warn but do not crash when model scan finds random cruft in models directory

invokeai/backend/install/invokeai_configure.py

@@ -7,8 +7,6 @@
 # Coauthor: Kevin Turner http://github.com/keturn
 #
 import sys
-print("Loading Python libraries...\n",file=sys.stderr)
-
 import argparse
 import io
 import os
@@ -706,18 +704,6 @@ def write_opts(opts: Namespace, init_file: Path):
 def default_output_dir() -> Path:
     return config.root_path / "outputs"
 
-# # -------------------------------------
-# def default_embedding_dir() -> Path:
-#     return config.root_path / "embeddings"
-
-# # -------------------------------------
-# def default_lora_dir() -> Path:
-#     return config.root_path / "loras"
-
-# # -------------------------------------
-# def default_controlnet_dir() -> Path:
-#     return config.root_path / "controlnets"
-
 # -------------------------------------
 def write_default_options(program_opts: Namespace, initfile: Path):
     opt = default_startup_options(initfile)

invokeai/backend/install/model_install_backend.py

@@ -155,8 +155,6 @@ class ModelInstall(object):
     def install(self, selections: InstallSelections):
         job = 1
         jobs = len(selections.remove_models) + len(selections.install_models)
-#        if selections.scan_directory:
-#            jobs += 1
         
         # remove requested models
         for key in selections.remove_models:
@@ -218,7 +216,7 @@ class ModelInstall(object):
     # the model from being probed twice in the event that it has already been probed.
     def _install_path(self, path: Path, info: ModelProbeInfo=None)->Path:
         try:
-            logger.info(f'Probing {path}')
+            # logger.debug(f'Probing {path}')
             info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
             model_name = path.stem if info.format=='checkpoint' else path.name
             if self.mgr.model_exists(model_name, info.base_type, info.model_type):

invokeai/backend/model_management/model_manager.py

@@ -714,9 +714,12 @@ class ModelManager(object):
 
                             if model_path.is_relative_to(self.app_config.root_path):
                                 model_path = model_path.relative_to(self.app_config.root_path)
-                            model_config: ModelConfigBase = model_class.probe_config(str(model_path))
+                                try:
-                            self.models[model_key] = model_config
+                                    model_config: ModelConfigBase = model_class.probe_config(str(model_path))
-                            new_models_found = True
+                                    self.models[model_key] = model_config
+                                    new_models_found = True
+                                except NotImplementedError as e:
+                                    self.logger.warning(e)
 
         imported_models = self.autoimport()
 
@@ -737,10 +740,10 @@ class ModelManager(object):
                                  )
         
         installed = set()
-
+        scanned_dirs = set()
+        
         config = self.app_config
         known_paths = {(self.app_config.root_path / x['path']) for x in self.list_models()}
-        scanned_dirs = set()
 
         for autodir in [config.autoimport_dir,
                         config.lora_dir,
@@ -748,19 +751,25 @@ class ModelManager(object):
                         config.controlnet_dir]:
             if autodir is None:
                 continue
+
+            self.logger.info(f'Scanning {autodir} for models to import')
         
             autodir = self.app_config.root_path / autodir
             if not autodir.exists():
                 continue
-        
+
+            items_scanned = 0
+            new_models_found = set()
+            
             for root, dirs, files in os.walk(autodir):
+                items_scanned += len(dirs) + len(files)
                 for d in dirs:
                     path = Path(root) / d
                     if path in known_paths or path.parent in scanned_dirs:
                         scanned_dirs.add(path)
                         continue
                     if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin'}]):
-                        installed.update(installer.heuristic_install(path))
+                        new_models_found.update(installer.heuristic_install(path))
                         scanned_dirs.add(path)
 
                 for f in files:
@@ -768,7 +777,11 @@ class ModelManager(object):
                     if path in known_paths or path.parent in scanned_dirs:
                         continue
                     if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
-                        installed.update(installer.heuristic_install(path))
+                        new_models_found.update(installer.heuristic_install(path))
+
+            self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
+            installed.update(new_models_found)
+
         return installed
 
     def heuristic_import(self,

invokeai/backend/model_management/models/stable_diffusion.py

@@ -69,7 +69,7 @@ class StableDiffusion1Model(DiffusersModel):
                 in_channels = unet_config['in_channels']
 
             else:
-                raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
+                raise NotImplementedError(f"{path} is not a supported stable diffusion diffusers format")
 
         else:
             raise NotImplementedError(f"Unknown stable diffusion 1.* format: {model_format}")

invokeai/frontend/install/model_install.py

@@ -316,31 +316,6 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             **kwargs,
         )
 
-        # label = "Directory to scan for models to automatically import (<tab> autocompletes):"
-        # self.nextrely += 1
-        # widgets.update(
-        #     autoload_directory = self.add_widget_intelligent(
-        #         FileBox,
-        #         max_height=3,
-        #         name=label,
-        #         value=str(config.root_path / config.autoimport_dir) if config.autoimport_dir else None,
-        #         select_dir=True,
-        #         must_exist=True,
-        #         use_two_lines=False,
-        #         labelColor="DANGER",
-        #         begin_entry_at=len(label)+1,
-        #         scroll_exit=True,
-        #     )
-        # )
-        # widgets.update(
-        #     autoscan_on_startup = self.add_widget_intelligent(
-        #         npyscreen.Checkbox,
-        #         name="Scan and import from this directory each time InvokeAI starts",
-        #         value=config.autoimport_dir is not None,
-        #         relx=4,
-        #         scroll_exit=True,
-        #     )
-        # )
         return widgets
 
     def resize(self):