Run ruff format

Makefile

@@ -10,11 +10,10 @@ 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!)"
 
@@ -54,9 +53,6 @@ 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

@@ -25,7 +25,6 @@ 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
@@ -72,8 +71,6 @@ 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
@@ -95,7 +92,6 @@ 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),
@@ -122,7 +118,6 @@ 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

@@ -1,16 +1,12 @@
 # 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, UploadFile
-from fastapi.responses import FileResponse
+from fastapi import Body, Path, Query, Response
 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
@@ -35,9 +31,6 @@ 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."""
@@ -112,9 +105,6 @@ 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)
 
 
@@ -158,8 +148,6 @@ 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))
@@ -278,75 +266,6 @@ 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",
@@ -377,29 +296,6 @@ 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",
@@ -643,7 +539,7 @@ 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.load_model(model_config, submodel_type=SubModelType.Scheduler)
+    model_manager.load_model_by_config(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

@@ -2,9 +2,12 @@
 # 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()
@@ -17,7 +20,6 @@ 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
@@ -38,7 +40,6 @@ 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
@@ -58,7 +59,6 @@ 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)
+        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
         tokenizer_model = tokenizer_info.model
         assert isinstance(tokenizer_model, CLIPTokenizer)
-        text_encoder_info = context.models.load(self.clip.text_encoder)
+        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
         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.lora)
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                 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)
+        tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
         tokenizer_model = tokenizer_info.model
         assert isinstance(tokenizer_model, CLIPTokenizer)
-        text_encoder_info = context.models.load(clip_field.text_encoder)
+        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
         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.lora)
+                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                 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

@@ -34,7 +34,6 @@ 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
@@ -52,9 +51,15 @@ 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: ModelField = Field(description="The ControlNet model to use")
+    control_model: ControlNetModelField = 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)"
@@ -90,7 +95,7 @@ class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
-    control_model: ModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
+    control_model: ControlNetModelField = 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[dict[str, Any]]):
+class MetadataField(RootModel):
     """
     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

@@ -11,17 +11,25 @@ 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, IPAdapterConfig, ModelType
+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")
 
 
 class IPAdapterField(BaseModel):
     image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
-    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.")
+    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.")
     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)"
@@ -54,7 +62,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: ModelField = InputField(
+    ip_adapter_model: IPAdapterModelField = InputField(
         description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
     )
 
@@ -82,18 +90,18 @@ 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=ModelField(key=image_encoder_models[0].key),
+                image_encoder_model=image_encoder_model,
                 weight=self.weight,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,

invokeai/app/invocations/latent.py

@@ -26,7 +26,6 @@ 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 (
@@ -76,7 +75,7 @@ from .baseinvocation import (
     invocation_output,
 )
 from .controlnet_image_processors import ControlField
-from .model import ModelField, UNetField, VAEField
+from .model import ModelInfo, UNetField, VaeField
 
 if choose_torch_device() == torch.device("mps"):
     from torch import mps
@@ -119,7 +118,7 @@ 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)
@@ -154,7 +153,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
         if image_tensor is not None:
-            vae_info = context.models.load(self.vae.vae)
+            vae_info = context.models.load(**self.vae.vae.model_dump())
 
             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)
@@ -245,12 +244,12 @@ class CreateGradientMaskInvocation(BaseInvocation):
 
 def get_scheduler(
     context: InvocationContext,
-    scheduler_info: ModelField,
+    scheduler_info: ModelInfo,
     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)
+    orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -462,7 +461,7 @@ 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(control_info.control_model))
+            control_model = exit_stack.enter_context(context.models.load(key=control_info.control_model.key))
 
             # control_models.append(control_model)
             control_image_field = control_info.image
@@ -524,10 +523,11 @@ 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(single_ip_adapter.ip_adapter_model)
+                context.models.load(key=single_ip_adapter.ip_adapter_model.key)
             )
 
-            image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
+            image_encoder_model_info = context.models.load(key=single_ip_adapter.image_encoder_model.key)
+
             # `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):
@@ -538,7 +538,6 @@ 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
@@ -578,8 +577,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            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)
+            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)
             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.
@@ -732,13 +731,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
                 for lora in self.unet.loras:
-                    lora_info = context.models.load(lora.lora)
-                    assert isinstance(lora_info.model, LoRAModelRaw)
+                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
                     yield (lora_info.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.models.load(self.unet.unet)
+            unet_info = context.models.load(**self.unet.unet.model_dump())
             assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
                 ExitStack() as exit_stack,
@@ -832,7 +830,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.latents,
         input=Input.Connection,
     )
-    vae: VAEField = InputField(
+    vae: VaeField = InputField(
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
@@ -843,8 +841,8 @@ 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)
-        assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL))
+        vae_info = context.models.load(**self.vae.vae.model_dump())
+
         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)
@@ -1010,7 +1008,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     image: ImageField = InputField(
         description="The image to encode",
     )
-    vae: VAEField = InputField(
+    vae: VaeField = InputField(
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
@@ -1066,7 +1064,7 @@ 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)
+        vae_info = context.models.load(**self.vae.vae.model_dump())
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:

invokeai/app/invocations/metadata.py

@@ -8,10 +8,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.controlnet_image_processors import (
-    CONTROLNET_MODE_VALUES,
-    CONTROLNET_RESIZE_VALUES,
-)
+from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -20,8 +17,10 @@ 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__
 
@@ -31,20 +30,10 @@ 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: ModelMetadataField = Field(description=FieldDescriptions.lora_model)
+    model: LoRAModelField = Field(description=FieldDescriptions.lora_model)
     weight: float = Field(description=FieldDescriptions.lora_weight)
 
 
@@ -52,7 +41,7 @@ class IPAdapterMetadataField(BaseModel):
     """IP Adapter Field, minus the CLIP Vision Encoder model"""
 
     image: ImageField = Field(description="The IP-Adapter image prompt.")
-    ip_adapter_model: ModelMetadataField = Field(
+    ip_adapter_model: IPAdapterModelField = Field(
         description="The IP-Adapter model.",
     )
     weight: Union[float, list[float]] = Field(
@@ -62,33 +51,6 @@ 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"""
@@ -178,14 +140,14 @@ class CoreMetadataInvocation(BaseInvocation):
         default=None,
         description="The number of skipped CLIP layers",
     )
-    model: Optional[ModelMetadataField] = InputField(default=None, description="The main model used for inference")
-    controlnets: Optional[list[ControlNetMetadataField]] = InputField(
+    model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
+    controlnets: Optional[list[ControlField]] = InputField(
         default=None, description="The ControlNets used for inference"
     )
     ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
         default=None, description="The IP Adapters used for inference"
     )
-    t2iAdapters: Optional[list[T2IAdapterMetadataField]] = InputField(
+    t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
         default=None, description="The IP Adapters used for inference"
     )
     loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
@@ -197,7 +159,7 @@ class CoreMetadataInvocation(BaseInvocation):
         default=None,
         description="The name of the initial image",
     )
-    vae: Optional[ModelMetadataField] = InputField(
+    vae: Optional[VAEModelField] = InputField(
         default=None,
         description="The VAE used for decoding, if the main model's default was not used",
     )
@@ -228,7 +190,7 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # SDXL Refiner
-    refiner_model: Optional[ModelMetadataField] = InputField(
+    refiner_model: Optional[MainModelField] = InputField(
         default=None,
         description="The SDXL Refiner model used",
     )
@@ -260,9 +222,10 @@ class CoreMetadataInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> MetadataOutput:
         """Collects and outputs a CoreMetadata object"""
 
-        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))
+        return MetadataOutput(
+            metadata=MetadataField.model_validate(
+                self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
+            )
+        )
 
     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 ModelField(BaseModel):
-    key: str = Field(description="Key of the model")
-    submodel_type: Optional[SubModelType] = Field(description="Submodel type", default=None)
+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 LoRAField(BaseModel):
-    lora: ModelField = Field(description="Info to load lora model")
-    weight: float = Field(description="Weight to apply to lora model")
+class LoraInfo(ModelInfo):
+    weight: float = Field(description="Lora's weight which to use when apply to model")
 
 
 class UNetField(BaseModel):
-    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")
+    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")
     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: ModelField = Field(description="Info to load tokenizer submodel")
-    text_encoder: ModelField = Field(description="Info to load text_encoder submodel")
+class ClipField(BaseModel):
+    tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
+    text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
-    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
+    loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
 
 
-class VAEField(BaseModel):
-    vae: ModelField = Field(description="Info to load vae submodel")
+class VaeField(BaseModel):
+    # TODO: better naming?
+    vae: ModelInfo = 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")
@@ -74,6 +74,18 @@ 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",
@@ -84,40 +96,62 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: ModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
+    model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
-        # TODO: not found exceptions
-        if not context.models.exists(self.model.key):
-            raise Exception(f"Unknown model {self.model.key}")
+        key = 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})
+        # TODO: not found exceptions
+        if not context.models.exists(key):
+            raise Exception(f"Unknown model {key}")
 
         return ModelLoaderOutput(
-            unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
-            clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
-            vae=VAEField(vae=vae),
+            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,
+                ),
+            ),
         )
 
 
 @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: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
+    lora: LoRAModelField = 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,
@@ -125,41 +159,46 @@ 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:
+    def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
+        if self.lora is None:
+            raise Exception("No LoRA provided")
+
         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.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.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.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.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 = self.unet.model_copy(deep=True)
+            output.unet = copy.deepcopy(self.unet)
             output.unet.loras.append(
-                LoRAField(
-                    lora=self.lora,
+                LoraInfo(
+                    key=lora_key,
+                    submodel_type=None,
                     weight=self.weight,
                 )
             )
 
         if self.clip is not None:
-            output.clip = self.clip.model_copy(deep=True)
+            output.clip = copy.deepcopy(self.clip)
             output.clip.loras.append(
-                LoRAField(
-                    lora=self.lora,
+                LoraInfo(
+                    key=lora_key,
+                    submodel_type=None,
                     weight=self.weight,
                 )
             )
@@ -168,12 +207,12 @@ 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(
@@ -183,10 +222,10 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
     category="model",
     version="1.0.1",
 )
-class SDXLLoRALoaderInvocation(BaseInvocation):
+class SDXLLoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
-    lora: ModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
+    lora: LoRAModelField = 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,
@@ -194,59 +233,65 @@ 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:
+    def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
+        if self.lora is None:
+            raise Exception("No LoRA provided")
+
         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.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.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.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.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.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.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 = self.unet.model_copy(deep=True)
+            output.unet = copy.deepcopy(self.unet)
             output.unet.loras.append(
-                LoRAField(
-                    lora=self.lora,
+                LoraInfo(
+                    key=lora_key,
+                    submodel_type=None,
                     weight=self.weight,
                 )
             )
 
         if self.clip is not None:
-            output.clip = self.clip.model_copy(deep=True)
+            output.clip = copy.deepcopy(self.clip)
             output.clip.loras.append(
-                LoRAField(
-                    lora=self.lora,
+                LoraInfo(
+                    key=lora_key,
+                    submodel_type=None,
                     weight=self.weight,
                 )
             )
 
         if self.clip2 is not None:
-            output.clip2 = self.clip2.model_copy(deep=True)
+            output.clip2 = copy.deepcopy(self.clip2)
             output.clip2.loras.append(
-                LoRAField(
-                    lora=self.lora,
+                LoraInfo(
+                    key=lora_key,
+                    submodel_type=None,
                     weight=self.weight,
                 )
             )
@@ -254,11 +299,17 @@ 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: ModelField = InputField(
+    vae_model: VAEModelField = InputField(
         description=FieldDescriptions.vae_model,
         input=Input.Direct,
         title="VAE",
@@ -270,7 +321,7 @@ class VAELoaderInvocation(BaseInvocation):
         if not context.models.exists(key):
             raise Exception(f"Unkown vae: {key}!")
 
-        return VAEOutput(vae=VAEField(vae=self.vae_model))
+        return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))
 
 
 @invocation_output("seamless_output")
@@ -278,7 +329,7 @@ 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(
@@ -297,7 +348,7 @@ 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, ModelField, UNetField, VAEField
+from .model import ClipField, MainModelField, ModelInfo, 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: ModelField = InputField(
+    model: MainModelField = InputField(
         description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
@@ -46,19 +46,48 @@ 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=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),
+            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,
+                ),
+            ),
         )
 
 
@@ -72,8 +101,10 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
-    model: ModelField = InputField(
-        description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
+    model: MainModelField = InputField(
+        description=FieldDescriptions.sdxl_refiner_model,
+        input=Input.Direct,
+        ui_type=UIType.SDXLRefinerModel,
     )
     # TODO: precision?
 
@@ -84,14 +115,34 @@ 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=unet, scheduler=scheduler, loras=[]),
-            clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
-            vae=VAEField(vae=vae),
+            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,
+                ),
+            ),
         )

invokeai/app/invocations/t2i_adapter.py

@@ -10,14 +10,17 @@ 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: ModelField = Field(description="The T2I-Adapter model to use.")
+    t2i_adapter_model: T2IAdapterModelField = 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)"
@@ -52,7 +55,7 @@ class T2IAdapterInvocation(BaseInvocation):
 
     # Inputs
     image: ImageField = InputField(description="The IP-Adapter image prompt.")
-    t2i_adapter_model: ModelField = InputField(
+    t2i_adapter_model: T2IAdapterModelField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
         input=Input.Direct,

invokeai/app/services/invocation_cache/invocation_cache_base.py

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

invokeai/app/services/invocation_cache/invocation_cache_memory.py

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

invokeai/app/services/invocation_services.py

@@ -25,7 +25,6 @@ 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
@@ -50,7 +49,6 @@ class InvocationServices:
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
         logger: "Logger",
-        model_images: "ModelImageFileStorageBase",
         model_manager: "ModelManagerServiceBase",
         download_queue: "DownloadQueueServiceBase",
         performance_statistics: "InvocationStatsServiceBase",
@@ -74,7 +72,6 @@ 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

@@ -1,33 +0,0 @@
-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

@@ -1,20 +0,0 @@
-# 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

@@ -1,79 +0,0 @@
-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_install/model_install_default.py

@@ -4,7 +4,6 @@ import os
 import re
 import threading
 import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from hashlib import sha256
 from pathlib import Path
 from queue import Empty, Queue
@@ -281,18 +280,12 @@ class ModelInstallService(ModelInstallServiceBase):
         self._scan_models_directory()
         if autoimport := self._app_config.autoimport_dir:
             self._logger.info("Scanning autoimport directory for new models")
-            installed: List[str] = []
-            # Use ThreadPoolExecutor to scan dirs in parallel
-            with ThreadPoolExecutor() as executor:
-                future_models = [executor.submit(self.scan_directory, self._app_config.root_path / autoimport / cur_model_type.value) for cur_model_type in ModelType]
-                [installed.extend(models.result()) for models in as_completed(future_models)]
+            installed = self.scan_directory(self._app_config.root_path / autoimport)
             self._logger.info(f"{len(installed)} new models registered")
         self._logger.info("Model installer (re)initialized")
 
     def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:  # noqa D102
         self._cached_model_paths = {Path(x.path).absolute() for x in self.record_store.all_models()}
-        if len([entry for entry in os.scandir(scan_dir) if not entry.name.startswith(".")]) == 0:
-            return []
         callback = self._scan_install if install else self._scan_register
         search = ModelSearch(on_model_found=callback, config=self._app_config)
         self._models_installed.clear()
@@ -455,10 +448,10 @@ class ModelInstallService(ModelInstallServiceBase):
                 self.unregister(key)
 
             self._logger.info(f"Scanning {self._app_config.models_path} for new and orphaned models")
-            # Use ThreadPoolExecutor to scan dirs in parallel
-            with ThreadPoolExecutor() as executor:
-                future_models = [executor.submit(self.scan_directory, Path(cur_base_model.value, cur_model_type.value)) for cur_base_model in BaseModelType for cur_model_type in ModelType]
-                [installed.update(models.result()) for models in as_completed(future_models)]
+            for cur_base_model in BaseModelType:
+                for cur_model_type in ModelType:
+                    models_dir = Path(cur_base_model.value, cur_model_type.value)
+                    installed.update(self.scan_directory(models_dir))
             self._logger.info(f"{len(installed)} new models registered; {len(defunct_models)} unregistered")
 
     def _sync_model_path(self, key: str) -> AnyModelConfig:

invokeai/app/services/model_manager/model_manager_base.py

@@ -1,11 +1,15 @@
 # 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
@@ -66,3 +70,32 @@ 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

@@ -1,10 +1,14 @@
 # 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
@@ -14,7 +18,7 @@ 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
+from ..model_records import ModelRecordServiceBase, UnknownModelException
 from .model_manager_base import ModelManagerServiceBase
 
 
@@ -60,6 +64,56 @@ 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

@@ -79,7 +79,6 @@ 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):
@@ -130,17 +129,6 @@ 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

@@ -203,21 +203,6 @@ 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/session_processor/session_processor_base.py

@@ -1,6 +1,35 @@
 from abc import ABC, abstractmethod
+from threading import Event
 
+from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
+from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
+
+
+class SessionRunnerBase(ABC):
+    """
+    Base class for session runner.
+    """
+
+    @abstractmethod
+    def start(self, services: InvocationServices, cancel_event: Event) -> None:
+        """Starts the session runner"""
+        pass
+
+    @abstractmethod
+    def run(self, queue_item: SessionQueueItem) -> None:
+        """Runs the session"""
+        pass
+
+    @abstractmethod
+    def complete(self, queue_item: SessionQueueItem) -> None:
+        """Completes the session"""
+        pass
+
+    @abstractmethod
+    def run_node(self, node_id: str, queue_item: SessionQueueItem) -> None:
+        """Runs an already prepared node on the session"""
+        pass
 
 
 class SessionProcessorBase(ABC):

invokeai/app/services/session_processor/session_processor_default.py

@@ -2,13 +2,14 @@ import traceback
 from contextlib import suppress
 from threading import BoundedSemaphore, Thread
 from threading import Event as ThreadEvent
-from typing import Optional
+from typing import Callable, Optional, Union
 
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event as FastAPIEvent
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.events.events_base import EventServiceBase
+from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.invocation_stats.invocation_stats_common import GESStatsNotFoundError
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
 from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
@@ -16,15 +17,164 @@ from invokeai.app.services.shared.invocation_context import InvocationContextDat
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
-from .session_processor_base import SessionProcessorBase
+from .session_processor_base import SessionProcessorBase, SessionRunnerBase
 from .session_processor_common import SessionProcessorStatus
 
 
+class DefaultSessionRunner(SessionRunnerBase):
+    """Processes a single session's invocations"""
+
+    def __init__(
+        self,
+        on_before_run_node: Union[Callable[[BaseInvocation, SessionQueueItem], bool], None] = None,
+        on_after_run_node: Union[Callable[[BaseInvocation, SessionQueueItem], bool], None] = None,
+    ):
+        self.on_before_run_node = on_before_run_node
+        self.on_after_run_node = on_after_run_node
+
+    def start(self, services: InvocationServices, cancel_event: ThreadEvent):
+        """Start the session runner"""
+        self.services = services
+        self.cancel_event = cancel_event
+
+    def run(self, queue_item: SessionQueueItem):
+        """Run the graph"""
+        if not queue_item.session:
+            raise ValueError("Queue item has no session")
+        # Loop over invocations until the session is complete or canceled
+        while not (queue_item.session.is_complete() or self.cancel_event.is_set()):
+            # Prepare the next node
+            invocation = queue_item.session.next()
+            if invocation is None:
+                # If there are no more invocations, complete the graph
+                break
+            # Build invocation context (the node-facing API
+            self.run_node(invocation.id, queue_item)
+        self.complete(queue_item)
+
+    def complete(self, queue_item: SessionQueueItem):
+        """Complete the graph"""
+        self.services.events.emit_graph_execution_complete(
+            queue_batch_id=queue_item.batch_id,
+            queue_item_id=queue_item.item_id,
+            queue_id=queue_item.queue_id,
+            graph_execution_state_id=queue_item.session.id,
+        )
+
+    def _on_before_run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
+        """Run before a node is executed"""
+        # Send starting event
+        self.services.events.emit_invocation_started(
+            queue_batch_id=queue_item.batch_id,
+            queue_item_id=queue_item.item_id,
+            queue_id=queue_item.queue_id,
+            graph_execution_state_id=queue_item.session_id,
+            node=invocation.model_dump(),
+            source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
+        )
+        if self.on_before_run_node is not None:
+            self.on_before_run_node(invocation, queue_item)
+
+    def _on_after_run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
+        """Run after a node is executed"""
+        if self.on_after_run_node is not None:
+            self.on_after_run_node(invocation, queue_item)
+
+    def run_node(self, node_id: str, queue_item: SessionQueueItem):
+        """Run a single node in the graph"""
+        # If this error raises a NodeNotFoundError that's handled by the processor
+        invocation = queue_item.session.execution_graph.get_node(node_id)
+        try:
+            self._on_before_run_node(invocation, queue_item)
+            data = InvocationContextData(
+                invocation=invocation,
+                source_invocation_id=queue_item.session.prepared_source_mapping[invocation.id],
+                queue_item=queue_item,
+            )
+
+            # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
+            with self.services.performance_statistics.collect_stats(invocation, queue_item.session_id):
+                context = build_invocation_context(
+                    data=data,
+                    services=self.services,
+                    cancel_event=self.cancel_event,
+                )
+
+                # Invoke the node
+                outputs = invocation.invoke_internal(context=context, services=self.services)
+
+                # Save outputs and history
+                queue_item.session.complete(invocation.id, outputs)
+
+            self._on_after_run_node(invocation, queue_item)
+            # Send complete event on successful runs
+            self.services.events.emit_invocation_complete(
+                queue_batch_id=queue_item.batch_id,
+                queue_item_id=queue_item.item_id,
+                queue_id=queue_item.queue_id,
+                graph_execution_state_id=queue_item.session.id,
+                node=invocation.model_dump(),
+                source_node_id=data.source_invocation_id,
+                result=outputs.model_dump(),
+            )
+        except KeyboardInterrupt:
+            # TODO(MM2): Create an event for this
+            pass
+        except CanceledException:
+            # When the user cancels the graph, we first set the cancel event. The event is checked
+            # between invocations, in this loop. Some invocations are long-running, and we need to
+            # be able to cancel them mid-execution.
+            #
+            # For example, denoising is a long-running invocation with many steps. A step callback
+            # is executed after each step. This step callback checks if the canceled event is set,
+            # then raises a CanceledException to stop execution immediately.
+            #
+            # When we get a CanceledException, we don't need to do anything - just pass and let the
+            # loop go to its next iteration, and the cancel event will be handled correctly.
+            pass
+        except Exception as e:
+            error = traceback.format_exc()
+
+            # Save error
+            queue_item.session.set_node_error(invocation.id, error)
+            self.services.logger.error(
+                f"Error while invoking session {queue_item.session_id}, invocation {invocation.id} ({invocation.get_type()}):\n{e}"
+            )
+            self.services.logger.error(error)
+
+            # Send error event
+            self.services.events.emit_invocation_error(
+                queue_batch_id=queue_item.session_id,
+                queue_item_id=queue_item.item_id,
+                queue_id=queue_item.queue_id,
+                graph_execution_state_id=queue_item.session.id,
+                node=invocation.model_dump(),
+                source_node_id=queue_item.session.prepared_source_mapping[invocation.id],
+                error_type=e.__class__.__name__,
+                error=error,
+            )
+
+
 class DefaultSessionProcessor(SessionProcessorBase):
-    def start(self, invoker: Invoker, thread_limit: int = 1, polling_interval: int = 1) -> None:
+    """Processes sessions from the session queue"""
+
+    def __init__(self, session_runner: Union[SessionRunnerBase, None] = None) -> None:
+        super().__init__()
+        self.session_runner = session_runner if session_runner else DefaultSessionRunner()
+
+    def start(
+        self,
+        invoker: Invoker,
+        thread_limit: int = 1,
+        polling_interval: int = 1,
+        on_before_run_session: Union[Callable[[SessionQueueItem], bool], None] = None,
+        on_after_run_session: Union[Callable[[SessionQueueItem], bool], None] = None,
+    ) -> None:
         self._invoker: Invoker = invoker
         self._queue_item: Optional[SessionQueueItem] = None
         self._invocation: Optional[BaseInvocation] = None
+        self.on_before_run_session = on_before_run_session
+        self.on_after_run_session = on_after_run_session
 
         self._resume_event = ThreadEvent()
         self._stop_event = ThreadEvent()
@@ -59,6 +209,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
                 "cancel_event": self._cancel_event,
             },
         )
+        self.session_runner.start(services=invoker.services, cancel_event=self._cancel_event)
         self._thread.start()
 
     def stop(self, *args, **kwargs) -> None:
@@ -117,131 +268,34 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
                         cancel_event.clear()
 
+                        # If we have a on_before_run_session callback, call it
+                        if self.on_before_run_session is not None:
+                            self.on_before_run_session(self._queue_item)
+
                         # If profiling is enabled, start the profiler
                         if self._profiler is not None:
                             self._profiler.start(profile_id=self._queue_item.session_id)
 
-                        # Prepare invocations and take the first
-                        self._invocation = self._queue_item.session.next()
+                        # Run the graph
+                        self.session_runner.run(queue_item=self._queue_item)
 
-                        # Loop over invocations until the session is complete or canceled
-                        while self._invocation is not None and not cancel_event.is_set():
-                            # get the source node id to provide to clients (the prepared node id is not as useful)
-                            source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
-
-                            # Send starting event
-                            self._invoker.services.events.emit_invocation_started(
-                                queue_batch_id=self._queue_item.batch_id,
-                                queue_item_id=self._queue_item.item_id,
-                                queue_id=self._queue_item.queue_id,
-                                graph_execution_state_id=self._queue_item.session_id,
-                                node=self._invocation.model_dump(),
-                                source_node_id=source_invocation_id,
+                        # If we are profiling, stop the profiler and dump the profile & stats
+                        if self._profiler:
+                            profile_path = self._profiler.stop()
+                            stats_path = profile_path.with_suffix(".json")
+                            self._invoker.services.performance_statistics.dump_stats(
+                                graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
                             )
 
-                            # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
-                            try:
-                                with self._invoker.services.performance_statistics.collect_stats(
-                                    self._invocation, self._queue_item.session.id
-                                ):
-                                    # Build invocation context (the node-facing API)
-                                    data = InvocationContextData(
-                                        invocation=self._invocation,
-                                        source_invocation_id=source_invocation_id,
-                                        queue_item=self._queue_item,
-                                    )
-                                    context = build_invocation_context(
-                                        data=data,
-                                        services=self._invoker.services,
-                                        cancel_event=self._cancel_event,
-                                    )
+                        # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
+                        # we don't care about that - suppress the error.
+                        with suppress(GESStatsNotFoundError):
+                            self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
+                            self._invoker.services.performance_statistics.reset_stats()
 
-                                    # Invoke the node
-                                    outputs = self._invocation.invoke_internal(
-                                        context=context, services=self._invoker.services
-                                    )
-
-                                    # Save outputs and history
-                                    self._queue_item.session.complete(self._invocation.id, outputs)
-
-                                    # Send complete event
-                                    self._invoker.services.events.emit_invocation_complete(
-                                        queue_batch_id=self._queue_item.batch_id,
-                                        queue_item_id=self._queue_item.item_id,
-                                        queue_id=self._queue_item.queue_id,
-                                        graph_execution_state_id=self._queue_item.session.id,
-                                        node=self._invocation.model_dump(),
-                                        source_node_id=source_invocation_id,
-                                        result=outputs.model_dump(),
-                                    )
-
-                            except KeyboardInterrupt:
-                                # TODO(MM2): Create an event for this
-                                pass
-
-                            except CanceledException:
-                                # When the user cancels the graph, we first set the cancel event. The event is checked
-                                # between invocations, in this loop. Some invocations are long-running, and we need to
-                                # be able to cancel them mid-execution.
-                                #
-                                # For example, denoising is a long-running invocation with many steps. A step callback
-                                # is executed after each step. This step callback checks if the canceled event is set,
-                                # then raises a CanceledException to stop execution immediately.
-                                #
-                                # When we get a CanceledException, we don't need to do anything - just pass and let the
-                                # loop go to its next iteration, and the cancel event will be handled correctly.
-                                pass
-
-                            except Exception as e:
-                                error = traceback.format_exc()
-
-                                # Save error
-                                self._queue_item.session.set_node_error(self._invocation.id, error)
-                                self._invoker.services.logger.error(
-                                    f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
-                                )
-                                self._invoker.services.logger.error(error)
-
-                                # Send error event
-                                self._invoker.services.events.emit_invocation_error(
-                                    queue_batch_id=self._queue_item.session_id,
-                                    queue_item_id=self._queue_item.item_id,
-                                    queue_id=self._queue_item.queue_id,
-                                    graph_execution_state_id=self._queue_item.session.id,
-                                    node=self._invocation.model_dump(),
-                                    source_node_id=source_invocation_id,
-                                    error_type=e.__class__.__name__,
-                                    error=error,
-                                )
-                                pass
-
-                            # The session is complete if the all invocations are complete or there was an error
-                            if self._queue_item.session.is_complete() or cancel_event.is_set():
-                                # Send complete event
-                                self._invoker.services.events.emit_graph_execution_complete(
-                                    queue_batch_id=self._queue_item.batch_id,
-                                    queue_item_id=self._queue_item.item_id,
-                                    queue_id=self._queue_item.queue_id,
-                                    graph_execution_state_id=self._queue_item.session.id,
-                                )
-                                # If we are profiling, stop the profiler and dump the profile & stats
-                                if self._profiler:
-                                    profile_path = self._profiler.stop()
-                                    stats_path = profile_path.with_suffix(".json")
-                                    self._invoker.services.performance_statistics.dump_stats(
-                                        graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
-                                    )
-                                # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
-                                # we don't care about that - suppress the error.
-                                with suppress(GESStatsNotFoundError):
-                                    self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
-                                    self._invoker.services.performance_statistics.reset_stats()
-
-                                # Set the invocation to None to prepare for the next session
-                                self._invocation = None
-                            else:
-                                # Prepare the next invocation
-                                self._invocation = self._queue_item.session.next()
+                        # If we have a on_after_run_session callback, call it
+                        if self.on_after_run_session is not None:
+                            self.on_after_run_session(self._queue_item)
 
                         # The session is complete, immediately poll for next session
                         self._queue_item = None
@@ -275,3 +329,4 @@ class DefaultSessionProcessor(SessionProcessorBase):
             poll_now_event.clear()
             self._queue_item = None
             self._thread_semaphore.release()
+            self._invoker.services.logger.debug("Session processor stopped")

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, Union
+from typing import TYPE_CHECKING, Optional
 
 from PIL.Image import Image
 from torch import Tensor
@@ -13,16 +13,15 @@ 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
 
 """
@@ -300,25 +299,22 @@ class ConditioningInterface(InvocationContextInterface):
 
 
 class ModelsInterface(InvocationContextInterface):
-    def exists(self, identifier: Union[str, "ModelField"]) -> bool:
+    def exists(self, key: str) -> bool:
         """Checks if a model exists.
 
         Args:
-            identifier: The key or ModelField representing the model.
+            key: The key of the model.
 
         Returns:
             True if the model exists, False if not.
         """
-        if isinstance(identifier, str):
-            return self._services.model_manager.store.exists(identifier)
+        return self._services.model_manager.store.exists(key)
 
-        return self._services.model_manager.store.exists(identifier.key)
-
-    def load(self, identifier: Union[str, "ModelField"], submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+    def load(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """Loads a model.
 
         Args:
-            identifier: The key or ModelField representing the model.
+            key: The key of the model.
             submodel_type: The submodel of the model to get.
 
         Returns:
@@ -328,13 +324,9 @@ class ModelsInterface(InvocationContextInterface):
         # The model manager emits events as it loads the model. It needs the context data to build
         # the event payloads.
 
-        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)
+        return self._services.model_manager.load_model_by_key(
+            key=key, submodel_type=submodel_type, context_data=self._data
+        )
 
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
@@ -351,29 +343,35 @@ 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,
+        )
 
-        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:
+    def get_config(self, key: str) -> AnyModelConfig:
         """Gets a model's config.
 
         Args:
-            identifier: The key or ModelField representing the model.
+            key: The key of the model.
 
         Returns:
             The model's config.
         """
-        if isinstance(identifier, str):
-            return self._services.model_manager.store.get_model(identifier)
+        return self._services.model_manager.store.get_model(key=key)
 
-        return self._services.model_manager.store.get_model(identifier.key)
+    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)
 
     def search_by_path(self, path: Path) -> list[AnyModelConfig]:
         """Searches for models by path.

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py

@@ -17,7 +17,8 @@ class MigrateCallback(Protocol):
     See :class:`Migration` for an example.
     """
 
-    def __call__(self, cursor: sqlite3.Cursor) -> None: ...
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        ...
 
 
 class MigrationError(RuntimeError):

invokeai/app/services/urls/urls_base.py

@@ -8,8 +8,3 @@ 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

@@ -4,9 +4,8 @@ from .urls_base import UrlServiceBase
 
 
 class LocalUrlService(UrlServiceBase):
-    def __init__(self, base_url: str = "api/v1", base_url_v2: str = "api/v2"):
+    def __init__(self, base_url: str = "api/v1"):
         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)
@@ -16,6 +15,3 @@ 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(name_or_key)
+            loaded_model = context.models.load(key=name_or_key)
             model = loaded_model.model
             assert isinstance(model, TextualInversionModelRaw)
             assert loaded_model.config.base == base

invokeai/backend/install/install_helper.py

@@ -19,6 +19,7 @@ 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 (
@@ -38,7 +39,7 @@ 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)
+    obj: ModelRecordServiceBase = ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
     return obj
 
 

invokeai/backend/model_manager/config.py

@@ -161,7 +161,6 @@ 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:
@@ -311,7 +310,7 @@ 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/hash.py

@@ -12,8 +12,6 @@ import hashlib
 import os
 from pathlib import Path
 from typing import Callable, Literal, Optional, Union
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
 
 from blake3 import blake3
 
@@ -107,14 +105,13 @@ class ModelHash:
         """
         model_component_paths = self._get_file_paths(dir, self._file_filter)
 
-        # Use ThreadPoolExecutor to hash files in parallel
-        with ThreadPoolExecutor(min(((os.cpu_count() or 1) + 4), len(model_component_paths))) as executor:
-            future_to_component = {executor.submit(self._hash_file, component): component for component in sorted(model_component_paths)}
-            component_hashes = [future.result() for future in as_completed(future_to_component)]
+        component_hashes: list[str] = []
+        for component in sorted(model_component_paths):
+            component_hashes.append(self._hash_file(component))
 
-        # BLAKE3 to hash the hashes
+        # BLAKE3 is cryptographically secure. We may as well fall back on a secure algorithm
+        # for the composite hash
         composite_hasher = blake3()
-        component_hashes.sort()
         for h in component_hashes:
             composite_hasher.update(h.encode("utf-8"))
         return composite_hasher.hexdigest()
@@ -132,12 +129,10 @@ class ModelHash:
         """
 
         files: list[Path] = []
-        entries = [entry for entry in os.scandir(model_path.as_posix()) if not entry.name.startswith(".")]
-        dirs = [entry for entry in entries if entry.is_dir()]
-        file_paths = [entry.path for entry in entries if entry.is_file() and file_filter(entry.path)]
-        files.extend([Path(file) for file in file_paths])
-        for dir in dirs:
-            files.extend(ModelHash._get_file_paths(Path(dir.path), file_filter))
+        for root, _dirs, _files in os.walk(model_path):
+            for file in _files:
+                if file_filter(file):
+                    files.append(Path(root, file))
         return files
 
     @staticmethod
@@ -166,11 +161,13 @@ class ModelHash:
         """
 
         def hashlib_hasher(file_path: Path) -> str:
-            """Hashes a file using a hashlib algorithm."""
+            """Hashes a file using a hashlib algorithm. Uses `memoryview` to avoid reading the entire file into memory."""
             hasher = hashlib.new(algorithm)
-            with open(file_path, "rb") as f:
-                for chunk in iter(lambda: f.read(8 * 1024), b""):
-                    hasher.update(chunk)
+            buffer = bytearray(128 * 1024)
+            mv = memoryview(buffer)
+            with open(file_path, "rb", buffering=0) as f:
+                while n := f.readinto(mv):
+                    hasher.update(mv[:n])
             return hasher.hexdigest()
 
         return hashlib_hasher

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/model_manager/probe.py

@@ -84,9 +84,6 @@ class ProbeBase(object):
 
 
 class ModelProbe(object):
-
-    hasher = ModelHash()
-
     PROBES: Dict[str, Dict[ModelType, type[ProbeBase]]] = {
         "diffusers": {},
         "checkpoint": {},
@@ -160,7 +157,7 @@ class ModelProbe(object):
             fields.get("description") or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
         )
         fields["format"] = fields.get("format") or probe.get_format()
-        fields["hash"] = fields.get("hash") or cls.hasher.hash(model_path)
+        fields["hash"] = fields.get("hash") or ModelHash().hash(model_path)
 
         if format_type == ModelFormat.Diffusers and isinstance(probe, FolderProbeBase):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()

invokeai/backend/training/textual_inversion_training.py

@@ -858,9 +858,9 @@ def do_textual_inversion_training(
                 # Let's make sure we don't update any embedding weights besides the newly added token
                 index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
-                        orig_embeds_params[index_no_updates]
-                    )
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                        index_no_updates
+                    ] = orig_embeds_params[index_no_updates]
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

invokeai/backend/util/test_utils.py

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

invokeai/frontend/merge/merge_diffusers.py

@@ -20,6 +20,7 @@ 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 (
@@ -412,7 +413,7 @@ 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)
+    return ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
 
 
 def get_model_merger(record_store: ModelRecordServiceBase) -> ModelMerger:

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

@@ -746,7 +746,6 @@
         "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",
@@ -787,10 +786,6 @@
         "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",
@@ -823,7 +818,6 @@
         "oliveModels": "Olives",
         "onnxModels": "Onnx",
         "path": "Path",
-        "pathToConfig": "Path To Config",
         "pathToCustomConfig": "Path To Custom Config",
         "pickModelType": "Pick Model Type",
         "predictionType": "Prediction Type",
@@ -858,7 +852,6 @@
         "triggerPhrases": "Trigger Phrases",
         "typePhraseHere": "Type phrase here",
         "upcastAttention": "Upcast Attention",
-        "uploadImage": "Upload Image",
         "updateModel": "Update Model",
         "useCustomConfig": "Use Custom Config",
         "useDefaultSettings": "Use Default Settings",
@@ -951,7 +944,6 @@
         "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.",
@@ -1027,7 +1019,6 @@
         "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",
@@ -1815,7 +1806,6 @@
         "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",
@@ -1825,7 +1815,6 @@
         "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

@@ -115,8 +115,7 @@
         "safetensors": "Safetensors",
         "ai": "ia",
         "file": "File",
-        "toResolve": "Da risolvere",
-        "add": "Aggiungi"
+        "toResolve": "Da risolvere"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -154,12 +153,7 @@
         "starImage": "Immagine preferita",
         "dropToUpload": "$t(gallery.drop) per aggiornare",
         "problemDeletingImagesDesc": "Impossibile eliminare una o più 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"
+        "problemDeletingImages": "Problema durante l'eliminazione delle immagini"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti di scelta rapida",
@@ -511,12 +505,12 @@
         "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 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 aggiorni manualmente il tuo file models.yaml o 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",
+        "predictionType": "Tipo di previsione (per modelli Stable Diffusion 2.x ed alcuni modelli Stable Diffusion 1.x)",
         "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",
@@ -527,34 +521,7 @@
         "vaePrecision": "Precisione VAE",
         "noModelSelected": "Nessun modello selezionato",
         "conversionNotSupported": "Conversione non supportata",
-        "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"
+        "configFile": "File di configurazione"
     },
     "parameters": {
         "images": "Immagini",
@@ -636,8 +603,8 @@
         "clipSkip": "CLIP Skip",
         "aspectRatio": "Proporzioni",
         "maskAdjustmentsHeader": "Regolazioni della maschera",
-        "maskBlur": "Sfocatura maschera",
-        "maskBlurMethod": "Metodo sfocatura maschera",
+        "maskBlur": "Sfocatura",
+        "maskBlurMethod": "Metodo di sfocatura",
         "seamLowThreshold": "Basso",
         "seamHighThreshold": "Alto",
         "coherencePassHeader": "Passaggio di coerenza",
@@ -694,8 +661,7 @@
         "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (potrebbe essere troppo grande)",
         "boxBlur": "Box",
         "gaussianBlur": "Gaussian",
-        "remixImage": "Remixa l'immagine",
-        "coherenceEdgeSize": "Dimensione bordo"
+        "remixImage": "Remixa l'immagine"
     },
     "settings": {
         "models": "Modelli",
@@ -778,8 +744,8 @@
         "canceled": "Elaborazione annullata",
         "problemCopyingImageLink": "Impossibile copiare il collegamento dell'immagine",
         "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
-        "parameterSet": "{{parameter}} impostato",
-        "parameterNotSet": "{{parameter}} non impostato",
+        "parameterSet": "Parametro impostato",
+        "parameterNotSet": "Parametro non impostato",
         "nodesLoadedFailed": "Impossibile caricare i nodi",
         "nodesSaved": "Nodi salvati",
         "nodesLoaded": "Nodi caricati",
@@ -832,10 +798,7 @@
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
         "uploadInitialImage": "Carica l'immagine iniziale",
-        "problemDownloadingImage": "Impossibile scaricare l'immagine",
-        "prunedQueue": "Coda ripulita",
-        "modelImportCanceled": "Importazione del modello annullata",
-        "modelImportRemoved": "Importazione del modello rimossa"
+        "problemDownloadingImage": "Impossibile scaricare l'immagine"
     },
     "tooltip": {
         "feature": {
@@ -913,10 +876,7 @@
         "antialiasing": "Anti aliasing",
         "showResultsOn": "Mostra i risultati (attivato)",
         "showResultsOff": "Mostra i risultati (disattivato)",
-        "saveMask": "Salva $t(unifiedCanvas.mask)",
-        "coherenceModeGaussianBlur": "Sfocatura Gaussiana",
-        "coherenceModeBoxBlur": "Sfocatura Box",
-        "coherenceModeStaged": "Maschera espansa"
+        "saveMask": "Salva $t(unifiedCanvas.mask)"
     },
     "accessibility": {
         "modelSelect": "Seleziona modello",
@@ -1385,8 +1345,7 @@
         "allLoRAsAdded": "Tutti i LoRA aggiunti",
         "defaultVAE": "VAE predefinito",
         "incompatibleBaseModel": "Modello base incompatibile",
-        "loraAlreadyAdded": "LoRA già aggiunto",
-        "concepts": "Concetti"
+        "loraAlreadyAdded": "LoRA già aggiunto"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1739,25 +1698,6 @@
             "paragraphs": [
                 "Valuta le generazioni in modo che siano più simili alle immagini con un punteggio estetico elevato, in base ai dati di addestramento."
             ]
-        },
-        "compositingCoherenceMinDenoise": {
-            "heading": "Livello minimo di riduzione del rumore",
-            "paragraphs": [
-                "Intensità minima di riduzione rumore per la modalità di Coerenza",
-                "L'intensità minima di riduzione del rumore per la regione di coerenza durante l'inpainting o l'outpainting"
-            ]
-        },
-        "compositingMaskBlur": {
-            "paragraphs": [
-                "Il raggio di sfocatura della maschera."
-            ],
-            "heading": "Sfocatura maschera"
-        },
-        "compositingCoherenceEdgeSize": {
-            "heading": "Dimensione del bordo",
-            "paragraphs": [
-                "La dimensione del bordo del passaggio di coerenza."
-            ]
         }
     },
     "sdxl": {
@@ -1806,12 +1746,7 @@
         "scheduler": "Campionatore",
         "recallParameters": "Richiama i parametri",
         "noRecallParameters": "Nessun parametro da richiamare trovato",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
-        "allPrompts": "Tutti i prompt",
-        "imageDimensions": "Dimensioni dell'immagine",
-        "parameterSet": "Parametro {{parameter}} impostato",
-        "parsingFailed": "Analisi non riuscita",
-        "recallParameter": "Richiama {{label}}"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
     },
     "hrf": {
         "enableHrf": "Abilita Correzione Alta Risoluzione",
@@ -1883,11 +1818,5 @@
         "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": "Линейный вид",
+        "linear": "Линейная обработка",
         "dontAskMeAgain": "Больше не спрашивать",
         "areYouSure": "Вы уверены?",
         "random": "Случайное",
@@ -117,8 +117,7 @@
         "toResolve": "Чтоб решить",
         "copy": "Копировать",
         "localSystem": "Локальная система",
-        "aboutDesc": "Используя Invoke для работы? Проверьте это:",
-        "add": "Добавить"
+        "aboutDesc": "Используя Invoke для работы? Проверьте это:"
     },
     "gallery": {
         "generations": "Генерации",
@@ -156,12 +155,7 @@
         "noImageSelected": "Изображение не выбрано",
         "setCurrentImage": "Установить как текущее изображение",
         "starImage": "Добавить в избранное",
-        "dropToUpload": "$t(gallery.drop) чтоб загрузить",
-        "bulkDownloadFailed": "Загрузка не удалась",
-        "bulkDownloadStarting": "Начало загрузки",
-        "bulkDownloadRequested": "Подготовка к скачиванию",
-        "bulkDownloadRequestedDesc": "Ваш запрос на скачивание готовится. Это может занять несколько минут.",
-        "bulkDownloadRequestFailed": "Возникла проблема при подготовке скачивания"
+        "dropToUpload": "$t(gallery.drop) чтоб загрузить"
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -510,7 +504,7 @@
         "settings": "Настройки",
         "selectModel": "Выберите модель",
         "syncModels": "Синхронизация моделей",
-        "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.",
+        "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.",
         "modelUpdateFailed": "Не удалось обновить модель",
         "modelConversionFailed": "Не удалось сконвертировать модель",
         "modelsMergeFailed": "Не удалось выполнить слияние моделей",
@@ -519,7 +513,7 @@
         "oliveModels": "Модели Olives",
         "conversionNotSupported": "Преобразование не поддерживается",
         "noModels": "Нет моделей",
-        "predictionType": "Тип прогноза",
+        "predictionType": "Тип прогноза (для моделей Stable Diffusion 2.x и периодических моделей Stable Diffusion 1.x)",
         "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",
@@ -530,33 +524,7 @@
         "customConfigFileLocation": "Расположение пользовательского файла конфигурации",
         "vaePrecision": "Точность VAE",
         "noModelSelected": "Модель не выбрана",
-        "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"
+        "configFile": "Файл конфигурации"
     },
     "parameters": {
         "images": "Изображения",
@@ -623,7 +591,7 @@
         "hSymmetryStep": "Шаг гор. симметрии",
         "hidePreview": "Скрыть предпросмотр",
         "imageToImage": "Изображение в изображение",
-        "denoisingStrength": "Сила зашумления",
+        "denoisingStrength": "Сила шумоподавления",
         "copyImage": "Скопировать изображение",
         "showPreview": "Показать предпросмотр",
         "noiseSettings": "Шум",
@@ -638,8 +606,8 @@
         "clipSkip": "CLIP Пропуск",
         "aspectRatio": "Соотношение",
         "maskAdjustmentsHeader": "Настройка маски",
-        "maskBlur": "Размытие маски",
-        "maskBlurMethod": "Метод размытия маски",
+        "maskBlur": "Размытие",
+        "maskBlurMethod": "Метод размытия",
         "seamLowThreshold": "Низкий",
         "seamHighThreshold": "Высокий",
         "coherencePassHeader": "Порог Coherence",
@@ -698,9 +666,7 @@
         "lockAspectRatio": "Заблокировать соотношение",
         "boxBlur": "Размытие прямоугольника",
         "gaussianBlur": "Размытие по Гауссу",
-        "remixImage": "Ремикс изображения",
-        "coherenceMinDenoise": "Мин. шумоподавление",
-        "coherenceEdgeSize": "Размер края"
+        "remixImage": "Ремикс изображения"
     },
     "settings": {
         "models": "Модели",
@@ -783,8 +749,8 @@
         "canceled": "Обработка отменена",
         "problemCopyingImageLink": "Не удалось скопировать ссылку на изображение",
         "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
-        "parameterNotSet": "Параметр {{parameter}} не задан",
-        "parameterSet": "Параметр {{parameter}} задан",
+        "parameterNotSet": "Параметр не задан",
+        "parameterSet": "Параметр задан",
         "nodesLoaded": "Узлы загружены",
         "problemCopyingImage": "Не удается скопировать изображение",
         "nodesLoadedFailed": "Не удалось загрузить Узлы",
@@ -837,10 +803,7 @@
         "problemImportingMask": "Проблема с импортом маски",
         "problemDownloadingImage": "Не удается скачать изображение",
         "uploadInitialImage": "Загрузить начальное изображение",
-        "resetInitialImage": "Сбросить начальное изображение",
-        "prunedQueue": "Урезанная очередь",
-        "modelImportCanceled": "Импорт модели отменен",
-        "modelImportRemoved": "Импорт модели удален"
+        "resetInitialImage": "Сбросить начальное изображение"
     },
     "tooltip": {
         "feature": {
@@ -1182,11 +1145,7 @@
         "reorderLinearView": "Изменить порядок линейного просмотра",
         "viewMode": "Использовать в линейном представлении",
         "editMode": "Открыть в редакторе узлов",
-        "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."
+        "resetToDefaultValue": "Сбросить к стандартному значкнию"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -1335,8 +1294,7 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "Планировщик, используемый в процессе генерации.",
-                "Каждый планировщик определяет, как итеративно добавлять шум к изображению или как обновлять образец на основе выходных данных модели."
+                "Планировщик определяет, как итеративно добавлять шум к изображению или как обновлять образец на основе выходных данных модели."
             ],
             "heading": "Планировщик"
         },
@@ -1389,7 +1347,7 @@
         "compositingCoherenceMode": {
             "heading": "Режим",
             "paragraphs": [
-                "Метод, используемый для создания связного изображения с вновь созданной замаскированной областью."
+                "Режим прохождения когерентности."
             ]
         },
         "paramSeed": {
@@ -1407,7 +1365,7 @@
         },
         "controlNetBeginEnd": {
             "paragraphs": [
-                "Часть процесса шумоподавления, к которой будет применен адаптер контроля.",
+                "На каких этапах процесса шумоподавления будет применена ControlNet.",
                 "ControlNet, применяемые в начале процесса, направляют композицию, а ControlNet, применяемые в конце, направляют детали."
             ],
             "heading": "Процент начала/конца шага"
@@ -1423,8 +1381,8 @@
         },
         "clipSkip": {
             "paragraphs": [
-                "Сколько слоев модели CLIP пропустить.",
-                "Некоторые модели лучше подходят для использования с CLIP Skip."
+                "Выберите, сколько слоев модели CLIP нужно пропустить.",
+                "Некоторые модели работают лучше с определенными настройками пропуска CLIP."
             ],
             "heading": "CLIP пропуск"
         },
@@ -1521,25 +1479,6 @@
             "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": {
@@ -1570,12 +1509,7 @@
         "steps": "Шаги",
         "scheduler": "Планировщик",
         "noRecallParameters": "Параметры для вызова не найдены",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
-        "parameterSet": "Параметр {{parameter}} установлен",
-        "parsingFailed": "Не удалось выполнить синтаксический анализ",
-        "recallParameter": "Отозвать {{label}}",
-        "allPrompts": "Все запросы",
-        "imageDimensions": "Размеры изображения"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
     },
     "queue": {
         "status": "Статус",
@@ -1654,11 +1588,10 @@
         "denoisingStrength": "Шумоподавление",
         "refinermodel": "Модель перерисовщик",
         "posAestheticScore": "Положительная эстетическая оценка",
-        "concatPromptStyle": "Связывание запроса и стиля",
+        "concatPromptStyle": "Объединение запроса и стиля",
         "loading": "Загрузка...",
         "steps": "Шаги",
-        "posStylePrompt": "Запрос стиля",
-        "freePromptStyle": "Ручной запрос стиля"
+        "posStylePrompt": "Запрос стиля"
     },
     "invocationCache": {
         "useCache": "Использовать кэш",
@@ -1745,8 +1678,7 @@
         "allLoRAsAdded": "Все LoRA добавлены",
         "defaultVAE": "Стандартное VAE",
         "incompatibleBaseModel": "Несовместимая базовая модель",
-        "loraAlreadyAdded": "LoRA уже добавлена",
-        "concepts": "Концепты"
+        "loraAlreadyAdded": "LoRA уже добавлена"
     },
     "app": {
         "storeNotInitialized": "Магазин не инициализирован"
@@ -1764,7 +1696,7 @@
         },
         "generation": {
             "title": "Генерация",
-            "conceptsTab": "LoRA",
+            "conceptsTab": "Концепты",
             "modelTab": "Модель"
         },
         "advanced": {

invokeai/frontend/web/scripts/typegen.js

@@ -5,55 +5,18 @@ 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 generateTypes(schema) {
-  process.stdout.write(`Generating types ${OUTPUT_FILE}...`);
-  const types = await openapiTS(schema, {
+async function main() {
+  process.stdout.write(`Generating types "${OPENAPI_URL}" --> "${OUTPUT_FILE}"...`);
+  const types = await openapiTS(OPENAPI_URL, {
     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/listeners/canvasImageToControlNet.ts

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

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

@@ -101,7 +101,7 @@ export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartLis
         ).unwrap();
       }
 
-      const graph = await buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
+      const graph = 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 = await buildLinearSDXLTextToImageGraph(state);
+          graph = buildLinearSDXLTextToImageGraph(state);
         } else {
-          graph = await buildLinearSDXLImageToImageGraph(state);
+          graph = buildLinearSDXLImageToImageGraph(state);
         }
       } else {
         if (action.payload.tabName === 'txt2img') {
-          graph = await buildLinearTextToImageGraph(state);
+          graph = buildLinearTextToImageGraph(state);
         } else {
-          graph = await buildLinearImageToImageGraph(state);
+          graph = 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: 5,
+  gap: 2,
   flexDir: 'column',
 };
 
@@ -39,8 +39,8 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
     <Flex sx={sx}>
       <RgbaColorPicker color={color} onChange={onChange} style={colorPickerStyles} {...rest} />
       {withNumberInput && (
-        <Flex gap={5}>
-          <FormControl gap={0}>
+        <Flex>
+          <FormControl>
             <FormLabel>{t('common.red')}</FormLabel>
             <CompositeNumberInput
               value={color.r}
@@ -52,7 +52,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={90}
             />
           </FormControl>
-          <FormControl gap={0}>
+          <FormControl>
             <FormLabel>{t('common.green')}</FormLabel>
             <CompositeNumberInput
               value={color.g}
@@ -64,7 +64,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={90}
             />
           </FormControl>
-          <FormControl gap={0}>
+          <FormControl>
             <FormLabel>{t('common.blue')}</FormLabel>
             <CompositeNumberInput
               value={color.b}
@@ -76,7 +76,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
               defaultValue={255}
             />
           </FormControl>
-          <FormControl gap={0}>
+          <FormControl>
             <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 IAICanvasMaskCompositor from './IAICanvasMaskCompositor';
+import IAICanvasMaskCompositer from './IAICanvasMaskCompositer';
 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} />
-            <IAICanvasMaskCompositor listening={false} />
+            <IAICanvasMaskCompositer listening={false} />
           </Layer>
           <Layer listening={false}>
             <IAICanvasBoundingBoxOverlay />

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

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

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

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

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

@@ -18,7 +18,6 @@ import {
   setShouldAutoSave,
   setShouldCropToBoundingBoxOnSave,
   setShouldDarkenOutsideBoundingBox,
-  setShouldInvertBrushSizeScrollDirection,
   setShouldRestrictStrokesToBox,
   setShouldShowCanvasDebugInfo,
   setShouldShowGrid,
@@ -41,7 +40,6 @@ 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);
@@ -78,10 +76,6 @@ 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]
@@ -150,13 +144,6 @@ 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

@@ -15,7 +15,6 @@ 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>) => {
@@ -29,16 +28,10 @@ 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(delta) * 0.7363 * Math.pow(1.0394, brushSize);
+        const targetDelta = Math.sign(e.evt.deltaY) * 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
@@ -74,7 +67,7 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
         dispatch(setStageCoordinates(newCoordinates));
       }
     },
-    [stageRef, isMoveStageKeyHeld, brushSize, dispatch, stageScale, shouldInvertBrushSizeScrollDirection]
+    [stageRef, isMoveStageKeyHeld, stageScale, dispatch, brushSize]
   );
 };
 

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

@@ -65,7 +65,6 @@ const initialCanvasState: CanvasState = {
   shouldAutoSave: false,
   shouldCropToBoundingBoxOnSave: false,
   shouldDarkenOutsideBoundingBox: false,
-  shouldInvertBrushSizeScrollDirection: false,
   shouldLockBoundingBox: false,
   shouldPreserveMaskedArea: false,
   shouldRestrictStrokesToBox: true,
@@ -221,9 +220,6 @@ 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 = [];
@@ -292,31 +288,6 @@ 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;
 
@@ -684,7 +655,6 @@ export const {
   commitColorPickerColor,
   commitStagingAreaImage,
   discardStagedImages,
-  discardStagedImage,
   nextStagingAreaImage,
   prevStagingAreaImage,
   redo,
@@ -704,7 +674,6 @@ export const {
   setShouldAutoSave,
   setShouldCropToBoundingBoxOnSave,
   setShouldDarkenOutsideBoundingBox,
-  setShouldInvertBrushSizeScrollDirection,
   setShouldPreserveMaskedArea,
   setShouldShowBoundingBox,
   setShouldShowCanvasDebugInfo,

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

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

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.400">
+      <Badge variant="solid" bg="invokeBlue.500">
         {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.400' : 'base.600'}
-                  color={isSelected ? 'base.800' : 'base.100'}
+                  bg={isSelected ? 'invokeBlue.500' : 'base.600'}
+                  color={isSelected ? 'base.50' : 'base.100'}
                   lineHeight="short"
                   fontSize="xs"
                 >
@@ -193,7 +193,6 @@ 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.400' : 'base.600'}
-                  color={isSelected ? 'base.800' : 'base.100'}
+                  bg={isSelected ? 'invokeBlue.500' : 'base.600'}
+                  color={isSelected ? 'base.50' : '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} fontSize="sm">
+        <Flex direction={direction}>
           <Text fontWeight="semibold" whiteSpace="pre-wrap" pr={2}>
             {label}:
           </Text>

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

@@ -1,27 +0,0 @@
-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

@@ -22,8 +22,6 @@ 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;
 };
@@ -75,7 +73,6 @@ 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

@@ -1,134 +0,0 @@
-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

@@ -4,7 +4,6 @@ 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';
@@ -26,22 +25,19 @@ export const Model = () => {
 
   return (
     <>
-      <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>
+      <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>
-        </Flex>
-        <ModelImageUpload model_key={selectedModelKey} model_image={data.cover_image} />
+        {data.source && (
+          <Text variant="subtext">
+            {t('modelManager.source')}: {data?.source}
+          </Text>
+        )}
+        <Box mt="4">
+          <ModelAttrView label="Description" value={data.description} />
+        </Box>
       </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 gap="4" alignItems="center">
+          <Flex>
             <FormControl flexDir="column" alignItems="flex-start" gap={1}>
               <FormLabel>{t('modelManager.description')}</FormLabel>
               <Textarea fontSize="md" resize="none" {...register('description')} />
@@ -145,32 +145,20 @@ 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.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 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>
       </form>

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

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

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

@@ -1,55 +0,0 @@
-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

@@ -7,7 +7,6 @@ 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) => {
@@ -31,7 +30,7 @@ export const WorkflowViewMode = () => {
               <WorkflowField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
             ))
           ) : (
-            <EmptyState />
+            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
           )}
         </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.linear')}</Tab>
           <Tab>{t('common.details')}</Tab>
+          <Tab>{t('common.linear')}</Tab>
           <Tab>JSON</Tab>
         </TabList>
 
         <TabPanels>
           <TabPanel>
-            <WorkflowLinearTab />
+            <WorkflowGeneralTab />
           </TabPanel>
           <TabPanel>
-            <WorkflowGeneralTab />
+            <WorkflowLinearTab />
           </TabPanel>
           <TabPanel>
             <WorkflowJSONTab />

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

@@ -55,22 +55,8 @@ 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

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

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

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

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

@@ -1,22 +1,16 @@
 import type { RootState } from 'app/store/store';
-import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { filter, size } from 'lodash-es';
-import {
-  type CoreMetadataInvocation,
-  isLoRAModelConfig,
-  type LoRALoaderInvocation,
-  type NonNullableGraph,
-} from 'services/api/types';
+import type { CoreMetadataInvocation, LoraLoaderInvocation, NonNullableGraph } from 'services/api/types';
 
 import { CLIP_SKIP, LORA_LOADER, MAIN_MODEL_LOADER, NEGATIVE_CONDITIONING, POSITIVE_CONDITIONING } from './constants';
-import { getModelMetadataField, upsertMetadata } from './metadata';
+import { upsertMetadata } from './metadata';
 
-export const addLoRAsToGraph = async (
+export const addLoRAsToGraph = (
   state: RootState,
   graph: NonNullableGraph,
   baseNodeId: string,
   modelLoaderNodeId: string = MAIN_MODEL_LOADER
-): Promise<void> => {
+): 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,
@@ -45,12 +39,12 @@ export const addLoRAsToGraph = async (
   let currentLoraIndex = 0;
   const loraMetadata: CoreMetadataInvocation['loras'] = [];
 
-  enabledLoRAs.forEach(async (lora) => {
+  enabledLoRAs.forEach((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,
@@ -58,10 +52,8 @@ export const addLoRAsToGraph = async (
       weight,
     };
 
-    const modelConfig = await fetchModelConfigWithTypeGuard(key, isLoRAModelConfig);
-
     loraMetadata.push({
-      model: getModelMetadataField(modelConfig),
+      model: { key },
       weight,
     });
 

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

@@ -1,12 +1,6 @@
 import type { RootState } from 'app/store/store';
-import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { filter, size } from 'lodash-es';
-import {
-  type CoreMetadataInvocation,
-  isLoRAModelConfig,
-  type NonNullableGraph,
-  type SDXLLoRALoaderInvocation,
-} from 'services/api/types';
+import type { CoreMetadataInvocation, NonNullableGraph, SDXLLoraLoaderInvocation } from 'services/api/types';
 
 import {
   LORA_LOADER,
@@ -16,14 +10,14 @@ import {
   SDXL_REFINER_INPAINT_CREATE_MASK,
   SEAMLESS,
 } from './constants';
-import { getModelMetadataField, upsertMetadata } from './metadata';
+import { upsertMetadata } from './metadata';
 
-export const addSDXLLoRAsToGraph = async (
+export const addSDXLLoRAsToGraph = (
   state: RootState,
   graph: NonNullableGraph,
   baseNodeId: string,
   modelLoaderNodeId: string = SDXL_MODEL_LOADER
-): Promise<void> => {
+): 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,
@@ -61,12 +55,12 @@ export const addSDXLLoRAsToGraph = async (
   let lastLoraNodeId = '';
   let currentLoraIndex = 0;
 
-  enabledLoRAs.forEach(async (lora) => {
+  enabledLoRAs.forEach((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,
@@ -74,9 +68,7 @@ export const addSDXLLoRAsToGraph = async (
       weight,
     };
 
-    const modelConfig = await fetchModelConfigWithTypeGuard(key, isLoRAModelConfig);
-
-    loraMetadata.push({ model: getModelMetadataField(modelConfig), weight });
+    loraMetadata.push({ model: { key }, weight });
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;

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

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

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

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

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

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

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 = async (
+export const buildCanvasGraph = (
   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 = await buildCanvasSDXLTextToImageGraph(state);
+      graph = buildCanvasSDXLTextToImageGraph(state);
     } else {
-      graph = await buildCanvasTextToImageGraph(state);
+      graph = 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 = await buildCanvasSDXLImageToImageGraph(state, canvasInitImage);
+      graph = buildCanvasSDXLImageToImageGraph(state, canvasInitImage);
     } else {
-      graph = await buildCanvasImageToImageGraph(state, canvasInitImage);
+      graph = 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 = await buildCanvasSDXLInpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = buildCanvasSDXLInpaintGraph(state, canvasInitImage, canvasMaskImage);
     } else {
-      graph = await buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage);
     }
   } else {
     if (!canvasInitImage) {
       throw new Error('Missing canvas init image');
     }
     if (state.generation.model && state.generation.model.base === 'sdxl') {
-      graph = await buildCanvasSDXLOutpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = buildCanvasSDXLOutpaintGraph(state, canvasInitImage, canvasMaskImage);
     } else {
-      graph = await buildCanvasOutpaintGraph(state, canvasInitImage, canvasMaskImage);
+      graph = buildCanvasOutpaintGraph(state, canvasInitImage, canvasMaskImage);
     }
   }
 

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

@@ -1,13 +1,7 @@
 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,
-  type ImageToLatentsInvocation,
-  isNonRefinerMainModelConfig,
-  type NonNullableGraph,
-} from 'services/api/types';
+import type { ImageDTO, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -31,15 +25,12 @@ import {
   POSITIVE_CONDITIONING,
   SEAMLESS,
 } from './constants';
-import { addCoreMetadataNode, getModelMetadataField } from './metadata';
+import { addCoreMetadataNode } from './metadata';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
  */
-export const buildCanvasImageToImageGraph = async (
-  state: RootState,
-  initialImage: ImageDTO
-): Promise<NonNullableGraph> => {
+export const buildCanvasImageToImageGraph = (state: RootState, initialImage: ImageDTO): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -315,8 +306,6 @@ export const buildCanvasImageToImageGraph = async (
     });
   }
 
-  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
-
   addCoreMetadataNode(
     graph,
     {
@@ -327,7 +316,7 @@ export const buildCanvasImageToImageGraph = async (
       height: !isUsingScaledDimensions ? height : scaledBoundingBoxDimensions.height,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model: getModelMetadataField(modelConfig),
+      model,
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -346,17 +335,17 @@ export const buildCanvasImageToImageGraph = async (
   }
 
   // add LoRA support
-  await addLoRAsToGraph(state, graph, DENOISE_LATENTS);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS);
 
   // optionally add custom VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  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 = async (
+export const buildCanvasInpaintGraph = (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage: ImageDTO
-): Promise<NonNullableGraph> => {
+): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -414,17 +414,17 @@ export const buildCanvasInpaintGraph = async (
   }
 
   // Add VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  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 = async (
+export const buildCanvasOutpaintGraph = (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage?: ImageDTO
-): Promise<NonNullableGraph> => {
+): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -545,18 +545,18 @@ export const buildCanvasOutpaintGraph = async (
   }
 
   // Add VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  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

@@ -1,12 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
-import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
-import {
-  type ImageDTO,
-  type ImageToLatentsInvocation,
-  isNonRefinerMainModelConfig,
-  type NonNullableGraph,
-} from 'services/api/types';
+import type { ImageDTO, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -32,15 +26,12 @@ import {
   SEAMLESS,
 } from './constants';
 import { getBoardField, getIsIntermediate, getSDXLStylePrompts } from './graphBuilderUtils';
-import { addCoreMetadataNode, getModelMetadataField } from './metadata';
+import { addCoreMetadataNode } from './metadata';
 
 /**
  * Builds the Canvas tab's Image to Image graph.
  */
-export const buildCanvasSDXLImageToImageGraph = async (
-  state: RootState,
-  initialImage: ImageDTO
-): Promise<NonNullableGraph> => {
+export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage: ImageDTO): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -316,8 +307,6 @@ export const buildCanvasSDXLImageToImageGraph = async (
     });
   }
 
-  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
-
   addCoreMetadataNode(
     graph,
     {
@@ -328,7 +317,7 @@ export const buildCanvasSDXLImageToImageGraph = async (
       height: !isUsingScaledDimensions ? height : scaledBoundingBoxDimensions.height,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model: getModelMetadataField(modelConfig),
+      model,
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -349,24 +338,24 @@ export const buildCanvasSDXLImageToImageGraph = async (
 
   // Add Refiner if enabled
   if (refinerModel) {
-    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // optionally add custom VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  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 = async (
+export const buildCanvasSDXLInpaintGraph = (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage: ImageDTO
-): Promise<NonNullableGraph> => {
+): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -426,31 +426,24 @@ export const buildCanvasSDXLInpaintGraph = async (
 
   // Add Refiner if enabled
   if (refinerModel) {
-    await addSDXLRefinerToGraph(
-      state,
-      graph,
-      SDXL_DENOISE_LATENTS,
-      modelLoaderNodeId,
-      canvasInitImage,
-      canvasMaskImage
-    );
+    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage, canvasMaskImage);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // Add VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  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 = async (
+export const buildCanvasSDXLOutpaintGraph = (
   state: RootState,
   canvasInitImage: ImageDTO,
   canvasMaskImage?: ImageDTO
-): Promise<NonNullableGraph> => {
+): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -555,25 +555,25 @@ export const buildCanvasSDXLOutpaintGraph = async (
 
   // Add Refiner if enabled
   if (refinerModel) {
-    await addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage);
+    addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId, canvasInitImage);
     if (seamlessXAxis || seamlessYAxis) {
       modelLoaderNodeId = SDXL_REFINER_SEAMLESS;
     }
   }
 
   // Add VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
+  addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
-  await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
+  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

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

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

@@ -1,13 +1,7 @@
 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,
-  type ImageToLatentsInvocation,
-  isNonRefinerMainModelConfig,
-  type NonNullableGraph,
-} from 'services/api/types';
+import type { ImageResizeInvocation, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addIPAdapterToLinearGraph } from './addIPAdapterToLinearGraph';
@@ -30,12 +24,12 @@ import {
   RESIZE,
   SEAMLESS,
 } from './constants';
-import { addCoreMetadataNode, getModelMetadataField } from './metadata';
+import { addCoreMetadataNode } from './metadata';
 
 /**
  * Builds the Image to Image tab graph.
  */
-export const buildLinearImageToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
+export const buildLinearImageToImageGraph = (state: RootState): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -313,8 +307,6 @@ export const buildLinearImageToImageGraph = async (state: RootState): Promise<No
     });
   }
 
-  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
-
   addCoreMetadataNode(
     graph,
     {
@@ -325,7 +317,7 @@ export const buildLinearImageToImageGraph = async (state: RootState): Promise<No
       width,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model: getModelMetadataField(modelConfig),
+      model,
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -344,17 +336,17 @@ export const buildLinearImageToImageGraph = async (state: RootState): Promise<No
   }
 
   // optionally add custom VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // Add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
-  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  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

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

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

@@ -1,8 +1,7 @@
 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 { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
+import type { NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
 import { addHrfToGraph } from './addHrfToGraph';
@@ -24,9 +23,9 @@ import {
   SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
 } from './constants';
-import { addCoreMetadataNode, getModelMetadataField } from './metadata';
+import { addCoreMetadataNode } from './metadata';
 
-export const buildLinearTextToImageGraph = async (state: RootState): Promise<NonNullableGraph> => {
+export const buildLinearTextToImageGraph = (state: RootState): NonNullableGraph => {
   const log = logger('nodes');
   const {
     positivePrompt,
@@ -213,8 +212,6 @@ export const buildLinearTextToImageGraph = async (state: RootState): Promise<Non
     ],
   };
 
-  const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
-
   addCoreMetadataNode(
     graph,
     {
@@ -225,7 +222,7 @@ export const buildLinearTextToImageGraph = async (state: RootState): Promise<Non
       width,
       positive_prompt: positivePrompt,
       negative_prompt: negativePrompt,
-      model: getModelMetadataField(modelConfig),
+      model,
       seed,
       steps,
       rand_device: use_cpu ? 'cpu' : 'cuda',
@@ -242,18 +239,18 @@ export const buildLinearTextToImageGraph = async (state: RootState): Promise<Non
   }
 
   // optionally add custom VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
+  addVAEToGraph(state, graph, modelLoaderNodeId);
 
   // add LoRA support
-  await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
+  addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
   // add controlnet, mutating `graph`
-  await addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
+  addControlNetToLinearGraph(state, graph, DENOISE_LATENTS);
 
   // add IP Adapter
-  await addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
+  addIPAdapterToLinearGraph(state, graph, DENOISE_LATENTS);
 
-  await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
+  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 { AnyModelConfig, CoreMetadataInvocation, ModelMetadataField, NonNullableGraph } from 'services/api/types';
+import type { CoreMetadataInvocation, NonNullableGraph } from 'services/api/types';
 
 import { METADATA } from './constants';
 
@@ -71,11 +71,3 @@ 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

@@ -38,7 +38,6 @@ 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

@@ -54,7 +54,6 @@ 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

@@ -41,7 +41,6 @@ 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

@@ -38,7 +38,6 @@ 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', 'InvocationCacheStatus'],
+      invalidatesTags: ['IntermediatesCount'],
     }),
     getImageDTO: build.query<ImageDTO, string>({
       query: (image_name) => ({ url: buildImagesUrl(`i/${image_name}`) }),
@@ -125,7 +125,7 @@ export const imagesApi = api.injectEndpoints({
       paths['/api/v1/images/i/{image_name}/workflow']['get']['responses']['200']['content']['application/json'],
       string
     >({
-      query: (image_name) => ({ url: buildImagesUrl(`i/${image_name}/workflow`) }),
+      query: (image_name) => ({ url: `images/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

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

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

@@ -38,6 +38,7 @@ 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
 
@@ -119,18 +120,17 @@ 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'];
-
-// Metadata fields
-export type ModelMetadataField = S['ModelMetadataField'];
+export type IPAdapterMetadataField = S['IPAdapterMetadataField'];
+export type T2IAdapterField = S['T2IAdapterField'];
 
 // ControlNet Nodes
 export type ControlNetInvocation = S['ControlNetInvocation'];

invokeai/invocation_api/__init__.py

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