feat(nodes): "ModelField" -> "ModelIdentifierField", add hash/name/base/type

invokeai/app/invocations/controlnet_image_processors.py

@@ -35,7 +35,7 @@ from invokeai.app.invocations.fields import (
     WithBoard,
     WithMetadata,
 )
-from invokeai.app.invocations.model import ModelField
+from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -55,7 +55,7 @@ CONTROLNET_RESIZE_VALUES = Literal[
 
 class ControlField(BaseModel):
     image: ImageField = Field(description="The control image")
-    control_model: ModelField = Field(description="The ControlNet model to use")
+    control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
     control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
@@ -91,7 +91,7 @@ class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
-    control_model: ModelField = InputField(
+    control_model: ModelIdentifierField = InputField(
         description=FieldDescriptions.controlnet_model, input=Input.Direct, ui_type=UIType.ControlNetModel
     )
     control_weight: Union[float, List[float]] = InputField(

invokeai/app/invocations/ip_adapter.py

@@ -11,7 +11,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import ModelField
+from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -20,8 +20,8 @@ from invokeai.backend.model_manager.config import BaseModelType, IPAdapterConfig
 
 class IPAdapterField(BaseModel):
     image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: ModelField = Field(description="The IP-Adapter model to use.")
-    image_encoder_model: ModelField = Field(description="The name of the CLIP image encoder model.")
+    ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
+    image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
@@ -54,7 +54,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: ModelField = InputField(
+    ip_adapter_model: ModelIdentifierField = InputField(
         description="The IP-Adapter model.",
         title="IP-Adapter Model",
         input=Input.Direct,
@@ -97,7 +97,7 @@ class IPAdapterInvocation(BaseInvocation):
             ip_adapter=IPAdapterField(
                 image=self.image,
                 ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=ModelField(key=image_encoder_models[0].key),
+                image_encoder_model=ModelIdentifierField(key=image_encoder_models[0].key),
                 weight=self.weight,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,

invokeai/app/invocations/latent.py

@@ -76,7 +76,7 @@ from .baseinvocation import (
     invocation_output,
 )
 from .controlnet_image_processors import ControlField
-from .model import ModelField, UNetField, VAEField
+from .model import ModelIdentifierField, UNetField, VAEField
 
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
@@ -245,7 +245,7 @@ class CreateGradientMaskInvocation(BaseInvocation):
 
 def get_scheduler(
     context: InvocationContext,
-    scheduler_info: ModelField,
+    scheduler_info: ModelIdentifierField,
     scheduler_name: str,
     seed: int,
 ) -> Scheduler:

invokeai/app/invocations/model.py

@@ -6,7 +6,7 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.model_manager.config import SubModelType
+from invokeai.backend.model_manager.config import BaseModelType, ModelType, SubModelType
 
 from .baseinvocation import (
     BaseInvocation,
@@ -16,33 +16,39 @@ from .baseinvocation import (
 )
 
 
-class ModelField(BaseModel):
-    key: str = Field(description="Key of the model")
-    submodel_type: Optional[SubModelType] = Field(description="Submodel type", default=None)
+class ModelIdentifierField(BaseModel):
+    key: str = Field(description="The model's unique key")
+    hash: str = Field(description="The model's BLAKE3 hash")
+    name: str = Field(description="The model's name")
+    base: BaseModelType = Field(description="The model's base model type")
+    type: ModelType = Field(description="The model's type")
+    submodel_type: Optional[SubModelType] = Field(
+        description="The submodel to load, if this is a main model", default=None
+    )
 
 
 class LoRAField(BaseModel):
-    lora: ModelField = Field(description="Info to load lora model")
+    lora: ModelIdentifierField = Field(description="Info to load lora model")
     weight: float = Field(description="Weight to apply to lora model")
 
 
 class UNetField(BaseModel):
-    unet: ModelField = Field(description="Info to load unet submodel")
-    scheduler: ModelField = Field(description="Info to load scheduler submodel")
+    unet: ModelIdentifierField = Field(description="Info to load unet submodel")
+    scheduler: ModelIdentifierField = Field(description="Info to load scheduler submodel")
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
     freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
 
 
 class CLIPField(BaseModel):
-    tokenizer: ModelField = Field(description="Info to load tokenizer submodel")
-    text_encoder: ModelField = Field(description="Info to load text_encoder submodel")
+    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
 class VAEField(BaseModel):
-    vae: ModelField = Field(description="Info to load vae submodel")
+    vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
@@ -84,7 +90,7 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: ModelField = InputField(
+    model: ModelIdentifierField = InputField(
         description=FieldDescriptions.main_model, input=Input.Direct, ui_type=UIType.MainModel
     )
     # TODO: precision?
@@ -119,7 +125,7 @@ class LoRALoaderOutput(BaseInvocationOutput):
 class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: ModelField = InputField(
+    lora: ModelIdentifierField = InputField(
         description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
@@ -190,7 +196,7 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: ModelField = InputField(
+    lora: ModelIdentifierField = InputField(
         description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
@@ -264,7 +270,7 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
-    vae_model: ModelField = InputField(
+    vae_model: ModelIdentifierField = InputField(
         description=FieldDescriptions.vae_model, input=Input.Direct, title="VAE", ui_type=UIType.VAEModel
     )
 

invokeai/app/invocations/sdxl.py

@@ -8,7 +8,7 @@ from .baseinvocation import (
     invocation,
     invocation_output,
 )
-from .model import CLIPField, ModelField, UNetField, VAEField
+from .model import CLIPField, ModelIdentifierField, UNetField, VAEField
 
 
 @invocation_output("sdxl_model_loader_output")
@@ -34,7 +34,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
-    model: ModelField = InputField(
+    model: ModelIdentifierField = InputField(
         description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
@@ -72,7 +72,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
-    model: ModelField = InputField(
+    model: ModelIdentifierField = InputField(
         description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
     )
     # TODO: precision?

invokeai/app/invocations/t2i_adapter.py

@@ -10,14 +10,14 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import ModelField
+from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
 
 
 class T2IAdapterField(BaseModel):
     image: ImageField = Field(description="The T2I-Adapter image prompt.")
-    t2i_adapter_model: ModelField = Field(description="The T2I-Adapter model to use.")
+    t2i_adapter_model: ModelIdentifierField = Field(description="The T2I-Adapter model to use.")
     weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
@@ -52,7 +52,7 @@ class T2IAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: ImageField = InputField(description="The IP-Adapter image prompt.")
-    t2i_adapter_model: ModelField = InputField(
+    t2i_adapter_model: ModelIdentifierField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
         input=Input.Direct,

invokeai/app/services/shared/invocation_context.py

@@ -22,7 +22,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
 
 if TYPE_CHECKING:
     from invokeai.app.invocations.baseinvocation import BaseInvocation
-    from invokeai.app.invocations.model import ModelField
+    from invokeai.app.invocations.model import ModelIdentifierField
     from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 
 """
@@ -300,7 +300,7 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
-    def exists(self, identifier: Union[str, "ModelField"]) -> bool:
+    def exists(self, identifier: Union[str, "ModelIdentifierField"]) -> bool:
         """Checks if a model exists.
 
         Args:
@@ -314,7 +314,9 @@ class ModelsInterface(InvocationContextInterface):
 
         return self._services.model_manager.store.exists(identifier.key)
 
-    def load(self, identifier: Union[str, "ModelField"], submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+    def load(
+        self, identifier: Union[str, "ModelIdentifierField"], submodel_type: Optional[SubModelType] = None
+    ) -> LoadedModel:
         """Loads a model.
 
         Args:
@@ -361,7 +363,7 @@ class ModelsInterface(InvocationContextInterface):
 
         return self._services.model_manager.load.load_model(configs[0], submodel_type, self._data)
 
-    def get_config(self, identifier: Union[str, "ModelField"]) -> AnyModelConfig:
+    def get_config(self, identifier: Union[str, "ModelIdentifierField"]) -> AnyModelConfig:
         """Gets a model's config.
 
         Args:

invokeai/invocation_api/__init__.py

@@ -36,7 +36,7 @@ from invokeai.app.invocations.model import (
     CLIPField,
     CLIPOutput,
     LoRALoaderOutput,
-    ModelField,
+    ModelIdentifierField,
     ModelLoaderOutput,
     SDXLLoRALoaderOutput,
     UNetField,
@@ -114,7 +114,7 @@ __all__ = [
     "MetadataItemOutput",
     "MetadataOutput",
     # invokeai.app.invocations.model
-    "ModelField",
+    "ModelIdentifierField",
     "UNetField",
     "CLIPField",
     "VAEField",