Apply suggestions from code review

Co-authored-by: Dhruv Nair <[email protected]>

Author: a-r-r-o-w

src/diffusers/hooks/layerwise_casting.py

@@ -74,7 +74,7 @@ def apply_layerwise_casting(
     module: torch.nn.Module,
     storage_dtype: torch.dtype,
     compute_dtype: torch.dtype,
-    skip_modules_pattern: Union[str, Tuple[str, ...]] = "default",
+    skip_modules_pattern: Union[str, Tuple[str, ...]] = "auto",
     skip_modules_classes: Optional[Tuple[Type[torch.nn.Module], ...]] = None,
     non_blocking: bool = False,
 ) -> None:
@@ -109,17 +109,17 @@ def apply_layerwise_casting(
             The dtype to cast the module to before/after the forward pass for storage.
         compute_dtype (`torch.dtype`):
             The dtype to cast the module to during the forward pass for computation.
-        skip_modules_pattern (`Tuple[str, ...]`, defaults to `"default"`):
+        skip_modules_pattern (`Tuple[str, ...]`, defaults to `"auto"`):
             A list of patterns to match the names of the modules to skip during the layerwise casting process. If set
-            to `"default"`, the default patterns are used. If set to `None`, no modules are skipped. If set to `None`
+            to `"auto"`, the default patterns are used. If set to `None`, no modules are skipped. If set to `None`
             alongside `skip_modules_classes` being `None`, the layerwise casting is applied directly to the module
             instead of its internal submodules.
         skip_modules_classes (`Tuple[Type[torch.nn.Module], ...]`, defaults to `None`):
             A list of module classes to skip during the layerwise casting process.
         non_blocking (`bool`, defaults to `False`):
             If `True`, the weight casting operations are non-blocking.
     """
-    if skip_modules_pattern == "default":
+    if skip_modules_pattern == "auto":
         skip_modules_pattern = DEFAULT_SKIP_MODULES_PATTERN
 
     if skip_modules_classes is None and skip_modules_pattern is None: