feat(nodes,ui): use new metadata nodes for all metadata

- Invocations that want metadata or workflows now must inherit from `WithMetadata` and `WithWorkflow` models (in addition to `BaseInvocation`). These provide the appropriate `workflow` and `metadata` fields to the invocation. The alternative API is to use the `@invocation` decorator, but this doesn't give type hints in invocation definitions and is also much harder to understand. I tried and scrapped it.
- Add `MergeMetadataDictInvocation`, which takes a collection of `MetadataDict` and merges them together. This adds additional flexibility to the construction of metadata. Without this, the linear UI would need a separate MetadataItemInvocation for *every* piece of metadata - with this merge node, far fewer nodes are needed to construct metadata.
- Update all invocations to use this new setup
- Update UI with new field types and logic to handle this new way of specifying workflows and metadata should be used

invokeai/app/api/routers/images.py

@@ -45,13 +45,17 @@ async def upload_image(
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
-    contents = await file.read()
+    metadata: Optional[str] = None
+    workflow: Optional[str] = None
 
+    contents = await file.read()
     try:
         pil_image = Image.open(io.BytesIO(contents))
         if crop_visible:
             bbox = pil_image.getbbox()
             pil_image = pil_image.crop(bbox)
+        metadata = pil_image.info.get("invokeai_metadata", None)
+        workflow = pil_image.info.get("invokeai_workflow", None)
     except Exception:
         # Error opening the image
         raise HTTPException(status_code=415, detail="Failed to read image")
@@ -63,6 +67,8 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
+            metadata=metadata,
+            workflow=workflow,
             is_intermediate=is_intermediate,
         )
 

invokeai/app/invocations/baseinvocation.py

@@ -71,7 +71,12 @@ class FieldDescriptions:
     denoised_latents = "Denoised latents tensor"
     latents = "Latents tensor"
     strength = "Strength of denoising (proportional to steps)"
-    core_metadata = "Optional core metadata to be written to image"
+    metadata = "Optional metadata to be saved with the image"
+    metadata_dict_collection = "Collection of MetadataDicts"
+    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
+    metadata_item_label = "Label for this metadata item"
+    metadata_item_value = "The value for this metadata item (may be any type)"
+    workflow = "Optional workflow to be saved with the image"
     interp_mode = "Interpolation mode"
     torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
     fp32 = "Whether or not to use full float32 precision"
@@ -88,6 +93,9 @@ class FieldDescriptions:
     num_1 = "The first number"
     num_2 = "The second number"
     mask = "The mask to use for the operation"
+    board = "The board to save the image to"
+    image = "The image to process"
+    tile_size = "Tile size"
 
 
 class Input(str, Enum):
@@ -172,7 +180,12 @@ class UIType(str, Enum):
     Scheduler = "Scheduler"
     WorkflowField = "WorkflowField"
     IsIntermediate = "IsIntermediate"
-    MetadataField = "MetadataField"
+    BoardField = "BoardField"
+    Any = "Any"
+    MetadataItem = "MetadataItem"
+    MetadataItemCollection = "MetadataItemCollection"
+    MetadataItemPolymorphic = "MetadataItemPolymorphic"
+    MetadataDict = "MetadataDict"
     # endregion
 
 
@@ -618,23 +631,8 @@ class BaseInvocation(ABC, BaseModel):
     is_intermediate: bool = InputField(
         default=False, description="Whether or not this is an intermediate invocation.", ui_type=UIType.IsIntermediate
     )
-    workflow: Optional[str] = InputField(
-        default=None,
-        description="The workflow to save with the image",
-        ui_type=UIType.WorkflowField,
-    )
     use_cache: bool = InputField(default=True, description="Whether or not to use the cache")
 
-    @validator("workflow", pre=True)
-    def validate_workflow_is_json(cls, v):
-        if v is None:
-            return None
-        try:
-            json.loads(v)
-        except json.decoder.JSONDecodeError:
-            raise ValueError("Workflow must be valid JSON")
-        return v
-
     UIConfig: ClassVar[Type[UIConfigBase]]
 
 
@@ -656,6 +654,8 @@ def invocation(
     :param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
     :param Optional[list[str]] tags: Adds tags to the invocation. Invocations may be searched for by their tags. Defaults to None.
     :param Optional[str] category: Adds a category to the invocation. Used to group the invocations in the UI. Defaults to None.
+    :param Optional[str] version: Adds a version to the invocation. Must be a valid semver string. Defaults to None.
+    :param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
     """
 
     def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]:
@@ -737,3 +737,19 @@ def invocation_output(
         return cls
 
     return wrapper
+
+
+class WithWorkflow(BaseModel):
+    workflow: Optional[str] = InputField(
+        default=None, description=FieldDescriptions.workflow, ui_type=UIType.WorkflowField
+    )
+
+    @validator("workflow", pre=True)
+    def validate_workflow_is_json(cls, v):
+        if v is None:
+            return None
+        try:
+            json.loads(v)
+        except json.decoder.JSONDecodeError:
+            raise ValueError("Workflow must be valid JSON")
+        return v

invokeai/app/invocations/controlnet_image_processors.py

@@ -25,6 +25,7 @@ from controlnet_aux import (
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
 from pydantic import BaseModel, Field, validator
+from invokeai.app.invocations.metadata import WithMetadata
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
@@ -38,6 +39,7 @@ from .baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -127,7 +129,7 @@ class ControlNetInvocation(BaseInvocation):
 @invocation(
     "image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
 )
-class ImageProcessorInvocation(BaseInvocation):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
             session_id=context.graph_execution_state_id,
             node_id=self.id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -559,3 +562,33 @@ class SamDetectorReproducibleColors(SamDetector):
             img[:, :] = ann_color
             final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
         return np.array(final_img, dtype=np.uint8)
+
+
+@invocation(
+    "color_map_image_processor",
+    title="Color Map Processor",
+    tags=["controlnet"],
+    category="controlnet",
+    version="1.0.0",
+)
+class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates a color map from the provided image"""
+
+    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
+
+    def run_processor(self, image: Image.Image):
+        image = image.convert("RGB")
+        image = np.array(image, dtype=np.uint8)
+        height, width = image.shape[:2]
+
+        width_tile_size = min(self.color_map_tile_size, width)
+        height_tile_size = min(self.color_map_tile_size, height)
+
+        color_map = cv2.resize(
+            image,
+            (width // width_tile_size, height // height_tile_size),
+            interpolation=cv2.INTER_CUBIC,
+        )
+        color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
+        color_map = Image.fromarray(color_map)
+        return color_map

invokeai/app/invocations/image.py

@@ -7,13 +7,21 @@ import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from invokeai.app.invocations.metadata import CoreMetadata
-from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
+from invokeai.app.invocations.metadata import WithMetadata
+from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
 from ..models.image import ImageCategory, ResourceOrigin
-from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
+from .baseinvocation import (
+    BaseInvocation,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    WithWorkflow,
+    invocation,
+)
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -37,7 +45,7 @@ class ShowImageInvocation(BaseInvocation):
 
 
 @invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
-class BlankImageInvocation(BaseInvocation):
+class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Creates a blank image and forwards it to the pipeline"""
 
     width: int = InputField(default=512, description="The width of the image")
@@ -55,6 +63,7 @@ class BlankImageInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -66,7 +75,7 @@ class BlankImageInvocation(BaseInvocation):
 
 
 @invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
-class ImageCropInvocation(BaseInvocation):
+class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Crops an image to a specified box. The box can be outside of the image."""
 
     image: ImageField = InputField(description="The image to crop")
@@ -88,6 +97,7 @@ class ImageCropInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -99,7 +109,7 @@ class ImageCropInvocation(BaseInvocation):
 
 
 @invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
-class ImagePasteInvocation(BaseInvocation):
+class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Pastes an image into another image."""
 
     base_image: ImageField = InputField(description="The base image")
@@ -141,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -152,7 +163,7 @@ class ImagePasteInvocation(BaseInvocation):
 
 
 @invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
-class MaskFromAlphaInvocation(BaseInvocation):
+class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Extracts the alpha channel of an image as a mask."""
 
     image: ImageField = InputField(description="The image to create the mask from")
@@ -172,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -183,7 +195,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
 
 
 @invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
-class ImageMultiplyInvocation(BaseInvocation):
+class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
     image1: ImageField = InputField(description="The first image to multiply")
@@ -202,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -216,7 +229,7 @@ IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
 @invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
-class ImageChannelInvocation(BaseInvocation):
+class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Gets a channel from an image."""
 
     image: ImageField = InputField(description="The image to get the channel from")
@@ -234,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -248,7 +262,7 @@ IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F
 
 
 @invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
-class ImageConvertInvocation(BaseInvocation):
+class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Converts an image to a different mode."""
 
     image: ImageField = InputField(description="The image to convert")
@@ -266,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -277,7 +292,7 @@ class ImageConvertInvocation(BaseInvocation):
 
 
 @invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
-class ImageBlurInvocation(BaseInvocation):
+class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Blurs an image"""
 
     image: ImageField = InputField(description="The image to blur")
@@ -300,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -331,16 +347,13 @@ PIL_RESAMPLING_MAP = {
 
 
 @invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
-class ImageResizeInvocation(BaseInvocation):
+class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
     width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
     height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -359,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -371,7 +384,7 @@ class ImageResizeInvocation(BaseInvocation):
 
 
 @invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
-class ImageScaleInvocation(BaseInvocation):
+class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Scales an image by a factor"""
 
     image: ImageField = InputField(description="The image to scale")
@@ -401,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -412,7 +426,7 @@ class ImageScaleInvocation(BaseInvocation):
 
 
 @invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
-class ImageLerpInvocation(BaseInvocation):
+class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -434,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -445,7 +460,7 @@ class ImageLerpInvocation(BaseInvocation):
 
 
 @invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
-class ImageInverseLerpInvocation(BaseInvocation):
+class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Inverse linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -467,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -478,13 +494,10 @@ class ImageInverseLerpInvocation(BaseInvocation):
 
 
 @invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
-class ImageNSFWBlurInvocation(BaseInvocation):
+class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add blur to NSFW-flagged images"""
 
     image: ImageField = InputField(description="The image to check")
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -505,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -525,14 +538,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
 @invocation(
     "img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0"
 )
-class ImageWatermarkInvocation(BaseInvocation):
+class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Add an invisible watermark to an image"""
 
     image: ImageField = InputField(description="The image to check")
     text: str = InputField(default="InvokeAI", description="Watermark text")
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -544,7 +554,7 @@ class ImageWatermarkInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -556,7 +566,7 @@ class ImageWatermarkInvocation(BaseInvocation):
 
 
 @invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
-class MaskEdgeInvocation(BaseInvocation):
+class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Applies an edge mask to an image"""
 
     image: ImageField = InputField(description="The image to apply the mask to")
@@ -590,6 +600,7 @@ class MaskEdgeInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -603,7 +614,7 @@ class MaskEdgeInvocation(BaseInvocation):
 @invocation(
     "mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0"
 )
-class MaskCombineInvocation(BaseInvocation):
+class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
 
     mask1: ImageField = InputField(description="The first mask to combine")
@@ -622,6 +633,7 @@ class MaskCombineInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -633,7 +645,7 @@ class MaskCombineInvocation(BaseInvocation):
 
 
 @invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
-class ColorCorrectInvocation(BaseInvocation):
+class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """
     Shifts the colors of a target image to match the reference image, optionally
     using a mask to only color-correct certain regions of the target image.
@@ -732,6 +744,7 @@ class ColorCorrectInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -743,7 +756,7 @@ class ColorCorrectInvocation(BaseInvocation):
 
 
 @invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
-class ImageHueAdjustmentInvocation(BaseInvocation):
+class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Adjusts the Hue of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -771,6 +784,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -846,7 +860,7 @@ CHANNEL_FORMATS = {
     category="image",
     version="1.0.0",
 )
-class ImageChannelOffsetInvocation(BaseInvocation):
+class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Add or subtract a value from a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -880,6 +894,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -916,7 +931,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
     category="image",
     version="1.0.0",
 )
-class ImageChannelMultiplyInvocation(BaseInvocation):
+class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Scale a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -956,6 +971,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             workflow=self.workflow,
+            metadata=self.metadata.data if self.metadata else None,
         )
 
         return ImageOutput(
@@ -972,18 +988,14 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
     use_cache=False,
 )
-class SaveImageInvocation(BaseInvocation):
+class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
 
-    image: ImageField = InputField(description="The image to load")
-    metadata: CoreMetadata = InputField(
-        default=None,
-        description=FieldDescriptions.core_metadata,
-        ui_hidden=True,
-    )
+    image: ImageField = InputField(description=FieldDescriptions.image)
+    board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -992,10 +1004,11 @@ class SaveImageInvocation(BaseInvocation):
             image=image,
             image_origin=ResourceOrigin.INTERNAL,
             image_category=ImageCategory.GENERAL,
+            board_id=self.board.board_id if self.board else None,
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 

invokeai/app/invocations/infill.py

@@ -5,6 +5,7 @@ from typing import Literal, Optional, get_args
 
 import numpy as np
 from PIL import Image, ImageOps
+from invokeai.app.invocations.metadata import WithMetadata
 
 from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
@@ -13,7 +14,7 @@ from invokeai.backend.image_util.lama import LaMA
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
 from ..models.image import ImageCategory, ResourceOrigin
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithWorkflow, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -119,7 +120,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
 
 
 @invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
-class InfillColorInvocation(BaseInvocation):
+class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -143,6 +144,7 @@ class InfillColorInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -154,7 +156,7 @@ class InfillColorInvocation(BaseInvocation):
 
 
 @invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
-class InfillTileInvocation(BaseInvocation):
+class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -179,6 +181,7 @@ class InfillTileInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -192,7 +195,7 @@ class InfillTileInvocation(BaseInvocation):
 @invocation(
     "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation):
+class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -232,6 +235,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -243,7 +247,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
 
 
 @invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
-class LaMaInfillInvocation(BaseInvocation):
+class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -260,6 +264,8 @@ class LaMaInfillInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
+            workflow=self.workflow,
         )
 
         return ImageOutput(
@@ -270,7 +276,7 @@ class LaMaInfillInvocation(BaseInvocation):
 
 
 @invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
-class CV2InfillInvocation(BaseInvocation):
+class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -287,6 +293,8 @@ class CV2InfillInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
+            workflow=self.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/latent.py

@@ -23,7 +23,7 @@ from pydantic import validator
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.invocations.metadata import WithMetadata
 from invokeai.app.invocations.primitives import (
     DenoiseMaskField,
     DenoiseMaskOutput,
@@ -62,6 +62,7 @@ from .baseinvocation import (
     InvocationContext,
     OutputField,
     UIType,
+    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -621,7 +622,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 @invocation(
     "l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0"
 )
-class LatentsToImageInvocation(BaseInvocation):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -634,11 +635,6 @@ class LatentsToImageInvocation(BaseInvocation):
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
     fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
-    metadata: CoreMetadata = InputField(
-        default=None,
-        description=FieldDescriptions.core_metadata,
-        ui_hidden=True,
-    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -707,7 +703,7 @@ class LatentsToImageInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 

invokeai/app/invocations/metadata.py

@@ -1,18 +1,19 @@
-from typing import Optional
+from typing import Any, Optional, Union
 
-from pydantic import Field
+from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.controlnet_image_processors import ControlField
-from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
+from invokeai.app.invocations.model import LoRAModelField
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
 from ...version import __version__
@@ -25,159 +26,78 @@ class LoRAMetadataField(BaseModelExcludeNull):
     weight: float = Field(description="The weight of the LoRA model")
 
 
-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(
-        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")
-    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")
-    loras: list[LoRAMetadataField] = Field(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",
-    )
-
-    # Latents-to-Latents
-    strength: Optional[float] = Field(
-        default=None,
-        description="The strength used for latents-to-latents",
-    )
-    init_image: Optional[str] = Field(default=None, description="The name of the initial image")
-
-    # SDXL
-    positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
-    negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
-
-    # SDXL Refiner
-    refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
-    refiner_cfg_scale: Optional[float] = Field(
-        default=None,
-        description="The classifier-free guidance scale parameter used for the refiner",
-    )
-    refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
-    refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
-    refiner_positive_aesthetic_score: Optional[float] = Field(
-        default=None, description="The aesthetic score used for the refiner"
-    )
-    refiner_negative_aesthetic_score: Optional[float] = Field(
-        default=None, description="The aesthetic score used for the refiner"
-    )
-    refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
-
-
 class ImageMetadata(BaseModelExcludeNull):
     """An image's generation metadata"""
 
-    metadata: Optional[dict] = Field(
-        default=None,
-        description="The image's core metadata, if it was created in the Linear or Canvas UI",
-    )
-    graph: Optional[dict] = Field(default=None, description="The graph that created the image")
+    metadata: Optional[dict] = Field(default=None, description="The metadata associated with the image")
+    workflow: Optional[dict] = Field(default=None, description="The workflow associated with the image")
 
 
-@invocation_output("metadata_accumulator_output")
-class MetadataAccumulatorOutput(BaseInvocationOutput):
-    """The output of the MetadataAccumulator node"""
-
-    metadata: CoreMetadata = OutputField(description="The core metadata for the image")
+class MetadataItem(BaseModel):
+    label: str = Field(description=FieldDescriptions.metadata_item_label)
+    value: Any = Field(description=FieldDescriptions.metadata_item_value)
 
 
-@invocation(
-    "metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
-)
-class MetadataAccumulatorInvocation(BaseInvocation):
-    """Outputs a Core Metadata Object"""
+@invocation_output("metadata_item_output")
+class MetadataItemOutput(BaseInvocationOutput):
+    """Metadata Item Output"""
 
-    generation_mode: str = InputField(
-        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(
-        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")
-    loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference")
-    strength: Optional[float] = InputField(
-        default=None,
-        description="The strength used for latents-to-latents",
-    )
-    init_image: Optional[str] = InputField(
-        default=None,
-        description="The name of the initial image",
-    )
-    vae: Optional[VAEModelField] = InputField(
-        default=None,
-        description="The VAE used for decoding, if the main model's default was not used",
-    )
+    item: MetadataItem = OutputField(description="Metadata Item")
 
-    # SDXL
-    positive_style_prompt: Optional[str] = InputField(
-        default=None,
-        description="The positive style prompt parameter",
-    )
-    negative_style_prompt: Optional[str] = InputField(
-        default=None,
-        description="The negative style prompt parameter",
-    )
 
-    # SDXL Refiner
-    refiner_model: Optional[MainModelField] = InputField(
-        default=None,
-        description="The SDXL Refiner model used",
-    )
-    refiner_cfg_scale: Optional[float] = InputField(
-        default=None,
-        description="The classifier-free guidance scale parameter used for the refiner",
-    )
-    refiner_steps: Optional[int] = InputField(
-        default=None,
-        description="The number of steps used for the refiner",
-    )
-    refiner_scheduler: Optional[str] = InputField(
-        default=None,
-        description="The scheduler used for the refiner",
-    )
-    refiner_positive_aesthetic_score: Optional[float] = InputField(
-        default=None,
-        description="The aesthetic score used for the refiner",
-    )
-    refiner_negative_aesthetic_score: Optional[float] = InputField(
-        default=None,
-        description="The aesthetic score used for the refiner",
-    )
-    refiner_start: Optional[float] = InputField(
-        default=None,
-        description="The start value used for refiner denoising",
-    )
+@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
+class MetadataItemInvocation(BaseInvocation):
+    """Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
 
-    def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
-        """Collects and outputs a CoreMetadata object"""
+    label: str = InputField(description=FieldDescriptions.metadata_item_label)
+    value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
 
-        return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
+    def invoke(self, context: InvocationContext) -> MetadataItemOutput:
+        return MetadataItemOutput(item=MetadataItem(label=self.label, value=self.value))
+
+
+class MetadataDict(BaseModel):
+    """Accepts a single MetadataItem or collection of MetadataItems (use a Collect node)."""
+
+    data: dict[str, Any] = Field(description="Metadata dict")
+
+
+@invocation_output("metadata_dict")
+class MetadataDictOutput(BaseInvocationOutput):
+    metadata_dict: MetadataDict = OutputField(description="Metadata Dict")
+
+
+@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
+class MetadataInvocation(BaseInvocation):
+    """Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
+
+    items: Union[list[MetadataItem], MetadataItem] = InputField(description=FieldDescriptions.metadata_item_polymorphic)
+
+    def invoke(self, context: InvocationContext) -> MetadataDictOutput:
+        if isinstance(self.items, MetadataItem):
+            # single metadata item
+            data = {self.items.label: self.items.value}
+        else:
+            # collection of metadata items
+            data = {item.label: item.value for item in self.items}
+
+        data.update({"app_version": __version__})
+        return MetadataDictOutput(metadata_dict=(MetadataDict(data=data)))
+
+
+@invocation("merge_metadata_dict", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
+class MergeMetadataDictInvocation(BaseInvocation):
+    """Merged a collection of MetadataDict into a single MetadataDict."""
+
+    collection: list[MetadataDict] = InputField(description=FieldDescriptions.metadata_dict_collection)
+
+    def invoke(self, context: InvocationContext) -> MetadataDictOutput:
+        data = {}
+        for item in self.collection:
+            data.update(item.data)
+
+        return MetadataDictOutput(metadata_dict=MetadataDict(data=data))
+
+
+class WithMetadata(BaseModel):
+    metadata: Optional[MetadataDict] = InputField(default=None, description=FieldDescriptions.metadata)

invokeai/app/invocations/onnx.py

@@ -12,7 +12,7 @@ from diffusers.image_processor import VaeImageProcessor
 from pydantic import BaseModel, Field, validator
 from tqdm import tqdm
 
-from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.invocations.metadata import WithMetadata
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend import BaseModelType, ModelType, SubModelType
@@ -28,6 +28,7 @@ from .baseinvocation import (
     Input,
     InputField,
     InvocationContext,
+    WithWorkflow,
     OutputField,
     UIComponent,
     UIType,
@@ -321,7 +322,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     category="image",
     version="1.0.0",
 )
-class ONNXLatentsToImageInvocation(BaseInvocation):
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -332,11 +333,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
-    metadata: Optional[CoreMetadata] = InputField(
-        default=None,
-        description=FieldDescriptions.core_metadata,
-        ui_hidden=True,
-    )
     # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -375,7 +371,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 

invokeai/app/invocations/primitives.py

@@ -226,6 +226,12 @@ class ImageField(BaseModel):
     image_name: str = Field(description="The name of the image")
 
 
+class BoardField(BaseModel):
+    """A board primitive field"""
+
+    board_id: str = Field(description="The id of the board")
+
+
 @invocation_output("image_output")
 class ImageOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
@@ -245,7 +251,9 @@ class ImageCollectionOutput(BaseInvocationOutput):
 
 
 @invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
-class ImageInvocation(BaseInvocation):
+class ImageInvocation(
+    BaseInvocation,
+):
     """An image primitive value"""
 
     image: ImageField = InputField(description="The image to load")

invokeai/app/invocations/upscale.py

@@ -7,11 +7,12 @@ import numpy as np
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from realesrgan import RealESRGANer
+from invokeai.app.invocations.metadata import WithMetadata
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithWorkflow, invocation
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -24,7 +25,7 @@ ESRGAN_MODELS = Literal[
 
 
 @invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
-class ESRGANInvocation(BaseInvocation):
+class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -106,6 +107,7 @@ class ESRGANInvocation(BaseInvocation):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
+            metadata=self.metadata.data if self.metadata else None,
             workflow=self.workflow,
         )
 

invokeai/app/services/graph.py

@@ -421,6 +421,14 @@ class Graph(BaseModel):
 
         return True
 
+    def _is_destination_field_Any(self, edge: Edge) -> bool:
+        """Checks if the destination field for an edge is of type typing.Any"""
+        return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
+
+    def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
+        """Checks if the destination field for an edge is of type typing.Any"""
+        return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
+
     def _validate_edge(self, edge: Edge):
         """Validates that a new edge doesn't create a cycle in the graph"""
 
@@ -473,8 +481,19 @@ class Graph(BaseModel):
                     f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
                 )
 
-        # Validate if collector output type matches input type (if this edge results in both being set)
-        if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
+        # Validate that we are not connecting collector to iterator (currently unsupported)
+        if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
+            raise InvalidEdgeError(
+                f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
+            )
+
+        # Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
+        if (
+            isinstance(from_node, CollectInvocation)
+            and edge.source.field == "collection"
+            and not self._is_destination_field_list_of_Any(edge)
+            and not self._is_destination_field_Any(edge)
+        ):
             if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
                 raise InvalidEdgeError(
                     f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
@@ -707,16 +726,15 @@ class Graph(BaseModel):
         # Get the input root type
         input_root_type = next(t[0] for t in type_degrees if t[1] == 0)  # type: ignore
 
-        # Verify that all outputs are lists
-        # if not all((get_origin(f) == list for f in output_fields)):
-        #     return False
-
         # Verify that all outputs are lists
         if not all(is_list_or_contains_list(f) for f in output_fields):
             return False
 
         # Verify that all outputs match the input type (are a base class or the same class)
-        if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
+        if not all(
+            is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
+            for f in output_fields
+        ):
             return False
 
         return True

invokeai/app/services/image_file_storage.py

@@ -59,7 +59,7 @@ class ImageFileStorageBase(ABC):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[dict] = None,
+        metadata: Optional[Union[str, dict]] = None,
         workflow: Optional[str] = None,
         thumbnail_size: int = 256,
     ) -> None:
@@ -109,7 +109,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[dict] = None,
+        metadata: Optional[Union[str, dict]] = None,
         workflow: Optional[str] = None,
         thumbnail_size: int = 256,
     ) -> None:
@@ -119,20 +119,10 @@ class DiskImageFileStorage(ImageFileStorageBase):
 
             pnginfo = PngImagePlugin.PngInfo()
 
-            if metadata is not None or workflow is not None:
-                if metadata is not None:
-                    pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
-                if workflow is not None:
-                    pnginfo.add_text("invokeai_workflow", workflow)
-            else:
-                # For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back
-                # TODO: retain non-invokeai metadata on save...
-                original_metadata = image.info.get("invokeai_metadata", None)
-                if original_metadata is not None:
-                    pnginfo.add_text("invokeai_metadata", original_metadata)
-                original_workflow = image.info.get("invokeai_workflow", None)
-                if original_workflow is not None:
-                    pnginfo.add_text("invokeai_workflow", original_workflow)
+            if metadata is not None:
+                pnginfo.add_text("invokeai_metadata", json.dumps(metadata) if type(metadata) is dict else metadata)
+            if workflow is not None:
+                pnginfo.add_text("invokeai_workflow", workflow)
 
             image.save(image_path, "PNG", pnginfo=pnginfo)
 

invokeai/app/services/image_record_storage.py

@@ -3,11 +3,12 @@ import sqlite3
 import threading
 from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Generic, Optional, TypeVar, cast
+from typing import Generic, Optional, TypeVar, Union, cast
 
 from pydantic import BaseModel, Field
 from pydantic.generics import GenericModel
 
+from invokeai.app.invocations.metadata import ImageMetadata
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
 
@@ -81,7 +82,7 @@ class ImageRecordStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_metadata(self, image_name: str) -> Optional[dict]:
+    def get_metadata(self, image_name: str) -> ImageMetadata:
         """Gets an image's metadata'."""
         pass
 
@@ -134,7 +135,8 @@ class ImageRecordStorageBase(ABC):
         height: int,
         session_id: Optional[str],
         node_id: Optional[str],
-        metadata: Optional[dict],
+        metadata: Optional[Union[str, dict]],
+        workflow: Optional[str],
         is_intermediate: bool = False,
         starred: bool = False,
     ) -> datetime:
@@ -204,6 +206,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 """
             )
 
+        if "workflow" not in columns:
+            self._cursor.execute(
+                """--sql
+                ALTER TABLE images ADD COLUMN workflow TEXT;
+                """
+            )
+
         # Create the `images` table indices.
         self._cursor.execute(
             """--sql
@@ -269,22 +278,31 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
         return deserialize_image_record(dict(result))
 
-    def get_metadata(self, image_name: str) -> Optional[dict]:
+    def get_metadata(self, image_name: str) -> ImageMetadata:
         try:
             self._lock.acquire()
 
             self._cursor.execute(
                 """--sql
-                SELECT images.metadata FROM images
+                SELECT metadata, workflow FROM images
                 WHERE image_name = ?;
                 """,
                 (image_name,),
             )
 
             result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
-            if not result or not result[0]:
-                return None
-            return json.loads(result[0])
+
+            if not result:
+                return ImageMetadata()
+
+            as_dict = dict(result)
+            metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
+            workflow_raw = cast(Optional[str], as_dict.get("workflow", None))
+
+            return ImageMetadata(
+                metadata=json.loads(metadata_raw) if metadata_raw is not None else None,
+                workflow=json.loads(workflow_raw) if workflow_raw is not None else None,
+            )
         except sqlite3.Error as e:
             self._conn.rollback()
             raise ImageRecordNotFoundException from e
@@ -519,12 +537,15 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         width: int,
         height: int,
         node_id: Optional[str],
-        metadata: Optional[dict],
+        metadata: Optional[Union[str, dict]],
+        workflow: Optional[str],
         is_intermediate: bool = False,
         starred: bool = False,
     ) -> datetime:
         try:
-            metadata_json = None if metadata is None else json.dumps(metadata)
+            metadata_json: Optional[str] = None
+            if metadata is not None:
+                metadata_json = metadata if type(metadata) is str else json.dumps(metadata)
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
@@ -537,10 +558,11 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     node_id,
                     session_id,
                     metadata,
+                    workflow,
                     is_intermediate,
                     starred
                     )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
                 """,
                 (
                     image_name,
@@ -551,6 +573,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     node_id,
                     session_id,
                     metadata_json,
+                    workflow,
                     is_intermediate,
                     starred,
                 ),

invokeai/app/services/images.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from logging import Logger
-from typing import TYPE_CHECKING, Callable, Optional
+from typing import TYPE_CHECKING, Callable, Optional, Union
 
 from PIL.Image import Image as PILImageType
 
@@ -29,7 +29,6 @@ from invokeai.app.services.item_storage import ItemStorageABC
 from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
 from invokeai.app.services.resource_name import NameServiceBase
 from invokeai.app.services.urls import UrlServiceBase
-from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
 
 if TYPE_CHECKING:
     from invokeai.app.services.graph import GraphExecutionState
@@ -71,7 +70,7 @@ class ImageServiceABC(ABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: bool = False,
-        metadata: Optional[dict] = None,
+        metadata: Optional[Union[str, dict]] = None,
         workflow: Optional[str] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
@@ -196,7 +195,7 @@ class ImageService(ImageServiceABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: bool = False,
-        metadata: Optional[dict] = None,
+        metadata: Optional[Union[str, dict]] = None,
         workflow: Optional[str] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
@@ -234,6 +233,7 @@ class ImageService(ImageServiceABC):
                 # Nullable fields
                 node_id=node_id,
                 metadata=metadata,
+                workflow=workflow,
                 session_id=session_id,
             )
             if board_id is not None:
@@ -311,23 +311,7 @@ class ImageService(ImageServiceABC):
 
     def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
         try:
-            image_record = self._services.image_records.get(image_name)
-            metadata = self._services.image_records.get_metadata(image_name)
-
-            if not image_record.session_id:
-                return ImageMetadata(metadata=metadata)
-
-            session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
-            graph = None
-
-            if session_raw:
-                try:
-                    graph = get_metadata_graph_from_raw_session(session_raw)
-                except Exception as e:
-                    self._services.logger.warn(f"Failed to parse session graph: {e}")
-                    graph = None
-
-            return ImageMetadata(graph=graph, metadata=metadata)
+            return self._services.image_records.get_metadata(image_name)
         except ImageRecordNotFoundException:
             self._services.logger.error("Image record not found")
             raise

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -8,97 +8,97 @@ from invokeai.app.services.invoker import Invoker
 
 
 class MemoryInvocationCache(InvocationCacheBase):
-    __cache: dict[Union[int, str], tuple[BaseInvocationOutput, str]]
-    __max_cache_size: int
-    __disabled: bool
-    __hits: int
-    __misses: int
-    __cache_ids: Queue
-    __invoker: Invoker
+    _cache: dict[Union[int, str], tuple[BaseInvocationOutput, str]]
+    _max_cache_size: int
+    _disabled: bool
+    _hits: int
+    _misses: int
+    _cache_ids: Queue
+    _invoker: Invoker
 
     def __init__(self, max_cache_size: int = 0) -> None:
-        self.__cache = dict()
-        self.__max_cache_size = max_cache_size
-        self.__disabled = False
-        self.__hits = 0
-        self.__misses = 0
-        self.__cache_ids = Queue()
+        self._cache = dict()
+        self._max_cache_size = max_cache_size
+        self._disabled = False
+        self._hits = 0
+        self._misses = 0
+        self._cache_ids = Queue()
 
     def start(self, invoker: Invoker) -> None:
-        self.__invoker = invoker
-        if self.__max_cache_size == 0:
+        self._invoker = invoker
+        if self._max_cache_size == 0:
             return
-        self.__invoker.services.images.on_deleted(self._delete_by_match)
-        self.__invoker.services.latents.on_deleted(self._delete_by_match)
+        self._invoker.services.images.on_deleted(self._delete_by_match)
+        self._invoker.services.latents.on_deleted(self._delete_by_match)
 
     def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
-        if self.__max_cache_size == 0 or self.__disabled:
+        if self._max_cache_size == 0 or self._disabled:
             return
 
-        item = self.__cache.get(key, None)
+        item = self._cache.get(key, None)
         if item is not None:
-            self.__hits += 1
+            self._hits += 1
             return item[0]
-        self.__misses += 1
+        self._misses += 1
 
     def save(self, key: Union[int, str], invocation_output: BaseInvocationOutput) -> None:
-        if self.__max_cache_size == 0 or self.__disabled:
+        if self._max_cache_size == 0 or self._disabled:
             return
 
-        if key not in self.__cache:
-            self.__cache[key] = (invocation_output, invocation_output.json())
-            self.__cache_ids.put(key)
-            if self.__cache_ids.qsize() > self.__max_cache_size:
+        if key not in self._cache:
+            self._cache[key] = (invocation_output, invocation_output.json())
+            self._cache_ids.put(key)
+            if self._cache_ids.qsize() > self._max_cache_size:
                 try:
-                    self.__cache.pop(self.__cache_ids.get())
+                    self._cache.pop(self._cache_ids.get())
                 except KeyError:
                     # this means the cache_ids are somehow out of sync w/ the cache
                     pass
 
     def delete(self, key: Union[int, str]) -> None:
-        if self.__max_cache_size == 0 or self.__disabled:
+        if self._max_cache_size == 0:
             return
 
-        if key in self.__cache:
-            del self.__cache[key]
+        if key in self._cache:
+            del self._cache[key]
 
     def clear(self, *args, **kwargs) -> None:
-        if self.__max_cache_size == 0 or self.__disabled:
+        if self._max_cache_size == 0:
             return
 
-        self.__cache.clear()
-        self.__cache_ids = Queue()
-        self.__misses = 0
-        self.__hits = 0
+        self._cache.clear()
+        self._cache_ids = Queue()
+        self._misses = 0
+        self._hits = 0
 
     def create_key(self, invocation: BaseInvocation) -> int:
         return hash(invocation.json(exclude={"id"}))
 
     def disable(self) -> None:
-        if self.__max_cache_size == 0:
+        if self._max_cache_size == 0:
             return
-        self.__disabled = True
+        self._disabled = True
 
     def enable(self) -> None:
-        if self.__max_cache_size == 0:
+        if self._max_cache_size == 0:
             return
-        self.__disabled = False
+        self._disabled = False
 
     def get_status(self) -> InvocationCacheStatus:
         return InvocationCacheStatus(
-            hits=self.__hits,
-            misses=self.__misses,
-            enabled=not self.__disabled and self.__max_cache_size > 0,
-            size=len(self.__cache),
-            max_size=self.__max_cache_size,
+            hits=self._hits,
+            misses=self._misses,
+            enabled=not self._disabled and self._max_cache_size > 0,
+            size=len(self._cache),
+            max_size=self._max_cache_size,
         )
 
     def _delete_by_match(self, to_match: str) -> None:
-        if self.__max_cache_size == 0 or self.__disabled:
+        if self._max_cache_size == 0:
             return
 
         keys_to_delete = set()
-        for key, value_tuple in self.__cache.items():
+        for key, value_tuple in self._cache.items():
             if to_match in value_tuple[1]:
                 keys_to_delete.add(key)
 
@@ -108,4 +108,4 @@ class MemoryInvocationCache(InvocationCacheBase):
         for key in keys_to_delete:
             self.delete(key)
 
-        self.__invoker.services.logger.debug(f"Deleted {len(keys_to_delete)} cached invocation outputs for {to_match}")
+        self._invoker.services.logger.debug(f"Deleted {len(keys_to_delete)} cached invocation outputs for {to_match}")

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

@@ -81,6 +81,7 @@
         "load": "Load",
         "loading": "Loading",
         "loadingInvokeAI": "Loading Invoke AI",
+        "learnMore": "Learn More",
         "modelManager": "Model Manager",
         "nodeEditor": "Node Editor",
         "nodes": "Workflow Editor",
@@ -135,6 +136,8 @@
         "bgth": "bg_th",
         "canny": "Canny",
         "cannyDescription": "Canny edge detection",
+        "colorMap": "Color",
+        "colorMapDescription": "Generates a color map from the image",
         "coarse": "Coarse",
         "contentShuffle": "Content Shuffle",
         "contentShuffleDescription": "Shuffles the content in an image",
@@ -158,6 +161,7 @@
         "hideAdvanced": "Hide Advanced",
         "highThreshold": "High Threshold",
         "imageResolution": "Image Resolution",
+        "colorMapTileSize": "Tile Size",
         "importImageFromCanvas": "Import Image From Canvas",
         "importMaskFromCanvas": "Import Mask From Canvas",
         "incompatibleBaseModel": "Incompatible base model:",
@@ -701,6 +705,8 @@
         "addNodeToolTip": "Add Node (Shift+A, Space)",
         "animatedEdges": "Animated Edges",
         "animatedEdgesHelp": "Animate selected edges and edges connected to selected nodes",
+        "boardField": "Board",
+        "boardFieldDescription": "A gallery board",
         "boolean": "Booleans",
         "booleanCollection": "Boolean Collection",
         "booleanCollectionDescription": "A collection of booleans.",
@@ -888,7 +894,7 @@
         "zoomOutNodes": "Zoom Out"
     },
     "parameters": {
-        "aspectRatio": "Ratio",
+        "aspectRatio": "Aspect Ratio",
         "boundingBoxHeader": "Bounding Box",
         "boundingBoxHeight": "Bounding Box Height",
         "boundingBoxWidth": "Bounding Box Width",
@@ -1020,8 +1026,8 @@
             "label": "Seed Behaviour",
             "perIterationLabel": "Seed per Iteration",
             "perIterationDesc": "Use a different seed for each iteration",
-            "perPromptLabel": "Seed per Prompt",
-            "perPromptDesc": "Use a different seed for each prompt"
+            "perPromptLabel": "Seed per Image",
+            "perPromptDesc": "Use a different seed for each image"
         }
     },
     "sdxl": {
@@ -1173,131 +1179,205 @@
     "popovers": {
         "clipSkip": {
             "heading": "CLIP Skip",
-            "paragraph": "Choose how many layers of the CLIP model to skip. Certain models are better suited to be used with CLIP Skip."
-        },
-        "compositingBlur": {
-            "heading": "Blur",
-            "paragraph": "The blur radius of the mask."
-        },
-        "compositingBlurMethod": {
-            "heading": "Blur Method",
-            "paragraph": "The method of blur applied to the masked area."
-        },
-        "compositingCoherencePass": {
-            "heading": "Coherence Pass",
-            "paragraph": "Composite the Inpainted/Outpainted images."
-        },
-        "compositingCoherenceMode": {
-            "heading": "Mode",
-            "paragraph": "The mode of the Coherence Pass."
-        },
-        "compositingCoherenceSteps": {
-            "heading": "Steps",
-            "paragraph": "Number of steps in the Coherence Pass. Similar to Denoising Steps."
-        },
-        "compositingStrength": {
-            "heading": "Strength",
-            "paragraph": "Amount of noise added for the Coherence Pass. Similar to Denoising Strength."
-        },
-        "compositingMaskAdjustments": {
-            "heading": "Mask Adjustments",
-            "paragraph": "Adjust the mask."
-        },
-        "controlNetBeginEnd": {
-            "heading": "Begin / End Step Percentage",
-            "paragraph": "Which parts of the denoising process will have the ControlNet applied. ControlNets applied at the start of the process guide composition, and ControlNets applied at the end guide details."
-        },
-        "controlNetControlMode": {
-            "heading": "Control Mode",
-            "paragraph": "Lends more weight to either the prompt or ControlNet."
-        },
-        "controlNetResizeMode": {
-            "heading": "Resize Mode",
-            "paragraph": "How the ControlNet image will be fit to the image generation Ratio"
-        },
-        "controlNetToggle": {
-            "heading": "Enable ControlNet",
-            "paragraph": "ControlNets provide guidance to the generation process, helping create images with controlled composition, structure, or style, depending on the model selected."
-        },
-        "controlNetWeight": {
-            "heading": "Weight",
-            "paragraph": "How strongly the ControlNet will impact the generated image."
-        },
-        "dynamicPromptsToggle": {
-            "heading": "Enable Dynamic Prompts",
-            "paragraph": "Dynamic prompts allow multiple options within a prompt. Dynamic prompts can be used by: {option1|option2|option3}. Combinations of prompts will be randomly generated until the “Images” number has been reached."
-        },
-        "dynamicPromptsCombinatorial": {
-            "heading": "Combinatorial Generation",
-            "paragraph": "Generate an image for every possible combination of Dynamic Prompts until the Max Prompts is reached."
-        },
-        "infillMethod": {
-            "heading": "Infill Method",
-            "paragraph": "Method to infill the selected area."
-        },
-        "lora": {
-            "heading": "LoRA Weight",
-            "paragraph": "Weight of the LoRA. Higher weight will lead to larger impacts on the final image."
-        },
-        "noiseEnable": {
-            "heading": "Enable Noise Settings",
-            "paragraph": "Advanced control over noise generation."
-        },
-        "noiseUseCPU": {
-            "heading": "Use CPU Noise",
-            "paragraph": "Uses the CPU to generate random noise."
-        },
-        "paramCFGScale": {
-            "heading": "CFG Scale",
-            "paragraph": "Controls how much your prompt influences the generation process."
-        },
-        "paramDenoisingStrength": {
-            "heading": "Denoising Strength",
-            "paragraph": "How much noise is added to the input image. 0 will result in an identical image, while 1 will result in a completely new image."
-        },
-        "paramIterations": {
-            "heading": "Iterations",
-            "paragraph": "The number of images to generate. If Dynamic Prompts is enabled, each of the prompts will be generated this many times."
-        },
-        "paramModel": {
-            "heading": "Model",
-            "paragraph": "Model used for the denoising steps. Different models are trained to specialize in producing different aesthetic results and content."
+            "paragraphs": [
+                "Choose how many layers of the CLIP model to skip.",
+                "Some models work better with certain CLIP Skip settings.",
+                "A higher value typically results in a less detailed image."
+            ]
         },
         "paramNegativeConditioning": {
             "heading": "Negative Prompt",
-            "paragraph": "The generation process avoids the concepts in the negative prompt. Use this to exclude qualities or objects from the output. Supports Compel syntax and embeddings."
+            "paragraphs": [
+                "The generation process avoids the concepts in the negative prompt. Use this to exclude qualities or objects from the output.",
+                "Supports Compel syntax and embeddings."
+            ]
         },
         "paramPositiveConditioning": {
             "heading": "Positive Prompt",
-            "paragraph": "Guides the generation process. You may use any words or phrases. Supports Compel and Dynamic Prompts syntaxes and embeddings."
-        },
-        "paramRatio": {
-            "heading": "Ratio",
-            "paragraph": "The ratio of the dimensions of the image generated. An image size (in number of pixels) equivalent to 512x512 is recommended for SD1.5 models and a size equivalent to 1024x1024 is recommended for SDXL models."
+            "paragraphs": [
+                "Guides the generation process. You may use any words or phrases.",
+                "Compel and Dynamic Prompts syntaxes and embeddings."
+            ]
         },
         "paramScheduler": {
             "heading": "Scheduler",
-            "paragraph": "Scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output."
+            "paragraphs": [
+                "Scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output."
+            ]
+        },
+        "compositingBlur": {
+            "heading": "Blur",
+            "paragraphs": ["The blur radius of the mask."]
+        },
+        "compositingBlurMethod": {
+            "heading": "Blur Method",
+            "paragraphs": ["The method of blur applied to the masked area."]
+        },
+        "compositingCoherencePass": {
+            "heading": "Coherence Pass",
+            "paragraphs": [
+                "A second round of denoising helps to composite the Inpainted/Outpainted image."
+            ]
+        },
+        "compositingCoherenceMode": {
+            "heading": "Mode",
+            "paragraphs": ["The mode of the Coherence Pass."]
+        },
+        "compositingCoherenceSteps": {
+            "heading": "Steps",
+            "paragraphs": [
+                "Number of denoising steps used in the Coherence Pass.",
+                "Same as the main Steps parameter."
+            ]
+        },
+        "compositingStrength": {
+            "heading": "Strength",
+            "paragraphs": [
+                "Denoising strength for the Coherence Pass.",
+                "Same as the Image to Image Denoising Strength parameter."
+            ]
+        },
+        "compositingMaskAdjustments": {
+            "heading": "Mask Adjustments",
+            "paragraphs": ["Adjust the mask."]
+        },
+        "controlNetBeginEnd": {
+            "heading": "Begin / End Step Percentage",
+            "paragraphs": [
+                "Which steps of the denoising process will have the ControlNet applied.",
+                "ControlNets applied at the beginning of the process guide composition, and ControlNets applied at the end guide details."
+            ]
+        },
+        "controlNetControlMode": {
+            "heading": "Control Mode",
+            "paragraphs": [
+                "Lends more weight to either the prompt or ControlNet."
+            ]
+        },
+        "controlNetResizeMode": {
+            "heading": "Resize Mode",
+            "paragraphs": [
+                "How the ControlNet image will be fit to the image output size."
+            ]
+        },
+        "controlNet": {
+            "heading": "ControlNet",
+            "paragraphs": [
+                "ControlNets provide guidance to the generation process, helping create images with controlled composition, structure, or style, depending on the model selected."
+            ]
+        },
+        "controlNetWeight": {
+            "heading": "Weight",
+            "paragraphs": [
+                "How strongly the ControlNet will impact the generated image."
+            ]
+        },
+        "dynamicPrompts": {
+            "heading": "Dynamic Prompts",
+            "paragraphs": [
+                "Dynamic Prompts parses a single prompt into many.",
+                "The basic syntax is \"a {red|green|blue} ball\". This will produce three prompts: \"a red ball\", \"a green ball\" and \"a blue ball\".",
+                "You can use the syntax as many times as you like in a single prompt, but be sure to keep the number of prompts generated in check with the Max Prompts setting."
+            ]
+        },
+        "dynamicPromptsMaxPrompts": {
+            "heading": "Max Prompts",
+            "paragraphs": [
+                "Limits the number of prompts that can be generated by Dynamic Prompts."
+            ]
+        },
+        "dynamicPromptsSeedBehaviour": {
+            "heading": "Seed Behaviour",
+            "paragraphs": [
+                "Controls how the seed is used when generating prompts.",
+                "Per Iteration will use a unique seed for each iteration. Use this to explore prompt variations on a single seed.",
+                "For example, if you have 5 prompts, each image will use the same seed.",
+                "Per Image will use a unique seed for each image. This provides more variation."
+            ]
+        },
+        "infillMethod": {
+            "heading": "Infill Method",
+            "paragraphs": ["Method to infill the selected area."]
+        },
+        "lora": {
+            "heading": "LoRA Weight",
+            "paragraphs": [
+                "Higher LoRA weight will lead to larger impacts on the final image."
+            ]
+        },
+        "noiseUseCPU": {
+            "heading": "Use CPU Noise",
+            "paragraphs": [
+                "Controls whether noise is generated on the CPU or GPU.",
+                "With CPU Noise enabled, a particular seed will produce the same image on any machine.",
+                "There is no performance impact to enabling CPU Noise."
+            ]
+        },
+        "paramCFGScale": {
+            "heading": "CFG Scale",
+            "paragraphs": [
+                "Controls how much your prompt influences the generation process."
+            ]
+        },
+        "paramDenoisingStrength": {
+            "heading": "Denoising Strength",
+            "paragraphs": [
+                "How much noise is added to the input image.",
+                "0 will result in an identical image, while 1 will result in a completely new image."
+            ]
+        },
+        "paramIterations": {
+            "heading": "Iterations",
+            "paragraphs": [
+                "The number of images to generate.",
+                "If Dynamic Prompts is enabled, each of the prompts will be generated this many times."
+            ]
+        },
+        "paramModel": {
+            "heading": "Model",
+            "paragraphs": [
+                "Model used for the denoising steps.",
+                "Different models are typically trained to specialize in producing particular aesthetic results and content."
+            ]
+        },
+        "paramRatio": {
+            "heading": "Aspect Ratio",
+            "paragraphs": [
+                "The aspect ratio of the dimensions of the image generated.",
+                "An image size (in number of pixels) equivalent to 512x512 is recommended for SD1.5 models and a size equivalent to 1024x1024 is recommended for SDXL models."
+            ]
         },
         "paramSeed": {
             "heading": "Seed",
-            "paragraph": "Controls the starting noise used for generation. Disable “Random Seed” to produce identical results with the same generation settings."
+            "paragraphs": [
+                "Controls the starting noise used for generation.",
+                "Disable “Random Seed” to produce identical results with the same generation settings."
+            ]
         },
         "paramSteps": {
             "heading": "Steps",
-            "paragraph": "Number of steps that will be performed in each generation. Higher step counts will typically create better images but will require more generation time."
+            "paragraphs": [
+                "Number of steps that will be performed in each generation.",
+                "Higher step counts will typically create better images but will require more generation time."
+            ]
         },
         "paramVAE": {
             "heading": "VAE",
-            "paragraph": "Model used for translating AI output into the final image."
+            "paragraphs": [
+                "Model used for translating AI output into the final image."
+            ]
         },
         "paramVAEPrecision": {
             "heading": "VAE Precision",
-            "paragraph": "The precision used during VAE encoding and decoding. Fp16/Half precision is more efficient, at the expense of minor image variations."
+            "paragraphs": [
+                "The precision used during VAE encoding and decoding. FP16/half precision is more efficient, at the expense of minor image variations."
+            ]
         },
         "scaleBeforeProcessing": {
             "heading": "Scale Before Processing",
-            "paragraph": "Scales the selected area to the size best suited for the model before the image generation process."
+            "paragraphs": [
+                "Scales the selected area to the size best suited for the model before the image generation process."
+            ]
         }
     },
     "ui": {

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

@@ -17,7 +17,8 @@ import {
 } from 'services/events/actions';
 import { startAppListening } from '../..';
 
-const nodeDenylist = ['load_image'];
+// These nodes output an image, but do not actually *save* an image, so we don't want to handle the gallery logic on them
+const nodeDenylist = ['load_image', 'image'];
 
 export const addInvocationCompleteEventListener = () => {
   startAppListening({
@@ -37,6 +38,7 @@ export const addInvocationCompleteEventListener = () => {
         const { image_name } = result.image;
         const { canvas, gallery } = getState();
 
+        // This populates the `getImageDTO` cache
         const imageDTO = await dispatch(
           imagesApi.endpoints.getImageDTO.initiate(image_name)
         ).unwrap();
@@ -52,54 +54,36 @@ export const addInvocationCompleteEventListener = () => {
         if (!imageDTO.is_intermediate) {
           /**
            * Cache updates for when an image result is received
-           * - *add* to getImageDTO
-           * - IF `autoAddBoardId` is set:
-           *    - THEN add it to the board_id/images
-           * - ELSE (`autoAddBoardId` is not set):
-           *    - THEN add it to the no_board/images
+           * - add it to the no_board/images
            */
 
-          const { autoAddBoardId } = gallery;
-          if (autoAddBoardId && autoAddBoardId !== 'none') {
-            dispatch(
-              imagesApi.endpoints.addImageToBoard.initiate({
-                board_id: autoAddBoardId,
-                imageDTO,
-              })
-            );
-          } else {
-            dispatch(
-              imagesApi.util.updateQueryData(
-                'listImages',
-                {
-                  board_id: 'none',
-                  categories: IMAGE_CATEGORIES,
-                },
-                (draft) => {
-                  imagesAdapter.addOne(draft, imageDTO);
-                }
-              )
-            );
-          }
+          dispatch(
+            imagesApi.util.updateQueryData(
+              'listImages',
+              {
+                board_id: imageDTO.board_id ?? 'none',
+                categories: IMAGE_CATEGORIES,
+              },
+              (draft) => {
+                imagesAdapter.addOne(draft, imageDTO);
+              }
+            )
+          );
 
           dispatch(
             imagesApi.util.invalidateTags([
-              { type: 'BoardImagesTotal', id: autoAddBoardId },
-              { type: 'BoardAssetsTotal', id: autoAddBoardId },
+              { type: 'BoardImagesTotal', id: imageDTO.board_id },
+              { type: 'BoardAssetsTotal', id: imageDTO.board_id },
             ])
           );
 
-          const { selectedBoardId, shouldAutoSwitch } = gallery;
+          const { shouldAutoSwitch } = gallery;
 
           // If auto-switch is enabled, select the new image
           if (shouldAutoSwitch) {
             // if auto-add is enabled, switch the board as the image comes in
-            if (autoAddBoardId && autoAddBoardId !== selectedBoardId) {
-              dispatch(boardIdSelected(autoAddBoardId));
-              dispatch(galleryViewChanged('images'));
-            } else if (!autoAddBoardId) {
-              dispatch(galleryViewChanged('images'));
-            }
+            dispatch(galleryViewChanged('images'));
+            dispatch(boardIdSelected(imageDTO.board_id ?? 'none'));
             dispatch(imageSelected(imageDTO));
           }
         }

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

@@ -18,11 +18,14 @@ export const addUpscaleRequestedListener = () => {
       const log = logger('session');
 
       const { image_name } = action.payload;
-      const { esrganModelName } = getState().postprocessing;
+      const state = getState();
+      const { esrganModelName } = state.postprocessing;
+      const { autoAddBoardId } = state.gallery;
 
       const graph = buildAdHocUpscaleGraph({
         image_name,
         esrganModelName,
+        autoAddBoardId,
       });
 
       try {

invokeai/frontend/web/src/common/components/IAIInformationalPopover.tsx

@@ -1,124 +0,0 @@
-import {
-  Box,
-  Button,
-  Divider,
-  Flex,
-  Heading,
-  Image,
-  Popover,
-  PopoverArrow,
-  PopoverBody,
-  PopoverCloseButton,
-  PopoverContent,
-  PopoverProps,
-  PopoverTrigger,
-  Portal,
-  Text,
-} from '@chakra-ui/react';
-import { ReactNode, memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useAppSelector } from '../../app/store/storeHooks';
-
-const OPEN_DELAY = 1500;
-
-type Props = Omit<PopoverProps, 'children'> & {
-  details: string;
-  children: ReactNode;
-  image?: string;
-  buttonLabel?: string;
-  buttonHref?: string;
-  placement?: PopoverProps['placement'];
-};
-
-const IAIInformationalPopover = ({
-  details,
-  image,
-  buttonLabel,
-  buttonHref,
-  children,
-  placement,
-}: Props) => {
-  const shouldEnableInformationalPopovers = useAppSelector(
-    (state) => state.system.shouldEnableInformationalPopovers
-  );
-  const { t } = useTranslation();
-
-  const heading = t(`popovers.${details}.heading`);
-  const paragraph = t(`popovers.${details}.paragraph`);
-
-  if (!shouldEnableInformationalPopovers) {
-    return <>{children}</>;
-  }
-
-  return (
-    <Popover
-      placement={placement || 'top'}
-      closeOnBlur={false}
-      trigger="hover"
-      variant="informational"
-      openDelay={OPEN_DELAY}
-    >
-      <PopoverTrigger>
-        <Box w="full">{children}</Box>
-      </PopoverTrigger>
-      <Portal>
-        <PopoverContent>
-          <PopoverArrow />
-          <PopoverCloseButton />
-
-          <PopoverBody>
-            <Flex
-              sx={{
-                gap: 3,
-                flexDirection: 'column',
-                width: '100%',
-                alignItems: 'center',
-              }}
-            >
-              {image && (
-                <Image
-                  sx={{
-                    objectFit: 'contain',
-                    maxW: '60%',
-                    maxH: '60%',
-                    backgroundColor: 'white',
-                  }}
-                  src={image}
-                  alt="Optional Image"
-                />
-              )}
-              <Flex
-                sx={{
-                  gap: 3,
-                  flexDirection: 'column',
-                  width: '100%',
-                }}
-              >
-                {heading && (
-                  <>
-                    <Heading size="sm">{heading}</Heading>
-                    <Divider />
-                  </>
-                )}
-                <Text>{paragraph}</Text>
-                {buttonLabel && (
-                  <Flex justifyContent="flex-end">
-                    <Button
-                      onClick={() => window.open(buttonHref)}
-                      size="sm"
-                      variant="invokeAIOutline"
-                    >
-                      {buttonLabel}
-                    </Button>
-                  </Flex>
-                )}
-              </Flex>
-            </Flex>
-          </PopoverBody>
-        </PopoverContent>
-      </Portal>
-    </Popover>
-  );
-};
-
-export default memo(IAIInformationalPopover);

invokeai/frontend/web/src/common/components/IAIInformationalPopover/IAIInformationalPopover.tsx

@@ -0,0 +1,155 @@
+import {
+  Box,
+  BoxProps,
+  Button,
+  Divider,
+  Flex,
+  Heading,
+  Image,
+  Popover,
+  PopoverBody,
+  PopoverCloseButton,
+  PopoverContent,
+  PopoverProps,
+  PopoverTrigger,
+  Portal,
+  Text,
+  forwardRef,
+} from '@chakra-ui/react';
+import { merge, omit } from 'lodash-es';
+import { PropsWithChildren, memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaExternalLinkAlt } from 'react-icons/fa';
+import { useAppSelector } from '../../../app/store/storeHooks';
+import {
+  Feature,
+  OPEN_DELAY,
+  POPOVER_DATA,
+  POPPER_MODIFIERS,
+} from './constants';
+
+type Props = PropsWithChildren & {
+  feature: Feature;
+  wrapperProps?: BoxProps;
+  popoverProps?: PopoverProps;
+};
+
+const IAIInformationalPopover = forwardRef(
+  ({ feature, children, wrapperProps, ...rest }: Props, ref) => {
+    const { t } = useTranslation();
+    const shouldEnableInformationalPopovers = useAppSelector(
+      (state) => state.system.shouldEnableInformationalPopovers
+    );
+
+    const data = useMemo(() => POPOVER_DATA[feature], [feature]);
+
+    const popoverProps = useMemo(
+      () => merge(omit(data, ['image', 'href', 'buttonLabel']), rest),
+      [data, rest]
+    );
+
+    const heading = useMemo<string | undefined>(
+      () => t(`popovers.${feature}.heading`),
+      [feature, t]
+    );
+
+    const paragraphs = useMemo<string[]>(
+      () =>
+        t(`popovers.${feature}.paragraphs`, {
+          returnObjects: true,
+        }) ?? [],
+      [feature, t]
+    );
+
+    const handleClick = useCallback(() => {
+      if (!data?.href) {
+        return;
+      }
+      window.open(data.href);
+    }, [data?.href]);
+
+    if (!shouldEnableInformationalPopovers) {
+      return (
+        <Box ref={ref} w="full" {...wrapperProps}>
+          {children}
+        </Box>
+      );
+    }
+
+    return (
+      <Popover
+        isLazy
+        closeOnBlur={false}
+        trigger="hover"
+        variant="informational"
+        openDelay={OPEN_DELAY}
+        modifiers={POPPER_MODIFIERS}
+        placement="top"
+        {...popoverProps}
+      >
+        <PopoverTrigger>
+          <Box ref={ref} w="full" {...wrapperProps}>
+            {children}
+          </Box>
+        </PopoverTrigger>
+        <Portal>
+          <PopoverContent w={96}>
+            <PopoverCloseButton />
+            <PopoverBody>
+              <Flex
+                sx={{
+                  gap: 2,
+                  flexDirection: 'column',
+                  alignItems: 'flex-start',
+                }}
+              >
+                {heading && (
+                  <>
+                    <Heading size="sm">{heading}</Heading>
+                    <Divider />
+                  </>
+                )}
+                {data?.image && (
+                  <>
+                    <Image
+                      sx={{
+                        objectFit: 'contain',
+                        maxW: '60%',
+                        maxH: '60%',
+                        backgroundColor: 'white',
+                      }}
+                      src={data.image}
+                      alt="Optional Image"
+                    />
+                    <Divider />
+                  </>
+                )}
+                {paragraphs.map((p) => (
+                  <Text key={p}>{p}</Text>
+                ))}
+                {data?.href && (
+                  <>
+                    <Divider />
+                    <Button
+                      pt={1}
+                      onClick={handleClick}
+                      leftIcon={<FaExternalLinkAlt />}
+                      alignSelf="flex-end"
+                      variant="link"
+                    >
+                      {t('common.learnMore') ?? heading}
+                    </Button>
+                  </>
+                )}
+              </Flex>
+            </PopoverBody>
+          </PopoverContent>
+        </Portal>
+      </Popover>
+    );
+  }
+);
+
+IAIInformationalPopover.displayName = 'IAIInformationalPopover';
+
+export default memo(IAIInformationalPopover);

invokeai/frontend/web/src/common/components/IAIInformationalPopover/constants.ts

@@ -0,0 +1,98 @@
+import { PopoverProps } from '@chakra-ui/react';
+
+export type Feature =
+  | 'clipSkip'
+  | 'paramNegativeConditioning'
+  | 'paramPositiveConditioning'
+  | 'paramScheduler'
+  | 'compositingBlur'
+  | 'compositingBlurMethod'
+  | 'compositingCoherencePass'
+  | 'compositingCoherenceMode'
+  | 'compositingCoherenceSteps'
+  | 'compositingStrength'
+  | 'compositingMaskAdjustments'
+  | 'controlNetBeginEnd'
+  | 'controlNetControlMode'
+  | 'controlNetResizeMode'
+  | 'controlNet'
+  | 'controlNetWeight'
+  | 'dynamicPrompts'
+  | 'dynamicPromptsMaxPrompts'
+  | 'dynamicPromptsSeedBehaviour'
+  | 'infillMethod'
+  | 'lora'
+  | 'noiseUseCPU'
+  | 'paramCFGScale'
+  | 'paramDenoisingStrength'
+  | 'paramIterations'
+  | 'paramModel'
+  | 'paramRatio'
+  | 'paramSeed'
+  | 'paramSteps'
+  | 'paramVAE'
+  | 'paramVAEPrecision'
+  | 'scaleBeforeProcessing';
+
+export type PopoverData = PopoverProps & {
+  image?: string;
+  href?: string;
+  buttonLabel?: string;
+};
+
+export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
+  paramNegativeConditioning: {
+    placement: 'right',
+  },
+  controlNet: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000105880',
+  },
+  lora: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000159072',
+  },
+  compositingCoherenceMode: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158838',
+  },
+  infillMethod: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158841',
+  },
+  scaleBeforeProcessing: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158841',
+  },
+  paramIterations: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000159073',
+  },
+  paramPositiveConditioning: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000096606-tips-on-crafting-prompts',
+    placement: 'right',
+  },
+  paramScheduler: {
+    placement: 'right',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000159073',
+  },
+  paramModel: {
+    placement: 'right',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000096601-what-is-a-model-which-should-i-use-',
+  },
+  paramRatio: {
+    gutter: 16,
+  },
+  controlNetControlMode: {
+    placement: 'right',
+  },
+  controlNetResizeMode: {
+    placement: 'right',
+  },
+  paramVAE: {
+    placement: 'right',
+  },
+  paramVAEPrecision: {
+    placement: 'right',
+  },
+} as const;
+
+export const OPEN_DELAY = 1000; // in milliseconds
+
+export const POPPER_MODIFIERS: PopoverProps['modifiers'] = [
+  { name: 'preventOverflow', options: { padding: 10 } },
+];

invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx

@@ -44,23 +44,19 @@ const IAIMantineMultiSelect = forwardRef((props: IAIMultiSelectProps, ref) => {
 
   return (
     <Tooltip label={tooltip} placement="top" hasArrow isOpen={true}>
-      <MultiSelect
-        label={
-          label ? (
-            <FormControl ref={ref} isDisabled={disabled}>
-              <FormLabel>{label}</FormLabel>
-            </FormControl>
-          ) : undefined
-        }
-        ref={inputRef}
-        disabled={disabled}
-        onKeyDown={handleKeyDown}
-        onKeyUp={handleKeyUp}
-        searchable={searchable}
-        maxDropdownHeight={300}
-        styles={styles}
-        {...rest}
-      />
+      <FormControl ref={ref} isDisabled={disabled}>
+        {label && <FormLabel>{label}</FormLabel>}
+        <MultiSelect
+          ref={inputRef}
+          disabled={disabled}
+          onKeyDown={handleKeyDown}
+          onKeyUp={handleKeyUp}
+          searchable={searchable}
+          maxDropdownHeight={300}
+          styles={styles}
+          {...rest}
+        />
+      </FormControl>
     </Tooltip>
   );
 });

invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx

@@ -70,26 +70,23 @@ const IAIMantineSearchableSelect = forwardRef((props: IAISelectProps, ref) => {
 
   return (
     <Tooltip label={tooltip} placement="top" hasArrow>
-      <Select
-        ref={inputRef}
-        label={
-          label ? (
-            <FormControl ref={ref} isDisabled={disabled}>
-              <FormLabel>{label}</FormLabel>
-            </FormControl>
-          ) : undefined
-        }
-        disabled={disabled}
-        searchValue={searchValue}
-        onSearchChange={setSearchValue}
-        onChange={handleChange}
-        onKeyDown={handleKeyDown}
-        onKeyUp={handleKeyUp}
-        searchable={searchable}
-        maxDropdownHeight={300}
-        styles={styles}
-        {...rest}
-      />
+      <FormControl ref={ref} isDisabled={disabled}>
+        {label && <FormLabel>{label}</FormLabel>}
+        <Select
+          ref={inputRef}
+          withinPortal
+          disabled={disabled}
+          searchValue={searchValue}
+          onSearchChange={setSearchValue}
+          onChange={handleChange}
+          onKeyDown={handleKeyDown}
+          onKeyUp={handleKeyUp}
+          searchable={searchable}
+          maxDropdownHeight={300}
+          styles={styles}
+          {...rest}
+        />
+      </FormControl>
     </Tooltip>
   );
 });

invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx

@@ -22,19 +22,10 @@ const IAIMantineSelect = forwardRef((props: IAISelectProps, ref) => {
 
   return (
     <Tooltip label={tooltip} placement="top" hasArrow>
-      <Select
-        label={
-          label ? (
-            <FormControl ref={ref} isRequired={required} isDisabled={disabled}>
-              <FormLabel>{label}</FormLabel>
-            </FormControl>
-          ) : undefined
-        }
-        disabled={disabled}
-        ref={inputRef}
-        styles={styles}
-        {...rest}
-      />
+      <FormControl ref={ref} isRequired={required} isDisabled={disabled}>
+        <FormLabel>{label}</FormLabel>
+        <Select disabled={disabled} ref={inputRef} styles={styles} {...rest} />
+      </FormControl>
     </Tooltip>
   );
 });

invokeai/frontend/web/src/features/controlNet/components/ControlNetProcessorComponent.tsx

@@ -1,6 +1,7 @@
 import { memo } from 'react';
 import { ControlNetConfig } from '../store/controlNetSlice';
 import CannyProcessor from './processors/CannyProcessor';
+import ColorMapProcessor from './processors/ColorMapProcessor';
 import ContentShuffleProcessor from './processors/ContentShuffleProcessor';
 import HedProcessor from './processors/HedProcessor';
 import LineartAnimeProcessor from './processors/LineartAnimeProcessor';
@@ -30,6 +31,16 @@ const ControlNetProcessorComponent = (props: ControlNetProcessorProps) => {
     );
   }
 
+  if (processorNode.type === 'color_map_image_processor') {
+    return (
+      <ColorMapProcessor
+        controlNetId={controlNetId}
+        processorNode={processorNode}
+        isEnabled={isEnabled}
+      />
+    );
+  }
+
   if (processorNode.type === 'hed_image_processor') {
     return (
       <HedProcessor

invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx

@@ -10,7 +10,7 @@ import {
   Tooltip,
 } from '@chakra-ui/react';
 import { useAppDispatch } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import {
   ControlNetConfig,
   controlNetBeginStepPctChanged,
@@ -50,7 +50,7 @@ const ParamControlNetBeginEnd = (props: Props) => {
   );
 
   return (
-    <IAIInformationalPopover details="controlNetBeginEnd">
+    <IAIInformationalPopover feature="controlNetBeginEnd">
       <FormControl isDisabled={!isEnabled}>
         <FormLabel>{t('controlnet.beginEndStepPercent')}</FormLabel>
         <HStack w="100%" gap={2} alignItems="center">

invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetControlMode.tsx

@@ -1,5 +1,5 @@
 import { useAppDispatch } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import {
   ControlModes,
@@ -35,7 +35,7 @@ export default function ParamControlNetControlMode(
   );
 
   return (
-    <IAIInformationalPopover details="controlNetControlMode">
+    <IAIInformationalPopover feature="controlNetControlMode">
       <IAIMantineSelect
         disabled={!isEnabled}
         label={t('controlnet.controlMode')}

invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetFeatureToggle.tsx

@@ -3,7 +3,7 @@ import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAISwitch from 'common/components/IAISwitch';
 import { isControlNetEnabledToggled } from 'features/controlNet/store/controlNetSlice';
 import { memo, useCallback } from 'react';
@@ -28,7 +28,7 @@ const ParamControlNetFeatureToggle = () => {
 
   return (
     <Box width="100%">
-      <IAIInformationalPopover details="controlNetToggle">
+      <IAIInformationalPopover feature="controlNet">
         <IAISwitch
           label="Enable ControlNet"
           isChecked={isEnabled}

invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetResizeMode.tsx

@@ -1,5 +1,5 @@
 import { useAppDispatch } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import {
   ControlNetConfig,
@@ -34,7 +34,7 @@ export default function ParamControlNetResizeMode(
   );
 
   return (
-    <IAIInformationalPopover details="controlNetResizeMode">
+    <IAIInformationalPopover feature="controlNetResizeMode">
       <IAIMantineSelect
         disabled={!isEnabled}
         label={t('controlnet.resizeMode')}

invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx

@@ -1,5 +1,5 @@
 import { useAppDispatch } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAISlider from 'common/components/IAISlider';
 import {
   ControlNetConfig,
@@ -24,7 +24,7 @@ const ParamControlNetWeight = (props: ParamControlNetWeightProps) => {
   );
 
   return (
-    <IAIInformationalPopover details="controlNetWeight">
+    <IAIInformationalPopover feature="controlNetWeight">
       <IAISlider
         isDisabled={!isEnabled}
         label={t('controlnet.weight')}

invokeai/frontend/web/src/features/controlNet/components/processors/ColorMapProcessor.tsx

@@ -0,0 +1,59 @@
+import IAISlider from 'common/components/IAISlider';
+import { CONTROLNET_PROCESSORS } from 'features/controlNet/store/constants';
+import { RequiredColorMapImageProcessorInvocation } from 'features/controlNet/store/types';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useProcessorNodeChanged } from '../hooks/useProcessorNodeChanged';
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.color_map_image_processor
+  .default as RequiredColorMapImageProcessorInvocation;
+
+type ColorMapProcessorProps = {
+  controlNetId: string;
+  processorNode: RequiredColorMapImageProcessorInvocation;
+  isEnabled: boolean;
+};
+
+const ColorMapProcessor = (props: ColorMapProcessorProps) => {
+  const { controlNetId, processorNode, isEnabled } = props;
+  const { color_map_tile_size } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+  const { t } = useTranslation();
+
+  const handleColorMapTileSizeChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { color_map_tile_size: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleColorMapTileSizeReset = useCallback(() => {
+    processorChanged(controlNetId, {
+      color_map_tile_size: DEFAULTS.color_map_tile_size,
+    });
+  }, [controlNetId, processorChanged]);
+
+  return (
+    <ProcessorWrapper>
+      <IAISlider
+        isDisabled={!isEnabled}
+        label={t('controlnet.colorMapTileSize')}
+        value={color_map_tile_size}
+        onChange={handleColorMapTileSizeChanged}
+        handleReset={handleColorMapTileSizeReset}
+        withReset
+        min={1}
+        max={256}
+        step={1}
+        withInput
+        withSliderMarks
+        sliderNumberInputProps={{
+          max: 4096,
+        }}
+      />
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(ColorMapProcessor);

invokeai/frontend/web/src/features/controlNet/components/processors/common/ProcessorWrapper.tsx

@@ -4,5 +4,9 @@ import { PropsWithChildren } from 'react';
 type Props = PropsWithChildren;
 
 export default function ProcessorWrapper(props: Props) {
-  return <Flex sx={{ flexDirection: 'column', gap: 2 }}>{props.children}</Flex>;
+  return (
+    <Flex sx={{ flexDirection: 'column', gap: 2, pb: 2 }}>
+      {props.children}
+    </Flex>
+  );
 }

invokeai/frontend/web/src/features/controlNet/store/constants.ts

@@ -1,8 +1,8 @@
+import i18n from 'i18next';
 import {
   ControlNetProcessorType,
   RequiredControlNetProcessorNode,
 } from './types';
-import i18n from 'i18next';
 
 type ControlNetProcessorsDict = Record<
   ControlNetProcessorType,
@@ -50,6 +50,20 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
       high_threshold: 200,
     },
   },
+  color_map_image_processor: {
+    type: 'color_map_image_processor',
+    get label() {
+      return i18n.t('controlnet.colorMap');
+    },
+    get description() {
+      return i18n.t('controlnet.colorMapDescription');
+    },
+    default: {
+      id: 'color_map_image_processor',
+      type: 'color_map_image_processor',
+      color_map_tile_size: 64,
+    },
+  },
   content_shuffle_image_processor: {
     type: 'content_shuffle_image_processor',
     get label() {

invokeai/frontend/web/src/features/controlNet/store/types.ts

@@ -1,6 +1,7 @@
 import { isObject } from 'lodash-es';
 import {
   CannyImageProcessorInvocation,
+  ColorMapImageProcessorInvocation,
   ContentShuffleImageProcessorInvocation,
   HedImageProcessorInvocation,
   LineartAnimeImageProcessorInvocation,
@@ -20,6 +21,7 @@ import { O } from 'ts-toolbelt';
  */
 export type ControlNetProcessorNode =
   | CannyImageProcessorInvocation
+  | ColorMapImageProcessorInvocation
   | ContentShuffleImageProcessorInvocation
   | HedImageProcessorInvocation
   | LineartAnimeImageProcessorInvocation
@@ -47,6 +49,14 @@ export type RequiredCannyImageProcessorInvocation = O.Required<
   'type' | 'low_threshold' | 'high_threshold'
 >;
 
+/**
+ * The Color Map processor node, with parameters flagged as required
+ */
+export type RequiredColorMapImageProcessorInvocation = O.Required<
+  ColorMapImageProcessorInvocation,
+  'type' | 'color_map_tile_size'
+>;
+
 /**
  * The ContentShuffle processor node, with parameters flagged as required
  */
@@ -140,6 +150,7 @@ export type RequiredZoeDepthImageProcessorInvocation = O.Required<
  */
 export type RequiredControlNetProcessorNode = O.Required<
   | RequiredCannyImageProcessorInvocation
+  | RequiredColorMapImageProcessorInvocation
   | RequiredContentShuffleImageProcessorInvocation
   | RequiredHedImageProcessorInvocation
   | RequiredLineartAnimeImageProcessorInvocation
@@ -166,6 +177,22 @@ export const isCannyImageProcessorInvocation = (
   return false;
 };
 
+/**
+ * Type guard for ColorMapImageProcessorInvocation
+ */
+export const isColorMapImageProcessorInvocation = (
+  obj: unknown
+): obj is ColorMapImageProcessorInvocation => {
+  if (
+    isObject(obj) &&
+    'type' in obj &&
+    obj.type === 'color_map_image_processor'
+  ) {
+    return true;
+  }
+  return false;
+};
+
 /**
  * Type guard for ContentShuffleImageProcessorInvocation
  */

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx

@@ -43,8 +43,8 @@ const ParamDynamicPromptsCollapse = () => {
       activeLabel={activeLabel}
     >
       <Flex sx={{ gap: 2, flexDir: 'column' }}>
-        <ParamDynamicPromptsSeedBehaviour />
         <ParamDynamicPromptsPreview />
+        <ParamDynamicPromptsSeedBehaviour />
         <ParamDynamicPromptsMaxPrompts />
       </Flex>
     </IAICollapse>

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCombinatorial.tsx

@@ -4,9 +4,8 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAISwitch from 'common/components/IAISwitch';
 import { memo, useCallback } from 'react';
-import { combinatorialToggled } from '../store/dynamicPromptsSlice';
 import { useTranslation } from 'react-i18next';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import { combinatorialToggled } from '../store/dynamicPromptsSlice';
 
 const selector = createSelector(
   stateSelector,
@@ -28,13 +27,11 @@ const ParamDynamicPromptsCombinatorial = () => {
   }, [dispatch]);
 
   return (
-    <IAIInformationalPopover details="dynamicPromptsCombinatorial">
-      <IAISwitch
-        label={t('dynamicPrompts.combinatorial')}
-        isChecked={combinatorial}
-        onChange={handleChange}
-      />
-    </IAIInformationalPopover>
+    <IAISwitch
+      label={t('dynamicPrompts.combinatorial')}
+      isChecked={combinatorial}
+      onChange={handleChange}
+    />
   );
 };
 

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsMaxPrompts.tsx

@@ -9,6 +9,7 @@ import {
   maxPromptsReset,
 } from '../store/dynamicPromptsSlice';
 import { useTranslation } from 'react-i18next';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 
 const selector = createSelector(
   stateSelector,
@@ -46,19 +47,21 @@ const ParamDynamicPromptsMaxPrompts = () => {
   }, [dispatch]);
 
   return (
-    <IAISlider
-      label={t('dynamicPrompts.maxPrompts')}
-      isDisabled={isDisabled}
-      min={min}
-      max={sliderMax}
-      value={maxPrompts}
-      onChange={handleChange}
-      sliderNumberInputProps={{ max: inputMax }}
-      withSliderMarks
-      withInput
-      withReset
-      handleReset={handleReset}
-    />
+    <IAIInformationalPopover feature="dynamicPromptsMaxPrompts">
+      <IAISlider
+        label={t('dynamicPrompts.maxPrompts')}
+        isDisabled={isDisabled}
+        min={min}
+        max={sliderMax}
+        value={maxPrompts}
+        onChange={handleChange}
+        sliderNumberInputProps={{ max: inputMax }}
+        withSliderMarks
+        withInput
+        withReset
+        handleReset={handleReset}
+      />
+    </IAIInformationalPopover>
   );
 };
 

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsPreview.tsx

@@ -13,6 +13,7 @@ import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent';
 import { memo } from 'react';
 import { FaCircleExclamation } from 'react-icons/fa6';
@@ -42,58 +43,73 @@ const ParamDynamicPromptsPreview = () => {
 
   if (isError) {
     return (
-      <Flex
-        w="full"
-        h="full"
-        layerStyle="second"
-        alignItems="center"
-        justifyContent="center"
-        p={8}
-      >
-        <IAINoContentFallback
-          icon={FaCircleExclamation}
-          label="Problem generating prompts"
-        />
-      </Flex>
+      <IAIInformationalPopover feature="dynamicPrompts">
+        <Flex
+          w="full"
+          h="full"
+          layerStyle="second"
+          alignItems="center"
+          justifyContent="center"
+          p={8}
+        >
+          <IAINoContentFallback
+            icon={FaCircleExclamation}
+            label="Problem generating prompts"
+          />
+        </Flex>
+      </IAIInformationalPopover>
     );
   }
 
   return (
-    <FormControl isInvalid={Boolean(parsingError)}>
-      <FormLabel whiteSpace="nowrap" overflow="hidden" textOverflow="ellipsis">
-        Prompts Preview ({prompts.length}){parsingError && ` - ${parsingError}`}
-      </FormLabel>
-      <Flex h={64} pos="relative" layerStyle="third" borderRadius="base" p={2}>
-        <ScrollableContent>
-          <OrderedList stylePosition="inside" ms={0}>
-            {prompts.map((prompt, i) => (
-              <ListItem
-                fontSize="sm"
-                key={`${prompt}.${i}`}
-                sx={listItemStyles}
-              >
-                <Text as="span">{prompt}</Text>
-              </ListItem>
-            ))}
-          </OrderedList>
-        </ScrollableContent>
-        {isLoading && (
-          <Flex
-            pos="absolute"
-            w="full"
-            h="full"
-            top={0}
-            insetInlineStart={0}
-            layerStyle="second"
-            opacity={0.7}
-            alignItems="center"
-            justifyContent="center"
-          >
-            <Spinner />
-          </Flex>
-        )}
-      </Flex>
-    </FormControl>
+    <IAIInformationalPopover feature="dynamicPrompts">
+      <FormControl isInvalid={Boolean(parsingError)}>
+        <FormLabel
+          whiteSpace="nowrap"
+          overflow="hidden"
+          textOverflow="ellipsis"
+        >
+          Prompts Preview ({prompts.length})
+          {parsingError && ` - ${parsingError}`}
+        </FormLabel>
+        <Flex
+          h={64}
+          pos="relative"
+          layerStyle="third"
+          borderRadius="base"
+          p={2}
+        >
+          <ScrollableContent>
+            <OrderedList stylePosition="inside" ms={0}>
+              {prompts.map((prompt, i) => (
+                <ListItem
+                  fontSize="sm"
+                  key={`${prompt}.${i}`}
+                  sx={listItemStyles}
+                >
+                  <Text as="span">{prompt}</Text>
+                </ListItem>
+              ))}
+            </OrderedList>
+          </ScrollableContent>
+          {isLoading && (
+            <Flex
+              pos="absolute"
+              w="full"
+              h="full"
+              top={0}
+              insetInlineStart={0}
+              layerStyle="second"
+              opacity={0.7}
+              alignItems="center"
+              justifyContent="center"
+            >
+              <Spinner />
+            </Flex>
+          )}
+        </Flex>
+      </FormControl>
+    </IAIInformationalPopover>
   );
 };
 

invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsSeedBehaviour.tsx

@@ -7,6 +7,7 @@ import {
   seedBehaviourChanged,
 } from '../store/dynamicPromptsSlice';
 import IAIMantineSelectItemWithDescription from 'common/components/IAIMantineSelectItemWithDescription';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 
 type Item = {
   label: string;
@@ -47,13 +48,15 @@ const ParamDynamicPromptsSeedBehaviour = () => {
   );
 
   return (
-    <IAIMantineSelect
-      label={t('dynamicPrompts.seedBehaviour.label')}
-      value={seedBehaviour}
-      data={data}
-      itemComponent={IAIMantineSelectItemWithDescription}
-      onChange={handleChange}
-    />
+    <IAIInformationalPopover feature="dynamicPromptsSeedBehaviour">
+      <IAIMantineSelect
+        label={t('dynamicPrompts.seedBehaviour.label')}
+        value={seedBehaviour}
+        data={data}
+        itemComponent={IAIMantineSelectItemWithDescription}
+        onChange={handleChange}
+      />
+    </IAIInformationalPopover>
   );
 };
 

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -28,7 +28,7 @@ import {
   setShouldShowImageDetails,
   setShouldShowProgressInViewer,
 } from 'features/ui/store/uiSlice';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import {
@@ -41,9 +41,10 @@ import {
 import { FaCircleNodes, FaEllipsis } from 'react-icons/fa6';
 import {
   useGetImageDTOQuery,
-  useGetImageMetadataFromFileQuery,
+  useGetImageMetadataQuery,
 } from 'services/api/endpoints/images';
 import { menuListMotionProps } from 'theme/components/menu';
+import { useDebounce } from 'use-debounce';
 import { sentImageToImg2Img } from '../../store/actions';
 import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
 
@@ -92,7 +93,6 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     shouldShowImageDetails,
     lastSelectedImage,
     shouldShowProgressInViewer,
-    shouldFetchMetadataFromApi,
   } = useAppSelector(currentImageButtonsSelector);
 
   const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
@@ -107,16 +107,10 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     lastSelectedImage?.image_name ?? skipToken
   );
 
-  const getMetadataArg = useMemo(() => {
-    if (lastSelectedImage) {
-      return { image: lastSelectedImage, shouldFetchMetadataFromApi };
-    } else {
-      return skipToken;
-    }
-  }, [lastSelectedImage, shouldFetchMetadataFromApi]);
+  const [debouncedImageName] = useDebounce(lastSelectedImage?.image_name, 300);
 
-  const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
-    getMetadataArg,
+  const { metadata, workflow, isLoading } = useGetImageMetadataQuery(
+    debouncedImageName ?? skipToken,
     {
       selectFromResult: (res) => ({
         isLoading: res.isFetching,

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -1,8 +1,9 @@
 import { Flex, MenuItem, Spinner } from '@chakra-ui/react';
 import { useStore } from '@nanostores/react';
+import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { useAppToaster } from 'app/components/Toaster';
 import { $customStarUI } from 'app/store/nanostores/customStarUI';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import {
   imagesToChangeSelected,
@@ -32,12 +33,12 @@ import {
 import { FaCircleNodes } from 'react-icons/fa6';
 import { MdStar, MdStarBorder } from 'react-icons/md';
 import {
-  useGetImageMetadataFromFileQuery,
+  useGetImageMetadataQuery,
   useStarImagesMutation,
   useUnstarImagesMutation,
 } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
-import { configSelector } from '../../../system/store/configSelectors';
+import { useDebounce } from 'use-debounce';
 import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
 
 type SingleSelectionMenuItemsProps = {
@@ -53,11 +54,12 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const toaster = useAppToaster();
 
   const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
-  const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
   const customStarUi = useStore($customStarUI);
 
-  const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
-    { image: imageDTO, shouldFetchMetadataFromApi },
+  const [debouncedImageName] = useDebounce(imageDTO.image_name, 300);
+
+  const { metadata, workflow, isLoading } = useGetImageMetadataQuery(
+    debouncedImageName ?? skipToken,
     {
       selectFromResult: (res) => ({
         isLoading: res.isFetching,

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx

@@ -9,15 +9,15 @@ import {
   Tabs,
   Text,
 } from '@chakra-ui/react';
+import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import { memo } from 'react';
-import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images';
+import { useTranslation } from 'react-i18next';
+import { useGetImageMetadataQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
+import { useDebounce } from 'use-debounce';
 import DataViewer from './DataViewer';
 import ImageMetadataActions from './ImageMetadataActions';
-import { useAppSelector } from '../../../../app/store/storeHooks';
-import { configSelector } from '../../../system/store/configSelectors';
-import { useTranslation } from 'react-i18next';
 
 type ImageMetadataViewerProps = {
   image: ImageDTO;
@@ -31,10 +31,10 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
   // });
   const { t } = useTranslation();
 
-  const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
+  const [debouncedImageName] = useDebounce(image.image_name, 300);
 
-  const { metadata, workflow } = useGetImageMetadataFromFileQuery(
-    { image, shouldFetchMetadataFromApi },
+  const { metadata, workflow } = useGetImageMetadataQuery(
+    debouncedImageName ?? skipToken,
     {
       selectFromResult: (res) => ({
         metadata: res?.currentData?.metadata,

invokeai/frontend/web/src/features/lora/components/ParamLora.tsx

@@ -10,7 +10,7 @@ import {
   loraWeightChanged,
   loraWeightReset,
 } from '../store/loraSlice';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 
 type Props = {
   lora: LoRA;
@@ -36,7 +36,7 @@ const ParamLora = (props: Props) => {
   }, [dispatch, lora.id]);
 
   return (
-    <IAIInformationalPopover details="lora">
+    <IAIInformationalPopover feature="lora">
       <Flex sx={{ gap: 2.5, alignItems: 'flex-end' }}>
         <IAISlider
           label={lora.model_name}

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx

@@ -1,13 +1,13 @@
 import { Checkbox, Flex, FormControl, FormLabel } from '@chakra-ui/react';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useEmbedWorkflow } from 'features/nodes/hooks/useEmbedWorkflow';
-import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
+import { useWithWorkflow } from 'features/nodes/hooks/useWithWorkflow';
 import { nodeEmbedWorkflowChanged } from 'features/nodes/store/nodesSlice';
 import { ChangeEvent, memo, useCallback } from 'react';
 
 const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
   const dispatch = useAppDispatch();
-  const hasImageOutput = useHasImageOutput(nodeId);
+  const withWorkflow = useWithWorkflow(nodeId);
   const embedWorkflow = useEmbedWorkflow(nodeId);
   const handleChange = useCallback(
     (e: ChangeEvent<HTMLInputElement>) => {
@@ -21,7 +21,7 @@ const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => {
     [dispatch, nodeId]
   );
 
-  if (!hasImageOutput) {
+  if (!withWorkflow) {
     return null;
   }
 

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx

@@ -1,10 +1,10 @@
 import { Flex } from '@chakra-ui/react';
+import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
 import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants';
 import { memo } from 'react';
 import EmbedWorkflowCheckbox from './EmbedWorkflowCheckbox';
 import SaveToGalleryCheckbox from './SaveToGalleryCheckbox';
 import UseCacheCheckbox from './UseCacheCheckbox';
-import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput';
 
 type Props = {
   nodeId: string;
@@ -25,8 +25,8 @@ const InvocationNodeFooter = ({ nodeId }: Props) => {
         justifyContent: 'space-between',
       }}
     >
-      {hasImageOutput && <EmbedWorkflowCheckbox nodeId={nodeId} />}
       <UseCacheCheckbox nodeId={nodeId} />
+      {hasImageOutput && <EmbedWorkflowCheckbox nodeId={nodeId} />}
       {hasImageOutput && <SaveToGalleryCheckbox nodeId={nodeId} />}
     </Flex>
   );

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer.tsx

@@ -16,6 +16,7 @@ import SchedulerInputField from './inputs/SchedulerInputField';
 import StringInputField from './inputs/StringInputField';
 import VaeModelInputField from './inputs/VaeModelInputField';
 import IPAdapterModelInputField from './inputs/IPAdapterModelInputField';
+import BoardInputField from './inputs/BoardInputField';
 
 type InputFieldProps = {
   nodeId: string;
@@ -99,6 +100,16 @@ const InputFieldRenderer = ({ nodeId, fieldName }: InputFieldProps) => {
     );
   }
 
+  if (field?.type === 'BoardField' && fieldTemplate?.type === 'BoardField') {
+    return (
+      <BoardInputField
+        nodeId={nodeId}
+        field={field}
+        fieldTemplate={fieldTemplate}
+      />
+    );
+  }
+
   if (
     field?.type === 'MainModelField' &&
     fieldTemplate?.type === 'MainModelField'

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/BoardInputField.tsx

@@ -0,0 +1,64 @@
+import { SelectItem } from '@mantine/core';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect';
+import { fieldBoardValueChanged } from 'features/nodes/store/nodesSlice';
+import {
+  BoardInputFieldTemplate,
+  BoardInputFieldValue,
+  FieldComponentProps,
+} from 'features/nodes/types/types';
+import { memo, useCallback } from 'react';
+import { useListAllBoardsQuery } from 'services/api/endpoints/boards';
+
+const BoardInputFieldComponent = (
+  props: FieldComponentProps<BoardInputFieldValue, BoardInputFieldTemplate>
+) => {
+  const { nodeId, field } = props;
+  const dispatch = useAppDispatch();
+
+  const { data, hasBoards } = useListAllBoardsQuery(undefined, {
+    selectFromResult: ({ data }) => {
+      const boards: SelectItem[] = [
+        {
+          label: 'None',
+          value: 'none',
+        },
+      ];
+      data?.forEach(({ board_id, board_name }) => {
+        boards.push({
+          label: board_name,
+          value: board_id,
+        });
+      });
+      return {
+        data: boards,
+        hasBoards: boards.length > 1,
+      };
+    },
+  });
+
+  const handleChange = useCallback(
+    (v: string | null) => {
+      dispatch(
+        fieldBoardValueChanged({
+          nodeId,
+          fieldName: field.name,
+          value: v && v !== 'none' ? { board_id: v } : undefined,
+        })
+      );
+    },
+    [dispatch, field.name, nodeId]
+  );
+
+  return (
+    <IAIMantineSearchableSelect
+      className="nowheel nodrag"
+      value={field.value?.board_id ?? 'none'}
+      data={data}
+      onChange={handleChange}
+      disabled={!hasBoards}
+    />
+  );
+};
+
+export default memo(BoardInputFieldComponent);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/inputs/SchedulerInputField.tsx

@@ -65,11 +65,6 @@ const SchedulerInputField = (
   return (
     <IAIMantineSearchableSelect
       className="nowheel nodrag"
-      sx={{
-        '.mantine-Select-dropdown': {
-          width: '14rem !important',
-        },
-      }}
       value={field.value}
       data={data}
       onChange={handleChange}

invokeai/frontend/web/src/features/nodes/hooks/useBuildNodeData.ts

@@ -143,7 +143,7 @@ export const useBuildNodeData = () => {
           notes: '',
           isOpen: true,
           embedWorkflow: false,
-          isIntermediate: true,
+          isIntermediate: type === 'save_image' ? false : true,
           inputs,
           outputs,
           useCache: template.useCache,

invokeai/frontend/web/src/features/nodes/hooks/useHasImageOutput.ts

@@ -17,8 +17,12 @@ export const useHasImageOutput = (nodeId: string) => {
           if (!isInvocationNode(node)) {
             return false;
           }
-          return some(node.data.outputs, (output) =>
-            IMAGE_FIELDS.includes(output.type)
+          return some(
+            node.data.outputs,
+            (output) =>
+              IMAGE_FIELDS.includes(output.type) &&
+              // the image primitive node does not actually save the image, do not show the image-saving checkboxes
+              node.data.type !== 'image'
           );
         },
         defaultSelectorOptions

invokeai/frontend/web/src/features/nodes/hooks/useIsValidConnection.ts

@@ -122,13 +122,16 @@ export const useIsValidConnection = () => {
 
         const isIntToFloat = sourceType === 'integer' && targetType === 'float';
 
+        const isEitherAnyType = sourceType === 'Any' || targetType === 'Any';
+
         return (
           isCollectionItemToNonCollection ||
           isNonCollectionToCollectionItem ||
           isAnythingToPolymorphicOfSameBaseType ||
           isGenericCollectionToAnyCollectionOrPolymorphic ||
           isCollectionToGenericCollection ||
-          isIntToFloat
+          isIntToFloat ||
+          isEitherAnyType
         );
       }
 

invokeai/frontend/web/src/features/nodes/hooks/useWithWorkflow.ts

@@ -0,0 +1,31 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { useMemo } from 'react';
+import { isInvocationNode } from '../types/types';
+
+export const useWithWorkflow = (nodeId: string) => {
+  const selector = useMemo(
+    () =>
+      createSelector(
+        stateSelector,
+        ({ nodes }) => {
+          const node = nodes.nodes.find((node) => node.id === nodeId);
+          if (!isInvocationNode(node)) {
+            return false;
+          }
+          const nodeTemplate = nodes.nodeTemplates[node?.data.type ?? ''];
+          if (!nodeTemplate) {
+            return false;
+          }
+          return nodeTemplate.withWorkflow;
+        },
+        defaultSelectorOptions
+      ),
+    [nodeId]
+  );
+
+  const withWorkflow = useAppSelector(selector);
+  return withWorkflow;
+};

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -30,6 +30,7 @@ import {
 import { v4 as uuidv4 } from 'uuid';
 import { DRAG_HANDLE_CLASSNAME } from '../types/constants';
 import {
+  BoardInputFieldValue,
   BooleanInputFieldValue,
   ColorInputFieldValue,
   ControlNetModelInputFieldValue,
@@ -494,6 +495,12 @@ const nodesSlice = createSlice({
     ) => {
       fieldValueReducer(state, action);
     },
+    fieldBoardValueChanged: (
+      state,
+      action: FieldValueAction<BoardInputFieldValue>
+    ) => {
+      fieldValueReducer(state, action);
+    },
     fieldImageValueChanged: (
       state,
       action: FieldValueAction<ImageInputFieldValue>
@@ -871,7 +878,7 @@ const nodesSlice = createSlice({
     builder.addCase(appSocketQueueItemStatusChanged, (state, action) => {
       if (['in_progress'].includes(action.payload.data.status)) {
         forEach(state.nodeExecutionStates, (nes) => {
-          nes.status = NodeStatus.IN_PROGRESS;
+          nes.status = NodeStatus.PENDING;
           nes.error = null;
           nes.progress = null;
           nes.progressImage = null;
@@ -897,6 +904,7 @@ export const {
   imageCollectionFieldValueChanged,
   fieldStringValueChanged,
   fieldNumberValueChanged,
+  fieldBoardValueChanged,
   fieldBooleanValueChanged,
   fieldImageValueChanged,
   fieldColorValueChanged,

invokeai/frontend/web/src/features/nodes/store/util/makeIsConnectionValidSelector.ts

@@ -116,6 +116,8 @@ export const makeConnectionErrorSelector = (
 
       const isIntToFloat = sourceType === 'integer' && targetType === 'float';
 
+      const isEitherAnyType = sourceType === 'Any' || targetType === 'Any';
+
       if (
         !(
           isCollectionItemToNonCollection ||
@@ -123,7 +125,8 @@ export const makeConnectionErrorSelector = (
           isAnythingToPolymorphicOfSameBaseType ||
           isGenericCollectionToAnyCollectionOrPolymorphic ||
           isCollectionToGenericCollection ||
-          isIntToFloat
+          isIntToFloat ||
+          isEitherAnyType
         )
       ) {
         return i18n.t('nodes.fieldTypesMustMatch');

invokeai/frontend/web/src/features/nodes/types/constants.ts

@@ -1,4 +1,9 @@
-import { FieldType, FieldUIConfig } from './types';
+import {
+  FieldType,
+  FieldTypeMap,
+  FieldTypeMapWithNumber,
+  FieldUIConfig,
+} from './types';
 import { t } from 'i18next';
 
 export const HANDLE_TOOLTIP_OPEN_DELAY = 500;
@@ -26,9 +31,11 @@ export const COLLECTION_TYPES: FieldType[] = [
   'ConditioningCollection',
   'ControlCollection',
   'ColorCollection',
+  'MetadataItemCollection',
+  'MetadataDictCollection',
 ];
 
-export const POLYMORPHIC_TYPES = [
+export const POLYMORPHIC_TYPES: FieldType[] = [
   'IntegerPolymorphic',
   'BooleanPolymorphic',
   'FloatPolymorphic',
@@ -38,9 +45,10 @@ export const POLYMORPHIC_TYPES = [
   'ConditioningPolymorphic',
   'ControlPolymorphic',
   'ColorPolymorphic',
+  'MetadataItemPolymorphic',
 ];
 
-export const MODEL_TYPES = [
+export const MODEL_TYPES: FieldType[] = [
   'IPAdapterModelField',
   'ControlNetModelField',
   'LoRAModelField',
@@ -54,7 +62,7 @@ export const MODEL_TYPES = [
   'ClipField',
 ];
 
-export const COLLECTION_MAP = {
+export const COLLECTION_MAP: FieldTypeMapWithNumber = {
   integer: 'IntegerCollection',
   boolean: 'BooleanCollection',
   number: 'FloatCollection',
@@ -65,13 +73,15 @@ export const COLLECTION_MAP = {
   ConditioningField: 'ConditioningCollection',
   ControlField: 'ControlCollection',
   ColorField: 'ColorCollection',
+  MetadataItem: 'MetadataItemCollection',
+  MetadataDict: 'MetadataDictCollection',
 };
 export const isCollectionItemType = (
   itemType: string | undefined
 ): itemType is keyof typeof COLLECTION_MAP =>
   Boolean(itemType && itemType in COLLECTION_MAP);
 
-export const SINGLE_TO_POLYMORPHIC_MAP = {
+export const SINGLE_TO_POLYMORPHIC_MAP: FieldTypeMapWithNumber = {
   integer: 'IntegerPolymorphic',
   boolean: 'BooleanPolymorphic',
   number: 'FloatPolymorphic',
@@ -82,9 +92,10 @@ export const SINGLE_TO_POLYMORPHIC_MAP = {
   ConditioningField: 'ConditioningPolymorphic',
   ControlField: 'ControlPolymorphic',
   ColorField: 'ColorPolymorphic',
+  MetadataItem: 'MetadataItemPolymorphic',
 };
 
-export const POLYMORPHIC_TO_SINGLE_MAP = {
+export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = {
   IntegerPolymorphic: 'integer',
   BooleanPolymorphic: 'boolean',
   FloatPolymorphic: 'float',
@@ -94,9 +105,10 @@ export const POLYMORPHIC_TO_SINGLE_MAP = {
   ConditioningPolymorphic: 'ConditioningField',
   ControlPolymorphic: 'ControlField',
   ColorPolymorphic: 'ColorField',
+  MetadataItemPolymorphic: 'MetadataItem',
 };
 
-export const TYPES_WITH_INPUT_COMPONENTS = [
+export const TYPES_WITH_INPUT_COMPONENTS: FieldType[] = [
   'string',
   'StringPolymorphic',
   'boolean',
@@ -117,6 +129,7 @@ export const TYPES_WITH_INPUT_COMPONENTS = [
   'SDXLMainModelField',
   'Scheduler',
   'IPAdapterModelField',
+  'BoardField',
 ];
 
 export const isPolymorphicItemType = (
@@ -125,6 +138,37 @@ export const isPolymorphicItemType = (
   Boolean(itemType && itemType in SINGLE_TO_POLYMORPHIC_MAP);
 
 export const FIELDS: Record<FieldType, FieldUIConfig> = {
+  Any: {
+    color: 'gray.500',
+    description: 'Any field type is accepted.',
+    title: 'Any',
+  },
+  MetadataDict: {
+    color: 'gray.500',
+    description: 'A metadata dict.',
+    title: 'Metadata Dict',
+  },
+  MetadataDictCollection: {
+    color: 'gray.500',
+    description: 'A collection of metadata dicts.',
+    title: 'Metadata Dict Collection',
+  },
+  MetadataItem: {
+    color: 'gray.500',
+    description: 'A metadata item.',
+    title: 'Metadata Item',
+  },
+  MetadataItemCollection: {
+    color: 'gray.500',
+    description: 'Any field type is accepted.',
+    title: 'Metadata Item Collection',
+  },
+  MetadataItemPolymorphic: {
+    color: 'gray.500',
+    description:
+      'MetadataItem or MetadataItemCollection field types are accepted.',
+    title: 'Metadata Item Polymorphic',
+  },
   boolean: {
     color: 'green.500',
     description: t('nodes.booleanDescription'),
@@ -240,6 +284,11 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     description: t('nodes.imageFieldDescription'),
     title: t('nodes.imageField'),
   },
+  BoardField: {
+    color: 'purple.500',
+    description: t('nodes.imageFieldDescription'),
+    title: t('nodes.imageField'),
+  },
   ImagePolymorphic: {
     color: 'purple.500',
     description: t('nodes.imagePolymorphicDescription'),

invokeai/frontend/web/src/features/nodes/types/types.ts

@@ -54,6 +54,10 @@ export type InvocationTemplate = {
    * The type of this node's output
    */
   outputType: string; // TODO: generate a union of output types
+  /**
+   * Whether or not this invocation supports workflows
+   */
+  withWorkflow: boolean;
   /**
    * The invocation's version.
    */
@@ -72,6 +76,8 @@ export type FieldUIConfig = {
 
 // TODO: Get this from the OpenAPI schema? may be tricky...
 export const zFieldType = z.enum([
+  'Any',
+  'BoardField',
   'boolean',
   'BooleanCollection',
   'BooleanPolymorphic',
@@ -106,6 +112,11 @@ export const zFieldType = z.enum([
   'LatentsPolymorphic',
   'LoRAModelField',
   'MainModelField',
+  'MetadataDict',
+  'MetadataDictCollection',
+  'MetadataItem',
+  'MetadataItemCollection',
+  'MetadataItemPolymorphic',
   'ONNXModelField',
   'Scheduler',
   'SDXLMainModelField',
@@ -119,6 +130,10 @@ export const zFieldType = z.enum([
 ]);
 
 export type FieldType = z.infer<typeof zFieldType>;
+export type FieldTypeMap = { [key in FieldType]?: FieldType };
+export type FieldTypeMapWithNumber = {
+  [key in FieldType | 'number']?: FieldType;
+};
 
 export const zReservedFieldType = z.enum([
   'WorkflowField',
@@ -187,6 +202,11 @@ export const zImageField = z.object({
 });
 export type ImageField = z.infer<typeof zImageField>;
 
+export const zBoardField = z.object({
+  board_id: z.string().trim().min(1),
+});
+export type BoardField = z.infer<typeof zBoardField>;
+
 export const zLatentsField = z.object({
   latents_name: z.string().trim().min(1),
   seed: z.number().int().optional(),
@@ -494,6 +514,12 @@ export const zImageInputFieldValue = zInputFieldValueBase.extend({
 });
 export type ImageInputFieldValue = z.infer<typeof zImageInputFieldValue>;
 
+export const zBoardInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('BoardField'),
+  value: zBoardField.optional(),
+});
+export type BoardInputFieldValue = z.infer<typeof zBoardInputFieldValue>;
+
 export const zImagePolymorphicInputFieldValue = zInputFieldValueBase.extend({
   type: z.literal('ImagePolymorphic'),
   value: zImageField.optional(),
@@ -591,6 +617,58 @@ export type CollectionItemInputFieldValue = z.infer<
   typeof zCollectionItemInputFieldValue
 >;
 
+export const zMetadataItem = z.object({
+  label: z.string(),
+  value: z.any(),
+});
+export type MetadataItem = z.infer<typeof zMetadataItem>;
+
+export const zMetadataItemInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('MetadataItem'),
+  value: zMetadataItem.optional(),
+});
+export type MetadataItemInputFieldValue = z.infer<
+  typeof zMetadataItemInputFieldValue
+>;
+
+export const zMetadataItemCollectionInputFieldValue =
+  zInputFieldValueBase.extend({
+    type: z.literal('MetadataItemCollection'),
+    value: z.array(zMetadataItem).optional(),
+  });
+export type MetadataItemCollectionInputFieldValue = z.infer<
+  typeof zMetadataItemCollectionInputFieldValue
+>;
+
+export const zMetadataItemPolymorphicInputFieldValue =
+  zInputFieldValueBase.extend({
+    type: z.literal('MetadataItemPolymorphic'),
+    value: z.union([zMetadataItem, z.array(zMetadataItem)]).optional(),
+  });
+export type MetadataItemPolymorphicInputFieldValue = z.infer<
+  typeof zMetadataItemPolymorphicInputFieldValue
+>;
+
+export const zMetadataDict = z.record(z.any());
+export type MetadataDict = z.infer<typeof zMetadataDict>;
+
+export const zMetadataDictInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('MetadataDict'),
+  value: zMetadataDict.optional(),
+});
+export type MetadataDictInputFieldValue = z.infer<
+  typeof zMetadataDictInputFieldValue
+>;
+
+export const zMetadataDictCollectionInputFieldValue =
+  zInputFieldValueBase.extend({
+    type: z.literal('MetadataDictCollection'),
+    value: z.array(zMetadataDict).optional(),
+  });
+export type MetadataDictCollectionInputFieldValue = z.infer<
+  typeof zMetadataDictCollectionInputFieldValue
+>;
+
 export const zColorField = z.object({
   r: z.number().int().min(0).max(255),
   g: z.number().int().min(0).max(255),
@@ -629,7 +707,14 @@ export type SchedulerInputFieldValue = z.infer<
   typeof zSchedulerInputFieldValue
 >;
 
+export const zAnyInputFieldValue = zInputFieldValueBase.extend({
+  type: z.literal('Any'),
+  value: z.any().optional(),
+});
+
 export const zInputFieldValue = z.discriminatedUnion('type', [
+  zAnyInputFieldValue,
+  zBoardInputFieldValue,
   zBooleanCollectionInputFieldValue,
   zBooleanInputFieldValue,
   zBooleanPolymorphicInputFieldValue,
@@ -673,6 +758,11 @@ export const zInputFieldValue = z.discriminatedUnion('type', [
   zUNetInputFieldValue,
   zVaeInputFieldValue,
   zVaeModelInputFieldValue,
+  zMetadataItemInputFieldValue,
+  zMetadataItemCollectionInputFieldValue,
+  zMetadataItemPolymorphicInputFieldValue,
+  zMetadataDictInputFieldValue,
+  zMetadataDictCollectionInputFieldValue,
 ]);
 
 export type InputFieldValue = z.infer<typeof zInputFieldValue>;
@@ -685,6 +775,11 @@ export type InputFieldTemplateBase = {
   fieldKind: 'input';
 } & _InputField;
 
+export type AnyInputFieldTemplate = InputFieldTemplateBase & {
+  type: 'Any';
+  default: undefined;
+};
+
 export type IntegerInputFieldTemplate = InputFieldTemplateBase & {
   type: 'integer';
   default: number;
@@ -770,6 +865,11 @@ export type BooleanPolymorphicInputFieldTemplate = Omit<
   type: 'BooleanPolymorphic';
 };
 
+export type BoardInputFieldTemplate = InputFieldTemplateBase & {
+  default: BoardField;
+  type: 'BoardField';
+};
+
 export type ImageInputFieldTemplate = InputFieldTemplateBase & {
   default: ImageField;
   type: 'ImageField';
@@ -833,6 +933,11 @@ export type UNetInputFieldTemplate = InputFieldTemplateBase & {
   type: 'UNetField';
 };
 
+export type MetadataItemFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'UNetField';
+};
+
 export type ClipInputFieldTemplate = InputFieldTemplateBase & {
   default: undefined;
   type: 'ClipField';
@@ -945,6 +1050,35 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
   type: 'WorkflowField';
 };
 
+export type MetadataItemInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'MetadataItem';
+};
+
+export type MetadataItemCollectionInputFieldTemplate =
+  InputFieldTemplateBase & {
+    default: undefined;
+    type: 'MetadataItemCollection';
+  };
+
+export type MetadataItemPolymorphicInputFieldTemplate = Omit<
+  MetadataItemInputFieldTemplate,
+  'type'
+> & {
+  type: 'MetadataItemPolymorphic';
+};
+
+export type MetadataDictInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'MetadataDict';
+};
+
+export type MetadataDictCollectionInputFieldTemplate =
+  InputFieldTemplateBase & {
+    default: undefined;
+    type: 'MetadataDictCollection';
+  };
+
 /**
  * An input field template is generated on each page load from the OpenAPI schema.
  *
@@ -952,6 +1086,8 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & {
  * maximum length, pattern to match, etc).
  */
 export type InputFieldTemplate =
+  | AnyInputFieldTemplate
+  | BoardInputFieldTemplate
   | BooleanCollectionInputFieldTemplate
   | BooleanPolymorphicInputFieldTemplate
   | BooleanInputFieldTemplate
@@ -994,7 +1130,12 @@ export type InputFieldTemplate =
   | StringInputFieldTemplate
   | UNetInputFieldTemplate
   | VaeInputFieldTemplate
-  | VaeModelInputFieldTemplate;
+  | VaeModelInputFieldTemplate
+  | MetadataItemInputFieldTemplate
+  | MetadataItemCollectionInputFieldTemplate
+  | MetadataDictInputFieldTemplate
+  | MetadataItemPolymorphicInputFieldTemplate
+  | MetadataDictCollectionInputFieldTemplate;
 
 export const isInputFieldValue = (
   field?: InputFieldValue | OutputFieldValue
@@ -1111,7 +1252,7 @@ export const isInvocationFieldSchema = (
 
 export type InvocationEdgeExtra = { type: 'default' | 'collapsed' };
 
-const zLoRAMetadataItem = z.object({
+export const zLoRAMetadataItem = z.object({
   lora: zLoRAModelField.deepPartial(),
   weight: z.number(),
 });
@@ -1138,15 +1279,7 @@ export const zCoreMetadata = z
       .nullish()
       .catch(null),
     controlnets: z.array(zControlField.deepPartial()).nullish().catch(null),
-    loras: z
-      .array(
-        z.object({
-          lora: zLoRAModelField.deepPartial(),
-          weight: z.number(),
-        })
-      )
-      .nullish()
-      .catch(null),
+    loras: z.array(zLoRAMetadataItem).nullish().catch(null),
     vae: zVaeModelField.nullish().catch(null),
     strength: z.number().nullish().catch(null),
     init_image: z.string().nullish().catch(null),

invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts

@@ -1,5 +1,6 @@
 import { isBoolean, isInteger, isNumber, isString } from 'lodash-es';
 import { OpenAPIV3 } from 'openapi-types';
+import { ControlField } from 'services/api/types';
 import {
   COLLECTION_MAP,
   POLYMORPHIC_TYPES,
@@ -8,36 +9,61 @@ import {
   isPolymorphicItemType,
 } from '../types/constants';
 import {
+  AnyInputFieldTemplate,
+  BoardInputFieldTemplate,
   BooleanCollectionInputFieldTemplate,
   BooleanInputFieldTemplate,
+  BooleanPolymorphicInputFieldTemplate,
   ClipInputFieldTemplate,
   CollectionInputFieldTemplate,
   CollectionItemInputFieldTemplate,
+  ColorCollectionInputFieldTemplate,
   ColorInputFieldTemplate,
+  ColorPolymorphicInputFieldTemplate,
+  ConditioningCollectionInputFieldTemplate,
+  ConditioningField,
   ConditioningInputFieldTemplate,
+  ConditioningPolymorphicInputFieldTemplate,
+  ControlCollectionInputFieldTemplate,
   ControlInputFieldTemplate,
   ControlNetModelInputFieldTemplate,
+  ControlPolymorphicInputFieldTemplate,
   DenoiseMaskInputFieldTemplate,
   EnumInputFieldTemplate,
   FieldType,
   FloatCollectionInputFieldTemplate,
-  FloatPolymorphicInputFieldTemplate,
   FloatInputFieldTemplate,
+  FloatPolymorphicInputFieldTemplate,
+  IPAdapterInputFieldTemplate,
+  IPAdapterModelInputFieldTemplate,
   ImageCollectionInputFieldTemplate,
+  ImageField,
   ImageInputFieldTemplate,
+  ImagePolymorphicInputFieldTemplate,
+  InputFieldTemplate,
   InputFieldTemplateBase,
   IntegerCollectionInputFieldTemplate,
   IntegerInputFieldTemplate,
+  IntegerPolymorphicInputFieldTemplate,
   InvocationFieldSchema,
   InvocationSchemaObject,
+  LatentsCollectionInputFieldTemplate,
+  LatentsField,
   LatentsInputFieldTemplate,
+  LatentsPolymorphicInputFieldTemplate,
   LoRAModelInputFieldTemplate,
   MainModelInputFieldTemplate,
+  MetadataDictCollectionInputFieldTemplate,
+  MetadataDictInputFieldTemplate,
+  MetadataItemCollectionInputFieldTemplate,
+  MetadataItemInputFieldTemplate,
+  MetadataItemPolymorphicInputFieldTemplate,
   SDXLMainModelInputFieldTemplate,
   SDXLRefinerModelInputFieldTemplate,
   SchedulerInputFieldTemplate,
   StringCollectionInputFieldTemplate,
   StringInputFieldTemplate,
+  StringPolymorphicInputFieldTemplate,
   UNetInputFieldTemplate,
   VaeInputFieldTemplate,
   VaeModelInputFieldTemplate,
@@ -45,25 +71,7 @@ import {
   isNonArraySchemaObject,
   isRefObject,
   isSchemaObject,
-  ControlPolymorphicInputFieldTemplate,
-  ColorPolymorphicInputFieldTemplate,
-  ColorCollectionInputFieldTemplate,
-  IntegerPolymorphicInputFieldTemplate,
-  StringPolymorphicInputFieldTemplate,
-  BooleanPolymorphicInputFieldTemplate,
-  ImagePolymorphicInputFieldTemplate,
-  LatentsPolymorphicInputFieldTemplate,
-  LatentsCollectionInputFieldTemplate,
-  ConditioningPolymorphicInputFieldTemplate,
-  ConditioningCollectionInputFieldTemplate,
-  ControlCollectionInputFieldTemplate,
-  ImageField,
-  LatentsField,
-  ConditioningField,
-  IPAdapterInputFieldTemplate,
-  IPAdapterModelInputFieldTemplate,
 } from '../types/types';
-import { ControlField } from 'services/api/types';
 
 export type BaseFieldProperties = 'name' | 'title' | 'description';
 
@@ -450,6 +458,19 @@ const buildIPAdapterModelInputFieldTemplate = ({
   return template;
 };
 
+const buildBoardInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): BoardInputFieldTemplate => {
+  const template: BoardInputFieldTemplate = {
+    ...baseField,
+    type: 'BoardField',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildImageInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -716,6 +737,78 @@ const buildCollectionItemInputFieldTemplate = ({
   return template;
 };
 
+const buildAnyInputFieldTemplate = ({
+  baseField,
+}: BuildInputFieldArg): AnyInputFieldTemplate => {
+  const template: AnyInputFieldTemplate = {
+    ...baseField,
+    type: 'Any',
+    default: undefined,
+  };
+
+  return template;
+};
+
+const buildMetadataItemInputFieldTemplate = ({
+  baseField,
+}: BuildInputFieldArg): MetadataItemInputFieldTemplate => {
+  const template: MetadataItemInputFieldTemplate = {
+    ...baseField,
+    type: 'MetadataItem',
+    default: undefined,
+  };
+
+  return template;
+};
+
+const buildMetadataItemCollectionInputFieldTemplate = ({
+  baseField,
+}: BuildInputFieldArg): MetadataItemCollectionInputFieldTemplate => {
+  const template: MetadataItemCollectionInputFieldTemplate = {
+    ...baseField,
+    type: 'MetadataItemCollection',
+    default: undefined,
+  };
+
+  return template;
+};
+
+const buildMetadataItemPolymorphicInputFieldTemplate = ({
+  baseField,
+}: BuildInputFieldArg): MetadataItemPolymorphicInputFieldTemplate => {
+  const template: MetadataItemPolymorphicInputFieldTemplate = {
+    ...baseField,
+    type: 'MetadataItemPolymorphic',
+    default: undefined,
+  };
+
+  return template;
+};
+
+const buildMetadataDictInputFieldTemplate = ({
+  baseField,
+}: BuildInputFieldArg): MetadataDictInputFieldTemplate => {
+  const template: MetadataDictInputFieldTemplate = {
+    ...baseField,
+    type: 'MetadataDict',
+    default: undefined,
+  };
+
+  return template;
+};
+
+const buildMetadataDictCollectionInputFieldTemplate = ({
+  baseField,
+}: BuildInputFieldArg): MetadataDictCollectionInputFieldTemplate => {
+  const template: MetadataDictCollectionInputFieldTemplate = {
+    ...baseField,
+    type: 'MetadataDictCollection',
+    default: undefined,
+  };
+
+  return template;
+};
+
 const buildColorInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -851,7 +944,11 @@ export const getFieldType = (
   return;
 };
 
-const TEMPLATE_BUILDER_MAP = {
+const TEMPLATE_BUILDER_MAP: {
+  [key in FieldType]?: (arg: BuildInputFieldArg) => InputFieldTemplate;
+} = {
+  BoardField: buildBoardInputFieldTemplate,
+  Any: buildAnyInputFieldTemplate,
   boolean: buildBooleanInputFieldTemplate,
   BooleanCollection: buildBooleanCollectionInputFieldTemplate,
   BooleanPolymorphic: buildBooleanPolymorphicInputFieldTemplate,
@@ -885,6 +982,11 @@ const TEMPLATE_BUILDER_MAP = {
   LatentsField: buildLatentsInputFieldTemplate,
   LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate,
   LoRAModelField: buildLoRAModelInputFieldTemplate,
+  MetadataItem: buildMetadataItemInputFieldTemplate,
+  MetadataItemCollection: buildMetadataItemCollectionInputFieldTemplate,
+  MetadataItemPolymorphic: buildMetadataItemPolymorphicInputFieldTemplate,
+  MetadataDict: buildMetadataDictInputFieldTemplate,
+  MetadataDictCollection: buildMetadataDictCollectionInputFieldTemplate,
   MainModelField: buildMainModelInputFieldTemplate,
   Scheduler: buildSchedulerInputFieldTemplate,
   SDXLMainModelField: buildSDXLMainModelInputFieldTemplate,
@@ -937,7 +1039,13 @@ export const buildInputFieldTemplate = (
     return;
   }
 
-  return TEMPLATE_BUILDER_MAP[fieldType]({
+  const builder = TEMPLATE_BUILDER_MAP[fieldType];
+
+  if (!builder) {
+    return;
+  }
+
+  return builder({
     schemaObject: fieldSchema,
     baseField,
   });

invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts

@@ -1,7 +1,11 @@
-import { InputFieldTemplate, InputFieldValue } from '../types/types';
+import { FieldType, InputFieldTemplate, InputFieldValue } from '../types/types';
 
-const FIELD_VALUE_FALLBACK_MAP = {
+const FIELD_VALUE_FALLBACK_MAP: {
+  [key in FieldType]: InputFieldValue['value'];
+} = {
+  Any: undefined,
   enum: '',
+  BoardField: undefined,
   boolean: false,
   BooleanCollection: [],
   BooleanPolymorphic: false,
@@ -33,6 +37,11 @@ const FIELD_VALUE_FALLBACK_MAP = {
   LatentsCollection: [],
   LatentsField: undefined,
   LatentsPolymorphic: undefined,
+  MetadataItem: undefined,
+  MetadataItemCollection: [],
+  MetadataItemPolymorphic: undefined,
+  MetadataDict: undefined,
+  MetadataDictCollection: [],
   LoRAModelField: undefined,
   MainModelField: undefined,
   ONNXModelField: undefined,

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

@@ -1,18 +1,16 @@
 import { RootState } from 'app/store/store';
 import { getValidControlNets } from 'features/controlNet/util/getValidControlNets';
-import { omit } from 'lodash-es';
 import {
   CollectInvocation,
   ControlField,
   ControlNetInvocation,
-  MetadataAccumulatorInvocation,
 } from 'services/api/types';
-import { NonNullableGraph } from '../../types/types';
+import { NonNullableGraph, zControlField } from '../../types/types';
 import {
   CANVAS_COHERENCE_DENOISE_LATENTS,
   CONTROL_NET_COLLECT,
-  METADATA_ACCUMULATOR,
 } from './constants';
+import { addMainMetadata } from './metadata';
 
 export const addControlNetToLinearGraph = (
   state: RootState,
@@ -23,12 +21,9 @@ export const addControlNetToLinearGraph = (
 
   const validControlNets = getValidControlNets(controlNets);
 
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
-
   if (isControlNetEnabled && Boolean(validControlNets.length)) {
     if (validControlNets.length) {
+      const controlnets: ControlField[] = [];
       // We have multiple controlnets, add ControlNet collector
       const controlNetIterateNode: CollectInvocation = {
         id: CONTROL_NET_COLLECT,
@@ -87,15 +82,7 @@ export const addControlNetToLinearGraph = (
 
         graph.nodes[controlNetNode.id] = controlNetNode as ControlNetInvocation;
 
-        if (metadataAccumulator?.controlnets) {
-          // metadata accumulator only needs a control field - not the whole node
-          // extract what we need and add to the accumulator
-          const controlField = omit(controlNetNode, [
-            'id',
-            'type',
-          ]) as ControlField;
-          metadataAccumulator.controlnets.push(controlField);
-        }
+        controlnets.push(zControlField.parse(controlNetNode));
 
         graph.edges.push({
           source: { node_id: controlNetNode.id, field: 'control' },
@@ -115,6 +102,8 @@ export const addControlNetToLinearGraph = (
           });
         }
       });
+
+      addMainMetadata(graph, { controlnets });
     }
   }
 };

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

@@ -38,15 +38,7 @@ export const addIPAdapterToLinearGraph = (
 
     graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation;
 
-    // if (metadataAccumulator?.ip_adapters) {
-    //   // metadata accumulator only needs the ip_adapter field - not the whole node
-    //   // extract what we need and add to the accumulator
-    //   const ipAdapterField = omit(ipAdapterNode, [
-    //     'id',
-    //     'type',
-    //   ]) as IPAdapterField;
-    //   metadataAccumulator.ip_adapters.push(ipAdapterField);
-    // }
+    // TODO: add metadata
 
     graph.edges.push({
       source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },

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

@@ -1,21 +1,22 @@
 import { RootState } from 'app/store/store';
-import { NonNullableGraph } from 'features/nodes/types/types';
+import {
+  LoRAMetadataItem,
+  NonNullableGraph,
+  zLoRAMetadataItem,
+} from 'features/nodes/types/types';
 import { forEach, size } from 'lodash-es';
+import { LoraLoaderInvocation } from 'services/api/types';
 import {
-  LoraLoaderInvocation,
-  MetadataAccumulatorInvocation,
-} from 'services/api/types';
-import {
+  CANVAS_COHERENCE_DENOISE_LATENTS,
   CANVAS_INPAINT_GRAPH,
   CANVAS_OUTPAINT_GRAPH,
-  CANVAS_COHERENCE_DENOISE_LATENTS,
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
 } from './constants';
+import { addMainMetadata } from './metadata';
 
 export const addLoRAsToGraph = (
   state: RootState,
@@ -33,29 +34,29 @@ export const addLoRAsToGraph = (
 
   const { loras } = state.lora;
   const loraCount = size(loras);
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
 
-  if (loraCount > 0) {
-    // Remove modelLoaderNodeId unet connection to feed it to LoRAs
-    graph.edges = graph.edges.filter(
-      (e) =>
-        !(
-          e.source.node_id === modelLoaderNodeId &&
-          ['unet'].includes(e.source.field)
-        )
-    );
-    // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
-    graph.edges = graph.edges.filter(
-      (e) =>
-        !(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field))
-    );
+  if (loraCount === 0) {
+    return;
   }
 
+  // Remove modelLoaderNodeId unet connection to feed it to LoRAs
+  graph.edges = graph.edges.filter(
+    (e) =>
+      !(
+        e.source.node_id === modelLoaderNodeId &&
+        ['unet'].includes(e.source.field)
+      )
+  );
+  // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
+  graph.edges = graph.edges.filter(
+    (e) =>
+      !(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field))
+  );
+
   // we need to remember the last lora so we can chain from it
   let lastLoraNodeId = '';
   let currentLoraIndex = 0;
+  const loraMetadata: LoRAMetadataItem[] = [];
 
   forEach(loras, (lora) => {
     const { model_name, base_model, weight } = lora;
@@ -69,13 +70,12 @@ export const addLoRAsToGraph = (
       weight,
     };
 
-    // add the lora to the metadata accumulator
-    if (metadataAccumulator?.loras) {
-      metadataAccumulator.loras.push({
+    loraMetadata.push(
+      zLoRAMetadataItem.parse({
         lora: { model_name, base_model },
         weight,
-      });
-    }
+      })
+    );
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;
@@ -182,4 +182,6 @@ export const addLoRAsToGraph = (
     lastLoraNodeId = currentLoraNodeId;
     currentLoraIndex += 1;
   });
+
+  addMainMetadata(graph, { loras: loraMetadata });
 };

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

@@ -1,14 +1,14 @@
 import { RootState } from 'app/store/store';
-import { NonNullableGraph } from 'features/nodes/types/types';
-import { forEach, size } from 'lodash-es';
 import {
-  MetadataAccumulatorInvocation,
-  SDXLLoraLoaderInvocation,
-} from 'services/api/types';
+  LoRAMetadataItem,
+  NonNullableGraph,
+  zLoRAMetadataItem,
+} from 'features/nodes/types/types';
+import { forEach, size } from 'lodash-es';
+import { SDXLLoraLoaderInvocation } from 'services/api/types';
 import {
   CANVAS_COHERENCE_DENOISE_LATENTS,
   LORA_LOADER,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
   SDXL_CANVAS_INPAINT_GRAPH,
@@ -17,6 +17,7 @@ import {
   SDXL_REFINER_INPAINT_CREATE_MASK,
   SEAMLESS,
 } from './constants';
+import { addMainMetadata } from './metadata';
 
 export const addSDXLLoRAsToGraph = (
   state: RootState,
@@ -34,9 +35,12 @@ export const addSDXLLoRAsToGraph = (
 
   const { loras } = state.lora;
   const loraCount = size(loras);
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
+
+  if (loraCount === 0) {
+    return;
+  }
+
+  const loraMetadata: LoRAMetadataItem[] = [];
 
   // Handle Seamless Plugs
   const unetLoaderId = modelLoaderNodeId;
@@ -47,22 +51,17 @@ export const addSDXLLoRAsToGraph = (
     clipLoaderId = SDXL_MODEL_LOADER;
   }
 
-  if (loraCount > 0) {
-    // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
-    graph.edges = graph.edges.filter(
-      (e) =>
-        !(
-          e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field)
-        ) &&
-        !(
-          e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field)
-        ) &&
-        !(
-          e.source.node_id === clipLoaderId &&
-          ['clip2'].includes(e.source.field)
-        )
-    );
-  }
+  // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
+  graph.edges = graph.edges.filter(
+    (e) =>
+      !(
+        e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field)
+      ) &&
+      !(
+        e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field)
+      ) &&
+      !(e.source.node_id === clipLoaderId && ['clip2'].includes(e.source.field))
+  );
 
   // we need to remember the last lora so we can chain from it
   let lastLoraNodeId = '';
@@ -80,16 +79,12 @@ export const addSDXLLoRAsToGraph = (
       weight,
     };
 
-    // add the lora to the metadata accumulator
-    if (metadataAccumulator) {
-      if (!metadataAccumulator.loras) {
-        metadataAccumulator.loras = [];
-      }
-      metadataAccumulator.loras.push({
+    loraMetadata.push(
+      zLoRAMetadataItem.parse({
         lora: { model_name, base_model },
         weight,
-      });
-    }
+      })
+    );
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;
@@ -242,4 +237,6 @@ export const addSDXLLoRAsToGraph = (
     lastLoraNodeId = currentLoraNodeId;
     currentLoraIndex += 1;
   });
+
+  addMainMetadata(graph, { loras: loraMetadata });
 };

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

@@ -2,7 +2,6 @@ import { RootState } from 'app/store/store';
 import {
   CreateDenoiseMaskInvocation,
   ImageDTO,
-  MetadataAccumulatorInvocation,
   SeamlessModeInvocation,
 } from 'services/api/types';
 import { NonNullableGraph } from '../../types/types';
@@ -12,7 +11,6 @@ import {
   LATENTS_TO_IMAGE,
   MASK_COMBINE,
   MASK_RESIZE_UP,
-  METADATA_ACCUMULATOR,
   SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
   SDXL_CANVAS_INPAINT_GRAPH,
   SDXL_CANVAS_OUTPAINT_GRAPH,
@@ -26,6 +24,7 @@ import {
   SDXL_REFINER_SEAMLESS,
 } from './constants';
 import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
+import { addMainMetadata } from './metadata';
 
 export const addSDXLRefinerToGraph = (
   state: RootState,
@@ -57,21 +56,15 @@ export const addSDXLRefinerToGraph = (
     return;
   }
 
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
-
-  if (metadataAccumulator) {
-    metadataAccumulator.refiner_model = refinerModel;
-    metadataAccumulator.refiner_positive_aesthetic_score =
-      refinerPositiveAestheticScore;
-    metadataAccumulator.refiner_negative_aesthetic_score =
-      refinerNegativeAestheticScore;
-    metadataAccumulator.refiner_cfg_scale = refinerCFGScale;
-    metadataAccumulator.refiner_scheduler = refinerScheduler;
-    metadataAccumulator.refiner_start = refinerStart;
-    metadataAccumulator.refiner_steps = refinerSteps;
-  }
+  addMainMetadata(graph, {
+    refiner_model: refinerModel,
+    refiner_positive_aesthetic_score: refinerPositiveAestheticScore,
+    refiner_negative_aesthetic_score: refinerNegativeAestheticScore,
+    refiner_cfg_scale: refinerCFGScale,
+    refiner_scheduler: refinerScheduler,
+    refiner_start: refinerStart,
+    refiner_steps: refinerSteps,
+  });
 
   const modelLoaderId = modelLoaderNodeId
     ? modelLoaderNodeId

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

@@ -1,18 +1,14 @@
+import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import { SaveImageInvocation } from 'services/api/types';
 import {
   CANVAS_OUTPUT,
   LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
   NSFW_CHECKER,
   SAVE_IMAGE,
   WATERMARKER,
 } from './constants';
-import {
-  MetadataAccumulatorInvocation,
-  SaveImageInvocation,
-} from 'services/api/types';
-import { RootState } from 'app/store/store';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 
 /**
  * Set the `use_cache` field on the linear/canvas graph's final image output node to False.
@@ -24,33 +20,18 @@ export const addSaveImageNode = (
   const activeTabName = activeTabNameSelector(state);
   const is_intermediate =
     activeTabName === 'unifiedCanvas' ? !state.canvas.shouldAutoSave : false;
+  const { autoAddBoardId } = state.gallery;
 
   const saveImageNode: SaveImageInvocation = {
     id: SAVE_IMAGE,
     type: 'save_image',
     is_intermediate,
     use_cache: false,
+    board: autoAddBoardId === 'none' ? undefined : { board_id: autoAddBoardId },
   };
 
   graph.nodes[SAVE_IMAGE] = saveImageNode;
 
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
-
-  if (metadataAccumulator) {
-    graph.edges.push({
-      source: {
-        node_id: METADATA_ACCUMULATOR,
-        field: 'metadata',
-      },
-      destination: {
-        node_id: SAVE_IMAGE,
-        field: 'metadata',
-      },
-    });
-  }
-
   const destination = {
     node_id: SAVE_IMAGE,
     field: 'image',

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

@@ -1,6 +1,7 @@
 import { RootState } from 'app/store/store';
 import { SeamlessModeInvocation } from 'services/api/types';
 import { NonNullableGraph } from '../../types/types';
+import { addMainMetadata } from './metadata';
 import {
   CANVAS_COHERENCE_DENOISE_LATENTS,
   CANVAS_INPAINT_GRAPH,
@@ -31,6 +32,11 @@ export const addSeamlessToLinearGraph = (
     seamless_y: seamlessYAxis,
   } as SeamlessModeInvocation;
 
+  addMainMetadata(graph, {
+    seamless_x: seamlessXAxis,
+    seamless_y: seamlessYAxis,
+  });
+
   let denoisingNodeId = DENOISE_LATENTS;
 
   if (

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

@@ -1,6 +1,5 @@
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
-import { MetadataAccumulatorInvocation } from 'services/api/types';
 import {
   CANVAS_COHERENCE_INPAINT_CREATE_MASK,
   CANVAS_IMAGE_TO_IMAGE_GRAPH,
@@ -14,7 +13,6 @@ import {
   INPAINT_IMAGE,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  METADATA_ACCUMULATOR,
   ONNX_MODEL_LOADER,
   SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH,
   SDXL_CANVAS_INPAINT_GRAPH,
@@ -26,6 +24,7 @@ import {
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
+import { addMainMetadata } from './metadata';
 
 export const addVAEToGraph = (
   state: RootState,
@@ -41,9 +40,6 @@ export const addVAEToGraph = (
   );
 
   const isAutoVae = !vae;
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
 
   if (!isAutoVae) {
     graph.nodes[VAE_LOADER] = {
@@ -181,7 +177,7 @@ export const addVAEToGraph = (
     }
   }
 
-  if (vae && metadataAccumulator) {
-    metadataAccumulator.vae = vae;
+  if (vae) {
+    addMainMetadata(graph, { vae });
   }
 };

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

@@ -5,14 +5,8 @@ import {
   ImageNSFWBlurInvocation,
   ImageWatermarkInvocation,
   LatentsToImageInvocation,
-  MetadataAccumulatorInvocation,
 } from 'services/api/types';
-import {
-  LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
-  NSFW_CHECKER,
-  WATERMARKER,
-} from './constants';
+import { LATENTS_TO_IMAGE, NSFW_CHECKER, WATERMARKER } from './constants';
 
 export const addWatermarkerToGraph = (
   state: RootState,
@@ -32,10 +26,6 @@ export const addWatermarkerToGraph = (
     | ImageNSFWBlurInvocation
     | undefined;
 
-  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    | MetadataAccumulatorInvocation
-    | undefined;
-
   if (!nodeToAddTo) {
     // something has gone terribly awry
     return;
@@ -80,17 +70,4 @@ export const addWatermarkerToGraph = (
       },
     });
   }
-
-  if (metadataAccumulator) {
-    graph.edges.push({
-      source: {
-        node_id: METADATA_ACCUMULATOR,
-        field: 'metadata',
-      },
-      destination: {
-        node_id: WATERMARKER,
-        field: 'metadata',
-      },
-    });
-  }
 };

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

@@ -1,20 +1,24 @@
+import { BoardId } from 'features/gallery/store/types';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { ESRGANModelName } from 'features/parameters/store/postprocessingSlice';
 import {
-  Graph,
   ESRGANInvocation,
+  Graph,
   SaveImageInvocation,
 } from 'services/api/types';
 import { REALESRGAN as ESRGAN, SAVE_IMAGE } from './constants';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 type Arg = {
   image_name: string;
   esrganModelName: ESRGANModelName;
+  autoAddBoardId: BoardId;
 };
 
 export const buildAdHocUpscaleGraph = ({
   image_name,
   esrganModelName,
+  autoAddBoardId,
 }: Arg): Graph => {
   const realesrganNode: ESRGANInvocation = {
     id: ESRGAN,
@@ -28,6 +32,8 @@ export const buildAdHocUpscaleGraph = ({
     id: SAVE_IMAGE,
     type: 'save_image',
     use_cache: false,
+    is_intermediate: false,
+    board: autoAddBoardId === 'none' ? undefined : { board_id: autoAddBoardId },
   };
 
   const graph: NonNullableGraph = {
@@ -50,5 +56,9 @@ export const buildAdHocUpscaleGraph = ({
     ],
   };
 
+  addMainMetadataNodeToGraph(graph, {
+    model: esrganModelName,
+  });
+
   return graph;
 };

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

@@ -19,12 +19,12 @@ import {
   IMG2IMG_RESIZE,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
@@ -307,10 +307,7 @@ export const buildCanvasImageToImageGraph = (
     });
   }
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'img2img',
     cfg_scale,
     width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@@ -324,13 +321,10 @@ export const buildCanvasImageToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined, // option; set in addVAEToGraph
-    controlnets: [], // populated in addControlNetToLinearGraph
-    loras: [], // populated in addLoRAsToGraph
     clip_skip: clipSkip,
     strength,
     init_image: initialImage.image_name,
-  };
+  });
 
   // Add Seamless To Graph
   if (seamlessXAxis || seamlessYAxis) {

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

@@ -16,7 +16,6 @@ import {
   IMAGE_TO_LATENTS,
   IMG2IMG_RESIZE,
   LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -27,6 +26,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
@@ -318,10 +318,7 @@ export const buildCanvasSDXLImageToImageGraph = (
     });
   }
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'img2img',
     cfg_scale,
     width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@@ -335,22 +332,8 @@ export const buildCanvasSDXLImageToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined, // option; set in addVAEToGraph
-    controlnets: [], // populated in addControlNetToLinearGraph
-    loras: [], // populated in addLoRAsToGraph
     strength,
     init_image: initialImage.image_name,
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: CANVAS_OUTPUT,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -17,7 +17,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   CANVAS_OUTPUT,
   LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   ONNX_MODEL_LOADER,
@@ -29,6 +28,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
@@ -300,10 +300,7 @@ export const buildCanvasSDXLTextToImageGraph = (
     });
   }
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'txt2img',
     cfg_scale,
     width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@@ -317,20 +314,6 @@ export const buildCanvasSDXLTextToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined, // option; set in addVAEToGraph
-    controlnets: [], // populated in addControlNetToLinearGraph
-    loras: [], // populated in addLoRAsToGraph
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: CANVAS_OUTPUT,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -20,13 +20,13 @@ import {
   DENOISE_LATENTS,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   ONNX_MODEL_LOADER,
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
@@ -288,10 +288,7 @@ export const buildCanvasTextToImageGraph = (
     });
   }
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'txt2img',
     cfg_scale,
     width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width,
@@ -305,21 +302,7 @@ export const buildCanvasTextToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined, // option; set in addVAEToGraph
-    controlnets: [], // populated in addControlNetToLinearGraph
-    loras: [], // populated in addLoRAsToGraph
     clip_skip: clipSkip,
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: CANVAS_OUTPUT,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -4,13 +4,20 @@ import { generateSeeds } from 'common/util/generateSeeds';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { range, unset } from 'lodash-es';
 import { components } from 'services/api/schema';
-import { Batch, BatchConfig } from 'services/api/types';
+import { Batch, BatchConfig, MetadataItemInvocation } from 'services/api/types';
 import {
+  BATCH_PROMPT,
+  BATCH_SEED,
+  BATCH_STYLE_PROMPT,
   CANVAS_COHERENCE_NOISE,
   METADATA_ACCUMULATOR,
   NOISE,
   POSITIVE_CONDITIONING,
 } from './constants';
+import {
+  addBatchMetadataNodeToGraph,
+  removeMetadataFromMainMetadataNode,
+} from './metadata';
 
 export const prepareLinearUIBatch = (
   state: RootState,
@@ -23,8 +30,27 @@ export const prepareLinearUIBatch = (
 
   const data: Batch['data'] = [];
 
+  const seedMetadataItemNode: MetadataItemInvocation = {
+    id: BATCH_SEED,
+    type: 'metadata_item',
+    label: 'seed',
+  };
+
+  const promptMetadataItemNode: MetadataItemInvocation = {
+    id: BATCH_PROMPT,
+    type: 'metadata_item',
+    label: 'positive_prompt',
+  };
+
+  const stylePromptMetadataItemNode: MetadataItemInvocation = {
+    id: BATCH_STYLE_PROMPT,
+    type: 'metadata_item',
+    label: 'positive_style_prompt',
+  };
+
+  const itemNodesIds: string[] = [];
+
   if (prompts.length === 1) {
-    unset(graph.nodes[METADATA_ACCUMULATOR], 'seed');
     const seeds = generateSeeds({
       count: iterations,
       start: shouldRandomizeSeed ? undefined : seed,
@@ -40,13 +66,15 @@ export const prepareLinearUIBatch = (
       });
     }
 
-    if (graph.nodes[METADATA_ACCUMULATOR]) {
-      zipped.push({
-        node_path: METADATA_ACCUMULATOR,
-        field_name: 'seed',
-        items: seeds,
-      });
-    }
+    // add to metadata
+    removeMetadataFromMainMetadataNode(graph, 'seed');
+    itemNodesIds.push(BATCH_SEED);
+    graph.nodes[BATCH_SEED] = seedMetadataItemNode;
+    zipped.push({
+      node_path: BATCH_SEED,
+      field_name: 'value',
+      items: seeds,
+    });
 
     if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
       zipped.push({
@@ -77,13 +105,15 @@ export const prepareLinearUIBatch = (
         });
       }
 
-      if (graph.nodes[METADATA_ACCUMULATOR]) {
-        firstBatchDatumList.push({
-          node_path: METADATA_ACCUMULATOR,
-          field_name: 'seed',
-          items: seeds,
-        });
-      }
+      // add to metadata
+      removeMetadataFromMainMetadataNode(graph, 'seed');
+      itemNodesIds.push(BATCH_SEED);
+      graph.nodes[BATCH_SEED] = seedMetadataItemNode;
+      firstBatchDatumList.push({
+        node_path: BATCH_SEED,
+        field_name: 'value',
+        items: seeds,
+      });
 
       if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
         firstBatchDatumList.push({
@@ -106,13 +136,17 @@ export const prepareLinearUIBatch = (
           items: seeds,
         });
       }
-      if (graph.nodes[METADATA_ACCUMULATOR]) {
-        secondBatchDatumList.push({
-          node_path: METADATA_ACCUMULATOR,
-          field_name: 'seed',
-          items: seeds,
-        });
-      }
+
+      // add to metadata
+      removeMetadataFromMainMetadataNode(graph, 'seed');
+      itemNodesIds.push(BATCH_SEED);
+      graph.nodes[BATCH_SEED] = seedMetadataItemNode;
+      secondBatchDatumList.push({
+        node_path: BATCH_SEED,
+        field_name: 'value',
+        items: seeds,
+      });
+
       if (graph.nodes[CANVAS_COHERENCE_NOISE]) {
         secondBatchDatumList.push({
           node_path: CANVAS_COHERENCE_NOISE,
@@ -137,13 +171,15 @@ export const prepareLinearUIBatch = (
       });
     }
 
-    if (graph.nodes[METADATA_ACCUMULATOR]) {
-      firstBatchDatumList.push({
-        node_path: METADATA_ACCUMULATOR,
-        field_name: 'positive_prompt',
-        items: extendedPrompts,
-      });
-    }
+    // add to metadata
+    removeMetadataFromMainMetadataNode(graph, 'positive_prompt');
+    itemNodesIds.push(BATCH_PROMPT);
+    graph.nodes[BATCH_PROMPT] = promptMetadataItemNode;
+    firstBatchDatumList.push({
+      node_path: BATCH_PROMPT,
+      field_name: 'value',
+      items: extendedPrompts,
+    });
 
     if (shouldConcatSDXLStylePrompt && model?.base_model === 'sdxl') {
       unset(graph.nodes[METADATA_ACCUMULATOR], 'positive_style_prompt');
@@ -160,18 +196,22 @@ export const prepareLinearUIBatch = (
         });
       }
 
-      if (graph.nodes[METADATA_ACCUMULATOR]) {
-        firstBatchDatumList.push({
-          node_path: METADATA_ACCUMULATOR,
-          field_name: 'positive_style_prompt',
-          items: stylePrompts,
-        });
-      }
+      // add to metadata
+      removeMetadataFromMainMetadataNode(graph, 'positive_style_prompt');
+      itemNodesIds.push(BATCH_STYLE_PROMPT);
+      graph.nodes[BATCH_STYLE_PROMPT] = stylePromptMetadataItemNode;
+      firstBatchDatumList.push({
+        node_path: BATCH_STYLE_PROMPT,
+        field_name: 'value',
+        items: extendedPrompts,
+      });
     }
 
     data.push(firstBatchDatumList);
   }
 
+  addBatchMetadataNodeToGraph(graph, itemNodesIds);
+
   const enqueueBatchArg: BatchConfig = {
     prepend,
     batch: {

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

@@ -20,13 +20,13 @@ import {
   IMAGE_TO_LATENTS,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
   RESIZE,
   SEAMLESS,
 } from './constants';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 /**
  * Builds the Image to Image tab graph.
@@ -310,10 +310,7 @@ export const buildLinearImageToImageGraph = (
     });
   }
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'img2img',
     cfg_scale,
     height,
@@ -325,23 +322,9 @@ export const buildLinearImageToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined, // option; set in addVAEToGraph
-    controlnets: [], // populated in addControlNetToLinearGraph
-    loras: [], // populated in addLoRAsToGraph
     clip_skip: clipSkip,
     strength,
     init_image: initialImage.imageName,
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: LATENTS_TO_IMAGE,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -17,7 +17,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
   IMAGE_TO_LATENTS,
   LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -29,6 +28,7 @@ import {
   SEAMLESS,
 } from './constants';
 import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt';
+import { addMainMetadataNodeToGraph } from './metadata';
 
 /**
  * Builds the Image to Image tab graph.
@@ -330,10 +330,7 @@ export const buildLinearSDXLImageToImageGraph = (
     });
   }
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'sdxl_img2img',
     cfg_scale,
     height,
@@ -345,24 +342,10 @@ export const buildLinearSDXLImageToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined,
-    controlnets: [],
-    loras: [],
-    strength: strength,
+    strength,
     init_image: initialImage.imageName,
     positive_style_prompt: positiveStylePrompt,
     negative_style_prompt: negativeStylePrompt,
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: LATENTS_TO_IMAGE,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -10,9 +10,9 @@ import { addSaveImageNode } from './addSaveImageNode';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
+import { addMainMetadataNodeToGraph } from './metadata';
 import {
   LATENTS_TO_IMAGE,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -224,10 +224,7 @@ export const buildLinearSDXLTextToImageGraph = (
     ],
   };
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'sdxl_txt2img',
     cfg_scale,
     height,
@@ -239,22 +236,8 @@ export const buildLinearSDXLTextToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined,
-    controlnets: [],
-    loras: [],
     positive_style_prompt: positiveStylePrompt,
     negative_style_prompt: negativeStylePrompt,
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: LATENTS_TO_IMAGE,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -13,12 +13,12 @@ import { addSaveImageNode } from './addSaveImageNode';
 import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
+import { addMainMetadataNodeToGraph } from './metadata';
 import {
   CLIP_SKIP,
   DENOISE_LATENTS,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
-  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   ONNX_MODEL_LOADER,
@@ -232,10 +232,7 @@ export const buildLinearTextToImageGraph = (
     ],
   };
 
-  // add metadata accumulator, which is only mostly populated - some fields are added later
-  graph.nodes[METADATA_ACCUMULATOR] = {
-    id: METADATA_ACCUMULATOR,
-    type: 'metadata_accumulator',
+  addMainMetadataNodeToGraph(graph, {
     generation_mode: 'txt2img',
     cfg_scale,
     height,
@@ -247,21 +244,7 @@ export const buildLinearTextToImageGraph = (
     steps,
     rand_device: use_cpu ? 'cpu' : 'cuda',
     scheduler,
-    vae: undefined, // option; set in addVAEToGraph
-    controlnets: [], // populated in addControlNetToLinearGraph
-    loras: [], // populated in addLoRAsToGraph
     clip_skip: clipSkip,
-  };
-
-  graph.edges.push({
-    source: {
-      node_id: METADATA_ACCUMULATOR,
-      field: 'metadata',
-    },
-    destination: {
-      node_id: LATENTS_TO_IMAGE,
-      field: 'metadata',
-    },
   });
 
   // Add Seamless To Graph

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

@@ -50,7 +50,15 @@ export const IP_ADAPTER = 'ip_adapter';
 export const DYNAMIC_PROMPT = 'dynamic_prompt';
 export const IMAGE_COLLECTION = 'image_collection';
 export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate';
+export const METADATA = 'metadata';
+export const BATCH_METADATA = 'batch_metadata';
+export const BATCH_METADATA_COLLECT = 'batch_metadata_collect';
+export const BATCH_SEED = 'batch_seed';
+export const BATCH_PROMPT = 'batch_prompt';
+export const BATCH_STYLE_PROMPT = 'batch_style_prompt';
+export const METADATA_COLLECT = 'metadata_collect';
 export const METADATA_ACCUMULATOR = 'metadata_accumulator';
+export const MERGE_METADATA = 'merge_metadata';
 export const REALESRGAN = 'esrgan';
 export const DIVIDE = 'divide';
 export const SCALE = 'scale_image';

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

@@ -0,0 +1,151 @@
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { map } from 'lodash-es';
+import { MetadataInvocationAsCollection } from 'services/api/types';
+import { JsonObject } from 'type-fest';
+import {
+  BATCH_METADATA,
+  BATCH_METADATA_COLLECT,
+  MERGE_METADATA,
+  METADATA,
+  METADATA_COLLECT,
+  SAVE_IMAGE,
+} from './constants';
+
+export const addMainMetadataNodeToGraph = (
+  graph: NonNullableGraph,
+  metadata: JsonObject
+): void => {
+  graph.nodes[METADATA] = {
+    id: METADATA,
+    type: 'metadata',
+    items: map(metadata, (value, label) => ({ label, value })),
+  };
+
+  graph.nodes[METADATA_COLLECT] = {
+    id: METADATA_COLLECT,
+    type: 'collect',
+  };
+
+  graph.nodes[MERGE_METADATA] = {
+    id: MERGE_METADATA,
+    type: 'merge_metadata_dict',
+  };
+
+  graph.edges.push({
+    source: {
+      node_id: METADATA,
+      field: 'metadata_dict',
+    },
+    destination: {
+      node_id: METADATA_COLLECT,
+      field: 'item',
+    },
+  });
+
+  graph.edges.push({
+    source: {
+      node_id: METADATA_COLLECT,
+      field: 'collection',
+    },
+    destination: {
+      node_id: MERGE_METADATA,
+      field: 'collection',
+    },
+  });
+
+  graph.edges.push({
+    source: {
+      node_id: MERGE_METADATA,
+      field: 'metadata_dict',
+    },
+    destination: {
+      node_id: SAVE_IMAGE,
+      field: 'metadata',
+    },
+  });
+
+  return;
+};
+
+export const addMainMetadata = (
+  graph: NonNullableGraph,
+  metadata: JsonObject
+): void => {
+  const metadataNode = graph.nodes[METADATA] as
+    | MetadataInvocationAsCollection
+    | undefined;
+
+  if (!metadataNode) {
+    return;
+  }
+
+  metadataNode.items.push(
+    ...map(metadata, (value, label) => ({ label, value }))
+  );
+};
+
+export const removeMetadataFromMainMetadataNode = (
+  graph: NonNullableGraph,
+  label: string
+): void => {
+  const metadataNode = graph.nodes[METADATA] as
+    | MetadataInvocationAsCollection
+    | undefined;
+
+  if (!metadataNode) {
+    return;
+  }
+
+  metadataNode.items = metadataNode.items.filter(
+    (item) => item.label !== label
+  );
+};
+
+export const addBatchMetadataNodeToGraph = (
+  graph: NonNullableGraph,
+  itemNodeIds: string[]
+) => {
+  graph.nodes[BATCH_METADATA] = {
+    id: BATCH_METADATA,
+    type: 'metadata',
+  };
+  graph.nodes[BATCH_METADATA_COLLECT] = {
+    id: BATCH_METADATA_COLLECT,
+    type: 'collect',
+  };
+
+  itemNodeIds.forEach((id) => {
+    graph.edges.push({
+      source: {
+        node_id: id,
+        field: 'item',
+      },
+      destination: {
+        node_id: BATCH_METADATA_COLLECT,
+        field: 'item',
+      },
+    });
+  });
+
+  graph.edges.push({
+    source: {
+      node_id: BATCH_METADATA_COLLECT,
+      field: 'collection',
+    },
+    destination: {
+      node_id: BATCH_METADATA,
+      field: 'items',
+    },
+  });
+
+  graph.edges.push({
+    source: {
+      node_id: BATCH_METADATA,
+      field: 'metadata_dict',
+    },
+    destination: {
+      node_id: METADATA_COLLECT,
+      field: 'item',
+    },
+  });
+};

invokeai/frontend/web/src/features/nodes/util/parseSchema.ts

@@ -4,7 +4,6 @@ import { reduce } from 'lodash-es';
 import { OpenAPIV3 } from 'openapi-types';
 import { AnyInvocationType } from 'services/events/types';
 import {
-  FieldType,
   InputFieldTemplate,
   InvocationSchemaObject,
   InvocationTemplate,
@@ -16,18 +15,11 @@ import {
 } from '../types/types';
 import { buildInputFieldTemplate, getFieldType } from './fieldTemplateBuilders';
 
-const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'metadata', 'use_cache'];
+const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'use_cache'];
 const RESERVED_OUTPUT_FIELD_NAMES = ['type'];
-const RESERVED_FIELD_TYPES = [
-  'WorkflowField',
-  'MetadataField',
-  'IsIntermediate',
-];
+const RESERVED_FIELD_TYPES = ['IsIntermediate', 'WorkflowField'];
 
-const invocationDenylist: AnyInvocationType[] = [
-  'graph',
-  'metadata_accumulator',
-];
+const invocationDenylist: AnyInvocationType[] = ['graph'];
 
 const isReservedInputField = (nodeType: string, fieldName: string) => {
   if (RESERVED_INPUT_FIELD_NAMES.includes(fieldName)) {
@@ -42,7 +34,7 @@ const isReservedInputField = (nodeType: string, fieldName: string) => {
   return false;
 };
 
-const isReservedFieldType = (fieldType: FieldType) => {
+const isReservedFieldType = (fieldType: string) => {
   if (RESERVED_FIELD_TYPES.includes(fieldType)) {
     return true;
   }
@@ -86,6 +78,7 @@ export const parseSchema = (
     const tags = schema.tags ?? [];
     const description = schema.description ?? '';
     const version = schema.version;
+    let withWorkflow = false;
 
     const inputs = reduce(
       schema.properties,
@@ -112,7 +105,7 @@ export const parseSchema = (
 
         const fieldType = getFieldType(property);
 
-        if (!isFieldType(fieldType)) {
+        if (!fieldType) {
           logger('nodes').warn(
             {
               node: type,
@@ -120,11 +113,16 @@ export const parseSchema = (
               fieldType,
               field: parseify(property),
             },
-            'Skipping unknown input field type'
+            'Missing input field type'
           );
           return inputsAccumulator;
         }
 
+        if (fieldType === 'WorkflowField') {
+          withWorkflow = true;
+          return inputsAccumulator;
+        }
+
         if (isReservedFieldType(fieldType)) {
           logger('nodes').trace(
             {
@@ -133,7 +131,20 @@ export const parseSchema = (
               fieldType,
               field: parseify(property),
             },
-            'Skipping reserved field type'
+            `Skipping reserved input field type: ${fieldType}`
+          );
+          return inputsAccumulator;
+        }
+
+        if (!isFieldType(fieldType)) {
+          logger('nodes').warn(
+            {
+              node: type,
+              fieldName: propertyName,
+              fieldType,
+              field: parseify(property),
+            },
+            `Skipping unknown input field type: ${fieldType}`
           );
           return inputsAccumulator;
         }
@@ -146,7 +157,7 @@ export const parseSchema = (
         );
 
         if (!field) {
-          logger('nodes').debug(
+          logger('nodes').warn(
             {
               node: type,
               fieldName: propertyName,
@@ -247,6 +258,7 @@ export const parseSchema = (
       inputs,
       outputs,
       useCache,
+      withWorkflow,
     };
 
     Object.assign(invocationsAccumulator, { [type]: invocation });

invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamClipSkip.tsx

@@ -1,6 +1,6 @@
 import { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAISlider from 'common/components/IAISlider';
 import { setClipSkip } from 'features/parameters/store/generationSlice';
 import { clipSkipMap } from 'features/parameters/types/constants';
@@ -47,7 +47,7 @@ export default function ParamClipSkip() {
   }
 
   return (
-    <IAIInformationalPopover details="clipSkip">
+    <IAIInformationalPopover feature="clipSkip" placement="top">
       <IAISlider
         label={t('parameters.clipSkip')}
         aria-label={t('parameters.clipSkip')}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxSize.tsx

@@ -1,7 +1,8 @@
-import { Box, Flex, Spacer, Text } from '@chakra-ui/react';
+import { Flex, FormControl, FormLabel, Spacer } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIIconButton from 'common/components/IAIIconButton';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { flipBoundingBoxAxes } from 'features/canvas/store/canvasSlice';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
@@ -18,7 +19,6 @@ import ParamAspectRatio, {
 } from '../../Core/ParamAspectRatio';
 import ParamBoundingBoxHeight from './ParamBoundingBoxHeight';
 import ParamBoundingBoxWidth from './ParamBoundingBoxWidth';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
 
 const sizeOptsSelector = createSelector(
   [generationSelector, canvasSelector],
@@ -93,42 +93,29 @@ export default function ParamBoundingBoxSize() {
         },
       }}
     >
-      <Flex alignItems="center" gap={2}>
-        <Box width="full">
-          <IAIInformationalPopover details="paramRatio">
-            <Text
-              sx={{
-                fontSize: 'sm',
-                width: 'full',
-                color: 'base.700',
-                _dark: {
-                  color: 'base.300',
-                },
-              }}
-            >
-              {t('parameters.aspectRatio')}
-            </Text>
-          </IAIInformationalPopover>
-        </Box>
-        <Spacer />
-        <ParamAspectRatio />
-        <IAIIconButton
-          tooltip={t('ui.swapSizes')}
-          aria-label={t('ui.swapSizes')}
-          size="sm"
-          icon={<MdOutlineSwapVert />}
-          fontSize={20}
-          onClick={handleToggleSize}
-        />
-        <IAIIconButton
-          tooltip={t('ui.lockRatio')}
-          aria-label={t('ui.lockRatio')}
-          size="sm"
-          icon={<FaLock />}
-          isChecked={shouldLockAspectRatio}
-          onClick={handleLockRatio}
-        />
-      </Flex>
+      <IAIInformationalPopover feature="paramRatio">
+        <FormControl as={Flex} flexDir="row" alignItems="center" gap={2}>
+          <FormLabel>{t('parameters.aspectRatio')}</FormLabel>
+          <Spacer />
+          <ParamAspectRatio />
+          <IAIIconButton
+            tooltip={t('ui.swapSizes')}
+            aria-label={t('ui.swapSizes')}
+            size="sm"
+            icon={<MdOutlineSwapVert />}
+            fontSize={20}
+            onClick={handleToggleSize}
+          />
+          <IAIIconButton
+            tooltip={t('ui.lockRatio')}
+            aria-label={t('ui.lockRatio')}
+            size="sm"
+            icon={<FaLock />}
+            isChecked={shouldLockAspectRatio}
+            onClick={handleLockRatio}
+          />
+        </FormControl>
+      </IAIInformationalPopover>
       <ParamBoundingBoxWidth />
       <ParamBoundingBoxHeight />
     </Flex>

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/Compositing/CoherencePass/ParamCanvasCoherenceMode.tsx

@@ -1,6 +1,6 @@
 import type { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import { IAISelectDataType } from 'common/components/IAIMantineSearchableSelect';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import { setCanvasCoherenceMode } from 'features/parameters/store/generationSlice';
@@ -31,7 +31,7 @@ const ParamCanvasCoherenceMode = () => {
   };
 
   return (
-    <IAIInformationalPopover details="compositingCoherenceMode">
+    <IAIInformationalPopover feature="compositingCoherenceMode">
       <IAIMantineSelect
         label={t('parameters.coherenceMode')}
         data={coherenceModeSelectData}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/Compositing/CoherencePass/ParamCanvasCoherenceSteps.tsx

@@ -1,6 +1,6 @@
 import type { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAISlider from 'common/components/IAISlider';
 import { setCanvasCoherenceSteps } from 'features/parameters/store/generationSlice';
 import { memo } from 'react';
@@ -14,7 +14,7 @@ const ParamCanvasCoherenceSteps = () => {
   const { t } = useTranslation();
 
   return (
-    <IAIInformationalPopover details="compositingCoherenceSteps">
+    <IAIInformationalPopover feature="compositingCoherenceSteps">
       <IAISlider
         label={t('parameters.coherenceSteps')}
         min={1}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/Compositing/CoherencePass/ParamCanvasCoherenceStrength.tsx

@@ -1,6 +1,6 @@
 import type { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAISlider from 'common/components/IAISlider';
 import { setCanvasCoherenceStrength } from 'features/parameters/store/generationSlice';
 import { memo } from 'react';
@@ -14,7 +14,7 @@ const ParamCanvasCoherenceStrength = () => {
   const { t } = useTranslation();
 
   return (
-    <IAIInformationalPopover details="compositingStrength">
+    <IAIInformationalPopover feature="compositingStrength">
       <IAISlider
         label={t('parameters.coherenceStrength')}
         min={0}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/Compositing/MaskAdjustment/ParamMaskBlur.tsx

@@ -1,6 +1,6 @@
 import type { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAISlider from 'common/components/IAISlider';
 import { setMaskBlur } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
@@ -13,7 +13,7 @@ export default function ParamMaskBlur() {
   const { t } = useTranslation();
 
   return (
-    <IAIInformationalPopover details="compositingBlur">
+    <IAIInformationalPopover feature="compositingBlur">
       <IAISlider
         label={t('parameters.maskBlur')}
         min={0}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/Compositing/MaskAdjustment/ParamMaskBlurMethod.tsx

@@ -2,7 +2,7 @@ import { SelectItem } from '@mantine/core';
 import { RootState } from 'app/store/store';
 
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import { setMaskBlurMethod } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
@@ -29,7 +29,7 @@ export default function ParamMaskBlurMethod() {
   };
 
   return (
-    <IAIInformationalPopover details="compositingBlurMethod">
+    <IAIInformationalPopover feature="compositingBlurMethod">
       <IAIMantineSelect
         value={maskBlurMethod}
         onChange={handleMaskBlurMethodChange}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/Compositing/ParamCompositingSettingsCollapse.tsx

@@ -15,19 +15,13 @@ const ParamCompositingSettingsCollapse = () => {
   return (
     <IAICollapse label={t('parameters.compositingSettingsHeader')}>
       <Flex sx={{ flexDirection: 'column', gap: 2 }}>
-        <SubParametersWrapper
-          label={t('parameters.coherencePassHeader')}
-          headerInfoPopover="compositingCoherencePass"
-        >
+        <SubParametersWrapper label={t('parameters.coherencePassHeader')}>
           <ParamCanvasCoherenceMode />
           <ParamCanvasCoherenceSteps />
           <ParamCanvasCoherenceStrength />
         </SubParametersWrapper>
         <Divider />
-        <SubParametersWrapper
-          label={t('parameters.maskAdjustmentsHeader')}
-          headerInfoPopover="compositingMaskAdjustments"
-        >
+        <SubParametersWrapper label={t('parameters.maskAdjustmentsHeader')}>
           <ParamMaskBlur />
           <ParamMaskBlurMethod />
         </SubParametersWrapper>

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillMethod.tsx

@@ -2,7 +2,7 @@ import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
 import { setInfillMethod } from 'features/parameters/store/generationSlice';
 
@@ -40,7 +40,7 @@ const ParamInfillMethod = () => {
   );
 
   return (
-    <IAIInformationalPopover details="infillMethod">
+    <IAIInformationalPopover feature="infillMethod">
       <IAIMantineSelect
         disabled={infill_methods?.length === 0}
         placeholder={isLoading ? 'Loading...' : undefined}

invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamScaleBeforeProcessing.tsx

@@ -1,7 +1,7 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect';
 import { canvasSelector } from 'features/canvas/store/canvasSelectors';
 import { setBoundingBoxScaleMethod } from 'features/canvas/store/canvasSlice';
@@ -36,7 +36,7 @@ const ParamScaleBeforeProcessing = () => {
   };
 
   return (
-    <IAIInformationalPopover details="scaleBeforeProcessing">
+    <IAIInformationalPopover feature="scaleBeforeProcessing">
       <IAIMantineSearchableSelect
         label={t('parameters.scaleBeforeProcessing')}
         data={BOUNDING_BOX_SCALES_DICT}

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamAspectRatio.tsx

@@ -1,4 +1,4 @@
-import { ButtonGroup, Flex } from '@chakra-ui/react';
+import { ButtonGroup } from '@chakra-ui/react';
 import { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIButton from 'common/components/IAIButton';
@@ -29,25 +29,23 @@ export default function ParamAspectRatio() {
   const activeTabName = useAppSelector(activeTabNameSelector);
 
   return (
-    <Flex gap={2} flexGrow={1}>
-      <ButtonGroup isAttached>
-        {aspectRatios.map((ratio) => (
-          <IAIButton
-            key={ratio.name}
-            size="sm"
-            isChecked={aspectRatio === ratio.value}
-            isDisabled={
-              activeTabName === 'img2img' ? !shouldFitToWidthHeight : false
-            }
-            onClick={() => {
-              dispatch(setAspectRatio(ratio.value));
-              dispatch(setShouldLockAspectRatio(false));
-            }}
-          >
-            {ratio.name}
-          </IAIButton>
-        ))}
-      </ButtonGroup>
-    </Flex>
+    <ButtonGroup isAttached>
+      {aspectRatios.map((ratio) => (
+        <IAIButton
+          key={ratio.name}
+          size="sm"
+          isChecked={aspectRatio === ratio.value}
+          isDisabled={
+            activeTabName === 'img2img' ? !shouldFitToWidthHeight : false
+          }
+          onClick={() => {
+            dispatch(setAspectRatio(ratio.value));
+            dispatch(setShouldLockAspectRatio(false));
+          }}
+        >
+          {ratio.name}
+        </IAIButton>
+      ))}
+    </ButtonGroup>
   );
 }

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamCFGScale.tsx

@@ -2,7 +2,7 @@ import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAINumberInput from 'common/components/IAINumberInput';
 import IAISlider from 'common/components/IAISlider';
 import { setCfgScale } from 'features/parameters/store/generationSlice';
@@ -54,7 +54,7 @@ const ParamCFGScale = () => {
   );
 
   return shouldUseSliders ? (
-    <IAIInformationalPopover details="paramCFGScale">
+    <IAIInformationalPopover feature="paramCFGScale">
       <IAISlider
         label={t('parameters.cfgScale')}
         step={shift ? 0.1 : 0.5}
@@ -71,7 +71,7 @@ const ParamCFGScale = () => {
       />
     </IAIInformationalPopover>
   ) : (
-    <IAIInformationalPopover details="paramCFGScale">
+    <IAIInformationalPopover feature="paramCFGScale">
       <IAINumberInput
         label={t('parameters.cfgScale')}
         step={0.5}

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamIterations.tsx

@@ -2,7 +2,7 @@ import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAINumberInput from 'common/components/IAINumberInput';
 import IAISlider from 'common/components/IAISlider';
 import { setIterations } from 'features/parameters/store/generationSlice';
@@ -61,7 +61,7 @@ const ParamIterations = ({ asSlider }: Props) => {
   }, [dispatch, initial]);
 
   return asSlider || shouldUseSliders ? (
-    <IAIInformationalPopover details="paramIterations">
+    <IAIInformationalPopover feature="paramIterations">
       <IAISlider
         label={t('parameters.iterations')}
         step={step}
@@ -77,7 +77,7 @@ const ParamIterations = ({ asSlider }: Props) => {
       />
     </IAIInformationalPopover>
   ) : (
-    <IAIInformationalPopover details="paramIterations">
+    <IAIInformationalPopover feature="paramIterations">
       <IAINumberInput
         label={t('parameters.iterations')}
         step={step}

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamNegativeConditioning.tsx

@@ -1,7 +1,7 @@
 import { Box, FormControl, useDisclosure } from '@chakra-ui/react';
 import type { RootState } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAITextarea from 'common/components/IAITextarea';
 import AddEmbeddingButton from 'features/embedding/components/AddEmbeddingButton';
 import ParamEmbeddingPopover from 'features/embedding/components/ParamEmbeddingPopover';
@@ -76,15 +76,15 @@ const ParamNegativeConditioning = () => {
   const isEmbeddingEnabled = useFeatureStatus('embedding').isFeatureEnabled;
 
   return (
-    <IAIInformationalPopover
-      placement="right"
-      details="paramNegativeConditioning"
-    >
-      <FormControl>
-        <ParamEmbeddingPopover
-          isOpen={isOpen}
-          onClose={onClose}
-          onSelect={handleSelectEmbedding}
+    <FormControl>
+      <ParamEmbeddingPopover
+        isOpen={isOpen}
+        onClose={onClose}
+        onSelect={handleSelectEmbedding}
+      >
+        <IAIInformationalPopover
+          feature="paramNegativeConditioning"
+          placement="right"
         >
           <IAITextarea
             id="negativePrompt"
@@ -98,20 +98,20 @@ const ParamNegativeConditioning = () => {
             minH={16}
             {...(isEmbeddingEnabled && { onKeyDown: handleKeyDown })}
           />
-        </ParamEmbeddingPopover>
-        {!isOpen && isEmbeddingEnabled && (
-          <Box
-            sx={{
-              position: 'absolute',
-              top: 0,
-              insetInlineEnd: 0,
-            }}
-          >
-            <AddEmbeddingButton onClick={onOpen} />
-          </Box>
-        )}
-      </FormControl>
-    </IAIInformationalPopover>
+        </IAIInformationalPopover>
+      </ParamEmbeddingPopover>
+      {!isOpen && isEmbeddingEnabled && (
+        <Box
+          sx={{
+            position: 'absolute',
+            top: 0,
+            insetInlineEnd: 0,
+          }}
+        >
+          <AddEmbeddingButton onClick={onOpen} />
+        </Box>
+      )}
+    </FormControl>
   );
 };
 

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamPositiveConditioning.tsx

@@ -2,6 +2,7 @@ import { Box, FormControl, useDisclosure } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAITextarea from 'common/components/IAITextarea';
 import AddEmbeddingButton from 'features/embedding/components/AddEmbeddingButton';
 import ParamEmbeddingPopover from 'features/embedding/components/ParamEmbeddingPopover';
@@ -12,7 +13,6 @@ import { flushSync } from 'react-dom';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus';
-import IAIInformationalPopover from '../../../../../common/components/IAIInformationalPopover';
 
 const promptInputSelector = createSelector(
   [stateSelector],
@@ -104,15 +104,15 @@ const ParamPositiveConditioning = () => {
 
   return (
     <Box position="relative">
-      <IAIInformationalPopover
-        placement="right"
-        details="paramPositiveConditioning"
-      >
-        <FormControl>
-          <ParamEmbeddingPopover
-            isOpen={isOpen}
-            onClose={onClose}
-            onSelect={handleSelectEmbedding}
+      <FormControl>
+        <ParamEmbeddingPopover
+          isOpen={isOpen}
+          onClose={onClose}
+          onSelect={handleSelectEmbedding}
+        >
+          <IAIInformationalPopover
+            feature="paramPositiveConditioning"
+            placement="right"
           >
             <IAITextarea
               id="prompt"
@@ -125,9 +125,9 @@ const ParamPositiveConditioning = () => {
               resize="vertical"
               minH={32}
             />
-          </ParamEmbeddingPopover>
-        </FormControl>
-      </IAIInformationalPopover>
+          </IAIInformationalPopover>
+        </ParamEmbeddingPopover>
+      </FormControl>
       {!isOpen && isEmbeddingEnabled && (
         <Box
           sx={{

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamScheduler.tsx

@@ -1,7 +1,7 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import { setScheduler } from 'features/parameters/store/generationSlice';
@@ -52,7 +52,7 @@ const ParamScheduler = () => {
   );
 
   return (
-    <IAIInformationalPopover details="paramScheduler">
+    <IAIInformationalPopover feature="paramScheduler">
       <IAIMantineSearchableSelect
         label={t('parameters.scheduler')}
         value={scheduler}

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSize.tsx

@@ -1,7 +1,9 @@
-import { Box, Flex, Spacer, Text } from '@chakra-ui/react';
+import { Flex, FormControl, FormLabel, Spacer } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAIIconButton from 'common/components/IAIIconButton';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import { generationSelector } from 'features/parameters/store/generationSelectors';
 import {
   setAspectRatio,
@@ -16,8 +18,6 @@ import { activeTabNameSelector } from '../../../../ui/store/uiSelectors';
 import ParamAspectRatio, { mappedAspectRatios } from './ParamAspectRatio';
 import ParamHeight from './ParamHeight';
 import ParamWidth from './ParamWidth';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 
 const sizeOptsSelector = createSelector(
   [generationSelector, activeTabNameSelector],
@@ -83,47 +83,35 @@ export default function ParamSize() {
         },
       }}
     >
-      <Flex alignItems="center" gap={2}>
-        <Box width="full">
-          <IAIInformationalPopover details="paramRatio">
-            <Text
-              sx={{
-                fontSize: 'sm',
-                color: 'base.700',
-                _dark: {
-                  color: 'base.300',
-                },
-              }}
-            >
-              {t('parameters.aspectRatio')}
-            </Text>
-          </IAIInformationalPopover>
-        </Box>
-        <Spacer />
-        <ParamAspectRatio />
-        <IAIIconButton
-          tooltip={t('ui.swapSizes')}
-          aria-label={t('ui.swapSizes')}
-          size="sm"
-          icon={<MdOutlineSwapVert />}
-          fontSize={20}
-          isDisabled={
-            activeTabName === 'img2img' ? !shouldFitToWidthHeight : false
-          }
-          onClick={handleToggleSize}
-        />
-        <IAIIconButton
-          tooltip={t('ui.lockRatio')}
-          aria-label={t('ui.lockRatio')}
-          size="sm"
-          icon={<FaLock />}
-          isChecked={shouldLockAspectRatio}
-          isDisabled={
-            activeTabName === 'img2img' ? !shouldFitToWidthHeight : false
-          }
-          onClick={handleLockRatio}
-        />
-      </Flex>
+      <IAIInformationalPopover feature="paramRatio">
+        <FormControl as={Flex} flexDir="row" alignItems="center" gap={2}>
+          <FormLabel>{t('parameters.aspectRatio')}</FormLabel>
+          <Spacer />
+          <ParamAspectRatio />
+          <IAIIconButton
+            tooltip={t('ui.swapSizes')}
+            aria-label={t('ui.swapSizes')}
+            size="sm"
+            icon={<MdOutlineSwapVert />}
+            fontSize={20}
+            isDisabled={
+              activeTabName === 'img2img' ? !shouldFitToWidthHeight : false
+            }
+            onClick={handleToggleSize}
+          />
+          <IAIIconButton
+            tooltip={t('ui.lockRatio')}
+            aria-label={t('ui.lockRatio')}
+            size="sm"
+            icon={<FaLock />}
+            isChecked={shouldLockAspectRatio}
+            isDisabled={
+              activeTabName === 'img2img' ? !shouldFitToWidthHeight : false
+            }
+            onClick={handleLockRatio}
+          />
+        </FormControl>
+      </IAIInformationalPopover>
       <Flex gap={2} alignItems="center">
         <Flex gap={2} flexDirection="column" width="full">
           <ParamWidth

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamSteps.tsx

@@ -2,7 +2,7 @@ import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 import IAINumberInput from 'common/components/IAINumberInput';
 
 import IAISlider from 'common/components/IAISlider';
@@ -57,7 +57,7 @@ const ParamSteps = () => {
   }, [dispatch]);
 
   return shouldUseSliders ? (
-    <IAIInformationalPopover details="paramSteps">
+    <IAIInformationalPopover feature="paramSteps">
       <IAISlider
         label={t('parameters.steps')}
         min={min}
@@ -73,7 +73,7 @@ const ParamSteps = () => {
       />
     </IAIInformationalPopover>
   ) : (
-    <IAIInformationalPopover details="paramSteps">
+    <IAIInformationalPopover feature="paramSteps">
       <IAINumberInput
         label={t('parameters.steps')}
         min={min}

invokeai/frontend/web/src/features/parameters/components/Parameters/ImageToImage/ImageToImageStrength.tsx

@@ -7,7 +7,7 @@ import { setImg2imgStrength } from 'features/parameters/store/generationSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import SubParametersWrapper from '../SubParametersWrapper';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 
 const selector = createSelector(
   [stateSelector],
@@ -46,8 +46,8 @@ const ImageToImageStrength = () => {
   }, [dispatch, initial]);
 
   return (
-    <SubParametersWrapper>
-      <IAIInformationalPopover details="paramDenoisingStrength">
+    <IAIInformationalPopover feature="paramDenoisingStrength">
+      <SubParametersWrapper>
         <IAISlider
           label={`${t('parameters.denoisingStrength')}`}
           step={step}
@@ -62,8 +62,8 @@ const ImageToImageStrength = () => {
           withReset
           sliderNumberInputProps={{ max: inputMax }}
         />
-      </IAIInformationalPopover>
-    </SubParametersWrapper>
+      </SubParametersWrapper>
+    </IAIInformationalPopover>
   );
 };
 

invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx

@@ -21,7 +21,7 @@ import {
   useGetOnnxModelsQuery,
 } from 'services/api/endpoints/models';
 import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus';
-import IAIInformationalPopover from 'common/components/IAIInformationalPopover';
+import IAIInformationalPopover from 'common/components/IAIInformationalPopover/IAIInformationalPopover';
 
 const selector = createSelector(
   stateSelector,
@@ -120,7 +120,7 @@ const ParamMainModelSelect = () => {
     />
   ) : (
     <Flex w="100%" alignItems="center" gap={3}>
-      <IAIInformationalPopover details="paramModel" placement="bottom">
+      <IAIInformationalPopover feature="paramModel">
         <IAIMantineSearchableSelect
           tooltip={selectedModel?.description}
           label={t('modelManager.model')}
@@ -136,7 +136,7 @@ const ParamMainModelSelect = () => {
         />
       </IAIInformationalPopover>
       {isSyncModelEnabled && (
-        <Box mt={7}>
+        <Box mt={6}>
           <SyncModelsButton iconMode />
         </Box>
       )}