(ci) remove python 3.10 from the test matrix; comment out GPU tests for now

## Summary

- Extend `ModelOnDisk` with caching, type hints, default args
- Fail early if there is an error classifying a config

## Related Issues / Discussions

<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->

## QA Instructions

<!--WHEN APPLICABLE: Describe how you have tested the changes in this
PR. Provide enough detail that a reviewer can reproduce your tests.-->

## Merge Plan

<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->

## Checklist

- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_

.git-blame-ignore-revs

@@ -1,2 +1,5 @@
 b3dccfaeb636599c02effc377cdd8a87d658256c
 218b6d0546b990fc449c876fb99f44b50c4daa35
+182580ff6970caed400be178c5b888514b75d7f2
+8e9d5c1187b0d36da80571ce4c8ba9b3a37b6c46
+99aac5870e1092b182e6c5f21abcaab6936a4ad1

.github/workflows/python-checks.yml

@@ -61,13 +61,13 @@ jobs:
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.12'
           cache: pip
           cache-dependency-path: pyproject.toml
 
       - name: install ruff
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: pip install ruff==0.9.9
+        run: pip install ruff==0.11.2
         shell: bash
 
       - name: ruff check

.github/workflows/python-tests.yml

@@ -39,26 +39,25 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - '3.10'
           - '3.11'
         platform:
-          - linux-cuda-11_7
-          - linux-rocm-5_2
+          # - linux-cuda-12_6
+          # - linux-rocm-6_2
           - linux-cpu
           - macos-default
           - windows-cpu
         include:
-          - platform: linux-cuda-11_7
-            os: ubuntu-22.04
-            github-env: $GITHUB_ENV
-          - platform: linux-rocm-5_2
-            os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
-            github-env: $GITHUB_ENV
+          # - platform: linux-cuda-12_6
+          #   os: ubuntu-24.04
+          #   github-env: $GITHUB_ENV
+          # - platform: linux-rocm-6_2
+          #   os: ubuntu-24.04
+          #   extra-index-url: 'https://download.pytorch.org/whl/rocm6.2'
+          #   github-env: $GITHUB_ENV
           - platform: linux-cpu
-            os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/cpu'
+            os: ubuntu-24.04
             github-env: $GITHUB_ENV
+            extra-index-url: 'https://download.pytorch.org/whl/cpu'
           - platform: macos-default
             os: macOS-14
             github-env: $GITHUB_ENV

invokeai/app/api/routers/app_info.py

@@ -12,6 +12,7 @@ from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
+from invokeai.app.services.config.config_default import InvokeAIAppConfig, get_config
 from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch
 from invokeai.backend.util.logging import logging
@@ -99,7 +100,7 @@ async def get_app_deps() -> AppDependencyVersions:
 
 
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
-async def get_config() -> AppConfig:
+async def get_config_() -> AppConfig:
     infill_methods = ["lama", "tile", "cv2", "color"]  # TODO: add mosaic back
     if PatchMatch.patchmatch_available():
         infill_methods.append("patchmatch")
@@ -121,6 +122,21 @@ async def get_config() -> AppConfig:
     )
 
 
+class InvokeAIAppConfigWithSetFields(BaseModel):
+    """InvokeAI App Config with model fields set"""
+
+    set_fields: set[str] = Field(description="The set fields")
+    config: InvokeAIAppConfig = Field(description="The InvokeAI App Config")
+
+
+@app_router.get(
+    "/runtime_config", operation_id="get_runtime_config", status_code=200, response_model=InvokeAIAppConfigWithSetFields
+)
+async def get_runtime_config() -> InvokeAIAppConfigWithSetFields:
+    config = get_config()
+    return InvokeAIAppConfigWithSetFields(set_fields=config.model_fields_set, config=config)
+
+
 @app_router.get(
     "/logging",
     operation_id="get_log_level",

invokeai/app/api/routers/images.py

@@ -96,6 +96,22 @@ async def upload_image(
         raise HTTPException(status_code=500, detail="Failed to create image")
 
 
+class ImageUploadEntry(BaseModel):
+    image_dto: ImageDTO = Body(description="The image DTO")
+    presigned_url: str = Body(description="The URL to get the presigned URL for the image upload")
+
+
+@images_router.post("/", operation_id="create_image_upload_entry")
+async def create_image_upload_entry(
+    width: int = Body(description="The width of the image"),
+    height: int = Body(description="The height of the image"),
+    board_id: Optional[str] = Body(default=None, description="The board to add this image to, if any"),
+) -> ImageUploadEntry:
+    """Uploads an image from a URL, not implemented"""
+
+    raise HTTPException(status_code=501, detail="Not implemented")
+
+
 @images_router.delete("/i/{image_name}", operation_id="delete_image")
 async def delete_image(
     image_name: str = Path(description="The name of the image to delete"),

invokeai/app/api/routers/model_manager.py

@@ -28,12 +28,10 @@ from invokeai.app.services.model_records import (
     UnknownModelException,
 )
 from invokeai.app.util.suppress_output import SuppressOutput
+from invokeai.backend.model_manager import BaseModelType, ModelFormat, ModelType
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     MainCheckpointConfig,
-    ModelFormat,
-    ModelType,
 )
 from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch

invokeai/app/invocations/create_gradient_mask.py

@@ -19,7 +19,8 @@ from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import LoadedModel
-from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
+from invokeai.backend.model_manager.config import MainConfigBase
+from invokeai.backend.model_manager.taxonomy import ModelVariantType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 
 

invokeai/app/invocations/denoise_latents.py

@@ -39,8 +39,8 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.model_manager import BaseModelType, ModelVariantType
 from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw

invokeai/app/invocations/flux_control_lora_loader.py

@@ -1,7 +1,6 @@
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -25,7 +24,6 @@ class FluxControlLoRALoaderOutput(BaseInvocationOutput):
     tags=["lora", "model", "flux"],
     category="model",
     version="1.1.1",
-    classification=Classification.Prototype,
 )
 class FluxControlLoRALoaderInvocation(BaseInvocation):
     """LoRA model and Image to use with FLUX transformer generation."""

invokeai/app/invocations/flux_controlnet.py

@@ -3,7 +3,6 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -52,7 +51,6 @@ class FluxControlNetOutput(BaseInvocationOutput):
     tags=["controlnet", "flux"],
     category="controlnet",
     version="1.0.0",
-    classification=Classification.Prototype,
 )
 class FluxControlNetInvocation(BaseInvocation):
     """Collect FLUX ControlNet info to pass to other nodes."""

invokeai/app/invocations/flux_denoise.py

@@ -10,7 +10,7 @@ from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
@@ -49,7 +49,7 @@ from invokeai.backend.flux.sampling_utils import (
     unpack,
 )
 from invokeai.backend.flux.text_conditioning import FluxReduxConditioning, FluxTextConditioning
-from invokeai.backend.model_manager.config import ModelFormat, ModelVariantType
+from invokeai.backend.model_manager.taxonomy import ModelFormat, ModelVariantType
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -64,7 +64,6 @@ from invokeai.backend.util.devices import TorchDevice
     tags=["image", "flux"],
     category="image",
     version="3.3.0",
-    classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a FLUX transformer model."""

invokeai/app/invocations/flux_fill.py

@@ -31,7 +31,7 @@ class FluxFillOutput(BaseInvocationOutput):
     tags=["inpaint"],
     category="inpaint",
     version="1.0.0",
-    classification=Classification.Prototype,
+    classification=Classification.Beta,
 )
 class FluxFillInvocation(BaseInvocation):
     """Prepare the FLUX Fill conditioning data."""

invokeai/app/invocations/flux_ip_adapter.py

@@ -4,7 +4,7 @@ from typing import List, Literal, Union
 from pydantic import field_validator, model_validator
 from typing_extensions import Self
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import InputField, UIType
 from invokeai.app.invocations.ip_adapter import (
     CLIP_VISION_MODEL_MAP,
@@ -28,7 +28,6 @@ from invokeai.backend.model_manager.config import (
     tags=["ip_adapter", "control"],
     category="ip_adapter",
     version="1.0.0",
-    classification=Classification.Prototype,
 )
 class FluxIPAdapterInvocation(BaseInvocation):
     """Collects FLUX IP-Adapter info to pass to other nodes."""

invokeai/app/invocations/flux_lora_loader.py

@@ -3,14 +3,13 @@ from typing import Optional
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, LoRAField, ModelIdentifierField, T5EncoderField, TransformerField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 
 
 @invocation_output("flux_lora_loader_output")
@@ -32,7 +31,6 @@ class FluxLoRALoaderOutput(BaseInvocationOutput):
     tags=["lora", "model", "flux"],
     category="model",
     version="1.2.1",
-    classification=Classification.Prototype,
 )
 class FluxLoRALoaderInvocation(BaseInvocation):
     """Apply a LoRA model to a FLUX transformer and/or text encoder."""
@@ -111,7 +109,6 @@ class FluxLoRALoaderInvocation(BaseInvocation):
     tags=["lora", "model", "flux"],
     category="model",
     version="1.3.1",
-    classification=Classification.Prototype,
 )
 class FLUXLoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to a FLUX transformer."""

invokeai/app/invocations/flux_model_loader.py

@@ -3,7 +3,6 @@ from typing import Literal
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -17,8 +16,8 @@ from invokeai.app.util.t5_model_identifier import (
 from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
-    SubModelType,
 )
+from invokeai.backend.model_manager.taxonomy import SubModelType
 
 
 @invocation_output("flux_model_loader_output")
@@ -41,7 +40,6 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
     tags=["model", "flux"],
     category="model",
     version="1.0.6",
-    classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):
     """Loads a flux base model, outputting its submodels."""

invokeai/app/invocations/flux_redux.py

@@ -23,7 +23,8 @@ from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.redux.flux_redux_model import FluxReduxModel
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType
+from invokeai.backend.model_manager import BaseModelType, ModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.starter_models import siglip
 from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
 from invokeai.backend.util.devices import TorchDevice
@@ -44,7 +45,7 @@ class FluxReduxOutput(BaseInvocationOutput):
     tags=["ip_adapter", "control"],
     category="ip_adapter",
     version="2.0.0",
-    classification=Classification.Prototype,
+    classification=Classification.Beta,
 )
 class FluxReduxInvocation(BaseInvocation):
     """Runs a FLUX Redux model to generate a conditioning tensor."""

invokeai/app/invocations/flux_text_encoder.py

@@ -4,7 +4,7 @@ from typing import Iterator, Literal, Optional, Tuple
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     FluxConditioningField,
@@ -17,7 +17,7 @@ from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.model_manager import ModelFormat
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_T5_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -30,7 +30,6 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
     version="1.1.2",
-    classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
     """Encodes and preps a prompt for a flux image."""

invokeai/app/invocations/ideal_size.py

@@ -6,7 +6,7 @@ from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 
 
 @invocation_output("ideal_size_output")

invokeai/app/invocations/image.py

@@ -355,7 +355,6 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     tags=["image", "unsharp_mask"],
     category="image",
     version="1.2.2",
-    classification=Classification.Beta,
 )
 class UnsharpMaskInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Applies an unsharp mask filter to an image"""
@@ -1096,6 +1095,7 @@ class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Expands a mask with a fade effect. The mask uses black to indicate areas to keep from the generated image and white for areas to discard.
     The mask is thresholded to create a binary mask, and then a distance transform is applied to create a fade effect.
     The fade size is specified in pixels, and the mask is expanded by that amount. The result is a mask with a smooth transition from black to white.
+    If the fade size is 0, the mask is returned as-is.
     """
 
     mask: ImageField = InputField(description="The mask to expand")
@@ -1105,6 +1105,11 @@ class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         pil_mask = context.images.get_pil(self.mask.image_name, mode="L")
 
+        if self.fade_size_px == 0:
+            # If the fade size is 0, just return the mask as-is.
+            image_dto = context.images.save(image=pil_mask, image_category=ImageCategory.MASK)
+            return ImageOutput.build(image_dto)
+
         np_mask = numpy.array(pil_mask)
 
         # Threshold the mask to create a binary mask - 0 for black, 255 for white
@@ -1265,7 +1270,6 @@ class ImageNoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     category="image",
     version="1.0.0",
     tags=["image", "crop"],
-    classification=Classification.Beta,
 )
 class CropImageToBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Crop an image to the given bounding box. If the bounding box is omitted, the image is cropped to the non-transparent pixels."""
@@ -1292,7 +1296,6 @@ class CropImageToBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
     category="image",
     version="1.0.0",
     tags=["image", "crop"],
-    classification=Classification.Beta,
 )
 class PasteImageIntoBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Paste the source image into the target image at the given bounding box.

invokeai/app/invocations/ip_adapter.py

@@ -13,10 +13,8 @@ from invokeai.app.services.model_records.model_records_base import ModelRecordCh
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     IPAdapterCheckpointConfig,
     IPAdapterInvokeAIConfig,
-    ModelType,
 )
 from invokeai.backend.model_manager.starter_models import (
     StarterModel,
@@ -24,6 +22,7 @@ from invokeai.backend.model_manager.starter_models import (
     ip_adapter_sd_image_encoder,
     ip_adapter_sdxl_image_encoder,
 )
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
 
 
 class IPAdapterField(BaseModel):

invokeai/app/invocations/llava_onevision_vllm.py

@@ -4,7 +4,7 @@ import torch
 from PIL.Image import Image
 from pydantic import field_validator
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, UIComponent, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import StringOutput
@@ -13,7 +13,14 @@ from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
 from invokeai.backend.util.devices import TorchDevice
 
 
-@invocation("llava_onevision_vllm", title="LLaVA OneVision VLLM", tags=["vllm"], category="vllm", version="1.0.0")
+@invocation(
+    "llava_onevision_vllm",
+    title="LLaVA OneVision VLLM",
+    tags=["vllm"],
+    category="vllm",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
 class LlavaOnevisionVllmInvocation(BaseInvocation):
     """Run a LLaVA OneVision VLLM model."""
 

invokeai/app/invocations/mask.py

@@ -4,7 +4,6 @@ from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
-    Classification,
     InvocationContext,
     invocation,
 )
@@ -58,7 +57,6 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
     tags=["conditioning"],
     category="conditioning",
     version="1.0.0",
-    classification=Classification.Beta,
 )
 class AlphaMaskToTensorInvocation(BaseInvocation):
     """Convert a mask image to a tensor. Opaque regions are 1 and transparent regions are 0."""
@@ -87,7 +85,6 @@ class AlphaMaskToTensorInvocation(BaseInvocation):
     tags=["conditioning"],
     category="conditioning",
     version="1.1.0",
-    classification=Classification.Beta,
 )
 class InvertTensorMaskInvocation(BaseInvocation):
     """Inverts a tensor mask."""
@@ -234,7 +231,6 @@ WHITE = ColorField(r=255, g=255, b=255, a=255)
     tags=["mask"],
     category="mask",
     version="1.0.0",
-    classification=Classification.Beta,
 )
 class GetMaskBoundingBoxInvocation(BaseInvocation):
     """Gets the bounding box of the given mask image."""

invokeai/app/invocations/metadata_linked.py

@@ -43,7 +43,7 @@ from invokeai.app.invocations.primitives import BooleanOutput, FloatOutput, Inte
 from invokeai.app.invocations.scheduler import SchedulerOutput
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField, T2IAdapterInvocation
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import ModelType, SubModelType
+from invokeai.backend.model_manager.taxonomy import ModelType, SubModelType
 from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 from invokeai.version import __version__
 

invokeai/app/invocations/model.py

@@ -6,7 +6,6 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -15,10 +14,8 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
-    ModelType,
-    SubModelType,
 )
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType
 
 
 class ModelIdentifierField(BaseModel):
@@ -126,7 +123,6 @@ class ModelIdentifierOutput(BaseInvocationOutput):
     tags=["model"],
     category="model",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class ModelIdentifierInvocation(BaseInvocation):
     """Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as

invokeai/app/invocations/sd3_denoise.py

@@ -6,7 +6,7 @@ from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
 from torchvision.transforms.functional import resize as tv_resize
 from tqdm import tqdm
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
@@ -23,7 +23,7 @@ from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.invocations.sd3_text_encoder import SD3_T5_MAX_SEQ_LEN
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager import BaseModelType
 from invokeai.backend.sd3.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import SD3ConditioningInfo
@@ -36,7 +36,6 @@ from invokeai.backend.util.devices import TorchDevice
     tags=["image", "sd3"],
     category="image",
     version="1.1.1",
-    classification=Classification.Prototype,
 )
 class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a SD3 model."""

invokeai/app/invocations/sd3_image_to_latents.py

@@ -2,7 +2,7 @@ import einops
 import torch
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -25,7 +25,6 @@ from invokeai.backend.util.devices import TorchDevice
     tags=["image", "latents", "vae", "i2l", "sd3"],
     category="image",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates latents from an image."""

invokeai/app/invocations/sd3_model_loader.py

@@ -3,7 +3,6 @@ from typing import Optional
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -14,7 +13,7 @@ from invokeai.app.util.t5_model_identifier import (
     preprocess_t5_encoder_model_identifier,
     preprocess_t5_tokenizer_model_identifier,
 )
-from invokeai.backend.model_manager.config import SubModelType
+from invokeai.backend.model_manager.taxonomy import SubModelType
 
 
 @invocation_output("sd3_model_loader_output")
@@ -34,7 +33,6 @@ class Sd3ModelLoaderOutput(BaseInvocationOutput):
     tags=["model", "sd3"],
     category="model",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class Sd3ModelLoaderInvocation(BaseInvocation):
     """Loads a SD3 base model, outputting its submodels."""

invokeai/app/invocations/sd3_text_encoder.py

@@ -11,12 +11,12 @@ from transformers import (
     T5TokenizerFast,
 )
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import SD3ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.model_manager.taxonomy import ModelFormat
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -33,7 +33,6 @@ SD3_T5_MAX_SEQ_LEN = 256
     tags=["prompt", "conditioning", "sd3"],
     category="conditioning",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class Sd3TextEncoderInvocation(BaseInvocation):
     """Encodes and preps a prompt for a SD3 image."""

invokeai/app/invocations/sdxl.py

@@ -2,7 +2,7 @@ from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocati
 from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import SubModelType
+from invokeai.backend.model_manager.taxonomy import SubModelType
 
 
 @invocation_output("sdxl_model_loader_output")

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -7,7 +7,7 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from pydantic import field_validator
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler
@@ -56,7 +56,6 @@ def crop_controlnet_data(control_data: ControlNetData, latent_region: TBLR) -> C
     title="Tiled Multi-Diffusion Denoise - SD1.5, SDXL",
     tags=["upscale", "denoise"],
     category="latents",
-    classification=Classification.Beta,
     version="1.0.1",
 )
 class TiledMultiDiffusionDenoiseLatents(BaseInvocation):

invokeai/app/invocations/tiles.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -40,7 +39,6 @@ class CalculateImageTilesOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class CalculateImageTilesInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
@@ -74,7 +72,6 @@ class CalculateImageTilesInvocation(BaseInvocation):
     tags=["tiles"],
     category="tiles",
     version="1.1.1",
-    classification=Classification.Beta,
 )
 class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
@@ -117,7 +114,6 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
@@ -168,7 +164,6 @@ class TileToPropertiesOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class TileToPropertiesInvocation(BaseInvocation):
     """Split a Tile into its individual properties."""
@@ -201,7 +196,6 @@ class PairTileImageOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class PairTileImageInvocation(BaseInvocation):
     """Pair an image with its tile properties."""
@@ -230,7 +224,6 @@ BLEND_MODES = Literal["Linear", "Seam"]
     tags=["tiles"],
     category="tiles",
     version="1.1.1",
-    classification=Classification.Beta,
 )
 class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Merge multiple tile images into a single image."""

invokeai/app/run_app.py

@@ -41,16 +41,15 @@ def run_app() -> None:
     )
 
     # Find an open port, and modify the config accordingly.
-    orig_config_port = app_config.port
-    app_config.port = find_open_port(app_config.port)
-    if orig_config_port != app_config.port:
+    first_open_port = find_open_port(app_config.port)
+    if app_config.port != first_open_port:
+        orig_config_port = app_config.port
+        app_config.port = first_open_port
         logger.warning(f"Port {orig_config_port} is already in use. Using port {app_config.port}.")
 
     # Miscellaneous startup tasks.
     apply_monkeypatches()
     register_mime_types()
-    if app_config.dev_reload:
-        enable_dev_reload()
     check_cudnn(logger)
 
     # Initialize the app and event loop.
@@ -61,6 +60,11 @@ def run_app() -> None:
     # core nodes have been imported so that we can catch when a custom node clobbers a core node.
     load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path, logger=logger)
 
+    if app_config.dev_reload:
+        # load_custom_nodes seems to bypass jurrigged's import sniffer, so be sure to call it *after* they're already
+        # imported.
+        enable_dev_reload(custom_nodes_path=app_config.custom_nodes_path)
+
     # Start the server.
     config = uvicorn.Config(
         app=app,

invokeai/app/services/events/events_base.py

@@ -44,7 +44,8 @@ if TYPE_CHECKING:
         SessionQueueItem,
         SessionQueueStatus,
     )
-    from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
+    from invokeai.backend.model_manager import SubModelType
+    from invokeai.backend.model_manager.config import AnyModelConfig
 
 
 class EventServiceBase:

invokeai/app/services/events/events_common.py

@@ -16,7 +16,8 @@ from invokeai.app.services.session_queue.session_queue_common import (
 )
 from invokeai.app.services.shared.graph import AnyInvocation, AnyInvocationOutput
 from invokeai.app.util.misc import get_timestamp
-from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
+from invokeai.backend.model_manager import SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 
 if TYPE_CHECKING:
     from invokeai.app.services.download.download_base import DownloadJob

invokeai/app/services/model_install/model_install_common.py

@@ -10,9 +10,9 @@ from typing_extensions import Annotated
 
 from invokeai.app.services.download import DownloadJob, MultiFileDownloadJob
 from invokeai.app.services.model_records import ModelRecordChanges
-from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
-from invokeai.backend.model_manager.config import ModelSourceType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
 
 
 class InstallStatus(str, Enum):

invokeai/app/services/model_install/model_install_default.py

@@ -39,8 +39,6 @@ from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
     InvalidModelConfigException,
     ModelConfigBase,
-    ModelRepoVariant,
-    ModelSourceType,
 )
 from invokeai.backend.model_manager.legacy_probe import ModelProbe
 from invokeai.backend.model_manager.metadata import (
@@ -52,6 +50,7 @@ from invokeai.backend.model_manager.metadata import (
 )
 from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
 from invokeai.backend.model_manager.search import ModelSearch
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice

invokeai/app/services/model_load/model_load_base.py

@@ -5,9 +5,10 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Callable, Optional
 
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 
 
 class ModelLoadServiceBase(ABC):

invokeai/app/services/model_load/model_load_default.py

@@ -11,7 +11,7 @@ from torch import load as torch_load
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_load.model_load_base import ModelLoadServiceBase
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load import (
     LoadedModel,
     LoadedModelWithoutConfig,
@@ -20,6 +20,7 @@ from invokeai.backend.model_manager.load import (
 )
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 

invokeai/app/services/model_manager/__init__.py

@@ -1,16 +1,12 @@
 """Initialization file for model manager service."""
 
 from invokeai.app.services.model_manager.model_manager_default import ModelManagerService, ModelManagerServiceBase
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager import AnyModelConfig
 from invokeai.backend.model_manager.load import LoadedModel
 
 __all__ = [
     "ModelManagerServiceBase",
     "ModelManagerService",
-    "AnyModel",
     "AnyModelConfig",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
     "LoadedModel",
 ]

invokeai/app/services/model_records/model_records_base.py

@@ -14,10 +14,12 @@ from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
-    ClipVariantType,
     ControlAdapterDefaultSettings,
     MainModelDefaultSettings,
+)
+from invokeai.backend.model_manager.taxonomy import (
+    BaseModelType,
+    ClipVariantType,
     ModelFormat,
     ModelSourceType,
     ModelType,

invokeai/app/services/model_records/model_records_sql.py

@@ -60,11 +60,9 @@ from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     ModelConfigFactory,
-    ModelFormat,
-    ModelType,
 )
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType
 
 
 class ModelRecordServiceSQL(ModelRecordServiceBase):

invokeai/app/services/shared/invocation_context.py

@@ -20,14 +20,10 @@ from invokeai.app.services.session_processor.session_processor_common import Pro
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.step_callback import flux_step_callback, stable_diffusion_step_callback
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 

invokeai/app/util/startup_utils.py

@@ -1,6 +1,7 @@
 import logging
 import mimetypes
 import socket
+from pathlib import Path
 
 import torch
 
@@ -33,7 +34,16 @@ def check_cudnn(logger: logging.Logger) -> None:
             )
 
 
-def enable_dev_reload() -> None:
+def invokeai_source_dir() -> Path:
+    # `invokeai.__file__` doesn't always work for editable installs
+    this_module_path = Path(__file__).resolve()
+    # https://youtrack.jetbrains.com/issue/PY-38382/Unresolved-reference-spec-but-this-is-standard-builtin
+    # noinspection PyUnresolvedReferences
+    depth = len(__spec__.parent.split("."))
+    return this_module_path.parents[depth - 1]
+
+
+def enable_dev_reload(custom_nodes_path=None) -> None:
     """Enable hot reloading on python file changes during development."""
     from invokeai.backend.util.logging import InvokeAILogger
 
@@ -44,7 +54,10 @@ def enable_dev_reload() -> None:
             'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.'
         ) from e
     else:
-        jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+        paths = [str(invokeai_source_dir() / "*.py")]
+        if custom_nodes_path:
+            paths.append(str(custom_nodes_path / "*.py"))
+        jurigged.watch(pattern=paths, logger=InvokeAILogger.get_logger(name="jurigged").info)
 
 
 def apply_monkeypatches() -> None:

invokeai/app/util/step_callback.py

@@ -5,7 +5,7 @@ import torch
 from PIL import Image
 
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 
 # fast latents preview matrix for sdxl

invokeai/app/util/t5_model_identifier.py

@@ -1,5 +1,5 @@
 from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.backend.model_manager.config import BaseModelType, SubModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, SubModelType
 
 
 def preprocess_t5_encoder_model_identifier(model_identifier: ModelIdentifierField) -> ModelIdentifierField:

invokeai/app/util/ti_utils.py

@@ -4,7 +4,7 @@ from typing import List, Tuple
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.model_records import UnknownModelException
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 
 

invokeai/backend/image_util/infill_methods/lama.py

@@ -6,8 +6,8 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+from invokeai.backend.model_manager.taxonomy import AnyModel
 
 
 def norm_img(np_img):

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/conv2d_layers.py

@@ -16,7 +16,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .config import *
+from .config import is_exportable, is_scriptable
 
 
 # From PyTorch internals

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/efficientnet_builder.py

@@ -5,8 +5,8 @@ Copyright 2020 Ross Wightman
 import re
 from copy import deepcopy
 
-from .conv2d_layers import *
-from geffnet.activations import *
+from .conv2d_layers import CondConv2d, get_condconv_initializer, math, partial, select_conv2d
+from geffnet.activations import F, get_act_layer, nn, sigmoid, torch
 
 __all__ = ['get_bn_args_tf', 'resolve_bn_args', 'resolve_se_args', 'resolve_act_layer', 'make_divisible',
            'round_channels', 'drop_connect', 'SqueezeExcite', 'ConvBnAct', 'DepthwiseSeparableConv',

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/gen_efficientnet.py

@@ -32,7 +32,9 @@ import torch.nn.functional as F
 from .config import layer_config_kwargs, is_scriptable
 from .conv2d_layers import select_conv2d
 from .helpers import load_pretrained
-from .efficientnet_builder import *
+from .efficientnet_builder import (BN_EPS_TF_DEFAULT, EfficientNetBuilder, decode_arch_def,
+                                   initialize_weight_default, initialize_weight_goog,
+                                   resolve_act_layer, resolve_bn_args, round_channels)
 
 __all__ = ['GenEfficientNet', 'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140',
            'semnasnet_050', 'semnasnet_075', 'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small',

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/mobilenetv3.py

@@ -13,7 +13,9 @@ from .activations import get_act_fn, get_act_layer, HardSwish
 from .config import layer_config_kwargs
 from .conv2d_layers import select_conv2d
 from .helpers import load_pretrained
-from .efficientnet_builder import *
+from .efficientnet_builder import (BN_EPS_TF_DEFAULT, EfficientNetBuilder, decode_arch_def,
+                                   initialize_weight_default, initialize_weight_goog,
+                                   resolve_act_layer, resolve_bn_args, round_channels)
 
 __all__ = ['mobilenetv3_rw', 'mobilenetv3_large_075', 'mobilenetv3_large_100', 'mobilenetv3_large_minimal_100',
            'mobilenetv3_small_075', 'mobilenetv3_small_100', 'mobilenetv3_small_minimal_100',

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -10,7 +10,7 @@ from cv2.typing import MatLike
 from tqdm import tqdm
 
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
-from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.model_manager.taxonomy import AnyModel
 from invokeai.backend.util.devices import TorchDevice
 
 """

invokeai/backend/llava_onevision_model.py

@@ -47,3 +47,10 @@ class LlavaOnevisionModel(RawModel):
 
     def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
         self._vllm_model.to(device=device, dtype=dtype)
+
+    def calc_size(self) -> int:
+        """Get size of the model in memory in bytes."""
+        # HACK(ryand): Fix this issue with circular imports.
+        from invokeai.backend.model_manager.load.model_util import calc_module_size
+
+        return calc_module_size(self._vllm_model)

invokeai/backend/model_manager/__init__.py

@@ -1,37 +1,45 @@
 """Re-export frequently-used symbols from the Model Manager backend."""
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
     InvalidModelConfigException,
     ModelConfigBase,
     ModelConfigFactory,
+)
+from invokeai.backend.model_manager.legacy_probe import ModelProbe
+from invokeai.backend.model_manager.load import LoadedModel
+from invokeai.backend.model_manager.search import ModelSearch
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    AnyVariant,
+    BaseModelType,
+    ClipVariantType,
     ModelFormat,
     ModelRepoVariant,
+    ModelSourceType,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
     SubModelType,
 )
-from invokeai.backend.model_manager.legacy_probe import ModelProbe
-from invokeai.backend.model_manager.load import LoadedModel
-from invokeai.backend.model_manager.search import ModelSearch
 
 __all__ = [
-    "AnyModel",
     "AnyModelConfig",
-    "BaseModelType",
-    "ModelRepoVariant",
     "InvalidModelConfigException",
     "LoadedModel",
     "ModelConfigFactory",
-    "ModelFormat",
     "ModelProbe",
     "ModelSearch",
+    "ModelConfigBase",
+    "AnyModel",
+    "AnyVariant",
+    "BaseModelType",
+    "ClipVariantType",
+    "ModelFormat",
+    "ModelRepoVariant",
+    "ModelSourceType",
     "ModelType",
     "ModelVariantType",
     "SchedulerPredictionType",
     "SubModelType",
-    "ModelConfigBase",
 ]

invokeai/backend/model_manager/config.py

@@ -30,11 +30,8 @@ from inspect import isabstract
 from pathlib import Path
 from typing import ClassVar, Literal, Optional, TypeAlias, Union
 
-import diffusers
-import onnxruntime as ort
 import safetensors.torch
 import torch
-from diffusers.models.modeling_utils import ModelMixin
 from picklescan.scanner import scan_file_path
 from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
@@ -42,139 +39,37 @@ from typing_extensions import Annotated, Any, Dict
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.hash_validator import validate_hash
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
+from invokeai.backend.model_manager.taxonomy import (
+    AnyVariant,
+    BaseModelType,
+    ClipVariantType,
+    ModelFormat,
+    ModelRepoVariant,
+    ModelSourceType,
+    ModelType,
+    ModelVariantType,
+    SchedulerPredictionType,
+    SubModelType,
+)
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
-from invokeai.backend.raw_model import RawModel
 from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 logger = logging.getLogger(__name__)
 
-# ModelMixin is the base class for all diffusers and transformers models
-# RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
-AnyModel = Union[
-    ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor], diffusers.DiffusionPipeline, ort.InferenceSession
-]
-
 
 class InvalidModelConfigException(Exception):
     """Exception for when config parser doesn't recognize this combination of model type and format."""
 
-
-class BaseModelType(str, Enum):
-    """Base model type."""
-
-    Any = "any"
-    StableDiffusion1 = "sd-1"
-    StableDiffusion2 = "sd-2"
-    StableDiffusion3 = "sd-3"
-    StableDiffusionXL = "sdxl"
-    StableDiffusionXLRefiner = "sdxl-refiner"
-    Flux = "flux"
-    # Kandinsky2_1 = "kandinsky-2.1"
-
-
-class ModelType(str, Enum):
-    """Model type."""
-
-    ONNX = "onnx"
-    Main = "main"
-    VAE = "vae"
-    LoRA = "lora"
-    ControlLoRa = "control_lora"
-    ControlNet = "controlnet"  # used by model_probe
-    TextualInversion = "embedding"
-    IPAdapter = "ip_adapter"
-    CLIPVision = "clip_vision"
-    CLIPEmbed = "clip_embed"
-    T2IAdapter = "t2i_adapter"
-    T5Encoder = "t5_encoder"
-    SpandrelImageToImage = "spandrel_image_to_image"
-    SigLIP = "siglip"
-    FluxRedux = "flux_redux"
-    LlavaOnevision = "llava_onevision"
-
-
-class SubModelType(str, Enum):
-    """Submodel type."""
-
-    UNet = "unet"
-    Transformer = "transformer"
-    TextEncoder = "text_encoder"
-    TextEncoder2 = "text_encoder_2"
-    TextEncoder3 = "text_encoder_3"
-    Tokenizer = "tokenizer"
-    Tokenizer2 = "tokenizer_2"
-    Tokenizer3 = "tokenizer_3"
-    VAE = "vae"
-    VAEDecoder = "vae_decoder"
-    VAEEncoder = "vae_encoder"
-    Scheduler = "scheduler"
-    SafetyChecker = "safety_checker"
-
-
-class ClipVariantType(str, Enum):
-    """Variant type."""
-
-    L = "large"
-    G = "gigantic"
-
-
-class ModelVariantType(str, Enum):
-    """Variant type."""
-
-    Normal = "normal"
-    Inpaint = "inpaint"
-    Depth = "depth"
-
-
-class ModelFormat(str, Enum):
-    """Storage format of model."""
-
-    Diffusers = "diffusers"
-    Checkpoint = "checkpoint"
-    LyCORIS = "lycoris"
-    ONNX = "onnx"
-    Olive = "olive"
-    EmbeddingFile = "embedding_file"
-    EmbeddingFolder = "embedding_folder"
-    InvokeAI = "invokeai"
-    T5Encoder = "t5_encoder"
-    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
-    BnbQuantizednf4b = "bnb_quantized_nf4b"
-    GGUFQuantized = "gguf_quantized"
-
-
-class SchedulerPredictionType(str, Enum):
-    """Scheduler prediction type."""
-
-    Epsilon = "epsilon"
-    VPrediction = "v_prediction"
-    Sample = "sample"
-
-
-class ModelRepoVariant(str, Enum):
-    """Various hugging face variants on the diffusers format."""
-
-    Default = ""  # model files without "fp16" or other qualifier
-    FP16 = "fp16"
-    FP32 = "fp32"
-    ONNX = "onnx"
-    OpenVINO = "openvino"
-    Flax = "flax"
-
-
-class ModelSourceType(str, Enum):
-    """Model source type."""
-
-    Path = "path"
-    Url = "url"
-    HFRepoID = "hf_repo_id"
+    pass
 
 
 DEFAULTS_PRECISION = Literal["fp16", "fp32"]
 
 
-AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]
+class FSLayout(Enum):
+    FILE = "file"
+    DIRECTORY = "directory"
 
 
 class SubmodelDefinition(BaseModel):
@@ -212,29 +107,31 @@ class ModelOnDisk:
 
     def __init__(self, path: Path, hash_algo: HASHING_ALGORITHMS = "blake3_single"):
         self.path = path
-        self.format_type = ModelFormat.Diffusers if path.is_dir() else ModelFormat.Checkpoint
+        # TODO: Revisit checkpoint vs diffusers terminology
+        self.layout = FSLayout.DIRECTORY if path.is_dir() else FSLayout.FILE
         if self.path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
             self.name = path.stem
         else:
             self.name = path.name
         self.hash_algo = hash_algo
+        self._state_dict_cache = {}
 
-    def hash(self):
+    def hash(self) -> str:
         return ModelHash(algorithm=self.hash_algo).hash(self.path)
 
-    def size(self):
-        if self.format_type == ModelFormat.Checkpoint:
+    def size(self) -> int:
+        if self.layout == FSLayout.FILE:
             return self.path.stat().st_size
         return sum(file.stat().st_size for file in self.path.rglob("*"))
 
-    def component_paths(self):
-        if self.format_type == ModelFormat.Checkpoint:
+    def component_paths(self) -> set[Path]:
+        if self.layout == FSLayout.FILE:
             return {self.path}
         extensions = {".safetensors", ".pt", ".pth", ".ckpt", ".bin", ".gguf"}
         return {f for f in self.path.rglob("*") if f.suffix in extensions}
 
-    def repo_variant(self):
-        if self.format_type == ModelFormat.Checkpoint:
+    def repo_variant(self) -> Optional[ModelRepoVariant]:
+        if self.layout == FSLayout.FILE:
             return None
 
         weight_files = list(self.path.glob("**/*.safetensors"))
@@ -250,14 +147,30 @@ class ModelOnDisk:
                 return ModelRepoVariant.ONNX
         return ModelRepoVariant.Default
 
-    @staticmethod
-    def load_state_dict(path: Path):
+    def load_state_dict(self, path: Optional[Path] = None) -> Dict[str | int, Any]:
+        if path in self._state_dict_cache:
+            return self._state_dict_cache[path]
+
+        if not path:
+            components = list(self.component_paths())
+            match components:
+                case []:
+                    raise ValueError("No weight files found for this model")
+                case [p]:
+                    path = p
+                case ps if len(ps) >= 2:
+                    raise ValueError(
+                        f"Multiple weight files found for this model: {ps}. "
+                        f"Please specify the intended file using the 'path' argument"
+                    )
+
         with SilenceWarnings():
             if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
                 scan_result = scan_file_path(path)
                 if scan_result.infected_files != 0 or scan_result.scan_err:
                     raise RuntimeError(f"The model {path.stem} is potentially infected by malware. Aborting import.")
                 checkpoint = torch.load(path, map_location="cpu")
+                assert isinstance(checkpoint, dict)
             elif path.suffix.endswith(".gguf"):
                 checkpoint = gguf_sd_loader(path, compute_dtype=torch.float32)
             elif path.suffix.endswith(".safetensors"):
@@ -266,6 +179,7 @@ class ModelOnDisk:
                 raise ValueError(f"Unrecognized model extension: {path.suffix}")
 
         state_dict = checkpoint.get("state_dict", checkpoint)
+        self._state_dict_cache[path] = state_dict
         return state_dict
 
 
@@ -348,11 +262,13 @@ class ModelConfigBase(ABC, BaseModel):
 
         for config_cls in sorted_by_match_speed:
             try:
-                return config_cls.from_model_on_disk(mod, **overrides)
-            except InvalidModelConfigException:
-                logger.debug(f"ModelConfig '{config_cls.__name__}' failed to parse '{mod.path}', trying next config")
+                if not config_cls.matches(mod):
+                    continue
             except Exception as e:
-                logger.error(f"Unexpected exception while parsing '{config_cls.__name__}': {e}, trying next config")
+                logger.warning(f"Unexpected exception while matching {mod.name} to '{config_cls.__name__}': {e}")
+                continue
+            else:
+                return config_cls.from_model_on_disk(mod, **overrides)
 
         raise InvalidModelConfigException("No valid config found")
 
@@ -395,9 +311,6 @@ class ModelConfigBase(ABC, BaseModel):
     @classmethod
     def from_model_on_disk(cls, mod: ModelOnDisk, **overrides):
         """Creates an instance of this config or raises InvalidModelConfigException."""
-        if not cls.matches(mod):
-            raise InvalidModelConfigException(f"Path {mod.path} does not match {cls.__name__} format")
-
         fields = cls.parse(mod)
         cls.cast_overrides(overrides)
         fields.update(overrides)
@@ -673,7 +586,7 @@ class LlavaOnevisionConfig(DiffusersConfigBase, ModelConfigBase):
 
     @classmethod
     def matches(cls, mod: ModelOnDisk) -> bool:
-        if mod.format_type == ModelFormat.Checkpoint:
+        if mod.layout == FSLayout.FILE:
             return False
 
         config_path = mod.path / "config.json"

invokeai/backend/model_manager/legacy_probe.py

@@ -19,22 +19,24 @@ from invokeai.backend.flux.redux.flux_redux_state_dict_utils import is_state_dic
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    AnyVariant,
-    BaseModelType,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
     MainModelDefaultSettings,
     ModelConfigFactory,
+    SubmodelDefinition,
+)
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyVariant,
+    BaseModelType,
     ModelFormat,
     ModelRepoVariant,
     ModelSourceType,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
-    SubmodelDefinition,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
 from invokeai.backend.model_manager.util.model_util import (
     get_clip_variant_type,
     lora_token_vector_length,

invokeai/backend/model_manager/load/load_base.py

@@ -13,12 +13,11 @@ import torch
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 
 
 class LoadedModelWithoutConfig:

invokeai/backend/model_manager/load/load_default.py

@@ -6,18 +6,16 @@ from pathlib import Path
 from typing import Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    InvalidModelConfigException,
-    SubModelType,
-)
-from invokeai.backend.model_manager.config import DiffusersConfigBase
+from invokeai.backend.model_manager.config import AnyModelConfig, DiffusersConfigBase, InvalidModelConfigException
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
 from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache, get_model_cache_key
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    SubModelType,
+)
 from invokeai.backend.util.devices import TorchDevice
 
 

invokeai/backend/model_manager/load/model_cache/model_cache.py

@@ -9,7 +9,6 @@ from typing import Any, Callable, Dict, List, Optional
 import psutil
 import torch
 
-from invokeai.backend.model_manager import AnyModel, SubModelType
 from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
 from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
 from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
@@ -23,6 +22,7 @@ from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch
     apply_custom_layers_to_model,
 )
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.backend.util.prefix_logger_adapter import PrefixedLoggerAdapter

invokeai/backend/model_manager/load/model_loader_registry.py

@@ -20,13 +20,10 @@ from typing import Callable, Dict, Optional, Tuple, Type, TypeVar
 
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     ModelConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load import ModelLoaderBase
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType, SubModelType
 
 
 class ModelLoaderRegistryBase(ABC):

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

@@ -4,16 +4,12 @@ from typing import Optional
 from transformers import CLIPVisionModelWithProjection
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
     DiffusersConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)

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

@@ -5,19 +5,19 @@ from typing import Optional
 
 from diffusers import ControlNetModel
 
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-)
 from invokeai.backend.model_manager.config import (
-    BaseModelType,
+    AnyModelConfig,
     ControlNetCheckpointConfig,
+)
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(

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

@@ -27,15 +27,8 @@ from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.redux.flux_redux_model import FluxReduxModel
 from invokeai.backend.flux.util import ae_params, params
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
 from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
     CheckpointConfigBase,
     CLIPEmbedDiffusersConfig,
     ControlNetCheckpointConfig,
@@ -51,6 +44,13 @@ from invokeai.backend.model_manager.config import (
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
 from invokeai.backend.model_manager.util.model_util import (
     convert_bundle_to_flux_transformer_checkpoint,
 )

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

@@ -8,18 +8,16 @@ from typing import Any, Optional
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.models.modeling_utils import ModelMixin
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig, DiffusersConfigBase, InvalidModelConfigException
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
-    InvalidModelConfigException,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import DiffusersConfigBase
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)

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

@@ -7,8 +7,9 @@ from typing import Optional
 import torch
 
 from invokeai.backend.ip_adapter.ip_adapter import build_ip_adapter
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load import ModelLoader, ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.raw_model import RawModel
 
 

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

@@ -3,15 +3,11 @@ from typing import Optional
 
 from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LlavaOnevision, format=ModelFormat.Diffusers)

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

@@ -9,17 +9,17 @@ import torch
 from safetensors.torch import load_file
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
     is_state_dict_likely_flux_control,
     lora_model_from_flux_control_state_dict,

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

@@ -5,16 +5,16 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.ONNX)

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

@@ -2,15 +2,11 @@ from pathlib import Path
 from typing import Optional
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
 
 

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

@@ -4,15 +4,11 @@ from typing import Optional
 import torch
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 
 

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

@@ -11,16 +11,8 @@ from diffusers import (
     StableDiffusionXLPipeline,
 )
 
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    ModelVariantType,
-    SubModelType,
-)
 from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
     CheckpointConfigBase,
     DiffusersConfigBase,
     MainCheckpointConfig,
@@ -28,6 +20,14 @@ from invokeai.backend.model_manager.config import (
 from invokeai.backend.model_manager.load.model_cache.model_cache import get_model_cache_key
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    ModelVariantType,
+    SubModelType,
+)
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 VARIANT_TO_IN_CHANNEL_MAP = {

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

@@ -4,16 +4,16 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 
 

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

@@ -5,15 +5,16 @@ from typing import Optional
 
 from diffusers import AutoencoderKL
 
-from invokeai.backend.model_manager import (
-    AnyModelConfig,
+from invokeai.backend.model_manager.config import AnyModelConfig, VAECheckpointConfig
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
     BaseModelType,
     ModelFormat,
     ModelType,
+    SubModelType,
 )
-from invokeai.backend.model_manager.config import AnyModel, SubModelType, VAECheckpointConfig
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)

invokeai/backend/model_manager/load/model_util.py

@@ -15,7 +15,8 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
+from invokeai.backend.model_manager.taxonomy import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
@@ -50,6 +51,7 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
             SegmentAnythingPipeline,
             DepthAnythingPipeline,
             SigLipPipeline,
+            LlavaOnevisionModel,
         ),
     ):
         return model.calc_size()

invokeai/backend/model_manager/metadata/fetch/fetch_base.py

@@ -17,12 +17,12 @@ from typing import Optional
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager import ModelRepoVariant
 from invokeai.backend.model_manager.metadata.metadata_base import (
     AnyModelRepoMetadata,
     AnyModelRepoMetadataValidator,
     BaseMetadata,
 )
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 
 
 class ModelMetadataFetchBase(ABC):

invokeai/backend/model_manager/metadata/fetch/huggingface.py

@@ -24,7 +24,6 @@ from huggingface_hub.errors import RepositoryNotFoundError, RevisionNotFoundErro
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager.config import ModelRepoVariant
 from invokeai.backend.model_manager.metadata.fetch.fetch_base import ModelMetadataFetchBase
 from invokeai.backend.model_manager.metadata.metadata_base import (
     AnyModelRepoMetadata,
@@ -32,6 +31,7 @@ from invokeai.backend.model_manager.metadata.metadata_base import (
     RemoteModelFile,
     UnknownMetadataException,
 )
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 
 HF_MODEL_RE = r"https?://huggingface.co/([\w\-.]+/[\w\-.]+)"
 

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -23,7 +23,7 @@ from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 from typing_extensions import Annotated
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 from invokeai.backend.model_manager.util.select_hf_files import filter_files
 
 

invokeai/backend/model_manager/starter_models.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from invokeai.backend.model_manager.config import BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType
 
 
 class StarterModelWithoutDependencies(BaseModel):

invokeai/backend/model_manager/taxonomy.py

@@ -0,0 +1,129 @@
+from enum import Enum
+from typing import Dict, TypeAlias, Union
+
+import diffusers
+import onnxruntime as ort
+import torch
+from diffusers import ModelMixin
+
+from invokeai.backend.raw_model import RawModel
+
+# ModelMixin is the base class for all diffusers and transformers models
+# RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
+AnyModel = Union[
+    ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor], diffusers.DiffusionPipeline, ort.InferenceSession
+]
+
+
+class BaseModelType(str, Enum):
+    """Base model type."""
+
+    Any = "any"
+    StableDiffusion1 = "sd-1"
+    StableDiffusion2 = "sd-2"
+    StableDiffusion3 = "sd-3"
+    StableDiffusionXL = "sdxl"
+    StableDiffusionXLRefiner = "sdxl-refiner"
+    Flux = "flux"
+    # Kandinsky2_1 = "kandinsky-2.1"
+
+
+class ModelType(str, Enum):
+    """Model type."""
+
+    ONNX = "onnx"
+    Main = "main"
+    VAE = "vae"
+    LoRA = "lora"
+    ControlLoRa = "control_lora"
+    ControlNet = "controlnet"  # used by model_probe
+    TextualInversion = "embedding"
+    IPAdapter = "ip_adapter"
+    CLIPVision = "clip_vision"
+    CLIPEmbed = "clip_embed"
+    T2IAdapter = "t2i_adapter"
+    T5Encoder = "t5_encoder"
+    SpandrelImageToImage = "spandrel_image_to_image"
+    SigLIP = "siglip"
+    FluxRedux = "flux_redux"
+    LlavaOnevision = "llava_onevision"
+
+
+class SubModelType(str, Enum):
+    """Submodel type."""
+
+    UNet = "unet"
+    Transformer = "transformer"
+    TextEncoder = "text_encoder"
+    TextEncoder2 = "text_encoder_2"
+    TextEncoder3 = "text_encoder_3"
+    Tokenizer = "tokenizer"
+    Tokenizer2 = "tokenizer_2"
+    Tokenizer3 = "tokenizer_3"
+    VAE = "vae"
+    VAEDecoder = "vae_decoder"
+    VAEEncoder = "vae_encoder"
+    Scheduler = "scheduler"
+    SafetyChecker = "safety_checker"
+
+
+class ClipVariantType(str, Enum):
+    """Variant type."""
+
+    L = "large"
+    G = "gigantic"
+
+
+class ModelVariantType(str, Enum):
+    """Variant type."""
+
+    Normal = "normal"
+    Inpaint = "inpaint"
+    Depth = "depth"
+
+
+class ModelFormat(str, Enum):
+    """Storage format of model."""
+
+    Diffusers = "diffusers"
+    Checkpoint = "checkpoint"
+    LyCORIS = "lycoris"
+    ONNX = "onnx"
+    Olive = "olive"
+    EmbeddingFile = "embedding_file"
+    EmbeddingFolder = "embedding_folder"
+    InvokeAI = "invokeai"
+    T5Encoder = "t5_encoder"
+    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
+    BnbQuantizednf4b = "bnb_quantized_nf4b"
+    GGUFQuantized = "gguf_quantized"
+
+
+class SchedulerPredictionType(str, Enum):
+    """Scheduler prediction type."""
+
+    Epsilon = "epsilon"
+    VPrediction = "v_prediction"
+    Sample = "sample"
+
+
+class ModelRepoVariant(str, Enum):
+    """Various hugging face variants on the diffusers format."""
+
+    Default = ""  # model files without "fp16" or other qualifier
+    FP16 = "fp16"
+    FP32 = "fp32"
+    ONNX = "onnx"
+    OpenVINO = "openvino"
+    Flax = "flax"
+
+
+class ModelSourceType(str, Enum):
+    """Model source type."""
+
+    Path = "path"
+    Url = "url"
+    HFRepoID = "hf_repo_id"
+
+
+AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]

invokeai/backend/model_manager/util/model_util.py

@@ -8,7 +8,7 @@ import picklescan.scanner as pscan
 import safetensors
 import torch
 
-from invokeai.backend.model_manager.config import ClipVariantType
+from invokeai.backend.model_manager.taxonomy import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 
 

invokeai/backend/model_manager/util/select_hf_files.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Dict, List, Optional, Set
 
-from invokeai.backend.model_manager.config import ModelRepoVariant
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 
 
 def filter_files(

invokeai/backend/stable_diffusion/extensions/t2i_adapter.py

@@ -8,7 +8,7 @@ from diffusers import T2IAdapter
 from PIL.Image import Image
 
 from invokeai.app.util.controlnet_utils import prepare_control_image
-from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningMode
 from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase, callback

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

@@ -196,7 +196,8 @@
         "row": "Row",
         "column": "Column",
         "value": "Value",
-        "label": "Label"
+        "label": "Label",
+        "systemInformation": "System Information"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -2343,8 +2344,9 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "Workflows: New and improved Workflow Library.",
-            "FLUX: Support for FLUX Redux & FLUX Fill in Workflows and Canvas."
+            "Workflows: Support for custom string drop-downs in Workflow Builder.",
+            "FLUX: Support for FLUX Fill in Workflows and Canvas.",
+            "LLaVA OneVision VLLM: Beta support in Workflows."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

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

@@ -113,7 +113,9 @@
         "saveChanges": "Salva modifiche",
         "error_withCount_one": "{{count}} errore",
         "error_withCount_many": "{{count}} errori",
-        "error_withCount_other": "{{count}} errori"
+        "error_withCount_other": "{{count}} errori",
+        "value": "Valore",
+        "label": "Etichetta"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -848,7 +850,8 @@
         "pasteSuccess": "Incollato su {{destination}}",
         "unableToCopy": "Impossibile copiare",
         "unableToCopyDesc": "Il tuo browser non supporta l'accesso agli appunti. Gli utenti di Firefox potrebbero risolvere il problema seguendo ",
-        "unableToCopyDesc_theseSteps": "questi passaggi"
+        "unableToCopyDesc_theseSteps": "questi passaggi",
+        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill non è compatibile con Testo a Immagine o Immagine a Immagine. Per queste attività, utilizzare altri modelli FLUX."
     },
     "accessibility": {
         "invokeProgressBar": "Barra di avanzamento generazione",
@@ -1038,7 +1041,11 @@
         "generatorImages_many": "{{count}} immagini",
         "generatorImages_other": "{{count}} immagini",
         "generatorImagesFromBoard": "Immagini dalla Bacheca",
-        "missingSourceOrTargetNode": "Nodo sorgente o di destinazione mancante"
+        "missingSourceOrTargetNode": "Nodo sorgente o di destinazione mancante",
+        "unknownField_withName": "Campo \"{{name}}\" sconosciuto",
+        "missingField_withName": "Campo \"{{name}}\" mancante",
+        "unknownFieldEditWorkflowToFix_withName": "Il flusso di lavoro contiene un campo \"{{name}}\" sconosciuto .\nModifica il flusso di lavoro per risolvere il problema.",
+        "unexpectedField_withName": "Campo \"{{name}}\" inaspettato"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1778,7 +1785,10 @@
             "containerRowLayout": "Contenitore (disposizione riga)",
             "containerColumnLayout": "Contenitore (disposizione colonna)",
             "minimum": "Minimo",
-            "maximum": "Massimo"
+            "maximum": "Massimo",
+            "dropdown": "Elenco a discesa",
+            "addOption": "Aggiungi opzione",
+            "resetOptions": "Reimposta opzioni"
         },
         "loadMore": "Carica altro",
         "searchPlaceholder": "Cerca per nome, descrizione o etichetta",
@@ -1794,7 +1804,8 @@
         "deselectAll": "Deseleziona tutto",
         "noRecentWorkflows": "Nessun flusso di lavoro recente",
         "view": "Visualizza",
-        "recommended": "Consigliato per te"
+        "recommended": "Consigliato per te",
+        "emptyStringPlaceholder": "<stringa vuota>"
     },
     "accordions": {
         "compositing": {
@@ -2238,7 +2249,8 @@
             "rgNegativePromptNotSupported": "Prompt negativo non supportato per il modello base selezionato",
             "ipAdapterIncompatibleBaseModel": "modello base dell'immagine di riferimento incompatibile",
             "ipAdapterNoImageSelected": "nessuna immagine di riferimento selezionata",
-            "rgAutoNegativeNotSupported": "Auto-Negativo non supportato per il modello base selezionato"
+            "rgAutoNegativeNotSupported": "Auto-Negativo non supportato per il modello base selezionato",
+            "fluxFillIncompatibleWithControlLoRA": "Il controllo LoRA non è compatibile con FLUX Fill"
         },
         "pasteTo": "Incolla su",
         "pasteToBboxDesc": "Nuovo livello (nel riquadro di delimitazione)",
@@ -2354,7 +2366,7 @@
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
             "Flussi di lavoro: nuova e migliorata libreria dei flussi di lavoro.",
-            "FLUX: supporto per FLUX Redux in Flussi di lavoro e Tela."
+            "FLUX: supporto per FLUX Redux e FLUX Fill in Flussi di lavoro e Tela."
         ]
     },
     "system": {

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

@@ -1020,7 +1020,11 @@
         "downloadWorkflowError": "Lỗi tải xuống workflow",
         "generatorImagesFromBoard": "Ảnh Từ Bảng",
         "generatorImagesCategory": "Phân Loại",
-        "generatorImages_other": "{{count}} ảnh"
+        "generatorImages_other": "{{count}} ảnh",
+        "unknownField_withName": "Vùng Dữ Liệu Không Rõ \"{{name}}\"",
+        "unexpectedField_withName": "Sai Vùng Dữ Liệu \"{{name}}\"",
+        "unknownFieldEditWorkflowToFix_withName": "Workflow chứa vùng dữ liệu không rõ \"{{name}}\".\nHãy biên tập workflow để sửa lỗi.",
+        "missingField_withName": "Thiếu Vùng Dữ Liệu \"{{name}}\""
     },
     "popovers": {
         "paramCFGRescaleMultiplier": {
@@ -2050,7 +2054,8 @@
             "rgNegativePromptNotSupported": "Lệnh Tiêu Cực không được hỗ trợ cho model cơ sở được chọn",
             "rgReferenceImagesNotSupported": "Ảnh Mẫu Khu Vực không được hỗ trợ cho model cơ sở được chọn",
             "rgAutoNegativeNotSupported": "Tự Động Đảo Chiều không được hỗ trợ cho model cơ sở được chọn",
-            "rgNoRegion": "không có khu vực được vẽ"
+            "rgNoRegion": "không có khu vực được vẽ",
+            "fluxFillIncompatibleWithControlLoRA": "LoRA Điều Khiển Được không tương tích với FLUX Fill"
         },
         "pasteTo": "Dán Vào",
         "pasteToAssets": "Tài Nguyên",
@@ -2201,7 +2206,8 @@
         "unableToCopyDesc_theseSteps": "các bước sau",
         "unableToCopyDesc": "Trình duyệt của bạn không hỗ trợ tính năng clipboard. Người dùng Firefox có thể khắc phục theo ",
         "pasteSuccess": "Dán Vào {{destination}}",
-        "pasteFailed": "Dán Thất Bại"
+        "pasteFailed": "Dán Thất Bại",
+        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill không tương tích với Từ Ngữ Sang Hình Ảnh và Hình Ảnh Sang Hình Ảnh. Dùng model FLUX khác cho các tính năng này."
     },
     "ui": {
         "tabs": {
@@ -2347,7 +2353,7 @@
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
             "Workflow: Thư Viện Workflow mới và đã được cải tiến.",
-            "FLUX: Hỗ trợ FLUX Redux trong Workflow và Canvas."
+            "FLUX: Hỗ trợ FLUX Redux & FLUX Fill trong Workflow và Canvas."
         ]
     },
     "upsell": {

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

@@ -1,6 +1,8 @@
+import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { RootState } from 'app/store/store';
+import { imageUploadedClientSide } from 'features/gallery/store/actions';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { boardIdSelected, galleryViewChanged } from 'features/gallery/store/gallerySlice';
 import { toast } from 'features/toast/toast';
@@ -8,7 +10,8 @@ import { t } from 'i18next';
 import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
-
+import type { ImageDTO } from 'services/api/types';
+import { getCategories, getListImagesUrl } from 'services/api/util';
 const log = logger('gallery');
 
 /**
@@ -34,19 +37,56 @@ let lastUploadedToastTimeout: number | null = null;
 
 export const addImageUploadedFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
-    matcher: imagesApi.endpoints.uploadImage.matchFulfilled,
+    matcher: isAnyOf(imagesApi.endpoints.uploadImage.matchFulfilled, imageUploadedClientSide),
     effect: (action, { dispatch, getState }) => {
-      const imageDTO = action.payload;
+      let imageDTO: ImageDTO;
+      let silent;
+      let isFirstUploadOfBatch = true;
+
+      if (imageUploadedClientSide.match(action)) {
+        imageDTO = action.payload.imageDTO;
+        silent = action.payload.silent;
+        isFirstUploadOfBatch = action.payload.isFirstUploadOfBatch;
+      } else if (imagesApi.endpoints.uploadImage.matchFulfilled(action)) {
+        imageDTO = action.payload;
+        silent = action.meta.arg.originalArgs.silent;
+        isFirstUploadOfBatch = action.meta.arg.originalArgs.isFirstUploadOfBatch ?? true;
+      } else {
+        return;
+      }
+
+      if (silent || imageDTO.is_intermediate) {
+        // If the image is silent or intermediate, we don't want to show a toast
+        return;
+      }
+
+      if (imageUploadedClientSide.match(action)) {
+        const categories = getCategories(imageDTO);
+        const boardId = imageDTO.board_id ?? 'none';
+        dispatch(
+          imagesApi.util.invalidateTags([
+            {
+              type: 'ImageList',
+              id: getListImagesUrl({
+                board_id: boardId,
+                categories,
+              }),
+            },
+            {
+              type: 'Board',
+              id: boardId,
+            },
+            {
+              type: 'BoardImagesTotal',
+              id: boardId,
+            },
+          ])
+        );
+      }
       const state = getState();
 
       log.debug({ imageDTO }, 'Image uploaded');
 
-      if (action.meta.arg.originalArgs.silent || imageDTO.is_intermediate) {
-        // When a "silent" upload is requested, or the image is intermediate, we can skip all post-upload actions,
-        // like toasts and switching the gallery view
-        return;
-      }
-
       const boardId = imageDTO.board_id ?? 'none';
 
       const DEFAULT_UPLOADED_TOAST = {
@@ -80,7 +120,7 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
        *
        * Default to true to not require _all_ image upload handlers to set this value
        */
-      const isFirstUploadOfBatch = action.meta.arg.originalArgs.isFirstUploadOfBatch ?? true;
+
       if (isFirstUploadOfBatch) {
         dispatch(boardIdSelected({ boardId }));
         dispatch(galleryViewChanged('assets'));

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

@@ -73,6 +73,7 @@ export type AppConfig = {
   maxUpscaleDimension?: number;
   allowPrivateBoards: boolean;
   allowPrivateStylePresets: boolean;
+  allowClientSideUpload: boolean;
   disabledTabs: TabName[];
   disabledFeatures: AppFeature[];
   disabledSDFeatures: SDFeature[];
@@ -81,7 +82,6 @@ export type AppConfig = {
   metadataFetchDebounce?: number;
   workflowFetchDebounce?: number;
   isLocal?: boolean;
-  maxImageUploadCount?: number;
   sd: {
     defaultModel?: string;
     disabledControlNetModels: string[];

invokeai/frontend/web/src/common/hooks/useClientSideUpload.ts

@@ -0,0 +1,105 @@
+import { useStore } from '@nanostores/react';
+import { $authToken } from 'app/store/nanostores/authToken';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { imageUploadedClientSide } from 'features/gallery/store/actions';
+import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
+import { useCallback } from 'react';
+import { useCreateImageUploadEntryMutation } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
+export const useClientSideUpload = () => {
+  const dispatch = useAppDispatch();
+  const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
+  const authToken = useStore($authToken);
+  const [createImageUploadEntry] = useCreateImageUploadEntryMutation();
+
+  const clientSideUpload = useCallback(
+    async (file: File, i: number): Promise<ImageDTO> => {
+      const image = new Image();
+      const objectURL = URL.createObjectURL(file);
+      image.src = objectURL;
+      let width = 0;
+      let height = 0;
+      let thumbnail: Blob | undefined;
+
+      await new Promise<void>((resolve) => {
+        image.onload = () => {
+          width = image.naturalWidth;
+          height = image.naturalHeight;
+
+          // Calculate thumbnail dimensions maintaining aspect ratio
+          let thumbWidth = width;
+          let thumbHeight = height;
+          if (width > height && width > 256) {
+            thumbWidth = 256;
+            thumbHeight = Math.round((height * 256) / width);
+          } else if (height > 256) {
+            thumbHeight = 256;
+            thumbWidth = Math.round((width * 256) / height);
+          }
+
+          const canvas = document.createElement('canvas');
+          canvas.width = thumbWidth;
+          canvas.height = thumbHeight;
+          const ctx = canvas.getContext('2d');
+          ctx?.drawImage(image, 0, 0, thumbWidth, thumbHeight);
+
+          canvas.toBlob(
+            (blob) => {
+              if (blob) {
+                thumbnail = blob;
+                // Clean up resources
+                URL.revokeObjectURL(objectURL);
+                image.src = ''; // Clear image source
+                image.remove(); // Remove the image element
+                canvas.width = 0; // Clear canvas
+                canvas.height = 0;
+                resolve();
+              }
+            },
+            'image/webp',
+            0.8
+          );
+        };
+
+        // Handle load errors
+        image.onerror = () => {
+          URL.revokeObjectURL(objectURL);
+          image.remove();
+          resolve();
+        };
+      });
+      const { presigned_url, image_dto } = await createImageUploadEntry({
+        width,
+        height,
+        board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+      }).unwrap();
+
+      await fetch(`${presigned_url}/?type=full`, {
+        method: 'PUT',
+        body: file,
+        ...(authToken && {
+          headers: {
+            Authorization: `Bearer ${authToken}`,
+          },
+        }),
+      });
+
+      await fetch(`${presigned_url}/?type=thumbnail`, {
+        method: 'PUT',
+        body: thumbnail,
+        ...(authToken && {
+          headers: {
+            Authorization: `Bearer ${authToken}`,
+          },
+        }),
+      });
+
+      dispatch(imageUploadedClientSide({ imageDTO: image_dto, silent: false, isFirstUploadOfBatch: i === 0 }));
+
+      return image_dto;
+    },
+    [autoAddBoardId, authToken, createImageUploadEntry, dispatch]
+  );
+
+  return clientSideUpload;
+};

invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx

@@ -3,7 +3,7 @@ import { IconButton } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
-import { selectMaxImageUploadCount } from 'features/system/store/configSlice';
+import { selectIsClientSideUploadEnabled } from 'features/system/store/configSlice';
 import { toast } from 'features/toast/toast';
 import { useCallback } from 'react';
 import type { FileRejection } from 'react-dropzone';
@@ -15,6 +15,7 @@ import type { ImageDTO } from 'services/api/types';
 import { assert } from 'tsafe';
 import type { SetOptional } from 'type-fest';
 
+import { useClientSideUpload } from './useClientSideUpload';
 type UseImageUploadButtonArgs =
   | {
       isDisabled?: boolean;
@@ -50,8 +51,9 @@ const log = logger('gallery');
  */
 export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: UseImageUploadButtonArgs) => {
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
+  const isClientSideUploadEnabled = useAppSelector(selectIsClientSideUploadEnabled);
   const [uploadImage, request] = useUploadImageMutation();
-  const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
+  const clientSideUpload = useClientSideUpload();
   const { t } = useTranslation();
 
   const onDropAccepted = useCallback(
@@ -79,22 +81,27 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           onUpload(imageDTO);
         }
       } else {
-        const imageDTOs = await uploadImages(
-          files.map((file, i) => ({
-            file,
-            image_category: 'user',
-            is_intermediate: false,
-            board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-            silent: false,
-            isFirstUploadOfBatch: i === 0,
-          }))
-        );
+        let imageDTOs: ImageDTO[] = [];
+        if (isClientSideUploadEnabled) {
+          imageDTOs = await Promise.all(files.map((file, i) => clientSideUpload(file, i)));
+        } else {
+          imageDTOs = await uploadImages(
+            files.map((file, i) => ({
+              file,
+              image_category: 'user',
+              is_intermediate: false,
+              board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+              silent: false,
+              isFirstUploadOfBatch: i === 0,
+            }))
+          );
+        }
         if (onUpload) {
           onUpload(imageDTOs);
         }
       }
     },
-    [allowMultiple, autoAddBoardId, onUpload, uploadImage]
+    [allowMultiple, autoAddBoardId, onUpload, uploadImage, isClientSideUploadEnabled, clientSideUpload]
   );
 
   const onDropRejected = useCallback(
@@ -105,10 +112,7 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           file: rejection.file.path,
         }));
         log.error({ errors }, 'Invalid upload');
-        const description =
-          maxImageUploadCount === undefined
-            ? t('toast.uploadFailedInvalidUploadDesc')
-            : t('toast.uploadFailedInvalidUploadDesc_withCount', { count: maxImageUploadCount });
+        const description = t('toast.uploadFailedInvalidUploadDesc');
 
         toast({
           id: 'UPLOAD_FAILED',
@@ -120,7 +124,7 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
         return;
       }
     },
-    [maxImageUploadCount, t]
+    [t]
   );
 
   const {
@@ -137,8 +141,7 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
     onDropRejected,
     disabled: isDisabled,
     noDrag: true,
-    multiple: allowMultiple && (maxImageUploadCount === undefined || maxImageUploadCount > 1),
-    maxFiles: maxImageUploadCount,
+    multiple: allowMultiple,
   });
 
   return { getUploadButtonProps, getUploadInputProps, openUploader, request };

invokeai/frontend/web/src/features/dnd/FullscreenDropzone.tsx

@@ -8,12 +8,13 @@ import { useStore } from '@nanostores/react';
 import { getStore } from 'app/store/nanostores/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { $focusedRegion } from 'common/hooks/focus';
+import { useClientSideUpload } from 'common/hooks/useClientSideUpload';
 import { setFileToPaste } from 'features/controlLayers/components/CanvasPasteModal';
 import { DndDropOverlay } from 'features/dnd/DndDropOverlay';
 import type { DndTargetState } from 'features/dnd/types';
 import { $imageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
-import { selectMaxImageUploadCount } from 'features/system/store/configSlice';
+import { selectIsClientSideUploadEnabled } from 'features/system/store/configSlice';
 import { toast } from 'features/toast/toast';
 import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import { memo, useCallback, useEffect, useRef, useState } from 'react';
@@ -53,13 +54,6 @@ const zUploadFile = z
     (file) => ({ message: `File extension .${file.name.split('.').at(-1)} is not supported` })
   );
 
-const getFilesSchema = (max?: number) => {
-  if (max === undefined) {
-    return z.array(zUploadFile);
-  }
-  return z.array(zUploadFile).max(max);
-};
-
 const sx = {
   position: 'absolute',
   top: 2,
@@ -74,22 +68,19 @@ const sx = {
 export const FullscreenDropzone = memo(() => {
   const { t } = useTranslation();
   const ref = useRef<HTMLDivElement>(null);
-  const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
   const [dndState, setDndState] = useState<DndTargetState>('idle');
   const activeTab = useAppSelector(selectActiveTab);
   const isImageViewerOpen = useStore($imageViewer);
+  const isClientSideUploadEnabled = useAppSelector(selectIsClientSideUploadEnabled);
+  const clientSideUpload = useClientSideUpload();
 
   const validateAndUploadFiles = useCallback(
-    (files: File[]) => {
+    async (files: File[]) => {
       const { getState } = getStore();
-      const uploadFilesSchema = getFilesSchema(maxImageUploadCount);
-      const parseResult = uploadFilesSchema.safeParse(files);
+      const parseResult = z.array(zUploadFile).safeParse(files);
 
       if (!parseResult.success) {
-        const description =
-          maxImageUploadCount === undefined
-            ? t('toast.uploadFailedInvalidUploadDesc')
-            : t('toast.uploadFailedInvalidUploadDesc_withCount', { count: maxImageUploadCount });
+        const description = t('toast.uploadFailedInvalidUploadDesc');
 
         toast({
           id: 'UPLOAD_FAILED',
@@ -118,17 +109,23 @@ export const FullscreenDropzone = memo(() => {
 
       const autoAddBoardId = selectAutoAddBoardId(getState());
 
-      const uploadArgs: UploadImageArg[] = files.map((file, i) => ({
-        file,
-        image_category: 'user',
-        is_intermediate: false,
-        board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-        isFirstUploadOfBatch: i === 0,
-      }));
+      if (isClientSideUploadEnabled) {
+        for (const [i, file] of files.entries()) {
+          await clientSideUpload(file, i);
+        }
+      } else {
+        const uploadArgs: UploadImageArg[] = files.map((file, i) => ({
+          file,
+          image_category: 'user',
+          is_intermediate: false,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          isFirstUploadOfBatch: i === 0,
+        }));
 
-      uploadImages(uploadArgs);
+        uploadImages(uploadArgs);
+      }
     },
-    [activeTab, isImageViewerOpen, maxImageUploadCount, t]
+    [activeTab, isImageViewerOpen, t, isClientSideUploadEnabled, clientSideUpload]
   );
 
   const onPaste = useCallback(

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

@@ -1,31 +1,18 @@
 import { IconButton } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
 import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
-import { selectMaxImageUploadCount } from 'features/system/store/configSlice';
 import { t } from 'i18next';
-import { useMemo } from 'react';
 import { PiUploadBold } from 'react-icons/pi';
 
 export const GalleryUploadButton = () => {
-  const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
-  const uploadOptions = useMemo(() => ({ allowMultiple: maxImageUploadCount !== 1 }), [maxImageUploadCount]);
-  const uploadApi = useImageUploadButton(uploadOptions);
+  const uploadApi = useImageUploadButton({ allowMultiple: true });
   return (
     <>
       <IconButton
         size="sm"
         alignSelf="stretch"
         variant="link"
-        aria-label={
-          maxImageUploadCount === undefined || maxImageUploadCount > 1
-            ? t('accessibility.uploadImages')
-            : t('accessibility.uploadImage')
-        }
-        tooltip={
-          maxImageUploadCount === undefined || maxImageUploadCount > 1
-            ? t('accessibility.uploadImages')
-            : t('accessibility.uploadImage')
-        }
+        aria-label={t('accessibility.uploadImages')}
+        tooltip={t('accessibility.uploadImages')}
         icon={<PiUploadBold />}
         {...uploadApi.getUploadButtonProps()}
       />

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

@@ -26,6 +26,7 @@ const useFocusRegionOptions = {
 };
 
 const FOCUS_REGION_STYLES: SystemStyleObject = {
+  display: 'flex',
   width: 'full',
   height: 'full',
   position: 'absolute',
@@ -45,7 +46,7 @@ export const ImageViewer = memo(({ closeButton }: Props) => {
     <FocusRegionWrapper region="viewer" sx={FOCUS_REGION_STYLES} layerStyle="first" {...useFocusRegionOptions}>
       {hasImageToCompare && <CompareToolbar />}
       {!hasImageToCompare && <ViewerToolbar closeButton={closeButton} />}
-      <Box ref={containerRef} w="full" h="full" p={2}>
+      <Box ref={containerRef} w="full" h="full" p={2} overflow="hidden">
         {!hasImageToCompare && <CurrentImagePreview />}
         {hasImageToCompare && <ImageComparison containerDims={containerDims} />}
       </Box>

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

@@ -1,4 +1,5 @@
 import { createAction } from '@reduxjs/toolkit';
+import type { ImageDTO } from 'services/api/types';
 
 export const sentImageToCanvas = createAction('gallery/sentImageToCanvas');
 
@@ -7,3 +8,9 @@ export const imageDownloaded = createAction('gallery/imageDownloaded');
 export const imageCopiedToClipboard = createAction('gallery/imageCopiedToClipboard');
 
 export const imageOpenedInNewTab = createAction('gallery/imageOpenedInNewTab');
+
+export const imageUploadedClientSide = createAction<{
+  imageDTO: ImageDTO;
+  silent: boolean;
+  isFirstUploadOfBatch: boolean;
+}>('gallery/imageUploadedClientSide');

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

@@ -14,6 +14,7 @@ import BottomLeftPanel from './flow/panels/BottomLeftPanel/BottomLeftPanel';
 import MinimapPanel from './flow/panels/MinimapPanel/MinimapPanel';
 
 const FOCUS_REGION_STYLES: SystemStyleObject = {
+  display: 'flex',
   position: 'relative',
   width: 'full',
   height: 'full',

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

@@ -109,6 +109,7 @@ export const StringGeneratorFieldInputComponent = memo(
                 fontFamily="monospace"
                 userSelect="text"
                 cursor="text"
+                whiteSpace="pre"
               >
                 {resolvedValuesAsString}
               </Text>

invokeai/frontend/web/src/features/nodes/components/flow/nodes/common/NodeTitle.tsx

@@ -56,6 +56,7 @@ const NodeTitle = ({ nodeId, title }: Props) => {
           fontWeight="semibold"
           color={batchGroupColorToken}
           onDoubleClick={editable.startEditing}
+          noOfLines={1}
         >
           {titleWithBatchGroupId}
         </Text>

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

@@ -4,7 +4,6 @@ import {
   Grid,
   GridItem,
   Heading,
-  IconButton,
   Image,
   Modal,
   ModalBody,
@@ -13,21 +12,17 @@ import {
   ModalFooter,
   ModalHeader,
   ModalOverlay,
-  Spacer,
   Text,
-  Tooltip,
   useDisclosure,
 } from '@invoke-ai/ui-library';
-import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
-import { useClipboard } from 'common/hooks/useClipboard';
+import { deepClone } from 'common/util/deepClone';
+import DataViewer from 'features/gallery/components/ImageMetadataViewer/DataViewer';
 import { discordLink, githubLink, websiteLink } from 'features/system/store/constants';
-import { map } from 'lodash-es';
 import InvokeLogoYellow from 'public/assets/images/invoke-tag-lrg.svg';
 import type { ReactElement } from 'react';
-import { cloneElement, memo, useCallback } from 'react';
+import { cloneElement, memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiCopyBold } from 'react-icons/pi';
-import { useGetAppDepsQuery, useGetAppVersionQuery } from 'services/api/endpoints/appInfo';
+import { useGetAppDepsQuery, useGetAppVersionQuery, useGetRuntimeConfigQuery } from 'services/api/endpoints/appInfo';
 
 type AboutModalProps = {
   /* The button to open the Settings Modal */
@@ -37,18 +32,26 @@ type AboutModalProps = {
 const AboutModal = ({ children }: AboutModalProps) => {
   const { isOpen, onOpen, onClose } = useDisclosure();
   const { t } = useTranslation();
-  const clipboard = useClipboard();
-  const { depsArray, depsObject } = useGetAppDepsQuery(undefined, {
-    selectFromResult: ({ data }) => ({
-      depsObject: data,
-      depsArray: data ? map(data, (version, name) => ({ name, version })) : [],
-    }),
-  });
+  const { data: runtimeConfig } = useGetRuntimeConfigQuery();
+  const { data: dependencies } = useGetAppDepsQuery();
   const { data: appVersion } = useGetAppVersionQuery();
 
-  const handleCopy = useCallback(() => {
-    clipboard.writeText(JSON.stringify(depsObject, null, 2));
-  }, [clipboard, depsObject]);
+  const localData = useMemo(() => {
+    const clonedRuntimeConfig = deepClone(runtimeConfig);
+
+    if (clonedRuntimeConfig && clonedRuntimeConfig.config.remote_api_tokens) {
+      clonedRuntimeConfig.config.remote_api_tokens.forEach((remote_api_token) => {
+        remote_api_token.token = 'REDACTED';
+      });
+    }
+
+    return {
+      version: appVersion?.version,
+      dependencies,
+      config: clonedRuntimeConfig?.config,
+      set_config_fields: clonedRuntimeConfig?.set_fields,
+    };
+  }, [appVersion, dependencies, runtimeConfig]);
 
   return (
     <>
@@ -63,27 +66,7 @@ const AboutModal = ({ children }: AboutModalProps) => {
           <ModalBody display="flex" flexDir="column" gap={4}>
             <Grid templateColumns="repeat(2, 1fr)" h="full">
               <GridItem backgroundColor="base.750" borderRadius="base" p="4" h="full">
-                <ScrollableContent>
-                  <Flex position="sticky" top="0" backgroundColor="base.750" p={1} alignItems="center">
-                    <Heading size="md">{t('common.localSystem')}</Heading>
-                    <Spacer />
-                    <Tooltip label={t('common.copy')}>
-                      <IconButton
-                        onClick={handleCopy}
-                        isDisabled={!depsObject}
-                        aria-label={t('common.copy')}
-                        icon={<PiCopyBold />}
-                        variant="ghost"
-                      />
-                    </Tooltip>
-                  </Flex>
-                  {depsArray.map(({ name, version }, i) => (
-                    <Grid key={i} py="2" px="1" w="full" templateColumns="repeat(2, 1fr)">
-                      <Text>{name}</Text>
-                      <Text>{version ? version : t('common.notInstalled')}</Text>
-                    </Grid>
-                  ))}
-                </ScrollableContent>
+                <DataViewer label={t('common.systemInformation')} data={localData} />
               </GridItem>
               <GridItem>
                 <Flex flexDir="column" gap={3} justifyContent="center" alignItems="center" h="full">

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

@@ -20,6 +20,7 @@ const initialConfigState: AppConfig = {
   shouldFetchMetadataFromApi: false,
   allowPrivateBoards: false,
   allowPrivateStylePresets: false,
+  allowClientSideUpload: false,
   disabledTabs: [],
   disabledFeatures: ['lightbox', 'faceRestore', 'batches'],
   disabledSDFeatures: ['variation', 'symmetry', 'hires', 'perlinNoise', 'noiseThreshold'],
@@ -218,6 +219,5 @@ export const selectWorkflowFetchDebounce = createConfigSelector((config) => conf
 export const selectMetadataFetchDebounce = createConfigSelector((config) => config.metadataFetchDebounce ?? 300);
 
 export const selectIsModelsTabDisabled = createConfigSelector((config) => config.disabledTabs.includes('models'));
-export const selectMaxImageUploadCount = createConfigSelector((config) => config.maxImageUploadCount);
-
+export const selectIsClientSideUploadEnabled = createConfigSelector((config) => config.allowClientSideUpload);
 export const selectIsLocal = createSelector(selectConfigSlice, (config) => config.isLocal);

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

@@ -36,6 +36,15 @@ export const appInfoApi = api.injectEndpoints({
       }),
       providesTags: ['FetchOnReconnect'],
     }),
+    getRuntimeConfig: build.query<
+      paths['/api/v1/app/runtime_config']['get']['responses']['200']['content']['application/json'],
+      void
+    >({
+      query: () => ({
+        url: buildAppInfoUrl('runtime_config'),
+        method: 'GET',
+      }),
+    }),
     getInvocationCacheStatus: build.query<
       paths['/api/v1/app/invocation_cache/status']['get']['responses']['200']['content']['application/json'],
       void
@@ -82,6 +91,7 @@ export const {
   useGetAppVersionQuery,
   useGetAppDepsQuery,
   useGetAppConfigQuery,
+  useGetRuntimeConfigQuery,
   useClearInvocationCacheMutation,
   useDisableInvocationCacheMutation,
   useEnableInvocationCacheMutation,

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

@@ -7,6 +7,8 @@ import type {
   DeleteBoardResult,
   GraphAndWorkflowResponse,
   ImageDTO,
+  ImageUploadEntryRequest,
+  ImageUploadEntryResponse,
   ListImagesArgs,
   ListImagesResponse,
   UploadImageArg,
@@ -287,6 +289,7 @@ export const imagesApi = api.injectEndpoints({
           },
         };
       },
+
       invalidatesTags: (result) => {
         if (!result || result.is_intermediate) {
           // Don't add it to anything
@@ -314,7 +317,13 @@ export const imagesApi = api.injectEndpoints({
         ];
       },
     }),
-
+    createImageUploadEntry: build.mutation<ImageUploadEntryResponse, ImageUploadEntryRequest>({
+      query: ({ width, height, board_id }) => ({
+        url: buildImagesUrl(),
+        method: 'POST',
+        body: { width, height, board_id },
+      }),
+    }),
     deleteBoard: build.mutation<DeleteBoardResult, string>({
       query: (board_id) => ({ url: buildBoardsUrl(board_id), method: 'DELETE' }),
       invalidatesTags: () => [
@@ -549,6 +558,7 @@ export const {
   useGetImageWorkflowQuery,
   useLazyGetImageWorkflowQuery,
   useUploadImageMutation,
+  useCreateImageUploadEntryMutation,
   useClearIntermediatesMutation,
   useAddImagesToBoardMutation,
   useRemoveImagesFromBoardMutation,