Re-enable classification API as fallback

Author: Billy

invokeai/app/services/model_install/model_install_default.py

@@ -40,6 +40,7 @@
     InvalidModelConfigException,
 )
 from invokeai.backend.model_manager.legacy_probe import ModelProbe
+from invokeai.backend.model_manager.config import ModelConfigBase
 from invokeai.backend.model_manager.metadata import (
     AnyModelRepoMetadata,
     HuggingFaceMetadataFetch,
@@ -646,14 +647,10 @@ def _probe(self, model_path: Path, config: Optional[ModelRecordChanges] = None):
         hash_algo = self._app_config.hashing_algorithm
         fields = config.model_dump()
 
-        return ModelProbe.probe(model_path=model_path, fields=fields, hash_algo=hash_algo)
-
-        # New model probe API is disabled pending resolution of issue caused by a change of the ordering of checks.
-        # See commit message for details.
-        # 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
+        try:
+            return ModelProbe.probe(model_path=model_path, fields=fields, hash_algo=hash_algo)  # type: ignore
+        except InvalidModelConfigException:
+            return ModelConfigBase.classify(model_path, hash_algo, **fields)
 
     def _register(
         self, model_path: Path, config: Optional[ModelRecordChanges] = None, info: Optional[AnyModelConfig] = None

invokeai/backend/model_manager/config.py

@@ -146,33 +146,39 @@ def json_schema_extra(schema: dict[str, Any]) -> None:
     )
     usage_info: Optional[str] = Field(default=None, description="Usage information for this model")
 
-    _USING_LEGACY_PROBE: ClassVar[set] = set()
-    _USING_CLASSIFY_API: ClassVar[set] = set()
+    USING_LEGACY_PROBE: ClassVar[set] = set()
+    USING_CLASSIFY_API: ClassVar[set] = set()
     _MATCH_SPEED: ClassVar[MatchSpeed] = MatchSpeed.MED
 
     def __init_subclass__(cls, **kwargs):
         super().__init_subclass__(**kwargs)
         if issubclass(cls, LegacyProbeMixin):
-            ModelConfigBase._USING_LEGACY_PROBE.add(cls)
+            ModelConfigBase.USING_LEGACY_PROBE.add(cls)
         else:
-            ModelConfigBase._USING_CLASSIFY_API.add(cls)
+            ModelConfigBase.USING_CLASSIFY_API.add(cls)
 
     @staticmethod
     def all_config_classes():
-        subclasses = ModelConfigBase._USING_LEGACY_PROBE | ModelConfigBase._USING_CLASSIFY_API
+        subclasses = ModelConfigBase.USING_LEGACY_PROBE | ModelConfigBase.USING_CLASSIFY_API
         concrete = {cls for cls in subclasses if not isabstract(cls)}
         return concrete
 
     @staticmethod
-    def classify(model_path: Path, hash_algo: HASHING_ALGORITHMS = "blake3_single", **overrides):
+    def classify(
+        mod: str | Path | ModelOnDisk,
+        hash_algo: HASHING_ALGORITHMS = "blake3_single",
+        **overrides
+    ):
         """
         Returns the best matching ModelConfig instance from a model's file/folder path.
         Raises InvalidModelConfigException if no valid configuration is found.
         Created to deprecate ModelProbe.probe
         """
-        candidates = ModelConfigBase._USING_CLASSIFY_API
+        if isinstance(mod, Path | str):
+            mod = ModelOnDisk(mod, hash_algo)
+
+        candidates = ModelConfigBase.USING_CLASSIFY_API
         sorted_by_match_speed = sorted(candidates, key=lambda cls: (cls._MATCH_SPEED, cls.__name__))
-        mod = ModelOnDisk(model_path, hash_algo)
 
         for config_cls in sorted_by_match_speed:
             try:

invokeai/backend/model_manager/model_on_disk.py

@@ -4,6 +4,7 @@
 import safetensors.torch
 import torch
 from picklescan.scanner import scan_file_path
+from safetensors import safe_open
 
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
@@ -35,12 +36,21 @@ def size(self) -> int:
             return self.path.stat().st_size
         return sum(file.stat().st_size for file in self.path.rglob("*"))
 
-    def component_paths(self) -> set[Path]:
+    def weight_files(self) -> set[Path]:
         if self.path.is_file():
             return {self.path}
         extensions = {".safetensors", ".pt", ".pth", ".ckpt", ".bin", ".gguf"}
         return {f for f in self.path.rglob("*") if f.suffix in extensions}
 
+    def metadata(self, path: Optional[Path] = None) -> dict[str, str]:
+        try:
+            with safe_open(self.path, framework="pt", device="cpu") as f:
+                metadata = f.metadata()
+                assert isinstance(metadata, dict)
+                return metadata
+        except Exception:
+            return {}
+
     def repo_variant(self) -> Optional[ModelRepoVariant]:
         if self.path.is_file():
             return None
@@ -64,18 +74,7 @@ def load_state_dict(self, path: Optional[Path] = None) -> StateDict:
         if path in sd_cache:
             return sd_cache[path]
 
-        if not path:
-            components = list(self.component_paths())
-            match components:
-                case []:
-                    raise ValueError("No weight files found for this model")
-                case [p]:
-                    path = p
-                case ps if len(ps) >= 2:
-                    raise ValueError(
-                        f"Multiple weight files found for this model: {ps}. "
-                        f"Please specify the intended file using the 'path' argument"
-                    )
+        path = self.resolve_weight_file(path)
 
         with SilenceWarnings():
             if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
@@ -94,3 +93,18 @@ def load_state_dict(self, path: Optional[Path] = None) -> StateDict:
         state_dict = checkpoint.get("state_dict", checkpoint)
         sd_cache[path] = state_dict
         return state_dict
+
+    def resolve_weight_file(self, path: Optional[Path] = None) -> Path:
+        if not path:
+            weight_files = list(self.weight_files())
+            match weight_files:
+                case []:
+                    raise ValueError("No weight files found for this model")
+                case [p]:
+                    return p
+                case ps if len(ps) >= 2:
+                    raise ValueError(
+                        f"Multiple weight files found for this model: {ps}. "
+                        f"Please specify the intended file using the 'path' argument"
+                    )
+        return path

scripts/classify-model.py

@@ -28,10 +28,9 @@
 
 def classify_with_fallback(path: Path, hash_algo: HASHING_ALGORITHMS):
     try:
-        return ModelConfigBase.classify(path, hash_algo)
-    except InvalidModelConfigException:
         return ModelProbe.probe(path, hash_algo=hash_algo)
-
+    except InvalidModelConfigException:
+        return ModelConfigBase.classify(path, hash_algo)
 
 for path in args.model_path:
     try:

scripts/strip_models.py

@@ -18,13 +18,16 @@
 import shutil
 import sys
 from pathlib import Path
+from typing import Optional
 
 import humanize
 import torch
 
-from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
+from invokeai.backend.model_manager.model_on_disk import ModelOnDisk, StateDict
 from invokeai.backend.model_manager.search import ModelSearch
 
+METADATA_KEY = "metadata_key_for_stripped_models"
+
 
 def strip(v):
     match v:
@@ -57,9 +60,22 @@ def dress(v):
 def load_stripped_model(path: Path, *args, **kwargs):
     with open(path, "r") as f:
         contents = json.load(f)
+        contents.pop(METADATA_KEY, None)
     return dress(contents)
 
 
+class StrippedModelOnDisk(ModelOnDisk):
+    def load_state_dict(self, path: Optional[Path] = None) -> StateDict:
+        path = self.resolve_weight_file(path)
+        return load_stripped_model(path)
+
+    def metadata(self, path: Optional[Path] = None) -> dict[str, str]:
+        path = self.resolve_weight_file(path)
+        with open(path, "r") as f:
+            contents = json.load(f)
+        return contents.get(METADATA_KEY, {})
+
+
 def create_stripped_model(original_model_path: Path, stripped_model_path: Path) -> ModelOnDisk:
     original = ModelOnDisk(original_model_path)
     if original.path.is_file():
@@ -69,11 +85,14 @@ def create_stripped_model(original_model_path: Path, stripped_model_path: Path)
     stripped = ModelOnDisk(stripped_model_path)
     print(f"Created clone of {original.name} at {stripped.path}")
 
-    for component_path in stripped.component_paths():
+    for component_path in stripped.weight_files():
         original_state_dict = stripped.load_state_dict(component_path)
+
         stripped_state_dict = strip(original_state_dict)  # type: ignore
+        metadata = stripped.metadata()
+        contents = {**stripped_state_dict, METADATA_KEY: metadata}
         with open(component_path, "w") as f:
-            json.dump(stripped_state_dict, f, indent=4)
+            json.dump(contents, f, indent=4)
 
     before_size = humanize.naturalsize(original.size())
     after_size = humanize.naturalsize(stripped.size())

tests/test_model_probe.py

@@ -29,6 +29,9 @@
 from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util.logging import InvokeAILogger
+from scripts.strip_models import StrippedModelOnDisk
+
+logger = InvokeAILogger.get_logger(__file__)
 
 
 @pytest.mark.parametrize(
@@ -156,7 +159,8 @@ def test_regression_against_model_probe(datadir: Path, override_model_loading):
             pass
 
         try:
-            new_config = ModelConfigBase.classify(path, hash=fake_hash, key=fake_key)
+            stripped_mod = StrippedModelOnDisk(path)
+            new_config = ModelConfigBase.classify(stripped_mod, hash=fake_hash, key=fake_key)
         except InvalidModelConfigException:
             pass
 
@@ -165,10 +169,10 @@ def test_regression_against_model_probe(datadir: Path, override_model_loading):
             assert legacy_config.model_dump_json() == new_config.model_dump_json()
 
         elif legacy_config:
-            assert type(legacy_config) in ModelConfigBase._USING_LEGACY_PROBE
+            assert type(legacy_config) in ModelConfigBase.USING_LEGACY_PROBE
 
         elif new_config:
-            assert type(new_config) in ModelConfigBase._USING_CLASSIFY_API
+            assert type(new_config) in ModelConfigBase.USING_CLASSIFY_API
 
         else:
             raise ValueError(f"Both probe and classify failed to classify model at path {path}.")
@@ -177,7 +181,6 @@ def test_regression_against_model_probe(datadir: Path, override_model_loading):
         configs_with_tests.add(config_type)
 
     untested_configs = ModelConfigBase.all_config_classes() - configs_with_tests - {MinimalConfigExample}
-    logger = InvokeAILogger.get_logger(__file__)
     logger.warning(f"Function test_regression_against_model_probe missing test case for: {untested_configs}")
 
 
@@ -255,3 +258,16 @@ def test_any_model_config_includes_all_config_classes():
 
     expected = set(ModelConfigBase.all_config_classes()) - {MinimalConfigExample}
     assert extracted == expected
+
+
+def test_config_uniquely_matches_model(datadir: Path):
+
+    model_paths = ModelSearch().search(datadir / "stripped_models")
+    for path in model_paths:
+
+        mod = StrippedModelOnDisk(path)
+        matches = { cls for cls in ModelConfigBase.USING_CLASSIFY_API if cls.matches(mod) }
+        assert len(matches) <= 1, f"Model at path {path} matches multiple config classes: {matches}"
+        if not matches:
+            logger.warning(f"Model at path {path} does not match any config classes using classify API.")
+