7.1 device_map shenanigans
-            if final_device_map is not None and len(final_device_map) > 0:
-                component_device = final_device_map.get(name, None)
-                if component_device is not None:
-                    current_device_map = {"": component_device}
-                else:
-                    current_device_map = None
+            if final_device_map is not None:
+                if isinstance(final_device_map, dict) and len(final_device_map) > 0:
+                    component_device = final_device_map.get(name, None)
+                    if component_device is not None:
+                        current_device_map = {"": component_device}
+                    else:
+                        current_device_map = None
+                elif isinstance(final_device_map, str):
+                    current_device_map = final_device_map
 
             # 7.2 - now that JAX/Flax is an official framework of the library, we might load from Flax names
             class_name = class_name[4:] if class_name.startswith("Flax") else class_name

tests/pipelines/test_pipelines_common.py

@@ -2339,6 +2339,29 @@ def test_torch_dtype_dict(self):
                     f"Component '{name}' has dtype {component.dtype} but expected {expected_dtype}",
                 )
 
+    @require_torch_accelerator
+    def test_pipeline_with_accelerator_device_map(self, expected_max_difference=1e-4):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        torch.manual_seed(0)
+        inputs = self.get_dummy_inputs(torch_device)
+        inputs["generator"] = torch.manual_seed(0)
+        out = pipe(**inputs)[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            loaded_pipe = self.pipeline_class.from_pretrained(tmpdir, device_map=torch_device)
+            for component in loaded_pipe.components.values():
+                if hasattr(component, "set_default_attn_processor"):
+                    component.set_default_attn_processor()
+        inputs["generator"] = torch.manual_seed(0)
+        loaded_out = loaded_pipe(**inputs)[0]
+        max_diff = np.abs(to_np(out) - to_np(loaded_out)).max()
+        self.assertLess(max_diff, expected_max_difference)
+
 
 @is_staging_test
 class PipelinePushToHubTester(unittest.TestCase):