translationBot(ui): update translation (Italian)

Currently translated at 98.7% (1800 of 1822 strings)

translationBot(ui): update translation (Italian)

Currently translated at 98.7% (1798 of 1820 strings)

translationBot(ui): update translation (Italian)

Currently translated at 98.7% (1796 of 1818 strings)

Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI

.github/CODEOWNERS

@@ -1,12 +1,12 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr @jazzhaiku
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @Millu
 /mkdocs.yml @lstein  @blessedcoolant @hipsterusername @Millu
 
 # nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
+/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername @jazzhaiku
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
@@ -22,7 +22,7 @@
 /invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername @jazzhaiku
 
 # front ends
 /invokeai/frontend/CLI @lstein @hipsterusername

.github/workflows/frontend-checks.yml

@@ -44,7 +44,12 @@ jobs:
       - name: check for changed frontend files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        uses: tj-actions/changed-files@v42
+        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
+        # See:
+        # - CVE-2025-30066
+        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
+        # - https://github.com/tj-actions/changed-files/issues/2463
+        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
         with:
           files_yaml: |
             frontend:

.github/workflows/frontend-tests.yml

@@ -44,7 +44,12 @@ jobs:
       - name: check for changed frontend files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        uses: tj-actions/changed-files@v42
+        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
+        # See:
+        # - CVE-2025-30066
+        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
+        # - https://github.com/tj-actions/changed-files/issues/2463
+        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
         with:
           files_yaml: |
             frontend:

.github/workflows/python-checks.yml

@@ -43,7 +43,12 @@ jobs:
       - name: check for changed python files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        uses: tj-actions/changed-files@v42
+        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
+        # See:
+        # - CVE-2025-30066
+        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
+        # - https://github.com/tj-actions/changed-files/issues/2463
+        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
         with:
           files_yaml: |
             python:
@@ -62,7 +67,7 @@ jobs:
 
       - name: install ruff
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: pip install ruff==0.6.0
+        run: pip install ruff==0.9.9
         shell: bash
 
       - name: ruff check

.github/workflows/python-tests.yml

@@ -77,7 +77,12 @@ jobs:
       - name: check for changed python files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        uses: tj-actions/changed-files@v42
+        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
+        # See:
+        # - CVE-2025-30066
+        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
+        # - https://github.com/tj-actions/changed-files/issues/2463
+        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
         with:
           files_yaml: |
             python:

.github/workflows/typegen-checks.yml

@@ -42,7 +42,12 @@ jobs:
       - name: check for changed files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        uses: tj-actions/changed-files@v42
+        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
+        # See:
+        # - CVE-2025-30066
+        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
+        # - https://github.com/tj-actions/changed-files/issues/2463
+        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
         with:
           files_yaml: |
             src:

invokeai/app/api/dependencies.py

@@ -36,6 +36,7 @@ from invokeai.app.services.style_preset_images.style_preset_images_disk import S
 from invokeai.app.services.style_preset_records.style_preset_records_sqlite import SqliteStylePresetRecordsStorage
 from invokeai.app.services.urls.urls_default import LocalUrlService
 from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
+from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_disk import WorkflowThumbnailFileStorageDisk
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
@@ -83,6 +84,7 @@ class ApiDependencies:
 
         model_images_folder = config.models_path
         style_presets_folder = config.style_presets_path
+        workflow_thumbnails_folder = config.workflow_thumbnails_path
 
         db = init_db(config=config, logger=logger, image_files=image_files)
 
@@ -120,6 +122,7 @@ class ApiDependencies:
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
         style_preset_records = SqliteStylePresetRecordsStorage(db=db)
         style_preset_image_files = StylePresetImageFileStorageDisk(style_presets_folder / "images")
+        workflow_thumbnails = WorkflowThumbnailFileStorageDisk(workflow_thumbnails_folder)
 
         services = InvocationServices(
             board_image_records=board_image_records,
@@ -147,6 +150,7 @@ class ApiDependencies:
             conditioning=conditioning,
             style_preset_records=style_preset_records,
             style_preset_image_files=style_preset_image_files,
+            workflow_thumbnails=workflow_thumbnails,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/extract_metadata_from_image.py

@@ -0,0 +1,124 @@
+import json
+import logging
+from dataclasses import dataclass
+
+from PIL import Image
+
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutIDValidator
+
+
+@dataclass
+class ExtractedMetadata:
+    invokeai_metadata: str | None
+    invokeai_workflow: str | None
+    invokeai_graph: str | None
+
+
+def extract_metadata_from_image(
+    pil_image: Image.Image,
+    invokeai_metadata_override: str | None,
+    invokeai_workflow_override: str | None,
+    invokeai_graph_override: str | None,
+    logger: logging.Logger,
+) -> ExtractedMetadata:
+    """
+    Extracts the "invokeai_metadata", "invokeai_workflow", and "invokeai_graph" data embedded in the PIL Image.
+
+    These items are stored as stringified JSON in the image file's metadata, so we need to do some parsing to validate
+    them. Once parsed, the values are returned as they came (as strings), or None if they are not present or invalid.
+
+    In some situations, we may prefer to override the values extracted from the image file with some other values.
+
+    For example, when uploading an image via API, the client can optionally provide the metadata directly in the request,
+    as opposed to embedding it in the image file. In this case, the client-provided metadata will be used instead of the
+    metadata embedded in the image file.
+
+    Args:
+        pil_image: The PIL Image object.
+        invokeai_metadata_override: The metadata override provided by the client.
+        invokeai_workflow_override: The workflow override provided by the client.
+        invokeai_graph_override: The graph override provided by the client.
+        logger: The logger to use for debug logging.
+
+    Returns:
+        ExtractedMetadata: The extracted metadata, workflow, and graph.
+    """
+
+    # The fallback value for metadata is None.
+    stringified_metadata: str | None = None
+
+    # Use the metadata override if provided, else attempt to extract it from the image file.
+    metadata_raw = invokeai_metadata_override or pil_image.info.get("invokeai_metadata", None)
+
+    # If the metadata is present in the image file, we will attempt to parse it as JSON. When we create images,
+    # we always store metadata as a stringified JSON dict. So, we expect it to be a string here.
+    if isinstance(metadata_raw, str):
+        try:
+            # Must be a JSON string
+            metadata_parsed = json.loads(metadata_raw)
+            # Must be a dict
+            if isinstance(metadata_parsed, dict):
+                # Looks good, overwrite the fallback value
+                stringified_metadata = metadata_raw
+        except Exception as e:
+            logger.debug(f"Failed to parse metadata for uploaded image, {e}")
+            pass
+
+    # We expect the workflow, if embedded in the image, to be a JSON-stringified WorkflowWithoutID. We will store it
+    # as a string.
+    workflow_raw: str | None = invokeai_workflow_override or pil_image.info.get("invokeai_workflow", None)
+
+    # The fallback value for workflow is None.
+    stringified_workflow: str | None = None
+
+    # If the workflow is present in the image file, we will attempt to parse it as JSON. When we create images, we
+    # always store workflows as a stringified JSON WorkflowWithoutID. So, we expect it to be a string here.
+    if isinstance(workflow_raw, str):
+        try:
+            # Validate the workflow JSON before storing it
+            WorkflowWithoutIDValidator.validate_json(workflow_raw)
+            # Looks good, overwrite the fallback value
+            stringified_workflow = workflow_raw
+        except Exception:
+            logger.debug("Failed to parse workflow for uploaded image")
+            pass
+
+    # We expect the workflow, if embedded in the image, to be a JSON-stringified Graph. We will store it as a
+    # string.
+    graph_raw: str | None = invokeai_graph_override or pil_image.info.get("invokeai_graph", None)
+
+    # The fallback value for graph is None.
+    stringified_graph: str | None = None
+
+    # If the graph is present in the image file, we will attempt to parse it as JSON. When we create images, we
+    # always store graphs as a stringified JSON Graph. So, we expect it to be a string here.
+    if isinstance(graph_raw, str):
+        try:
+            # TODO(psyche): Due to pydantic's handling of None values, it is possible for the graph to fail validation,
+            # even if it is a direct dump of a valid graph. Node fields in the graph are allowed to have be unset if
+            # they have incoming connections, but something about the ser/de process cannot adequately handle this.
+            #
+            # In lieu of fixing the graph validation, we will just do a simple check here to see if the graph is dict
+            # with the correct keys. This is not a perfect solution, but it should be good enough for now.
+
+            # FIX ME: Validate the graph JSON before storing it
+            # Graph.model_validate_json(graph_raw)
+
+            # Crappy workaround to validate JSON
+            graph_parsed = json.loads(graph_raw)
+            if not isinstance(graph_parsed, dict):
+                raise ValueError("Not a dict")
+            if not isinstance(graph_parsed.get("nodes", None), dict):
+                raise ValueError("'nodes' is not a dict")
+            if not isinstance(graph_parsed.get("edges", None), list):
+                raise ValueError("'edges' is not a list")
+
+            # Looks good, overwrite the fallback value
+            stringified_graph = graph_raw
+        except Exception as e:
+            logger.debug(f"Failed to parse graph for uploaded image, {e}")
+            pass
+
+    return ExtractedMetadata(
+        invokeai_metadata=stringified_metadata, invokeai_workflow=stringified_workflow, invokeai_graph=stringified_graph
+    )

invokeai/app/api/routers/images.py

@@ -6,9 +6,10 @@ from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request,
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
-from pydantic import BaseModel, Field, JsonValue
+from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.app.api.extract_metadata_from_image import extract_metadata_from_image
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
@@ -45,18 +46,16 @@ async def upload_image(
     board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
     session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
     crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
-    metadata: Optional[JsonValue] = Body(
-        default=None, description="The metadata to associate with the image", embed=True
+    metadata: Optional[str] = Body(
+        default=None,
+        description="The metadata to associate with the image, must be a stringified JSON dict",
+        embed=True,
     ),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type or not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
-    _metadata = None
-    _workflow = None
-    _graph = None
-
     contents = await file.read()
     try:
         pil_image = Image.open(io.BytesIO(contents))
@@ -67,30 +66,13 @@ async def upload_image(
         ApiDependencies.invoker.services.logger.error(traceback.format_exc())
         raise HTTPException(status_code=415, detail="Failed to read image")
 
-    # TODO: retain non-invokeai metadata on upload?
-    # attempt to parse metadata from image
-    metadata_raw = metadata if isinstance(metadata, str) else pil_image.info.get("invokeai_metadata", None)
-    if isinstance(metadata_raw, str):
-        _metadata = metadata_raw
-    else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse metadata for uploaded image")
-        pass
-
-    # attempt to parse workflow from image
-    workflow_raw = pil_image.info.get("invokeai_workflow", None)
-    if isinstance(workflow_raw, str):
-        _workflow = workflow_raw
-    else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse workflow for uploaded image")
-        pass
-
-    # attempt to extract graph from image
-    graph_raw = pil_image.info.get("invokeai_graph", None)
-    if isinstance(graph_raw, str):
-        _graph = graph_raw
-    else:
-        ApiDependencies.invoker.services.logger.debug("Failed to parse graph for uploaded image")
-        pass
+    extracted_metadata = extract_metadata_from_image(
+        pil_image=pil_image,
+        invokeai_metadata_override=metadata,
+        invokeai_workflow_override=None,
+        invokeai_graph_override=None,
+        logger=ApiDependencies.invoker.services.logger,
+    )
 
     try:
         image_dto = ApiDependencies.invoker.services.images.create(
@@ -99,9 +81,9 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
-            metadata=_metadata,
-            workflow=_workflow,
-            graph=_graph,
+            metadata=extracted_metadata.invokeai_metadata,
+            workflow=extracted_metadata.invokeai_workflow,
+            graph=extracted_metadata.invokeai_graph,
             is_intermediate=is_intermediate,
         )
 

invokeai/app/api/routers/workflows.py

@@ -1,6 +1,10 @@
+import io
+import traceback
 from typing import Optional
 
-from fastapi import APIRouter, Body, HTTPException, Path, Query
+from fastapi import APIRouter, Body, File, HTTPException, Path, Query, UploadFile
+from fastapi.responses import FileResponse
+from PIL import Image
 
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.services.shared.pagination import PaginatedResults
@@ -10,11 +14,14 @@ from invokeai.app.services.workflow_records.workflow_records_common import (
     WorkflowCategory,
     WorkflowNotFoundError,
     WorkflowRecordDTO,
-    WorkflowRecordListItemDTO,
+    WorkflowRecordListItemWithThumbnailDTO,
     WorkflowRecordOrderBy,
+    WorkflowRecordWithThumbnailDTO,
     WorkflowWithoutID,
 )
+from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_common import WorkflowThumbnailFileNotFoundException
 
+IMAGE_MAX_AGE = 31536000
 workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
 
 
@@ -22,15 +29,17 @@ workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
     "/i/{workflow_id}",
     operation_id="get_workflow",
     responses={
-        200: {"model": WorkflowRecordDTO},
+        200: {"model": WorkflowRecordWithThumbnailDTO},
     },
 )
 async def get_workflow(
     workflow_id: str = Path(description="The workflow to get"),
-) -> WorkflowRecordDTO:
+) -> WorkflowRecordWithThumbnailDTO:
     """Gets a workflow"""
     try:
-        return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+        thumbnail_url = ApiDependencies.invoker.services.workflow_thumbnails.get_url(workflow_id)
+        workflow = ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+        return WorkflowRecordWithThumbnailDTO(thumbnail_url=thumbnail_url, **workflow.model_dump())
     except WorkflowNotFoundError:
         raise HTTPException(status_code=404, detail="Workflow not found")
 
@@ -57,6 +66,11 @@ async def delete_workflow(
     workflow_id: str = Path(description="The workflow to delete"),
 ) -> None:
     """Deletes a workflow"""
+    try:
+        ApiDependencies.invoker.services.workflow_thumbnails.delete(workflow_id)
+    except WorkflowThumbnailFileNotFoundException:
+        # It's OK if the workflow has no thumbnail file. We can still delete the workflow.
+        pass
     ApiDependencies.invoker.services.workflow_records.delete(workflow_id)
 
 
@@ -78,7 +92,7 @@ async def create_workflow(
     "/",
     operation_id="list_workflows",
     responses={
-        200: {"model": PaginatedResults[WorkflowRecordListItemDTO]},
+        200: {"model": PaginatedResults[WorkflowRecordListItemWithThumbnailDTO]},
     },
 )
 async def list_workflows(
@@ -88,10 +102,158 @@ async def list_workflows(
         default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
     ),
     direction: SQLiteDirection = Query(default=SQLiteDirection.Ascending, description="The direction to order by"),
-    category: WorkflowCategory = Query(default=WorkflowCategory.User, description="The category of workflow to get"),
+    categories: Optional[list[WorkflowCategory]] = Query(default=None, description="The categories of workflow to get"),
+    tags: Optional[list[str]] = Query(default=None, description="The tags of workflow to get"),
     query: Optional[str] = Query(default=None, description="The text to query by (matches name and description)"),
-) -> PaginatedResults[WorkflowRecordListItemDTO]:
+    has_been_opened: Optional[bool] = Query(default=None, description="Whether to include/exclude recent workflows"),
+) -> PaginatedResults[WorkflowRecordListItemWithThumbnailDTO]:
     """Gets a page of workflows"""
-    return ApiDependencies.invoker.services.workflow_records.get_many(
-        order_by=order_by, direction=direction, page=page, per_page=per_page, query=query, category=category
+    workflows_with_thumbnails: list[WorkflowRecordListItemWithThumbnailDTO] = []
+    workflows = ApiDependencies.invoker.services.workflow_records.get_many(
+        order_by=order_by,
+        direction=direction,
+        page=page,
+        per_page=per_page,
+        query=query,
+        categories=categories,
+        tags=tags,
+        has_been_opened=has_been_opened,
     )
+    for workflow in workflows.items:
+        workflows_with_thumbnails.append(
+            WorkflowRecordListItemWithThumbnailDTO(
+                thumbnail_url=ApiDependencies.invoker.services.workflow_thumbnails.get_url(workflow.workflow_id),
+                **workflow.model_dump(),
+            )
+        )
+    return PaginatedResults[WorkflowRecordListItemWithThumbnailDTO](
+        items=workflows_with_thumbnails,
+        total=workflows.total,
+        page=workflows.page,
+        pages=workflows.pages,
+        per_page=workflows.per_page,
+    )
+
+
+@workflows_router.put(
+    "/i/{workflow_id}/thumbnail",
+    operation_id="set_workflow_thumbnail",
+    responses={
+        200: {"model": WorkflowRecordDTO},
+    },
+)
+async def set_workflow_thumbnail(
+    workflow_id: str = Path(description="The workflow to update"),
+    image: UploadFile = File(description="The image file to upload"),
+):
+    """Sets a workflow's thumbnail image"""
+    try:
+        ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+    except WorkflowNotFoundError:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+
+    if not image.content_type or not image.content_type.startswith("image"):
+        raise HTTPException(status_code=415, detail="Not an image")
+
+    contents = await image.read()
+    try:
+        pil_image = Image.open(io.BytesIO(contents))
+
+    except Exception:
+        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
+        raise HTTPException(status_code=415, detail="Failed to read image")
+
+    try:
+        ApiDependencies.invoker.services.workflow_thumbnails.save(workflow_id, pil_image)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@workflows_router.delete(
+    "/i/{workflow_id}/thumbnail",
+    operation_id="delete_workflow_thumbnail",
+    responses={
+        200: {"model": WorkflowRecordDTO},
+    },
+)
+async def delete_workflow_thumbnail(
+    workflow_id: str = Path(description="The workflow to update"),
+):
+    """Removes a workflow's thumbnail image"""
+    try:
+        ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+    except WorkflowNotFoundError:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+
+    try:
+        ApiDependencies.invoker.services.workflow_thumbnails.delete(workflow_id)
+    except ValueError as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@workflows_router.get(
+    "/i/{workflow_id}/thumbnail",
+    operation_id="get_workflow_thumbnail",
+    responses={
+        200: {
+            "description": "The workflow thumbnail was fetched successfully",
+        },
+        400: {"description": "Bad request"},
+        404: {"description": "The workflow thumbnail could not be found"},
+    },
+    status_code=200,
+)
+async def get_workflow_thumbnail(
+    workflow_id: str = Path(description="The id of the workflow thumbnail to get"),
+) -> FileResponse:
+    """Gets a workflow's thumbnail image"""
+
+    try:
+        path = ApiDependencies.invoker.services.workflow_thumbnails.get_path(workflow_id)
+
+        response = FileResponse(
+            path,
+            media_type="image/png",
+            filename=workflow_id + ".png",
+            content_disposition_type="inline",
+        )
+        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
+        return response
+    except Exception:
+        raise HTTPException(status_code=404)
+
+
+@workflows_router.get("/counts_by_tag", operation_id="get_counts_by_tag")
+async def get_counts_by_tag(
+    tags: list[str] = Query(description="The tags to get counts for"),
+    categories: Optional[list[WorkflowCategory]] = Query(default=None, description="The categories to include"),
+    has_been_opened: Optional[bool] = Query(default=None, description="Whether to include/exclude recent workflows"),
+) -> dict[str, int]:
+    """Counts workflows by tag"""
+
+    return ApiDependencies.invoker.services.workflow_records.counts_by_tag(
+        tags=tags, categories=categories, has_been_opened=has_been_opened
+    )
+
+
+@workflows_router.get("/counts_by_category", operation_id="counts_by_category")
+async def counts_by_category(
+    categories: list[WorkflowCategory] = Query(description="The categories to include"),
+    has_been_opened: Optional[bool] = Query(default=None, description="Whether to include/exclude recent workflows"),
+) -> dict[str, int]:
+    """Counts workflows by category"""
+
+    return ApiDependencies.invoker.services.workflow_records.counts_by_category(
+        categories=categories, has_been_opened=has_been_opened
+    )
+
+
+@workflows_router.put(
+    "/i/{workflow_id}/opened_at",
+    operation_id="update_opened_at",
+)
+async def update_opened_at(
+    workflow_id: str = Path(description="The workflow to update"),
+) -> None:
+    """Updates the opened_at field of a workflow"""
+    ApiDependencies.invoker.services.workflow_records.update_opened_at(workflow_id)

invokeai/app/invocations/baseinvocation.py

@@ -417,7 +417,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         ui_type = field.json_schema_extra.get("ui_type", None)
         if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
+            logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
             field.json_schema_extra.pop("ui_type")
     return None
 

invokeai/app/invocations/compel.py

@@ -40,10 +40,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "compel",
-    title="Prompt",
+    title="Prompt - SD1.5",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.2.0",
+    version="1.2.1",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -233,10 +233,10 @@ class SDXLPromptInvocationBase:
 
 @invocation(
     "sdxl_compel_prompt",
-    title="SDXL Prompt",
+    title="Prompt - SDXL",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.2.0",
+    version="1.2.1",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -327,10 +327,10 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
 @invocation(
     "sdxl_refiner_compel_prompt",
-    title="SDXL Refiner Prompt",
+    title="Prompt - SDXL Refiner",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.1.1",
+    version="1.1.2",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -376,10 +376,10 @@ class CLIPSkipInvocationOutput(BaseInvocationOutput):
 
 @invocation(
     "clip_skip",
-    title="CLIP Skip",
+    title="Apply CLIP Skip - SD1.5, SDXL",
     tags=["clipskip", "clip", "skip"],
     category="conditioning",
-    version="1.1.0",
+    version="1.1.1",
 )
 class CLIPSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""
@@ -513,7 +513,7 @@ def log_tokenization_for_text(
             usedTokens += 1
 
     if usedTokens > 0:
-        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f"\n>> [TOKENLOG] Tokens {display_label or ''} ({usedTokens}):")
         print(f"{tokenized}\x1b[0m")
 
     if discarded != "":

invokeai/app/invocations/controlnet_image_processors.py

@@ -87,7 +87,7 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
+@invocation("controlnet", title="ControlNet - SD1.5, SDXL", tags=["controlnet"], category="controlnet", version="1.1.3")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 

invokeai/app/invocations/custom_nodes/init.py

@@ -1,64 +0,0 @@
-"""
-Invoke-managed custom node loader. See README.md for more information.
-"""
-
-import sys
-import traceback
-from importlib.util import module_from_spec, spec_from_file_location
-from pathlib import Path
-
-from invokeai.backend.util.logging import InvokeAILogger
-
-logger = InvokeAILogger.get_logger()
-loaded_packs: list[str] = []
-failed_packs: list[str] = []
-
-custom_nodes_dir = Path(__file__).parent
-
-for d in custom_nodes_dir.iterdir():
-    # skip files
-    if not d.is_dir():
-        continue
-
-    # skip hidden directories
-    if d.name.startswith("_") or d.name.startswith("."):
-        continue
-
-    # skip directories without an `__init__.py`
-    init = d / "__init__.py"
-    if not init.exists():
-        continue
-
-    module_name = init.parent.stem
-
-    # skip if already imported
-    if module_name in globals():
-        continue
-
-    # load the module, appending adding a suffix to identify it as a custom node pack
-    spec = spec_from_file_location(module_name, init.absolute())
-
-    if spec is None or spec.loader is None:
-        logger.warn(f"Could not load {init}")
-        continue
-
-    logger.info(f"Loading node pack {module_name}")
-
-    try:
-        module = module_from_spec(spec)
-        sys.modules[spec.name] = module
-        spec.loader.exec_module(module)
-
-        loaded_packs.append(module_name)
-    except Exception:
-        failed_packs.append(module_name)
-        full_error = traceback.format_exc()
-        logger.error(f"Failed to load node pack {module_name} (may have partially loaded):\n{full_error}")
-
-    del init, module_name
-
-loaded_count = len(loaded_packs)
-if loaded_count > 0:
-    logger.info(
-        f"Loaded {loaded_count} node pack{'s' if loaded_count != 1 else ''} from {custom_nodes_dir}: {', '.join(loaded_packs)}"
-    )

invokeai/app/invocations/denoise_latents.py

@@ -127,10 +127,10 @@ def get_scheduler(
 
 @invocation(
     "denoise_latents",
-    title="Denoise Latents",
+    title="Denoise - SD1.5, SDXL",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.3",
+    version="1.5.4",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""

invokeai/app/invocations/fields.py

@@ -57,6 +57,8 @@ class UIType(str, Enum, metaclass=MetaEnum):
     CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     ControlLoRAModel = "ControlLoRAModelField"
+    SigLipModel = "SigLipModelField"
+    FluxReduxModel = "FluxReduxModelField"
     # endregion
 
     # region Misc Field Types
@@ -152,6 +154,7 @@ class FieldDescriptions:
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
     spandrel_image_to_image_model = "Image-to-Image model"
+    vllm_model = "VLLM model"
     lora_weight = "The weight at which the LoRA is applied to each model"
     compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
     raw_prompt = "Raw prompt text (no parsing)"
@@ -201,6 +204,7 @@ class FieldDescriptions:
     freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
     freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
     instantx_control_mode = "The control mode for InstantX ControlNet union models. Ignored for other ControlNet models. The standard mapping is: canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6). Negative values will be treated as 'None'."
+    flux_redux_conditioning = "FLUX Redux conditioning tensor"
 
 
 class ImageField(BaseModel):
@@ -259,6 +263,17 @@ class FluxConditioningField(BaseModel):
     )
 
 
+class FluxReduxConditioningField(BaseModel):
+    """A FLUX Redux conditioning tensor primitive value"""
+
+    conditioning: TensorField = Field(description="The Redux image conditioning tensor.")
+    mask: Optional[TensorField] = Field(
+        default=None,
+        description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
+        "included regions should be set to True.",
+    )
+
+
 class SD3ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_control_lora_loader.py

@@ -21,10 +21,10 @@ class FluxControlLoRALoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "flux_control_lora_loader",
-    title="Flux Control LoRA",
+    title="Control LoRA - FLUX",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.1.0",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class FluxControlLoRALoaderInvocation(BaseInvocation):

invokeai/app/invocations/flux_denoise.py

@@ -15,6 +15,7 @@ from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
     FluxConditioningField,
+    FluxReduxConditioningField,
     ImageField,
     Input,
     InputField,
@@ -46,7 +47,7 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
-from invokeai.backend.flux.text_conditioning import FluxTextConditioning
+from invokeai.backend.flux.text_conditioning import FluxReduxConditioning, FluxTextConditioning
 from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
@@ -61,7 +62,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.2.2",
+    version="3.2.3",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -103,6 +104,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description="Negative conditioning tensor. Can be None if cfg_scale is 1.0.",
         input=Input.Connection,
     )
+    redux_conditioning: FluxReduxConditioningField | list[FluxReduxConditioningField] | None = InputField(
+        default=None,
+        description="FLUX Redux conditioning tensor.",
+        input=Input.Connection,
+    )
     cfg_scale: float | list[float] = InputField(default=1.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
     cfg_scale_start_step: int = InputField(
         default=0,
@@ -190,11 +196,23 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 dtype=inference_dtype,
                 device=TorchDevice.choose_torch_device(),
             )
+        redux_conditionings: list[FluxReduxConditioning] = self._load_redux_conditioning(
+            context=context,
+            redux_cond_field=self.redux_conditioning,
+            packed_height=packed_h,
+            packed_width=packed_w,
+            device=TorchDevice.choose_torch_device(),
+            dtype=inference_dtype,
+        )
         pos_regional_prompting_extension = RegionalPromptingExtension.from_text_conditioning(
-            pos_text_conditionings, img_seq_len=packed_h * packed_w
+            text_conditioning=pos_text_conditionings,
+            redux_conditioning=redux_conditionings,
+            img_seq_len=packed_h * packed_w,
         )
         neg_regional_prompting_extension = (
-            RegionalPromptingExtension.from_text_conditioning(neg_text_conditionings, img_seq_len=packed_h * packed_w)
+            RegionalPromptingExtension.from_text_conditioning(
+                text_conditioning=neg_text_conditionings, redux_conditioning=[], img_seq_len=packed_h * packed_w
+            )
             if neg_text_conditionings
             else None
         )
@@ -400,6 +418,42 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return text_conditionings
 
+    def _load_redux_conditioning(
+        self,
+        context: InvocationContext,
+        redux_cond_field: FluxReduxConditioningField | list[FluxReduxConditioningField] | None,
+        packed_height: int,
+        packed_width: int,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> list[FluxReduxConditioning]:
+        # Normalize to a list of FluxReduxConditioningFields.
+        if redux_cond_field is None:
+            return []
+
+        redux_cond_list = (
+            [redux_cond_field] if isinstance(redux_cond_field, FluxReduxConditioningField) else redux_cond_field
+        )
+
+        redux_conditionings: list[FluxReduxConditioning] = []
+        for redux_cond_field in redux_cond_list:
+            # Load the Redux conditioning tensor.
+            redux_cond_data = context.tensors.load(redux_cond_field.conditioning.tensor_name)
+            redux_cond_data.to(device=device, dtype=dtype)
+
+            # Load the mask, if provided.
+            mask: Optional[torch.Tensor] = None
+            if redux_cond_field.mask is not None:
+                mask = context.tensors.load(redux_cond_field.mask.tensor_name)
+                mask = mask.to(device=device)
+                mask = RegionalPromptingExtension.preprocess_regional_prompt_mask(
+                    mask, packed_height, packed_width, dtype, device
+                )
+
+            redux_conditionings.append(FluxReduxConditioning(redux_embeddings=redux_cond_data, mask=mask))
+
+        return redux_conditionings
+
     @classmethod
     def prep_cfg_scale(
         cls, cfg_scale: float | list[float], timesteps: list[float], cfg_scale_start_step: int, cfg_scale_end_step: int

invokeai/app/invocations/flux_model_loader.py

@@ -37,10 +37,10 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "flux_model_loader",
-    title="Flux Main Model",
+    title="Main Model - FLUX",
     tags=["model", "flux"],
     category="model",
-    version="1.0.5",
+    version="1.0.6",
     classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):

invokeai/app/invocations/flux_redux.py

@@ -0,0 +1,119 @@
+from typing import Optional
+
+import torch
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxReduxConditioningField,
+    InputField,
+    OutputField,
+    TensorField,
+    UIType,
+)
+from invokeai.app.invocations.model import ModelIdentifierField
+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.starter_models import siglip
+from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation_output("flux_redux_output")
+class FluxReduxOutput(BaseInvocationOutput):
+    """The conditioning output of a FLUX Redux invocation."""
+
+    redux_cond: FluxReduxConditioningField = OutputField(
+        description=FieldDescriptions.flux_redux_conditioning, title="Conditioning"
+    )
+
+
+@invocation(
+    "flux_redux",
+    title="FLUX Redux",
+    tags=["ip_adapter", "control"],
+    category="ip_adapter",
+    version="2.0.0",
+    classification=Classification.Prototype,
+)
+class FluxReduxInvocation(BaseInvocation):
+    """Runs a FLUX Redux model to generate a conditioning tensor."""
+
+    image: ImageField = InputField(description="The FLUX Redux image prompt.")
+    mask: Optional[TensorField] = InputField(
+        default=None,
+        description="The bool mask associated with this FLUX Redux image prompt. Excluded regions should be set to "
+        "False, included regions should be set to True.",
+    )
+    redux_model: ModelIdentifierField = InputField(
+        description="The FLUX Redux model to use.",
+        title="FLUX Redux Model",
+        ui_type=UIType.FluxReduxModel,
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxReduxOutput:
+        image = context.images.get_pil(self.image.image_name, "RGB")
+
+        encoded_x = self._siglip_encode(context, image)
+        redux_conditioning = self._flux_redux_encode(context, encoded_x)
+
+        tensor_name = context.tensors.save(redux_conditioning)
+        return FluxReduxOutput(
+            redux_cond=FluxReduxConditioningField(conditioning=TensorField(tensor_name=tensor_name), mask=self.mask)
+        )
+
+    @torch.no_grad()
+    def _siglip_encode(self, context: InvocationContext, image: Image.Image) -> torch.Tensor:
+        siglip_model_config = self._get_siglip_model(context)
+        with context.models.load(siglip_model_config.key).model_on_device() as (_, siglip_pipeline):
+            assert isinstance(siglip_pipeline, SigLipPipeline)
+            return siglip_pipeline.encode_image(
+                x=image, device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
+            )
+
+    @torch.no_grad()
+    def _flux_redux_encode(self, context: InvocationContext, encoded_x: torch.Tensor) -> torch.Tensor:
+        with context.models.load(self.redux_model).model_on_device() as (_, flux_redux):
+            assert isinstance(flux_redux, FluxReduxModel)
+            dtype = next(flux_redux.parameters()).dtype
+            encoded_x = encoded_x.to(dtype=dtype)
+            return flux_redux(encoded_x)
+
+    def _get_siglip_model(self, context: InvocationContext) -> AnyModelConfig:
+        siglip_models = context.models.search_by_attrs(name=siglip.name, base=BaseModelType.Any, type=ModelType.SigLIP)
+
+        if not len(siglip_models) > 0:
+            context.logger.warning(
+                f"The SigLIP model required by FLUX Redux ({siglip.name}) is not installed. Downloading and installing now. This may take a while."
+            )
+
+            # TODO(psyche): Can the probe reliably determine the type of the model? Just hardcoding it bc I don't want to experiment now
+            config_overrides = ModelRecordChanges(name=siglip.name, type=ModelType.SigLIP)
+
+            # Queue the job
+            job = context._services.model_manager.install.heuristic_import(siglip.source, config=config_overrides)
+
+            # Wait for up to 10 minutes - model is ~3.5GB
+            context._services.model_manager.install.wait_for_job(job, timeout=600)
+
+            siglip_models = context.models.search_by_attrs(
+                name=siglip.name,
+                base=BaseModelType.Any,
+                type=ModelType.SigLIP,
+            )
+
+            if len(siglip_models) == 0:
+                context.logger.error("Error while fetching SigLIP for FLUX Redux")
+                assert len(siglip_models) == 1
+
+        return siglip_models[0]

invokeai/app/invocations/flux_text_encoder.py

@@ -26,10 +26,10 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
 
 @invocation(
     "flux_text_encoder",
-    title="FLUX Text Encoding",
+    title="Prompt - FLUX",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.1",
+    version="1.1.2",
     classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):

invokeai/app/invocations/flux_vae_decode.py

@@ -22,10 +22,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "flux_vae_decode",
-    title="FLUX Latents to Image",
+    title="Latents to Image - FLUX",
     tags=["latents", "image", "vae", "l2i", "flux"],
     category="latents",
-    version="1.0.1",
+    version="1.0.2",
 )
 class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""

invokeai/app/invocations/flux_vae_encode.py

@@ -19,10 +19,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "flux_vae_encode",
-    title="FLUX Image to Latents",
+    title="Image to Latents - FLUX",
     tags=["latents", "image", "vae", "i2l", "flux"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FluxVaeEncodeInvocation(BaseInvocation):
     """Encodes an image into latents."""

invokeai/app/invocations/ideal_size.py

@@ -19,9 +19,9 @@ class IdealSizeOutput(BaseInvocationOutput):
 
 @invocation(
     "ideal_size",
-    title="Ideal Size",
+    title="Ideal Size - SD1.5, SDXL",
     tags=["latents", "math", "ideal_size"],
-    version="1.0.4",
+    version="1.0.5",
 )
 class IdealSizeInvocation(BaseInvocation):
     """Calculates the ideal size for generation to avoid duplication"""

invokeai/app/invocations/image_to_latents.py

@@ -31,10 +31,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "i2l",
-    title="Image to Latents",
+    title="Image to Latents - SD1.5, SDXL",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.1.0",
+    version="1.1.1",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""

invokeai/app/invocations/ip_adapter.py

@@ -69,7 +69,13 @@ CLIP_VISION_MODEL_MAP: dict[Literal["ViT-L", "ViT-H", "ViT-G"], StarterModel] =
 }
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.5.0")
+@invocation(
+    "ip_adapter",
+    title="IP-Adapter - SD1.5, SDXL",
+    tags=["ip_adapter", "control"],
+    category="ip_adapter",
+    version="1.5.1",
+)
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 

invokeai/app/invocations/latents_to_image.py

@@ -31,10 +31,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "l2i",
-    title="Latents to Image",
+    title="Latents to Image - SD1.5, SDXL",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.3.1",
+    version="1.3.2",
 )
 class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""

invokeai/app/invocations/load_custom_nodes.py

@@ -1,40 +1,83 @@
+import logging
 import shutil
 import sys
+import traceback
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
 
-def load_custom_nodes(custom_nodes_path: Path):
+def load_custom_nodes(custom_nodes_path: Path, logger: logging.Logger):
     """
     Loads all custom nodes from the custom_nodes_path directory.
 
-    This function copies a custom __init__.py file to the custom_nodes_path directory, effectively turning it into a
-    python module.
+    If custom_nodes_path does not exist, it creates it.
 
-    The custom __init__.py file itself imports all the custom node packs as python modules from the custom_nodes_path
-    directory.
+    It also copies the custom_nodes/README.md file to the custom_nodes_path directory. Because this file may change,
+    it is _always_ copied to the custom_nodes_path directory.
 
-    Then,the custom __init__.py file is programmatically imported using importlib. As it executes, it imports all the
-    custom node packs as python modules.
+    Then, it crawls the custom_nodes_path directory and imports all top-level directories as python modules.
+
+    If the directory does not contain an __init__.py file or starts with an `_` or `.`, it is skipped.
     """
+
+    # create the custom nodes directory if it does not exist
     custom_nodes_path.mkdir(parents=True, exist_ok=True)
 
-    custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
-    custom_nodes_readme_path = str(custom_nodes_path / "README.md")
+    # Copy the README file to the custom nodes directory
+    source_custom_nodes_readme_path = Path(__file__).parent / "custom_nodes/README.md"
+    target_custom_nodes_readme_path = Path(custom_nodes_path) / "README.md"
 
-    # copy our custom nodes __init__.py to the custom nodes directory
-    shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
-    shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
+    # copy our custom nodes README to the custom nodes directory
+    shutil.copy(source_custom_nodes_readme_path, target_custom_nodes_readme_path)
 
-    # set the same permissions as the destination directory, in case our source is read-only,
-    # so that the files are user-writable
-    for p in custom_nodes_path.glob("**/*"):
-        p.chmod(custom_nodes_path.stat().st_mode)
+    loaded_packs: list[str] = []
+    failed_packs: list[str] = []
 
     # Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
-    spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
-    if spec is None or spec.loader is None:
-        raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}")
-    module = module_from_spec(spec)
-    sys.modules[spec.name] = module
-    spec.loader.exec_module(module)
+    for d in custom_nodes_path.iterdir():
+        # skip files
+        if not d.is_dir():
+            continue
+
+        # skip hidden directories
+        if d.name.startswith("_") or d.name.startswith("."):
+            continue
+
+        # skip directories without an `__init__.py`
+        init = d / "__init__.py"
+        if not init.exists():
+            continue
+
+        module_name = init.parent.stem
+
+        # skip if already imported
+        if module_name in globals():
+            continue
+
+        # load the module
+        spec = spec_from_file_location(module_name, init.absolute())
+
+        if spec is None or spec.loader is None:
+            logger.warning(f"Could not load {init}")
+            continue
+
+        logger.info(f"Loading node pack {module_name}")
+
+        try:
+            module = module_from_spec(spec)
+            sys.modules[spec.name] = module
+            spec.loader.exec_module(module)
+
+            loaded_packs.append(module_name)
+        except Exception:
+            failed_packs.append(module_name)
+            full_error = traceback.format_exc()
+            logger.error(f"Failed to load node pack {module_name} (may have partially loaded):\n{full_error}")
+
+        del init, module_name
+
+    loaded_count = len(loaded_packs)
+    if loaded_count > 0:
+        logger.info(
+            f"Loaded {loaded_count} node pack{'s' if loaded_count != 1 else ''} from {custom_nodes_path}: {', '.join(loaded_packs)}"
+        )

invokeai/app/invocations/metadata_linked.py

@@ -610,10 +610,10 @@ class LatentsMetaOutput(LatentsOutput, MetadataOutput):
 
 @invocation(
     "denoise_latents_meta",
-    title="Denoise Latents + metadata",
+    title=f"{DenoiseLatentsInvocation.UIConfig.title} + Metadata",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.1.0",
+    version="1.1.1",
 )
 class DenoiseLatentsMetaInvocation(DenoiseLatentsInvocation, WithMetadata):
     def invoke(self, context: InvocationContext) -> LatentsMetaOutput:
@@ -675,10 +675,10 @@ class DenoiseLatentsMetaInvocation(DenoiseLatentsInvocation, WithMetadata):
 
 @invocation(
     "flux_denoise_meta",
-    title="Flux Denoise + metadata",
+    title=f"{FluxDenoiseInvocation.UIConfig.title} + Metadata",
     tags=["flux", "latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FluxDenoiseLatentsMetaInvocation(FluxDenoiseInvocation, WithMetadata):
     """Run denoising process with a FLUX transformer model + metadata."""

invokeai/app/invocations/model.py

@@ -122,10 +122,10 @@ class ModelIdentifierOutput(BaseInvocationOutput):
 
 @invocation(
     "model_identifier",
-    title="Model identifier",
+    title="Any Model",
     tags=["model"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class ModelIdentifierInvocation(BaseInvocation):
@@ -144,10 +144,10 @@ class ModelIdentifierInvocation(BaseInvocation):
 
 @invocation(
     "main_model_loader",
-    title="Main Model",
+    title="Main Model - SD1.5",
     tags=["model"],
     category="model",
-    version="1.0.3",
+    version="1.0.4",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
@@ -244,7 +244,7 @@ class LoRASelectorOutput(BaseInvocationOutput):
     lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
 
 
-@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
+@invocation("lora_selector", title="LoRA Model - SD1.5", tags=["model"], category="model", version="1.0.2")
 class LoRASelectorInvocation(BaseInvocation):
     """Selects a LoRA model and weight."""
 
@@ -257,7 +257,9 @@ class LoRASelectorInvocation(BaseInvocation):
         return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
 
 
-@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.1.0")
+@invocation(
+    "lora_collection_loader", title="LoRA Collection - SD1.5", tags=["model"], category="model", version="1.1.1"
+)
 class LoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to the provided UNet and CLIP models."""
 
@@ -320,10 +322,10 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "sdxl_lora_loader",
-    title="SDXL LoRA",
+    title="LoRA Model - SDXL",
     tags=["lora", "model"],
     category="model",
-    version="1.0.3",
+    version="1.0.4",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
@@ -400,10 +402,10 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
 
 @invocation(
     "sdxl_lora_collection_loader",
-    title="SDXL LoRA Collection Loader",
+    title="LoRA Collection - SDXL",
     tags=["model"],
     category="model",
-    version="1.1.0",
+    version="1.1.1",
 )
 class SDXLLoRACollectionLoader(BaseInvocation):
     """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
@@ -469,7 +471,9 @@ class SDXLLoRACollectionLoader(BaseInvocation):
         return output
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
+@invocation(
+    "vae_loader", title="VAE Model - SD1.5, SDXL, SD3, FLUX", tags=["vae", "model"], category="model", version="1.0.4"
+)
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
@@ -496,10 +500,10 @@ class SeamlessModeOutput(BaseInvocationOutput):
 
 @invocation(
     "seamless",
-    title="Seamless",
+    title="Apply Seamless - SD1.5, SDXL",
     tags=["seamless", "model"],
     category="model",
-    version="1.0.1",
+    version="1.0.2",
 )
 class SeamlessModeInvocation(BaseInvocation):
     """Applies the seamless transformation to the Model UNet and VAE."""
@@ -539,7 +543,7 @@ class SeamlessModeInvocation(BaseInvocation):
         return SeamlessModeOutput(unet=unet, vae=vae)
 
 
-@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.1")
+@invocation("freeu", title="Apply FreeU - SD1.5, SDXL", tags=["freeu"], category="unet", version="1.0.2")
 class FreeUInvocation(BaseInvocation):
     """
     Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):

invokeai/app/invocations/noise.py

@@ -72,10 +72,10 @@ class NoiseOutput(BaseInvocationOutput):
 
 @invocation(
     "noise",
-    title="Noise",
+    title="Create Latent Noise",
     tags=["latents", "noise"],
     category="latents",
-    version="1.0.2",
+    version="1.0.3",
 )
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""

invokeai/app/invocations/sd3_denoise.py

@@ -32,10 +32,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_denoise",
-    title="SD3 Denoise",
+    title="Denoise - SD3",
     tags=["image", "sd3"],
     category="image",
-    version="1.1.0",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):

invokeai/app/invocations/sd3_image_to_latents.py

@@ -21,10 +21,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_i2l",
-    title="SD3 Image to Latents",
+    title="Image to Latents - SD3",
     tags=["image", "latents", "vae", "i2l", "sd3"],
     category="image",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):

invokeai/app/invocations/sd3_latents_to_image.py

@@ -24,10 +24,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_l2i",
-    title="SD3 Latents to Image",
+    title="Latents to Image - SD3",
     tags=["latents", "image", "vae", "l2i", "sd3"],
     category="latents",
-    version="1.3.1",
+    version="1.3.2",
 )
 class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""

invokeai/app/invocations/sd3_model_loader.py

@@ -30,10 +30,10 @@ class Sd3ModelLoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "sd3_model_loader",
-    title="SD3 Main Model",
+    title="Main Model - SD3",
     tags=["model", "sd3"],
     category="model",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class Sd3ModelLoaderInvocation(BaseInvocation):

invokeai/app/invocations/sd3_text_encoder.py

@@ -29,10 +29,10 @@ SD3_T5_MAX_SEQ_LEN = 256
 
 @invocation(
     "sd3_text_encoder",
-    title="SD3 Text Encoding",
+    title="Prompt - SD3",
     tags=["prompt", "conditioning", "sd3"],
     category="conditioning",
-    version="1.0.0",
+    version="1.0.1",
     classification=Classification.Prototype,
 )
 class Sd3TextEncoderInvocation(BaseInvocation):

invokeai/app/invocations/sdxl.py

@@ -24,7 +24,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
+@invocation("sdxl_model_loader", title="Main Model - SDXL", tags=["model", "sdxl"], category="model", version="1.0.4")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -58,10 +58,10 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 
 @invocation(
     "sdxl_refiner_model_loader",
-    title="SDXL Refiner Model",
+    title="Refiner Model - SDXL",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.3",
+    version="1.0.4",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""

invokeai/app/invocations/segment_anything.py

@@ -185,9 +185,9 @@ class SegmentAnythingInvocation(BaseInvocation):
             # Find the largest mask.
             return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
-            assert (
-                bounding_boxes is not None
-            ), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
+            assert bounding_boxes is not None, (
+                "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
+            )
             assert len(masks) == len(bounding_boxes)
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most

invokeai/app/invocations/t2i_adapter.py

@@ -45,7 +45,11 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
+    "t2i_adapter",
+    title="T2I-Adapter - SD1.5, SDXL",
+    tags=["t2i_adapter", "control"],
+    category="t2i_adapter",
+    version="1.0.4",
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -53,11 +53,11 @@ def crop_controlnet_data(control_data: ControlNetData, latent_region: TBLR) -> C
 
 @invocation(
     "tiled_multi_diffusion_denoise_latents",
-    title="Tiled Multi-Diffusion Denoise Latents",
+    title="Tiled Multi-Diffusion Denoise - SD1.5, SDXL",
     tags=["upscale", "denoise"],
     category="latents",
     classification=Classification.Beta,
-    version="1.0.0",
+    version="1.0.1",
 )
 class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
     """Tiled Multi-Diffusion denoising.

invokeai/app/run_app.py

@@ -59,7 +59,7 @@ def run_app() -> None:
     # Load custom nodes. This must be done after importing the Graph class, which itself imports all modules from the
     # invocations module. The ordering here is implicit, but important - we want to load custom nodes after all the
     # 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)
+    load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path, logger=logger)
 
     # Start the server.
     config = uvicorn.Config(

invokeai/app/services/config/config_default.py

@@ -72,6 +72,7 @@ class InvokeAIAppConfig(BaseSettings):
         outputs_dir: Path to directory for outputs.
         custom_nodes_dir: Path to directory for custom nodes.
         style_presets_dir: Path to directory for style presets.
+        workflow_thumbnails_dir: Path to directory for workflow thumbnails.
         log_handlers: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
         log_format: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.<br>Valid values: `plain`, `color`, `syslog`, `legacy`
         log_level: Emit logging messages at this level or higher.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
@@ -142,6 +143,7 @@ class InvokeAIAppConfig(BaseSettings):
     outputs_dir:                   Path = Field(default=Path("outputs"),    description="Path to directory for outputs.")
     custom_nodes_dir:              Path = Field(default=Path("nodes"),      description="Path to directory for custom nodes.")
     style_presets_dir:      Path = Field(default=Path("style_presets"),      description="Path to directory for style presets.")
+    workflow_thumbnails_dir: Path = Field(default=Path("workflow_thumbnails"), description="Path to directory for workflow thumbnails.")
 
     # LOGGING
     log_handlers:             list[str] = Field(default=["console"],        description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".')
@@ -304,6 +306,11 @@ class InvokeAIAppConfig(BaseSettings):
         """Path to the style presets directory, resolved to an absolute path.."""
         return self._resolve(self.style_presets_dir)
 
+    @property
+    def workflow_thumbnails_path(self) -> Path:
+        """Path to the workflow thumbnails directory, resolved to an absolute path.."""
+        return self._resolve(self.workflow_thumbnails_dir)
+
     @property
     def convert_cache_path(self) -> Path:
         """Path to the converted cache models directory, resolved to an absolute path.."""
@@ -476,9 +483,9 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     try:
         # Meta is not included in the model fields, so we need to validate it separately
         config = InvokeAIAppConfig.model_validate(loaded_config_dict)
-        assert (
-            config.schema_version == CONFIG_SCHEMA_VERSION
-        ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        assert config.schema_version == CONFIG_SCHEMA_VERSION, (
+            f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        )
         return config
     except Exception as e:
         raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e

invokeai/app/services/invocation_services.py

@@ -32,6 +32,7 @@ if TYPE_CHECKING:
     from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
     from invokeai.app.services.urls.urls_base import UrlServiceBase
     from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
+    from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_base import WorkflowThumbnailServiceBase
     from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 
 
@@ -65,6 +66,7 @@ class InvocationServices:
         conditioning: "ObjectSerializerBase[ConditioningFieldData]",
         style_preset_records: "StylePresetRecordsStorageBase",
         style_preset_image_files: "StylePresetImageFileStorageBase",
+        workflow_thumbnails: "WorkflowThumbnailServiceBase",
     ):
         self.board_images = board_images
         self.board_image_records = board_image_records
@@ -91,3 +93,4 @@ class InvocationServices:
         self.conditioning = conditioning
         self.style_preset_records = style_preset_records
         self.style_preset_image_files = style_preset_image_files
+        self.workflow_thumbnails = workflow_thumbnails

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -19,6 +19,8 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_13 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_14 import build_migration_14
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_15 import build_migration_15
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_16 import build_migration_16
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_17 import build_migration_17
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_18 import build_migration_18
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -55,6 +57,8 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_14())
     migrator.register_migration(build_migration_15())
     migrator.register_migration(build_migration_16())
+    migrator.register_migration(build_migration_17())
+    migrator.register_migration(build_migration_18())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_17.py

@@ -0,0 +1,35 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration17Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._add_workflows_tags_col(cursor)
+
+    def _add_workflows_tags_col(self, cursor: sqlite3.Cursor) -> None:
+        """
+        - Adds `tags` column to the workflow_library table. It is a generated column that extracts the tags from the
+            workflow JSON.
+        """
+
+        cursor.execute(
+            "ALTER TABLE workflow_library ADD COLUMN tags TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.tags')) VIRTUAL;"
+        )
+
+
+def build_migration_17() -> Migration:
+    """
+    Build the migration from database version 16 to 17.
+
+    This migration does the following:
+        - Adds `tags` column to the workflow_library table. It is a generated column that extracts the tags from the
+            workflow JSON.
+    """
+    migration_17 = Migration(
+        from_version=16,
+        to_version=17,
+        callback=Migration17Callback(),
+    )
+
+    return migration_17

invokeai/app/services/shared/sqlite_migrator/migrations/migration_18.py

@@ -0,0 +1,47 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration18Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._make_workflow_opened_at_nullable(cursor)
+
+    def _make_workflow_opened_at_nullable(self, cursor: sqlite3.Cursor) -> None:
+        """
+        Make the `opened_at` column nullable in the `workflow_library` table. This is accomplished by:
+            - Dropping the existing `idx_workflow_library_opened_at` index (must be done before dropping the column)
+            - Dropping the existing `opened_at` column
+            - Adding a new nullable column `opened_at` (no data migration needed, all values will be NULL)
+            - Adding a new `idx_workflow_library_opened_at` index on the `opened_at` column
+        """
+        # For index renaming in SQLite, we need to drop and recreate
+        cursor.execute("DROP INDEX IF EXISTS idx_workflow_library_opened_at;")
+        # Rename existing column to deprecated
+        cursor.execute("ALTER TABLE workflow_library DROP COLUMN opened_at;")
+        # Add new nullable column - all values will be NULL - no migration of data needed
+        cursor.execute("ALTER TABLE workflow_library ADD COLUMN opened_at DATETIME;")
+        # Create new index on the new column
+        cursor.execute(
+            "CREATE INDEX idx_workflow_library_opened_at ON workflow_library(opened_at);",
+        )
+
+
+def build_migration_18() -> Migration:
+    """
+    Build the migration from database version 17 to 18.
+
+    This migration does the following:
+        - Make the `opened_at` column nullable in the `workflow_library` table. This is accomplished by:
+            - Dropping the existing `idx_workflow_library_opened_at` index (must be done before dropping the column)
+            - Dropping the existing `opened_at` column
+            - Adding a new nullable column `opened_at` (no data migration needed, all values will be NULL)
+            - Adding a new `idx_workflow_library_opened_at` index on the `opened_at` column
+    """
+    migration_18 = Migration(
+        from_version=17,
+        to_version=18,
+        callback=Migration18Callback(),
+    )
+
+    return migration_18

invokeai/app/services/urls/urls_base.py

@@ -18,3 +18,8 @@ class UrlServiceBase(ABC):
     def get_style_preset_image_url(self, style_preset_id: str) -> str:
         """Gets the URL for a style preset image"""
         pass
+
+    @abstractmethod
+    def get_workflow_thumbnail_url(self, workflow_id: str) -> str:
+        """Gets the URL for a workflow thumbnail"""
+        pass

invokeai/app/services/urls/urls_default.py

@@ -22,3 +22,6 @@ class LocalUrlService(UrlServiceBase):
 
     def get_style_preset_image_url(self, style_preset_id: str) -> str:
         return f"{self._base_url}/style_presets/i/{style_preset_id}/image"
+
+    def get_workflow_thumbnail_url(self, workflow_id: str) -> str:
+        return f"{self._base_url}/workflows/i/{workflow_id}/thumbnail"

invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json

@@ -1,10 +1,11 @@
 {
-  "name": "ESRGAN Upscaling with Canny ControlNet",
+  "id": "default_686bb1d0-d086-4c70-9fa3-2f600b922023",
+  "name": "Upscaler - SD1.5, ESRGAN",
   "author": "InvokeAI",
-  "description": "Sample workflow for using Upscaling with ControlNet with SD1.5",
+  "description": "Sample workflow for using ESRGAN to upscale with ControlNet with SD1.5",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "upscale, controlnet, default",
+  "tags": "sd1.5, upscaling, control",
   "notes": "",
   "exposedFields": [
     {
@@ -184,14 +185,7 @@
           },
           "control_model": {
             "name": "control_model",
-            "label": "Control Model (select Canny)",
-            "value": {
-              "key": "a7b9c76f-4bc5-42aa-b918-c1c458a5bb24",
-              "hash": "blake3:260c7f8e10aefea9868cfc68d89970e91033bd37132b14b903e70ee05ebf530e",
-              "name": "sd-controlnet-canny",
-              "base": "sd-1",
-              "type": "controlnet"
-            }
+            "label": "Control Model (select Canny)"
           },
           "control_weight": {
             "name": "control_weight",
@@ -294,14 +288,7 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "",
-            "value": {
-              "key": "5cd43ca0-dd0a-418d-9f7e-35b2b9d5e106",
-              "hash": "blake3:6987f323017f597213cc3264250edf57056d21a40a0a85d83a1a33a7d44dc41a",
-              "name": "Deliberate_v5",
-              "base": "sd-1",
-              "type": "main"
-            }
+            "label": ""
           }
         },
         "isOpen": true,
@@ -848,4 +835,4 @@
       "targetHandle": "image_resolution"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/FLUX Image to Image.json

@@ -1,10 +1,11 @@
 {
-  "name": "FLUX Image to Image",
+  "id": "default_cbf0e034-7b54-4b2c-b670-3b1e2e4b4a88",
+  "name": "Image to Image - FLUX",
   "author": "InvokeAI",
   "description": "A simple image-to-image workflow using a FLUX dev model. ",
   "version": "1.1.0",
   "contact": "",
-  "tags": "image2image, flux, image-to-image",
+  "tags": "flux, image to image",
   "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend using FLUX dev models for image-to-image workflows. The image-to-image performance with FLUX schnell models is poor.",
   "exposedFields": [
     {
@@ -200,36 +201,15 @@
           },
           "t5_encoder_model": {
             "name": "t5_encoder_model",
-            "label": "",
-            "value": {
-              "key": "d18d5575-96b6-4da3-b3d8-eb58308d6705",
-              "hash": "random:f2f9ed74acdfb4bf6fec200e780f6c25f8dd8764a35e65d425d606912fdf573a",
-              "name": "t5_bnb_int8_quantized_encoder",
-              "base": "any",
-              "type": "t5_encoder"
-            }
+            "label": ""
           },
           "clip_embed_model": {
             "name": "clip_embed_model",
-            "label": "",
-            "value": {
-              "key": "5a19d7e5-8d98-43cd-8a81-87515e4b3b4e",
-              "hash": "random:4bd08514c08fb6ff04088db9aeb45def3c488e8b5fd09a35f2cc4f2dc346f99f",
-              "name": "clip-vit-large-patch14",
-              "base": "any",
-              "type": "clip_embed"
-            }
+            "label": ""
           },
           "vae_model": {
             "name": "vae_model",
-            "label": "",
-            "value": {
-              "key": "9172beab-5c1d-43f0-b2f0-6e0b956710d9",
-              "hash": "random:c54dde288e5fa2e6137f1c92e9d611f598049e6f16e360207b6d96c9f5a67ba0",
-              "name": "FLUX.1-schnell_ae",
-              "base": "flux",
-              "type": "vae"
-            }
+            "label": ""
           }
         }
       },

invokeai/app/services/workflow_records/default_workflows/Face Detailer with IP-Adapter & Canny (See Note in Details).json

@@ -1,10 +1,11 @@
 {
-  "name": "Face Detailer with IP-Adapter & Canny (See Note in Details)",
+  "id": "default_dec5a2e9-f59c-40d9-8869-a056751d79b8",
+  "name": "Face Detailer - SD1.5",
   "author": "kosmoskatten",
   "description": "A workflow to add detail to and improve faces. This workflow is most effective when used with a model that creates realistic outputs. ",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "face detailer, IP-Adapter, Canny",
+  "tags": "sd1.5, reference image, control",
   "notes": "Set this image as the blur mask: https://i.imgur.com/Gxi61zP.png",
   "exposedFields": [
     {
@@ -135,14 +136,7 @@
           },
           "control_model": {
             "name": "control_model",
-            "label": "Control Model (select canny)",
-            "value": {
-              "key": "5bdaacf7-a7a3-4fb8-b394-cc0ffbb8941d",
-              "hash": "blake3:260c7f8e10aefea9868cfc68d89970e91033bd37132b14b903e70ee05ebf530e",
-              "name": "sd-controlnet-canny",
-              "base": "sd-1",
-              "type": "controlnet"
-            }
+            "label": "Control Model (select canny)"
           },
           "control_weight": {
             "name": "control_weight",
@@ -196,14 +190,7 @@
           },
           "ip_adapter_model": {
             "name": "ip_adapter_model",
-            "label": "IP-Adapter Model (select IP Adapter Face)",
-            "value": {
-              "key": "1cc210bb-4d0a-4312-b36c-b5d46c43768e",
-              "hash": "blake3:3d669dffa7471b357b4df088b99ffb6bf4d4383d5e0ef1de5ec1c89728a3d5a5",
-              "name": "ip_adapter_sd15",
-              "base": "sd-1",
-              "type": "ip_adapter"
-            }
+            "label": "IP-Adapter Model (select IP Adapter Face)"
           },
           "clip_vision_model": {
             "name": "clip_vision_model",
@@ -1445,4 +1432,4 @@
       "targetHandle": "vae"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -1,10 +1,11 @@
 {
-  "name": "FLUX Text to Image",
+  "id": "default_444fe292-896b-44fd-bfc6-c0b5d220fffc",
+  "name": "Text to Image - FLUX",
   "author": "InvokeAI",
   "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
   "version": "1.1.0",
   "contact": "",
-  "tags": "text2image, flux",
+  "tags": "flux, text to image",
   "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
   "exposedFields": [
     {
@@ -168,36 +169,15 @@
           },
           "t5_encoder_model": {
             "name": "t5_encoder_model",
-            "label": "",
-            "value": {
-              "key": "d18d5575-96b6-4da3-b3d8-eb58308d6705",
-              "hash": "random:f2f9ed74acdfb4bf6fec200e780f6c25f8dd8764a35e65d425d606912fdf573a",
-              "name": "t5_bnb_int8_quantized_encoder",
-              "base": "any",
-              "type": "t5_encoder"
-            }
+            "label": ""
           },
           "clip_embed_model": {
             "name": "clip_embed_model",
-            "label": "",
-            "value": {
-              "key": "5a19d7e5-8d98-43cd-8a81-87515e4b3b4e",
-              "hash": "random:4bd08514c08fb6ff04088db9aeb45def3c488e8b5fd09a35f2cc4f2dc346f99f",
-              "name": "clip-vit-large-patch14",
-              "base": "any",
-              "type": "clip_embed"
-            }
+            "label": ""
           },
           "vae_model": {
             "name": "vae_model",
-            "label": "",
-            "value": {
-              "key": "9172beab-5c1d-43f0-b2f0-6e0b956710d9",
-              "hash": "random:c54dde288e5fa2e6137f1c92e9d611f598049e6f16e360207b6d96c9f5a67ba0",
-              "name": "FLUX.1-schnell_ae",
-              "base": "flux",
-              "type": "vae"
-            }
+            "label": ""
           }
         }
       },

invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json

@@ -1,10 +1,11 @@
 {
-  "name": "Multi ControlNet (Canny & Depth)",
+  "id": "default_2d05e719-a6b9-4e64-9310-b875d3b2f9d2",
+  "name": "Text to Image -  SD1.5, Control",
   "author": "InvokeAI",
   "description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "ControlNet, canny, depth",
+  "tags": "sd1.5, control, text to image",
   "notes": "",
   "exposedFields": [
     {
@@ -216,14 +217,7 @@
           },
           "control_model": {
             "name": "control_model",
-            "label": "Control Model (select canny)",
-            "value": {
-              "key": "5bdaacf7-a7a3-4fb8-b394-cc0ffbb8941d",
-              "hash": "blake3:260c7f8e10aefea9868cfc68d89970e91033bd37132b14b903e70ee05ebf530e",
-              "name": "sd-controlnet-canny",
-              "base": "sd-1",
-              "type": "controlnet"
-            }
+            "label": "Control Model (select canny)"
           },
           "control_weight": {
             "name": "control_weight",
@@ -370,14 +364,7 @@
           },
           "control_model": {
             "name": "control_model",
-            "label": "Control Model (select depth)",
-            "value": {
-              "key": "87e8855c-671f-4c9e-bbbb-8ed47ccb4aac",
-              "hash": "blake3:2550bf22a53942dfa28ab2fed9d10d80851112531f44d977168992edf9d0534c",
-              "name": "control_v11f1p_sd15_depth",
-              "base": "sd-1",
-              "type": "controlnet"
-            }
+            "label": "Control Model (select depth)"
           },
           "control_weight": {
             "name": "control_weight",
@@ -1014,4 +1001,4 @@
       "targetHandle": "image_resolution"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/MultiDiffusion SD1.5.json

@@ -1,10 +1,11 @@
 {
-  "name": "MultiDiffusion SD1.5",
+  "id": "default_f96e794f-eb3e-4d01-a960-9b4e43402bcf",
+  "name": "Upscaler - SD1.5, MultiDiffusion",
   "author": "Invoke",
   "description": "A workflow to upscale an input image with tiled upscaling, using SD1.5 based models.",
   "version": "1.0.0",
   "contact": "invoke@invoke.ai",
-  "tags": "tiled, upscaling, sdxl",
+  "tags": "sd1.5, upscaling",
   "notes": "",
   "exposedFields": [
     {
@@ -52,7 +53,6 @@
     "version": "3.0.0",
     "category": "default"
   },
-  "id": "e5b5fb01-8906-463a-963a-402dbc42f79b",
   "nodes": [
     {
       "id": "33fe76a0-5efd-4482-a7f0-e2abf1223dc2",
@@ -135,14 +135,7 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "",
-            "value": {
-              "key": "e7b402e5-62e5-4acb-8c39-bee6bdb758ab",
-              "hash": "c8659e796168d076368256b57edbc1b48d6dafc1712f1bb37cc57c7c06889a6b",
-              "name": "526mix",
-              "base": "sd-1",
-              "type": "main"
-            }
+            "label": ""
           }
         }
       },
@@ -384,21 +377,11 @@
           },
           "image": {
             "name": "image",
-            "label": "Image to Upscale",
-            "value": {
-              "image_name": "ee7009f7-a35d-488b-a2a6-21237ef5ae05.png"
-            }
+            "label": "Image to Upscale"
           },
           "image_to_image_model": {
             "name": "image_to_image_model",
-            "label": "",
-            "value": {
-              "key": "38bb1a29-8ede-42ba-b77f-64b3478896eb",
-              "hash": "blake3:e52fdbee46a484ebe9b3b20ea0aac0a35a453ab6d0d353da00acfd35ce7a91ed",
-              "name": "4xNomosWebPhoto_esrgan",
-              "base": "sdxl",
-              "type": "spandrel_image_to_image"
-            }
+            "label": ""
           },
           "tile_size": {
             "name": "tile_size",
@@ -437,14 +420,7 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "ControlNet Model - Choose a Tile ControlNet",
-            "value": {
-              "key": "20645e4d-ef97-4c5a-9243-b834a3483925",
-              "hash": "f0812e13758f91baf4e54b7dbb707b70642937d3b2098cd2b94cc36d3eba308e",
-              "name": "tile",
-              "base": "sd-1",
-              "type": "controlnet"
-            }
+            "label": "ControlNet Model - Choose a Tile ControlNet"
           }
         }
       },
@@ -1427,4 +1403,4 @@
       "targetHandle": "noise"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/MultiDiffusion SDXL.json

@@ -1,10 +1,11 @@
 {
-  "name": "MultiDiffusion SDXL",
+  "id": "default_35658541-6d41-4a20-8ec5-4bf2561faed0",
+  "name": "Upscaler - SDXL, MultiDiffusion",
   "author": "Invoke",
   "description": "A workflow to upscale an input image with tiled upscaling, using SDXL based models.",
   "version": "1.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "tiled, upscaling, sdxl",
+  "tags": "sdxl, upscaling",
   "notes": "",
   "exposedFields": [
     {
@@ -56,7 +57,6 @@
     "version": "3.0.0",
     "category": "default"
   },
-  "id": "dd607062-9e1b-48b9-89ad-9762cdfbb8f4",
   "nodes": [
     {
       "id": "71a116e1-c631-48b3-923d-acea4753b887",
@@ -341,14 +341,7 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "ControlNet Model - Choose a Tile ControlNet",
-            "value": {
-              "key": "74f4651f-0ace-4b7b-b616-e98360257797",
-              "hash": "blake3:167a5b84583aaed3e5c8d660b45830e82e1c602743c689d3c27773c6c8b85b4a",
-              "name": "controlnet-tile-sdxl-1.0",
-              "base": "sdxl",
-              "type": "controlnet"
-            }
+            "label": "ControlNet Model - Choose a Tile ControlNet"
           }
         }
       },
@@ -801,14 +794,7 @@
         "inputs": {
           "vae_model": {
             "name": "vae_model",
-            "label": "",
-            "value": {
-              "key": "ff926845-090e-4d46-b81e-30289ee47474",
-              "hash": "9705ab1c31fa96b308734214fb7571a958621c7a9247eed82b7d277145f8d9fa",
-              "name": "VAEFix",
-              "base": "sdxl",
-              "type": "vae"
-            }
+            "label": ""
           }
         }
       },
@@ -832,14 +818,7 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "SDXL Model",
-            "value": {
-              "key": "ab191f73-68d2-492c-8aec-b438a8cf0f45",
-              "hash": "blake3:2d50e940627e3bf555f015280ec0976d5c1fa100f7bc94e95ffbfc770e98b6fe",
-              "name": "CustomXLv7",
-              "base": "sdxl",
-              "type": "main"
-            }
+            "label": "SDXL Model"
           }
         }
       },
@@ -1642,4 +1621,4 @@
       "targetHandle": "noise"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/Prompt from File.json

@@ -1,10 +1,11 @@
 {
-  "name": "Prompt from File",
+  "id": "default_d7a1c60f-ca2f-4f90-9e33-75a826ca6d8f",
+  "name": "Text to Image - SD1.5, Prompt from File",
   "author": "InvokeAI",
   "description": "Sample workflow using Prompt from File node",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "text2image, prompt from file, default",
+  "tags": "sd1.5, text to image",
   "notes": "",
   "exposedFields": [
     {
@@ -512,4 +513,4 @@
       "targetHandle": "vae"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/README.md

@@ -3,12 +3,14 @@
 Workflows placed in this directory will be synced to the `workflow_library` as
 _default workflows_ on app startup.
 
+- Default workflows must have an id that starts with "default\_". The ID must be retained when the workflow is updated. You may need to do this manually.
 - Default workflows are not editable by users. If they are loaded and saved,
   they will save as a copy of the default workflow.
 - Default workflows must have the `meta.category` property set to `"default"`.
   An exception will be raised during sync if this is not set correctly.
 - Default workflows appear on the "Default Workflows" tab of the Workflow
   Library.
+- Default workflows should not reference any resources that are user-created or installed. That includes images and models. For example, if a default workflow references Juggernaut as an SDXL model, when a user loads the workflow, even if they have a version of Juggernaut installed, it will have a different UUID. They may see a warning. So, it's best to ship default workflows without any references to these types of resources.
 
 After adding or updating default workflows, you **must** start the app up and
 load them to ensure:

invokeai/app/services/workflow_records/default_workflows/SD3.5 Text to Image.json

@@ -1,382 +1,375 @@
 {
-    "name": "SD3.5 Text to Image",
-   "author": "InvokeAI",
-    "description": "Sample text to image workflow for Stable Diffusion 3.5",
-    "version": "1.0.0",
-    "contact": "invoke@invoke.ai",
-    "tags": "text2image, SD3.5, default",
+  "id": "default_dbe46d95-22aa-43fb-9c16-94400d0ce2fd",
+  "name": "Text to Image - SD3.5",
+  "author": "InvokeAI",
+  "description": "Sample text to image workflow for Stable Diffusion 3.5",
+  "version": "1.0.0",
+  "contact": "invoke@invoke.ai",
+  "tags": "SD3.5, text to image",
   "notes": "",
-    "exposedFields": [
-      {
-        "nodeId": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "fieldName": "model"
-      },
-      {
-        "nodeId": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "fieldName": "prompt"
-      }
-    ],
-    "meta": {
-      "version": "3.0.0",
-      "category": "default"
+  "exposedFields": [
+    {
+      "nodeId": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+      "fieldName": "model"
     },
-    "id": "e3a51d6b-8208-4d6d-b187-fcfe8b32934c",
-    "nodes": [
-      {
+    {
+      "nodeId": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+      "fieldName": "prompt"
+    }
+  ],
+  "meta": {
+    "version": "3.0.0",
+    "category": "default"
+  },
+  "nodes": [
+    {
+      "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+      "type": "invocation",
+      "data": {
         "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "type": "invocation",
-        "data": {
-          "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-          "type": "sd3_model_loader",
-          "version": "1.0.0",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": true,
-          "useCache": true,
-          "nodePack": "invokeai",
-          "inputs": {
-            "model": {
-              "name": "model",
-              "label": "",
-              "value": {
-                "key": "f7b20be9-92a8-4cfb-bca4-6c3b5535c10b",
-                "hash": "placeholder",
-                "name": "stable-diffusion-3.5-medium",
-                "base": "sd-3",
-                "type": "main"
-              }
-            },
-            "t5_encoder_model": {
-              "name": "t5_encoder_model",
-              "label": ""
-            },
-            "clip_l_model": {
-              "name": "clip_l_model",
-              "label": ""
-            },
-            "clip_g_model": {
-              "name": "clip_g_model",
-              "label": ""
-            },
-            "vae_model": {
-              "name": "vae_model",
-              "label": ""
-            }
+        "type": "sd3_model_loader",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": true,
+        "nodePack": "invokeai",
+        "inputs": {
+          "model": {
+            "name": "model",
+            "label": ""
+          },
+          "t5_encoder_model": {
+            "name": "t5_encoder_model",
+            "label": ""
+          },
+          "clip_l_model": {
+            "name": "clip_l_model",
+            "label": ""
+          },
+          "clip_g_model": {
+            "name": "clip_g_model",
+            "label": ""
+          },
+          "vae_model": {
+            "name": "vae_model",
+            "label": ""
           }
-        },
-        "position": {
-          "x": -55.58689609637031,
-          "y": -111.53602444662268
         }
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+      "source": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+      "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_conditioning"
+    },
+    {
+      "id": "reactflow__edge-3b4f7f27-cfc0-4373-a009-99c5290d0cd6conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fnegative_conditioning",
+      "type": "default",
+      "source": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+      "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+      "sourceHandle": "conditioning",
+      "targetHandle": "negative_conditioning"
+    }
+  ]
+}

invokeai/app/services/workflow_records/default_workflows/Text to Image - SD1.5.json

@@ -1,10 +1,11 @@
 {
+  "id": "default_7dde3e36-d78f-4152-9eea-00ef9c8124ed",
   "name": "Text to Image - SD1.5",
   "author": "InvokeAI",
   "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "text2image, SD1.5, SD2, default",
+  "tags": "SD1.5, text to image",
   "notes": "",
   "exposedFields": [
     {
@@ -416,4 +417,4 @@
       "targetHandle": "vae"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/Text to Image - SDXL.json

@@ -1,10 +1,11 @@
 {
+  "id": "default_5e8b008d-c697-45d0-8883-085a954c6ace",
   "name": "Text to Image - SDXL",
   "author": "InvokeAI",
   "description": "Sample text to image workflow for SDXL",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "text2image, SDXL, default",
+  "tags": "SDXL, text to image",
   "notes": "",
   "exposedFields": [
     {
@@ -45,14 +46,7 @@
         "inputs": {
           "vae_model": {
             "name": "vae_model",
-            "label": "VAE (use the FP16 model)",
-            "value": {
-              "key": "f20f9e5c-1bce-4c46-a84d-34ebfa7df069",
-              "hash": "blake3:9705ab1c31fa96b308734214fb7571a958621c7a9247eed82b7d277145f8d9fa",
-              "name": "sdxl-vae-fp16-fix",
-              "base": "sdxl",
-              "type": "vae"
-            }
+            "label": "VAE (use the FP16 model)"
           }
         },
         "isOpen": true,
@@ -202,14 +196,7 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "",
-            "value": {
-              "key": "4a63b226-e8ff-4da4-854e-0b9f04b562ba",
-              "hash": "blake3:d279309ea6e5ee6e8fd52504275865cc280dac71cbf528c5b07c98b888bddaba",
-              "name": "dreamshaper-xl-v2-turbo",
-              "base": "sdxl",
-              "type": "main"
-            }
+            "label": ""
           }
         },
         "isOpen": true,
@@ -714,4 +701,4 @@
       "targetHandle": "style"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json

@@ -1,10 +1,11 @@
 {
-  "name": "Text to Image with LoRA",
+  "id": "default_e71d153c-2089-43c7-bd2c-f61f37d4c1c1",
+  "name": "Text to Image - SD1.5, LoRA",
   "author": "InvokeAI",
   "description": "Simple text to image workflow with a LoRA",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "text to image, lora, default",
+  "tags": "sd1.5, text to image, lora",
   "notes": "",
   "exposedFields": [
     {

invokeai/app/services/workflow_records/default_workflows/Tiled Upscaling (Beta).json

@@ -1,10 +1,11 @@
 {
-  "name": "Tiled Upscaling (Beta)",
+  "id": "default_43b0d7f7-6a12-4dcf-a5a4-50c940cbee29",
+  "name": "Upscaler - SD1.5, Tiled",
   "author": "Invoke",
   "description": "A workflow to upscale an input image with tiled upscaling. ",
   "version": "2.1.0",
   "contact": "invoke@invoke.ai",
-  "tags": "tiled, upscaling, sd1.5",
+  "tags": "sd1.5, upscaling",
   "notes": "",
   "exposedFields": [
     {
@@ -85,14 +86,7 @@
           },
           "ip_adapter_model": {
             "name": "ip_adapter_model",
-            "label": "IP-Adapter Model (select ip_adapter_sd15)",
-            "value": {
-              "key": "1cc210bb-4d0a-4312-b36c-b5d46c43768e",
-              "hash": "blake3:3d669dffa7471b357b4df088b99ffb6bf4d4383d5e0ef1de5ec1c89728a3d5a5",
-              "name": "ip_adapter_sd15",
-              "base": "sd-1",
-              "type": "ip_adapter"
-            }
+            "label": "IP-Adapter Model (select ip_adapter_sd15)"
           },
           "clip_vision_model": {
             "name": "clip_vision_model",
@@ -200,14 +194,7 @@
           },
           "control_model": {
             "name": "control_model",
-            "label": "Control Model (select contro_v11f1e_sd15_tile)",
-            "value": {
-              "key": "773843c8-db1f-4502-8f65-59782efa7960",
-              "hash": "blake3:f0812e13758f91baf4e54b7dbb707b70642937d3b2098cd2b94cc36d3eba308e",
-              "name": "control_v11f1e_sd15_tile",
-              "base": "sd-1",
-              "type": "controlnet"
-            }
+            "label": "Control Model (select control_v11f1e_sd15_tile)"
           },
           "control_weight": {
             "name": "control_weight",
@@ -1815,4 +1802,4 @@
       "targetHandle": "unet"
     }
   ]
-}
+}

invokeai/app/services/workflow_records/workflow_records_base.py

@@ -41,10 +41,36 @@ class WorkflowRecordsStorageBase(ABC):
         self,
         order_by: WorkflowRecordOrderBy,
         direction: SQLiteDirection,
-        category: WorkflowCategory,
+        categories: Optional[list[WorkflowCategory]],
         page: int,
         per_page: Optional[int],
         query: Optional[str],
+        tags: Optional[list[str]],
+        has_been_opened: Optional[bool],
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
         """Gets many workflows."""
         pass
+
+    @abstractmethod
+    def counts_by_category(
+        self,
+        categories: list[WorkflowCategory],
+        has_been_opened: Optional[bool] = None,
+    ) -> dict[str, int]:
+        """Gets a dictionary of counts for each of the provided categories."""
+        pass
+
+    @abstractmethod
+    def counts_by_tag(
+        self,
+        tags: list[str],
+        categories: Optional[list[WorkflowCategory]] = None,
+        has_been_opened: Optional[bool] = None,
+    ) -> dict[str, int]:
+        """Gets a dictionary of counts for each of the provided tags."""
+        pass
+
+    @abstractmethod
+    def update_opened_at(self, workflow_id: str) -> None:
+        """Open a workflow."""
+        pass

invokeai/app/services/workflow_records/workflow_records_common.py

@@ -1,6 +1,6 @@
 import datetime
 from enum import Enum
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 import semver
 from pydantic import BaseModel, ConfigDict, Field, JsonValue, TypeAdapter, field_validator
@@ -36,9 +36,7 @@ class WorkflowCategory(str, Enum, metaclass=MetaEnum):
 
 class WorkflowMeta(BaseModel):
     version: str = Field(description="The version of the workflow schema.")
-    category: WorkflowCategory = Field(
-        default=WorkflowCategory.User, description="The category of the workflow (user or default)."
-    )
+    category: WorkflowCategory = Field(description="The category of the workflow (user or default).")
 
     @field_validator("version")
     def validate_version(cls, version: str):
@@ -100,7 +98,9 @@ class WorkflowRecordDTOBase(BaseModel):
     name: str = Field(description="The name of the workflow.")
     created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the workflow.")
     updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the workflow.")
-    opened_at: Union[datetime.datetime, str] = Field(description="The opened timestamp of the workflow.")
+    opened_at: Optional[Union[datetime.datetime, str]] = Field(
+        default=None, description="The opened timestamp of the workflow."
+    )
 
 
 class WorkflowRecordDTO(WorkflowRecordDTOBase):
@@ -118,6 +118,15 @@ WorkflowRecordDTOValidator = TypeAdapter(WorkflowRecordDTO)
 class WorkflowRecordListItemDTO(WorkflowRecordDTOBase):
     description: str = Field(description="The description of the workflow.")
     category: WorkflowCategory = Field(description="The description of the workflow.")
+    tags: str = Field(description="The tags of the workflow.")
 
 
 WorkflowRecordListItemDTOValidator = TypeAdapter(WorkflowRecordListItemDTO)
+
+
+class WorkflowRecordWithThumbnailDTO(WorkflowRecordDTO):
+    thumbnail_url: str | None = Field(default=None, description="The URL of the workflow thumbnail.")
+
+
+class WorkflowRecordListItemWithThumbnailDTO(WorkflowRecordListItemDTO):
+    thumbnail_url: str | None = Field(default=None, description="The URL of the workflow thumbnail.")

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -14,11 +14,13 @@ from invokeai.app.services.workflow_records.workflow_records_common import (
     WorkflowRecordListItemDTO,
     WorkflowRecordListItemDTOValidator,
     WorkflowRecordOrderBy,
+    WorkflowValidator,
     WorkflowWithoutID,
-    WorkflowWithoutIDValidator,
 )
 from invokeai.app.util.misc import uuid_string
 
+SQL_TIME_FORMAT = "%Y-%m-%d %H:%M:%f"
+
 
 class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
@@ -32,15 +34,6 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
     def get(self, workflow_id: str) -> WorkflowRecordDTO:
         """Gets a workflow by ID. Updates the opened_at column."""
         cursor = self._conn.cursor()
-        cursor.execute(
-            """--sql
-            UPDATE workflow_library
-            SET opened_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-            WHERE workflow_id = ?;
-            """,
-            (workflow_id,),
-        )
-        self._conn.commit()
         cursor.execute(
             """--sql
             SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
@@ -55,9 +48,10 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         return WorkflowRecordDTO.from_dict(dict(row))
 
     def create(self, workflow: WorkflowWithoutID) -> WorkflowRecordDTO:
+        if workflow.meta.category is WorkflowCategory.Default:
+            raise ValueError("Default workflows cannot be created via this method")
+
         try:
-            # Only user workflows may be created by this method
-            assert workflow.meta.category is WorkflowCategory.User
             workflow_with_id = Workflow(**workflow.model_dump(), id=uuid_string())
             cursor = self._conn.cursor()
             cursor.execute(
@@ -77,6 +71,9 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         return self.get(workflow_with_id.id)
 
     def update(self, workflow: Workflow) -> WorkflowRecordDTO:
+        if workflow.meta.category is WorkflowCategory.Default:
+            raise ValueError("Default workflows cannot be updated")
+
         try:
             cursor = self._conn.cursor()
             cursor.execute(
@@ -94,6 +91,9 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         return self.get(workflow.id)
 
     def delete(self, workflow_id: str) -> None:
+        if self.get(workflow_id).workflow.meta.category is WorkflowCategory.Default:
+            raise ValueError("Default workflows cannot be deleted")
+
         try:
             cursor = self._conn.cursor()
             cursor.execute(
@@ -113,45 +113,108 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         self,
         order_by: WorkflowRecordOrderBy,
         direction: SQLiteDirection,
-        category: WorkflowCategory,
+        categories: Optional[list[WorkflowCategory]],
         page: int = 0,
         per_page: Optional[int] = None,
         query: Optional[str] = None,
+        tags: Optional[list[str]] = None,
+        has_been_opened: Optional[bool] = None,
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
         # sanitize!
         assert order_by in WorkflowRecordOrderBy
         assert direction in SQLiteDirection
-        assert category in WorkflowCategory
-        count_query = "SELECT COUNT(*) FROM workflow_library WHERE category = ?"
-        main_query = """
-            SELECT
-                workflow_id,
-                category,
-                name,
-                description,
-                created_at,
-                updated_at,
-                opened_at
-            FROM workflow_library
-            WHERE category = ?
-            """
-        main_params: list[int | str] = [category.value]
-        count_params: list[int | str] = [category.value]
 
+        # We will construct the query dynamically based on the query params
+
+        # The main query to get the workflows / counts
+        main_query = """
+                SELECT
+                    workflow_id,
+                    category,
+                    name,
+                    description,
+                    created_at,
+                    updated_at,
+                    opened_at,
+                    tags
+                FROM workflow_library
+                """
+        count_query = "SELECT COUNT(*) FROM workflow_library"
+
+        # Start with an empty list of conditions and params
+        conditions: list[str] = []
+        params: list[str | int] = []
+
+        if categories:
+            # Categories is a list of WorkflowCategory enum values, and a single string in the DB
+
+            # Ensure all categories are valid (is this necessary?)
+            assert all(c in WorkflowCategory for c in categories)
+
+            # Construct a placeholder string for the number of categories
+            placeholders = ", ".join("?" for _ in categories)
+
+            # Construct the condition string & params
+            category_condition = f"category IN ({placeholders})"
+            category_params = [category.value for category in categories]
+
+            conditions.append(category_condition)
+            params.extend(category_params)
+
+        if tags:
+            # Tags is a list of strings, and a single string in the DB
+            # The string in the DB has no guaranteed format
+
+            # Construct a list of conditions for each tag
+            tags_conditions = ["tags LIKE ?" for _ in tags]
+            tags_conditions_joined = " OR ".join(tags_conditions)
+            tags_condition = f"({tags_conditions_joined})"
+
+            # And the params for the tags, case-insensitive
+            tags_params = [f"%{t.strip()}%" for t in tags]
+
+            conditions.append(tags_condition)
+            params.extend(tags_params)
+
+        if has_been_opened:
+            conditions.append("opened_at IS NOT NULL")
+        elif has_been_opened is False:
+            conditions.append("opened_at IS NULL")
+
+        # Ignore whitespace in the query
         stripped_query = query.strip() if query else None
         if stripped_query:
+            # Construct a wildcard query for the name, description, and tags
             wildcard_query = "%" + stripped_query + "%"
-            main_query += " AND name LIKE ? OR description LIKE ? "
-            count_query += " AND name LIKE ? OR description LIKE ?;"
-            main_params.extend([wildcard_query, wildcard_query])
-            count_params.extend([wildcard_query, wildcard_query])
+            query_condition = "(name LIKE ? OR description LIKE ? OR tags LIKE ?)"
 
+            conditions.append(query_condition)
+            params.extend([wildcard_query, wildcard_query, wildcard_query])
+
+        if conditions:
+            # If there are conditions, add a WHERE clause and then join the conditions
+            main_query += " WHERE "
+            count_query += " WHERE "
+
+            all_conditions = " AND ".join(conditions)
+            main_query += all_conditions
+            count_query += all_conditions
+
+        # After this point, the query and params differ for the main query and the count query
+        main_params = params.copy()
+        count_params = params.copy()
+
+        # Main query also gets ORDER BY and LIMIT/OFFSET
         main_query += f" ORDER BY {order_by.value} {direction.value}"
 
         if per_page:
             main_query += " LIMIT ? OFFSET ?"
             main_params.extend([per_page, page * per_page])
 
+        # Put a ring on it
+        main_query += ";"
+        count_query += ";"
+
         cursor = self._conn.cursor()
         cursor.execute(main_query, main_params)
         rows = cursor.fetchall()
@@ -173,6 +236,122 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             total=total,
         )
 
+    def counts_by_tag(
+        self,
+        tags: list[str],
+        categories: Optional[list[WorkflowCategory]] = None,
+        has_been_opened: Optional[bool] = None,
+    ) -> dict[str, int]:
+        if not tags:
+            return {}
+
+        cursor = self._conn.cursor()
+        result: dict[str, int] = {}
+        # Base conditions for categories and selected tags
+        base_conditions: list[str] = []
+        base_params: list[str | int] = []
+
+        # Add category conditions
+        if categories:
+            assert all(c in WorkflowCategory for c in categories)
+            placeholders = ", ".join("?" for _ in categories)
+            base_conditions.append(f"category IN ({placeholders})")
+            base_params.extend([category.value for category in categories])
+
+        if has_been_opened:
+            base_conditions.append("opened_at IS NOT NULL")
+        elif has_been_opened is False:
+            base_conditions.append("opened_at IS NULL")
+
+        # For each tag to count, run a separate query
+        for tag in tags:
+            # Start with the base conditions
+            conditions = base_conditions.copy()
+            params = base_params.copy()
+
+            # Add this specific tag condition
+            conditions.append("tags LIKE ?")
+            params.append(f"%{tag.strip()}%")
+
+            # Construct the full query
+            stmt = """--sql
+                SELECT COUNT(*)
+                FROM workflow_library
+                """
+
+            if conditions:
+                stmt += " WHERE " + " AND ".join(conditions)
+
+            cursor.execute(stmt, params)
+            count = cursor.fetchone()[0]
+            result[tag] = count
+
+        return result
+
+    def counts_by_category(
+        self,
+        categories: list[WorkflowCategory],
+        has_been_opened: Optional[bool] = None,
+    ) -> dict[str, int]:
+        cursor = self._conn.cursor()
+        result: dict[str, int] = {}
+        # Base conditions for categories
+        base_conditions: list[str] = []
+        base_params: list[str | int] = []
+
+        # Add category conditions
+        if categories:
+            assert all(c in WorkflowCategory for c in categories)
+            placeholders = ", ".join("?" for _ in categories)
+            base_conditions.append(f"category IN ({placeholders})")
+            base_params.extend([category.value for category in categories])
+
+        if has_been_opened:
+            base_conditions.append("opened_at IS NOT NULL")
+        elif has_been_opened is False:
+            base_conditions.append("opened_at IS NULL")
+
+        # For each category to count, run a separate query
+        for category in categories:
+            # Start with the base conditions
+            conditions = base_conditions.copy()
+            params = base_params.copy()
+
+            # Add this specific category condition
+            conditions.append("category = ?")
+            params.append(category.value)
+
+            # Construct the full query
+            stmt = """--sql
+                SELECT COUNT(*)
+                FROM workflow_library
+                """
+
+            if conditions:
+                stmt += " WHERE " + " AND ".join(conditions)
+
+            cursor.execute(stmt, params)
+            count = cursor.fetchone()[0]
+            result[category.value] = count
+
+        return result
+
+    def update_opened_at(self, workflow_id: str) -> None:
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                f"""--sql
+                UPDATE workflow_library
+                SET opened_at = STRFTIME('{SQL_TIME_FORMAT}', 'NOW')
+                WHERE workflow_id = ?;
+                """,
+                (workflow_id,),
+            )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+
     def _sync_default_workflows(self) -> None:
         """Syncs default workflows to the database. Internal use only."""
 
@@ -187,27 +366,68 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         """
 
         try:
-            workflows: list[Workflow] = []
+            cursor = self._conn.cursor()
+            workflows_from_file: list[Workflow] = []
+            workflows_to_update: list[Workflow] = []
+            workflows_to_add: list[Workflow] = []
             workflows_dir = Path(__file__).parent / Path("default_workflows")
             workflow_paths = workflows_dir.glob("*.json")
             for path in workflow_paths:
                 bytes_ = path.read_bytes()
-                workflow_without_id = WorkflowWithoutIDValidator.validate_json(bytes_)
-                workflow = Workflow(**workflow_without_id.model_dump(), id=uuid_string())
-                workflows.append(workflow)
-            # Only default workflows may be managed by this method
-            assert all(w.meta.category is WorkflowCategory.Default for w in workflows)
-            cursor = self._conn.cursor()
-            cursor.execute(
-                """--sql
-                DELETE FROM workflow_library
-                WHERE category = 'default';
-                """
-            )
-            for w in workflows:
+                workflow_from_file = WorkflowValidator.validate_json(bytes_)
+
+                assert workflow_from_file.id.startswith("default_"), (
+                    f'Invalid default workflow ID (must start with "default_"): {workflow_from_file.id}'
+                )
+
+                assert workflow_from_file.meta.category is WorkflowCategory.Default, (
+                    f"Invalid default workflow category: {workflow_from_file.meta.category}"
+                )
+
+                workflows_from_file.append(workflow_from_file)
+
+                try:
+                    workflow_from_db = self.get(workflow_from_file.id).workflow
+                    if workflow_from_file != workflow_from_db:
+                        self._invoker.services.logger.debug(
+                            f"Updating library workflow {workflow_from_file.name} ({workflow_from_file.id})"
+                        )
+                        workflows_to_update.append(workflow_from_file)
+                    continue
+                except WorkflowNotFoundError:
+                    self._invoker.services.logger.debug(
+                        f"Adding missing default workflow {workflow_from_file.name} ({workflow_from_file.id})"
+                    )
+                    workflows_to_add.append(workflow_from_file)
+                    continue
+
+            library_workflows_from_db = self.get_many(
+                order_by=WorkflowRecordOrderBy.Name,
+                direction=SQLiteDirection.Ascending,
+                categories=[WorkflowCategory.Default],
+            ).items
+
+            workflows_from_file_ids = [w.id for w in workflows_from_file]
+
+            for w in library_workflows_from_db:
+                if w.workflow_id not in workflows_from_file_ids:
+                    self._invoker.services.logger.debug(
+                        f"Deleting obsolete default workflow {w.name} ({w.workflow_id})"
+                    )
+                    # We cannot use the `delete` method here, as it only deletes non-default workflows
+                    cursor.execute(
+                        """--sql
+                        DELETE from workflow_library
+                        WHERE workflow_id = ?;
+                        """,
+                        (w.workflow_id,),
+                    )
+
+            for w in workflows_to_add:
+                # We cannot use the `create` method here, as it only creates non-default workflows
                 cursor.execute(
                     """--sql
-                    INSERT OR REPLACE INTO workflow_library (
+                    INSERT INTO workflow_library (
                         workflow_id,
                         workflow
                     )
@@ -215,6 +435,18 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                     """,
                     (w.id, w.model_dump_json()),
                 )
+
+            for w in workflows_to_update:
+                # We cannot use the `update` method here, as it only updates non-default workflows
+                cursor.execute(
+                    """--sql
+                    UPDATE workflow_library
+                    SET workflow = ?
+                    WHERE workflow_id = ?;
+                    """,
+                    (w.model_dump_json(), w.id),
+                )
+
             self._conn.commit()
         except Exception:
             self._conn.rollback()

invokeai/app/services/workflow_thumbnails/workflow_thumbnails_base.py

@@ -0,0 +1,28 @@
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from PIL import Image
+
+
+class WorkflowThumbnailServiceBase(ABC):
+    """Base class for workflow thumbnail services"""
+
+    @abstractmethod
+    def get_path(self, workflow_id: str, with_hash: bool = True) -> Path:
+        """Gets the path to a workflow thumbnail"""
+        pass
+
+    @abstractmethod
+    def get_url(self, workflow_id: str, with_hash: bool = True) -> str | None:
+        """Gets the URL of a workflow thumbnail"""
+        pass
+
+    @abstractmethod
+    def save(self, workflow_id: str, image: Image.Image) -> None:
+        """Saves a workflow thumbnail"""
+        pass
+
+    @abstractmethod
+    def delete(self, workflow_id: str) -> None:
+        """Deletes a workflow thumbnail"""
+        pass

invokeai/app/services/workflow_thumbnails/workflow_thumbnails_common.py

@@ -0,0 +1,22 @@
+class WorkflowThumbnailFileNotFoundException(Exception):
+    """Raised when a workflow thumbnail file is not found"""
+
+    def __init__(self, message: str = "Workflow thumbnail file not found"):
+        self.message = message
+        super().__init__(self.message)
+
+
+class WorkflowThumbnailFileSaveException(Exception):
+    """Raised when a workflow thumbnail file cannot be saved"""
+
+    def __init__(self, message: str = "Workflow thumbnail file cannot be saved"):
+        self.message = message
+        super().__init__(self.message)
+
+
+class WorkflowThumbnailFileDeleteException(Exception):
+    """Raised when a workflow thumbnail file cannot be deleted"""
+
+    def __init__(self, message: str = "Workflow thumbnail file cannot be deleted"):
+        self.message = message
+        super().__init__(self.message)

invokeai/app/services/workflow_thumbnails/workflow_thumbnails_disk.py

@@ -0,0 +1,87 @@
+from pathlib import Path
+
+from PIL import Image
+from PIL.Image import Image as PILImageType
+
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowCategory
+from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_base import WorkflowThumbnailServiceBase
+from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_common import (
+    WorkflowThumbnailFileDeleteException,
+    WorkflowThumbnailFileNotFoundException,
+    WorkflowThumbnailFileSaveException,
+)
+from invokeai.app.util.misc import uuid_string
+from invokeai.app.util.thumbnails import make_thumbnail
+
+
+class WorkflowThumbnailFileStorageDisk(WorkflowThumbnailServiceBase):
+    def __init__(self, thumbnails_path: Path):
+        self._workflow_thumbnail_folder = thumbnails_path
+        self._validate_storage_folders()
+
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+
+    def get(self, workflow_id: str) -> PILImageType:
+        try:
+            path = self.get_path(workflow_id)
+
+            return Image.open(path)
+        except FileNotFoundError as e:
+            raise WorkflowThumbnailFileNotFoundException from e
+
+    def save(self, workflow_id: str, image: PILImageType) -> None:
+        try:
+            self._validate_storage_folders()
+            image_path = self._workflow_thumbnail_folder / (workflow_id + ".webp")
+            thumbnail = make_thumbnail(image, 256)
+            thumbnail.save(image_path, format="webp")
+
+        except Exception as e:
+            raise WorkflowThumbnailFileSaveException from e
+
+    def get_path(self, workflow_id: str, with_hash: bool = True) -> Path:
+        workflow = self._invoker.services.workflow_records.get(workflow_id).workflow
+        if workflow.meta.category is WorkflowCategory.Default:
+            default_thumbnails_dir = Path(__file__).parent / Path("default_workflow_thumbnails")
+            path = default_thumbnails_dir / (workflow_id + ".png")
+        else:
+            path = self._workflow_thumbnail_folder / (workflow_id + ".webp")
+
+        return path
+
+    def get_url(self, workflow_id: str, with_hash: bool = True) -> str | None:
+        path = self.get_path(workflow_id)
+        if not self._validate_path(path):
+            return
+
+        url = self._invoker.services.urls.get_workflow_thumbnail_url(workflow_id)
+
+        # The image URL never changes, so we must add random query string to it to prevent caching
+        if with_hash:
+            url += f"?{uuid_string()}"
+
+        return url
+
+    def delete(self, workflow_id: str) -> None:
+        try:
+            path = self.get_path(workflow_id)
+
+            if not self._validate_path(path):
+                raise WorkflowThumbnailFileNotFoundException
+
+            path.unlink()
+
+        except WorkflowThumbnailFileNotFoundException as e:
+            raise WorkflowThumbnailFileNotFoundException from e
+        except Exception as e:
+            raise WorkflowThumbnailFileDeleteException from e
+
+    def _validate_path(self, path: Path) -> bool:
+        """Validates the path given for an image."""
+        return path.exists()
+
+    def _validate_storage_folders(self) -> None:
+        """Checks if the required folders exist and create them if they don't"""
+        self._workflow_thumbnail_folder.mkdir(parents=True, exist_ok=True)

invokeai/backend/flux/extensions/regional_prompting_extension.py

@@ -3,7 +3,11 @@ from typing import Optional
 import torch
 import torchvision
 
-from invokeai.backend.flux.text_conditioning import FluxRegionalTextConditioning, FluxTextConditioning
+from invokeai.backend.flux.text_conditioning import (
+    FluxReduxConditioning,
+    FluxRegionalTextConditioning,
+    FluxTextConditioning,
+)
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.mask import to_standard_float_mask
@@ -32,14 +36,19 @@ class RegionalPromptingExtension:
         return order[block_index % len(order)]
 
     @classmethod
-    def from_text_conditioning(cls, text_conditioning: list[FluxTextConditioning], img_seq_len: int):
+    def from_text_conditioning(
+        cls,
+        text_conditioning: list[FluxTextConditioning],
+        redux_conditioning: list[FluxReduxConditioning],
+        img_seq_len: int,
+    ):
         """Create a RegionalPromptingExtension from a list of text conditionings.
 
         Args:
             text_conditioning (list[FluxTextConditioning]): The text conditionings to use for regional prompting.
             img_seq_len (int): The image sequence length (i.e. packed_height * packed_width).
         """
-        regional_text_conditioning = cls._concat_regional_text_conditioning(text_conditioning)
+        regional_text_conditioning = cls._concat_regional_text_conditioning(text_conditioning, redux_conditioning)
         attn_mask_with_restricted_img_self_attn = cls._prepare_restricted_attn_mask(
             regional_text_conditioning, img_seq_len
         )
@@ -202,6 +211,7 @@ class RegionalPromptingExtension:
     def _concat_regional_text_conditioning(
         cls,
         text_conditionings: list[FluxTextConditioning],
+        redux_conditionings: list[FluxReduxConditioning],
     ) -> FluxRegionalTextConditioning:
         """Concatenate regional text conditioning data into a single conditioning tensor (with associated masks)."""
         concat_t5_embeddings: list[torch.Tensor] = []
@@ -217,18 +227,27 @@ class RegionalPromptingExtension:
                 global_clip_embedding = text_conditioning.clip_embeddings
                 break
 
+        # Handle T5 text embeddings.
         cur_t5_embedding_len = 0
         for text_conditioning in text_conditionings:
             concat_t5_embeddings.append(text_conditioning.t5_embeddings)
-
             concat_t5_embedding_ranges.append(
                 Range(start=cur_t5_embedding_len, end=cur_t5_embedding_len + text_conditioning.t5_embeddings.shape[1])
             )
-
             image_masks.append(text_conditioning.mask)
-
             cur_t5_embedding_len += text_conditioning.t5_embeddings.shape[1]
 
+        # Handle Redux embeddings.
+        for redux_conditioning in redux_conditionings:
+            concat_t5_embeddings.append(redux_conditioning.redux_embeddings)
+            concat_t5_embedding_ranges.append(
+                Range(
+                    start=cur_t5_embedding_len, end=cur_t5_embedding_len + redux_conditioning.redux_embeddings.shape[1]
+                )
+            )
+            image_masks.append(redux_conditioning.mask)
+            cur_t5_embedding_len += redux_conditioning.redux_embeddings.shape[1]
+
         t5_embeddings = torch.cat(concat_t5_embeddings, dim=1)
 
         # Initialize the txt_ids tensor.

invokeai/backend/flux/redux/flux_redux_model.py

@@ -0,0 +1,17 @@
+import torch
+
+# This model definition is based on:
+# https://github.com/black-forest-labs/flux/blob/716724eb276d94397be99710a0a54d352664e23b/src/flux/modules/image_embedders.py#L66
+
+
+class FluxReduxModel(torch.nn.Module):
+    def __init__(self, redux_dim: int = 1152, txt_in_features: int = 4096) -> None:
+        super().__init__()
+
+        self.redux_dim = redux_dim
+
+        self.redux_up = torch.nn.Linear(redux_dim, txt_in_features * 3)
+        self.redux_down = torch.nn.Linear(txt_in_features * 3, txt_in_features)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.redux_down(torch.nn.functional.silu(self.redux_up(x)))

invokeai/backend/flux/redux/flux_redux_state_dict_utils.py

@@ -0,0 +1,11 @@
+from typing import Any, Dict
+
+
+def is_state_dict_likely_flux_redux(state_dict: Dict[str, Any]) -> bool:
+    """Checks if the provided state dict is likely a FLUX Redux model."""
+
+    expected_keys = {"redux_down.bias", "redux_down.weight", "redux_up.bias", "redux_up.weight"}
+    if set(state_dict.keys()) == expected_keys:
+        return True
+
+    return False

invokeai/backend/flux/text_conditioning.py

@@ -13,6 +13,13 @@ class FluxTextConditioning:
     mask: torch.Tensor | None
 
 
+@dataclass
+class FluxReduxConditioning:
+    redux_embeddings: torch.Tensor
+    # If mask is None, the prompt is a global prompt.
+    mask: torch.Tensor | None
+
+
 @dataclass
 class FluxRegionalTextConditioning:
     # Concatenated text embeddings.

invokeai/backend/image_util/pngwriter.py

@@ -91,10 +91,10 @@ class PromptFormatter:
 
         switches = []
         switches.append(f'"{opt.prompt}"')
-        switches.append(f"-s{opt.steps        or t2i.steps}")
-        switches.append(f"-W{opt.width        or t2i.width}")
-        switches.append(f"-H{opt.height       or t2i.height}")
-        switches.append(f"-C{opt.cfg_scale    or t2i.cfg_scale}")
+        switches.append(f"-s{opt.steps or t2i.steps}")
+        switches.append(f"-W{opt.width or t2i.width}")
+        switches.append(f"-H{opt.height or t2i.height}")
+        switches.append(f"-C{opt.cfg_scale or t2i.cfg_scale}")
         switches.append(f"-A{opt.sampler_name or t2i.sampler_name}")
         # to do: put model name into the t2i object
         #        switches.append(f'--model{t2i.model_name}')
@@ -109,7 +109,7 @@ class PromptFormatter:
         if opt.gfpgan_strength:
             switches.append(f"-G{opt.gfpgan_strength}")
         if opt.upscale:
-            switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
+            switches.append(f"-U {' '.join([str(u) for u in opt.upscale])}")
         if opt.variation_amount > 0:
             switches.append(f"-v{opt.variation_amount}")
         if opt.with_variations:

invokeai/backend/model_manager/config.py

@@ -76,6 +76,8 @@ class ModelType(str, Enum):
     T2IAdapter = "t2i_adapter"
     T5Encoder = "t5_encoder"
     SpandrelImageToImage = "spandrel_image_to_image"
+    SigLIP = "siglip"
+    FluxRedux = "flux_redux"
 
 
 class SubModelType(str, Enum):
@@ -528,6 +530,28 @@ class SpandrelImageToImageConfig(ModelConfigBase):
         return Tag(f"{ModelType.SpandrelImageToImage.value}.{ModelFormat.Checkpoint.value}")
 
 
+class SigLIPConfig(DiffusersConfigBase):
+    """Model config for SigLIP."""
+
+    type: Literal[ModelType.SigLIP] = ModelType.SigLIP
+    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.SigLIP.value}.{ModelFormat.Diffusers.value}")
+
+
+class FluxReduxConfig(ModelConfigBase):
+    """Model config for FLUX Tools Redux model."""
+
+    type: Literal[ModelType.FluxRedux] = ModelType.FluxRedux
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.FluxRedux.value}.{ModelFormat.Checkpoint.value}")
+
+
 def get_model_discriminator_value(v: Any) -> str:
     """
     Computes the discriminator value for a model config.
@@ -575,6 +599,8 @@ AnyModelConfig = Annotated[
         Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
         Annotated[CLIPLEmbedDiffusersConfig, CLIPLEmbedDiffusersConfig.get_tag()],
         Annotated[CLIPGEmbedDiffusersConfig, CLIPGEmbedDiffusersConfig.get_tag()],
+        Annotated[SigLIPConfig, SigLIPConfig.get_tag()],
+        Annotated[FluxReduxConfig, FluxReduxConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

invokeai/backend/model_manager/load/memory_snapshot.py

@@ -70,7 +70,7 @@ def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: O
 
     def get_msg_line(prefix: str, val1: int, val2: int) -> str:
         diff = val2 - val1
-        return f"{prefix: <30} ({(diff/GB):+5.3f}): {(val1/GB):5.3f}GB -> {(val2/GB):5.3f}GB\n"
+        return f"{prefix: <30} ({(diff / GB):+5.3f}): {(val1 / GB):5.3f}GB -> {(val2 / GB):5.3f}GB\n"
 
     msg = ""
 

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

@@ -192,7 +192,7 @@ class ModelCache:
         self._cached_models[key] = cache_record
         self._cache_stack.append(key)
         self._logger.debug(
-            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size/MB:.2f}MB)"
+            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size / MB:.2f}MB)"
         )
 
     @synchronized
@@ -303,7 +303,7 @@ class ModelCache:
         # 2. If the model can't fit fully into VRAM, then unload all other models and load as much of the model as
         #    possible.
         vram_bytes_freed = self._offload_unlocked_models(model_vram_needed, working_mem_bytes)
-        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed/MB):.2f}MB")
+        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed / MB):.2f}MB")
 
         # Check the updated vram_available after offloading.
         vram_available = self._get_vram_available(working_mem_bytes)
@@ -317,7 +317,7 @@ class ModelCache:
             vram_bytes_freed_from_own_model = self._move_model_to_ram(cache_entry, -vram_available)
             vram_available = self._get_vram_available(working_mem_bytes)
             self._logger.debug(
-                f"Unloaded {vram_bytes_freed_from_own_model/MB:.2f}MB from the model being locked ({cache_entry.key})."
+                f"Unloaded {vram_bytes_freed_from_own_model / MB:.2f}MB from the model being locked ({cache_entry.key})."
             )
 
         # Move as much of the model as possible into VRAM.
@@ -333,10 +333,12 @@ class ModelCache:
         self._logger.info(
             f"Loaded model '{cache_entry.key}' ({cache_entry.cached_model.model.__class__.__name__}) onto "
             f"{self._execution_device.type} device in {(time.time() - start_time):.2f}s. "
-            f"Total model size: {model_total_bytes/MB:.2f}MB, "
-            f"VRAM: {model_cur_vram_bytes/MB:.2f}MB ({loaded_percent:.1%})"
+            f"Total model size: {model_total_bytes / MB:.2f}MB, "
+            f"VRAM: {model_cur_vram_bytes / MB:.2f}MB ({loaded_percent:.1%})"
+        )
+        self._logger.debug(
+            f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded / MB):.2f}MB, "
         )
-        self._logger.debug(f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded/MB):.2f}MB, ")
         self._logger.debug(
             f"After loading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
         )
@@ -495,10 +497,10 @@ class ModelCache:
         """Helper function for preparing a VRAM state log string."""
         model_cur_vram_bytes_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
         return (
-            f"model_total={model_total_bytes/MB:.0f} MB, "
-            + f"model_vram={model_cur_vram_bytes/MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
+            f"model_total={model_total_bytes / MB:.0f} MB, "
+            + f"model_vram={model_cur_vram_bytes / MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
             # + f"vram_total={int(self._max_vram_cache_size * GB)/MB:.0f} MB, "
-            + f"vram_available={(vram_available/MB):.0f} MB, "
+            + f"vram_available={(vram_available / MB):.0f} MB, "
         )
 
     def _offload_unlocked_models(self, vram_bytes_required: int, working_mem_bytes: Optional[int] = None) -> int:
@@ -509,7 +511,7 @@ class ModelCache:
             int: The number of bytes freed based on believed model sizes. The actual change in VRAM may be different.
         """
         self._logger.debug(
-            f"Offloading unlocked models with goal of making room for {vram_bytes_required/MB:.2f}MB of VRAM."
+            f"Offloading unlocked models with goal of making room for {vram_bytes_required / MB:.2f}MB of VRAM."
         )
         vram_bytes_freed = 0
         # TODO(ryand): Give more thought to the offloading policy used here.
@@ -527,7 +529,7 @@ class ModelCache:
             cache_entry_bytes_freed = self._move_model_to_ram(cache_entry, vram_bytes_to_free)
             if cache_entry_bytes_freed > 0:
                 self._logger.debug(
-                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed/MB):.0f} MB."
+                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed / MB):.0f} MB."
                 )
             vram_bytes_freed += cache_entry_bytes_freed
 
@@ -609,7 +611,7 @@ class ModelCache:
         external references to the model, there's nothing that the cache can do about it, and those models will not be
         garbage-collected.
         """
-        self._logger.debug(f"Making room for {bytes_needed/MB:.2f}MB of RAM.")
+        self._logger.debug(f"Making room for {bytes_needed / MB:.2f}MB of RAM.")
         self._log_cache_state(title="Before dropping models:")
 
         ram_bytes_available = self._get_ram_available()
@@ -625,7 +627,7 @@ class ModelCache:
             if not cache_entry.is_locked:
                 ram_bytes_freed += cache_entry.cached_model.total_bytes()
                 self._logger.debug(
-                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes()/MB):.2f}MB."
+                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes() / MB):.2f}MB."
                 )
                 self._delete_cache_entry(cache_entry)
                 del cache_entry
@@ -650,7 +652,7 @@ class ModelCache:
             gc.collect()
 
         TorchDevice.empty_cache()
-        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed/MB:.2f}MB of RAM.")
+        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed / MB:.2f}MB of RAM.")
         self._log_cache_state(title="After dropping models:")
 
     def _delete_cache_entry(self, cache_entry: CacheRecord) -> None:

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

@@ -25,6 +25,7 @@ from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import (
 )
 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,
@@ -39,6 +40,7 @@ from invokeai.backend.model_manager.config import (
     CLIPEmbedDiffusersConfig,
     ControlNetCheckpointConfig,
     ControlNetDiffusersConfig,
+    FluxReduxConfig,
     IPAdapterCheckpointConfig,
     MainBnbQuantized4bCheckpointConfig,
     MainCheckpointConfig,
@@ -391,3 +393,25 @@ class FluxIpAdapterModel(ModelLoader):
 
         model.load_xlabs_state_dict(sd, assign=True)
         return model
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.FluxRedux, format=ModelFormat.Checkpoint)
+class FluxReduxModelLoader(ModelLoader):
+    """Class to load FLUX Redux models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, FluxReduxConfig):
+            raise ValueError(f"Unexpected model config type: {type(config)}.")
+
+        sd = load_file(Path(config.path))
+
+        with accelerate.init_empty_weights():
+            model = FluxReduxModel()
+
+        model.load_state_dict(sd, assign=True)
+        model.to(dtype=torch.bfloat16)
+        return model

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

@@ -0,0 +1,32 @@
+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.sig_lip.sig_lip_pipeline import SigLipPipeline
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.SigLIP, format=ModelFormat.Diffusers)
+class SigLIPModelLoader(ModelLoader):
+    """Class for loading SigLIP models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if submodel_type is not None:
+            raise ValueError("Unexpected submodel requested for LLaVA OneVision model.")
+
+        model_path = Path(config.path)
+        model = SigLipPipeline.load_from_path(model_path)
+        model.to(dtype=self._torch_dtype)
+        return model

invokeai/backend/model_manager/load/model_util.py

@@ -18,6 +18,7 @@ from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 from invokeai.backend.model_manager.config 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
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 from invokeai.backend.util.calc_tensor_size import calc_tensor_size
@@ -48,6 +49,7 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
             GroundingDinoPipeline,
             SegmentAnythingPipeline,
             DepthAnythingPipeline,
+            SigLipPipeline,
         ),
     ):
         return model.calc_size()

invokeai/backend/model_manager/merge.py

@@ -115,19 +115,19 @@ class ModelMerger(object):
         base_models: Set[BaseModelType] = set()
         variant = None if self._installer.app_config.precision == "float32" else "fp16"
 
-        assert (
-            len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference
-        ), "When merging three models, only the 'add_difference' merge method is supported"
+        assert len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference, (
+            "When merging three models, only the 'add_difference' merge method is supported"
+        )
 
         for key in model_keys:
             info = store.get_model(key)
             model_names.append(info.name)
-            assert isinstance(
-                info, MainDiffusersConfig
-            ), f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
-            assert info.variant == ModelVariantType(
-                "normal"
-            ), f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
+            assert isinstance(info, MainDiffusersConfig), (
+                f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
+            )
+            assert info.variant == ModelVariantType("normal"), (
+                f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
+            )
 
             # tally base models used
             base_models.add(info.base)

invokeai/backend/model_manager/probe.py

@@ -15,6 +15,7 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_xlabs_controlnet,
 )
 from invokeai.backend.flux.ip_adapter.state_dict_utils import is_state_dict_xlabs_ip_adapter
+from invokeai.backend.flux.redux.flux_redux_state_dict_utils import is_state_dict_likely_flux_redux
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
@@ -139,6 +140,7 @@ class ModelProbe(object):
         "FluxControlNetModel": ModelType.ControlNet,
         "SD3Transformer2DModel": ModelType.Main,
         "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
+        "SiglipModel": ModelType.SigLIP,
     }
 
     TYPE2VARIANT: Dict[ModelType, Callable[[str], Optional[AnyVariant]]] = {ModelType.CLIPEmbed: get_clip_variant_type}
@@ -267,6 +269,9 @@ class ModelProbe(object):
         if isinstance(ckpt, dict) and is_state_dict_likely_flux_control(ckpt):
             return ModelType.ControlLoRa
 
+        if isinstance(ckpt, dict) and is_state_dict_likely_flux_redux(ckpt):
+            return ModelType.FluxRedux
+
         for key in [str(k) for k in ckpt.keys()]:
             if key.startswith(
                 (
@@ -752,6 +757,16 @@ class SpandrelImageToImageCheckpointProbe(CheckpointProbeBase):
         return BaseModelType.Any
 
 
+class SigLIPCheckpointProbe(CheckpointProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        raise NotImplementedError()
+
+
+class FluxReduxCheckpointProbe(CheckpointProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.Flux
+
+
 ########################################################
 # classes for probing folders
 #######################################################
@@ -1022,6 +1037,16 @@ class SpandrelImageToImageFolderProbe(FolderProbeBase):
         raise NotImplementedError()
 
 
+class SigLIPFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        return BaseModelType.Any
+
+
+class FluxReduxFolderProbe(FolderProbeBase):
+    def get_base_type(self) -> BaseModelType:
+        raise NotImplementedError()
+
+
 class T2IAdapterFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         config_file = self.model_path / "config.json"
@@ -1055,6 +1080,8 @@ ModelProbe.register_probe("diffusers", ModelType.CLIPEmbed, CLIPEmbedFolderProbe
 ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.SpandrelImageToImage, SpandrelImageToImageFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.SigLIP, SigLIPFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.FluxRedux, FluxReduxFolderProbe)
 
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.VAE, VaeCheckpointProbe)
@@ -1066,5 +1093,7 @@ ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpoint
 ModelProbe.register_probe("checkpoint", ModelType.CLIPVision, CLIPVisionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.T2IAdapter, T2IAdapterCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.SpandrelImageToImage, SpandrelImageToImageCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.SigLIP, SigLIPCheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.FluxRedux, FluxReduxCheckpointProbe)
 
 ModelProbe.register_probe("onnx", ModelType.ONNX, ONNXFolderProbe)

invokeai/backend/model_manager/starter_models.py

@@ -593,6 +593,26 @@ swinir = StarterModel(
 
 # endregion
 
+# region SigLIP
+siglip = StarterModel(
+    name="SigLIP - google/siglip-so400m-patch14-384",
+    base=BaseModelType.Any,
+    source="google/siglip-so400m-patch14-384",
+    description="A SigLIP model (used by FLUX Redux).",
+    type=ModelType.SigLIP,
+)
+# endregion
+
+# region FLUX Redux
+flux_redux = StarterModel(
+    name="FLUX Redux",
+    base=BaseModelType.Flux,
+    source="black-forest-labs/FLUX.1-Redux-dev::flux1-redux-dev.safetensors",
+    description="FLUX Redux model (for image variation).",
+    type=ModelType.FluxRedux,
+    dependencies=[siglip],
+)
+# endregion
 
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
@@ -661,6 +681,8 @@ STARTER_MODELS: list[StarterModel] = [
     t5_base_encoder,
     t5_8b_quantized_encoder,
     clip_l_encoder,
+    siglip,
+    flux_redux,
 ]
 
 sd1_bundle: list[StarterModel] = [
@@ -708,6 +730,7 @@ flux_bundle: list[StarterModel] = [
     ip_adapter_flux,
     flux_canny_control_lora,
     flux_depth_control_lora,
+    flux_redux,
 ]
 
 STARTER_BUNDLES: dict[str, list[StarterModel]] = {

invokeai/backend/model_manager/util/libc_util.py

@@ -37,19 +37,21 @@ class Struct_mallinfo2(ctypes.Structure):
 
     def __str__(self) -> str:
         s = ""
-        s += f"{'arena': <10}= {(self.arena/2**30):15.5f}   # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
+        s += (
+            f"{'arena': <10}= {(self.arena / 2**30):15.5f}   # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
+        )
         s += f"{'ordblks': <10}= {(self.ordblks): >15}   # Number of free chunks\n"
         s += f"{'smblks': <10}= {(self.smblks): >15}   # Number of free fastbin blocks \n"
         s += f"{'hblks': <10}= {(self.hblks): >15}   # Number of mmapped regions \n"
-        s += f"{'hblkhd': <10}= {(self.hblkhd/2**30):15.5f}   # Space allocated in mmapped regions (GB)\n"
+        s += f"{'hblkhd': <10}= {(self.hblkhd / 2**30):15.5f}   # Space allocated in mmapped regions (GB)\n"
         s += f"{'usmblks': <10}= {(self.usmblks): >15}   # Unused\n"
-        s += f"{'fsmblks': <10}= {(self.fsmblks/2**30):15.5f}   # Space in freed fastbin blocks (GB)\n"
+        s += f"{'fsmblks': <10}= {(self.fsmblks / 2**30):15.5f}   # Space in freed fastbin blocks (GB)\n"
         s += (
-            f"{'uordblks': <10}= {(self.uordblks/2**30):15.5f}   # Space used by in-use allocations (non-mmapped)"
+            f"{'uordblks': <10}= {(self.uordblks / 2**30):15.5f}   # Space used by in-use allocations (non-mmapped)"
             " (GB)\n"
         )
-        s += f"{'fordblks': <10}= {(self.fordblks/2**30):15.5f}   # Space in free blocks (non-mmapped) (GB)\n"
-        s += f"{'keepcost': <10}= {(self.keepcost/2**30):15.5f}   # Top-most, releasable space (GB)\n"
+        s += f"{'fordblks': <10}= {(self.fordblks / 2**30):15.5f}   # Space in free blocks (non-mmapped) (GB)\n"
+        s += f"{'keepcost': <10}= {(self.keepcost / 2**30):15.5f}   # Top-most, releasable space (GB)\n"
         return s
 
 

invokeai/backend/patches/lora_conversions/sdxl_lora_conversion_utils.py

@@ -73,36 +73,36 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
         for j in range(2):
             # loop over resnets/attentions for downblocks
             hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
+            sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
             unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
 
             if i < 3:
                 # no attention layers in down_blocks.3
                 hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
+                sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
                 unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
 
         for j in range(3):
             # loop over resnets/attentions for upblocks
             hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-            sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
+            sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
             unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
 
             # if i > 0: commentout for sdxl
             # no attention layers in up_blocks.0
             hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
+            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
             unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
 
         if i < 3:
             # no downsample in down_blocks.3
             hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
+            sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
             unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
 
             # no upsample in up_blocks.3
             hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-            sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}."  # change for sdxl
+            sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{2}."  # change for sdxl
             unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
 
     hf_mid_atn_prefix = "mid_block.attentions.0."
@@ -111,7 +111,7 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
 
     for j in range(2):
         hf_mid_res_prefix = f"mid_block.resnets.{j}."
-        sd_mid_res_prefix = f"middle_block.{2*j}."
+        sd_mid_res_prefix = f"middle_block.{2 * j}."
         unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
 
     unet_conversion_map_resnet = [
@@ -133,13 +133,13 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
             unet_conversion_map.append((sd, hf))
 
     for j in range(2):
-        hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
-        sd_time_embed_prefix = f"time_embed.{j*2}."
+        hf_time_embed_prefix = f"time_embedding.linear_{j + 1}."
+        sd_time_embed_prefix = f"time_embed.{j * 2}."
         unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
 
     for j in range(2):
-        hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
-        sd_label_embed_prefix = f"label_emb.0.{j*2}."
+        hf_label_embed_prefix = f"add_embedding.linear_{j + 1}."
+        sd_label_embed_prefix = f"label_emb.0.{j * 2}."
         unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
 
     unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))

invokeai/backend/sig_lip/sig_lip_pipeline.py

@@ -0,0 +1,43 @@
+from pathlib import Path
+from typing import Optional
+
+import torch
+from PIL import Image
+from transformers import SiglipImageProcessor, SiglipVisionModel
+
+from invokeai.backend.raw_model import RawModel
+
+
+class SigLipPipeline(RawModel):
+    """A wrapper for a SigLIP model + processor."""
+
+    def __init__(
+        self,
+        siglip_processor: SiglipImageProcessor,
+        siglip_model: SiglipVisionModel,
+    ):
+        self._siglip_processor = siglip_processor
+        self._siglip_model = siglip_model
+
+    @classmethod
+    def load_from_path(cls, path: str | Path):
+        siglip_model = SiglipVisionModel.from_pretrained(path, local_files_only=True)
+        assert isinstance(siglip_model, SiglipVisionModel)
+        siglip_processor = SiglipImageProcessor.from_pretrained(path, local_files_only=True)
+        assert isinstance(siglip_processor, SiglipImageProcessor)
+        return cls(siglip_processor, siglip_model)
+
+    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
+        self._siglip_model.to(device=device, dtype=dtype)
+
+    def encode_image(self, x: Image.Image, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+        imgs = self._siglip_processor.preprocess(images=[x], do_resize=True, return_tensors="pt", do_convert_rgb=True)
+        encoded_x = self._siglip_model(**imgs.to(device=device, dtype=dtype)).last_hidden_state
+        return encoded_x
+
+    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._siglip_model)

invokeai/frontend/web/package.json

@@ -60,14 +60,13 @@
     "@fontsource-variable/inter": "^5.1.0",
     "@invoke-ai/ui-library": "^0.0.46",
     "@nanostores/react": "^0.7.3",
-    "@reduxjs/toolkit": "2.5.1",
+    "@reduxjs/toolkit": "2.6.1",
     "@roarr/browser-log-writer": "^1.3.0",
     "@xyflow/react": "^12.4.2",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.0.0",
     "compare-versions": "^6.1.1",
-    "dateformat": "^5.0.3",
     "fracturedjsonjs": "^4.0.2",
     "framer-motion": "^11.10.0",
     "i18next": "^23.15.1",
@@ -101,7 +100,7 @@
     "react-resizable-panels": "^2.1.4",
     "react-textarea-autosize": "^8.5.7",
     "react-use": "^17.5.1",
-    "react-virtuoso": "^4.10.4",
+    "react-virtuoso": "^4.12.5",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
     "redux-undo": "^1.1.0",
@@ -131,7 +130,6 @@
     "@storybook/react": "^8.3.4",
     "@storybook/react-vite": "^8.5.5",
     "@storybook/theming": "^8.3.4",
-    "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
     "@types/node": "^20.16.10",
     "@types/react": "^18.3.11",

invokeai/frontend/web/pnpm-lock.yaml

@@ -30,8 +30,8 @@ dependencies:
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.3)(react@18.3.1)
   '@reduxjs/toolkit':
-    specifier: 2.5.1
-    version: 2.5.1(react-redux@9.1.2)(react@18.3.1)
+    specifier: 2.6.1
+    version: 2.6.1(react-redux@9.1.2)(react@18.3.1)
   '@roarr/browser-log-writer':
     specifier: ^1.3.0
     version: 1.3.0
@@ -50,9 +50,6 @@ dependencies:
   compare-versions:
     specifier: ^6.1.1
     version: 6.1.1
-  dateformat:
-    specifier: ^5.0.3
-    version: 5.0.3
   fracturedjsonjs:
     specifier: ^4.0.2
     version: 4.0.2
@@ -153,8 +150,8 @@ dependencies:
     specifier: ^17.5.1
     version: 17.5.1(react-dom@18.3.1)(react@18.3.1)
   react-virtuoso:
-    specifier: ^4.10.4
-    version: 4.10.4(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^4.12.5
+    version: 4.12.5(react-dom@18.3.1)(react@18.3.1)
   redux-dynamic-middlewares:
     specifier: ^2.2.0
     version: 2.2.0
@@ -226,9 +223,6 @@ devDependencies:
   '@storybook/theming':
     specifier: ^8.3.4
     version: 8.3.4(storybook@8.3.4)
-  '@types/dateformat':
-    specifier: ^5.0.2
-    version: 5.0.2
   '@types/lodash-es':
     specifier: ^4.17.12
     version: 4.17.12
@@ -2317,8 +2311,8 @@ packages:
       - supports-color
     dev: true
 
-  /@reduxjs/toolkit@2.5.1(react-redux@9.1.2)(react@18.3.1):
-    resolution: {integrity: sha512-UHhy3p0oUpdhnSxyDjaRDYaw8Xra75UiLbCiRozVPHjfDwNYkh0TsVm/1OmTW8Md+iDAJmYPWUKMvsMc2GtpNg==}
+  /@reduxjs/toolkit@2.6.1(react-redux@9.1.2)(react@18.3.1):
+    resolution: {integrity: sha512-SSlIqZNYhqm/oMkXbtofwZSt9lrncblzo6YcZ9zoX+zLngRBrCOjK4lNLdkNucJF58RHOWrD9txT3bT3piH7Zw==}
     peerDependencies:
       react: ^16.9.0 || ^17.0.0 || ^18 || ^19
       react-redux: ^7.2.1 || ^8.1.3 || ^9.0.0
@@ -3355,10 +3349,6 @@ packages:
       '@types/d3-selection': 3.0.10
     dev: false
 
-  /@types/dateformat@5.0.2:
-    resolution: {integrity: sha512-M95hNBMa/hnwErH+a+VOD/sYgTmo15OTYTM2Hr52/e0OdOuY+Crag+kd3/ioZrhg0WGbl9Sm3hR7UU+MH6rfOw==}
-    dev: true
-
   /@types/diff-match-patch@1.0.36:
     resolution: {integrity: sha512-xFdR6tkm0MWvBfO8xXCSsinYxHcqkQUlcHeSpMC2ukzOb6lwQAfDmW+Qt0AvlGd8HpsS28qKsB+oPeJn9I39jg==}
     dev: false
@@ -3435,7 +3425,7 @@ packages:
   /@types/lodash.mergewith@4.6.7:
     resolution: {integrity: sha512-3m+lkO5CLRRYU0fhGRp7zbsGi6+BZj0uTVSwvcKU+nSlhjA9/QRNfuSGnD2mX6hQA7ZbmcCkzk5h4ZYGOtk14A==}
     dependencies:
-      '@types/lodash': 4.17.15
+      '@types/lodash': 4.17.16
     dev: false
 
   /@types/lodash.mergewith@4.6.9:
@@ -3447,8 +3437,8 @@ packages:
   /@types/lodash@4.17.10:
     resolution: {integrity: sha512-YpS0zzoduEhuOWjAotS6A5AVCva7X4lVlYLF0FYHAY9sdraBfnatttHItlWeZdGhuEkf+OzMNg2ZYAx8t+52uQ==}
 
-  /@types/lodash@4.17.15:
-    resolution: {integrity: sha512-w/P33JFeySuhN6JLkysYUK2gEmy9kHHFN7E8ro0tkfmlDOgxBDzWEZ/J8cWA+fHqFevpswDTFZnDx+R9lbL6xw==}
+  /@types/lodash@4.17.16:
+    resolution: {integrity: sha512-HX7Em5NYQAXKW+1T+FiuG27NGwzJfCX3s1GjOa7ujxZa52kjJLOr4FUxT+giF6Tgxv1e+/czV/iTtBw27WTU9g==}
     dev: false
 
   /@types/mdx@2.0.13:
@@ -4856,11 +4846,6 @@ packages:
       '@babel/runtime': 7.25.7
     dev: true
 
-  /dateformat@5.0.3:
-    resolution: {integrity: sha512-Kvr6HmPXUMerlLcLF+Pwq3K7apHpYmGDVqrxcDasBg86UcKeTSNWbEzU8bwdXnxnR44FtMhJAxI4Bov6Y/KUfA==}
-    engines: {node: '>=12.20'}
-    dev: false
-
   /de-indent@1.0.2:
     resolution: {integrity: sha512-e/1zu3xH5MQryN2zdVaF0OrdNLUbvWxzMbi+iNA6Bky7l1RoP8a2fIbRocyHclXt/arDrrR6lL3TqFD9pMQTsg==}
     dev: true
@@ -7913,12 +7898,11 @@ packages:
       tslib: 2.7.0
     dev: false
 
-  /react-virtuoso@4.10.4(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-G/gprhTbK+lzMxoo/iStcZxVEGph/cIhc3WANEpt92RuMw+LiCZOmBfKoeoZOHlm/iyftTrDJhGaTCpxyucnkQ==}
-    engines: {node: '>=10'}
+  /react-virtuoso@4.12.5(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-YeCbRRsC9CLf0buD0Rct7WsDbzf+yBU1wGbo05/XjbcN2nJuhgh040m3y3+6HVogTZxEqVm45ac9Fpae4/MxRQ==}
     peerDependencies:
-      react: '>=16 || >=17 || >= 18'
-      react-dom: '>=16 || >=17 || >= 18'
+      react: '>=16 || >=17 || >= 18 || >= 19'
+      react-dom: '>=16 || >=17 || >= 18 || >=19'
     dependencies:
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)

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

@@ -113,7 +113,8 @@
         "end": "Ende",
         "layout": "Layout",
         "board": "Ordner",
-        "combinatorial": "Kombinatorisch"
+        "combinatorial": "Kombinatorisch",
+        "saveChanges": "Änderungen speichern"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -761,7 +762,16 @@
         "workflowDeleted": "Arbeitsablauf gelöscht",
         "errorCopied": "Fehler kopiert",
         "layerCopiedToClipboard": "Ebene in die Zwischenablage kopiert",
-        "sentToCanvas": "An Leinwand gesendet"
+        "sentToCanvas": "An Leinwand gesendet",
+        "problemDeletingWorkflow": "Problem beim Löschen des Arbeitsablaufs",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Es darf maximal 1 PNG- oder JPEG-Bild sein.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Es dürfen maximal {{count}} PNG- oder JPEG-Bilder sein.",
+        "problemRetrievingWorkflow": "Problem beim Abrufen des Arbeitsablaufs",
+        "uploadFailedInvalidUploadDesc": "Müssen PNG- oder JPEG-Bilder sein.",
+        "pasteSuccess": "Eingefügt in {{destination}}",
+        "pasteFailed": "Einfügen fehlgeschlagen",
+        "unableToCopy": "Kopieren nicht möglich",
+        "unableToCopyDesc_theseSteps": "diese Schritte"
     },
     "accessibility": {
         "uploadImage": "Bild hochladen",
@@ -1314,7 +1324,8 @@
         "nodeName": "Knotenname",
         "description": "Beschreibung",
         "loadWorkflowDesc": "Arbeitsablauf laden?",
-        "loadWorkflowDesc2": "Ihr aktueller Arbeitsablauf enthält nicht gespeicherte Änderungen."
+        "loadWorkflowDesc2": "Ihr aktueller Arbeitsablauf enthält nicht gespeicherte Änderungen.",
+        "loadingTemplates": "Lade {{name}}"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",

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

@@ -116,6 +116,8 @@
         "dontShowMeThese": "Don't show me these",
         "editor": "Editor",
         "error": "Error",
+        "error_withCount_one": "{{count}} error",
+        "error_withCount_other": "{{count}} errors",
         "file": "File",
         "folder": "Folder",
         "format": "format",
@@ -149,6 +151,7 @@
         "safetensors": "Safetensors",
         "save": "Save",
         "saveAs": "Save As",
+        "saveChanges": "Save Changes",
         "settingsLabel": "Settings",
         "simple": "Simple",
         "somethingWentWrong": "Something went wrong",
@@ -761,6 +764,7 @@
         "deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
         "description": "Description",
         "edit": "Edit",
+        "fluxRedux": "FLUX Redux",
         "height": "Height",
         "huggingFace": "HuggingFace",
         "huggingFacePlaceholder": "owner/model-name",
@@ -835,6 +839,7 @@
         "settings": "Settings",
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
+        "sigLip": "SigLIP",
         "spandrelImageToImage": "Image to Image (Spandrel)",
         "starterBundles": "Starter Bundles",
         "starterBundleHelpText": "Easily install all models needed to get started with a base model, including a main model, controlnets, IP adapters, and more. Selecting a bundle will skip any models that you already have installed.",
@@ -1202,6 +1207,7 @@
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
         "enableModelDescriptions": "Enable Model Descriptions in Dropdowns",
+        "enableHighlightFocusedRegions": "Highlight Focused Regions",
         "modelDescriptionsDisabled": "Model Descriptions in Dropdowns Disabled",
         "modelDescriptionsDisabledDesc": "Model descriptions in dropdowns have been disabled. Enable them in Settings.",
         "enableInvisibleWatermark": "Enable Invisible Watermark",
@@ -1283,9 +1289,9 @@
         "somethingWentWrong": "Something Went Wrong",
         "uploadFailed": "Upload failed",
         "imagesWillBeAddedTo": "Uploaded images will be added to board {{boardName}}'s assets.",
-        "uploadFailedInvalidUploadDesc_withCount_one": "Must be maximum of 1 PNG or JPEG image.",
-        "uploadFailedInvalidUploadDesc_withCount_other": "Must be maximum of {{count}} PNG or JPEG images.",
-        "uploadFailedInvalidUploadDesc": "Must be PNG or JPEG images.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Must be maximum of 1 PNG, JPEG or WEBP image.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Must be maximum of {{count}} PNG, JPEG or WEBP images.",
+        "uploadFailedInvalidUploadDesc": "Must be PNG, JPEG or WEBP images.",
         "workflowLoaded": "Workflow Loaded",
         "problemRetrievingWorkflow": "Problem Retrieving Workflow",
         "workflowDeleted": "Workflow Deleted",
@@ -1682,12 +1688,22 @@
         "created": "Created",
         "descending": "Descending",
         "workflows": "Workflows",
-        "workflowLibrary": "Library",
+        "workflowLibrary": "Workflow Library",
+        "loadMore": "Load More",
+        "allLoaded": "All Workflows Loaded",
+        "searchPlaceholder": "Search by name, description or tags",
+        "filterByTags": "Filter by Tags",
+        "yourWorkflows": "Your Workflows",
+        "recentlyOpened": "Recently Opened",
+        "noRecentWorkflows": "No Recent Workflows",
+        "private": "Private",
+        "shared": "Shared",
+        "browseWorkflows": "Browse Workflows",
+        "deselectAll": "Deselect All",
         "opened": "Opened",
         "openWorkflow": "Open Workflow",
         "updated": "Updated",
         "uploadWorkflow": "Load from File",
-        "uploadAndSaveWorkflow": "Upload to Library",
         "deleteWorkflow": "Delete Workflow",
         "deleteWorkflow2": "Are you sure you want to delete this workflow? This cannot be undone.",
         "unnamedWorkflow": "Unnamed Workflow",
@@ -1714,11 +1730,14 @@
         "loadWorkflow": "$t(common.load) Workflow",
         "autoLayout": "Auto Layout",
         "edit": "Edit",
+        "view": "View",
         "download": "Download",
         "copyShareLink": "Copy Share Link",
         "copyShareLinkForWorkflow": "Copy Share Link for Workflow",
         "delete": "Delete",
         "openLibrary": "Open Library",
+        "workflowThumbnail": "Workflow Thumbnail",
+        "saveChanges": "Save Changes",
         "builder": {
             "deleteAllElements": "Delete All Form Elements",
             "resetAllNodeFields": "Reset All Node Fields",
@@ -1727,6 +1746,8 @@
             "row": "Row",
             "column": "Column",
             "container": "Container",
+            "containerRowLayout": "Container (row layout)",
+            "containerColumnLayout": "Container (column layout)",
             "heading": "Heading",
             "text": "Text",
             "divider": "Divider",
@@ -1747,7 +1768,9 @@
             "containerPlaceholder": "Empty Container",
             "headingPlaceholder": "Empty Heading",
             "textPlaceholder": "Empty Text",
-            "workflowBuilderAlphaWarning": "The workflow builder is currently in alpha. There may be breaking changes before the stable release."
+            "workflowBuilderAlphaWarning": "The workflow builder is currently in alpha. There may be breaking changes before the stable release.",
+            "minimum": "Minimum",
+            "maximum": "Maximum"
         }
     },
     "controlLayers": {
@@ -2299,6 +2322,7 @@
     "newUserExperience": {
         "toGetStartedLocal": "To get started, make sure to download or import models needed to run Invoke. Then, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. Select a prompt template to improve results. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
         "toGetStarted": "To get started, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. Select a prompt template to improve results. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
+        "toGetStartedWorkflow": "To get started, fill in the fields on the left and press <StrongComponent>Invoke</StrongComponent> to generate your image. Want to explore more workflows? Click the <StrongComponent>folder icon</StrongComponent> next to the workflow title to see a list of other templates you can try.",
         "gettingStartedSeries": "Want more guidance? Check out our <LinkComponent>Getting Started Series</LinkComponent> for tips on unlocking the full potential of the Invoke Studio.",
         "lowVRAMMode": "For best performance, follow our <LinkComponent>Low VRAM guide</LinkComponent>.",
         "noModelsInstalled": "It looks like you don't have any models installed! You can <DownloadStarterModelsButton>download a starter model bundle</DownloadStarterModelsButton> or <ImportModelsButton>import models</ImportModelsButton>."
@@ -2306,8 +2330,8 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "Memory Management: New setting for users with Nvidia GPUs to reduce VRAM usage.",
-            "Performance: Continued improvements to overall application performance and responsiveness."
+            "Workflows: New and improved Workflow Library.",
+            "FLUX: Support for FLUX Redux in Workflows and Canvas."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

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

@@ -1771,7 +1771,6 @@
         "projectWorkflows": "Workflows du projet",
         "copyShareLink": "Copier le lien de partage",
         "chooseWorkflowFromLibrary": "Choisir le Workflow dans la Bibliothèque",
-        "uploadAndSaveWorkflow": "Importer dans la bibliothèque",
         "edit": "Modifer",
         "deleteWorkflow2": "Êtes-vous sûr de vouloir supprimer ce Workflow ? Cette action ne peut pas être annulé.",
         "download": "Télécharger",

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

@@ -109,7 +109,11 @@
         "board": "Bacheca",
         "layout": "Schema",
         "row": "Riga",
-        "column": "Colonna"
+        "column": "Colonna",
+        "saveChanges": "Salva modifiche",
+        "error_withCount_one": "{{count}} errore",
+        "error_withCount_many": "{{count}} errori",
+        "error_withCount_other": "{{count}} errori"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -784,7 +788,7 @@
         "serverError": "Errore del Server",
         "connected": "Connesso al server",
         "canceled": "Elaborazione annullata",
-        "uploadFailedInvalidUploadDesc": "Devono essere immagini PNG o JPEG.",
+        "uploadFailedInvalidUploadDesc": "Devono essere immagini PNG, JPEG o WEBP.",
         "parameterSet": "Parametro richiamato",
         "parameterNotSet": "Parametro non richiamato",
         "problemCopyingImage": "Impossibile copiare l'immagine",
@@ -835,9 +839,9 @@
         "linkCopied": "Collegamento copiato",
         "addedToUncategorized": "Aggiunto alle risorse della bacheca $t(boards.uncategorized)",
         "imagesWillBeAddedTo": "Le immagini caricate verranno aggiunte alle risorse della bacheca {{boardName}}.",
-        "uploadFailedInvalidUploadDesc_withCount_one": "Devi caricare al massimo 1 immagine PNG o JPEG.",
-        "uploadFailedInvalidUploadDesc_withCount_many": "Devi caricare al massimo {{count}} immagini PNG o JPEG.",
-        "uploadFailedInvalidUploadDesc_withCount_other": "Devi caricare al massimo {{count}} immagini PNG o JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Devi caricare al massimo 1 immagine PNG, JPEG o WEBP.",
+        "uploadFailedInvalidUploadDesc_withCount_many": "Devi caricare al massimo {{count}} immagini PNG, JPEG o WEBP.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Devi caricare al massimo {{count}} immagini PNG, JPEG o WEBP.",
         "outOfMemoryErrorDescLocal": "Segui la nostra <LinkComponent>guida per bassa VRAM</LinkComponent> per ridurre gli OOM.",
         "pasteFailed": "Incolla non riuscita",
         "pasteSuccess": "Incollato su {{destination}}",
@@ -1704,7 +1708,7 @@
         "saveWorkflow": "Salva flusso di lavoro",
         "openWorkflow": "Apri flusso di lavoro",
         "clearWorkflowSearchFilter": "Cancella il filtro di ricerca del flusso di lavoro",
-        "workflowLibrary": "Libreria",
+        "workflowLibrary": "Libreria flussi di lavoro",
         "workflowSaved": "Flusso di lavoro salvato",
         "unnamedWorkflow": "Flusso di lavoro senza nome",
         "savingWorkflow": "Salvataggio del flusso di lavoro...",
@@ -1734,7 +1738,6 @@
         "userWorkflows": "Flussi di lavoro utente",
         "projectWorkflows": "Flussi di lavoro del progetto",
         "defaultWorkflows": "Flussi di lavoro predefiniti",
-        "uploadAndSaveWorkflow": "Carica nella libreria",
         "chooseWorkflowFromLibrary": "Scegli il flusso di lavoro dalla libreria",
         "deleteWorkflow2": "Vuoi davvero eliminare questo flusso di lavoro? Questa operazione non può essere annullata.",
         "edit": "Modifica",
@@ -1771,8 +1774,24 @@
             "divider": "Divisore",
             "container": "Contenitore",
             "text": "Testo",
-            "numberInput": "Ingresso numerico"
-        }
+            "numberInput": "Ingresso numerico",
+            "containerRowLayout": "Contenitore (disposizione riga)",
+            "containerColumnLayout": "Contenitore (disposizione colonna)"
+        },
+        "loadMore": "Carica altro",
+        "searchPlaceholder": "Cerca per nome, descrizione o etichetta",
+        "filterByTags": "Filtra per etichetta",
+        "shared": "Condiviso",
+        "browseWorkflows": "Sfoglia i flussi di lavoro",
+        "allLoaded": "Tutti i flussi di lavoro caricati",
+        "saveChanges": "Salva modifiche",
+        "yourWorkflows": "I tuoi flussi di lavoro",
+        "recentlyOpened": "Aperto di recente",
+        "workflowThumbnail": "Miniatura del flusso di lavoro",
+        "private": "Privato",
+        "deselectAll": "Deseleziona tutto",
+        "noRecentWorkflows": "Nessun flusso di lavoro recente",
+        "view": "Visualizza"
     },
     "accordions": {
         "compositing": {
@@ -2322,7 +2341,8 @@
         "toGetStarted": "Per iniziare, inserisci un prompt nella casella e fai clic su <StrongComponent>Invoke</StrongComponent> per generare la tua prima immagine. Seleziona un modello di prompt per migliorare i risultati. Puoi scegliere di salvare le tue immagini direttamente nella <StrongComponent>Galleria</StrongComponent> o modificarle nella <StrongComponent>Tela</StrongComponent>.",
         "noModelsInstalled": "Sembra che non hai installato alcun modello! Puoi <DownloadStarterModelsButton>scaricare un pacchetto di modelli di avvio</DownloadStarterModelsButton> o <ImportModelsButton>importare modelli</ImportModelsButton>.",
         "toGetStartedLocal": "Per iniziare, assicurati di scaricare o importare i modelli necessari per eseguire Invoke. Quindi, inserisci un prompt nella casella e fai clic su <StrongComponent>Invoke</StrongComponent> per generare la tua prima immagine. Seleziona un modello di prompt per migliorare i risultati. Puoi scegliere di salvare le tue immagini direttamente nella <StrongComponent>Galleria</StrongComponent> o modificarle nella <StrongComponent>Tela</StrongComponent>.",
-        "lowVRAMMode": "Per prestazioni ottimali, segui la nostra <LinkComponent>guida per bassa VRAM</LinkComponent>."
+        "lowVRAMMode": "Per prestazioni ottimali, segui la nostra <LinkComponent>guida per bassa VRAM</LinkComponent>.",
+        "toGetStartedWorkflow": "Per iniziare, compila i campi a sinistra e premi <StrongComponent>Invoke</StrongComponent> per generare la tua immagine. Vuoi esplorare altri flussi di lavoro? Fai clic sull'<StrongComponent>icona della cartella</StrongComponent> accanto al titolo del flusso di lavoro per visualizzare un elenco di altri modelli che puoi provare."
     },
     "whatsNew": {
         "whatsNewInInvoke": "Novità in Invoke",
@@ -2330,8 +2350,8 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "Editor del flusso di lavoro: nuovo generatore di moduli trascina-e-rilascia per una creazione più facile del flusso di lavoro.",
-            "Altri miglioramenti: messa in coda dei lotti più rapida, migliore ampliamento, selettore colore migliorato e nodi metadati."
+            "Gestione della memoria: nuova impostazione per gli utenti con GPU Nvidia per ridurre l'utilizzo della VRAM.",
+            "Prestazioni: continui miglioramenti alle prestazioni e alla reattività complessive dell'applicazione."
         ]
     },
     "system": {

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

@@ -1566,7 +1566,6 @@
         "defaultWorkflows": "Стандартные рабочие процессы",
         "deleteWorkflow2": "Вы уверены, что хотите удалить этот рабочий процесс? Это нельзя отменить.",
         "chooseWorkflowFromLibrary": "Выбрать рабочий процесс из библиотеки",
-        "uploadAndSaveWorkflow": "Загрузить в библиотеку",
         "edit": "Редактировать",
         "download": "Скачать",
         "copyShareLink": "Скопировать ссылку на общий доступ",

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

@@ -235,7 +235,8 @@
         "column": "Cột",
         "layout": "Bố Cục",
         "row": "Hàng",
-        "board": "Bảng"
+        "board": "Bảng",
+        "saveChanges": "Lưu Thay Đổi"
     },
     "prompt": {
         "addPromptTrigger": "Thêm Prompt Trigger",
@@ -766,7 +767,9 @@
         "urlUnauthorizedErrorMessage2": "Tìm hiểu thêm.",
         "urlForbidden": "Bạn không có quyền truy cập vào model này",
         "urlForbiddenErrorMessage": "Bạn có thể cần yêu cầu quyền truy cập từ trang web đang cung cấp model.",
-        "urlUnauthorizedErrorMessage": "Bạn có thể cần thiếp lập một token API để dùng được model này."
+        "urlUnauthorizedErrorMessage": "Bạn có thể cần thiếp lập một token API để dùng được model này.",
+        "fluxRedux": "FLUX Redux",
+        "sigLip": "SigLIP"
     },
     "metadata": {
         "guidance": "Hướng Dẫn",
@@ -979,7 +982,7 @@
         "unknownInput": "Đầu Vào Không Rõ: {{name}}",
         "validateConnections": "Xác Thực Kết Nối Và Đồ Thị",
         "workflowNotes": "Ghi Chú",
-        "workflowTags": "Thẻ Tên",
+        "workflowTags": "Nhãn",
         "editMode": "Chỉnh sửa trong Trình Biên Tập Workflow",
         "edit": "Chỉnh Sửa",
         "executionStateInProgress": "Đang Xử Lý",
@@ -2021,7 +2024,7 @@
         },
         "mergingLayers": "Đang gộp layer",
         "controlLayerEmptyState": "<UploadButton>Tải lên ảnh</UploadButton>, kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này, hoặc vẽ trên canvas để bắt đầu.",
-        "referenceImageEmptyState": "<UploadButton>Tải lên ảnh</UploadButton> hoặc kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này để bắt đầu.",
+        "referenceImageEmptyState": "<UploadButton>Tải lên hình ảnh</UploadButton>, kéo ảnh từ <GalleryButton>thư viện ảnh</GalleryButton> vào layer này, hoặc <PullBboxButton>kéo hộp giới hạn vào layer này</PullBboxButton> để bắt đầu.",
         "useImage": "Dùng Hình Ảnh",
         "resetCanvasLayers": "Khởi Động Lại Layer Canvas",
         "asRasterLayer": "Như $t(controlLayers.rasterLayer)",
@@ -2137,7 +2140,7 @@
     "toast": {
         "imageUploadFailed": "Tải Lên Ảnh Thất Bại",
         "layerCopiedToClipboard": "Sao Chép Layer Vào Clipboard",
-        "uploadFailedInvalidUploadDesc_withCount_other": "Tối đa là {{count}} ảnh PNG hoặc JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Tối đa là {{count}} ảnh PNG, JPEG hoặc WEBP.",
         "imageCopied": "Ảnh Đã Được Sao Chép",
         "sentToUpscale": "Chuyển Vào Upscale",
         "unableToLoadImage": "Không Thể Tải Hình Ảnh",
@@ -2149,7 +2152,7 @@
         "unableToLoadImageMetadata": "Không Thể Tải Metadata Của Ảnh",
         "workflowLoaded": "Workflow Đã Tải",
         "uploadFailed": "Tải Lên Thất Bại",
-        "uploadFailedInvalidUploadDesc": "Phải là ảnh PNG hoặc JPEG.",
+        "uploadFailedInvalidUploadDesc": "Phải là ảnh PNG, JPEG hoặc WEBP.",
         "serverError": "Lỗi Server",
         "addedToBoard": "Thêm vào tài nguyên của bảng {{name}}",
         "sessionRef": "Phiên: {{sessionId}}",
@@ -2252,11 +2255,10 @@
         "convertGraph": "Chuyển Đổi Đồ Thị",
         "saveWorkflowToProject": "Lưu Workflow Vào Dự Án",
         "workflowName": "Tên Workflow",
-        "workflowLibrary": "Thư Viện",
+        "workflowLibrary": "Thư Viện Workflow",
         "opened": "Ngày Mở",
         "deleteWorkflow": "Xoá Workflow",
         "workflowEditorMenu": "Menu Biên Tập Workflow",
-        "uploadAndSaveWorkflow": "Tải Lên Thư Viện",
         "openLibrary": "Mở Thư Viện",
         "builder": {
             "resetAllNodeFields": "Tải Lại Các Vùng Node",
@@ -2287,7 +2289,18 @@
             "heading": "Đầu Dòng",
             "text": "Văn Bản",
             "divider": "Gạch Chia"
-        }
+        },
+        "yourWorkflows": "Workflow Của Bạn",
+        "browseWorkflows": "Khám Phá Workflow",
+        "workflowThumbnail": "Ảnh Minh Họa Workflow",
+        "saveChanges": "Lưu Thay Đổi",
+        "allLoaded": "Đã Tải Tất Cả Workflow",
+        "shared": "Nhóm",
+        "searchPlaceholder": "Tìm theo tên, mô tả, hoặc nhãn",
+        "filterByTags": "Lọc Theo Nhãn",
+        "recentlyOpened": "Mở Gần Đây",
+        "private": "Cá Nhân",
+        "loadMore": "Tải Thêm"
     },
     "upscaling": {
         "missingUpscaleInitialImage": "Thiếu ảnh dùng để upscale",
@@ -2322,8 +2335,8 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "Trình Biên Tập Workflow: trình tạo vùng nhập dưới dạng kéo thả nhằm tạo dựng workflow dễ dàng hơn.",
-            "Các nâng cấp khác: Xếp hàng tạo sinh theo nhóm nhanh hơn, upscale tốt hơn, trình chọn màu được cải thiện, và node chứa metadata."
+            "Trình Quản Lý Bộ Nhớ: Thiết lập mới cho người dùng với GPU Nvidia để giảm lượng VRAM sử dụng.",
+            "Hiệu suất: Các cải thiện tiếp theo nhằm gói gọn hiệu suất và khả năng phản hồi của ứng dụng."
         ]
     },
     "upsell": {

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

@@ -1629,7 +1629,6 @@
         "projectWorkflows": "项目工作流程",
         "copyShareLink": "复制分享链接",
         "chooseWorkflowFromLibrary": "从库中选择工作流程",
-        "uploadAndSaveWorkflow": "上传到库",
         "deleteWorkflow2": "您确定要删除此工作流程吗？此操作无法撤销。"
     },
     "accordions": {

invokeai/frontend/web/src/app/components/App.tsx

@@ -26,7 +26,8 @@ import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicP
 import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
 import { ImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
-import { ShareWorkflowModal } from 'features/nodes/components/sidePanel/WorkflowListMenu/ShareWorkflowModal';
+import { ShareWorkflowModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/ShareWorkflowModal';
+import { WorkflowLibraryModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/WorkflowLibraryModal';
 import { CancelAllExceptCurrentQueueItemConfirmationAlertDialog } from 'features/queue/components/CancelAllExceptCurrentQueueItemConfirmationAlertDialog';
 import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 import { useReadinessWatcher } from 'features/queue/store/readiness';
@@ -39,7 +40,9 @@ import { selectLanguage } from 'features/system/store/systemSelectors';
 import { AppContent } from 'features/ui/components/AppContent';
 import { DeleteWorkflowDialog } from 'features/workflowLibrary/components/DeleteLibraryWorkflowConfirmationAlertDialog';
 import { LoadWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
+import { LoadWorkflowFromGraphModal } from 'features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal';
 import { NewWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/NewWorkflowConfirmationAlertDialog';
+import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog';
 import i18n from 'i18n';
 import { size } from 'lodash-es';
 import { memo, useCallback, useEffect } from 'react';
@@ -48,7 +51,6 @@ import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 import { useSocketIO } from 'services/events/useSocketIO';
 
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
-
 const DEFAULT_CONFIG = {};
 
 interface Props {
@@ -73,28 +75,7 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
         {!didStudioInit && <Loading />}
       </Box>
       <HookIsolator config={config} studioInitAction={studioInitAction} />
-      <DeleteImageModal />
-      <ChangeBoardModal />
-      <DynamicPromptsModal />
-      <StylePresetModal />
-      <CancelAllExceptCurrentQueueItemConfirmationAlertDialog />
-      <ClearQueueConfirmationsAlertDialog />
-      <NewWorkflowConfirmationAlertDialog />
-      <LoadWorkflowConfirmationAlertDialog />
-      <DeleteStylePresetDialog />
-      <DeleteWorkflowDialog />
-      <ShareWorkflowModal />
-      <RefreshAfterResetModal />
-      <DeleteBoardModal />
-      <GlobalImageHotkeys />
-      <NewGallerySessionDialog />
-      <NewCanvasSessionDialog />
-      <ImageContextMenu />
-      <FullscreenDropzone />
-      <VideosModal />
-      <CanvasManagerProviderGate>
-        <CanvasPasteModal />
-      </CanvasManagerProviderGate>
+      <ModalIsolator />
     </ErrorBoundary>
   );
 };
@@ -140,3 +121,36 @@ const HookIsolator = memo(
   }
 );
 HookIsolator.displayName = 'HookIsolator';
+
+const ModalIsolator = memo(() => {
+  return (
+    <>
+      <DeleteImageModal />
+      <ChangeBoardModal />
+      <DynamicPromptsModal />
+      <StylePresetModal />
+      <WorkflowLibraryModal />
+      <CancelAllExceptCurrentQueueItemConfirmationAlertDialog />
+      <ClearQueueConfirmationsAlertDialog />
+      <NewWorkflowConfirmationAlertDialog />
+      <LoadWorkflowConfirmationAlertDialog />
+      <DeleteStylePresetDialog />
+      <DeleteWorkflowDialog />
+      <ShareWorkflowModal />
+      <RefreshAfterResetModal />
+      <DeleteBoardModal />
+      <GlobalImageHotkeys />
+      <NewGallerySessionDialog />
+      <NewCanvasSessionDialog />
+      <ImageContextMenu />
+      <FullscreenDropzone />
+      <VideosModal />
+      <SaveWorkflowAsDialog />
+      <CanvasManagerProviderGate>
+        <CanvasPasteModal />
+      </CanvasManagerProviderGate>
+      <LoadWorkflowFromGraphModal />
+    </>
+  );
+});
+ModalIsolator.displayName = 'ModalIsolator';

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -16,10 +16,18 @@ import { $openAPISchemaUrl } from 'app/store/nanostores/openAPISchemaUrl';
 import { $projectId, $projectName, $projectUrl } from 'app/store/nanostores/projectId';
 import { $queueId, DEFAULT_QUEUE_ID } from 'app/store/nanostores/queueId';
 import { $store } from 'app/store/nanostores/store';
-import { $workflowCategories } from 'app/store/nanostores/workflowCategories';
 import { createStore } from 'app/store/store';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
+import type { WorkflowSortOption, WorkflowTagCategory } from 'features/nodes/store/workflowLibrarySlice';
+import {
+  $workflowLibraryCategoriesOptions,
+  $workflowLibrarySortOptions,
+  $workflowLibraryTagCategoriesOptions,
+  DEFAULT_WORKFLOW_LIBRARY_CATEGORIES,
+  DEFAULT_WORKFLOW_LIBRARY_SORT_OPTIONS,
+  DEFAULT_WORKFLOW_LIBRARY_TAG_CATEGORIES,
+} from 'features/nodes/store/workflowLibrarySlice';
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
 import type { PropsWithChildren, ReactNode } from 'react';
 import React, { lazy, memo, useEffect, useLayoutEffect, useMemo } from 'react';
@@ -48,6 +56,8 @@ interface Props extends PropsWithChildren {
   isDebugging?: boolean;
   logo?: ReactNode;
   workflowCategories?: WorkflowCategory[];
+  workflowTagCategories?: WorkflowTagCategory[];
+  workflowSortOptions?: WorkflowSortOption[];
   loggingOverrides?: LoggingOverrides;
 }
 
@@ -68,6 +78,8 @@ const InvokeAIUI = ({
   isDebugging = false,
   logo,
   workflowCategories,
+  workflowTagCategories,
+  workflowSortOptions,
   loggingOverrides,
 }: Props) => {
   useLayoutEffect(() => {
@@ -195,14 +207,34 @@ const InvokeAIUI = ({
 
   useEffect(() => {
     if (workflowCategories) {
-      $workflowCategories.set(workflowCategories);
+      $workflowLibraryCategoriesOptions.set(workflowCategories);
     }
 
     return () => {
-      $workflowCategories.set([]);
+      $workflowLibraryCategoriesOptions.set(DEFAULT_WORKFLOW_LIBRARY_CATEGORIES);
     };
   }, [workflowCategories]);
 
+  useEffect(() => {
+    if (workflowTagCategories) {
+      $workflowLibraryTagCategoriesOptions.set(workflowTagCategories);
+    }
+
+    return () => {
+      $workflowLibraryTagCategoriesOptions.set(DEFAULT_WORKFLOW_LIBRARY_TAG_CATEGORIES);
+    };
+  }, [workflowTagCategories]);
+
+  useEffect(() => {
+    if (workflowSortOptions) {
+      $workflowLibrarySortOptions.set(workflowSortOptions);
+    }
+
+    return () => {
+      $workflowLibrarySortOptions.set(DEFAULT_WORKFLOW_LIBRARY_SORT_OPTIONS);
+    };
+  }, [workflowSortOptions]);
+
   useEffect(() => {
     if (socketOptions) {
       $socketOptions.set(socketOptions);

invokeai/frontend/web/src/app/hooks/useStudioInitAction.ts

@@ -11,11 +11,11 @@ import { $imageViewer } from 'features/gallery/components/ImageViewer/useImageVi
 import { sentImageToCanvas } from 'features/gallery/store/actions';
 import { parseAndRecallAllMetadata } from 'features/metadata/util/handlers';
 import { $hasTemplates } from 'features/nodes/store/nodesSlice';
-import { $isWorkflowListMenuIsOpen } from 'features/nodes/store/workflowListMenu';
+import { $isWorkflowLibraryModalOpen } from 'features/nodes/store/workflowLibraryModal';
 import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { toast } from 'features/toast/toast';
 import { activeTabCanvasRightPanelChanged, setActiveTab } from 'features/ui/store/uiSlice';
-import { useGetAndLoadLibraryWorkflow } from 'features/workflowLibrary/hooks/useGetAndLoadLibraryWorkflow';
+import { useLoadWorkflowWithDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
 import { atom } from 'nanostores';
 import { useCallback, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -57,7 +57,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   const { t } = useTranslation();
   const didParseOpenAPISchema = useStore($hasTemplates);
   const store = useAppStore();
-  const { getAndLoadWorkflow } = useGetAndLoadLibraryWorkflow();
+  const loadWorkflowWithDialog = useLoadWorkflowWithDialog();
 
   const handleSendToCanvas = useCallback(
     async (imageName: string) => {
@@ -113,10 +113,15 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   const handleLoadWorkflow = useCallback(
     async (workflowId: string) => {
       // This shows a toast
-      await getAndLoadWorkflow(workflowId);
-      store.dispatch(setActiveTab('workflows'));
+      await loadWorkflowWithDialog({
+        type: 'library',
+        data: workflowId,
+        onSuccess: () => {
+          store.dispatch(setActiveTab('workflows'));
+        },
+      });
     },
-    [getAndLoadWorkflow, store]
+    [loadWorkflowWithDialog, store]
   );
 
   const handleSelectStylePreset = useCallback(
@@ -166,7 +171,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
         case 'viewAllWorkflows':
           // Go to the workflows tab and open the workflow library modal
           store.dispatch(setActiveTab('workflows'));
-          $isWorkflowListMenuIsOpen.set(true);
+          $isWorkflowLibraryModalOpen.set(true);
           break;
         case 'viewAllStylePresets':
           // Go to the canvas tab and open the style presets menu

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

@@ -27,7 +27,6 @@ import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddlewa
 import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
 import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
 import { addUpdateAllNodesRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested';
-import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
 
 import { addArchivedOrDeletedBoardListener } from './listeners/addArchivedOrDeletedBoardListener';
@@ -89,7 +88,6 @@ addArchivedOrDeletedBoardListener(startAppListening);
 addGetOpenAPISchemaListener(startAppListening);
 
 // Workflows
-addWorkflowLoadRequestedListener(startAppListening);
 addUpdateAllNodesRequestedListener(startAppListening);
 
 // Models

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

@@ -31,6 +31,7 @@ import type { AnyModelConfig } from 'services/api/types';
 import {
   isCLIPEmbedModelConfig,
   isControlLayerModelConfig,
+  isFluxReduxModelConfig,
   isFluxVAEModelConfig,
   isIPAdapterModelConfig,
   isLoRAModelConfig,
@@ -77,6 +78,7 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
       handleT5EncoderModels(models, state, dispatch, log);
       handleCLIPEmbedModels(models, state, dispatch, log);
       handleFLUXVAEModels(models, state, dispatch, log);
+      handleFLUXReduxModels(models, state, dispatch, log);
     },
   });
 };
@@ -209,6 +211,10 @@ const handleControlAdapterModels: ModelHandler = (models, state, dispatch, log)
 const handleIPAdapterModels: ModelHandler = (models, state, dispatch, log) => {
   const ipaModels = models.filter(isIPAdapterModelConfig);
   selectCanvasSlice(state).referenceImages.entities.forEach((entity) => {
+    if (entity.ipAdapter.type !== 'ip_adapter') {
+      return;
+    }
+
     const selectedIPAdapterModel = entity.ipAdapter.model;
     // `null` is a valid IP adapter model - no need to do anything.
     if (!selectedIPAdapterModel) {
@@ -224,6 +230,10 @@ const handleIPAdapterModels: ModelHandler = (models, state, dispatch, log) => {
 
   selectCanvasSlice(state).regionalGuidance.entities.forEach((entity) => {
     entity.referenceImages.forEach(({ id: referenceImageId, ipAdapter }) => {
+      if (ipAdapter.type !== 'ip_adapter') {
+        return;
+      }
+
       const selectedIPAdapterModel = ipAdapter.model;
       // `null` is a valid IP adapter model - no need to do anything.
       if (!selectedIPAdapterModel) {
@@ -241,6 +251,49 @@ const handleIPAdapterModels: ModelHandler = (models, state, dispatch, log) => {
   });
 };
 
+const handleFLUXReduxModels: ModelHandler = (models, state, dispatch, log) => {
+  const fluxReduxModels = models.filter(isFluxReduxModelConfig);
+
+  selectCanvasSlice(state).referenceImages.entities.forEach((entity) => {
+    if (entity.ipAdapter.type !== 'flux_redux') {
+      return;
+    }
+    const selectedFLUXReduxModel = entity.ipAdapter.model;
+    // `null` is a valid FLUX Redux model - no need to do anything.
+    if (!selectedFLUXReduxModel) {
+      return;
+    }
+    const isModelAvailable = fluxReduxModels.some((m) => m.key === selectedFLUXReduxModel.key);
+    if (isModelAvailable) {
+      return;
+    }
+    log.debug({ selectedFLUXReduxModel }, 'Selected FLUX Redux model is not available, clearing');
+    dispatch(referenceImageIPAdapterModelChanged({ entityIdentifier: getEntityIdentifier(entity), modelConfig: null }));
+  });
+
+  selectCanvasSlice(state).regionalGuidance.entities.forEach((entity) => {
+    entity.referenceImages.forEach(({ id: referenceImageId, ipAdapter }) => {
+      if (ipAdapter.type !== 'flux_redux') {
+        return;
+      }
+
+      const selectedFLUXReduxModel = ipAdapter.model;
+      // `null` is a valid FLUX Redux model - no need to do anything.
+      if (!selectedFLUXReduxModel) {
+        return;
+      }
+      const isModelAvailable = fluxReduxModels.some((m) => m.key === selectedFLUXReduxModel.key);
+      if (isModelAvailable) {
+        return;
+      }
+      log.debug({ selectedFLUXReduxModel }, 'Selected FLUX Redux model is not available, clearing');
+      dispatch(
+        rgIPAdapterModelChanged({ entityIdentifier: getEntityIdentifier(entity), referenceImageId, modelConfig: null })
+      );
+    });
+  });
+};
+
 const handlePostProcessingModel: ModelHandler = (models, state, dispatch, log) => {
   const selectedPostProcessingModel = state.upscale.postProcessingModel;
   const allSpandrelModels = models.filter(isSpandrelImageToImageModelConfig);

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

@@ -1,4 +0,0 @@
-import type { WorkflowCategory } from 'features/nodes/types/workflow';
-import { atom } from 'nanostores';
-
-export const $workflowCategories = atom<WorkflowCategory[]>(['user', 'default']);