Merge branch 'main' into bugfix/invokeai-configure

Prevent legacy CLI crash caused by removal of convert option
    
- Compensatory change to the CLI that prevents it from crashing when it
tries to import a model.
- Bug introduced when the "convert" option removed from the model
manager.

.github/CODEOWNERS

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

.github/stale.yaml

@@ -0,0 +1,19 @@
+# Number of days of inactivity before an issue becomes stale
+daysUntilStale: 28
+# Number of days of inactivity before a stale issue is closed
+daysUntilClose: 14
+# Issues with these labels will never be considered stale
+exemptLabels:
+  - pinned
+  - security
+# Label to use when marking an issue as stale
+staleLabel: stale
+# Comment to post when marking an issue as stale. Set to `false` to disable
+markComment: >
+  This issue has been automatically marked as stale because it has not had
+  recent activity. It will be closed if no further activity occurs. Please
+  update the ticket if this is still a problem on the latest release.
+# Comment to post when closing a stale issue. Set to `false` to disable
+closeComment: >
+  Due to inactivity, this issue has been automatically closed. If this is
+  still a problem on the latest release, please recreate the issue.

.gitignore

@@ -9,6 +9,8 @@ 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 generaiton models.
+select a set of starting image generation 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
@@ -148,6 +148,11 @@ 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/contributing/INVOCATIONS.md

@@ -1,10 +1,18 @@
 # Invocations
 
-Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
+Invocations represent a single operation, its inputs, and its outputs. These
+operations and their outputs can be chained together to generate and modify
+images.
 
 ## Creating a new invocation
 
-To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
+To create a new invocation, either find the appropriate module file in
+`/ldm/invoke/app/invocations` to add your invocation to, or create a new one in
+that folder. All invocations in that folder will be discovered and made
+available to the CLI and API automatically. Invocations make use of
+[typing](https://docs.python.org/3/library/typing.html) and
+[pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration
+into the CLI and API.
 
 An invocation looks like this:
 
@@ -41,34 +49,54 @@ class UpscaleInvocation(BaseInvocation):
 Each portion is important to implement correctly.
 
 ### Class definition and type
+
 ```py
 class UpscaleInvocation(BaseInvocation):
     """Upscales an image."""
     type: Literal['upscale'] = 'upscale'
 ```
-All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
+
+All invocations must derive from `BaseInvocation`. They should have a docstring
+that declares what they do in a single, short line. They should also have a
+`type` with a type hint that's `Literal["command_name"]`, where `command_name`
+is what the user will type on the CLI or use in the API to create this
+invocation. The `command_name` must be unique. The `type` must be assigned to
+the value of the literal in the type hint.
 
 ### Inputs
+
 ```py
     # Inputs
     image: Union[ImageField,None] = Field(description="The input image")
     strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
     level: Literal[2,4]           = Field(default=2, description="The upscale level")
 ```
-Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
-| Part | Value | Description |
-| ---- | ----- | ----------- |
-| Name | `strength` | This field is referred to as `strength` |
-| Type Hint | `float` | This field must be of type `float` |
-| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
 
-Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
+Inputs consist of three parts: a name, a type hint, and a `Field` with default,
+description, and validation information. For example:
 
-The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
+| Part      | Value                                                         | Description                                                                                                               |
+| --------- | ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
+| Name      | `strength`                                                    | This field is referred to as `strength`                                                                                   |
+| Type Hint | `float`                                                       | This field must be of type `float`                                                                                        |
+| Field     | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
 
-Finally, note that for all linking, the `type` of the linked fields must match. If the `name` also matches, then the field can be **automatically linked** to a previous invocation by name and matching.
+Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this
+field to be parsed with `None` as a value, which enables linking to previous
+invocations. All fields should either provide a default value or allow `None` as
+a value, so that they can be overwritten with a linked output from another
+invocation.
+
+The special type `ImageField` is also used here. All images are passed as
+`ImageField`, which protects them from pydantic validation errors (since images
+only ever come from links).
+
+Finally, note that for all linking, the `type` of the linked fields must match.
+If the `name` also matches, then the field can be **automatically linked** to a
+previous invocation by name and matching.
 
 ### Invoke Function
+
 ```py
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(self.image.image_type, self.image.image_name)
@@ -88,13 +116,22 @@ Finally, note that for all linking, the `type` of the linked fields must match.
             image = ImageField(image_type = image_type, image_name = image_name)
         )
 ```
-The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
 
-Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
+The `invoke` function is the last portion of an invocation. It is provided an
+`InvocationContext` which contains services to perform work as well as a
+`session_id` for use as needed. It should return a class with output values that
+derives from `BaseInvocationOutput`.
 
-Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
+Before being called, the invocation will have all of its fields set from
+defaults, inputs, and finally links (overriding in that order).
+
+Assume that this invocation may be running simultaneously with other
+invocations, may be running on another machine, or in other interesting
+scenarios. If you need functionality, please provide it as a service in the
+`InvocationServices` class, and make sure it can be overridden.
 
 ### Outputs
+
 ```py
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
@@ -102,4 +139,64 @@ class ImageOutput(BaseInvocationOutput):
 
     image: ImageField = Field(default=None, description="The output image")
 ```
-Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.
+
+Output classes look like an invocation class without the invoke method. Prefer
+to use an existing output class if available, and prefer to name inputs the same
+as outputs when possible, to promote automatic invocation linking.
+
+## Schema Generation
+
+Invocation, output and related classes are used to generate an OpenAPI schema.
+
+### Required Properties
+
+The schema generation treat all properties with default values as optional. This
+makes sense internally, but when when using these classes via the generated
+schema, we end up with e.g. the `ImageOutput` class having its `image` property
+marked as optional.
+
+We know that this property will always be present, so the additional logic
+needed to always check if the property exists adds a lot of extraneous cruft.
+
+To fix this, we can leverage `pydantic`'s
+[schema customisation](https://docs.pydantic.dev/usage/schema/#schema-customization)
+to mark properties that we know will always be present as required.
+
+Here's that `ImageOutput` class, without the needed schema customisation:
+
+```python
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    type: Literal["image"] = "image"
+    image:      ImageField = Field(default=None, description="The output image")
+```
+
+The generated OpenAPI schema, and all clients/types generated from it, will have
+the `type` and `image` properties marked as optional, even though we know they
+will always have a value by the time we can interact with them via the API.
+
+Here's the same class, but with the schema customisation added:
+
+```python
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    type: Literal["image"] = "image"
+    image:      ImageField = Field(default=None, description="The output image")
+
+    class Config:
+        schema_extra = {
+            'required': [
+                'type',
+                'image',
+            ]
+        }
+```
+
+The resultant schema (and any API client or types generated from it) will now
+have see `type` as string literal `"image"` and `image` as an `ImageField`
+object.
+
+See this `pydantic` issue for discussion on this solution:
+<https://github.com/pydantic/pydantic/discussions/4577>

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` in your home directory). You can change the default at any
+`invokeai.init` 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`.
 

docs/installation/050_INSTALLING_MODELS.md

@@ -50,7 +50,7 @@ subset that are currently installed are found in
 |stable-diffusion-1.5|runwayml/stable-diffusion-v1-5|Stable Diffusion version 1.5 diffusers model (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-v1-5 |
 |sd-inpainting-1.5|runwayml/stable-diffusion-inpainting|RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-inpainting |
 |stable-diffusion-2.1|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
-|sd-inpainting-2.0|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
+|sd-inpainting-2.0|stabilityai/stable-diffusion-2-inpainting|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-inpainting |
 |analog-diffusion-1.0|wavymulder/Analog-Diffusion|An SD-1.5 model trained on diverse analog photographs (2.13 GB)|https://huggingface.co/wavymulder/Analog-Diffusion |
 |deliberate-1.0|XpucT/Deliberate|Versatile model that produces detailed images up to 768px (4.27 GB)|https://huggingface.co/XpucT/Deliberate |
 |d&d-diffusion-1.0|0xJustin/Dungeons-and-Diffusion|Dungeons & Dragons characters (2.13 GB)|https://huggingface.co/0xJustin/Dungeons-and-Diffusion |

invokeai/app/api/dependencies.py

@@ -3,12 +3,16 @@
 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
+from ..services.graph import GraphExecutionState, LibraryGraph
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
@@ -58,7 +62,9 @@ class ApiDependencies:
 
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents'))
 
-        images = DiskImageStorage(f'{output_folder}/images')
+        metadata = PngMetadataService()
+
+        images = DiskImageStorage(f'{output_folder}/images', metadata_service=metadata)
 
         # TODO: build a file/path manager?
         db_location = os.path.join(output_folder, "invokeai.db")
@@ -68,7 +74,11 @@ 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"
             ),
@@ -76,6 +86,8 @@ 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(0.001)
+                await asyncio.sleep(0.1)
                 pass
 
             except asyncio.CancelledError as e:

invokeai/app/api/models/images.py

@@ -0,0 +1,34 @@
+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"
+    )
+
+
+class ImageResponse(BaseModel):
+    """The response type for images"""
+
+    image_type: ImageType = Field(description="The type of the image")
+    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")

invokeai/app/api/routers/images.py

@@ -1,11 +1,18 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
+import io
 from datetime import datetime, timezone
+import json
+import os
+from typing import Any
+import uuid
 
-from fastapi import Path, Request, UploadFile
+from fastapi import HTTPException, 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.services.item_storage import PaginatedResults
 
 from ...services.image_storage import ImageType
 from ..dependencies import ApiDependencies
@@ -17,50 +24,105 @@ images_router = APIRouter(prefix="/v1/images", tags=["images"])
 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)
 
-@images_router.get("/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail")
+    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"
+)
 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"""
-    # 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)
+
+    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)
 
 
 @images_router.post(
     "/uploads/",
     operation_id="upload_image",
     responses={
-        201: {"description": "The image was uploaded successfully"},
-        404: {"description": "Session not found"},
+        201: {
+            "description": "The image was uploaded successfully",
+            "model": ImageResponse,
+        },
+        415: {"description": "Image upload failed"},
     },
+    status_code=201,
 )
-async def upload_image(file: UploadFile, request: Request):
+async def upload_image(
+    file: UploadFile, request: Request, response: Response
+) -> ImageResponse:
     if not file.content_type.startswith("image"):
-        return Response(status_code=415)
+        raise HTTPException(status_code=415, detail="Not an image")
 
     contents = await file.read()
+
     try:
-        im = Image.open(contents)
+        img = Image.open(io.BytesIO(contents))
     except:
         # Error opening the image
-        return Response(status_code=415)
+        raise HTTPException(status_code=415, detail="Failed to read image")
 
-    filename = f"{str(int(datetime.now(timezone.utc).timestamp()))}.png"
-    ApiDependencies.invoker.services.images.save(ImageType.UPLOAD, filename, im)
+    filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
 
-    return Response(
-        status_code=201,
-        headers={
-            "Location": request.url_for(
-                "get_image", image_type=ImageType.UPLOAD, image_name=filename
-            )
-        },
+    (image_path, thumbnail_path, ctime) = ApiDependencies.invoker.services.images.save(
+        ImageType.UPLOAD, filename, img
     )
+
+    invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
+
+    res = ImageResponse(
+        image_type=ImageType.UPLOAD,
+        image_name=filename,
+        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,
+            width=img.width,
+            height=img.height,
+            invokeai=invokeai_metadata,
+        ),
+    )
+
+    response.status_code = 201
+    response.headers["Location"] = request.url_for(
+        "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"
+    ),
+    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)
+    return result

invokeai/app/api/routers/models.py

@@ -1,11 +1,17 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and 2023 Kent Keirsey (https://github.com/hipsterusername)
 
+import shutil
+import asyncio
 from typing import Annotated, Any, List, Literal, Optional, Union
 
-from fastapi.routing import APIRouter
+from fastapi.routing import APIRouter, HTTPException
 from pydantic import BaseModel, Field, parse_obj_as
-
+from pathlib import Path
 from ..dependencies import ApiDependencies
+from invokeai.backend.globals import Globals, global_converted_ckpts_dir
+from invokeai.backend.args import Args
+
+
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
 
@@ -15,11 +21,9 @@ class VaeRepo(BaseModel):
     path: Optional[str] = Field(description="The path to the VAE")
     subfolder: Optional[str] = Field(description="The subfolder to use for this VAE")
 
-
 class ModelInfo(BaseModel):
     description: Optional[str] = Field(description="A description of the model")
     
-
 class CkptModelInfo(ModelInfo):
     format: Literal['ckpt'] = 'ckpt'
 
@@ -29,7 +33,6 @@ class CkptModelInfo(ModelInfo):
     width: Optional[int] = Field(description="The width of the model")
     height: Optional[int] = Field(description="The height of the model")
 
-
 class DiffusersModelInfo(ModelInfo):
     format: Literal['diffusers'] = 'diffusers'
 
@@ -37,12 +40,29 @@ class DiffusersModelInfo(ModelInfo):
     repo_id: Optional[str] = Field(description="The repo ID to use for this model")
     path: Optional[str] = Field(description="The path to the model")
 
+class CreateModelRequest(BaseModel):
+    name: str = Field(description="The name of the model")
+    info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
+
+class CreateModelResponse(BaseModel):
+    name: str = Field(description="The name of the new model")
+    info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
+    status: str = Field(description="The status of the API response")
+
+class ConversionRequest(BaseModel):
+    name: str = Field(description="The name of the new model")
+    info: CkptModelInfo = Field(description="The converted model info")
+    save_location: str = Field(description="The path to save the converted model weights")
+    
+
+class ConvertedModelResponse(BaseModel):
+    name: str = Field(description="The name of the new model")
+    info: DiffusersModelInfo = Field(description="The converted model info")
 
 class ModelsList(BaseModel):
     models: dict[str, Annotated[Union[(CkptModelInfo,DiffusersModelInfo)], Field(discriminator="format")]]
 
 
-
 @models_router.get(
     "/",
     operation_id="list_models",
@@ -54,108 +74,61 @@ async def list_models() -> ModelsList:
     models = parse_obj_as(ModelsList, { "models": models_raw })
     return models
 
-        # @socketio.on("requestSystemConfig")
-        # def handle_request_capabilities():
-        #     print(">> System config requested")
-        #     config = self.get_system_config()
-        #     config["model_list"] = self.generate.model_manager.list_models()
-        #     config["infill_methods"] = infill_methods()
-        #     socketio.emit("systemConfig", config)
 
-        # @socketio.on("searchForModels")
-        # def handle_search_models(search_folder: str):
-        #     try:
-        #         if not search_folder:
-        #             socketio.emit(
-        #                 "foundModels",
-        #                 {"search_folder": None, "found_models": None},
-        #             )
-        #         else:
-        #             (
-        #                 search_folder,
-        #                 found_models,
-        #             ) = self.generate.model_manager.search_models(search_folder)
-        #             socketio.emit(
-        #                 "foundModels",
-        #                 {"search_folder": search_folder, "found_models": found_models},
-        #             )
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
-        #         print("\n")
+@models_router.post(
+    "/",
+    operation_id="update_model",
+    responses={200: {"status": "success"}},
+)
+async def update_model(
+    model_request: CreateModelRequest
+) -> CreateModelResponse:
+    """ Add Model """
+    model_request_info = model_request.info
+    info_dict = model_request_info.dict()
+    model_response = CreateModelResponse(name=model_request.name, info=model_request.info, status="success")
 
-        # @socketio.on("addNewModel")
-        # def handle_add_model(new_model_config: dict):
-        #     try:
-        #         model_name = new_model_config["name"]
-        #         del new_model_config["name"]
-        #         model_attributes = new_model_config
-        #         if len(model_attributes["vae"]) == 0:
-        #             del model_attributes["vae"]
-        #         update = False
-        #         current_model_list = self.generate.model_manager.list_models()
-        #         if model_name in current_model_list:
-        #             update = True
+    ApiDependencies.invoker.services.model_manager.add_model(
+        model_name=model_request.name,
+        model_attributes=info_dict,
+        clobber=True,
+    )
 
-        #         print(f">> Adding New Model: {model_name}")
+    return model_response
 
-        #         self.generate.model_manager.add_model(
-        #             model_name=model_name,
-        #             model_attributes=model_attributes,
-        #             clobber=True,
-        #         )
-        #         self.generate.model_manager.commit(opt.conf)
 
-        #         new_model_list = self.generate.model_manager.list_models()
-        #         socketio.emit(
-        #             "newModelAdded",
-        #             {
-        #                 "new_model_name": model_name,
-        #                 "model_list": new_model_list,
-        #                 "update": update,
-        #             },
-        #         )
-        #         print(f">> New Model Added: {model_name}")
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
+@models_router.delete(
+    "/{model_name}",
+    operation_id="del_model",
+    responses={
+        204: {
+        "description": "Model deleted successfully"
+        }, 
+        404: {
+        "description": "Model not found"
+        }
+    },
+)
+async def delete_model(model_name: str) -> None:
+    """Delete Model"""
+    model_names = ApiDependencies.invoker.services.model_manager.model_names()
+    model_exists = model_name in model_names
 
-        # @socketio.on("deleteModel")
-        # def handle_delete_model(model_name: str):
-        #     try:
-        #         print(f">> Deleting Model: {model_name}")
-        #         self.generate.model_manager.del_model(model_name)
-        #         self.generate.model_manager.commit(opt.conf)
-        #         updated_model_list = self.generate.model_manager.list_models()
-        #         socketio.emit(
-        #             "modelDeleted",
-        #             {
-        #                 "deleted_model_name": model_name,
-        #                 "model_list": updated_model_list,
-        #             },
-        #         )
-        #         print(f">> Model Deleted: {model_name}")
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
+    # check if model exists
+    print(f">> Checking for model {model_name}...")
+           
+    if model_exists:
+        print(f">> Deleting Model: {model_name}")
+        ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
+        print(f">> Model Deleted: {model_name}")
+        raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully")
+    
+    else:
+        print(f">> Model not found")
+        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
+    
 
-        # @socketio.on("requestModelChange")
-        # def handle_set_model(model_name: str):
-        #     try:
-        #         print(f">> Model change requested: {model_name}")
-        #         model = self.generate.set_model(model_name)
-        #         model_list = self.generate.model_manager.list_models()
-        #         if model is None:
-        #             socketio.emit(
-        #                 "modelChangeFailed",
-        #                 {"model_name": model_name, "model_list": model_list},
-        #             )
-        #         else:
-        #             socketio.emit(
-        #                 "modelChanged",
-        #                 {"model_name": model_name, "model_list": model_list},
-        #             )
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
-
-        # @socketio.on("convertToDiffusers")
+            # @socketio.on("convertToDiffusers")
         # def convert_to_diffusers(model_to_convert: dict):
         #     try:
         #         if model_info := self.generate.model_manager.model_info(
@@ -275,5 +248,4 @@ async def list_models() -> ModelsList:
         #         )
         #         print(f">> Models Merged: {models_to_merge}")
         #         print(f">> New Model Added: {model_merge_info['merged_model_name']}")
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
+        #     except Exception as e:

invokeai/app/cli/commands.py

@@ -6,11 +6,41 @@ from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_t
 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
+from ..services.graph import GraphExecutionState, LibraryGraph, GraphInvocation, Edge
 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],
@@ -35,30 +65,26 @@ def add_parsers(
             if name in exclude_fields:
                 continue
 
-            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
+            add_field_argument(command_parser, name, field)
 
-                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,
-                )
+
+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)
 
 
 class CliContext:
@@ -66,17 +92,38 @@ 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

@@ -13,17 +13,22 @@ 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_parsers, get_graph_execution_history
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, 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, GraphExecutionState, are_connection_types_compatible
+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.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
@@ -58,7 +63,7 @@ def add_invocation_args(command_parser):
     )
 
 
-def get_command_parser() -> argparse.ArgumentParser:
+def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     # Create invocation parser
     parser = argparse.ArgumentParser()
 
@@ -76,20 +81,72 @@ def get_command_parser() -> 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
+    a: BaseInvocation, b: BaseInvocation, context: CliContext
 ) -> list[Edge]:
     """Generates all possible edges between two invocations"""
-    atype = type(a)
-    btype = type(b)
-
-    aoutputtype = atype.get_output_type()
-
-    afields = get_type_hints(aoutputtype)
-    bfields = get_type_hints(btype)
+    afields = get_node_outputs(a, context)
+    bfields = get_node_inputs(b, context)
 
     matching_fields = set(afields.keys()).intersection(bfields.keys())
 
@@ -98,14 +155,14 @@ 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], bfields[f])]
+    matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)]
 
     edges = [
         Edge(
-            source=EdgeConnection(node_id=a.id, field=field),
-            destination=EdgeConnection(node_id=b.id, field=field)
+            source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
+            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
         )
-        for field in matching_fields
+        for alias in matching_fields
     ]
     return edges
 
@@ -145,6 +202,8 @@ def invoke_cli():
 
     events = EventServiceBase()
 
+    metadata = PngMetadataService()
+
     output_folder = os.path.abspath(
         os.path.join(os.path.dirname(__file__), "../../../outputs")
     )
@@ -156,8 +215,12 @@ def invoke_cli():
         model_manager=model_manager,
         events=events,
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
-        images=DiskImageStorage(f'{output_folder}/images'),
+        images=DiskImageStorage(f'{output_folder}/images', metadata_service=metadata),
+        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"
         ),
@@ -165,9 +228,12 @@ 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()
+    parser = get_command_parser(services)
 
     re_negid = re.compile('^-[0-9]+$')
 
@@ -185,11 +251,12 @@ def invoke_cli():
 
         try:
             # Refresh the state of the session
-            history = list(get_graph_execution_history(context.session))
+            #history = list(get_graph_execution_history(context.session))
+            history = list(reversed(context.nodes_added))
 
             # Split the command for piping
             cmds = cmd_input.split("|")
-            start_id = len(history)
+            start_id = len(context.nodes_added)
             current_id = start_id
             new_invocations = list()
             for cmd in cmds:
@@ -205,8 +272,24 @@ def invoke_cli():
                         args[field_name] = field_default
 
                 # Parse invocation
-                args["id"] = current_id
-                command = CliCommand(command=args)
+                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
 
                 # Run any CLI commands immediately
                 if isinstance(command.command, BaseCommand):
@@ -217,6 +300,7 @@ 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:
@@ -229,7 +313,7 @@ def invoke_cli():
                         else context.session.graph.get_node(from_id)
                     )
                     matching_edges = generate_matching_edges(
-                        from_node, command.command
+                        from_node, command.command, context
                     )
                     edges.extend(matching_edges)
 
@@ -242,7 +326,7 @@ def invoke_cli():
 
                         link_node = context.session.graph.get_node(node_id)
                         matching_edges = generate_matching_edges(
-                            link_node, command.command
+                            link_node, command.command, context
                         )
                         matching_destinations = [e.destination for e in matching_edges]
                         edges = [e for e in edges if e.destination not in matching_destinations]
@@ -256,12 +340,14 @@ 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_id, field=link[1]),
-                                destination=EdgeConnection(
-                                    node_id=command.command.id, field=link[2]
-                                )
+                                source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
+                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
                             )
                         )
 
@@ -270,10 +356,10 @@ def invoke_cli():
                 current_id = current_id + 1
 
                 # Add the node to the session
-                context.session.add_node(command.command)
+                context.add_node(command.command)
                 for edge in edges:
                     print(edge)
-                    context.session.add_edge(edge)
+                    context.add_edge(edge)
 
             # Execute all remaining nodes
             invoke_all(context)
@@ -285,7 +371,7 @@ def invoke_cli():
         except SessionError:
             # Start a new session
             print("Session error: creating a new session")
-            context.session = context.invoker.create_execution_state()
+            context.reset()
 
         except ExitCli:
             break

invokeai/app/invocations/baseinvocation.py

@@ -2,7 +2,7 @@
 
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import get_args, get_type_hints
+from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict
 
 from pydantic import BaseModel, Field
 
@@ -76,3 +76,56 @@ class BaseInvocation(ABC, BaseModel):
     #fmt: off
     id: str = Field(description="The id of this node. Must be unique among all nodes.")
     #fmt: on
+
+
+# TODO: figure out a better way to provide these hints
+# TODO: when we can upgrade to python 3.11, we can use the`NotRequired` type instead of `total=False`
+class UIConfig(TypedDict, total=False):
+    type_hints: Dict[
+        str,
+        Literal[
+            "integer",
+            "float",
+            "boolean",
+            "string",
+            "enum",
+            "image",
+            "latents",
+            "model",
+        ],
+    ]
+    tags: List[str]
+    title: str
+
+class CustomisedSchemaExtra(TypedDict):
+    ui: UIConfig
+
+
+class InvocationConfig(BaseModel.Config):
+    """Customizes pydantic's BaseModel.Config class for use by Invocations.
+
+    Provide `schema_extra` a `ui` dict to add hints for generated UIs.
+
+    `tags`
+    - A list of strings, used to categorise invocations.
+
+    `type_hints`
+    - A dict of field types which override the types in the invocation definition.
+    - Each key should be the name of one of the invocation's fields.
+    - Each value should be one of the valid types:
+      - `integer`, `float`, `boolean`, `string`, `enum`, `image`, `latents`, `model`
+
+    ```python
+    class Config(InvocationConfig):
+      schema_extra = {
+          "ui": {
+              "tags": ["stable-diffusion", "image"],
+              "type_hints": {
+                  "initial_image": "image",
+              },
+          },
+      }
+    ```
+    """
+
+    schema_extra: CustomisedSchemaExtra

invokeai/app/invocations/collections.py

@@ -1,16 +1,17 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Literal
+from typing import Literal, Optional
 
-import cv2 as cv
 import numpy as np
 import numpy.random
-from PIL import Image, ImageOps
 from pydantic import Field
 
-from ..services.image_storage import ImageType
-from .baseinvocation import BaseInvocation, InvocationContext, BaseInvocationOutput
-from .image import ImageField, ImageOutput
+from .baseinvocation import (
+    BaseInvocation,
+    InvocationConfig,
+    InvocationContext,
+    BaseInvocationOutput,
+)
 
 
 class IntCollectionOutput(BaseInvocationOutput):
@@ -33,7 +34,9 @@ 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):
@@ -43,8 +46,19 @@ 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:
-        return IntCollectionOutput(collection=list(numpy.random.randint(self.low, self.high, size=self.size)))
+        rng = np.random.default_rng(self.seed)
+        return IntCollectionOutput(
+            collection=list(rng.integers(low=self.low, high=self.high, size=self.size))
+        )

invokeai/app/invocations/cv.py

@@ -5,14 +5,26 @@ from typing import Literal
 import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
-from pydantic import Field
+from pydantic import BaseModel, Field
 
-from ..services.image_storage import ImageType
-from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from invokeai.app.models.image import ImageField, ImageType
+from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
+from .image import ImageOutput, build_image_output
 
 
-class CvInpaintInvocation(BaseInvocation):
+class CvInvocationConfig(BaseModel):
+    """Helper class to provide all OpenCV invocations with additional config"""
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["cv", "image"],
+            },
+        }
+
+
+class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
     """Simple inpaint using opencv."""
     #fmt: off
     type: Literal["cv_inpaint"] = "cv_inpaint"
@@ -44,7 +56,14 @@ class CvInpaintInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, image_inpainted)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image_inpainted,
+        )

invokeai/app/invocations/generate.py

@@ -6,21 +6,36 @@ from typing import Literal, Optional, Union
 import numpy as np
 from torch import Tensor
 
-from pydantic import Field
+from pydantic import BaseModel, Field
 
-from ..services.image_storage import ImageType
-from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from invokeai.app.models.image import ImageField, ImageType
+from invokeai.app.invocations.util.choose_model import choose_model
+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.util import diffusers_step_callback_adapter, CanceledException
+from ..util.step_callback import stable_diffusion_step_callback
+
+SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
+
+
+class SDImageInvocation(BaseModel):
+    """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["stable-diffusion", "image"],
+                "type_hints": {
+                    "model": "model",
+                },
+            },
+        }
 
-SAMPLER_NAME_VALUES = Literal[
-    tuple(InvokeAIGenerator.schedulers())
-]
 
 # Text to image
-class TextToImageInvocation(BaseInvocation):
+class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     """Generates an image using text2img."""
 
     type: Literal["txt2img"] = "txt2img"
@@ -34,7 +49,7 @@ class TextToImageInvocation(BaseInvocation):
     width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
     height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler to use" )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
     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",  )
@@ -42,35 +57,31 @@ class TextToImageInvocation(BaseInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, intermediate_state: PipelineIntermediateState
+        self,
+        context: InvocationContext,
+        source_node_id: str,
+        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)
+        stable_diffusion_step_callback(
+            context=context,
+            intermediate_state=intermediate_state,
+            node=self.dict(),
+            source_node_id=source_node_id,
+        )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # def step_callback(state: PipelineIntermediateState):
-        #     if (context.services.queue.is_canceled(context.graph_execution_state_id)):
-        #         raise CanceledException
-        #     self.dispatch_progress(context, state.latents, state.step)
-
         # Handle invalid model parameter
-        # TODO: figure out if this can be done via a validator that uses the model_cache
-        # TODO: How to get the default model name now?
-        #       (right now uses whatever current model is set in model manager)
-        model= context.services.model_manager.get_model()
+        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]
+
         outputs = Txt2Img(model).generate(
             prompt=self.prompt,
-            step_callback=partial(self.dispatch_progress, context),
+            step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
@@ -86,9 +97,18 @@ class TextToImageInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, generate_output.image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        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,
         )
 
 
@@ -108,20 +128,17 @@ class ImageToImageInvocation(TextToImageInvocation):
     )
 
     def dispatch_progress(
-        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)
+        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,
+        )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
@@ -134,18 +151,23 @@ class ImageToImageInvocation(TextToImageInvocation):
         mask = None
 
         # Handle invalid model parameter
-        # TODO: figure out if this can be done via a validator that uses the model_cache
-        # TODO: How to get the default model name now?
-        model = context.services.model_manager.get_model()
+        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]
+
         outputs = Img2Img(model).generate(
-                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
-            )
+            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
+        )
 
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
         # each time it is called. We only need the first one.
@@ -160,11 +182,19 @@ class ImageToImageInvocation(TextToImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, result_image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=result_image,
+        )
+
+
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -180,20 +210,17 @@ class InpaintInvocation(ImageToImageInvocation):
     )
 
     def dispatch_progress(
-        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)
+        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,
+        )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
@@ -210,18 +237,23 @@ class InpaintInvocation(ImageToImageInvocation):
         )
 
         # Handle invalid model parameter
-        # TODO: figure out if this can be done via a validator that uses the model_cache
-        # TODO: How to get the default model name now?
-        model = context.services.model_manager.get_model()
+        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]
+
         outputs = Inpaint(model).generate(
-                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
-            )
+            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
+        )
 
         # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
         # each time it is called. We only need the first one.
@@ -236,7 +268,14 @@ class InpaintInvocation(ImageToImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, result_image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=result_image,
         )

invokeai/app/invocations/image.py

@@ -1,70 +1,97 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from datetime import datetime, timezone
 from typing import Literal, Optional
 
 import numpy
 from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field
 
-from ..services.image_storage import ImageType
-from ..services.invocation_services import InvocationServices
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+from ..models.image import ImageField, ImageType
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InvocationContext,
+    InvocationConfig,
+)
 
 
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
+class PILInvocationConfig(BaseModel):
+    """Helper class to provide all PIL invocations with additional config"""
 
-    image_type: str = Field(
-        default=ImageType.RESULT, description="The type of the image"
-    )
-    image_name: Optional[str] = Field(default=None, description="The name of the image")
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["PIL", "image"],
+            },
+        }
 
 
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    #fmt: on
+    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")
+    # fmt: on
 
     class Config:
         schema_extra = {
-            'required': [
-                'type',
-                'image',
-            ]
+            "required": ["type", "image", "width", "height", "mode"]
         }
 
+
+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,
+    )
+
+
 class MaskOutput(BaseInvocationOutput):
     """Base class for invocations that output a mask"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["mask"] = "mask"
     mask:      ImageField = Field(default=None, description="The output mask")
-    #fmt: on
+    # fmt: on
 
     class Config:
         schema_extra = {
-            'required': [
-                'type',
-                'mask',
+            "required": [
+                "type",
+                "mask",
             ]
         }
 
-# TODO: this isn't really necessary anymore
+
 class LoadImageInvocation(BaseInvocation):
-    """Load an image from a filename and provide it as output."""
-    #fmt: off
+    """Load an image and provide it as output."""
+
+    # 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
-
+    # fmt: on
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        return ImageOutput(
-            image=ImageField(image_type=self.image_type, image_name=self.image_name)
+        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,
         )
 
 
@@ -85,16 +112,17 @@ class ShowImageInvocation(BaseInvocation):
 
         # TODO: how to handle failure?
 
-        return ImageOutput(
-            image=ImageField(
-                image_type=self.image.image_type, image_name=self.image.image_name
-            )
+        return build_image_output(
+            image_type=self.image.image_type,
+            image_name=self.image.image_name,
+            image=image,
         )
 
 
-class CropImageInvocation(BaseInvocation):
+class CropImageInvocation(BaseInvocation, PILInvocationConfig):
     """Crops an image to a specified box. The box can be outside of the image."""
-    #fmt: off
+
+    # fmt: off
     type: Literal["crop"] = "crop"
 
     # Inputs
@@ -103,7 +131,7 @@ class CropImageInvocation(BaseInvocation):
     y:      int = Field(default=0, description="The top y coordinate of the crop rectangle")
     width:  int = Field(default=512, gt=0, description="The width of the crop rectangle")
     height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
@@ -119,15 +147,23 @@ class CropImageInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, image_crop)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image_crop,
         )
 
 
-class PasteImageInvocation(BaseInvocation):
+class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
     """Pastes an image into another image."""
-    #fmt: off
+
+    # fmt: off
     type: Literal["paste"] = "paste"
 
     # Inputs
@@ -136,7 +172,7 @@ class PasteImageInvocation(BaseInvocation):
     mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
     x:                     int = Field(default=0, description="The left x coordinate at which to paste the image")
     y:                     int = Field(default=0, description="The top y coordinate at which to paste the image")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         base_image = context.services.images.get(
@@ -149,7 +185,7 @@ class PasteImageInvocation(BaseInvocation):
             None
             if self.mask is None
             else ImageOps.invert(
-                services.images.get(self.mask.image_type, self.mask.image_name)
+                context.services.images.get(self.mask.image_type, self.mask.image_name)
             )
         )
         # TODO: probably shouldn't invert mask here... should user be required to do it?
@@ -169,21 +205,29 @@ class PasteImageInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, new_image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=new_image,
         )
 
 
-class MaskFromAlphaInvocation(BaseInvocation):
+class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
     """Extracts the alpha channel of an image as a mask."""
-    #fmt: off
+
+    # fmt: off
     type: Literal["tomask"] = "tomask"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to create the mask from")
     invert:      bool = Field(default=False, description="Whether or not to invert the mask")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
         image = context.services.images.get(
@@ -198,22 +242,27 @@ class MaskFromAlphaInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, image_mask)
+
+        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)
         return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
 
 
-class BlurInvocation(BaseInvocation):
+class BlurInvocation(BaseInvocation, PILInvocationConfig):
     """Blurs an image"""
 
-    #fmt: off
+    # fmt: off
     type: Literal["blur"] = "blur"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to blur")
     radius:     float = Field(default=8.0, ge=0, description="The blur radius")
     blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
-    #fmt: on
-    
+    # fmt: on
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -230,22 +279,28 @@ class BlurInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, blur_image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=blur_image
         )
 
 
-class LerpInvocation(BaseInvocation):
+class LerpInvocation(BaseInvocation, PILInvocationConfig):
     """Linear interpolation of all pixels of an image"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["lerp"] = "lerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
-    #fmt: on
+    # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
@@ -261,23 +316,29 @@ class LerpInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, lerp_image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=lerp_image
         )
 
 
-class InverseLerpInvocation(BaseInvocation):
+class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
     """Inverse linear interpolation of all pixels of an image"""
-    #fmt: off
+
+    # fmt: off
     type: Literal["ilerp"] = "ilerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
-    #fmt: on
-    
+    # fmt: on
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -297,7 +358,12 @@ class InverseLerpInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, ilerp_image)
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=ilerp_image
         )

invokeai/app/invocations/latent.py

@@ -1,25 +1,26 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
+import random
 from typing import Literal, Optional
 from pydantic import BaseModel, Field
-from torch import Tensor
 import torch
 
+from invokeai.app.invocations.util.choose_model import choose_model
+
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
+
 from ...backend.model_management.model_manager import ModelManager
-from ...backend.util.devices import CUDA_DEVICE, torch_dtype
+from ...backend.util.devices import choose_torch_device, torch_dtype
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.image_util.seamless import configure_model_padding
 from ...backend.prompting.conditioning import get_uc_and_c_and_ec
 from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
-from accelerate.utils import set_seed
 from ..services.image_storage import ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
-from ...backend.generator import Generator
+from .image import ImageField, ImageOutput, build_image_output
 from ...backend.stable_diffusion import PipelineIntermediateState
-from ...backend.util.util import image_to_dataURL
 from diffusers.schedulers import SchedulerMixin as Scheduler
 import diffusers
 from diffusers import DiffusionPipeline
@@ -30,6 +31,8 @@ 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"""
@@ -99,18 +102,31 @@ 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(default=0, ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", )
+    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
     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", )
 
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "noise"],
+            },
+        }
+
     def invoke(self, context: InvocationContext) -> NoiseOutput:
-        device = torch.device(CUDA_DEVICE)
+        device = torch.device(choose_torch_device())
         noise = get_noise(self.width, self.height, device, self.seed)
 
         name = f'{context.graph_execution_state_id}__{self.id}'
@@ -136,48 +152,45 @@ class TextToLatentsInvocation(BaseInvocation):
     width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
     height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler to use" )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
     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",  )
     # fmt: on
 
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "image"],
+                "type_hints": {
+                  "model": "model"
+                }
+            },
+        }
+
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self, context: InvocationContext, sample: Tensor, step: int
-    ) -> None:  
-        # TODO: only output a preview image when requested
-        image = Generator.sample_to_lowres_estimated_image(sample)
-
-        (width, height) = image.size
-        width *= 8
-        height *= 8
-
-        dataURL = image_to_dataURL(image, image_format="JPEG")
-
-        context.services.events.emit_generator_progress(
-            context.graph_execution_state_id,
-            self.id,
-            {
-                "width": width,
-                "height": height,
-                "dataURL": dataURL
-            },
-            step,
-            self.steps,
+        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,
         )
-    
+
     def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
-        model_info = model_manager.get_model(self.model)
+        model_info = choose_model(model_manager, self.model)
         model_name = model_info['model_name']
         model_hash = model_info['hash']
         model: StableDiffusionGeneratorPipeline = model_info['model']
         model.scheduler = get_scheduler(
             model=model,
-            scheduler_name=self.sampler_name
+            scheduler_name=self.scheduler
         )
-        
+
         if isinstance(model, DiffusionPipeline):
             for component in [model.unet, model.vae]:
                 configure_model_padding(component,
@@ -213,8 +226,12 @@ 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, state.latents, state.step)
+            self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
         conditioning_data = self.get_conditioning_data(model)
@@ -244,6 +261,17 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     type: Literal["l2l"] = "l2l"
 
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents"],
+                "type_hints": {
+                    "model": "model"
+                }
+            },
+        }
+
     # Inputs
     latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
     strength: float = Field(default=0.5, description="The strength of the latents to use")
@@ -252,8 +280,12 @@ 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, state.latents, state.step)
+            self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
         conditioning_data = self.get_conditioning_data(model)
@@ -263,7 +295,7 @@ 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,
@@ -299,12 +331,23 @@ class LatentsToImageInvocation(BaseInvocation):
     latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
     model: str = Field(default="", description="The model to use")
 
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["latents", "image"],
+                "type_hints": {
+                  "model": "model"
+                }
+            },
+        }
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.services.latents.get(self.latents.latents_name)
 
         # TODO: this only really needs the vae
-        model_info = context.services.model_manager.get_model(self.model)
+        model_info = choose_model(context.services.model_manager, self.model)
         model: StableDiffusionGeneratorPipeline = model_info['model']
 
         with torch.inference_mode():
@@ -315,7 +358,14 @@ class LatentsToImageInvocation(BaseInvocation):
             image_name = context.services.images.create_name(
                 context.graph_execution_state_id, self.id
             )
-            context.services.images.save(image_type, image_name, image)
-            return ImageOutput(
-                image=ImageField(image_type=image_type, image_name=image_name)
+
+            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)
+            return build_image_output(
+                image_type=image_type,
+                image_name=image_name,
+                image=image
             )

invokeai/app/invocations/math.py

@@ -1,15 +1,22 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from datetime import datetime, timezone
-from typing import Literal, Optional
+from typing import Literal
 
-import numpy
-from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field
 
-from ..services.image_storage import ImageType
-from ..services.invocation_services import InvocationServices
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+
+
+class MathInvocationConfig(BaseModel):
+    """Helper class to provide all math invocations with additional config"""
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["math"],
+            }
+        }
 
 
 class IntOutput(BaseInvocationOutput):
@@ -20,7 +27,7 @@ class IntOutput(BaseInvocationOutput):
     #fmt: on
 
 
-class AddInvocation(BaseInvocation):
+class AddInvocation(BaseInvocation, MathInvocationConfig):
     """Adds two numbers"""
     #fmt: off
     type: Literal["add"] = "add"
@@ -32,7 +39,7 @@ class AddInvocation(BaseInvocation):
         return IntOutput(a=self.a + self.b)
 
 
-class SubtractInvocation(BaseInvocation):
+class SubtractInvocation(BaseInvocation, MathInvocationConfig):
     """Subtracts two numbers"""
     #fmt: off
     type: Literal["sub"] = "sub"
@@ -44,7 +51,7 @@ class SubtractInvocation(BaseInvocation):
         return IntOutput(a=self.a - self.b)
 
 
-class MultiplyInvocation(BaseInvocation):
+class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
     """Multiplies two numbers"""
     #fmt: off
     type: Literal["mul"] = "mul"
@@ -56,7 +63,7 @@ class MultiplyInvocation(BaseInvocation):
         return IntOutput(a=self.a * self.b)
 
 
-class DivideInvocation(BaseInvocation):
+class DivideInvocation(BaseInvocation, MathInvocationConfig):
     """Divides two numbers"""
     #fmt: off
     type: Literal["div"] = "div"

invokeai/app/invocations/params.py

@@ -0,0 +1,18 @@
+# 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

@@ -1,12 +1,11 @@
-from datetime import datetime, timezone
 from typing import Literal, Union
 
 from pydantic import Field
 
-from ..services.image_storage import ImageType
-from ..services.invocation_services import InvocationServices
-from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from invokeai.app.models.image import ImageField, ImageType
+
+from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
+from .image import ImageOutput, build_image_output
 
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
@@ -18,6 +17,14 @@ class RestoreFaceInvocation(BaseInvocation):
     strength:                float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration"  )
     #fmt: on
     
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["restoration", "image"],
+            },
+        }
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -36,7 +43,14 @@ class RestoreFaceInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, results[0][0])
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=results[0][0]
+        )

invokeai/app/invocations/upscale.py

@@ -1,14 +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 ..services.image_storage import ImageType
-from ..services.invocation_services import InvocationServices
-from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
+from invokeai.app.models.image import ImageField, ImageType
+from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
+from .image import ImageOutput, build_image_output
 
 
 class UpscaleInvocation(BaseInvocation):
@@ -22,6 +20,15 @@ class UpscaleInvocation(BaseInvocation):
     level: Literal[2, 4] = Field(default=2, description="The upscale level")
     #fmt: on
 
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["upscaling", "image"],
+            },
+        }
+
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -40,7 +47,14 @@ class UpscaleInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-        context.services.images.save(image_type, image_name, results[0][0])
-        return ImageOutput(
-            image=ImageField(image_type=image_type, image_name=image_name)
+
+        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)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=results[0][0]
+        )

invokeai/app/invocations/util/choose_model.py

@@ -0,0 +1,14 @@
+from invokeai.backend.model_management.model_manager import ModelManager
+
+
+def choose_model(model_manager: ModelManager, model_name: str):
+    """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
+    if model_manager.valid_model(model_name):
+        model = model_manager.get_model(model_name)
+    else:
+        model = model_manager.get_model()
+        print(
+            f"* Warning: '{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead."
+        )
+
+    return model

invokeai/app/models/exceptions.py

@@ -0,0 +1,3 @@
+class CanceledException(Exception):
+    """Execution canceled by user."""
+    pass

invokeai/app/models/image.py

@@ -0,0 +1,29 @@
+from enum import Enum
+from typing import Optional
+from pydantic import BaseModel, Field
+
+
+class ImageType(str, Enum):
+    RESULT = "results"
+    INTERMEDIATE = "intermediates"
+    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"""
+
+    image_type: ImageType = Field(
+        default=ImageType.RESULT, description="The type of the image"
+    )
+    image_name: Optional[str] = Field(default=None, description="The name of the image")
+
+    class Config:
+        schema_extra = {"required": ["image_type", "image_name"]}

invokeai/app/services/default_graphs.py

@@ -0,0 +1,56 @@
+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

@@ -1,10 +1,9 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any, Dict, TypedDict
+from typing import Any
+from invokeai.app.api.models.images import ProgressImage
+from invokeai.app.util.misc import get_timestamp
 
-ProgressImage = TypedDict(
-    "ProgressImage", {"dataURL": str, "width": int, "height": int}
-)
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -14,7 +13,8 @@ class EventServiceBase:
     def dispatch(self, event_name: str, payload: Any) -> None:
         pass
 
-    def __emit_session_event(self, event_name: str, payload: Dict) -> None:
+    def __emit_session_event(self, event_name: str, payload: dict) -> None:
+        payload["timestamp"] = get_timestamp()
         self.dispatch(
             event_name=EventServiceBase.session_event,
             payload=dict(event=event_name, data=payload),
@@ -25,7 +25,8 @@ class EventServiceBase:
     def emit_generator_progress(
         self,
         graph_execution_state_id: str,
-        invocation_id: str,
+        node: dict,
+        source_node_id: str,
         progress_image: ProgressImage | None,
         step: int,
         total_steps: int,
@@ -35,48 +36,60 @@ class EventServiceBase:
             event_name="generator_progress",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                invocation_id=invocation_id,
-                progress_image=progress_image,
+                node=node,
+                source_node_id=source_node_id,
+                progress_image=progress_image.dict() if progress_image is not None else None,
                 step=step,
                 total_steps=total_steps,
             ),
         )
 
     def emit_invocation_complete(
-        self, graph_execution_state_id: str, invocation_id: str, result: Dict
+        self,
+        graph_execution_state_id: str,
+        result: dict,
+        node: dict,
+        source_node_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,
-                invocation_id=invocation_id,
+                node=node,
+                source_node_id=source_node_id,
                 result=result,
             ),
         )
 
     def emit_invocation_error(
-        self, graph_execution_state_id: str, invocation_id: str, error: str
+        self,
+        graph_execution_state_id: str,
+        node: dict,
+        source_node_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,
-                invocation_id=invocation_id,
+                node=node,
+                source_node_id=source_node_id,
                 error=error,
             ),
         )
 
     def emit_invocation_started(
-        self, graph_execution_state_id: str, invocation_id: str
+        self, graph_execution_state_id: str, node: dict, source_node_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,
-                invocation_id=invocation_id,
+                node=node,
+                source_node_id=source_node_id,
             ),
         )
 
@@ -84,5 +97,7 @@ 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

@@ -2,7 +2,6 @@
 
 import copy
 import itertools
-import traceback
 import uuid
 from types import NoneType
 from typing import (
@@ -17,7 +16,7 @@ from typing import (
 )
 
 import networkx as nx
-from pydantic import BaseModel, validator
+from pydantic import BaseModel, root_validator, validator
 from pydantic.fields import Field
 
 from ..invocations import *
@@ -26,7 +25,6 @@ from ..invocations.baseinvocation import (
     BaseInvocationOutput,
     InvocationContext,
 )
-from .invocation_services import InvocationServices
 
 
 class EdgeConnection(BaseModel):
@@ -215,7 +213,7 @@ InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()]
 
 
 class Graph(BaseModel):
-    id: str = Field(description="The id of this graph", default_factory=uuid.uuid4)
+    id: str = Field(description="The id of this graph", default_factory=lambda: uuid.uuid4().__str__())
     # 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
@@ -283,7 +281,8 @@ class Graph(BaseModel):
         :raises InvalidEdgeError: the provided edge is invalid.
         """
 
-        if self._is_edge_valid(edge) and edge not in self.edges:
+        self._validate_edge(edge)
+        if edge not in self.edges:
             self.edges.append(edge)
         else:
             raise InvalidEdgeError()
@@ -354,7 +353,7 @@ class Graph(BaseModel):
 
         return True
 
-    def _is_edge_valid(self, edge: Edge) -> bool:
+    def _validate_edge(self, edge: Edge):
         """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)
@@ -362,54 +361,53 @@ class Graph(BaseModel):
             from_node = self.get_node(edge.source.node_id)
             to_node = self.get_node(edge.destination.node_id)
         except NodeNotFoundError:
-            return False
+            raise InvalidEdgeError("One or both nodes don't exist")
 
         # 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):
-            return False
+            raise InvalidEdgeError(f'Edge to node {edge.destination.node_id} field {edge.destination.field} already exists')
 
         # 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):
-            return False
+            raise InvalidEdgeError(f'Edge creates a cycle in the graph')
 
         # Validate that the field types are compatible
         if not are_connections_compatible(
             from_node, edge.source.field, to_node, edge.destination.field
         ):
-            return False
+            raise InvalidEdgeError(f'Fields are incompatible')
 
         # 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
             ):
-                return False
+                raise InvalidEdgeError(f'Iterator input type does not match iterator output type')
 
         # 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
             ):
-                return False
+                raise InvalidEdgeError(f'Iterator output type does not match iterator input type')
 
         # 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
             ):
-                return False
+                raise InvalidEdgeError(f'Collector output type does not match collector input type')
 
         # 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
             ):
-                return False
+                raise InvalidEdgeError(f'Collector input type does not match collector output type')
 
-        return True
 
     def has_node(self, node_path: str) -> bool:
         """Determines whether or not a node exists in the graph."""
@@ -733,7 +731,7 @@ class Graph(BaseModel):
         for sgn in (
             gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)
         ):
-            sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
+            g = sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
 
         # TODO: figure out if iteration nodes need to be expanded
 
@@ -750,9 +748,7 @@ 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=uuid.uuid4
-    )
+    id: str = Field(description="The id of the execution state", default_factory=lambda: uuid.uuid4().__str__())
 
     # TODO: Store a reference to the graph instead of the actual graph?
     graph: Graph = Field(description="The graph being executed")
@@ -794,9 +790,6 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
-    # Declare all fields as required; necessary for OpenAPI schema generation build.
-    # Technically only fields without a `default_factory` need to be listed here.
-    # See: https://github.com/pydantic/pydantic/discussions/4577
     class Config:
         schema_extra = {
             'required': [
@@ -861,7 +854,8 @@ class GraphExecutionState(BaseModel):
 
     def is_complete(self) -> bool:
         """Returns true if the graph is complete"""
-        return self.has_error() or all((k in self.executed for k in self.graph.nodes))
+        node_ids = set(self.graph.nx_graph_flat().nodes)
+        return self.has_error() or all((k in self.executed for k in node_ids))
 
     def has_error(self) -> bool:
         """Returns true if the graph has any errors"""
@@ -949,11 +943,11 @@ 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()
+        g = self.graph.nx_graph_flat()
         collectors = (
             n
             for n in self.graph.nodes
-            if isinstance(self.graph.nodes[n], CollectInvocation)
+            if isinstance(self.graph.get_node(n), CollectInvocation)
         )
         for c in collectors:
             g.remove_edges_from(list(g.in_edges(c)))
@@ -965,7 +959,7 @@ class GraphExecutionState(BaseModel):
         iterators = [
             n
             for n in nx.ancestors(g, node_id)
-            if isinstance(self.graph.nodes[n], IterateInvocation)
+            if isinstance(self.graph.get_node(n), IterateInvocation)
         ]
         return iterators
 
@@ -1101,7 +1095,9 @@ 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:
-        if not self._is_edge_valid(edge):
+        try:
+            self.graph._validate_edge(edge)
+        except InvalidEdgeError:
             return False
 
         # Invalid if destination has already been prepared or executed
@@ -1147,4 +1143,52 @@ 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

@@ -1,23 +1,24 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-import datetime
 import os
+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
+from typing import Dict, List, Tuple
 
 from PIL.Image import Image
-from invokeai.app.util.save_thumbnail import save_thumbnail
-
-from invokeai.backend.image_util import PngWriter
-
-
-class ImageType(str, Enum):
-    RESULT = "results"
-    INTERMEDIATE = "intermediates"
-    UPLOAD = "uploads"
+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 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
 
 
 class ImageStorageBase(ABC):
@@ -25,40 +26,66 @@ 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
     @abstractmethod
-    def get_path(self, image_type: ImageType, image_name: str) -> str:
+    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) -> None:
+    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."""
         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:
-        return f"{context_id}_{node_id}_{str(int(datetime.datetime.now(datetime.timezone.utc).timestamp()))}.png"
+        """Creates a unique contextual image filename."""
+        return f"{context_id}_{node_id}_{str(get_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):
+    def __init__(self, output_folder: str, metadata_service: MetadataServiceBase):
         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)
 
@@ -71,42 +98,132 @@ class DiskImageStorage(ImageStorageBase):
                 parents=True, exist_ok=True
             )
 
+    def list(
+        self, image_type: ImageType, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ImageResponse]:
+        dir_path = os.path.join(self.__output_folder, image_type)
+        image_paths = glob(f"{dir_path}/*.png")
+        count = len(image_paths)
+
+        sorted_image_paths = sorted(
+            glob(f"{dir_path}/*.png"), key=os.path.getctime, reverse=True
+        )
+
+        page_of_image_paths = sorted_image_paths[
+            page * per_page : (page + 1) * per_page
+        ]
+
+        page_of_images: List[ImageResponse] = []
+
+        for path in page_of_image_paths:
+            filename = os.path.basename(path)
+            img = PILImage.open(path)
+
+            invokeai_metadata = self.__metadata_service.get_metadata(img)
+
+            page_of_images.append(
+                ImageResponse(
+                    image_type=image_type.value,
+                    image_name=filename,
+                    # 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(
+                        created=int(os.path.getctime(path)),
+                        width=img.width,
+                        height=img.height,
+                        invokeai=invokeai_metadata,
+                    ),
+                )
+            )
+
+        page_count_trunc = int(count / per_page)
+        page_count_mod = count % per_page
+        page_count = page_count_trunc if page_count_mod == 0 else page_count_trunc + 1
+
+        return PaginatedResults[ImageResponse](
+            items=page_of_images,
+            page=page,
+            pages=page_count,
+            per_page=per_page,
+            total=count,
+        )
+
     def get(self, image_type: ImageType, image_name: str) -> Image:
         image_path = self.get_path(image_type, image_name)
         cache_item = self.__get_cache(image_path)
         if cache_item:
             return cache_item
 
-        image = Image.open(image_path)
+        image = PILImage.open(image_path)
         self.__set_cache(image_path, image)
         return image
 
     # TODO: make this a bit more flexible for e.g. cloud storage
-    def get_path(self, image_type: ImageType, image_name: str) -> str:
-        path = os.path.join(self.__output_folder, image_type, image_name)
+    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
+            )
+        else:
+            path = os.path.join(self.__output_folder, image_type, basename)
+
         return path
 
-    def save(self, image_type: ImageType, image_name: str, image: Image) -> None:
-        image_subpath = os.path.join(image_type, image_name)
-        self.__pngWriter.save_image_and_prompt_to_png(
-            image, "", image_subpath, None
-        )  # 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"),
-        )
+    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]:
         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)))
 
     def delete(self, image_type: ImageType, image_name: str) -> None:
         image_path = self.get_path(image_type, image_name)
+        thumbnail_path = self.get_path(image_type, image_name, True)
         if os.path.exists(image_path):
             os.remove(image_path)
 
         if image_path in self.__cache:
             del self.__cache[image_path]
 
+        if os.path.exists(thumbnail_path):
+            os.remove(thumbnail_path)
+
+        if thumbnail_path in self.__cache:
+            del self.__cache[thumbnail_path]
+
     def __get_cache(self, image_name: str) -> Image:
         return None if image_name not in self.__cache else self.__cache[image_name]
 

invokeai/app/services/invocation_queue.py

@@ -1,30 +1,17 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import time
 from abc import ABC, abstractmethod
 from queue import Queue
-import time
+
+from pydantic import BaseModel, Field
 
 
-# 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 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)
 
 
 class InvocationQueueABC(ABC):

invokeai/app/services/invocation_services.py

@@ -1,4 +1,5 @@
 # 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
@@ -14,11 +15,13 @@ 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"
 
@@ -28,7 +31,9 @@ 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,
@@ -37,7 +42,9 @@ 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

@@ -0,0 +1,96 @@
+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

@@ -4,7 +4,7 @@ from threading import Event, Thread
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
-from ..util.util import CanceledException
+from ..models.exceptions import CanceledException
 
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
@@ -43,10 +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]
+
                 # Send starting event
                 self.__invoker.services.events.emit_invocation_started(
                     graph_execution_state_id=graph_execution_state.id,
-                    invocation_id=invocation.id,
+                    node=invocation.dict(),
+                    source_node_id=source_node_id
                 )
 
                 # Invoke
@@ -75,7 +79,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # Send complete event
                     self.__invoker.services.events.emit_invocation_complete(
                         graph_execution_state_id=graph_execution_state.id,
-                        invocation_id=invocation.id,
+                        node=invocation.dict(),
+                        source_node_id=source_node_id,
                         result=outputs.dict(),
                     )
 
@@ -99,7 +104,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # Send error event
                     self.__invoker.services.events.emit_invocation_error(
                         graph_execution_state_id=graph_execution_state.id,
-                        invocation_id=invocation.id,
+                        node=invocation.dict(),
+                        source_node_id=source_node_id,
                         error=error,
                     )
 

invokeai/app/util/misc.py

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

invokeai/app/util/save_thumbnail.py

@@ -1,25 +0,0 @@
-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

@@ -0,0 +1,55 @@
+from invokeai.app.api.models.images import ProgressImage
+from invokeai.app.models.exceptions import CanceledException
+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(
+    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)
+
+    (width, height) = image.size
+    width *= 8
+    height *= 8
+
+    dataURL = image_to_dataURL(image, image_format="JPEG")
+
+    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"],
+    )

invokeai/app/util/thumbnails.py

@@ -0,0 +1,15 @@
+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/app/util/util.py

@@ -1,42 +0,0 @@
-import torch
-from PIL import Image
-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
-
-class CanceledException(Exception):
-    pass
-
-def fast_latents_step_callback(sample: torch.Tensor, step: int, steps: int, id: str, context: InvocationContext, ):
-    # TODO: only output a preview image when requested
-    image = Generator.sample_to_lowres_estimated_image(sample)
-
-    (width, height) = image.size
-    width *= 8
-    height *= 8
-
-    dataURL = image_to_dataURL(image, image_format="JPEG")
-
-    context.services.events.emit_generator_progress(
-        context.graph_execution_state_id,
-        id,
-        {
-            "width": width,
-            "height": height,
-            "dataURL": dataURL
-        },
-        step,
-        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/backend/__init__.py

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

invokeai/backend/args.py

@@ -561,7 +561,7 @@ class Args(object):
             "--autoimport",
             default=None,
             type=str,
-            help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
+            help="(DEPRECATED - NONFUNCTIONAL). Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
         )
         model_group.add_argument(
             "--autoconvert",

invokeai/backend/config/model_install_backend.py

@@ -67,7 +67,6 @@ def install_requested_models(
     scan_directory: Path = None,
     external_models: List[str] = None,
     scan_at_startup: bool = False,
-    convert_to_diffusers: bool = False,
     precision: str = "float16",
     purge_deleted: bool = False,
     config_file_path: Path = None,
@@ -113,7 +112,6 @@ def install_requested_models(
             try:
                 model_manager.heuristic_import(
                     path_url_or_repo,
-                    convert=convert_to_diffusers,
                     commit_to_conf=config_file_path,
                 )
             except KeyboardInterrupt:
@@ -122,7 +120,7 @@ def install_requested_models(
                 pass
 
     if scan_at_startup and scan_directory.is_dir():
-        argument = "--autoconvert" if convert_to_diffusers else "--autoimport"
+        argument = "--autoconvert"
         initfile = Path(Globals.root, Globals.initfile)
         replacement = Path(Globals.root, f"{Globals.initfile}.new")
         directory = str(scan_directory).replace("\\", "/")

invokeai/backend/model_management/__init__.py

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

invokeai/backend/model_management/model_manager.py

@@ -1,4 +1,4 @@
-"""
+"""enum
 Manage a cache of Stable Diffusion model files for fast switching.
 They are moved between GPU and CPU as necessary. If CPU memory falls
 below a preset minimum, the least recently used model will be
@@ -15,7 +15,7 @@ import sys
 import textwrap
 import time
 import warnings
-from enum import Enum
+from enum import Enum, auto
 from pathlib import Path
 from shutil import move, rmtree
 from typing import Any, Optional, Union, Callable
@@ -24,8 +24,12 @@ import safetensors
 import safetensors.torch
 import torch
 import transformers
-from diffusers import AutoencoderKL
-from diffusers import logging as dlogging
+from diffusers import (
+    AutoencoderKL,
+    UNet2DConditionModel,
+    SchedulerMixin,
+    logging as dlogging,
+)    
 from huggingface_hub import scan_cache_dir
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
@@ -33,37 +37,58 @@ from picklescan.scanner import scan_file_path
 
 from invokeai.backend.globals import Globals, global_cache_dir
 
-from ..stable_diffusion import StableDiffusionGeneratorPipeline
+from transformers import (
+    CLIPTextModel,
+    CLIPTokenizer,
+    CLIPFeatureExtractor,
+)
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+    )
+from ..stable_diffusion import (
+    StableDiffusionGeneratorPipeline,
+)
 from ..util import CUDA_DEVICE, ask_user, download_with_resume
 
-class SDLegacyType(Enum):
-    V1 = 1
-    V1_INPAINT = 2
-    V2   = 3
-    V2_e = 4
-    V2_v = 5
-    UNKNOWN = 99
 
+class SDLegacyType(Enum):
+    V1 = auto()
+    V1_INPAINT = auto()
+    V2 = auto()
+    V2_e = auto()
+    V2_v = auto()
+    UNKNOWN = auto()
+
+class SDModelComponent(Enum):
+    vae="vae"
+    text_encoder="text_encoder"
+    tokenizer="tokenizer"
+    unet="unet"
+    scheduler="scheduler"
+    safety_checker="safety_checker"
+    feature_extractor="feature_extractor"
+    
 DEFAULT_MAX_MODELS = 2
 
 class ModelManager(object):
-    '''
+    """
     Model manager handles loading, caching, importing, deleting, converting, and editing models.
-    '''
+    """
+
     def __init__(
-            self,
-            config: OmegaConf|Path,
-            device_type: torch.device = CUDA_DEVICE,
-            precision: str = "float16",
-            max_loaded_models=DEFAULT_MAX_MODELS,
-            sequential_offload=False,
-            embedding_path: Path=None,
+        self,
+        config: OmegaConf | Path,
+        device_type: torch.device = CUDA_DEVICE,
+        precision: str = "float16",
+        max_loaded_models=DEFAULT_MAX_MODELS,
+        sequential_offload=False,
+        embedding_path: Path = None,
     ):
         """
         Initialize with the path to the models.yaml config file or
         an initialized OmegaConf dictionary. Optional parameters
         are the torch device type, precision, max_loaded_models,
-        and sequential_offload boolean. Note that the default device 
+        and sequential_offload boolean. Note that the default device
         type and precision are set up for a CUDA system running at half precision.
         """
         # prevent nasty-looking CLIP log message
@@ -87,15 +112,25 @@ class ModelManager(object):
         """
         return model_name in self.config
 
-    def get_model(self, model_name: str=None)->dict:
-        """
-        Given a model named identified in models.yaml, return
-        the model object. If in RAM will load into GPU VRAM.
-        If on disk, will load from there.
+    def get_model(self, model_name: str = None) -> dict:
+        """Given a model named identified in models.yaml, return a dict
+        containing the model object and some of its key features. If
+        in RAM will load into GPU VRAM.  If on disk, will load from
+        there.
+        The dict has the following keys:
+        'model': The StableDiffusionGeneratorPipeline object
+        'model_name': The name of the model in models.yaml
+        'width': The width of images trained by this model
+        'height': The height of images trained by this model
+        'hash': A unique hash of this model's files on disk.
         """
         if not model_name:
-            return self.get_model(self.current_model) if self.current_model else self.get_model(self.default_model())
-        
+            return (
+                self.get_model(self.current_model)
+                if self.current_model
+                else self.get_model(self.default_model())
+            )
+
         if not self.valid_model(model_name):
             print(
                 f'** "{model_name}" is not a known model name. Please check your models.yaml file'
@@ -135,6 +170,81 @@ class ModelManager(object):
             "hash": hash,
         }
 
+    def get_model_vae(self, model_name: str=None)->AutoencoderKL:
+        """Given a model name identified in models.yaml, load the model into
+        GPU if necessary and return its assigned VAE as an
+        AutoencoderKL object. If no model name is provided, return the
+        vae from the model currently in the GPU.
+        """
+        return self._get_sub_model(model_name, SDModelComponent.vae)
+                                   
+    def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer:
+        """Given a model name identified in models.yaml, load the model into
+        GPU if necessary and return its assigned CLIPTokenizer. If no
+        model name is provided, return the tokenizer from the model
+        currently in the GPU.
+        """
+        return self._get_sub_model(model_name, SDModelComponent.tokenizer)
+                                   
+    def get_model_unet(self, model_name: str=None)->UNet2DConditionModel:
+        """Given a model name identified in models.yaml, load the model into
+        GPU if necessary and return its assigned UNet2DConditionModel. If no model
+        name is provided, return the UNet from the model
+        currently in the GPU.  
+        """
+        return self._get_sub_model(model_name, SDModelComponent.unet)
+
+    def get_model_text_encoder(self, model_name: str=None)->CLIPTextModel:
+        """Given a model name identified in models.yaml, load the model into
+        GPU if necessary and return its assigned CLIPTextModel. If no
+        model name is provided, return the text encoder from the model
+        currently in the GPU.
+        """
+        return self._get_sub_model(model_name, SDModelComponent.text_encoder)
+
+    def get_model_feature_extractor(self, model_name: str=None)->CLIPFeatureExtractor:
+        """Given a model name identified in models.yaml, load the model into
+        GPU if necessary and return its assigned CLIPFeatureExtractor. If no
+        model name is provided, return the text encoder from the model
+        currently in the GPU.
+        """
+        return self._get_sub_model(model_name, SDModelComponent.feature_extractor)
+
+    def get_model_scheduler(self, model_name: str=None)->SchedulerMixin:
+        """Given a model name identified in models.yaml, load the model into
+        GPU if necessary and return its assigned scheduler. If no
+        model name is provided, return the text encoder from the model
+        currently in the GPU.
+        """
+        return self._get_sub_model(model_name, SDModelComponent.scheduler)
+    
+    def _get_sub_model(
+            self,
+            model_name: str=None,
+            model_part: SDModelComponent=SDModelComponent.vae,
+    ) -> Union[
+        AutoencoderKL,
+        CLIPTokenizer,
+        CLIPFeatureExtractor,
+        UNet2DConditionModel,
+        CLIPTextModel,
+        StableDiffusionSafetyChecker,
+    ]:
+        """Given a model name identified in models.yaml, and the part of the
+        model you wish to retrieve, return that part. Parts are in an Enum
+        class named SDModelComponent, and consist of:
+        SDModelComponent.vae
+        SDModelComponent.text_encoder
+        SDModelComponent.tokenizer
+        SDModelComponent.unet
+        SDModelComponent.scheduler
+        SDModelComponent.safety_checker
+        SDModelComponent.feature_extractor
+        """
+        model_dict = self.get_model(model_name)
+        model = model_dict["model"]
+        return getattr(model, model_part.value)
+
     def default_model(self) -> str | None:
         """
         Returns the name of the default model, or None
@@ -360,7 +470,7 @@ class ModelManager(object):
                 f"Unknown model format {model_name}: {model_format}"
             )
         self._add_embeddings_to_model(model)
-        
+
         # usage statistics
         toc = time.time()
         print(">> Model loaded in", "%4.2fs" % (toc - tic))
@@ -433,7 +543,7 @@ class ModelManager(object):
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
         print(f"   | Default image dimensions = {width} x {height}")
-        
+
         return pipeline, width, height, model_hash
 
     def _load_ckpt_model(self, model_name, mconfig):
@@ -454,14 +564,18 @@ class ModelManager(object):
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
         try:
-            if self.list_models()[self.current_model]['status'] == 'active':
+            if self.list_models()[self.current_model]["status"] == "active":
                 self.offload_model(self.current_model)
         except Exception as e:
             pass
-        
+
         vae_path = None
         if vae:
-            vae_path = vae if os.path.isabs(vae) else os.path.normpath(os.path.join(Globals.root, vae))
+            vae_path = (
+                vae
+                if os.path.isabs(vae)
+                else os.path.normpath(os.path.join(Globals.root, vae))
+            )
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
@@ -571,9 +685,7 @@ class ModelManager(object):
         models.yaml file.
         """
         model_name = model_name or Path(repo_or_path).stem
-        model_description = (
-            description or f"Imported diffusers model {model_name}"
-        )
+        model_description = description or f"Imported diffusers model {model_name}"
         new_config = dict(
             description=model_description,
             vae=vae,
@@ -602,7 +714,7 @@ class ModelManager(object):
         SDLegacyType.V2_v   (V2 using 'v_prediction' prediction type)
         SDLegacyType.UNKNOWN
         """
-        global_step = checkpoint.get('global_step')
+        global_step = checkpoint.get("global_step")
         state_dict = checkpoint.get("state_dict") or checkpoint
 
         try:
@@ -628,13 +740,13 @@ class ModelManager(object):
             return SDLegacyType.UNKNOWN
 
     def heuristic_import(
-            self,
-            path_url_or_repo: str,
-            model_name: str = None,
-            description: str = None,
-            model_config_file: Path = None,
-            commit_to_conf: Path = None,
-            config_file_callback: Callable[[Path], Path] = None,
+        self,
+        path_url_or_repo: str,
+        model_name: str = None,
+        description: str = None,
+        model_config_file: Path = None,
+        commit_to_conf: Path = None,
+        config_file_callback: Callable[[Path], Path] = None,
     ) -> str:
         """Accept a string which could be:
            - a HF diffusers repo_id
@@ -738,8 +850,8 @@ class ModelManager(object):
 
         # another round of heuristics to guess the correct config file.
         checkpoint = None
-        if model_path.suffix in [".ckpt",".pt"]:
-            self.scan_model(model_path,model_path)
+        if model_path.suffix in [".ckpt", ".pt"]:
+            self.scan_model(model_path, model_path)
             checkpoint = torch.load(model_path)
         else:
             checkpoint = safetensors.torch.load_file(model_path)
@@ -761,19 +873,16 @@ class ModelManager(object):
                 elif model_type == SDLegacyType.V1_INPAINT:
                     print("   | SD-v1 inpainting model detected")
                     model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml"
+                        Globals.root,
+                        "configs/stable-diffusion/v1-inpainting-inference.yaml",
                     )
                 elif model_type == SDLegacyType.V2_v:
-                    print(
-                        "   | SD-v2-v model detected"
-                    )
+                    print("   | SD-v2-v model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
                     )
                 elif model_type == SDLegacyType.V2_e:
-                    print(
-                        "   | SD-v2-e model detected"
-                    )
+                    print("   | SD-v2-e model detected")
                     model_config_file = Path(
                         Globals.root, "configs/stable-diffusion/v2-inference.yaml"
                     )
@@ -820,16 +929,16 @@ class ModelManager(object):
         return model_name
 
     def convert_and_import(
-            self,
-            ckpt_path: Path,
-            diffusers_path: Path,
-            model_name=None,
-            model_description=None,
-            vae:dict=None,
-            vae_path:Path=None,
-            original_config_file: Path = None,
-            commit_to_conf: Path = None,
-            scan_needed: bool=True,
+        self,
+        ckpt_path: Path,
+        diffusers_path: Path,
+        model_name=None,
+        model_description=None,
+        vae: dict = None,
+        vae_path: Path = None,
+        original_config_file: Path = None,
+        commit_to_conf: Path = None,
+        scan_needed: bool = True,
     ) -> str:
         """
         Convert a legacy ckpt weights file to diffuser model and import
@@ -857,10 +966,10 @@ class ModelManager(object):
         try:
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
-            vae_model=None
+            vae_model = None
             if vae:
-                vae_model=self._load_vae(vae)
-                vae_path=None
+                vae_model = self._load_vae(vae)
+                vae_path = None
             convert_ckpt_to_diffusers(
                 ckpt_path,
                 diffusers_path,
@@ -976,16 +1085,16 @@ class ModelManager(object):
         legacy_locations = [
             Path(
                 models_dir,
-                "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker"
+                "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker",
             ),
             Path(models_dir, "bert-base-uncased/models--bert-base-uncased"),
             Path(
                 models_dir,
-                "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14"
+                "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14",
             ),
         ]
-        legacy_locations.extend(list(global_cache_dir("diffusers").glob('*')))
-        
+        legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
+
         legacy_layout = False
         for model in legacy_locations:
             legacy_layout = legacy_layout or model.exists()
@@ -1003,7 +1112,7 @@ class ModelManager(object):
 >> make adjustments, please press ctrl-C now to abort and relaunch InvokeAI when you are ready.
 >> Otherwise press <enter> to continue."""
         )
-        input('continue> ')
+        input("continue> ")
 
         # transformer files get moved into the hub directory
         if cls._is_huggingface_hub_directory_present():
@@ -1090,12 +1199,12 @@ class ModelManager(object):
             print(
                 f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
             )
-            
+
     def _has_cuda(self) -> bool:
         return self.device.type == "cuda"
 
     def _diffuser_sha256(
-        self, name_or_path: Union[str, Path], chunksize=4096
+        self, name_or_path: Union[str, Path], chunksize=16777216
     ) -> Union[str, bytes]:
         path = None
         if isinstance(name_or_path, Path):

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
-        else:
+        elif Globals.internet_available is True:
             try:
                 models = self.hf_api.list_models(
                     filter=ModelFilter(model_name="sd-concepts-library/")
@@ -73,6 +73,8 @@ 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

@@ -445,8 +445,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @property
     def _submodels(self) -> Sequence[torch.nn.Module]:
         module_names, _, _ = self.extract_init_dict(dict(self.config))
-        values = [getattr(self, name) for name in module_names.keys()]
-        return [m for m in values if isinstance(m, torch.nn.Module)]
+        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
 
     def image_from_embeddings(
         self,
@@ -544,7 +551,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             yield PipelineIntermediateState(
                 run_id=run_id,
                 step=-1,
-                timestep=self.scheduler.num_train_timesteps,
+                timestep=self.scheduler.config.num_train_timesteps,
                 latents=latents,
             )
 
@@ -915,7 +922,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @property
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""
-        return self.unet.in_channels
+        return self.unet.config.in_channels
 
     def decode_latents(self, latents):
         # Explicit call to get the vae loaded, since `decode` isn't the forward method.

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -10,8 +10,7 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
-from diffusers.models.cross_attention import AttnProcessor
-from diffusers.models.unet_2d_condition import UNet2DConditionModel
+from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
 from ...util import torch_dtype
@@ -188,7 +187,7 @@ class Context:
 
 class InvokeAICrossAttentionMixin:
     """
-    Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
+    Enable InvokeAI-flavoured Attention 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.
     """
@@ -209,7 +208,7 @@ 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 CrossAttention module for which the callback is being invoked.
+            `module` is the current Attention 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.
@@ -345,11 +344,11 @@ class InvokeAICrossAttentionMixin:
 def restore_default_cross_attention(
     model,
     is_running_diffusers: bool,
-    restore_attention_processor: Optional[AttnProcessor] = None,
+    restore_attention_processor: Optional[AttentionProcessor] = None,
 ):
     if is_running_diffusers:
         unet = model
-        unet.set_attn_processor(restore_attention_processor or CrossAttnProcessor())
+        unet.set_attn_processor(restore_attention_processor or AttnProcessor())
     else:
         remove_attention_function(model)
 
@@ -408,12 +407,9 @@ 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 = [
@@ -428,10 +424,10 @@ 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 "
-            + f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
+            + "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 "
-            + 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."
+            + "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."
         )
     return attention_module_tuples
 
@@ -550,7 +546,7 @@ def get_mem_free_total(device):
 
 
 class InvokeAIDiffusersCrossAttention(
-    diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin
+    diffusers.models.attention.Attention, InvokeAICrossAttentionMixin
 ):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -572,8 +568,8 @@ class InvokeAIDiffusersCrossAttention(
 """
 # base implementation
 
-class CrossAttnProcessor:
-    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
+class AttnProcessor:
+    def __call__(self, attn: Attention, 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)
 
@@ -601,9 +597,9 @@ class CrossAttnProcessor:
 from dataclasses import dataclass, field
 
 import torch
-from diffusers.models.cross_attention import (
-    CrossAttention,
-    CrossAttnProcessor,
+from diffusers.models.attention_processor import (
+    Attention,
+    AttnProcessor,
     SlicedAttnProcessor,
 )
 
@@ -653,7 +649,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 
     def __call__(
         self,
-        attn: CrossAttention,
+        attn: Attention,
         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.cross_attention import AttnProcessor
+from diffusers.models.attention_processor import AttentionProcessor
 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, AttnProcessor]:
+    ) -> Dict[str, AttentionProcessor]:
         """
         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["AttnProcessor"] = None
+        self, restore_attention_processor: Optional["AttentionProcessor"] = None
     ):
         self.conditioning = None
         self.cross_attention_control_context = None
@@ -262,7 +262,7 @@ class InvokeAIDiffuserComponent:
             # TODO remove when compvis codepath support is dropped
             if step_index is None and sigma is None:
                 raise ValueError(
-                    f"Either step_index or sigma is required when doing cross attention control, but both are None."
+                    "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
@@ -599,7 +599,6 @@ 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/CLI/CLI.py

@@ -158,14 +158,9 @@ def main():
         report_model_error(opt, e)
 
     # try to autoconvert new models
-    if path := opt.autoimport:
-        gen.model_manager.heuristic_import(
-            str(path), convert=False, commit_to_conf=opt.conf
-        )
-
     if path := opt.autoconvert:
         gen.model_manager.heuristic_import(
-            str(path), convert=True, commit_to_conf=opt.conf
+            str(path), commit_to_conf=opt.conf
         )
 
     # web server loops forever
@@ -581,6 +576,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
 
     elif command.startswith("!replay"):
         file_path = command.replace("!replay", "", 1).strip()
+        file_path = os.path.join(opt.outdir, file_path)
         if infile is None and os.path.isfile(file_path):
             infile = open(file_path, "r", encoding="utf-8")
         completer.add_history(command)

invokeai/frontend/install/invokeai_update.py

@@ -1,10 +1,9 @@
-"""
+'''
 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
@@ -16,8 +15,10 @@ from rich.text import Text
 
 from invokeai.version import __version__
 
-INVOKE_AI_SRC = "https://github.com/invoke-ai/InvokeAI/archive"
-INVOKE_AI_REL = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+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"
 
 OS = platform.uname().system
 ARCH = platform.uname().machine
@@ -28,22 +29,22 @@ 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 tag or branch name you wish to update"""
+[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'''        
 
     console.rule()
     print(
@@ -59,33 +60,41 @@ def welcome(versions: dict):
     )
     console.line()
 
-
 def main():
     versions = get_versions()
     welcome(versions)
 
     tag = None
-    choice = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
+    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')
 
-    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")
+    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'
     else:
-        print(f":exclamation: [bold red]Upgrade failed[/red bold]")
-
-
+        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]')
+    
 if __name__ == "__main__":
     try:
         main()
     except KeyboardInterrupt:
         pass
+

invokeai/frontend/install/model_install.py

@@ -199,17 +199,6 @@ class addModelsForm(npyscreen.FormMultiPage):
             relx=4,
             scroll_exit=True,
         )
-        self.nextrely += 1
-        self.convert_models = self.add_widget_intelligent(
-            npyscreen.TitleSelectOne,
-            name="== CONVERT IMPORTED MODELS INTO DIFFUSERS==",
-            values=["Keep original format", "Convert to diffusers"],
-            value=0,
-            begin_entry_at=4,
-            max_height=4,
-            hidden=True,  # will appear when imported models box is edited
-            scroll_exit=True,
-        )
         self.cancel = self.add_widget_intelligent(
             npyscreen.ButtonPress,
             name="CANCEL",
@@ -244,8 +233,6 @@ class addModelsForm(npyscreen.FormMultiPage):
             self.show_directory_fields.addVisibleWhenSelected(i)
 
         self.show_directory_fields.when_value_edited = self._clear_scan_directory
-        self.import_model_paths.when_value_edited = self._show_hide_convert
-        self.autoload_directory.when_value_edited = self._show_hide_convert
 
     def resize(self):
         super().resize()
@@ -256,13 +243,6 @@ class addModelsForm(npyscreen.FormMultiPage):
         if not self.show_directory_fields.value:
             self.autoload_directory.value = ""
 
-    def _show_hide_convert(self):
-        model_paths = self.import_model_paths.value or ""
-        autoload_directory = self.autoload_directory.value or ""
-        self.convert_models.hidden = (
-            len(model_paths) == 0 and len(autoload_directory) == 0
-        )
-
     def _get_starter_model_labels(self) -> List[str]:
         window_width, window_height = get_terminal_size()
         label_width = 25
@@ -322,7 +302,6 @@ class addModelsForm(npyscreen.FormMultiPage):
         .scan_directory: Path to a directory of models to scan and import
         .autoscan_on_startup:  True if invokeai should scan and import at startup time
         .import_model_paths:   list of URLs, repo_ids and file paths to import
-        .convert_to_diffusers: if True, convert legacy checkpoints into diffusers
         """
         # we're using a global here rather than storing the result in the parentapp
         # due to some bug in npyscreen that is causing attributes to be lost
@@ -359,7 +338,6 @@ class addModelsForm(npyscreen.FormMultiPage):
 
         # URLs and the like
         selections.import_model_paths = self.import_model_paths.value.split()
-        selections.convert_to_diffusers = self.convert_models.value[0] == 1
 
 
 class AddModelApplication(npyscreen.NPSAppManaged):
@@ -372,7 +350,6 @@ class AddModelApplication(npyscreen.NPSAppManaged):
             scan_directory=None,
             autoscan_on_startup=None,
             import_model_paths=None,
-            convert_to_diffusers=None,
         )
 
     def onStart(self):
@@ -393,7 +370,6 @@ def process_and_execute(opt: Namespace, selections: Namespace):
     directory_to_scan = selections.scan_directory
     scan_at_startup = selections.autoscan_on_startup
     potential_models_to_install = selections.import_model_paths
-    convert_to_diffusers = selections.convert_to_diffusers
 
     install_requested_models(
         install_initial_models=models_to_install,
@@ -401,7 +377,6 @@ def process_and_execute(opt: Namespace, selections: Namespace):
         scan_directory=Path(directory_to_scan) if directory_to_scan else None,
         external_models=potential_models_to_install,
         scan_at_startup=scan_at_startup,
-        convert_to_diffusers=convert_to_diffusers,
         precision="float32"
         if opt.full_precision
         else choose_precision(torch.device(choose_torch_device())),

invokeai/frontend/web/.eslintignore

@@ -6,3 +6,5 @@ stats.html
 index.html
 .yarn/
 *.scss
+src/services/api/
+src/services/fixtures/*

invokeai/frontend/web/.prettierignore

@@ -3,4 +3,8 @@ dist/
 node_modules/
 patches/
 stats.html
+index.html
 .yarn/
+*.scss
+src/services/api/
+src/services/fixtures/*

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-7791bd91.js

@@ -1,4 +1,4 @@
-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=`
+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=`
   :root {
     --chakra-vh: 100vh;
   }

invokeai/frontend/web/dist/index.html

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

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

@@ -8,7 +8,6 @@
         "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

@@ -7,7 +7,6 @@
         "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

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

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

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

@@ -8,7 +8,6 @@
         "darkTheme": "Sombre",
         "lightTheme": "Clair",
         "greenTheme": "Vert",
-        "text2img": "Texte en image",
         "img2img": "Image en image",
         "unifiedCanvas": "Canvas unifié",
         "nodes": "Nœuds",
@@ -47,7 +46,19 @@
         "statusLoadingModel": "Chargement du modèle",
         "statusModelChanged": "Modèle changé",
         "discordLabel": "Discord",
-        "githubLabel": "Github"
+        "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"
     },
     "gallery": {
         "generations": "Générations",
@@ -518,5 +529,15 @@
         "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

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

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

@@ -8,7 +8,6 @@
         "darkTheme": "Scuro",
         "lightTheme": "Chiaro",
         "greenTheme": "Verde",
-        "text2img": "Testo a Immagine",
         "img2img": "Immagine a Immagine",
         "unifiedCanvas": "Tela unificata",
         "nodes": "Nodi",
@@ -70,7 +69,11 @@
         "loading": "Caricamento in corso",
         "oceanTheme": "Oceano",
         "langHebrew": "Ebraico",
-        "loadingInvokeAI": "Caricamento Invoke AI"
+        "loadingInvokeAI": "Caricamento Invoke AI",
+        "postprocessing": "Post Elaborazione",
+        "txt2img": "Testo a Immagine",
+        "accept": "Accetta",
+        "cancel": "Annulla"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -404,7 +407,8 @@
         "v2_768": "v2 (768px)",
         "none": "niente",
         "addDifference": "Aggiungi differenza",
-        "pickModelType": "Scegli il tipo di modello"
+        "pickModelType": "Scegli il tipo di modello",
+        "scanForModels": "Cerca modelli"
     },
     "parameters": {
         "images": "Immagini",
@@ -574,7 +578,7 @@
         "autoSaveToGallery": "Salvataggio automatico nella Galleria",
         "saveBoxRegionOnly": "Salva solo l'area di selezione",
         "limitStrokesToBox": "Limita i tratti all'area di selezione",
-        "showCanvasDebugInfo": "Mostra informazioni di debug della Tela",
+        "showCanvasDebugInfo": "Mostra ulteriori informazioni sulla 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.",
@@ -612,7 +616,7 @@
         "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

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

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

@@ -8,7 +8,6 @@
         "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

@@ -8,7 +8,6 @@
         "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

@@ -20,7 +20,6 @@
         "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

@@ -8,7 +8,6 @@
         "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

@@ -8,7 +8,6 @@
         "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

@@ -8,7 +8,6 @@
         "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

@@ -8,7 +8,6 @@
         "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

@@ -33,7 +33,6 @@
         "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/docs/API_CLIENT.md

@@ -0,0 +1,87 @@
+# Generated axios API client
+
+- [Generated axios API client](#generated-axios-api-client)
+  - [Generation](#generation)
+    - [Generate the API client from the nodes web server](#generate-the-api-client-from-the-nodes-web-server)
+    - [Generate the API client from JSON](#generate-the-api-client-from-json)
+      - [Getting the JSON from the nodes web server](#getting-the-json-from-the-nodes-web-server)
+      - [Getting the JSON with a python script](#getting-the-json-with-a-python-script)
+      - [Generate the API client](#generate-the-api-client)
+  - [The generated client](#the-generated-client)
+  - [API client customisation](#api-client-customisation)
+
+This API client is generated by an [openapi code generator](https://github.com/ferdikoomen/openapi-typescript-codegen).
+
+All files in `invokeai/frontend/web/src/services/api/` are made by the generator.
+
+## Generation
+
+The axios client may be generated by from the OpenAPI schema from the nodes web server, or from JSON.
+
+### Generate the API client from the nodes web server
+
+We need to start the nodes web server, which serves the OpenAPI schema to the generator.
+
+1. Start the nodes web server.
+
+```bash
+# from the repo root
+python scripts/invoke-new.py --web
+```
+
+2. Generate the API client.
+
+```bash
+# from invokeai/frontend/web/
+yarn api:web
+```
+
+### Generate the API client from JSON
+
+The JSON can be acquired from the nodes web server, or with a python script.
+
+#### Getting the JSON from the nodes web server
+
+Start the nodes web server as described above, then download the file.
+
+```bash
+# from invokeai/frontend/web/
+curl http://localhost:9090/openapi.json -o openapi.json
+```
+
+#### Getting the JSON with a python script
+
+Run this python script from the repo root, so it can access the nodes server modules.
+
+The script will output `openapi.json` in the repo root. Then we need to move it to `invokeai/frontend/web/`.
+
+```bash
+# from the repo root
+python invokeai/app/util/generate_openapi_json.py
+mv invokeai/app/util/openapi.json invokeai/frontend/web/services/fixtures/
+```
+
+#### Generate the API client
+
+Now we can generate the API client from the JSON.
+
+```bash
+# from invokeai/frontend/web/
+yarn api:file
+```
+
+## The generated client
+
+The client will be written to `invokeai/frontend/web/services/api/`:
+
+- `axios` client
+- TS types
+- An easily parseable schema, which we can use to generate UI
+
+## API client customisation
+
+The generator has a default `request.ts` file that implements a base `axios` client. The generated client uses this base client.
+
+One shortcoming of this is base client is it does not provide response headers unless the response body is empty. To fix this, we provide our own lightly-patched `request.ts`.
+
+To access the headers, call `getHeaders(response)` on any response from the generated api client. This function is exported from `invokeai/frontend/web/src/services/util/getHeaders.ts`.

invokeai/frontend/web/docs/EVENTS.md

@@ -0,0 +1,21 @@
+# Events
+
+Events via `socket.io`
+
+## `actions.ts`
+
+Redux actions for all socket events. Payloads all include a timestamp, and optionally some other data.
+
+Any reducer (or middleware) can respond to the actions.
+
+## `middleware.ts`
+
+Redux middleware for events.
+
+Handles dispatching the event actions. Only put logic here if it can't really go anywhere else.
+
+For example, on connect we want to load images to the gallery if it's not populated. This requires dispatching a thunk, so we need to directly dispatch this in the middleware.
+
+## `types.ts`
+
+Hand-written types for the socket events. Cannot generate these from the server, but fortunately they are few and simple.

invokeai/frontend/web/docs/NODE_EDITOR.md

@@ -0,0 +1,17 @@
+# Node Editor Design
+
+WIP
+
+nodes
+
+everything in `src/features/nodes/`
+
+have a look at `state.nodes.invocation`
+
+- on socket connect, if no schema saved, fetch `localhost:9090/openapi.json`, save JSON to `state.nodes.schema`
+- on fulfilled schema fetch, `parseSchema()` the schema. this outputs a `Record<string, Invocation>` which is saved to `state.nodes.invocations` - `Invocation` is like a template for the node
+- when you add a node, the the `Invocation` template is passed to `InvocationComponent.tsx` to build the UI component for that node
+- inputs/outputs have field types - and each field type gets an `FieldComponent` which includes a dispatcher to write state changes to redux `nodesSlice`
+- `reactflow` sends changes to nodes/edges to redux
+- to invoke, `buildNodesGraph()` state, then send this
+- changed onClick Invoke button actions to build the schema, then when schema builds it dispatches the actual network request to create the session - see `session.ts`

invokeai/frontend/web/docs/PACKAGE_SCRIPTS.md

@@ -0,0 +1,29 @@
+# Package Scripts
+
+WIP walkthrough of `package.json` scripts.
+
+## `theme` & `theme:watch`
+
+These run the Chakra CLI to generate types for the theme, or watch for code change and re-generate the types.
+
+The CLI essentially monkeypatches Chakra's files in `node_modules`.
+
+## `postinstall`
+
+The `postinstall` script patches a few packages and runs the Chakra CLI to generate types for the theme.
+
+### Patch `@chakra-ui/cli`
+
+See: <https://github.com/chakra-ui/chakra-ui/issues/7394>
+
+### Patch `redux-persist`
+
+We want to persist the canvas state to `localStorage` but many canvas operations change data very quickly, so we need to debounce the writes to `localStorage`.
+
+`redux-persist` is unfortunately unmaintained. The repo's current code is nonfunctional, but the last release's code depends on a package that was removed from `npm` for being malware, so we cannot just fork it.
+
+So, we have to patch it directly. Perhaps a better way would be to write a debounced storage adapter, but I couldn't figure out how to do that.
+
+### Patch `redux-deep-persist`
+
+This package makes blacklisting and whitelisting persist configs very simple, but we have to patch it to match `redux-persist` for the types to work.

invokeai/frontend/web/docs/README.md

@@ -1,10 +1,16 @@
 # InvokeAI Web UI
 
+- [InvokeAI Web UI](#invokeai-web-ui)
+  - [Stack](#stack)
+  - [Contributing](#contributing)
+    - [Dev Environment](#dev-environment)
+    - [Production builds](#production-builds)
+
 The UI is a fairly straightforward Typescript React app. The only really fancy stuff is the Unified Canvas.
 
 Code in `invokeai/frontend/web/` if you want to have a look.
 
-## Details
+## Stack
 
 State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a custom redux middleware to help).
 
@@ -32,7 +38,7 @@ Start everything in dev mode:
 
 1. Start the dev server: `yarn dev`
 2. Start the InvokeAI UI per usual: `invokeai --web`
-3. Point your browser to the dev server address e.g. `http://localhost:5173/`
+3. Point your browser to the dev server address e.g. <http://localhost:5173/>
 
 ### Production builds
 

invokeai/frontend/web/index.d.ts

@@ -1,6 +1,7 @@
 import React, { PropsWithChildren } from 'react';
 import { IAIPopoverProps } from '../web/src/common/components/IAIPopover';
 import { IAIIconButtonProps } from '../web/src/common/components/IAIIconButton';
+import { InvokeTabName } from 'features/ui/store/tabMap';
 
 export {};
 
@@ -64,9 +65,26 @@ declare module '@invoke-ai/invoke-ai-ui' {
   declare class SettingsModal extends React.Component<SettingsModalProps> {
     public constructor(props: SettingsModalProps);
   }
+
+  declare class StatusIndicator extends React.Component<StatusIndicatorProps> {
+    public constructor(props: StatusIndicatorProps);
+  }
+
+  declare class ModelSelect extends React.Component<ModelSelectProps> {
+    public constructor(props: ModelSelectProps);
+  }
 }
 
-declare function Invoke(props: PropsWithChildren): JSX.Element;
+interface InvokeProps extends PropsWithChildren {
+  apiUrl?: string;
+  disabledPanels?: string[];
+  disabledTabs?: InvokeTabName[];
+  token?: string;
+  shouldTransformUrls?: boolean;
+  shouldFetchImages?: boolean;
+}
+
+declare function Invoke(props: InvokeProps): JSX.Element;
 
 export {
   ThemeChanger,
@@ -74,5 +92,7 @@ export {
   IAIPopover,
   IAIIconButton,
   SettingsModal,
+  StatusIndicator,
+  ModelSelect,
 };
 export = Invoke;

invokeai/frontend/web/package.json

@@ -5,7 +5,11 @@
   "scripts": {
     "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",
     "preview": "vite preview",
     "lint:madge": "madge --circular src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
@@ -41,13 +45,16 @@
     "@chakra-ui/react": "^2.5.1",
     "@chakra-ui/styled-system": "^2.6.1",
     "@chakra-ui/theme-tools": "^2.0.16",
+    "@dagrejs/graphlib": "^2.1.12",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
-    "@reduxjs/toolkit": "^1.9.2",
+    "@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",
@@ -67,15 +74,17 @@
     "react-redux": "^8.0.5",
     "react-transition-group": "^4.4.5",
     "react-zoom-pan-pinch": "^2.6.1",
+    "reactflow": "^11.7.0",
     "redux-deep-persist": "^1.0.7",
+    "redux-dynamic-middlewares": "^2.2.0",
     "redux-persist": "^6.0.0",
     "socket.io-client": "^4.6.0",
     "use-image": "^1.1.0",
     "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",
@@ -83,6 +92,7 @@
     "@typescript-eslint/eslint-plugin": "^5.52.0",
     "@typescript-eslint/parser": "^5.52.0",
     "@vitejs/plugin-react-swc": "^3.2.0",
+    "axios": "^1.3.4",
     "babel-plugin-transform-imports": "^2.0.0",
     "concurrently": "^7.6.0",
     "eslint": "^8.34.0",
@@ -90,13 +100,17 @@
     "eslint-plugin-prettier": "^4.2.1",
     "eslint-plugin-react": "^7.32.2",
     "eslint-plugin-react-hooks": "^4.6.0",
+    "form-data": "^4.0.0",
     "husky": "^8.0.3",
     "lint-staged": "^13.1.2",
     "madge": "^6.0.0",
+    "openapi-types": "^12.1.0",
+    "openapi-typescript-codegen": "^0.23.0",
     "postinstall-postinstall": "^2.1.0",
     "prettier": "^2.8.4",
     "rollup-plugin-visualizer": "^5.9.0",
     "terser": "^5.16.4",
+    "typescript": "4.9.5",
     "vite": "^4.1.2",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.0.5",

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": "Upload",
+        "upload": "Hochladen",
         "close": "Schließen",
         "load": "Laden",
         "statusConnected": "Verbunden",
@@ -41,12 +41,34 @@
         "statusUpscaling": "Hochskalierung",
         "statusUpscalingESRGAN": "Hochskalierung (ESRGAN)",
         "statusLoadingModel": "Laden des Modells",
-        "statusModelChanged": "Modell Geändert"
+        "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"
     },
     "gallery": {
         "generations": "Erzeugungen",
         "showGenerations": "Zeige Erzeugnisse",
-        "uploads": "Uploads",
+        "uploads": "Hochgelades",
         "showUploads": "Zeige Uploads",
         "galleryImageSize": "Bildgröße",
         "galleryImageResetSize": "Größe zurücksetzen",
@@ -312,7 +334,11 @@
         "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."
+        "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"
     },
     "parameters": {
         "images": "Bilder",
@@ -370,7 +396,10 @@
         "useInitImg": "Ausgangsbild verwenden",
         "deleteImage": "Bild löschen",
         "initialImage": "Ursprüngliches Bild",
-        "showOptionsPanel": "Optionsleiste zeigen"
+        "showOptionsPanel": "Optionsleiste zeigen",
+        "cancel": {
+            "setType": "Abbruchart festlegen"
+        }
     },
     "settings": {
         "displayInProgress": "Bilder in Bearbeitung anzeigen",
@@ -489,5 +518,25 @@
         "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": "ExitViewer",
+        "exitViewer": "Exit Viewer",
         "zoomIn": "Zoom In",
         "zoomOut": "Zoom Out",
         "rotateCounterClockwise": "Rotate Counter-Clockwise",
@@ -19,7 +19,8 @@
         "toggleAutoscroll": "Toggle autoscroll",
         "toggleLogViewer": "Toggle Log Viewer",
         "showGallery": "Show Gallery",
-        "showOptionsPanel": "Show Options Panel"
+        "showOptionsPanel": "Show Options Panel",
+        "menu": "Menu"
     },
     "common": {
         "hotkeysLabel": "Hotkeys",
@@ -52,6 +53,7 @@
         "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.",
@@ -505,7 +507,9 @@
         "info": "Info",
         "deleteImage": "Delete Image",
         "initialImage": "Initial Image",
-        "showOptionsPanel": "Show Options Panel"
+        "showOptionsPanel": "Show Options Panel",
+        "hidePreview": "Hide Preview",
+        "showPreview": "Show Preview"
     },
     "settings": {
         "models": "Models",
@@ -522,6 +526,10 @@
         "resetComplete": "Web UI has been reset. Refresh the page to reload."
     },
     "toast": {
+        "serverError": "Server Error",
+        "disconnected": "Disconnected from Server",
+        "connected": "Connected to Server",
+        "canceled": "Processing Canceled",
         "tempFoldersEmptied": "Temp Folder Emptied",
         "uploadFailed": "Upload failed",
         "uploadFailedMultipleImagesDesc": "Multiple images pasted, may only upload one image at a time",

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

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

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

@@ -0,0 +1,122 @@
+{
+    "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

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

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

@@ -37,7 +37,43 @@
         "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"
+        "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"
     },
     "gallery": {
         "uploads": "アップロード",
@@ -46,11 +82,14 @@
         "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": "シングルカラムレイアウト",
+        "singleColumnLayout": "1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"
+        "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"
     },
     "hotkeys": {
         "keyboardShortcuts": "キーボードショートカット",
@@ -59,14 +98,16 @@
         "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"
+            "desc": "画像を生成",
+            "title": "Invoke"
         },
         "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"
+            "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"
         },
         "toggleOptions": {
             "title": "オプションパネルのトグル",
@@ -410,5 +451,27 @@
         "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

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

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

@@ -62,7 +62,18 @@
         "statusConvertingModel": "Omzetten van model",
         "statusModelConverted": "Model omgezet",
         "statusMergingModels": "Samenvoegen van modellen",
-        "statusMergedModels": "Modellen samengevoegd"
+        "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"
     },
     "gallery": {
         "generations": "Gegenereerde afbeeldingen",
@@ -301,7 +312,7 @@
         "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",
@@ -391,7 +402,13 @@
         "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"
+        "weightedSum": "Gewogen som",
+        "v2_base": "v2 (512px)",
+        "v2_768": "v2 (768px)",
+        "none": "geen",
+        "addDifference": "Voeg verschil toe",
+        "scanForModels": "Scan naar modellen",
+        "pickModelType": "Kies modelsoort"
     },
     "parameters": {
         "images": "Afbeeldingen",
@@ -561,7 +578,7 @@
         "autoSaveToGallery": "Bewaar automatisch naar galerij",
         "saveBoxRegionOnly": "Bewaar alleen tekengebied",
         "limitStrokesToBox": "Beperk streken tot tekenvak",
-        "showCanvasDebugInfo": "Toon foutopsporingsgegevens canvas",
+        "showCanvasDebugInfo": "Toon aanvullende canvasgegevens",
         "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.",
@@ -587,5 +604,27 @@
         "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": "Универсальный холст",
+        "unifiedCanvas": "Единый холст",
         "nodes": "Ноды",
         "langRussian": "Русский",
         "nodesDesc": "Cистема генерации изображений на основе нодов (узлов) уже разрабатывается. Следите за новостями об этой замечательной функции.",
@@ -53,7 +53,28 @@
         "loading": "Загрузка",
         "loadingInvokeAI": "Загрузка Invoke AI",
         "back": "Назад",
-        "statusConvertingModel": "Конвертация модели"
+        "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"
     },
     "gallery": {
         "generations": "Генерации",
@@ -72,11 +93,11 @@
         "noImagesInGallery": "Изображений нет"
     },
     "hotkeys": {
-        "keyboardShortcuts": "Клавиатурные сокращения",
+        "keyboardShortcuts": "Горячие клавиши",
         "appHotkeys": "Горячие клавиши приложения",
         "generalHotkeys": "Общие горячие клавиши",
         "galleryHotkeys": "Горячие клавиши галереи",
-        "unifiedCanvasHotkeys": "Горячие клавиши универсального холста",
+        "unifiedCanvasHotkeys": "Горячие клавиши Единого холста",
         "invoke": {
             "title": "Invoke",
             "desc": "Сгенерировать изображение"
@@ -266,12 +287,12 @@
             "desc": "Сбросить вид холста"
         },
         "previousStagingImage": {
-            "title": "Previous Staging Image",
-            "desc": "Предыдущее изображение"
+            "title": "Предыдущее изображение",
+            "desc": "Предыдущая область изображения"
         },
         "nextStagingImage": {
-            "title": "Next Staging Image",
-            "desc": "Следующее изображение"
+            "title": "Следующее изображение",
+            "desc": "Следующая область изображения"
         },
         "acceptStagingImage": {
             "title": "Принять изображение",
@@ -353,7 +374,42 @@
         "modelConverted": "Модель преобразована",
         "invokeRoot": "Каталог InvokeAI",
         "modelsMerged": "Модели объединены",
-        "mergeModels": "Объединить модели"
+        "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)"
     },
     "parameters": {
         "images": "Изображения",
@@ -380,7 +436,7 @@
         "scale": "Масштаб",
         "otherOptions": "Другие параметры",
         "seamlessTiling": "Бесшовный узор",
-        "hiresOptim": "Высокое разрешение",
+        "hiresOptim": "Оптимизация High Res",
         "imageFit": "Уместить изображение",
         "codeformerFidelity": "Точность",
         "seamSize": "Размер шва",
@@ -397,11 +453,11 @@
         "infillScalingHeader": "Заполнение и масштабирование",
         "img2imgStrength": "Сила обработки img2img",
         "toggleLoopback": "Зациклить обработку",
-        "invoke": "Вызвать",
+        "invoke": "Invoke",
         "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 холст",
+        "sendToUnifiedCanvas": "Отправить на Единый холст",
         "copyImageToLink": "Скопировать ссылку",
         "downloadImage": "Скачать",
         "openInViewer": "Открыть в просмотрщике",
@@ -413,7 +469,24 @@
         "info": "Метаданные",
         "deleteImage": "Удалить изображение",
         "initialImage": "Исходное изображение",
-        "showOptionsPanel": "Показать панель настроек"
+        "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"
     },
     "settings": {
         "models": "Модели",
@@ -423,10 +496,11 @@
         "displayHelpIcons": "Показывать значки подсказок",
         "useCanvasBeta": "Показывать инструменты слева (Beta UI)",
         "enableImageDebugging": "Включить отладку",
-        "resetWebUI": "Вернуть умолчания",
+        "resetWebUI": "Сброс настроек Web UI",
         "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."
+        "resetComplete": "Интерфейс сброшен. Обновите эту страницу.",
+        "useSlidersForAll": "Использовать ползунки для всех параметров"
     },
     "toast": {
         "tempFoldersEmptied": "Временная папка очищена",
@@ -441,7 +515,7 @@
         "imageSavedToGallery": "Изображение сохранено в галерею",
         "canvasMerged": "Холст объединен",
         "sentToImageToImage": "Отправить в img2img",
-        "sentToUnifiedCanvas": "Отправить на холст",
+        "sentToUnifiedCanvas": "Отправлено на Единый холст",
         "parametersSet": "Параметры заданы",
         "parametersNotSet": "Параметры не заданы",
         "parametersNotSetDesc": "Не найдены метаданные изображения.",
@@ -458,7 +532,11 @@
         "metadataLoadFailed": "Не удалось загрузить метаданные",
         "initialImageSet": "Исходное изображение задано",
         "initialImageNotSet": "Исходное изображение не задано",
-        "initialImageNotSetDesc": "Не получилось загрузить исходное изображение"
+        "initialImageNotSetDesc": "Не получилось загрузить исходное изображение",
+        "serverError": "Ошибка сервера",
+        "disconnected": "Отключено от сервера",
+        "connected": "Подключено к серверу",
+        "canceled": "Обработка отменена"
     },
     "tooltip": {
         "feature": {
@@ -507,7 +585,7 @@
         "autoSaveToGallery": "Автосохранение в галерее",
         "saveBoxRegionOnly": "Сохранять только выделение",
         "limitStrokesToBox": "Ограничить штрихи выделением",
-        "showCanvasDebugInfo": "Показать отладку холста",
+        "showCanvasDebugInfo": "Показать доп. информацию о холсте",
         "clearCanvasHistory": "Очистить историю холста",
         "clearHistory": "Очистить историю",
         "clearCanvasHistoryMessage": "Очистка истории холста оставляет текущий холст нетронутым, но удаляет историю отмен и повторов.",
@@ -535,6 +613,26 @@
         "betaPreserveMasked": "Сохранять маскируемую область"
     },
     "accessibility": {
-        "modelSelect": "Выбор модели"
+        "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"
     }
 }

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

@@ -0,0 +1,254 @@
+{
+    "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

@@ -0,0 +1,64 @@
+{
+    "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",
@@ -43,7 +43,38 @@
         "statusUpscaling": "Збільшення",
         "statusUpscalingESRGAN": "Збільшення (ESRGAN)",
         "statusLoadingModel": "Завантаження моделі",
-        "statusModelChanged": "Модель змінено"
+        "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"
     },
     "gallery": {
         "generations": "Генерації",
@@ -284,15 +315,15 @@
         "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",
@@ -319,7 +350,66 @@
         "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."
+        "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"
     },
     "parameters": {
         "images": "Зображення",
@@ -346,7 +436,7 @@
         "scale": "Масштаб",
         "otherOptions": "інші параметри",
         "seamlessTiling": "Безшовний узор",
-        "hiresOptim": "Висока роздільна здатність",
+        "hiresOptim": "Оптимізація High Res",
         "imageFit": "Вмістити зображення",
         "codeformerFidelity": "Точність",
         "seamSize": "Размір шву",
@@ -379,7 +469,24 @@
         "info": "Метадані",
         "deleteImage": "Видалити зображення",
         "initialImage": "Початкове зображення",
-        "showOptionsPanel": "Показати панель налаштувань"
+        "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"
     },
     "settings": {
         "models": "Моделі",
@@ -392,7 +499,8 @@
         "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."
+        "resetComplete": "Інтерфейс скинуто. Оновіть цю сторінку.",
+        "useSlidersForAll": "Використовувати повзунки для всіх параметрів"
     },
     "toast": {
         "tempFoldersEmptied": "Тимчасова папка очищена",
@@ -410,21 +518,25 @@
         "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"
+        "initialImageNotSetDesc": "Не вдалося завантажити початкове зображення",
+        "serverError": "Помилка сервера",
+        "disconnected": "Відключено від сервера",
+        "connected": "Підключено до сервера",
+        "canceled": "Обробку скасовано"
     },
     "tooltip": {
         "feature": {
@@ -473,10 +585,10 @@
         "autoSaveToGallery": "Автозбереження до галереї",
         "saveBoxRegionOnly": "Зберiгати тiльки видiлення",
         "limitStrokesToBox": "Обмежити штрихи виділенням",
-        "showCanvasDebugInfo": "Показати налаштування полотна",
+        "showCanvasDebugInfo": "Показати дод. інформацію про полотно",
         "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",
@@ -499,5 +611,28 @@
         "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

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

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

@@ -481,5 +481,22 @@
         "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

@@ -13,16 +13,48 @@ import { Box, Flex, Grid, Portal, useColorMode } from '@chakra-ui/react';
 import { APP_HEIGHT, APP_WIDTH } from 'theme/util/constants';
 import ImageGalleryPanel from 'features/gallery/components/ImageGalleryPanel';
 import Lightbox from 'features/lightbox/components/Lightbox';
-import { useAppSelector } from './storeHooks';
+import { useAppDispatch, useAppSelector } from './storeHooks';
 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();
 
-const App = (props: PropsWithChildren) => {
+interface Props extends PropsWithChildren {
+  options: {
+    disabledPanels: string[];
+    disabledTabs: InvokeTabName[];
+    shouldTransformUrls?: boolean;
+    shouldFetchImages: boolean;
+  };
+}
+
+const App = (props: Props) => {
   useToastWatcher();
 
   const currentTheme = useAppSelector((state) => state.ui.currentTheme);
   const { setColorMode } = useColorMode();
+  const dispatch = useAppDispatch();
+
+  useEffect(() => {
+    dispatch(setDisabledPanels(props.options.disabledPanels));
+  }, [dispatch, props.options.disabledPanels]);
+
+  useEffect(() => {
+    dispatch(setDisabledTabs(props.options.disabledTabs));
+  }, [dispatch, props.options.disabledTabs]);
+
+  useEffect(() => {
+    dispatch(
+      shouldTransformUrlsChanged(Boolean(props.options.shouldTransformUrls))
+    );
+  }, [dispatch, props.options.shouldTransformUrls]);
+
+  useEffect(() => {
+    dispatch(setShouldFetchImages(props.options.shouldFetchImages));
+  }, [dispatch, props.options.shouldFetchImages]);
 
   useEffect(() => {
     setColorMode(['light'].includes(currentTheme) ? 'light' : 'dark');
@@ -41,7 +73,12 @@ const App = (props: PropsWithChildren) => {
           h={APP_HEIGHT}
         >
           {props.children || <SiteHeader />}
-          <Flex gap={4} w="full" h="full">
+          <Flex
+            gap={4}
+            w={{ base: '100vw', xl: 'full' }}
+            h="full"
+            flexDir={{ base: 'column', xl: 'row' }}
+          >
             <InvokeTabs />
             <ImageGalleryPanel />
           </Flex>