feat(ui, nodes): metadata wip

it is not fully baked just yet

.github/CODEOWNERS

@@ -1,16 +1,16 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant
+/.github/workflows/ @mauwii @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein  @tildebyte @blessedcoolant
-/mkdocs.yml @lstein  @blessedcoolant
+/docs/ @lstein @mauwii @tildebyte @blessedcoolant
+/mkdocs.yml @lstein @mauwii @blessedcoolant
 
 # nodes
 /invokeai/app/ @Kyle0654 @blessedcoolant
 
 # installation and configuration
-/pyproject.toml  @lstein @blessedcoolant
-/docker/  @lstein @blessedcoolant
+/pyproject.toml @mauwii @lstein @blessedcoolant
+/docker/ @mauwii @lstein @blessedcoolant
 /scripts/ @ebr @lstein
 /installer/ @lstein @ebr
 /invokeai/assets @lstein @ebr
@@ -22,11 +22,11 @@
 /invokeai/backend @blessedcoolant @psychedelicious @lstein
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2
+/invokeai/backend @keturn @damian0815 @lstein @blessedcoolant @jpphoto
 
 # front ends
 /invokeai/frontend/CLI @lstein
-/invokeai/frontend/install @lstein @ebr  
+/invokeai/frontend/install @lstein @ebr @mauwii 
 /invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/web @psychedelicious @blessedcoolant

.gitignore

@@ -9,8 +9,6 @@ models/ldm/stable-diffusion-v1/model.ckpt
 configs/models.user.yaml
 config/models.user.yml
 invokeai.init
-.version
-.last_model
 
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh

README.md

@@ -84,7 +84,7 @@ installing lots of models.
 
 6. Wait while the installer does its thing. After installing the software,
 the installer will launch a script that lets you configure InvokeAI and
-select a set of starting image generation models.
+select a set of starting image generaiton models.
 
 7. Find the folder that InvokeAI was installed into (it is not the
 same as the unpacked zip file directory!) The default location of this
@@ -148,11 +148,6 @@ not supported.
     pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
-    _For non-GPU systems:_
-    ```terminal
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
-    ``` 
-
     _For Macintoshes, either Intel or M1/M2:_
 
     ```sh

docs/features/NSFW.md

@@ -32,7 +32,7 @@ turned on and off on the command line using `--nsfw_checker` and
 At installation time, InvokeAI will ask whether the checker should be
 activated by default (neither argument given on the command line). The
 response is stored in the InvokeAI initialization file (usually
-`invokeai.init` in your home directory). You can change the default at any
+`.invokeai` in your home directory). You can change the default at any
 time by opening this file in a text editor and commenting or
 uncommenting the line `--nsfw_checker`.
 

invokeai/app/api/dependencies.py

@@ -3,16 +3,12 @@
 import os
 from argparse import Namespace
 
-from invokeai.app.services.metadata import PngMetadataService, MetadataServiceBase
-
-from ..services.default_graphs import create_system_graphs
-
 from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
 from ...backend import Globals
 from ..services.model_manager_initializer import get_model_manager
 from ..services.restoration_services import RestorationServices
-from ..services.graph import GraphExecutionState, LibraryGraph
+from ..services.graph import GraphExecutionState
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
@@ -62,9 +58,7 @@ class ApiDependencies:
 
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents'))
 
-        metadata = PngMetadataService()
-
-        images = DiskImageStorage(f'{output_folder}/images', metadata_service=metadata)
+        images = DiskImageStorage(f'{output_folder}/images')
 
         # TODO: build a file/path manager?
         db_location = os.path.join(output_folder, "invokeai.db")
@@ -74,11 +68,7 @@ class ApiDependencies:
             events=events,
             latents=latents,
             images=images,
-            metadata=metadata,
             queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](
-                filename=db_location, table_name="graphs"
-            ),
             graph_execution_manager=SqliteItemStorage[GraphExecutionState](
                 filename=db_location, table_name="graph_executions"
             ),
@@ -86,8 +76,6 @@ class ApiDependencies:
             restoration=RestorationServices(config),
         )
 
-        create_system_graphs(services.graph_library)
-
         ApiDependencies.invoker = Invoker(services)
 
     @staticmethod

invokeai/app/api/events.py

@@ -45,7 +45,7 @@ class FastAPIEventService(EventServiceBase):
                 )
 
             except Empty:
-                await asyncio.sleep(0.1)
+                await asyncio.sleep(0.001)
                 pass
 
             except asyncio.CancelledError as e:

invokeai/app/api/models/images.py

@@ -1,19 +1,7 @@
-from typing import Optional
 from pydantic import BaseModel, Field
 
 from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import InvokeAIMetadata
-
-
-class ImageResponseMetadata(BaseModel):
-    """An image's metadata. Used only in HTTP responses."""
-
-    created: int = Field(description="The creation timestamp of the image")
-    width: int = Field(description="The width of the image in pixels")
-    height: int = Field(description="The height of the image in pixels")
-    invokeai: Optional[InvokeAIMetadata] = Field(
-        description="The image's InvokeAI-specific metadata"
-    )
+from invokeai.app.models.metadata import ImageMetadata
 
 
 class ImageResponse(BaseModel):
@@ -23,12 +11,4 @@ class ImageResponse(BaseModel):
     image_name: str = Field(description="The name of the image")
     image_url: str = Field(description="The url of the image")
     thumbnail_url: str = Field(description="The url of the image's thumbnail")
-    metadata: ImageResponseMetadata = Field(description="The image's metadata")
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
+    metadata: ImageMetadata = Field(description="The image's metadata")

invokeai/app/api/routers/images.py

@@ -3,15 +3,14 @@ import io
 from datetime import datetime, timezone
 import json
 import os
-from typing import Any
 import uuid
 
-from fastapi import HTTPException, Path, Query, Request, UploadFile
+from fastapi import Path, Query, Request, UploadFile
 from fastapi.responses import FileResponse, Response
 from fastapi.routing import APIRouter
 from PIL import Image
-from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
-from invokeai.app.services.metadata import InvokeAIMetadata
+from invokeai.app.api.models.images import ImageResponse
+from invokeai.app.models.metadata import ImageMetadata
 from invokeai.app.services.item_storage import PaginatedResults
 
 from ...services.image_storage import ImageType
@@ -19,110 +18,85 @@ from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
 
-
 @images_router.get("/{image_type}/{image_name}", operation_id="get_image")
 async def get_image(
     image_type: ImageType = Path(description="The type of image to get"),
     image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse | Response:
+):
     """Gets a result"""
+    # TODO: This is not really secure at all. At least make sure only output results are served
+    filename = ApiDependencies.invoker.services.images.get_path(image_type, image_name)
+    return FileResponse(filename)
 
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
-
-
-@images_router.get(
-    "/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail"
-)
+@images_router.get("/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail")
 async def get_thumbnail(
     image_type: ImageType = Path(description="The type of image to get"),
     image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse | Response:
+):
     """Gets a thumbnail"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name, is_thumbnail=True
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
+    # TODO: This is not really secure at all. At least make sure only output results are served
+    filename = ApiDependencies.invoker.services.images.get_path(image_type, 'thumbnails/' + image_name)
+    return FileResponse(filename)
 
 
 @images_router.post(
     "/uploads/",
     operation_id="upload_image",
     responses={
-        201: {
-            "description": "The image was uploaded successfully",
-            "model": ImageResponse,
-        },
-        415: {"description": "Image upload failed"},
+        201: {"description": "The image was uploaded successfully", "model": ImageResponse},
+        404: {"description": "Session not found"},
     },
-    status_code=201,
+    status_code=201
 )
-async def upload_image(
-    file: UploadFile, request: Request, response: Response
-) -> ImageResponse:
+async def upload_image(file: UploadFile, request: Request, response: Response) -> ImageResponse:
     if not file.content_type.startswith("image"):
-        raise HTTPException(status_code=415, detail="Not an image")
+        return Response(status_code=415)
 
     contents = await file.read()
-
     try:
         img = Image.open(io.BytesIO(contents))
     except:
         # Error opening the image
-        raise HTTPException(status_code=415, detail="Failed to read image")
+        return Response(status_code=415)
 
     filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
-
-    (image_path, thumbnail_path, ctime) = ApiDependencies.invoker.services.images.save(
-        ImageType.UPLOAD, filename, img
-    )
-
-    invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
+    image_path = ApiDependencies.invoker.services.images.save(ImageType.UPLOAD, filename, img)
+    invokeai_metadata = json.loads(img.info.get("invokeai", "{}"))
 
     res = ImageResponse(
         image_type=ImageType.UPLOAD,
         image_name=filename,
+        # TODO: DiskImageStorage should not be building URLs...?
         image_url=f"api/v1/images/{ImageType.UPLOAD.value}/{filename}",
         thumbnail_url=f"api/v1/images/{ImageType.UPLOAD.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
-        metadata=ImageResponseMetadata(
-            created=ctime,
+        # TODO: Creation of this object should happen elsewhere, just making it fit here so it works
+        metadata=ImageMetadata(
+            created=int(os.path.getctime(image_path)),
             width=img.width,
             height=img.height,
-            invokeai=invokeai_metadata,
+            invokeai=invokeai_metadata
         ),
     )
 
     response.status_code = 201
     response.headers["Location"] = request.url_for(
-        "get_image", image_type=ImageType.UPLOAD.value, image_name=filename
-    )
+                "get_image", image_type=ImageType.UPLOAD.value, image_name=filename
+            )
 
     return res
 
-
 @images_router.get(
     "/",
     operation_id="list_images",
     responses={200: {"model": PaginatedResults[ImageResponse]}},
 )
 async def list_images(
-    image_type: ImageType = Query(
-        default=ImageType.RESULT, description="The type of images to get"
-    ),
+    image_type: ImageType = Query(default=ImageType.RESULT, description="The type of images to get"),
     page: int = Query(default=0, description="The page of images to get"),
     per_page: int = Query(default=10, description="The number of images per page"),
 ) -> PaginatedResults[ImageResponse]:
     """Gets a list of images"""
-    result = ApiDependencies.invoker.services.images.list(image_type, page, per_page)
+    result = ApiDependencies.invoker.services.images.list(
+        image_type, page, per_page
+    )
     return result

invokeai/app/cli/commands.py

@@ -7,40 +7,11 @@ from pydantic import BaseModel, Field
 import networkx as nx
 import matplotlib.pyplot as plt
 
-from ..invocations.baseinvocation import BaseInvocation
-from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, GraphInvocation, Edge
+from ..models.image import ImageField
+from ..services.graph import GraphExecutionState
 from ..services.invoker import Invoker
 
 
-def add_field_argument(command_parser, name: str, field, default_override = None):
-    default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
-    if get_origin(field.type_) == Literal:
-        allowed_values = get_args(field.type_)
-        allowed_types = set()
-        for val in allowed_values:
-            allowed_types.add(type(val))
-        allowed_types_list = list(allowed_types)
-        field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
-
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field_type,
-            default=default,
-            choices=allowed_values,
-            help=field.field_info.description,
-        )
-    else:
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field.type_,
-            default=default,
-            help=field.field_info.description,
-        )
-
-
 def add_parsers(
     subparsers,
     commands: list[type],
@@ -65,26 +36,30 @@ def add_parsers(
             if name in exclude_fields:
                 continue
 
-            add_field_argument(command_parser, name, field)
+            if get_origin(field.type_) == Literal:
+                allowed_values = get_args(field.type_)
+                allowed_types = set()
+                for val in allowed_values:
+                    allowed_types.add(type(val))
+                allowed_types_list = list(allowed_types)
+                field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
 
-
-def add_graph_parsers(
-    subparsers,
-    graphs: list[LibraryGraph],
-    add_arguments: Callable[[argparse.ArgumentParser], None]|None = None
-):
-    for graph in graphs:
-        command_parser = subparsers.add_parser(graph.name, help=graph.description)
-        
-        if add_arguments is not None:
-            add_arguments(command_parser)
-
-        # Add arguments for inputs
-        for exposed_input in graph.exposed_inputs:
-            node = graph.graph.get_node(exposed_input.node_path)
-            field = node.__fields__[exposed_input.field]
-            default_override = getattr(node, exposed_input.field)
-            add_field_argument(command_parser, exposed_input.alias, field, default_override)
+                command_parser.add_argument(
+                    f"--{name}",
+                    dest=name,
+                    type=field_type,
+                    default=field.default if field.default_factory is None else field.default_factory(),
+                    choices=allowed_values,
+                    help=field.field_info.description,
+                )
+            else:
+                command_parser.add_argument(
+                    f"--{name}",
+                    dest=name,
+                    type=field.type_,
+                    default=field.default if field.default_factory is None else field.default_factory(),
+                    help=field.field_info.description,
+                )
 
 
 class CliContext:
@@ -92,38 +67,17 @@ class CliContext:
     session: GraphExecutionState
     parser: argparse.ArgumentParser
     defaults: dict[str, Any]
-    graph_nodes: dict[str, str]
-    nodes_added: list[str]
 
     def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser):
         self.invoker = invoker
         self.session = session
         self.parser = parser
         self.defaults = dict()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
 
     def get_session(self):
         self.session = self.invoker.services.graph_execution_manager.get(self.session.id)
         return self.session
 
-    def reset(self):
-        self.session = self.invoker.create_execution_state()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
-        # Leave defaults unchanged
-
-    def add_node(self, node: BaseInvocation):
-        self.get_session()
-        self.session.graph.add_node(node)
-        self.nodes_added.append(node.id)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
-    def add_edge(self, edge: Edge):
-        self.get_session()
-        self.session.add_edge(edge)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
 
 class ExitCli(Exception):
     """Exception to exit the CLI"""

invokeai/app/cli_app.py

@@ -13,22 +13,17 @@ from typing import (
 from pydantic import BaseModel
 from pydantic.fields import Field
 
-from invokeai.app.services.metadata import PngMetadataService
-
-from .services.default_graphs import create_system_graphs
-
 from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
 from ..backend import Args
-from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, get_graph_execution_history
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_parsers, get_graph_execution_history
 from .cli.completer import set_autocompleter
 from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
 from .services.model_manager_initializer import get_model_manager
 from .services.restoration_services import RestorationServices
-from .services.graph import Edge, EdgeConnection, ExposedNodeInput, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
-from .services.default_graphs import default_text_to_image_graph_id
+from .services.graph import Edge, EdgeConnection, GraphExecutionState, are_connection_types_compatible
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
@@ -63,7 +58,7 @@ def add_invocation_args(command_parser):
     )
 
 
-def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
+def get_command_parser() -> argparse.ArgumentParser:
     # Create invocation parser
     parser = argparse.ArgumentParser()
 
@@ -81,72 +76,20 @@ def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     commands = BaseCommand.get_all_subclasses()
     add_parsers(subparsers, commands, exclude_fields=["type"])
 
-    # Create subparsers for exposed CLI graphs
-    # TODO: add a way to identify these graphs
-    text_to_image = services.graph_library.get(default_text_to_image_graph_id)
-    add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args)
-
     return parser
 
 
-class NodeField():
-    alias: str
-    node_path: str
-    field: str
-    field_type: type
-
-    def __init__(self, alias: str, node_path: str, field: str, field_type: type):
-        self.alias = alias
-        self.node_path = node_path
-        self.field = field
-        self.field_type = field_type
-
-
-def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str,NodeField]:
-    return {k:NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
-
-
-def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_input.node_path))
-    return NodeField(alias=exposed_input.alias, node_path=f'{node_id}.{exposed_input.node_path}', field=exposed_input.field, field_type=get_type_hints(node_type)[exposed_input.field])
-
-
-def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_output.node_path))
-    node_output_type = node_type.get_output_type()
-    return NodeField(alias=exposed_output.alias, node_path=f'{node_id}.{exposed_output.node_path}', field=exposed_output.field, field_type=get_type_hints(node_output_type)[exposed_output.field])
-
-
-def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the inputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs}
-
-
-def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the outputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
-
-
 def generate_matching_edges(
-    a: BaseInvocation, b: BaseInvocation, context: CliContext
+    a: BaseInvocation, b: BaseInvocation
 ) -> list[Edge]:
     """Generates all possible edges between two invocations"""
-    afields = get_node_outputs(a, context)
-    bfields = get_node_inputs(b, context)
+    atype = type(a)
+    btype = type(b)
+
+    aoutputtype = atype.get_output_type()
+
+    afields = get_type_hints(aoutputtype)
+    bfields = get_type_hints(btype)
 
     matching_fields = set(afields.keys()).intersection(bfields.keys())
 
@@ -155,14 +98,14 @@ def generate_matching_edges(
     matching_fields = matching_fields.difference(invalid_fields)
 
     # Validate types
-    matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)]
+    matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f], bfields[f])]
 
     edges = [
         Edge(
-            source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
-            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
+            source=EdgeConnection(node_id=a.id, field=field),
+            destination=EdgeConnection(node_id=b.id, field=field)
         )
-        for alias in matching_fields
+        for field in matching_fields
     ]
     return edges
 
@@ -202,8 +145,6 @@ def invoke_cli():
 
     events = EventServiceBase()
 
-    metadata = PngMetadataService()
-
     output_folder = os.path.abspath(
         os.path.join(os.path.dirname(__file__), "../../../outputs")
     )
@@ -215,12 +156,8 @@ def invoke_cli():
         model_manager=model_manager,
         events=events,
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
-        images=DiskImageStorage(f'{output_folder}/images', metadata_service=metadata),
-        metadata=metadata,
+        images=DiskImageStorage(f'{output_folder}/images'),
         queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=db_location, table_name="graphs"
-        ),
         graph_execution_manager=SqliteItemStorage[GraphExecutionState](
             filename=db_location, table_name="graph_executions"
         ),
@@ -228,12 +165,9 @@ def invoke_cli():
         restoration=RestorationServices(config),
     )
 
-    system_graphs = create_system_graphs(services.graph_library)
-    system_graph_names = set([g.name for g in system_graphs])
-
     invoker = Invoker(services)
     session: GraphExecutionState = invoker.create_execution_state()
-    parser = get_command_parser(services)
+    parser = get_command_parser()
 
     re_negid = re.compile('^-[0-9]+$')
 
@@ -251,12 +185,11 @@ def invoke_cli():
 
         try:
             # Refresh the state of the session
-            #history = list(get_graph_execution_history(context.session))
-            history = list(reversed(context.nodes_added))
+            history = list(get_graph_execution_history(context.session))
 
             # Split the command for piping
             cmds = cmd_input.split("|")
-            start_id = len(context.nodes_added)
+            start_id = len(history)
             current_id = start_id
             new_invocations = list()
             for cmd in cmds:
@@ -272,24 +205,8 @@ def invoke_cli():
                         args[field_name] = field_default
 
                 # Parse invocation
-                command: CliCommand = None # type:ignore
-                system_graph: LibraryGraph|None = None
-                if args['type'] in system_graph_names:
-                    system_graph = next(filter(lambda g: g.name == args['type'], system_graphs))
-                    invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
-                    for exposed_input in system_graph.exposed_inputs:
-                        if exposed_input.alias in args:
-                            node = invocation.graph.get_node(exposed_input.node_path)
-                            field = exposed_input.field
-                            setattr(node, field, args[exposed_input.alias])
-                    command = CliCommand(command = invocation)
-                    context.graph_nodes[invocation.id] = system_graph.id
-                else:
-                    args["id"] = current_id
-                    command = CliCommand(command=args)
-
-                if command is None:
-                    continue
+                args["id"] = current_id
+                command = CliCommand(command=args)
 
                 # Run any CLI commands immediately
                 if isinstance(command.command, BaseCommand):
@@ -300,7 +217,6 @@ def invoke_cli():
                     command.command.run(context)
                     continue
 
-                # TODO: handle linking with library graphs
                 # Pipe previous command output (if there was a previous command)
                 edges: list[Edge] = list()
                 if len(history) > 0 or current_id != start_id:
@@ -313,7 +229,7 @@ def invoke_cli():
                         else context.session.graph.get_node(from_id)
                     )
                     matching_edges = generate_matching_edges(
-                        from_node, command.command, context
+                        from_node, command.command
                     )
                     edges.extend(matching_edges)
 
@@ -326,7 +242,7 @@ def invoke_cli():
 
                         link_node = context.session.graph.get_node(node_id)
                         matching_edges = generate_matching_edges(
-                            link_node, command.command, context
+                            link_node, command.command
                         )
                         matching_destinations = [e.destination for e in matching_edges]
                         edges = [e for e in edges if e.destination not in matching_destinations]
@@ -340,14 +256,12 @@ def invoke_cli():
                         if re_negid.match(node_id):
                             node_id = str(current_id + int(node_id))
 
-                        # TODO: handle missing input/output
-                        node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]]
-                        node_input = get_node_inputs(command.command, context)[link[2]]
-
                         edges.append(
                             Edge(
-                                source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
-                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
+                                source=EdgeConnection(node_id=node_id, field=link[1]),
+                                destination=EdgeConnection(
+                                    node_id=command.command.id, field=link[2]
+                                )
                             )
                         )
 
@@ -356,10 +270,10 @@ def invoke_cli():
                 current_id = current_id + 1
 
                 # Add the node to the session
-                context.add_node(command.command)
+                context.session.add_node(command.command)
                 for edge in edges:
                     print(edge)
-                    context.add_edge(edge)
+                    context.session.add_edge(edge)
 
             # Execute all remaining nodes
             invoke_all(context)
@@ -371,7 +285,7 @@ def invoke_cli():
         except SessionError:
             # Start a new session
             print("Session error: creating a new session")
-            context.reset()
+            context.session = context.invoker.create_execution_state()
 
         except ExitCli:
             break

invokeai/app/invocations/baseinvocation.py

@@ -95,7 +95,7 @@ class UIConfig(TypedDict, total=False):
         ],
     ]
     tags: List[str]
-    title: str
+
 
 class CustomisedSchemaExtra(TypedDict):
     ui: UIConfig

invokeai/app/invocations/collections.py

@@ -1,17 +1,16 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Literal, Optional
+from typing import Literal
 
+import cv2 as cv
 import numpy as np
 import numpy.random
+from PIL import Image, ImageOps
 from pydantic import Field
 
-from .baseinvocation import (
-    BaseInvocation,
-    InvocationConfig,
-    InvocationContext,
-    BaseInvocationOutput,
-)
+from ..services.image_storage import ImageType
+from .baseinvocation import BaseInvocation, InvocationContext, BaseInvocationOutput
+from .image import ImageField, ImageOutput
 
 
 class IntCollectionOutput(BaseInvocationOutput):
@@ -34,9 +33,7 @@ class RangeInvocation(BaseInvocation):
     step: int = Field(default=1, description="The step of the range")
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        return IntCollectionOutput(
-            collection=list(range(self.start, self.stop, self.step))
-        )
+        return IntCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
 
 
 class RandomRangeInvocation(BaseInvocation):
@@ -46,19 +43,8 @@ class RandomRangeInvocation(BaseInvocation):
 
     # Inputs
     low: int = Field(default=0, description="The inclusive low value")
-    high: int = Field(
-        default=np.iinfo(np.int32).max, description="The exclusive high value"
-    )
+    high: int = Field(default=np.iinfo(np.int32).max, description="The exclusive high value")
     size: int = Field(default=1, description="The number of values to generate")
-    seed: Optional[int] = Field(
-        ge=0,
-        le=np.iinfo(np.int32).max,
-        description="The seed for the RNG",
-        default_factory=lambda: numpy.random.randint(0, np.iinfo(np.int32).max),
-    )
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        rng = np.random.default_rng(self.seed)
-        return IntCollectionOutput(
-            collection=list(rng.integers(low=self.low, high=self.high, size=self.size))
-        )
+        return IntCollectionOutput(collection=list(numpy.random.randint(self.low, self.high, size=self.size)))

invokeai/app/invocations/cv.py

@@ -56,14 +56,9 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, image_inpainted, metadata)
+        context.services.images.save(image_type, image_name, image_inpainted, self.dict())
         return build_image_output(
             image_type=image_type,
             image_name=image_name,
             image=image_inpainted,
-        )
+        )

invokeai/app/invocations/generate.py

@@ -4,9 +4,7 @@ from functools import partial
 from typing import Literal, Optional, Union
 
 import numpy as np
-from diffusers import ControlNetModel
 from torch import Tensor
-import torch
 
 from pydantic import BaseModel, Field
 
@@ -16,7 +14,8 @@ from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
 from .image import ImageOutput, build_image_output
 from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
 from ...backend.stable_diffusion import PipelineIntermediateState
-from ..util.step_callback import stable_diffusion_step_callback
+from ..models.exceptions import CanceledException
+from ..util.step_callback import diffusers_step_callback_adapter
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
 
@@ -55,56 +54,35 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     model:       str = Field(default="", description="The model to use (currently ignored)")
     progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-    control_model: Optional[str] = Field(default=None, description="The control model to use")
-    control_image: Optional[ImageField] = Field(default=None, description="The processed control image")
-    # control_strength: Optional[float] = Field(default=1.0, ge=0, le=1, description="The strength of the controlnet")
     # fmt: on
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
     ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+            raise CanceledException
+
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
+        
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
+        # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
+        self.model_name = model["model_name"]
 
-        # loading controlnet image (currently requires pre-processed image)
-        control_image = (
-            None if self.control_image is None
-            else context.services.images.get(
-                self.control_image.image_type, self.control_image.image_name
-            )
-        )
-        # loading controlnet model
-        if (self.control_model is None or self.control_model==''):
-            control_model = None
-        else:
-            # FIXME: change this to dropdown menu?
-            control_model = ControlNetModel.from_pretrained(self.control_model,
-                                                            torch_dtype=torch.float16).to("cuda")
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        txt2img = Txt2Img(model, control_model=control_model)
-        outputs = txt2img.generate(
+        outputs = Txt2Img(model).generate(
             prompt=self.prompt,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            control_image=control_image,
+            step_callback=partial(self.dispatch_progress, context),
             **self.dict(
-                exclude={"prompt", "control_image" }
+                exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
         )
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
@@ -119,17 +97,21 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
             context.graph_execution_state_id, self.id
         )
 
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        source_id = graph_execution_state.prepared_source_mapping[self.id]
+        invocation = graph_execution_state.execution_graph.get_node(self.id)
+        
+        metadata = {
+          "session": context.graph_execution_state_id,
+          "source_id": source_id,
+          "invocation": invocation.dict()
+        }
 
-        context.services.images.save(
-            image_type, image_name, generate_output.image, metadata
-        )
+        context.services.images.save(image_type, image_name, generate_output.image, metadata)
         return build_image_output(
             image_type=image_type,
             image_name=image_name,
-            image=generate_output.image,
+            image=generate_output.image
         )
 
 
@@ -149,17 +131,20 @@ class ImageToImageInvocation(TextToImageInvocation):
     )
 
     def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+    ) -> None:  
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+            raise CanceledException
+
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
+
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
@@ -173,22 +158,17 @@ class ImageToImageInvocation(TextToImageInvocation):
 
         # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+        self.model = model["model_name"]
 
         outputs = Img2Img(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            init_mask=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
+                prompt=self.prompt,
+                init_image=image,
+                init_mask=mask,
+                step_callback=partial(self.dispatch_progress, context),
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
 
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
         # each time it is called. We only need the first one.
@@ -203,19 +183,13 @@ class ImageToImageInvocation(TextToImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, result_image, metadata)
+        context.services.images.save(image_type, image_name, result_image, self.dict())
         return build_image_output(
             image_type=image_type,
             image_name=image_name,
-            image=result_image,
+            image=result_image
         )
 
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -231,17 +205,20 @@ class InpaintInvocation(ImageToImageInvocation):
     )
 
     def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+    ) -> None:  
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+            raise CanceledException
+
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
+
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
@@ -259,22 +236,17 @@ class InpaintInvocation(ImageToImageInvocation):
 
         # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+        self.model = model["model_name"]
 
         outputs = Inpaint(model).generate(
-            prompt=self.prompt,
-            init_img=image,
-            init_mask=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
+                prompt=self.prompt,
+                init_img=image,
+                init_mask=mask,
+                step_callback=partial(self.dispatch_progress, context),
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
 
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
         # each time it is called. We only need the first one.
@@ -289,14 +261,9 @@ class InpaintInvocation(ImageToImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, result_image, metadata)
+        context.services.images.save(image_type, image_name, result_image, self.dict())
         return build_image_output(
             image_type=image_type,
             image_name=image_name,
-            image=result_image,
+            image=result_image
         )

invokeai/app/invocations/image.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from datetime import datetime, timezone
 from typing import Literal, Optional
 
 import numpy
@@ -7,6 +8,7 @@ from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field
 
 from ..models.image import ImageField, ImageType
+from ..services.invocation_services import InvocationServices
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -32,30 +34,27 @@ class ImageOutput(BaseInvocationOutput):
     # fmt: off
     type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
-    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
     # fmt: on
 
     class Config:
         schema_extra = {
-            "required": ["type", "image", "width", "height", "mode"]
+            "required": [
+                "type",
+                "image",
+                "width",
+                "height",
+            ]
         }
 
 
 def build_image_output(
     image_type: ImageType, image_name: str, image: Image.Image
 ) -> ImageOutput:
-    """Builds an ImageOutput and its ImageField"""
-    image_field = ImageField(
-        image_name=image_name,
-        image_type=image_type,
-    )
-    return ImageOutput(
-        image=image_field,
-        width=image.width,
-        height=image.height,
-        mode=image.mode,
-    )
+    image_field = ImageField(image_name=image_name, image_type=image_type)
+
+    return ImageOutput(image=image_field, width=image.width, height=image.height)
 
 
 class MaskOutput(BaseInvocationOutput):
@@ -75,24 +74,23 @@ class MaskOutput(BaseInvocationOutput):
         }
 
 
-class LoadImageInvocation(BaseInvocation):
-    """Load an image and provide it as output."""
+# # TODO: this isn't really necessary anymore
+# class LoadImageInvocation(BaseInvocation):
+#     """Load an image from a filename and provide it as output."""
+#     #fmt: off
+#     type: Literal["load_image"] = "load_image"
 
-    # fmt: off
-    type: Literal["load_image"] = "load_image"
+#     # Inputs
+#     image_type: ImageType = Field(description="The type of the image")
+#     image_name:       str = Field(description="The name of the image")
+#     #fmt: on
 
-    # Inputs
-    image_type: ImageType = Field(description="The type of the image")
-    image_name:       str = Field(description="The name of the image")
-    # fmt: on
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image_type, self.image_name)
-
-        return build_image_output(
-            image_type=self.image_type,
-            image_name=self.image_name,
-            image=image,
-        )
+#     def invoke(self, context: InvocationContext) -> ImageOutput:
+#         return ImageOutput(
+#             image_type=self.image_type,
+#             image_name=self.image_name,
+#             image=result_image
+#         )
 
 
 class ShowImageInvocation(BaseInvocation):
@@ -147,16 +145,9 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, image_crop, metadata)
+        context.services.images.save(image_type, image_name, image_crop, self.dict())
         return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=image_crop,
+            image_type=image_type, image_name=image_name, image=image_crop
         )
 
 
@@ -205,16 +196,9 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, new_image, metadata)
+        context.services.images.save(image_type, image_name, new_image, self.dict())
         return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=new_image,
+            image_type=image_type, image_name=image_name, image=new_image
         )
 
 
@@ -242,12 +226,7 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, image_mask, metadata)
+        context.services.images.save(image_type, image_name, image_mask, self.dict())
         return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
 
 
@@ -279,12 +258,7 @@ class BlurInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, blur_image, metadata)
+        context.services.images.save(image_type, image_name, blur_image, self.dict())
         return build_image_output(
             image_type=image_type, image_name=image_name, image=blur_image
         )
@@ -316,12 +290,7 @@ class LerpInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, lerp_image, metadata)
+        context.services.images.save(image_type, image_name, lerp_image, self.dict())
         return build_image_output(
             image_type=image_type, image_name=image_name, image=lerp_image
         )
@@ -358,12 +327,7 @@ class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, ilerp_image, metadata)
+        context.services.images.save(image_type, image_name, ilerp_image, self.dict())
         return build_image_output(
             image_type=image_type, image_name=image_name, image=ilerp_image
         )

invokeai/app/invocations/latent.py

@@ -1,13 +1,12 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-import random
 from typing import Literal, Optional
 from pydantic import BaseModel, Field
 import torch
 
+from invokeai.app.models.exceptions import CanceledException
 from invokeai.app.invocations.util.choose_model import choose_model
-
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.app.util.step_callback import diffusers_step_callback_adapter
 
 from ...backend.model_management.model_manager import ModelManager
 from ...backend.util.devices import choose_torch_device, torch_dtype
@@ -31,8 +30,6 @@ class LatentsField(BaseModel):
 
     latents_name: Optional[str] = Field(default=None, description="The name of the latents")
 
-    class Config:
-        schema_extra = {"required": ["latents_name"]}
 
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
@@ -102,17 +99,13 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c
     return x
 
 
-def random_seed():
-    return random.randint(0, np.iinfo(np.uint32).max)
-
-
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     type: Literal["noise"] = "noise"
 
     # Inputs
-    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
+    seed:        int = Field(default=0, ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", )
     width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting noise", )
     height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting noise", )
 
@@ -172,15 +165,22 @@ class TextToLatentsInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
+        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+    ) -> None:  
+        if (context.services.queue.is_canceled(context.graph_execution_state_id)):
+            raise CanceledException
 
+        step = intermediate_state.step
+        if intermediate_state.predicted_original is not None:
+            # Some schedulers report not only the noisy latents at the current timestep,
+            # but also their estimate so far of what the de-noised latents will be.
+            sample = intermediate_state.predicted_original
+        else:
+            sample = intermediate_state.latents
+
+        diffusers_step_callback_adapter(sample, step, steps=self.steps, id=self.id, context=context)
+
+    
     def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
         model_info = choose_model(model_manager, self.model)
         model_name = model_info['model_name']
@@ -190,7 +190,7 @@ class TextToLatentsInvocation(BaseInvocation):
             model=model,
             scheduler_name=self.scheduler
         )
-
+        
         if isinstance(model, DiffusionPipeline):
             for component in [model.unet, model.vae]:
                 configure_model_padding(component,
@@ -226,12 +226,8 @@ class TextToLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
         def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+            self.dispatch_progress(context, state)
 
         model = self.get_model(context.services.model_manager)
         conditioning_data = self.get_conditioning_data(model)
@@ -280,12 +276,8 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         noise = context.services.latents.get(self.noise.latents_name)
         latent = context.services.latents.get(self.latents.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
         def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+            self.dispatch_progress(context, state)
 
         model = self.get_model(context.services.model_manager)
         conditioning_data = self.get_conditioning_data(model)
@@ -295,7 +287,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
             latent, device=model.device, dtype=latent.dtype
         )
-
+        
         timesteps, _ = model.get_img2img_timesteps(
             self.steps,
             self.strength,
@@ -358,12 +350,7 @@ class LatentsToImageInvocation(BaseInvocation):
             image_name = context.services.images.create_name(
                 context.graph_execution_state_id, self.id
             )
-
-            metadata = context.services.metadata.build_metadata(
-                session_id=context.graph_execution_state_id, node=self
-            )
-
-            context.services.images.save(image_type, image_name, image, metadata)
+            context.services.images.save(image_type, image_name, image, self.dict())
             return build_image_output(
                 image_type=image_type,
                 image_name=image_name,

invokeai/app/invocations/params.py

@@ -1,18 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-from pydantic import Field
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
-from .math import IntOutput
-
-# Pass-through parameter nodes - used by subgraphs
-
-class ParamIntInvocation(BaseInvocation):
-    """An integer parameter"""
-    #fmt: off
-    type: Literal["param_int"] = "param_int"
-    a: int = Field(default=0, description="The integer value")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a)

invokeai/app/invocations/reconstruct.py

@@ -1,9 +1,10 @@
+from datetime import datetime, timezone
 from typing import Literal, Union
 
 from pydantic import Field
 
 from invokeai.app.models.image import ImageField, ImageType
-
+from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
 from .image import ImageOutput, build_image_output
 
@@ -43,12 +44,7 @@ class RestoreFaceInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, results[0][0], metadata)
+        context.services.images.save(image_type, image_name, results[0][0], self.dict())
         return build_image_output(
             image_type=image_type,
             image_name=image_name,

invokeai/app/invocations/upscale.py

@@ -1,10 +1,12 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from datetime import datetime, timezone
 from typing import Literal, Union
 
 from pydantic import Field
 
 from invokeai.app.models.image import ImageField, ImageType
+from ..services.invocation_services import InvocationServices
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
 from .image import ImageOutput, build_image_output
 
@@ -47,12 +49,7 @@ class UpscaleInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, results[0][0], metadata)
+        context.services.images.save(image_type, image_name, results[0][0], self.dict())
         return build_image_output(
             image_type=image_type,
             image_name=image_name,

invokeai/app/models/image.py

@@ -9,14 +9,6 @@ class ImageType(str, Enum):
     UPLOAD = "uploads"
 
 
-def is_image_type(obj):
-    try:
-        ImageType(obj)
-    except ValueError:
-        return False
-    return True
-
-
 class ImageField(BaseModel):
     """An image field used for passing image objects between invocations"""
 
@@ -26,4 +18,9 @@ class ImageField(BaseModel):
     image_name: Optional[str] = Field(default=None, description="The name of the image")
 
     class Config:
-        schema_extra = {"required": ["image_type", "image_name"]}
+        schema_extra = {
+            "required": [
+                "image_type",
+                "image_name",
+            ]
+        }

invokeai/app/models/metadata.py

@@ -0,0 +1,26 @@
+from typing import Any, Optional, Dict
+from pydantic import BaseModel, Field
+
+
+class InvokeAIMetadata(BaseModel):
+    """An image's InvokeAI-specific metadata"""
+
+    session: Optional[str] = Field(description="The session that generated this image")
+    source_id: Optional[str] = Field(
+        description="The source id of the invocation that generated this image"
+    )
+    # TODO: figure out metadata
+    invocation: Optional[Dict[str, Any]] = Field(
+        default={}, description="The prepared invocation that generated this image"
+    )
+
+
+class ImageMetadata(BaseModel):
+    """An image's general metadata"""
+
+    created: int = Field(description="The creation timestamp of the image")
+    width: int = Field(description="The width of the image in pixels")
+    height: int = Field(description="The height of the image in pixels")
+    invokeai: Optional[InvokeAIMetadata] = Field(
+        default={}, description="The image's InvokeAI-specific metadata"
+    )

invokeai/app/services/default_graphs.py

@@ -1,56 +0,0 @@
-from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
-from ..invocations.params import ParamIntInvocation
-from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
-from .item_storage import ItemStorageABC
-
-
-default_text_to_image_graph_id = '539b2af5-2b4d-4d8c-8071-e54a3255fc74'
-
-
-def create_text_to_image() -> LibraryGraph:
-    return LibraryGraph(
-        id=default_text_to_image_graph_id,
-        name='t2i',
-        description='Converts text to an image',
-        graph=Graph(
-            nodes={
-                'width': ParamIntInvocation(id='width', a=512),
-                'height': ParamIntInvocation(id='height', a=512),
-                '3': NoiseInvocation(id='3'),
-                '4': TextToLatentsInvocation(id='4'),
-                '5': LatentsToImageInvocation(id='5')
-            },
-            edges=[
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='4', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='4', field='height')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
-                Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
-            ]
-        ),
-        exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='prompt'),
-            ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height')
-        ],
-        exposed_outputs=[
-            ExposedNodeOutput(node_path='5', field='image', alias='image')
-        ])
-
-
-def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
-    """Creates the default system graphs, or adds new versions if the old ones don't match"""
-
-    graphs: list[LibraryGraph] = list()
-
-    text_to_image = graph_library.get(default_text_to_image_graph_id)
-    
-    # TODO: Check if the graph is the same as the default one, and if not, update it
-    #if text_to_image is None:
-    text_to_image = create_text_to_image()
-    graph_library.set(text_to_image)
-
-    graphs.append(text_to_image)
-
-    return graphs

invokeai/app/services/events.py

@@ -1,9 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any
-from invokeai.app.api.models.images import ProgressImage
-from invokeai.app.util.misc import get_timestamp
+from typing import Any, Dict, TypedDict
 
+ProgressImage = TypedDict(
+    "ProgressImage", {"dataURL": str, "width": int, "height": int}
+)
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -13,8 +14,7 @@ class EventServiceBase:
     def dispatch(self, event_name: str, payload: Any) -> None:
         pass
 
-    def __emit_session_event(self, event_name: str, payload: dict) -> None:
-        payload["timestamp"] = get_timestamp()
+    def __emit_session_event(self, event_name: str, payload: Dict) -> None:
         self.dispatch(
             event_name=EventServiceBase.session_event,
             payload=dict(event=event_name, data=payload),
@@ -25,8 +25,8 @@ class EventServiceBase:
     def emit_generator_progress(
         self,
         graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
+        invocation_dict: dict,
+        source_id: str,
         progress_image: ProgressImage | None,
         step: int,
         total_steps: int,
@@ -36,60 +36,52 @@ class EventServiceBase:
             event_name="generator_progress",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
-                progress_image=progress_image.dict() if progress_image is not None else None,
+                invocation=invocation_dict,
+                source_id=source_id,
+                progress_image=progress_image,
                 step=step,
                 total_steps=total_steps,
             ),
         )
 
     def emit_invocation_complete(
-        self,
-        graph_execution_state_id: str,
-        result: dict,
-        node: dict,
-        source_node_id: str,
+        self, graph_execution_state_id: str, result: Dict, invocation_dict: Dict, source_id: str,
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_session_event(
             event_name="invocation_complete",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
+                invocation=invocation_dict,
+                source_id=source_id,
                 result=result,
             ),
         )
 
     def emit_invocation_error(
-        self,
-        graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
-        error: str,
+        self, graph_execution_state_id: str, invocation_dict: Dict, source_id: str, error: str
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_session_event(
             event_name="invocation_error",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
+                invocation=invocation_dict,
+                source_id=source_id,
                 error=error,
             ),
         )
 
     def emit_invocation_started(
-        self, graph_execution_state_id: str, node: dict, source_node_id: str
+        self, graph_execution_state_id: str, invocation_dict: Dict, source_id: str
     ) -> None:
         """Emitted when an invocation has started"""
         self.__emit_session_event(
             event_name="invocation_started",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
+                invocation=invocation_dict,
+                source_id=source_id,
             ),
         )
 
@@ -97,7 +89,5 @@ class EventServiceBase:
         """Emitted when a session has completed all invocations"""
         self.__emit_session_event(
             event_name="graph_execution_state_complete",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-            ),
+            payload=dict(graph_execution_state_id=graph_execution_state_id),
         )

invokeai/app/services/graph.py

@@ -2,6 +2,7 @@
 
 import copy
 import itertools
+import traceback
 import uuid
 from types import NoneType
 from typing import (
@@ -16,7 +17,7 @@ from typing import (
 )
 
 import networkx as nx
-from pydantic import BaseModel, root_validator, validator
+from pydantic import BaseModel, validator
 from pydantic.fields import Field
 
 from ..invocations import *
@@ -25,6 +26,7 @@ from ..invocations.baseinvocation import (
     BaseInvocationOutput,
     InvocationContext,
 )
+from .invocation_services import InvocationServices
 
 
 class EdgeConnection(BaseModel):
@@ -213,7 +215,7 @@ InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()]
 
 
 class Graph(BaseModel):
-    id: str = Field(description="The id of this graph", default_factory=lambda: uuid.uuid4().__str__())
+    id: str = Field(description="The id of this graph", default_factory=uuid.uuid4)
     # TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
     nodes: dict[str, Annotated[InvocationsUnion, Field(discriminator="type")]] = Field(
         description="The nodes in this graph", default_factory=dict
@@ -281,8 +283,7 @@ class Graph(BaseModel):
         :raises InvalidEdgeError: the provided edge is invalid.
         """
 
-        self._validate_edge(edge)
-        if edge not in self.edges:
+        if self._is_edge_valid(edge) and edge not in self.edges:
             self.edges.append(edge)
         else:
             raise InvalidEdgeError()
@@ -353,7 +354,7 @@ class Graph(BaseModel):
 
         return True
 
-    def _validate_edge(self, edge: Edge):
+    def _is_edge_valid(self, edge: Edge) -> bool:
         """Validates that a new edge doesn't create a cycle in the graph"""
 
         # Validate that the nodes exist (edges may contain node paths, so we can't just check for nodes directly)
@@ -361,53 +362,54 @@ class Graph(BaseModel):
             from_node = self.get_node(edge.source.node_id)
             to_node = self.get_node(edge.destination.node_id)
         except NodeNotFoundError:
-            raise InvalidEdgeError("One or both nodes don't exist")
+            return False
 
         # Validate that an edge to this node+field doesn't already exist
         input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
         if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
-            raise InvalidEdgeError(f'Edge to node {edge.destination.node_id} field {edge.destination.field} already exists')
+            return False
 
         # Validate that no cycles would be created
         g = self.nx_graph_flat()
         g.add_edge(edge.source.node_id, edge.destination.node_id)
         if not nx.is_directed_acyclic_graph(g):
-            raise InvalidEdgeError(f'Edge creates a cycle in the graph')
+            return False
 
         # Validate that the field types are compatible
         if not are_connections_compatible(
             from_node, edge.source.field, to_node, edge.destination.field
         ):
-            raise InvalidEdgeError(f'Fields are incompatible')
+            return False
 
         # Validate if iterator output type matches iterator input type (if this edge results in both being set)
         if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
             if not self._is_iterator_connection_valid(
                 edge.destination.node_id, new_input=edge.source
             ):
-                raise InvalidEdgeError(f'Iterator input type does not match iterator output type')
+                return False
 
         # Validate if iterator input type matches output type (if this edge results in both being set)
         if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
             if not self._is_iterator_connection_valid(
                 edge.source.node_id, new_output=edge.destination
             ):
-                raise InvalidEdgeError(f'Iterator output type does not match iterator input type')
+                return False
 
         # Validate if collector input type matches output type (if this edge results in both being set)
         if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
             if not self._is_collector_connection_valid(
                 edge.destination.node_id, new_input=edge.source
             ):
-                raise InvalidEdgeError(f'Collector output type does not match collector input type')
+                return False
 
         # Validate if collector output type matches input type (if this edge results in both being set)
         if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
             if not self._is_collector_connection_valid(
                 edge.source.node_id, new_output=edge.destination
             ):
-                raise InvalidEdgeError(f'Collector input type does not match collector output type')
+                return False
 
+        return True
 
     def has_node(self, node_path: str) -> bool:
         """Determines whether or not a node exists in the graph."""
@@ -731,7 +733,7 @@ class Graph(BaseModel):
         for sgn in (
             gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)
         ):
-            g = sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
+            sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
 
         # TODO: figure out if iteration nodes need to be expanded
 
@@ -748,7 +750,9 @@ class Graph(BaseModel):
 class GraphExecutionState(BaseModel):
     """Tracks the state of a graph execution"""
 
-    id: str = Field(description="The id of the execution state", default_factory=lambda: uuid.uuid4().__str__())
+    id: str = Field(
+        description="The id of the execution state", default_factory=uuid.uuid4
+    )
 
     # TODO: Store a reference to the graph instead of the actual graph?
     graph: Graph = Field(description="The graph being executed")
@@ -854,8 +858,7 @@ class GraphExecutionState(BaseModel):
 
     def is_complete(self) -> bool:
         """Returns true if the graph is complete"""
-        node_ids = set(self.graph.nx_graph_flat().nodes)
-        return self.has_error() or all((k in self.executed for k in node_ids))
+        return self.has_error() or all((k in self.executed for k in self.graph.nodes))
 
     def has_error(self) -> bool:
         """Returns true if the graph has any errors"""
@@ -943,11 +946,11 @@ class GraphExecutionState(BaseModel):
 
     def _iterator_graph(self) -> nx.DiGraph:
         """Gets a DiGraph with edges to collectors removed so an ancestor search produces all active iterators for any node"""
-        g = self.graph.nx_graph_flat()
+        g = self.graph.nx_graph()
         collectors = (
             n
             for n in self.graph.nodes
-            if isinstance(self.graph.get_node(n), CollectInvocation)
+            if isinstance(self.graph.nodes[n], CollectInvocation)
         )
         for c in collectors:
             g.remove_edges_from(list(g.in_edges(c)))
@@ -959,7 +962,7 @@ class GraphExecutionState(BaseModel):
         iterators = [
             n
             for n in nx.ancestors(g, node_id)
-            if isinstance(self.graph.get_node(n), IterateInvocation)
+            if isinstance(self.graph.nodes[n], IterateInvocation)
         ]
         return iterators
 
@@ -1095,9 +1098,7 @@ class GraphExecutionState(BaseModel):
 
     # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
     def _is_edge_valid(self, edge: Edge) -> bool:
-        try:
-            self.graph._validate_edge(edge)
-        except InvalidEdgeError:
+        if not self._is_edge_valid(edge):
             return False
 
         # Invalid if destination has already been prepared or executed
@@ -1143,52 +1144,4 @@ class GraphExecutionState(BaseModel):
         self.graph.delete_edge(edge)
 
 
-class ExposedNodeInput(BaseModel):
-    node_path: str = Field(description="The node path to the node with the input")
-    field: str = Field(description="The field name of the input")
-    alias: str = Field(description="The alias of the input")
-
-
-class ExposedNodeOutput(BaseModel):
-    node_path: str = Field(description="The node path to the node with the output")
-    field: str = Field(description="The field name of the output")
-    alias: str = Field(description="The alias of the output")
-
-class LibraryGraph(BaseModel):
-    id: str = Field(description="The unique identifier for this library graph", default_factory=uuid.uuid4)
-    graph: Graph = Field(description="The graph")
-    name: str = Field(description="The name of the graph")
-    description: str = Field(description="The description of the graph")
-    exposed_inputs: list[ExposedNodeInput] = Field(description="The inputs exposed by this graph", default_factory=list)
-    exposed_outputs: list[ExposedNodeOutput] = Field(description="The outputs exposed by this graph", default_factory=list)
-
-    @validator('exposed_inputs', 'exposed_outputs')
-    def validate_exposed_aliases(cls, v):
-        if len(v) != len(set(i.alias for i in v)):
-            raise ValueError("Duplicate exposed alias")
-        return v
-
-    @root_validator
-    def validate_exposed_nodes(cls, values):
-        graph = values['graph']
-
-        # Validate exposed inputs
-        for exposed_input in values['exposed_inputs']:
-            if not graph.has_node(exposed_input.node_path):
-                raise ValueError(f"Exposed input node {exposed_input.node_path} does not exist")
-            node = graph.get_node(exposed_input.node_path)
-            if get_input_field(node, exposed_input.field) is None:
-                raise ValueError(f"Exposed input field {exposed_input.field} does not exist on node {exposed_input.node_path}")
-
-        # Validate exposed outputs
-        for exposed_output in values['exposed_outputs']:
-            if not graph.has_node(exposed_output.node_path):
-                raise ValueError(f"Exposed output node {exposed_output.node_path} does not exist")
-            node = graph.get_node(exposed_output.node_path)
-            if get_output_field(node, exposed_output.field) is None:
-                raise ValueError(f"Exposed output field {exposed_output.field} does not exist on node {exposed_output.node_path}")
-
-        return values
-
-
 GraphInvocation.update_forward_refs()

invokeai/app/services/image_storage.py

@@ -1,24 +1,25 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import datetime
 import os
+import json
 from glob import glob
 from abc import ABC, abstractmethod
+from enum import Enum
 from pathlib import Path
 from queue import Queue
-from typing import Dict, List, Tuple
+from typing import Any, Callable, Dict, List, Union
 
 from PIL.Image import Image
 import PIL.Image as PILImage
-from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
-from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import (
-    InvokeAIMetadata,
-    MetadataServiceBase,
-    build_invokeai_metadata_pnginfo,
-)
+from pydantic import BaseModel, Json
+from invokeai.app.api.models.images import ImageResponse
+from invokeai.app.models.image import ImageField, ImageType
+from invokeai.app.models.metadata import ImageMetadata
 from invokeai.app.services.item_storage import PaginatedResults
-from invokeai.app.util.misc import get_timestamp
-from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
+from invokeai.app.util.save_thumbnail import save_thumbnail
+
+from invokeai.backend.image_util import PngWriter
 
 
 class ImageStorageBase(ABC):
@@ -26,14 +27,12 @@ class ImageStorageBase(ABC):
 
     @abstractmethod
     def get(self, image_type: ImageType, image_name: str) -> Image:
-        """Retrieves an image as PIL Image."""
         pass
 
     @abstractmethod
     def list(
         self, image_type: ImageType, page: int = 0, per_page: int = 10
     ) -> PaginatedResults[ImageResponse]:
-        """Gets a paginated list of images."""
         pass
 
     # TODO: make this a bit more flexible for e.g. cloud storage
@@ -41,51 +40,35 @@ class ImageStorageBase(ABC):
     def get_path(
         self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
     ) -> str:
-        """Gets the path to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image path."""
         pass
 
     @abstractmethod
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> Tuple[str, str, int]:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image path, thumbnail path, and created timestamp."""
+    def save(self, image_type: ImageType, image_name: str, image: Image, metadata: Dict[str, Any] | None = None) -> str:
         pass
 
     @abstractmethod
     def delete(self, image_type: ImageType, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
         pass
 
     def create_name(self, context_id: str, node_id: str) -> str:
-        """Creates a unique contextual image filename."""
-        return f"{context_id}_{node_id}_{str(get_timestamp())}.png"
+        return f"{context_id}_{node_id}_{str(int(datetime.datetime.now(datetime.timezone.utc).timestamp()))}.png"
 
 
 class DiskImageStorage(ImageStorageBase):
     """Stores images on disk"""
 
     __output_folder: str
+    __pngWriter: PngWriter
     __cache_ids: Queue  # TODO: this is an incredibly naive cache
     __cache: Dict[str, Image]
     __max_cache_size: int
-    __metadata_service: MetadataServiceBase
 
-    def __init__(self, output_folder: str, metadata_service: MetadataServiceBase):
+    def __init__(self, output_folder: str):
         self.__output_folder = output_folder
+        self.__pngWriter = PngWriter(output_folder)
         self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = 10  # TODO: get this from config
-        self.__metadata_service = metadata_service
 
         Path(output_folder).mkdir(parents=True, exist_ok=True)
 
@@ -118,8 +101,7 @@ class DiskImageStorage(ImageStorageBase):
         for path in page_of_image_paths:
             filename = os.path.basename(path)
             img = PILImage.open(path)
-
-            invokeai_metadata = self.__metadata_service.get_metadata(img)
+            invokeai_metadata = json.loads(img.info.get("invokeai", "{}"))
 
             page_of_images.append(
                 ImageResponse(
@@ -128,12 +110,12 @@ class DiskImageStorage(ImageStorageBase):
                     # TODO: DiskImageStorage should not be building URLs...?
                     image_url=f"api/v1/images/{image_type.value}/{filename}",
                     thumbnail_url=f"api/v1/images/{image_type.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
-                    # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
-                    metadata=ImageResponseMetadata(
+                    # TODO: Creation of this object should happen elsewhere, just making it fit here so it works
+                    metadata=ImageMetadata(
                         created=int(os.path.getctime(path)),
                         width=img.width,
                         height=img.height,
-                        invokeai=invokeai_metadata,
+                        invokeai=invokeai_metadata
                     ),
                 )
             )
@@ -164,50 +146,28 @@ class DiskImageStorage(ImageStorageBase):
     def get_path(
         self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
     ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
         if is_thumbnail:
             path = os.path.join(
-                self.__output_folder, image_type, "thumbnails", basename
+                self.__output_folder, image_type, "thumbnails", image_name
             )
         else:
-            path = os.path.join(self.__output_folder, image_type, basename)
-
+            path = os.path.join(self.__output_folder, image_type, image_name)
         return path
 
-    def validate_path(self, path: str) -> bool:
-        try:
-            os.stat(path)
-            return True
-        except Exception:
-            return False
-
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> Tuple[str, str, int]:
+    def save(self, image_type: ImageType, image_name: str, image: Image, metadata: Dict[str, Any] | None = None) -> str:
+        print(metadata)
+        image_subpath = os.path.join(image_type, image_name)
+        self.__pngWriter.save_image_and_prompt_to_png(
+            image, "", image_subpath, metadata
+        )  # TODO: just pass full path to png writer
+        save_thumbnail(
+            image=image,
+            filename=image_name,
+            path=os.path.join(self.__output_folder, image_type, "thumbnails"),
+        )
         image_path = self.get_path(image_type, image_name)
-
-        # TODO: Reading the image and then saving it strips the metadata...
-        if metadata:
-            pnginfo = build_invokeai_metadata_pnginfo(metadata=metadata)
-            image.save(image_path, "PNG", pnginfo=pnginfo)
-        else:
-            image.save(image_path) # this saved image has an empty info
-
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, is_thumbnail=True)
-        thumbnail_image = make_thumbnail(image)
-        thumbnail_image.save(thumbnail_path)
-
         self.__set_cache(image_path, image)
-        self.__set_cache(thumbnail_path, thumbnail_image)
-
-        return (image_path, thumbnail_path, int(os.path.getctime(image_path)))
+        return image_path
 
     def delete(self, image_type: ImageType, image_name: str) -> None:
         image_path = self.get_path(image_type, image_name)

invokeai/app/services/invocation_queue.py

@@ -1,17 +1,30 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-import time
 from abc import ABC, abstractmethod
 from queue import Queue
-
-from pydantic import BaseModel, Field
+import time
 
 
-class InvocationQueueItem(BaseModel):
-    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
-    invocation_id: str = Field(description="The ID of the node being invoked")
-    invoke_all: bool = Field(default=False)
-    timestamp: float = Field(default_factory=time.time)
+# TODO: make this serializable
+class InvocationQueueItem:
+    # session_id: str
+    graph_execution_state_id: str
+    invocation_id: str
+    invoke_all: bool
+    timestamp: float
+
+    def __init__(
+        self,
+        # session_id: str,
+        graph_execution_state_id: str,
+        invocation_id: str,
+        invoke_all: bool = False,
+    ):
+        # self.session_id = session_id
+        self.graph_execution_state_id = graph_execution_state_id
+        self.invocation_id = invocation_id
+        self.invoke_all = invoke_all
+        self.timestamp = time.time()
 
 
 class InvocationQueueABC(ABC):

invokeai/app/services/invocation_services.py

@@ -1,5 +1,4 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-from invokeai.app.services.metadata import MetadataServiceBase
 from invokeai.backend import ModelManager
 
 from .events import EventServiceBase
@@ -15,13 +14,11 @@ class InvocationServices:
     events: EventServiceBase
     latents: LatentsStorageBase
     images: ImageStorageBase
-    metadata: MetadataServiceBase
     queue: InvocationQueueABC
     model_manager: ModelManager
     restoration: RestorationServices
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
-    graph_library: ItemStorageABC["LibraryGraph"]
     graph_execution_manager: ItemStorageABC["GraphExecutionState"]
     processor: "InvocationProcessorABC"
 
@@ -31,9 +28,7 @@ class InvocationServices:
             events: EventServiceBase,
             latents: LatentsStorageBase,
             images: ImageStorageBase,
-            metadata: MetadataServiceBase,
             queue: InvocationQueueABC,
-            graph_library: ItemStorageABC["LibraryGraph"],
             graph_execution_manager: ItemStorageABC["GraphExecutionState"],
             processor: "InvocationProcessorABC",
             restoration: RestorationServices,
@@ -42,9 +37,7 @@ class InvocationServices:
         self.events = events
         self.latents = latents
         self.images = images
-        self.metadata = metadata
         self.queue = queue
-        self.graph_library = graph_library
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor
         self.restoration = restoration

invokeai/app/services/metadata.py

@@ -1,96 +0,0 @@
-import json
-from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, TypedDict
-from PIL import Image, PngImagePlugin
-from pydantic import BaseModel
-
-from invokeai.app.models.image import ImageType, is_image_type
-
-
-class MetadataImageField(TypedDict):
-    """Pydantic-less ImageField, used for metadata parsing."""
-
-    image_type: ImageType
-    image_name: str
-
-
-class MetadataLatentsField(TypedDict):
-    """Pydantic-less LatentsField, used for metadata parsing."""
-
-    latents_name: str
-
-
-# TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
-NodeMetadata = Dict[
-    str, str | int | float | bool | MetadataImageField | MetadataLatentsField
-]
-
-
-class InvokeAIMetadata(TypedDict, total=False):
-    """InvokeAI-specific metadata format."""
-
-    session_id: Optional[str]
-    node: Optional[NodeMetadata]
-
-
-def build_invokeai_metadata_pnginfo(
-    metadata: InvokeAIMetadata | None,
-) -> PngImagePlugin.PngInfo:
-    """Builds a PngInfo object with key `"invokeai"` and value `metadata`"""
-    pnginfo = PngImagePlugin.PngInfo()
-
-    if metadata is not None:
-        pnginfo.add_text("invokeai", json.dumps(metadata))
-
-    return pnginfo
-
-
-class MetadataServiceBase(ABC):
-    @abstractmethod
-    def get_metadata(self, image: Image.Image) -> InvokeAIMetadata | None:
-        """Gets the InvokeAI metadata from a PIL Image, skipping invalid values"""
-        pass
-
-    @abstractmethod
-    def build_metadata(
-        self, session_id: str, node: BaseModel
-    ) -> InvokeAIMetadata | None:
-        """Builds an InvokeAIMetadata object"""
-        pass
-
-
-class PngMetadataService(MetadataServiceBase):
-    """Handles loading and building metadata for images."""
-
-    # TODO: Use `InvocationsUnion` to **validate** metadata as representing a fully-functioning node
-    def _load_metadata(self, image: Image.Image) -> dict | None:
-        """Loads a specific info entry from a PIL Image."""
-
-        try:
-            info = image.info.get("invokeai")
-
-            if type(info) is not str:
-                return None
-
-            loaded_metadata = json.loads(info)
-
-            if type(loaded_metadata) is not dict:
-                return None
-
-            if len(loaded_metadata.items()) == 0:
-                return None
-
-            return loaded_metadata
-        except:
-            return None
-
-    def get_metadata(self, image: Image.Image) -> dict | None:
-        """Retrieves an image's metadata as a dict"""
-        loaded_metadata = self._load_metadata(image)
-
-        return loaded_metadata
-
-    def build_metadata(self, session_id: str, node: BaseModel) -> InvokeAIMetadata:
-        metadata = InvokeAIMetadata(session_id=session_id, node=node.dict())
-
-        return metadata

invokeai/app/services/processor.py

@@ -43,14 +43,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     queue_item.invocation_id
                 )
 
-                # get the source node id to provide to clients (the prepared node id is not as useful)
-                source_node_id = graph_execution_state.prepared_source_mapping[invocation.id]
+                # get the source node to provide to cliepnts (the prepared node is not as useful)
+                source_id = graph_execution_state.prepared_source_mapping[invocation.id]
 
                 # Send starting event
                 self.__invoker.services.events.emit_invocation_started(
                     graph_execution_state_id=graph_execution_state.id,
-                    node=invocation.dict(),
-                    source_node_id=source_node_id
+                    invocation_dict=invocation.dict(),
+                    source_id=source_id
                 )
 
                 # Invoke
@@ -79,8 +79,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # Send complete event
                     self.__invoker.services.events.emit_invocation_complete(
                         graph_execution_state_id=graph_execution_state.id,
-                        node=invocation.dict(),
-                        source_node_id=source_node_id,
+                        invocation_dict=invocation.dict(),
+                        source_id=source_id,
                         result=outputs.dict(),
                     )
 
@@ -104,8 +104,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # Send error event
                     self.__invoker.services.events.emit_invocation_error(
                         graph_execution_state_id=graph_execution_state.id,
-                        node=invocation.dict(),
-                        source_node_id=source_node_id,
+                        invocation_dict=invocation.dict(),
+                        source_id=source_id,
                         error=error,
                     )
 

invokeai/app/services/sqlite.py

@@ -1,23 +1,25 @@
-import sqlite3
-from threading import Lock
 from typing import Generic, TypeVar, Union, get_args
-
 from pydantic import BaseModel, parse_raw_as
 
 from .item_storage import ItemStorageABC, PaginatedResults
 
+from sqlalchemy import create_engine, String, TEXT, Engine, select
+from sqlalchemy.orm import DeclarativeBase, mapped_column, Session
+
+
 T = TypeVar("T", bound=BaseModel)
 
-sqlite_memory = ":memory:"
+
+class Base(DeclarativeBase):
+    pass
 
 
 class SqliteItemStorage(ItemStorageABC, Generic[T]):
     _filename: str
     _table_name: str
-    _conn: sqlite3.Connection
-    _cursor: sqlite3.Cursor
     _id_field: str
-    _lock: Lock
+    _engine: Engine
+    # _table: ??? # TODO: figure out how to type this
 
     def __init__(self, filename: str, table_name: str, id_field: str = "id"):
         super().__init__()
@@ -25,86 +27,79 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
         self._filename = filename
         self._table_name = table_name
         self._id_field = id_field  # TODO: validate that T has this field
-        self._lock = Lock()
-
-        self._conn = sqlite3.connect(
-            self._filename, check_same_thread=False
-        )  # TODO: figure out a better threading solution
-        self._cursor = self._conn.cursor()
 
+        self._engine = create_engine(f"sqlite+pysqlite:///{self._filename}")
         self._create_table()
 
     def _create_table(self):
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""CREATE TABLE IF NOT EXISTS {self._table_name} (
-                item TEXT,
-                id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);"""
-            )
-            self._cursor.execute(
-                f"""CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);"""
-            )
-        finally:
-            self._lock.release()
+        # dynamically create the ORM model class to avoid name collisions
+
+        # cannot access `self.__orig_class__` in `__init__` or `__new__` so
+        # format the table name into the class name
+        pascal_table_name = self._table_name.replace("_", " ").title()
+        pascal_table_name = pascal_table_name.replace(" ", "")
+
+        table_dict = dict(
+            __tablename__=self._table_name,
+            id=mapped_column(String, primary_key=True),
+            item=mapped_column(TEXT, nullable=False),
+        )
+
+        self._table = type(pascal_table_name, (Base,), table_dict)
+
+        Base.metadata.create_all(self._engine)
 
     def _parse_item(self, item: str) -> T:
         item_type = get_args(self.__orig_class__)[0]
         return parse_raw_as(item_type, item)
 
     def set(self, item: T):
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
-                (item.json(),),
-            )
-            self._conn.commit()
-        finally:
-            self._lock.release()
+        session = Session(self._engine)
+
+        item_id = str(getattr(item, self._id_field))
+        new_item = self._table(id=item_id, item=item.json())
+
+        session.merge(new_item)
+
+        session.commit()
+        session.close()
+
         self._on_changed(item)
 
     def get(self, id: str) -> Union[T, None]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
-            result = self._cursor.fetchone()
-        finally:
-            self._lock.release()
+        session = Session(self._engine)
 
-        if not result:
+        item = session.get(self._table, str(id))
+
+        session.close()
+
+        if not item:
             return None
 
-        return self._parse_item(result[0])
+        return self._parse_item(item.item)
 
     def delete(self, id: str):
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),)
-            )
-            self._conn.commit()
-        finally:
-            self._lock.release()
+        session = Session(self._engine)
+
+        item = session.get(self._table, id)
+        session.delete(item)
+
+        session.commit()
+        session.close()
+
         self._on_deleted(id)
 
     def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;""",
-                (per_page, page * per_page),
-            )
-            result = self._cursor.fetchall()
+        session = Session(self._engine)
 
-            items = list(map(lambda r: self._parse_item(r[0]), result))
+        stmt = select(self._table.item).limit(per_page).offset(page * per_page)
+        result = session.execute(stmt)
 
-            self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
-            count = self._cursor.fetchone()[0]
-        finally:
-            self._lock.release()
+        items = list(map(lambda r: self._parse_item(r[0]), result))
+        count = session.query(self._table.item).count()
+
+        session.commit()
+        session.close()
 
         pageCount = int(count / per_page) + 1
 
@@ -115,23 +110,19 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
     def search(
         self, query: str, page: int = 0, per_page: int = 10
     ) -> PaginatedResults[T]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                f"""SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;""",
-                (f"%{query}%", per_page, page * per_page),
-            )
-            result = self._cursor.fetchall()
+        session = Session(self._engine)
 
-            items = list(map(lambda r: self._parse_item(r[0]), result))
+        stmt = (
+            session.query(self._table)
+            .where(self._table.item.like(f"%{query}%"))
+            .limit(per_page)
+            .offset(page * per_page)
+        )
 
-            self._cursor.execute(
-                f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",
-                (f"%{query}%",),
-            )
-            count = self._cursor.fetchone()[0]
-        finally:
-            self._lock.release()
+        result = session.execute(stmt)
+
+        items = list(map(lambda r: self._parse_item(r[0].item), result))
+        count = session.query(self._table.item).count()
 
         pageCount = int(count / per_page) + 1
 

invokeai/app/util/misc.py

@@ -1,5 +0,0 @@
-import datetime
-
-
-def get_timestamp():
-    return int(datetime.datetime.now(datetime.timezone.utc).timestamp())

invokeai/app/util/save_thumbnail.py

@@ -0,0 +1,25 @@
+import os
+from PIL import Image
+
+
+def save_thumbnail(
+    image: Image.Image,
+    filename: str,
+    path: str,
+    size: int = 256,
+) -> str:
+    """
+    Saves a thumbnail of an image, returning its path.
+    """
+    base_filename = os.path.splitext(filename)[0]
+    thumbnail_path = os.path.join(path, base_filename + ".webp")
+
+    if os.path.exists(thumbnail_path):
+        return thumbnail_path
+
+    image_copy = image.copy()
+    image_copy.thumbnail(size=(size, size))
+
+    image_copy.save(thumbnail_path, "WEBP")
+
+    return thumbnail_path

invokeai/app/util/step_callback.py

@@ -1,41 +1,17 @@
-from invokeai.app.api.models.images import ProgressImage
-from invokeai.app.models.exceptions import CanceledException
+from re import S
+import torch
 from ..invocations.baseinvocation import InvocationContext
 from ...backend.util.util import image_to_dataURL
 from ...backend.generator.base import Generator
 from ...backend.stable_diffusion import PipelineIntermediateState
 
-
-def stable_diffusion_step_callback(
+def fast_latents_step_callback(
+    sample: torch.Tensor,
+    step: int,
+    steps: int,
+    id: str,
     context: InvocationContext,
-    intermediate_state: PipelineIntermediateState,
-    node: dict,
-    source_node_id: str,
 ):
-    if context.services.queue.is_canceled(context.graph_execution_state_id):
-        raise CanceledException
-
-    # Some schedulers report not only the noisy latents at the current timestep,
-    # but also their estimate so far of what the de-noised latents will be. Use
-    # that estimate if it is available.
-    if intermediate_state.predicted_original is not None:
-        sample = intermediate_state.predicted_original
-    else:
-        sample = intermediate_state.latents
-
-    # TODO: This does not seem to be needed any more?
-    # # txt2img provides a Tensor in the step_callback
-    # # img2img provides a PipelineIntermediateState
-    # if isinstance(sample, PipelineIntermediateState):
-    #     # this was an img2img
-    #     print('img2img')
-    #     latents = sample.latents
-    #     step = sample.step
-    # else:
-    #     print('txt2img')
-    #     latents = sample
-    #     step = intermediate_state.step
-
     # TODO: only output a preview image when requested
     image = Generator.sample_to_lowres_estimated_image(sample)
 
@@ -45,11 +21,30 @@ def stable_diffusion_step_callback(
 
     dataURL = image_to_dataURL(image, image_format="JPEG")
 
+    graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+    source_id = graph_execution_state.prepared_source_mapping[id]
+
+    invocation = graph_execution_state.execution_graph.get_node(id)
+
     context.services.events.emit_generator_progress(
         graph_execution_state_id=context.graph_execution_state_id,
-        node=node,
-        source_node_id=source_node_id,
-        progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
-        step=intermediate_state.step,
-        total_steps=node["steps"],
+        invocation_dict=invocation.dict(),
+        source_id=source_id,
+        progress_image={"width": width, "height": height, "dataURL": dataURL},
+        step=step,
+        total_steps=steps,
     )
+
+
+def diffusers_step_callback_adapter(*cb_args, **kwargs):
+    """
+    txt2img gives us a Tensor in the step_callbak, while img2img gives us a PipelineIntermediateState.
+    This adapter grabs the needed data and passes it along to the callback function.
+    """
+    if isinstance(cb_args[0], PipelineIntermediateState):
+        progress_state: PipelineIntermediateState = cb_args[0]
+        return fast_latents_step_callback(
+            progress_state.latents, progress_state.step, **kwargs
+        )
+    else:
+        return fast_latents_step_callback(*cb_args, **kwargs)

invokeai/app/util/thumbnails.py

@@ -1,15 +0,0 @@
-import os
-from PIL import Image
-
-
-def get_thumbnail_name(image_name: str) -> str:
-    """Formats given an image name, returns the appropriate thumbnail image name"""
-    thumbnail_name = os.path.splitext(image_name)[0] + ".webp"
-    return thumbnail_name
-
-
-def make_thumbnail(image: Image.Image, size: int = 256) -> Image.Image:
-    """Makes a thumbnail from a PIL Image"""
-    thumbnail = image.copy()
-    thumbnail.thumbnail(size=(size, size))
-    return thumbnail

invokeai/backend/__init__.py

@@ -10,7 +10,7 @@ from .generator import (
     Img2Img,
     Inpaint
 )
-from .model_management import ModelManager, SDModelComponent
+from .model_management import ModelManager
 from .safety_checker import SafetyChecker
 from .args import Args
 from .globals import Globals

invokeai/backend/generator/base.py

@@ -86,11 +86,9 @@ class InvokeAIGenerator(metaclass=ABCMeta):
     def __init__(self,
                  model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
-                 **kwargs,
                  ):
         self.model_info=model_info
         self.params=params
-        self.kwargs = kwargs
 
     def generate(self,
                  prompt: str='',
@@ -131,12 +129,9 @@ class InvokeAIGenerator(metaclass=ABCMeta):
             model=model,
             scheduler_name=generator_args.get('scheduler')
         )
-
-        # get conditioning from prompt via Compel package
-        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt, model=model)
-
+        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(prompt,model=model)
         gen_class = self._generator_class()
-        generator = gen_class(model, self.params.precision, **self.kwargs)
+        generator = gen_class(model, self.params.precision)
         if self.params.variation_amount > 0:
             generator.set_variation(generator_args.get('seed'),
                                     generator_args.get('variation_amount'),
@@ -286,7 +281,7 @@ class Generator:
     precision: str
     model: DiffusionPipeline
 
-    def __init__(self, model: DiffusionPipeline, precision: str, **kwargs):
+    def __init__(self, model: DiffusionPipeline, precision: str):
         self.model = model
         self.precision = precision
         self.seed = None
@@ -359,6 +354,7 @@ class Generator:
         seed = seed if seed is not None and seed >= 0 else self.new_seed()
         first_seed = seed
         seed, initial_noise = self.generate_initial_noise(seed, width, height)
+
         # There used to be an additional self.model.ema_scope() here, but it breaks
         # the inpaint-1.5 model. Not sure what it did.... ?
         with scope(self.model.device.type):

invokeai/backend/generator/txt2img.py

@@ -4,10 +4,6 @@ invokeai.backend.generator.txt2img inherits from invokeai.backend.generator
 import PIL.Image
 import torch
 
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-
 from ..stable_diffusion import (
     ConditioningData,
     PostprocessingSettings,
@@ -17,13 +13,8 @@ from .base import Generator
 
 
 class Txt2Img(Generator):
-    def __init__(self, model, precision,
-                 control_model: Optional[Union[ControlNetModel, List[ControlNetModel]]] = None,
-                 **kwargs):
-        self.control_model = control_model
-        if isinstance(self.control_model, list):
-            self.control_model = MultiControlNetModel(self.control_model)
-        super().__init__(model, precision, **kwargs)
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
 
     @torch.no_grad()
     def get_make_image(
@@ -51,12 +42,9 @@ class Txt2Img(Generator):
         kwargs are 'width' and 'height'
         """
         self.perlin = perlin
-        control_image = kwargs.get("control_image", None)
-        do_classifier_free_guidance = cfg_scale > 1.0
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.control_model = self.control_model
         pipeline.scheduler = sampler
 
         uc, c, extra_conditioning_info = conditioning
@@ -73,37 +61,6 @@ class Txt2Img(Generator):
             ),
         ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
 
-        # FIXME: still need to test with different widths, heights, devices, dtypes
-        #        and add in batch_size, num_images_per_prompt?
-        if control_image is not None:
-            if isinstance(self.control_model, ControlNetModel):
-                control_image = pipeline.prepare_control_image(
-                    image=control_image,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    width=width,
-                    height=height,
-                    # batch_size=batch_size * num_images_per_prompt,
-                    # num_images_per_prompt=num_images_per_prompt,
-                    device=self.control_model.device,
-                    dtype=self.control_model.dtype,
-                )
-            elif isinstance(self.control_model, MultiControlNetModel):
-                images = []
-                for image_ in control_image:
-                    image_ = self.model.prepare_control_image(
-                        image=image_,
-                        do_classifier_free_guidance=do_classifier_free_guidance,
-                        width=width,
-                        height=height,
-                        # batch_size=batch_size * num_images_per_prompt,
-                        # num_images_per_prompt=num_images_per_prompt,
-                        device=self.control_model.device,
-                        dtype=self.control_model.dtype,
-                    )
-                    images.append(image_)
-                control_image = images
-            kwargs["control_image"] = control_image
-
         def make_image(x_T: torch.Tensor, _: int) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
                 latents=torch.zeros_like(x_T, dtype=self.torch_dtype()),
@@ -111,7 +68,6 @@ class Txt2Img(Generator):
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
                 callback=step_callback,
-                **kwargs,
             )
 
             if (

invokeai/backend/image_util/pngwriter.py

@@ -41,7 +41,7 @@ class PngWriter:
         info = PngImagePlugin.PngInfo()
         info.add_text("Dream", dream_prompt)
         if metadata:
-            info.add_text("sd-metadata", json.dumps(metadata))
+            info.add_text("invokeai", json.dumps(metadata))
         image.save(path, "PNG", pnginfo=info, compress_level=compress_level)
         return path
 

invokeai/backend/model_management/__init__.py

@@ -5,7 +5,6 @@ from .convert_ckpt_to_diffusers import (
     convert_ckpt_to_diffusers,
     load_pipeline_from_original_stable_diffusion_ckpt,
 )
-from .model_manager import ModelManager,SDModelComponent
-
+from .model_manager import ModelManager
 
 

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -57,7 +57,7 @@ class HuggingFaceConceptsLibrary(object):
                 self.concept_list.extend(list(local_concepts_to_add))
                 return self.concept_list
             return self.concept_list
-        elif Globals.internet_available is True:
+        else:
             try:
                 models = self.hf_api.list_models(
                     filter=ModelFilter(model_name="sd-concepts-library/")
@@ -73,8 +73,6 @@ class HuggingFaceConceptsLibrary(object):
                     " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument."
                 )
             return self.concept_list
-        else: 
-            return self.concept_list
 
     def get_concept_model_path(self, concept_name: str) -> str:
         """

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -9,20 +9,16 @@ from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
 
 import einops
 import PIL.Image
-import numpy as np
 from accelerate.utils import set_seed
 import psutil
 import torch
 import torchvision.transforms as T
 from compel import EmbeddingsProvider
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.controlnet import ControlNetModel, ControlNetOutput
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
     StableDiffusionPipeline,
 )
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import (
     StableDiffusionImg2ImgPipeline,
 )
@@ -31,7 +27,6 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
 )
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
-from diffusers.utils import PIL_INTERPOLATION
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.outputs import BaseOutput
 from torchvision.transforms.functional import resize as tv_resize
@@ -309,7 +304,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         feature_extractor: Optional[CLIPFeatureExtractor],
         requires_safety_checker: bool = False,
         precision: str = "float32",
-        control_model: ControlNetModel = None,
     ):
         super().__init__(
             vae,
@@ -330,8 +324,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
-            # FIXME: can't currently register control module
-            # control_model=control_model,
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(
             self.unet, self._unet_forward, is_running_diffusers=True
@@ -351,7 +343,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         self._model_group = FullyLoadedModelGroup(self.unet.device)
         self._model_group.install(*self._submodels)
-        self.control_model = control_model
 
     def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
         """
@@ -454,15 +445,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @property
     def _submodels(self) -> Sequence[torch.nn.Module]:
         module_names, _, _ = self.extract_init_dict(dict(self.config))
-        submodels = []
-        for name in module_names.keys():
-            if hasattr(self, name):
-                value = getattr(self, name)
-            else:
-                value = getattr(self.config, name)
-            if isinstance(value, torch.nn.Module):
-                submodels.append(value)
-        return submodels
+        values = [getattr(self, name) for name in module_names.keys()]
+        return [m for m in values if isinstance(m, torch.nn.Module)]
 
     def image_from_embeddings(
         self,
@@ -473,7 +457,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise: torch.Tensor,
         callback: Callable[[PipelineIntermediateState], None] = None,
         run_id=None,
-        **kwargs,
     ) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
@@ -494,7 +477,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             noise=noise,
             run_id=run_id,
             callback=callback,
-            **kwargs,
         )
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -519,7 +501,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None] = None,
-        **kwargs,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if timesteps is None:
             self.scheduler.set_timesteps(
@@ -537,7 +518,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance=additional_guidance,
             run_id=run_id,
             callback=callback,
-            **kwargs,
         )
         return result.latents, result.attention_map_saver
 
@@ -550,7 +530,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise: torch.Tensor,
         run_id: str = None,
         additional_guidance: List[Callable] = None,
-        **kwargs,
     ):
         self._adjust_memory_efficient_attention(latents)
         if run_id is None:
@@ -565,7 +544,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             yield PipelineIntermediateState(
                 run_id=run_id,
                 step=-1,
-                timestep=self.scheduler.config.num_train_timesteps,
+                timestep=self.scheduler.num_train_timesteps,
                 latents=latents,
             )
 
@@ -589,7 +568,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     step_index=i,
                     total_step_count=len(timesteps),
                     additional_guidance=additional_guidance,
-                    **kwargs,
                 )
                 latents = step_output.prev_sample
 
@@ -630,7 +608,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         step_index: int,
         total_step_count: int,
         additional_guidance: List[Callable] = None,
-        **kwargs,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
@@ -642,33 +619,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         #     i.e. before or after passing it to InvokeAIDiffuserComponent
         latent_model_input = self.scheduler.scale_model_input(latents, timestep)
 
-        if (self.control_model is not None) and (kwargs.get("control_image") is not None):
-            control_image = kwargs.get("control_image") # should be a processed tensor derived from the control image(s)
-            control_scale = kwargs.get("control_scale", 1.0)  # control_scale default is 1.0
-            # handling case where using multiple control models but only specifying single control_scale
-            #     so reshape control_scale to match number of control models
-            if isinstance(self.control_model, MultiControlNetModel) and isinstance(control_scale, float):
-                control_scale = [control_scale] * len(self.control_model.nets)
-            if conditioning_data.guidance_scale > 1.0:
-                # expand the latents input to control model if doing classifier free guidance
-                #    (which I think for now is always true, there is conditional elsewhere that stops execution if
-                #     classifier_free_guidance is <= 1.0 ?)
-                latent_control_input = torch.cat([latent_model_input] * 2)
-            else:
-                latent_control_input = latent_model_input
-            # controlnet inference
-            down_block_res_samples, mid_block_res_sample = self.control_model(
-                latent_control_input,
-                timestep,
-                encoder_hidden_states=torch.cat([conditioning_data.unconditioned_embeddings,
-                                                 conditioning_data.text_embeddings]),
-                controlnet_cond=control_image,
-                conditioning_scale=control_scale,
-                return_dict=False,
-            )
-        else:
-            down_block_res_samples, mid_block_res_sample = None, None
-
         # predict the noise residual
         noise_pred = self.invokeai_diffuser.do_diffusion_step(
             latent_model_input,
@@ -678,8 +628,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             conditioning_data.guidance_scale,
             step_index=step_index,
             total_step_count=total_step_count,
-            down_block_additional_residuals=down_block_res_samples,
-            mid_block_additional_residual=mid_block_res_sample,
         )
 
         # compute the previous noisy sample x_t -> x_t-1
@@ -701,7 +649,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         t,
         text_embeddings,
         cross_attention_kwargs: Optional[dict[str, Any]] = None,
-        **kwargs,
     ):
         """predict the noise residual"""
         if is_inpainting_model(self.unet) and latents.size(1) == 4:
@@ -721,8 +668,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         # First three args should be positional, not keywords, so torch hooks can see them.
         return self.unet(
-            latents, t, text_embeddings, cross_attention_kwargs=cross_attention_kwargs,
-            **kwargs,
+            latents, t, text_embeddings, cross_attention_kwargs=cross_attention_kwargs
         ).sample
 
     def img2img_from_embeddings(
@@ -969,7 +915,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @property
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""
-        return self.unet.config.in_channels
+        return self.unet.in_channels
 
     def decode_latents(self, latents):
         # Explicit call to get the vae loaded, since `decode` isn't the forward method.
@@ -984,48 +930,3 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             debug_image(
                 img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
             )
-
-    # Copied from diffusers pipeline_stable_diffusion_controlnet.py
-    # Returns torch.Tensor of shape (batch_size, 3, height, width)
-    def prepare_control_image(
-        self,
-        image,
-        width=512,
-        height=512,
-        batch_size=1,
-        num_images_per_prompt=1,
-        device="cuda",
-        dtype=torch.float16,
-        do_classifier_free_guidance=True,
-    ):
-        if not isinstance(image, torch.Tensor):
-            if isinstance(image, PIL.Image.Image):
-                image = [image]
-
-            if isinstance(image[0], PIL.Image.Image):
-                images = []
-                for image_ in image:
-                    image_ = image_.convert("RGB")
-                    image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-                    image_ = np.array(image_)
-                    image_ = image_[None, :]
-                    images.append(image_)
-                image = images
-                image = np.concatenate(image, axis=0)
-                image = np.array(image).astype(np.float32) / 255.0
-                image = image.transpose(0, 3, 1, 2)
-                image = torch.from_numpy(image)
-            elif isinstance(image[0], torch.Tensor):
-                image = torch.cat(image, dim=0)
-
-        image_batch_size = image.shape[0]
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-        image = image.repeat_interleave(repeat_by, dim=0)
-        image = image.to(device=device, dtype=dtype)
-        if do_classifier_free_guidance:
-            image = torch.cat([image] * 2)
-        return image

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -1,6 +1,7 @@
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
 
+
 import enum
 import math
 from typing import Callable, Optional
@@ -9,7 +10,8 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
-from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.cross_attention import AttnProcessor
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from torch import nn
 
 from ...util import torch_dtype
@@ -186,7 +188,7 @@ class Context:
 
 class InvokeAICrossAttentionMixin:
     """
-    Enable InvokeAI-flavoured Attention calculation, which does aggressive low-memory slicing and calls
+    Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
     through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
     and dymamic slicing strategy selection.
     """
@@ -207,7 +209,7 @@ class InvokeAICrossAttentionMixin:
         Set custom attention calculator to be called when attention is calculated
         :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
         which returns either the suggested_attention_slice or an adjusted equivalent.
-            `module` is the current Attention module for which the callback is being invoked.
+            `module` is the current CrossAttention module for which the callback is being invoked.
             `suggested_attention_slice` is the default-calculated attention slice
             `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
                 If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
@@ -343,11 +345,11 @@ class InvokeAICrossAttentionMixin:
 def restore_default_cross_attention(
     model,
     is_running_diffusers: bool,
-    restore_attention_processor: Optional[AttentionProcessor] = None,
+    restore_attention_processor: Optional[AttnProcessor] = None,
 ):
     if is_running_diffusers:
         unet = model
-        unet.set_attn_processor(restore_attention_processor or AttnProcessor())
+        unet.set_attn_processor(restore_attention_processor or CrossAttnProcessor())
     else:
         remove_attention_function(model)
 
@@ -406,9 +408,12 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
 def get_cross_attention_modules(
     model, which: CrossAttentionType
 ) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
+    from ldm.modules.attention import CrossAttention  # avoid circular import
 
     cross_attention_class: type = (
         InvokeAIDiffusersCrossAttention
+        if isinstance(model, UNet2DConditionModel)
+        else CrossAttention
     )
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
     attention_module_tuples = [
@@ -423,10 +428,10 @@ def get_cross_attention_modules(
         print(
             f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
             + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
-            + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
+            + f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
             + f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows "
-            + "what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
-            + "work properly until it is fixed."
+            + f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
+            + f"work properly until it is fixed."
         )
     return attention_module_tuples
 
@@ -545,7 +550,7 @@ def get_mem_free_total(device):
 
 
 class InvokeAIDiffusersCrossAttention(
-    diffusers.models.attention.Attention, InvokeAICrossAttentionMixin
+    diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin
 ):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -567,8 +572,8 @@ class InvokeAIDiffusersCrossAttention(
 """
 # base implementation
 
-class AttnProcessor:
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
+class CrossAttnProcessor:
+    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
         batch_size, sequence_length, _ = hidden_states.shape
         attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
 
@@ -596,9 +601,9 @@ class AttnProcessor:
 from dataclasses import dataclass, field
 
 import torch
-from diffusers.models.attention_processor import (
-    Attention,
-    AttnProcessor,
+from diffusers.models.cross_attention import (
+    CrossAttention,
+    CrossAttnProcessor,
     SlicedAttnProcessor,
 )
 
@@ -648,7 +653,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 
     def __call__(
         self,
-        attn: Attention,
+        attn: CrossAttention,
         hidden_states,
         encoder_hidden_states=None,
         attention_mask=None,

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -5,7 +5,7 @@ from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
-from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.cross_attention import AttnProcessor
 from typing_extensions import TypeAlias
 
 from invokeai.backend.globals import Globals
@@ -101,7 +101,7 @@ class InvokeAIDiffuserComponent:
 
     def override_cross_attention(
         self, conditioning: ExtraConditioningInfo, step_count: int
-    ) -> Dict[str, AttentionProcessor]:
+    ) -> Dict[str, AttnProcessor]:
         """
         setup cross attention .swap control. for diffusers this replaces the attention processor, so
         the previous attention processor is returned so that the caller can restore it later.
@@ -118,7 +118,7 @@ class InvokeAIDiffuserComponent:
         )
 
     def restore_default_cross_attention(
-        self, restore_attention_processor: Optional["AttentionProcessor"] = None
+        self, restore_attention_processor: Optional["AttnProcessor"] = None
     ):
         self.conditioning = None
         self.cross_attention_control_context = None
@@ -168,7 +168,6 @@ class InvokeAIDiffuserComponent:
         unconditional_guidance_scale: float,
         step_index: Optional[int] = None,
         total_step_count: Optional[int] = None,
-        **kwargs,
     ):
         """
         :param x: current latents
@@ -197,7 +196,7 @@ class InvokeAIDiffuserComponent:
 
         if wants_hybrid_conditioning:
             unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(
-                x, sigma, unconditioning, conditioning, **kwargs,
+                x, sigma, unconditioning, conditioning
             )
         elif wants_cross_attention_control:
             (
@@ -209,14 +208,13 @@ class InvokeAIDiffuserComponent:
                 unconditioning,
                 conditioning,
                 cross_attention_control_types_to_do,
-                **kwargs,
             )
         elif self.sequential_guidance:
             (
                 unconditioned_next_x,
                 conditioned_next_x,
             ) = self._apply_standard_conditioning_sequentially(
-                x, sigma, unconditioning, conditioning, **kwargs,
+                x, sigma, unconditioning, conditioning
             )
 
         else:
@@ -224,7 +222,7 @@ class InvokeAIDiffuserComponent:
                 unconditioned_next_x,
                 conditioned_next_x,
             ) = self._apply_standard_conditioning(
-                x, sigma, unconditioning, conditioning, **kwargs,
+                x, sigma, unconditioning, conditioning
             )
 
         combined_next_x = self._combine(
@@ -264,20 +262,20 @@ class InvokeAIDiffuserComponent:
             # TODO remove when compvis codepath support is dropped
             if step_index is None and sigma is None:
                 raise ValueError(
-                    "Either step_index or sigma is required when doing cross attention control, but both are None."
+                    f"Either step_index or sigma is required when doing cross attention control, but both are None."
                 )
             percent_through = self.estimate_percent_through(step_index, sigma)
         return percent_through
 
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
-    def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
+    def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
         # fast batched path
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
         both_conditionings = torch.cat([unconditioning, conditioning])
         both_results = self.model_forward_callback(
-            x_twice, sigma_twice, both_conditionings, **kwargs,
+            x_twice, sigma_twice, both_conditionings
         )
         unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
         if conditioned_next_x.device.type == "mps":
@@ -291,17 +289,16 @@ class InvokeAIDiffuserComponent:
         sigma,
         unconditioning: torch.Tensor,
         conditioning: torch.Tensor,
-        **kwargs,
     ):
         # low-memory sequential path
-        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning, **kwargs)
-        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning, **kwargs)
+        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
+        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning)
         if conditioned_next_x.device.type == "mps":
             # prevent a result filled with zeros. seems to be a torch bug.
             conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
-    def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
+    def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
         assert isinstance(conditioning, dict)
         assert isinstance(unconditioning, dict)
         x_twice = torch.cat([x] * 2)
@@ -316,7 +313,7 @@ class InvokeAIDiffuserComponent:
             else:
                 both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
         unconditioned_next_x, conditioned_next_x = self.model_forward_callback(
-            x_twice, sigma_twice, both_conditionings, **kwargs,
+            x_twice, sigma_twice, both_conditionings
         ).chunk(2)
         return unconditioned_next_x, conditioned_next_x
 
@@ -327,7 +324,6 @@ class InvokeAIDiffuserComponent:
         unconditioning,
         conditioning,
         cross_attention_control_types_to_do,
-        **kwargs,
     ):
         if self.is_running_diffusers:
             return self._apply_cross_attention_controlled_conditioning__diffusers(
@@ -336,7 +332,6 @@ class InvokeAIDiffuserComponent:
                 unconditioning,
                 conditioning,
                 cross_attention_control_types_to_do,
-                **kwargs,
             )
         else:
             return self._apply_cross_attention_controlled_conditioning__compvis(
@@ -345,7 +340,6 @@ class InvokeAIDiffuserComponent:
                 unconditioning,
                 conditioning,
                 cross_attention_control_types_to_do,
-                **kwargs,
             )
 
     def _apply_cross_attention_controlled_conditioning__diffusers(
@@ -355,7 +349,6 @@ class InvokeAIDiffuserComponent:
         unconditioning,
         conditioning,
         cross_attention_control_types_to_do,
-        **kwargs,
     ):
         context: Context = self.cross_attention_control_context
 
@@ -371,7 +364,6 @@ class InvokeAIDiffuserComponent:
             sigma,
             unconditioning,
             {"swap_cross_attn_context": cross_attn_processor_context},
-            **kwargs,
         )
 
         # do requested cross attention types for conditioning (positive prompt)
@@ -383,7 +375,6 @@ class InvokeAIDiffuserComponent:
             sigma,
             conditioning,
             {"swap_cross_attn_context": cross_attn_processor_context},
-            **kwargs,
         )
         return unconditioned_next_x, conditioned_next_x
 
@@ -394,7 +385,6 @@ class InvokeAIDiffuserComponent:
         unconditioning,
         conditioning,
         cross_attention_control_types_to_do,
-        **kwargs,
     ):
         # print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
         # slower non-batched path (20% slower on mac MPS)
@@ -408,13 +398,13 @@ class InvokeAIDiffuserComponent:
         context: Context = self.cross_attention_control_context
 
         try:
-            unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning, **kwargs)
+            unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
 
             # process x using the original prompt, saving the attention maps
             # print("saving attention maps for", cross_attention_control_types_to_do)
             for ca_type in cross_attention_control_types_to_do:
                 context.request_save_attention_maps(ca_type)
-            _ = self.model_forward_callback(x, sigma, conditioning, **kwargs,)
+            _ = self.model_forward_callback(x, sigma, conditioning)
             context.clear_requests(cleanup=False)
 
             # process x again, using the saved attention maps to control where self.edited_conditioning will be applied
@@ -425,7 +415,7 @@ class InvokeAIDiffuserComponent:
                 self.conditioning.cross_attention_control_args.edited_conditioning
             )
             conditioned_next_x = self.model_forward_callback(
-                x, sigma, edited_conditioning, **kwargs,
+                x, sigma, edited_conditioning
             )
             context.clear_requests(cleanup=True)
 
@@ -609,6 +599,7 @@ class InvokeAIDiffuserComponent:
         )
 
         # below is fugly omg
+        num_actual_conditionings = len(c_or_weighted_c_list)
         conditionings = [uc] + [c for c, weight in weighted_cond_list]
         weights = [1] + [weight for c, weight in weighted_cond_list]
         chunk_count = ceil(len(conditionings) / 2)

invokeai/frontend/install/invokeai_update.py

@@ -1,9 +1,10 @@
-'''
+"""
 Minimalist updater script. Prompts user for the tag or branch to update to and runs
 pip install <path_to_git_source>.
-'''
+"""
 import os
 import platform
+
 import requests
 from rich import box, print
 from rich.console import Console, Group, group
@@ -15,10 +16,8 @@ from rich.text import Text
 
 from invokeai.version import __version__
 
-INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
-INVOKE_AI_TAG="https://github.com/invoke-ai/InvokeAI/archive/refs/tags"
-INVOKE_AI_BRANCH="https://github.com/invoke-ai/InvokeAI/archive/refs/heads"
-INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+INVOKE_AI_SRC = "https://github.com/invoke-ai/InvokeAI/archive"
+INVOKE_AI_REL = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
 
 OS = platform.uname().system
 ARCH = platform.uname().machine
@@ -29,22 +28,22 @@ if OS == "Windows":
 else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
-def get_versions()->dict:
+
+def get_versions() -> dict:
     return requests.get(url=INVOKE_AI_REL).json()
 
+
 def welcome(versions: dict):
-    
     @group()
     def text():
-        yield f'InvokeAI Version: [bold yellow]{__version__}'
-        yield ''
-        yield 'This script will update InvokeAI to the latest release, or to a development version of your choice.'
-        yield ''
-        yield '[bold yellow]Options:'
-        yield f'''[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
+        yield f"InvokeAI Version: [bold yellow]{__version__}"
+        yield ""
+        yield "This script will update InvokeAI to the latest release, or to a development version of your choice."
+        yield ""
+        yield "[bold yellow]Options:"
+        yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
 [2] Update to the bleeding-edge development version ([italic]main[/italic])
-[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
-[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to'''        
+[3] Manually enter the tag or branch name you wish to update"""
 
     console.rule()
     print(
@@ -60,41 +59,33 @@ def welcome(versions: dict):
     )
     console.line()
 
+
 def main():
     versions = get_versions()
     welcome(versions)
 
     tag = None
-    branch = None
-    release = None
-    choice = Prompt.ask('Choice:',choices=['1','2','3','4'],default='1')
-    
-    if choice=='1':
-        release = versions[0]['tag_name']
-    elif choice=='2':
-        release = 'main'
-    elif choice=='3':
-        tag = Prompt.ask('Enter an InvokeAI tag name')
-    elif choice=='4':
-        branch = Prompt.ask('Enter an InvokeAI branch name')
+    choice = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
 
-    print(f':crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]')
-    if release:
-        cmd = f'pip install {INVOKE_AI_SRC}/{release}.zip --use-pep517 --upgrade'
-    elif tag:
-        cmd = f'pip install {INVOKE_AI_TAG}/{tag}.zip --use-pep517 --upgrade'
+    if choice == "1":
+        tag = versions[0]["tag_name"]
+    elif choice == "2":
+        tag = "main"
+    elif choice == "3":
+        tag = Prompt.ask("Enter an InvokeAI tag or branch name")
+
+    print(f":crossed_fingers: Upgrading to [yellow]{tag}[/yellow]")
+    cmd = f"pip install {INVOKE_AI_SRC}/{tag}.zip --use-pep517"
+    print("")
+    print("")
+    if os.system(cmd) == 0:
+        print(f":heavy_check_mark: Upgrade successful")
     else:
-        cmd = f'pip install {INVOKE_AI_BRANCH}/{branch}.zip --use-pep517 --upgrade'
-    print('')
-    print('')
-    if os.system(cmd)==0:
-        print(f':heavy_check_mark: Upgrade successful')
-    else:
-        print(f':exclamation: [bold red]Upgrade failed[/red bold]')
-    
+        print(f":exclamation: [bold red]Upgrade failed[/red bold]")
+
+
 if __name__ == "__main__":
     try:
         main()
     except KeyboardInterrupt:
         pass
-

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-680f01c6.js

@@ -1,4 +1,4 @@
-import{j as y,cO as Ie,r as _,cP as bt,q as Lr,cQ as o,cR as b,cS as v,cT as S,cU as Vr,cV as ut,cW as vt,cN as ft,cX as mt,n as gt,cY as ht,E as pt}from"./index-e53e8108.js";import{d as yt,i as St,T as xt,j as $t,h as kt}from"./storeHooks-5cde7d31.js";var Or=`
+import{j as y,cN as Ie,r as _,cO as bt,q as Lr,cP as o,cQ as b,cR as v,cS as S,cT as Vr,cU as ut,cV as vt,cM as ft,cW as mt,n as gt,cX as ht,E as pt}from"./index-f7f41e1f.js";import{d as yt,i as St,T as xt,j as $t,h as kt}from"./storeHooks-eaf47ae3.js";var Or=`
   :root {
     --chakra-vh: 100vh;
   }

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-e53e8108.js"></script>
+    <script type="module" crossorigin src="./assets/index-f7f41e1f.js"></script>
     <link rel="stylesheet" href="./assets/index-5483945c.css">
   </head>
 

invokeai/frontend/web/dist/locales/ar.json

@@ -8,6 +8,7 @@
         "darkTheme": "داكن",
         "lightTheme": "فاتح",
         "greenTheme": "أخضر",
+        "text2img": "نص إلى صورة",
         "img2img": "صورة إلى صورة",
         "unifiedCanvas": "لوحة موحدة",
         "nodes": "عقد",

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

@@ -7,6 +7,7 @@
         "darkTheme": "Dunkel",
         "lightTheme": "Hell",
         "greenTheme": "Grün",
+        "text2img": "Text zu Bild",
         "img2img": "Bild zu Bild",
         "nodes": "Knoten",
         "langGerman": "Deutsch",

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

@@ -505,9 +505,7 @@
         "info": "Info",
         "deleteImage": "Delete Image",
         "initialImage": "Initial Image",
-        "showOptionsPanel": "Show Options Panel",
-        "hidePreview": "Hide Preview",
-        "showPreview": "Show Preview"
+        "showOptionsPanel": "Show Options Panel"
     },
     "settings": {
         "models": "Models",

invokeai/frontend/web/dist/locales/es.json

@@ -8,6 +8,7 @@
         "darkTheme": "Oscuro",
         "lightTheme": "Claro",
         "greenTheme": "Verde",
+        "text2img": "Texto a Imagen",
         "img2img": "Imagen a Imagen",
         "unifiedCanvas": "Lienzo Unificado",
         "nodes": "Nodos",
@@ -69,11 +70,7 @@
         "langHebrew": "Hebreo",
         "pinOptionsPanel": "Pin del panel de opciones",
         "loading": "Cargando",
-        "loadingInvokeAI": "Cargando invocar a la IA",
-        "postprocessing": "Tratamiento posterior",
-        "txt2img": "De texto a imagen",
-        "accept": "Aceptar",
-        "cancel": "Cancelar"
+        "loadingInvokeAI": "Cargando invocar a la IA"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -407,8 +404,7 @@
         "none": "ninguno",
         "pickModelType": "Elige el tipo de modelo",
         "v2_768": "v2 (768px)",
-        "addDifference": "Añadir una diferencia",
-        "scanForModels": "Buscar modelos"
+        "addDifference": "Añadir una diferencia"
     },
     "parameters": {
         "images": "Imágenes",
@@ -578,7 +574,7 @@
         "autoSaveToGallery": "Guardar automáticamente en galería",
         "saveBoxRegionOnly": "Guardar solo región dentro de la caja",
         "limitStrokesToBox": "Limitar trazos a la caja",
-        "showCanvasDebugInfo": "Mostrar la información adicional del lienzo",
+        "showCanvasDebugInfo": "Mostrar información de depuración de lienzo",
         "clearCanvasHistory": "Limpiar historial de lienzo",
         "clearHistory": "Limpiar historial",
         "clearCanvasHistoryMessage": "Limpiar el historial de lienzo también restablece completamente el lienzo unificado. Esto incluye todo el historial de deshacer/rehacer, las imágenes en el área de preparación y la capa base del lienzo.",

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

@@ -8,6 +8,7 @@
         "darkTheme": "Sombre",
         "lightTheme": "Clair",
         "greenTheme": "Vert",
+        "text2img": "Texte en image",
         "img2img": "Image en image",
         "unifiedCanvas": "Canvas unifié",
         "nodes": "Nœuds",
@@ -46,19 +47,7 @@
         "statusLoadingModel": "Chargement du modèle",
         "statusModelChanged": "Modèle changé",
         "discordLabel": "Discord",
-        "githubLabel": "Github",
-        "accept": "Accepter",
-        "statusMergingModels": "Mélange des modèles",
-        "loadingInvokeAI": "Chargement de Invoke AI",
-        "cancel": "Annuler",
-        "langEnglish": "Anglais",
-        "statusConvertingModel": "Conversion du modèle",
-        "statusModelConverted": "Modèle converti",
-        "loading": "Chargement",
-        "pinOptionsPanel": "Épingler la page d'options",
-        "statusMergedModels": "Modèles mélangés",
-        "txt2img": "Texte vers image",
-        "postprocessing": "Post-Traitement"
+        "githubLabel": "Github"
     },
     "gallery": {
         "generations": "Générations",
@@ -529,15 +518,5 @@
         "betaDarkenOutside": "Assombrir à l'extérieur",
         "betaLimitToBox": "Limiter à la boîte",
         "betaPreserveMasked": "Conserver masqué"
-    },
-    "accessibility": {
-        "uploadImage": "Charger une image",
-        "reset": "Réinitialiser",
-        "nextImage": "Image suivante",
-        "previousImage": "Image précédente",
-        "useThisParameter": "Utiliser ce paramètre",
-        "zoomIn": "Zoom avant",
-        "zoomOut": "Zoom arrière",
-        "showOptionsPanel": "Montrer la page d'options"
     }
 }

invokeai/frontend/web/dist/locales/he.json

@@ -125,6 +125,7 @@
         "langSimplifiedChinese": "סינית",
         "langUkranian": "אוקראינית",
         "langSpanish": "ספרדית",
+        "text2img": "טקסט לתמונה",
         "img2img": "תמונה לתמונה",
         "unifiedCanvas": "קנבס מאוחד",
         "nodes": "צמתים",

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

@@ -8,6 +8,7 @@
         "darkTheme": "Scuro",
         "lightTheme": "Chiaro",
         "greenTheme": "Verde",
+        "text2img": "Testo a Immagine",
         "img2img": "Immagine a Immagine",
         "unifiedCanvas": "Tela unificata",
         "nodes": "Nodi",
@@ -69,11 +70,7 @@
         "loading": "Caricamento in corso",
         "oceanTheme": "Oceano",
         "langHebrew": "Ebraico",
-        "loadingInvokeAI": "Caricamento Invoke AI",
-        "postprocessing": "Post Elaborazione",
-        "txt2img": "Testo a Immagine",
-        "accept": "Accetta",
-        "cancel": "Annulla"
+        "loadingInvokeAI": "Caricamento Invoke AI"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -407,8 +404,7 @@
         "v2_768": "v2 (768px)",
         "none": "niente",
         "addDifference": "Aggiungi differenza",
-        "pickModelType": "Scegli il tipo di modello",
-        "scanForModels": "Cerca modelli"
+        "pickModelType": "Scegli il tipo di modello"
     },
     "parameters": {
         "images": "Immagini",
@@ -578,7 +574,7 @@
         "autoSaveToGallery": "Salvataggio automatico nella Galleria",
         "saveBoxRegionOnly": "Salva solo l'area di selezione",
         "limitStrokesToBox": "Limita i tratti all'area di selezione",
-        "showCanvasDebugInfo": "Mostra ulteriori informazioni sulla Tela",
+        "showCanvasDebugInfo": "Mostra informazioni di debug della Tela",
         "clearCanvasHistory": "Cancella cronologia Tela",
         "clearHistory": "Cancella la cronologia",
         "clearCanvasHistoryMessage": "La cancellazione della cronologia della tela lascia intatta la tela corrente, ma cancella in modo irreversibile la cronologia degli annullamenti e dei ripristini.",
@@ -616,7 +612,7 @@
         "copyMetadataJson": "Copia i metadati JSON",
         "exitViewer": "Esci dal visualizzatore",
         "zoomIn": "Zoom avanti",
-        "zoomOut": "Zoom indietro",
+        "zoomOut": "Zoom Indietro",
         "rotateCounterClockwise": "Ruotare in senso antiorario",
         "rotateClockwise": "Ruotare in senso orario",
         "flipHorizontally": "Capovolgi orizzontalmente",

invokeai/frontend/web/dist/locales/ko.json

@@ -11,6 +11,7 @@
         "langArabic": "العربية",
         "langEnglish": "English",
         "langDutch": "Nederlands",
+        "text2img": "텍스트->이미지",
         "unifiedCanvas": "통합 캔버스",
         "langFrench": "Français",
         "langGerman": "Deutsch",

invokeai/frontend/web/dist/locales/nl.json

@@ -8,6 +8,7 @@
         "darkTheme": "Donker",
         "lightTheme": "Licht",
         "greenTheme": "Groen",
+        "text2img": "Tekst naar afbeelding",
         "img2img": "Afbeelding naar afbeelding",
         "unifiedCanvas": "Centraal canvas",
         "nodes": "Knooppunten",

invokeai/frontend/web/dist/locales/pl.json

@@ -8,6 +8,7 @@
         "darkTheme": "Ciemny",
         "lightTheme": "Jasny",
         "greenTheme": "Zielony",
+        "text2img": "Tekst na obraz",
         "img2img": "Obraz na obraz",
         "unifiedCanvas": "Tryb uniwersalny",
         "nodes": "Węzły",

invokeai/frontend/web/dist/locales/pt.json

@@ -20,6 +20,7 @@
         "langSpanish": "Espanhol",
         "langRussian": "Русский",
         "langUkranian": "Украї́нська",
+        "text2img": "Texto para Imagem",
         "img2img": "Imagem para Imagem",
         "unifiedCanvas": "Tela Unificada",
         "nodes": "Nós",

invokeai/frontend/web/dist/locales/pt_BR.json

@@ -8,6 +8,7 @@
         "darkTheme": "Noite",
         "lightTheme": "Dia",
         "greenTheme": "Verde",
+        "text2img": "Texto Para Imagem",
         "img2img": "Imagem Para Imagem",
         "unifiedCanvas": "Tela Unificada",
         "nodes": "Nódulos",

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

@@ -8,6 +8,7 @@
         "darkTheme": "Темная",
         "lightTheme": "Светлая",
         "greenTheme": "Зеленая",
+        "text2img": "Изображение из текста (text2img)",
         "img2img": "Изображение в изображение (img2img)",
         "unifiedCanvas": "Универсальный холст",
         "nodes": "Ноды",

invokeai/frontend/web/dist/locales/uk.json

@@ -8,6 +8,7 @@
         "darkTheme": "Темна",
         "lightTheme": "Світла",
         "greenTheme": "Зелена",
+        "text2img": "Зображення із тексту (text2img)",
         "img2img": "Зображення із зображення (img2img)",
         "unifiedCanvas": "Універсальне полотно",
         "nodes": "Вузли",

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

@@ -8,6 +8,7 @@
         "darkTheme": "暗色",
         "lightTheme": "亮色",
         "greenTheme": "绿色",
+        "text2img": "文字到图像",
         "img2img": "图像到图像",
         "unifiedCanvas": "统一画布",
         "nodes": "节点",

invokeai/frontend/web/dist/locales/zh_Hant.json

@@ -33,6 +33,7 @@
         "langBrPortuguese": "巴西葡萄牙語",
         "langRussian": "俄語",
         "langSpanish": "西班牙語",
+        "text2img": "文字到圖像",
         "unifiedCanvas": "統一畫布"
     }
 }

invokeai/frontend/web/index.d.ts

@@ -80,8 +80,6 @@ interface InvokeProps extends PropsWithChildren {
   disabledPanels?: string[];
   disabledTabs?: InvokeTabName[];
   token?: string;
-  shouldTransformUrls?: boolean;
-  shouldFetchImages?: boolean;
 }
 
 declare function Invoke(props: InvokeProps): JSX.Element;

invokeai/frontend/web/package.json

@@ -6,7 +6,6 @@
     "prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
     "dev:nodes": "concurrently \"vite dev --mode nodes\" \"yarn run theme:watch\"",
-    "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
     "api:web": "openapi -i http://localhost:9090/openapi.json -o src/services/api --client axios --useOptions --useUnionTypes --exportSchemas true --indent 2 --request src/services/fixtures/request.ts",
     "api:file": "openapi -i src/services/fixtures/openapi.json -o src/services/api --client axios --useOptions --useUnionTypes --exportSchemas true --indent 2 --request src/services/fixtures/request.ts",
@@ -48,13 +47,11 @@
     "@dagrejs/graphlib": "^2.1.12",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
-    "@fontsource/inter": "^4.5.15",
     "@reduxjs/toolkit": "^1.9.3",
     "chakra-ui-contextmenu": "^1.0.5",
     "dateformat": "^5.0.3",
     "formik": "^2.2.9",
     "framer-motion": "^9.0.4",
-    "fuse.js": "^6.6.2",
     "i18next": "^22.4.10",
     "i18next-browser-languagedetector": "^7.0.1",
     "i18next-http-backend": "^2.1.1",
@@ -83,8 +80,8 @@
     "uuid": "^9.0.0"
   },
   "devDependencies": {
+    "@fontsource/inter": "^4.5.15",
     "@types/dateformat": "^5.0.0",
-    "@types/lodash": "^4.14.194",
     "@types/react": "^18.0.28",
     "@types/react-dom": "^18.0.11",
     "@types/react-transition-group": "^4.4.5",

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

@@ -18,7 +18,7 @@
         "training": "Training",
         "trainingDesc1": "Ein spezieller Arbeitsablauf zum Trainieren Ihrer eigenen Embeddings und Checkpoints mit Textual Inversion und Dreambooth über die Weboberfläche.",
         "trainingDesc2": "InvokeAI unterstützt bereits das Training von benutzerdefinierten Embeddings mit Textual Inversion unter Verwendung des Hauptskripts.",
-        "upload": "Hochladen",
+        "upload": "Upload",
         "close": "Schließen",
         "load": "Laden",
         "statusConnected": "Verbunden",
@@ -41,34 +41,12 @@
         "statusUpscaling": "Hochskalierung",
         "statusUpscalingESRGAN": "Hochskalierung (ESRGAN)",
         "statusLoadingModel": "Laden des Modells",
-        "statusModelChanged": "Modell Geändert",
-        "cancel": "Abbruch",
-        "accept": "Annehmen",
-        "back": "Zurück",
-        "langEnglish": "Englisch",
-        "langDutch": "Niederländisch",
-        "langFrench": "Französisch",
-        "oceanTheme": "Ozean",
-        "langItalian": "Italienisch",
-        "langPortuguese": "Portogisisch",
-        "langRussian": "Russisch",
-        "langUkranian": "Ukrainisch",
-        "hotkeysLabel": "Tastenkombinationen",
-        "githubLabel": "Github",
-        "discordLabel": "Discord",
-        "txt2img": "Text zu Bild",
-        "postprocessing": "Nachbearbeitung",
-        "langPolish": "Polnisch",
-        "langJapanese": "Japanisch",
-        "langArabic": "Arabisch",
-        "langKorean": "Koreanisch",
-        "langHebrew": "Hebräisch",
-        "langSpanish": "Spanisch"
+        "statusModelChanged": "Modell Geändert"
     },
     "gallery": {
         "generations": "Erzeugungen",
         "showGenerations": "Zeige Erzeugnisse",
-        "uploads": "Hochgelades",
+        "uploads": "Uploads",
         "showUploads": "Zeige Uploads",
         "galleryImageSize": "Bildgröße",
         "galleryImageResetSize": "Größe zurücksetzen",
@@ -334,11 +312,7 @@
         "deleteModel": "Model löschen",
         "deleteConfig": "Konfiguration löschen",
         "deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?",
-        "deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.",
-        "customConfig": "Benutzerdefinierte Konfiguration",
-        "invokeRoot": "InvokeAI Ordner",
-        "formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.",
-        "checkpointModels": "Kontrollpunkte"
+        "deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen."
     },
     "parameters": {
         "images": "Bilder",
@@ -396,10 +370,7 @@
         "useInitImg": "Ausgangsbild verwenden",
         "deleteImage": "Bild löschen",
         "initialImage": "Ursprüngliches Bild",
-        "showOptionsPanel": "Optionsleiste zeigen",
-        "cancel": {
-            "setType": "Abbruchart festlegen"
-        }
+        "showOptionsPanel": "Optionsleiste zeigen"
     },
     "settings": {
         "displayInProgress": "Bilder in Bearbeitung anzeigen",
@@ -518,25 +489,5 @@
         "betaDarkenOutside": "Außen abdunkeln",
         "betaLimitToBox": "Begrenzung auf das Feld",
         "betaPreserveMasked": "Maskiertes bewahren"
-    },
-    "accessibility": {
-        "modelSelect": "Model Auswahl",
-        "uploadImage": "Bild hochladen",
-        "previousImage": "Voriges Bild",
-        "useThisParameter": "Benutze diesen Parameter",
-        "copyMetadataJson": "Kopiere metadata JSON",
-        "zoomIn": "Vergrößern",
-        "rotateClockwise": "Im Uhrzeigersinn drehen",
-        "flipHorizontally": "Horizontal drehen",
-        "flipVertically": "Vertikal drehen",
-        "modifyConfig": "Optionen einstellen",
-        "toggleAutoscroll": "Auroscroll ein/ausschalten",
-        "toggleLogViewer": "Log Betrachter ein/ausschalten",
-        "showGallery": "Zeige Galerie",
-        "showOptionsPanel": "Zeige Optionen",
-        "reset": "Zurücksetzen",
-        "nextImage": "Nächstes Bild",
-        "zoomOut": "Verkleinern",
-        "rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen"
     }
 }

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

@@ -8,7 +8,7 @@
         "nextImage": "Next Image",
         "useThisParameter": "Use this parameter",
         "copyMetadataJson": "Copy metadata JSON",
-        "exitViewer": "Exit Viewer",
+        "exitViewer": "ExitViewer",
         "zoomIn": "Zoom In",
         "zoomOut": "Zoom Out",
         "rotateCounterClockwise": "Rotate Counter-Clockwise",
@@ -19,8 +19,7 @@
         "toggleAutoscroll": "Toggle autoscroll",
         "toggleLogViewer": "Toggle Log Viewer",
         "showGallery": "Show Gallery",
-        "showOptionsPanel": "Show Options Panel",
-        "menu": "Menu"
+        "showOptionsPanel": "Show Options Panel"
     },
     "common": {
         "hotkeysLabel": "Hotkeys",
@@ -53,7 +52,6 @@
         "txt2img": "Text To Image",
         "img2img": "Image To Image",
         "unifiedCanvas": "Unified Canvas",
-        "linear": "Linear",
         "nodes": "Nodes",
         "postprocessing": "Post Processing",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
@@ -507,9 +505,7 @@
         "info": "Info",
         "deleteImage": "Delete Image",
         "initialImage": "Initial Image",
-        "showOptionsPanel": "Show Options Panel",
-        "hidePreview": "Hide Preview",
-        "showPreview": "Show Preview"
+        "showOptionsPanel": "Show Options Panel"
     },
     "settings": {
         "models": "Models",

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

@@ -73,8 +73,7 @@
         "postprocessing": "Tratamiento posterior",
         "txt2img": "De texto a imagen",
         "accept": "Aceptar",
-        "cancel": "Cancelar",
-        "linear": "Lineal"
+        "cancel": "Cancelar"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -484,9 +483,7 @@
         "negativePrompts": "Preguntas negativas",
         "imageToImage": "Imagen a imagen",
         "denoisingStrength": "Intensidad de la eliminación del ruido",
-        "hiresStrength": "Alta resistencia",
-        "showPreview": "Mostrar la vista previa",
-        "hidePreview": "Ocultar la vista previa"
+        "hiresStrength": "Alta resistencia"
     },
     "settings": {
         "models": "Modelos",
@@ -532,11 +529,7 @@
         "metadataLoadFailed": "Error al cargar metadatos",
         "initialImageSet": "Imágen inicial establecida",
         "initialImageNotSet": "Imagen inicial no establecida",
-        "initialImageNotSetDesc": "Error al establecer la imágen inicial",
-        "serverError": "Error en el servidor",
-        "disconnected": "Desconectado del servidor",
-        "canceled": "Procesando la cancelación",
-        "connected": "Conectado al servidor"
+        "initialImageNotSetDesc": "Error al establecer la imágen inicial"
     },
     "tooltip": {
         "feature": {
@@ -632,7 +625,6 @@
         "toggleAutoscroll": "Activar el autodesplazamiento",
         "toggleLogViewer": "Alternar el visor de registros",
         "showGallery": "Mostrar galería",
-        "showOptionsPanel": "Mostrar el panel de opciones",
-        "menu": "Menú"
+        "showOptionsPanel": "Mostrar el panel de opciones"
     }
 }

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

@@ -1,122 +0,0 @@
-{
-    "accessibility": {
-        "reset": "Resetoi",
-        "useThisParameter": "Käytä tätä parametria",
-        "modelSelect": "Mallin Valinta",
-        "exitViewer": "Poistu katselimesta",
-        "uploadImage": "Lataa kuva",
-        "copyMetadataJson": "Kopioi metadata JSON:iin",
-        "invokeProgressBar": "Invoken edistymispalkki",
-        "nextImage": "Seuraava kuva",
-        "previousImage": "Edellinen kuva",
-        "zoomIn": "Lähennä",
-        "flipHorizontally": "Käännä vaakasuoraan",
-        "zoomOut": "Loitonna",
-        "rotateCounterClockwise": "Kierrä vastapäivään",
-        "rotateClockwise": "Kierrä myötäpäivään",
-        "flipVertically": "Käännä pystysuoraan",
-        "showGallery": "Näytä galleria",
-        "modifyConfig": "Muokkaa konfiguraatiota",
-        "toggleAutoscroll": "Kytke automaattinen vieritys",
-        "toggleLogViewer": "Kytke lokin katselutila",
-        "showOptionsPanel": "Näytä asetukset"
-    },
-    "common": {
-        "postProcessDesc2": "Erillinen käyttöliittymä tullaan julkaisemaan helpottaaksemme työnkulkua jälkikäsittelyssä.",
-        "training": "Kouluta",
-        "statusLoadingModel": "Ladataan mallia",
-        "statusModelChanged": "Malli vaihdettu",
-        "statusConvertingModel": "Muunnetaan mallia",
-        "statusModelConverted": "Malli muunnettu",
-        "langFrench": "Ranska",
-        "langItalian": "Italia",
-        "languagePickerLabel": "Kielen valinta",
-        "hotkeysLabel": "Pikanäppäimet",
-        "reportBugLabel": "Raportoi Bugista",
-        "langPolish": "Puola",
-        "themeLabel": "Teema",
-        "langDutch": "Hollanti",
-        "settingsLabel": "Asetukset",
-        "githubLabel": "Github",
-        "darkTheme": "Tumma",
-        "lightTheme": "Vaalea",
-        "greenTheme": "Vihreä",
-        "langGerman": "Saksa",
-        "langPortuguese": "Portugali",
-        "discordLabel": "Discord",
-        "langEnglish": "Englanti",
-        "oceanTheme": "Meren sininen",
-        "langRussian": "Venäjä",
-        "langUkranian": "Ukraina",
-        "langSpanish": "Espanja",
-        "upload": "Lataa",
-        "statusMergedModels": "Mallit yhdistelty",
-        "img2img": "Kuva kuvaksi",
-        "nodes": "Solmut",
-        "nodesDesc": "Solmupohjainen järjestelmä kuvien generoimiseen on parhaillaan kehitteillä. Pysy kuulolla päivityksistä tähän uskomattomaan ominaisuuteen liittyen.",
-        "postProcessDesc1": "Invoke AI tarjoaa monenlaisia jälkikäsittelyominaisuukisa. Kuvan laadun skaalaus sekä kasvojen korjaus ovat jo saatavilla WebUI:ssä. Voit ottaa ne käyttöön lisäasetusten valikosta teksti kuvaksi sekä kuva kuvaksi -välilehdiltä. Voit myös suoraan prosessoida kuvia käyttämällä kuvan toimintapainikkeita nykyisen kuvan yläpuolella tai tarkastelussa.",
-        "postprocessing": "Jälkikäsitellään",
-        "postProcessing": "Jälkikäsitellään",
-        "cancel": "Peruuta",
-        "close": "Sulje",
-        "accept": "Hyväksy",
-        "statusConnected": "Yhdistetty",
-        "statusError": "Virhe",
-        "statusProcessingComplete": "Prosessointi valmis",
-        "load": "Lataa",
-        "back": "Takaisin",
-        "statusGeneratingTextToImage": "Generoidaan tekstiä kuvaksi",
-        "trainingDesc2": "InvokeAI tukee jo mukautettujen upotusten kouluttamista tekstin inversiolla käyttäen pääskriptiä.",
-        "statusDisconnected": "Yhteys katkaistu",
-        "statusPreparing": "Valmistellaan",
-        "statusIterationComplete": "Iteraatio valmis",
-        "statusMergingModels": "Yhdistellään malleja",
-        "statusProcessingCanceled": "Valmistelu peruutettu",
-        "statusSavingImage": "Tallennetaan kuvaa",
-        "statusGeneratingImageToImage": "Generoidaan kuvaa kuvaksi",
-        "statusRestoringFacesGFPGAN": "Korjataan kasvoja (GFPGAN)",
-        "statusRestoringFacesCodeFormer": "Korjataan kasvoja (CodeFormer)",
-        "statusGeneratingInpainting": "Generoidaan sisällemaalausta",
-        "statusGeneratingOutpainting": "Generoidaan ulosmaalausta",
-        "statusRestoringFaces": "Korjataan kasvoja",
-        "pinOptionsPanel": "Kiinnitä asetukset -paneeli",
-        "loadingInvokeAI": "Ladataan Invoke AI:ta",
-        "loading": "Ladataan",
-        "statusGenerating": "Generoidaan",
-        "txt2img": "Teksti kuvaksi",
-        "trainingDesc1": "Erillinen työnkulku omien upotusten ja tarkastuspisteiden kouluttamiseksi käyttäen tekstin inversiota ja dreamboothia selaimen käyttöliittymässä.",
-        "postProcessDesc3": "Invoke AI:n komentorivi tarjoaa paljon muita ominaisuuksia, kuten esimerkiksi Embiggenin.",
-        "unifiedCanvas": "Yhdistetty kanvas",
-        "statusGenerationComplete": "Generointi valmis"
-    },
-    "gallery": {
-        "uploads": "Lataukset",
-        "showUploads": "Näytä lataukset",
-        "galleryImageResetSize": "Resetoi koko",
-        "maintainAspectRatio": "Säilytä kuvasuhde",
-        "galleryImageSize": "Kuvan koko",
-        "pinGallery": "Kiinnitä galleria",
-        "showGenerations": "Näytä generaatiot",
-        "singleColumnLayout": "Yhden sarakkeen asettelu",
-        "generations": "Generoinnit",
-        "gallerySettings": "Gallerian asetukset",
-        "autoSwitchNewImages": "Vaihda uusiin kuviin automaattisesti",
-        "allImagesLoaded": "Kaikki kuvat ladattu",
-        "noImagesInGallery": "Ei kuvia galleriassa",
-        "loadMore": "Lataa lisää"
-    },
-    "hotkeys": {
-        "keyboardShortcuts": "näppäimistön pikavalinnat",
-        "appHotkeys": "Sovelluksen pikanäppäimet",
-        "generalHotkeys": "Yleiset pikanäppäimet",
-        "galleryHotkeys": "Gallerian pikanäppäimet",
-        "unifiedCanvasHotkeys": "Yhdistetyn kanvaan pikanäppäimet",
-        "cancel": {
-            "desc": "Peruuta kuvan luominen",
-            "title": "Peruuta"
-        },
-        "invoke": {
-            "desc": "Luo kuva"
-        }
-    }
-}

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

@@ -73,8 +73,7 @@
         "postprocessing": "Post Elaborazione",
         "txt2img": "Testo a Immagine",
         "accept": "Accetta",
-        "cancel": "Annulla",
-        "linear": "Lineare"
+        "cancel": "Annulla"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -484,9 +483,7 @@
         },
         "hSymmetryStep": "Passi Simmetria Orizzontale",
         "vSymmetryStep": "Passi Simmetria Verticale",
-        "symmetry": "Simmetria",
-        "hidePreview": "Nascondi l'anteprima",
-        "showPreview": "Mostra l'anteprima"
+        "symmetry": "Simmetria"
     },
     "settings": {
         "models": "Modelli",
@@ -532,11 +529,7 @@
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "initialImageSet": "Immagine iniziale impostata",
         "initialImageNotSet": "Immagine iniziale non impostata",
-        "initialImageNotSetDesc": "Impossibile caricare l'immagine iniziale",
-        "serverError": "Errore del Server",
-        "disconnected": "Disconnesso dal Server",
-        "connected": "Connesso al Server",
-        "canceled": "Elaborazione annullata"
+        "initialImageNotSetDesc": "Impossibile caricare l'immagine iniziale"
     },
     "tooltip": {
         "feature": {
@@ -632,7 +625,6 @@
         "showOptionsPanel": "Mostra il pannello opzioni",
         "flipVertically": "Capovolgi verticalmente",
         "toggleAutoscroll": "Attiva/disattiva lo scorrimento automatico",
-        "modifyConfig": "Modifica configurazione",
-        "menu": "Menu"
+        "modifyConfig": "Modifica configurazione"
     }
 }

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

@@ -37,43 +37,7 @@
         "statusUpscaling": "アップスケーリング",
         "statusUpscalingESRGAN": "アップスケーリング (ESRGAN)",
         "statusLoadingModel": "モデルを読み込む",
-        "statusModelChanged": "モデルを変更",
-        "cancel": "キャンセル",
-        "accept": "同意",
-        "langBrPortuguese": "Português do Brasil",
-        "langRussian": "Русский",
-        "langSimplifiedChinese": "简体中文",
-        "langUkranian": "Украї́нська",
-        "langSpanish": "Español",
-        "img2img": "img2img",
-        "unifiedCanvas": "Unified Canvas",
-        "statusMergingModels": "モデルのマージ",
-        "statusModelConverted": "変換済モデル",
-        "statusGeneratingInpainting": "Inpaintingを生成",
-        "statusIterationComplete": "Iteration Complete",
-        "statusGeneratingOutpainting": "Outpaintingを生成",
-        "loading": "ロード中",
-        "loadingInvokeAI": "Invoke AIをロード中",
-        "statusConvertingModel": "モデルの変換",
-        "statusMergedModels": "マージ済モデル",
-        "pinOptionsPanel": "オプションパネルを固定",
-        "githubLabel": "Github",
-        "hotkeysLabel": "ホットキー",
-        "langHebrew": "עברית",
-        "discordLabel": "Discord",
-        "langItalian": "Italiano",
-        "langEnglish": "English",
-        "oceanTheme": "オーシャン",
-        "langArabic": "アラビア語",
-        "langDutch": "Nederlands",
-        "langFrench": "Français",
-        "langGerman": "Deutsch",
-        "langPortuguese": "Português",
-        "nodes": "ノード",
-        "langKorean": "한국어",
-        "langPolish": "Polski",
-        "txt2img": "txt2img",
-        "postprocessing": "Post Processing"
+        "statusModelChanged": "モデルを変更"
     },
     "gallery": {
         "uploads": "アップロード",
@@ -82,14 +46,11 @@
         "galleryImageResetSize": "サイズをリセット",
         "gallerySettings": "ギャラリーの設定",
         "maintainAspectRatio": "アスペクト比を維持",
-        "singleColumnLayout": "1カラムレイアウト",
+        "singleColumnLayout": "シングルカラムレイアウト",
         "pinGallery": "ギャラリーにピン留め",
         "allImagesLoaded": "すべての画像を読み込む",
         "loadMore": "さらに読み込む",
-        "noImagesInGallery": "ギャラリーに画像がありません",
-        "generations": "生成",
-        "showGenerations": "生成過程を見る",
-        "autoSwitchNewImages": "新しい画像に自動切替"
+        "noImagesInGallery": "ギャラリーに画像がありません"
     },
     "hotkeys": {
         "keyboardShortcuts": "キーボードショートカット",
@@ -98,16 +59,14 @@
         "galleryHotkeys": "ギャラリーのホットキー",
         "unifiedCanvasHotkeys": "Unified Canvasのホットキー",
         "invoke": {
-            "desc": "画像を生成",
-            "title": "Invoke"
+            "desc": "画像を生成"
         },
         "cancel": {
             "title": "キャンセル",
             "desc": "画像の生成をキャンセル"
         },
         "focusPrompt": {
-            "desc": "プロンプトテキストボックスにフォーカス",
-            "title": "プロジェクトにフォーカス"
+            "desc": "プロンプトテキストボックスにフォーカス"
         },
         "toggleOptions": {
             "title": "オプションパネルのトグル",
@@ -451,27 +410,5 @@
         "accept": "同意",
         "showHide": "表示/非表示",
         "discardAll": "すべて破棄"
-    },
-    "accessibility": {
-        "modelSelect": "モデルを選択",
-        "invokeProgressBar": "進捗バー",
-        "reset": "リセット",
-        "uploadImage": "画像をアップロード",
-        "previousImage": "前の画像",
-        "nextImage": "次の画像",
-        "useThisParameter": "このパラメータを使用する",
-        "copyMetadataJson": "メタデータをコピー(JSON)",
-        "zoomIn": "ズームイン",
-        "exitViewer": "ExitViewer",
-        "zoomOut": "ズームアウト",
-        "rotateCounterClockwise": "反時計回りに回転",
-        "rotateClockwise": "時計回りに回転",
-        "flipHorizontally": "水平方向に反転",
-        "flipVertically": "垂直方向に反転",
-        "toggleAutoscroll": "自動スクロールの切替",
-        "modifyConfig": "Modify Config",
-        "toggleLogViewer": "Log Viewerの切替",
-        "showGallery": "ギャラリーを表示",
-        "showOptionsPanel": "オプションパネルを表示"
     }
 }

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

@@ -1 +0,0 @@
-{}

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

@@ -62,18 +62,7 @@
         "statusConvertingModel": "Omzetten van model",
         "statusModelConverted": "Model omgezet",
         "statusMergingModels": "Samenvoegen van modellen",
-        "statusMergedModels": "Modellen samengevoegd",
-        "cancel": "Annuleer",
-        "accept": "Akkoord",
-        "langPortuguese": "Português",
-        "pinOptionsPanel": "Zet deelscherm Opties vast",
-        "loading": "Bezig met laden",
-        "loadingInvokeAI": "Bezig met laden van Invoke AI",
-        "oceanTheme": "Oceaan",
-        "langHebrew": "עברית",
-        "langKorean": "한국어",
-        "txt2img": "Tekst naar afbeelding",
-        "postprocessing": "Nabewerking"
+        "statusMergedModels": "Modellen samengevoegd"
     },
     "gallery": {
         "generations": "Gegenereerde afbeeldingen",
@@ -312,7 +301,7 @@
         "name": "Naam",
         "nameValidationMsg": "Geef een naam voor je model",
         "description": "Beschrijving",
-        "descriptionValidationMsg": "Voeg een beschrijving toe voor je model",
+        "descriptionValidationMsg": "Voeg een beschrijving toe voor je model.",
         "config": "Configuratie",
         "configValidationMsg": "Pad naar het configuratiebestand van je model.",
         "modelLocation": "Locatie model",
@@ -402,13 +391,7 @@
         "modelMergeInterpAddDifferenceHelp": "In deze stand wordt model 3 eerst van model 2 afgehaald. Wat daar uitkomt wordt gemengd met model 1, gebruikmakend van de hierboven ingestelde alfawaarde.",
         "inverseSigmoid": "Keer Sigmoid om",
         "sigmoid": "Sigmoid",
-        "weightedSum": "Gewogen som",
-        "v2_base": "v2 (512px)",
-        "v2_768": "v2 (768px)",
-        "none": "geen",
-        "addDifference": "Voeg verschil toe",
-        "scanForModels": "Scan naar modellen",
-        "pickModelType": "Kies modelsoort"
+        "weightedSum": "Gewogen som"
     },
     "parameters": {
         "images": "Afbeeldingen",
@@ -578,7 +561,7 @@
         "autoSaveToGallery": "Bewaar automatisch naar galerij",
         "saveBoxRegionOnly": "Bewaar alleen tekengebied",
         "limitStrokesToBox": "Beperk streken tot tekenvak",
-        "showCanvasDebugInfo": "Toon aanvullende canvasgegevens",
+        "showCanvasDebugInfo": "Toon foutopsporingsgegevens canvas",
         "clearCanvasHistory": "Wis canvasgeschiedenis",
         "clearHistory": "Wis geschiedenis",
         "clearCanvasHistoryMessage": "Het wissen van de canvasgeschiedenis laat het huidige canvas ongemoeid, maar wist onherstelbaar de geschiedenis voor het ongedaan maken en herhalen.",
@@ -604,27 +587,5 @@
         "betaDarkenOutside": "Verduister buiten tekenvak",
         "betaLimitToBox": "Beperk tot tekenvak",
         "betaPreserveMasked": "Behoud masker"
-    },
-    "accessibility": {
-        "exitViewer": "Stop viewer",
-        "zoomIn": "Zoom in",
-        "rotateCounterClockwise": "Draai tegen de klok in",
-        "modelSelect": "Modelkeuze",
-        "invokeProgressBar": "Voortgangsbalk Invoke",
-        "reset": "Herstel",
-        "uploadImage": "Upload afbeelding",
-        "previousImage": "Vorige afbeelding",
-        "nextImage": "Volgende afbeelding",
-        "useThisParameter": "Gebruik deze parameter",
-        "copyMetadataJson": "Kopieer metagegevens-JSON",
-        "zoomOut": "Zoom uit",
-        "rotateClockwise": "Draai met de klok mee",
-        "flipHorizontally": "Spiegel horizontaal",
-        "flipVertically": "Spiegel verticaal",
-        "modifyConfig": "Wijzig configuratie",
-        "toggleAutoscroll": "Autom. scrollen aan/uit",
-        "toggleLogViewer": "Logboekviewer aan/uit",
-        "showGallery": "Toon galerij",
-        "showOptionsPanel": "Toon deelscherm Opties"
     }
 }

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

@@ -9,7 +9,7 @@
         "lightTheme": "Светлая",
         "greenTheme": "Зеленая",
         "img2img": "Изображение в изображение (img2img)",
-        "unifiedCanvas": "Единый холст",
+        "unifiedCanvas": "Универсальный холст",
         "nodes": "Ноды",
         "langRussian": "Русский",
         "nodesDesc": "Cистема генерации изображений на основе нодов (узлов) уже разрабатывается. Следите за новостями об этой замечательной функции.",
@@ -53,28 +53,7 @@
         "loading": "Загрузка",
         "loadingInvokeAI": "Загрузка Invoke AI",
         "back": "Назад",
-        "statusConvertingModel": "Конвертация модели",
-        "cancel": "Отменить",
-        "accept": "Принять",
-        "oceanTheme": "Океан",
-        "langUkranian": "Украинский",
-        "langEnglish": "Английский",
-        "postprocessing": "Постобработка",
-        "langArabic": "Арабский",
-        "langSpanish": "Испанский",
-        "langSimplifiedChinese": "Китайский (упрощенный)",
-        "langDutch": "Нидерландский",
-        "langFrench": "Французский",
-        "langGerman": "Немецкий",
-        "langHebrew": "Иврит",
-        "langItalian": "Итальянский",
-        "langJapanese": "Японский",
-        "langKorean": "Корейский",
-        "langPolish": "Польский",
-        "langPortuguese": "Португальский",
-        "txt2img": "Текст в изображение (txt2img)",
-        "langBrPortuguese": "Португальский (Бразилия)",
-        "linear": "Линейная обработка"
+        "statusConvertingModel": "Конвертация модели"
     },
     "gallery": {
         "generations": "Генерации",
@@ -93,11 +72,11 @@
         "noImagesInGallery": "Изображений нет"
     },
     "hotkeys": {
-        "keyboardShortcuts": "Горячие клавиши",
+        "keyboardShortcuts": "Клавиатурные сокращения",
         "appHotkeys": "Горячие клавиши приложения",
         "generalHotkeys": "Общие горячие клавиши",
         "galleryHotkeys": "Горячие клавиши галереи",
-        "unifiedCanvasHotkeys": "Горячие клавиши Единого холста",
+        "unifiedCanvasHotkeys": "Горячие клавиши универсального холста",
         "invoke": {
             "title": "Invoke",
             "desc": "Сгенерировать изображение"
@@ -287,12 +266,12 @@
             "desc": "Сбросить вид холста"
         },
         "previousStagingImage": {
-            "title": "Предыдущее изображение",
-            "desc": "Предыдущая область изображения"
+            "title": "Previous Staging Image",
+            "desc": "Предыдущее изображение"
         },
         "nextStagingImage": {
-            "title": "Следующее изображение",
-            "desc": "Следующая область изображения"
+            "title": "Next Staging Image",
+            "desc": "Следующее изображение"
         },
         "acceptStagingImage": {
             "title": "Принять изображение",
@@ -374,42 +353,7 @@
         "modelConverted": "Модель преобразована",
         "invokeRoot": "Каталог InvokeAI",
         "modelsMerged": "Модели объединены",
-        "mergeModels": "Объединить модели",
-        "scanForModels": "Просканировать модели",
-        "sigmoid": "Сигмоид",
-        "formMessageDiffusersModelLocation": "Расположение Diffusers-модели",
-        "modelThree": "Модель 3",
-        "modelMergeHeaderHelp2": "Только Diffusers-модели доступны для объединения. Если вы хотите объединить checkpoint-модели, сначала преобразуйте их в Diffusers.",
-        "pickModelType": "Выбрать тип модели",
-        "formMessageDiffusersVAELocation": "Расположение VAE",
-        "v1": "v1",
-        "convertToDiffusersSaveLocation": "Путь сохранения",
-        "customSaveLocation": "Пользовательский путь сохранения",
-        "alpha": "Альфа",
-        "diffusersModels": "Diffusers",
-        "customConfig": "Пользовательский конфиг",
-        "pathToCustomConfig": "Путь к пользовательскому конфигу",
-        "inpainting": "v1 Inpainting",
-        "sameFolder": "В ту же папку",
-        "modelOne": "Модель 1",
-        "mergedModelCustomSaveLocation": "Пользовательский путь",
-        "none": "пусто",
-        "addDifference": "Добавить разницу",
-        "vaeRepoIDValidationMsg": "Онлайн репозиторий VAE",
-        "convertToDiffusersHelpText2": "Этот процесс заменит вашу запись в Model Manager на версию той же модели в Diffusers.",
-        "custom": "Пользовательский",
-        "modelTwo": "Модель 2",
-        "mergedModelSaveLocation": "Путь сохранения",
-        "merge": "Объединить",
-        "interpolationType": "Тип интерполяции",
-        "modelMergeInterpAddDifferenceHelp": "В этом режиме Модель 3 сначала вычитается из Модели 2. Результирующая версия смешивается с Моделью 1 с установленным выше коэффициентом Альфа.",
-        "modelMergeHeaderHelp1": "Вы можете объединить до трех разных моделей, чтобы создать смешанную, соответствующую вашим потребностям.",
-        "modelMergeAlphaHelp": "Альфа влияет на силу смешивания моделей. Более низкие значения альфа приводят к меньшему влиянию второй модели.",
-        "inverseSigmoid": "Обратный Сигмоид",
-        "weightedSum": "Взвешенная сумма",
-        "safetensorModels": "SafeTensors",
-        "v2_768": "v2 (768px)",
-        "v2_base": "v2 (512px)"
+        "mergeModels": "Объединить модели"
     },
     "parameters": {
         "images": "Изображения",
@@ -436,7 +380,7 @@
         "scale": "Масштаб",
         "otherOptions": "Другие параметры",
         "seamlessTiling": "Бесшовный узор",
-        "hiresOptim": "Оптимизация High Res",
+        "hiresOptim": "Высокое разрешение",
         "imageFit": "Уместить изображение",
         "codeformerFidelity": "Точность",
         "seamSize": "Размер шва",
@@ -453,11 +397,11 @@
         "infillScalingHeader": "Заполнение и масштабирование",
         "img2imgStrength": "Сила обработки img2img",
         "toggleLoopback": "Зациклить обработку",
-        "invoke": "Invoke",
+        "invoke": "Вызвать",
         "promptPlaceholder": "Введите запрос здесь (на английском). [исключенные токены], (более значимые)++, (менее значимые)--, swap и blend тоже доступны (смотрите Github)",
         "sendTo": "Отправить",
         "sendToImg2Img": "Отправить в img2img",
-        "sendToUnifiedCanvas": "Отправить на Единый холст",
+        "sendToUnifiedCanvas": "Отправить на холст",
         "copyImageToLink": "Скопировать ссылку",
         "downloadImage": "Скачать",
         "openInViewer": "Открыть в просмотрщике",
@@ -469,24 +413,7 @@
         "info": "Метаданные",
         "deleteImage": "Удалить изображение",
         "initialImage": "Исходное изображение",
-        "showOptionsPanel": "Показать панель настроек",
-        "vSymmetryStep": "Шаг верт. симметрии",
-        "cancel": {
-            "immediate": "Отменить немедленно",
-            "schedule": "Отменить после текущей итерации",
-            "isScheduled": "Отмена",
-            "setType": "Установить тип отмены"
-        },
-        "general": "Основное",
-        "hiresStrength": "Сила High Res",
-        "symmetry": "Симметрия",
-        "hSymmetryStep": "Шаг гор. симметрии",
-        "hidePreview": "Скрыть предпросмотр",
-        "imageToImage": "Изображение в изображение",
-        "denoisingStrength": "Сила шумоподавления",
-        "copyImage": "Скопировать изображение",
-        "negativePrompts": "Исключающий запрос",
-        "showPreview": "Показать предпросмотр"
+        "showOptionsPanel": "Показать панель настроек"
     },
     "settings": {
         "models": "Модели",
@@ -496,11 +423,10 @@
         "displayHelpIcons": "Показывать значки подсказок",
         "useCanvasBeta": "Показывать инструменты слева (Beta UI)",
         "enableImageDebugging": "Включить отладку",
-        "resetWebUI": "Сброс настроек Web UI",
+        "resetWebUI": "Вернуть умолчания",
         "resetWebUIDesc1": "Сброс настроек веб-интерфейса удаляет только локальный кэш браузера с вашими изображениями и настройками. Он не удаляет изображения с диска.",
         "resetWebUIDesc2": "Если изображения не отображаются в галерее или не работает что-то еще, пожалуйста, попробуйте сбросить настройки, прежде чем сообщать о проблеме на GitHub.",
-        "resetComplete": "Интерфейс сброшен. Обновите эту страницу.",
-        "useSlidersForAll": "Использовать ползунки для всех параметров"
+        "resetComplete": "Интерфейс сброшен. Обновите эту страницу."
     },
     "toast": {
         "tempFoldersEmptied": "Временная папка очищена",
@@ -515,7 +441,7 @@
         "imageSavedToGallery": "Изображение сохранено в галерею",
         "canvasMerged": "Холст объединен",
         "sentToImageToImage": "Отправить в img2img",
-        "sentToUnifiedCanvas": "Отправлено на Единый холст",
+        "sentToUnifiedCanvas": "Отправить на холст",
         "parametersSet": "Параметры заданы",
         "parametersNotSet": "Параметры не заданы",
         "parametersNotSetDesc": "Не найдены метаданные изображения.",
@@ -532,11 +458,7 @@
         "metadataLoadFailed": "Не удалось загрузить метаданные",
         "initialImageSet": "Исходное изображение задано",
         "initialImageNotSet": "Исходное изображение не задано",
-        "initialImageNotSetDesc": "Не получилось загрузить исходное изображение",
-        "serverError": "Ошибка сервера",
-        "disconnected": "Отключено от сервера",
-        "connected": "Подключено к серверу",
-        "canceled": "Обработка отменена"
+        "initialImageNotSetDesc": "Не получилось загрузить исходное изображение"
     },
     "tooltip": {
         "feature": {
@@ -585,7 +507,7 @@
         "autoSaveToGallery": "Автосохранение в галерее",
         "saveBoxRegionOnly": "Сохранять только выделение",
         "limitStrokesToBox": "Ограничить штрихи выделением",
-        "showCanvasDebugInfo": "Показать доп. информацию о холсте",
+        "showCanvasDebugInfo": "Показать отладку холста",
         "clearCanvasHistory": "Очистить историю холста",
         "clearHistory": "Очистить историю",
         "clearCanvasHistoryMessage": "Очистка истории холста оставляет текущий холст нетронутым, но удаляет историю отмен и повторов.",
@@ -613,26 +535,6 @@
         "betaPreserveMasked": "Сохранять маскируемую область"
     },
     "accessibility": {
-        "modelSelect": "Выбор модели",
-        "uploadImage": "Загрузить изображение",
-        "nextImage": "Следующее изображение",
-        "previousImage": "Предыдущее изображение",
-        "zoomIn": "Приблизить",
-        "zoomOut": "Отдалить",
-        "rotateClockwise": "Повернуть по часовой стрелке",
-        "rotateCounterClockwise": "Повернуть против часовой стрелки",
-        "flipVertically": "Перевернуть вертикально",
-        "flipHorizontally": "Отразить горизонтально",
-        "toggleAutoscroll": "Включить автопрокрутку",
-        "toggleLogViewer": "Показать или скрыть просмотрщик логов",
-        "showOptionsPanel": "Показать опции",
-        "showGallery": "Показать галерею",
-        "invokeProgressBar": "Индикатор выполнения",
-        "reset": "Сброс",
-        "modifyConfig": "Изменить конфиг",
-        "useThisParameter": "Использовать этот параметр",
-        "copyMetadataJson": "Скопировать метаданные JSON",
-        "exitViewer": "Закрыть просмотрщик",
-        "menu": "Меню"
+        "modelSelect": "Выбор модели"
     }
 }

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

@@ -1,254 +0,0 @@
-{
-    "accessibility": {
-        "copyMetadataJson": "Kopiera metadata JSON",
-        "zoomIn": "Zooma in",
-        "exitViewer": "Avslutningsvisare",
-        "modelSelect": "Välj modell",
-        "uploadImage": "Ladda upp bild",
-        "invokeProgressBar": "Invoke förloppsmätare",
-        "nextImage": "Nästa bild",
-        "toggleAutoscroll": "Växla automatisk rullning",
-        "flipHorizontally": "Vänd vågrätt",
-        "flipVertically": "Vänd lodrätt",
-        "zoomOut": "Zooma ut",
-        "toggleLogViewer": "Växla logvisare",
-        "reset": "Starta om",
-        "previousImage": "Föregående bild",
-        "useThisParameter": "Använd denna parametern",
-        "showGallery": "Visa galleri",
-        "rotateCounterClockwise": "Rotera moturs",
-        "rotateClockwise": "Rotera medurs",
-        "modifyConfig": "Ändra konfiguration",
-        "showOptionsPanel": "Visa inställningspanelen"
-    },
-    "common": {
-        "hotkeysLabel": "Snabbtangenter",
-        "reportBugLabel": "Rapportera bugg",
-        "githubLabel": "Github",
-        "discordLabel": "Discord",
-        "settingsLabel": "Inställningar",
-        "darkTheme": "Mörk",
-        "lightTheme": "Ljus",
-        "greenTheme": "Grön",
-        "oceanTheme": "Hav",
-        "langEnglish": "Engelska",
-        "langDutch": "Nederländska",
-        "langFrench": "Franska",
-        "langGerman": "Tyska",
-        "langItalian": "Italienska",
-        "langArabic": "العربية",
-        "langHebrew": "עברית",
-        "langPolish": "Polski",
-        "langPortuguese": "Português",
-        "langBrPortuguese": "Português do Brasil",
-        "langSimplifiedChinese": "简体中文",
-        "langJapanese": "日本語",
-        "langKorean": "한국어",
-        "langRussian": "Русский",
-        "unifiedCanvas": "Förenad kanvas",
-        "nodesDesc": "Ett nodbaserat system för bildgenerering är under utveckling. Håll utkik för uppdateringar om denna fantastiska funktion.",
-        "langUkranian": "Украї́нська",
-        "langSpanish": "Español",
-        "postProcessDesc2": "Ett dedikerat användargränssnitt kommer snart att släppas för att underlätta mer avancerade arbetsflöden av efterbehandling.",
-        "trainingDesc1": "Ett dedikerat arbetsflöde för träning av dina egna inbäddningar och kontrollpunkter genom Textual Inversion eller Dreambooth från webbgränssnittet.",
-        "trainingDesc2": "InvokeAI stöder redan träning av anpassade inbäddningar med hjälp av Textual Inversion genom huvudscriptet.",
-        "upload": "Ladda upp",
-        "close": "Stäng",
-        "cancel": "Avbryt",
-        "accept": "Acceptera",
-        "statusDisconnected": "Frånkopplad",
-        "statusGeneratingTextToImage": "Genererar text till bild",
-        "statusGeneratingImageToImage": "Genererar Bild till bild",
-        "statusGeneratingInpainting": "Genererar Måla i",
-        "statusGenerationComplete": "Generering klar",
-        "statusModelConverted": "Modell konverterad",
-        "statusMergingModels": "Sammanfogar modeller",
-        "pinOptionsPanel": "Nåla fast inställningspanelen",
-        "loading": "Laddar",
-        "loadingInvokeAI": "Laddar Invoke AI",
-        "statusRestoringFaces": "Återskapar ansikten",
-        "languagePickerLabel": "Språkväljare",
-        "themeLabel": "Tema",
-        "txt2img": "Text till bild",
-        "nodes": "Noder",
-        "img2img": "Bild till bild",
-        "postprocessing": "Efterbehandling",
-        "postProcessing": "Efterbehandling",
-        "load": "Ladda",
-        "training": "Träning",
-        "postProcessDesc1": "Invoke AI erbjuder ett brett utbud av efterbehandlingsfunktioner. Uppskalning och ansiktsåterställning finns redan tillgängligt i webbgränssnittet. Du kommer åt dem ifrån Avancerade inställningar-menyn under Bild till bild-fliken. Du kan också behandla bilder direkt genom att använda knappen bildåtgärder ovanför nuvarande bild eller i bildvisaren.",
-        "postProcessDesc3": "Invoke AI's kommandotolk erbjuder många olika funktioner, bland annat \"Förstora\".",
-        "statusGenerating": "Genererar",
-        "statusError": "Fel",
-        "back": "Bakåt",
-        "statusConnected": "Ansluten",
-        "statusPreparing": "Förbereder",
-        "statusProcessingCanceled": "Bearbetning avbruten",
-        "statusProcessingComplete": "Bearbetning färdig",
-        "statusGeneratingOutpainting": "Genererar Fyll ut",
-        "statusIterationComplete": "Itterering klar",
-        "statusSavingImage": "Sparar bild",
-        "statusRestoringFacesGFPGAN": "Återskapar ansikten (GFPGAN)",
-        "statusRestoringFacesCodeFormer": "Återskapar ansikten (CodeFormer)",
-        "statusUpscaling": "Skala upp",
-        "statusUpscalingESRGAN": "Uppskalning (ESRGAN)",
-        "statusModelChanged": "Modell ändrad",
-        "statusLoadingModel": "Laddar modell",
-        "statusConvertingModel": "Konverterar modell",
-        "statusMergedModels": "Modeller sammanfogade"
-    },
-    "gallery": {
-        "generations": "Generationer",
-        "showGenerations": "Visa generationer",
-        "uploads": "Uppladdningar",
-        "showUploads": "Visa uppladdningar",
-        "galleryImageSize": "Bildstorlek",
-        "allImagesLoaded": "Alla bilder laddade",
-        "loadMore": "Ladda mer",
-        "galleryImageResetSize": "Återställ storlek",
-        "gallerySettings": "Galleriinställningar",
-        "maintainAspectRatio": "Behåll bildförhållande",
-        "pinGallery": "Nåla fast galleri",
-        "noImagesInGallery": "Inga bilder i galleriet",
-        "autoSwitchNewImages": "Ändra automatiskt till nya bilder",
-        "singleColumnLayout": "Enkolumnslayout"
-    },
-    "hotkeys": {
-        "generalHotkeys": "Allmänna snabbtangenter",
-        "galleryHotkeys": "Gallerisnabbtangenter",
-        "unifiedCanvasHotkeys": "Snabbtangenter för sammanslagskanvas",
-        "invoke": {
-            "title": "Anropa",
-            "desc": "Genererar en bild"
-        },
-        "cancel": {
-            "title": "Avbryt",
-            "desc": "Avbryt bildgenerering"
-        },
-        "focusPrompt": {
-            "desc": "Fokusera området för promptinmatning",
-            "title": "Fokusprompt"
-        },
-        "pinOptions": {
-            "desc": "Nåla fast alternativpanelen",
-            "title": "Nåla fast alternativ"
-        },
-        "toggleOptions": {
-            "title": "Växla inställningar",
-            "desc": "Öppna och stäng alternativpanelen"
-        },
-        "toggleViewer": {
-            "title": "Växla visaren",
-            "desc": "Öppna och stäng bildvisaren"
-        },
-        "toggleGallery": {
-            "title": "Växla galleri",
-            "desc": "Öppna eller stäng galleribyrån"
-        },
-        "maximizeWorkSpace": {
-            "title": "Maximera arbetsyta",
-            "desc": "Stäng paneler och maximera arbetsyta"
-        },
-        "changeTabs": {
-            "title": "Växla flik",
-            "desc": "Byt till en annan arbetsyta"
-        },
-        "consoleToggle": {
-            "title": "Växla konsol",
-            "desc": "Öppna och stäng konsol"
-        },
-        "setSeed": {
-            "desc": "Använd seed för nuvarande bild",
-            "title": "välj seed"
-        },
-        "setParameters": {
-            "title": "Välj parametrar",
-            "desc": "Använd alla parametrar från nuvarande bild"
-        },
-        "setPrompt": {
-            "desc": "Använd prompt för nuvarande bild",
-            "title": "Välj prompt"
-        },
-        "restoreFaces": {
-            "title": "Återskapa ansikten",
-            "desc": "Återskapa nuvarande bild"
-        },
-        "upscale": {
-            "title": "Skala upp",
-            "desc": "Skala upp nuvarande bild"
-        },
-        "showInfo": {
-            "title": "Visa info",
-            "desc": "Visa metadata för nuvarande bild"
-        },
-        "sendToImageToImage": {
-            "title": "Skicka till Bild till bild",
-            "desc": "Skicka nuvarande bild till Bild till bild"
-        },
-        "deleteImage": {
-            "title": "Radera bild",
-            "desc": "Radera nuvarande bild"
-        },
-        "closePanels": {
-            "title": "Stäng paneler",
-            "desc": "Stäng öppna paneler"
-        },
-        "previousImage": {
-            "title": "Föregående bild",
-            "desc": "Visa föregående bild"
-        },
-        "nextImage": {
-            "title": "Nästa bild",
-            "desc": "Visa nästa bild"
-        },
-        "toggleGalleryPin": {
-            "title": "Växla gallerinål",
-            "desc": "Nålar fast eller nålar av galleriet i gränssnittet"
-        },
-        "increaseGalleryThumbSize": {
-            "title": "Förstora galleriets bildstorlek",
-            "desc": "Förstora miniatyrbildernas storlek"
-        },
-        "decreaseGalleryThumbSize": {
-            "title": "Minska gelleriets bildstorlek",
-            "desc": "Minska miniatyrbildernas storlek i galleriet"
-        },
-        "decreaseBrushSize": {
-            "desc": "Förminska storleken på kanvas- pensel eller suddgummi",
-            "title": "Minska penselstorlek"
-        },
-        "increaseBrushSize": {
-            "title": "Öka penselstorlek",
-            "desc": "Öka stoleken på kanvas- pensel eller suddgummi"
-        },
-        "increaseBrushOpacity": {
-            "title": "Öka penselns opacitet",
-            "desc": "Öka opaciteten för kanvaspensel"
-        },
-        "decreaseBrushOpacity": {
-            "desc": "Minska kanvaspenselns opacitet",
-            "title": "Minska penselns opacitet"
-        },
-        "moveTool": {
-            "title": "Flytta",
-            "desc": "Tillåt kanvasnavigation"
-        },
-        "fillBoundingBox": {
-            "title": "Fyll ram",
-            "desc": "Fyller ramen med pensels färg"
-        },
-        "keyboardShortcuts": "Snabbtangenter",
-        "appHotkeys": "Appsnabbtangenter",
-        "selectBrush": {
-            "desc": "Välj kanvaspensel",
-            "title": "Välj pensel"
-        },
-        "selectEraser": {
-            "desc": "Välj kanvassuddgummi",
-            "title": "Välj suddgummi"
-        },
-        "eraseBoundingBox": {
-            "title": "Ta bort ram"
-        }
-    }
-}

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

@@ -1,64 +0,0 @@
-{
-    "accessibility": {
-        "invokeProgressBar": "Invoke ilerleme durumu",
-        "nextImage": "Sonraki Resim",
-        "useThisParameter": "Kullanıcı parametreleri",
-        "copyMetadataJson": "Metadata verilerini kopyala (JSON)",
-        "exitViewer": "Görüntüleme Modundan Çık",
-        "zoomIn": "Yakınlaştır",
-        "zoomOut": "Uzaklaştır",
-        "rotateCounterClockwise": "Döndür (Saat yönünün tersine)",
-        "rotateClockwise": "Döndür (Saat yönünde)",
-        "flipHorizontally": "Yatay Çevir",
-        "flipVertically": "Dikey Çevir",
-        "modifyConfig": "Ayarları Değiştir",
-        "toggleAutoscroll": "Otomatik kaydırmayı aç/kapat",
-        "toggleLogViewer": "Günlük Görüntüleyici Aç/Kapa",
-        "showOptionsPanel": "Ayarlar Panelini Göster",
-        "modelSelect": "Model Seçin",
-        "reset": "Sıfırla",
-        "uploadImage": "Resim Yükle",
-        "previousImage": "Önceki Resim",
-        "menu": "Menü",
-        "showGallery": "Galeriyi Göster"
-    },
-    "common": {
-        "hotkeysLabel": "Kısayol Tuşları",
-        "themeLabel": "Tema",
-        "languagePickerLabel": "Dil Seçimi",
-        "reportBugLabel": "Hata Bildir",
-        "githubLabel": "Github",
-        "discordLabel": "Discord",
-        "settingsLabel": "Ayarlar",
-        "darkTheme": "Karanlık Tema",
-        "lightTheme": "Aydınlık Tema",
-        "greenTheme": "Yeşil Tema",
-        "oceanTheme": "Okyanus Tema",
-        "langArabic": "Arapça",
-        "langEnglish": "İngilizce",
-        "langDutch": "Hollandaca",
-        "langFrench": "Fransızca",
-        "langGerman": "Almanca",
-        "langItalian": "İtalyanca",
-        "langJapanese": "Japonca",
-        "langPolish": "Lehçe",
-        "langPortuguese": "Portekizce",
-        "langBrPortuguese": "Portekizcr (Brezilya)",
-        "langRussian": "Rusça",
-        "langSimplifiedChinese": "Çince (Basit)",
-        "langUkranian": "Ukraynaca",
-        "langSpanish": "İspanyolca",
-        "txt2img": "Metinden Resime",
-        "img2img": "Resimden Metine",
-        "linear": "Çizgisel",
-        "nodes": "Düğümler",
-        "postprocessing": "İşlem Sonrası",
-        "postProcessing": "İşlem Sonrası",
-        "postProcessDesc2": "Daha gelişmiş özellikler için ve iş akışını kolaylaştırmak için özel bir kullanıcı arayüzü çok yakında yayınlanacaktır.",
-        "postProcessDesc3": "Invoke AI komut satırı arayüzü, bir çok yeni özellik sunmaktadır.",
-        "langKorean": "Korece",
-        "unifiedCanvas": "Akıllı Tuval",
-        "nodesDesc": "Görüntülerin oluşturulmasında hazırladığımız yeni bir sistem geliştirme aşamasındadır. Bu harika özellikler ve çok daha fazlası için bizi takip etmeye devam edin.",
-        "postProcessDesc1": "Invoke AI son kullanıcıya yönelik bir çok özellik sunar. Görüntü kalitesi yükseltme, yüz restorasyonu WebUI üzerinden kullanılabilir. Metinden resime ve resimden metne araçlarına gelişmiş seçenekler menüsünden ulaşabilirsiniz. İsterseniz mevcut görüntü ekranının üzerindeki veya görüntüleyicideki görüntüyü doğrudan düzenleyebilirsiniz."
-    }
-}

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

@@ -16,9 +16,9 @@
         "postProcessing": "Постобробка",
         "postProcessDesc1": "Invoke AI пропонує широкий спектр функцій постобробки. Збільшення зображення (upscale) та відновлення облич вже доступні в інтерфейсі. Отримайте доступ до них з меню 'Додаткові параметри' на вкладках 'Зображення із тексту' та 'Зображення із зображення'. Обробляйте зображення безпосередньо, використовуючи кнопки дій із зображеннями над поточним зображенням або в режимі перегляду.",
         "postProcessDesc2": "Найближчим часом буде випущено спеціальний інтерфейс для більш сучасних процесів постобробки.",
-        "postProcessDesc3": "Інтерфейс командного рядка Invoke AI пропонує різні інші функції, включаючи збільшення Embiggen.",
+        "postProcessDesc3": "Інтерфейс командного рядка Invoke AI пропонує різні інші функції, включаючи збільшення Embiggen",
         "training": "Навчання",
-        "trainingDesc1": "Спеціальний інтерфейс для навчання власних моделей з використанням Textual Inversion та Dreambooth.",
+        "trainingDesc1": "Спеціальний інтерфейс для навчання власних моделей з використанням Textual Inversion та Dreambooth",
         "trainingDesc2": "InvokeAI вже підтримує навчання моделей за допомогою TI, через інтерфейс командного рядка.",
         "upload": "Завантажити",
         "close": "Закрити",
@@ -43,38 +43,7 @@
         "statusUpscaling": "Збільшення",
         "statusUpscalingESRGAN": "Збільшення (ESRGAN)",
         "statusLoadingModel": "Завантаження моделі",
-        "statusModelChanged": "Модель змінено",
-        "cancel": "Скасувати",
-        "accept": "Підтвердити",
-        "back": "Назад",
-        "postprocessing": "Постобробка",
-        "statusModelConverted": "Модель сконвертована",
-        "statusMergingModels": "Злиття моделей",
-        "loading": "Завантаження",
-        "loadingInvokeAI": "Завантаження Invoke AI",
-        "langHebrew": "Іврит",
-        "langKorean": "Корейська",
-        "langPortuguese": "Португальська",
-        "pinOptionsPanel": "Закріпити панель налаштувань",
-        "oceanTheme": "Океан",
-        "langArabic": "Арабська",
-        "langSimplifiedChinese": "Китайська (спрощена)",
-        "langSpanish": "Іспанська",
-        "langEnglish": "Англійська",
-        "langGerman": "Німецька",
-        "langItalian": "Італійська",
-        "langJapanese": "Японська",
-        "langPolish": "Польська",
-        "langBrPortuguese": "Португальська (Бразилія)",
-        "langRussian": "Російська",
-        "githubLabel": "Github",
-        "txt2img": "Текст в зображення (txt2img)",
-        "discordLabel": "Discord",
-        "langDutch": "Голландська",
-        "langFrench": "Французька",
-        "statusMergedModels": "Моделі об'єднані",
-        "statusConvertingModel": "Конвертація моделі",
-        "linear": "Лінійна обробка"
+        "statusModelChanged": "Модель змінено"
     },
     "gallery": {
         "generations": "Генерації",
@@ -315,15 +284,15 @@
         "description": "Опис",
         "descriptionValidationMsg": "Введіть опис моделі",
         "config": "Файл конфігурації",
-        "configValidationMsg": "Шлях до файлу конфігурації.",
+        "configValidationMsg": "Шлях до файлу конфігурації",
         "modelLocation": "Розташування моделі",
-        "modelLocationValidationMsg": "Шлях до файлу з моделлю.",
+        "modelLocationValidationMsg": "Шлях до файлу з моделлю",
         "vaeLocation": "Розтышування VAE",
-        "vaeLocationValidationMsg": "Шлях до VAE.",
+        "vaeLocationValidationMsg": "Шлях до VAE",
         "width": "Ширина",
-        "widthValidationMsg": "Початкова ширина зображень.",
+        "widthValidationMsg": "Початкова ширина зображень",
         "height": "Висота",
-        "heightValidationMsg": "Початкова висота зображень.",
+        "heightValidationMsg": "Початкова висота зображень",
         "addModel": "Додати модель",
         "updateModel": "Оновити модель",
         "availableModels": "Доступні моделі",
@@ -350,66 +319,7 @@
         "deleteModel": "Видалити модель",
         "deleteConfig": "Видалити конфігурацію",
         "deleteMsg1": "Ви точно хочете видалити модель із InvokeAI?",
-        "deleteMsg2": "Це не призведе до видалення файлу моделі з диску. Позніше ви можете додати його знову.",
-        "allModels": "Усі моделі",
-        "diffusersModels": "Diffusers",
-        "scanForModels": "Сканувати моделі",
-        "convert": "Конвертувати",
-        "convertToDiffusers": "Конвертувати в Diffusers",
-        "formMessageDiffusersVAELocationDesc": "Якщо не надано, InvokeAI буде шукати файл VAE в розташуванні моделі, вказаній вище.",
-        "convertToDiffusersHelpText3": "Файл моделі на диску НЕ буде видалено або змінено. Ви можете знову додати його в Model Manager, якщо потрібно.",
-        "customConfig": "Користувальницький конфіг",
-        "invokeRoot": "Каталог InvokeAI",
-        "custom": "Користувальницький",
-        "modelTwo": "Модель 2",
-        "modelThree": "Модель 3",
-        "mergedModelName": "Назва об'єднаної моделі",
-        "alpha": "Альфа",
-        "interpolationType": "Тип інтерполяції",
-        "mergedModelSaveLocation": "Шлях збереження",
-        "mergedModelCustomSaveLocation": "Користувальницький шлях",
-        "invokeAIFolder": "Каталог InvokeAI",
-        "ignoreMismatch": "Ігнорувати невідповідності між вибраними моделями",
-        "modelMergeHeaderHelp2": "Тільки Diffusers-моделі доступні для об'єднання. Якщо ви хочете об'єднати checkpoint-моделі, спочатку перетворіть їх на Diffusers.",
-        "checkpointModels": "Checkpoints",
-        "repo_id": "ID репозиторію",
-        "v2_base": "v2 (512px)",
-        "repoIDValidationMsg": "Онлайн-репозиторій моделі",
-        "formMessageDiffusersModelLocationDesc": "Вкажіть хоча б одне.",
-        "formMessageDiffusersModelLocation": "Шлях до Diffusers-моделі",
-        "v2_768": "v2 (768px)",
-        "formMessageDiffusersVAELocation": "Шлях до VAE",
-        "convertToDiffusersHelpText5": "Переконайтеся, що у вас достатньо місця на диску. Моделі зазвичай займають від 4 до 7 Гб.",
-        "convertToDiffusersSaveLocation": "Шлях збереження",
-        "v1": "v1",
-        "convertToDiffusersHelpText6": "Ви хочете перетворити цю модель?",
-        "inpainting": "v1 Inpainting",
-        "modelConverted": "Модель перетворено",
-        "sameFolder": "У ту ж папку",
-        "statusConverting": "Перетворення",
-        "merge": "Об'єднати",
-        "mergeModels": "Об'єднати моделі",
-        "modelOne": "Модель 1",
-        "sigmoid": "Сігмоїд",
-        "weightedSum": "Зважена сума",
-        "none": "пусто",
-        "addDifference": "Додати різницю",
-        "pickModelType": "Вибрати тип моделі",
-        "convertToDiffusersHelpText4": "Це одноразова дія. Вона може зайняти від 30 до 60 секунд в залежності від характеристик вашого комп'ютера.",
-        "pathToCustomConfig": "Шлях до конфігу користувача",
-        "safetensorModels": "SafeTensors",
-        "addCheckpointModel": "Додати модель Checkpoint/Safetensor",
-        "addDiffuserModel": "Додати Diffusers",
-        "vaeRepoID": "ID репозиторію VAE",
-        "vaeRepoIDValidationMsg": "Онлайн-репозиторій VAE",
-        "modelMergeInterpAddDifferenceHelp": "У цьому режимі Модель 3 спочатку віднімається з Моделі 2. Результуюча версія змішується з Моделью 1 із встановленим вище коефіцієнтом Альфа.",
-        "customSaveLocation": "Користувальницький шлях збереження",
-        "modelMergeAlphaHelp": "Альфа впливає силу змішування моделей. Нижчі значення альфа призводять до меншого впливу другої моделі.",
-        "convertToDiffusersHelpText1": "Ця модель буде конвертована в формат 🧨 Diffusers.",
-        "convertToDiffusersHelpText2": "Цей процес замінить ваш запис в Model Manager на версію тієї ж моделі в Diffusers.",
-        "modelsMerged": "Моделі об'єднані",
-        "modelMergeHeaderHelp1": "Ви можете об'єднати до трьох різних моделей, щоб створити змішану, що відповідає вашим потребам.",
-        "inverseSigmoid": "Зворотній Сігмоїд"
+        "deleteMsg2": "Це не призведе до видалення файлу моделі з диску. Позніше ви можете додати його знову."
     },
     "parameters": {
         "images": "Зображення",
@@ -436,7 +346,7 @@
         "scale": "Масштаб",
         "otherOptions": "інші параметри",
         "seamlessTiling": "Безшовний узор",
-        "hiresOptim": "Оптимізація High Res",
+        "hiresOptim": "Висока роздільна здатність",
         "imageFit": "Вмістити зображення",
         "codeformerFidelity": "Точність",
         "seamSize": "Размір шву",
@@ -469,24 +379,7 @@
         "info": "Метадані",
         "deleteImage": "Видалити зображення",
         "initialImage": "Початкове зображення",
-        "showOptionsPanel": "Показати панель налаштувань",
-        "general": "Основне",
-        "cancel": {
-            "immediate": "Скасувати негайно",
-            "schedule": "Скасувати після поточної ітерації",
-            "isScheduled": "Відміна",
-            "setType": "Встановити тип скасування"
-        },
-        "vSymmetryStep": "Крок верт. симетрії",
-        "hiresStrength": "Сила High Res",
-        "hidePreview": "Сховати попередній перегляд",
-        "showPreview": "Показати попередній перегляд",
-        "imageToImage": "Зображення до зображення",
-        "denoisingStrength": "Сила шумоподавлення",
-        "copyImage": "Копіювати зображення",
-        "symmetry": "Симетрія",
-        "hSymmetryStep": "Крок гор. симетрії",
-        "negativePrompts": "Виключний запит"
+        "showOptionsPanel": "Показати панель налаштувань"
     },
     "settings": {
         "models": "Моделі",
@@ -499,8 +392,7 @@
         "resetWebUI": "Повернути початкові",
         "resetWebUIDesc1": "Скидання настройок веб-інтерфейсу видаляє лише локальний кеш браузера з вашими зображеннями та налаштуваннями. Це не призводить до видалення зображень з диску.",
         "resetWebUIDesc2": "Якщо зображення не відображаються в галереї або не працює ще щось, спробуйте скинути налаштування, перш ніж повідомляти про проблему на GitHub.",
-        "resetComplete": "Інтерфейс скинуто. Оновіть цю сторінку.",
-        "useSlidersForAll": "Використовувати повзунки для всіх параметрів"
+        "resetComplete": "Інтерфейс скинуто. Оновіть цю сторінку."
     },
     "toast": {
         "tempFoldersEmptied": "Тимчасова папка очищена",
@@ -518,25 +410,21 @@
         "sentToUnifiedCanvas": "Надіслати на полотно",
         "parametersSet": "Параметри задані",
         "parametersNotSet": "Параметри не задані",
-        "parametersNotSetDesc": "Не знайдені метадані цього зображення.",
+        "parametersNotSetDesc": "Не знайдені метадані цього зображення",
         "parametersFailed": "Проблема із завантаженням параметрів",
-        "parametersFailedDesc": "Неможливо завантажити початкове зображення.",
+        "parametersFailedDesc": "Неможливо завантажити початкове зображення",
         "seedSet": "Сід заданий",
         "seedNotSet": "Сід не заданий",
-        "seedNotSetDesc": "Не вдалося знайти сід для зображення.",
+        "seedNotSetDesc": "Не вдалося знайти сід для зображення",
         "promptSet": "Запит заданий",
         "promptNotSet": "Запит не заданий",
-        "promptNotSetDesc": "Не вдалося знайти запит для зображення.",
+        "promptNotSetDesc": "Не вдалося знайти запит для зображення",
         "upscalingFailed": "Збільшення не вдалося",
         "faceRestoreFailed": "Відновлення облич не вдалося",
         "metadataLoadFailed": "Не вдалося завантажити метадані",
         "initialImageSet": "Початкове зображення задане",
         "initialImageNotSet": "Початкове зображення не задане",
-        "initialImageNotSetDesc": "Не вдалося завантажити початкове зображення",
-        "serverError": "Помилка сервера",
-        "disconnected": "Відключено від сервера",
-        "connected": "Підключено до сервера",
-        "canceled": "Обробку скасовано"
+        "initialImageNotSetDesc": "Не вдалося завантажити початкове зображення"
     },
     "tooltip": {
         "feature": {
@@ -585,10 +473,10 @@
         "autoSaveToGallery": "Автозбереження до галереї",
         "saveBoxRegionOnly": "Зберiгати тiльки видiлення",
         "limitStrokesToBox": "Обмежити штрихи виділенням",
-        "showCanvasDebugInfo": "Показати дод. інформацію про полотно",
+        "showCanvasDebugInfo": "Показати налаштування полотна",
         "clearCanvasHistory": "Очистити iсторiю полотна",
         "clearHistory": "Очистити iсторiю",
-        "clearCanvasHistoryMessage": "Очищення історії полотна залишає поточне полотно незайманим, але видаляє історію скасування та повтору.",
+        "clearCanvasHistoryMessage": "Очищення історії полотна залишає поточне полотно незайманим, але видаляє історію скасування та повтору",
         "clearCanvasHistoryConfirm": "Ви впевнені, що хочете очистити історію полотна?",
         "emptyTempImageFolder": "Очистити тимчасову папку",
         "emptyFolder": "Очистити папку",
@@ -611,28 +499,5 @@
         "betaDarkenOutside": "Затемнити зовні",
         "betaLimitToBox": "Обмежити виділенням",
         "betaPreserveMasked": "Зберiгати замасковану область"
-    },
-    "accessibility": {
-        "nextImage": "Наступне зображення",
-        "modelSelect": "Вибір моделі",
-        "invokeProgressBar": "Індикатор виконання",
-        "reset": "Скинути",
-        "uploadImage": "Завантажити зображення",
-        "useThisParameter": "Використовувати цей параметр",
-        "exitViewer": "Вийти з переглядача",
-        "zoomIn": "Збільшити",
-        "zoomOut": "Зменшити",
-        "rotateCounterClockwise": "Обертати проти годинникової стрілки",
-        "rotateClockwise": "Обертати за годинниковою стрілкою",
-        "toggleAutoscroll": "Увімкнути автопрокручування",
-        "toggleLogViewer": "Показати або приховати переглядач журналів",
-        "showGallery": "Показати галерею",
-        "previousImage": "Попереднє зображення",
-        "copyMetadataJson": "Скопіювати метадані JSON",
-        "flipVertically": "Перевернути по вертикалі",
-        "flipHorizontally": "Відобразити по горизонталі",
-        "showOptionsPanel": "Показати опції",
-        "modifyConfig": "Змінити конфігурацію",
-        "menu": "Меню"
     }
 }

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

@@ -1 +0,0 @@
-{}

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

@@ -481,22 +481,5 @@
         "betaDarkenOutside": "暗化外部区域",
         "betaLimitToBox": "限制在框内",
         "betaPreserveMasked": "保留遮罩层"
-    },
-    "accessibility": {
-        "modelSelect": "模型选择",
-        "invokeProgressBar": "Invoke 进度条",
-        "reset": "重置",
-        "nextImage": "下一张图片",
-        "useThisParameter": "使用此参数",
-        "uploadImage": "上传图片",
-        "previousImage": "上一张图片",
-        "copyMetadataJson": "复制JSON元数据",
-        "exitViewer": "退出视口（ExitViewer）",
-        "zoomIn": "放大",
-        "zoomOut": "缩小",
-        "rotateCounterClockwise": "逆时针旋转",
-        "rotateClockwise": "顺时针旋转",
-        "flipHorizontally": "水平翻转",
-        "flipVertically": "垂直翻转"
     }
 }

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

@@ -18,7 +18,6 @@ import { PropsWithChildren, useEffect } from 'react';
 import { setDisabledPanels, setDisabledTabs } from 'features/ui/store/uiSlice';
 import { InvokeTabName } from 'features/ui/store/tabMap';
 import { shouldTransformUrlsChanged } from 'features/system/store/systemSlice';
-import { setShouldFetchImages } from 'features/gallery/store/resultsSlice';
 
 keepGUIAlive();
 
@@ -27,7 +26,6 @@ interface Props extends PropsWithChildren {
     disabledPanels: string[];
     disabledTabs: InvokeTabName[];
     shouldTransformUrls?: boolean;
-    shouldFetchImages: boolean;
   };
 }
 
@@ -52,10 +50,6 @@ const App = (props: Props) => {
     );
   }, [dispatch, props.options.shouldTransformUrls]);
 
-  useEffect(() => {
-    dispatch(setShouldFetchImages(props.options.shouldFetchImages));
-  }, [dispatch, props.options.shouldFetchImages]);
-
   useEffect(() => {
     setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
   }, [setColorMode, currentTheme]);
@@ -73,12 +67,7 @@ const App = (props: Props) => {
           h={APP_HEIGHT}
         >
           {props.children || <SiteHeader />}
-          <Flex
-            gap={4}
-            w={{ base: '100vw', xl: 'full' }}
-            h="full"
-            flexDir={{ base: 'column', xl: 'row' }}
-          >
+          <Flex gap={4} w="full" h="full">
             <InvokeTabs />
             <ImageGalleryPanel />
           </Flex>

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

@@ -31,13 +31,13 @@ export const DIFFUSERS_SAMPLERS: Array<string> = [
 ];
 
 // Valid image widths
-export const WIDTHS: Array<number> = Array.from(Array(64)).map(
-  (_x, i) => (i + 1) * 64
+export const WIDTHS: Array<number> = Array.from(Array(65)).map(
+  (_x, i) => i * 64
 );
 
 // Valid image heights
-export const HEIGHTS: Array<number> = Array.from(Array(64)).map(
-  (_x, i) => (i + 1) * 64
+export const HEIGHTS: Array<number> = Array.from(Array(65)).map(
+  (_x, i) => i * 64
 );
 
 // Valid upscaling levels
@@ -60,5 +60,3 @@ export const IN_PROGRESS_IMAGE_TYPES: Array<{
   { key: 'Fast', value: 'latents' },
   { key: 'Accurate', value: 'full-res' },
 ];
-
-export const NODE_MIN_WIDTH = 250;

invokeai/frontend/web/src/app/selectors/readinessSelector.ts

@@ -20,7 +20,6 @@ export const readinessSelector = createSelector(
       seedWeights,
       initialImage,
       seed,
-      isImageToImageEnabled,
     } = generation;
 
     const { isProcessing, isConnected } = system;
@@ -34,7 +33,7 @@ export const readinessSelector = createSelector(
       reasonsWhyNotReady.push('Missing prompt');
     }
 
-    if (isImageToImageEnabled && !initialImage) {
+    if (activeTabName === 'img2img' && !initialImage) {
       isReady = false;
       reasonsWhyNotReady.push('No initial image selected');
     }

invokeai/frontend/web/src/app/socketio/listeners.ts

@@ -143,17 +143,16 @@ const makeSocketIOListeners = (
           }
         }
 
-        // TODO: fix
-        // if (shouldLoopback) {
-        //   const activeTabName = tabMap[activeTab];
-        //   switch (activeTabName) {
-        //     case 'img2img': {
-        //       dispatch(initialImageSelected(newImage.uuid));
-        //       // dispatch(setInitialImage(newImage));
-        //       break;
-        //     }
-        //   }
-        // }
+        if (shouldLoopback) {
+          const activeTabName = tabMap[activeTab];
+          switch (activeTabName) {
+            case 'img2img': {
+              dispatch(initialImageSelected(newImage.uuid));
+              // dispatch(setInitialImage(newImage));
+              break;
+            }
+          }
+        }
 
         dispatch(clearIntermediateImage());
 

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

@@ -6,28 +6,31 @@ import dynamicMiddlewares from 'redux-dynamic-middlewares';
 import { getPersistConfig } from 'redux-deep-persist';
 
 import canvasReducer from 'features/canvas/store/canvasSlice';
-import galleryReducer from 'features/gallery/store/gallerySlice';
-import resultsReducer from 'features/gallery/store/resultsSlice';
+import galleryReducer, {
+  GalleryState,
+} from 'features/gallery/store/gallerySlice';
+import resultsReducer, {
+  resultsAdapter,
+  ResultsState,
+} from 'features/gallery/store/resultsSlice';
 import uploadsReducer from 'features/gallery/store/uploadsSlice';
-import lightboxReducer from 'features/lightbox/store/lightboxSlice';
-import generationReducer from 'features/parameters/store/generationSlice';
-import postprocessingReducer from 'features/parameters/store/postprocessingSlice';
-import systemReducer from 'features/system/store/systemSlice';
+import lightboxReducer, {
+  LightboxState,
+} from 'features/lightbox/store/lightboxSlice';
+import generationReducer, {
+  GenerationState,
+} from 'features/parameters/store/generationSlice';
+import postprocessingReducer, {
+  PostprocessingState,
+} from 'features/parameters/store/postprocessingSlice';
+import systemReducer, { SystemState } from 'features/system/store/systemSlice';
 import uiReducer from 'features/ui/store/uiSlice';
 import modelsReducer from 'features/system/store/modelSlice';
-import nodesReducer from 'features/nodes/store/nodesSlice';
+import nodesReducer, { NodesState } from 'features/nodes/store/nodesSlice';
 
 import { socketioMiddleware } from './socketio/middleware';
 import { socketMiddleware } from 'services/events/middleware';
-import { canvasBlacklist } from 'features/canvas/store/canvasPersistBlacklist';
-import { galleryBlacklist } from 'features/gallery/store/galleryPersistBlacklist';
-import { generationBlacklist } from 'features/parameters/store/generationPersistBlacklist';
-import { lightboxBlacklist } from 'features/lightbox/store/lightboxPersistBlacklist';
-import { modelsBlacklist } from 'features/system/store/modelsPersistBlacklist';
-import { nodesBlacklist } from 'features/nodes/store/nodesPersistBlacklist';
-import { postprocessingBlacklist } from 'features/parameters/store/postprocessingPersistBlacklist';
-import { systemBlacklist } from 'features/system/store/systemPersistsBlacklist';
-import { uiBlacklist } from 'features/ui/store/uiPersistBlacklist';
+import { CanvasState } from 'features/canvas/store/canvasTypes';
 
 /**
  * redux-persist provides an easy and reliable way to persist state across reloads.
@@ -43,6 +46,116 @@ import { uiBlacklist } from 'features/ui/store/uiPersistBlacklist';
  * The necesssary nested persistors with blacklists are configured below.
  */
 
+/**
+ * Canvas slice persist blacklist
+ */
+const canvasBlacklist: (keyof CanvasState)[] = [
+  'cursorPosition',
+  'isCanvasInitialized',
+  'doesCanvasNeedScaling',
+];
+
+canvasBlacklist.map((blacklistItem) => `canvas.${blacklistItem}`);
+
+/**
+ * System slice persist blacklist
+ */
+const systemBlacklist: (keyof SystemState)[] = [
+  'currentIteration',
+  'currentStatus',
+  'currentStep',
+  'isCancelable',
+  'isConnected',
+  'isESRGANAvailable',
+  'isGFPGANAvailable',
+  'isProcessing',
+  'socketId',
+  'totalIterations',
+  'totalSteps',
+  'openModel',
+  'isCancelScheduled',
+  'sessionId',
+  'progressImage',
+];
+
+systemBlacklist.map((blacklistItem) => `system.${blacklistItem}`);
+
+/**
+ * Gallery slice persist blacklist
+ */
+const galleryBlacklist: (keyof GalleryState)[] = [
+  'categories',
+  'currentCategory',
+  'currentImage',
+  'currentImageUuid',
+  'shouldAutoSwitchToNewImages',
+  'intermediateImage',
+];
+
+galleryBlacklist.map((blacklistItem) => `gallery.${blacklistItem}`);
+
+/**
+ * Lightbox slice persist blacklist
+ */
+const lightboxBlacklist: (keyof LightboxState)[] = ['isLightboxOpen'];
+
+lightboxBlacklist.map((blacklistItem) => `lightbox.${blacklistItem}`);
+
+/**
+ * Nodes slice persist blacklist
+ */
+const nodesBlacklist: (keyof NodesState)[] = ['schema', 'invocations'];
+
+nodesBlacklist.map((blacklistItem) => `nodes.${blacklistItem}`);
+
+/**
+ * Generation slice persist blacklist
+ */
+const generationBlacklist: (keyof GenerationState)[] = [];
+
+generationBlacklist.map((blacklistItem) => `generation.${blacklistItem}`);
+
+/**
+ * Postprocessing slice persist blacklist
+ */
+const postprocessingBlacklist: (keyof PostprocessingState)[] = [];
+
+postprocessingBlacklist.map(
+  (blacklistItem) => `postprocessing.${blacklistItem}`
+);
+
+/**
+ * Results slice persist blacklist
+ *
+ * Currently blacklisting results slice entirely, see persist config below
+ */
+const resultsBlacklist: (keyof ResultsState)[] = [];
+
+resultsBlacklist.map((blacklistItem) => `results.${blacklistItem}`);
+
+/**
+ * Uploads slice persist blacklist
+ *
+ * Currently blacklisting uploads slice entirely, see persist config below
+ */
+const uploadsBlacklist: (keyof NodesState)[] = [];
+
+uploadsBlacklist.map((blacklistItem) => `uploads.${blacklistItem}`);
+
+/**
+ * Models slice persist blacklist
+ */
+const modelsBlacklist: (keyof NodesState)[] = [];
+
+modelsBlacklist.map((blacklistItem) => `models.${blacklistItem}`);
+
+/**
+ * UI slice persist blacklist
+ */
+const uiBlacklist: (keyof NodesState)[] = [];
+
+uiBlacklist.map((blacklistItem) => `ui.${blacklistItem}`);
+
 const rootReducer = combineReducers({
   canvas: canvasReducer,
   gallery: galleryReducer,

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

@@ -44,10 +44,12 @@ export type IAIFullSliderProps = {
   inputReadOnly?: boolean;
   withReset?: boolean;
   handleReset?: () => void;
+  isResetDisabled?: boolean;
+  isSliderDisabled?: boolean;
+  isInputDisabled?: boolean;
   tooltipSuffix?: string;
   hideTooltip?: boolean;
   isCompact?: boolean;
-  isDisabled?: boolean;
   sliderFormControlProps?: FormControlProps;
   sliderFormLabelProps?: FormLabelProps;
   sliderMarkProps?: Omit<SliderMarkProps, 'value'>;
@@ -78,8 +80,10 @@ const IAISlider = (props: IAIFullSliderProps) => {
     withReset = false,
     hideTooltip = false,
     isCompact = false,
-    isDisabled = false,
     handleReset,
+    isResetDisabled,
+    isSliderDisabled,
+    isInputDisabled,
     sliderFormControlProps,
     sliderFormLabelProps,
     sliderMarkProps,
@@ -145,7 +149,6 @@ const IAISlider = (props: IAIFullSliderProps) => {
             }
           : {}
       }
-      isDisabled={isDisabled}
       {...sliderFormControlProps}
     >
       <FormLabel {...sliderFormLabelProps} mb={-1}>
@@ -163,13 +166,15 @@ const IAISlider = (props: IAIFullSliderProps) => {
           onMouseEnter={() => setShowTooltip(true)}
           onMouseLeave={() => setShowTooltip(false)}
           focusThumbOnChange={false}
-          isDisabled={isDisabled}
+          isDisabled={isSliderDisabled}
+          // width={width}
           {...rest}
         >
           {withSliderMarks && (
             <>
               <SliderMark
                 value={min}
+                // insetInlineStart={0}
                 sx={{
                   insetInlineStart: '0 !important',
                   insetInlineEnd: 'unset !important',
@@ -180,6 +185,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
               </SliderMark>
               <SliderMark
                 value={max}
+                // insetInlineEnd={0}
                 sx={{
                   insetInlineStart: 'unset !important',
                   insetInlineEnd: '0 !important',
@@ -215,6 +221,7 @@ const IAISlider = (props: IAIFullSliderProps) => {
             value={localInputValue}
             onChange={handleInputChange}
             onBlur={handleInputBlur}
+            isDisabled={isInputDisabled}
             {...sliderNumberInputProps}
           >
             <NumberInputField
@@ -239,8 +246,8 @@ const IAISlider = (props: IAIFullSliderProps) => {
             aria-label={t('accessibility.reset')}
             tooltip="Reset"
             icon={<BiReset />}
-            isDisabled={isDisabled}
             onClick={handleResetDisable}
+            isDisabled={isResetDisabled}
             {...sliderIAIIconButtonProps}
           />
         )}

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

@@ -1,79 +0,0 @@
-import { Badge, Box, ButtonGroup, Flex } from '@chakra-ui/react';
-import { RootState } from 'app/store';
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
-import { useCallback } from 'react';
-import IAIIconButton from 'common/components/IAIIconButton';
-import { FaUndo, FaUpload } from 'react-icons/fa';
-import { useTranslation } from 'react-i18next';
-import { Image } from 'app/invokeai';
-
-type ImageToImageOverlayProps = {
-  setIsLoaded: (isLoaded: boolean) => void;
-  image: Image;
-};
-
-const ImageToImageOverlay = ({
-  setIsLoaded,
-  image,
-}: ImageToImageOverlayProps) => {
-  const isImageToImageEnabled = useAppSelector(
-    (state: RootState) => state.generation.isImageToImageEnabled
-  );
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-  const handleResetInitialImage = useCallback(() => {
-    dispatch(clearInitialImage());
-    setIsLoaded(false);
-  }, [dispatch, setIsLoaded]);
-
-  return (
-    <Box
-      sx={{
-        top: 0,
-        left: 0,
-        w: 'full',
-        h: 'full',
-        position: 'absolute',
-      }}
-    >
-      <ButtonGroup
-        sx={{
-          position: 'absolute',
-          top: 0,
-          right: 0,
-          p: 2,
-        }}
-      >
-        <IAIIconButton
-          size="sm"
-          isDisabled={!isImageToImageEnabled}
-          icon={<FaUndo />}
-          aria-label={t('accessibility.reset')}
-          onClick={handleResetInitialImage}
-        />
-        <IAIIconButton
-          size="sm"
-          isDisabled={!isImageToImageEnabled}
-          icon={<FaUpload />}
-          aria-label={t('common.upload')}
-        />
-      </ButtonGroup>
-      <Flex
-        sx={{
-          position: 'absolute',
-          bottom: 0,
-          left: 0,
-          p: 2,
-          alignItems: 'flex-start',
-        }}
-      >
-        <Badge variant="solid" colorScheme="base">
-          {image.metadata?.width} × {image.metadata?.height}
-        </Badge>
-      </Flex>
-    </Box>
-  );
-};
-
-export default ImageToImageOverlay;

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

@@ -1,12 +0,0 @@
-import { Flex, Icon } from '@chakra-ui/react';
-import { FaImage } from 'react-icons/fa';
-
-const SelectImagePlaceholder = () => {
-  return (
-    <Flex sx={{ h: 36, alignItems: 'center', justifyContent: 'center' }}>
-      <Icon color="base.400" boxSize={32} as={FaImage}></Icon>
-    </Flex>
-  );
-};
-
-export default SelectImagePlaceholder;

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

@@ -1,18 +0,0 @@
-import { useBreakpoint } from '@chakra-ui/react';
-
-export default function useResolution():
-  | 'mobile'
-  | 'tablet'
-  | 'desktop'
-  | 'unknown' {
-  const breakpointValue = useBreakpoint();
-
-  const mobileResolutions = ['base', 'sm'];
-  const tabletResolutions = ['md', 'lg'];
-  const desktopResolutions = ['xl', '2xl'];
-
-  if (mobileResolutions.includes(breakpointValue)) return 'mobile';
-  if (tabletResolutions.includes(breakpointValue)) return 'tablet';
-  if (desktopResolutions.includes(breakpointValue)) return 'desktop';
-  return 'unknown';
-}

invokeai/frontend/web/src/common/util/_parseMetadataZod.ts

@@ -1,119 +0,0 @@
-/**
- * PARTIAL ZOD IMPLEMENTATION
- *
- * doesn't work well bc like most validators, zod is not built to skip invalid values.
- * it mostly works but just seems clearer and simpler to manually parse for now.
- *
- * in the future it would be really nice if we could use zod for some things:
- * - zodios (axios + zod): https://github.com/ecyrbe/zodios
- * - openapi to zodios: https://github.com/astahmer/openapi-zod-client
- */
-
-// import { z } from 'zod';
-
-// const zMetadataStringField = z.string();
-// export type MetadataStringField = z.infer<typeof zMetadataStringField>;
-
-// const zMetadataIntegerField = z.number().int();
-// export type MetadataIntegerField = z.infer<typeof zMetadataIntegerField>;
-
-// const zMetadataFloatField = z.number();
-// export type MetadataFloatField = z.infer<typeof zMetadataFloatField>;
-
-// const zMetadataBooleanField = z.boolean();
-// export type MetadataBooleanField = z.infer<typeof zMetadataBooleanField>;
-
-// const zMetadataImageField = z.object({
-//   image_type: z.union([
-//     z.literal('results'),
-//     z.literal('uploads'),
-//     z.literal('intermediates'),
-//   ]),
-//   image_name: z.string().min(1),
-// });
-// export type MetadataImageField = z.infer<typeof zMetadataImageField>;
-
-// const zMetadataLatentsField = z.object({
-//   latents_name: z.string().min(1),
-// });
-// export type MetadataLatentsField = z.infer<typeof zMetadataLatentsField>;
-
-// /**
-//  * zod Schema for any node field. Use a `transform()` to manually parse, skipping invalid values.
-//  */
-// const zAnyMetadataField = z.any().transform((val, ctx) => {
-//   // Grab the field name from the path
-//   const fieldName = String(ctx.path[ctx.path.length - 1]);
-
-//   // `id` and `type` must be strings if they exist
-//   if (['id', 'type'].includes(fieldName)) {
-//     const reservedStringPropertyResult = zMetadataStringField.safeParse(val);
-//     if (reservedStringPropertyResult.success) {
-//       return reservedStringPropertyResult.data;
-//     }
-
-//     return;
-//   }
-
-//   // Parse the rest of the fields, only returning the data if the parsing is successful
-
-//   const stringFieldResult = zMetadataStringField.safeParse(val);
-//   if (stringFieldResult.success) {
-//     return stringFieldResult.data;
-//   }
-
-//   const integerFieldResult = zMetadataIntegerField.safeParse(val);
-//   if (integerFieldResult.success) {
-//     return integerFieldResult.data;
-//   }
-
-//   const floatFieldResult = zMetadataFloatField.safeParse(val);
-//   if (floatFieldResult.success) {
-//     return floatFieldResult.data;
-//   }
-
-//   const booleanFieldResult = zMetadataBooleanField.safeParse(val);
-//   if (booleanFieldResult.success) {
-//     return booleanFieldResult.data;
-//   }
-
-//   const imageFieldResult = zMetadataImageField.safeParse(val);
-//   if (imageFieldResult.success) {
-//     return imageFieldResult.data;
-//   }
-
-//   const latentsFieldResult = zMetadataImageField.safeParse(val);
-//   if (latentsFieldResult.success) {
-//     return latentsFieldResult.data;
-//   }
-// });
-
-// /**
-//  * The node metadata schema.
-//  */
-// const zNodeMetadata = z.object({
-//   session_id: z.string().min(1).optional(),
-//   node: z.record(z.string().min(1), zAnyMetadataField).optional(),
-// });
-
-// export type NodeMetadata = z.infer<typeof zNodeMetadata>;
-
-// const zMetadata = z.object({
-//   invokeai: zNodeMetadata.optional(),
-//   'sd-metadata': z.record(z.string().min(1), z.any()).optional(),
-// });
-// export type Metadata = z.infer<typeof zMetadata>;
-
-// export const parseMetadata = (
-//   metadata: Record<string, any>
-// ): Metadata | undefined => {
-//   const result = zMetadata.safeParse(metadata);
-//   if (!result.success) {
-//     console.log(result.error.issues);
-//     return;
-//   }
-
-//   return result.data;
-// };
-
-export default {};

invokeai/frontend/web/src/common/util/buildGraph.ts

@@ -0,0 +1,72 @@
+import { RootState } from 'app/store';
+import { InvokeTabName, tabMap } from 'features/ui/store/tabMap';
+import { find } from 'lodash';
+import {
+  Graph,
+  ImageToImageInvocation,
+  TextToImageInvocation,
+} from 'services/api';
+import { buildHiResNode, buildImg2ImgNode } from './nodes/image2Image';
+import { buildIteration } from './nodes/iteration';
+import { buildTxt2ImgNode } from './nodes/text2Image';
+
+function mapTabToFunction(activeTabName: InvokeTabName) {
+  switch (activeTabName) {
+    case 'txt2img':
+      return buildTxt2ImgNode;
+
+    case 'img2img':
+      return buildImg2ImgNode;
+
+    default:
+      return buildTxt2ImgNode;
+  }
+}
+
+const buildBaseNode = (
+  state: RootState
+): Record<string, TextToImageInvocation | ImageToImageInvocation> => {
+  const { activeTab } = state.ui;
+  const activeTabName = tabMap[activeTab];
+
+  return mapTabToFunction(activeTabName)(state);
+};
+
+type BuildGraphOutput = {
+  graph: Graph;
+  nodeIdsToSubscribe: string[];
+};
+
+export const buildGraph = (state: RootState): BuildGraphOutput => {
+  const { generation, postprocessing } = state;
+  const { iterations } = generation;
+  const { hiresFix, hiresStrength } = postprocessing;
+
+  const baseNode = buildBaseNode(state);
+
+  let graph: Graph = { nodes: baseNode };
+  const nodeIdsToSubscribe: string[] = [];
+
+  if (iterations > 1) {
+    graph = buildIteration({ graph, iterations });
+  }
+
+  if (hiresFix) {
+    const { node, edge } = buildHiResNode(
+      baseNode as Record<string, TextToImageInvocation>,
+      hiresStrength
+    );
+    graph = {
+      nodes: {
+        ...graph.nodes,
+        ...node,
+      },
+      edges: [...(graph.edges || []), edge],
+    };
+    nodeIdsToSubscribe.push(Object.keys(node)[0]);
+  }
+
+  console.log('buildGraph: ', graph);
+
+  return { graph, nodeIdsToSubscribe };
+};

invokeai/frontend/web/src/common/util/getUrl.ts

@@ -17,7 +17,7 @@ export const useGetUrl = () => {
 
   return {
     shouldTransformUrls,
-    getUrl: (url?: string) => {
+    getUrl: (url: string) => {
       if (OpenAPI.BASE && shouldTransformUrls) {
         return [OpenAPI.BASE, url].join('/');
       }

invokeai/frontend/web/src/common/util/nodes/image2Image.ts

@@ -8,11 +8,14 @@ import {
 import { _Image } from 'app/invokeai';
 import { initialImageSelector } from 'features/parameters/store/generationSelectors';
 
-export const buildImg2ImgNode = (state: RootState): ImageToImageInvocation => {
+export const buildImg2ImgNode = (
+  state: RootState
+): Record<string, ImageToImageInvocation> => {
   const nodeId = uuidv4();
   const { generation, system, models } = state;
 
-  const { selectedModelName } = models;
+  const { shouldDisplayInProgressType } = system;
+  const { currentModel: model } = models;
 
   const {
     prompt,
@@ -35,31 +38,28 @@ export const buildImg2ImgNode = (state: RootState): ImageToImageInvocation => {
     throw 'no initial image';
   }
 
-  const imageToImageNode: ImageToImageInvocation = {
-    id: nodeId,
-    type: 'img2img',
-    prompt,
-    steps,
-    width,
-    height,
-    cfg_scale: cfgScale,
-    scheduler: sampler as ImageToImageInvocation['scheduler'],
-    seamless,
-    model: selectedModelName,
-    progress_images: true,
-    image: {
-      image_name: initialImage.name,
-      image_type: initialImage.type,
+  return {
+    [nodeId]: {
+      id: nodeId,
+      type: 'img2img',
+      prompt,
+      seed: shouldRandomizeSeed ? -1 : seed,
+      steps,
+      width,
+      height,
+      cfg_scale: cfgScale,
+      scheduler: sampler as ImageToImageInvocation['scheduler'],
+      seamless,
+      model,
+      progress_images: shouldDisplayInProgressType === 'full-res',
+      image: {
+        image_name: initialImage.name,
+        image_type: initialImage.type,
+      },
+      strength,
+      fit,
     },
-    strength,
-    fit,
   };
-
-  if (!shouldRandomizeSeed) {
-    imageToImageNode.seed = seed;
-  }
-
-  return imageToImageNode;
 };
 
 type hiresReturnType = {
@@ -82,7 +82,6 @@ export const buildHiResNode = (
         id: nodeId,
         type: 'img2img',
         strength,
-        fit: true,
       },
     },
     edge: {

invokeai/frontend/web/src/common/util/nodes/iteration.ts

@@ -0,0 +1,81 @@
+import { v4 as uuidv4 } from 'uuid';
+import {
+  Edge,
+  Graph,
+  ImageToImageInvocation,
+  IterateInvocation,
+  RangeInvocation,
+  TextToImageInvocation,
+} from 'services/api';
+import { buildImg2ImgNode } from './image2Image';
+
+type BuildIteration = {
+  graph: Graph;
+  iterations: number;
+};
+
+const buildRangeNode = (
+  iterations: number
+): Record<string, RangeInvocation> => {
+  const nodeId = uuidv4();
+  return {
+    [nodeId]: {
+      id: nodeId,
+      type: 'range',
+      start: 0,
+      stop: iterations,
+      step: 1,
+    },
+  };
+};
+
+const buildIterateNode = (): Record<string, IterateInvocation> => {
+  const nodeId = uuidv4();
+  return {
+    [nodeId]: {
+      id: nodeId,
+      type: 'iterate',
+      collection: [],
+      index: 0,
+    },
+  };
+};
+
+export const buildIteration = ({
+  graph,
+  iterations,
+}: BuildIteration): Graph => {
+  const rangeNode = buildRangeNode(iterations);
+  const iterateNode = buildIterateNode();
+  const baseNode: Graph['nodes'] = graph.nodes;
+  const edges: Edge[] = [
+    {
+      source: {
+        field: 'collection',
+        node_id: Object.keys(rangeNode)[0],
+      },
+      destination: {
+        field: 'collection',
+        node_id: Object.keys(iterateNode)[0],
+      },
+    },
+    {
+      source: {
+        field: 'item',
+        node_id: Object.keys(iterateNode)[0],
+      },
+      destination: {
+        field: 'seed',
+        node_id: Object.keys(baseNode!)[0],
+      },
+    },
+  ];
+  return {
+    nodes: {
+      ...rangeNode,
+      ...iterateNode,
+      ...graph.nodes,
+    },
+    edges,
+  };
+};

invokeai/frontend/web/src/common/util/nodes/text2Image.ts

@@ -0,0 +1,43 @@
+import { v4 as uuidv4 } from 'uuid';
+import { RootState } from 'app/store';
+import { TextToImageInvocation } from 'services/api';
+
+export const buildTxt2ImgNode = (
+  state: RootState
+): Record<string, TextToImageInvocation> => {
+  const nodeId = uuidv4();
+  const { generation, system, models } = state;
+
+  const { shouldDisplayInProgressType } = system;
+  const { currentModel: model } = models;
+
+  const {
+    prompt,
+    seed,
+    steps,
+    width,
+    height,
+    cfgScale: cfg_scale,
+    sampler,
+    seamless,
+    shouldRandomizeSeed,
+  } = generation;
+
+  // missing fields in TextToImageInvocation: strength, hires_fix
+  return {
+    [nodeId]: {
+      id: nodeId,
+      type: 'txt2img',
+      prompt,
+      seed: shouldRandomizeSeed ? -1 : seed,
+      steps,
+      width,
+      height,
+      cfg_scale,
+      scheduler: sampler as TextToImageInvocation['scheduler'],
+      seamless,
+      model,
+      progress_images: shouldDisplayInProgressType === 'full-res',
+    },
+  };
+};

invokeai/frontend/web/src/common/util/parseMetadata.ts

@@ -1,169 +0,0 @@
-import { forEach, size } from 'lodash';
-import { ImageField, LatentsField } from 'services/api';
-
-const OBJECT_TYPESTRING = '[object Object]';
-const STRING_TYPESTRING = '[object String]';
-const NUMBER_TYPESTRING = '[object Number]';
-const BOOLEAN_TYPESTRING = '[object Boolean]';
-const ARRAY_TYPESTRING = '[object Array]';
-
-const isObject = (obj: unknown): obj is Record<string | number, any> =>
-  Object.prototype.toString.call(obj) === OBJECT_TYPESTRING;
-
-const isString = (obj: unknown): obj is string =>
-  Object.prototype.toString.call(obj) === STRING_TYPESTRING;
-
-const isNumber = (obj: unknown): obj is number =>
-  Object.prototype.toString.call(obj) === NUMBER_TYPESTRING;
-
-const isBoolean = (obj: unknown): obj is boolean =>
-  Object.prototype.toString.call(obj) === BOOLEAN_TYPESTRING;
-
-const isArray = (obj: unknown): obj is Array<any> =>
-  Object.prototype.toString.call(obj) === ARRAY_TYPESTRING;
-
-const parseImageField = (imageField: unknown): ImageField | undefined => {
-  // Must be an object
-  if (!isObject(imageField)) {
-    return;
-  }
-
-  // An ImageField must have both `image_name` and `image_type`
-  if (!('image_name' in imageField && 'image_type' in imageField)) {
-    return;
-  }
-
-  // An ImageField's `image_type` must be one of the allowed values
-  if (
-    !['results', 'uploads', 'intermediates'].includes(imageField.image_type)
-  ) {
-    return;
-  }
-
-  // An ImageField's `image_name` must be a string
-  if (typeof imageField.image_name !== 'string') {
-    return;
-  }
-
-  // Build a valid ImageField
-  return {
-    image_type: imageField.image_type,
-    image_name: imageField.image_name,
-  };
-};
-
-const parseLatentsField = (latentsField: unknown): LatentsField | undefined => {
-  // Must be an object
-  if (!isObject(latentsField)) {
-    return;
-  }
-
-  // A LatentsField must have a `latents_name`
-  if (!('latents_name' in latentsField)) {
-    return;
-  }
-
-  // A LatentsField's `latents_name` must be a string
-  if (typeof latentsField.latents_name !== 'string') {
-    return;
-  }
-
-  // Build a valid LatentsField
-  return {
-    latents_name: latentsField.latents_name,
-  };
-};
-
-type NodeMetadata = {
-  [key: string]: string | number | boolean | ImageField | LatentsField;
-};
-
-type InvokeAIMetadata = {
-  session_id?: string;
-  node?: NodeMetadata;
-};
-
-export const parseNodeMetadata = (
-  nodeMetadata: Record<string | number, any>
-): NodeMetadata | undefined => {
-  if (!isObject(nodeMetadata)) {
-    return;
-  }
-
-  const parsed: NodeMetadata = {};
-
-  forEach(nodeMetadata, (nodeItem, nodeKey) => {
-    // `id` and `type` must be strings if they are present
-    if (['id', 'type'].includes(nodeKey)) {
-      if (isString(nodeItem)) {
-        parsed[nodeKey] = nodeItem;
-      }
-      return;
-    }
-
-    // the only valid object types are ImageField and LatentsField
-    if (isObject(nodeItem)) {
-      if ('image_name' in nodeItem || 'image_type' in nodeItem) {
-        const imageField = parseImageField(nodeItem);
-        if (imageField) {
-          parsed[nodeKey] = imageField;
-        }
-        return;
-      }
-
-      if ('latents_name' in nodeItem) {
-        const latentsField = parseLatentsField(nodeItem);
-        if (latentsField) {
-          parsed[nodeKey] = latentsField;
-        }
-        return;
-      }
-    }
-
-    // otherwise we accept any string, number or boolean
-    if (isString(nodeItem) || isNumber(nodeItem) || isBoolean(nodeItem)) {
-      parsed[nodeKey] = nodeItem;
-      return;
-    }
-  });
-
-  if (size(parsed) === 0) {
-    return;
-  }
-
-  return parsed;
-};
-
-export const parseInvokeAIMetadata = (
-  metadata: Record<string | number, any> | undefined
-): InvokeAIMetadata | undefined => {
-  if (metadata === undefined) {
-    return;
-  }
-
-  if (!isObject(metadata)) {
-    return;
-  }
-
-  const parsed: InvokeAIMetadata = {};
-
-  forEach(metadata, (item, key) => {
-    if (key === 'session_id' && isString(item)) {
-      parsed['session_id'] = item;
-    }
-
-    if (key === 'node' && isObject(item)) {
-      const nodeMetadata = parseNodeMetadata(item);
-
-      if (nodeMetadata) {
-        parsed['node'] = nodeMetadata;
-      }
-    }
-  });
-
-  if (size(parsed) === 0) {
-    return;
-  }
-
-  return parsed;
-};

invokeai/frontend/web/src/component.tsx

@@ -30,7 +30,6 @@ interface Props extends PropsWithChildren {
   disabledTabs?: InvokeTabName[];
   token?: string;
   shouldTransformUrls?: boolean;
-  shouldFetchImages?: boolean;
 }
 
 export default function Component({
@@ -40,7 +39,6 @@ export default function Component({
   token,
   children,
   shouldTransformUrls,
-  shouldFetchImages = false,
 }: Props) {
   useEffect(() => {
     // configure API client token
@@ -72,12 +70,7 @@ export default function Component({
           <React.Suspense fallback={<Loading showText />}>
             <ThemeLocaleProvider>
               <App
-                options={{
-                  disabledPanels,
-                  disabledTabs,
-                  shouldTransformUrls,
-                  shouldFetchImages,
-                }}
+                options={{ disabledPanels, disabledTabs, shouldTransformUrls }}
               >
                 {children}
               </App>

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

@@ -1,14 +0,0 @@
-import { CanvasState } from './canvasTypes';
-
-/**
- * Canvas slice persist blacklist
- */
-const itemsToBlacklist: (keyof CanvasState)[] = [
-  'cursorPosition',
-  'isCanvasInitialized',
-  'doesCanvasNeedScaling',
-];
-
-export const canvasBlacklist = itemsToBlacklist.map(
-  (blacklistItem) => `canvas.${blacklistItem}`
-);