tidy(mm): consistent class names

Author: psychedelicious

invokeai/app/invocations/create_gradient_mask.py

@@ -21,7 +21,7 @@
 from invokeai.app.invocations.model import UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import LoadedModel
-from invokeai.backend.model_manager.config import MainConfigBase
+from invokeai.backend.model_manager.config import Main_Config_Base
 from invokeai.backend.model_manager.taxonomy import ModelVariantType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 
@@ -182,7 +182,7 @@ def invoke(self, context: InvocationContext) -> GradientMaskOutput:
         if self.unet is not None and self.vae is not None and self.image is not None:
             # all three fields must be present at the same time
             main_model_config = context.models.get_config(self.unet.unet.key)
-            assert isinstance(main_model_config, MainConfigBase)
+            assert isinstance(main_model_config, Main_Config_Base)
             if main_model_config.variant is ModelVariantType.Inpaint:
                 mask = dilated_mask_tensor
                 vae_info: LoadedModel = context.models.load(self.vae.vae)

invokeai/app/invocations/flux_model_loader.py

@@ -15,7 +15,7 @@
 )
 from invokeai.backend.flux.util import get_flux_max_seq_length
 from invokeai.backend.model_manager.config import (
-    CheckpointConfigBase,
+    Checkpoint_Config_Base,
 )
 from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType
 
@@ -87,7 +87,7 @@ def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
         t5_encoder = preprocess_t5_encoder_model_identifier(self.t5_encoder_model)
 
         transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
+        assert isinstance(transformer_config, Checkpoint_Config_Base)
 
         return FluxModelLoaderOutput(
             transformer=TransformerField(transformer=transformer, loras=[]),

invokeai/app/services/model_install/model_install_default.py

@@ -37,7 +37,7 @@
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    CheckpointConfigBase,
+    Checkpoint_Config_Base,
     InvalidModelConfigException,
     ModelConfigFactory,
 )
@@ -625,7 +625,7 @@ def _register(
 
         info.path = model_path.as_posix()
 
-        if isinstance(info, CheckpointConfigBase) and info.config_path is not None:
+        if isinstance(info, Checkpoint_Config_Base) and info.config_path is not None:
             # Checkpoints have a config file needed for conversion. Same handling as the model weights - if it's in the
             # invoke-managed legacy config dir, we use a relative path.
             legacy_config_path = self.app_config.legacy_conf_path / info.config_path

invokeai/app/services/shared/invocation_context.py

@@ -21,7 +21,7 @@
 from invokeai.app.util.step_callback import diffusion_step_callback
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    ModelConfigBase,
+    Config_Base,
 )
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
@@ -558,7 +558,7 @@ def get_absolute_path(self, config_or_path: AnyModelConfig | Path | str) -> Path
             The absolute path to the model.
         """
 
-        model_path = Path(config_or_path.path) if isinstance(config_or_path, ModelConfigBase) else Path(config_or_path)
+        model_path = Path(config_or_path.path) if isinstance(config_or_path, Config_Base) else Path(config_or_path)
 
         if model_path.is_absolute():
             return model_path.resolve()

invokeai/backend/model_manager/__init__.py

@@ -3,7 +3,7 @@
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     InvalidModelConfigException,
-    ModelConfigBase,
+    Config_Base,
     ModelConfigFactory,
 )
 from invokeai.backend.model_manager.legacy_probe import ModelProbe
@@ -30,7 +30,7 @@
     "ModelConfigFactory",
     "ModelProbe",
     "ModelSearch",
-    "ModelConfigBase",
+    "Config_Base",
     "AnyModel",
     "AnyVariant",
     "BaseModelType",