final tidying before marking PR as ready for review

- Replace AnyModelLoader with ModelLoaderRegistry
- Fix type check errors in multiple files
- Remove apparently unneeded `get_model_config_enum()` method from model manager
- Remove last vestiges of old model manager
- Updated tests and documentation

resolve conflict with seamless.py

invokeai/backend/model_manager/util/libc_util.py

@@ -35,7 +35,7 @@ class Struct_mallinfo2(ctypes.Structure):
         ("keepcost", ctypes.c_size_t),
     ]
 
-    def __str__(self):
+    def __str__(self) -> str:
         s = ""
         s += f"{'arena': <10}= {(self.arena/2**30):15.5f}   # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
         s += f"{'ordblks': <10}= {(self.ordblks): >15}   # Number of free chunks\n"
@@ -62,7 +62,7 @@ class LibcUtil:
     TODO: Improve cross-OS compatibility of this class.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self._libc = ctypes.cdll.LoadLibrary("libc.so.6")
 
     def mallinfo2(self) -> Struct_mallinfo2:
@@ -72,4 +72,5 @@ class LibcUtil:
         """
         mallinfo2 = self._libc.mallinfo2
         mallinfo2.restype = Struct_mallinfo2
-        return mallinfo2()
+        result: Struct_mallinfo2 = mallinfo2()
+        return result

invokeai/backend/model_manager/util/model_util.py

@@ -1,12 +1,15 @@
 """Utilities for parsing model files, used mostly by probe.py"""
 
 import json
-import torch
-from typing import Union
 from pathlib import Path
+from typing import Dict, Optional, Union
+
+import safetensors
+import torch
 from picklescan.scanner import scan_file_path
 
-def _fast_safetensors_reader(path: str):
+
+def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
     checkpoint = {}
     device = torch.device("meta")
     with open(path, "rb") as f:
@@ -37,10 +40,12 @@ def _fast_safetensors_reader(path: str):
 
     return checkpoint
 
-def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
+
+def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str, torch.Tensor]:
     if str(path).endswith(".safetensors"):
         try:
-            checkpoint = _fast_safetensors_reader(path)
+            path_str = path.as_posix() if isinstance(path, Path) else path
+            checkpoint = _fast_safetensors_reader(path_str)
         except Exception:
             # TODO: create issue for support "meta"?
             checkpoint = safetensors.torch.load_file(path, device="cpu")
@@ -52,14 +57,15 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
         checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint
 
-def lora_token_vector_length(checkpoint: dict) -> int:
+
+def lora_token_vector_length(checkpoint: Dict[str, torch.Tensor]) -> Optional[int]:
     """
     Given a checkpoint in memory, return the lora token vector length
 
     :param checkpoint: The checkpoint
     """
 
-    def _get_shape_1(key: str, tensor, checkpoint) -> int:
+    def _get_shape_1(key: str, tensor: torch.Tensor, checkpoint: Dict[str, torch.Tensor]) -> Optional[int]:
         lora_token_vector_length = None
 
         if "." not in key: