Merge branch 'main' into fix/controlnet_cfg_inj_cond

invokeai/app/api/dependencies.py

@@ -13,7 +13,6 @@ from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
 from invokeai.app.services.boards import BoardService, BoardServiceDependencies
 from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
-from invokeai.app.services.metadata import CoreMetadataService
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
 from invokeai.backend.util.logging import InvokeAILogger
@@ -75,7 +74,6 @@ class ApiDependencies:
         )
 
         urls = LocalUrlService()
-        metadata = CoreMetadataService()
         image_record_storage = SqliteImageRecordStorage(db_location)
         image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
         names = SimpleNameService()
@@ -111,7 +109,6 @@ class ApiDependencies:
                 board_image_record_storage=board_image_record_storage,
                 image_record_storage=image_record_storage,
                 image_file_storage=image_file_storage,
-                metadata=metadata,
                 url=urls,
                 logger=logger,
                 names=names,

invokeai/app/api/routers/images.py

@@ -1,20 +1,19 @@
 import io
 from typing import Optional
-from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
-from fastapi.routing import APIRouter
+
+from fastapi import (Body, HTTPException, Path, Query, Request, Response,
+                     UploadFile)
 from fastapi.responses import FileResponse
+from fastapi.routing import APIRouter
 from PIL import Image
-from invokeai.app.models.image import (
-    ImageCategory,
-    ResourceOrigin,
-)
+
+from invokeai.app.invocations.metadata import ImageMetadata
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import (
-    ImageDTO,
-    ImageRecordChanges,
-    ImageUrlsDTO,
-)
 from invokeai.app.services.item_storage import PaginatedResults
+from invokeai.app.services.models.image_record import (ImageDTO,
+                                                       ImageRecordChanges,
+                                                       ImageUrlsDTO)
 
 from ..dependencies import ApiDependencies
 
@@ -103,23 +102,38 @@ async def update_image(
 
 
 @images_router.get(
-    "/{image_name}/metadata",
-    operation_id="get_image_metadata",
+    "/{image_name}",
+    operation_id="get_image_dto",
     response_model=ImageDTO,
 )
-async def get_image_metadata(
+async def get_image_dto(
     image_name: str = Path(description="The name of image to get"),
 ) -> ImageDTO:
-    """Gets an image's metadata"""
+    """Gets an image's DTO"""
 
     try:
         return ApiDependencies.invoker.services.images.get_dto(image_name)
     except Exception as e:
         raise HTTPException(status_code=404)
 
+@images_router.get(
+    "/{image_name}/metadata",
+    operation_id="get_image_metadata",
+    response_model=ImageMetadata,
+)
+async def get_image_metadata(
+    image_name: str = Path(description="The name of image to get"),
+) -> ImageMetadata:
+    """Gets an image's metadata"""
+
+    try:
+        return ApiDependencies.invoker.services.images.get_metadata(image_name)
+    except Exception as e:
+        raise HTTPException(status_code=404)
+
 
 @images_router.get(
-    "/{image_name}",
+    "/{image_name}/full",
     operation_id="get_image_full",
     response_class=Response,
     responses={
@@ -208,10 +222,10 @@ async def get_image_urls(
 
 @images_router.get(
     "/",
-    operation_id="list_images_with_metadata",
+    operation_id="list_image_dtos",
     response_model=OffsetPaginatedResults[ImageDTO],
 )
-async def list_images_with_metadata(
+async def list_image_dtos(
     image_origin: Optional[ResourceOrigin] = Query(
         default=None, description="The origin of images to list"
     ),
@@ -227,7 +241,7 @@ async def list_images_with_metadata(
     offset: int = Query(default=0, description="The page offset"),
     limit: int = Query(default=10, description="The number of images per page"),
 ) -> OffsetPaginatedResults[ImageDTO]:
-    """Gets a list of images"""
+    """Gets a list of image DTOs"""
 
     image_dtos = ApiDependencies.invoker.services.images.get_many(
         offset,

invokeai/app/cli_app.py

@@ -34,7 +34,6 @@ from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
 from invokeai.app.services.boards import BoardService, BoardServiceDependencies
 from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
-from invokeai.app.services.metadata import CoreMetadataService
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
 from .services.default_graphs import (default_text_to_image_graph_id,
@@ -244,7 +243,6 @@ def invoke_cli():
         )
 
     urls = LocalUrlService()
-    metadata = CoreMetadataService()
     image_record_storage = SqliteImageRecordStorage(db_location)
     image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
     names = SimpleNameService()
@@ -277,7 +275,6 @@ def invoke_cli():
             board_image_record_storage=board_image_record_storage,
             image_record_storage=image_record_storage,
             image_file_storage=image_file_storage,
-            metadata=metadata,
             url=urls,
             logger=logger,
             names=names,

invokeai/app/invocations/generate.py

@@ -154,40 +154,42 @@ class InpaintInvocation(BaseInvocation):
 
     @contextmanager
     def load_model_old_way(self, context, scheduler):
+        def _lora_loader():
+            for lora in self.unet.loras:
+                lora_info = context.services.model_manager.get_model(
+                    **lora.dict(exclude={"weight"}))
+                yield (lora_info.context.model, lora.weight)
+                del lora_info
+            return
+        
         unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
         vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
 
-        #unet = unet_info.context.model
-        #vae = vae_info.context.model
+        with vae_info as vae,\
+                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
+                unet_info as unet:
 
-        with ExitStack() as stack:
-            loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
+            device = context.services.model_manager.mgr.cache.execution_device
+            dtype = context.services.model_manager.mgr.cache.precision
 
-            with vae_info as vae,\
-                 unet_info as unet,\
-                 ModelPatcher.apply_lora_unet(unet, loras):
+            pipeline = StableDiffusionGeneratorPipeline(
+                vae=vae,
+                text_encoder=None,
+                tokenizer=None,
+                unet=unet,
+                scheduler=scheduler,
+                safety_checker=None,
+                feature_extractor=None,
+                requires_safety_checker=False,
+                precision="float16" if dtype == torch.float16 else "float32",
+                execution_device=device,
+            )
 
-                device = context.services.model_manager.mgr.cache.execution_device
-                dtype = context.services.model_manager.mgr.cache.precision
-
-                pipeline = StableDiffusionGeneratorPipeline(
-                    vae=vae,
-                    text_encoder=None,
-                    tokenizer=None,
-                    unet=unet,
-                    scheduler=scheduler,
-                    safety_checker=None,
-                    feature_extractor=None,
-                    requires_safety_checker=False,
-                    precision="float16" if dtype == torch.float16 else "float32",
-                    execution_device=device,
-                )
-
-                yield OldModelInfo(
-                    name=self.unet.unet.model_name,
-                    hash="<NO-HASH>",
-                    model=pipeline,
-                )
+            yield OldModelInfo(
+                name=self.unet.unet.model_name,
+                hash="<NO-HASH>",
+                model=pipeline,
+            )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
@@ -226,21 +228,21 @@ class InpaintInvocation(BaseInvocation):
                 ),  # Shorthand for passing all of the parameters above manually
             )
 
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
+            # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
+            # each time it is called. We only need the first one.
+            generator_output = next(outputs)
 
-        image_dto = context.services.images.create(
-            image=generator_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-        )
+            image_dto = context.services.images.create(
+                image=generator_output.image,
+                image_origin=ResourceOrigin.INTERNAL,
+                image_category=ImageCategory.GENERAL,
+                session_id=context.graph_execution_state_id,
+                node_id=self.id,
+                is_intermediate=self.is_intermediate,
+            )
 
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+            return ImageOutput(
+                image=ImageField(image_name=image_dto.image_name),
+                width=image_dto.width,
+                height=image_dto.height,
+            )

invokeai/app/invocations/latent.py

@@ -9,9 +9,9 @@ from diffusers.image_processor import VaeImageProcessor
 from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import BaseModel, Field, validator
 
+from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
@@ -21,6 +21,7 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
     PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import torch_dtype
+from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
 from .compel import ConditioningField
@@ -449,6 +450,8 @@ class LatentsToImageInvocation(BaseInvocation):
     tiled: bool = Field(
         default=False,
         description="Decode latents by overlaping tiles(less memory consumption)")
+    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
+    
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -493,7 +496,8 @@ class LatentsToImageInvocation(BaseInvocation):
             image_category=ImageCategory.GENERAL,
             node_id=self.id,
             session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata.dict() if self.metadata else None,
         )
 
         return ImageOutput(

invokeai/app/invocations/metadata.py

@@ -0,0 +1,124 @@
+from typing import Literal, Optional, Union
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (BaseInvocation,
+                                                     BaseInvocationOutput,
+                                                     InvocationContext)
+from invokeai.app.invocations.controlnet_image_processors import ControlField
+from invokeai.app.invocations.model import (LoRAModelField, MainModelField,
+                                            VAEModelField)
+
+
+class LoRAMetadataField(BaseModel):
+    """LoRA metadata for an image generated in InvokeAI."""
+    lora: LoRAModelField = Field(description="The LoRA model")
+    weight: float = Field(description="The weight of the LoRA model")
+
+
+class CoreMetadata(BaseModel):
+    """Core generation metadata for an image generated in InvokeAI."""
+
+    generation_mode: str = Field(description="The generation mode that output this 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: int = Field(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")
+    strength: Union[float, None] = Field(
+        default=None,
+        description="The strength used for latents-to-latents",
+    )
+    init_image: Union[str, None] = Field(
+        default=None, description="The name of the initial image"
+    )
+    vae: Union[VAEModelField, None] = Field(
+        default=None,
+        description="The VAE used for decoding, if the main model's default was not used",
+    )
+
+
+class ImageMetadata(BaseModel):
+    """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"
+    )
+
+
+class MetadataAccumulatorOutput(BaseInvocationOutput):
+    """The output of the MetadataAccumulator node"""
+
+    type: Literal["metadata_accumulator_output"] = "metadata_accumulator_output"
+
+    metadata: CoreMetadata = Field(description="The core metadata for the image")
+
+
+class MetadataAccumulatorInvocation(BaseInvocation):
+    """Outputs a Core Metadata Object"""
+
+    type: Literal["metadata_accumulator"] = "metadata_accumulator"
+
+    generation_mode: str = Field(description="The generation mode that output this 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: int = Field(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")
+    strength: Union[float, None] = Field(
+        default=None,
+        description="The strength used for latents-to-latents",
+    )
+    init_image: Union[str, None] = Field(
+        default=None, description="The name of the initial image"
+    )
+    vae: Union[VAEModelField, None] = Field(
+        default=None,
+        description="The VAE used for decoding, if the main model's default was not used",
+    )
+
+
+    def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
+        """Collects and outputs a CoreMetadata object"""
+
+        return MetadataAccumulatorOutput(
+            metadata=CoreMetadata(
+                generation_mode=self.generation_mode,
+                positive_prompt=self.positive_prompt,
+                negative_prompt=self.negative_prompt,
+                width=self.width,
+                height=self.height,
+                seed=self.seed,
+                rand_device=self.rand_device,
+                cfg_scale=self.cfg_scale,
+                steps=self.steps,
+                scheduler=self.scheduler,
+                model=self.model,
+                strength=self.strength,
+                init_image=self.init_image,
+                vae=self.vae,
+                controlnets=self.controlnets,
+                loras=self.loras,
+                clip_skip=self.clip_skip,
+            )
+        )

invokeai/app/models/metadata.py

@@ -1,93 +0,0 @@
-from typing import Optional, Union, List
-from pydantic import BaseModel, Extra, Field, StrictFloat, StrictInt, StrictStr
-
-
-class ImageMetadata(BaseModel):
-    """
-    Core generation metadata for an image/tensor generated in InvokeAI.
-
-    Also includes any metadata from the image's PNG tEXt chunks.
-
-    Generated by traversing the execution graph, collecting the parameters of the nearest ancestors
-    of a given node.
-
-    Full metadata may be accessed by querying for the session in the `graph_executions` table.
-    """
-
-    class Config:
-        extra = Extra.allow
-        """
-        This lets the ImageMetadata class accept arbitrary additional fields. The CoreMetadataService
-        won't add any fields that are not already defined, but other a different metadata service
-        implementation might.
-        """
-
-    type: Optional[StrictStr] = Field(
-        default=None,
-        description="The type of the ancestor node of the image output node.",
-    )
-    """The type of the ancestor node of the image output node."""
-    positive_conditioning: Optional[StrictStr] = Field(
-        default=None, description="The positive conditioning."
-    )
-    """The positive conditioning"""
-    negative_conditioning: Optional[StrictStr] = Field(
-        default=None, description="The negative conditioning."
-    )
-    """The negative conditioning"""
-    width: Optional[StrictInt] = Field(
-        default=None, description="Width of the image/latents in pixels."
-    )
-    """Width of the image/latents in pixels"""
-    height: Optional[StrictInt] = Field(
-        default=None, description="Height of the image/latents in pixels."
-    )
-    """Height of the image/latents in pixels"""
-    seed: Optional[StrictInt] = Field(
-        default=None, description="The seed used for noise generation."
-    )
-    """The seed used for noise generation"""
-    # cfg_scale: Optional[StrictFloat] = Field(
-    # cfg_scale: Union[float, list[float]] = Field(
-    cfg_scale: Union[StrictFloat, List[StrictFloat]] = Field(
-        default=None, description="The classifier-free guidance scale."
-    )
-    """The classifier-free guidance scale"""
-    steps: Optional[StrictInt] = Field(
-        default=None, description="The number of steps used for inference."
-    )
-    """The number of steps used for inference"""
-    scheduler: Optional[StrictStr] = Field(
-        default=None, description="The scheduler used for inference."
-    )
-    """The scheduler used for inference"""
-    model: Optional[StrictStr] = Field(
-        default=None, description="The model used for inference."
-    )
-    """The model used for inference"""
-    strength: Optional[StrictFloat] = Field(
-        default=None,
-        description="The strength used for image-to-image/latents-to-latents.",
-    )
-    """The strength used for image-to-image/latents-to-latents."""
-    latents: Optional[StrictStr] = Field(
-        default=None, description="The ID of the initial latents."
-    )
-    """The ID of the initial latents"""
-    vae: Optional[StrictStr] = Field(
-        default=None, description="The VAE used for decoding."
-    )
-    """The VAE used for decoding"""
-    unet: Optional[StrictStr] = Field(
-        default=None, description="The UNet used dor inference."
-    )
-    """The UNet used dor inference"""
-    clip: Optional[StrictStr] = Field(
-        default=None, description="The CLIP Encoder used for conditioning."
-    )
-    """The CLIP Encoder used for conditioning"""
-    extra: Optional[StrictStr] = Field(
-        default=None,
-        description="Uploaded image metadata, extracted from the PNG tEXt chunk.",
-    )
-    """Uploaded image metadata, extracted from the PNG tEXt chunk."""

invokeai/app/services/image_file_storage.py

@@ -1,14 +1,14 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+import json
 from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
 from typing import Dict, Optional, Union
 
-from PIL.Image import Image as PILImageType
 from PIL import Image, PngImagePlugin
+from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
-from invokeai.app.models.metadata import ImageMetadata
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 
@@ -59,7 +59,8 @@ class ImageFileStorageBase(ABC):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[ImageMetadata] = None,
+        metadata: Optional[dict] = None,
+        graph: Optional[dict] = None,
         thumbnail_size: int = 256,
     ) -> None:
         """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@@ -110,20 +111,22 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[ImageMetadata] = None,
+        metadata: Optional[dict] = None,
+        graph: Optional[dict] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
             self.__validate_storage_folders()
             image_path = self.get_path(image_name)
 
+            pnginfo = PngImagePlugin.PngInfo()
+            
             if metadata is not None:
-                pnginfo = PngImagePlugin.PngInfo()
-                pnginfo.add_text("invokeai", metadata.json())
-                image.save(image_path, "PNG", pnginfo=pnginfo)
-            else:
-                image.save(image_path, "PNG")
+                pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
+            if graph is not None:
+                pnginfo.add_text("invokeai_graph", json.dumps(graph))
 
+            image.save(image_path, "PNG", pnginfo=pnginfo)
             thumbnail_name = get_thumbnail_name(image_name)
             thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)
             thumbnail_image = make_thumbnail(image, thumbnail_size)

invokeai/app/services/image_record_storage.py

@@ -1,3 +1,4 @@
+import json
 import sqlite3
 import threading
 from abc import ABC, abstractmethod
@@ -8,7 +9,6 @@ from pydantic import BaseModel, Field
 from pydantic.generics import GenericModel
 
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.models.metadata import ImageMetadata
 from invokeai.app.services.models.image_record import (
     ImageRecord, ImageRecordChanges, deserialize_image_record)
 
@@ -48,6 +48,28 @@ class ImageRecordDeleteException(Exception):
         super().__init__(message)
 
 
+IMAGE_DTO_COLS = ", ".join(
+    list(
+        map(
+            lambda c: "images." + c,
+            [
+                "image_name",
+                "image_origin",
+                "image_category",
+                "width",
+                "height",
+                "session_id",
+                "node_id",
+                "is_intermediate",
+                "created_at",
+                "updated_at",
+                "deleted_at",
+            ],
+        )
+    )
+)
+
+
 class ImageRecordStorageBase(ABC):
     """Low-level service responsible for interfacing with the image record store."""
 
@@ -58,6 +80,11 @@ class ImageRecordStorageBase(ABC):
         """Gets an image record."""
         pass
 
+    @abstractmethod
+    def get_metadata(self, image_name: str) -> Optional[dict]:
+        """Gets an image's metadata'."""
+        pass
+
     @abstractmethod
     def update(
         self,
@@ -102,7 +129,7 @@ class ImageRecordStorageBase(ABC):
         height: int,
         session_id: Optional[str],
         node_id: Optional[str],
-        metadata: Optional[ImageMetadata],
+        metadata: Optional[dict],
         is_intermediate: bool = False,
     ) -> datetime:
         """Saves an image record."""
@@ -206,7 +233,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
             self._cursor.execute(
                 f"""--sql
-                SELECT * FROM images
+                SELECT {IMAGE_DTO_COLS} FROM images
                 WHERE image_name = ?;
                 """,
                 (image_name,),
@@ -224,6 +251,28 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
         return deserialize_image_record(dict(result))
 
+    def get_metadata(self, image_name: str) -> Optional[dict]:
+        try:
+            self._lock.acquire()
+
+            self._cursor.execute(
+                f"""--sql
+                SELECT images.metadata 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])
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordNotFoundException from e
+        finally:
+            self._lock.release()
+
     def update(
         self,
         image_name: str,
@@ -291,8 +340,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             WHERE 1=1
             """
 
-            images_query = """--sql
-            SELECT images.*
+            images_query = f"""--sql
+            SELECT {IMAGE_DTO_COLS}
             FROM images
             LEFT JOIN board_images ON board_images.image_name = images.image_name
             WHERE 1=1
@@ -410,12 +459,12 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         width: int,
         height: int,
         node_id: Optional[str],
-        metadata: Optional[ImageMetadata],
+        metadata: Optional[dict],
         is_intermediate: bool = False,
     ) -> datetime:
         try:
             metadata_json = (
-                None if metadata is None else metadata.json(exclude_none=True)
+                None if metadata is None else json.dumps(metadata)
             )
             self._lock.acquire()
             self._cursor.execute(
@@ -465,9 +514,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         finally:
             self._lock.release()
 
-    def get_most_recent_image_for_board(
-        self, board_id: str
-    ) -> Optional[ImageRecord]:
+    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
         try:
             self._lock.acquire()
             self._cursor.execute(

invokeai/app/services/images.py

@@ -1,39 +1,30 @@
+import json
 from abc import ABC, abstractmethod
 from logging import Logger
-from typing import Optional, TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, Optional
+
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.models.image import (
-    ImageCategory,
-    ResourceOrigin,
-    InvalidImageCategoryException,
-    InvalidOriginException,
-)
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
-from invokeai.app.services.image_record_storage import (
-    ImageRecordDeleteException,
-    ImageRecordNotFoundException,
-    ImageRecordSaveException,
-    ImageRecordStorageBase,
-    OffsetPaginatedResults,
-)
-from invokeai.app.services.models.image_record import (
-    ImageRecord,
-    ImageDTO,
-    ImageRecordChanges,
-    image_record_to_dto,
-)
+from invokeai.app.invocations.metadata import ImageMetadata
+from invokeai.app.models.image import (ImageCategory,
+                                       InvalidImageCategoryException,
+                                       InvalidOriginException, ResourceOrigin)
+from invokeai.app.services.board_image_record_storage import \
+    BoardImageRecordStorageBase
+from invokeai.app.services.graph import Graph
 from invokeai.app.services.image_file_storage import (
-    ImageFileDeleteException,
-    ImageFileNotFoundException,
-    ImageFileSaveException,
-    ImageFileStorageBase,
-)
-from invokeai.app.services.item_storage import ItemStorageABC, PaginatedResults
-from invokeai.app.services.metadata import MetadataServiceBase
+    ImageFileDeleteException, ImageFileNotFoundException,
+    ImageFileSaveException, ImageFileStorageBase)
+from invokeai.app.services.image_record_storage import (
+    ImageRecordDeleteException, ImageRecordNotFoundException,
+    ImageRecordSaveException, ImageRecordStorageBase, OffsetPaginatedResults)
+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
@@ -51,6 +42,7 @@ class ImageServiceABC(ABC):
         node_id: Optional[str] = None,
         session_id: Optional[str] = None,
         is_intermediate: bool = False,
+        metadata: Optional[dict] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -79,6 +71,11 @@ class ImageServiceABC(ABC):
         """Gets an image DTO."""
         pass
 
+    @abstractmethod
+    def get_metadata(self, image_name: str) -> ImageMetadata:
+        """Gets an image's metadata."""
+        pass
+
     @abstractmethod
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         """Gets an image's path."""
@@ -124,7 +121,6 @@ class ImageServiceDependencies:
     image_records: ImageRecordStorageBase
     image_files: ImageFileStorageBase
     board_image_records: BoardImageRecordStorageBase
-    metadata: MetadataServiceBase
     urls: UrlServiceBase
     logger: Logger
     names: NameServiceBase
@@ -135,7 +131,6 @@ class ImageServiceDependencies:
         image_record_storage: ImageRecordStorageBase,
         image_file_storage: ImageFileStorageBase,
         board_image_record_storage: BoardImageRecordStorageBase,
-        metadata: MetadataServiceBase,
         url: UrlServiceBase,
         logger: Logger,
         names: NameServiceBase,
@@ -144,7 +139,6 @@ class ImageServiceDependencies:
         self.image_records = image_record_storage
         self.image_files = image_file_storage
         self.board_image_records = board_image_record_storage
-        self.metadata = metadata
         self.urls = url
         self.logger = logger
         self.names = names
@@ -165,6 +159,7 @@ class ImageService(ImageServiceABC):
         node_id: Optional[str] = None,
         session_id: Optional[str] = None,
         is_intermediate: bool = False,
+        metadata: Optional[dict] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
             raise InvalidOriginException
@@ -174,7 +169,16 @@ class ImageService(ImageServiceABC):
 
         image_name = self._services.names.create_image_name()
 
-        metadata = self._get_metadata(session_id, node_id)
+        graph = None
+
+        if session_id is not None:
+            session_raw = self._services.graph_execution_manager.get_raw(session_id)
+            if session_raw is not None:
+                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
 
         (width, height) = image.size
 
@@ -191,14 +195,12 @@ class ImageService(ImageServiceABC):
                 is_intermediate=is_intermediate,
                 # Nullable fields
                 node_id=node_id,
-                session_id=session_id,
                 metadata=metadata,
+                session_id=session_id,
             )
 
             self._services.image_files.save(
-                image_name=image_name,
-                image=image,
-                metadata=metadata,
+                image_name=image_name, image=image, metadata=metadata, graph=graph
             )
 
             image_dto = self.get_dto(image_name)
@@ -268,6 +270,34 @@ class ImageService(ImageServiceABC):
             self._services.logger.error("Problem getting image DTO")
             raise e
 
+    def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
+        try:
+            image_record = self._services.image_records.get(image_name)
+
+            if not image_record.session_id:
+                return ImageMetadata()
+
+            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
+
+            metadata = self._services.image_records.get_metadata(image_name)
+            return ImageMetadata(graph=graph, metadata=metadata)
+        except ImageRecordNotFoundException:
+            self._services.logger.error("Image record not found")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image DTO")
+            raise e
+
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         try:
             return self._services.image_files.get_path(image_name, thumbnail)
@@ -367,15 +397,3 @@ class ImageService(ImageServiceABC):
         except Exception as e:
             self._services.logger.error("Problem deleting image records and files")
             raise e
-
-    def _get_metadata(
-        self, session_id: Optional[str] = None, node_id: Optional[str] = None
-    ) -> Optional[ImageMetadata]:
-        """Get the metadata for a node."""
-        metadata = None
-
-        if node_id is not None and session_id is not None:
-            session = self._services.graph_execution_manager.get(session_id)
-            metadata = self._services.metadata.create_image_metadata(session, node_id)
-
-        return metadata

invokeai/app/services/item_storage.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Callable, Generic, TypeVar
+from typing import Callable, Generic, Optional, TypeVar
 
 from pydantic import BaseModel, Field
 from pydantic.generics import GenericModel
@@ -29,14 +29,22 @@ class ItemStorageABC(ABC, Generic[T]):
 
     @abstractmethod
     def get(self, item_id: str) -> T:
+        """Gets the item, parsing it into a Pydantic model"""
+        pass
+
+    @abstractmethod
+    def get_raw(self, item_id: str) -> Optional[str]:
+        """Gets the raw item as a string, skipping Pydantic parsing"""
         pass
 
     @abstractmethod
     def set(self, item: T) -> None:
+        """Sets the item"""
         pass
 
     @abstractmethod
     def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        """Gets a paginated list of items"""
         pass
 
     @abstractmethod

invokeai/app/services/metadata.py

@@ -1,142 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, Optional
-import networkx as nx
-
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.services.graph import Graph, GraphExecutionState
-
-
-class MetadataServiceBase(ABC):
-    """Handles building metadata for nodes, images, and outputs."""
-
-    @abstractmethod
-    def create_image_metadata(
-        self, session: GraphExecutionState, node_id: str
-    ) -> ImageMetadata:
-        """Builds an ImageMetadata object for a node."""
-        pass
-
-
-class CoreMetadataService(MetadataServiceBase):
-    _ANCESTOR_TYPES = ["t2l", "l2l"]
-    """The ancestor types that contain the core metadata"""
-
-    _ANCESTOR_PARAMS = ["type", "steps", "model", "cfg_scale", "scheduler", "strength"]
-    """The core metadata parameters in the ancestor types"""
-
-    _NOISE_FIELDS = ["seed", "width", "height"]
-    """The core metadata parameters in the noise node"""
-
-    def create_image_metadata(
-        self, session: GraphExecutionState, node_id: str
-    ) -> ImageMetadata:
-        metadata = self._build_metadata_from_graph(session, node_id)
-
-        return metadata
-
-    def _find_nearest_ancestor(self, G: nx.DiGraph, node_id: str) -> Optional[str]:
-        """
-        Finds the id of the nearest ancestor (of a valid type) of a given node.
-
-        Parameters:
-        G (nx.DiGraph): The execution graph, converted in to a networkx DiGraph. Its nodes must
-        have the same data as the execution graph.
-        node_id (str): The ID of the node.
-
-        Returns:
-        str | None: The ID of the nearest ancestor, or None if there are no valid ancestors.
-        """
-
-        # Retrieve the node from the graph
-        node = G.nodes[node_id]
-
-        # If the node type is one of the core metadata node types, return its id
-        if node.get("type") in self._ANCESTOR_TYPES:
-            return node.get("id")
-
-        # Else, look for the ancestor in the predecessor nodes
-        for predecessor in G.predecessors(node_id):
-            result = self._find_nearest_ancestor(G, predecessor)
-            if result:
-                return result
-
-        # If there are no valid ancestors, return None
-        return None
-
-    def _get_additional_metadata(
-        self, graph: Graph, node_id: str
-    ) -> Optional[dict[str, Any]]:
-        """
-        Returns additional metadata for a given node.
-
-        Parameters:
-        graph (Graph): The execution graph.
-        node_id (str): The ID of the node.
-
-        Returns:
-        dict[str, Any] | None: A dictionary of additional metadata.
-        """
-
-        metadata = {}
-
-        # Iterate over all edges in the graph
-        for edge in graph.edges:
-            dest_node_id = edge.destination.node_id
-            dest_field = edge.destination.field
-            source_node_dict = graph.nodes[edge.source.node_id].dict()
-
-            # If the destination node ID matches the given node ID, gather necessary metadata
-            if dest_node_id == node_id:
-                # Prompt
-                if dest_field == "positive_conditioning":
-                    metadata["positive_conditioning"] = source_node_dict.get("prompt")
-                # Negative prompt
-                if dest_field == "negative_conditioning":
-                    metadata["negative_conditioning"] = source_node_dict.get("prompt")
-                # Seed, width and height
-                if dest_field == "noise":
-                    for field in self._NOISE_FIELDS:
-                        metadata[field] = source_node_dict.get(field)
-        return metadata
-
-    def _build_metadata_from_graph(
-        self, session: GraphExecutionState, node_id: str
-    ) -> ImageMetadata:
-        """
-        Builds an ImageMetadata object for a node.
-
-        Parameters:
-        session (GraphExecutionState): The session.
-        node_id (str): The ID of the node.
-
-        Returns:
-        ImageMetadata: The metadata for the node.
-        """
-
-        # We need to do all the traversal on the execution graph
-        graph = session.execution_graph
-
-        # Find the nearest `t2l`/`l2l` ancestor of the given node
-        ancestor_id = self._find_nearest_ancestor(graph.nx_graph_with_data(), node_id)
-
-        # If no ancestor was found, return an empty ImageMetadata object
-        if ancestor_id is None:
-            return ImageMetadata()
-
-        ancestor_node = graph.get_node(ancestor_id)
-
-        # Grab all the core metadata from the ancestor node
-        ancestor_metadata = {
-            param: val
-            for param, val in ancestor_node.dict().items()
-            if param in self._ANCESTOR_PARAMS
-        }
-
-        # Get this image's prompts and noise parameters
-        addl_metadata = self._get_additional_metadata(graph, ancestor_id)
-
-        # If additional metadata was found, add it to the main metadata
-        if addl_metadata is not None:
-            ancestor_metadata.update(addl_metadata)
-
-        return ImageMetadata(**ancestor_metadata)

invokeai/app/services/models/image_record.py

@@ -1,13 +1,14 @@
 import datetime
 from typing import Optional, Union
+
 from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr
+
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.models.metadata import ImageMetadata
 from invokeai.app.util.misc import get_iso_timestamp
 
 
 class ImageRecord(BaseModel):
-    """Deserialized image record."""
+    """Deserialized image record without metadata."""
 
     image_name: str = Field(description="The unique name of the image.")
     """The unique name of the image."""
@@ -43,11 +44,6 @@ class ImageRecord(BaseModel):
         description="The node ID that generated this image, if it is a generated image.",
     )
     """The node ID that generated this image, if it is a generated image."""
-    metadata: Optional[ImageMetadata] = Field(
-        default=None,
-        description="A limited subset of the image's generation metadata. Retrieve the image's session for full metadata.",
-    )
-    """A limited subset of the image's generation metadata. Retrieve the image's session for full metadata."""
 
 
 class ImageRecordChanges(BaseModel, extra=Extra.forbid):
@@ -112,6 +108,7 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
 
     # Retrieve all the values, setting "reasonable" defaults if they are not present.
 
+    # TODO: do we really need to handle default values here? ideally the data is the correct shape...
     image_name = image_dict.get("image_name", "unknown")
     image_origin = ResourceOrigin(
         image_dict.get("image_origin", ResourceOrigin.INTERNAL.value)
@@ -128,13 +125,6 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
     deleted_at = image_dict.get("deleted_at", get_iso_timestamp())
     is_intermediate = image_dict.get("is_intermediate", False)
 
-    raw_metadata = image_dict.get("metadata")
-
-    if raw_metadata is not None:
-        metadata = ImageMetadata.parse_raw(raw_metadata)
-    else:
-        metadata = None
-
     return ImageRecord(
         image_name=image_name,
         image_origin=image_origin,
@@ -143,7 +133,6 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
         height=height,
         session_id=session_id,
         node_id=node_id,
-        metadata=metadata,
         created_at=created_at,
         updated_at=updated_at,
         deleted_at=deleted_at,

invokeai/app/services/sqlite.py

@@ -1,6 +1,6 @@
 import sqlite3
 from threading import Lock
-from typing import Generic, TypeVar, Optional, Union, get_args
+from typing import Generic, Optional, TypeVar, get_args
 
 from pydantic import BaseModel, parse_raw_as
 
@@ -78,6 +78,21 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
 
         return self._parse_item(result[0])
 
+    def get_raw(self, id: str) -> Optional[str]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
+            )
+            result = self._cursor.fetchone()
+        finally:
+            self._lock.release()
+
+        if not result:
+            return None
+
+        return result[0]
+
     def delete(self, id: str):
         try:
             self._lock.acquire()

invokeai/app/services/urls.py

@@ -22,4 +22,4 @@ class LocalUrlService(UrlServiceBase):
         if thumbnail:
             return f"{self._base_url}/images/{image_basename}/thumbnail"
 
-        return f"{self._base_url}/images/{image_basename}"
+        return f"{self._base_url}/images/{image_basename}/full"

invokeai/app/util/metadata.py

@@ -0,0 +1,55 @@
+import json
+from typing import Optional
+
+from pydantic import ValidationError
+
+from invokeai.app.services.graph import Edge
+
+
+def get_metadata_graph_from_raw_session(session_raw: str) -> Optional[dict]:
+    """
+    Parses raw session string, returning a dict of the graph.
+
+    Only the general graph shape is validated; none of the fields are validated.
+
+    Any `metadata_accumulator` nodes and edges are removed.
+
+    Any validation failure will return None.
+    """
+
+    graph = json.loads(session_raw).get("graph", None)
+
+    # sanity check make sure the graph is at least reasonably shaped
+    if (
+        type(graph) is not dict
+        or "nodes" not in graph
+        or type(graph["nodes"]) is not dict
+        or "edges" not in graph
+        or type(graph["edges"]) is not list
+    ):
+        # something has gone terribly awry, return an empty dict
+        return None
+
+    try:
+        # delete the `metadata_accumulator` node
+        del graph["nodes"]["metadata_accumulator"]
+    except KeyError:
+        # no accumulator node, all good
+        pass
+
+    # delete any edges to or from it
+    for i, edge in enumerate(graph["edges"]):
+        try:
+            # try to parse the edge
+            Edge(**edge)
+        except ValidationError:
+            # something has gone terribly awry, return an empty dict
+            return None
+
+        if (
+            edge["source"]["node_id"] == "metadata_accumulator"
+            or edge["destination"]["node_id"] == "metadata_accumulator"
+        ):
+            del graph["edges"][i]
+
+    return graph

invokeai/backend/install/model_install_backend.py

@@ -121,8 +121,8 @@ class ModelInstall(object):
         installed_models = self.mgr.list_models()
         for md in installed_models:
             base = md['base_model']
-            model_type = md['type']
-            name = md['name']
+            model_type = md['model_type']
+            name = md['model_name']
             key = ModelManager.create_key(name, base, model_type)
             if key in model_dict:
                 model_dict[key].installed = True

invokeai/backend/model_management/model_manager.py

@@ -250,8 +250,8 @@ from .model_cache import ModelCache, ModelLocker
 from .models import (
     BaseModelType, ModelType, SubModelType,
     ModelError, SchedulerPredictionType, MODEL_CLASSES,
-    ModelConfigBase, ModelNotFoundException,
-    )
+    ModelConfigBase, ModelNotFoundException, InvalidModelException,
+)
 
 # We are only starting to number the config file with release 3.
 # The config file version doesn't have to start at release version, but it will help
@@ -275,10 +275,6 @@ class ModelInfo():
     def __exit__(self,*args, **kwargs):
         self.context.__exit__(*args, **kwargs)
 
-class InvalidModelError(Exception):
-    "Raised when an invalid model is requested"
-    pass
-
 class AddModelResult(BaseModel):
     name: str = Field(description="The name of the model after installation")
     model_type: ModelType = Field(description="The type of model")
@@ -542,9 +538,9 @@ class ModelManager(object):
             model_dict = dict(
                 **model_config.dict(exclude_defaults=True),
                 # OpenAPIModelInfoBase
-                name=cur_model_name,
+                model_name=cur_model_name,
                 base_model=cur_base_model,
-                type=cur_model_type,
+                model_type=cur_model_type,
             )
 
             models.append(model_dict)
@@ -817,6 +813,8 @@ class ModelManager(object):
                                 model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                 self.models[model_key] = model_config
                                 new_models_found = True
+                            except InvalidModelException:
+                                self.logger.warning(f"Not a valid model: {model_path}")
                             except NotImplementedError as e:
                                 self.logger.warning(e)
 

invokeai/backend/model_management/models/__init__.py

@@ -2,7 +2,7 @@ import inspect
 from enum import Enum
 from pydantic import BaseModel
 from typing import Literal, get_origin
-from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException
+from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException, InvalidModelException
 from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
 from .vae import VaeModel
 from .lora import LoRAModel
@@ -37,9 +37,9 @@ MODEL_CONFIGS = list()
 OPENAPI_MODEL_CONFIGS = list()
 
 class OpenAPIModelInfoBase(BaseModel):
-    name: str
+    model_name: str
     base_model: BaseModelType
-    type: ModelType
+    model_type: ModelType
 
 
 for base_model, models in MODEL_CLASSES.items():
@@ -56,7 +56,7 @@ for base_model, models in MODEL_CLASSES.items():
 
             api_wrapper = type(openapi_cfg_name, (cfg, OpenAPIModelInfoBase), dict(
                 __annotations__ = dict(
-                    type=Literal[model_type.value],
+                    model_type=Literal[model_type.value],
                 ),
             ))
 

invokeai/backend/model_management/models/base.py

@@ -15,6 +15,9 @@ from contextlib import suppress
 from pydantic import BaseModel, Field
 from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
 
+class InvalidModelException(Exception):
+    pass
+
 class ModelNotFoundException(Exception):
     pass
 

invokeai/backend/model_management/models/controlnet.py

@@ -13,6 +13,7 @@ from .base import (
     calc_model_size_by_fs,
     calc_model_size_by_data,
     classproperty,
+    InvalidModelException,
 )
 
 class ControlNetModelFormat(str, Enum):
@@ -73,10 +74,18 @@ class ControlNetModel(ModelBase):
 
     @classmethod
     def detect_format(cls, path: str):
+        if not os.path.exists(path):
+            raise ModelNotFoundException()
+
         if os.path.isdir(path):
-            return ControlNetModelFormat.Diffusers
-        else:
-            return ControlNetModelFormat.Checkpoint
+            if os.path.exists(os.path.join(path, "config.json")):
+                return ControlNetModelFormat.Diffusers
+
+        if os.path.isfile(path):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "pth"]]):
+                return ControlNetModelFormat.Checkpoint
+
+        raise InvalidModelException(f"Not a valid model: {path}")
 
     @classmethod
     def convert_if_required(

invokeai/backend/model_management/models/lora.py

@@ -9,6 +9,7 @@ from .base import (
     ModelType,
     SubModelType,
     classproperty,
+    InvalidModelException,
 )
 # TODO: naming
 from ..lora import LoRAModel as LoRAModelRaw
@@ -56,10 +57,18 @@ class LoRAModel(ModelBase):
 
     @classmethod
     def detect_format(cls, path: str):
+        if not os.path.exists(path):
+            raise ModelNotFoundException()
+
         if os.path.isdir(path):
-            return LoRAModelFormat.Diffusers
-        else:
-            return LoRAModelFormat.LyCORIS
+            if os.path.exists(os.path.join(path, "pytorch_lora_weights.bin")):
+                return LoRAModelFormat.Diffusers
+
+        if os.path.isfile(path):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
+                return LoRAModelFormat.LyCORIS
+
+        raise InvalidModelException(f"Not a valid model: {path}")
 
     @classmethod
     def convert_if_required(

invokeai/backend/model_management/models/stable_diffusion.py

@@ -16,6 +16,7 @@ from .base import (
     SilenceWarnings,
     read_checkpoint_meta,
     classproperty,
+    InvalidModelException,
 )
 from invokeai.app.services.config import InvokeAIAppConfig
 from omegaconf import OmegaConf
@@ -98,10 +99,18 @@ class StableDiffusion1Model(DiffusersModel):
 
     @classmethod
     def detect_format(cls, model_path: str):
+        if not os.path.exists(model_path):
+            raise ModelNotFoundException()
+
         if os.path.isdir(model_path):
-            return StableDiffusion1ModelFormat.Diffusers
-        else:
-            return StableDiffusion1ModelFormat.Checkpoint
+            if os.path.exists(os.path.join(model_path, "model_index.json")):
+                return StableDiffusion1ModelFormat.Diffusers
+
+        if os.path.isfile(model_path):
+            if any([model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
+                return StableDiffusion1ModelFormat.Checkpoint
+
+        raise InvalidModelException(f"Not a valid model: {model_path}")
 
     @classmethod
     def convert_if_required(
@@ -200,10 +209,18 @@ class StableDiffusion2Model(DiffusersModel):
 
     @classmethod
     def detect_format(cls, model_path: str):
+        if not os.path.exists(model_path):
+            raise ModelNotFoundException()
+
         if os.path.isdir(model_path):
-            return StableDiffusion2ModelFormat.Diffusers
-        else:
-            return StableDiffusion2ModelFormat.Checkpoint
+            if os.path.exists(os.path.join(model_path, "model_index.json")):
+                return StableDiffusion2ModelFormat.Diffusers
+
+        if os.path.isfile(model_path):
+            if any([model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
+                return StableDiffusion2ModelFormat.Checkpoint
+
+        raise InvalidModelException(f"Not a valid model: {model_path}")
 
     @classmethod
     def convert_if_required(

invokeai/backend/model_management/models/textual_inversion.py

@@ -9,6 +9,7 @@ from .base import (
     SubModelType,
     classproperty,
     ModelNotFoundException,
+    InvalidModelException,
 )
 # TODO: naming
 from ..lora import TextualInversionModel as TextualInversionModelRaw
@@ -59,7 +60,18 @@ class TextualInversionModel(ModelBase):
 
     @classmethod
     def detect_format(cls, path: str):
-        return None
+        if not os.path.exists(path):
+            raise ModelNotFoundException()
+
+        if os.path.isdir(path):
+            if os.path.exists(os.path.join(path, "learned_embeds.bin")):
+                return None # diffusers-ti
+
+        if os.path.isfile(path):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
+                return None
+
+        raise InvalidModelException(f"Not a valid model: {path}")
 
     @classmethod
     def convert_if_required(

invokeai/backend/model_management/models/vae.py

@@ -15,6 +15,7 @@ from .base import (
     calc_model_size_by_fs,
     calc_model_size_by_data,
     classproperty,
+    InvalidModelException,
 )
 from invokeai.app.services.config import InvokeAIAppConfig
 from diffusers.utils import is_safetensors_available
@@ -75,10 +76,18 @@ class VaeModel(ModelBase):
 
     @classmethod
     def detect_format(cls, path: str):
+        if not os.path.exists(path):
+            raise ModelNotFoundException()
+
         if os.path.isdir(path):
-            return VaeModelFormat.Diffusers
-        else:
-            return VaeModelFormat.Checkpoint
+            if os.path.exists(os.path.join(path, "config.json")):
+                return VaeModelFormat.Diffusers
+
+        if os.path.isfile(path):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
+                return VaeModelFormat.Checkpoint
+
+        raise InvalidModelException(f"Not a valid model: {path}")
 
     @classmethod
     def convert_if_required(

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -127,7 +127,7 @@ class AddsMaskGuidance:
 
     def _t_for_field(self, field_name: str, t):
         if field_name == "pred_original_sample":
-            return torch.zeros_like(t, dtype=t.dtype)  # it represents t=0
+            return self.scheduler.timesteps[-1]
         return t
 
     def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:

invokeai/frontend/web/package.json

@@ -108,6 +108,7 @@
     "roarr": "^7.15.0",
     "serialize-error": "^11.0.0",
     "socket.io-client": "^4.7.0",
+    "use-debounce": "^9.0.4",
     "use-image": "^1.1.1",
     "uuid": "^9.0.0",
     "zod": "^3.21.4"

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

@@ -102,7 +102,8 @@
         "openInNewTab": "Open in New Tab",
         "dontAskMeAgain": "Don't ask me again",
         "areYouSure": "Are you sure?",
-        "imagePrompt": "Image Prompt"
+        "imagePrompt": "Image Prompt",
+        "clearNodes": "Are you sure you want to clear all nodes?"
     },
     "gallery": {
         "generations": "Generations",
@@ -528,7 +529,7 @@
         "hidePreview": "Hide Preview",
         "showPreview": "Show Preview",
         "controlNetControlMode": "Control Mode",
-        "clipSkip": "Clip Skip",
+        "clipSkip": "CLIP Skip",
         "aspectRatio": "Ratio"
     },
     "settings": {
@@ -593,7 +594,12 @@
         "metadataLoadFailed": "Failed to load metadata",
         "initialImageSet": "Initial Image Set",
         "initialImageNotSet": "Initial Image Not Set",
-        "initialImageNotSetDesc": "Could not load initial image"
+        "initialImageNotSetDesc": "Could not load initial image",
+        "nodesSaved": "Nodes Saved",
+        "nodesLoaded": "Nodes Loaded",
+        "nodesLoadedFailed": "Failed To Load Nodes",
+        "nodesCleared": "Nodes Cleared"
+
     },
     "tooltip": {
         "feature": {
@@ -674,5 +680,11 @@
         "showProgressImages": "Show Progress Images",
         "hideProgressImages": "Hide Progress Images",
         "swapSizes": "Swap Sizes"
+    },
+    "nodes": {
+        "reloadSchema": "Reload Schema",
+        "saveNodes": "Save Nodes",
+        "loadNodes": "Load Nodes",
+        "clearNodes": "Clear Nodes"
     }
 }

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -51,6 +51,7 @@ import {
 } from './listeners/imageUrlsReceived';
 import { addInitialImageSelectedListener } from './listeners/initialImageSelected';
 import { addModelSelectedListener } from './listeners/modelSelected';
+import { addModelsLoadedListener } from './listeners/modelsLoaded';
 import { addReceivedOpenAPISchemaListener } from './listeners/receivedOpenAPISchema';
 import {
   addReceivedPageOfImagesFulfilledListener,
@@ -224,3 +225,4 @@ addModelSelectedListener();
 
 // app startup
 addAppStartedListener();
+addModelsLoadedListener();

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

@@ -1,13 +1,13 @@
-import { startAppListening } from '..';
-import { imageMetadataReceived } from 'services/api/thunks/image';
 import { log } from 'app/logging/useLogger';
 import { controlNetImageProcessed } from 'features/controlNet/store/actions';
-import { Graph } from 'services/api/types';
-import { sessionCreated } from 'services/api/thunks/session';
-import { sessionReadyToInvoke } from 'features/system/store/actions';
-import { socketInvocationComplete } from 'services/events/actions';
-import { isImageOutput } from 'services/api/guards';
 import { controlNetProcessedImageChanged } from 'features/controlNet/store/controlNetSlice';
+import { sessionReadyToInvoke } from 'features/system/store/actions';
+import { isImageOutput } from 'services/api/guards';
+import { imageDTOReceived } from 'services/api/thunks/image';
+import { sessionCreated } from 'services/api/thunks/session';
+import { Graph } from 'services/api/types';
+import { socketInvocationComplete } from 'services/events/actions';
+import { startAppListening } from '..';
 
 const moduleLog = log.child({ namespace: 'controlNet' });
 
@@ -63,10 +63,8 @@ export const addControlNetImageProcessedListener = () => {
 
         // Wait for the ImageDTO to be received
         const [imageMetadataReceivedAction] = await take(
-          (
-            action
-          ): action is ReturnType<typeof imageMetadataReceived.fulfilled> =>
-            imageMetadataReceived.fulfilled.match(action) &&
+          (action): action is ReturnType<typeof imageDTOReceived.fulfilled> =>
+            imageDTOReceived.fulfilled.match(action) &&
             action.payload.image_name === image_name
         );
         const processedControlImage = imageMetadataReceivedAction.payload;

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

@@ -1,7 +1,7 @@
 import { log } from 'app/logging/useLogger';
-import { startAppListening } from '..';
-import { imageMetadataReceived } from 'services/api/thunks/image';
 import { boardImagesApi } from 'services/api/endpoints/boardImages';
+import { imageDTOReceived } from 'services/api/thunks/image';
+import { startAppListening } from '..';
 
 const moduleLog = log.child({ namespace: 'boards' });
 
@@ -17,7 +17,7 @@ export const addImageAddedToBoardFulfilledListener = () => {
       );
 
       dispatch(
-        imageMetadataReceived({
+        imageDTOReceived({
           image_name,
         })
       );

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

@@ -1,13 +1,13 @@
 import { log } from 'app/logging/useLogger';
-import { startAppListening } from '..';
-import { imageMetadataReceived, imageUpdated } from 'services/api/thunks/image';
 import { imageUpserted } from 'features/gallery/store/gallerySlice';
+import { imageDTOReceived, imageUpdated } from 'services/api/thunks/image';
+import { startAppListening } from '..';
 
 const moduleLog = log.child({ namespace: 'image' });
 
 export const addImageMetadataReceivedFulfilledListener = () => {
   startAppListening({
-    actionCreator: imageMetadataReceived.fulfilled,
+    actionCreator: imageDTOReceived.fulfilled,
     effect: (action, { getState, dispatch }) => {
       const image = action.payload;
 
@@ -40,7 +40,7 @@ export const addImageMetadataReceivedFulfilledListener = () => {
 
 export const addImageMetadataReceivedRejectedListener = () => {
   startAppListening({
-    actionCreator: imageMetadataReceived.rejected,
+    actionCreator: imageDTOReceived.rejected,
     effect: (action, { getState, dispatch }) => {
       moduleLog.debug(
         { data: { image: action.meta.arg } },

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

@@ -1,7 +1,7 @@
 import { log } from 'app/logging/useLogger';
-import { startAppListening } from '..';
-import { imageMetadataReceived } from 'services/api/thunks/image';
 import { boardImagesApi } from 'services/api/endpoints/boardImages';
+import { imageDTOReceived } from 'services/api/thunks/image';
+import { startAppListening } from '..';
 
 const moduleLog = log.child({ namespace: 'boards' });
 
@@ -17,7 +17,7 @@ export const addImageRemovedFromBoardFulfilledListener = () => {
       );
 
       dispatch(
-        imageMetadataReceived({
+        imageDTOReceived({
           image_name,
         })
       );

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

@@ -14,7 +14,7 @@ export const addModelSelectedListener = () => {
     actionCreator: modelSelected,
     effect: (action, { getState, dispatch }) => {
       const state = getState();
-      const [base_model, type, name] = action.payload.split('/');
+      const { base_model, model_name } = action.payload;
 
       if (state.generation.model?.base_model !== base_model) {
         dispatch(
@@ -30,11 +30,7 @@ export const addModelSelectedListener = () => {
         // TODO: controlnet cleared
       }
 
-      const newModel = zMainModel.parse({
-        id: action.payload,
-        base_model,
-        name,
-      });
+      const newModel = zMainModel.parse(action.payload);
 
       dispatch(modelChanged(newModel));
     },

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

@@ -0,0 +1,42 @@
+import { modelChanged } from 'features/parameters/store/generationSlice';
+import { some } from 'lodash-es';
+import { modelsApi } from 'services/api/endpoints/models';
+import { startAppListening } from '..';
+
+export const addModelsLoadedListener = () => {
+  startAppListening({
+    matcher: modelsApi.endpoints.getMainModels.matchFulfilled,
+    effect: async (action, { getState, dispatch }) => {
+      // models loaded, we need to ensure the selected model is available and if not, select the first one
+
+      const currentModel = getState().generation.model;
+
+      const isCurrentModelAvailable = some(
+        action.payload.entities,
+        (m) =>
+          m?.model_name === currentModel?.model_name &&
+          m?.base_model === currentModel?.base_model
+      );
+
+      if (isCurrentModelAvailable) {
+        return;
+      }
+
+      const firstModelId = action.payload.ids[0];
+      const firstModel = action.payload.entities[firstModelId];
+
+      if (!firstModel) {
+        // No models loaded at all
+        dispatch(modelChanged(null));
+        return;
+      }
+
+      dispatch(
+        modelChanged({
+          base_model: firstModel.base_model,
+          model_name: firstModel.model_name,
+        })
+      );
+    },
+  });
+};

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

@@ -30,6 +30,7 @@ export const addSessionCreatedRejectedListener = () => {
     effect: (action, { getState, dispatch }) => {
       if (action.payload) {
         const { arg, error } = action.payload;
+        const stringifiedError = JSON.stringify(error);
         moduleLog.error(
           {
             data: {
@@ -37,7 +38,7 @@ export const addSessionCreatedRejectedListener = () => {
               error: serializeError(error),
             },
           },
-          `Problem creating session`
+          `Problem creating session: ${stringifiedError}`
         );
       }
     },

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

@@ -33,6 +33,7 @@ export const addSessionInvokedRejectedListener = () => {
     effect: (action, { getState, dispatch }) => {
       if (action.payload) {
         const { arg, error } = action.payload;
+        const stringifiedError = JSON.stringify(error);
         moduleLog.error(
           {
             data: {
@@ -40,7 +41,7 @@ export const addSessionInvokedRejectedListener = () => {
               error: serializeError(error),
             },
           },
-          `Problem invoking session`
+          `Problem invoking session: ${stringifiedError}`
         );
       }
     },

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

@@ -1,15 +1,15 @@
-import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
-import { startAppListening } from '../..';
 import { log } from 'app/logging/useLogger';
+import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
+import { progressImageSet } from 'features/system/store/systemSlice';
+import { boardImagesApi } from 'services/api/endpoints/boardImages';
+import { isImageOutput } from 'services/api/guards';
+import { imageDTOReceived } from 'services/api/thunks/image';
+import { sessionCanceled } from 'services/api/thunks/session';
 import {
   appSocketInvocationComplete,
   socketInvocationComplete,
 } from 'services/events/actions';
-import { imageMetadataReceived } from 'services/api/thunks/image';
-import { sessionCanceled } from 'services/api/thunks/session';
-import { isImageOutput } from 'services/api/guards';
-import { progressImageSet } from 'features/system/store/systemSlice';
-import { boardImagesApi } from 'services/api/endpoints/boardImages';
+import { startAppListening } from '../..';
 
 const moduleLog = log.child({ namespace: 'socketio' });
 const nodeDenylist = ['dataURL_image'];
@@ -42,13 +42,13 @@ export const addInvocationCompleteEventListener = () => {
 
         // Get its metadata
         dispatch(
-          imageMetadataReceived({
+          imageDTOReceived({
             image_name,
           })
         );
 
         const [{ payload: imageDTO }] = await take(
-          imageMetadataReceived.fulfilled.match
+          imageDTOReceived.fulfilled.match
         );
 
         // Handle canvas image

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

@@ -13,7 +13,7 @@ export const addInvocationErrorEventListener = () => {
     effect: (action, { dispatch, getState }) => {
       moduleLog.error(
         action.payload,
-        `Invocation error (${action.payload.data.node.type})`
+        `Invocation error (${action.payload.data.node.type}): ${action.payload.data.error}`
       );
       dispatch(appSocketInvocationError(action.payload));
     },

invokeai/frontend/web/src/app/store/store.ts

@@ -1,6 +1,7 @@
 import {
   AnyAction,
   ThunkDispatch,
+  autoBatchEnhancer,
   combineReducers,
   configureStore,
 } from '@reduxjs/toolkit';
@@ -79,14 +80,18 @@ const rememberedKeys: (keyof typeof allReducers)[] = [
 
 export const store = configureStore({
   reducer: rememberedRootReducer,
-  enhancers: [
-    rememberEnhancer(window.localStorage, rememberedKeys, {
-      persistDebounce: 300,
-      serialize,
-      unserialize,
-      prefix: LOCALSTORAGE_PREFIX,
-    }),
-  ],
+  enhancers: (existingEnhancers) => {
+    return existingEnhancers
+      .concat(
+        rememberEnhancer(window.localStorage, rememberedKeys, {
+          persistDebounce: 300,
+          serialize,
+          unserialize,
+          prefix: LOCALSTORAGE_PREFIX,
+        })
+      )
+      .concat(autoBatchEnhancer());
+  },
   middleware: (getDefaultMiddleware) =>
     getDefaultMiddleware({
       immutableCheck: false,

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -102,6 +102,8 @@ export type AppFeature =
 export type SDFeature =
   | 'controlNet'
   | 'noise'
+  | 'perlinNoise'
+  | 'noiseThreshold'
   | 'variation'
   | 'symmetry'
   | 'seamless'

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasBoundingBox.tsx

@@ -12,6 +12,7 @@ import {
   setIsMovingBoundingBox,
   setIsTransformingBoundingBox,
 } from 'features/canvas/store/canvasSlice';
+import { uiSelector } from 'features/ui/store/uiSelectors';
 import Konva from 'konva';
 import { GroupConfig } from 'konva/lib/Group';
 import { KonvaEventObject } from 'konva/lib/Node';
@@ -22,8 +23,8 @@ import { useCallback, useEffect, useRef, useState } from 'react';
 import { Group, Rect, Transformer } from 'react-konva';
 
 const boundingBoxPreviewSelector = createSelector(
-  canvasSelector,
-  (canvas) => {
+  [canvasSelector, uiSelector],
+  (canvas, ui) => {
     const {
       boundingBoxCoordinates,
       boundingBoxDimensions,
@@ -35,6 +36,8 @@ const boundingBoxPreviewSelector = createSelector(
       shouldSnapToGrid,
     } = canvas;
 
+    const { aspectRatio } = ui;
+
     return {
       boundingBoxCoordinates,
       boundingBoxDimensions,
@@ -45,6 +48,7 @@ const boundingBoxPreviewSelector = createSelector(
       shouldSnapToGrid,
       tool,
       hitStrokeWidth: 20 / stageScale,
+      aspectRatio,
     };
   },
   {
@@ -70,6 +74,7 @@ const IAICanvasBoundingBox = (props: IAICanvasBoundingBoxPreviewProps) => {
     shouldSnapToGrid,
     tool,
     hitStrokeWidth,
+    aspectRatio,
   } = useAppSelector(boundingBoxPreviewSelector);
 
   const transformerRef = useRef<Konva.Transformer>(null);
@@ -137,12 +142,22 @@ const IAICanvasBoundingBox = (props: IAICanvasBoundingBoxPreviewProps) => {
     const x = Math.round(rect.x());
     const y = Math.round(rect.y());
 
-    dispatch(
-      setBoundingBoxDimensions({
-        width,
-        height,
-      })
-    );
+    if (aspectRatio) {
+      const newHeight = roundToMultiple(width / aspectRatio, 64);
+      dispatch(
+        setBoundingBoxDimensions({
+          width: width,
+          height: newHeight,
+        })
+      );
+    } else {
+      dispatch(
+        setBoundingBoxDimensions({
+          width,
+          height,
+        })
+      );
+    }
 
     dispatch(
       setBoundingBoxCoordinates({
@@ -154,7 +169,7 @@ const IAICanvasBoundingBox = (props: IAICanvasBoundingBoxPreviewProps) => {
     // Reset the scale now that the coords/dimensions have been un-scaled
     rect.scaleX(1);
     rect.scaleY(1);
-  }, [dispatch, shouldSnapToGrid]);
+  }, [dispatch, shouldSnapToGrid, aspectRatio]);
 
   const anchorDragBoundFunc = useCallback(
     (

invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts

@@ -7,7 +7,14 @@ import {
 import { IRect, Vector2d } from 'konva/lib/types';
 import { clamp, cloneDeep } from 'lodash-es';
 //
+import {
+  setActiveTab,
+  setAspectRatio,
+  setShouldUseCanvasBetaLayout,
+} from 'features/ui/store/uiSlice';
 import { RgbaColor } from 'react-colorful';
+import { sessionCanceled } from 'services/api/thunks/session';
+import { ImageDTO } from 'services/api/types';
 import calculateCoordinates from '../util/calculateCoordinates';
 import calculateScale from '../util/calculateScale';
 import { STAGE_PADDING_PERCENTAGE } from '../util/constants';
@@ -28,13 +35,6 @@ import {
   isCanvasBaseImage,
   isCanvasMaskLine,
 } from './canvasTypes';
-import { ImageDTO } from 'services/api/types';
-import { sessionCanceled } from 'services/api/thunks/session';
-import {
-  setActiveTab,
-  setShouldUseCanvasBetaLayout,
-} from 'features/ui/store/uiSlice';
-import { imageUrlsReceived } from 'services/api/thunks/image';
 
 export const initialLayerState: CanvasLayerState = {
   objects: [],
@@ -240,6 +240,16 @@ export const canvasSlice = createSlice({
         state.scaledBoundingBoxDimensions = scaledDimensions;
       }
     },
+    flipBoundingBoxAxes: (state) => {
+      const [currWidth, currHeight] = [
+        state.boundingBoxDimensions.width,
+        state.boundingBoxDimensions.height,
+      ];
+      state.boundingBoxDimensions = {
+        width: currHeight,
+        height: currWidth,
+      };
+    },
     setBoundingBoxCoordinates: (state, action: PayloadAction<Vector2d>) => {
       state.boundingBoxCoordinates = floorCoordinates(action.payload);
     },
@@ -864,6 +874,15 @@ export const canvasSlice = createSlice({
     builder.addCase(setActiveTab, (state, action) => {
       state.doesCanvasNeedScaling = true;
     });
+    builder.addCase(setAspectRatio, (state, action) => {
+      const ratio = action.payload;
+      if (ratio) {
+        state.boundingBoxDimensions.height = roundToMultiple(
+          state.boundingBoxDimensions.width / ratio,
+          64
+        );
+      }
+    });
 
     // builder.addCase(imageUrlsReceived.fulfilled, (state, action) => {
     //   const { image_name, image_url, thumbnail_url } = action.payload;
@@ -912,6 +931,7 @@ export const {
   setBoundingBoxDimensions,
   setBoundingBoxPreviewFill,
   setBoundingBoxScaleMethod,
+  flipBoundingBoxAxes,
   setBrushColor,
   setBrushSize,
   setCanvasContainerDimensions,

invokeai/frontend/web/src/features/embedding/components/ParamEmbeddingPopover.tsx

@@ -47,8 +47,8 @@ const ParamEmbeddingPopover = (props: Props) => {
       const disabled = currentMainModel?.base_model !== embedding.base_model;
 
       data.push({
-        value: embedding.name,
-        label: embedding.name,
+        value: embedding.model_name,
+        label: embedding.model_name,
         group: MODEL_TYPE_MAP[embedding.base_model],
         disabled,
         tooltip: disabled

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

@@ -45,7 +45,11 @@ import {
   FaShare,
   FaShareAlt,
 } from 'react-icons/fa';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import {
+  useGetImageDTOQuery,
+  useGetImageMetadataQuery,
+} from 'services/api/endpoints/images';
+import { useDebounce } from 'use-debounce';
 import { sentImageToCanvas, sentImageToImg2Img } from '../store/actions';
 
 const currentImageButtonsSelector = createSelector(
@@ -128,10 +132,23 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   const { recallBothPrompts, recallSeed, recallAllParameters } =
     useRecallParameters();
 
-  const { currentData: image } = useGetImageDTOQuery(
+  const [debouncedMetadataQueryArg, debounceState] = useDebounce(
+    lastSelectedImage,
+    500
+  );
+
+  const { currentData: image, isFetching } = useGetImageDTOQuery(
     lastSelectedImage ?? skipToken
   );
 
+  const { currentData: metadataData } = useGetImageMetadataQuery(
+    debounceState.isPending()
+      ? skipToken
+      : debouncedMetadataQueryArg ?? skipToken
+  );
+
+  const metadata = metadataData?.metadata;
+
   // const handleCopyImage = useCallback(async () => {
   //   if (!image?.url) {
   //     return;
@@ -193,29 +210,26 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   }, [toaster, t, image]);
 
   const handleClickUseAllParameters = useCallback(() => {
-    recallAllParameters(image);
-  }, [image, recallAllParameters]);
+    recallAllParameters(metadata);
+  }, [metadata, recallAllParameters]);
 
   useHotkeys(
     'a',
     () => {
       handleClickUseAllParameters;
     },
-    [image, recallAllParameters]
+    [metadata, recallAllParameters]
   );
 
   const handleUseSeed = useCallback(() => {
-    recallSeed(image?.metadata?.seed);
-  }, [image, recallSeed]);
+    recallSeed(metadata?.seed);
+  }, [metadata?.seed, recallSeed]);
 
   useHotkeys('s', handleUseSeed, [image]);
 
   const handleUsePrompt = useCallback(() => {
-    recallBothPrompts(
-      image?.metadata?.positive_conditioning,
-      image?.metadata?.negative_conditioning
-    );
-  }, [image, recallBothPrompts]);
+    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
+  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
 
   useHotkeys('p', handleUsePrompt, [image]);
 
@@ -440,7 +454,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
             icon={<FaQuoteRight />}
             tooltip={`${t('parameters.usePrompt')} (P)`}
             aria-label={`${t('parameters.usePrompt')} (P)`}
-            isDisabled={!image?.metadata?.positive_conditioning}
+            isDisabled={!metadata?.positive_prompt}
             onClick={handleUsePrompt}
           />
 
@@ -448,7 +462,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
             icon={<FaSeedling />}
             tooltip={`${t('parameters.useSeed')} (S)`}
             aria-label={`${t('parameters.useSeed')} (S)`}
-            isDisabled={!image?.metadata?.seed}
+            isDisabled={!metadata?.seed}
             onClick={handleUseSeed}
           />
 
@@ -456,10 +470,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
             icon={<FaAsterisk />}
             tooltip={`${t('parameters.useAll')} (A)`}
             aria-label={`${t('parameters.useAll')} (A)`}
-            isDisabled={
-              // not sure what this list should be
-              !['t2l', 'l2l', 'inpaint'].includes(String(image?.metadata?.type))
-            }
+            isDisabled={!metadata}
             onClick={handleClickUseAllParameters}
           />
         </ButtonGroup>

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

@@ -11,7 +11,9 @@ import IAIDndImage from 'common/components/IAIDndImage';
 import { selectLastSelectedImage } from 'features/gallery/store/gallerySlice';
 import { isEqual } from 'lodash-es';
 import { memo, useMemo } from 'react';
+import { useHotkeys } from 'react-hotkeys-hook';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import { useNextPrevImage } from '../hooks/useNextPrevImage';
 import ImageMetadataViewer from './ImageMetaDataViewer/ImageMetadataViewer';
 import NextPrevImageButtons from './NextPrevImageButtons';
 
@@ -49,6 +51,45 @@ const CurrentImagePreview = () => {
     shouldAntialiasProgressImage,
   } = useAppSelector(imagesSelector);
 
+  const {
+    handlePrevImage,
+    handleNextImage,
+    prevImageId,
+    nextImageId,
+    isOnLastImage,
+    handleLoadMoreImages,
+    areMoreImagesAvailable,
+    isFetching,
+  } = useNextPrevImage();
+
+  useHotkeys(
+    'left',
+    () => {
+      handlePrevImage();
+    },
+    [prevImageId]
+  );
+
+  useHotkeys(
+    'right',
+    () => {
+      if (isOnLastImage && areMoreImagesAvailable && !isFetching) {
+        handleLoadMoreImages();
+        return;
+      }
+      if (!isOnLastImage) {
+        handleNextImage();
+      }
+    },
+    [
+      nextImageId,
+      isOnLastImage,
+      areMoreImagesAvailable,
+      handleLoadMoreImages,
+      isFetching,
+    ]
+  );
+
   const {
     currentData: imageDTO,
     isLoading,
@@ -118,7 +159,6 @@ const CurrentImagePreview = () => {
             width: 'full',
             height: 'full',
             borderRadius: 'base',
-            overflow: 'scroll',
           }}
         >
           <ImageMetadataViewer image={imageDTO} />

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

@@ -6,10 +6,7 @@ import { stateSelector } from 'app/store/store';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { ContextMenu, ContextMenuProps } from 'chakra-ui-contextmenu';
-import {
-  imagesAddedToBatch,
-  selectionAddedToBatch,
-} from 'features/batch/store/batchSlice';
+import { imagesAddedToBatch } from 'features/batch/store/batchSlice';
 import {
   resizeAndScaleCanvas,
   setInitialCanvasImage,
@@ -24,6 +21,7 @@ import { useTranslation } from 'react-i18next';
 import { FaExpand, FaFolder, FaShare, FaTrash } from 'react-icons/fa';
 import { IoArrowUndoCircleOutline } from 'react-icons/io5';
 import { useRemoveImageFromBoardMutation } from 'services/api/endpoints/boardImages';
+import { useGetImageMetadataQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
 import { AddImageToBoardContext } from '../../../app/contexts/AddImageToBoardContext';
 import { sentImageToCanvas, sentImageToImg2Img } from '../store/actions';
@@ -38,24 +36,17 @@ const ImageContextMenu = ({ image, children }: Props) => {
     () =>
       createSelector(
         [stateSelector],
-        ({ gallery, batch }) => {
+        ({ gallery }) => {
           const selectionCount = gallery.selection.length;
-          const isInBatch = batch.imageNames.includes(image.image_name);
 
-          return { selectionCount, isInBatch };
+          return { selectionCount };
         },
         defaultSelectorOptions
       ),
-    [image.image_name]
+    []
   );
-  const { selectionCount, isInBatch } = useAppSelector(selector);
+  const { selectionCount } = useAppSelector(selector);
   const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-
-  const toaster = useAppToaster();
-
-  const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
-  const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
 
   const { onClickAddToBoard } = useContext(AddImageToBoardContext);
 
@@ -66,178 +57,17 @@ const ImageContextMenu = ({ image, children }: Props) => {
     dispatch(imageToDeleteSelected(image));
   }, [dispatch, image]);
 
-  const { recallBothPrompts, recallSeed, recallAllParameters } =
-    useRecallParameters();
-
-  const [removeFromBoard] = useRemoveImageFromBoardMutation();
-
-  // Recall parameters handlers
-  const handleRecallPrompt = useCallback(() => {
-    recallBothPrompts(
-      image.metadata?.positive_conditioning,
-      image.metadata?.negative_conditioning
-    );
-  }, [
-    image.metadata?.negative_conditioning,
-    image.metadata?.positive_conditioning,
-    recallBothPrompts,
-  ]);
-
-  const handleRecallSeed = useCallback(() => {
-    recallSeed(image.metadata?.seed);
-  }, [image, recallSeed]);
-
-  const handleSendToImageToImage = useCallback(() => {
-    dispatch(sentImageToImg2Img());
-    dispatch(initialImageSelected(image));
-  }, [dispatch, image]);
-
-  // const handleRecallInitialImage = useCallback(() => {
-  //   recallInitialImage(image.metadata.invokeai?.node?.image);
-  // }, [image, recallInitialImage]);
-
-  const handleSendToCanvas = () => {
-    dispatch(sentImageToCanvas());
-    dispatch(setInitialCanvasImage(image));
-    dispatch(resizeAndScaleCanvas());
-    dispatch(setActiveTab('unifiedCanvas'));
-
-    toaster({
-      title: t('toast.sentToUnifiedCanvas'),
-      status: 'success',
-      duration: 2500,
-      isClosable: true,
-    });
-  };
-
-  const handleUseAllParameters = useCallback(() => {
-    recallAllParameters(image);
-  }, [image, recallAllParameters]);
-
-  const handleLightBox = () => {
-    // dispatch(setCurrentImage(image));
-    // dispatch(setIsLightboxOpen(true));
-  };
-
   const handleAddToBoard = useCallback(() => {
     onClickAddToBoard(image);
   }, [image, onClickAddToBoard]);
 
-  const handleRemoveFromBoard = useCallback(() => {
-    if (!image.board_id) {
-      return;
-    }
-    removeFromBoard({ board_id: image.board_id, image_name: image.image_name });
-  }, [image.board_id, image.image_name, removeFromBoard]);
-
-  const handleOpenInNewTab = () => {
-    window.open(image.image_url, '_blank');
-  };
-
-  const handleAddSelectionToBatch = useCallback(() => {
-    dispatch(selectionAddedToBatch());
-  }, [dispatch]);
-
-  const handleAddToBatch = useCallback(() => {
-    dispatch(imagesAddedToBatch([image.image_name]));
-  }, [dispatch, image.image_name]);
-
   return (
     <ContextMenu<HTMLDivElement>
       menuProps={{ size: 'sm', isLazy: true }}
       renderMenu={() => (
         <MenuList sx={{ visibility: 'visible !important' }}>
           {selectionCount === 1 ? (
-            <>
-              <MenuItem
-                icon={<ExternalLinkIcon />}
-                onClickCapture={handleOpenInNewTab}
-              >
-                {t('common.openInNewTab')}
-              </MenuItem>
-              {isLightboxEnabled && (
-                <MenuItem icon={<FaExpand />} onClickCapture={handleLightBox}>
-                  {t('parameters.openInViewer')}
-                </MenuItem>
-              )}
-              <MenuItem
-                icon={<IoArrowUndoCircleOutline />}
-                onClickCapture={handleRecallPrompt}
-                isDisabled={
-                  image?.metadata?.positive_conditioning === undefined
-                }
-              >
-                {t('parameters.usePrompt')}
-              </MenuItem>
-
-              <MenuItem
-                icon={<IoArrowUndoCircleOutline />}
-                onClickCapture={handleRecallSeed}
-                isDisabled={image?.metadata?.seed === undefined}
-              >
-                {t('parameters.useSeed')}
-              </MenuItem>
-              {/* <MenuItem
-              icon={<IoArrowUndoCircleOutline />}
-              onClickCapture={handleRecallInitialImage}
-              isDisabled={image?.metadata?.type !== 'img2img'}
-            >
-              {t('parameters.useInitImg')}
-            </MenuItem> */}
-              <MenuItem
-                icon={<IoArrowUndoCircleOutline />}
-                onClickCapture={handleUseAllParameters}
-                isDisabled={
-                  // what should these be
-                  !['t2l', 'l2l', 'inpaint'].includes(
-                    String(image?.metadata?.type)
-                  )
-                }
-              >
-                {t('parameters.useAll')}
-              </MenuItem>
-              <MenuItem
-                icon={<FaShare />}
-                onClickCapture={handleSendToImageToImage}
-                id="send-to-img2img"
-              >
-                {t('parameters.sendToImg2Img')}
-              </MenuItem>
-              {isCanvasEnabled && (
-                <MenuItem
-                  icon={<FaShare />}
-                  onClickCapture={handleSendToCanvas}
-                  id="send-to-canvas"
-                >
-                  {t('parameters.sendToUnifiedCanvas')}
-                </MenuItem>
-              )}
-              {/* <MenuItem
-                icon={<FaFolder />}
-                isDisabled={isInBatch}
-                onClickCapture={handleAddToBatch}
-              >
-                Add to Batch
-              </MenuItem> */}
-              <MenuItem icon={<FaFolder />} onClickCapture={handleAddToBoard}>
-                {image.board_id ? 'Change Board' : 'Add to Board'}
-              </MenuItem>
-              {image.board_id && (
-                <MenuItem
-                  icon={<FaFolder />}
-                  onClickCapture={handleRemoveFromBoard}
-                >
-                  Remove from Board
-                </MenuItem>
-              )}
-              <MenuItem
-                sx={{ color: 'error.600', _dark: { color: 'error.300' } }}
-                icon={<FaTrash />}
-                onClickCapture={handleDelete}
-              >
-                {t('gallery.deleteImage')}
-              </MenuItem>
-            </>
+            <SingleSelectionMenuItems image={image} />
           ) : (
             <>
               <MenuItem
@@ -271,3 +101,185 @@ const ImageContextMenu = ({ image, children }: Props) => {
 };
 
 export default memo(ImageContextMenu);
+
+type SingleSelectionMenuItemsProps = {
+  image: ImageDTO;
+};
+
+const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
+  const { image } = props;
+
+  const selector = useMemo(
+    () =>
+      createSelector(
+        [stateSelector],
+        ({ batch }) => {
+          const isInBatch = batch.imageNames.includes(image.image_name);
+
+          return { isInBatch };
+        },
+        defaultSelectorOptions
+      ),
+    [image.image_name]
+  );
+
+  const { isInBatch } = useAppSelector(selector);
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const toaster = useAppToaster();
+
+  const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
+  const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
+
+  const { onClickAddToBoard } = useContext(AddImageToBoardContext);
+
+  const { currentData } = useGetImageMetadataQuery(image.image_name);
+
+  const metadata = currentData?.metadata;
+
+  const handleDelete = useCallback(() => {
+    if (!image) {
+      return;
+    }
+    dispatch(imageToDeleteSelected(image));
+  }, [dispatch, image]);
+
+  const { recallBothPrompts, recallSeed, recallAllParameters } =
+    useRecallParameters();
+
+  const [removeFromBoard] = useRemoveImageFromBoardMutation();
+
+  // Recall parameters handlers
+  const handleRecallPrompt = useCallback(() => {
+    recallBothPrompts(metadata?.positive_prompt, metadata?.negative_prompt);
+  }, [metadata?.negative_prompt, metadata?.positive_prompt, recallBothPrompts]);
+
+  const handleRecallSeed = useCallback(() => {
+    recallSeed(metadata?.seed);
+  }, [metadata?.seed, recallSeed]);
+
+  const handleSendToImageToImage = useCallback(() => {
+    dispatch(sentImageToImg2Img());
+    dispatch(initialImageSelected(image));
+  }, [dispatch, image]);
+
+  const handleSendToCanvas = () => {
+    dispatch(sentImageToCanvas());
+    dispatch(setInitialCanvasImage(image));
+    dispatch(resizeAndScaleCanvas());
+    dispatch(setActiveTab('unifiedCanvas'));
+
+    toaster({
+      title: t('toast.sentToUnifiedCanvas'),
+      status: 'success',
+      duration: 2500,
+      isClosable: true,
+    });
+  };
+
+  const handleUseAllParameters = useCallback(() => {
+    console.log(metadata);
+    recallAllParameters(metadata);
+  }, [metadata, recallAllParameters]);
+
+  const handleLightBox = () => {
+    // dispatch(setCurrentImage(image));
+    // dispatch(setIsLightboxOpen(true));
+  };
+
+  const handleAddToBoard = useCallback(() => {
+    onClickAddToBoard(image);
+  }, [image, onClickAddToBoard]);
+
+  const handleRemoveFromBoard = useCallback(() => {
+    if (!image.board_id) {
+      return;
+    }
+    removeFromBoard({ board_id: image.board_id, image_name: image.image_name });
+  }, [image.board_id, image.image_name, removeFromBoard]);
+
+  const handleOpenInNewTab = () => {
+    window.open(image.image_url, '_blank');
+  };
+
+  const handleAddToBatch = useCallback(() => {
+    dispatch(imagesAddedToBatch([image.image_name]));
+  }, [dispatch, image.image_name]);
+
+  return (
+    <>
+      <MenuItem icon={<ExternalLinkIcon />} onClickCapture={handleOpenInNewTab}>
+        {t('common.openInNewTab')}
+      </MenuItem>
+      {isLightboxEnabled && (
+        <MenuItem icon={<FaExpand />} onClickCapture={handleLightBox}>
+          {t('parameters.openInViewer')}
+        </MenuItem>
+      )}
+      <MenuItem
+        icon={<IoArrowUndoCircleOutline />}
+        onClickCapture={handleRecallPrompt}
+        isDisabled={
+          metadata?.positive_prompt === undefined &&
+          metadata?.negative_prompt === undefined
+        }
+      >
+        {t('parameters.usePrompt')}
+      </MenuItem>
+
+      <MenuItem
+        icon={<IoArrowUndoCircleOutline />}
+        onClickCapture={handleRecallSeed}
+        isDisabled={metadata?.seed === undefined}
+      >
+        {t('parameters.useSeed')}
+      </MenuItem>
+      <MenuItem
+        icon={<IoArrowUndoCircleOutline />}
+        onClickCapture={handleUseAllParameters}
+        isDisabled={!metadata}
+      >
+        {t('parameters.useAll')}
+      </MenuItem>
+      <MenuItem
+        icon={<FaShare />}
+        onClickCapture={handleSendToImageToImage}
+        id="send-to-img2img"
+      >
+        {t('parameters.sendToImg2Img')}
+      </MenuItem>
+      {isCanvasEnabled && (
+        <MenuItem
+          icon={<FaShare />}
+          onClickCapture={handleSendToCanvas}
+          id="send-to-canvas"
+        >
+          {t('parameters.sendToUnifiedCanvas')}
+        </MenuItem>
+      )}
+      <MenuItem
+        icon={<FaFolder />}
+        isDisabled={isInBatch}
+        onClickCapture={handleAddToBatch}
+      >
+        Add to Batch
+      </MenuItem>
+      <MenuItem icon={<FaFolder />} onClickCapture={handleAddToBoard}>
+        {image.board_id ? 'Change Board' : 'Add to Board'}
+      </MenuItem>
+      {image.board_id && (
+        <MenuItem icon={<FaFolder />} onClickCapture={handleRemoveFromBoard}>
+          Remove from Board
+        </MenuItem>
+      )}
+      <MenuItem
+        sx={{ color: 'error.600', _dark: { color: 'error.300' } }}
+        icon={<FaTrash />}
+        onClickCapture={handleDelete}
+      >
+        {t('gallery.deleteImage')}
+      </MenuItem>
+    </>
+  );
+};

invokeai/frontend/web/src/features/gallery/components/ImageMetaDataViewer/ImageMetadataActions.tsx

@@ -0,0 +1,212 @@
+import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
+import { useCallback } from 'react';
+import { UnsafeImageMetadata } from 'services/api/endpoints/images';
+import MetadataItem from './MetadataItem';
+
+type Props = {
+  metadata?: UnsafeImageMetadata['metadata'];
+};
+
+const ImageMetadataActions = (props: Props) => {
+  const { metadata } = props;
+
+  const {
+    recallBothPrompts,
+    recallPositivePrompt,
+    recallNegativePrompt,
+    recallSeed,
+    recallInitialImage,
+    recallCfgScale,
+    recallModel,
+    recallScheduler,
+    recallSteps,
+    recallWidth,
+    recallHeight,
+    recallStrength,
+    recallAllParameters,
+  } = useRecallParameters();
+
+  const handleRecallPositivePrompt = useCallback(() => {
+    recallPositivePrompt(metadata?.positive_prompt);
+  }, [metadata?.positive_prompt, recallPositivePrompt]);
+
+  const handleRecallNegativePrompt = useCallback(() => {
+    recallNegativePrompt(metadata?.negative_prompt);
+  }, [metadata?.negative_prompt, recallNegativePrompt]);
+
+  const handleRecallSeed = useCallback(() => {
+    recallSeed(metadata?.seed);
+  }, [metadata?.seed, recallSeed]);
+
+  const handleRecallModel = useCallback(() => {
+    recallModel(metadata?.model);
+  }, [metadata?.model, recallModel]);
+
+  const handleRecallWidth = useCallback(() => {
+    recallWidth(metadata?.width);
+  }, [metadata?.width, recallWidth]);
+
+  const handleRecallHeight = useCallback(() => {
+    recallHeight(metadata?.height);
+  }, [metadata?.height, recallHeight]);
+
+  const handleRecallScheduler = useCallback(() => {
+    recallScheduler(metadata?.scheduler);
+  }, [metadata?.scheduler, recallScheduler]);
+
+  const handleRecallSteps = useCallback(() => {
+    recallSteps(metadata?.steps);
+  }, [metadata?.steps, recallSteps]);
+
+  const handleRecallCfgScale = useCallback(() => {
+    recallCfgScale(metadata?.cfg_scale);
+  }, [metadata?.cfg_scale, recallCfgScale]);
+
+  const handleRecallStrength = useCallback(() => {
+    recallStrength(metadata?.strength);
+  }, [metadata?.strength, recallStrength]);
+
+  if (!metadata || Object.keys(metadata).length === 0) {
+    return null;
+  }
+
+  return (
+    <>
+      {metadata.generation_mode && (
+        <MetadataItem
+          label="Generation Mode"
+          value={metadata.generation_mode}
+        />
+      )}
+      {metadata.positive_prompt && (
+        <MetadataItem
+          label="Positive Prompt"
+          labelPosition="top"
+          value={metadata.positive_prompt}
+          onClick={handleRecallPositivePrompt}
+        />
+      )}
+      {metadata.negative_prompt && (
+        <MetadataItem
+          label="Negative Prompt"
+          labelPosition="top"
+          value={metadata.negative_prompt}
+          onClick={handleRecallNegativePrompt}
+        />
+      )}
+      {metadata.seed !== undefined && (
+        <MetadataItem
+          label="Seed"
+          value={metadata.seed}
+          onClick={handleRecallSeed}
+        />
+      )}
+      {metadata.model !== undefined && (
+        <MetadataItem
+          label="Model"
+          value={metadata.model.model_name}
+          onClick={handleRecallModel}
+        />
+      )}
+      {metadata.width && (
+        <MetadataItem
+          label="Width"
+          value={metadata.width}
+          onClick={handleRecallWidth}
+        />
+      )}
+      {metadata.height && (
+        <MetadataItem
+          label="Height"
+          value={metadata.height}
+          onClick={handleRecallHeight}
+        />
+      )}
+      {/* {metadata.threshold !== undefined && (
+          <MetadataItem
+            label="Noise Threshold"
+            value={metadata.threshold}
+            onClick={() => dispatch(setThreshold(Number(metadata.threshold)))}
+          />
+        )}
+        {metadata.perlin !== undefined && (
+          <MetadataItem
+            label="Perlin Noise"
+            value={metadata.perlin}
+            onClick={() => dispatch(setPerlin(Number(metadata.perlin)))}
+          />
+        )} */}
+      {metadata.scheduler && (
+        <MetadataItem
+          label="Scheduler"
+          value={metadata.scheduler}
+          onClick={handleRecallScheduler}
+        />
+      )}
+      {metadata.steps && (
+        <MetadataItem
+          label="Steps"
+          value={metadata.steps}
+          onClick={handleRecallSteps}
+        />
+      )}
+      {metadata.cfg_scale !== undefined && (
+        <MetadataItem
+          label="CFG scale"
+          value={metadata.cfg_scale}
+          onClick={handleRecallCfgScale}
+        />
+      )}
+      {/* {metadata.variations && metadata.variations.length > 0 && (
+          <MetadataItem
+            label="Seed-weight pairs"
+            value={seedWeightsToString(metadata.variations)}
+            onClick={() =>
+              dispatch(
+                setSeedWeights(seedWeightsToString(metadata.variations))
+              )
+            }
+          />
+        )}
+        {metadata.seamless && (
+          <MetadataItem
+            label="Seamless"
+            value={metadata.seamless}
+            onClick={() => dispatch(setSeamless(metadata.seamless))}
+          />
+        )}
+        {metadata.hires_fix && (
+          <MetadataItem
+            label="High Resolution Optimization"
+            value={metadata.hires_fix}
+            onClick={() => dispatch(setHiresFix(metadata.hires_fix))}
+          />
+        )} */}
+
+      {/* {init_image_path && (
+          <MetadataItem
+            label="Initial image"
+            value={init_image_path}
+            isLink
+            onClick={() => dispatch(setInitialImage(init_image_path))}
+          />
+        )} */}
+      {metadata.strength && (
+        <MetadataItem
+          label="Image to image strength"
+          value={metadata.strength}
+          onClick={handleRecallStrength}
+        />
+      )}
+      {/* {metadata.fit && (
+          <MetadataItem
+            label="Image to image fit"
+            value={metadata.fit}
+            onClick={() => dispatch(setShouldFitToWidthHeight(metadata.fit))}
+          />
+        )} */}
+    </>
+  );
+};
+
+export default ImageMetadataActions;

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

@@ -1,131 +1,74 @@
 import { ExternalLinkIcon } from '@chakra-ui/icons';
 import {
-  Box,
-  Center,
   Flex,
-  IconButton,
   Link,
+  Tab,
+  TabList,
+  TabPanel,
+  TabPanels,
+  Tabs,
   Text,
-  Tooltip,
 } from '@chakra-ui/react';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
-import { setShouldShowImageDetails } from 'features/ui/store/uiSlice';
-import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
-import { memo } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
-import { useTranslation } from 'react-i18next';
-import { FaCopy } from 'react-icons/fa';
-import { IoArrowUndoCircleOutline } from 'react-icons/io5';
+import { skipToken } from '@reduxjs/toolkit/dist/query';
+import { memo, useMemo } from 'react';
+import { useGetImageMetadataQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
-
-type MetadataItemProps = {
-  isLink?: boolean;
-  label: string;
-  onClick?: () => void;
-  value: number | string | boolean;
-  labelPosition?: string;
-  withCopy?: boolean;
-};
-
-/**
- * Component to display an individual metadata item or parameter.
- */
-const MetadataItem = ({
-  label,
-  value,
-  onClick,
-  isLink,
-  labelPosition,
-  withCopy = false,
-}: MetadataItemProps) => {
-  const { t } = useTranslation();
-
-  if (!value) {
-    return null;
-  }
-
-  return (
-    <Flex gap={2}>
-      {onClick && (
-        <Tooltip label={`Recall ${label}`}>
-          <IconButton
-            aria-label={t('accessibility.useThisParameter')}
-            icon={<IoArrowUndoCircleOutline />}
-            size="xs"
-            variant="ghost"
-            fontSize={20}
-            onClick={onClick}
-          />
-        </Tooltip>
-      )}
-      {withCopy && (
-        <Tooltip label={`Copy ${label}`}>
-          <IconButton
-            aria-label={`Copy ${label}`}
-            icon={<FaCopy />}
-            size="xs"
-            variant="ghost"
-            fontSize={14}
-            onClick={() => navigator.clipboard.writeText(value.toString())}
-          />
-        </Tooltip>
-      )}
-      <Flex direction={labelPosition ? 'column' : 'row'}>
-        <Text fontWeight="semibold" whiteSpace="pre-wrap" pr={2}>
-          {label}:
-        </Text>
-        {isLink ? (
-          <Link href={value.toString()} isExternal wordBreak="break-all">
-            {value.toString()} <ExternalLinkIcon mx="2px" />
-          </Link>
-        ) : (
-          <Text overflowY="scroll" wordBreak="break-all">
-            {value.toString()}
-          </Text>
-        )}
-      </Flex>
-    </Flex>
-  );
-};
+import { useDebounce } from 'use-debounce';
+import ImageMetadataActions from './ImageMetadataActions';
+import MetadataJSONViewer from './MetadataJSONViewer';
 
 type ImageMetadataViewerProps = {
   image: ImageDTO;
 };
 
-/**
- * Image metadata viewer overlays currently selected image and provides
- * access to any of its metadata for use in processing.
- */
 const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
-  const dispatch = useAppDispatch();
-  const {
-    recallBothPrompts,
-    recallPositivePrompt,
-    recallNegativePrompt,
-    recallSeed,
-    recallInitialImage,
-    recallCfgScale,
-    recallModel,
-    recallScheduler,
-    recallSteps,
-    recallWidth,
-    recallHeight,
-    recallStrength,
-    recallAllParameters,
-  } = useRecallParameters();
+  // TODO: fix hotkeys
+  // const dispatch = useAppDispatch();
+  // useHotkeys('esc', () => {
+  //   dispatch(setShouldShowImageDetails(false));
+  // });
 
-  useHotkeys('esc', () => {
-    dispatch(setShouldShowImageDetails(false));
-  });
+  const [debouncedMetadataQueryArg, debounceState] = useDebounce(
+    image.image_name,
+    500
+  );
 
-  const sessionId = image?.session_id;
+  const { currentData } = useGetImageMetadataQuery(
+    debounceState.isPending()
+      ? skipToken
+      : debouncedMetadataQueryArg ?? skipToken
+  );
+  const metadata = currentData?.metadata;
+  const graph = currentData?.graph;
 
-  const metadata = image?.metadata;
+  const tabData = useMemo(() => {
+    const _tabData: { label: string; data: object; copyTooltip: string }[] = [];
 
-  const { t } = useTranslation();
+    if (metadata) {
+      _tabData.push({
+        label: 'Core Metadata',
+        data: metadata,
+        copyTooltip: 'Copy Core Metadata JSON',
+      });
+    }
 
-  const metadataJSON = JSON.stringify(image, null, 2);
+    if (image) {
+      _tabData.push({
+        label: 'Image Details',
+        data: image,
+        copyTooltip: 'Copy Image Details JSON',
+      });
+    }
+
+    if (graph) {
+      _tabData.push({
+        label: 'Graph',
+        data: graph,
+        copyTooltip: 'Copy Graph JSON',
+      });
+    }
+    return _tabData;
+  }, [metadata, graph, image]);
 
   return (
     <Flex
@@ -136,11 +79,13 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
         width: 'full',
         height: 'full',
         backdropFilter: 'blur(20px)',
-        bg: 'whiteAlpha.600',
+        bg: 'baseAlpha.200',
         _dark: {
           bg: 'blackAlpha.600',
         },
-        overflow: 'scroll',
+        borderRadius: 'base',
+        position: 'absolute',
+        overflow: 'hidden',
       }}
     >
       <Flex gap={2}>
@@ -150,179 +95,42 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
           <ExternalLinkIcon mx="2px" />
         </Link>
       </Flex>
-      {metadata && Object.keys(metadata).length > 0 ? (
-        <>
-          {metadata.type && (
-            <MetadataItem label="Invocation type" value={metadata.type} />
-          )}
-          {sessionId && <MetadataItem label="Session ID" value={sessionId} />}
-          {metadata.positive_conditioning && (
-            <MetadataItem
-              label="Positive Prompt"
-              labelPosition="top"
-              value={metadata.positive_conditioning}
-              onClick={() =>
-                recallPositivePrompt(metadata.positive_conditioning)
-              }
-            />
-          )}
-          {metadata.negative_conditioning && (
-            <MetadataItem
-              label="Negative Prompt"
-              labelPosition="top"
-              value={metadata.negative_conditioning}
-              onClick={() =>
-                recallNegativePrompt(metadata.negative_conditioning)
-              }
-            />
-          )}
-          {metadata.seed !== undefined && (
-            <MetadataItem
-              label="Seed"
-              value={metadata.seed}
-              onClick={() => recallSeed(metadata.seed)}
-            />
-          )}
-          {metadata.model !== undefined && (
-            <MetadataItem
-              label="Model"
-              value={metadata.model}
-              onClick={() => recallModel(metadata.model)}
-            />
-          )}
-          {metadata.width && (
-            <MetadataItem
-              label="Width"
-              value={metadata.width}
-              onClick={() => recallWidth(metadata.width)}
-            />
-          )}
-          {metadata.height && (
-            <MetadataItem
-              label="Height"
-              value={metadata.height}
-              onClick={() => recallHeight(metadata.height)}
-            />
-          )}
-          {/* {metadata.threshold !== undefined && (
-            <MetadataItem
-              label="Noise Threshold"
-              value={metadata.threshold}
-              onClick={() => dispatch(setThreshold(Number(metadata.threshold)))}
-            />
-          )}
-          {metadata.perlin !== undefined && (
-            <MetadataItem
-              label="Perlin Noise"
-              value={metadata.perlin}
-              onClick={() => dispatch(setPerlin(Number(metadata.perlin)))}
-            />
-          )} */}
-          {metadata.scheduler && (
-            <MetadataItem
-              label="Scheduler"
-              value={metadata.scheduler}
-              onClick={() => recallScheduler(metadata.scheduler)}
-            />
-          )}
-          {metadata.steps && (
-            <MetadataItem
-              label="Steps"
-              value={metadata.steps}
-              onClick={() => recallSteps(metadata.steps)}
-            />
-          )}
-          {metadata.cfg_scale !== undefined && (
-            <MetadataItem
-              label="CFG scale"
-              value={metadata.cfg_scale}
-              onClick={() => recallCfgScale(metadata.cfg_scale)}
-            />
-          )}
-          {/* {metadata.variations && metadata.variations.length > 0 && (
-            <MetadataItem
-              label="Seed-weight pairs"
-              value={seedWeightsToString(metadata.variations)}
-              onClick={() =>
-                dispatch(
-                  setSeedWeights(seedWeightsToString(metadata.variations))
-                )
-              }
-            />
-          )}
-          {metadata.seamless && (
-            <MetadataItem
-              label="Seamless"
-              value={metadata.seamless}
-              onClick={() => dispatch(setSeamless(metadata.seamless))}
-            />
-          )}
-          {metadata.hires_fix && (
-            <MetadataItem
-              label="High Resolution Optimization"
-              value={metadata.hires_fix}
-              onClick={() => dispatch(setHiresFix(metadata.hires_fix))}
-            />
-          )} */}
 
-          {/* {init_image_path && (
-            <MetadataItem
-              label="Initial image"
-              value={init_image_path}
-              isLink
-              onClick={() => dispatch(setInitialImage(init_image_path))}
-            />
-          )} */}
-          {metadata.strength && (
-            <MetadataItem
-              label="Image to image strength"
-              value={metadata.strength}
-              onClick={() => recallStrength(metadata.strength)}
-            />
-          )}
-          {/* {metadata.fit && (
-            <MetadataItem
-              label="Image to image fit"
-              value={metadata.fit}
-              onClick={() => dispatch(setShouldFitToWidthHeight(metadata.fit))}
-            />
-          )} */}
-        </>
-      ) : (
-        <Center width="100%" pt={10}>
-          <Text fontSize="lg" fontWeight="semibold">
-            No metadata available
-          </Text>
-        </Center>
-      )}
-      <Flex gap={2} direction="column" overflow="auto">
-        <Flex gap={2}>
-          <Tooltip label="Copy metadata JSON">
-            <IconButton
-              aria-label={t('accessibility.copyMetadataJson')}
-              icon={<FaCopy />}
-              size="xs"
-              variant="ghost"
-              fontSize={14}
-              onClick={() => navigator.clipboard.writeText(metadataJSON)}
-            />
-          </Tooltip>
-          <Text fontWeight="semibold">Metadata JSON:</Text>
-        </Flex>
-        <OverlayScrollbarsComponent defer>
-          <Box
-            sx={{
-              padding: 4,
-              borderRadius: 'base',
-              bg: 'whiteAlpha.500',
-              _dark: { bg: 'blackAlpha.500' },
-              w: 'full',
-            }}
-          >
-            <pre>{metadataJSON}</pre>
-          </Box>
-        </OverlayScrollbarsComponent>
-      </Flex>
+      <ImageMetadataActions metadata={metadata} />
+
+      <Tabs
+        variant="line"
+        sx={{ display: 'flex', flexDir: 'column', w: 'full', h: 'full' }}
+      >
+        <TabList>
+          {tabData.map((tab) => (
+            <Tab
+              key={tab.label}
+              sx={{
+                borderTopRadius: 'base',
+              }}
+            >
+              <Text sx={{ color: 'base.700', _dark: { color: 'base.300' } }}>
+                {tab.label}
+              </Text>
+            </Tab>
+          ))}
+        </TabList>
+
+        <TabPanels sx={{ w: 'full', h: 'full' }}>
+          {tabData.map((tab) => (
+            <TabPanel
+              key={tab.label}
+              sx={{ w: 'full', h: 'full', p: 0, pt: 4 }}
+            >
+              <MetadataJSONViewer
+                jsonObject={tab.data}
+                copyTooltip={tab.copyTooltip}
+              />
+            </TabPanel>
+          ))}
+        </TabPanels>
+      </Tabs>
     </Flex>
   );
 };

invokeai/frontend/web/src/features/gallery/components/ImageMetaDataViewer/MetadataItem.tsx

@@ -0,0 +1,77 @@
+import { ExternalLinkIcon } from '@chakra-ui/icons';
+import { Flex, IconButton, Link, Text, Tooltip } from '@chakra-ui/react';
+import { useTranslation } from 'react-i18next';
+import { FaCopy } from 'react-icons/fa';
+import { IoArrowUndoCircleOutline } from 'react-icons/io5';
+
+type MetadataItemProps = {
+  isLink?: boolean;
+  label: string;
+  onClick?: () => void;
+  value: number | string | boolean;
+  labelPosition?: string;
+  withCopy?: boolean;
+};
+
+/**
+ * Component to display an individual metadata item or parameter.
+ */
+const MetadataItem = ({
+  label,
+  value,
+  onClick,
+  isLink,
+  labelPosition,
+  withCopy = false,
+}: MetadataItemProps) => {
+  const { t } = useTranslation();
+
+  if (!value) {
+    return null;
+  }
+
+  return (
+    <Flex gap={2}>
+      {onClick && (
+        <Tooltip label={`Recall ${label}`}>
+          <IconButton
+            aria-label={t('accessibility.useThisParameter')}
+            icon={<IoArrowUndoCircleOutline />}
+            size="xs"
+            variant="ghost"
+            fontSize={20}
+            onClick={onClick}
+          />
+        </Tooltip>
+      )}
+      {withCopy && (
+        <Tooltip label={`Copy ${label}`}>
+          <IconButton
+            aria-label={`Copy ${label}`}
+            icon={<FaCopy />}
+            size="xs"
+            variant="ghost"
+            fontSize={14}
+            onClick={() => navigator.clipboard.writeText(value.toString())}
+          />
+        </Tooltip>
+      )}
+      <Flex direction={labelPosition ? 'column' : 'row'}>
+        <Text fontWeight="semibold" whiteSpace="pre-wrap" pr={2}>
+          {label}:
+        </Text>
+        {isLink ? (
+          <Link href={value.toString()} isExternal wordBreak="break-all">
+            {value.toString()} <ExternalLinkIcon mx="2px" />
+          </Link>
+        ) : (
+          <Text overflowY="scroll" wordBreak="break-all">
+            {value.toString()}
+          </Text>
+        )}
+      </Flex>
+    </Flex>
+  );
+};
+
+export default MetadataItem;

invokeai/frontend/web/src/features/gallery/components/ImageMetaDataViewer/MetadataJSONViewer.tsx

@@ -0,0 +1,70 @@
+import { Box, Flex, IconButton, Tooltip } from '@chakra-ui/react';
+import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
+import { useMemo } from 'react';
+import { FaCopy } from 'react-icons/fa';
+
+type Props = {
+  copyTooltip: string;
+  jsonObject: object;
+};
+
+const MetadataJSONViewer = (props: Props) => {
+  const { copyTooltip, jsonObject } = props;
+  const jsonString = useMemo(
+    () => JSON.stringify(jsonObject, null, 2),
+    [jsonObject]
+  );
+
+  return (
+    <Flex
+      sx={{
+        borderRadius: 'base',
+        bg: 'whiteAlpha.500',
+        _dark: { bg: 'blackAlpha.500' },
+        flexGrow: 1,
+        w: 'full',
+        h: 'full',
+        position: 'relative',
+      }}
+    >
+      <Box
+        sx={{
+          position: 'absolute',
+          top: 0,
+          left: 0,
+          right: 0,
+          bottom: 0,
+          overflow: 'auto',
+          p: 4,
+        }}
+      >
+        <OverlayScrollbarsComponent
+          defer
+          style={{ height: '100%', width: '100%' }}
+          options={{
+            scrollbars: {
+              visibility: 'auto',
+              autoHide: 'move',
+              autoHideDelay: 1300,
+              theme: 'os-theme-dark',
+            },
+          }}
+        >
+          <pre>{jsonString}</pre>
+        </OverlayScrollbarsComponent>
+      </Box>
+      <Flex sx={{ position: 'absolute', top: 0, insetInlineEnd: 0, p: 2 }}>
+        <Tooltip label={copyTooltip}>
+          <IconButton
+            aria-label={copyTooltip}
+            icon={<FaCopy />}
+            variant="ghost"
+            onClick={() => navigator.clipboard.writeText(jsonString)}
+          />
+        </Tooltip>
+      </Flex>
+    </Flex>
+  );
+};
+
+export default MetadataJSONViewer;

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

@@ -1,18 +1,8 @@
 import { ChakraProps, Flex, Grid, IconButton, Spinner } from '@chakra-ui/react';
-import { createSelector } from '@reduxjs/toolkit';
-import { stateSelector } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  imageSelected,
-  selectFilteredImages,
-  selectImagesById,
-} from 'features/gallery/store/gallerySlice';
-import { clamp, isEqual } from 'lodash-es';
 import { memo, useCallback, useState } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { FaAngleDoubleRight, FaAngleLeft, FaAngleRight } from 'react-icons/fa';
-import { receivedPageOfImages } from 'services/api/thunks/image';
+import { useNextPrevImage } from '../hooks/useNextPrevImage';
 
 const nextPrevButtonTriggerAreaStyles: ChakraProps['sx'] = {
   height: '100%',
@@ -24,74 +14,18 @@ const nextPrevButtonStyles: ChakraProps['sx'] = {
   color: 'base.100',
 };
 
-export const nextPrevImageButtonsSelector = createSelector(
-  [stateSelector, selectFilteredImages],
-  (state, filteredImages) => {
-    const { total, isFetching } = state.gallery;
-    const lastSelectedImage =
-      state.gallery.selection[state.gallery.selection.length - 1];
-
-    if (!lastSelectedImage || filteredImages.length === 0) {
-      return {
-        isOnFirstImage: true,
-        isOnLastImage: true,
-      };
-    }
-
-    const currentImageIndex = filteredImages.findIndex(
-      (i) => i.image_name === lastSelectedImage
-    );
-    const nextImageIndex = clamp(
-      currentImageIndex + 1,
-      0,
-      filteredImages.length - 1
-    );
-
-    const prevImageIndex = clamp(
-      currentImageIndex - 1,
-      0,
-      filteredImages.length - 1
-    );
-
-    const nextImageId = filteredImages[nextImageIndex].image_name;
-    const prevImageId = filteredImages[prevImageIndex].image_name;
-
-    const nextImage = selectImagesById(state, nextImageId);
-    const prevImage = selectImagesById(state, prevImageId);
-
-    const imagesLength = filteredImages.length;
-
-    return {
-      isOnFirstImage: currentImageIndex === 0,
-      isOnLastImage:
-        !isNaN(currentImageIndex) && currentImageIndex === imagesLength - 1,
-      areMoreImagesAvailable: total > imagesLength,
-      isFetching,
-      nextImage,
-      prevImage,
-      nextImageId,
-      prevImageId,
-    };
-  },
-  {
-    memoizeOptions: {
-      resultEqualityCheck: isEqual,
-    },
-  }
-);
-
 const NextPrevImageButtons = () => {
-  const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
   const {
+    handlePrevImage,
+    handleNextImage,
     isOnFirstImage,
     isOnLastImage,
-    nextImageId,
-    prevImageId,
+    handleLoadMoreImages,
     areMoreImagesAvailable,
     isFetching,
-  } = useAppSelector(nextPrevImageButtonsSelector);
+  } = useNextPrevImage();
 
   const [shouldShowNextPrevButtons, setShouldShowNextPrevButtons] =
     useState<boolean>(false);
@@ -104,50 +38,6 @@ const NextPrevImageButtons = () => {
     setShouldShowNextPrevButtons(false);
   }, []);
 
-  const handlePrevImage = useCallback(() => {
-    prevImageId && dispatch(imageSelected(prevImageId));
-  }, [dispatch, prevImageId]);
-
-  const handleNextImage = useCallback(() => {
-    nextImageId && dispatch(imageSelected(nextImageId));
-  }, [dispatch, nextImageId]);
-
-  const handleLoadMoreImages = useCallback(() => {
-    dispatch(
-      receivedPageOfImages({
-        is_intermediate: false,
-      })
-    );
-  }, [dispatch]);
-
-  useHotkeys(
-    'left',
-    () => {
-      handlePrevImage();
-    },
-    [prevImageId]
-  );
-
-  useHotkeys(
-    'right',
-    () => {
-      if (isOnLastImage && areMoreImagesAvailable && !isFetching) {
-        handleLoadMoreImages();
-        return;
-      }
-      if (!isOnLastImage) {
-        handleNextImage();
-      }
-    },
-    [
-      nextImageId,
-      isOnLastImage,
-      areMoreImagesAvailable,
-      handleLoadMoreImages,
-      isFetching,
-    ]
-  );
-
   return (
     <Flex
       sx={{

invokeai/frontend/web/src/features/gallery/hooks/useNextPrevImage.ts

@@ -0,0 +1,108 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import {
+  imageSelected,
+  selectFilteredImages,
+  selectImagesById,
+} from 'features/gallery/store/gallerySlice';
+import { clamp, isEqual } from 'lodash-es';
+import { useCallback } from 'react';
+import { receivedPageOfImages } from 'services/api/thunks/image';
+
+export const nextPrevImageButtonsSelector = createSelector(
+  [stateSelector, selectFilteredImages],
+  (state, filteredImages) => {
+    const { total, isFetching } = state.gallery;
+    const lastSelectedImage =
+      state.gallery.selection[state.gallery.selection.length - 1];
+
+    if (!lastSelectedImage || filteredImages.length === 0) {
+      return {
+        isOnFirstImage: true,
+        isOnLastImage: true,
+      };
+    }
+
+    const currentImageIndex = filteredImages.findIndex(
+      (i) => i.image_name === lastSelectedImage
+    );
+    const nextImageIndex = clamp(
+      currentImageIndex + 1,
+      0,
+      filteredImages.length - 1
+    );
+
+    const prevImageIndex = clamp(
+      currentImageIndex - 1,
+      0,
+      filteredImages.length - 1
+    );
+
+    const nextImageId = filteredImages[nextImageIndex].image_name;
+    const prevImageId = filteredImages[prevImageIndex].image_name;
+
+    const nextImage = selectImagesById(state, nextImageId);
+    const prevImage = selectImagesById(state, prevImageId);
+
+    const imagesLength = filteredImages.length;
+
+    return {
+      isOnFirstImage: currentImageIndex === 0,
+      isOnLastImage:
+        !isNaN(currentImageIndex) && currentImageIndex === imagesLength - 1,
+      areMoreImagesAvailable: total > imagesLength,
+      isFetching,
+      nextImage,
+      prevImage,
+      nextImageId,
+      prevImageId,
+    };
+  },
+  {
+    memoizeOptions: {
+      resultEqualityCheck: isEqual,
+    },
+  }
+);
+
+export const useNextPrevImage = () => {
+  const dispatch = useAppDispatch();
+
+  const {
+    isOnFirstImage,
+    isOnLastImage,
+    nextImageId,
+    prevImageId,
+    areMoreImagesAvailable,
+    isFetching,
+  } = useAppSelector(nextPrevImageButtonsSelector);
+
+  const handlePrevImage = useCallback(() => {
+    prevImageId && dispatch(imageSelected(prevImageId));
+  }, [dispatch, prevImageId]);
+
+  const handleNextImage = useCallback(() => {
+    nextImageId && dispatch(imageSelected(nextImageId));
+  }, [dispatch, nextImageId]);
+
+  const handleLoadMoreImages = useCallback(() => {
+    dispatch(
+      receivedPageOfImages({
+        is_intermediate: false,
+      })
+    );
+  }, [dispatch]);
+
+  return {
+    handlePrevImage,
+    handleNextImage,
+    isOnFirstImage,
+    isOnLastImage,
+    nextImageId,
+    prevImageId,
+    areMoreImagesAvailable,
+    handleLoadMoreImages,
+    isFetching,
+  };
+};

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

@@ -37,7 +37,7 @@ const ParamLora = (props: Props) => {
   return (
     <Flex sx={{ gap: 2.5, alignItems: 'flex-end' }}>
       <IAISlider
-        label={lora.name}
+        label={lora.model_name}
         value={lora.weight}
         onChange={handleChange}
         min={-1}

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

@@ -18,7 +18,7 @@ const selector = createSelector(
 const ParamLoraList = () => {
   const { loras } = useAppSelector(selector);
 
-  return map(loras, (lora) => <ParamLora key={lora.name} lora={lora} />);
+  return map(loras, (lora) => <ParamLora key={lora.model_name} lora={lora} />);
 };
 
 export default ParamLoraList;

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

@@ -45,7 +45,7 @@ const ParamLoraSelect = () => {
 
       data.push({
         value: id,
-        label: lora.name,
+        label: lora.model_name,
         disabled,
         group: MODEL_TYPE_MAP[lora.base_model],
         tooltip: disabled

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -1,12 +1,8 @@
 import { PayloadAction, createSlice } from '@reduxjs/toolkit';
 import { LoRAModelParam } from 'features/parameters/store/parameterZodSchemas';
 import { LoRAModelConfigEntity } from 'services/api/endpoints/models';
-import { BaseModelType } from 'services/api/types';
 
-export type Lora = {
-  id: string;
-  base_model: BaseModelType;
-  name: string;
+export type Lora = LoRAModelParam & {
   weight: number;
 };
 
@@ -27,8 +23,8 @@ export const loraSlice = createSlice({
   initialState: intialLoraState,
   reducers: {
     loraAdded: (state, action: PayloadAction<LoRAModelConfigEntity>) => {
-      const { name, id, base_model } = action.payload;
-      state.loras[id] = { id, name, base_model, ...defaultLoRAConfig };
+      const { model_name, id, base_model } = action.payload;
+      state.loras[id] = { id, model_name, base_model, ...defaultLoRAConfig };
     },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;

invokeai/frontend/web/src/features/nodes/components/Flow.tsx

@@ -7,6 +7,7 @@ import {
   OnConnectEnd,
   OnConnectStart,
   OnEdgesChange,
+  OnInit,
   OnNodesChange,
   ReactFlow,
 } from 'reactflow';
@@ -16,6 +17,7 @@ import {
   connectionStarted,
   edgesChanged,
   nodesChanged,
+  setEditorInstance,
 } from '../store/nodesSlice';
 import { InvocationComponent } from './InvocationComponent';
 import ProgressImageNode from './ProgressImageNode';
@@ -67,6 +69,14 @@ export const Flow = () => {
     dispatch(connectionEnded());
   }, [dispatch]);
 
+  const onInit: OnInit = useCallback(
+    (v) => {
+      dispatch(setEditorInstance(v));
+      if (v) v.fitView();
+    },
+    [dispatch]
+  );
+
   return (
     <ReactFlow
       nodeTypes={nodeTypes}
@@ -77,6 +87,7 @@ export const Flow = () => {
       onConnectStart={onConnectStart}
       onConnect={onConnect}
       onConnectEnd={onConnectEnd}
+      onInit={onInit}
       defaultEdgeOptions={{
         style: { strokeWidth: 2 },
       }}

invokeai/frontend/web/src/features/nodes/components/fields/LoRAModelInputFieldComponent.tsx

@@ -45,7 +45,7 @@ const LoRAModelInputFieldComponent = (
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: BASE_MODEL_NAME_MAP[model.base_model],
       });
     });

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -38,7 +38,7 @@ const ModelInputFieldComponent = (
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: BASE_MODEL_NAME_MAP[model.base_model],
       });
     });

invokeai/frontend/web/src/features/nodes/components/fields/VaeModelInputFieldComponent.tsx

@@ -45,7 +45,7 @@ const VaeModelInputFieldComponent = (
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: BASE_MODEL_NAME_MAP[model.base_model],
       });
     });

invokeai/frontend/web/src/features/nodes/components/panels/TopCenterPanel.tsx

@@ -1,25 +1,23 @@
 import { HStack } from '@chakra-ui/react';
-import { useAppDispatch } from 'app/store/storeHooks';
-import IAIButton from 'common/components/IAIButton';
-import { memo, useCallback } from 'react';
-import { Panel } from 'reactflow';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
-import NodeInvokeButton from '../ui/NodeInvokeButton';
 import CancelButton from 'features/parameters/components/ProcessButtons/CancelButton';
+import { memo } from 'react';
+import { Panel } from 'reactflow';
+import LoadNodesButton from '../ui/LoadNodesButton';
+import NodeInvokeButton from '../ui/NodeInvokeButton';
+import ReloadSchemaButton from '../ui/ReloadSchemaButton';
+import SaveNodesButton from '../ui/SaveNodesButton';
+import ClearNodesButton from '../ui/ClearNodesButton';
 
 const TopCenterPanel = () => {
-  const dispatch = useAppDispatch();
-
-  const handleReloadSchema = useCallback(() => {
-    dispatch(receivedOpenAPISchema());
-  }, [dispatch]);
-
   return (
     <Panel position="top-center">
       <HStack>
         <NodeInvokeButton />
         <CancelButton />
-        <IAIButton onClick={handleReloadSchema}>Reload Schema</IAIButton>
+        <ReloadSchemaButton />
+        <SaveNodesButton />
+        <LoadNodesButton />
+        <ClearNodesButton />
       </HStack>
     </Panel>
   );

invokeai/frontend/web/src/features/nodes/components/ui/ClearNodesButton.tsx

@@ -0,0 +1,86 @@
+import {
+  AlertDialog,
+  AlertDialogBody,
+  AlertDialogContent,
+  AlertDialogFooter,
+  AlertDialogHeader,
+  AlertDialogOverlay,
+  Button,
+  Text,
+  useDisclosure,
+} from '@chakra-ui/react';
+import { makeToast } from 'app/components/Toaster';
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { addToast } from 'features/system/store/systemSlice';
+import { memo, useRef, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaTrash } from 'react-icons/fa';
+
+const ClearNodesButton = () => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { isOpen, onOpen, onClose } = useDisclosure();
+  const cancelRef = useRef<HTMLButtonElement | null>(null);
+
+  const nodes = useAppSelector((state: RootState) => state.nodes.nodes);
+
+  const handleConfirmClear = useCallback(() => {
+    dispatch(nodeEditorReset());
+
+    dispatch(
+      addToast(
+        makeToast({
+          title: t('toast.nodesCleared'),
+          status: 'success',
+        })
+      )
+    );
+
+    onClose();
+  }, [dispatch, t, onClose]);
+
+  return (
+    <>
+      <IAIIconButton
+        icon={<FaTrash />}
+        tooltip={t('nodes.clearNodes')}
+        aria-label={t('nodes.clearNodes')}
+        onClick={onOpen}
+        isDisabled={nodes.length === 0}
+      />
+
+      <AlertDialog
+        isOpen={isOpen}
+        onClose={onClose}
+        leastDestructiveRef={cancelRef}
+        isCentered
+      >
+        <AlertDialogOverlay />
+
+        <AlertDialogContent>
+          <AlertDialogHeader fontSize="lg" fontWeight="bold">
+            {t('nodes.clearNodes')}
+          </AlertDialogHeader>
+
+          <AlertDialogBody>
+            <Text>{t('common.clearNodes')}</Text>
+          </AlertDialogBody>
+
+          <AlertDialogFooter>
+            <Button ref={cancelRef} onClick={onClose}>
+              {t('common.cancel')}
+            </Button>
+            <Button colorScheme="red" ml={3} onClick={handleConfirmClear}>
+              {t('common.accept')}
+            </Button>
+          </AlertDialogFooter>
+        </AlertDialogContent>
+      </AlertDialog>
+    </>
+  );
+};
+
+export default memo(ClearNodesButton);

invokeai/frontend/web/src/features/nodes/components/ui/LoadNodesButton.tsx

@@ -0,0 +1,79 @@
+import { FileButton } from '@mantine/core';
+import { makeToast } from 'app/components/Toaster';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { loadFileEdges, loadFileNodes } from 'features/nodes/store/nodesSlice';
+import { addToast } from 'features/system/store/systemSlice';
+import { memo, useCallback, useRef } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaUpload } from 'react-icons/fa';
+import { useReactFlow } from 'reactflow';
+
+const LoadNodesButton = () => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { fitView } = useReactFlow();
+
+  const uploadedFileRef = useRef<() => void>(null);
+
+  const restoreJSONToEditor = useCallback(
+    (v: File | null) => {
+      if (!v) return;
+      const reader = new FileReader();
+      reader.onload = async () => {
+        const json = reader.result;
+        const retrievedNodeTree = await JSON.parse(String(json));
+
+        if (!retrievedNodeTree) {
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('toast.nodesLoadedFailed'),
+                status: 'error',
+              })
+            )
+          );
+        }
+
+        if (retrievedNodeTree) {
+          dispatch(loadFileNodes(retrievedNodeTree.nodes));
+          dispatch(loadFileEdges(retrievedNodeTree.edges));
+          fitView();
+
+          dispatch(
+            addToast(
+              makeToast({ title: t('toast.nodesLoaded'), status: 'success' })
+            )
+          );
+        }
+
+        // Cleanup
+        reader.abort();
+      };
+
+      reader.readAsText(v);
+
+      // Cleanup
+      uploadedFileRef.current?.();
+    },
+    [fitView, dispatch, t]
+  );
+  return (
+    <FileButton
+      resetRef={uploadedFileRef}
+      accept="application/json"
+      onChange={restoreJSONToEditor}
+    >
+      {(props) => (
+        <IAIIconButton
+          icon={<FaUpload />}
+          tooltip={t('nodes.loadNodes')}
+          aria-label={t('nodes.loadNodes')}
+          {...props}
+        />
+      )}
+    </FileButton>
+  );
+};
+
+export default memo(LoadNodesButton);

invokeai/frontend/web/src/features/nodes/components/ui/ReloadSchemaButton.tsx

@@ -0,0 +1,24 @@
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaSyncAlt } from 'react-icons/fa';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+
+export default function ReloadSchemaButton() {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  const handleReloadSchema = useCallback(() => {
+    dispatch(receivedOpenAPISchema());
+  }, [dispatch]);
+
+  return (
+    <IAIIconButton
+      icon={<FaSyncAlt />}
+      tooltip={t('nodes.reloadSchema')}
+      aria-label={t('nodes.reloadSchema')}
+      onClick={handleReloadSchema}
+    />
+  );
+}

invokeai/frontend/web/src/features/nodes/components/ui/SaveNodesButton.tsx

@@ -0,0 +1,48 @@
+import { RootState } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { map, omit } from 'lodash-es';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { FaSave } from 'react-icons/fa';
+
+const SaveNodesButton = () => {
+  const { t } = useTranslation();
+  const editorInstance = useAppSelector(
+    (state: RootState) => state.nodes.editorInstance
+  );
+
+  const nodes = useAppSelector((state: RootState) => state.nodes.nodes);
+
+  const saveEditorToJSON = useCallback(() => {
+    if (editorInstance) {
+      const editorState = editorInstance.toObject();
+
+      editorState.edges = map(editorState.edges, (edge) => {
+        return omit(edge, ['style']);
+      });
+
+      const nodeSetupJSON = new Blob([JSON.stringify(editorState)]);
+      const nodeDownloadElement = document.createElement('a');
+      nodeDownloadElement.href = URL.createObjectURL(nodeSetupJSON);
+      nodeDownloadElement.download = 'MyNodes.json';
+      document.body.appendChild(nodeDownloadElement);
+      nodeDownloadElement.click();
+      // Cleanup
+      nodeDownloadElement.remove();
+    }
+  }, [editorInstance]);
+
+  return (
+    <IAIIconButton
+      icon={<FaSave />}
+      fontSize={18}
+      tooltip={t('nodes.saveNodes')}
+      aria-label={t('nodes.saveNodes')}
+      onClick={saveEditorToJSON}
+      isDisabled={nodes.length === 0}
+    />
+  );
+};
+
+export default memo(SaveNodesButton);

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

@@ -13,6 +13,7 @@ import {
   Node,
   NodeChange,
   OnConnectStartParams,
+  ReactFlowInstance,
 } from 'reactflow';
 import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { ImageField } from 'services/api/types';
@@ -25,6 +26,7 @@ export type NodesState = {
   invocationTemplates: Record<string, InvocationTemplate>;
   connectionStartParams: OnConnectStartParams | null;
   shouldShowGraphOverlay: boolean;
+  editorInstance: ReactFlowInstance | undefined;
 };
 
 export const initialNodesState: NodesState = {
@@ -34,6 +36,7 @@ export const initialNodesState: NodesState = {
   invocationTemplates: {},
   connectionStartParams: null,
   shouldShowGraphOverlay: false,
+  editorInstance: undefined,
 };
 
 const nodesSlice = createSlice({
@@ -118,8 +121,18 @@ const nodesSlice = createSlice({
     ) => {
       state.invocationTemplates = action.payload;
     },
-    nodeEditorReset: () => {
-      return { ...initialNodesState };
+    nodeEditorReset: (state) => {
+      state.nodes = [];
+      state.edges = [];
+    },
+    setEditorInstance: (state, action) => {
+      state.editorInstance = action.payload;
+    },
+    loadFileNodes: (state, action: PayloadAction<Node<InvocationValue>[]>) => {
+      state.nodes = action.payload;
+    },
+    loadFileEdges: (state, action: PayloadAction<Edge[]>) => {
+      state.edges = action.payload;
     },
   },
   extraReducers: (builder) => {
@@ -141,6 +154,9 @@ export const {
   nodeTemplatesBuilt,
   nodeEditorReset,
   imageCollectionFieldValueChanged,
+  setEditorInstance,
+  loadFileNodes,
+  loadFileEdges,
 } = nodesSlice.actions;
 
 export default nodesSlice.reducer;

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

@@ -1,94 +0,0 @@
-import { RootState } from 'app/store/store';
-import { getValidControlNets } from 'features/controlNet/util/getValidControlNets';
-import { CollectInvocation, ControlNetInvocation } from 'services/api/types';
-import { NonNullableGraph } from '../types/types';
-import { CONTROL_NET_COLLECT } from './graphBuilders/constants';
-
-export const addControlNetToLinearGraph = (
-  graph: NonNullableGraph,
-  baseNodeId: string,
-  state: RootState
-): void => {
-  const { isEnabled: isControlNetEnabled, controlNets } = state.controlNet;
-
-  const validControlNets = getValidControlNets(controlNets);
-
-  if (isControlNetEnabled && Boolean(validControlNets.length)) {
-    if (validControlNets.length > 1) {
-      // We have multiple controlnets, add ControlNet collector
-      const controlNetIterateNode: CollectInvocation = {
-        id: CONTROL_NET_COLLECT,
-        type: 'collect',
-      };
-      graph.nodes[controlNetIterateNode.id] = controlNetIterateNode;
-      graph.edges.push({
-        source: { node_id: controlNetIterateNode.id, field: 'collection' },
-        destination: {
-          node_id: baseNodeId,
-          field: 'control',
-        },
-      });
-    }
-
-    validControlNets.forEach((controlNet) => {
-      const {
-        controlNetId,
-        controlImage,
-        processedControlImage,
-        beginStepPct,
-        endStepPct,
-        controlMode,
-        model,
-        processorType,
-        weight,
-      } = controlNet;
-
-      const controlNetNode: ControlNetInvocation = {
-        id: `control_net_${controlNetId}`,
-        type: 'controlnet',
-        begin_step_percent: beginStepPct,
-        end_step_percent: endStepPct,
-        control_mode: controlMode,
-        control_model: model as ControlNetInvocation['control_model'],
-        control_weight: weight,
-      };
-
-      if (processedControlImage && processorType !== 'none') {
-        // We've already processed the image in the app, so we can just use the processed image
-        controlNetNode.image = {
-          image_name: processedControlImage,
-        };
-      } else if (controlImage) {
-        // The control image is preprocessed
-        controlNetNode.image = {
-          image_name: controlImage,
-        };
-      } else {
-        // Skip ControlNets without an unprocessed image - should never happen if everything is working correctly
-        return;
-      }
-
-      graph.nodes[controlNetNode.id] = controlNetNode;
-
-      if (validControlNets.length > 1) {
-        // if we have multiple controlnets, link to the collector
-        graph.edges.push({
-          source: { node_id: controlNetNode.id, field: 'control' },
-          destination: {
-            node_id: CONTROL_NET_COLLECT,
-            field: 'item',
-          },
-        });
-      } else {
-        // otherwise, link directly to the base node
-        graph.edges.push({
-          source: { node_id: controlNetNode.id, field: 'control' },
-          destination: {
-            node_id: baseNodeId,
-            field: 'control',
-          },
-        });
-      }
-    });
-  }
-};

invokeai/frontend/web/src/features/nodes/util/edgeBuilders/buildEdges.ts

@@ -1,40 +0,0 @@
-import {
-  Edge,
-  ImageToImageInvocation,
-  InpaintInvocation,
-  IterateInvocation,
-  RandomRangeInvocation,
-  RangeInvocation,
-  TextToImageInvocation,
-} from 'services/api/types';
-
-export const buildEdges = (
-  baseNode: TextToImageInvocation | ImageToImageInvocation | InpaintInvocation,
-  rangeNode: RangeInvocation | RandomRangeInvocation,
-  iterateNode: IterateInvocation
-): Edge[] => {
-  const edges: Edge[] = [
-    {
-      source: {
-        node_id: rangeNode.id,
-        field: 'collection',
-      },
-      destination: {
-        node_id: iterateNode.id,
-        field: 'collection',
-      },
-    },
-    {
-      source: {
-        node_id: iterateNode.id,
-        field: 'item',
-      },
-      destination: {
-        node_id: baseNode.id,
-        field: 'seed',
-      },
-    },
-  ];
-
-  return edges;
-};

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

@@ -0,0 +1,102 @@
+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 { CONTROL_NET_COLLECT, METADATA_ACCUMULATOR } from './constants';
+
+export const addControlNetToLinearGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  baseNodeId: string
+): void => {
+  const { isEnabled: isControlNetEnabled, controlNets } = state.controlNet;
+
+  const validControlNets = getValidControlNets(controlNets);
+
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
+
+  if (isControlNetEnabled && Boolean(validControlNets.length)) {
+    if (validControlNets.length) {
+      // We have multiple controlnets, add ControlNet collector
+      const controlNetIterateNode: CollectInvocation = {
+        id: CONTROL_NET_COLLECT,
+        type: 'collect',
+      };
+      graph.nodes[CONTROL_NET_COLLECT] = controlNetIterateNode;
+      graph.edges.push({
+        source: { node_id: CONTROL_NET_COLLECT, field: 'collection' },
+        destination: {
+          node_id: baseNodeId,
+          field: 'control',
+        },
+      });
+
+      validControlNets.forEach((controlNet) => {
+        const {
+          controlNetId,
+          controlImage,
+          processedControlImage,
+          beginStepPct,
+          endStepPct,
+          controlMode,
+          model,
+          processorType,
+          weight,
+        } = controlNet;
+
+        const controlNetNode: ControlNetInvocation = {
+          id: `control_net_${controlNetId}`,
+          type: 'controlnet',
+          begin_step_percent: beginStepPct,
+          end_step_percent: endStepPct,
+          control_mode: controlMode,
+          control_model: model as ControlNetInvocation['control_model'],
+          control_weight: weight,
+        };
+
+        if (processedControlImage && processorType !== 'none') {
+          // We've already processed the image in the app, so we can just use the processed image
+          controlNetNode.image = {
+            image_name: processedControlImage,
+          };
+        } else if (controlImage) {
+          // The control image is preprocessed
+          controlNetNode.image = {
+            image_name: controlImage,
+          };
+        } else {
+          // Skip ControlNets without an unprocessed image - should never happen if everything is working correctly
+          return;
+        }
+
+        graph.nodes[controlNetNode.id] = controlNetNode;
+
+        if (metadataAccumulator) {
+          // 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);
+        }
+
+        graph.edges.push({
+          source: { node_id: controlNetNode.id, field: 'control' },
+          destination: {
+            node_id: CONTROL_NET_COLLECT,
+            field: 'item',
+          },
+        });
+      });
+    }
+  }
+};

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

@@ -1,8 +1,10 @@
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
+import { unset } from 'lodash-es';
 import {
   DynamicPromptInvocation,
   IterateInvocation,
+  MetadataAccumulatorInvocation,
   NoiseInvocation,
   RandomIntInvocation,
   RangeOfSizeInvocation,
@@ -10,16 +12,16 @@ import {
 import {
   DYNAMIC_PROMPT,
   ITERATE,
+  METADATA_ACCUMULATOR,
   NOISE,
   POSITIVE_CONDITIONING,
   RANDOM_INT,
   RANGE_OF_SIZE,
 } from './constants';
-import { unset } from 'lodash-es';
 
 export const addDynamicPromptsToGraph = (
-  graph: NonNullableGraph,
-  state: RootState
+  state: RootState,
+  graph: NonNullableGraph
 ): void => {
   const { positivePrompt, iterations, seed, shouldRandomizeSeed } =
     state.generation;
@@ -30,6 +32,10 @@ export const addDynamicPromptsToGraph = (
     maxPrompts,
   } = state.dynamicPrompts;
 
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
+
   if (isDynamicPromptsEnabled) {
     // iteration is handled via dynamic prompts
     unset(graph.nodes[POSITIVE_CONDITIONING], 'prompt');
@@ -74,6 +80,18 @@ export const addDynamicPromptsToGraph = (
       }
     );
 
+    // hook up positive prompt to metadata
+    graph.edges.push({
+      source: {
+        node_id: ITERATE,
+        field: 'item',
+      },
+      destination: {
+        node_id: METADATA_ACCUMULATOR,
+        field: 'positive_prompt',
+      },
+    });
+
     if (shouldRandomizeSeed) {
       // Random int node to generate the starting seed
       const randomIntNode: RandomIntInvocation = {
@@ -88,11 +106,26 @@ export const addDynamicPromptsToGraph = (
         source: { node_id: RANDOM_INT, field: 'a' },
         destination: { node_id: NOISE, field: 'seed' },
       });
+
+      graph.edges.push({
+        source: { node_id: RANDOM_INT, field: 'a' },
+        destination: { node_id: METADATA_ACCUMULATOR, field: 'seed' },
+      });
     } else {
       // User specified seed, so set the start of the range of size to the seed
       (graph.nodes[NOISE] as NoiseInvocation).seed = seed;
+
+      // hook up seed to metadata
+      if (metadataAccumulator) {
+        metadataAccumulator.seed = seed;
+      }
     }
   } else {
+    // no dynamic prompt - hook up positive prompt
+    if (metadataAccumulator) {
+      metadataAccumulator.positive_prompt = positivePrompt;
+    }
+
     const rangeOfSizeNode: RangeOfSizeInvocation = {
       id: RANGE_OF_SIZE,
       type: 'range_of_size',
@@ -130,6 +163,18 @@ export const addDynamicPromptsToGraph = (
       },
     });
 
+    // hook up seed to metadata
+    graph.edges.push({
+      source: {
+        node_id: ITERATE,
+        field: 'item',
+      },
+      destination: {
+        node_id: METADATA_ACCUMULATOR,
+        field: 'seed',
+      },
+    });
+
     // handle seed
     if (shouldRandomizeSeed) {
       // Random int node to generate the starting seed

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

@@ -1,19 +1,23 @@
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } 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 { modelIdToLoRAModelField } from '../modelIdToLoRAName';
 import {
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
+  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
 } from './constants';
 
 export const addLoRAsToGraph = (
-  graph: NonNullableGraph,
   state: RootState,
+  graph: NonNullableGraph,
   baseNodeId: string
 ): void => {
   /**
@@ -26,6 +30,9 @@ export const addLoRAsToGraph = (
 
   const { loras } = state.lora;
   const loraCount = size(loras);
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
 
   if (loraCount > 0) {
     // Remove MAIN_MODEL_LOADER unet connection to feed it to LoRAs
@@ -62,6 +69,12 @@ export const addLoRAsToGraph = (
       weight,
     };
 
+    // add the lora to the metadata accumulator
+    if (metadataAccumulator) {
+      metadataAccumulator.loras.push({ lora: loraField, weight });
+    }
+
+    // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;
 
     if (currentLoraIndex === 0) {

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

@@ -1,5 +1,6 @@
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
+import { MetadataAccumulatorInvocation } from 'services/api/types';
 import { modelIdToVAEModelField } from '../modelIdToVAEModelField';
 import {
   IMAGE_TO_IMAGE_GRAPH,
@@ -8,18 +9,22 @@ import {
   INPAINT_GRAPH,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  METADATA_ACCUMULATOR,
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
 
 export const addVAEToGraph = (
-  graph: NonNullableGraph,
-  state: RootState
+  state: RootState,
+  graph: NonNullableGraph
 ): void => {
   const { vae } = state.generation;
   const vae_model = modelIdToVAEModelField(vae?.id || '');
 
   const isAutoVae = !vae;
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
 
   if (!isAutoVae) {
     graph.nodes[VAE_LOADER] = {
@@ -67,4 +72,8 @@ export const addVAEToGraph = (
       },
     });
   }
+
+  if (vae && metadataAccumulator) {
+    metadataAccumulator.vae = vae_model;
+  }
 };

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

@@ -7,8 +7,7 @@ import {
   ImageResizeInvocation,
   ImageToLatentsInvocation,
 } from 'services/api/types';
-import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
-import { modelIdToMainModelField } from '../modelIdToMainModelField';
+import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
@@ -19,6 +18,7 @@ import {
   LATENTS_TO_IMAGE,
   LATENTS_TO_LATENTS,
   MAIN_MODEL_LOADER,
+  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -37,7 +37,7 @@ export const buildCanvasImageToImageGraph = (
   const {
     positivePrompt,
     negativePrompt,
-    model: currentModel,
+    model,
     cfgScale: cfg_scale,
     scheduler,
     steps,
@@ -50,7 +50,10 @@ export const buildCanvasImageToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const model = modelIdToMainModelField(currentModel?.id || '');
+  if (!model) {
+    moduleLog.error('No model found in state');
+    throw new Error('No model found in state');
+  }
 
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
@@ -275,16 +278,51 @@ export const buildCanvasImageToImageGraph = (
     });
   }
 
-  addLoRAsToGraph(graph, state, LATENTS_TO_LATENTS);
+  // add metadata accumulator, which is only mostly populated - some fields are added later
+  graph.nodes[METADATA_ACCUMULATOR] = {
+    id: METADATA_ACCUMULATOR,
+    type: 'metadata_accumulator',
+    generation_mode: 'img2img',
+    cfg_scale,
+    height,
+    width,
+    positive_prompt: '', // set in addDynamicPromptsToGraph
+    negative_prompt: negativePrompt,
+    model,
+    seed: 0, // set in addDynamicPromptsToGraph
+    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 VAE
-  addVAEToGraph(graph, state);
+  graph.edges.push({
+    source: {
+      node_id: METADATA_ACCUMULATOR,
+      field: 'metadata',
+    },
+    destination: {
+      node_id: LATENTS_TO_IMAGE,
+      field: 'metadata',
+    },
+  });
 
-  // add dynamic prompts, mutating `graph`
-  addDynamicPromptsToGraph(graph, state);
+  // add LoRA support
+  addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
+
+  // optionally add custom VAE
+  addVAEToGraph(state, graph);
+
+  // add dynamic prompts - also sets up core iteration and seed
+  addDynamicPromptsToGraph(state, graph);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
+  addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
 
   return graph;
 };

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

@@ -7,7 +7,6 @@ import {
   RandomIntInvocation,
   RangeOfSizeInvocation,
 } from 'services/api/types';
-import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
@@ -35,7 +34,7 @@ export const buildCanvasInpaintGraph = (
   const {
     positivePrompt,
     negativePrompt,
-    model: currentModel,
+    model,
     cfgScale: cfg_scale,
     scheduler,
     steps,
@@ -53,14 +52,17 @@ export const buildCanvasInpaintGraph = (
     clipSkip,
   } = state.generation;
 
+  if (!model) {
+    moduleLog.error('No model found in state');
+    throw new Error('No model found in state');
+  }
+
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
   // We may need to set the inpaint width and height to scale the image
   const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
-  const model = modelIdToMainModelField(currentModel?.id || '');
-
   const graph: NonNullableGraph = {
     id: INPAINT_GRAPH,
     nodes: {
@@ -212,10 +214,10 @@ export const buildCanvasInpaintGraph = (
     ],
   };
 
-  addLoRAsToGraph(graph, state, INPAINT);
+  addLoRAsToGraph(state, graph, INPAINT);
 
   // Add VAE
-  addVAEToGraph(graph, state);
+  addVAEToGraph(state, graph);
 
   // handle seed
   if (shouldRandomizeSeed) {

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

@@ -1,8 +1,8 @@
+import { log } from 'app/logging/useLogger';
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
-import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
-import { modelIdToMainModelField } from '../modelIdToMainModelField';
+import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
@@ -10,6 +10,7 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -17,6 +18,8 @@ import {
   TEXT_TO_LATENTS,
 } from './constants';
 
+const moduleLog = log.child({ namespace: 'nodes' });
+
 /**
  * Builds the Canvas tab's Text to Image graph.
  */
@@ -26,7 +29,7 @@ export const buildCanvasTextToImageGraph = (
   const {
     positivePrompt,
     negativePrompt,
-    model: currentModel,
+    model,
     cfgScale: cfg_scale,
     scheduler,
     steps,
@@ -38,7 +41,10 @@ export const buildCanvasTextToImageGraph = (
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
-  const model = modelIdToMainModelField(currentModel?.id || '');
+  if (!model) {
+    moduleLog.error('No model found in state');
+    throw new Error('No model found in state');
+  }
 
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
@@ -180,16 +186,49 @@ export const buildCanvasTextToImageGraph = (
     ],
   };
 
-  addLoRAsToGraph(graph, state, TEXT_TO_LATENTS);
+  // add metadata accumulator, which is only mostly populated - some fields are added later
+  graph.nodes[METADATA_ACCUMULATOR] = {
+    id: METADATA_ACCUMULATOR,
+    type: 'metadata_accumulator',
+    generation_mode: 'txt2img',
+    cfg_scale,
+    height,
+    width,
+    positive_prompt: '', // set in addDynamicPromptsToGraph
+    negative_prompt: negativePrompt,
+    model,
+    seed: 0, // set in addDynamicPromptsToGraph
+    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,
+  };
 
-  // Add VAE
-  addVAEToGraph(graph, state);
+  graph.edges.push({
+    source: {
+      node_id: METADATA_ACCUMULATOR,
+      field: 'metadata',
+    },
+    destination: {
+      node_id: LATENTS_TO_IMAGE,
+      field: 'metadata',
+    },
+  });
 
-  // add dynamic prompts, mutating `graph`
-  addDynamicPromptsToGraph(graph, state);
+  // add LoRA support
+  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
+
+  // optionally add custom VAE
+  addVAEToGraph(state, graph);
+
+  // add dynamic prompts - also sets up core iteration and seed
+  addDynamicPromptsToGraph(state, graph);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
+  addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
 
   return graph;
 };

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

@@ -3,25 +3,21 @@ import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
 import {
-  ImageCollectionInvocation,
   ImageResizeInvocation,
   ImageToLatentsInvocation,
-  IterateInvocation,
 } from 'services/api/types';
-import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
-import { modelIdToMainModelField } from '../modelIdToMainModelField';
+import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
   CLIP_SKIP,
-  IMAGE_COLLECTION,
-  IMAGE_COLLECTION_ITERATE,
   IMAGE_TO_IMAGE_GRAPH,
   IMAGE_TO_LATENTS,
   LATENTS_TO_IMAGE,
   LATENTS_TO_LATENTS,
   MAIN_MODEL_LOADER,
+  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -39,7 +35,7 @@ export const buildLinearImageToImageGraph = (
   const {
     positivePrompt,
     negativePrompt,
-    model: currentModel,
+    model,
     cfgScale: cfg_scale,
     scheduler,
     steps,
@@ -53,14 +49,15 @@ export const buildLinearImageToImageGraph = (
     shouldUseNoiseSettings,
   } = state.generation;
 
-  const {
-    isEnabled: isBatchEnabled,
-    imageNames: batchImageNames,
-    asInitialImage,
-  } = state.batch;
+  // TODO: add batch functionality
+  // const {
+  //   isEnabled: isBatchEnabled,
+  //   imageNames: batchImageNames,
+  //   asInitialImage,
+  // } = state.batch;
 
-  const shouldBatch =
-    isBatchEnabled && batchImageNames.length > 0 && asInitialImage;
+  // const shouldBatch =
+  //   isBatchEnabled && batchImageNames.length > 0 && asInitialImage;
 
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
@@ -71,12 +68,15 @@ export const buildLinearImageToImageGraph = (
    * the `fit` param. These are added to the graph at the end.
    */
 
-  if (!initialImage && !shouldBatch) {
+  if (!initialImage) {
     moduleLog.error('No initial image found in state');
     throw new Error('No initial image found in state');
   }
 
-  const model = modelIdToMainModelField(currentModel?.id || '');
+  if (!model) {
+    moduleLog.error('No model found in state');
+    throw new Error('No model found in state');
+  }
 
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
@@ -295,51 +295,87 @@ export const buildLinearImageToImageGraph = (
     });
   }
 
-  if (isBatchEnabled && asInitialImage && batchImageNames.length > 0) {
-    // we are going to connect an iterate up to the init image
-    delete (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image;
+  // TODO: add batch functionality
+  // if (isBatchEnabled && asInitialImage && batchImageNames.length > 0) {
+  //   // we are going to connect an iterate up to the init image
+  //   delete (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image;
 
-    const imageCollection: ImageCollectionInvocation = {
-      id: IMAGE_COLLECTION,
-      type: 'image_collection',
-      images: batchImageNames.map((image_name) => ({ image_name })),
-    };
+  //   const imageCollection: ImageCollectionInvocation = {
+  //     id: IMAGE_COLLECTION,
+  //     type: 'image_collection',
+  //     images: batchImageNames.map((image_name) => ({ image_name })),
+  //   };
 
-    const imageCollectionIterate: IterateInvocation = {
-      id: IMAGE_COLLECTION_ITERATE,
-      type: 'iterate',
-    };
+  //   const imageCollectionIterate: IterateInvocation = {
+  //     id: IMAGE_COLLECTION_ITERATE,
+  //     type: 'iterate',
+  //   };
 
-    graph.nodes[IMAGE_COLLECTION] = imageCollection;
-    graph.nodes[IMAGE_COLLECTION_ITERATE] = imageCollectionIterate;
+  //   graph.nodes[IMAGE_COLLECTION] = imageCollection;
+  //   graph.nodes[IMAGE_COLLECTION_ITERATE] = imageCollectionIterate;
 
-    graph.edges.push({
-      source: { node_id: IMAGE_COLLECTION, field: 'collection' },
-      destination: {
-        node_id: IMAGE_COLLECTION_ITERATE,
-        field: 'collection',
-      },
-    });
+  //   graph.edges.push({
+  //     source: { node_id: IMAGE_COLLECTION, field: 'collection' },
+  //     destination: {
+  //       node_id: IMAGE_COLLECTION_ITERATE,
+  //       field: 'collection',
+  //     },
+  //   });
 
-    graph.edges.push({
-      source: { node_id: IMAGE_COLLECTION_ITERATE, field: 'item' },
-      destination: {
-        node_id: IMAGE_TO_LATENTS,
-        field: 'image',
-      },
-    });
-  }
+  //   graph.edges.push({
+  //     source: { node_id: IMAGE_COLLECTION_ITERATE, field: 'item' },
+  //     destination: {
+  //       node_id: IMAGE_TO_LATENTS,
+  //       field: 'image',
+  //     },
+  //   });
+  // }
 
-  addLoRAsToGraph(graph, state, LATENTS_TO_LATENTS);
+  // add metadata accumulator, which is only mostly populated - some fields are added later
+  graph.nodes[METADATA_ACCUMULATOR] = {
+    id: METADATA_ACCUMULATOR,
+    type: 'metadata_accumulator',
+    generation_mode: 'img2img',
+    cfg_scale,
+    height,
+    width,
+    positive_prompt: '', // set in addDynamicPromptsToGraph
+    negative_prompt: negativePrompt,
+    model,
+    seed: 0, // set in addDynamicPromptsToGraph
+    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,
+  };
 
-  // Add VAE
-  addVAEToGraph(graph, state);
+  graph.edges.push({
+    source: {
+      node_id: METADATA_ACCUMULATOR,
+      field: 'metadata',
+    },
+    destination: {
+      node_id: LATENTS_TO_IMAGE,
+      field: 'metadata',
+    },
+  });
 
-  // add dynamic prompts, mutating `graph`
-  addDynamicPromptsToGraph(graph, state);
+  // add LoRA support
+  addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS);
+
+  // optionally add custom VAE
+  addVAEToGraph(state, graph);
+
+  // add dynamic prompts - also sets up core iteration and seed
+  addDynamicPromptsToGraph(state, graph);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(graph, LATENTS_TO_LATENTS, state);
+  addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS);
 
   return graph;
 };

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

@@ -1,8 +1,8 @@
+import { log } from 'app/logging/useLogger';
 import { RootState } from 'app/store/store';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
-import { addControlNetToLinearGraph } from '../addControlNetToLinearGraph';
-import { modelIdToMainModelField } from '../modelIdToMainModelField';
+import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
@@ -10,6 +10,7 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
   POSITIVE_CONDITIONING,
@@ -17,13 +18,15 @@ import {
   TEXT_TO_LATENTS,
 } from './constants';
 
+const moduleLog = log.child({ namespace: 'nodes' });
+
 export const buildLinearTextToImageGraph = (
   state: RootState
 ): NonNullableGraph => {
   const {
     positivePrompt,
     negativePrompt,
-    model: currentModel,
+    model,
     cfgScale: cfg_scale,
     scheduler,
     steps,
@@ -34,12 +37,15 @@ export const buildLinearTextToImageGraph = (
     shouldUseNoiseSettings,
   } = state.generation;
 
-  const model = modelIdToMainModelField(currentModel?.id || '');
-
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
 
+  if (!model) {
+    moduleLog.error('No model found in state');
+    throw new Error('No model found in state');
+  }
+
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -176,16 +182,49 @@ export const buildLinearTextToImageGraph = (
     ],
   };
 
-  addLoRAsToGraph(graph, state, TEXT_TO_LATENTS);
+  // add metadata accumulator, which is only mostly populated - some fields are added later
+  graph.nodes[METADATA_ACCUMULATOR] = {
+    id: METADATA_ACCUMULATOR,
+    type: 'metadata_accumulator',
+    generation_mode: 'txt2img',
+    cfg_scale,
+    height,
+    width,
+    positive_prompt: '', // set in addDynamicPromptsToGraph
+    negative_prompt: negativePrompt,
+    model,
+    seed: 0, // set in addDynamicPromptsToGraph
+    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,
+  };
 
-  // Add Custom VAE Support
-  addVAEToGraph(graph, state);
+  graph.edges.push({
+    source: {
+      node_id: METADATA_ACCUMULATOR,
+      field: 'metadata',
+    },
+    destination: {
+      node_id: LATENTS_TO_IMAGE,
+      field: 'metadata',
+    },
+  });
 
-  // add dynamic prompts, mutating `graph`
-  addDynamicPromptsToGraph(graph, state);
+  // add LoRA support
+  addLoRAsToGraph(state, graph, TEXT_TO_LATENTS);
+
+  // optionally add custom VAE
+  addVAEToGraph(state, graph);
+
+  // add dynamic prompts - also sets up core iteration and seed
+  addDynamicPromptsToGraph(state, graph);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(graph, TEXT_TO_LATENTS, state);
+  addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS);
 
   return graph;
 };

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

@@ -19,6 +19,7 @@ export const CONTROL_NET_COLLECT = 'control_net_collect';
 export const DYNAMIC_PROMPT = 'dynamic_prompt';
 export const IMAGE_COLLECTION = 'image_collection';
 export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate';
+export const METADATA_ACCUMULATOR = 'metadata_accumulator';
 
 // friendly graph ids
 export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';

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

@@ -5,17 +5,21 @@ import {
   InputFieldTemplate,
   InvocationSchemaObject,
   InvocationTemplate,
-  isInvocationSchemaObject,
   OutputFieldTemplate,
+  isInvocationSchemaObject,
 } from '../types/types';
 import {
   buildInputFieldTemplate,
   buildOutputFieldTemplates,
 } from './fieldTemplateBuilders';
 
-const RESERVED_FIELD_NAMES = ['id', 'type', 'is_intermediate'];
+const RESERVED_FIELD_NAMES = ['id', 'type', 'is_intermediate', 'metadata'];
 
-const invocationDenylist = ['Graph', 'InvocationMeta'];
+const invocationDenylist = [
+  'Graph',
+  'InvocationMeta',
+  'MetadataAccumulatorInvocation',
+];
 
 export const parseSchema = (openAPI: OpenAPIV3.Document) => {
   // filter out non-invocation schemas, plus some tricky invocations for now

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

@@ -2,22 +2,26 @@ import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAISlider from 'common/components/IAISlider';
+import { roundToMultiple } from 'common/util/roundDownToMultiple';
 import {
   canvasSelector,
   isStagingSelector,
 } from 'features/canvas/store/canvasSelectors';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
+import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo } from 'react';
 
 import { useTranslation } from 'react-i18next';
 
 const selector = createSelector(
-  [canvasSelector, isStagingSelector],
-  (canvas, isStaging) => {
+  [canvasSelector, isStagingSelector, uiSelector],
+  (canvas, isStaging, ui) => {
     const { boundingBoxDimensions } = canvas;
+    const { aspectRatio } = ui;
     return {
       boundingBoxDimensions,
       isStaging,
+      aspectRatio,
     };
   },
   defaultSelectorOptions
@@ -25,7 +29,8 @@ const selector = createSelector(
 
 const ParamBoundingBoxWidth = () => {
   const dispatch = useAppDispatch();
-  const { boundingBoxDimensions, isStaging } = useAppSelector(selector);
+  const { boundingBoxDimensions, isStaging, aspectRatio } =
+    useAppSelector(selector);
 
   const { t } = useTranslation();
 
@@ -36,6 +41,15 @@ const ParamBoundingBoxWidth = () => {
         height: Math.floor(v),
       })
     );
+    if (aspectRatio) {
+      const newWidth = roundToMultiple(v * aspectRatio, 64);
+      dispatch(
+        setBoundingBoxDimensions({
+          width: newWidth,
+          height: Math.floor(v),
+        })
+      );
+    }
   };
 
   const handleResetHeight = () => {
@@ -45,6 +59,15 @@ const ParamBoundingBoxWidth = () => {
         height: Math.floor(512),
       })
     );
+    if (aspectRatio) {
+      const newWidth = roundToMultiple(512 * aspectRatio, 64);
+      dispatch(
+        setBoundingBoxDimensions({
+          width: newWidth,
+          height: Math.floor(512),
+        })
+      );
+    }
   };
 
   return (

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

@@ -0,0 +1,57 @@
+import { Flex, Spacer, Text } from '@chakra-ui/react';
+import { useAppDispatch } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { flipBoundingBoxAxes } from 'features/canvas/store/canvasSlice';
+import { useTranslation } from 'react-i18next';
+import { MdOutlineSwapVert } from 'react-icons/md';
+import ParamAspectRatio from '../../Core/ParamAspectRatio';
+import ParamBoundingBoxHeight from './ParamBoundingBoxHeight';
+import ParamBoundingBoxWidth from './ParamBoundingBoxWidth';
+
+export default function ParamBoundingBoxSize() {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  return (
+    <Flex
+      sx={{
+        gap: 2,
+        p: 4,
+        borderRadius: 4,
+        flexDirection: 'column',
+        w: 'full',
+        bg: 'base.150',
+        _dark: {
+          bg: 'base.750',
+        },
+      }}
+    >
+      <Flex alignItems="center" gap={2}>
+        <Text
+          sx={{
+            fontSize: 'sm',
+            width: 'full',
+            color: 'base.700',
+            _dark: {
+              color: 'base.300',
+            },
+          }}
+        >
+          {t('parameters.aspectRatio')}
+        </Text>
+        <Spacer />
+        <ParamAspectRatio />
+        <IAIIconButton
+          tooltip={t('ui.swapSizes')}
+          aria-label={t('ui.swapSizes')}
+          size="sm"
+          icon={<MdOutlineSwapVert />}
+          fontSize={20}
+          onClick={() => dispatch(flipBoundingBoxAxes())}
+        />
+      </Flex>
+      <ParamBoundingBoxWidth />
+      <ParamBoundingBoxHeight />
+    </Flex>
+  );
+}

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

@@ -2,22 +2,26 @@ import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAISlider from 'common/components/IAISlider';
+import { roundToMultiple } from 'common/util/roundDownToMultiple';
 import {
   canvasSelector,
   isStagingSelector,
 } from 'features/canvas/store/canvasSelectors';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
+import { uiSelector } from 'features/ui/store/uiSelectors';
 import { memo } from 'react';
 
 import { useTranslation } from 'react-i18next';
 
 const selector = createSelector(
-  [canvasSelector, isStagingSelector],
-  (canvas, isStaging) => {
+  [canvasSelector, isStagingSelector, uiSelector],
+  (canvas, isStaging, ui) => {
     const { boundingBoxDimensions } = canvas;
+    const { aspectRatio } = ui;
     return {
       boundingBoxDimensions,
       isStaging,
+      aspectRatio,
     };
   },
   defaultSelectorOptions
@@ -25,7 +29,8 @@ const selector = createSelector(
 
 const ParamBoundingBoxWidth = () => {
   const dispatch = useAppDispatch();
-  const { boundingBoxDimensions, isStaging } = useAppSelector(selector);
+  const { boundingBoxDimensions, isStaging, aspectRatio } =
+    useAppSelector(selector);
 
   const { t } = useTranslation();
 
@@ -36,6 +41,15 @@ const ParamBoundingBoxWidth = () => {
         width: Math.floor(v),
       })
     );
+    if (aspectRatio) {
+      const newHeight = roundToMultiple(v / aspectRatio, 64);
+      dispatch(
+        setBoundingBoxDimensions({
+          width: Math.floor(v),
+          height: newHeight,
+        })
+      );
+    }
   };
 
   const handleResetWidth = () => {
@@ -45,6 +59,15 @@ const ParamBoundingBoxWidth = () => {
         width: Math.floor(512),
       })
     );
+    if (aspectRatio) {
+      const newHeight = roundToMultiple(512 / aspectRatio, 64);
+      dispatch(
+        setBoundingBoxDimensions({
+          width: Math.floor(512),
+          height: newHeight,
+        })
+      );
+    }
   };
 
   return (

invokeai/frontend/web/src/features/parameters/components/Parameters/Noise/ParamNoiseCollapse.tsx

@@ -27,6 +27,9 @@ const ParamNoiseCollapse = () => {
   const { t } = useTranslation();
 
   const isNoiseEnabled = useFeatureStatus('noise').isFeatureEnabled;
+  const isPerlinNoiseEnabled = useFeatureStatus('perlinNoise').isFeatureEnabled;
+  const isNoiseThresholdEnabled =
+    useFeatureStatus('noiseThreshold').isFeatureEnabled;
 
   const { activeLabel } = useAppSelector(selector);
 
@@ -42,8 +45,8 @@ const ParamNoiseCollapse = () => {
       <Flex sx={{ gap: 2, flexDirection: 'column' }}>
         <ParamNoiseToggle />
         <ParamCpuNoiseToggle />
-        <ParamPerlinNoise />
-        <ParamNoiseThreshold />
+        {isPerlinNoiseEnabled && <ParamPerlinNoise />}
+        {isNoiseThresholdEnabled && <ParamNoiseThreshold />}
       </Flex>
     </IAICollapse>
   );

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -2,6 +2,7 @@ import { useAppToaster } from 'app/components/Toaster';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
+import { UnsafeImageMetadata } from 'services/api/endpoints/images';
 import { isImageField } from 'services/api/guards';
 import { ImageDTO } from 'services/api/types';
 import { initialImageSelected, modelSelected } from '../store/actions';
@@ -162,7 +163,7 @@ export const useRecallParameters = () => {
         parameterNotSetToast();
         return;
       }
-      dispatch(modelSelected(model?.id || ''));
+      dispatch(modelSelected(model));
       parameterSetToast();
     },
     [dispatch, parameterSetToast, parameterNotSetToast]
@@ -269,28 +270,24 @@ export const useRecallParameters = () => {
   );
 
   const recallAllParameters = useCallback(
-    (image: ImageDTO | undefined) => {
-      if (!image || !image.metadata) {
+    (metadata: UnsafeImageMetadata['metadata'] | undefined) => {
+      if (!metadata) {
         allParameterNotSetToast();
         return;
       }
+
       const {
         cfg_scale,
         height,
         model,
-        positive_conditioning,
-        negative_conditioning,
+        positive_prompt,
+        negative_prompt,
         scheduler,
         seed,
         steps,
         width,
         strength,
-        clip,
-        extra,
-        latents,
-        unet,
-        vae,
-      } = image.metadata;
+      } = metadata;
 
       if (isValidCfgScale(cfg_scale)) {
         dispatch(setCfgScale(cfg_scale));
@@ -298,11 +295,11 @@ export const useRecallParameters = () => {
       if (isValidMainModel(model)) {
         dispatch(modelSelected(model));
       }
-      if (isValidPositivePrompt(positive_conditioning)) {
-        dispatch(setPositivePrompt(positive_conditioning));
+      if (isValidPositivePrompt(positive_prompt)) {
+        dispatch(setPositivePrompt(positive_prompt));
       }
-      if (isValidNegativePrompt(negative_conditioning)) {
-        dispatch(setNegativePrompt(negative_conditioning));
+      if (isValidNegativePrompt(negative_prompt)) {
+        dispatch(setNegativePrompt(negative_prompt));
       }
       if (isValidScheduler(scheduler)) {
         dispatch(setScheduler(scheduler));

invokeai/frontend/web/src/features/parameters/store/actions.ts

@@ -1,8 +1,10 @@
 import { createAction } from '@reduxjs/toolkit';
-import { ImageDTO } from 'services/api/types';
+import { ImageDTO, MainModelField } from 'services/api/types';
 
 export const initialImageSelected = createAction<ImageDTO | string | undefined>(
   'generation/initialImageSelected'
 );
 
-export const modelSelected = createAction<string>('generation/modelSelected');
+export const modelSelected = createAction<MainModelField>(
+  'generation/modelSelected'
+);

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -8,12 +8,11 @@ import {
   setShouldShowAdvancedOptions,
 } from 'features/ui/store/uiSlice';
 import { clamp } from 'lodash-es';
-import { ImageDTO } from 'services/api/types';
+import { ImageDTO, MainModelField } from 'services/api/types';
 import { clipSkipMap } from '../components/Parameters/Advanced/ParamClipSkip';
 import {
   CfgScaleParam,
   HeightParam,
-  MainModelParam,
   NegativePromptParam,
   PositivePromptParam,
   SchedulerParam,
@@ -54,7 +53,7 @@ export interface GenerationState {
   shouldUseSymmetry: boolean;
   horizontalSymmetrySteps: number;
   verticalSymmetrySteps: number;
-  model: MainModelParam | null;
+  model: MainModelField | null;
   vae: VaeModelParam | null;
   seamlessXAxis: boolean;
   seamlessYAxis: boolean;
@@ -227,23 +226,17 @@ export const generationSlice = createSlice({
       const { image_name, width, height } = action.payload;
       state.initialImage = { imageName: image_name, width, height };
     },
-    modelSelected: (state, action: PayloadAction<string>) => {
-      const [base_model, type, name] = action.payload.split('/');
+    modelChanged: (state, action: PayloadAction<MainModelField | null>) => {
+      if (!action.payload) {
+        state.model = null;
+      }
 
-      state.model = zMainModel.parse({
-        id: action.payload,
-        base_model,
-        name,
-        type,
-      });
+      state.model = zMainModel.parse(action.payload);
 
       // Clamp ClipSkip Based On Selected Model
       const { maxClip } = clipSkipMap[state.model.base_model];
       state.clipSkip = clamp(state.clipSkip, 0, maxClip);
     },
-    modelChanged: (state, action: PayloadAction<MainModelParam>) => {
-      state.model = action.payload;
-    },
     vaeSelected: (state, action: PayloadAction<VaeModelParam | null>) => {
       state.vae = action.payload;
     },

invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts

@@ -135,8 +135,7 @@ export type BaseModelParam = z.infer<typeof zBaseModel>;
  * TODO: Make this a dynamically generated enum?
  */
 export const zMainModel = z.object({
-  id: z.string(),
-  name: z.string(),
+  model_name: z.string(),
   base_model: zBaseModel,
 });
 
@@ -171,7 +170,7 @@ export const isValidVaeModel = (val: unknown): val is VaeModelParam =>
  */
 export const zLoRAModel = z.object({
   id: z.string(),
-  name: z.string(),
+  model_name: z.string(),
   base_model: zBaseModel,
 });
 /**

invokeai/frontend/web/src/features/system/components/ModelSelect.tsx

@@ -1,13 +1,16 @@
-import { memo, useCallback, useEffect, useMemo } from 'react';
+import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIMantineSelect from 'common/components/IAIMantineSelect';
 
 import { SelectItem } from '@mantine/core';
-import { RootState } from 'app/store/store';
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import { modelIdToMainModelField } from 'features/nodes/util/modelIdToMainModelField';
 import { modelSelected } from 'features/parameters/store/actions';
-import { forEach, isString } from 'lodash-es';
+import { forEach } from 'lodash-es';
 import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 
 export const MODEL_TYPE_MAP = {
@@ -15,13 +18,17 @@ export const MODEL_TYPE_MAP = {
   'sd-2': 'Stable Diffusion 2.x',
 };
 
+const selector = createSelector(
+  stateSelector,
+  (state) => ({ currentModel: state.generation.model }),
+  defaultSelectorOptions
+);
+
 const ModelSelect = () => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const currentModel = useAppSelector(
-    (state: RootState) => state.generation.model
-  );
+  const { currentModel } = useAppSelector(selector);
 
   const { data: mainModels, isLoading } = useGetMainModelsQuery();
 
@@ -39,7 +46,7 @@ const ModelSelect = () => {
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: MODEL_TYPE_MAP[model.base_model],
       });
     });
@@ -48,7 +55,10 @@ const ModelSelect = () => {
   }, [mainModels]);
 
   const selectedModel = useMemo(
-    () => mainModels?.entities[currentModel?.id || ''],
+    () =>
+      mainModels?.entities[
+        `${currentModel?.base_model}/main/${currentModel?.model_name}`
+      ],
     [mainModels?.entities, currentModel]
   );
 
@@ -57,31 +67,13 @@ const ModelSelect = () => {
       if (!v) {
         return;
       }
-      dispatch(modelSelected(v));
+
+      const modelField = modelIdToMainModelField(v);
+      dispatch(modelSelected(modelField));
     },
     [dispatch]
   );
 
-  useEffect(() => {
-    if (isLoading) {
-      // return early here to avoid resetting model selection before we've loaded the available models
-      return;
-    }
-
-    if (selectedModel && mainModels?.ids.includes(selectedModel?.id)) {
-      // the selected model is an available model, no need to change it
-      return;
-    }
-
-    const firstModel = mainModels?.ids[0];
-
-    if (!isString(firstModel)) {
-      return;
-    }
-
-    handleChangeModel(firstModel);
-  }, [handleChangeModel, isLoading, mainModels?.ids, selectedModel]);
-
   return isLoading ? (
     <IAIMantineSelect
       label={t('modelManager.model')}
@@ -94,9 +86,10 @@ const ModelSelect = () => {
       tooltip={selectedModel?.description}
       label={t('modelManager.model')}
       value={selectedModel?.id}
-      placeholder={data.length > 0 ? 'Select a model' : 'No models detected!'}
+      placeholder={data.length > 0 ? 'Select a model' : 'No models available'}
       data={data}
       error={data.length === 0}
+      disabled={data.length === 0}
       onChange={handleChangeModel}
     />
   );

invokeai/frontend/web/src/features/system/components/VAESelect.tsx

@@ -50,7 +50,7 @@ const VAESelect = () => {
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: MODEL_TYPE_MAP[model.base_model],
         disabled,
         tooltip: disabled

invokeai/frontend/web/src/features/system/store/configSlice.ts

@@ -6,8 +6,15 @@ import { merge } from 'lodash-es';
 export const initialConfigState: AppConfig = {
   shouldUpdateImagesOnConnect: false,
   disabledTabs: [],
-  disabledFeatures: [],
-  disabledSDFeatures: [],
+  disabledFeatures: ['lightbox', 'faceRestore'],
+  disabledSDFeatures: [
+    'variation',
+    'seamless',
+    'symmetry',
+    'hires',
+    'perlinNoise',
+    'noiseThreshold',
+  ],
   canRestoreDeletedImagesFromBin: true,
   sd: {
     disabledControlNetModels: [],

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasCoreParameters.tsx

@@ -4,8 +4,7 @@ import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
-import ParamBoundingBoxHeight from 'features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxHeight';
-import ParamBoundingBoxWidth from 'features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxWidth';
+import ParamBoundingBoxSize from 'features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxSize';
 import ParamCFGScale from 'features/parameters/components/Parameters/Core/ParamCFGScale';
 import ParamIterations from 'features/parameters/components/Parameters/Core/ParamIterations';
 import ParamModelandVAEandScheduler from 'features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler';
@@ -51,8 +50,7 @@ const UnifiedCanvasCoreParameters = () => {
             <Box pt={2}>
               <ParamSeedFull />
             </Box>
-            <ParamBoundingBoxWidth />
-            <ParamBoundingBoxHeight />
+            <ParamBoundingBoxSize />
           </>
         ) : (
           <>
@@ -65,8 +63,7 @@ const UnifiedCanvasCoreParameters = () => {
             <Box pt={2}>
               <ParamSeedFull />
             </Box>
-            <ParamBoundingBoxWidth />
-            <ParamBoundingBoxHeight />
+            <ParamBoundingBoxSize />
           </>
         )}
         <ImageToImageStrength />

invokeai/frontend/web/src/services/api/endpoints/images.ts

@@ -1,13 +1,22 @@
 import { ApiFullTagDescription, api } from '..';
+import { components } from '../schema';
 import { ImageDTO } from '../types';
 
+/**
+ * This is an unsafe type; the object inside is not guaranteed to be valid.
+ */
+export type UnsafeImageMetadata = {
+  metadata: components['schemas']['CoreMetadata'];
+  graph: NonNullable<components['schemas']['Graph']>;
+};
+
 export const imagesApi = api.injectEndpoints({
   endpoints: (build) => ({
     /**
      * Image Queries
      */
     getImageDTO: build.query<ImageDTO, string>({
-      query: (image_name) => ({ url: `images/${image_name}/metadata` }),
+      query: (image_name) => ({ url: `images/${image_name}` }),
       providesTags: (result, error, arg) => {
         const tags: ApiFullTagDescription[] = [{ type: 'Image', id: arg }];
         if (result?.board_id) {
@@ -17,7 +26,17 @@ export const imagesApi = api.injectEndpoints({
       },
       keepUnusedDataFor: 86400, // 24 hours
     }),
+    getImageMetadata: build.query<UnsafeImageMetadata, string>({
+      query: (image_name) => ({ url: `images/${image_name}/metadata` }),
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { type: 'ImageMetadata', id: arg },
+        ];
+        return tags;
+      },
+      keepUnusedDataFor: 86400, // 24 hours
+    }),
   }),
 });
 
-export const { useGetImageDTOQuery } = imagesApi;
+export const { useGetImageDTOQuery, useGetImageMetadataQuery } = imagesApi;

invokeai/frontend/web/src/services/api/endpoints/models.ts

@@ -33,25 +33,28 @@ type AnyModelConfigEntity =
   | VaeModelConfigEntity;
 
 const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
-  sortComparer: (a, b) => a.name.localeCompare(b.name),
+  sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
 const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
-  sortComparer: (a, b) => a.name.localeCompare(b.name),
+  sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
 const controlNetModelsAdapter =
   createEntityAdapter<ControlNetModelConfigEntity>({
-    sortComparer: (a, b) => a.name.localeCompare(b.name),
+    sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
   });
 const textualInversionModelsAdapter =
   createEntityAdapter<TextualInversionModelConfigEntity>({
-    sortComparer: (a, b) => a.name.localeCompare(b.name),
+    sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
   });
 const vaeModelsAdapter = createEntityAdapter<VaeModelConfigEntity>({
-  sortComparer: (a, b) => a.name.localeCompare(b.name),
+  sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
 
-export const getModelId = ({ base_model, type, name }: AnyModelConfig) =>
-  `${base_model}/${type}/${name}`;
+export const getModelId = ({
+  base_model,
+  model_type,
+  model_name,
+}: AnyModelConfig) => `${base_model}/${model_type}/${model_name}`;
 
 const createModelEntities = <T extends AnyModelConfigEntity>(
   models: AnyModelConfig[]

invokeai/frontend/web/src/services/api/index.ts

@@ -1,3 +1,4 @@
+import { FullTagDescription } from '@reduxjs/toolkit/dist/query/endpointDefinitions';
 import {
   BaseQueryFn,
   FetchArgs,
@@ -5,10 +6,9 @@ import {
   createApi,
   fetchBaseQuery,
 } from '@reduxjs/toolkit/query/react';
-import { FullTagDescription } from '@reduxjs/toolkit/dist/query/endpointDefinitions';
 import { $authToken, $baseUrl } from 'services/api/client';
 
-export const tagTypes = ['Board', 'Image', 'Model'];
+export const tagTypes = ['Board', 'Image', 'ImageMetadata', 'Model'];
 export type ApiFullTagDescription = FullTagDescription<
   (typeof tagTypes)[number]
 >;

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -109,10 +109,10 @@ export type paths = {
   };
   "/api/v1/images/": {
     /**
-     * List Images With Metadata 
-     * @description Gets a list of images
+     * List Image Dtos 
+     * @description Gets a list of image DTOs
      */
-    get: operations["list_images_with_metadata"];
+    get: operations["list_image_dtos"];
     /**
      * Upload Image 
      * @description Uploads an image
@@ -121,10 +121,10 @@ export type paths = {
   };
   "/api/v1/images/{image_name}": {
     /**
-     * Get Image Full 
-     * @description Gets a full-resolution image file
+     * Get Image Dto 
+     * @description Gets an image's DTO
      */
-    get: operations["get_image_full"];
+    get: operations["get_image_dto"];
     /**
      * Delete Image 
      * @description Deletes an image
@@ -143,6 +143,13 @@ export type paths = {
      */
     get: operations["get_image_metadata"];
   };
+  "/api/v1/images/{image_name}/full": {
+    /**
+     * Get Image Full 
+     * @description Gets a full-resolution image file
+     */
+    get: operations["get_image_full"];
+  };
   "/api/v1/images/{image_name}/thumbnail": {
     /**
      * Get Image Thumbnail 
@@ -798,14 +805,14 @@ export type components = {
     };
     /** ControlNetModelConfig */
     ControlNetModelConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "controlnet";
+      model_type: "controlnet";
       /** Path */
       path: string;
       /** Description */
@@ -836,6 +843,97 @@ export type components = {
        */
       control?: components["schemas"]["ControlField"];
     };
+    /**
+     * CoreMetadata 
+     * @description Core generation metadata for an image generated in InvokeAI.
+     */
+    CoreMetadata: {
+      /**
+       * Generation Mode 
+       * @description The generation mode that output this image
+       */
+      generation_mode: string;
+      /**
+       * Positive Prompt 
+       * @description The positive prompt parameter
+       */
+      positive_prompt: string;
+      /**
+       * Negative Prompt 
+       * @description The negative prompt parameter
+       */
+      negative_prompt: string;
+      /**
+       * Width 
+       * @description The width parameter
+       */
+      width: number;
+      /**
+       * Height 
+       * @description The height parameter
+       */
+      height: number;
+      /**
+       * Seed 
+       * @description The seed used for noise generation
+       */
+      seed: number;
+      /**
+       * Rand Device 
+       * @description The device used for random number generation
+       */
+      rand_device: string;
+      /**
+       * Cfg Scale 
+       * @description The classifier-free guidance scale parameter
+       */
+      cfg_scale: number;
+      /**
+       * Steps 
+       * @description The number of steps used for inference
+       */
+      steps: number;
+      /**
+       * Scheduler 
+       * @description The scheduler used for inference
+       */
+      scheduler: string;
+      /**
+       * Clip Skip 
+       * @description The number of skipped CLIP layers
+       */
+      clip_skip: number;
+      /**
+       * Model 
+       * @description The main model used for inference
+       */
+      model: components["schemas"]["MainModelField"];
+      /**
+       * Controlnets 
+       * @description The ControlNets used for inference
+       */
+      controlnets: (components["schemas"]["ControlField"])[];
+      /**
+       * Loras 
+       * @description The LoRAs used for inference
+       */
+      loras: (components["schemas"]["LoRAMetadataField"])[];
+      /**
+       * Strength 
+       * @description The strength used for latents-to-latents
+       */
+      strength?: number;
+      /**
+       * Init Image 
+       * @description The name of the initial image
+       */
+      init_image?: string;
+      /**
+       * Vae 
+       * @description The VAE used for decoding, if the main model's default was not used
+       */
+      vae?: components["schemas"]["VAEModelField"];
+    };
     /**
      * CvInpaintInvocation 
      * @description Simple inpaint using opencv.
@@ -1058,7 +1156,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1101,7 +1199,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -1502,11 +1600,6 @@ export type components = {
        * @description The node ID that generated this image, if it is a generated image.
        */
       node_id?: string;
-      /**
-       * Metadata 
-       * @description A limited subset of the image's generation metadata. Retrieve the image's session for full metadata.
-       */
-      metadata?: components["schemas"]["ImageMetadata"];
       /**
        * Board Id 
        * @description The id of the board the image belongs to, if one exists.
@@ -1606,96 +1699,19 @@ export type components = {
     };
     /**
      * ImageMetadata 
-     * @description Core generation metadata for an image/tensor generated in InvokeAI.
-     * 
-     * Also includes any metadata from the image's PNG tEXt chunks.
-     * 
-     * Generated by traversing the execution graph, collecting the parameters of the nearest ancestors
-     * of a given node.
-     * 
-     * Full metadata may be accessed by querying for the session in the `graph_executions` table.
+     * @description An image's generation metadata
      */
     ImageMetadata: {
       /**
-       * Type 
-       * @description The type of the ancestor node of the image output node.
+       * Metadata 
+       * @description The image's core metadata, if it was created in the Linear or Canvas UI
        */
-      type?: string;
+      metadata?: Record<string, never>;
       /**
-       * Positive Conditioning 
-       * @description The positive conditioning.
+       * Graph 
+       * @description The graph that created the image
        */
-      positive_conditioning?: string;
-      /**
-       * Negative Conditioning 
-       * @description The negative conditioning.
-       */
-      negative_conditioning?: string;
-      /**
-       * Width 
-       * @description Width of the image/latents in pixels.
-       */
-      width?: number;
-      /**
-       * Height 
-       * @description Height of the image/latents in pixels.
-       */
-      height?: number;
-      /**
-       * Seed 
-       * @description The seed used for noise generation.
-       */
-      seed?: number;
-      /**
-       * Cfg Scale 
-       * @description The classifier-free guidance scale.
-       */
-      cfg_scale?: number | (number)[];
-      /**
-       * Steps 
-       * @description The number of steps used for inference.
-       */
-      steps?: number;
-      /**
-       * Scheduler 
-       * @description The scheduler used for inference.
-       */
-      scheduler?: string;
-      /**
-       * Model 
-       * @description The model used for inference.
-       */
-      model?: string;
-      /**
-       * Strength 
-       * @description The strength used for image-to-image/latents-to-latents.
-       */
-      strength?: number;
-      /**
-       * Latents 
-       * @description The ID of the initial latents.
-       */
-      latents?: string;
-      /**
-       * Vae 
-       * @description The VAE used for decoding.
-       */
-      vae?: string;
-      /**
-       * Unet 
-       * @description The UNet used dor inference.
-       */
-      unet?: string;
-      /**
-       * Clip 
-       * @description The CLIP Encoder used for conditioning.
-       */
-      clip?: string;
-      /**
-       * Extra 
-       * @description Uploaded image metadata, extracted from the PNG tEXt chunk.
-       */
-      extra?: string;
+      graph?: Record<string, never>;
     };
     /**
      * ImageMultiplyInvocation 
@@ -2436,6 +2452,11 @@ export type components = {
        * @default false
        */
       tiled?: boolean;
+      /**
+       * Metadata 
+       * @description Optional core metadata to be written to the image
+       */
+      metadata?: components["schemas"]["CoreMetadata"];
     };
     /**
      * LatentsToLatentsInvocation 
@@ -2659,16 +2680,32 @@ export type components = {
        */
       coarse?: boolean;
     };
+    /**
+     * LoRAMetadataField 
+     * @description LoRA metadata for an image generated in InvokeAI.
+     */
+    LoRAMetadataField: {
+      /**
+       * Lora 
+       * @description The LoRA model
+       */
+      lora: components["schemas"]["LoRAModelField"];
+      /**
+       * Weight 
+       * @description The weight of the LoRA model
+       */
+      weight: number;
+    };
     /** LoRAModelConfig */
     LoRAModelConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "lora";
+      model_type: "lora";
       /** Path */
       path: string;
       /** Description */
@@ -2956,6 +2993,131 @@ export type components = {
      * @enum {string}
      */
     MergeInterpolationMethod: "weighted_sum" | "sigmoid" | "inv_sigmoid" | "add_difference";
+    /**
+     * MetadataAccumulatorInvocation 
+     * @description Outputs a Core Metadata Object
+     */
+    MetadataAccumulatorInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default metadata_accumulator 
+       * @enum {string}
+       */
+      type?: "metadata_accumulator";
+      /**
+       * Generation Mode 
+       * @description The generation mode that output this image
+       */
+      generation_mode: string;
+      /**
+       * Positive Prompt 
+       * @description The positive prompt parameter
+       */
+      positive_prompt: string;
+      /**
+       * Negative Prompt 
+       * @description The negative prompt parameter
+       */
+      negative_prompt: string;
+      /**
+       * Width 
+       * @description The width parameter
+       */
+      width: number;
+      /**
+       * Height 
+       * @description The height parameter
+       */
+      height: number;
+      /**
+       * Seed 
+       * @description The seed used for noise generation
+       */
+      seed: number;
+      /**
+       * Rand Device 
+       * @description The device used for random number generation
+       */
+      rand_device: string;
+      /**
+       * Cfg Scale 
+       * @description The classifier-free guidance scale parameter
+       */
+      cfg_scale: number;
+      /**
+       * Steps 
+       * @description The number of steps used for inference
+       */
+      steps: number;
+      /**
+       * Scheduler 
+       * @description The scheduler used for inference
+       */
+      scheduler: string;
+      /**
+       * Clip Skip 
+       * @description The number of skipped CLIP layers
+       */
+      clip_skip: number;
+      /**
+       * Model 
+       * @description The main model used for inference
+       */
+      model: components["schemas"]["MainModelField"];
+      /**
+       * Controlnets 
+       * @description The ControlNets used for inference
+       */
+      controlnets: (components["schemas"]["ControlField"])[];
+      /**
+       * Loras 
+       * @description The LoRAs used for inference
+       */
+      loras: (components["schemas"]["LoRAMetadataField"])[];
+      /**
+       * Strength 
+       * @description The strength used for latents-to-latents
+       */
+      strength?: number;
+      /**
+       * Init Image 
+       * @description The name of the initial image
+       */
+      init_image?: string;
+      /**
+       * Vae 
+       * @description The VAE used for decoding, if the main model's default was not used
+       */
+      vae?: components["schemas"]["VAEModelField"];
+    };
+    /**
+     * MetadataAccumulatorOutput 
+     * @description The output of the MetadataAccumulator node
+     */
+    MetadataAccumulatorOutput: {
+      /**
+       * Type 
+       * @default metadata_accumulator_output 
+       * @enum {string}
+       */
+      type?: "metadata_accumulator_output";
+      /**
+       * Metadata 
+       * @description The core metadata for the image
+       */
+      metadata: components["schemas"]["CoreMetadata"];
+    };
     /**
      * MidasDepthImageProcessorInvocation 
      * @description Applies Midas depth processing to image
@@ -3110,7 +3272,7 @@ export type components = {
     /** ModelsList */
     ModelsList: {
       /** Models */
-      models: (components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"])[];
+      models: (components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"])[];
     };
     /**
      * MultiplyInvocation 
@@ -3900,14 +4062,14 @@ export type components = {
     };
     /** StableDiffusion1ModelCheckpointConfig */
     StableDiffusion1ModelCheckpointConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "main";
+      model_type: "main";
       /** Path */
       path: string;
       /** Description */
@@ -3926,14 +4088,14 @@ export type components = {
     };
     /** StableDiffusion1ModelDiffusersConfig */
     StableDiffusion1ModelDiffusersConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "main";
+      model_type: "main";
       /** Path */
       path: string;
       /** Description */
@@ -3950,14 +4112,14 @@ export type components = {
     };
     /** StableDiffusion2ModelCheckpointConfig */
     StableDiffusion2ModelCheckpointConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "main";
+      model_type: "main";
       /** Path */
       path: string;
       /** Description */
@@ -3976,14 +4138,14 @@ export type components = {
     };
     /** StableDiffusion2ModelDiffusersConfig */
     StableDiffusion2ModelDiffusersConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "main";
+      model_type: "main";
       /** Path */
       path: string;
       /** Description */
@@ -4190,14 +4352,14 @@ export type components = {
     };
     /** TextualInversionModelConfig */
     TextualInversionModelConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "embedding";
+      model_type: "embedding";
       /** Path */
       path: string;
       /** Description */
@@ -4367,14 +4529,14 @@ export type components = {
     };
     /** VaeModelConfig */
     VaeModelConfig: {
-      /** Name */
-      name: string;
+      /** Model Name */
+      model_name: string;
       base_model: components["schemas"]["BaseModelType"];
       /**
-       * Type 
+       * Model Type 
        * @enum {string}
        */
-      type: "vae";
+      model_type: "vae";
       /** Path */
       path: string;
       /** Description */
@@ -4425,18 +4587,18 @@ export type components = {
        */
       image?: components["schemas"]["ImageField"];
     };
-    /**
-     * StableDiffusion2ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusion1ModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
     StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion2ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -4547,7 +4709,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -4584,7 +4746,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -4817,7 +4979,7 @@ export type operations = {
       /** @description The model imported successfully */
       201: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
         };
       };
       /** @description The model could not be found */
@@ -4885,14 +5047,14 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
+        "application/json": components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
       };
     };
     responses: {
       /** @description The model was updated successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
         };
       };
       /** @description Bad request */
@@ -4926,7 +5088,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
         };
       };
       /** @description Bad request */
@@ -4961,7 +5123,7 @@ export type operations = {
       /** @description Model converted successfully */
       200: {
         content: {
-          "application/json": components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
+          "application/json": components["schemas"]["StableDiffusion1ModelDiffusersConfig"] | components["schemas"]["StableDiffusion1ModelCheckpointConfig"] | components["schemas"]["VaeModelConfig"] | components["schemas"]["LoRAModelConfig"] | components["schemas"]["ControlNetModelConfig"] | components["schemas"]["TextualInversionModelConfig"] | components["schemas"]["StableDiffusion2ModelDiffusersConfig"] | components["schemas"]["StableDiffusion2ModelCheckpointConfig"];
         };
       };
       /** @description Incompatible models */
@@ -4977,10 +5139,10 @@ export type operations = {
     };
   };
   /**
-   * List Images With Metadata 
-   * @description Gets a list of images
+   * List Image Dtos 
+   * @description Gets a list of image DTOs
    */
-  list_images_with_metadata: {
+  list_image_dtos: {
     parameters: {
       query?: {
         /** @description The origin of images to list */
@@ -5050,25 +5212,23 @@ export type operations = {
     };
   };
   /**
-   * Get Image Full 
-   * @description Gets a full-resolution image file
+   * Get Image Dto 
+   * @description Gets an image's DTO
    */
-  get_image_full: {
+  get_image_dto: {
     parameters: {
       path: {
-        /** @description The name of full-resolution image file to get */
+        /** @description The name of image to get */
         image_name: string;
       };
     };
     responses: {
-      /** @description Return the full-resolution image */
+      /** @description Successful Response */
       200: {
         content: {
-          "image/png": unknown;
+          "application/json": components["schemas"]["ImageDTO"];
         };
       };
-      /** @description Image not found */
-      404: never;
       /** @description Validation Error */
       422: {
         content: {
@@ -5149,7 +5309,7 @@ export type operations = {
       /** @description Successful Response */
       200: {
         content: {
-          "application/json": components["schemas"]["ImageDTO"];
+          "application/json": components["schemas"]["ImageMetadata"];
         };
       };
       /** @description Validation Error */
@@ -5160,6 +5320,34 @@ export type operations = {
       };
     };
   };
+  /**
+   * Get Image Full 
+   * @description Gets a full-resolution image file
+   */
+  get_image_full: {
+    parameters: {
+      path: {
+        /** @description The name of full-resolution image file to get */
+        image_name: string;
+      };
+    };
+    responses: {
+      /** @description Return the full-resolution image */
+      200: {
+        content: {
+          "image/png": unknown;
+        };
+      };
+      /** @description Image not found */
+      404: never;
+      /** @description Validation Error */
+      422: {
+        content: {
+          "application/json": components["schemas"]["HTTPValidationError"];
+        };
+      };
+    };
+  };
   /**
    * Get Image Thumbnail 
    * @description Gets a thumbnail image file

invokeai/frontend/web/src/services/api/thunks/image.ts

@@ -1,9 +1,9 @@
-import queryString from 'query-string';
 import { createAppAsyncThunk } from 'app/store/storeUtils';
 import { selectImagesAll } from 'features/gallery/store/gallerySlice';
 import { size } from 'lodash-es';
-import { paths } from 'services/api/schema';
+import queryString from 'query-string';
 import { $client } from 'services/api/client';
+import { paths } from 'services/api/schema';
 
 type GetImageUrlsArg =
   paths['/api/v1/images/{image_name}/urls']['get']['parameters']['path'];
@@ -24,7 +24,7 @@ export const imageUrlsReceived = createAppAsyncThunk<
   GetImageUrlsResponse,
   GetImageUrlsArg,
   GetImageUrlsThunkConfig
->('api/imageUrlsReceived', async (arg, { rejectWithValue }) => {
+>('thunkApi/imageUrlsReceived', async (arg, { rejectWithValue }) => {
   const { image_name } = arg;
   const { get } = $client.get();
   const { data, error, response } = await get(
@@ -46,10 +46,10 @@ export const imageUrlsReceived = createAppAsyncThunk<
 });
 
 type GetImageMetadataArg =
-  paths['/api/v1/images/{image_name}/metadata']['get']['parameters']['path'];
+  paths['/api/v1/images/{image_name}']['get']['parameters']['path'];
 
 type GetImageMetadataResponse =
-  paths['/api/v1/images/{image_name}/metadata']['get']['responses']['200']['content']['application/json'];
+  paths['/api/v1/images/{image_name}']['get']['responses']['200']['content']['application/json'];
 
 type GetImageMetadataThunkConfig = {
   rejectValue: {
@@ -58,21 +58,18 @@ type GetImageMetadataThunkConfig = {
   };
 };
 
-export const imageMetadataReceived = createAppAsyncThunk<
+export const imageDTOReceived = createAppAsyncThunk<
   GetImageMetadataResponse,
   GetImageMetadataArg,
   GetImageMetadataThunkConfig
->('api/imageMetadataReceived', async (arg, { rejectWithValue }) => {
+>('thunkApi/imageMetadataReceived', async (arg, { rejectWithValue }) => {
   const { image_name } = arg;
   const { get } = $client.get();
-  const { data, error, response } = await get(
-    '/api/v1/images/{image_name}/metadata',
-    {
-      params: {
-        path: { image_name },
-      },
-    }
-  );
+  const { data, error, response } = await get('/api/v1/images/{image_name}', {
+    params: {
+      path: { image_name },
+    },
+  });
 
   if (error) {
     return rejectWithValue({ arg, error });
@@ -148,7 +145,7 @@ export const imageUploaded = createAppAsyncThunk<
   UploadImageResponse,
   UploadImageArg,
   UploadImageThunkConfig
->('api/imageUploaded', async (arg, { rejectWithValue }) => {
+>('thunkApi/imageUploaded', async (arg, { rejectWithValue }) => {
   const {
     postUploadAction,
     file,
@@ -199,7 +196,7 @@ export const imageDeleted = createAppAsyncThunk<
   DeleteImageResponse,
   DeleteImageArg,
   DeleteImageThunkConfig
->('api/imageDeleted', async (arg, { rejectWithValue }) => {
+>('thunkApi/imageDeleted', async (arg, { rejectWithValue }) => {
   const { image_name } = arg;
   const { del } = $client.get();
   const { data, error, response } = await del('/api/v1/images/{image_name}', {
@@ -235,7 +232,7 @@ export const imageUpdated = createAppAsyncThunk<
   UpdateImageResponse,
   UpdateImageArg,
   UpdateImageThunkConfig
->('api/imageUpdated', async (arg, { rejectWithValue }) => {
+>('thunkApi/imageUpdated', async (arg, { rejectWithValue }) => {
   const { image_name, image_category, is_intermediate, session_id } = arg;
   const { patch } = $client.get();
   const { data, error, response } = await patch('/api/v1/images/{image_name}', {
@@ -284,46 +281,49 @@ export const receivedPageOfImages = createAppAsyncThunk<
   ListImagesResponse,
   ListImagesArg,
   ListImagesThunkConfig
->('api/receivedPageOfImages', async (arg, { getState, rejectWithValue }) => {
-  const { get } = $client.get();
+>(
+  'thunkApi/receivedPageOfImages',
+  async (arg, { getState, rejectWithValue }) => {
+    const { get } = $client.get();
 
-  const state = getState();
-  const { categories, selectedBoardId } = state.gallery;
+    const state = getState();
+    const { categories, selectedBoardId } = state.gallery;
 
-  const images = selectImagesAll(state).filter((i) => {
-    const isInCategory = categories.includes(i.image_category);
-    const isInSelectedBoard = selectedBoardId
-      ? i.board_id === selectedBoardId
-      : true;
-    return isInCategory && isInSelectedBoard;
-  });
+    const images = selectImagesAll(state).filter((i) => {
+      const isInCategory = categories.includes(i.image_category);
+      const isInSelectedBoard = selectedBoardId
+        ? i.board_id === selectedBoardId
+        : true;
+      return isInCategory && isInSelectedBoard;
+    });
 
-  let query: ListImagesArg = {};
+    let query: ListImagesArg = {};
 
-  if (size(arg)) {
-    query = {
-      ...DEFAULT_IMAGES_LISTED_ARG,
-      offset: images.length,
-      ...arg,
-    };
-  } else {
-    query = {
-      ...DEFAULT_IMAGES_LISTED_ARG,
-      categories,
-      offset: images.length,
-    };
+    if (size(arg)) {
+      query = {
+        ...DEFAULT_IMAGES_LISTED_ARG,
+        offset: images.length,
+        ...arg,
+      };
+    } else {
+      query = {
+        ...DEFAULT_IMAGES_LISTED_ARG,
+        categories,
+        offset: images.length,
+      };
+    }
+
+    const { data, error, response } = await get('/api/v1/images/', {
+      params: {
+        query,
+      },
+      querySerializer: (q) => queryString.stringify(q, { arrayFormat: 'none' }),
+    });
+
+    if (error) {
+      return rejectWithValue({ arg, error });
+    }
+
+    return data;
   }
-
-  const { data, error, response } = await get('/api/v1/images/', {
-    params: {
-      query,
-    },
-    querySerializer: (q) => queryString.stringify(q, { arrayFormat: 'none' }),
-  });
-
-  if (error) {
-    return rejectWithValue({ arg, error });
-  }
-
-  return data;
-});
+);

invokeai/frontend/web/src/services/api/types.d.ts

@@ -19,6 +19,7 @@ export type ImageChanges = components['schemas']['ImageRecordChanges'];
 export type ImageCategory = components['schemas']['ImageCategory'];
 export type ResourceOrigin = components['schemas']['ResourceOrigin'];
 export type ImageField = components['schemas']['ImageField'];
+export type ImageMetadata = components['schemas']['ImageMetadata'];
 export type OffsetPaginatedResults_BoardDTO_ =
   components['schemas']['OffsetPaginatedResults_BoardDTO_'];
 export type OffsetPaginatedResults_ImageDTO_ =
@@ -31,6 +32,7 @@ export type MainModelField = components['schemas']['MainModelField'];
 export type VAEModelField = components['schemas']['VAEModelField'];
 export type LoRAModelField = components['schemas']['LoRAModelField'];
 export type ModelsList = components['schemas']['ModelsList'];
+export type ControlField = components['schemas']['ControlField'];
 
 // Model Configs
 export type LoRAModelConfig = components['schemas']['LoRAModelConfig'];
@@ -107,6 +109,9 @@ export type MainModelLoaderInvocation = TypeReq<
 export type LoraLoaderInvocation = TypeReq<
   components['schemas']['LoraLoaderInvocation']
 >;
+export type MetadataAccumulatorInvocation = TypeReq<
+  components['schemas']['MetadataAccumulatorInvocation']
+>;
 
 // ControlNet Nodes
 export type ControlNetInvocation = TypeReq<

invokeai/frontend/web/yarn.lock

@@ -6409,6 +6409,11 @@ use-composed-ref@^1.3.0:
   resolved "https://registry.yarnpkg.com/use-composed-ref/-/use-composed-ref-1.3.0.tgz#3d8104db34b7b264030a9d916c5e94fbe280dbda"
   integrity sha512-GLMG0Jc/jiKov/3Ulid1wbv3r54K9HlMW29IWcDFPEqFkSO2nS0MuefWgMJpeHQ9YJeXDL3ZUF+P3jdXlZX/cQ==
 
+use-debounce@^9.0.4:
+  version "9.0.4"
+  resolved "https://registry.yarnpkg.com/use-debounce/-/use-debounce-9.0.4.tgz#51d25d856fbdfeb537553972ce3943b897f1ac85"
+  integrity sha512-6X8H/mikbrt0XE8e+JXRtZ8yYVvKkdYRfmIhWZYsP8rcNs9hk3APV8Ua2mFkKRLcJKVdnX2/Vwrmg2GWKUQEaQ==
+
 use-image@^1.1.1:
   version "1.1.1"
   resolved "https://registry.yarnpkg.com/use-image/-/use-image-1.1.1.tgz#bdd3f2e1718393ffc0e56136f993467103d9d2df"