fix(nodes,ui): optional metadata

- Make all metadata items optional. This will reduce errors related to metadata not being provided when we update the backend but old queue items still exist
- Fix a bug in t2i adapter metadata handling where it checked for ip adapter metadata instaed of t2i adapter metadata
- Fix some metadata fields that were not using `InputField`

invokeai/app/invocations/metadata.py

@@ -44,28 +44,31 @@ class CoreMetadata(BaseModelExcludeNull):
     """Core generation metadata for an image generated in InvokeAI."""
 
     app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
-    generation_mode: str = Field(
+    generation_mode: Optional[str] = Field(
+        default=None,
         description="The generation mode that output this image",
     )
     created_by: Optional[str] = Field(description="The name of the creator of the image")
-    positive_prompt: str = Field(description="The positive prompt parameter")
-    negative_prompt: str = Field(description="The negative prompt parameter")
-    width: int = Field(description="The width parameter")
-    height: int = Field(description="The height parameter")
-    seed: int = Field(description="The seed used for noise generation")
-    rand_device: str = Field(description="The device used for random number generation")
-    cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
-    steps: int = Field(description="The number of steps used for inference")
-    scheduler: str = Field(description="The scheduler used for inference")
+    positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
+    negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
+    width: Optional[int] = Field(default=None, description="The width parameter")
+    height: Optional[int] = Field(default=None, description="The height parameter")
+    seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
+    rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
+    cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
+    steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
+    scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
     clip_skip: Optional[int] = Field(
         default=None,
         description="The number of skipped CLIP layers",
     )
-    model: MainModelField = Field(description="The main model used for inference")
-    controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
-    ipAdapters: list[IPAdapterMetadataField] = Field(description="The IP Adapters used for inference")
-    t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference")
-    loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
+    model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
+    controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
+    ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
+        default=None, description="The IP Adapters used for inference"
+    )
+    t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
+    loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
     vae: Optional[VAEModelField] = Field(
         default=None,
         description="The VAE used for decoding, if the main model's default was not used",
@@ -122,27 +125,34 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
 class MetadataAccumulatorInvocation(BaseInvocation):
     """Outputs a Core Metadata Object"""
 
-    generation_mode: str = InputField(
+    generation_mode: Optional[str] = InputField(
+        default=None,
         description="The generation mode that output this image",
     )
-    positive_prompt: str = InputField(description="The positive prompt parameter")
-    negative_prompt: str = InputField(description="The negative prompt parameter")
-    width: int = InputField(description="The width parameter")
-    height: int = InputField(description="The height parameter")
-    seed: int = InputField(description="The seed used for noise generation")
-    rand_device: str = InputField(description="The device used for random number generation")
-    cfg_scale: float = InputField(description="The classifier-free guidance scale parameter")
-    steps: int = InputField(description="The number of steps used for inference")
-    scheduler: str = InputField(description="The scheduler used for inference")
-    clip_skip: Optional[int] = Field(
+    positive_prompt: Optional[str] = InputField(default=None, description="The positive prompt parameter")
+    negative_prompt: Optional[str] = InputField(default=None, description="The negative prompt parameter")
+    width: Optional[int] = InputField(default=None, description="The width parameter")
+    height: Optional[int] = InputField(default=None, description="The height parameter")
+    seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
+    rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
+    cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
+    steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
+    scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
+    clip_skip: Optional[int] = InputField(
         default=None,
         description="The number of skipped CLIP layers",
     )
-    model: MainModelField = InputField(description="The main model used for inference")
-    controlnets: list[ControlField] = InputField(description="The ControlNets used for inference")
-    ipAdapters: list[IPAdapterMetadataField] = InputField(description="The IP Adapters used for inference")
-    t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference")
-    loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference")
+    model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
+    controlnets: Optional[list[ControlField]] = InputField(
+        default=None, description="The ControlNets used for inference"
+    )
+    ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
+        default=None, description="The IP Adapters used for inference"
+    )
+    t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
+        default=None, description="The IP Adapters used for inference"
+    )
+    loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
     strength: Optional[float] = InputField(
         default=None,
         description="The strength used for latents-to-latents",
@@ -158,9 +168,11 @@ class MetadataAccumulatorInvocation(BaseInvocation):
 
     # High resolution fix metadata.
     hrf_width: Optional[int] = InputField(
+        default=None,
         description="The high resolution fix height and width multipler.",
     )
     hrf_height: Optional[int] = InputField(
+        default=None,
         description="The high resolution fix height and width multipler.",
     )
     hrf_strength: Optional[float] = InputField(

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts

@@ -86,7 +86,7 @@ export const addT2IAdaptersToLinearGraph = (
 
       graph.nodes[t2iAdapterNode.id] = t2iAdapterNode as T2IAdapterInvocation;
 
-      if (metadataAccumulator?.ipAdapters) {
+      if (metadataAccumulator?.t2iAdapters) {
         // metadata accumulator only needs a control field - not the whole node
         // extract what we need and add to the accumulator
         const t2iAdapterField = omit(t2iAdapterNode, [