feat(ui): connection validation rework

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,7 +24,6 @@
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
-    Input,
     InputField,
     OutputField,
     UIType,
@@ -80,13 +79,13 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
     control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, input=Input.Direct, ui_type=UIType.ControlNetModel
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
     )
     control_weight: Union[float, List[float]] = InputField(
         default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"

invokeai/app/invocations/ip_adapter.py

@@ -5,7 +5,7 @@
 from typing_extensions import Self
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, TensorField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, TensorField, UIType
 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
@@ -58,7 +58,7 @@ class IPAdapterOutput(BaseInvocationOutput):
 CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -67,7 +67,6 @@ class IPAdapterInvocation(BaseInvocation):
     ip_adapter_model: ModelIdentifierField = InputField(
         description="The IP-Adapter model.",
         title="IP-Adapter Model",
-        input=Input.Direct,
         ui_order=-1,
         ui_type=UIType.IPAdapterModel,
     )

invokeai/app/invocations/model.py

@@ -11,6 +11,7 @@
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Classification,
     invocation,
     invocation_output,
 )
@@ -93,19 +94,46 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
     pass
 
 
+@invocation_output("model_identifier_output")
+class ModelIdentifierOutput(BaseInvocationOutput):
+    """Model identifier output"""
+
+    model: ModelIdentifierField = OutputField(description="Model identifier", title="Model")
+
+
+@invocation(
+    "model_identifier",
+    title="Model identifier",
+    tags=["model"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class ModelIdentifierInvocation(BaseInvocation):
+    """Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as
+    input for any model, even if the model types don't match. If you connect this to a mismatched input, you'll get an
+    error."""
+
+    model: ModelIdentifierField = InputField(description="The model to select", title="Model")
+
+    def invoke(self, context: InvocationContext) -> ModelIdentifierOutput:
+        if not context.models.exists(self.model.key):
+            raise Exception(f"Unknown model {self.model.key}")
+
+        return ModelIdentifierOutput(model=self.model)
+
+
 @invocation(
     "main_model_loader",
     title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.2",
+    version="1.0.3",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.main_model, input=Input.Direct, ui_type=UIType.MainModel
-    )
+    model: ModelIdentifierField = InputField(description=FieldDescriptions.main_model, ui_type=UIType.MainModel)
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
@@ -134,12 +162,12 @@ class LoRALoaderOutput(BaseInvocationOutput):
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.2")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.3")
 class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -197,12 +225,12 @@ class LoRASelectorOutput(BaseInvocationOutput):
     lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
 
 
-@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.0")
+@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
 class LoRASelectorInvocation(BaseInvocation):
     """Selects a LoRA model and weight."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
 
@@ -273,13 +301,13 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.2",
+    version="1.0.3",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -414,12 +442,12 @@ def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
         return output
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
     vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, input=Input.Direct, title="VAE", ui_type=UIType.VAEModel
+        description=FieldDescriptions.vae_model, title="VAE", ui_type=UIType.VAEModel
     )
 
     def invoke(self, context: InvocationContext) -> VAEOutput:

invokeai/app/invocations/sdxl.py

@@ -1,4 +1,4 @@
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import SubModelType
 
@@ -30,12 +30,12 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.2")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
     model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
+        description=FieldDescriptions.sdxl_main_model, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
 
@@ -67,13 +67,13 @@ def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
     title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.2",
+    version="1.0.3",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
     model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
+        description=FieldDescriptions.sdxl_refiner_model, ui_type=UIType.SDXLRefinerModel
     )
     # TODO: precision?
 

invokeai/app/invocations/t2i_adapter.py

@@ -8,7 +8,7 @@
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
 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
@@ -45,7 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.2"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -55,7 +55,6 @@ class T2IAdapterInvocation(BaseInvocation):
     t2i_adapter_model: ModelIdentifierField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
-        input=Input.Direct,
         ui_order=-1,
         ui_type=UIType.T2IAdapterModel,
     )

invokeai/frontend/web/public/locales/en.json

@@ -775,6 +775,9 @@
         "cannotConnectToSelf": "Cannot connect to self",
         "cannotDuplicateConnection": "Cannot create duplicate connections",
         "cannotMixAndMatchCollectionItemTypes": "Cannot mix and match collection item types",
+        "missingNode": "Missing invocation node",
+        "missingInvocationTemplate": "Missing invocation template",
+        "missingFieldTemplate": "Missing field template",
         "nodePack": "Node pack",
         "collection": "Collection",
         "collectionFieldType": "{{name}} Collection",

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketGeneratorProgress.ts

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketGeneratorProgress } from 'services/events/actions';
 
@@ -18,6 +18,7 @@ export const addGeneratorProgressEventListener = (startAppListening: AppStartLis
         nes.status = zNodeStatus.enum.IN_PROGRESS;
         nes.progress = (step + 1) / total_steps;
         nes.progressImage = progress_image ?? null;
+        upsertExecutionState(nes.nodeId, nes);
       }
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested.ts

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { updateAllNodesRequested } from 'features/nodes/store/actions';
-import { $templates, nodeReplaced } from 'features/nodes/store/nodesSlice';
+import { $templates, nodesChanged } from 'features/nodes/store/nodesSlice';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
@@ -31,7 +31,12 @@ export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartLi
         }
         try {
           const updatedNode = updateNode(node, template);
-          dispatch(nodeReplaced({ nodeId: updatedNode.id, node: updatedNode }));
+          dispatch(
+            nodesChanged([
+              { type: 'remove', id: updatedNode.id },
+              { type: 'add', item: updatedNode },
+            ])
+          );
         } catch (e) {
           if (e instanceof NodeUpdateError) {
             unableToUpdateCount++;