feat(mm): add UnknownModelConfig

invokeai/backend/model_manager/config.py

@@ -28,7 +28,7 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import isabstract
 from pathlib import Path
-from typing import ClassVar, Literal, Optional, TypeAlias, Union
+from typing import ClassVar, Literal, Optional, Type, TypeAlias, Union
 
 from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
@@ -109,6 +109,18 @@ class MatchSpeed(int, Enum):
     SLOW = 2
 
 
+class LegacyProbeMixin:
+    """Mixin for classes using the legacy probe for model classification."""
+
+    @classmethod
+    def matches(cls, *args, **kwargs):
+        raise NotImplementedError(f"Method 'matches' not implemented for {cls.__name__}")
+
+    @classmethod
+    def parse(cls, *args, **kwargs):
+        raise NotImplementedError(f"Method 'parse' not implemented for {cls.__name__}")
+
+
 class ModelConfigBase(ABC, BaseModel):
     """
     Abstract Base class for model configurations.
@@ -152,15 +164,15 @@ class ModelConfigBase(ABC, BaseModel):
     )
     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[Type["ModelConfigBase"]]] = set()
+    USING_CLASSIFY_API: ClassVar[set[Type["ModelConfigBase"]]] = 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)
-        else:
+        elif cls is not UnknownModelConfig:
             ModelConfigBase.USING_CLASSIFY_API.add(cls)
 
     @staticmethod
@@ -170,7 +182,9 @@ class ModelConfigBase(ABC, BaseModel):
         return concrete
 
     @staticmethod
-    def classify(mod: str | Path | ModelOnDisk, hash_algo: HASHING_ALGORITHMS = "blake3_single", **overrides):
+    def classify(
+        mod: str | Path | ModelOnDisk, hash_algo: HASHING_ALGORITHMS = "blake3_single", **overrides
+    ) -> "AnyModelConfig":
         """
         Returns the best matching ModelConfig instance from a model's file/folder path.
         Raises InvalidModelConfigException if no valid configuration is found.
@@ -192,7 +206,10 @@ class ModelConfigBase(ABC, BaseModel):
             else:
                 return config_cls.from_model_on_disk(mod, **overrides)
 
-        raise InvalidModelConfigException("Unable to determine model type")
+        try:
+            return UnknownModelConfig.from_model_on_disk(mod, **overrides)
+        except Exception:
+            raise InvalidModelConfigException("Unable to determine model type")
 
     @classmethod
     def get_tag(cls) -> Tag:
@@ -256,16 +273,17 @@ class ModelConfigBase(ABC, BaseModel):
         return cls(**fields)
 
 
-class LegacyProbeMixin:
-    """Mixin for classes using the legacy probe for model classification."""
+class UnknownModelConfig(ModelConfigBase):
+    type: Literal[ModelType.Unknown] = ModelType.Unknown
+    format: Literal[ModelFormat.Unknown] = ModelFormat.Unknown
 
     @classmethod
-    def matches(cls, *args, **kwargs):
-        raise NotImplementedError(f"Method 'matches' not implemented for {cls.__name__}")
+    def matches(cls, *args, **kwargs) -> bool:
+        raise NotImplementedError("UnknownModelConfig cannot match anything")
 
     @classmethod
-    def parse(cls, *args, **kwargs):
-        raise NotImplementedError(f"Method 'parse' not implemented for {cls.__name__}")
+    def parse(cls, *args, **kwargs) -> dict[str, Any]:
+        raise NotImplementedError("UnknownModelConfig cannot parse anything")
 
 
 class CheckpointConfigBase(ABC, BaseModel):
@@ -353,7 +371,7 @@ class LoRAOmiConfig(LoRAConfigBase, ModelConfigBase):
 
         metadata = mod.metadata()
         return (
-            metadata.get("modelspec.sai_model_spec")
+            bool(metadata.get("modelspec.sai_model_spec"))
             and metadata.get("ot_branch") == "omi_format"
             and metadata["modelspec.architecture"].split("/")[1].lower() == "lora"
         )
@@ -751,6 +769,7 @@ AnyModelConfig = Annotated[
         Annotated[LlavaOnevisionConfig, LlavaOnevisionConfig.get_tag()],
         Annotated[ApiModelConfig, ApiModelConfig.get_tag()],
         Annotated[VideoApiModelConfig, VideoApiModelConfig.get_tag()],
+        Annotated[UnknownModelConfig, UnknownModelConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

invokeai/backend/model_manager/taxonomy.py

@@ -55,6 +55,7 @@ class ModelType(str, Enum):
     FluxRedux = "flux_redux"
     LlavaOnevision = "llava_onevision"
     Video = "video"
+    Unknown = "unknown"
 
 
 class SubModelType(str, Enum):
@@ -107,6 +108,7 @@ class ModelFormat(str, Enum):
     BnbQuantizednf4b = "bnb_quantized_nf4b"
     GGUFQuantized = "gguf_quantized"
     Api = "api"
+    Unknown = "unknown"
 
 
 class SchedulerPredictionType(str, Enum):