simplify device_manager usage

scripts/demo/streamlit_helpers.py

@@ -36,7 +36,7 @@ def init_st(
             pipeline = SamplingPipeline(
                 model_spec=spec,
                 use_fp16=True,
-                device_manager=CudaModelManager(device="cuda", swap_device="cpu"),
+                device=CudaModelManager(device="cuda", swap_device="cpu"),
             )
         else:
             pipeline = SamplingPipeline(model_spec=spec, use_fp16=False)

sgm/inference/api.py

@@ -57,6 +57,13 @@ class Thresholder(str, Enum):
 
 @dataclass
 class SamplingParams:
+    """
+    Parameters for sampling.
+    The defaults here are derived from user preference testing.
+    They will be subject to change in the future, likely pulled
+    from model specs instead of global defaults.
+    """
+
     width: int = 1024
     height: int = 1024
     steps: int = 40
@@ -167,7 +174,9 @@ def __init__(
         model_path: Optional[str] = None,
         config_path: Optional[str] = None,
         use_fp16: bool = True,
-        device_manager: DeviceModelManager = CudaModelManager(device="cuda"),
+        device: Union[DeviceModelManager, str, torch.device] = CudaModelManager(
+            device="cuda"
+        ),
     ) -> None:
         """
         Sampling pipeline for generating images from a model.
@@ -177,7 +186,7 @@ def __init__(
         @param model_path: Path to model checkpoints folder.
         @param config_path: Path to model config folder.
         @param use_fp16: Whether to use fp16 for sampling.
-        @param model_loader: Model loader class to use. Defaults to CudaModelLoader.
+        @param device: Device manager to use with this pipeline. If a string or torch.device is passed, a device  manager will be created based on device type if possible.
         """
 
         self.model_id = model_id
@@ -205,7 +214,13 @@ def __init__(
                 f"Checkpoint {self.ckpt} not found, check model spec or config_path"
             )
 
-        self.device_manager = device_manager
+        if isinstance(device, torch.device) or isinstance(device, str):
+            if torch.device(device).type == "cuda":
+                self.device_manager = CudaModelManager(device=device)
+            else:
+                self.device_manager = DeviceModelManager(device=device)
+        else:
+            self.device_manager = device
         self.model = self._load_model(
             device_manager=self.device_manager, use_fp16=use_fp16
         )