Remove unused check_internet() function.

README.md

@@ -12,7 +12,7 @@
 
 Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
 
-[Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
+[Installation][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
 
 <div align="center">
 

docs/contributing/frontend/WORKFLOWS.md

@@ -117,13 +117,13 @@ Stateless fields do not store their value in the node, so their field instances
 
 "Custom" fields will always be treated as stateless fields.
 
-##### Single and Collection Fields
+##### Collection and Scalar Fields
 
-Field types have a name and cardinality property which may identify it as a **SINGLE**, **COLLECTION** or **SINGLE_OR_COLLECTION** field.
+Field types have a name and two flags which may identify it as a **collection** or **collection or scalar** field.
 
-- If a field is annotated in python as a singular value or class, its field type is parsed as a **SINGLE** type (e.g. `int`, `ImageField`, `str`).
-- If a field is annotated in python as a list, its field type is parsed as a **COLLECTION** type (e.g. `list[int]`).
-- If it is annotated as a union of a type and list, the type will be parsed as a **SINGLE_OR_COLLECTION** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
+If a field is annotated in python as a list, its field type is parsed and flagged as a **collection** type (e.g. `list[int]`).
+
+If it is annotated as a union of a type and list, the type will be flagged as a **collection or scalar** type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
 
 ## Implementation
 
@@ -173,7 +173,8 @@ Field types are represented as structured objects:
 ```ts
 type FieldType = {
   name: string;
-  cardinality: 'SINGLE' | 'COLLECTION' | 'SINGLE_OR_COLLECTION';
+  isCollection: boolean;
+  isCollectionOrScalar: boolean;
 };
 ```
 
@@ -185,7 +186,7 @@ There are 4 general cases for field type parsing.
 
 When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
 
-We create a field type name from this `type` string (e.g. `string` -> `StringField`). The cardinality is `"SINGLE"`.
+We create a field type name from this `type` string (e.g. `string` -> `StringField`).
 
 ##### Complex Types
 
@@ -199,13 +200,13 @@ We need to **dereference** the schema to pull these out. Dereferencing may requi
 
 When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
 
-We use the item type for field type name. The cardinality is `"COLLECTION"`.
+We use the item type for field type name, adding `isCollection: true` to the field type.
 
-##### Single or Collection Types
+##### Collection or Scalar Types
 
 When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
 
-After verifying that the union has two members (a type and list of the same type), we use the type for field type name, with cardinality `"SINGLE_OR_COLLECTION"`.
+After verifying that the union has two members (a type and list of the same type), we use the type for field type name, adding `isCollectionOrScalar: true` to the field type.
 
 ##### Optional Fields
 

docs/features/CONFIGURATION.md

@@ -51,11 +51,13 @@ The settings in this file will override the defaults. You only need
 to change this file if the default for a particular setting doesn't
 work for you.
 
-You'll find an example file next to `invokeai.yaml` that shows the default values.
-
 Some settings, like [Model Marketplace API Keys], require the YAML
 to be formatted correctly. Here is a [basic guide to YAML files].
 
+You can fix a broken `invokeai.yaml` by deleting it and running the
+configuration script again -- option [6] in the launcher, "Re-run the
+configure script".
+
 #### Custom Config File Location
 
 You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.

docs/features/TRAINING.md

@@ -4,6 +4,278 @@ title: Training
 
 # :material-file-document: Training
 
-Invoke Training has moved to its own repository, with a dedicated UI for accessing common scripts like Textual Inversion and LoRA training.
+# Textual Inversion Training
+## **Personalizing Text-to-Image Generation**
 
-You can find more by visiting the repo at https://github.com/invoke-ai/invoke-training
+You may personalize the generated images to provide your own styles or objects
+by training a new LDM checkpoint and introducing a new vocabulary to the fixed
+model as a (.pt) embeddings file. Alternatively, you may use or train
+HuggingFace Concepts embeddings files (.bin) from
+<https://huggingface.co/sd-concepts-library> and its associated
+notebooks.
+
+## **Hardware and Software Requirements**
+
+You will need a GPU to perform training in a reasonable length of
+time, and at least 12 GB of VRAM. We recommend using the [`xformers`
+library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
+training process further. During training, about ~8 GB is temporarily
+needed in order to store intermediate models, checkpoints and logs.
+
+## **Preparing for Training**
+
+To train, prepare a folder that contains 3-5 images that illustrate
+the object or concept. It is good to provide a variety of examples or
+poses to avoid overtraining the system. Format these images as PNG
+(preferred) or JPG. You do not need to resize or crop the images in
+advance, but for more control you may wish to do so.
+
+Place the training images in a directory on the machine InvokeAI runs
+on. We recommend placing them in a subdirectory of the
+`text-inversion-training-data` folder located in the InvokeAI root
+directory, ordinarily `~/invokeai` (Linux/Mac), or
+`C:\Users\your_name\invokeai` (Windows). For example, to create an
+embedding for the "psychedelic" style, you'd place the training images
+into the directory
+`~invokeai/text-inversion-training-data/psychedelic`.
+
+## **Launching Training Using the Console Front End**
+
+InvokeAI 2.3 and higher comes with a text console-based training front
+end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
+start training tool selecting choice (3):
+
+```sh
+1 "Generate images with a browser-based interface"
+2 "Explore InvokeAI nodes using a command-line interface"
+3 "Textual inversion training"
+4 "Merge models (diffusers type only)"
+5 "Download and install models"
+6 "Change InvokeAI startup options"
+7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
+8 "Open the developer console"
+9 "Update InvokeAI"
+```
+
+Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
+you can then launch the front end with the command `invokeai-ti --gui`.
+
+This will launch a text-based front end that will look like this:
+
+<figure markdown>
+![ti-frontend](../assets/textual-inversion/ti-frontend.png)
+</figure>
+
+The interface is keyboard-based. Move from field to field using
+control-N (^N) to move to the next field and control-P (^P) to the
+previous one. <Tab> and <shift-TAB> work as well. Once a field is
+active, use the cursor keys. In a checkbox group, use the up and down
+cursor keys to move from choice to choice, and <space> to select a
+choice. In a scrollbar, use the left and right cursor keys to increase
+and decrease the value of the scroll. In textfields, type the desired
+values.
+
+The number of parameters may look intimidating, but in most cases the
+predefined defaults work fine. The red circled fields in the above
+illustration are the ones you will adjust most frequently.
+
+### Model Name
+
+This will list all the diffusers models that are currently
+installed. Select the one you wish to use as the basis for your
+embedding. Be aware that if you use a SD-1.X-based model for your
+training, you will only be able to use this embedding with other
+SD-1.X-based models. Similarly, if you train on SD-2.X, you will only
+be able to use the embeddings with models based on SD-2.X.
+
+### Trigger Term
+
+This is the prompt term you will use to trigger the embedding. Type a
+single word or phrase you wish to use as the trigger, example
+"psychedelic" (without angle brackets). Within InvokeAI, you will then
+be able to activate the trigger using the syntax `<psychedelic>`.
+
+### Initializer
+
+This is a single character that is used internally during the training
+process as a placeholder for the trigger term. It defaults to "*" and
+can usually be left alone.
+
+### Resume from last saved checkpoint
+
+As training proceeds, textual inversion will write a series of
+intermediate files that can be used to resume training from where it
+was left off in the case of an interruption. This checkbox will be
+automatically selected if you provide a previously used trigger term
+and at least one checkpoint file is found on disk.
+
+Note that as of 20 January 2023, resume does not seem to be working
+properly due to an issue with the upstream code.
+
+### Data Training Directory
+
+This is the location of the images to be used for training. When you
+select a trigger term like "my-trigger", the frontend will prepopulate
+this field with `~/invokeai/text-inversion-training-data/my-trigger`,
+but you can change the path to wherever you want.
+
+### Output Destination Directory
+
+This is the location of the logs, checkpoint files, and embedding
+files created during training. When you select a trigger term like
+"my-trigger", the frontend will prepopulate this field with
+`~/invokeai/text-inversion-output/my-trigger`, but you can change the
+path to wherever you want.
+
+### Image resolution
+
+The images in the training directory will be automatically scaled to
+the value you use here. For best results, you will want to use the
+same default resolution of the underlying model (512 pixels for
+SD-1.5, 768 for the larger version of SD-2.1).
+
+### Center crop images
+
+If this is selected, your images will be center cropped to make them
+square before resizing them to the desired resolution. Center cropping
+can indiscriminately cut off the top of subjects' heads for portrait
+aspect images, so if you have images like this, you may wish to use a
+photoeditor to manually crop them to a square aspect ratio.
+
+### Mixed precision
+
+Select the floating point precision for the embedding. "no" will
+result in a full 32-bit precision, "fp16" will provide 16-bit
+precision, and "bf16" will provide mixed precision (only available
+when XFormers is used).
+
+### Max training steps
+
+How many steps the training will take before the model converges. Most
+training sets will converge with 2000-3000 steps.
+
+### Batch size
+
+This adjusts how many training images are processed simultaneously in
+each step. Higher values will cause the training process to run more
+quickly, but use more memory. The default size will run with GPUs with
+as little as 12 GB.
+
+### Learning rate
+
+The rate at which the system adjusts its internal weights during
+training. Higher values risk overtraining (getting the same image each
+time), and lower values will take more steps to train a good
+model. The default of 0.0005 is conservative; you may wish to increase
+it to 0.005 to speed up training.
+
+### Scale learning rate by number of GPUs, steps and batch size
+
+If this is selected (the default) the system will adjust the provided
+learning rate to improve performance.
+
+### Use xformers acceleration
+
+This will activate XFormers memory-efficient attention. You need to
+have XFormers installed for this to have an effect.
+
+### Learning rate scheduler
+
+This adjusts how the learning rate changes over the course of
+training. The default "constant" means to use a constant learning rate
+for the entire training session. The other values scale the learning
+rate according to various formulas.
+
+Only "constant" is supported by the XFormers library.
+
+### Gradient accumulation steps
+
+This is a parameter that allows you to use bigger batch sizes than
+your GPU's VRAM would ordinarily accommodate, at the cost of some
+performance.
+
+### Warmup steps
+
+If "constant_with_warmup" is selected in the learning rate scheduler,
+then this provides the number of warmup steps. Warmup steps have a
+very low learning rate, and are one way of preventing early
+overtraining.
+
+## The training run
+
+Start the training run by advancing to the OK button (bottom right)
+and pressing <enter>. A series of progress messages will be displayed
+as the training process proceeds. This may take an hour or two,
+depending on settings and the speed of your system. Various log and
+checkpoint files will be written into the output directory (ordinarily
+`~/invokeai/text-inversion-output/my-model/`)
+
+At the end of successful training, the system will copy the file
+`learned_embeds.bin` into the InvokeAI root directory's `embeddings`
+directory, using a subdirectory named after the trigger token. For
+example, if the trigger token was `psychedelic`, then look for the
+embeddings file in
+`~/invokeai/embeddings/psychedelic/learned_embeds.bin`
+
+You may now launch InvokeAI and try out a prompt that uses the trigger
+term. For example `a plate of banana sushi in <psychedelic> style`.
+
+## **Training with the Command-Line Script**
+
+Training can also be done using a traditional command-line script. It
+can be launched from within the "developer's console", or from the
+command line after activating InvokeAI's virtual environment.
+
+It accepts a large number of arguments, which can be summarized by
+passing the `--help` argument:
+
+```sh
+invokeai-ti --help
+```
+
+Typical usage is shown here:
+```sh
+invokeai-ti \
+       --model=stable-diffusion-1.5 \
+       --resolution=512 \
+       --learnable_property=style \
+       --initializer_token='*' \
+       --placeholder_token='<psychedelic>' \
+       --train_data_dir=/home/lstein/invokeai/training-data/psychedelic \
+       --output_dir=/home/lstein/invokeai/text-inversion-training/psychedelic \
+       --scale_lr \
+       --train_batch_size=8 \
+       --gradient_accumulation_steps=4 \
+       --max_train_steps=3000 \
+       --learning_rate=0.0005 \
+       --resume_from_checkpoint=latest \
+       --lr_scheduler=constant \
+       --mixed_precision=fp16 \
+       --only_save_embeds
+```
+
+## Troubleshooting
+
+### `Cannot load embedding for <trigger>. It was trained on a model with token dimension 1024, but the current model has token dimension 768`
+
+Messages like this indicate you trained the embedding on a different base model than the currently selected one.
+
+For example, in the error above, the training was done on SD2.1 (768x768) but it was used on SD1.5 (512x512).
+
+## Reading
+
+For more information on textual inversion, please see the following
+resources:
+
+* The [textual inversion repository](https://github.com/rinongal/textual_inversion) and
+  associated paper for details and limitations.
+* [HuggingFace's textual inversion training
+  page](https://huggingface.co/docs/diffusers/training/text_inversion)
+* [HuggingFace example script
+  documentation](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
+  (Note that this script is similar to, but not identical, to
+  `textual_inversion`, but produces embed files that are completely compatible.
+
+---
+
+copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/installation/010_INSTALL_AUTOMATED.md

@@ -1,10 +1,8 @@
-# Automatic Install & Updates
+# Automatic Install
 
-**The same packaged installer file can be used for both new installs and updates.**
-Using the installer for updates will leave everything you've added since installation, and just update the core libraries used to run Invoke.
-Simply use the same path you installed to originally.
+The installer is used for both new installs and updates.
 
-Both release and pre-release versions can be installed using the installer. It also supports install through a wheel if needed.
+Both release and pre-release versions can be installed using it. It also supports install a wheel if needed.
 
 Be sure to review the [installation requirements] and ensure your system has everything it needs to install Invoke.
 
@@ -98,7 +96,7 @@ Updating is exactly the same as installing - download the latest installer, choo
 
 If you have installation issues, please review the [FAQ]. You can also [create an issue] or ask for help on [discord].
 
-[installation requirements]: INSTALL_REQUIREMENTS.md
+[installation requirements]: INSTALLATION.md#installation-requirements
 [FAQ]: ../help/FAQ.md
 [install some models]: 050_INSTALLING_MODELS.md
 [configuration docs]: ../features/CONFIGURATION.md

docs/installation/020_INSTALL_MANUAL.md

@@ -10,7 +10,7 @@ InvokeAI is distributed as a python package on PyPI, installable with `pip`. The
 
 ### Requirements
 
-Before you start, go through the [installation requirements](./INSTALL_REQUIREMENTS.md).
+Before you start, go through the [installation requirements].
 
 ### Installation Walkthrough
 
@@ -79,7 +79,7 @@ Before you start, go through the [installation requirements](./INSTALL_REQUIREME
 
 1. Install the InvokeAI Package. The base command is `pip install InvokeAI --use-pep517`, but you may need to change this depending on your system and the desired features.
 
-    - You may need to provide an [extra index URL](https://pip.pypa.io/en/stable/cli/pip_install/#cmdoption-extra-index-url). Select your platform configuration using [this tool on the PyTorch website](https://pytorch.org/get-started/locally/). Copy the `--extra-index-url` string from this and append it to your install command.
+    - You may need to provide an [extra index URL]. Select your platform configuration using [this tool on the PyTorch website]. Copy the `--extra-index-url` string from this and append it to your install command.
 
         !!! example "Install with an extra index URL"
 
@@ -116,4 +116,4 @@ Before you start, go through the [installation requirements](./INSTALL_REQUIREME
 
     !!! warning
 
-        If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.
+        If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root_dir \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.

docs/installation/INSTALLATION.md

@@ -1,4 +1,4 @@
-# Installation and Updating Overview
+# Installation Overview
 
 Before installing, review the [installation requirements] to ensure your system is set up properly.
 
@@ -6,21 +6,14 @@ See the [FAQ] for frequently-encountered installation issues.
 
 If you need more help, join our [discord] or [create an issue].
 
-<h2>Automatic Install & Updates </h2>
+<h2>Automatic Install</h2>
 
 ✅ The automatic install is the best way to run InvokeAI. Check out the [installation guide] to get started.
 
-⬆️ The same installer is also the best way to update InvokeAI - Simply rerun it for the same folder you installed to.
-
-The installation process simply manages installation for the core libraries & application dependencies that run Invoke.
-Any models, images, or other assets in the Invoke root folder won't be affected by the installation process.
-
 <h2>Manual Install</h2>
 
 If you are familiar with python and want more control over the packages that are installed, you can [install InvokeAI manually via PyPI].
 
-Updates are managed by reinstalling the latest version through PyPi.
-
 <h2>Developer Install</h2>
 
 If you want to contribute to InvokeAI, consult the [developer install guide].

docs/installation/INSTALL_REQUIREMENTS.md

@@ -37,13 +37,13 @@ Invoke runs best with a dedicated GPU, but will fall back to running on CPU, alb
     === "Nvidia"
 
         ```
-        Any GPU with at least 8GB VRAM.
+        Any GPU with at least 8GB VRAM. Linux only.
         ```
 
     === "AMD"
 
         ```
-        Any GPU with at least 16GB VRAM. Linux only.
+        Any GPU with at least 16GB VRAM.
         ```
 
     === "Mac"

invokeai/app/api/dependencies.py

@@ -35,23 +35,6 @@ from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
 
-
-# TODO: is there a better way to achieve this?
-def check_internet() -> bool:
-    """
-    Return true if the internet is reachable.
-    It does this by pinging huggingface.co.
-    """
-    import urllib.request
-
-    host = "http://huggingface.co"
-    try:
-        urllib.request.urlopen(host, timeout=1)
-        return True
-    except Exception:
-        return False
-
-
 logger = InvokeAILogger.get_logger()
 
 

invokeai/app/api/routers/app_info.py

@@ -13,6 +13,7 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
 from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch
+from invokeai.backend.image_util.safety_checker import SafetyChecker
 from invokeai.backend.util.logging import logging
 from invokeai.version import __version__
 
@@ -108,7 +109,9 @@ async def get_config() -> AppConfig:
         upscaling_models.append(str(Path(model).stem))
     upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
 
-    nsfw_methods = ["nsfw_checker"]
+    nsfw_methods = []
+    if SafetyChecker.safety_checker_available():
+        nsfw_methods.append("nsfw_checker")
 
     watermarking_methods = ["invisible_watermark"]
 

invokeai/app/api/routers/images.py

@@ -6,12 +6,13 @@ from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request,
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
-from pydantic import BaseModel, Field, JsonValue
+from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID, WorkflowWithoutIDValidator
 
 from ..dependencies import ApiDependencies
 
@@ -41,17 +42,13 @@ async def upload_image(
     board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
     session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
     crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
-    metadata: Optional[JsonValue] = Body(
-        default=None, description="The metadata to associate with the image", embed=True
-    ),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type or not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
-    _metadata = None
-    _workflow = None
-    _graph = None
+    metadata = None
+    workflow = None
 
     contents = await file.read()
     try:
@@ -65,28 +62,22 @@ async def upload_image(
 
     # TODO: retain non-invokeai metadata on upload?
     # attempt to parse metadata from image
-    metadata_raw = metadata if isinstance(metadata, str) else pil_image.info.get("invokeai_metadata", None)
-    if isinstance(metadata_raw, str):
-        _metadata = metadata_raw
-    else:
-        ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
-        pass
+    metadata_raw = pil_image.info.get("invokeai_metadata", None)
+    if metadata_raw:
+        try:
+            metadata = MetadataFieldValidator.validate_json(metadata_raw)
+        except ValidationError:
+            ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
+            pass
 
     # attempt to parse workflow from image
     workflow_raw = pil_image.info.get("invokeai_workflow", None)
-    if isinstance(workflow_raw, str):
-        _workflow = workflow_raw
-    else:
-        ApiDependencies.invoker.services.logger.warn("Failed to parse workflow for uploaded image")
-        pass
-
-    # attempt to extract graph from image
-    graph_raw = pil_image.info.get("invokeai_graph", None)
-    if isinstance(graph_raw, str):
-        _graph = graph_raw
-    else:
-        ApiDependencies.invoker.services.logger.warn("Failed to parse graph for uploaded image")
-        pass
+    if workflow_raw is not None:
+        try:
+            workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw)
+        except ValidationError:
+            ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
+            pass
 
     try:
         image_dto = ApiDependencies.invoker.services.images.create(
@@ -95,9 +86,8 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
-            metadata=_metadata,
-            workflow=_workflow,
-            graph=_graph,
+            metadata=metadata,
+            workflow=workflow,
             is_intermediate=is_intermediate,
         )
 
@@ -195,21 +185,14 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-class WorkflowAndGraphResponse(BaseModel):
-    workflow: Optional[str] = Field(description="The workflow used to generate the image, as stringified JSON")
-    graph: Optional[str] = Field(description="The graph used to generate the image, as stringified JSON")
-
-
 @images_router.get(
-    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=WorkflowAndGraphResponse
+    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=Optional[WorkflowWithoutID]
 )
 async def get_image_workflow(
     image_name: str = Path(description="The name of image whose workflow to get"),
-) -> WorkflowAndGraphResponse:
+) -> Optional[WorkflowWithoutID]:
     try:
-        workflow = ApiDependencies.invoker.services.images.get_workflow(image_name)
-        graph = ApiDependencies.invoker.services.images.get_graph(image_name)
-        return WorkflowAndGraphResponse(workflow=workflow, graph=graph)
+        return ApiDependencies.invoker.services.images.get_workflow(image_name)
     except Exception:
         raise HTTPException(status_code=404)
 

invokeai/app/api/routers/model_manager.py

@@ -6,7 +6,7 @@ import pathlib
 import shutil
 import traceback
 from copy import deepcopy
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, Dict, List, Optional
 
 from fastapi import Body, Path, Query, Response, UploadFile
 from fastapi.responses import FileResponse
@@ -16,7 +16,6 @@ from pydantic import AnyHttpUrl, BaseModel, ConfigDict, Field
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
-from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install import ModelInstallJob
 from invokeai.app.services.model_records import (
     DuplicateModelException,
@@ -53,13 +52,6 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
-def add_cover_image_to_model_config(config: AnyModelConfig, dependencies: Type[ApiDependencies]) -> AnyModelConfig:
-    """Add a cover image URL to a model configuration."""
-    cover_image = dependencies.invoker.services.model_images.get_url(config.key)
-    config.cover_image = cover_image
-    return config
-
-
 ##############################################################################
 # These are example inputs and outputs that are used in places where Swagger
 # is unable to generate a correct example.
@@ -126,7 +118,8 @@ async def list_model_records(
             record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
         )
     for model in found_models:
-        model = add_cover_image_to_model_config(model, ApiDependencies)
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
+        model.cover_image = cover_image
     return ModelsList(models=found_models)
 
 
@@ -167,9 +160,12 @@ async def get_model_record(
     key: str = Path(description="Key of the model record to fetch."),
 ) -> AnyModelConfig:
     """Get a model record"""
+    record_store = ApiDependencies.invoker.services.model_manager.store
     try:
-        config = ApiDependencies.invoker.services.model_manager.store.get_model(key)
-        return add_cover_image_to_model_config(config, ApiDependencies)
+        config: AnyModelConfig = record_store.get_model(key)
+        cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
+        config.cover_image = cover_image
+        return config
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
 
@@ -298,15 +294,14 @@ async def update_model_record(
     installer = ApiDependencies.invoker.services.model_manager.install
     try:
         record_store.update_model(key, changes=changes)
-        config = installer.sync_model_path(key)
-        config = add_cover_image_to_model_config(config, ApiDependencies)
+        model_response: AnyModelConfig = installer.sync_model_path(key)
         logger.info(f"Updated model: {key}")
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
-    return config
+    return model_response
 
 
 @model_manager_router.get(
@@ -653,14 +648,6 @@ async def convert_model(
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
 
-    # Update the model image if the model had one
-    try:
-        model_image = ApiDependencies.invoker.services.model_images.get(key)
-        ApiDependencies.invoker.services.model_images.save(model_image, new_key)
-        ApiDependencies.invoker.services.model_images.delete(key)
-    except ModelImageFileNotFoundException:
-        pass
-
     # delete the original safetensors file
     installer.delete(key)
 
@@ -668,8 +655,7 @@ async def convert_model(
     shutil.rmtree(cache_path)
 
     # return the config record for the new diffusers directory
-    new_config = store.get_model(new_key)
-    new_config = add_cover_image_to_model_config(new_config, ApiDependencies)
+    new_config: AnyModelConfig = store.get_model(new_key)
     return new_config
 
 

invokeai/app/api_app.py

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import mimetypes
 import socket
+import time
 from contextlib import asynccontextmanager
 from inspect import signature
 from pathlib import Path
@@ -20,13 +21,10 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
 from pydantic.json_schema import models_json_schema
 from torch.backends.mps import is_available as is_mps_available
 
-# for PyCharm:
-# noinspection PyUnresolvedReferences
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 import invokeai.frontend.web as web_dir
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.services.config.config_default import get_config
+from invokeai.app.services.config.config_default import InvokeAIAppConfig, get_config
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.backend.util.devices import TorchDevice
 
@@ -44,196 +42,189 @@ from .api.routers import (
     workflows,
 )
 from .api.sockets import SocketIO
-from .invocations.baseinvocation import (
-    BaseInvocation,
-    UIConfigBase,
-)
+from .invocations.baseinvocation import BaseInvocation, UIConfigBase
 from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 
-app_config = get_config()
+
+# TODO(ryand): Search for imports from api_app.py in the rest of the codebase and make sure I didn't break any of them.
+def build_app(app_config: InvokeAIAppConfig, logger: logging.Logger) -> FastAPI:
+    @asynccontextmanager
+    async def lifespan(app: FastAPI):
+        # Add startup event to load dependencies
+        ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
+        yield
+        # Shut down threads
+        ApiDependencies.shutdown()
+
+    app = FastAPI(
+        title="Invoke - Community Edition",
+        docs_url=None,
+        redoc_url=None,
+        separate_input_output_schemas=False,
+        lifespan=lifespan,
+    )
+
+    # Add event handler
+    event_handler_id: int = id(app)
+    app.add_middleware(
+        EventHandlerASGIMiddleware,
+        handlers=[local_handler],  # TODO: consider doing this in services to support different configurations
+        middleware_id=event_handler_id,
+    )
+
+    socket_io = SocketIO(app)
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=app_config.allow_origins,
+        allow_credentials=app_config.allow_credentials,
+        allow_methods=app_config.allow_methods,
+        allow_headers=app_config.allow_headers,
+    )
+
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+
+    # Include all routers
+    app.include_router(utilities.utilities_router, prefix="/api")
+    app.include_router(model_manager.model_manager_router, prefix="/api")
+    app.include_router(download_queue.download_queue_router, prefix="/api")
+    app.include_router(images.images_router, prefix="/api")
+    app.include_router(boards.boards_router, prefix="/api")
+    app.include_router(board_images.board_images_router, prefix="/api")
+    app.include_router(app_info.app_router, prefix="/api")
+    app.include_router(session_queue.session_queue_router, prefix="/api")
+    app.include_router(workflows.workflows_router, prefix="/api")
+
+    add_custom_openapi(app)
+
+    @app.get("/docs", include_in_schema=False)
+    def overridden_swagger() -> HTMLResponse:
+        return get_swagger_ui_html(
+            openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
+            title=f"{app.title} - Swagger UI",
+            swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
+        )
+
+    @app.get("/redoc", include_in_schema=False)
+    def overridden_redoc() -> HTMLResponse:
+        return get_redoc_html(
+            openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
+            title=f"{app.title} - Redoc",
+            redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
+        )
+
+    web_root_path = Path(list(web_dir.__path__)[0])
+
+    try:
+        app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
+    except RuntimeError:
+        logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
+
+    app.mount(
+        "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
+    )  # docs favicon is in here
+
+    return app
 
 
-if is_mps_available():
-    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+def apply_monkeypatches() -> None:
+    # TODO(ryand): Don't monkeypatch on import!
+    if is_mps_available():
+        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+    import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 
 
-logger = InvokeAILogger.get_logger(config=app_config)
-# fix for windows mimetypes registry entries being borked
-# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
-mimetypes.add_type("application/javascript", ".js")
-mimetypes.add_type("text/css", ".css")
-
-torch_device_name = TorchDevice.get_torch_device_name()
-logger.info(f"Using torch device: {torch_device_name}")
+def fix_mimetypes():
+    # fix for windows mimetypes registry entries being borked
+    # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
+    mimetypes.add_type("application/javascript", ".js")
+    mimetypes.add_type("text/css", ".css")
 
 
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    # Add startup event to load dependencies
-    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
-    yield
-    # Shut down threads
-    ApiDependencies.shutdown()
+def add_custom_openapi(app: FastAPI) -> None:
+    """Add a custom .openapi() method to the FastAPI app.
 
+    This is done based on the guidance here:
+    https://fastapi.tiangolo.com/how-to/extending-openapi/#normal-fastapi
+    """
 
-# Create the app
-# TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(
-    title="Invoke - Community Edition",
-    docs_url=None,
-    redoc_url=None,
-    separate_input_output_schemas=False,
-    lifespan=lifespan,
-)
+    # Build a custom OpenAPI to include all outputs
+    # TODO: can outputs be included on metadata of invocation schemas somehow?
+    def custom_openapi() -> dict[str, Any]:
+        if app.openapi_schema:
+            return app.openapi_schema
+        openapi_schema = get_openapi(
+            title=app.title,
+            description="An API for invoking AI image operations",
+            version="1.0.0",
+            routes=app.routes,
+            separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
+        )
 
-# Add event handler
-event_handler_id: int = id(app)
-app.add_middleware(
-    EventHandlerASGIMiddleware,
-    handlers=[local_handler],  # TODO: consider doing this in services to support different configurations
-    middleware_id=event_handler_id,
-)
+        # Add all outputs
+        all_invocations = BaseInvocation.get_invocations()
+        output_types = set()
+        output_type_titles = {}
+        for invoker in all_invocations:
+            output_type = signature(invoker.invoke).return_annotation
+            output_types.add(output_type)
 
-socket_io = SocketIO(app)
+        output_schemas = models_json_schema(
+            models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
+        )
+        for schema_key, output_schema in output_schemas[1]["$defs"].items():
+            # TODO: note that we assume the schema_key here is the TYPE.__name__
+            # This could break in some cases, figure out a better way to do it
+            output_type_titles[schema_key] = output_schema["title"]
+            openapi_schema["components"]["schemas"][schema_key] = output_schema
+            openapi_schema["components"]["schemas"][schema_key]["class"] = "output"
 
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=app_config.allow_origins,
-    allow_credentials=app_config.allow_credentials,
-    allow_methods=app_config.allow_methods,
-    allow_headers=app_config.allow_headers,
-)
+        # Some models don't end up in the schemas as standalone definitions
+        additional_schemas = models_json_schema(
+            [
+                (UIConfigBase, "serialization"),
+                (InputFieldJSONSchemaExtra, "serialization"),
+                (OutputFieldJSONSchemaExtra, "serialization"),
+                (ModelIdentifierField, "serialization"),
+                (ProgressImage, "serialization"),
+            ],
+            ref_template="#/components/schemas/{model}",
+        )
+        for schema_key, schema_json in additional_schemas[1]["$defs"].items():
+            openapi_schema["components"]["schemas"][schema_key] = schema_json
 
-app.add_middleware(GZipMiddleware, minimum_size=1000)
+        # Add a reference to the output type to additionalProperties of the invoker schema
+        for invoker in all_invocations:
+            invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
+            output_type = signature(obj=invoker.invoke).return_annotation
+            output_type_title = output_type_titles[output_type.__name__]
+            invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
+            outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
+            invoker_schema["output"] = outputs_ref
+            invoker_schema["class"] = "invocation"
 
+        # This code no longer seems to be necessary?
+        # Leave it here just in case
+        #
+        # from invokeai.backend.model_manager import get_model_config_formats
+        # formats = get_model_config_formats()
+        # for model_config_name, enum_set in formats.items():
 
-# Include all routers
-app.include_router(utilities.utilities_router, prefix="/api")
-app.include_router(model_manager.model_manager_router, prefix="/api")
-app.include_router(download_queue.download_queue_router, prefix="/api")
-app.include_router(images.images_router, prefix="/api")
-app.include_router(boards.boards_router, prefix="/api")
-app.include_router(board_images.board_images_router, prefix="/api")
-app.include_router(app_info.app_router, prefix="/api")
-app.include_router(session_queue.session_queue_router, prefix="/api")
-app.include_router(workflows.workflows_router, prefix="/api")
+        #     if model_config_name in openapi_schema["components"]["schemas"]:
+        #         # print(f"Config with name {name} already defined")
+        #         continue
 
+        #     openapi_schema["components"]["schemas"][model_config_name] = {
+        #         "title": model_config_name,
+        #         "description": "An enumeration.",
+        #         "type": "string",
+        #         "enum": [v.value for v in enum_set],
+        #     }
 
-# Build a custom OpenAPI to include all outputs
-# TODO: can outputs be included on metadata of invocation schemas somehow?
-def custom_openapi() -> dict[str, Any]:
-    if app.openapi_schema:
+        app.openapi_schema = openapi_schema
         return app.openapi_schema
-    openapi_schema = get_openapi(
-        title=app.title,
-        description="An API for invoking AI image operations",
-        version="1.0.0",
-        routes=app.routes,
-        separate_input_output_schemas=False,  # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
-    )
 
-    # Add all outputs
-    all_invocations = BaseInvocation.get_invocations()
-    output_types = set()
-    output_type_titles = {}
-    for invoker in all_invocations:
-        output_type = signature(invoker.invoke).return_annotation
-        output_types.add(output_type)
-
-    output_schemas = models_json_schema(
-        models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
-    )
-    for schema_key, output_schema in output_schemas[1]["$defs"].items():
-        # TODO: note that we assume the schema_key here is the TYPE.__name__
-        # This could break in some cases, figure out a better way to do it
-        output_type_titles[schema_key] = output_schema["title"]
-        openapi_schema["components"]["schemas"][schema_key] = output_schema
-        openapi_schema["components"]["schemas"][schema_key]["class"] = "output"
-
-    # Some models don't end up in the schemas as standalone definitions
-    additional_schemas = models_json_schema(
-        [
-            (UIConfigBase, "serialization"),
-            (InputFieldJSONSchemaExtra, "serialization"),
-            (OutputFieldJSONSchemaExtra, "serialization"),
-            (ModelIdentifierField, "serialization"),
-            (ProgressImage, "serialization"),
-        ],
-        ref_template="#/components/schemas/{model}",
-    )
-    for schema_key, schema_json in additional_schemas[1]["$defs"].items():
-        openapi_schema["components"]["schemas"][schema_key] = schema_json
-
-    openapi_schema["components"]["schemas"]["InvocationOutputMap"] = {
-        "type": "object",
-        "properties": {},
-        "required": [],
-    }
-
-    # Add a reference to the output type to additionalProperties of the invoker schema
-    for invoker in all_invocations:
-        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
-        output_type = signature(obj=invoker.invoke).return_annotation
-        output_type_title = output_type_titles[output_type.__name__]
-        invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
-        outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
-        invoker_schema["output"] = outputs_ref
-        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["properties"][invoker.get_type()] = outputs_ref
-        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["required"].append(invoker.get_type())
-        invoker_schema["class"] = "invocation"
-
-    # This code no longer seems to be necessary?
-    # Leave it here just in case
-    #
-    # from invokeai.backend.model_manager import get_model_config_formats
-    # formats = get_model_config_formats()
-    # for model_config_name, enum_set in formats.items():
-
-    #     if model_config_name in openapi_schema["components"]["schemas"]:
-    #         # print(f"Config with name {name} already defined")
-    #         continue
-
-    #     openapi_schema["components"]["schemas"][model_config_name] = {
-    #         "title": model_config_name,
-    #         "description": "An enumeration.",
-    #         "type": "string",
-    #         "enum": [v.value for v in enum_set],
-    #     }
-
-    app.openapi_schema = openapi_schema
-    return app.openapi_schema
-
-
-app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
-
-
-@app.get("/docs", include_in_schema=False)
-def overridden_swagger() -> HTMLResponse:
-    return get_swagger_ui_html(
-        openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=f"{app.title} - Swagger UI",
-        swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
-    )
-
-
-@app.get("/redoc", include_in_schema=False)
-def overridden_redoc() -> HTMLResponse:
-    return get_redoc_html(
-        openapi_url=app.openapi_url,  # type: ignore [arg-type] # this is always a string
-        title=f"{app.title} - Redoc",
-        redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
-    )
-
-
-web_root_path = Path(list(web_dir.__path__)[0])
-
-try:
-    app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
-except RuntimeError:
-    logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
-app.mount(
-    "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
-)  # docs favicon is in here
+    app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
 
 
 def check_cudnn(logger: logging.Logger) -> None:
@@ -252,27 +243,39 @@ def check_cudnn(logger: logging.Logger) -> None:
             )
 
 
+def find_port(port: int) -> int:
+    """Find a port not in use starting at given port"""
+    # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
+    # https://github.com/WaylonWalker
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        if s.connect_ex(("localhost", port)) == 0:
+            return find_port(port=port + 1)
+        else:
+            return port
+
+
+def init_dev_reload(logger: logging.Logger) -> None:
+    try:
+        import jurigged
+    except ImportError as e:
+        logger.error(
+            'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
+            exc_info=e,
+        )
+    else:
+        jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+
+
 def invoke_api() -> None:
-    def find_port(port: int) -> int:
-        """Find a port not in use starting at given port"""
-        # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
-        # https://github.com/WaylonWalker
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            if s.connect_ex(("localhost", port)) == 0:
-                return find_port(port=port + 1)
-            else:
-                return port
+    start = time.time()
+    app_config = get_config()
+    logger = InvokeAILogger.get_logger(config=app_config)
+
+    apply_monkeypatches()
+    fix_mimetypes()
 
     if app_config.dev_reload:
-        try:
-            import jurigged
-        except ImportError as e:
-            logger.error(
-                'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
-                exc_info=e,
-            )
-        else:
-            jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+        init_dev_reload(logger)
 
     port = find_port(app_config.port)
     if port != app_config.port:
@@ -280,6 +283,11 @@ def invoke_api() -> None:
 
     check_cudnn(logger)
 
+    torch_device_name = TorchDevice.get_torch_device_name()
+    logger.info(f"Using torch device: {torch_device_name}")
+
+    app = build_app(app_config, logger)
+
     # Start our own event loop for eventing usage
     loop = asyncio.new_event_loop()
     config = uvicorn.Config(
@@ -299,6 +307,7 @@ def invoke_api() -> None:
         log.handlers.clear()
         for ch in logger.handlers:
             log.addHandler(ch)
+    logger.info(f"API started in {time.time() - start:.2f} seconds")
 
     loop.run_until_complete(server.serve())
 

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,6 +24,7 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
+    Input,
     InputField,
     OutputField,
     UIType,
@@ -34,16 +35,15 @@ from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
 from invokeai.backend.image_util.canny import get_canny_edges
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 from invokeai.backend.image_util.hed import HEDProcessor
 from invokeai.backend.image_util.lineart import LineartProcessor
 from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
-from invokeai.backend.image_util.util import np_to_pil, pil_to_np
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, Classification, invocation, invocation_output
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 
 
 class ControlField(BaseModel):
@@ -79,13 +79,13 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 
     image: ImageField = InputField(description="The control image")
     control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
+        description=FieldDescriptions.controlnet_model, input=Input.Direct, ui_type=UIType.ControlNetModel
     )
     control_weight: Union[float, List[float]] = InputField(
         default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
@@ -164,13 +164,13 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.3.3",
+    version="1.3.2",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     low_threshold: int = InputField(
         default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
     )
@@ -198,13 +198,13 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     # safe not supported in controlnet_aux v0.0.3
     # safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
@@ -227,13 +227,13 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     coarse: bool = InputField(default=False, description="Whether to use coarse mode")
 
     def run_processor(self, image: Image.Image) -> Image.Image:
@@ -249,13 +249,13 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image) -> Image.Image:
         processor = LineartAnimeProcessor()
@@ -272,15 +272,15 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.4",
+    version="1.2.3",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
 
     a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
     bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     # depth_and_normal not supported in controlnet_aux v0.0.3
     # depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
 
@@ -303,13 +303,13 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
@@ -320,13 +320,13 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.3"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.2"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
     thr_d: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_d`")
 
@@ -343,13 +343,13 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.3"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.2"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
 
@@ -370,13 +370,13 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
     w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
     f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
@@ -400,7 +400,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -416,15 +416,15 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.4",
+    version="1.2.3",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
 
     max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
     min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         mediapipe_face_processor = MediapipeFaceDetector()
@@ -443,7 +443,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -451,8 +451,8 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     thr_a: float = InputField(default=0, description="Leres parameter `thr_a`")
     thr_b: float = InputField(default=0, description="Leres parameter `thr_b`")
     boost: bool = InputField(default=False, description="Whether to use boost mode")
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
@@ -472,7 +472,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -512,13 +512,13 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.4",
+    version="1.2.3",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
@@ -559,12 +559,12 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""
 
-    color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
+    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
 
     def run_processor(self, image: Image.Image):
         np_image = np.array(image, dtype=np.uint8)
@@ -591,7 +591,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
     title="Depth Anything Processor",
     tags=["controlnet", "depth", "depth anything"],
     category="controlnet",
-    version="1.1.2",
+    version="1.1.1",
 )
 class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a depth map based on the Depth Anything algorithm"""
@@ -599,7 +599,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
         default="small", description="The size of the depth model to use"
     )
-    resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
@@ -614,7 +614,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     title="DW Openpose Image Processor",
     tags=["controlnet", "dwpose", "openpose"],
     category="controlnet",
-    version="1.1.1",
+    version="1.1.0",
 )
 class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Generates an openpose pose from an image using DWPose"""
@@ -622,7 +622,7 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     draw_body: bool = InputField(default=True)
     draw_face: bool = InputField(default=False)
     draw_hands: bool = InputField(default=False)
-    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         dw_openpose = DWOpenposeDetector()
@@ -634,27 +634,3 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
             resolution=self.image_resolution,
         )
         return processed_image
-
-
-@invocation(
-    "heuristic_resize",
-    title="Heuristic Resize",
-    tags=["image, controlnet"],
-    category="image",
-    version="1.0.1",
-    classification=Classification.Prototype,
-)
-class HeuristicResizeInvocation(BaseInvocation):
-    """Resize an image using a heuristic method. Preserves edge maps."""
-
-    image: ImageField = InputField(description="The image to resize")
-    width: int = InputField(default=512, ge=1, description="The width to resize to (px)")
-    height: int = InputField(default=512, ge=1, description="The height to resize to (px)")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, "RGB")
-        np_img = pil_to_np(image)
-        np_resized = heuristic_resize(np_img, (self.width, self.height))
-        resized = np_to_pil(np_resized)
-        image_dto = context.images.save(image=resized)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/image.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from pathlib import Path
 from typing import Literal, Optional
 
 import cv2
@@ -503,7 +504,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Blur NSFW Image",
     tags=["image", "nsfw"],
     category="image",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add blur to NSFW-flagged images"""
@@ -515,12 +516,23 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         logger = context.logger
         logger.debug("Running NSFW checker")
-        image = SafetyChecker.blur_if_nsfw(image)
+        if SafetyChecker.has_nsfw_concept(image):
+            logger.info("A potentially NSFW image has been detected. Image will be blurred.")
+            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
+            caution = self._get_caution_img()
+            blurry_image.paste(caution, (0, 0), caution)
+            image = blurry_image
 
         image_dto = context.images.save(image=image)
 
         return ImageOutput.build(image_dto)
 
+    def _get_caution_img(self) -> Image.Image:
+        import invokeai.app.assets.images as image_assets
+
+        caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
+        return caution.resize((caution.width // 2, caution.height // 2))
+
 
 @invocation(
     "img_watermark",

invokeai/app/invocations/ip_adapter.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from typing_extensions import Self
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, TensorField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, TensorField, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
@@ -58,7 +58,7 @@ class IPAdapterOutput(BaseInvocationOutput):
 CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -67,6 +67,7 @@ class IPAdapterInvocation(BaseInvocation):
     ip_adapter_model: ModelIdentifierField = InputField(
         description="The IP-Adapter model.",
         title="IP-Adapter Model",
+        input=Input.Direct,
         ui_order=-1,
         ui_type=UIType.IPAdapterModel,
     )

invokeai/app/invocations/latent.py

@@ -3,7 +3,7 @@ import inspect
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
+from typing import Any, Iterator, List, Literal, Optional, Tuple, Union
 
 import einops
 import numpy as np
@@ -11,6 +11,7 @@ import numpy.typing as npt
 import torch
 import torchvision
 import torchvision.transforms as T
+from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.adapter import T2IAdapter
@@ -20,12 +21,9 @@ from diffusers.models.attention_processor import (
     LoRAXFormersAttnProcessor,
     XFormersAttnProcessor,
 )
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
-from diffusers.schedulers.scheduling_tcd import TCDScheduler
-from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
+from diffusers.schedulers import DPMSolverSDEScheduler
+from diffusers.schedulers import SchedulerMixin as Scheduler
 from PIL import Image, ImageFilter
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
@@ -343,7 +341,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
     cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale"
+        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
     )
     denoising_start: float = InputField(
         default=0.0,
@@ -523,10 +521,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
 
         if is_sdxl:
-            return (
-                SDXLConditioningInfo(embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids),
-                regions,
-            )
+            return SDXLConditioningInfo(
+                embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids
+            ), regions
         return BasicConditioningInfo(embeds=text_embedding), regions
 
     def get_conditioning_data(
@@ -566,11 +563,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             dtype=unet.dtype,
         )
 
-        if isinstance(self.cfg_scale, list):
-            assert (
-                len(self.cfg_scale) == self.steps
-            ), "cfg_scale (list) must have the same length as the number of steps"
-
         conditioning_data = TextConditioningData(
             uncond_text=uncond_text_embedding,
             cond_text=cond_text_embedding,
@@ -586,6 +578,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
         unet: UNet2DConditionModel,
         scheduler: Scheduler,
     ) -> StableDiffusionGeneratorPipeline:
+        # TODO:
+        # configure_model_padding(
+        #    unet,
+        #    self.seamless,
+        #    self.seamless_axes,
+        # )
+
         class FakeVae:
             class FakeVaeConfig:
                 def __init__(self) -> None:
@@ -821,7 +820,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         denoising_start: float,
         denoising_end: float,
         seed: int,
-    ) -> Tuple[int, List[int], int, Dict[str, Any]]:
+    ) -> Tuple[int, List[int], int]:
         assert isinstance(scheduler, ConfigMixin)
         if scheduler.config.get("cpu_only", False):
             scheduler.set_timesteps(steps, device="cpu")
@@ -849,15 +848,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
         timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
         num_inference_steps = len(timesteps) // scheduler.order
 
-        scheduler_step_kwargs: Dict[str, Any] = {}
+        scheduler_step_kwargs = {}
         scheduler_step_signature = inspect.signature(scheduler.step)
         if "generator" in scheduler_step_signature.parameters:
             # At some point, someone decided that schedulers that accept a generator should use the original seed with
             # all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
             # reproducibility.
-            scheduler_step_kwargs.update({"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)})
-        if isinstance(scheduler, TCDScheduler):
-            scheduler_step_kwargs.update({"eta": 1.0})
+            scheduler_step_kwargs = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
 
         return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
 

invokeai/app/invocations/model.py

@@ -11,7 +11,6 @@ from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType,
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -94,46 +93,19 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
     pass
 
 
-@invocation_output("model_identifier_output")
-class ModelIdentifierOutput(BaseInvocationOutput):
-    """Model identifier output"""
-
-    model: ModelIdentifierField = OutputField(description="Model identifier", title="Model")
-
-
-@invocation(
-    "model_identifier",
-    title="Model identifier",
-    tags=["model"],
-    category="model",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class ModelIdentifierInvocation(BaseInvocation):
-    """Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as
-    input for any model, even if the model types don't match. If you connect this to a mismatched input, you'll get an
-    error."""
-
-    model: ModelIdentifierField = InputField(description="The model to select", title="Model")
-
-    def invoke(self, context: InvocationContext) -> ModelIdentifierOutput:
-        if not context.models.exists(self.model.key):
-            raise Exception(f"Unknown model {self.model.key}")
-
-        return ModelIdentifierOutput(model=self.model)
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.3",
+    version="1.0.2",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: ModelIdentifierField = InputField(description=FieldDescriptions.main_model, ui_type=UIType.MainModel)
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.main_model, input=Input.Direct, ui_type=UIType.MainModel
+    )
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
@@ -162,12 +134,12 @@ class LoRALoaderOutput(BaseInvocationOutput):
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.3")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.2")
 class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -218,75 +190,6 @@ class LoRALoaderInvocation(BaseInvocation):
         return output
 
 
-@invocation_output("lora_selector_output")
-class LoRASelectorOutput(BaseInvocationOutput):
-    """Model loader output"""
-
-    lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
-
-
-@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
-class LoRASelectorInvocation(BaseInvocation):
-    """Selects a LoRA model and weight."""
-
-    lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
-    )
-    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
-
-    def invoke(self, context: InvocationContext) -> LoRASelectorOutput:
-        return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
-
-
-@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.0.0")
-class LoRACollectionLoader(BaseInvocation):
-    """Applies a collection of LoRAs to the provided UNet and CLIP models."""
-
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
-    )
-    unet: Optional[UNetField] = InputField(
-        default=None,
-        description=FieldDescriptions.unet,
-        input=Input.Connection,
-        title="UNet",
-    )
-    clip: Optional[CLIPField] = InputField(
-        default=None,
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-        title="CLIP",
-    )
-
-    def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
-        output = LoRALoaderOutput()
-        loras = self.loras if isinstance(self.loras, list) else [self.loras]
-        added_loras: list[str] = []
-
-        for lora in loras:
-            if lora.lora.key in added_loras:
-                continue
-
-            if not context.models.exists(lora.lora.key):
-                raise Exception(f"Unknown lora: {lora.lora.key}!")
-
-            assert lora.lora.base in (BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2)
-
-            added_loras.append(lora.lora.key)
-
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
-                output.unet.loras.append(lora)
-
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
-                output.clip.loras.append(lora)
-
-        return output
-
-
 @invocation_output("sdxl_lora_loader_output")
 class SDXLLoRALoaderOutput(BaseInvocationOutput):
     """SDXL LoRA Loader Output"""
@@ -301,13 +204,13 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.3",
+    version="1.0.2",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -376,78 +279,12 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
         return output
 
 
-@invocation(
-    "sdxl_lora_collection_loader",
-    title="SDXL LoRA Collection Loader",
-    tags=["model"],
-    category="model",
-    version="1.0.0",
-)
-class SDXLLoRACollectionLoader(BaseInvocation):
-    """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
-
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
-    )
-    unet: Optional[UNetField] = InputField(
-        default=None,
-        description=FieldDescriptions.unet,
-        input=Input.Connection,
-        title="UNet",
-    )
-    clip: Optional[CLIPField] = InputField(
-        default=None,
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-        title="CLIP",
-    )
-    clip2: Optional[CLIPField] = InputField(
-        default=None,
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-        title="CLIP 2",
-    )
-
-    def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
-        output = SDXLLoRALoaderOutput()
-        loras = self.loras if isinstance(self.loras, list) else [self.loras]
-        added_loras: list[str] = []
-
-        for lora in loras:
-            if lora.lora.key in added_loras:
-                continue
-
-            if not context.models.exists(lora.lora.key):
-                raise Exception(f"Unknown lora: {lora.lora.key}!")
-
-            assert lora.lora.base is BaseModelType.StableDiffusionXL
-
-            added_loras.append(lora.lora.key)
-
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
-                output.unet.loras.append(lora)
-
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
-                output.clip.loras.append(lora)
-
-            if self.clip2 is not None:
-                if output.clip2 is None:
-                    output.clip2 = self.clip2.model_copy(deep=True)
-                output.clip2.loras.append(lora)
-
-        return output
-
-
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
     vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, title="VAE", ui_type=UIType.VAEModel
+        description=FieldDescriptions.vae_model, input=Input.Direct, title="VAE", ui_type=UIType.VAEModel
     )
 
     def invoke(self, context: InvocationContext) -> VAEOutput:

invokeai/app/invocations/sdxl.py

@@ -1,4 +1,4 @@
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import SubModelType
 
@@ -30,12 +30,12 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.2")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
     model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sdxl_main_model, ui_type=UIType.SDXLMainModel
+        description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
     )
     # TODO: precision?
 
@@ -67,13 +67,13 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.3",
+    version="1.0.2",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
 
     model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sdxl_refiner_model, ui_type=UIType.SDXLRefinerModel
+        description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
     )
     # TODO: precision?
 

invokeai/app/invocations/t2i_adapter.py

@@ -8,7 +8,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -45,7 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.2"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -55,6 +55,7 @@ class T2IAdapterInvocation(BaseInvocation):
     t2i_adapter_model: ModelIdentifierField = InputField(
         description="The T2I-Adapter model.",
         title="T2I-Adapter Model",
+        input=Input.Direct,
         ui_order=-1,
         ui_type=UIType.T2IAdapterModel,
     )

invokeai/app/services/download/download_default.py

@@ -318,8 +318,10 @@ class DownloadQueueService(DownloadQueueServiceBase):
         in_progress_path.rename(job.download_path)
 
     def _validate_filename(self, directory: str, filename: str) -> bool:
-        pc_name_max = get_pc_name_max(directory)
-        pc_path_max = get_pc_path_max(directory)
+        pc_name_max = os.pathconf(directory, "PC_NAME_MAX") if hasattr(os, "pathconf") else 260  # hardcoded for windows
+        pc_path_max = (
+            os.pathconf(directory, "PC_PATH_MAX") if hasattr(os, "pathconf") else 32767
+        )  # hardcoded for windows with long names enabled
         if "/" in filename:
             return False
         if filename.startswith(".."):
@@ -417,26 +419,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
             self._logger.warning(excp)
 
 
-def get_pc_name_max(directory: str) -> int:
-    if hasattr(os, "pathconf"):
-        try:
-            return os.pathconf(directory, "PC_NAME_MAX")
-        except OSError:
-            # macOS w/ external drives raise OSError
-            pass
-    return 260  # hardcoded for windows
-
-
-def get_pc_path_max(directory: str) -> int:
-    if hasattr(os, "pathconf"):
-        try:
-            return os.pathconf(directory, "PC_PATH_MAX")
-        except OSError:
-            # some platforms may not have this value
-            pass
-    return 32767  # hardcoded for windows with long names enabled
-
-
 # Example on_progress event handler to display a TQDM status bar
 # Activate with:
 #   download_service.download(DownloadJob('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().update))

invokeai/app/services/events/events_base.py

@@ -122,8 +122,6 @@ class EventServiceBase:
         source_node_id: str,
         error_type: str,
         error: str,
-        user_id: str | None,
-        project_id: str | None,
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_queue_event(
@@ -137,8 +135,6 @@ class EventServiceBase:
                 "source_node_id": source_node_id,
                 "error_type": error_type,
                 "error": error,
-                "user_id": user_id,
-                "project_id": project_id,
             },
         )
 

invokeai/app/services/image_files/image_files_base.py

@@ -4,6 +4,9 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
+from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+
 
 class ImageFileStorageBase(ABC):
     """Low-level service responsible for storing and retrieving image files."""
@@ -30,9 +33,8 @@ class ImageFileStorageBase(ABC):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
         thumbnail_size: int = 256,
     ) -> None:
         """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@@ -44,11 +46,6 @@ class ImageFileStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[str]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         """Gets the workflow of an image."""
         pass
-
-    @abstractmethod
-    def get_graph(self, image_name: str) -> Optional[str]:
-        """Gets the graph of an image."""
-        pass

invokeai/app/services/image_files/image_files_disk.py

@@ -7,7 +7,9 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
+from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 from .image_files_base import ImageFileStorageBase
@@ -54,9 +56,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
@@ -67,14 +68,13 @@ class DiskImageFileStorage(ImageFileStorageBase):
             info_dict = {}
 
             if metadata is not None:
-                info_dict["invokeai_metadata"] = metadata
-                pnginfo.add_text("invokeai_metadata", metadata)
+                metadata_json = metadata.model_dump_json()
+                info_dict["invokeai_metadata"] = metadata_json
+                pnginfo.add_text("invokeai_metadata", metadata_json)
             if workflow is not None:
-                info_dict["invokeai_workflow"] = workflow
-                pnginfo.add_text("invokeai_workflow", workflow)
-            if graph is not None:
-                info_dict["invokeai_graph"] = graph
-                pnginfo.add_text("invokeai_graph", graph)
+                workflow_json = workflow.model_dump_json()
+                info_dict["invokeai_workflow"] = workflow_json
+                pnginfo.add_text("invokeai_workflow", workflow_json)
 
             # When saving the image, the image object's info field is not populated. We need to set it
             image.info = info_dict
@@ -129,18 +129,11 @@ class DiskImageFileStorage(ImageFileStorageBase):
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
 
-    def get_workflow(self, image_name: str) -> str | None:
+    def get_workflow(self, image_name: str) -> WorkflowWithoutID | None:
         image = self.get(image_name)
         workflow = image.info.get("invokeai_workflow", None)
-        if isinstance(workflow, str):
-            return workflow
-        return None
-
-    def get_graph(self, image_name: str) -> str | None:
-        image = self.get(image_name)
-        graph = image.info.get("invokeai_graph", None)
-        if isinstance(graph, str):
-            return graph
+        if workflow is not None:
+            return WorkflowWithoutID.model_validate_json(workflow)
         return None
 
     def __validate_storage_folders(self) -> None:

invokeai/app/services/image_records/image_records_base.py

@@ -80,7 +80,7 @@ class ImageRecordStorageBase(ABC):
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
         node_id: Optional[str] = None,
-        metadata: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
     ) -> datetime:
         """Saves an image record."""
         pass

invokeai/app/services/image_records/image_records_sqlite.py

@@ -328,9 +328,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
         node_id: Optional[str] = None,
-        metadata: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
     ) -> datetime:
         try:
+            metadata_json = metadata.model_dump_json() if metadata is not None else None
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
@@ -357,7 +358,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     height,
                     node_id,
                     session_id,
-                    metadata,
+                    metadata_json,
                     is_intermediate,
                     starred,
                     has_workflow,

invokeai/app/services/images/images_base.py

@@ -12,6 +12,7 @@ from invokeai.app.services.image_records.image_records_common import (
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 
 class ImageServiceABC(ABC):
@@ -50,9 +51,8 @@ class ImageServiceABC(ABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -87,12 +87,7 @@ class ImageServiceABC(ABC):
         pass
 
     @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[str]:
-        """Gets an image's workflow."""
-        pass
-
-    @abstractmethod
-    def get_graph(self, image_name: str) -> Optional[str]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         """Gets an image's workflow."""
         pass
 

invokeai/app/services/images/images_default.py

@@ -5,6 +5,7 @@ from PIL.Image import Image as PILImageType
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 from ..image_files.image_files_common import (
     ImageFileDeleteException,
@@ -41,9 +42,8 @@ class ImageService(ImageServiceABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
-        metadata: Optional[str] = None,
-        workflow: Optional[str] = None,
-        graph: Optional[str] = None,
+        metadata: Optional[MetadataField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
             raise InvalidOriginException
@@ -64,7 +64,7 @@ class ImageService(ImageServiceABC):
                 image_category=image_category,
                 width=width,
                 height=height,
-                has_workflow=workflow is not None or graph is not None,
+                has_workflow=workflow is not None,
                 # Meta fields
                 is_intermediate=is_intermediate,
                 # Nullable fields
@@ -75,7 +75,7 @@ class ImageService(ImageServiceABC):
             if board_id is not None:
                 self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
             self.__invoker.services.image_files.save(
-                image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
+                image_name=image_name, image=image, metadata=metadata, workflow=workflow
             )
             image_dto = self.get_dto(image_name)
 
@@ -157,7 +157,7 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image metadata")
             raise e
 
-    def get_workflow(self, image_name: str) -> Optional[str]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         try:
             return self.__invoker.services.image_files.get_workflow(image_name)
         except ImageFileNotFoundException:
@@ -167,16 +167,6 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image workflow")
             raise
 
-    def get_graph(self, image_name: str) -> Optional[str]:
-        try:
-            return self.__invoker.services.image_files.get_graph(image_name)
-        except ImageFileNotFoundException:
-            self.__invoker.services.logger.error("Image file not found")
-            raise
-        except Exception:
-            self.__invoker.services.logger.error("Problem getting image graph")
-            raise
-
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         try:
             return str(self.__invoker.services.image_files.get_path(image_name, thumbnail))

invokeai/app/services/session_processor/session_processor_default.py

@@ -237,8 +237,6 @@ class DefaultSessionProcessor(SessionProcessorBase):
                                 source_node_id=source_invocation_id,
                                 error_type=e.__class__.__name__,
                                 error=error,
-                                user_id=None,
-                                project_id=None,
                             )
                             pass
 

invokeai/app/services/shared/invocation_context.py

@@ -180,9 +180,9 @@ class ImagesInterface(InvocationContextInterface):
         # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
         metadata_ = None
         if metadata:
-            metadata_ = metadata.model_dump_json()
-        elif isinstance(self._data.invocation, WithMetadata) and self._data.invocation.metadata:
-            metadata_ = self._data.invocation.metadata.model_dump_json()
+            metadata_ = metadata
+        elif isinstance(self._data.invocation, WithMetadata):
+            metadata_ = self._data.invocation.metadata
 
         # If `board_id` is provided directly, use that. Else, use the board provided by `WithBoard`, falling back to None.
         board_id_ = None
@@ -191,14 +191,6 @@ class ImagesInterface(InvocationContextInterface):
         elif isinstance(self._data.invocation, WithBoard) and self._data.invocation.board:
             board_id_ = self._data.invocation.board.board_id
 
-        workflow_ = None
-        if self._data.queue_item.workflow:
-            workflow_ = self._data.queue_item.workflow.model_dump_json()
-
-        graph_ = None
-        if self._data.queue_item.session.graph:
-            graph_ = self._data.queue_item.session.graph.model_dump_json()
-
         return self._services.images.create(
             image=image,
             is_intermediate=self._data.invocation.is_intermediate,
@@ -206,8 +198,7 @@ class ImagesInterface(InvocationContextInterface):
             board_id=board_id_,
             metadata=metadata_,
             image_origin=ResourceOrigin.INTERNAL,
-            workflow=workflow_,
-            graph=graph_,
+            workflow=self._data.queue_item.workflow,
             session_id=self._data.queue_item.session_id,
             node_id=self._data.invocation.id,
         )

invokeai/backend/image_util/__init__.py

@@ -4,4 +4,5 @@ Initialization file for invokeai.backend.image_util methods.
 
 from .infill_methods.patchmatch import PatchMatch  # noqa: F401
 from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata  # noqa: F401
+from .seamless import configure_model_padding  # noqa: F401
 from .util import InitImageResizer, make_grid  # noqa: F401

invokeai/backend/image_util/safety_checker.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 import numpy as np
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from PIL import Image, ImageFilter
+from PIL import Image
 from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
@@ -16,7 +16,6 @@ from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
-repo_id = "CompVis/stable-diffusion-safety-checker"
 CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
 
 
@@ -25,30 +24,30 @@ class SafetyChecker:
     Wrapper around SafetyChecker model.
     """
 
-    feature_extractor = None
     safety_checker = None
+    feature_extractor = None
+    tried_load: bool = False
 
     @classmethod
     def _load_safety_checker(cls):
-        if cls.safety_checker is not None and cls.feature_extractor is not None:
+        if cls.tried_load:
             return
 
         try:
-            model_path = get_config().models_path / CHECKER_PATH
-            if model_path.exists():
-                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(model_path)
-                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_path)
-            else:
-                model_path.mkdir(parents=True, exist_ok=True)
-                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
-                cls.feature_extractor.save_pretrained(model_path, safe_serialization=True)
-                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(repo_id)
-                cls.safety_checker.save_pretrained(model_path, safe_serialization=True)
+            cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(get_config().models_path / CHECKER_PATH)
+            cls.feature_extractor = AutoFeatureExtractor.from_pretrained(get_config().models_path / CHECKER_PATH)
         except Exception as e:
             logger.warning(f"Could not load NSFW checker: {str(e)}")
+        cls.tried_load = True
+
+    @classmethod
+    def safety_checker_available(cls) -> bool:
+        return Path(get_config().models_path, CHECKER_PATH).exists()
 
     @classmethod
     def has_nsfw_concept(cls, image: Image.Image) -> bool:
+        if not cls.safety_checker_available() and cls.tried_load:
+            return False
         cls._load_safety_checker()
         if cls.safety_checker is None or cls.feature_extractor is None:
             return False
@@ -61,24 +60,3 @@ class SafetyChecker:
         with SilenceWarnings():
             checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
         return has_nsfw_concept[0]
-
-    @classmethod
-    def blur_if_nsfw(cls, image: Image.Image) -> Image.Image:
-        if cls.has_nsfw_concept(image):
-            logger.warning("A potentially NSFW image has been detected. Image will be blurred.")
-            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
-            caution = cls._get_caution_img()
-            # Center the caution image on the blurred image
-            x = (blurry_image.width - caution.width) // 2
-            y = (blurry_image.height - caution.height) // 2
-            blurry_image.paste(caution, (x, y), caution)
-            image = blurry_image
-
-        return image
-
-    @classmethod
-    def _get_caution_img(cls) -> Image.Image:
-        import invokeai.app.assets.images as image_assets
-
-        caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
-        return caution.resize((caution.width // 2, caution.height // 2))

invokeai/backend/image_util/seamless.py

@@ -0,0 +1,52 @@
+import torch.nn as nn
+
+
+def _conv_forward_asymmetric(self, input, weight, bias):
+    """
+    Patch for Conv2d._conv_forward that supports asymmetric padding
+    """
+    working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
+    working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
+    return nn.functional.conv2d(
+        working,
+        weight,
+        bias,
+        self.stride,
+        nn.modules.utils._pair(0),
+        self.dilation,
+        self.groups,
+    )
+
+
+def configure_model_padding(model, seamless, seamless_axes):
+    """
+    Modifies the 2D convolution layers to use a circular padding mode based on
+    the `seamless` and `seamless_axes` options.
+    """
+    # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+            if seamless:
+                m.asymmetric_padding_mode = {}
+                m.asymmetric_padding = {}
+                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
+                m.asymmetric_padding["x"] = (
+                    m._reversed_padding_repeated_twice[0],
+                    m._reversed_padding_repeated_twice[1],
+                    0,
+                    0,
+                )
+                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
+                m.asymmetric_padding["y"] = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+            else:
+                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
+                if hasattr(m, "asymmetric_padding_mode"):
+                    del m.asymmetric_padding_mode
+                if hasattr(m, "asymmetric_padding"):
+                    del m.asymmetric_padding

invokeai/backend/image_util/util.py

@@ -144,8 +144,10 @@ def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.n
     h = float(input_image.shape[0])
     w = float(input_image.shape[1])
     scaling_factor = float(resolution) / min(h, w)
-    h = int(h * scaling_factor)
-    w = int(w * scaling_factor)
+    h *= scaling_factor
+    w *= scaling_factor
+    h = int(np.round(h / 64.0)) * 64
+    w = int(np.round(w / 64.0)) * 64
     if scaling_factor > 1:
         return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_LANCZOS4)
     else:

invokeai/backend/model_manager/probe.py

@@ -51,7 +51,6 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched
     },
     BaseModelType.StableDiffusionXL: {
         ModelVariantType.Normal: "sd_xl_base.yaml",
-        ModelVariantType.Inpaint: "sd_xl_inpaint.yaml",
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelVariantType.Normal: "sd_xl_refiner.yaml",

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -13,7 +13,6 @@ from diffusers import (
     LCMScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
-    TCDScheduler,
     UniPCMultistepScheduler,
 )
 
@@ -41,5 +40,4 @@ SCHEDULER_MAP = {
     "dpmpp_sde_k": (DPMSolverSDEScheduler, {"use_karras_sigmas": True, "noise_sampler_seed": 0}),
     "unipc": (UniPCMultistepScheduler, {"cpu_only": True}),
     "lcm": (LCMScheduler, {}),
-    "tcd": (TCDScheduler, {}),
 }

invokeai/backend/stable_diffusion/seamless.py

@@ -1,51 +1,89 @@
-from contextlib import contextmanager
-from typing import Callable, List, Optional, Tuple, Union
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import Callable, List, Union
 
-import torch
 import torch.nn as nn
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
-from diffusers.models.lora import LoRACompatibleConv
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 
 
+def _conv_forward_asymmetric(self, input, weight, bias):
+    """
+    Patch for Conv2d._conv_forward that supports asymmetric padding
+    """
+    working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
+    working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
+    return nn.functional.conv2d(
+        working,
+        weight,
+        bias,
+        self.stride,
+        nn.modules.utils._pair(0),
+        self.dilation,
+        self.groups,
+    )
+
+
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
     if not seamless_axes:
         yield
         return
 
-    # override conv_forward
-    # https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
-    def _conv_forward_asymmetric(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
-        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
-        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
-        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
-        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
-        return torch.nn.functional.conv2d(
-            working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
-        )
-
-    original_layers: List[Tuple[nn.Conv2d, Callable]] = []
-
+    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
+    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:
-        x_mode = "circular" if "x" in seamless_axes else "constant"
-        y_mode = "circular" if "y" in seamless_axes else "constant"
+        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
+        skipped_layers = 1
+        for m_name, m in model.named_modules():
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                continue
 
-        conv_layers: List[torch.nn.Conv2d] = []
+            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
+                # down_blocks.1.resnets.1.conv1
+                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
+                block_num = int(block_num)
+                resnet_num = int(resnet_num)
 
-        for module in model.modules():
-            if isinstance(module, torch.nn.Conv2d):
-                conv_layers.append(module)
+                if block_num >= len(model.down_blocks) - skipped_layers:
+                    continue
 
-        for layer in conv_layers:
-            if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
-                layer.lora_layer = lambda *x: 0
-            original_layers.append((layer, layer._conv_forward))
-            layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)
+                # Skip the second resnet (could be configurable)
+                if resnet_num > 0:
+                    continue
+
+                # Skip Conv2d layers (could be configurable)
+                if submodule_name == "conv2":
+                    continue
+
+            m.asymmetric_padding_mode = {}
+            m.asymmetric_padding = {}
+            m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
+            m.asymmetric_padding["x"] = (
+                m._reversed_padding_repeated_twice[0],
+                m._reversed_padding_repeated_twice[1],
+                0,
+                0,
+            )
+            m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
+            m.asymmetric_padding["y"] = (
+                0,
+                0,
+                m._reversed_padding_repeated_twice[2],
+                m._reversed_padding_repeated_twice[3],
+            )
+
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 
     finally:
-        for layer, orig_conv_forward in original_layers:
-            layer._conv_forward = orig_conv_forward
+        for module, orig_conv_forward in to_restore:
+            module._conv_forward = orig_conv_forward
+            if hasattr(module, "asymmetric_padding_mode"):
+                del module.asymmetric_padding_mode
+            if hasattr(module, "asymmetric_padding"):
+                del module.asymmetric_padding

invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml

@@ -1,98 +0,0 @@
-model:
-  target: sgm.models.diffusion.DiffusionEngine
-  params:
-    scale_factor: 0.13025
-    disable_first_stage_autocast: True
-
-    denoiser_config:
-      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
-      params:
-        num_idx: 1000
-
-        weighting_config:
-          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
-        scaling_config:
-          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
-        discretization_config:
-          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
-
-    network_config:
-      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        adm_in_channels: 2816
-        num_classes: sequential
-        use_checkpoint: True
-        in_channels: 9
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [4, 2]
-        num_res_blocks: 2
-        channel_mult: [1, 2, 4]
-        num_head_channels: 64
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
-        context_dim: 2048
-        spatial_transformer_attn_type: softmax-xformers
-        legacy: False
-
-    conditioner_config:
-      target: sgm.modules.GeneralConditioner
-      params:
-        emb_models:
-          # crossattn cond
-          - is_trainable: False
-            input_key: txt
-            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
-            params:
-              layer: hidden
-              layer_idx: 11
-          # crossattn and vector cond
-          - is_trainable: False
-            input_key: txt
-            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
-            params:
-              arch: ViT-bigG-14
-              version: laion2b_s39b_b160k
-              freeze: True
-              layer: penultimate
-              always_return_pooled: True
-              legacy: False
-          # vector cond
-          - is_trainable: False
-            input_key: original_size_as_tuple
-            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-            params:
-              outdim: 256  # multiplied by two
-          # vector cond
-          - is_trainable: False
-            input_key: crop_coords_top_left
-            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-            params:
-              outdim: 256  # multiplied by two
-          # vector cond
-          - is_trainable: False
-            input_key: target_size_as_tuple
-            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
-            params:
-              outdim: 256  # multiplied by two
-
-    first_stage_config:
-      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          attn_type: vanilla-xformers
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult: [1, 2, 4, 4]
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity

invokeai/frontend/web/.eslintrc.js

@@ -10,8 +10,6 @@ module.exports = {
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
     // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
     'i18next/no-literal-string': 'error',
-    // https://eslint.org/docs/latest/rules/no-console
-    'no-console': 'error',
   },
   overrides: [
     /**

invokeai/frontend/web/.gitignore

@@ -43,5 +43,4 @@ stats.html
 yalc.lock
 
 # vitest
-tsconfig.vitest-temp.json
-coverage/
+tsconfig.vitest-temp.json

invokeai/frontend/web/package.json

@@ -25,7 +25,7 @@
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
     "lint:knip": "knip",
-    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
+    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:0 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
@@ -35,7 +35,6 @@
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "test": "vitest",
-    "test:ui": "vitest --coverage --ui",
     "test:no-watch": "vitest --no-watch"
   },
   "madge": {
@@ -53,48 +52,47 @@
   },
   "dependencies": {
     "@chakra-ui/react-use-size": "^2.1.0",
-    "@dagrejs/dagre": "^1.1.2",
-    "@dagrejs/graphlib": "^2.2.2",
+    "@dagrejs/dagre": "^1.1.1",
+    "@dagrejs/graphlib": "^2.2.1",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
-    "@fontsource-variable/inter": "^5.0.18",
-    "@invoke-ai/ui-library": "^0.0.25",
+    "@fontsource-variable/inter": "^5.0.17",
+    "@invoke-ai/ui-library": "^0.0.21",
     "@nanostores/react": "^0.7.2",
-    "@reduxjs/toolkit": "2.2.3",
+    "@reduxjs/toolkit": "2.2.2",
     "@roarr/browser-log-writer": "^1.3.0",
     "chakra-react-select": "^4.7.6",
     "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
-    "fracturedjsonjs": "^4.0.1",
-    "framer-motion": "^11.1.8",
-    "i18next": "^23.11.3",
-    "i18next-http-backend": "^2.5.1",
+    "framer-motion": "^11.0.22",
+    "i18next": "^23.10.1",
+    "i18next-http-backend": "^2.5.0",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.6",
     "lodash-es": "^4.17.21",
-    "nanostores": "^0.10.3",
+    "nanostores": "^0.10.0",
     "new-github-issue-url": "^1.0.0",
-    "overlayscrollbars": "^2.7.3",
-    "overlayscrollbars-react": "^0.5.6",
+    "overlayscrollbars": "^2.6.1",
+    "overlayscrollbars-react": "^0.5.5",
     "query-string": "^9.0.0",
-    "react": "^18.3.1",
+    "react": "^18.2.0",
     "react-colorful": "^5.6.1",
-    "react-dom": "^18.3.1",
+    "react-dom": "^18.2.0",
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.51.4",
+    "react-hook-form": "^7.51.2",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^14.1.1",
-    "react-icons": "^5.2.0",
+    "react-i18next": "^14.1.0",
+    "react-icons": "^5.0.1",
     "react-konva": "^18.2.10",
-    "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.0.19",
+    "react-redux": "9.1.0",
+    "react-resizable-panels": "^2.0.16",
     "react-select": "5.8.0",
     "react-use": "^17.5.0",
-    "react-virtuoso": "^4.7.10",
-    "reactflow": "^11.11.3",
+    "react-virtuoso": "^4.7.5",
+    "reactflow": "^11.10.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
     "redux-undo": "^1.1.0",
@@ -103,11 +101,10 @@
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
     "use-debounce": "^10.0.0",
-    "use-device-pixel-ratio": "^1.1.2",
     "use-image": "^1.1.1",
     "uuid": "^9.0.1",
-    "zod": "^3.23.6",
-    "zod-validation-error": "^3.2.0"
+    "zod": "^3.22.4",
+    "zod-validation-error": "^3.0.3"
   },
   "peerDependencies": {
     "@chakra-ui/react": "^2.8.2",
@@ -118,42 +115,40 @@
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.0.10",
-    "@storybook/addon-interactions": "^8.0.10",
-    "@storybook/addon-links": "^8.0.10",
-    "@storybook/addon-storysource": "^8.0.10",
-    "@storybook/manager-api": "^8.0.10",
-    "@storybook/react": "^8.0.10",
-    "@storybook/react-vite": "^8.0.10",
-    "@storybook/theming": "^8.0.10",
+    "@storybook/addon-essentials": "^8.0.4",
+    "@storybook/addon-interactions": "^8.0.4",
+    "@storybook/addon-links": "^8.0.4",
+    "@storybook/addon-storysource": "^8.0.4",
+    "@storybook/manager-api": "^8.0.4",
+    "@storybook/react": "^8.0.4",
+    "@storybook/react-vite": "^8.0.4",
+    "@storybook/theming": "^8.0.4",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.12.10",
-    "@types/react": "^18.3.1",
-    "@types/react-dom": "^18.3.0",
+    "@types/node": "^20.11.30",
+    "@types/react": "^18.2.73",
+    "@types/react-dom": "^18.2.22",
     "@types/uuid": "^9.0.8",
     "@vitejs/plugin-react-swc": "^3.6.0",
-    "@vitest/coverage-v8": "^1.5.0",
-    "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.0",
     "eslint-plugin-i18next": "^6.0.3",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.12.3",
+    "knip": "^5.6.1",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.7.5",
     "prettier": "^3.2.5",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.0.10",
+    "storybook": "^8.0.4",
     "ts-toolbelt": "^9.6.0",
     "tsafe": "^1.6.6",
-    "typescript": "^5.4.5",
-    "vite": "^5.2.11",
-    "vite-plugin-css-injected-by-js": "^3.5.1",
-    "vite-plugin-dts": "^3.9.1",
+    "typescript": "^5.4.3",
+    "vite": "^5.2.6",
+    "vite-plugin-css-injected-by-js": "^3.5.0",
+    "vite-plugin-dts": "^3.8.0",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.3.2",
-    "vitest": "^1.6.0"
+    "vitest": "^1.4.0"
   }
 }

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

@@ -76,9 +76,7 @@
         "aboutHeading": "Nutzen Sie Ihre kreative Energie",
         "toResolve": "Lösen",
         "add": "Hinzufügen",
-        "loglevel": "Protokoll Stufe",
-        "selected": "Ausgewählt",
-        "beta": "Beta"
+        "loglevel": "Protokoll Stufe"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -88,7 +86,7 @@
         "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
         "deleteImage_one": "Lösche Bild",
-        "deleteImage_other": "Lösche {{count}} Bilder",
+        "deleteImage_other": "",
         "copy": "Kopieren",
         "download": "Runterladen",
         "setCurrentImage": "Setze aktuelle Bild",
@@ -399,14 +397,7 @@
         "cancel": "Stornieren",
         "defaultSettingsSaved": "Standardeinstellungen gespeichert",
         "addModels": "Model hinzufügen",
-        "deleteModelImage": "Lösche Model Bild",
-        "hfTokenInvalidErrorMessage": "Falscher oder fehlender HuggingFace Schlüssel.",
-        "huggingFaceRepoID": "HuggingFace Repo ID",
-        "hfToken": "HuggingFace Schlüssel",
-        "hfTokenInvalid": "Falscher oder fehlender HF Schlüssel",
-        "huggingFacePlaceholder": "besitzer/model-name",
-        "hfTokenSaved": "HF Schlüssel gespeichert",
-        "hfTokenUnableToVerify": "Konnte den HF Schlüssel nicht validieren"
+        "deleteModelImage": "Lösche Model Bild"
     },
     "parameters": {
         "images": "Bilder",
@@ -695,11 +686,7 @@
         "hands": "Hände",
         "dwOpenpose": "DW Openpose",
         "dwOpenposeDescription": "Posenschätzung mit DW Openpose",
-        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus",
-        "ipAdapterMethod": "Methode",
-        "composition": "Nur Komposition",
-        "full": "Voll",
-        "style": "Nur Style"
+        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus"
     },
     "queue": {
         "status": "Status",
@@ -730,6 +717,7 @@
         "resume": "Wieder aufnehmen",
         "item": "Auftrag",
         "notReady": "Warteschlange noch nicht bereit",
+        "queueCountPrediction": "{{promptsCount}} Prompts × {{iterations}} Iterationen -> {{count}} Generationen",
         "clearQueueAlertDialog": "\"Die Warteschlange leeren\" stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
         "completedIn": "Fertig in",
         "cancelBatchSucceeded": "Stapel abgebrochen",

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

@@ -88,13 +88,11 @@
         "negativePrompt": "Negative Prompt",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
-        "editor": "Editor",
         "error": "Error",
         "file": "File",
         "folder": "Folder",
         "format": "format",
         "githubLabel": "Github",
-        "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
@@ -142,11 +140,7 @@
         "blue": "Blue",
         "alpha": "Alpha",
         "selected": "Selected",
-        "tab": "Tab",
-        "viewing": "Viewing",
-        "viewingDesc": "Review images in a large gallery view",
-        "editing": "Editing",
-        "editingDesc": "Edit on the Control Layers canvas"
+        "viewer": "Viewer"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -162,7 +156,6 @@
         "balanced": "Balanced",
         "base": "Base",
         "beginEndStepPercent": "Begin / End Step Percentage",
-        "beginEndStepPercentShort": "Begin/End %",
         "bgth": "bg_th",
         "canny": "Canny",
         "cannyDescription": "Canny edge detection",
@@ -231,11 +224,10 @@
         "composition": "Composition Only",
         "safe": "Safe",
         "saveControlImage": "Save Control Image",
-        "scribble": "Scribble",
+        "scribble": "scribble",
         "selectModel": "Select a model",
         "selectCLIPVisionModel": "Select a CLIP Vision model",
-        "setControlImageDimensions": "Copy size to W/H (optimize for model)",
-        "setControlImageDimensionsForce": "Copy size to W/H (ignore model)",
+        "setControlImageDimensions": "Set Control Image Dimensions To W/H",
         "showAdvanced": "Show Advanced",
         "small": "Small",
         "toggleControlNet": "Toggle this ControlNet",
@@ -261,6 +253,7 @@
         "queue": "Queue",
         "queueFront": "Add to Front of Queue",
         "queueBack": "Add to Queue",
+        "queueCountPrediction": "{{promptsCount}} prompts \u00d7 {{iterations}} iterations -> {{count}} generations",
         "queueEmpty": "Queue Empty",
         "enqueueing": "Queueing Batch",
         "resume": "Resume",
@@ -313,13 +306,7 @@
         "batchFailedToQueue": "Failed to Queue Batch",
         "graphQueued": "Graph queued",
         "graphFailedToQueue": "Failed to queue graph",
-        "openQueue": "Open Queue",
-        "prompts_one": "Prompt",
-        "prompts_other": "Prompts",
-        "iterations_one": "Iteration",
-        "iterations_other": "Iterations",
-        "generations_one": "Generation",
-        "generations_other": "Generations"
+        "openQueue": "Open Queue"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -595,10 +582,6 @@
         "upscale": {
             "desc": "Upscale the current image",
             "title": "Upscale"
-        },
-        "toggleViewer": {
-            "desc": "Switches between the Image Viewer and workspace for the current tab.",
-            "title": "Toggle Image Viewer"
         }
     },
     "metadata": {
@@ -774,15 +757,10 @@
         "cannotConnectOutputToOutput": "Cannot connect output to output",
         "cannotConnectToSelf": "Cannot connect to self",
         "cannotDuplicateConnection": "Cannot create duplicate connections",
-        "cannotMixAndMatchCollectionItemTypes": "Cannot mix and match collection item types",
-        "missingNode": "Missing invocation node",
-        "missingInvocationTemplate": "Missing invocation template",
-        "missingFieldTemplate": "Missing field template",
         "nodePack": "Node pack",
         "collection": "Collection",
-        "singleFieldType": "{{name}} (Single)",
-        "collectionFieldType": "{{name}} (Collection)",
-        "collectionOrScalarFieldType": "{{name}} (Single or Collection)",
+        "collectionFieldType": "{{name}} Collection",
+        "collectionOrScalarFieldType": "{{name}} Collection|Scalar",
         "colorCodeEdges": "Color-Code Edges",
         "colorCodeEdgesHelp": "Color-code edges according to their connected fields",
         "connectionWouldCreateCycle": "Connection would create a cycle",
@@ -884,7 +862,6 @@
         "versionUnknown": " Version Unknown",
         "workflow": "Workflow",
         "graph": "Graph",
-        "noGraph": "No Graph",
         "workflowAuthor": "Author",
         "workflowContact": "Contact",
         "workflowDescription": "Short Description",
@@ -938,37 +915,23 @@
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} missing input",
             "missingNodeTemplate": "Missing node template",
             "noControlImageForControlAdapter": "Control Adapter #{{number}} has no control image",
-            "imageNotProcessedForControlAdapter": "Control Adapter #{{number}}'s image is not processed",
             "noInitialImageSelected": "No initial image selected",
             "noModelForControlAdapter": "Control Adapter #{{number}} has no model selected.",
             "incompatibleBaseModelForControlAdapter": "Control Adapter #{{number}} model is incompatible with main model.",
             "noModelSelected": "No model selected",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected",
-            "layer": {
-                "initialImageNoImageSelected": "no initial image selected",
-                "controlAdapterNoModelSelected": "no Control Adapter model selected",
-                "controlAdapterIncompatibleBaseModel": "incompatible Control Adapter base model",
-                "controlAdapterNoImageSelected": "no Control Adapter image selected",
-                "controlAdapterImageNotProcessed": "Control Adapter image not processed",
-                "t2iAdapterIncompatibleDimensions": "T2I Adapter requires image dimension to be multiples of {{multiple}}",
-                "ipAdapterNoModelSelected": "no IP adapter selected",
-                "ipAdapterIncompatibleBaseModel": "incompatible IP Adapter base model",
-                "ipAdapterNoImageSelected": "no IP Adapter image selected",
-                "rgNoPromptsOrIPAdapters": "no text prompts or IP Adapters",
-                "rgNoRegion": "no region selected"
-            }
+            "systemDisconnected": "System disconnected"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
-        "globalNegativePromptPlaceholder": "Global Negative Prompt",
         "noiseThreshold": "Noise Threshold",
         "patchmatchDownScaleSize": "Downscale",
         "perlinNoise": "Perlin Noise",
         "positivePromptPlaceholder": "Positive Prompt",
-        "globalPositivePromptPlaceholder": "Global Positive Prompt",
         "iterations": "Iterations",
+        "iterationsWithCount_one": "{{count}} Iteration",
+        "iterationsWithCount_other": "{{count}} Iterations",
         "scale": "Scale",
         "scaleBeforeProcessing": "Scale Before Processing",
         "scaledHeight": "Scaled H",
@@ -1548,7 +1511,7 @@
     "app": {
         "storeNotInitialized": "Store is not initialized"
     },
-    "controlLayers": {
+    "regionalPrompts": {
         "deleteAll": "Delete All",
         "addLayer": "Add Layer",
         "moveToFront": "Move to Front",
@@ -1556,7 +1519,8 @@
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "controlLayers": "Control Layers",
+        "regionalControl": "Regional Control (ALPHA)",
+        "enableRegionalPrompts": "Enable $t(regionalPrompts.regionalPrompts)",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
         "toggleVisibility": "Toggle Layer Visibility",
@@ -1567,35 +1531,6 @@
         "maskPreviewColor": "Mask Preview Color",
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
-        "addIPAdapter": "Add $t(common.ipAdapter)",
-        "regionalGuidance": "Regional Guidance",
-        "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "opacity": "Opacity",
-        "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
-        "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
-        "globalIPAdapter": "Global $t(common.ipAdapter)",
-        "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
-        "globalInitialImage": "Global Initial Image",
-        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "opacityFilter": "Opacity Filter",
-        "clearProcessor": "Clear Processor",
-        "resetProcessor": "Reset Processor to Defaults",
-        "noLayersAdded": "No Layers Added",
-        "layers_one": "Layer",
-        "layers_other": "Layers"
-    },
-    "ui": {
-        "tabs": {
-            "generation": "Generation",
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "canvas": "Canvas",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "workflows": "Workflows",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "models": "Models",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Queue",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)"
-        }
+        "addIPAdapter": "Add $t(common.ipAdapter)"
     }
 }

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

@@ -25,24 +25,7 @@
         "areYouSure": "¿Estas seguro?",
         "batch": "Administrador de lotes",
         "modelManager": "Administrador de modelos",
-        "communityLabel": "Comunidad",
-        "direction": "Dirección",
-        "ai": "Ia",
-        "add": "Añadir",
-        "auto": "Automático",
-        "copyError": "Error $t(gallery.copy)",
-        "details": "Detalles",
-        "or": "o",
-        "checkpoint": "Punto de control",
-        "controlNet": "ControlNet",
-        "aboutHeading": "Sea dueño de su poder creativo",
-        "advanced": "Avanzado",
-        "data": "Fecha",
-        "delete": "Borrar",
-        "copy": "Copiar",
-        "beta": "Beta",
-        "on": "En",
-        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:"
+        "communityLabel": "Comunidad"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -460,13 +443,7 @@
         "previousImage": "Imagen anterior",
         "nextImage": "Siguiente imagen",
         "showOptionsPanel": "Mostrar el panel lateral",
-        "menu": "Menú",
-        "showGalleryPanel": "Mostrar panel de galería",
-        "loadMore": "Cargar más",
-        "about": "Acerca de",
-        "createIssue": "Crear un problema",
-        "resetUI": "Interfaz de usuario $t(accessibility.reset)",
-        "mode": "Modo"
+        "menu": "Menú"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -479,68 +456,5 @@
         "reloadNodeTemplates": "Recargar las plantillas de nodos",
         "loadWorkflow": "Cargar el flujo de trabajo",
         "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON"
-    },
-    "boards": {
-        "autoAddBoard": "Agregar panel automáticamente",
-        "changeBoard": "Cambiar el panel",
-        "clearSearch": "Borrar la búsqueda",
-        "deleteBoard": "Borrar el panel",
-        "selectBoard": "Seleccionar un panel",
-        "uncategorized": "Sin categoría",
-        "cancel": "Cancelar",
-        "addBoard": "Agregar un panel",
-        "movingImagesToBoard_one": "Moviendo {{count}} imagen al panel:",
-        "movingImagesToBoard_many": "Moviendo {{count}} imágenes al panel:",
-        "movingImagesToBoard_other": "Moviendo {{count}} imágenes al panel:",
-        "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
-        "deleteBoardAndImages": "Borrar el panel y las imágenes",
-        "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar",
-        "move": "Mover",
-        "menuItemAutoAdd": "Agregar automáticamente a este panel",
-        "searchBoard": "Buscando paneles…",
-        "topMessage": "Este panel contiene imágenes utilizadas en las siguientes funciones:",
-        "downloadBoard": "Descargar panel",
-        "deleteBoardOnly": "Borrar solo el panel",
-        "myBoard": "Mi panel",
-        "noMatching": "No hay paneles que coincidan"
-    },
-    "accordions": {
-        "compositing": {
-            "title": "Composición",
-            "infillTab": "Relleno"
-        },
-        "generation": {
-            "title": "Generación"
-        },
-        "image": {
-            "title": "Imagen"
-        },
-        "control": {
-            "title": "Control"
-        },
-        "advanced": {
-            "options": "$t(accordions.advanced.title) opciones",
-            "title": "Avanzado"
-        }
-    },
-    "ui": {
-        "tabs": {
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "canvas": "Lienzo",
-            "generation": "Generación",
-            "queue": "Cola",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "workflows": "Flujos de trabajo",
-            "models": "Modelos",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)"
-        }
-    },
-    "controlLayers": {
-        "layers_one": "Capa",
-        "layers_many": "Capas",
-        "layers_other": "Capas"
     }
 }

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

@@ -5,7 +5,7 @@
         "reportBugLabel": "Segnala un errore",
         "settingsLabel": "Impostazioni",
         "img2img": "Immagine a Immagine",
-        "unifiedCanvas": "Tela",
+        "unifiedCanvas": "Tela unificata",
         "nodes": "Flussi di lavoro",
         "upload": "Caricamento",
         "load": "Carica",
@@ -74,18 +74,7 @@
         "file": "File",
         "toResolve": "Da risolvere",
         "add": "Aggiungi",
-        "loglevel": "Livello di log",
-        "beta": "Beta",
-        "positivePrompt": "Prompt positivo",
-        "negativePrompt": "Prompt negativo",
-        "selected": "Selezionato",
-        "goTo": "Vai a",
-        "editor": "Editor",
-        "tab": "Scheda",
-        "viewing": "Visualizza",
-        "viewingDesc": "Rivedi le immagini in un'ampia vista della galleria",
-        "editing": "Modifica",
-        "editingDesc": "Modifica nell'area Livelli di controllo"
+        "loglevel": "Livello di log"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -191,8 +180,8 @@
             "desc": "Mostra le informazioni sui metadati dell'immagine corrente"
         },
         "sendToImageToImage": {
-            "title": "Invia a Generazione da immagine",
-            "desc": "Invia l'immagine corrente a Generazione da immagine"
+            "title": "Invia a Immagine a Immagine",
+            "desc": "Invia l'immagine corrente a da Immagine a Immagine"
         },
         "deleteImage": {
             "title": "Elimina immagine",
@@ -345,10 +334,6 @@
         "remixImage": {
             "desc": "Utilizza tutti i parametri tranne il seme dell'immagine corrente",
             "title": "Remixa l'immagine"
-        },
-        "toggleViewer": {
-            "title": "Attiva/disattiva il visualizzatore di immagini",
-            "desc": "Passa dal Visualizzatore immagini all'area di lavoro per la scheda corrente."
         }
     },
     "modelManager": {
@@ -486,8 +471,8 @@
         "scaledHeight": "Altezza ridimensionata",
         "infillMethod": "Metodo di riempimento",
         "tileSize": "Dimensione piastrella",
-        "sendToImg2Img": "Invia a Generazione da immagine",
-        "sendToUnifiedCanvas": "Invia alla Tela",
+        "sendToImg2Img": "Invia a Immagine a Immagine",
+        "sendToUnifiedCanvas": "Invia a Tela Unificata",
         "downloadImage": "Scarica l'immagine",
         "usePrompt": "Usa Prompt",
         "useSeed": "Usa Seme",
@@ -523,11 +508,13 @@
             "incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo #{{number}} non è compatibile con il modello principale.",
             "missingNodeTemplate": "Modello di nodo mancante",
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante",
-            "missingFieldTemplate": "Modello di campo mancante",
-            "imageNotProcessedForControlAdapter": "L'immagine dell'adattatore di controllo #{{number}} non è stata elaborata"
+            "missingFieldTemplate": "Modello di campo mancante"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
+        "iterationsWithCount_one": "{{count}} Iterazione",
+        "iterationsWithCount_many": "{{count}} Iterazioni",
+        "iterationsWithCount_other": "{{count}} Iterazioni",
         "isAllowedToUpscale": {
             "useX2Model": "L'immagine è troppo grande per l'ampliamento con il modello x4, utilizza il modello x2",
             "tooLarge": "L'immagine è troppo grande per l'ampliamento, seleziona un'immagine più piccola"
@@ -547,10 +534,7 @@
         "infillMosaicMinColor": "Colore minimo",
         "infillMosaicMaxColor": "Colore massimo",
         "infillMosaicTileHeight": "Altezza piastrella",
-        "infillColorValue": "Colore di riempimento",
-        "globalSettings": "Impostazioni globali",
-        "globalPositivePromptPlaceholder": "Prompt positivo globale",
-        "globalNegativePromptPlaceholder": "Prompt negativo globale"
+        "infillColorValue": "Colore di riempimento"
     },
     "settings": {
         "models": "Modelli",
@@ -575,7 +559,7 @@
         "intermediatesCleared_one": "Cancellata {{count}} immagine intermedia",
         "intermediatesCleared_many": "Cancellate {{count}} immagini intermedie",
         "intermediatesCleared_other": "Cancellate {{count}} immagini intermedie",
-        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato della Tela e degli Adattatori di Controllo.",
+        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato di Tela Unificata e ControlNet.",
         "intermediatesClearedFailed": "Problema con la cancellazione delle immagini intermedie",
         "clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
         "clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
@@ -591,8 +575,8 @@
         "imageCopied": "Immagine copiata",
         "imageNotLoadedDesc": "Impossibile trovare l'immagine",
         "canvasMerged": "Tela unita",
-        "sentToImageToImage": "Inviato a Generazione da immagine",
-        "sentToUnifiedCanvas": "Inviato alla Tela",
+        "sentToImageToImage": "Inviato a Immagine a Immagine",
+        "sentToUnifiedCanvas": "Inviato a Tela Unificata",
         "parametersNotSet": "Parametri non impostati",
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "serverError": "Errore del Server",
@@ -811,7 +795,7 @@
         "float": "In virgola mobile",
         "currentImageDescription": "Visualizza l'immagine corrente nell'editor dei nodi",
         "fieldTypesMustMatch": "I tipi di campo devono corrispondere",
-        "edge": "Collegamento",
+        "edge": "Bordo",
         "currentImage": "Immagine corrente",
         "integer": "Numero Intero",
         "inputMayOnlyHaveOneConnection": "L'ingresso può avere solo una connessione",
@@ -861,9 +845,7 @@
         "resetToDefaultValue": "Ripristina il valore predefinito",
         "noFieldsViewMode": "Questo flusso di lavoro non ha campi selezionati da visualizzare. Visualizza il flusso di lavoro completo per configurare i valori.",
         "edit": "Modifica",
-        "graph": "Grafico",
-        "showEdgeLabelsHelp": "Mostra etichette sui collegamenti, che indicano i nodi collegati",
-        "showEdgeLabels": "Mostra le etichette del collegamento"
+        "graph": "Grafico"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -940,7 +922,7 @@
         "colorMapTileSize": "Dimensione piastrella",
         "mediapipeFaceDescription": "Rilevamento dei volti tramite Mediapipe",
         "hedDescription": "Rilevamento dei bordi nidificati olisticamente",
-        "setControlImageDimensions": "Copia le dimensioni in L/A (ottimizza per il modello)",
+        "setControlImageDimensions": "Imposta le dimensioni dell'immagine di controllo su L/A",
         "maxFaces": "Numero massimo di volti",
         "addT2IAdapter": "Aggiungi $t(common.t2iAdapter)",
         "addControlNet": "Aggiungi $t(common.controlNet)",
@@ -969,17 +951,12 @@
         "mediapipeFace": "Mediapipe Volto",
         "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
         "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
-        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision",
-        "ipAdapterMethod": "Metodo",
-        "full": "Completo",
-        "composition": "Solo la composizione",
-        "style": "Solo lo stile",
-        "beginEndStepPercentShort": "Inizio/Fine %",
-        "setControlImageDimensionsForce": "Copia le dimensioni in L/A (ignora il modello)"
+        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
         "queueBack": "Aggiungi alla coda",
+        "queueCountPrediction": "{{promptsCount}} prompt × {{iterations}} iterazioni -> {{count}} generazioni",
         "queue": "Coda",
         "status": "Stato",
         "pruneSucceeded": "Rimossi {{item_count}} elementi completati dalla coda",
@@ -1016,7 +993,7 @@
         "cancelBatchSucceeded": "Lotto annullato",
         "clearTooltip": "Annulla e cancella tutti gli elementi",
         "current": "Attuale",
-        "pauseTooltip": "Sospendi l'elaborazione",
+        "pauseTooltip": "Sospende l'elaborazione",
         "failed": "Falliti",
         "cancelItem": "Annulla l'elemento",
         "next": "Prossimo",
@@ -1417,12 +1394,6 @@
             "paragraphs": [
                 "La dimensione del bordo del passaggio di coerenza."
             ]
-        },
-        "ipAdapterMethod": {
-            "heading": "Metodo",
-            "paragraphs": [
-                "Metodo con cui applicare l'adattatore IP corrente."
-            ]
         }
     },
     "sdxl": {
@@ -1551,56 +1522,5 @@
         "compatibleEmbeddings": "Incorporamenti compatibili",
         "addPromptTrigger": "Aggiungi Trigger nel prompt",
         "noMatchingTriggers": "Nessun Trigger corrispondente"
-    },
-    "controlLayers": {
-        "opacityFilter": "Filtro opacità",
-        "deleteAll": "Cancella tutto",
-        "addLayer": "Aggiungi Livello",
-        "moveToFront": "Sposta in primo piano",
-        "moveToBack": "Sposta in fondo",
-        "moveForward": "Sposta avanti",
-        "moveBackward": "Sposta indietro",
-        "brushSize": "Dimensioni del pennello",
-        "globalMaskOpacity": "Opacità globale della maschera",
-        "autoNegative": "Auto Negativo",
-        "toggleVisibility": "Attiva/disattiva la visibilità dei livelli",
-        "deletePrompt": "Cancella il prompt",
-        "debugLayers": "Debug dei Livelli",
-        "rectangle": "Rettangolo",
-        "maskPreviewColor": "Colore anteprima maschera",
-        "addPositivePrompt": "Aggiungi $t(common.positivePrompt)",
-        "addNegativePrompt": "Aggiungi $t(common.negativePrompt)",
-        "addIPAdapter": "Aggiungi $t(common.ipAdapter)",
-        "regionalGuidance": "Guida regionale",
-        "regionalGuidanceLayer": "$t(unifiedCanvas.layer) $t(controlLayers.regionalGuidance)",
-        "opacity": "Opacità",
-        "globalControlAdapter": "$t(controlnet.controlAdapter_one) Globale",
-        "globalControlAdapterLayer": "$t(controlnet.controlAdapter_one) - $t(unifiedCanvas.layer) Globale",
-        "globalIPAdapter": "$t(common.ipAdapter) Globale",
-        "globalIPAdapterLayer": "$t(common.ipAdapter) - $t(unifiedCanvas.layer) Globale",
-        "globalInitialImage": "Immagine iniziale",
-        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) - $t(unifiedCanvas.layer) Globale",
-        "clearProcessor": "Cancella processore",
-        "resetProcessor": "Ripristina il processore alle impostazioni predefinite",
-        "noLayersAdded": "Nessun livello aggiunto",
-        "resetRegion": "Reimposta la regione",
-        "controlLayers": "Livelli di controllo",
-        "layers_one": "Livello",
-        "layers_many": "Livelli",
-        "layers_other": "Livelli"
-    },
-    "ui": {
-        "tabs": {
-            "generation": "Generazione",
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "canvas": "Tela",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "workflows": "Flussi di lavoro",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "models": "Modelli",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Coda",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)"
-        }
     }
 }

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

@@ -570,6 +570,7 @@
         "pauseSucceeded": "処理が一時停止されました",
         "queueFront": "キューの先頭へ追加",
         "queueBack": "キューに追加",
+        "queueCountPrediction": "{{promptsCount}} プロンプト × {{iterations}} イテレーション -> {{count}} 枚生成",
         "pause": "一時停止",
         "queue": "キュー",
         "pauseTooltip": "処理を一時停止",

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

@@ -505,6 +505,7 @@
         "completed": "완성된",
         "queueBack": "Queue에 추가",
         "cancelFailed": "항목 취소 중 발생한 문제",
+        "queueCountPrediction": "Queue에 {{predicted}} 추가",
         "batchQueued": "Batch Queued",
         "pauseFailed": "프로세서 중지 중 발생한 문제",
         "clearFailed": "Queue 제거 중 발생한 문제",

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

@@ -383,6 +383,8 @@
         "useCpuNoise": "Gebruik CPU-ruis",
         "imageActions": "Afbeeldingshandeling",
         "iterations": "Iteraties",
+        "iterationsWithCount_one": "{{count}} iteratie",
+        "iterationsWithCount_other": "{{count}} iteraties",
         "coherenceMode": "Modus"
     },
     "settings": {
@@ -938,6 +940,7 @@
         "completed": "Voltooid",
         "queueBack": "Voeg toe aan wachtrij",
         "cancelFailed": "Fout bij annuleren onderdeel",
+        "queueCountPrediction": "Voeg {{predicted}} toe aan wachtrij",
         "batchQueued": "Reeks in wachtrij geplaatst",
         "pauseFailed": "Fout bij onderbreken verwerker",
         "clearFailed": "Fout bij wissen van wachtrij",

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

@@ -76,18 +76,7 @@
         "localSystem": "Локальная система",
         "aboutDesc": "Используя Invoke для работы? Проверьте это:",
         "add": "Добавить",
-        "loglevel": "Уровень логов",
-        "beta": "Бета",
-        "selected": "Выбрано",
-        "positivePrompt": "Позитивный запрос",
-        "negativePrompt": "Негативный запрос",
-        "editor": "Редактор",
-        "goTo": "Перейти к",
-        "tab": "Вкладка",
-        "viewing": "Просмотр",
-        "editing": "Редактирование",
-        "viewingDesc": "Просмотр изображений в режиме большой галереи",
-        "editingDesc": "Редактировать на холсте слоёв управления"
+        "loglevel": "Уровень логов"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -98,8 +87,8 @@
         "deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
         "deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
         "deleteImage_one": "Удалить изображение",
-        "deleteImage_few": "Удалить {{count}} изображения",
-        "deleteImage_many": "Удалить {{count}} изображений",
+        "deleteImage_few": "",
+        "deleteImage_many": "",
         "assets": "Ресурсы",
         "autoAssignBoardOnClick": "Авто-назначение доски по клику",
         "deleteSelection": "Удалить выделенное",
@@ -347,10 +336,6 @@
         "remixImage": {
             "desc": "Используйте все параметры, кроме сида из текущего изображения",
             "title": "Ремикс изображения"
-        },
-        "toggleViewer": {
-            "title": "Переключить просмотр изображений",
-            "desc": "Переключение между средством просмотра изображений и рабочей областью для текущей вкладки."
         }
     },
     "modelManager": {
@@ -527,8 +512,7 @@
             "missingNodeTemplate": "Отсутствует шаблон узла",
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
-            "invoke": "Создать",
-            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
+            "invoke": "Создать"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -539,6 +523,9 @@
         "useCpuNoise": "Использовать шум CPU",
         "imageActions": "Действия с изображениями",
         "iterations": "Кол-во",
+        "iterationsWithCount_one": "{{count}} Интеграция",
+        "iterationsWithCount_few": "{{count}} Итерации",
+        "iterationsWithCount_many": "{{count}} Итераций",
         "useSize": "Использовать размер",
         "coherenceMode": "Режим",
         "aspect": "Соотношение",
@@ -554,10 +541,7 @@
         "infillMosaicTileHeight": "Высота плиток",
         "infillMosaicMinColor": "Мин цвет",
         "infillMosaicMaxColor": "Макс цвет",
-        "infillColorValue": "Цвет заливки",
-        "globalSettings": "Глобальные настройки",
-        "globalNegativePromptPlaceholder": "Глобальный негативный запрос",
-        "globalPositivePromptPlaceholder": "Глобальный запрос"
+        "infillColorValue": "Цвет заливки"
     },
     "settings": {
         "models": "Модели",
@@ -722,9 +706,7 @@
         "coherenceModeBoxBlur": "коробчатое размытие",
         "discardCurrent": "Отбросить текущее",
         "invertBrushSizeScrollDirection": "Инвертировать прокрутку для размера кисти",
-        "initialFitImageSize": "Подогнать размер изображения при перебросе",
-        "hideBoundingBox": "Скрыть ограничительную рамку",
-        "showBoundingBox": "Показать ограничительную рамку"
+        "initialFitImageSize": "Подогнать размер изображения при перебросе"
     },
     "accessibility": {
         "uploadImage": "Загрузить изображение",
@@ -867,10 +849,7 @@
         "editMode": "Открыть в редакторе узлов",
         "resetToDefaultValue": "Сбросить к стандартному значкнию",
         "edit": "Редактировать",
-        "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений.",
-        "graph": "График",
-        "showEdgeLabels": "Показать метки на ребрах",
-        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы"
+        "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений."
     },
     "controlnet": {
         "amult": "a_mult",
@@ -938,8 +917,8 @@
         "lineartAnime": "Контурный рисунок в стиле аниме",
         "mediapipeFaceDescription": "Обнаружение лиц с помощью Mediapipe",
         "hedDescription": "Целостное обнаружение границ",
-        "setControlImageDimensions": "Скопируйте размер в Ш/В (оптимизируйте для модели)",
-        "scribble": "Штрихи",
+        "setControlImageDimensions": "Установите размеры контрольного изображения на Ш/В",
+        "scribble": "каракули",
         "maxFaces": "Макс Лица",
         "mlsdDescription": "Минималистичный детектор отрезков линии",
         "resizeSimple": "Изменить размер (простой)",
@@ -954,18 +933,7 @@
         "small": "Маленький",
         "body": "Тело",
         "hands": "Руки",
-        "selectCLIPVisionModel": "Выбрать модель CLIP Vision",
-        "ipAdapterMethod": "Метод",
-        "full": "Всё",
-        "mlsd": "M-LSD",
-        "h": "H",
-        "style": "Только стиль",
-        "dwOpenpose": "DW Openpose",
-        "pidi": "PIDI",
-        "composition": "Только композиция",
-        "hed": "HED",
-        "beginEndStepPercentShort": "Начало/конец %",
-        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)"
+        "selectCLIPVisionModel": "Выбрать модель CLIP Vision"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1344,12 +1312,6 @@
             "paragraphs": [
                 "Плавно укладывайте изображение вдоль вертикальной оси."
             ]
-        },
-        "ipAdapterMethod": {
-            "heading": "Метод",
-            "paragraphs": [
-                "Метод, с помощью которого применяется текущий IP-адаптер."
-            ]
         }
     },
     "metadata": {
@@ -1397,6 +1359,7 @@
         "completed": "Выполнено",
         "queueBack": "Добавить в очередь",
         "cancelFailed": "Проблема с отменой элемента",
+        "queueCountPrediction": "{{promptsCount}} запросов × {{iterations}} изображений -> {{count}} генераций",
         "batchQueued": "Пакетная очередь",
         "pauseFailed": "Проблема с приостановкой рендеринга",
         "clearFailed": "Проблема с очисткой очереди",
@@ -1512,11 +1475,7 @@
         "projectWorkflows": "Рабочие процессы проекта",
         "defaultWorkflows": "Стандартные рабочие процессы",
         "name": "Имя",
-        "noRecentWorkflows": "Нет последних рабочих процессов",
-        "loadWorkflow": "Рабочий процесс $t(common.load)",
-        "convertGraph": "Конвертировать график",
-        "loadFromGraph": "Загрузка рабочего процесса из графика",
-        "autoLayout": "Автоматическое расположение"
+        "noRecentWorkflows": "Нет последних рабочих процессов"
     },
     "hrf": {
         "enableHrf": "Включить исправление высокого разрешения",
@@ -1569,56 +1528,5 @@
         "addPromptTrigger": "Добавить триггер запроса",
         "compatibleEmbeddings": "Совместимые встраивания",
         "noMatchingTriggers": "Нет соответствующих триггеров"
-    },
-    "controlLayers": {
-        "moveToBack": "На задний план",
-        "moveForward": "Переместить вперёд",
-        "moveBackward": "Переместить назад",
-        "brushSize": "Размер кисти",
-        "controlLayers": "Слои управления",
-        "globalMaskOpacity": "Глобальная непрозрачность маски",
-        "autoNegative": "Авто негатив",
-        "deletePrompt": "Удалить запрос",
-        "resetRegion": "Сбросить регион",
-        "debugLayers": "Слои отладки",
-        "rectangle": "Прямоугольник",
-        "maskPreviewColor": "Цвет предпросмотра маски",
-        "addNegativePrompt": "Добавить $t(common.negativePrompt)",
-        "regionalGuidance": "Региональная точность",
-        "opacity": "Непрозрачность",
-        "globalControlAdapter": "Глобальный $t(controlnet.controlAdapter_one)",
-        "globalControlAdapterLayer": "Глобальный $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
-        "globalIPAdapter": "Глобальный $t(common.ipAdapter)",
-        "globalIPAdapterLayer": "Глобальный $t(common.ipAdapter) $t(unifiedCanvas.layer)",
-        "opacityFilter": "Фильтр непрозрачности",
-        "deleteAll": "Удалить всё",
-        "addLayer": "Добавить слой",
-        "moveToFront": "На передний план",
-        "toggleVisibility": "Переключить видимость слоя",
-        "addPositivePrompt": "Добавить $t(common.positivePrompt)",
-        "addIPAdapter": "Добавить $t(common.ipAdapter)",
-        "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "resetProcessor": "Сброс процессора по умолчанию",
-        "clearProcessor": "Чистый процессор",
-        "globalInitialImage": "Глобальное исходное изображение",
-        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "noLayersAdded": "Без слоев",
-        "layers_one": "Слой",
-        "layers_few": "Слоя",
-        "layers_many": "Слоев"
-    },
-    "ui": {
-        "tabs": {
-            "generation": "Генерация",
-            "canvas": "Холст",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "models": "Модели",
-            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
-            "workflows": "Рабочие процессы",
-            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
-            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Очередь"
-        }
     }
 }

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

@@ -66,7 +66,7 @@
         "saveAs": "保存为",
         "ai": "ai",
         "or": "或",
-        "aboutDesc": "使用 Invoke 工作？来看看：",
+        "aboutDesc": "使用 Invoke 工作？查看：",
         "add": "添加",
         "loglevel": "日志级别",
         "copy": "复制",
@@ -445,6 +445,7 @@
             "useX2Model": "图像太大，无法使用 x4 模型，使用 x2 模型作为替代",
             "tooLarge": "图像太大无法进行放大，请选择更小的图像"
         },
+        "iterationsWithCount_other": "{{count}} 次迭代生成",
         "cfgRescaleMultiplier": "CFG 重缩放倍数",
         "useSize": "使用尺寸",
         "setToOptimalSize": "优化模型大小",
@@ -852,6 +853,7 @@
         "pruneSucceeded": "从队列修剪 {{item_count}} 个已完成的项目",
         "notReady": "无法排队",
         "batchFailedToQueue": "批次加入队列失败",
+        "queueCountPrediction": "{{promptsCount}} 提示词 × {{iterations}} 迭代次数 -> {{count}} 次生成",
         "batchQueued": "加入队列的批次",
         "front": "前",
         "pruneTooltip": "修剪 {{item_count}} 个已完成的项目",

invokeai/frontend/web/scripts/typegen.js

@@ -1,4 +1,3 @@
-/* eslint-disable no-console */
 import fs from 'node:fs';
 
 import openapiTS from 'openapi-typescript';

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

@@ -21,7 +21,6 @@ import i18n from 'i18n';
 import { size } from 'lodash-es';
 import { memo, useCallback, useEffect } from 'react';
 import { ErrorBoundary } from 'react-error-boundary';
-import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
 import PreselectedImage from './PreselectedImage';
@@ -47,7 +46,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
   useSocketIO();
   useGlobalModifiersInit();
   useGlobalHotkeys();
-  useGetOpenAPISchemaQuery();
 
   const { dropzone, isHandlingUpload, setIsHandlingUpload } = useFullscreenDropzone();
 

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

@@ -67,8 +67,6 @@ export const useSocketIO = () => {
 
     if ($isDebugging.get() || import.meta.env.MODE === 'development') {
       window.$socketOptions = $socketOptions;
-      // This is only enabled manually for debugging, console is allowed.
-      /* eslint-disable-next-line no-console */
       console.log('Socket initialized', socket);
     }
 
@@ -77,8 +75,6 @@ export const useSocketIO = () => {
     return () => {
       if ($isDebugging.get() || import.meta.env.MODE === 'development') {
         window.$socketOptions = undefined;
-        // This is only enabled manually for debugging, console is allowed.
-        /* eslint-disable-next-line no-console */
         console.log('Socket teardown', socket);
       }
       socket.disconnect();

invokeai/frontend/web/src/app/logging/logger.ts

@@ -20,14 +20,15 @@ export type LoggerNamespace =
   | 'models'
   | 'config'
   | 'canvas'
-  | 'generation'
+  | 'txt2img'
+  | 'img2img'
   | 'nodes'
   | 'system'
   | 'socketio'
   | 'session'
   | 'queue'
   | 'dnd'
-  | 'controlLayers';
+  | 'regionalPrompts';
 
 export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 

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

@@ -1,6 +1,3 @@
-/* eslint-disable no-console */
-// This is only enabled manually for debugging, console is allowed.
-
 import type { Middleware, MiddlewareAPI } from '@reduxjs/toolkit';
 import { diff } from 'jsondiffpatch';
 

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,6 +1,7 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
 import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
@@ -24,6 +25,13 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     };
   }
 
+  if (nodeTemplatesBuilt.match(action)) {
+    return {
+      ...action,
+      payload: '<Node templates omitted>',
+    };
+  }
+
   if (socketGeneratorProgress.match(action)) {
     const sanitized = deepClone(action);
     if (sanitized.payload.data.progress_image) {

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

@@ -16,7 +16,6 @@ import { addCanvasMaskSavedToGalleryListener } from 'app/store/middleware/listen
 import { addCanvasMaskToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet';
 import { addCanvasMergedListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMerged';
 import { addCanvasSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery';
-import { addControlAdapterPreprocessor } from 'app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor';
 import { addControlNetAutoProcessListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess';
 import { addControlNetImageProcessedListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed';
 import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas';
@@ -32,6 +31,7 @@ import { addImagesStarredListener } from 'app/store/middleware/listenerMiddlewar
 import { addImagesUnstarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesUnstarred';
 import { addImageToDeleteSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected';
 import { addImageUploadedFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageUploaded';
+import { addInitialImageSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/initialImageSelected';
 import { addModelSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelSelected';
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
@@ -72,6 +72,9 @@ const startAppListening = listenerMiddleware.startListening as AppStartListening
 // Image uploaded
 addImageUploadedFulfilledListener(startAppListening);
 
+// Image selected
+addInitialImageSelectedListener(startAppListening);
+
 // Image deleted
 addRequestedSingleImageDeletionListener(startAppListening);
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
@@ -154,4 +157,3 @@ addUpscaleRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
-addControlAdapterPreprocessor(startAppListening);

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

@@ -1,9 +1,9 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { allLayersDeleted } from 'features/controlLayers/store/controlLayersSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppStartListening) => {
@@ -14,14 +14,19 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
 
       // Remove all deleted images from the UI
 
+      let wasInitialImageReset = false;
       let wasCanvasReset = false;
       let wasNodeEditorReset = false;
       let wereControlAdaptersReset = false;
-      let wereControlLayersReset = false;
 
-      const { canvas, nodes, controlAdapters, controlLayers } = getState();
+      const { generation, canvas, nodes, controlAdapters } = getState();
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
+        const imageUsage = getImageUsage(generation, canvas, nodes, controlAdapters, image_name);
+
+        if (imageUsage.isInitialImage && !wasInitialImageReset) {
+          dispatch(clearInitialImage());
+          wasInitialImageReset = true;
+        }
 
         if (imageUsage.isCanvasImage && !wasCanvasReset) {
           dispatch(resetCanvas());
@@ -37,11 +42,6 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
           dispatch(controlAdaptersReset());
           wereControlAdaptersReset = true;
         }
-
-        if (imageUsage.isControlLayerImage && !wereControlLayersReset) {
-          dispatch(allLayersDeleted());
-          wereControlLayersReset = true;
-        }
       });
     },
   });

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

@@ -1,6 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
 import { canvasSavedToGallery } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
 import { addToast } from 'features/system/store/systemSlice';
@@ -44,9 +43,6 @@ export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListe
             type: 'TOAST',
             toastOptions: { title: t('toast.canvasSavedGallery') },
           },
-          metadata: {
-            _canvas_objects: parseify(state.canvas.layerState.objects),
-          },
         })
       );
     },

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

@@ -1,189 +0,0 @@
-import { isAnyOf } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { AppDispatch } from 'app/store/store';
-import { parseify } from 'common/util/serialize';
-import {
-  caLayerImageChanged,
-  caLayerModelChanged,
-  caLayerProcessedImageChanged,
-  caLayerProcessorConfigChanged,
-  caLayerProcessorPendingBatchIdChanged,
-  caLayerRecalled,
-  isControlAdapterLayer,
-} from 'features/controlLayers/store/controlLayersSlice';
-import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
-import { isImageOutput } from 'features/nodes/types/common';
-import { addToast } from 'features/system/store/systemSlice';
-import { t } from 'i18next';
-import { isEqual } from 'lodash-es';
-import { getImageDTO } from 'services/api/endpoints/images';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { BatchConfig } from 'services/api/types';
-import { socketInvocationComplete } from 'services/events/actions';
-import { assert } from 'tsafe';
-
-const matcher = isAnyOf(caLayerImageChanged, caLayerProcessorConfigChanged, caLayerModelChanged, caLayerRecalled);
-
-const DEBOUNCE_MS = 300;
-const log = logger('session');
-
-/**
- * Simple helper to cancel a batch and reset the pending batch ID
- */
-const cancelProcessorBatch = async (dispatch: AppDispatch, layerId: string, batchId: string) => {
-  const req = dispatch(queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: [batchId] }));
-  log.trace({ batchId }, 'Cancelling existing preprocessor batch');
-  try {
-    await req.unwrap();
-  } catch {
-    // no-op
-  } finally {
-    req.reset();
-    // Always reset the pending batch ID - the cancel req could fail if the batch doesn't exist
-    dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
-  }
-};
-
-export const addControlAdapterPreprocessor = (startAppListening: AppStartListening) => {
-  startAppListening({
-    matcher,
-    effect: async (action, { dispatch, getState, getOriginalState, cancelActiveListeners, delay, take, signal }) => {
-      const layerId = caLayerRecalled.match(action) ? action.payload.id : action.payload.layerId;
-      const state = getState();
-      const originalState = getOriginalState();
-
-      // Cancel any in-progress instances of this listener
-      cancelActiveListeners();
-      log.trace('Control Layer CA auto-process triggered');
-
-      // Delay before starting actual work
-      await delay(DEBOUNCE_MS);
-
-      const layer = state.controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
-
-      if (!layer) {
-        return;
-      }
-
-      // We should only process if the processor settings or image have changed
-      const originalLayer = originalState.controlLayers.present.layers
-        .filter(isControlAdapterLayer)
-        .find((l) => l.id === layerId);
-      const originalImage = originalLayer?.controlAdapter.image;
-      const originalConfig = originalLayer?.controlAdapter.processorConfig;
-
-      const image = layer.controlAdapter.image;
-      const config = layer.controlAdapter.processorConfig;
-
-      if (isEqual(config, originalConfig) && isEqual(image, originalImage)) {
-        // Neither config nor image have changed, we can bail
-        return;
-      }
-
-      if (!image || !config) {
-        // - If we have no image, we have nothing to process
-        // - If we have no processor config, we have nothing to process
-        // Clear the processed image and bail
-        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
-        return;
-      }
-
-      // At this point, the user has stopped fiddling with the processor settings and there is a processor selected.
-
-      // If there is a pending processor batch, cancel it.
-      if (layer.controlAdapter.processorPendingBatchId) {
-        cancelProcessorBatch(dispatch, layerId, layer.controlAdapter.processorPendingBatchId);
-      }
-
-      // TODO(psyche): I can't get TS to be happy, it thinkgs `config` is `never` but it should be inferred from the generic... I'll just cast it for now
-      const processorNode = CA_PROCESSOR_DATA[config.type].buildNode(image, config as never);
-      const enqueueBatchArg: BatchConfig = {
-        prepend: true,
-        batch: {
-          graph: {
-            nodes: {
-              [processorNode.id]: {
-                ...processorNode,
-                // Control images are always intermediate - do not save to gallery
-                is_intermediate: true,
-              },
-            },
-            edges: [],
-          },
-          runs: 1,
-        },
-      };
-
-      // Kick off the processor batch
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
-          fixedCacheKey: 'enqueueBatch',
-        })
-      );
-
-      try {
-        const enqueueResult = await req.unwrap();
-        // TODO(psyche): Update the pydantic models, pretty sure we will _always_ have a batch_id here, but the model says it's optional
-        assert(enqueueResult.batch.batch_id, 'Batch ID not returned from queue');
-        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: enqueueResult.batch.batch_id }));
-        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
-
-        // Wait for the processor node to complete
-        const [invocationCompleteAction] = await take(
-          (action): action is ReturnType<typeof socketInvocationComplete> =>
-            socketInvocationComplete.match(action) &&
-            action.payload.data.queue_batch_id === enqueueResult.batch.batch_id &&
-            action.payload.data.source_node_id === processorNode.id
-        );
-
-        // We still have to check the output type
-        assert(
-          isImageOutput(invocationCompleteAction.payload.data.result),
-          `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
-        );
-        const { image_name } = invocationCompleteAction.payload.data.result.image;
-
-        const imageDTO = await getImageDTO(image_name);
-        assert(imageDTO, "Failed to fetch processor output's image DTO");
-
-        // Whew! We made it. Update the layer with the processed image
-        log.debug({ layerId, imageDTO }, 'ControlNet image processed');
-        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO }));
-        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
-      } catch (error) {
-        if (signal.aborted) {
-          // The listener was canceled - we need to cancel the pending processor batch, if there is one (could have changed by now).
-          const pendingBatchId = getState()
-            .controlLayers.present.layers.filter(isControlAdapterLayer)
-            .find((l) => l.id === layerId)?.controlAdapter.processorPendingBatchId;
-          if (pendingBatchId) {
-            cancelProcessorBatch(dispatch, layerId, pendingBatchId);
-          }
-          log.trace('Control Adapter preprocessor cancelled');
-        } else {
-          // Some other error condition...
-          log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
-
-          if (error instanceof Object) {
-            if ('data' in error && 'status' in error) {
-              if (error.status === 403) {
-                dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
-                return;
-              }
-            }
-          }
-
-          dispatch(
-            addToast({
-              title: t('queue.graphFailedToQueue'),
-              status: 'error',
-            })
-          );
-        }
-      } finally {
-        req.reset();
-      }
-    },
-  });
-};

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

@@ -8,8 +8,8 @@ import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
 import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
 import { canvasGraphBuilt } from 'features/nodes/store/actions';
+import { buildCanvasGraph } from 'features/nodes/util/graph/buildCanvasGraph';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildCanvasGraph } from 'features/nodes/util/graph/canvas/buildCanvasGraph';
 import { imagesApi } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
@@ -30,7 +30,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'canvas',
+      enqueueRequested.match(action) && action.payload.tabName === 'unifiedCanvas',
     effect: async (action, { getState, dispatch }) => {
       const log = logger('queue');
       const { prepend } = action.payload;

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

@@ -1,27 +1,35 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildGenerationTabGraph } from 'features/nodes/util/graph/generation/buildGenerationTabGraph';
-import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/generation/buildGenerationTabSDXLGraph';
+import { buildLinearImageToImageGraph } from 'features/nodes/util/graph/buildLinearImageToImageGraph';
+import { buildLinearSDXLImageToImageGraph } from 'features/nodes/util/graph/buildLinearSDXLImageToImageGraph';
+import { buildLinearSDXLTextToImageGraph } from 'features/nodes/util/graph/buildLinearSDXLTextToImageGraph';
+import { buildLinearTextToImageGraph } from 'features/nodes/util/graph/buildLinearTextToImageGraph';
 import { queueApi } from 'services/api/endpoints/queue';
 
 export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'generation',
+      enqueueRequested.match(action) && (action.payload.tabName === 'txt2img' || action.payload.tabName === 'img2img'),
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { shouldShowProgressInViewer } = state.ui;
       const model = state.generation.model;
       const { prepend } = action.payload;
 
       let graph;
 
-      if (model?.base === 'sdxl') {
-        graph = await buildGenerationTabSDXLGraph(state);
+      if (model && model.base === 'sdxl') {
+        if (action.payload.tabName === 'txt2img') {
+          graph = await buildLinearSDXLTextToImageGraph(state);
+        } else {
+          graph = await buildLinearSDXLImageToImageGraph(state);
+        }
       } else {
-        graph = await buildGenerationTabGraph(state);
+        if (action.payload.tabName === 'txt2img') {
+          graph = await buildLinearTextToImageGraph(state);
+        } else {
+          graph = await buildLinearImageToImageGraph(state);
+        }
       }
 
       const batchConfig = prepareLinearUIBatch(state, graph, prepend);
@@ -31,14 +39,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      try {
-        await req.unwrap();
-        if (shouldShowProgressInViewer) {
-          dispatch(isImageViewerOpenChanged(true));
-        }
-      } finally {
-        req.reset();
-      }
+      req.reset();
     },
   });
 };

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

@@ -8,12 +8,12 @@ import type { BatchConfig } from 'services/api/types';
 export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'workflows',
+      enqueueRequested.match(action) && action.payload.tabName === 'nodes',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { nodes, edges } = state.nodes.present;
+      const { nodes, edges } = state.nodes;
       const workflow = state.workflow;
-      const graph = buildNodesGraph(state.nodes.present);
+      const graph = buildNodesGraph(state.nodes);
       const builtWorkflow = buildWorkflowWithValidation({
         nodes,
         edges,
@@ -39,11 +39,7 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      try {
-        await req.unwrap();
-      } finally {
-        req.reset();
-      }
+      req.reset();
     },
   });
 };

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

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
-import { $templates } from 'features/nodes/store/nodesSlice';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
@@ -9,7 +9,7 @@ import { appInfoApi } from 'services/api/endpoints/appInfo';
 export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
-    effect: (action, { getState }) => {
+    effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
@@ -20,7 +20,7 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
 
       log.debug({ nodeTemplates: parseify(nodeTemplates) }, `Built ${size(nodeTemplates)} node templates`);
 
-      $templates.set(nodeTemplates);
+      dispatch(nodeTemplatesBuilt(nodeTemplates));
     },
   });
 

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

@@ -1,6 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { AppDispatch, RootState } from 'app/store/store';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import {
   controlAdapterImageChanged,
@@ -8,13 +7,6 @@ import {
   selectControlAdapterAll,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-import {
-  isControlAdapterLayer,
-  isInitialImageLayer,
-  isIPAdapterLayer,
-  isRegionalGuidanceLayer,
-  layerDeleted,
-} from 'features/controlLayers/store/controlLayersSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
@@ -22,82 +14,12 @@ import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { isImageFieldInputInstance } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
+import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { clamp, forEach } from 'lodash-es';
 import { api } from 'services/api';
 import { imagesApi } from 'services/api/endpoints/images';
-import type { ImageDTO } from 'services/api/types';
 import { imagesSelectors } from 'services/api/util';
 
-const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  state.nodes.present.nodes.forEach((node) => {
-    if (!isInvocationNode(node)) {
-      return;
-    }
-
-    forEach(node.data.inputs, (input) => {
-      if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
-        dispatch(
-          fieldImageValueChanged({
-            nodeId: node.data.id,
-            fieldName: input.name,
-            value: undefined,
-          })
-        );
-      }
-    });
-  });
-};
-
-const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  forEach(selectControlAdapterAll(state.controlAdapters), (ca) => {
-    if (
-      ca.controlImage === imageDTO.image_name ||
-      (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
-    ) {
-      dispatch(
-        controlAdapterImageChanged({
-          id: ca.id,
-          controlImage: null,
-        })
-      );
-      dispatch(
-        controlAdapterProcessedImageChanged({
-          id: ca.id,
-          processedControlImage: null,
-        })
-      );
-    }
-  });
-};
-
-const deleteControlLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  state.controlLayers.present.layers.forEach((l) => {
-    if (isRegionalGuidanceLayer(l)) {
-      if (l.ipAdapters.some((ipa) => ipa.image?.name === imageDTO.image_name)) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-    if (isControlAdapterLayer(l)) {
-      if (
-        l.controlAdapter.image?.name === imageDTO.image_name ||
-        l.controlAdapter.processedImage?.name === imageDTO.image_name
-      ) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-    if (isIPAdapterLayer(l)) {
-      if (l.ipAdapter.image?.name === imageDTO.image_name) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-    if (isInitialImageLayer(l)) {
-      if (l.image?.name === imageDTO.image_name) {
-        dispatch(layerDeleted(l.id));
-      }
-    }
-  });
-};
-
 export const addRequestedSingleImageDeletionListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: imageDeletionConfirmed,
@@ -151,9 +73,50 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
       }
 
       imageDTOs.forEach((imageDTO) => {
-        deleteControlAdapterImages(state, dispatch, imageDTO);
-        deleteNodesImages(state, dispatch, imageDTO);
-        deleteControlLayerImages(state, dispatch, imageDTO);
+        // reset init image if we deleted it
+        if (getState().generation.initialImage?.imageName === imageDTO.image_name) {
+          dispatch(clearInitialImage());
+        }
+
+        // reset control adapters that use the deleted images
+        forEach(selectControlAdapterAll(getState().controlAdapters), (ca) => {
+          if (
+            ca.controlImage === imageDTO.image_name ||
+            (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+          ) {
+            dispatch(
+              controlAdapterImageChanged({
+                id: ca.id,
+                controlImage: null,
+              })
+            );
+            dispatch(
+              controlAdapterProcessedImageChanged({
+                id: ca.id,
+                processedControlImage: null,
+              })
+            );
+          }
+        });
+
+        // reset nodes that use the deleted images
+        getState().nodes.nodes.forEach((node) => {
+          if (!isInvocationNode(node)) {
+            return;
+          }
+
+          forEach(node.data.inputs, (input) => {
+            if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
+              dispatch(
+                fieldImageValueChanged({
+                  nodeId: node.data.id,
+                  fieldName: input.name,
+                  value: undefined,
+                })
+              );
+            }
+          });
+        });
       });
 
       // Delete from server
@@ -205,9 +168,50 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
         }
 
         imageDTOs.forEach((imageDTO) => {
-          deleteControlAdapterImages(state, dispatch, imageDTO);
-          deleteNodesImages(state, dispatch, imageDTO);
-          deleteControlLayerImages(state, dispatch, imageDTO);
+          // reset init image if we deleted it
+          if (getState().generation.initialImage?.imageName === imageDTO.image_name) {
+            dispatch(clearInitialImage());
+          }
+
+          // reset control adapters that use the deleted images
+          forEach(selectControlAdapterAll(getState().controlAdapters), (ca) => {
+            if (
+              ca.controlImage === imageDTO.image_name ||
+              (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+            ) {
+              dispatch(
+                controlAdapterImageChanged({
+                  id: ca.id,
+                  controlImage: null,
+                })
+              );
+              dispatch(
+                controlAdapterProcessedImageChanged({
+                  id: ca.id,
+                  processedControlImage: null,
+                })
+              );
+            }
+          });
+
+          // reset nodes that use the deleted images
+          getState().nodes.nodes.forEach((node) => {
+            if (!isInvocationNode(node)) {
+              return;
+            }
+
+            forEach(node.data.inputs, (input) => {
+              if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
+                dispatch(
+                  fieldImageValueChanged({
+                    nodeId: node.data.id,
+                    fieldName: input.name,
+                    value: undefined,
+                  })
+                );
+              }
+            });
+          });
         });
       } catch {
         // no-op

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

@@ -7,16 +7,10 @@ import {
   controlAdapterImageChanged,
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import {
-  caLayerImageChanged,
-  iiLayerImageChanged,
-  ipaLayerImageChanged,
-  rgLayerIPAdapterImageChanged,
-} from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const dndDropped = createAction<{
@@ -53,6 +47,18 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
+      /**
+       * Image dropped on initial image
+       */
+      if (
+        overData.actionType === 'SET_INITIAL_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        dispatch(initialImageChanged(activeData.payload.imageDTO));
+        return;
+      }
+
       /**
        * Image dropped on ControlNet
        */
@@ -77,79 +83,6 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
-      /**
-       * Image dropped on Control Adapter Layer
-       */
-      if (
-        overData.actionType === 'SET_CA_LAYER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId } = overData.context;
-        dispatch(
-          caLayerImageChanged({
-            layerId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on IP Adapter Layer
-       */
-      if (
-        overData.actionType === 'SET_IPA_LAYER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId } = overData.context;
-        dispatch(
-          ipaLayerImageChanged({
-            layerId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on RG Layer IP Adapter
-       */
-      if (
-        overData.actionType === 'SET_RG_LAYER_IP_ADAPTER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId, ipAdapterId } = overData.context;
-        dispatch(
-          rgLayerIPAdapterImageChanged({
-            layerId,
-            ipAdapterId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on II Layer Image
-       */
-      if (
-        overData.actionType === 'SET_II_LAYER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { layerId } = overData.context;
-        dispatch(
-          iiLayerImageChanged({
-            layerId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
       /**
        * Image dropped on Canvas
        */

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

@@ -14,6 +14,7 @@ export const addImageToDeleteSelectedListener = (startAppListening: AppStartList
 
       const isImageInUse =
         imagesUsage.some((i) => i.isCanvasImage) ||
+        imagesUsage.some((i) => i.isInitialImage) ||
         imagesUsage.some((i) => i.isControlImage) ||
         imagesUsage.some((i) => i.isNodesImage);
 

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

@@ -6,14 +6,8 @@ import {
   controlAdapterImageChanged,
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import {
-  caLayerImageChanged,
-  iiLayerImageChanged,
-  ipaLayerImageChanged,
-  rgLayerIPAdapterImageChanged,
-} from 'features/controlLayers/store/controlLayersSlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { omit } from 'lodash-es';
@@ -114,48 +108,15 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
-      if (postUploadAction?.type === 'SET_CA_LAYER_IMAGE') {
-        const { layerId } = postUploadAction;
-        dispatch(caLayerImageChanged({ layerId, imageDTO }));
+      if (postUploadAction?.type === 'SET_INITIAL_IMAGE') {
+        dispatch(initialImageChanged(imageDTO));
         dispatch(
           addToast({
             ...DEFAULT_UPLOADED_TOAST,
-            description: t('toast.setControlImage'),
-          })
-        );
-      }
-
-      if (postUploadAction?.type === 'SET_IPA_LAYER_IMAGE') {
-        const { layerId } = postUploadAction;
-        dispatch(ipaLayerImageChanged({ layerId, imageDTO }));
-        dispatch(
-          addToast({
-            ...DEFAULT_UPLOADED_TOAST,
-            description: t('toast.setControlImage'),
-          })
-        );
-      }
-
-      if (postUploadAction?.type === 'SET_RG_LAYER_IP_ADAPTER_IMAGE') {
-        const { layerId, ipAdapterId } = postUploadAction;
-        dispatch(rgLayerIPAdapterImageChanged({ layerId, ipAdapterId, imageDTO }));
-        dispatch(
-          addToast({
-            ...DEFAULT_UPLOADED_TOAST,
-            description: t('toast.setControlImage'),
-          })
-        );
-      }
-
-      if (postUploadAction?.type === 'SET_II_LAYER_IMAGE') {
-        const { layerId } = postUploadAction;
-        dispatch(iiLayerImageChanged({ layerId, imageDTO }));
-        dispatch(
-          addToast({
-            ...DEFAULT_UPLOADED_TOAST,
-            description: t('toast.setControlImage'),
+            description: t('toast.setInitialImage'),
           })
         );
+        return;
       }
 
       if (postUploadAction?.type === 'SET_NODES_IMAGE') {

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

@@ -0,0 +1,21 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { initialImageSelected } from 'features/parameters/store/actions';
+import { initialImageChanged } from 'features/parameters/store/generationSlice';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
+import { t } from 'i18next';
+
+export const addInitialImageSelectedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: initialImageSelected,
+    effect: (action, { dispatch }) => {
+      if (!action.payload) {
+        dispatch(addToast(makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })));
+        return;
+      }
+
+      dispatch(initialImageChanged(action.payload));
+      dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
+    },
+  });
+};

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

@@ -6,10 +6,9 @@ import {
   controlAdapterModelCleared,
   selectControlAdapterAll,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
 import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
-import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
+import { heightChanged, modelChanged, vaeSelected, widthChanged } from 'features/parameters/store/generationSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice';
@@ -70,22 +69,16 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
       dispatch(modelChanged(defaultModelInList, currentModel));
 
       const optimalDimension = getOptimalDimension(defaultModelInList);
-      if (
-        getIsSizeOptimal(
-          state.controlLayers.present.size.width,
-          state.controlLayers.present.size.height,
-          optimalDimension
-        )
-      ) {
+      if (getIsSizeOptimal(state.generation.width, state.generation.height, optimalDimension)) {
         return;
       }
       const { width, height } = calculateNewSize(
-        state.controlLayers.present.size.aspectRatio.value,
+        state.generation.aspectRatio.value,
         optimalDimension * optimalDimension
       );
 
-      dispatch(widthChanged({ width }));
-      dispatch(heightChanged({ height }));
+      dispatch(widthChanged(width));
+      dispatch(heightChanged(height));
       return;
     }
   }

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

@@ -1,6 +1,5 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { positivePromptChanged } from 'features/controlLayers/store/controlLayersSlice';
 import {
   combinatorialToggled,
   isErrorChanged,
@@ -11,16 +10,11 @@ import {
   promptsChanged,
 } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
+import { setPositivePrompt } from 'features/parameters/store/generationSlice';
 import { utilitiesApi } from 'services/api/endpoints/utilities';
 import { socketConnected } from 'services/events/actions';
 
-const matcher = isAnyOf(
-  positivePromptChanged,
-  combinatorialToggled,
-  maxPromptsChanged,
-  maxPromptsReset,
-  socketConnected
-);
+const matcher = isAnyOf(setPositivePrompt, combinatorialToggled, maxPromptsChanged, maxPromptsReset, socketConnected);
 
 export const addDynamicPromptsListener = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -28,7 +22,7 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
     effect: async (action, { dispatch, getState, cancelActiveListeners, delay }) => {
       cancelActiveListeners();
       const state = getState();
-      const { positivePrompt } = state.controlLayers.present;
+      const { positivePrompt } = state.generation;
       const { maxPrompts } = state.dynamicPrompts;
 
       if (state.config.disabledFeatures.includes('dynamicPrompting')) {
@@ -38,7 +32,7 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
       const cachedPrompts = utilitiesApi.endpoints.dynamicPrompts.select({
         prompt: positivePrompt,
         max_prompts: maxPrompts,
-      })(state).data;
+      })(getState()).data;
 
       if (cachedPrompts) {
         dispatch(promptsChanged(cachedPrompts.prompts));
@@ -46,8 +40,8 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
         return;
       }
 
-      if (!getShouldProcessPrompt(positivePrompt)) {
-        dispatch(promptsChanged([positivePrompt]));
+      if (!getShouldProcessPrompt(state.generation.positivePrompt)) {
+        dispatch(promptsChanged([state.generation.positivePrompt]));
         dispatch(parsingErrorChanged(undefined));
         dispatch(isErrorChanged(false));
         return;

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

@@ -1,13 +1,14 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { setDefaultSettings } from 'features/parameters/store/actions';
 import {
+  heightRecalled,
   setCfgRescaleMultiplier,
   setCfgScale,
   setScheduler,
   setSteps,
   vaePrecisionChanged,
   vaeSelected,
+  widthRecalled,
 } from 'features/parameters/store/generationSlice';
 import {
   isParameterCFGRescaleMultiplier,
@@ -96,16 +97,16 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
             dispatch(setScheduler(scheduler));
           }
         }
-        const setSizeOptions = { updateAspectRatio: true, clamp: true };
+
         if (width) {
           if (isParameterWidth(width)) {
-            dispatch(widthChanged({ width, ...setSizeOptions }));
+            dispatch(widthRecalled(width));
           }
         }
 
         if (height) {
           if (isParameterHeight(height)) {
-            dispatch(heightChanged({ height, ...setSizeOptions }));
+            dispatch(heightRecalled(height));
           }
         }
 

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

@@ -1,8 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
-import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketGeneratorProgress } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -12,14 +9,6 @@ export const addGeneratorProgressEventListener = (startAppListening: AppStartLis
     actionCreator: socketGeneratorProgress,
     effect: (action) => {
       log.trace(action.payload, `Generator progress`);
-      const { source_node_id, step, total_steps, progress_image } = action.payload.data;
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.IN_PROGRESS;
-        nes.progress = (step + 1) / total_steps;
-        nes.progressImage = progress_image ?? null;
-        upsertExecutionState(nes.nodeId, nes);
-      }
     },
   });
 };

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

@@ -1,18 +1,10 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
 import { parseify } from 'common/util/serialize';
 import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
-import {
-  boardIdSelected,
-  galleryViewChanged,
-  imageSelected,
-  isImageViewerOpenChanged,
-} from 'features/gallery/store/gallerySlice';
+import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { isImageOutput } from 'features/nodes/types/common';
-import { zNodeStatus } from 'features/nodes/types/invocation';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -31,7 +23,7 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
       const { data } = action.payload;
       log.debug({ data: parseify(data) }, `Invocation complete (${action.payload.data.node.type})`);
 
-      const { result, node, queue_batch_id, source_node_id } = data;
+      const { result, node, queue_batch_id } = data;
       // This complete event has an associated image output
       if (isImageOutput(result) && !nodeTypeDenylist.includes(node.type)) {
         const { image_name } = result.image;
@@ -109,20 +101,9 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
             }
 
             dispatch(imageSelected(imageDTO));
-            dispatch(isImageViewerOpenChanged(true));
           }
         }
       }
-
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.COMPLETED;
-        if (nes.progress !== null) {
-          nes.progress = 1;
-        }
-        nes.outputs.push(result);
-        upsertExecutionState(nes.nodeId, nes);
-      }
     },
   });
 };

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

@@ -1,8 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
-import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketInvocationError } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -12,15 +9,6 @@ export const addInvocationErrorEventListener = (startAppListening: AppStartListe
     actionCreator: socketInvocationError,
     effect: (action) => {
       log.error(action.payload, `Invocation error (${action.payload.data.node.type})`);
-      const { source_node_id } = action.payload.data;
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.FAILED;
-        nes.error = action.payload.data.error;
-        nes.progress = null;
-        nes.progressImage = null;
-        upsertExecutionState(nes.nodeId, nes);
-      }
     },
   });
 };

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

@@ -1,8 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
-import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketInvocationStarted } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -12,12 +9,6 @@ export const addInvocationStartedEventListener = (startAppListening: AppStartLis
     actionCreator: socketInvocationStarted,
     effect: (action) => {
       log.debug(action.payload, `Invocation started (${action.payload.data.node.type})`);
-      const { source_node_id } = action.payload.data;
-      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
-      if (nes) {
-        nes.status = zNodeStatus.enum.IN_PROGRESS;
-        upsertExecutionState(nes.nodeId, nes);
-      }
     },
   });
 };

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

@@ -1,9 +1,5 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
-import { zNodeStatus } from 'features/nodes/types/invocation';
-import { forEach } from 'lodash-es';
 import { queueApi, queueItemsAdapter } from 'services/api/endpoints/queue';
 import { socketQueueItemStatusChanged } from 'services/events/actions';
 
@@ -58,21 +54,6 @@ export const addSocketQueueItemStatusChangedEventListener = (startAppListening:
       dispatch(
         queueApi.util.invalidateTags(['CurrentSessionQueueItem', 'NextSessionQueueItem', 'InvocationCacheStatus'])
       );
-
-      if (['in_progress'].includes(action.payload.data.queue_item.status)) {
-        forEach($nodeExecutionStates.get(), (nes) => {
-          if (!nes) {
-            return;
-          }
-          const clone = deepClone(nes);
-          clone.status = zNodeStatus.enum.PENDING;
-          clone.error = null;
-          clone.progress = null;
-          clone.progressImage = null;
-          clone.outputs = [];
-          $nodeExecutionStates.setKey(clone.nodeId, clone);
-        });
-      }
     },
   });
 };

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

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { updateAllNodesRequested } from 'features/nodes/store/actions';
-import { $templates, nodesChanged } from 'features/nodes/store/nodesSlice';
+import { nodeReplaced } from 'features/nodes/store/nodesSlice';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
@@ -14,8 +14,7 @@ export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartLi
     actionCreator: updateAllNodesRequested,
     effect: (action, { dispatch, getState }) => {
       const log = logger('nodes');
-      const { nodes } = getState().nodes.present;
-      const templates = $templates.get();
+      const { nodes, templates } = getState().nodes;
 
       let unableToUpdateCount = 0;
 
@@ -25,18 +24,13 @@ export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartLi
           unableToUpdateCount++;
           return;
         }
-        if (!getNeedsUpdate(node.data, template)) {
+        if (!getNeedsUpdate(node, template)) {
           // No need to increment the count here, since we're not actually updating
           return;
         }
         try {
           const updatedNode = updateNode(node, template);
-          dispatch(
-            nodesChanged([
-              { type: 'remove', id: updatedNode.id },
-              { type: 'add', item: updatedNode },
-            ])
-          );
+          dispatch(nodeReplaced({ nodeId: updatedNode.id, node: updatedNode }));
         } catch (e) {
           if (e instanceof NodeUpdateError) {
             unableToUpdateCount++;

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

@@ -2,51 +2,32 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
 import { workflowLoaded, workflowLoadRequested } from 'features/nodes/store/actions';
-import { $templates } from 'features/nodes/store/nodesSlice';
 import { $flow } from 'features/nodes/store/reactFlowInstance';
-import type { Templates } from 'features/nodes/store/types';
 import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/types/error';
-import { graphToWorkflow } from 'features/nodes/util/workflow/graphToWorkflow';
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
 import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
 import { t } from 'i18next';
-import type { GraphAndWorkflowResponse, NonNullableGraph } from 'services/api/types';
 import { z } from 'zod';
 import { fromZodError } from 'zod-validation-error';
 
-const getWorkflow = (data: GraphAndWorkflowResponse, templates: Templates) => {
-  if (data.workflow) {
-    // Prefer to load the workflow if it's available - it has more information
-    const parsed = JSON.parse(data.workflow);
-    return validateWorkflow(parsed, templates);
-  } else if (data.graph) {
-    // Else we fall back on the graph, using the graphToWorkflow function to convert and do layout
-    const parsed = JSON.parse(data.graph);
-    const workflow = graphToWorkflow(parsed as NonNullableGraph, true);
-    return validateWorkflow(workflow, templates);
-  } else {
-    throw new Error('No workflow or graph provided');
-  }
-};
-
 export const addWorkflowLoadRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: workflowLoadRequested,
-    effect: (action, { dispatch }) => {
+    effect: (action, { dispatch, getState }) => {
       const log = logger('nodes');
-      const { data, asCopy } = action.payload;
-      const nodeTemplates = $templates.get();
+      const { workflow, asCopy } = action.payload;
+      const nodeTemplates = getState().nodes.templates;
 
       try {
-        const { workflow, warnings } = getWorkflow(data, nodeTemplates);
+        const { workflow: validatedWorkflow, warnings } = validateWorkflow(workflow, nodeTemplates);
 
         if (asCopy) {
           // If we're loading a copy, we need to remove the ID so that the backend will create a new workflow
-          delete workflow.id;
+          delete validatedWorkflow.id;
         }
 
-        dispatch(workflowLoaded(workflow));
+        dispatch(workflowLoaded(validatedWorkflow));
         if (!warnings.length) {
           dispatch(
             addToast(

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

@@ -10,23 +10,22 @@ import {
   controlAdaptersPersistConfig,
   controlAdaptersSlice,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import {
-  controlLayersPersistConfig,
-  controlLayersSlice,
-  controlLayersUndoableConfig,
-} from 'features/controlLayers/store/controlLayersSlice';
 import { deleteImageModalSlice } from 'features/deleteImageModal/store/slice';
 import { dynamicPromptsPersistConfig, dynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { galleryPersistConfig, gallerySlice } from 'features/gallery/store/gallerySlice';
 import { hrfPersistConfig, hrfSlice } from 'features/hrf/store/hrfSlice';
 import { loraPersistConfig, loraSlice } from 'features/lora/store/loraSlice';
 import { modelManagerV2PersistConfig, modelManagerV2Slice } from 'features/modelManagerV2/store/modelManagerV2Slice';
-import { nodesPersistConfig, nodesSlice, nodesUndoableConfig } from 'features/nodes/store/nodesSlice';
-import { workflowSettingsPersistConfig, workflowSettingsSlice } from 'features/nodes/store/workflowSettingsSlice';
+import { nodesPersistConfig, nodesSlice } from 'features/nodes/store/nodesSlice';
 import { workflowPersistConfig, workflowSlice } from 'features/nodes/store/workflowSlice';
 import { generationPersistConfig, generationSlice } from 'features/parameters/store/generationSlice';
 import { postprocessingPersistConfig, postprocessingSlice } from 'features/parameters/store/postprocessingSlice';
 import { queueSlice } from 'features/queue/store/queueSlice';
+import {
+  regionalPromptsPersistConfig,
+  regionalPromptsSlice,
+  regionalPromptsUndoableConfig,
+} from 'features/regionalPrompts/store/regionalPromptsSlice';
 import { sdxlPersistConfig, sdxlSlice } from 'features/sdxl/store/sdxlSlice';
 import { configSlice } from 'features/system/store/configSlice';
 import { systemPersistConfig, systemSlice } from 'features/system/store/systemSlice';
@@ -51,7 +50,7 @@ const allReducers = {
   [canvasSlice.name]: canvasSlice.reducer,
   [gallerySlice.name]: gallerySlice.reducer,
   [generationSlice.name]: generationSlice.reducer,
-  [nodesSlice.name]: undoable(nodesSlice.reducer, nodesUndoableConfig),
+  [nodesSlice.name]: nodesSlice.reducer,
   [postprocessingSlice.name]: postprocessingSlice.reducer,
   [systemSlice.name]: systemSlice.reducer,
   [configSlice.name]: configSlice.reducer,
@@ -66,8 +65,7 @@ const allReducers = {
   [queueSlice.name]: queueSlice.reducer,
   [workflowSlice.name]: workflowSlice.reducer,
   [hrfSlice.name]: hrfSlice.reducer,
-  [controlLayersSlice.name]: undoable(controlLayersSlice.reducer, controlLayersUndoableConfig),
-  [workflowSettingsSlice.name]: workflowSettingsSlice.reducer,
+  [regionalPromptsSlice.name]: undoable(regionalPromptsSlice.reducer, regionalPromptsUndoableConfig),
   [api.reducerPath]: api.reducer,
 };
 
@@ -112,8 +110,7 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [loraPersistConfig.name]: loraPersistConfig,
   [modelManagerV2PersistConfig.name]: modelManagerV2PersistConfig,
   [hrfPersistConfig.name]: hrfPersistConfig,
-  [controlLayersPersistConfig.name]: controlLayersPersistConfig,
-  [workflowSettingsPersistConfig.name]: workflowSettingsPersistConfig,
+  [regionalPromptsPersistConfig.name]: regionalPromptsPersistConfig,
 };
 
 const unserialize: UnserializeFunction = (data, key) => {

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

@@ -70,7 +70,6 @@ const IAIDndImage = (props: IAIDndImageProps) => {
     onMouseOver,
     onMouseOut,
     dataTestId,
-    ...rest
   } = props;
 
   const [isHovered, setIsHovered] = useState(false);
@@ -139,7 +138,6 @@ const IAIDndImage = (props: IAIDndImageProps) => {
           minH={minSize ? minSize : undefined}
           userSelect="none"
           cursor={isDragDisabled || !imageDTO ? 'default' : 'pointer'}
-          {...rest}
         >
           {imageDTO && (
             <Flex

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

@@ -17,10 +17,14 @@ const accept: Accept = {
 const selectPostUploadAction = createMemoizedSelector(activeTabNameSelector, (activeTabName) => {
   let postUploadAction: PostUploadAction = { type: 'TOAST' };
 
-  if (activeTabName === 'canvas') {
+  if (activeTabName === 'unifiedCanvas') {
     postUploadAction = { type: 'SET_CANVAS_INITIAL_IMAGE' };
   }
 
+  if (activeTabName === 'img2img') {
+    postUploadAction = { type: 'SET_INITIAL_IMAGE' };
+  }
+
   return postUploadAction;
 });
 

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

@@ -67,7 +67,7 @@ export const useGlobalHotkeys = () => {
   useHotkeys(
     '1',
     () => {
-      dispatch(setActiveTab('generation'));
+      dispatch(setActiveTab('txt2img'));
     },
     [dispatch]
   );
@@ -75,7 +75,7 @@ export const useGlobalHotkeys = () => {
   useHotkeys(
     '2',
     () => {
-      dispatch(setActiveTab('canvas'));
+      dispatch(setActiveTab('img2img'));
     },
     [dispatch]
   );
@@ -83,23 +83,31 @@ export const useGlobalHotkeys = () => {
   useHotkeys(
     '3',
     () => {
-      dispatch(setActiveTab('workflows'));
+      dispatch(setActiveTab('unifiedCanvas'));
     },
     [dispatch]
   );
 
   useHotkeys(
     '4',
+    () => {
+      dispatch(setActiveTab('nodes'));
+    },
+    [dispatch]
+  );
+
+  useHotkeys(
+    '5',
     () => {
       if (isModelManagerEnabled) {
-        dispatch(setActiveTab('models'));
+        dispatch(setActiveTab('modelManager'));
       }
     },
     [dispatch, isModelManagerEnabled]
   );
 
   useHotkeys(
-    isModelManagerEnabled ? '5' : '4',
+    isModelManagerEnabled ? '6' : '5',
     () => {
       dispatch(setActiveTab('queue'));
     },

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

@@ -13,7 +13,6 @@ type UseGroupedModelComboboxArg<T extends AnyModelConfig> = {
   onChange: (value: T | null) => void;
   getIsDisabled?: (model: T) => boolean;
   isLoading?: boolean;
-  groupByType?: boolean;
 };
 
 type UseGroupedModelComboboxReturn = {
@@ -24,21 +23,17 @@ type UseGroupedModelComboboxReturn = {
   noOptionsMessage: () => string;
 };
 
-const groupByBaseFunc = <T extends AnyModelConfig>(model: T) => model.base.toUpperCase();
-const groupByBaseAndTypeFunc = <T extends AnyModelConfig>(model: T) =>
-  `${model.base.toUpperCase()} / ${model.type.replaceAll('_', ' ').toUpperCase()}`;
-
 export const useGroupedModelCombobox = <T extends AnyModelConfig>(
   arg: UseGroupedModelComboboxArg<T>
 ): UseGroupedModelComboboxReturn => {
   const { t } = useTranslation();
   const base_model = useAppSelector((s) => s.generation.model?.base ?? 'sdxl');
-  const { modelConfigs, selectedModel, getIsDisabled, onChange, isLoading, groupByType = false } = arg;
+  const { modelConfigs, selectedModel, getIsDisabled, onChange, isLoading } = arg;
   const options = useMemo<GroupBase<ComboboxOption>[]>(() => {
     if (!modelConfigs) {
       return [];
     }
-    const groupedModels = groupBy(modelConfigs, groupByType ? groupByBaseAndTypeFunc : groupByBaseFunc);
+    const groupedModels = groupBy(modelConfigs, 'base');
     const _options = reduce(
       groupedModels,
       (acc, val, label) => {
@@ -54,9 +49,9 @@ export const useGroupedModelCombobox = <T extends AnyModelConfig>(
       },
       [] as GroupBase<ComboboxOption>[]
     );
-    _options.sort((a) => (a.label?.split('/')[0]?.toLowerCase().includes(base_model) ? -1 : 1));
+    _options.sort((a) => (a.label === base_model ? -1 : 1));
     return _options;
-  }, [modelConfigs, groupByType, getIsDisabled, base_model]);
+  }, [getIsDisabled, modelConfigs, base_model]);
 
   const value = useMemo(
     () =>

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

@@ -1,4 +1,3 @@
-import { useStore } from '@nanostores/react';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import {
@@ -6,231 +5,133 @@ import {
   selectControlAdaptersSlice,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
-import { selectControlLayersSlice } from 'features/controlLayers/store/controlLayersSlice';
-import type { Layer } from 'features/controlLayers/store/types';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
-import { $templates, selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import type { Templates } from 'features/nodes/store/types';
-import { selectWorkflowSettingsSlice } from 'features/nodes/store/workflowSettingsSlice';
+import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { selectGenerationSlice } from 'features/parameters/store/generationSlice';
 import { selectSystemSlice } from 'features/system/store/systemSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import i18n from 'i18next';
-import { forEach, upperFirst } from 'lodash-es';
-import { useMemo } from 'react';
+import { forEach } from 'lodash-es';
 import { getConnectedEdges } from 'reactflow';
 
-const LAYER_TYPE_TO_TKEY: Record<Layer['type'], string> = {
-  initial_image_layer: 'controlLayers.globalInitialImage',
-  control_adapter_layer: 'controlLayers.globalControlAdapter',
-  ip_adapter_layer: 'controlLayers.globalIPAdapter',
-  regional_guidance_layer: 'controlLayers.regionalGuidance',
-};
+const selector = createMemoizedSelector(
+  [
+    selectControlAdaptersSlice,
+    selectGenerationSlice,
+    selectSystemSlice,
+    selectNodesSlice,
+    selectDynamicPromptsSlice,
+    activeTabNameSelector,
+  ],
+  (controlAdapters, generation, system, nodes, dynamicPrompts, activeTabName) => {
+    const { initialImage, model, positivePrompt } = generation;
 
-const createSelector = (templates: Templates) =>
-  createMemoizedSelector(
-    [
-      selectControlAdaptersSlice,
-      selectGenerationSlice,
-      selectSystemSlice,
-      selectNodesSlice,
-      selectWorkflowSettingsSlice,
-      selectDynamicPromptsSlice,
-      selectControlLayersSlice,
-      activeTabNameSelector,
-    ],
-    (controlAdapters, generation, system, nodes, workflowSettings, dynamicPrompts, controlLayers, activeTabName) => {
-      const { model } = generation;
-      const { size } = controlLayers.present;
-      const { positivePrompt } = controlLayers.present;
+    const { isConnected } = system;
 
-      const { isConnected } = system;
+    const reasons: string[] = [];
 
-      const reasons: { prefix?: string; content: string }[] = [];
+    // Cannot generate if not connected
+    if (!isConnected) {
+      reasons.push(i18n.t('parameters.invoke.systemDisconnected'));
+    }
 
-      // Cannot generate if not connected
-      if (!isConnected) {
-        reasons.push({ content: i18n.t('parameters.invoke.systemDisconnected') });
-      }
+    if (activeTabName === 'img2img' && !initialImage) {
+      reasons.push(i18n.t('parameters.invoke.noInitialImageSelected'));
+    }
 
-      if (activeTabName === 'workflows') {
-        if (workflowSettings.shouldValidateGraph) {
-          if (!nodes.nodes.length) {
-            reasons.push({ content: i18n.t('parameters.invoke.noNodesInGraph') });
+    if (activeTabName === 'nodes') {
+      if (nodes.shouldValidateGraph) {
+        if (!nodes.nodes.length) {
+          reasons.push(i18n.t('parameters.invoke.noNodesInGraph'));
+        }
+
+        nodes.nodes.forEach((node) => {
+          if (!isInvocationNode(node)) {
+            return;
           }
 
-          nodes.nodes.forEach((node) => {
-            if (!isInvocationNode(node)) {
+          const nodeTemplate = nodes.templates[node.data.type];
+
+          if (!nodeTemplate) {
+            // Node type not found
+            reasons.push(i18n.t('parameters.invoke.missingNodeTemplate'));
+            return;
+          }
+
+          const connectedEdges = getConnectedEdges([node], nodes.edges);
+
+          forEach(node.data.inputs, (field) => {
+            const fieldTemplate = nodeTemplate.inputs[field.name];
+            const hasConnection = connectedEdges.some(
+              (edge) => edge.target === node.id && edge.targetHandle === field.name
+            );
+
+            if (!fieldTemplate) {
+              reasons.push(i18n.t('parameters.invoke.missingFieldTemplate'));
               return;
             }
 
-            const nodeTemplate = templates[node.data.type];
-
-            if (!nodeTemplate) {
-              // Node type not found
-              reasons.push({ content: i18n.t('parameters.invoke.missingNodeTemplate') });
-              return;
-            }
-
-            const connectedEdges = getConnectedEdges([node], nodes.edges);
-
-            forEach(node.data.inputs, (field) => {
-              const fieldTemplate = nodeTemplate.inputs[field.name];
-              const hasConnection = connectedEdges.some(
-                (edge) => edge.target === node.id && edge.targetHandle === field.name
+            if (fieldTemplate.required && field.value === undefined && !hasConnection) {
+              reasons.push(
+                i18n.t('parameters.invoke.missingInputForField', {
+                  nodeLabel: node.data.label || nodeTemplate.title,
+                  fieldLabel: field.label || fieldTemplate.title,
+                })
               );
-
-              if (!fieldTemplate) {
-                reasons.push({ content: i18n.t('parameters.invoke.missingFieldTemplate') });
-                return;
-              }
-
-              if (fieldTemplate.required && field.value === undefined && !hasConnection) {
-                reasons.push({
-                  content: i18n.t('parameters.invoke.missingInputForField', {
-                    nodeLabel: node.data.label || nodeTemplate.title,
-                    fieldLabel: field.label || fieldTemplate.title,
-                  }),
-                });
-                return;
-              }
-            });
+              return;
+            }
           });
-        }
-      } else {
-        if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
-          reasons.push({ content: i18n.t('parameters.invoke.noPrompts') });
-        }
-
-        if (!model) {
-          reasons.push({ content: i18n.t('parameters.invoke.noModelSelected') });
-        }
-
-        if (activeTabName === 'generation') {
-          // Handling for generation tab
-          controlLayers.present.layers
-            .filter((l) => l.isEnabled)
-            .forEach((l, i) => {
-              const layerLiteral = i18n.t('controlLayers.layers_one');
-              const layerNumber = i + 1;
-              const layerType = i18n.t(LAYER_TYPE_TO_TKEY[l.type]);
-              const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-              const problems: string[] = [];
-              if (l.type === 'control_adapter_layer') {
-                // Must have model
-                if (!l.controlAdapter.model) {
-                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterNoModelSelected'));
-                }
-                // Model base must match
-                if (l.controlAdapter.model?.base !== model?.base) {
-                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterIncompatibleBaseModel'));
-                }
-                // Must have a control image OR, if it has a processor, it must have a processed image
-                if (!l.controlAdapter.image) {
-                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterNoImageSelected'));
-                } else if (l.controlAdapter.processorConfig && !l.controlAdapter.processedImage) {
-                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterImageNotProcessed'));
-                }
-                // T2I Adapters require images have dimensions that are multiples of 64 (SD1.5) or 32 (SDXL)
-                if (l.controlAdapter.type === 't2i_adapter') {
-                  const multiple = model?.base === 'sdxl' ? 32 : 64;
-                  if (size.width % multiple !== 0 || size.height % multiple !== 0) {
-                    problems.push(i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleDimensions', { multiple }));
-                  }
-                }
-              }
-
-              if (l.type === 'ip_adapter_layer') {
-                // Must have model
-                if (!l.ipAdapter.model) {
-                  problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
-                }
-                // Model base must match
-                if (l.ipAdapter.model?.base !== model?.base) {
-                  problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
-                }
-                // Must have an image
-                if (!l.ipAdapter.image) {
-                  problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
-                }
-              }
-
-              if (l.type === 'initial_image_layer') {
-                // Must have an image
-                if (!l.image) {
-                  problems.push(i18n.t('parameters.invoke.layer.initialImageNoImageSelected'));
-                }
-              }
-
-              if (l.type === 'regional_guidance_layer') {
-                // Must have a region
-                if (l.maskObjects.length === 0) {
-                  problems.push(i18n.t('parameters.invoke.layer.rgNoRegion'));
-                }
-                // Must have at least 1 prompt or IP Adapter
-                if (l.positivePrompt === null && l.negativePrompt === null && l.ipAdapters.length === 0) {
-                  problems.push(i18n.t('parameters.invoke.layer.rgNoPromptsOrIPAdapters'));
-                }
-                l.ipAdapters.forEach((ipAdapter) => {
-                  // Must have model
-                  if (!ipAdapter.model) {
-                    problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
-                  }
-                  // Model base must match
-                  if (ipAdapter.model?.base !== model?.base) {
-                    problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
-                  }
-                  // Must have an image
-                  if (!ipAdapter.image) {
-                    problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
-                  }
-                });
-              }
-
-              if (problems.length) {
-                const content = upperFirst(problems.join(', '));
-                reasons.push({ prefix, content });
-              }
-            });
-        } else {
-          // Handling for all other tabs
-          selectControlAdapterAll(controlAdapters)
-            .filter((ca) => ca.isEnabled)
-            .forEach((ca, i) => {
-              if (!ca.isEnabled) {
-                return;
-              }
-
-              if (!ca.model) {
-                reasons.push({ content: i18n.t('parameters.invoke.noModelForControlAdapter', { number: i + 1 }) });
-              } else if (ca.model.base !== model?.base) {
-                // This should never happen, just a sanity check
-                reasons.push({
-                  content: i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', { number: i + 1 }),
-                });
-              }
-
-              if (
-                !ca.controlImage ||
-                (isControlNetOrT2IAdapter(ca) && !ca.processedControlImage && ca.processorType !== 'none')
-              ) {
-                reasons.push({
-                  content: i18n.t('parameters.invoke.noControlImageForControlAdapter', { number: i + 1 }),
-                });
-              }
-            });
-        }
+        });
+      }
+    } else {
+      if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
+        reasons.push(i18n.t('parameters.invoke.noPrompts'));
       }
 
-      return { isReady: !reasons.length, reasons };
+      if (!model) {
+        reasons.push(i18n.t('parameters.invoke.noModelSelected'));
+      }
+
+      selectControlAdapterAll(controlAdapters).forEach((ca, i) => {
+        if (!ca.isEnabled) {
+          return;
+        }
+
+        if (!ca.model) {
+          reasons.push(
+            i18n.t('parameters.invoke.noModelForControlAdapter', {
+              number: i + 1,
+            })
+          );
+        } else if (ca.model.base !== model?.base) {
+          // This should never happen, just a sanity check
+          reasons.push(
+            i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', {
+              number: i + 1,
+            })
+          );
+        }
+
+        if (
+          !ca.controlImage ||
+          (isControlNetOrT2IAdapter(ca) && !ca.processedControlImage && ca.processorType !== 'none')
+        ) {
+          reasons.push(
+            i18n.t('parameters.invoke.noControlImageForControlAdapter', {
+              number: i + 1,
+            })
+          );
+        }
+      });
     }
-  );
+
+    return { isReady: !reasons.length, reasons };
+  }
+);
 
 export const useIsReadyToEnqueue = () => {
-  const templates = useStore($templates);
-  const selector = useMemo(() => createSelector(templates), [templates]);
-  const value = useAppSelector(selector);
-  return value;
+  const { isReady, reasons } = useAppSelector(selector);
+  return { isReady, reasons };
 };

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

@@ -1,3 +0,0 @@
-export const stopPropagation = (e: React.MouseEvent) => {
-  e.stopPropagation();
-};

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

@@ -21,6 +21,7 @@ import {
   setShouldShowBoundingBox,
 } from 'features/canvas/store/canvasSlice';
 import type { CanvasLayer } from 'features/canvas/store/canvasTypes';
+import { LAYER_NAMES_DICT } from 'features/canvas/store/canvasTypes';
 import { memo, useCallback, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
@@ -215,20 +216,13 @@ const IAICanvasToolbar = () => {
     [dispatch, isMaskEnabled]
   );
 
-  const layerOptions = useMemo<{ label: string; value: CanvasLayer }[]>(
-    () => [
-      { label: t('unifiedCanvas.base'), value: 'base' },
-      { label: t('unifiedCanvas.mask'), value: 'mask' },
-    ],
-    [t]
-  );
-  const layerValue = useMemo(() => layerOptions.filter((o) => o.value === layer)[0] ?? null, [layer, layerOptions]);
+  const value = useMemo(() => LAYER_NAMES_DICT.filter((o) => o.value === layer)[0], [layer]);
 
   return (
     <Flex alignItems="center" gap={2} flexWrap="wrap">
       <Tooltip label={`${t('unifiedCanvas.layer')} (Q)`}>
         <FormControl isDisabled={isStaging} w="5rem">
-          <Combobox value={layerValue} options={layerOptions} onChange={handleChangeLayer} />
+          <Combobox value={value} options={LAYER_NAMES_DICT} onChange={handleChangeLayer} />
         </FormControl>
       </Tooltip>
 

invokeai/frontend/web/src/features/canvas/hooks/useCanvasHotkeys.ts

@@ -75,7 +75,7 @@ const useInpaintingCanvasHotkeys = () => {
 
   const onKeyDown = useCallback(
     (e: KeyboardEvent) => {
-      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'canvas') {
+      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'unifiedCanvas') {
         return;
       }
       if ($toolStash.get() || $tool.get() === 'move') {
@@ -90,7 +90,7 @@ const useInpaintingCanvasHotkeys = () => {
   );
   const onKeyUp = useCallback(
     (e: KeyboardEvent) => {
-      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'canvas') {
+      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'unifiedCanvas') {
         return;
       }
       if (!$toolStash.get() || $tool.get() !== 'move') {

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

@@ -8,7 +8,6 @@ import calculateScale from 'features/canvas/util/calculateScale';
 import { STAGE_PADDING_PERCENTAGE } from 'features/canvas/util/constants';
 import floorCoordinates from 'features/canvas/util/floorCoordinates';
 import getScaledBoundingBoxDimensions from 'features/canvas/util/getScaledBoundingBoxDimensions';
-import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
 import { initialAspectRatioState } from 'features/parameters/components/ImageSize/constants';
 import type { AspectRatioState } from 'features/parameters/components/ImageSize/types';
 import { modelChanged } from 'features/parameters/store/generationSlice';
@@ -589,9 +588,8 @@ export const canvasSlice = createSlice({
   },
   extraReducers: (builder) => {
     builder.addCase(modelChanged, (state, action) => {
-      const newModel = action.payload;
-      if (!newModel || action.meta.previousModel?.base === newModel.base) {
-        // Model was cleared or the base didn't change
+      if (action.meta.previousModel?.base === action.payload?.base) {
+        // The base model hasn't changed, we don't need to optimize the size
         return;
       }
       const optimalDimension = getOptimalDimension(action.payload);
@@ -599,8 +597,14 @@ export const canvasSlice = createSlice({
       if (getIsSizeOptimal(width, height, optimalDimension)) {
         return;
       }
-      const newSize = calculateNewSize(state.aspectRatio.value, optimalDimension * optimalDimension);
-      setBoundingBoxDimensionsReducer(state, newSize, optimalDimension);
+      setBoundingBoxDimensionsReducer(
+        state,
+        {
+          width,
+          height,
+        },
+        optimalDimension
+      );
     });
 
     builder.addCase(socketQueueItemStatusChanged, (state, action) => {

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

@@ -5,6 +5,11 @@ import { z } from 'zod';
 
 export type CanvasLayer = 'base' | 'mask';
 
+export const LAYER_NAMES_DICT: { label: string; value: CanvasLayer }[] = [
+  { label: 'Base', value: 'base' },
+  { label: 'Mask', value: 'mask' },
+];
+
 const zBoundingBoxScaleMethod = z.enum(['none', 'auto', 'manual']);
 export type BoundingBoxScaleMethod = z.infer<typeof zBoundingBoxScaleMethod>;
 export const isBoundingBoxScaleMethod = (v: unknown): v is BoundingBoxScaleMethod =>

invokeai/frontend/web/src/features/controlAdapters/components/ControlAdapterConfig.tsx

@@ -76,7 +76,7 @@ const ControlAdapterConfig = (props: { id: string; number: number }) => {
         <Box minW={0} w="full" transitionProperty="common" transitionDuration="0.1s">
           <ParamControlAdapterModel id={id} />
         </Box>
-        {activeTabName === 'canvas' && <ControlNetCanvasImageImports id={id} />}
+        {activeTabName === 'unifiedCanvas' && <ControlNetCanvasImageImports id={id} />}
         <IconButton
           size="sm"
           tooltip={t('controlnet.duplicate')}

invokeai/frontend/web/src/features/controlAdapters/components/ControlAdapterImagePreview.tsx

@@ -13,10 +13,9 @@ import {
   controlAdapterImageChanged,
   selectControlAdaptersSlice,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
-import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { heightChanged, selectOptimalDimension, widthChanged } from 'features/parameters/store/generationSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { memo, useCallback, useEffect, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -93,16 +92,15 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
       return;
     }
 
-    if (activeTabName === 'canvas') {
+    if (activeTabName === 'unifiedCanvas') {
       dispatch(setBoundingBoxDimensions({ width: controlImage.width, height: controlImage.height }, optimalDimension));
     } else {
-      const options = { updateAspectRatio: true, clamp: true };
       const { width, height } = calculateNewSize(
         controlImage.width / controlImage.height,
         optimalDimension * optimalDimension
       );
-      dispatch(widthChanged({ width, ...options }));
-      dispatch(heightChanged({ height, ...options }));
+      dispatch(widthChanged(width));
+      dispatch(heightChanged(height));
     }
   }, [controlImage, activeTabName, dispatch, optimalDimension]);
 

invokeai/frontend/web/src/features/controlAdapters/components/parameters/ParamControlAdapterIPMethod.tsx

@@ -52,7 +52,7 @@ const ParamControlAdapterIPMethod = ({ id }: Props) => {
 
   return (
     <FormControl>
-      <InformationalPopover feature="controlNetResizeMode">
+      <InformationalPopover feature="ipAdapterMethod">
         <FormLabel>{t('controlnet.ipAdapterMethod')}</FormLabel>
       </InformationalPopover>
       <Combobox value={value} options={options} isDisabled={!isEnabled} onChange={handleIPMethodChanged} />

invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts

@@ -1,11 +1,12 @@
 import type { PayloadAction, Update } from '@reduxjs/toolkit';
-import { createEntityAdapter, createSlice } from '@reduxjs/toolkit';
+import { createEntityAdapter, createSlice, isAnyOf } from '@reduxjs/toolkit';
 import { getSelectorsOptions } from 'app/store/createMemoizedSelector';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { deepClone } from 'common/util/deepClone';
 import { buildControlAdapter } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
 import { zModelIdentifierField } from 'features/nodes/types/common';
+import { maskLayerIPAdapterAdded } from 'features/regionalPrompts/store/regionalPromptsSlice';
 import { merge, uniq } from 'lodash-es';
 import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { socketInvocationError } from 'services/events/actions';
@@ -382,6 +383,10 @@ export const controlAdaptersSlice = createSlice({
     builder.addCase(socketInvocationError, (state) => {
       state.pendingControlImages = [];
     });
+
+    builder.addCase(maskLayerIPAdapterAdded, (state, action) => {
+      caAdapter.addOne(state, buildControlAdapter(action.meta.uuid, 'ip_adapter'));
+    });
   },
 });
 
@@ -412,6 +417,8 @@ export const {
   t2iAdaptersReset,
 } = controlAdaptersSlice.actions;
 
+export const isAnyControlAdapterAdded = isAnyOf(controlAdapterAdded, controlAdapterRecalled);
+
 export const selectControlAdaptersSlice = (state: RootState) => state.controlAdapters;
 
 /* eslint-disable-next-line @typescript-eslint/no-explicit-any */

invokeai/frontend/web/src/features/controlAdapters/store/types.ts

@@ -5,7 +5,22 @@ import type {
   ParameterT2IAdapterModel,
 } from 'features/parameters/types/parameterSchemas';
 import type { components } from 'services/api/schema';
-import type { Invocation } from 'services/api/types';
+import type {
+  CannyImageProcessorInvocation,
+  ColorMapImageProcessorInvocation,
+  ContentShuffleImageProcessorInvocation,
+  DepthAnythingImageProcessorInvocation,
+  DWOpenposeImageProcessorInvocation,
+  HedImageProcessorInvocation,
+  LineartAnimeImageProcessorInvocation,
+  LineartImageProcessorInvocation,
+  MediapipeFaceProcessorInvocation,
+  MidasDepthImageProcessorInvocation,
+  MlsdImageProcessorInvocation,
+  NormalbaeImageProcessorInvocation,
+  PidiImageProcessorInvocation,
+  ZoeDepthImageProcessorInvocation,
+} from 'services/api/types';
 import type { O } from 'ts-toolbelt';
 import { z } from 'zod';
 
@@ -13,20 +28,20 @@ import { z } from 'zod';
  * Any ControlNet processor node
  */
 export type ControlAdapterProcessorNode =
-  | Invocation<'canny_image_processor'>
-  | Invocation<'color_map_image_processor'>
-  | Invocation<'content_shuffle_image_processor'>
-  | Invocation<'depth_anything_image_processor'>
-  | Invocation<'hed_image_processor'>
-  | Invocation<'lineart_anime_image_processor'>
-  | Invocation<'lineart_image_processor'>
-  | Invocation<'mediapipe_face_processor'>
-  | Invocation<'midas_depth_image_processor'>
-  | Invocation<'mlsd_image_processor'>
-  | Invocation<'normalbae_image_processor'>
-  | Invocation<'dw_openpose_image_processor'>
-  | Invocation<'pidi_image_processor'>
-  | Invocation<'zoe_depth_image_processor'>;
+  | CannyImageProcessorInvocation
+  | ColorMapImageProcessorInvocation
+  | ContentShuffleImageProcessorInvocation
+  | DepthAnythingImageProcessorInvocation
+  | HedImageProcessorInvocation
+  | LineartAnimeImageProcessorInvocation
+  | LineartImageProcessorInvocation
+  | MediapipeFaceProcessorInvocation
+  | MidasDepthImageProcessorInvocation
+  | MlsdImageProcessorInvocation
+  | NormalbaeImageProcessorInvocation
+  | DWOpenposeImageProcessorInvocation
+  | PidiImageProcessorInvocation
+  | ZoeDepthImageProcessorInvocation;
 
 /**
  * Any ControlNet processor type
@@ -56,7 +71,7 @@ export const isControlAdapterProcessorType = (v: unknown): v is ControlAdapterPr
  * The Canny processor node, with parameters flagged as required
  */
 export type RequiredCannyImageProcessorInvocation = O.Required<
-  Invocation<'canny_image_processor'>,
+  CannyImageProcessorInvocation,
   'type' | 'low_threshold' | 'high_threshold' | 'image_resolution' | 'detect_resolution'
 >;
 
@@ -64,7 +79,7 @@ export type RequiredCannyImageProcessorInvocation = O.Required<
  * The Color Map processor node, with parameters flagged as required
  */
 export type RequiredColorMapImageProcessorInvocation = O.Required<
-  Invocation<'color_map_image_processor'>,
+  ColorMapImageProcessorInvocation,
   'type' | 'color_map_tile_size'
 >;
 
@@ -72,7 +87,7 @@ export type RequiredColorMapImageProcessorInvocation = O.Required<
  * The ContentShuffle processor node, with parameters flagged as required
  */
 export type RequiredContentShuffleImageProcessorInvocation = O.Required<
-  Invocation<'content_shuffle_image_processor'>,
+  ContentShuffleImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution' | 'w' | 'h' | 'f'
 >;
 
@@ -80,7 +95,7 @@ export type RequiredContentShuffleImageProcessorInvocation = O.Required<
  * The DepthAnything processor node, with parameters flagged as required
  */
 export type RequiredDepthAnythingImageProcessorInvocation = O.Required<
-  Invocation<'depth_anything_image_processor'>,
+  DepthAnythingImageProcessorInvocation,
   'type' | 'model_size' | 'resolution' | 'offload'
 >;
 
@@ -93,7 +108,7 @@ export const isDepthAnythingModelSize = (v: unknown): v is DepthAnythingModelSiz
  * The HED processor node, with parameters flagged as required
  */
 export type RequiredHedImageProcessorInvocation = O.Required<
-  Invocation<'hed_image_processor'>,
+  HedImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution' | 'scribble'
 >;
 
@@ -101,7 +116,7 @@ export type RequiredHedImageProcessorInvocation = O.Required<
  * The Lineart Anime processor node, with parameters flagged as required
  */
 export type RequiredLineartAnimeImageProcessorInvocation = O.Required<
-  Invocation<'lineart_anime_image_processor'>,
+  LineartAnimeImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution'
 >;
 
@@ -109,7 +124,7 @@ export type RequiredLineartAnimeImageProcessorInvocation = O.Required<
  * The Lineart processor node, with parameters flagged as required
  */
 export type RequiredLineartImageProcessorInvocation = O.Required<
-  Invocation<'lineart_image_processor'>,
+  LineartImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution' | 'coarse'
 >;
 
@@ -117,7 +132,7 @@ export type RequiredLineartImageProcessorInvocation = O.Required<
  * The MediapipeFace processor node, with parameters flagged as required
  */
 export type RequiredMediapipeFaceProcessorInvocation = O.Required<
-  Invocation<'mediapipe_face_processor'>,
+  MediapipeFaceProcessorInvocation,
   'type' | 'max_faces' | 'min_confidence' | 'image_resolution' | 'detect_resolution'
 >;
 
@@ -125,7 +140,7 @@ export type RequiredMediapipeFaceProcessorInvocation = O.Required<
  * The MidasDepth processor node, with parameters flagged as required
  */
 export type RequiredMidasDepthImageProcessorInvocation = O.Required<
-  Invocation<'midas_depth_image_processor'>,
+  MidasDepthImageProcessorInvocation,
   'type' | 'a_mult' | 'bg_th' | 'image_resolution' | 'detect_resolution'
 >;
 
@@ -133,7 +148,7 @@ export type RequiredMidasDepthImageProcessorInvocation = O.Required<
  * The MLSD processor node, with parameters flagged as required
  */
 export type RequiredMlsdImageProcessorInvocation = O.Required<
-  Invocation<'mlsd_image_processor'>,
+  MlsdImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution' | 'thr_v' | 'thr_d'
 >;
 
@@ -141,7 +156,7 @@ export type RequiredMlsdImageProcessorInvocation = O.Required<
  * The NormalBae processor node, with parameters flagged as required
  */
 export type RequiredNormalbaeImageProcessorInvocation = O.Required<
-  Invocation<'normalbae_image_processor'>,
+  NormalbaeImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution'
 >;
 
@@ -149,7 +164,7 @@ export type RequiredNormalbaeImageProcessorInvocation = O.Required<
  * The DW Openpose processor node, with parameters flagged as required
  */
 export type RequiredDWOpenposeImageProcessorInvocation = O.Required<
-  Invocation<'dw_openpose_image_processor'>,
+  DWOpenposeImageProcessorInvocation,
   'type' | 'image_resolution' | 'draw_body' | 'draw_face' | 'draw_hands'
 >;
 
@@ -157,14 +172,14 @@ export type RequiredDWOpenposeImageProcessorInvocation = O.Required<
  * The Pidi processor node, with parameters flagged as required
  */
 export type RequiredPidiImageProcessorInvocation = O.Required<
-  Invocation<'pidi_image_processor'>,
+  PidiImageProcessorInvocation,
   'type' | 'detect_resolution' | 'image_resolution' | 'safe' | 'scribble'
 >;
 
 /**
  * The ZoeDepth processor node, with parameters flagged as required
  */
-export type RequiredZoeDepthImageProcessorInvocation = O.Required<Invocation<'zoe_depth_image_processor'>, 'type'>;
+export type RequiredZoeDepthImageProcessorInvocation = O.Required<ZoeDepthImageProcessorInvocation, 'type'>;
 
 /**
  * Any ControlNet Processor node, with its parameters flagged as required

invokeai/frontend/web/src/features/controlLayers/components/AddLayerButton.tsx

@@ -1,47 +0,0 @@
-import { Button, Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useAddCALayer, useAddIILayer, useAddIPALayer } from 'features/controlLayers/hooks/addLayerHooks';
-import { rgLayerAdded } from 'features/controlLayers/store/controlLayersSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiPlusBold } from 'react-icons/pi';
-
-export const AddLayerButton = memo(() => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const [addCALayer, isAddCALayerDisabled] = useAddCALayer();
-  const [addIPALayer, isAddIPALayerDisabled] = useAddIPALayer();
-  const [addIILayer, isAddIILayerDisabled] = useAddIILayer();
-  const addRGLayer = useCallback(() => {
-    dispatch(rgLayerAdded());
-  }, [dispatch]);
-
-  return (
-    <Menu>
-      <MenuButton
-        as={Button}
-        leftIcon={<PiPlusBold />}
-        variant="ghost"
-        data-testid="control-layers-add-layer-menu-button"
-      >
-        {t('controlLayers.addLayer')}
-      </MenuButton>
-      <MenuList>
-        <MenuItem icon={<PiPlusBold />} onClick={addRGLayer}>
-          {t('controlLayers.regionalGuidanceLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addCALayer} isDisabled={isAddCALayerDisabled}>
-          {t('controlLayers.globalControlAdapterLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addIPALayer} isDisabled={isAddIPALayerDisabled}>
-          {t('controlLayers.globalIPAdapterLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addIILayer} isDisabled={isAddIILayerDisabled}>
-          {t('controlLayers.globalInitialImageLayer')}
-        </MenuItem>
-      </MenuList>
-    </Menu>
-  );
-});
-
-AddLayerButton.displayName = 'AddLayerButton';

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayer.tsx

@@ -1,45 +0,0 @@
-import { Flex, Spacer, useDisclosure } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { CALayerControlAdapterWrapper } from 'features/controlLayers/components/CALayer/CALayerControlAdapterWrapper';
-import { LayerDeleteButton } from 'features/controlLayers/components/LayerCommon/LayerDeleteButton';
-import { LayerMenu } from 'features/controlLayers/components/LayerCommon/LayerMenu';
-import { LayerTitle } from 'features/controlLayers/components/LayerCommon/LayerTitle';
-import { LayerVisibilityToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
-import { LayerWrapper } from 'features/controlLayers/components/LayerCommon/LayerWrapper';
-import { layerSelected, selectCALayerOrThrow } from 'features/controlLayers/store/controlLayersSlice';
-import { memo, useCallback } from 'react';
-
-import CALayerOpacity from './CALayerOpacity';
-
-type Props = {
-  layerId: string;
-};
-
-export const CALayer = memo(({ layerId }: Props) => {
-  const dispatch = useAppDispatch();
-  const isSelected = useAppSelector((s) => selectCALayerOrThrow(s.controlLayers.present, layerId).isSelected);
-  const onClick = useCallback(() => {
-    dispatch(layerSelected(layerId));
-  }, [dispatch, layerId]);
-  const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });
-
-  return (
-    <LayerWrapper onClick={onClick} borderColor={isSelected ? 'base.400' : 'base.800'}>
-      <Flex gap={3} alignItems="center" p={3} cursor="pointer" onDoubleClick={onToggle}>
-        <LayerVisibilityToggle layerId={layerId} />
-        <LayerTitle type="control_adapter_layer" />
-        <Spacer />
-        <CALayerOpacity layerId={layerId} />
-        <LayerMenu layerId={layerId} />
-        <LayerDeleteButton layerId={layerId} />
-      </Flex>
-      {isOpen && (
-        <Flex flexDir="column" gap={3} px={3} pb={3}>
-          <CALayerControlAdapterWrapper layerId={layerId} />
-        </Flex>
-      )}
-    </LayerWrapper>
-  );
-});
-
-CALayer.displayName = 'CALayer';

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayerControlAdapterWrapper.tsx

@@ -1,121 +0,0 @@
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { ControlAdapter } from 'features/controlLayers/components/ControlAndIPAdapter/ControlAdapter';
-import {
-  caLayerControlModeChanged,
-  caLayerImageChanged,
-  caLayerModelChanged,
-  caLayerProcessorConfigChanged,
-  caOrIPALayerBeginEndStepPctChanged,
-  caOrIPALayerWeightChanged,
-  selectCALayerOrThrow,
-} from 'features/controlLayers/store/controlLayersSlice';
-import type { ControlModeV2, ProcessorConfig } from 'features/controlLayers/util/controlAdapters';
-import type { CALayerImageDropData } from 'features/dnd/types';
-import { memo, useCallback, useMemo } from 'react';
-import type {
-  CALayerImagePostUploadAction,
-  ControlNetModelConfig,
-  ImageDTO,
-  T2IAdapterModelConfig,
-} from 'services/api/types';
-
-type Props = {
-  layerId: string;
-};
-
-export const CALayerControlAdapterWrapper = memo(({ layerId }: Props) => {
-  const dispatch = useAppDispatch();
-  const controlAdapter = useAppSelector((s) => selectCALayerOrThrow(s.controlLayers.present, layerId).controlAdapter);
-
-  const onChangeBeginEndStepPct = useCallback(
-    (beginEndStepPct: [number, number]) => {
-      dispatch(
-        caOrIPALayerBeginEndStepPctChanged({
-          layerId,
-          beginEndStepPct,
-        })
-      );
-    },
-    [dispatch, layerId]
-  );
-
-  const onChangeControlMode = useCallback(
-    (controlMode: ControlModeV2) => {
-      dispatch(
-        caLayerControlModeChanged({
-          layerId,
-          controlMode,
-        })
-      );
-    },
-    [dispatch, layerId]
-  );
-
-  const onChangeWeight = useCallback(
-    (weight: number) => {
-      dispatch(caOrIPALayerWeightChanged({ layerId, weight }));
-    },
-    [dispatch, layerId]
-  );
-
-  const onChangeProcessorConfig = useCallback(
-    (processorConfig: ProcessorConfig | null) => {
-      dispatch(caLayerProcessorConfigChanged({ layerId, processorConfig }));
-    },
-    [dispatch, layerId]
-  );
-
-  const onChangeModel = useCallback(
-    (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig) => {
-      dispatch(
-        caLayerModelChanged({
-          layerId,
-          modelConfig,
-        })
-      );
-    },
-    [dispatch, layerId]
-  );
-
-  const onChangeImage = useCallback(
-    (imageDTO: ImageDTO | null) => {
-      dispatch(caLayerImageChanged({ layerId, imageDTO }));
-    },
-    [dispatch, layerId]
-  );
-
-  const droppableData = useMemo<CALayerImageDropData>(
-    () => ({
-      actionType: 'SET_CA_LAYER_IMAGE',
-      context: {
-        layerId,
-      },
-      id: layerId,
-    }),
-    [layerId]
-  );
-
-  const postUploadAction = useMemo<CALayerImagePostUploadAction>(
-    () => ({
-      layerId,
-      type: 'SET_CA_LAYER_IMAGE',
-    }),
-    [layerId]
-  );
-
-  return (
-    <ControlAdapter
-      controlAdapter={controlAdapter}
-      onChangeBeginEndStepPct={onChangeBeginEndStepPct}
-      onChangeControlMode={onChangeControlMode}
-      onChangeWeight={onChangeWeight}
-      onChangeProcessorConfig={onChangeProcessorConfig}
-      onChangeModel={onChangeModel}
-      onChangeImage={onChangeImage}
-      droppableData={droppableData}
-      postUploadAction={postUploadAction}
-    />
-  );
-});
-
-CALayerControlAdapterWrapper.displayName = 'CALayerControlAdapterWrapper';

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayerOpacity.tsx

@@ -1,98 +0,0 @@
-import {
-  CompositeNumberInput,
-  CompositeSlider,
-  Flex,
-  FormControl,
-  FormLabel,
-  IconButton,
-  Popover,
-  PopoverArrow,
-  PopoverBody,
-  PopoverContent,
-  PopoverTrigger,
-  Switch,
-} from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { stopPropagation } from 'common/util/stopPropagation';
-import { useLayerOpacity } from 'features/controlLayers/hooks/layerStateHooks';
-import { caLayerIsFilterEnabledChanged, caLayerOpacityChanged } from 'features/controlLayers/store/controlLayersSlice';
-import type { ChangeEvent } from 'react';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiDropHalfFill } from 'react-icons/pi';
-
-type Props = {
-  layerId: string;
-};
-
-const marks = [0, 25, 50, 75, 100];
-const formatPct = (v: number | string) => `${v} %`;
-
-const CALayerOpacity = ({ layerId }: Props) => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const { opacity, isFilterEnabled } = useLayerOpacity(layerId);
-  const onChangeOpacity = useCallback(
-    (v: number) => {
-      dispatch(caLayerOpacityChanged({ layerId, opacity: v / 100 }));
-    },
-    [dispatch, layerId]
-  );
-  const onChangeFilter = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      dispatch(caLayerIsFilterEnabledChanged({ layerId, isFilterEnabled: e.target.checked }));
-    },
-    [dispatch, layerId]
-  );
-  return (
-    <Popover isLazy>
-      <PopoverTrigger>
-        <IconButton
-          aria-label={t('controlLayers.opacity')}
-          size="sm"
-          icon={<PiDropHalfFill size={16} />}
-          variant="ghost"
-          onDoubleClick={stopPropagation}
-        />
-      </PopoverTrigger>
-      <PopoverContent onDoubleClick={stopPropagation}>
-        <PopoverArrow />
-        <PopoverBody>
-          <Flex direction="column" gap={2}>
-            <FormControl orientation="horizontal" w="full">
-              <FormLabel m={0} flexGrow={1} cursor="pointer">
-                {t('controlLayers.opacityFilter')}
-              </FormLabel>
-              <Switch isChecked={isFilterEnabled} onChange={onChangeFilter} />
-            </FormControl>
-            <FormControl orientation="horizontal">
-              <FormLabel m={0}>{t('controlLayers.opacity')}</FormLabel>
-              <CompositeSlider
-                min={0}
-                max={100}
-                step={1}
-                value={opacity}
-                defaultValue={100}
-                onChange={onChangeOpacity}
-                marks={marks}
-                w={48}
-              />
-              <CompositeNumberInput
-                min={0}
-                max={100}
-                step={1}
-                value={opacity}
-                defaultValue={100}
-                onChange={onChangeOpacity}
-                w={24}
-                format={formatPct}
-              />
-            </FormControl>
-          </Flex>
-        </PopoverBody>
-      </PopoverContent>
-    </Popover>
-  );
-};
-
-export default memo(CALayerOpacity);