Model classification api (#7742)

## Summary
The _goal_ of this PR is to make it easier to add an new config type.
This _scope_ of this PR is to integrate the API and does not include
adding new configs (outside tests) or porting existing ones.


One of the glaring issues of the existing *legacy probe* is that the
logic for each type is spread across multiple classes and intertwined
with the other configs. This means that adding a new config type (or
modifying an existing one) is complex and error prone.

This PR attempts to remedy this by providing a new API for adding
configs that:

- Is backwards compatible with the existing probe.
- Encapsulates fields and logic in a single class, keeping things
self-contained and easy to modify safely.

Below is a minimal toy example illustrating the proposed new structure:

```python
class MinimalConfigExample(ModelConfigBase):
    type: ModelType = ModelType.Main
    format: ModelFormat = ModelFormat.Checkpoint
    fun_quote: str

    @classmethod
    def matches(cls, mod: ModelOnDisk) -> bool:
        return mod.path.suffix == ".json"

    @classmethod
    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
        with open(mod.path, "r") as f:
            contents = json.load(f)

        return {
            "fun_quote": contents["quote"],
            "base": BaseModelType.Any,
        }
```

To create a new config type, one needs to inherit from `ModelConfigBase`
and implement its interface.

The code falls back to the legacy model probe for existing models using
the old API.
This allows us to incrementally port the configs one by one.



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## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_

Author: psychedelicious

invokeai/app/services/model_install/model_install_default.py

@@ -38,9 +38,11 @@
     AnyModelConfig,
     CheckpointConfigBase,
     InvalidModelConfigException,
+    ModelConfigBase,
     ModelRepoVariant,
     ModelSourceType,
 )
+from invokeai.backend.model_manager.legacy_probe import ModelProbe
 from invokeai.backend.model_manager.metadata import (
     AnyModelRepoMetadata,
     HuggingFaceMetadataFetch,
@@ -49,7 +51,6 @@
     RemoteModelFile,
 )
 from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
-from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
@@ -182,9 +183,7 @@ def install_path(
     ) -> str:  # noqa D102
         model_path = Path(model_path)
         config = config or ModelRecordChanges()
-        info: AnyModelConfig = ModelProbe.probe(
-            Path(model_path), config.model_dump(), hash_algo=self._app_config.hashing_algorithm
-        )  # type: ignore
+        info: AnyModelConfig = self._probe(Path(model_path), config)  # type: ignore
 
         if preferred_name := config.name:
             preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
@@ -644,12 +643,22 @@ def _move_model(self, old_path: Path, new_path: Path) -> Path:
         move(old_path, new_path)
         return new_path
 
+    def _probe(self, model_path: Path, config: Optional[ModelRecordChanges] = None):
+        config = config or ModelRecordChanges()
+        hash_algo = self._app_config.hashing_algorithm
+        fields = config.model_dump()
+
+        try:
+            return ModelConfigBase.classify(model_path=model_path, hash_algo=hash_algo, **fields)
+        except InvalidModelConfigException:
+            return ModelProbe.probe(model_path=model_path, fields=fields, hash_algo=hash_algo)  # type: ignore
+
     def _register(
         self, model_path: Path, config: Optional[ModelRecordChanges] = None, info: Optional[AnyModelConfig] = None
     ) -> str:
         config = config or ModelRecordChanges()
 
-        info = info or ModelProbe.probe(model_path, config.model_dump(), hash_algo=self._app_config.hashing_algorithm)  # type: ignore
+        info = info or self._probe(model_path, config)
 
         model_path = model_path.resolve()
 

invokeai/backend/model_manager/__init__.py

@@ -13,8 +13,8 @@
     SchedulerPredictionType,
     SubModelType,
 )
+from invokeai.backend.model_manager.legacy_probe import ModelProbe
 from invokeai.backend.model_manager.load import LoadedModel
-from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 
 __all__ = [