lint fix

Currently translated at 94.6% (1431 of 1512 strings)

translationBot(ui): update translation (Russian)

Currently translated at 94.6% (1431 of 1512 strings)

Co-authored-by: Васянатор <ilabulanov339@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI

Makefile

@@ -10,10 +10,11 @@ help:
 	@echo "ruff-unsafe       Run ruff, fixing all fixable errors and formatting"
 	@echo "mypy              Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
 	@echo "mypy-all          Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
-	@echo "test"             Run the unit tests.
-	@echo "frontend-install" Install the pnpm modules needed for the front end
+	@echo "test              Run the unit tests."
+	@echo "frontend-install  Install the pnpm modules needed for the front end"
 	@echo "frontend-build    Build the frontend in order to run on localhost:9090"
 	@echo "frontend-dev      Run the frontend in developer mode on localhost:5173"
+	@echo "frontend-typegen  Generate types for the frontend from the OpenAPI schema"
 	@echo "installer-zip     Build the installer .zip file for the current version"
 	@echo "tag-release       Tag the GitHub repository with the current version (use at release time only!)"
 
@@ -53,6 +54,9 @@ frontend-build:
 frontend-dev:
 	cd invokeai/frontend/web && pnpm dev
 
+frontend-typegen:
+	cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
+
 # Installer zip file
 installer-zip:
 	cd installer && ./create_installer.sh

invokeai/app/api/dependencies.py

@@ -25,6 +25,7 @@ from ..services.invocation_cache.invocation_cache_memory import MemoryInvocation
 from ..services.invocation_services import InvocationServices
 from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
+from ..services.model_images.model_images_default import ModelImageFileStorageDisk
 from ..services.model_manager.model_manager_default import ModelManagerService
 from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
@@ -71,6 +72,8 @@ class ApiDependencies:
 
         image_files = DiskImageFileStorage(f"{output_folder}/images")
 
+        model_images_folder = config.models_path
+
         db = init_db(config=config, logger=logger, image_files=image_files)
 
         configuration = config
@@ -92,6 +95,7 @@ class ApiDependencies:
             ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
         )
         download_queue_service = DownloadQueueService(event_bus=events)
+        model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")
         model_manager = ModelManagerService.build_model_manager(
             app_config=configuration,
             model_record_service=ModelRecordServiceSQL(db=db),
@@ -118,6 +122,7 @@ class ApiDependencies:
             images=images,
             invocation_cache=invocation_cache,
             logger=logger,
+            model_images=model_images_service,
             model_manager=model_manager,
             download_queue=download_queue_service,
             names=names,

invokeai/app/api/routers/model_manager.py

@@ -1,12 +1,16 @@
 # Copyright (c) 2023 Lincoln D. Stein
 """FastAPI route for model configuration records."""
 
+import io
 import pathlib
 import shutil
+import traceback
 from typing import Any, Dict, List, Optional
 
-from fastapi import Body, Path, Query, Response
+from fastapi import Body, Path, Query, Response, UploadFile
+from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
+from PIL import Image
 from pydantic import BaseModel, ConfigDict, Field
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
@@ -31,6 +35,9 @@ from ..dependencies import ApiDependencies
 
 model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
 
+# images are immutable; set a high max-age
+IMAGE_MAX_AGE = 31536000
+
 
 class ModelsList(BaseModel):
     """Return list of configs."""
@@ -105,6 +112,9 @@ async def list_model_records(
         found_models.extend(
             record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
         )
+    for model in found_models:
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
+        model.cover_image = cover_image
     return ModelsList(models=found_models)
 
 
@@ -148,6 +158,8 @@ async def get_model_record(
     record_store = ApiDependencies.invoker.services.model_manager.store
     try:
         config: AnyModelConfig = record_store.get_model(key)
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
+        config.cover_image = cover_image
         return config
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
@@ -266,6 +278,75 @@ async def update_model_record(
     return model_response
 
 
+@model_manager_router.get(
+    "/i/{key}/image",
+    operation_id="get_model_image",
+    responses={
+        200: {
+            "description": "The model image was fetched successfully",
+        },
+        400: {"description": "Bad request"},
+        404: {"description": "The model image could not be found"},
+    },
+    status_code=200,
+)
+async def get_model_image(
+    key: str = Path(description="The name of model image file to get"),
+) -> FileResponse:
+    """Gets an image file that previews the model"""
+
+    try:
+        path = ApiDependencies.invoker.services.model_images.get_path(key)
+
+        response = FileResponse(
+            path,
+            media_type="image/png",
+            filename=key + ".png",
+            content_disposition_type="inline",
+        )
+        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
+        return response
+    except Exception:
+        raise HTTPException(status_code=404)
+
+
+@model_manager_router.patch(
+    "/i/{key}/image",
+    operation_id="update_model_image",
+    responses={
+        200: {
+            "description": "The model image was updated successfully",
+        },
+        400: {"description": "Bad request"},
+    },
+    status_code=200,
+)
+async def update_model_image(
+    key: Annotated[str, Path(description="Unique key of model")],
+    image: UploadFile,
+) -> None:
+    if not image.content_type or not image.content_type.startswith("image"):
+        raise HTTPException(status_code=415, detail="Not an image")
+
+    contents = await image.read()
+    try:
+        pil_image = Image.open(io.BytesIO(contents))
+
+    except Exception:
+        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
+        raise HTTPException(status_code=415, detail="Failed to read image")
+
+    logger = ApiDependencies.invoker.services.logger
+    model_images = ApiDependencies.invoker.services.model_images
+    try:
+        model_images.save(pil_image, key)
+        logger.info(f"Updated image for model: {key}")
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    return
+
+
 @model_manager_router.delete(
     "/i/{key}",
     operation_id="delete_model",
@@ -296,6 +377,29 @@ async def delete_model(
         raise HTTPException(status_code=404, detail=str(e))
 
 
+@model_manager_router.delete(
+    "/i/{key}/image",
+    operation_id="delete_model_image",
+    responses={
+        204: {"description": "Model image deleted successfully"},
+        404: {"description": "Model image not found"},
+    },
+    status_code=204,
+)
+async def delete_model_image(
+    key: str = Path(description="Unique key of model image to remove from model_images directory."),
+) -> None:
+    logger = ApiDependencies.invoker.services.logger
+    model_images = ApiDependencies.invoker.services.model_images
+    try:
+        model_images.delete(key)
+        logger.info(f"Deleted model image: {key}")
+        return
+    except UnknownModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=404, detail=str(e))
+
+
 # @model_manager_router.post(
 #     "/i/",
 #     operation_id="add_model_record",
@@ -539,7 +643,7 @@ async def convert_model(
         raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
 
     # loading the model will convert it into a cached diffusers file
-    model_manager.load_model_by_config(model_config, submodel_type=SubModelType.Scheduler)
+    model_manager.load.load_model(model_config, submodel_type=SubModelType.Scheduler)
 
     # Get the path of the converted model from the loader
     cache_path = loader.convert_cache.cache_path(key)

invokeai/app/api_app.py

@@ -2,12 +2,9 @@
 # which are imported/used before parse_args() is called will get the default config values instead of the
 # values from the command line or config file.
 import sys
-from contextlib import asynccontextmanager
 
-from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.version.invokeai_version import __version__
 
-from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 from .services.config import InvokeAIAppConfig
 
 app_config = InvokeAIAppConfig.get_config()
@@ -20,6 +17,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     import asyncio
     import mimetypes
     import socket
+    from contextlib import asynccontextmanager
     from inspect import signature
     from pathlib import Path
     from typing import Any
@@ -40,6 +38,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
     import invokeai.frontend.web as web_dir
+    from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 
     from ..backend.util.logging import InvokeAILogger
     from .api.dependencies import ApiDependencies
@@ -59,6 +58,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         BaseInvocation,
         UIConfigBase,
     )
+    from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 
     if is_mps_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)

invokeai/app/invocations/compel.py

@@ -20,7 +20,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 from invokeai.backend.util.devices import torch_dtype
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from .model import ClipField
+from .model import CLIPField
 
 # unconditioned: Optional[torch.Tensor]
 
@@ -46,7 +46,7 @@ class CompelInvocation(BaseInvocation):
         description=FieldDescriptions.compel_prompt,
         ui_component=UIComponent.Textarea,
     )
-    clip: ClipField = InputField(
+    clip: CLIPField = InputField(
         title="CLIP",
         description=FieldDescriptions.clip,
         input=Input.Connection,
@@ -54,16 +54,16 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
+        tokenizer_info = context.models.load(self.clip.tokenizer)
         tokenizer_model = tokenizer_info.model
         assert isinstance(tokenizer_model, CLIPTokenizer)
-        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
+        text_encoder_info = context.models.load(self.clip.text_encoder)
         text_encoder_model = text_encoder_info.model
         assert isinstance(text_encoder_model, CLIPTextModel)
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
-                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
+                lora_info = context.models.load(lora.lora)
                 assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
@@ -127,16 +127,16 @@ class SDXLPromptInvocationBase:
     def run_clip_compel(
         self,
         context: InvocationContext,
-        clip_field: ClipField,
+        clip_field: CLIPField,
         prompt: str,
         get_pooled: bool,
         lora_prefix: str,
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
-        tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
+        tokenizer_info = context.models.load(clip_field.tokenizer)
         tokenizer_model = tokenizer_info.model
         assert isinstance(tokenizer_model, CLIPTokenizer)
-        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
+        text_encoder_info = context.models.load(clip_field.text_encoder)
         text_encoder_model = text_encoder_info.model
         assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
 
@@ -163,7 +163,7 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in clip_field.loras:
-                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
+                lora_info = context.models.load(lora.lora)
                 lora_model = lora_info.model
                 assert isinstance(lora_model, LoRAModelRaw)
                 yield (lora_model, lora.weight)
@@ -253,8 +253,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = InputField(default=0, description="")
     target_width: int = InputField(default=1024, description="")
     target_height: int = InputField(default=1024, description="")
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
-    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
+    clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
+    clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -340,7 +340,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
     crop_top: int = InputField(default=0, description="")
     crop_left: int = InputField(default=0, description="")
     aesthetic_score: float = InputField(default=6.0, description=FieldDescriptions.sdxl_aesthetic)
-    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
+    clip2: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
@@ -370,10 +370,10 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
 
 @invocation_output("clip_skip_output")
-class ClipSkipInvocationOutput(BaseInvocationOutput):
-    """Clip skip node output"""
+class CLIPSkipInvocationOutput(BaseInvocationOutput):
+    """CLIP skip node output"""
 
-    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
 @invocation(
@@ -383,15 +383,15 @@ class ClipSkipInvocationOutput(BaseInvocationOutput):
     category="conditioning",
     version="1.0.0",
 )
-class ClipSkipInvocation(BaseInvocation):
+class CLIPSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""
 
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
+    clip: CLIPField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
     skipped_layers: int = InputField(default=0, ge=0, description=FieldDescriptions.skipped_layers)
 
-    def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
+    def invoke(self, context: InvocationContext) -> CLIPSkipInvocationOutput:
         self.clip.skipped_layers += self.skipped_layers
-        return ClipSkipInvocationOutput(
+        return CLIPSkipInvocationOutput(
             clip=self.clip,
         )
 

invokeai/app/invocations/controlnet_image_processors.py

@@ -34,6 +34,7 @@ from invokeai.app.invocations.fields import (
     WithBoard,
     WithMetadata,
 )
+from invokeai.app.invocations.model import ModelField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -51,15 +52,9 @@ CONTROLNET_RESIZE_VALUES = Literal[
 ]
 
 
-class ControlNetModelField(BaseModel):
-    """ControlNet model field"""
-
-    key: str = Field(description="Model config record key for the ControlNet model")
-
-
 class ControlField(BaseModel):
     image: ImageField = Field(description="The control image")
-    control_model: ControlNetModelField = Field(description="The ControlNet model to use")
+    control_model: ModelField = Field(description="The ControlNet model to use")
     control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
@@ -95,7 +90,7 @@ class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
-    control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
+    control_model: ModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
         default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
     )

invokeai/app/invocations/fields.py

@@ -228,7 +228,7 @@ class ConditioningField(BaseModel):
     # endregion
 
 
-class MetadataField(RootModel):
+class MetadataField(RootModel[dict[str, Any]]):
     """
     Pydantic model for metadata with custom root of type dict[str, Any].
     Metadata is stored without a strict schema.

invokeai/app/invocations/ip_adapter.py

@@ -11,25 +11,17 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
+from invokeai.app.invocations.model import ModelField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType, ModelType
-
-
-# LS: Consider moving these two classes into model.py
-class IPAdapterModelField(BaseModel):
-    key: str = Field(description="Key to the IP-Adapter model")
-
-
-class CLIPVisionModelField(BaseModel):
-    key: str = Field(description="Key to the CLIP Vision image encoder model")
+from invokeai.backend.model_manager.config import BaseModelType, IPAdapterConfig, ModelType
 
 
 class IPAdapterField(BaseModel):
     image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
-    image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
+    ip_adapter_model: ModelField = Field(description="The IP-Adapter model to use.")
+    image_encoder_model: ModelField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
@@ -62,7 +54,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: IPAdapterModelField = InputField(
+    ip_adapter_model: ModelField = InputField(
         description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
     )
 
@@ -90,18 +82,18 @@ class IPAdapterInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
         ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
+        assert isinstance(ip_adapter_info, IPAdapterConfig)
         image_encoder_model_id = ip_adapter_info.image_encoder_model_id
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         image_encoder_models = context.models.search_by_attrs(
             name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
         )
         assert len(image_encoder_models) == 1
-        image_encoder_model = CLIPVisionModelField(key=image_encoder_models[0].key)
         return IPAdapterOutput(
             ip_adapter=IPAdapterField(
                 image=self.image,
                 ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=image_encoder_model,
+                image_encoder_model=ModelField(key=image_encoder_models[0].key),
                 weight=self.weight,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,

invokeai/app/invocations/latent.py

@@ -26,6 +26,7 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 from PIL import Image, ImageFilter
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
+from transformers import CLIPVisionModelWithProjection
 
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
 from invokeai.app.invocations.fields import (
@@ -75,7 +76,7 @@ from .baseinvocation import (
     invocation_output,
 )
 from .controlnet_image_processors import ControlField
-from .model import ModelInfo, UNetField, VaeField
+from .model import ModelField, UNetField, VAEField
 
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
@@ -118,7 +119,7 @@ class SchedulerInvocation(BaseInvocation):
 class CreateDenoiseMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
 
-    vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
+    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
     image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
     mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
@@ -153,7 +154,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
         if image_tensor is not None:
-            vae_info = context.models.load(**self.vae.vae.model_dump())
+            vae_info = context.models.load(self.vae.vae)
 
             img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
@@ -244,12 +245,12 @@ class CreateGradientMaskInvocation(BaseInvocation):
 
 def get_scheduler(
     context: InvocationContext,
-    scheduler_info: ModelInfo,
+    scheduler_info: ModelField,
     scheduler_name: str,
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
+    orig_scheduler_info = context.models.load(scheduler_info)
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -461,7 +462,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         #        and if weight is None, populate with default 1.0?
         controlnet_data = []
         for control_info in control_list:
-            control_model = exit_stack.enter_context(context.models.load(key=control_info.control_model.key))
+            control_model = exit_stack.enter_context(context.models.load(control_info.control_model))
 
             # control_models.append(control_model)
             control_image_field = control_info.image
@@ -523,11 +524,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
         conditioning_data.ip_adapter_conditioning = []
         for single_ip_adapter in ip_adapter:
             ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-                context.models.load(key=single_ip_adapter.ip_adapter_model.key)
+                context.models.load(single_ip_adapter.ip_adapter_model)
             )
 
-            image_encoder_model_info = context.models.load(key=single_ip_adapter.image_encoder_model.key)
-
+            image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
             # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
             single_ipa_image_fields = single_ip_adapter.image
             if not isinstance(single_ipa_image_fields, list):
@@ -538,6 +538,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
             # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
             with image_encoder_model_info as image_encoder_model:
+                assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                 # Get image embeddings from CLIP and ImageProjModel.
                 image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
                     single_ipa_images, image_encoder_model
@@ -577,8 +578,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            t2i_adapter_model_config = context.models.get_config(key=t2i_adapter_field.t2i_adapter_model.key)
-            t2i_adapter_loaded_model = context.models.load(key=t2i_adapter_field.t2i_adapter_model.key)
+            t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
+            t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
             image = context.images.get_pil(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
@@ -731,12 +732,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
                 for lora in self.unet.loras:
-                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
+                    lora_info = context.models.load(lora.lora)
+                    assert isinstance(lora_info.model, LoRAModelRaw)
                     yield (lora_info.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.models.load(**self.unet.unet.model_dump())
+            unet_info = context.models.load(self.unet.unet)
             assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
                 ExitStack() as exit_stack,
@@ -830,7 +832,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.latents,
         input=Input.Connection,
     )
-    vae: VaeField = InputField(
+    vae: VAEField = InputField(
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
@@ -841,8 +843,8 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        vae_info = context.models.load(**self.vae.vae.model_dump())
-
+        vae_info = context.models.load(self.vae.vae)
+        assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL))
         with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             assert isinstance(vae, torch.nn.Module)
             latents = latents.to(vae.device)
@@ -1008,7 +1010,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     image: ImageField = InputField(
         description="The image to encode",
     )
-    vae: VaeField = InputField(
+    vae: VAEField = InputField(
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
@@ -1064,7 +1066,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.models.load(**self.vae.vae.model_dump())
+        vae_info = context.models.load(self.vae.vae)
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:

invokeai/app/invocations/metadata.py

@@ -8,7 +8,10 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.controlnet_image_processors import ControlField
+from invokeai.app.invocations.controlnet_image_processors import (
+    CONTROLNET_MODE_VALUES,
+    CONTROLNET_RESIZE_VALUES,
+)
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -17,10 +20,8 @@ from invokeai.app.invocations.fields import (
     OutputField,
     UIType,
 )
-from invokeai.app.invocations.ip_adapter import IPAdapterModelField
-from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
-from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
 
 from ...version import __version__
 
@@ -30,10 +31,20 @@ class MetadataItemField(BaseModel):
     value: Any = Field(description=FieldDescriptions.metadata_item_value)
 
 
+class ModelMetadataField(BaseModel):
+    """Model Metadata Field"""
+
+    key: str
+    hash: str
+    name: str
+    base: BaseModelType
+    type: ModelType
+
+
 class LoRAMetadataField(BaseModel):
     """LoRA Metadata Field"""
 
-    model: LoRAModelField = Field(description=FieldDescriptions.lora_model)
+    model: ModelMetadataField = Field(description=FieldDescriptions.lora_model)
     weight: float = Field(description=FieldDescriptions.lora_weight)
 
 
@@ -41,7 +52,7 @@ class IPAdapterMetadataField(BaseModel):
     """IP Adapter Field, minus the CLIP Vision Encoder model"""
 
     image: ImageField = Field(description="The IP-Adapter image prompt.")
-    ip_adapter_model: IPAdapterModelField = Field(
+    ip_adapter_model: ModelMetadataField = Field(
         description="The IP-Adapter model.",
     )
     weight: Union[float, list[float]] = Field(
@@ -51,6 +62,33 @@ class IPAdapterMetadataField(BaseModel):
     end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
 
 
+class T2IAdapterMetadataField(BaseModel):
+    image: ImageField = Field(description="The T2I-Adapter image prompt.")
+    t2i_adapter_model: ModelMetadataField = Field(description="The T2I-Adapter model to use.")
+    weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
+class ControlNetMetadataField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    control_model: ModelMetadataField = Field(description="The ControlNet model to use")
+    control_weight: Union[float, list[float]] = Field(default=1, description="The weight given to the ControlNet")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
 @invocation_output("metadata_item_output")
 class MetadataItemOutput(BaseInvocationOutput):
     """Metadata Item Output"""
@@ -140,14 +178,14 @@ class CoreMetadataInvocation(BaseInvocation):
         default=None,
         description="The number of skipped CLIP layers",
     )
-    model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
-    controlnets: Optional[list[ControlField]] = InputField(
+    model: Optional[ModelMetadataField] = InputField(default=None, description="The main model used for inference")
+    controlnets: Optional[list[ControlNetMetadataField]] = InputField(
         default=None, description="The ControlNets used for inference"
     )
     ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
         default=None, description="The IP Adapters used for inference"
     )
-    t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
+    t2iAdapters: Optional[list[T2IAdapterMetadataField]] = InputField(
         default=None, description="The IP Adapters used for inference"
     )
     loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
@@ -159,7 +197,7 @@ class CoreMetadataInvocation(BaseInvocation):
         default=None,
         description="The name of the initial image",
     )
-    vae: Optional[VAEModelField] = InputField(
+    vae: Optional[ModelMetadataField] = InputField(
         default=None,
         description="The VAE used for decoding, if the main model's default was not used",
     )
@@ -190,7 +228,7 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # SDXL Refiner
-    refiner_model: Optional[MainModelField] = InputField(
+    refiner_model: Optional[ModelMetadataField] = InputField(
         default=None,
         description="The SDXL Refiner model used",
     )
@@ -222,10 +260,9 @@ class CoreMetadataInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> MetadataOutput:
         """Collects and outputs a CoreMetadata object"""
 
-        return MetadataOutput(
-            metadata=MetadataField.model_validate(
-                self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
-            )
-        )
+        as_dict = self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
+        as_dict["app_version"] = __version__
+
+        return MetadataOutput(metadata=MetadataField.model_validate(as_dict))
 
     model_config = ConfigDict(extra="allow")

invokeai/app/invocations/model.py

@@ -6,8 +6,8 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
+from invokeai.backend.model_manager.config import SubModelType
 
-from ...backend.model_manager import SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -16,33 +16,33 @@ from .baseinvocation import (
 )
 
 
-class ModelInfo(BaseModel):
-    key: str = Field(description="Key of model as returned by ModelRecordServiceBase.get_model()")
-    submodel_type: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
+class ModelField(BaseModel):
+    key: str = Field(description="Key of the model")
+    submodel_type: Optional[SubModelType] = Field(description="Submodel type", default=None)
 
 
-class LoraInfo(ModelInfo):
-    weight: float = Field(description="Lora's weight which to use when apply to model")
+class LoRAField(BaseModel):
+    lora: ModelField = Field(description="Info to load lora model")
+    weight: float = Field(description="Weight to apply to lora model")
 
 
 class UNetField(BaseModel):
-    unet: ModelInfo = Field(description="Info to load unet submodel")
-    scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
-    loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
+    unet: ModelField = Field(description="Info to load unet submodel")
+    scheduler: ModelField = Field(description="Info to load scheduler submodel")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
     freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
 
 
-class ClipField(BaseModel):
-    tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
-    text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
+class CLIPField(BaseModel):
+    tokenizer: ModelField = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelField = Field(description="Info to load text_encoder submodel")
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
-    loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
-class VaeField(BaseModel):
-    # TODO: better naming?
-    vae: ModelInfo = Field(description="Info to load vae submodel")
+class VAEField(BaseModel):
+    vae: ModelField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
@@ -57,14 +57,14 @@ class UNetOutput(BaseInvocationOutput):
 class VAEOutput(BaseInvocationOutput):
     """Base class for invocations that output a VAE field"""
 
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation_output("clip_output")
 class CLIPOutput(BaseInvocationOutput):
     """Base class for invocations that output a CLIP field"""
 
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
 
 
 @invocation_output("model_loader_output")
@@ -74,18 +74,6 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
     pass
 
 
-class MainModelField(BaseModel):
-    """Main model field"""
-
-    key: str = Field(description="Model key")
-
-
-class LoRAModelField(BaseModel):
-    """LoRA model field"""
-
-    key: str = Field(description="LoRA model key")
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",
@@ -96,62 +84,40 @@ class LoRAModelField(BaseModel):
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
+    model: ModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
-        key = self.model.key
-
         # TODO: not found exceptions
-        if not context.models.exists(key):
-            raise Exception(f"Unknown model {key}")
+        if not context.models.exists(self.model.key):
+            raise Exception(f"Unknown model {self.model.key}")
+
+        unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
+        scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
 
         return ModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.TextEncoder,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae=VaeField(
-                vae=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.VAE,
-                ),
-            ),
+            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
+            vae=VAEField(vae=vae),
         )
 
 
 @invocation_output("lora_loader_output")
-class LoraLoaderOutput(BaseInvocationOutput):
+class LoRALoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
     unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
 @invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
-class LoraLoaderInvocation(BaseInvocation):
+class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
+    lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -159,46 +125,41 @@ class LoraLoaderInvocation(BaseInvocation):
         input=Input.Connection,
         title="UNet",
     )
-    clip: Optional[ClipField] = InputField(
+    clip: Optional[CLIPField] = InputField(
         default=None,
         description=FieldDescriptions.clip,
         input=Input.Connection,
         title="CLIP",
     )
 
-    def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
-        if self.lora is None:
-            raise Exception("No LoRA provided")
-
+    def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
             raise Exception(f"Unkown lora: {lora_key}!")
 
-        if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to unet')
+        if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to unet')
 
-        if self.clip is not None and any(lora.key == lora_key for lora in self.clip.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to clip')
+        if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to clip')
 
-        output = LoraLoaderOutput()
+        output = LoRALoaderOutput()
 
         if self.unet is not None:
-            output.unet = copy.deepcopy(self.unet)
+            output.unet = self.unet.model_copy(deep=True)
             output.unet.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
 
         if self.clip is not None:
-            output.clip = copy.deepcopy(self.clip)
+            output.clip = self.clip.model_copy(deep=True)
             output.clip.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
@@ -207,12 +168,12 @@ class LoraLoaderInvocation(BaseInvocation):
 
 
 @invocation_output("sdxl_lora_loader_output")
-class SDXLLoraLoaderOutput(BaseInvocationOutput):
+class SDXLLoRALoaderOutput(BaseInvocationOutput):
     """SDXL LoRA Loader Output"""
 
     unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
-    clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
+    clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
+    clip2: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
 
 
 @invocation(
@@ -222,10 +183,10 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
     category="model",
     version="1.0.1",
 )
-class SDXLLoraLoaderInvocation(BaseInvocation):
+class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
+    lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -233,65 +194,59 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         input=Input.Connection,
         title="UNet",
     )
-    clip: Optional[ClipField] = InputField(
+    clip: Optional[CLIPField] = InputField(
         default=None,
         description=FieldDescriptions.clip,
         input=Input.Connection,
         title="CLIP 1",
     )
-    clip2: Optional[ClipField] = InputField(
+    clip2: Optional[CLIPField] = InputField(
         default=None,
         description=FieldDescriptions.clip,
         input=Input.Connection,
         title="CLIP 2",
     )
 
-    def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
-        if self.lora is None:
-            raise Exception("No LoRA provided")
-
+    def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
             raise Exception(f"Unknown lora: {lora_key}!")
 
-        if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to unet')
+        if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to unet')
 
-        if self.clip is not None and any(lora.key == lora_key for lora in self.clip.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to clip')
+        if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to clip')
 
-        if self.clip2 is not None and any(lora.key == lora_key for lora in self.clip2.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to clip2')
+        if self.clip2 is not None and any(lora.lora.key == lora_key for lora in self.clip2.loras):
+            raise Exception(f'LoRA "{lora_key}" already applied to clip2')
 
-        output = SDXLLoraLoaderOutput()
+        output = SDXLLoRALoaderOutput()
 
         if self.unet is not None:
-            output.unet = copy.deepcopy(self.unet)
+            output.unet = self.unet.model_copy(deep=True)
             output.unet.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
 
         if self.clip is not None:
-            output.clip = copy.deepcopy(self.clip)
+            output.clip = self.clip.model_copy(deep=True)
             output.clip.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
 
         if self.clip2 is not None:
-            output.clip2 = copy.deepcopy(self.clip2)
+            output.clip2 = self.clip2.model_copy(deep=True)
             output.clip2.loras.append(
-                LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                LoRAField(
+                    lora=self.lora,
                     weight=self.weight,
                 )
             )
@@ -299,17 +254,11 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         return output
 
 
-class VAEModelField(BaseModel):
-    """Vae model field"""
-
-    key: str = Field(description="Model's key")
-
-
 @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
-class VaeLoaderInvocation(BaseInvocation):
+class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
-    vae_model: VAEModelField = InputField(
+    vae_model: ModelField = InputField(
         description=FieldDescriptions.vae_model,
         input=Input.Direct,
         title="VAE",
@@ -321,7 +270,7 @@ class VaeLoaderInvocation(BaseInvocation):
         if not context.models.exists(key):
             raise Exception(f"Unkown vae: {key}!")
 
-        return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))
+        return VAEOutput(vae=VAEField(vae=self.vae_model))
 
 
 @invocation_output("seamless_output")
@@ -329,7 +278,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
     """Modified Seamless Model output"""
 
     unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
-    vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
+    vae: Optional[VAEField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation(
@@ -348,7 +297,7 @@ class SeamlessModeInvocation(BaseInvocation):
         input=Input.Connection,
         title="UNet",
     )
-    vae: Optional[VaeField] = InputField(
+    vae: Optional[VAEField] = InputField(
         default=None,
         description=FieldDescriptions.vae_model,
         input=Input.Connection,

invokeai/app/invocations/sdxl.py

@@ -8,7 +8,7 @@ from .baseinvocation import (
     invocation,
     invocation_output,
 )
-from .model import ClipField, MainModelField, ModelInfo, UNetField, VaeField
+from .model import CLIPField, ModelField, UNetField, VAEField
 
 
 @invocation_output("sdxl_model_loader_output")
@@ -16,9 +16,9 @@ class SDXLModelLoaderOutput(BaseInvocationOutput):
     """SDXL base model loader output"""
 
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
-    clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
+    clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation_output("sdxl_refiner_model_loader_output")
@@ -26,15 +26,15 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     """SDXL refiner model loader output"""
 
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-    clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 @invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
-    model: MainModelField = InputField(
+    model: ModelField = InputField(
         description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
@@ -46,48 +46,19 @@ class SDXLModelLoaderInvocation(BaseInvocation):
         if not context.models.exists(model_key):
             raise Exception(f"Unknown model: {model_key}")
 
+        unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
+        scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        tokenizer2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        text_encoder2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+
         return SDXLModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip=ClipField(
-                tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer,
-                ),
-                text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            clip2=ClipField(
-                tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer2,
-                ),
-                text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder2,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae=VaeField(
-                vae=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.VAE,
-                ),
-            ),
+            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
+            clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
+            vae=VAEField(vae=vae),
         )
 
 
@@ -101,10 +72,8 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
-    model: MainModelField = InputField(
-        description=FieldDescriptions.sdxl_refiner_model,
-        input=Input.Direct,
-        ui_type=UIType.SDXLRefinerModel,
+    model: ModelField = InputField(
+        description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
     )
     # TODO: precision?
 
@@ -115,34 +84,14 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
         if not context.models.exists(model_key):
             raise Exception(f"Unknown model: {model_key}")
 
+        unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
+        scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
+        tokenizer2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        text_encoder2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+
         return SDXLRefinerModelLoaderOutput(
-            unet=UNetField(
-                unet=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.UNet,
-                ),
-                scheduler=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Scheduler,
-                ),
-                loras=[],
-            ),
-            clip2=ClipField(
-                tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer2,
-                ),
-                text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder2,
-                ),
-                loras=[],
-                skipped_layers=0,
-            ),
-            vae=VaeField(
-                vae=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.VAE,
-                ),
-            ),
+            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
+            clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
+            vae=VAEField(vae=vae),
         )

invokeai/app/invocations/t2i_adapter.py

@@ -10,17 +10,14 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
+from invokeai.app.invocations.model import ModelField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
 
 
-class T2IAdapterModelField(BaseModel):
-    key: str = Field(description="Model record key for the T2I-Adapter model")
-
-
 class T2IAdapterField(BaseModel):
     image: ImageField = Field(description="The T2I-Adapter image prompt.")
-    t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
+    t2i_adapter_model: ModelField = Field(description="The T2I-Adapter model to use.")
     weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
@@ -55,7 +52,7 @@ class T2IAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: ImageField = InputField(description="The IP-Adapter image prompt.")
-    t2i_adapter_model: T2IAdapterModelField = InputField(
+    t2i_adapter_model: ModelField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
         input=Input.Direct,

invokeai/app/services/invocation_cache/invocation_cache_base.py

@@ -41,8 +41,9 @@ class InvocationCacheBase(ABC):
         """Clears the cache"""
         pass
 
+    @staticmethod
     @abstractmethod
-    def create_key(self, invocation: BaseInvocation) -> int:
+    def create_key(invocation: BaseInvocation) -> int:
         """Gets the key for the invocation's cache item"""
         pass
 

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -61,9 +61,7 @@ class MemoryInvocationCache(InvocationCacheBase):
             self._delete_oldest_access(number_to_delete)
             self._cache[key] = CachedItem(
                 invocation_output,
-                invocation_output.model_dump_json(
-                    warnings=False, exclude_defaults=True, exclude_unset=True, include={"type"}
-                ),
+                invocation_output.model_dump_json(warnings=False, exclude_defaults=True, exclude_unset=True),
             )
 
     def _delete_oldest_access(self, number_to_delete: int) -> None:
@@ -81,7 +79,7 @@ class MemoryInvocationCache(InvocationCacheBase):
         with self._lock:
             return self._delete(key)
 
-    def clear(self, *args, **kwargs) -> None:
+    def clear(self) -> None:
         with self._lock:
             if self._max_cache_size == 0:
                 return

invokeai/app/services/invocation_services.py

@@ -25,6 +25,7 @@ if TYPE_CHECKING:
     from .images.images_base import ImageServiceABC
     from .invocation_cache.invocation_cache_base import InvocationCacheBase
     from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
+    from .model_images.model_images_base import ModelImageFileStorageBase
     from .model_manager.model_manager_base import ModelManagerServiceBase
     from .names.names_base import NameServiceBase
     from .session_processor.session_processor_base import SessionProcessorBase
@@ -49,6 +50,7 @@ class InvocationServices:
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
         logger: "Logger",
+        model_images: "ModelImageFileStorageBase",
         model_manager: "ModelManagerServiceBase",
         download_queue: "DownloadQueueServiceBase",
         performance_statistics: "InvocationStatsServiceBase",
@@ -72,6 +74,7 @@ class InvocationServices:
         self.image_files = image_files
         self.image_records = image_records
         self.logger = logger
+        self.model_images = model_images
         self.model_manager = model_manager
         self.download_queue = download_queue
         self.performance_statistics = performance_statistics

invokeai/app/services/model_images/model_images_base.py

@@ -0,0 +1,33 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from PIL.Image import Image as PILImageType
+
+
+class ModelImageFileStorageBase(ABC):
+    """Low-level service responsible for storing and retrieving image files."""
+
+    @abstractmethod
+    def get(self, model_key: str) -> PILImageType:
+        """Retrieves a model image as PIL Image."""
+        pass
+
+    @abstractmethod
+    def get_path(self, model_key: str) -> Path:
+        """Gets the internal path to a model image."""
+        pass
+
+    @abstractmethod
+    def get_url(self, model_key: str) -> str | None:
+        """Gets the URL to fetch a model image."""
+        pass
+
+    @abstractmethod
+    def save(self, image: PILImageType, model_key: str) -> None:
+        """Saves a model image."""
+        pass
+
+    @abstractmethod
+    def delete(self, model_key: str) -> None:
+        """Deletes a model image."""
+        pass

invokeai/app/services/model_images/model_images_common.py

@@ -0,0 +1,20 @@
+# TODO: Should these excpetions subclass existing python exceptions?
+class ModelImageFileNotFoundException(Exception):
+    """Raised when an image file is not found in storage."""
+
+    def __init__(self, message="Model image file not found"):
+        super().__init__(message)
+
+
+class ModelImageFileSaveException(Exception):
+    """Raised when an image cannot be saved."""
+
+    def __init__(self, message="Model image file not saved"):
+        super().__init__(message)
+
+
+class ModelImageFileDeleteException(Exception):
+    """Raised when an image cannot be deleted."""
+
+    def __init__(self, message="Model image file not deleted"):
+        super().__init__(message)

invokeai/app/services/model_images/model_images_default.py

@@ -0,0 +1,79 @@
+from pathlib import Path
+
+from PIL import Image
+from PIL.Image import Image as PILImageType
+from send2trash import send2trash
+
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.util.thumbnails import make_thumbnail
+
+from .model_images_base import ModelImageFileStorageBase
+from .model_images_common import (
+    ModelImageFileDeleteException,
+    ModelImageFileNotFoundException,
+    ModelImageFileSaveException,
+)
+
+
+class ModelImageFileStorageDisk(ModelImageFileStorageBase):
+    """Stores images on disk"""
+
+    def __init__(self, model_images_folder: Path):
+        self._model_images_folder = model_images_folder
+        self._validate_storage_folders()
+
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+
+    def get(self, model_key: str) -> PILImageType:
+        try:
+            path = self.get_path(model_key)
+
+            if not self._validate_path(path):
+                raise ModelImageFileNotFoundException
+
+            return Image.open(path)
+        except FileNotFoundError as e:
+            raise ModelImageFileNotFoundException from e
+
+    def save(self, image: PILImageType, model_key: str) -> None:
+        try:
+            self._validate_storage_folders()
+            image_path = self._model_images_folder / (model_key + ".webp")
+            thumbnail = make_thumbnail(image, 256)
+            thumbnail.save(image_path, format="webp")
+
+        except Exception as e:
+            raise ModelImageFileSaveException from e
+
+    def get_path(self, model_key: str) -> Path:
+        path = self._model_images_folder / (model_key + ".webp")
+
+        return path
+
+    def get_url(self, model_key: str) -> str | None:
+        path = self.get_path(model_key)
+        if not self._validate_path(path):
+            return
+
+        return self._invoker.services.urls.get_model_image_url(model_key)
+
+    def delete(self, model_key: str) -> None:
+        try:
+            path = self.get_path(model_key)
+
+            if not self._validate_path(path):
+                raise ModelImageFileNotFoundException
+
+            send2trash(path)
+
+        except Exception as e:
+            raise ModelImageFileDeleteException from e
+
+    def _validate_path(self, path: Path) -> bool:
+        """Validates the path given for an image."""
+        return path.exists()
+
+    def _validate_storage_folders(self) -> None:
+        """Checks if the required folders exist and create them if they don't"""
+        self._model_images_folder.mkdir(parents=True, exist_ok=True)

invokeai/app/services/model_manager/model_manager_base.py

@@ -1,15 +1,11 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 
 from abc import ABC, abstractmethod
-from typing import Optional
 
 import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.invocation_context import InvocationContextData
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType
-from invokeai.backend.model_manager.load.load_base import LoadedModel
 
 from ..config import InvokeAIAppConfig
 from ..download import DownloadQueueServiceBase
@@ -70,32 +66,3 @@ class ModelManagerServiceBase(ABC):
     @abstractmethod
     def stop(self, invoker: Invoker) -> None:
         pass
-
-    @abstractmethod
-    def load_model_by_config(
-        self,
-        model_config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        pass
-
-    @abstractmethod
-    def load_model_by_key(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        pass
-
-    @abstractmethod
-    def load_model_by_attr(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        pass

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,14 +1,10 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
-from typing import Optional
-
 import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.invocation_context import InvocationContextData
-from invokeai.backend.model_manager import AnyModelConfig, BaseModelType, LoadedModel, ModelType, SubModelType
 from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
 from invokeai.backend.util.devices import choose_torch_device
 from invokeai.backend.util.logging import InvokeAILogger
@@ -18,7 +14,7 @@ from ..download import DownloadQueueServiceBase
 from ..events.events_base import EventServiceBase
 from ..model_install import ModelInstallService, ModelInstallServiceBase
 from ..model_load import ModelLoadService, ModelLoadServiceBase
-from ..model_records import ModelRecordServiceBase, UnknownModelException
+from ..model_records import ModelRecordServiceBase
 from .model_manager_base import ModelManagerServiceBase
 
 
@@ -64,56 +60,6 @@ class ModelManagerService(ModelManagerServiceBase):
             if hasattr(service, "stop"):
                 service.stop(invoker)
 
-    def load_model_by_config(
-        self,
-        model_config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        return self.load.load_model(model_config, submodel_type, context_data)
-
-    def load_model_by_key(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        config = self.store.get_model(key)
-        return self.load.load_model(config, submodel_type, context_data)
-
-    def load_model_by_attr(
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        """
-        Given a model's attributes, search the database for it, and if found, load and return the LoadedModel object.
-
-        This is provided for API compatability with the get_model() method
-        in the original model manager. However, note that LoadedModel is
-        not the same as the original ModelInfo that ws returned.
-
-        :param model_name: Name of to be fetched.
-        :param base_model: Base model
-        :param model_type: Type of the model
-        :param submodel: For main (pipeline models), the submodel to fetch
-        :param context: The invocation context.
-
-        Exceptions: UnknownModelException -- model with this key not known
-                    NotImplementedException -- a model loader was not provided at initialization time
-                    ValueError -- more than one model matches this combination
-        """
-        configs = self.store.search_by_attr(model_name, base_model, model_type)
-        if len(configs) == 0:
-            raise UnknownModelException(f"{base_model}/{model_type}/{model_name}: Unknown model")
-        elif len(configs) > 1:
-            raise ValueError(f"{base_model}/{model_type}/{model_name}: More than one model matches.")
-        else:
-            return self.load.load_model(configs[0], submodel, context_data)
-
     @classmethod
     def build_model_manager(
         cls,

invokeai/app/services/model_records/model_records_base.py

@@ -79,6 +79,7 @@ class ModelRecordChanges(BaseModelExcludeNull):
         description="The prediction type of the model.", default=None
     )
     upcast_attention: Optional[bool] = Field(description="Whether to upcast attention.", default=None)
+    config_path: Optional[str] = Field(description="Path to config file for model", default=None)
 
 
 class ModelRecordServiceBase(ABC):
@@ -129,6 +130,17 @@ class ModelRecordServiceBase(ABC):
         """
         pass
 
+    @abstractmethod
+    def get_model_by_hash(self, hash: str) -> AnyModelConfig:
+        """
+        Retrieve the configuration for the indicated model.
+
+        :param hash: Hash of model config to be fetched.
+
+        Exceptions: UnknownModelException
+        """
+        pass
+
     @abstractmethod
     def list_models(
         self, page: int = 0, per_page: int = 10, order_by: ModelRecordOrderBy = ModelRecordOrderBy.Default

invokeai/app/services/model_records/model_records_sql.py

@@ -203,6 +203,21 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
         return model
 
+    def get_model_by_hash(self, hash: str) -> AnyModelConfig:
+        with self._db.lock:
+            self._cursor.execute(
+                """--sql
+                SELECT config, strftime('%s',updated_at) FROM models
+                WHERE hash=?;
+                """,
+                (hash,),
+            )
+            rows = self._cursor.fetchone()
+            if not rows:
+                raise UnknownModelException("model not found")
+            model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
+        return model
+
     def exists(self, key: str) -> bool:
         """
         Return True if a model with the indicated key exists in the databse.

invokeai/app/services/shared/invocation_context.py

@@ -1,7 +1,7 @@
 import threading
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Optional, Union
 
 from PIL.Image import Image
 from torch import Tensor
@@ -13,15 +13,16 @@ from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
+from invokeai.app.services.model_records.model_records_base import UnknownModelException
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.model_manager.load.load_base import LoadedModel
-from invokeai.backend.model_manager.metadata.metadata_base import AnyModelRepoMetadata
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 
 if TYPE_CHECKING:
     from invokeai.app.invocations.baseinvocation import BaseInvocation
+    from invokeai.app.invocations.model import ModelField
     from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
 
 """
@@ -299,22 +300,25 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
-    def exists(self, key: str) -> bool:
+    def exists(self, identifier: Union[str, "ModelField"]) -> bool:
         """Checks if a model exists.
 
         Args:
-            key: The key of the model.
+            identifier: The key or ModelField representing the model.
 
         Returns:
             True if the model exists, False if not.
         """
-        return self._services.model_manager.store.exists(key)
+        if isinstance(identifier, str):
+            return self._services.model_manager.store.exists(identifier)
 
-    def load(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+        return self._services.model_manager.store.exists(identifier.key)
+
+    def load(self, identifier: Union[str, "ModelField"], submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """Loads a model.
 
         Args:
-            key: The key of the model.
+            identifier: The key or ModelField representing the model.
             submodel_type: The submodel of the model to get.
 
         Returns:
@@ -324,9 +328,13 @@ class ModelsInterface(InvocationContextInterface):
         # The model manager emits events as it loads the model. It needs the context data to build
         # the event payloads.
 
-        return self._services.model_manager.load_model_by_key(
-            key=key, submodel_type=submodel_type, context_data=self._data
-        )
+        if isinstance(identifier, str):
+            model = self._services.model_manager.store.get_model(identifier)
+            return self._services.model_manager.load.load_model(model, submodel_type, self._data)
+        else:
+            _submodel_type = submodel_type or identifier.submodel_type
+            model = self._services.model_manager.store.get_model(identifier.key)
+            return self._services.model_manager.load.load_model(model, _submodel_type, self._data)
 
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
@@ -343,35 +351,29 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             An object representing the loaded model.
         """
-        return self._services.model_manager.load_model_by_attr(
-            model_name=name,
-            base_model=base,
-            model_type=type,
-            submodel=submodel_type,
-            context_data=self._data,
-        )
 
-    def get_config(self, key: str) -> AnyModelConfig:
+        configs = self._services.model_manager.store.search_by_attr(model_name=name, base_model=base, model_type=type)
+        if len(configs) == 0:
+            raise UnknownModelException(f"No model found with name {name}, base {base}, and type {type}")
+
+        if len(configs) > 1:
+            raise ValueError(f"More than one model found with name {name}, base {base}, and type {type}")
+
+        return self._services.model_manager.load.load_model(configs[0], submodel_type, self._data)
+
+    def get_config(self, identifier: Union[str, "ModelField"]) -> AnyModelConfig:
         """Gets a model's config.
 
         Args:
-            key: The key of the model.
+            identifier: The key or ModelField representing the model.
 
         Returns:
             The model's config.
         """
-        return self._services.model_manager.store.get_model(key=key)
+        if isinstance(identifier, str):
+            return self._services.model_manager.store.get_model(identifier)
 
-    def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
-        """Gets a model's metadata, if it has any.
-
-        Args:
-            key: The key of the model.
-
-        Returns:
-            The model's metadata, if it has any.
-        """
-        return self._services.model_manager.store.get_metadata(key=key)
+        return self._services.model_manager.store.get_model(identifier.key)
 
     def search_by_path(self, path: Path) -> list[AnyModelConfig]:
         """Searches for models by path.

invokeai/app/services/urls/urls_base.py

@@ -8,3 +8,8 @@ class UrlServiceBase(ABC):
     def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:
         """Gets the URL for an image or thumbnail."""
         pass
+
+    @abstractmethod
+    def get_model_image_url(self, model_key: str) -> str:
+        """Gets the URL for a model image"""
+        pass

invokeai/app/services/urls/urls_default.py

@@ -4,8 +4,9 @@ from .urls_base import UrlServiceBase
 
 
 class LocalUrlService(UrlServiceBase):
-    def __init__(self, base_url: str = "api/v1"):
+    def __init__(self, base_url: str = "api/v1", base_url_v2: str = "api/v2"):
         self._base_url = base_url
+        self._base_url_v2 = base_url_v2
 
     def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:
         image_basename = os.path.basename(image_name)
@@ -15,3 +16,6 @@ class LocalUrlService(UrlServiceBase):
             return f"{self._base_url}/images/i/{image_basename}/thumbnail"
 
         return f"{self._base_url}/images/i/{image_basename}/full"
+
+    def get_model_image_url(self, model_key: str) -> str:
+        return f"{self._base_url_v2}/models/i/{model_key}/image"

invokeai/app/util/ti_utils.py

@@ -22,7 +22,7 @@ def generate_ti_list(
     for trigger in extract_ti_triggers_from_prompt(prompt):
         name_or_key = trigger[1:-1]
         try:
-            loaded_model = context.models.load(key=name_or_key)
+            loaded_model = context.models.load(name_or_key)
             model = loaded_model.model
             assert isinstance(model, TextualInversionModelRaw)
             assert loaded_model.config.base == base

invokeai/backend/install/install_helper.py

@@ -19,7 +19,6 @@ from invokeai.app.services.model_install import (
     ModelInstallService,
     ModelInstallServiceBase,
 )
-from invokeai.app.services.model_metadata import ModelMetadataStoreSQL
 from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.backend.model_manager import (
@@ -39,7 +38,7 @@ def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordService
     logger = InvokeAILogger.get_logger(config=app_config)
     image_files = DiskImageFileStorage(f"{app_config.output_path}/images")
     db = init_db(config=app_config, logger=logger, image_files=image_files)
-    obj: ModelRecordServiceBase = ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
+    obj: ModelRecordServiceBase = ModelRecordServiceSQL(db)
     return obj
 
 

invokeai/backend/model_manager/config.py

@@ -161,6 +161,7 @@ class ModelConfigBase(BaseModel):
     default_settings: Optional[ModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
+    cover_image: Optional[str] = Field(description="Url for image to preview model", default=None)
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
@@ -310,7 +311,7 @@ class IPAdapterConfig(ModelConfigBase):
 
 
 class CLIPVisionDiffusersConfig(ModelConfigBase):
-    """Model config for ClipVision."""
+    """Model config for CLIPVision."""
 
     type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
     format: Literal[ModelFormat.Diffusers]

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -24,7 +24,7 @@ from .. import ModelLoader, ModelLoaderRegistry
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
-class LoraLoader(ModelLoader):
+class LoRALoader(ModelLoader):
     """Class to load LoRA models."""
 
     # We cheat a little bit to get access to the model base

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -23,7 +23,7 @@ from .generic_diffusers import GenericDiffusersLoader
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.VAE, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.VAE, format=ModelFormat.Checkpoint)
-class VaeLoader(GenericDiffusersLoader):
+class VAELoader(GenericDiffusersLoader):
     """Class to load VAE models."""
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:

invokeai/backend/util/test_utils.py

@@ -42,9 +42,10 @@ def install_and_load_model(
     # If the requested model is already installed, return its LoadedModel
     with contextlib.suppress(UnknownModelException):
         # TODO: Replace with wrapper call
-        loaded_model: LoadedModel = model_manager.load_model_by_attr(
+        configs = model_manager.store.search_by_attr(
             model_name=model_name, base_model=base_model, model_type=model_type
         )
+        loaded_model: LoadedModel = model_manager.load.load_model(configs[0])
         return loaded_model
 
     # Install the requested model.
@@ -53,7 +54,7 @@ def install_and_load_model(
     assert job.complete
 
     try:
-        loaded_model = model_manager.load_model_by_config(job.config_out)
+        loaded_model = model_manager.load.load_model(job.config_out)
         return loaded_model
     except UnknownModelException as e:
         raise Exception(

invokeai/frontend/merge/merge_diffusers.py

@@ -20,7 +20,6 @@ from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.download import DownloadQueueService
 from invokeai.app.services.image_files.image_files_disk import DiskImageFileStorage
 from invokeai.app.services.model_install import ModelInstallService
-from invokeai.app.services.model_metadata import ModelMetadataStoreSQL
 from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.backend.model_manager import (
@@ -413,7 +412,7 @@ def get_config_store() -> ModelRecordServiceSQL:
     assert output_path is not None
     image_files = DiskImageFileStorage(output_path / "images")
     db = init_db(config=config, logger=InvokeAILogger.get_logger(), image_files=image_files)
-    return ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
+    return ModelRecordServiceSQL(db)
 
 
 def get_model_merger(record_store: ModelRecordServiceBase) -> ModelMerger:

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

@@ -746,6 +746,7 @@
         "delete": "Delete",
         "deleteConfig": "Delete Config",
         "deleteModel": "Delete Model",
+        "deleteModelImage": "Delete Model Image",
         "deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
         "deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
         "description": "Description",
@@ -786,6 +787,10 @@
         "modelDeleteFailed": "Failed to delete model",
         "modelEntryDeleted": "Model Entry Deleted",
         "modelExists": "Model Exists",
+        "modelImageDeleted": "Model Image Deleted",
+        "modelImageDeleteFailed": "Model Image Delete Failed",
+        "modelImageUpdated": "Model Image Updated",
+        "modelImageUpdateFailed": "Model Image Update Failed",
         "modelLocation": "Model Location",
         "modelLocationValidationMsg": "Provide the path to a local folder where your Diffusers Model is stored",
         "modelManager": "Model Manager",
@@ -818,6 +823,7 @@
         "oliveModels": "Olives",
         "onnxModels": "Onnx",
         "path": "Path",
+        "pathToConfig": "Path To Config",
         "pathToCustomConfig": "Path To Custom Config",
         "pickModelType": "Pick Model Type",
         "predictionType": "Prediction Type",
@@ -852,6 +858,7 @@
         "triggerPhrases": "Trigger Phrases",
         "typePhraseHere": "Type phrase here",
         "upcastAttention": "Upcast Attention",
+        "uploadImage": "Upload Image",
         "updateModel": "Update Model",
         "useCustomConfig": "Use Custom Config",
         "useDefaultSettings": "Use Default Settings",
@@ -944,6 +951,7 @@
         "doesNotExist": "does not exist",
         "downloadWorkflow": "Download Workflow JSON",
         "edge": "Edge",
+        "edit": "Edit",
         "editMode": "Edit in Workflow Editor",
         "enum": "Enum",
         "enumDescription": "Enums are values that may be one of a number of options.",
@@ -1019,6 +1027,7 @@
         "nodeTemplate": "Node Template",
         "nodeType": "Node Type",
         "noFieldsLinearview": "No fields added to Linear View",
+        "noFieldsViewMode": "This workflow has no selected fields to display. View the full workflow to configure values.",
         "noFieldType": "No field type",
         "noImageFoundState": "No initial image found in state",
         "noMatchingNodes": "No matching nodes",
@@ -1806,6 +1815,7 @@
         "cursorPosition": "Cursor Position",
         "darkenOutsideSelection": "Darken Outside Selection",
         "discardAll": "Discard All",
+        "discardCurrent": "Discard Current",
         "downloadAsImage": "Download As Image",
         "emptyFolder": "Empty Folder",
         "emptyTempImageFolder": "Empty Temp Image Folder",
@@ -1815,6 +1825,7 @@
         "eraseBoundingBox": "Erase Bounding Box",
         "eraser": "Eraser",
         "fillBoundingBox": "Fill Bounding Box",
+        "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size",
         "layer": "Layer",
         "limitStrokesToBox": "Limit Strokes to Box",
         "mask": "Mask",

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

@@ -115,7 +115,8 @@
         "safetensors": "Safetensors",
         "ai": "ia",
         "file": "File",
-        "toResolve": "Da risolvere"
+        "toResolve": "Da risolvere",
+        "add": "Aggiungi"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -153,7 +154,12 @@
         "starImage": "Immagine preferita",
         "dropToUpload": "$t(gallery.drop) per aggiornare",
         "problemDeletingImagesDesc": "Impossibile eliminare una o più immagini",
-        "problemDeletingImages": "Problema durante l'eliminazione delle immagini"
+        "problemDeletingImages": "Problema durante l'eliminazione delle immagini",
+        "bulkDownloadRequested": "Preparazione del download",
+        "bulkDownloadRequestedDesc": "La tua richiesta di download è in preparazione. L'operazione potrebbe richiedere alcuni istanti.",
+        "bulkDownloadRequestFailed": "Problema durante la preparazione del download",
+        "bulkDownloadStarting": "Avvio scaricamento",
+        "bulkDownloadFailed": "Scaricamento fallito"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti di scelta rapida",
@@ -505,12 +511,12 @@
         "modelSyncFailed": "Sincronizzazione modello non riuscita",
         "settings": "Impostazioni",
         "syncModels": "Sincronizza Modelli",
-        "syncModelsDesc": "Se i tuoi modelli non sono sincronizzati con il back-end, puoi aggiornarli utilizzando questa opzione. Questo è generalmente utile nei casi in cui aggiorni manualmente il tuo file models.yaml o aggiungi modelli alla cartella principale di InvokeAI dopo l'avvio dell'applicazione.",
+        "syncModelsDesc": "Se i tuoi modelli non sono sincronizzati con il back-end, puoi aggiornarli utilizzando questa opzione. Questo è generalmente utile nei casi in cui aggiungi modelli alla cartella principale di InvokeAI dopo l'avvio dell'applicazione.",
         "loraModels": "LoRA",
         "oliveModels": "Olive",
         "onnxModels": "ONNX",
         "noModels": "Nessun modello trovato",
-        "predictionType": "Tipo di previsione (per modelli Stable Diffusion 2.x ed alcuni modelli Stable Diffusion 1.x)",
+        "predictionType": "Tipo di previsione",
         "quickAdd": "Aggiunta rapida",
         "simpleModelDesc": "Fornire un percorso a un modello diffusori locale, un modello checkpoint/safetensor locale, un ID repository HuggingFace o un URL del modello checkpoint/diffusori.",
         "advanced": "Avanzate",
@@ -521,7 +527,34 @@
         "vaePrecision": "Precisione VAE",
         "noModelSelected": "Nessun modello selezionato",
         "conversionNotSupported": "Conversione non supportata",
-        "configFile": "File di configurazione"
+        "configFile": "File di configurazione",
+        "modelName": "Nome del modello",
+        "modelSettings": "Impostazioni del modello",
+        "advancedImportInfo": "La scheda opzioni avanzate consente la configurazione manuale delle impostazioni del modello principale. Utilizza questa scheda solo se sei sicuro di conoscere il tipo di modello e la configurazione corretti per il modello selezionato.",
+        "addAll": "Aggiungi tutto",
+        "addModels": "Aggiungi modelli",
+        "cancel": "Annulla",
+        "edit": "Modifica",
+        "imageEncoderModelId": "ID modello codificatore di immagini",
+        "importQueue": "Coda di importazione",
+        "modelMetadata": "Metadati del modello",
+        "path": "Percorso",
+        "prune": "Elimina",
+        "pruneTooltip": "Elimina dalla coda le importazioni completate",
+        "removeFromQueue": "Rimuovi dalla coda",
+        "repoVariant": "Variante del repository",
+        "scan": "Scansiona",
+        "scanFolder": "Scansione cartella",
+        "scanResults": "Risultati della scansione",
+        "source": "Sorgente",
+        "upcastAttention": "Eleva l'attenzione",
+        "ztsnrTraining": "Addestramento ZTSNR",
+        "typePhraseHere": "Digita la frase qui",
+        "defaultSettingsSaved": "Impostazioni predefinite salvate",
+        "defaultSettings": "Impostazioni predefinite",
+        "metadata": "Metadati",
+        "useDefaultSettings": "Usa le impostazioni predefinite",
+        "triggerPhrases": "Frasi trigger"
     },
     "parameters": {
         "images": "Immagini",
@@ -603,8 +636,8 @@
         "clipSkip": "CLIP Skip",
         "aspectRatio": "Proporzioni",
         "maskAdjustmentsHeader": "Regolazioni della maschera",
-        "maskBlur": "Sfocatura",
-        "maskBlurMethod": "Metodo di sfocatura",
+        "maskBlur": "Sfocatura maschera",
+        "maskBlurMethod": "Metodo sfocatura maschera",
         "seamLowThreshold": "Basso",
         "seamHighThreshold": "Alto",
         "coherencePassHeader": "Passaggio di coerenza",
@@ -661,7 +694,8 @@
         "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (potrebbe essere troppo grande)",
         "boxBlur": "Box",
         "gaussianBlur": "Gaussian",
-        "remixImage": "Remixa l'immagine"
+        "remixImage": "Remixa l'immagine",
+        "coherenceEdgeSize": "Dimensione bordo"
     },
     "settings": {
         "models": "Modelli",
@@ -744,8 +778,8 @@
         "canceled": "Elaborazione annullata",
         "problemCopyingImageLink": "Impossibile copiare il collegamento dell'immagine",
         "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
-        "parameterSet": "Parametro impostato",
-        "parameterNotSet": "Parametro non impostato",
+        "parameterSet": "{{parameter}} impostato",
+        "parameterNotSet": "{{parameter}} non impostato",
         "nodesLoadedFailed": "Impossibile caricare i nodi",
         "nodesSaved": "Nodi salvati",
         "nodesLoaded": "Nodi caricati",
@@ -798,7 +832,10 @@
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
         "uploadInitialImage": "Carica l'immagine iniziale",
-        "problemDownloadingImage": "Impossibile scaricare l'immagine"
+        "problemDownloadingImage": "Impossibile scaricare l'immagine",
+        "prunedQueue": "Coda ripulita",
+        "modelImportCanceled": "Importazione del modello annullata",
+        "modelImportRemoved": "Importazione del modello rimossa"
     },
     "tooltip": {
         "feature": {
@@ -876,7 +913,10 @@
         "antialiasing": "Anti aliasing",
         "showResultsOn": "Mostra i risultati (attivato)",
         "showResultsOff": "Mostra i risultati (disattivato)",
-        "saveMask": "Salva $t(unifiedCanvas.mask)"
+        "saveMask": "Salva $t(unifiedCanvas.mask)",
+        "coherenceModeGaussianBlur": "Sfocatura Gaussiana",
+        "coherenceModeBoxBlur": "Sfocatura Box",
+        "coherenceModeStaged": "Maschera espansa"
     },
     "accessibility": {
         "modelSelect": "Seleziona modello",
@@ -1345,7 +1385,8 @@
         "allLoRAsAdded": "Tutti i LoRA aggiunti",
         "defaultVAE": "VAE predefinito",
         "incompatibleBaseModel": "Modello base incompatibile",
-        "loraAlreadyAdded": "LoRA già aggiunto"
+        "loraAlreadyAdded": "LoRA già aggiunto",
+        "concepts": "Concetti"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1698,6 +1739,25 @@
             "paragraphs": [
                 "Valuta le generazioni in modo che siano più simili alle immagini con un punteggio estetico elevato, in base ai dati di addestramento."
             ]
+        },
+        "compositingCoherenceMinDenoise": {
+            "heading": "Livello minimo di riduzione del rumore",
+            "paragraphs": [
+                "Intensità minima di riduzione rumore per la modalità di Coerenza",
+                "L'intensità minima di riduzione del rumore per la regione di coerenza durante l'inpainting o l'outpainting"
+            ]
+        },
+        "compositingMaskBlur": {
+            "paragraphs": [
+                "Il raggio di sfocatura della maschera."
+            ],
+            "heading": "Sfocatura maschera"
+        },
+        "compositingCoherenceEdgeSize": {
+            "heading": "Dimensione del bordo",
+            "paragraphs": [
+                "La dimensione del bordo del passaggio di coerenza."
+            ]
         }
     },
     "sdxl": {
@@ -1746,7 +1806,12 @@
         "scheduler": "Campionatore",
         "recallParameters": "Richiama i parametri",
         "noRecallParameters": "Nessun parametro da richiamare trovato",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "allPrompts": "Tutti i prompt",
+        "imageDimensions": "Dimensioni dell'immagine",
+        "parameterSet": "Parametro {{parameter}} impostato",
+        "parsingFailed": "Analisi non riuscita",
+        "recallParameter": "Richiama {{label}}"
     },
     "hrf": {
         "enableHrf": "Abilita Correzione Alta Risoluzione",
@@ -1818,5 +1883,11 @@
         "image": {
             "title": "Immagine"
         }
+    },
+    "prompt": {
+        "compatibleEmbeddings": "Incorporamenti compatibili",
+        "addPromptTrigger": "Aggiungi parola chiave nel prompt",
+        "noPromptTriggers": "Nessuna parola chiave disponibile",
+        "noMatchingTriggers": "Nessuna parola chiave corrispondente"
     }
 }

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

@@ -52,7 +52,7 @@
         "accept": "Принять",
         "postprocessing": "Постобработка",
         "txt2img": "Текст в изображение (txt2img)",
-        "linear": "Линейная обработка",
+        "linear": "Линейный вид",
         "dontAskMeAgain": "Больше не спрашивать",
         "areYouSure": "Вы уверены?",
         "random": "Случайное",
@@ -117,7 +117,8 @@
         "toResolve": "Чтоб решить",
         "copy": "Копировать",
         "localSystem": "Локальная система",
-        "aboutDesc": "Используя Invoke для работы? Проверьте это:"
+        "aboutDesc": "Используя Invoke для работы? Проверьте это:",
+        "add": "Добавить"
     },
     "gallery": {
         "generations": "Генерации",
@@ -155,7 +156,12 @@
         "noImageSelected": "Изображение не выбрано",
         "setCurrentImage": "Установить как текущее изображение",
         "starImage": "Добавить в избранное",
-        "dropToUpload": "$t(gallery.drop) чтоб загрузить"
+        "dropToUpload": "$t(gallery.drop) чтоб загрузить",
+        "bulkDownloadFailed": "Загрузка не удалась",
+        "bulkDownloadStarting": "Начало загрузки",
+        "bulkDownloadRequested": "Подготовка к скачиванию",
+        "bulkDownloadRequestedDesc": "Ваш запрос на скачивание готовится. Это может занять несколько минут.",
+        "bulkDownloadRequestFailed": "Возникла проблема при подготовке скачивания"
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -504,7 +510,7 @@
         "settings": "Настройки",
         "selectModel": "Выберите модель",
         "syncModels": "Синхронизация моделей",
-        "syncModelsDesc": "Если ваши модели не синхронизированы с серверной частью, вы можете обновить их, используя эту опцию. Обычно это удобно в тех случаях, когда вы вручную обновляете свой файл \"models.yaml\" или добавляете модели в корневую папку InvokeAI после загрузки приложения.",
+        "syncModelsDesc": "Если ваши модели не синхронизированы с серверной частью, вы можете обновить их с помощью этой опции. Обычно это удобно в тех случаях, когда вы добавляете модели в корневую папку InvokeAI или каталог автоимпорта после загрузки приложения.",
         "modelUpdateFailed": "Не удалось обновить модель",
         "modelConversionFailed": "Не удалось сконвертировать модель",
         "modelsMergeFailed": "Не удалось выполнить слияние моделей",
@@ -513,7 +519,7 @@
         "oliveModels": "Модели Olives",
         "conversionNotSupported": "Преобразование не поддерживается",
         "noModels": "Нет моделей",
-        "predictionType": "Тип прогноза (для моделей Stable Diffusion 2.x и периодических моделей Stable Diffusion 1.x)",
+        "predictionType": "Тип прогноза",
         "quickAdd": "Быстрое добавление",
         "simpleModelDesc": "Укажите путь к локальной модели Diffusers , локальной модели checkpoint / safetensors, идентификатор репозитория HuggingFace или URL-адрес модели контрольной checkpoint / diffusers.",
         "advanced": "Продвинутый",
@@ -524,7 +530,33 @@
         "customConfigFileLocation": "Расположение пользовательского файла конфигурации",
         "vaePrecision": "Точность VAE",
         "noModelSelected": "Модель не выбрана",
-        "configFile": "Файл конфигурации"
+        "configFile": "Файл конфигурации",
+        "addAll": "Добавить всё",
+        "addModels": "Добавить модели",
+        "cancel": "Отмена",
+        "defaultSettings": "Стандартные настройки",
+        "importQueue": "Импортировать очередь",
+        "metadata": "Метаданные",
+        "imageEncoderModelId": "ID модели-энкодера изображений",
+        "typePhraseHere": "Введите фразы здесь",
+        "advancedImportInfo": "Вкладка «Дополнительно» позволяет вручную настроить основные параметры модели. Используйте эту вкладку только в том случае, если вы уверены, что знаете правильный тип модели и конфигурацию выбранной модели.",
+        "defaultSettingsSaved": "Стандартные настройки сохранены",
+        "edit": "Редактировать",
+        "path": "Путь",
+        "prune": "Удалить",
+        "pruneTooltip": "Удалить готовые импорты из очереди",
+        "removeFromQueue": "Удалить из очереди",
+        "repoVariant": "Вариант репозитория",
+        "scan": "Сканировать",
+        "scanFolder": "Сканировать папку",
+        "scanResults": "Результаты сканирования",
+        "source": "Источник",
+        "triggerPhrases": "Триггерные фразы",
+        "useDefaultSettings": "Использовать стандартные настройки",
+        "modelMetadata": "Метаданные модели",
+        "modelName": "Название модели",
+        "modelSettings": "Настройки модели",
+        "upcastAttention": "Внимание"
     },
     "parameters": {
         "images": "Изображения",
@@ -591,7 +623,7 @@
         "hSymmetryStep": "Шаг гор. симметрии",
         "hidePreview": "Скрыть предпросмотр",
         "imageToImage": "Изображение в изображение",
-        "denoisingStrength": "Сила шумоподавления",
+        "denoisingStrength": "Сила зашумления",
         "copyImage": "Скопировать изображение",
         "showPreview": "Показать предпросмотр",
         "noiseSettings": "Шум",
@@ -606,8 +638,8 @@
         "clipSkip": "CLIP Пропуск",
         "aspectRatio": "Соотношение",
         "maskAdjustmentsHeader": "Настройка маски",
-        "maskBlur": "Размытие",
-        "maskBlurMethod": "Метод размытия",
+        "maskBlur": "Размытие маски",
+        "maskBlurMethod": "Метод размытия маски",
         "seamLowThreshold": "Низкий",
         "seamHighThreshold": "Высокий",
         "coherencePassHeader": "Порог Coherence",
@@ -666,7 +698,9 @@
         "lockAspectRatio": "Заблокировать соотношение",
         "boxBlur": "Размытие прямоугольника",
         "gaussianBlur": "Размытие по Гауссу",
-        "remixImage": "Ремикс изображения"
+        "remixImage": "Ремикс изображения",
+        "coherenceMinDenoise": "Мин. шумоподавление",
+        "coherenceEdgeSize": "Размер края"
     },
     "settings": {
         "models": "Модели",
@@ -749,8 +783,8 @@
         "canceled": "Обработка отменена",
         "problemCopyingImageLink": "Не удалось скопировать ссылку на изображение",
         "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
-        "parameterNotSet": "Параметр не задан",
-        "parameterSet": "Параметр задан",
+        "parameterNotSet": "Параметр {{parameter}} не задан",
+        "parameterSet": "Параметр {{parameter}} задан",
         "nodesLoaded": "Узлы загружены",
         "problemCopyingImage": "Не удается скопировать изображение",
         "nodesLoadedFailed": "Не удалось загрузить Узлы",
@@ -803,7 +837,10 @@
         "problemImportingMask": "Проблема с импортом маски",
         "problemDownloadingImage": "Не удается скачать изображение",
         "uploadInitialImage": "Загрузить начальное изображение",
-        "resetInitialImage": "Сбросить начальное изображение"
+        "resetInitialImage": "Сбросить начальное изображение",
+        "prunedQueue": "Урезанная очередь",
+        "modelImportCanceled": "Импорт модели отменен",
+        "modelImportRemoved": "Импорт модели удален"
     },
     "tooltip": {
         "feature": {
@@ -1145,7 +1182,11 @@
         "reorderLinearView": "Изменить порядок линейного просмотра",
         "viewMode": "Использовать в линейном представлении",
         "editMode": "Открыть в редакторе узлов",
-        "resetToDefaultValue": "Сбросить к стандартному значкнию"
+        "resetToDefaultValue": "Сбросить к стандартному значкнию",
+        "latentsField": "Латенты",
+        "latentsCollectionDescription": "Латенты могут передаваться между узлами.",
+        "latentsPolymorphicDescription": "Латенты могут передаваться между узлами.",
+        "latentsFieldDescription": "Латенты могут передаваться между узлами."
     },
     "controlnet": {
         "amult": "a_mult",
@@ -1294,7 +1335,8 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "Планировщик определяет, как итеративно добавлять шум к изображению или как обновлять образец на основе выходных данных модели."
+                "Планировщик, используемый в процессе генерации.",
+                "Каждый планировщик определяет, как итеративно добавлять шум к изображению или как обновлять образец на основе выходных данных модели."
             ],
             "heading": "Планировщик"
         },
@@ -1347,7 +1389,7 @@
         "compositingCoherenceMode": {
             "heading": "Режим",
             "paragraphs": [
-                "Режим прохождения когерентности."
+                "Метод, используемый для создания связного изображения с вновь созданной замаскированной областью."
             ]
         },
         "paramSeed": {
@@ -1365,7 +1407,7 @@
         },
         "controlNetBeginEnd": {
             "paragraphs": [
-                "На каких этапах процесса шумоподавления будет применена ControlNet.",
+                "Часть процесса шумоподавления, к которой будет применен адаптер контроля.",
                 "ControlNet, применяемые в начале процесса, направляют композицию, а ControlNet, применяемые в конце, направляют детали."
             ],
             "heading": "Процент начала/конца шага"
@@ -1381,8 +1423,8 @@
         },
         "clipSkip": {
             "paragraphs": [
-                "Выберите, сколько слоев модели CLIP нужно пропустить.",
-                "Некоторые модели работают лучше с определенными настройками пропуска CLIP."
+                "Сколько слоев модели CLIP пропустить.",
+                "Некоторые модели лучше подходят для использования с CLIP Skip."
             ],
             "heading": "CLIP пропуск"
         },
@@ -1479,6 +1521,25 @@
             "paragraphs": [
                 "Более высокий вес LoRA приведет к большему влиянию на конечное изображение."
             ]
+        },
+        "compositingMaskBlur": {
+            "heading": "Размытие маски",
+            "paragraphs": [
+                "Радиус размытия маски."
+            ]
+        },
+        "compositingCoherenceMinDenoise": {
+            "heading": "Минимальное шумоподавление",
+            "paragraphs": [
+                "Минимальный уровень шумоподавления для режима Coherence",
+                "Минимальный уровень шумоподавления для области когерентности при перерисовывании или дорисовке"
+            ]
+        },
+        "compositingCoherenceEdgeSize": {
+            "heading": "Размер края",
+            "paragraphs": [
+                "Размер края прохода когерентности."
+            ]
         }
     },
     "metadata": {
@@ -1509,7 +1570,12 @@
         "steps": "Шаги",
         "scheduler": "Планировщик",
         "noRecallParameters": "Параметры для вызова не найдены",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "parameterSet": "Параметр {{parameter}} установлен",
+        "parsingFailed": "Не удалось выполнить синтаксический анализ",
+        "recallParameter": "Отозвать {{label}}",
+        "allPrompts": "Все запросы",
+        "imageDimensions": "Размеры изображения"
     },
     "queue": {
         "status": "Статус",
@@ -1588,10 +1654,11 @@
         "denoisingStrength": "Шумоподавление",
         "refinermodel": "Модель перерисовщик",
         "posAestheticScore": "Положительная эстетическая оценка",
-        "concatPromptStyle": "Объединение запроса и стиля",
+        "concatPromptStyle": "Связывание запроса и стиля",
         "loading": "Загрузка...",
         "steps": "Шаги",
-        "posStylePrompt": "Запрос стиля"
+        "posStylePrompt": "Запрос стиля",
+        "freePromptStyle": "Ручной запрос стиля"
     },
     "invocationCache": {
         "useCache": "Использовать кэш",
@@ -1678,7 +1745,8 @@
         "allLoRAsAdded": "Все LoRA добавлены",
         "defaultVAE": "Стандартное VAE",
         "incompatibleBaseModel": "Несовместимая базовая модель",
-        "loraAlreadyAdded": "LoRA уже добавлена"
+        "loraAlreadyAdded": "LoRA уже добавлена",
+        "concepts": "Концепты"
     },
     "app": {
         "storeNotInitialized": "Магазин не инициализирован"
@@ -1696,7 +1764,7 @@
         },
         "generation": {
             "title": "Генерация",
-            "conceptsTab": "Концепты",
+            "conceptsTab": "LoRA",
             "modelTab": "Модель"
         },
         "advanced": {

invokeai/frontend/web/scripts/typegen.js

@@ -5,18 +5,55 @@ import openapiTS from 'openapi-typescript';
 const OPENAPI_URL = 'http://127.0.0.1:9090/openapi.json';
 const OUTPUT_FILE = 'src/services/api/schema.ts';
 
-async function main() {
-  process.stdout.write(`Generating types "${OPENAPI_URL}" --> "${OUTPUT_FILE}"...`);
-  const types = await openapiTS(OPENAPI_URL, {
+async function generateTypes(schema) {
+  process.stdout.write(`Generating types ${OUTPUT_FILE}...`);
+  const types = await openapiTS(schema, {
     exportType: true,
     transform: (schemaObject) => {
       if ('format' in schemaObject && schemaObject.format === 'binary') {
         return schemaObject.nullable ? 'Blob | null' : 'Blob';
       }
+      if (schemaObject.title === 'MetadataField') {
+        // This is `Record<string, never>` by default, but it actually accepts any a dict of any valid JSON value.
+        return 'Record<string, unknown>';
+      }
     },
   });
   fs.writeFileSync(OUTPUT_FILE, types);
   process.stdout.write(`\nOK!\r\n`);
 }
 
+async function main() {
+  const encoding = 'utf-8';
+
+  if (process.stdin.isTTY) {
+    // Handle generating types with an arg (e.g. URL or path to file)
+    if (process.argv.length > 3) {
+      console.error('Usage: typegen.js <openapi.json>');
+      process.exit(1);
+    }
+    if (process.argv[2]) {
+      const schema = new Buffer.from(process.argv[2], encoding);
+      generateTypes(schema);
+    } else {
+      generateTypes(OPENAPI_URL);
+    }
+  } else {
+    // Handle generating types from stdin
+    let schema = '';
+    process.stdin.setEncoding(encoding);
+
+    process.stdin.on('readable', function () {
+      const chunk = process.stdin.read();
+      if (chunk !== null) {
+        schema += chunk;
+      }
+    });
+
+    process.stdin.on('end', function () {
+      generateTypes(JSON.parse(schema));
+    });
+  }
+}
+
 main();

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

@@ -55,6 +55,7 @@ import { addUpscaleRequestedListener } from 'app/store/middleware/listenerMiddle
 import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
 
+import { addControlAdapterAutoProcessorUpdateListener } from './listeners/controlAdapterAutoProcessorUpdateListener';
 import { addSetDefaultSettingsListener } from './listeners/setDefaultSettings';
 
 export const listenerMiddleware = createListenerMiddleware();
@@ -125,6 +126,7 @@ addBulkDownloadListeners(startAppListening);
 // ControlNet
 addControlNetImageProcessedListener(startAppListening);
 addControlNetAutoProcessListener(startAppListening);
+addControlAdapterAutoProcessorUpdateListener(startAppListening);
 
 // Boards
 addImageAddedToBoardFulfilledListener(startAppListening);

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

@@ -38,7 +38,7 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
             type: 'image/png',
           }),
           image_category: 'control',
-          is_intermediate: false,
+          is_intermediate: true,
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
           crop_visible: false,
           postUploadAction: {

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

@@ -48,7 +48,7 @@ export const addCanvasMaskToControlNetListener = (startAppListening: AppStartLis
             type: 'image/png',
           }),
           image_category: 'mask',
-          is_intermediate: false,
+          is_intermediate: true,
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
           crop_visible: false,
           postUploadAction: {

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

@@ -0,0 +1,77 @@
+import { isAnyOf } from '@reduxjs/toolkit';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { CONTROLNET_MODEL_DEFAULT_PROCESSORS } from 'features/controlAdapters/store/constants';
+import {
+  controlAdapterAutoConfigToggled,
+  controlAdapterModelChanged,
+  controlAdapterProcessortTypeChanged,
+  selectControlAdapterById,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import type { ControlAdapterProcessorType } from 'features/controlAdapters/store/types';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import { modelsApi } from 'services/api/endpoints/models';
+
+export const addControlAdapterAutoProcessorUpdateListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher: isAnyOf(controlAdapterModelChanged, controlAdapterAutoConfigToggled),
+    effect: async (action, { getState, dispatch }) => {
+      let id;
+      let model;
+
+      const state = getState();
+      const { controlAdapters: controlAdaptersState } = state;
+
+      if (controlAdapterModelChanged.match(action)) {
+        model = action.payload.model;
+        id = action.payload.id;
+
+        const cn = selectControlAdapterById(controlAdaptersState, id);
+        if (!cn) {
+          return;
+        }
+
+        if (!isControlNetOrT2IAdapter(cn)) {
+          return;
+        }
+      }
+
+      if (controlAdapterAutoConfigToggled.match(action)) {
+        id = action.payload.id;
+
+        const cn = selectControlAdapterById(controlAdaptersState, id);
+        if (!cn) {
+          return;
+        }
+
+        if (!isControlNetOrT2IAdapter(cn)) {
+          return;
+        }
+
+        // if they turned off autoconfig, return
+        if (!cn.shouldAutoConfig) {
+          return;
+        }
+
+        model = cn.model;
+      }
+
+      if (!model || !id) {
+        return;
+      }
+
+      let processorType: ControlAdapterProcessorType | undefined = undefined;
+      const { data: modelConfig } = modelsApi.endpoints.getModelConfig.select(model.key)(state);
+
+      for (const modelSubstring in CONTROLNET_MODEL_DEFAULT_PROCESSORS) {
+        if (modelConfig?.name.includes(modelSubstring)) {
+          processorType = CONTROLNET_MODEL_DEFAULT_PROCESSORS[modelSubstring];
+          break;
+        }
+      }
+
+      dispatch(
+        controlAdapterProcessortTypeChanged({ id, processorType: processorType || 'none', shouldAutoConfig: true })
+      );
+    },
+  });
+};

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

@@ -101,7 +101,7 @@ export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartLis
         ).unwrap();
       }
 
-      const graph = buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
+      const graph = await buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
 
       log.debug({ graph: parseify(graph) }, `Canvas graph built`);
 

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

@@ -20,15 +20,15 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
 
       if (model && model.base === 'sdxl') {
         if (action.payload.tabName === 'txt2img') {
-          graph = buildLinearSDXLTextToImageGraph(state);
+          graph = await buildLinearSDXLTextToImageGraph(state);
         } else {
-          graph = buildLinearSDXLImageToImageGraph(state);
+          graph = await buildLinearSDXLImageToImageGraph(state);
         }
       } else {
         if (action.payload.tabName === 'txt2img') {
-          graph = buildLinearTextToImageGraph(state);
+          graph = await buildLinearTextToImageGraph(state);
         } else {
-          graph = buildLinearImageToImageGraph(state);
+          graph = await buildLinearImageToImageGraph(state);
         }
       }
 

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

@@ -20,7 +20,7 @@ const sx: ChakraProps['sx'] = {
   '.react-colorful__hue-pointer': colorPickerPointerStyles,
   '.react-colorful__saturation-pointer': colorPickerPointerStyles,
   '.react-colorful__alpha-pointer': colorPickerPointerStyles,
-  gap: 2,
+  gap: 5,
   flexDir: 'column',
 };
 
@@ -39,8 +39,8 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
     <Flex sx={sx}>
       <RgbaColorPicker color={color} onChange={onChange} style={colorPickerStyles} {...rest} />
       {withNumberInput && (
-        <Flex>
-          <FormControl>
+        <Flex gap={5}>
+          <FormControl gap={0}>
             <FormLabel>{t('common.red')}</FormLabel>
             <CompositeNumberInput
               value={color.r}
@@ -52,7 +52,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={90}
             />
           </FormControl>
-          <FormControl>
+          <FormControl gap={0}>
             <FormLabel>{t('common.green')}</FormLabel>
             <CompositeNumberInput
               value={color.g}
@@ -64,7 +64,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={90}
             />
           </FormControl>
-          <FormControl>
+          <FormControl gap={0}>
             <FormLabel>{t('common.blue')}</FormLabel>
             <CompositeNumberInput
               value={color.b}
@@ -76,7 +76,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={255}
             />
           </FormControl>
-          <FormControl>
+          <FormControl gap={0}>
             <FormLabel>{t('common.alpha')}</FormLabel>
             <CompositeNumberInput
               value={color.a}

invokeai/frontend/web/src/features/canvas/components/IAICanvas.tsx

@@ -29,7 +29,7 @@ import { Layer, Stage } from 'react-konva';
 import IAICanvasBoundingBoxOverlay from './IAICanvasBoundingBoxOverlay';
 import IAICanvasGrid from './IAICanvasGrid';
 import IAICanvasIntermediateImage from './IAICanvasIntermediateImage';
-import IAICanvasMaskCompositer from './IAICanvasMaskCompositer';
+import IAICanvasMaskCompositor from './IAICanvasMaskCompositor';
 import IAICanvasMaskLines from './IAICanvasMaskLines';
 import IAICanvasObjectRenderer from './IAICanvasObjectRenderer';
 import IAICanvasStagingArea from './IAICanvasStagingArea';
@@ -176,7 +176,7 @@ const IAICanvas = () => {
           </Layer>
           <Layer id="mask" visible={isMaskEnabled && !isStaging} listening={false}>
             <IAICanvasMaskLines visible={true} listening={false} />
-            <IAICanvasMaskCompositer listening={false} />
+            <IAICanvasMaskCompositor listening={false} />
           </Layer>
           <Layer listening={false}>
             <IAICanvasBoundingBoxOverlay />

invokeai/frontend/web/src/features/canvas/components/IAICanvasMaskCompositor.tsx

@@ -16,9 +16,9 @@ const canvasMaskCompositerSelector = createMemoizedSelector(selectCanvasSlice, (
   };
 });
 
-type IAICanvasMaskCompositerProps = RectConfig;
+type IAICanvasMaskCompositorProps = RectConfig;
 
-const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
+const IAICanvasMaskCompositor = (props: IAICanvasMaskCompositorProps) => {
   const { ...rest } = props;
 
   const { stageCoordinates, stageDimensions } = useAppSelector(canvasMaskCompositerSelector);
@@ -89,4 +89,4 @@ const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
   );
 };
 
-export default memo(IAICanvasMaskCompositer);
+export default memo(IAICanvasMaskCompositor);

invokeai/frontend/web/src/features/canvas/components/IAICanvasStagingAreaToolbar.tsx

@@ -5,6 +5,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { stagingAreaImageSaved } from 'features/canvas/store/actions';
 import {
   commitStagingAreaImage,
+  discardStagedImage,
   discardStagedImages,
   nextStagingAreaImage,
   prevStagingAreaImage,
@@ -22,6 +23,7 @@ import {
   PiEyeBold,
   PiEyeSlashBold,
   PiFloppyDiskBold,
+  PiTrashSimpleBold,
   PiXBold,
 } from 'react-icons/pi';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
@@ -44,6 +46,40 @@ const selector = createMemoizedSelector(selectCanvasSlice, (canvas) => {
   };
 });
 
+const ClearStagingIntermediatesIconButton = () => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const handleDiscardStagingArea = useCallback(() => {
+    dispatch(discardStagedImages());
+  }, [dispatch]);
+
+  const handleDiscardStagingImage = useCallback(() => {
+    dispatch(discardStagedImage());
+  }, [dispatch]);
+
+  return (
+    <>
+      <IconButton
+        tooltip={`${t('unifiedCanvas.discardCurrent')}`}
+        aria-label={t('unifiedCanvas.discardCurrent')}
+        icon={<PiXBold />}
+        onClick={handleDiscardStagingImage}
+        colorScheme="invokeBlue"
+        fontSize={16}
+      />
+      <IconButton
+        tooltip={`${t('unifiedCanvas.discardAll')} (Esc)`}
+        aria-label={t('unifiedCanvas.discardAll')}
+        icon={<PiTrashSimpleBold />}
+        onClick={handleDiscardStagingArea}
+        colorScheme="error"
+        fontSize={16}
+      />
+    </>
+  );
+};
+
 const IAICanvasStagingAreaToolbar = () => {
   const dispatch = useAppDispatch();
   const { currentStagingAreaImage, shouldShowStagingImage, currentIndex, total } = useAppSelector(selector);
@@ -185,14 +221,7 @@ const IAICanvasStagingAreaToolbar = () => {
           onClick={handleSaveToGallery}
           colorScheme="invokeBlue"
         />
-        <IconButton
-          tooltip={`${t('unifiedCanvas.discardAll')} (Esc)`}
-          aria-label={t('unifiedCanvas.discardAll')}
-          icon={<PiXBold />}
-          onClick={handleDiscardStagingArea}
-          colorScheme="error"
-          fontSize={20}
-        />
+        <ClearStagingIntermediatesIconButton />
       </ButtonGroup>
     </Flex>
   );

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

@@ -18,6 +18,7 @@ import {
   setShouldAutoSave,
   setShouldCropToBoundingBoxOnSave,
   setShouldDarkenOutsideBoundingBox,
+  setShouldInvertBrushSizeScrollDirection,
   setShouldRestrictStrokesToBox,
   setShouldShowCanvasDebugInfo,
   setShouldShowGrid,
@@ -40,6 +41,7 @@ const IAICanvasSettingsButtonPopover = () => {
   const shouldAutoSave = useAppSelector((s) => s.canvas.shouldAutoSave);
   const shouldCropToBoundingBoxOnSave = useAppSelector((s) => s.canvas.shouldCropToBoundingBoxOnSave);
   const shouldDarkenOutsideBoundingBox = useAppSelector((s) => s.canvas.shouldDarkenOutsideBoundingBox);
+  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
   const shouldShowCanvasDebugInfo = useAppSelector((s) => s.canvas.shouldShowCanvasDebugInfo);
   const shouldShowGrid = useAppSelector((s) => s.canvas.shouldShowGrid);
   const shouldShowIntermediates = useAppSelector((s) => s.canvas.shouldShowIntermediates);
@@ -76,6 +78,10 @@ const IAICanvasSettingsButtonPopover = () => {
     (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldDarkenOutsideBoundingBox(e.target.checked)),
     [dispatch]
   );
+  const handleChangeShouldInvertBrushSizeScrollDirection = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldInvertBrushSizeScrollDirection(e.target.checked)),
+    [dispatch]
+  );
   const handleChangeShouldAutoSave = useCallback(
     (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldAutoSave(e.target.checked)),
     [dispatch]
@@ -144,6 +150,13 @@ const IAICanvasSettingsButtonPopover = () => {
                 <FormLabel>{t('unifiedCanvas.limitStrokesToBox')}</FormLabel>
                 <Checkbox isChecked={shouldRestrictStrokesToBox} onChange={handleChangeShouldRestrictStrokesToBox} />
               </FormControl>
+              <FormControl>
+                <FormLabel>{t('unifiedCanvas.invertBrushSizeScrollDirection')}</FormLabel>
+                <Checkbox
+                  isChecked={shouldInvertBrushSizeScrollDirection}
+                  onChange={handleChangeShouldInvertBrushSizeScrollDirection}
+                />
+              </FormControl>
               <FormControl>
                 <FormLabel>{t('unifiedCanvas.showCanvasDebugInfo')}</FormLabel>
                 <Checkbox isChecked={shouldShowCanvasDebugInfo} onChange={handleChangeShouldShowCanvasDebugInfo} />

invokeai/frontend/web/src/features/canvas/hooks/useCanvasZoom.ts

@@ -15,6 +15,7 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
   const stageScale = useAppSelector((s) => s.canvas.stageScale);
   const isMoveStageKeyHeld = useStore($isMoveStageKeyHeld);
   const brushSize = useAppSelector((s) => s.canvas.brushSize);
+  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
 
   return useCallback(
     (e: KonvaEventObject<WheelEvent>) => {
@@ -28,10 +29,16 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
       // checking for ctrl key is pressed or not,
       // so that brush size can be controlled using ctrl + scroll up/down
 
+      // Invert the delta if the property is set to true
+      let delta = e.evt.deltaY;
+      if (shouldInvertBrushSizeScrollDirection) {
+        delta = -delta;
+      }
+
       if ($ctrl.get() || $meta.get()) {
         // This equation was derived by fitting a curve to the desired brush sizes and deltas
         // see https://github.com/invoke-ai/InvokeAI/pull/5542#issuecomment-1915847565
-        const targetDelta = Math.sign(e.evt.deltaY) * 0.7363 * Math.pow(1.0394, brushSize);
+        const targetDelta = Math.sign(delta) * 0.7363 * Math.pow(1.0394, brushSize);
         // This needs to be clamped to prevent the delta from getting too large
         const finalDelta = clamp(targetDelta, -20, 20);
         // The new brush size is also clamped to prevent it from getting too large or small
@@ -67,7 +74,7 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
         dispatch(setStageCoordinates(newCoordinates));
       }
     },
-    [stageRef, isMoveStageKeyHeld, stageScale, dispatch, brushSize]
+    [stageRef, isMoveStageKeyHeld, brushSize, dispatch, stageScale, shouldInvertBrushSizeScrollDirection]
   );
 };
 

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

@@ -65,6 +65,7 @@ const initialCanvasState: CanvasState = {
   shouldAutoSave: false,
   shouldCropToBoundingBoxOnSave: false,
   shouldDarkenOutsideBoundingBox: false,
+  shouldInvertBrushSizeScrollDirection: false,
   shouldLockBoundingBox: false,
   shouldPreserveMaskedArea: false,
   shouldRestrictStrokesToBox: true,
@@ -220,6 +221,9 @@ export const canvasSlice = createSlice({
     setShouldDarkenOutsideBoundingBox: (state, action: PayloadAction<boolean>) => {
       state.shouldDarkenOutsideBoundingBox = action.payload;
     },
+    setShouldInvertBrushSizeScrollDirection: (state, action: PayloadAction<boolean>) => {
+      state.shouldInvertBrushSizeScrollDirection = action.payload;
+    },
     clearCanvasHistory: (state) => {
       state.pastLayerStates = [];
       state.futureLayerStates = [];
@@ -288,6 +292,31 @@ export const canvasSlice = createSlice({
       state.shouldShowStagingImage = true;
       state.batchIds = [];
     },
+    discardStagedImage: (state) => {
+      const { images, selectedImageIndex } = state.layerState.stagingArea;
+      state.pastLayerStates.push(cloneDeep(state.layerState));
+
+      if (state.pastLayerStates.length > MAX_HISTORY) {
+        state.pastLayerStates.shift();
+      }
+
+      if (!images.length) {
+        return;
+      }
+
+      images.splice(selectedImageIndex, 1);
+
+      if (selectedImageIndex >= images.length) {
+        state.layerState.stagingArea.selectedImageIndex = images.length - 1;
+      }
+
+      if (!images.length) {
+        state.shouldShowStagingImage = false;
+        state.shouldShowStagingOutline = false;
+      }
+
+      state.futureLayerStates = [];
+    },
     addFillRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions, brushColor } = state;
 
@@ -655,6 +684,7 @@ export const {
   commitColorPickerColor,
   commitStagingAreaImage,
   discardStagedImages,
+  discardStagedImage,
   nextStagingAreaImage,
   prevStagingAreaImage,
   redo,
@@ -674,6 +704,7 @@ export const {
   setShouldAutoSave,
   setShouldCropToBoundingBoxOnSave,
   setShouldDarkenOutsideBoundingBox,
+  setShouldInvertBrushSizeScrollDirection,
   setShouldPreserveMaskedArea,
   setShouldShowBoundingBox,
   setShouldShowCanvasDebugInfo,

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

@@ -120,6 +120,7 @@ export interface CanvasState {
   shouldAutoSave: boolean;
   shouldCropToBoundingBoxOnSave: boolean;
   shouldDarkenOutsideBoundingBox: boolean;
+  shouldInvertBrushSizeScrollDirection: boolean;
   shouldLockBoundingBox: boolean;
   shouldPreserveMaskedArea: boolean;
   shouldRestrictStrokesToBox: boolean;

invokeai/frontend/web/src/features/controlAdapters/hooks/useAddControlAdapter.ts

@@ -1,6 +1,12 @@
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { CONTROLNET_MODEL_DEFAULT_PROCESSORS, CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
 import { controlAdapterAdded } from 'features/controlAdapters/store/controlAdaptersSlice';
-import type { ControlAdapterType } from 'features/controlAdapters/store/types';
+import type {
+  ControlAdapterProcessorType,
+  ControlAdapterType,
+  RequiredControlAdapterProcessorNode,
+} from 'features/controlAdapters/store/types';
+import { cloneDeep } from 'lodash-es';
 import { useCallback, useMemo } from 'react';
 
 import { useControlAdapterModels } from './useControlAdapterModels';
@@ -22,6 +28,29 @@ export const useAddControlAdapter = (type: ControlAdapterType) => {
     return models[0];
   }, [baseModel, models]);
 
+  const processor = useMemo(() => {
+    let processorType;
+    for (const modelSubstring in CONTROLNET_MODEL_DEFAULT_PROCESSORS) {
+      if (firstModel?.name.includes(modelSubstring)) {
+        processorType = CONTROLNET_MODEL_DEFAULT_PROCESSORS[modelSubstring];
+        break;
+      }
+    }
+
+    if (!processorType) {
+      processorType = 'none';
+    }
+
+    const processorNode =
+      processorType === 'none'
+        ? (cloneDeep(CONTROLNET_PROCESSORS.none.default) as RequiredControlAdapterProcessorNode)
+        : (cloneDeep(
+            CONTROLNET_PROCESSORS[processorType as ControlAdapterProcessorType].default
+          ) as RequiredControlAdapterProcessorNode);
+
+    return { processorType, processorNode };
+  }, [firstModel]);
+
   const isDisabled = useMemo(() => !firstModel, [firstModel]);
 
   const addControlAdapter = useCallback(() => {
@@ -31,10 +60,10 @@ export const useAddControlAdapter = (type: ControlAdapterType) => {
     dispatch(
       controlAdapterAdded({
         type,
-        overrides: { model: firstModel },
+        overrides: { model: firstModel, ...processor },
       })
     );
-  }, [dispatch, firstModel, isDisabled, type]);
+  }, [dispatch, firstModel, isDisabled, type, processor]);
 
   return [addControlAdapter, isDisabled] as const;
 };

invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts

@@ -13,10 +13,7 @@ import { socketInvocationError } from 'services/events/actions';
 import { v4 as uuidv4 } from 'uuid';
 
 import { controlAdapterImageProcessed } from './actions';
-import {
-  CONTROLNET_MODEL_DEFAULT_PROCESSORS as CONTROLADAPTER_MODEL_DEFAULT_PROCESSORS,
-  CONTROLNET_PROCESSORS,
-} from './constants';
+import { CONTROLNET_PROCESSORS } from './constants';
 import type {
   ControlAdapterConfig,
   ControlAdapterProcessorType,
@@ -215,25 +212,6 @@ export const controlAdaptersSlice = createSlice({
 
       update.changes.processedControlImage = null;
 
-      let processorType: ControlAdapterProcessorType | undefined = undefined;
-
-      for (const modelSubstring in CONTROLADAPTER_MODEL_DEFAULT_PROCESSORS) {
-        // TODO(MM2): matching modelSubstring to the model key is no longer a valid way to figure out the default processorType
-        if (model.key.includes(modelSubstring)) {
-          processorType = CONTROLADAPTER_MODEL_DEFAULT_PROCESSORS[modelSubstring];
-          break;
-        }
-      }
-
-      if (processorType) {
-        update.changes.processorType = processorType;
-        update.changes.processorNode = CONTROLNET_PROCESSORS[processorType]
-          .default as RequiredControlAdapterProcessorNode;
-      } else {
-        update.changes.processorType = 'none';
-        update.changes.processorNode = CONTROLNET_PROCESSORS.none.default as RequiredControlAdapterProcessorNode;
-      }
-
       caAdapter.updateOne(state, update);
     },
     controlAdapterWeightChanged: (state, action: PayloadAction<{ id: string; weight: number }>) => {
@@ -298,17 +276,19 @@ export const controlAdaptersSlice = createSlice({
       action: PayloadAction<{
         id: string;
         processorType: ControlAdapterProcessorType;
+        shouldAutoConfig?: boolean;
       }>
     ) => {
-      const { id, processorType } = action.payload;
+      const { id, processorType, shouldAutoConfig = false } = action.payload;
       const cn = selectControlAdapterById(state, id);
       if (!cn || !isControlNetOrT2IAdapter(cn)) {
         return;
       }
 
-      const processorNode = cloneDeep(
-        CONTROLNET_PROCESSORS[processorType].default
-      ) as RequiredControlAdapterProcessorNode;
+      const processorNode =
+        processorType === 'none'
+          ? (cloneDeep(CONTROLNET_PROCESSORS.none.default) as RequiredControlAdapterProcessorNode)
+          : (cloneDeep(CONTROLNET_PROCESSORS[processorType].default) as RequiredControlAdapterProcessorNode);
 
       caAdapter.updateOne(state, {
         id,
@@ -316,7 +296,7 @@ export const controlAdaptersSlice = createSlice({
           processorType,
           processedControlImage: null,
           processorNode,
-          shouldAutoConfig: false,
+          shouldAutoConfig,
         },
       });
     },
@@ -337,28 +317,6 @@ export const controlAdaptersSlice = createSlice({
         changes: { shouldAutoConfig: !cn.shouldAutoConfig },
       };
 
-      if (update.changes.shouldAutoConfig) {
-        // manage the processor for the user
-        let processorType: ControlAdapterProcessorType | undefined = undefined;
-
-        for (const modelSubstring in CONTROLADAPTER_MODEL_DEFAULT_PROCESSORS) {
-          // TODO(MM2): matching modelSubstring to the model key is no longer a valid way to figure out the default processorType
-          if (cn.model?.key.includes(modelSubstring)) {
-            processorType = CONTROLADAPTER_MODEL_DEFAULT_PROCESSORS[modelSubstring];
-            break;
-          }
-        }
-
-        if (processorType) {
-          update.changes.processorType = processorType;
-          update.changes.processorNode = CONTROLNET_PROCESSORS[processorType]
-            .default as RequiredControlAdapterProcessorNode;
-        } else {
-          update.changes.processorType = 'none';
-          update.changes.processorNode = CONTROLNET_PROCESSORS.none.default as RequiredControlAdapterProcessorNode;
-        }
-      }
-
       caAdapter.updateOne(state, update);
     },
     controlAdaptersReset: () => {

invokeai/frontend/web/src/features/gallery/components/Boards/AutoAddIcon.tsx

@@ -6,7 +6,7 @@ const AutoAddIcon = () => {
   const { t } = useTranslation();
   return (
     <Flex position="absolute" insetInlineEnd={0} top={0} p={1}>
-      <Badge variant="solid" bg="invokeBlue.500">
+      <Badge variant="solid" bg="invokeBlue.400">
         {t('common.auto')}
       </Badge>
     </Flex>

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/GalleryBoard.tsx

@@ -173,8 +173,8 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
                   w="full"
                   maxW="full"
                   borderBottomRadius="base"
-                  bg={isSelected ? 'invokeBlue.500' : 'base.600'}
-                  color={isSelected ? 'base.50' : 'base.100'}
+                  bg={isSelected ? 'invokeBlue.400' : 'base.600'}
+                  color={isSelected ? 'base.800' : 'base.100'}
                   lineHeight="short"
                   fontSize="xs"
                 >
@@ -193,6 +193,7 @@ const GalleryBoard = ({ board, isSelected, setBoardToDelete }: GalleryBoardProps
                       overflow="hidden"
                       textOverflow="ellipsis"
                       noOfLines={1}
+                      color="inherit"
                     />
                     <EditableInput sx={editableInputStyles} />
                   </Editable>

invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx

@@ -109,8 +109,8 @@ const NoBoardBoard = memo(({ isSelected }: Props) => {
                   w="full"
                   maxW="full"
                   borderBottomRadius="base"
-                  bg={isSelected ? 'invokeBlue.500' : 'base.600'}
-                  color={isSelected ? 'base.50' : 'base.100'}
+                  bg={isSelected ? 'invokeBlue.400' : 'base.600'}
+                  color={isSelected ? 'base.800' : 'base.100'}
                   lineHeight="short"
                   fontSize="xs"
                   fontWeight={isSelected ? 'bold' : 'normal'}

invokeai/frontend/web/src/features/metadata/components/MetadataItemView.tsx

@@ -15,7 +15,7 @@ export const MetadataItemView = memo(
     return (
       <Flex gap={2}>
         {onRecall && <RecallButton label={label} onClick={onRecall} isDisabled={isDisabled} />}
-        <Flex direction={direction}>
+        <Flex direction={direction} fontSize="sm">
           <Text fontWeight="semibold" whiteSpace="pre-wrap" pr={2}>
             {label}:
           </Text>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelImage.tsx

@@ -0,0 +1,27 @@
+import { Box, Image } from '@invoke-ai/ui-library';
+import { typedMemo } from 'common/util/typedMemo';
+
+type Props = {
+  image_url?: string;
+};
+
+const ModelImage = ({ image_url }: Props) => {
+  if (!image_url) {
+    return <Box height="50px" minWidth="50px" />;
+  }
+
+  return (
+    <Image
+      src={image_url}
+      objectFit="cover"
+      objectPosition="50% 50%"
+      height="50px"
+      width="50px"
+      minHeight="50px"
+      minWidth="50px"
+      borderRadius="base"
+    />
+  );
+};
+
+export default typedMemo(ModelImage);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelManagerPanel/ModelListItem.tsx

@@ -22,6 +22,8 @@ import { PiTrashSimpleBold } from 'react-icons/pi';
 import { useDeleteModelsMutation } from 'services/api/endpoints/models';
 import type { AnyModelConfig } from 'services/api/types';
 
+import ModelImage from './ModelImage';
+
 type ModelListItemProps = {
   model: AnyModelConfig;
 };
@@ -73,6 +75,7 @@ const ModelListItem = (props: ModelListItemProps) => {
 
   return (
     <Flex gap={2} alignItems="center" w="full">
+      <ModelImage image_url={model.cover_image || ''} />
       <Flex
         as={Button}
         isChecked={isSelected}

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Fields/ModelImageUpload.tsx

@@ -0,0 +1,134 @@
+import { Box, Button, IconButton, Image } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { typedMemo } from 'common/util/typedMemo';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
+import { useCallback, useState } from 'react';
+import { useDropzone } from 'react-dropzone';
+import { useTranslation } from 'react-i18next';
+import { PiArrowCounterClockwiseBold, PiUploadSimpleBold } from 'react-icons/pi';
+import { useDeleteModelImageMutation, useUpdateModelImageMutation } from 'services/api/endpoints/models';
+
+type Props = {
+  model_key: string | null;
+  model_image?: string | null;
+};
+
+const ModelImageUpload = ({ model_key, model_image }: Props) => {
+  const dispatch = useAppDispatch();
+  const [image, setImage] = useState<string | null>(model_image || null);
+  const { t } = useTranslation();
+
+  const [updateModelImage] = useUpdateModelImageMutation();
+  const [deleteModelImage] = useDeleteModelImageMutation();
+
+  const onDropAccepted = useCallback(
+    (files: File[]) => {
+      const file = files[0];
+
+      if (!file || !model_key) {
+        return;
+      }
+
+      updateModelImage({ key: model_key, image: file })
+        .unwrap()
+        .then(() => {
+          setImage(URL.createObjectURL(file));
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelImageUpdated'),
+                status: 'success',
+              })
+            )
+          );
+        })
+        .catch((_) => {
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelImageUpdateFailed'),
+                status: 'error',
+              })
+            )
+          );
+        });
+    },
+    [dispatch, model_key, t, updateModelImage]
+  );
+
+  const handleResetImage = useCallback(() => {
+    if (!model_key) {
+      return;
+    }
+    setImage(null);
+    deleteModelImage(model_key)
+      .unwrap()
+      .then(() => {
+        dispatch(
+          addToast(
+            makeToast({
+              title: t('modelManager.modelImageDeleted'),
+              status: 'success',
+            })
+          )
+        );
+      })
+      .catch((_) => {
+        dispatch(
+          addToast(
+            makeToast({
+              title: t('modelManager.modelImageDeleteFailed'),
+              status: 'error',
+            })
+          )
+        );
+      });
+  }, [dispatch, model_key, t, deleteModelImage]);
+
+  const { getInputProps, getRootProps } = useDropzone({
+    accept: { 'image/png': ['.png'], 'image/jpeg': ['.jpg', '.jpeg', '.png'] },
+    onDropAccepted,
+    noDrag: true,
+    multiple: false,
+  });
+
+  if (image) {
+    return (
+      <Box position="relative">
+        <Image
+          src={image}
+          objectFit="cover"
+          objectPosition="50% 50%"
+          height="100px"
+          width="100px"
+          minWidth="100px"
+          borderRadius="base"
+        />
+        <IconButton
+          position="absolute"
+          top="1"
+          right="1"
+          onClick={handleResetImage}
+          aria-label={t('modelManager.deleteModelImage')}
+          tooltip={t('modelManager.deleteModelImage')}
+          icon={<PiArrowCounterClockwiseBold size={16} />}
+          size="sm"
+          variant="link"
+          _hover={{ color: 'base.100' }}
+        />
+      </Box>
+    );
+  }
+
+  return (
+    <>
+      <Button leftIcon={<PiUploadSimpleBold />} {...getRootProps()} pointerEvents="auto">
+        {t('modelManager.uploadImage')}
+      </Button>
+      <input {...getInputProps()} />
+    </>
+  );
+};
+
+export default typedMemo(ModelImageUpload);

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/Model.tsx

@@ -4,6 +4,7 @@ import { useAppSelector } from 'app/store/storeHooks';
 import { useTranslation } from 'react-i18next';
 import { useGetModelConfigQuery } from 'services/api/endpoints/models';
 
+import ModelImageUpload from './Fields/ModelImageUpload';
 import { ModelMetadata } from './Metadata/ModelMetadata';
 import { ModelAttrView } from './ModelAttrView';
 import { ModelEdit } from './ModelEdit';
@@ -25,19 +26,22 @@ export const Model = () => {
 
   return (
     <>
-      <Flex flexDir="column" gap={1} p={2}>
-        <Heading as="h2" fontSize="lg">
-          {data.name}
-        </Heading>
+      <Flex alignItems="center" justifyContent="space-between" gap="4" paddingRight="5">
+        <Flex flexDir="column" gap={1} p={2}>
+          <Heading as="h2" fontSize="lg">
+            {data.name}
+          </Heading>
 
-        {data.source && (
-          <Text variant="subtext">
-            {t('modelManager.source')}: {data?.source}
-          </Text>
-        )}
-        <Box mt="4">
-          <ModelAttrView label="Description" value={data.description} />
-        </Box>
+          {data.source && (
+            <Text variant="subtext">
+              {t('modelManager.source')}: {data?.source}
+            </Text>
+          )}
+          <Box mt="4">
+            <ModelAttrView label="Description" value={data.description} />
+          </Box>
+        </Flex>
+        <ModelImageUpload model_key={selectedModelKey} model_image={data.cover_image} />
       </Flex>
 
       <Tabs mt="4" h="100%">

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelEdit.tsx

@@ -129,7 +129,7 @@ export const ModelEdit = () => {
         </Flex>
 
         <Flex flexDir="column" gap={3} mt="4">
-          <Flex>
+          <Flex gap="4" alignItems="center">
             <FormControl flexDir="column" alignItems="flex-start" gap={1}>
               <FormLabel>{t('modelManager.description')}</FormLabel>
               <Textarea fontSize="md" resize="none" {...register('description')} />
@@ -145,20 +145,32 @@ export const ModelEdit = () => {
             </FormControl>
           </Flex>
           {data.type === 'main' && data.format === 'checkpoint' && (
-            <Flex gap={4}>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('modelManager.variant')}</FormLabel>
-                <ModelVariantSelect control={control} />
-              </FormControl>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('modelManager.predictionType')}</FormLabel>
-                <PredictionTypeSelect control={control} />
-              </FormControl>
-              <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                <FormLabel>{t('modelManager.upcastAttention')}</FormLabel>
-                <Checkbox {...register('upcast_attention')} />
-              </FormControl>
-            </Flex>
+            <>
+              <Flex gap={4}>
+                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                  <FormLabel>{t('modelManager.pathToConfig')}</FormLabel>
+                  <Input
+                    {...register('config_path', {
+                      validate: (value) => (value && value.trim().length > 3) || 'Must be at least 3 characters',
+                    })}
+                  />
+                </FormControl>
+                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                  <FormLabel>{t('modelManager.variant')}</FormLabel>
+                  <ModelVariantSelect control={control} />
+                </FormControl>
+              </Flex>
+              <Flex gap={4}>
+                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                  <FormLabel>{t('modelManager.predictionType')}</FormLabel>
+                  <PredictionTypeSelect control={control} />
+                </FormControl>
+                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+                  <FormLabel>{t('modelManager.upcastAttention')}</FormLabel>
+                  <Checkbox {...register('upcast_attention')} />
+                </FormControl>
+              </Flex>
+            </>
           )}
         </Flex>
       </form>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelView.tsx

@@ -90,21 +90,26 @@ export const ModelView = () => {
             <ModelAttrView label={t('common.format')} value={modelData.format} />
             <ModelAttrView label={t('modelManager.path')} value={modelData.path} />
           </Flex>
-          {modelData.type === 'main' && (
+
+          {modelData.type === 'main' && modelData.format === 'diffusers' && modelData.repo_variant && (
             <Flex gap={2}>
-              {modelData.format === 'diffusers' && modelData.repo_variant && (
-                <ModelAttrView label={t('modelManager.repoVariant')} value={modelData.repo_variant} />
-              )}
-              {modelData.format === 'checkpoint' && (
-                <>
-                  <ModelAttrView label={t('modelManager.pathToConfig')} value={modelData.config_path} />
-                  <ModelAttrView label={t('modelManager.variant')} value={modelData.variant} />
-                  <ModelAttrView label={t('modelManager.predictionType')} value={modelData.prediction_type} />
-                  <ModelAttrView label={t('modelManager.upcastAttention')} value={`${modelData.upcast_attention}`} />
-                </>
-              )}
+              <ModelAttrView label={t('modelManager.repoVariant')} value={modelData.repo_variant} />
             </Flex>
           )}
+
+          {modelData.type === 'main' && modelData.format === 'checkpoint' && (
+            <>
+              <Flex gap={2}>
+                <ModelAttrView label={t('modelManager.pathToConfig')} value={modelData.config_path} />
+                <ModelAttrView label={t('modelManager.variant')} value={modelData.variant} />
+              </Flex>
+              <Flex gap={2}>
+                <ModelAttrView label={t('modelManager.predictionType')} value={modelData.prediction_type} />
+                <ModelAttrView label={t('modelManager.upcastAttention')} value={`${modelData.upcast_attention}`} />
+              </Flex>
+            </>
+          )}
+
           {modelData.type === 'ip_adapter' && (
             <Flex gap={2}>
               <ModelAttrView label={t('modelManager.imageEncoderModelId')} value={modelData.image_encoder_model_id} />
@@ -112,9 +117,11 @@ export const ModelView = () => {
           )}
         </Flex>
       </Box>
-      <Box layerStyle="second" borderRadius="base" p={3}>
-        <DefaultSettings />
-      </Box>
+      {modelData.type === 'main' && (
+        <Box layerStyle="second" borderRadius="base" p={3}>
+          <DefaultSettings />
+        </Box>
+      )}
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/EmptyState.tsx

@@ -0,0 +1,55 @@
+import { Button, Flex, Image, Text } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { workflowModeChanged } from 'features/nodes/store/workflowSlice';
+import InvokeLogoSVG from 'public/assets/images/invoke-symbol-wht-lrg.svg';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+export const EmptyState = () => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  const onClick = useCallback(() => {
+    dispatch(workflowModeChanged('edit'));
+  }, [dispatch]);
+
+  return (
+    <Flex
+      sx={{
+        w: 'full',
+        h: 'full',
+        userSelect: 'none',
+      }}
+    >
+      <Flex
+        sx={{
+          alignItems: 'center',
+          justifyContent: 'center',
+          borderRadius: 'base',
+          flexDir: 'column',
+          gap: 5,
+          maxW: '230px',
+          margin: '0 auto',
+        }}
+      >
+        <Image
+          src={InvokeLogoSVG}
+          alt="invoke-ai-logo"
+          opacity={0.2}
+          mixBlendMode="overlay"
+          w={16}
+          h={16}
+          minW={16}
+          minH={16}
+          userSelect="none"
+        />
+        <Text textAlign="center" fontSize="md">
+          {t('nodes.noFieldsViewMode')}
+        </Text>
+        <Button colorScheme="invokeBlue" onClick={onClick}>
+          {t('nodes.edit')}
+        </Button>
+      </Flex>
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowViewMode.tsx

@@ -7,6 +7,7 @@ import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
 import { t } from 'i18next';
 import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
+import { EmptyState } from './EmptyState';
 import WorkflowField from './WorkflowField';
 
 const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
@@ -30,7 +31,7 @@ export const WorkflowViewMode = () => {
               <WorkflowField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
             ))
           ) : (
-            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
+            <EmptyState />
           )}
         </Flex>
       </ScrollableContent>

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/WorkflowPanel.tsx

@@ -12,17 +12,17 @@ const WorkflowPanel = () => {
     <Flex layerStyle="first" flexDir="column" w="full" h="full" borderRadius="base" p={2} gap={2}>
       <Tabs variant="line" display="flex" w="full" h="full" flexDir="column">
         <TabList>
-          <Tab>{t('common.details')}</Tab>
           <Tab>{t('common.linear')}</Tab>
+          <Tab>{t('common.details')}</Tab>
           <Tab>JSON</Tab>
         </TabList>
 
         <TabPanels>
           <TabPanel>
-            <WorkflowGeneralTab />
+            <WorkflowLinearTab />
           </TabPanel>
           <TabPanel>
-            <WorkflowLinearTab />
+            <WorkflowGeneralTab />
           </TabPanel>
           <TabPanel>
             <WorkflowJSONTab />

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

@@ -55,8 +55,22 @@ export type SchedulerField = z.infer<typeof zSchedulerField>;
 
 // #region Model-related schemas
 const zBaseModel = z.enum(['any', 'sd-1', 'sd-2', 'sdxl', 'sdxl-refiner']);
+const zSubModelType = z.enum([
+  'unet',
+  'text_encoder',
+  'text_encoder_2',
+  'tokenizer',
+  'tokenizer_2',
+  'vae',
+  'vae_decoder',
+  'vae_encoder',
+  'scheduler',
+  'safety_checker',
+]);
+
 const zModelIdentifier = z.object({
   key: z.string().min(1),
+  submodel_type: zSubModelType.nullish(),
 });
 export const isModelIdentifier = (field: unknown): field is ModelIdentifier =>
   zModelIdentifier.safeParse(field).success;

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

@@ -1,18 +1,22 @@
 import type { RootState } from 'app/store/store';
 import { selectValidControlNets } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { omit } from 'lodash-es';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import type {
   CollectInvocation,
-  ControlField,
   ControlNetInvocation,
   CoreMetadataInvocation,
   NonNullableGraph,
 } from 'services/api/types';
+import { isControlNetModelConfig } from 'services/api/types';
 
 import { CONTROL_NET_COLLECT } from './constants';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addControlNetToLinearGraph = (state: RootState, graph: NonNullableGraph, baseNodeId: string): void => {
+export const addControlNetToLinearGraph = async (
+  state: RootState,
+  graph: NonNullableGraph,
+  baseNodeId: string
+): Promise<void> => {
   const validControlNets = selectValidControlNets(state.controlAdapters).filter(
     (ca) => ca.model?.base === state.generation.model?.base
   );
@@ -39,7 +43,7 @@ export const addControlNetToLinearGraph = (state: RootState, graph: NonNullableG
       },
     });
 
-    validControlNets.forEach((controlNet) => {
+    validControlNets.forEach(async (controlNet) => {
       if (!controlNet.model) {
         return;
       }
@@ -85,7 +89,17 @@ export const addControlNetToLinearGraph = (state: RootState, graph: NonNullableG
 
       graph.nodes[controlNetNode.id] = controlNetNode as ControlNetInvocation;
 
-      controlNetMetadata.push(omit(controlNetNode, ['id', 'type', 'is_intermediate']) as ControlField);
+      const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isControlNetModelConfig);
+
+      controlNetMetadata.push({
+        control_model: getModelMetadataField(modelConfig),
+        control_weight: weight,
+        control_mode: controlMode,
+        begin_step_percent: beginStepPct,
+        end_step_percent: endStepPct,
+        resize_mode: resizeMode,
+        image: controlNetNode.image,
+      });
 
       graph.edges.push({
         source: { node_id: controlNetNode.id, field: 'control' },

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

@@ -1,18 +1,22 @@
 import type { RootState } from 'app/store/store';
 import { selectValidIPAdapters } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { omit } from 'lodash-es';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import type {
   CollectInvocation,
   CoreMetadataInvocation,
   IPAdapterInvocation,
-  IPAdapterMetadataField,
   NonNullableGraph,
 } from 'services/api/types';
+import { isIPAdapterModelConfig } from 'services/api/types';
 
 import { IP_ADAPTER_COLLECT } from './constants';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addIPAdapterToLinearGraph = (state: RootState, graph: NonNullableGraph, baseNodeId: string): void => {
+export const addIPAdapterToLinearGraph = async (
+  state: RootState,
+  graph: NonNullableGraph,
+  baseNodeId: string
+): Promise<void> => {
   const validIPAdapters = selectValidIPAdapters(state.controlAdapters).filter(
     (ca) => ca.model?.base === state.generation.model?.base
   );
@@ -35,7 +39,7 @@ export const addIPAdapterToLinearGraph = (state: RootState, graph: NonNullableGr
 
     const ipAdapterMetdata: CoreMetadataInvocation['ipAdapters'] = [];
 
-    validIPAdapters.forEach((ipAdapter) => {
+    validIPAdapters.forEach(async (ipAdapter) => {
       if (!ipAdapter.model) {
         return;
       }
@@ -58,9 +62,17 @@ export const addIPAdapterToLinearGraph = (state: RootState, graph: NonNullableGr
         return;
       }
 
-      graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation;
+      graph.nodes[ipAdapterNode.id] = ipAdapterNode;
 
-      ipAdapterMetdata.push(omit(ipAdapterNode, ['id', 'type', 'is_intermediate']) as IPAdapterMetadataField);
+      const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isIPAdapterModelConfig);
+
+      ipAdapterMetdata.push({
+        weight: weight,
+        ip_adapter_model: getModelMetadataField(modelConfig),
+        begin_step_percent: beginStepPct,
+        end_step_percent: endStepPct,
+        image: ipAdapterNode.image,
+      });
 
       graph.edges.push({
         source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },

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

@@ -1,16 +1,22 @@
 import type { RootState } from 'app/store/store';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { filter, size } from 'lodash-es';
-import type { CoreMetadataInvocation, LoraLoaderInvocation, NonNullableGraph } from 'services/api/types';
+import {
+  type CoreMetadataInvocation,
+  isLoRAModelConfig,
+  type LoRALoaderInvocation,
+  type NonNullableGraph,
+} from 'services/api/types';
 
 import { CLIP_SKIP, LORA_LOADER, MAIN_MODEL_LOADER, NEGATIVE_CONDITIONING, POSITIVE_CONDITIONING } from './constants';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addLoRAsToGraph = (
+export const addLoRAsToGraph = async (
   state: RootState,
   graph: NonNullableGraph,
   baseNodeId: string,
   modelLoaderNodeId: string = MAIN_MODEL_LOADER
-): void => {
+): Promise<void> => {
   /**
    * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
    * They then output the UNet and CLIP models references on to either the next LoRA in the chain,
@@ -39,12 +45,12 @@ export const addLoRAsToGraph = (
   let currentLoraIndex = 0;
   const loraMetadata: CoreMetadataInvocation['loras'] = [];
 
-  enabledLoRAs.forEach((lora) => {
+  enabledLoRAs.forEach(async (lora) => {
     const { weight } = lora;
     const { key } = lora.model;
     const currentLoraNodeId = `${LORA_LOADER}_${key}`;
 
-    const loraLoaderNode: LoraLoaderInvocation = {
+    const loraLoaderNode: LoRALoaderInvocation = {
       type: 'lora_loader',
       id: currentLoraNodeId,
       is_intermediate: true,
@@ -52,8 +58,10 @@ export const addLoRAsToGraph = (
       weight,
     };
 
+    const modelConfig = await fetchModelConfigWithTypeGuard(key, isLoRAModelConfig);
+
     loraMetadata.push({
-      model: { key },
+      model: getModelMetadataField(modelConfig),
       weight,
     });
 

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

@@ -1,6 +1,12 @@
 import type { RootState } from 'app/store/store';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { filter, size } from 'lodash-es';
-import type { CoreMetadataInvocation, NonNullableGraph, SDXLLoraLoaderInvocation } from 'services/api/types';
+import {
+  type CoreMetadataInvocation,
+  isLoRAModelConfig,
+  type NonNullableGraph,
+  type SDXLLoRALoaderInvocation,
+} from 'services/api/types';
 
 import {
   LORA_LOADER,
@@ -10,14 +16,14 @@ import {
   SDXL_REFINER_INPAINT_CREATE_MASK,
   SEAMLESS,
 } from './constants';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addSDXLLoRAsToGraph = (
+export const addSDXLLoRAsToGraph = async (
   state: RootState,
   graph: NonNullableGraph,
   baseNodeId: string,
   modelLoaderNodeId: string = SDXL_MODEL_LOADER
-): void => {
+): Promise<void> => {
   /**
    * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
    * They then output the UNet and CLIP models references on to either the next LoRA in the chain,
@@ -55,12 +61,12 @@ export const addSDXLLoRAsToGraph = (
   let lastLoraNodeId = '';
   let currentLoraIndex = 0;
 
-  enabledLoRAs.forEach((lora) => {
+  enabledLoRAs.forEach(async (lora) => {
     const { weight } = lora;
     const { key } = lora.model;
     const currentLoraNodeId = `${LORA_LOADER}_${key}`;
 
-    const loraLoaderNode: SDXLLoraLoaderInvocation = {
+    const loraLoaderNode: SDXLLoRALoaderInvocation = {
       type: 'sdxl_lora_loader',
       id: currentLoraNodeId,
       is_intermediate: true,
@@ -68,7 +74,9 @@ export const addSDXLLoRAsToGraph = (
       weight,
     };
 
-    loraMetadata.push({ model: { key }, weight });
+    const modelConfig = await fetchModelConfigWithTypeGuard(key, isLoRAModelConfig);
+
+    loraMetadata.push({ model: getModelMetadataField(modelConfig), weight });
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;

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

@@ -1,9 +1,11 @@
 import type { RootState } from 'app/store/store';
-import type {
-  CreateDenoiseMaskInvocation,
-  ImageDTO,
-  NonNullableGraph,
-  SeamlessModeInvocation,
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import {
+  type CreateDenoiseMaskInvocation,
+  type ImageDTO,
+  isRefinerMainModelModelConfig,
+  type NonNullableGraph,
+  type SeamlessModeInvocation,
 } from 'services/api/types';
 
 import {
@@ -25,16 +27,16 @@ import {
   SDXL_REFINER_SEAMLESS,
 } from './constants';
 import { getSDXLStylePrompts } from './graphBuilderUtils';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addSDXLRefinerToGraph = (
+export const addSDXLRefinerToGraph = async (
   state: RootState,
   graph: NonNullableGraph,
   baseNodeId: string,
   modelLoaderNodeId?: string,
   canvasInitImage?: ImageDTO,
   canvasMaskImage?: ImageDTO
-): void => {
+): Promise<void> => {
   const {
     refinerModel,
     refinerPositiveAestheticScore,
@@ -55,9 +57,10 @@ export const addSDXLRefinerToGraph = (
   const fp32 = vaePrecision === 'fp32';
 
   const isUsingScaledDimensions = ['auto', 'manual'].includes(boundingBoxScaleMethod);
+  const modelConfig = await fetchModelConfigWithTypeGuard(refinerModel.key, isRefinerMainModelModelConfig);
 
   upsertMetadata(graph, {
-    refiner_model: refinerModel,
+    refiner_model: getModelMetadataField(modelConfig),
     refiner_positive_aesthetic_score: refinerPositiveAestheticScore,
     refiner_negative_aesthetic_score: refinerNegativeAestheticScore,
     refiner_cfg_scale: refinerCFGScale,

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

@@ -1,18 +1,22 @@
 import type { RootState } from 'app/store/store';
 import { selectValidT2IAdapters } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { omit } from 'lodash-es';
-import type {
-  CollectInvocation,
-  CoreMetadataInvocation,
-  NonNullableGraph,
-  T2IAdapterField,
-  T2IAdapterInvocation,
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import {
+  type CollectInvocation,
+  type CoreMetadataInvocation,
+  isT2IAdapterModelConfig,
+  type NonNullableGraph,
+  type T2IAdapterInvocation,
 } from 'services/api/types';
 
 import { T2I_ADAPTER_COLLECT } from './constants';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addT2IAdaptersToLinearGraph = (state: RootState, graph: NonNullableGraph, baseNodeId: string): void => {
+export const addT2IAdaptersToLinearGraph = async (
+  state: RootState,
+  graph: NonNullableGraph,
+  baseNodeId: string
+): Promise<void> => {
   const validT2IAdapters = selectValidT2IAdapters(state.controlAdapters).filter(
     (ca) => ca.model?.base === state.generation.model?.base
   );
@@ -33,9 +37,9 @@ export const addT2IAdaptersToLinearGraph = (state: RootState, graph: NonNullable
       },
     });
 
-    const t2iAdapterMetdata: CoreMetadataInvocation['t2iAdapters'] = [];
+    const t2iAdapterMetadata: CoreMetadataInvocation['t2iAdapters'] = [];
 
-    validT2IAdapters.forEach((t2iAdapter) => {
+    validT2IAdapters.forEach(async (t2iAdapter) => {
       if (!t2iAdapter.model) {
         return;
       }
@@ -77,9 +81,18 @@ export const addT2IAdaptersToLinearGraph = (state: RootState, graph: NonNullable
         return;
       }
 
-      graph.nodes[t2iAdapterNode.id] = t2iAdapterNode as T2IAdapterInvocation;
+      graph.nodes[t2iAdapterNode.id] = t2iAdapterNode;
 
-      t2iAdapterMetdata.push(omit(t2iAdapterNode, ['id', 'type', 'is_intermediate']) as T2IAdapterField);
+      const modelConfig = await fetchModelConfigWithTypeGuard(t2iAdapter.model.key, isT2IAdapterModelConfig);
+
+      t2iAdapterMetadata.push({
+        begin_step_percent: beginStepPct,
+        end_step_percent: endStepPct,
+        resize_mode: resizeMode,
+        t2i_adapter_model: getModelMetadataField(modelConfig),
+        weight: weight,
+        image: t2iAdapterNode.image,
+      });
 
       graph.edges.push({
         source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' },
@@ -90,6 +103,6 @@ export const addT2IAdaptersToLinearGraph = (state: RootState, graph: NonNullable
       });
     });
 
-    upsertMetadata(graph, { t2iAdapters: t2iAdapterMetdata });
+    upsertMetadata(graph, { t2iAdapters: t2iAdapterMetadata });
   }
 };

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

@@ -1,5 +1,7 @@
 import type { RootState } from 'app/store/store';
-import type { NonNullableGraph } from 'services/api/types';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import type { ModelMetadataField, NonNullableGraph } from 'services/api/types';
+import { isVAEModelConfig } from 'services/api/types';
 
 import {
   CANVAS_IMAGE_TO_IMAGE_GRAPH,
@@ -23,13 +25,13 @@ import {
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
-import { upsertMetadata } from './metadata';
+import { getModelMetadataField, upsertMetadata } from './metadata';
 
-export const addVAEToGraph = (
+export const addVAEToGraph = async (
   state: RootState,
   graph: NonNullableGraph,
   modelLoaderNodeId: string = MAIN_MODEL_LOADER
-): void => {
+): Promise<void> => {
   const { vae, seamlessXAxis, seamlessYAxis } = state.generation;
   const { boundingBoxScaleMethod } = state.canvas;
   const { refinerModel } = state.sdxl;
@@ -149,6 +151,8 @@ export const addVAEToGraph = (
   }
 
   if (vae) {
-    upsertMetadata(graph, { vae });
+    const modelConfig = await fetchModelConfigWithTypeGuard(vae.key, isVAEModelConfig);
+    const vaeMetadata: ModelMetadataField = getModelMetadataField(modelConfig);
+    upsertMetadata(graph, { vae: vaeMetadata });
   }
 };

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasGraph.ts

@@ -10,46 +10,46 @@ import { buildCanvasSDXLOutpaintGraph } from './buildCanvasSDXLOutpaintGraph';
 import { buildCanvasSDXLTextToImageGraph } from './buildCanvasSDXLTextToImageGraph';
 import { buildCanvasTextToImageGraph } from './buildCanvasTextToImageGraph';
 
-export const buildCanvasGraph = (
+export const buildCanvasGraph = async (
   state: RootState,
   generationMode: 'txt2img' | 'img2img' | 'inpaint' | 'outpaint',
   canvasInitImage: ImageDTO | undefined,
   canvasMaskImage: ImageDTO | undefined
-) => {
+): Promise<NonNullableGraph> => {
   let graph: NonNullableGraph;
 
   if (generationMode === 'txt2img') {
     if (state.generation.model && state.generation.model.base === 'sdxl') {
-      graph = buildCanvasSDXLTextToImageGraph(state);
+      graph = await buildCanvasSDXLTextToImageGraph(state);
     } else {
-      graph = buildCanvasTextToImageGraph(state);
+      graph = await buildCanvasTextToImageGraph(state);
     }
   } else if (generationMode === 'img2img') {
     if (!canvasInitImage) {
       throw new Error('Missing canvas init image');
     }
     if (state.generation.model && state.generation.model.base === 'sdxl') {
-      graph = buildCanvasSDXLImageToImageGraph(state, canvasInitImage);
+      graph = await buildCanvasSDXLImageToImageGraph(state, canvasInitImage);
     } else {
-      graph = buildCanvasImageToImageGraph(state, canvasInitImage);
+      graph = await buildCanvasImageToImageGraph(state, canvasInitImage);
     }
   } else if (generationMode === 'inpaint') {
     if (!canvasInitImage || !canvasMaskImage) {
       throw new Error('Missing canvas init and mask images');
     }
     if (state.generation.model && state.generation.model.base === 'sdxl') {
-      graph = buildCanvasSDXLInpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = await buildCanvasSDXLInpaintGraph(state, canvasInitImage, canvasMaskImage);
     } else {
-      graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = await buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
     }
   } else {
     if (!canvasInitImage) {
       throw new Error('Missing canvas init image');
     }
     if (state.generation.model && state.generation.model.base === 'sdxl') {
-      graph = buildCanvasSDXLOutpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = await buildCanvasSDXLOutpaintGraph(state, canvasInitImage, canvasMaskImage);
     } else {
-      graph = buildCanvasOutpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = await buildCanvasOutpaintGraph(state, canvasInitImage, canvasMaskImage);
     }
   }
 

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

@@ -1,7 +1,13 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
-import type { ImageDTO, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
+import {
+  type ImageDTO,
+  type ImageToLatentsInvocation,
+  isNonRefinerMainModelConfig,
+  type NonNullableGraph,
+} from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -25,12 +31,15 @@ import {
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
  */
-export const buildCanvasImageToImageGraph = (state: RootState, initialImage: ImageDTO): NonNullableGraph => {
+export const buildCanvasImageToImageGraph = async (
+  state: RootState,
+  initialImage: ImageDTO
+): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -306,6 +315,8 @@ export const buildCanvasImageToImageGraph = (state: RootState, initialImage: Ima
     });
   }
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -316,7 +327,7 @@ export const buildCanvasImageToImageGraph = (state: RootState, initialImage: Ima
       height: !isUsingScaledDimensions ? height : scaledBoundingBoxDimensions.height,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -335,17 +346,17 @@ export const buildCanvasImageToImageGraph = (state: RootState, initialImage: Ima
   }
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS);
+  await addLoRAsToGraph(state, graph, DENOISE_LATENTS);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -40,11 +40,11 @@ import { getBoardField, getIsIntermediate } from './graphBuilderUtils';
 /**
  * Builds the Canvas tab's Inpaint graph.
  */
-export const buildCanvasInpaintGraph = (
+export const buildCanvasInpaintGraph = async (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage: ImageDTO
-): NonNullableGraph => {
+): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -414,17 +414,17 @@ export const buildCanvasInpaintGraph = (
   }
 
   // Add VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasOutpaintGraph.ts

@@ -44,11 +44,11 @@ import { getBoardField, getIsIntermediate } from './graphBuilderUtils';
 /**
  * Builds the Canvas tab's Outpaint graph.
  */
-export const buildCanvasOutpaintGraph = (
+export const buildCanvasOutpaintGraph = async (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage?: ImageDTO
-): NonNullableGraph => {
+): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -545,18 +545,18 @@ export const buildCanvasOutpaintGraph = (
   }
 
   // Add VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,6 +1,12 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
-import type { ImageDTO, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import {
+  type ImageDTO,
+  type ImageToLatentsInvocation,
+  isNonRefinerMainModelConfig,
+  type NonNullableGraph,
+} from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -26,12 +32,15 @@ import {
   SEAMLESS,
 } from './constants';
 import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBuilderUtils';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
  */
-export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage: ImageDTO): NonNullableGraph => {
+export const buildCanvasSDXLImageToImageGraph = async (
+  state: RootState,
+  initialImage: ImageDTO
+): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -307,6 +316,8 @@ export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage:
     });
   }
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -317,7 +328,7 @@ export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage:
       height: !isUsingScaledDimensions ? height : scaledBoundingBoxDimensions.height,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -338,24 +349,24 @@ export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage:
 
   // Add Refiner if enabled
   if (refinerModel) {
-    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLInpaintGraph.ts

@@ -41,11 +41,11 @@ import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBu
 /**
  * Builds the Canvas tab's Inpaint graph.
  */
-export const buildCanvasSDXLInpaintGraph = (
+export const buildCanvasSDXLInpaintGraph = async (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage: ImageDTO
-): NonNullableGraph => {
+): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -426,24 +426,31 @@ export const buildCanvasSDXLInpaintGraph = (
 
   // Add Refiner if enabled
   if (refinerModel) {
-    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage, canvasMaskImage);
+    await addSDXLRefinerToGraph(
+      state,
+      graph,
+      SDXL_DENOISE_LATENTS,
+      modelLoaderNodeId,
+      canvasInitImage,
+      canvasMaskImage
+    );
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // Add VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLOutpaintGraph.ts

@@ -45,11 +45,11 @@ import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBu
 /**
  * Builds the Canvas tab's Outpaint graph.
  */
-export const buildCanvasSDXLOutpaintGraph = (
+export const buildCanvasSDXLOutpaintGraph = async (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage?: ImageDTO
-): NonNullableGraph => {
+): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -555,25 +555,25 @@ export const buildCanvasSDXLOutpaintGraph = (
 
   // Add Refiner if enabled
   if (refinerModel) {
-    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage);
+    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // Add VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,6 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
-import type { NonNullableGraph } from 'services/api/types';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -24,12 +25,12 @@ import {
   SEAMLESS,
 } from './constants';
 import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBuilderUtils';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
  */
-export const buildCanvasSDXLTextToImageGraph = (state: RootState): NonNullableGraph => {
+export const buildCanvasSDXLTextToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -272,6 +273,8 @@ export const buildCanvasSDXLTextToImageGraph = (state: RootState): NonNullableGr
     });
   }
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -284,7 +287,7 @@ export const buildCanvasSDXLTextToImageGraph = (state: RootState): NonNullableGr
       negative_prompt: negativePrompt,
       positive_style_prompt: positiveStylePrompt,
       negative_style_prompt: negativeStylePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -301,24 +304,24 @@ export const buildCanvasSDXLTextToImageGraph = (state: RootState): NonNullableGr
 
   // Add Refiner if enabled
   if (refinerModel) {
-    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,7 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
-import type { NonNullableGraph } from 'services/api/types';
+import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -23,12 +24,12 @@ import {
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
 /**
  * Builds the Canvas tab's Text to Image graph.
  */
-export const buildCanvasTextToImageGraph = (state: RootState): NonNullableGraph => {
+export const buildCanvasTextToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -262,6 +263,8 @@ export const buildCanvasTextToImageGraph = (state: RootState): NonNullableGraph
     });
   }
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -272,7 +275,7 @@ export const buildCanvasTextToImageGraph = (state: RootState): NonNullableGraph
       height: !isUsingScaledDimensions ? height : scaledBoundingBoxDimensions.height,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -289,17 +292,17 @@ export const buildCanvasTextToImageGraph = (state: RootState): NonNullableGraph
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,7 +1,13 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
-import type { ImageResizeInvocation, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
+import {
+  type ImageResizeInvocation,
+  type ImageToLatentsInvocation,
+  isNonRefinerMainModelConfig,
+  type NonNullableGraph,
+} from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -24,12 +30,12 @@ import {
   RESIZE,
   SEAMLESS,
 } from './constants';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
 /**
  * Builds the Image to Image tab graph.
  */
-export const buildLinearImageToImageGraph = (state: RootState): NonNullableGraph => {
+export const buildLinearImageToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -307,6 +313,8 @@ export const buildLinearImageToImageGraph = (state: RootState): NonNullableGraph
     });
   }
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -317,7 +325,7 @@ export const buildLinearImageToImageGraph = (state: RootState): NonNullableGraph
       width,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -336,17 +344,17 @@ export const buildLinearImageToImageGraph = (state: RootState): NonNullableGraph
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,6 +1,12 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
-import type { ImageResizeInvocation, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import {
+  type ImageResizeInvocation,
+  type ImageToLatentsInvocation,
+  isNonRefinerMainModelConfig,
+  type NonNullableGraph,
+} from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -25,12 +31,12 @@ import {
   SEAMLESS,
 } from './constants';
 import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBuilderUtils';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
 /**
  * Builds the Image to Image tab graph.
  */
-export const buildLinearSDXLImageToImageGraph = (state: RootState): NonNullableGraph => {
+export const buildLinearSDXLImageToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -318,6 +324,8 @@ export const buildLinearSDXLImageToImageGraph = (state: RootState): NonNullableG
     });
   }
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -328,7 +336,7 @@ export const buildLinearSDXLImageToImageGraph = (state: RootState): NonNullableG
       width,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -349,25 +357,25 @@ export const buildLinearSDXLImageToImageGraph = (state: RootState): NonNullableG
 
   // Add Refiner if enabled
   if (refinerModel) {
-    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
+    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // Add LoRA Support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,6 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
-import type { NonNullableGraph } from 'services/api/types';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -23,9 +24,9 @@ import {
   SEAMLESS,
 } from './constants';
 import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBuilderUtils';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
-export const buildLinearSDXLTextToImageGraph = (state: RootState): NonNullableGraph => {
+export const buildLinearSDXLTextToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -221,6 +222,8 @@ export const buildLinearSDXLTextToImageGraph = (state: RootState): NonNullableGr
     ],
   };
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -231,7 +234,7 @@ export const buildLinearSDXLTextToImageGraph = (state: RootState): NonNullableGr
       width,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -250,25 +253,25 @@ export const buildLinearSDXLTextToImageGraph = (state: RootState): NonNullableGr
 
   // Add Refiner if enabled
   if (refinerModel) {
-    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
+    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {

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

@@ -1,7 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
+import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
-import type { NonNullableGraph } from 'services/api/types';
+import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addHrfToGraph } from './addHrfToGraph';
@@ -23,9 +24,9 @@ import {
   SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
 } from './constants';
-import { addCoreMetadataNode } from './metadata';
+import { addCoreMetadataNode, getModelMetadataField } from './metadata';
 
-export const buildLinearTextToImageGraph = (state: RootState): NonNullableGraph => {
+export const buildLinearTextToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -212,6 +213,8 @@ export const buildLinearTextToImageGraph = (state: RootState): NonNullableGraph
     ],
   };
 
+  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
+
   addCoreMetadataNode(
     graph,
     {
@@ -222,7 +225,7 @@ export const buildLinearTextToImageGraph = (state: RootState): NonNullableGraph
       width,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model,
+      model: getModelMetadataField(modelConfig),
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -239,18 +242,18 @@ export const buildLinearTextToImageGraph = (state: RootState): NonNullableGraph
   }
 
   // optionally add custom VAE
-  addVAEToGraph(state, graph, modelLoaderNodeId);
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // add IP Adapter
-  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // High resolution fix.
   if (state.hrf.hrfEnabled) {

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

@@ -1,5 +1,5 @@
 import type { JSONObject } from 'common/types';
-import type { CoreMetadataInvocation, NonNullableGraph } from 'services/api/types';
+import type { AnyModelConfig, CoreMetadataInvocation, ModelMetadataField, NonNullableGraph } from 'services/api/types';
 
 import { METADATA } from './constants';
 
@@ -71,3 +71,11 @@ export const setMetadataReceivingNode = (graph: NonNullableGraph, nodeId: string
     },
   });
 };
+
+export const getModelMetadataField = ({ key, hash, name, base, type }: AnyModelConfig): ModelMetadataField => ({
+  key,
+  hash,
+  name,
+  base,
+  type,
+});

invokeai/frontend/web/src/features/parameters/components/Core/ParamNegativePrompt.tsx

@@ -38,6 +38,7 @@ export const ParamNegativePrompt = memo(() => {
           onKeyDown={onKeyDown}
           fontSize="sm"
           variant="darkFilled"
+          paddingRight={30}
         />
         <PromptOverlayButtonWrapper>
           <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />

invokeai/frontend/web/src/features/parameters/components/Core/ParamPositivePrompt.tsx

@@ -54,6 +54,7 @@ export const ParamPositivePrompt = memo(() => {
           minH={28}
           onKeyDown={onKeyDown}
           variant="darkFilled"
+          paddingRight={30}
         />
         <PromptOverlayButtonWrapper>
           <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />

invokeai/frontend/web/src/features/sdxl/components/SDXLPrompts/ParamSDXLNegativeStylePrompt.tsx

@@ -41,6 +41,7 @@ export const ParamSDXLNegativeStylePrompt = memo(() => {
           onKeyDown={onKeyDown}
           fontSize="sm"
           variant="darkFilled"
+          paddingRight={30}
         />
         <PromptOverlayButtonWrapper>
           <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />

invokeai/frontend/web/src/features/sdxl/components/SDXLPrompts/ParamSDXLPositiveStylePrompt.tsx

@@ -38,6 +38,7 @@ export const ParamSDXLPositiveStylePrompt = memo(() => {
           onKeyDown={onKeyDown}
           fontSize="sm"
           variant="darkFilled"
+          paddingRight={30}
         />
         <PromptOverlayButtonWrapper>
           <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />

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

@@ -109,7 +109,7 @@ export const imagesApi = api.injectEndpoints({
     }),
     clearIntermediates: build.mutation<number, void>({
       query: () => ({ url: buildImagesUrl('intermediates'), method: 'DELETE' }),
-      invalidatesTags: ['IntermediatesCount'],
+      invalidatesTags: ['IntermediatesCount', 'InvocationCacheStatus'],
     }),
     getImageDTO: build.query<ImageDTO, string>({
       query: (image_name) => ({ url: buildImagesUrl(`i/${image_name}`) }),
@@ -125,7 +125,7 @@ export const imagesApi = api.injectEndpoints({
       paths['/api/v1/images/i/{image_name}/workflow']['get']['responses']['200']['content']['application/json'],
       string
     >({
-      query: (image_name) => ({ url: `images/i/${image_name}/workflow` }),
+      query: (image_name) => ({ url: buildImagesUrl(`i/${image_name}/workflow`) }),
       providesTags: (result, error, image_name) => [{ type: 'ImageWorkflow', id: image_name }],
       keepUnusedDataFor: 86400, // 24 hours
     }),

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

@@ -23,7 +23,14 @@ export type UpdateModelArg = {
   body: paths['/api/v2/models/i/{key}']['patch']['requestBody']['content']['application/json'];
 };
 
+export type UpdateModelImageArg = {
+  key: string;
+  image: Blob;
+};
+
 type UpdateModelResponse = paths['/api/v2/models/i/{key}']['patch']['responses']['200']['content']['application/json'];
+type UpdateModelImageResponse =
+  paths['/api/v2/models/i/{key}/image']['patch']['responses']['200']['content']['application/json'];
 
 type GetModelConfigResponse = paths['/api/v2/models/i/{key}']['get']['responses']['200']['content']['application/json'];
 
@@ -33,6 +40,7 @@ type DeleteModelArg = {
   key: string;
 };
 type DeleteModelResponse = void;
+type DeleteModelImageResponse = void;
 
 type ConvertMainModelResponse =
   paths['/api/v2/models/convert/{key}']['put']['responses']['200']['content']['application/json'];
@@ -144,6 +152,18 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: ['Model'],
     }),
+    updateModelImage: build.mutation<UpdateModelImageResponse, UpdateModelImageArg>({
+      query: ({ key, image }) => {
+        const formData = new FormData();
+        formData.append('image', image);
+        return {
+          url: buildModelsUrl(`i/${key}/image`),
+          method: 'PATCH',
+          body: formData,
+        };
+      },
+      invalidatesTags: ['Model'],
+    }),
     installModel: build.mutation<InstallModelResponse, InstallModelArg>({
       query: ({ source }) => {
         return {
@@ -163,6 +183,18 @@ export const modelsApi = api.injectEndpoints({
       },
       invalidatesTags: ['Model'],
     }),
+    deleteModelImage: build.mutation<DeleteModelImageResponse, string>({
+      query: (key) => {
+        return {
+          url: buildModelsUrl(`i/${key}/image`),
+          method: 'DELETE',
+        };
+      },
+      invalidatesTags: ['Model'],
+    }),
+    getModelImage: build.query<string, string>({
+      query: (key) => buildModelsUrl(`i/${key}/image`),
+    }),
     convertModel: build.mutation<ConvertMainModelResponse, string>({
       query: (key) => {
         return {
@@ -330,7 +362,9 @@ export const {
   useGetTextualInversionModelsQuery,
   useGetVaeModelsQuery,
   useDeleteModelsMutation,
+  useDeleteModelImageMutation,
   useUpdateModelMutation,
+  useUpdateModelImageMutation,
   useInstallModelMutation,
   useConvertModelMutation,
   useSyncModelsMutation,

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

@@ -38,7 +38,6 @@ export type OffsetPaginatedResults_ImageDTO_ = S['OffsetPaginatedResults_ImageDT
 export type ModelType = S['ModelType'];
 export type SubModelType = S['SubModelType'];
 export type BaseModelType = S['BaseModelType'];
-export type ControlField = S['ControlField'];
 
 // Model Configs
 
@@ -120,17 +119,18 @@ export type CreateGradientMaskInvocation = S['CreateGradientMaskInvocation'];
 export type CanvasPasteBackInvocation = S['CanvasPasteBackInvocation'];
 export type NoiseInvocation = S['NoiseInvocation'];
 export type DenoiseLatentsInvocation = S['DenoiseLatentsInvocation'];
-export type SDXLLoraLoaderInvocation = S['SDXLLoraLoaderInvocation'];
+export type SDXLLoRALoaderInvocation = S['SDXLLoRALoaderInvocation'];
 export type ImageToLatentsInvocation = S['ImageToLatentsInvocation'];
 export type LatentsToImageInvocation = S['LatentsToImageInvocation'];
-export type LoraLoaderInvocation = S['LoraLoaderInvocation'];
+export type LoRALoaderInvocation = S['LoRALoaderInvocation'];
 export type ESRGANInvocation = S['ESRGANInvocation'];
 export type ImageNSFWBlurInvocation = S['ImageNSFWBlurInvocation'];
 export type ImageWatermarkInvocation = S['ImageWatermarkInvocation'];
 export type SeamlessModeInvocation = S['SeamlessModeInvocation'];
 export type CoreMetadataInvocation = S['CoreMetadataInvocation'];
-export type IPAdapterMetadataField = S['IPAdapterMetadataField'];
-export type T2IAdapterField = S['T2IAdapterField'];
+
+// Metadata fields
+export type ModelMetadataField = S['ModelMetadataField'];
 
 // ControlNet Nodes
 export type ControlNetInvocation = S['ControlNetInvocation'];

invokeai/invocation_api/__init__.py

@@ -33,19 +33,15 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.latent import SchedulerOutput
 from invokeai.app.invocations.metadata import MetadataItemField, MetadataItemOutput, MetadataOutput
 from invokeai.app.invocations.model import (
-    ClipField,
+    CLIPField,
     CLIPOutput,
-    LoraInfo,
-    LoraLoaderOutput,
-    LoRAModelField,
-    MainModelField,
-    ModelInfo,
+    LoRALoaderOutput,
+    ModelField,
     ModelLoaderOutput,
-    SDXLLoraLoaderOutput,
+    SDXLLoRALoaderOutput,
     UNetField,
     UNetOutput,
-    VaeField,
-    VAEModelField,
+    VAEField,
     VAEOutput,
 )
 from invokeai.app.invocations.primitives import (
@@ -73,8 +69,8 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
-from invokeai.backend.model_management.model_manager import LoadedModelInfo
-from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager.config import BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
@@ -118,20 +114,16 @@ __all__ = [
     "MetadataItemOutput",
     "MetadataOutput",
     # invokeai.app.invocations.model
-    "ModelInfo",
-    "LoraInfo",
+    "ModelField",
     "UNetField",
-    "ClipField",
-    "VaeField",
-    "MainModelField",
-    "LoRAModelField",
-    "VAEModelField",
+    "CLIPField",
+    "VAEField",
     "UNetOutput",
     "VAEOutput",
     "CLIPOutput",
     "ModelLoaderOutput",
-    "LoraLoaderOutput",
-    "SDXLLoraLoaderOutput",
+    "LoRALoaderOutput",
+    "SDXLLoRALoaderOutput",
     # invokeai.app.invocations.primitives
     "BooleanCollectionOutput",
     "BooleanOutput",
@@ -166,7 +158,7 @@ __all__ = [
     # invokeai.app.services.config.config_default
     "InvokeAIAppConfig",
     # invokeai.backend.model_management.model_manager
-    "LoadedModelInfo",
+    "LoadedModel",
     # invokeai.backend.model_management.models.base
     "BaseModelType",
     "ModelType",

scripts/generate_openapi_schema.py

@@ -0,0 +1,17 @@
+import json
+import os
+import sys
+
+
+def main():
+    # Change working directory to the repo root
+    os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+    from invokeai.app.api_app import custom_openapi
+
+    schema = custom_openapi()
+    json.dump(schema, sys.stdout, indent=2)
+
+
+if __name__ == "__main__":
+    main()

tests/backend/model_manager/model_loading/test_model_load.py

@@ -14,17 +14,13 @@ def test_loading(mm2_model_manager: ModelManagerServiceBase, embedding_file: Pat
     matches = store.search_by_attr(model_name="test_embedding")
     assert len(matches) == 0
     key = mm2_model_manager.install.register_path(embedding_file)
-    loaded_model = mm2_model_manager.load_model_by_config(store.get_model(key))
+    loaded_model = mm2_model_manager.load.load_model(store.get_model(key))
     assert loaded_model is not None
     assert loaded_model.config.key == key
     with loaded_model as model:
         assert isinstance(model, TextualInversionModelRaw)
-    loaded_model_2 = mm2_model_manager.load_model_by_key(key)
-    assert loaded_model.config.key == loaded_model_2.config.key
 
-    loaded_model_3 = mm2_model_manager.load_model_by_attr(
-        model_name=loaded_model.config.name,
-        model_type=loaded_model.config.type,
-        base_model=loaded_model.config.base,
-    )
-    assert loaded_model.config.key == loaded_model_3.config.key
+    config = mm2_model_manager.store.get_model(key)
+    loaded_model_2 = mm2_model_manager.load.load_model(config)
+
+    assert loaded_model.config.key == loaded_model_2.config.key

tests/conftest.py

@@ -44,6 +44,7 @@ def mock_services() -> InvocationServices:
         images=ImageService(),
         invocation_cache=MemoryInvocationCache(max_cache_size=0),
         logger=logging,  # type: ignore
+        model_images=None,  # type: ignore
         model_manager=None,  # type: ignore
         download_queue=None,  # type: ignore
         names=None,  # type: ignore