translationBot(ui): update translation (Spanish)

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Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI

README.md

@@ -2,21 +2,102 @@
 
 ![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
 
-# Invoke - Professional Creative AI Tools for Visual Media 
-##  To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
-  
+# Invoke - Professional Creative AI Tools for Visual Media
 
+#### To learn more about Invoke, or implement our Business solutions, visit [invoke.com]
 
-[![discord badge]][discord link]
+[![discord badge]][discord link] [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link] [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link] [![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
 
-[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
+</div>
 
-[![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link]
+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.
 
-[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
+[Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
 
+<div align="center">
+
+![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
+
+</div>
+
+## Quick Start
+
+1. Download and unzip the installer from the bottom of the [latest release][latest release link].
+2. Run the installer script.
+
+   - **Windows**: Double-click on the `install.bat` script.
+   - **macOS**: Open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press enter.
+   - **Linux**: Run `install.sh`.
+
+3. When prompted, enter a location for the install and select your GPU type.
+4. Once the install finishes, find the directory you selected during install. The default location is `C:\Users\Username\invokeai` for Windows or `~/invokeai` for Linux/macOS.
+5. Run the launcher script (`invoke.bat` for Windows, `invoke.sh` for macOS and Linux) the same way you ran the installer script in step 2.
+6. Select option 1 to start the application. Once it starts up, open your browser and go to <http://localhost:9090>.
+7. Open the model manager tab to install a starter model and then you'll be ready to generate.
+
+More detail, including hardware requirements and manual install instructions, are available in the [installation documentation][installation docs].
+
+## Troubleshooting, FAQ and Support
+
+Please review our [FAQ][faq] for solutions to common installation problems and other issues.
+
+For more help, please join our [Discord][discord link].
+
+## Features
+
+Full details on features can be found in [our documentation][features docs].
+
+### Web Server & UI
+
+Invoke runs a locally hosted web server & React UI with an industry-leading user experience.
+
+### Unified Canvas
+
+The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/out-painting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
+
+### Workflows & Nodes
+
+Invoke offers a fully featured workflow management solution, enabling users to combine the power of node-based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
+
+### Board & Gallery Management
+
+Invoke features an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
+
+### Other features
+
+- Support for both ckpt and diffusers models
+- SD1.5, SD2.0, and SDXL support
+- Upscaling Tools
+- Embedding Manager & Support
+- Model Manager & Support
+- Workflow creation & management
+- Node-Based Architecture
+
+## Contributing
+
+Anyone who wishes to contribute to this project - whether documentation, features, bug fixes, code cleanup, testing, or code reviews - is very much encouraged to do so.
+
+Get started with contributing by reading our [contribution documentation][contributing docs], joining the [#dev-chat] or the GitHub discussion board.
+
+We hope you enjoy using Invoke as much as we enjoy creating it, and we hope you will elect to become part of our community.
+
+## Thanks
+
+Invoke is a combined effort of [passionate and talented people from across the world][contributors]. We thank them for their time, hard work and effort.
+
+Original portions of the software are Copyright © 2024 by respective contributors.
+
+[features docs]: https://invoke-ai.github.io/InvokeAI/features/
+[faq]: https://invoke-ai.github.io/InvokeAI/help/FAQ/
+[contributors]: https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/
+[invoke.com]: https://www.invoke.com/about
+[github issues]: https://github.com/invoke-ai/InvokeAI/issues
+[docs home]: https://invoke-ai.github.io/InvokeAI
+[installation docs]: https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/
+[#dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
+[contributing docs]: https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/
 [CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
-[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
+[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
 [github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
@@ -30,402 +111,6 @@
 [latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
 [latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
 [latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
-[latest release link]: https://github.com/invoke-ai/InvokeAI/releases
+[latest release link]: https://github.com/invoke-ai/InvokeAI/releases/latest
 [translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
 [translation status link]: https://hosted.weblate.org/engage/invokeai/
-
-</div>
-
-InvokeAI is a leading creative engine built to empower professionals
-and enthusiasts alike. Generate and create stunning visual media using
-the latest AI-driven technologies. InvokeAI offers an industry leading
-Web Interface, interactive Command Line Interface, and also serves as
-the foundation for multiple commercial products.
-
-**Quick links**: [[How to
-  Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
-  href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
-  href="https://invoke-ai.github.io/InvokeAI/">Documentation and
-  Tutorials</a>]
-  [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
-  [<a
-  href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
-  Ideas & Q&A</a>] 
-   [<a
-  href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>] 
-
-<div align="center">
-
-
-![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
-
-
-</div>
-
-## Table of Contents
-
-Table of Contents 📝
-
-**Getting Started**
-1. 🏁 [Quick Start](#quick-start) 
-3. 🖥️ [Hardware Requirements](#hardware-requirements) 
-
-**More About Invoke**
-1. 🌟 [Features](#features) 
-2. 📣 [Latest Changes](#latest-changes) 
-3. 🛠️ [Troubleshooting](#troubleshooting) 
-
-**Supporting the Project**
-1. 🤝 [Contributing](#contributing) 
-2. 👥 [Contributors](#contributors) 
-3. 💕 [Support](#support) 
-
-## Quick Start
-
-For full installation and upgrade instructions, please see:
-[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)
-
-If upgrading from version 2.3, please read [Migrating a 2.3 root
-directory to 3.0](#migrating-to-3) first.
-
-### Automatic Installer (suggested for 1st time users)
-
-1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
-
-2. Download the .zip file for your OS (Windows/macOS/Linux).
-
-3. Unzip the file.
-
-4. **Windows:** double-click on the `install.bat` script. **macOS:** Open a Terminal window, drag the file `install.sh` from Finder
-into the Terminal, and press return. **Linux:** run `install.sh`.
-
-5. You'll be asked to confirm the location of the folder in which
-to install InvokeAI and its image generation model files. Pick a
-location with at least 15 GB of free memory. More if you plan on
-installing lots of models.
-
-6. Wait while the installer does its thing. After installing the software,
-the installer will launch a script that lets you configure InvokeAI and
-select a set of starting image generation models.
-
-7. Find the folder that InvokeAI was installed into (it is not the
-same as the unpacked zip file directory!) The default location of this
-folder (if you didn't change it in step 5) is `~/invokeai` on
-Linux/Mac systems, and `C:\Users\YourName\invokeai` on Windows. This directory will contain launcher scripts named `invoke.sh` and `invoke.bat`.
-
-8. On Windows systems, double-click on the `invoke.bat` file. On
-macOS, open a Terminal window, drag `invoke.sh` from the folder into
-the Terminal, and press return. On Linux, run `invoke.sh`
-
-9. Press 2 to open the "browser-based UI", press enter/return, wait a
-minute or two for Stable Diffusion to start up, then open your browser
-and go to http://localhost:9090.
-
-10. Type `banana sushi` in the box on the top left and click `Invoke`
-
-### Command-Line Installation (for developers and users familiar with Terminals)
-
-You must have Python 3.10 through 3.11 installed on your machine. Earlier or
-later versions are not supported.
-Node.js also needs to be installed along with `pnpm` (can be installed with
-the command `npm install -g pnpm` if needed)
-
-1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
-2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
-
-    ```terminal
-    mkdir invokeai
-    ````
-
-3. Create a virtual environment named `.venv` inside this directory and activate it:
-
-    ```terminal
-    cd invokeai
-    python -m venv .venv --prompt InvokeAI
-    ```
-
-4. Activate the virtual environment (do it every time you run InvokeAI)
-
-    _For Linux/Mac users:_
-
-    ```sh
-    source .venv/bin/activate
-    ```
-
-    _For Windows users:_
-
-    ```ps
-    .venv\Scripts\activate
-    ```
-
-5. Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
-
-    _For Windows/Linux with an NVIDIA GPU:_
-
-    ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
-    ```
-
-    _For Linux with an AMD GPU:_
-
-    ```sh
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
-    ```
-
-    _For non-GPU systems:_
-    ```terminal
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
-    ``` 
-
-    _For Macintoshes, either Intel or M1/M2/M3:_
-
-    ```sh
-    pip install InvokeAI --use-pep517
-    ```
-
-6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
-
-    ```terminal
-    invokeai-configure --root .
-    ```
-	Don't miss the dot at the end!
-
-7. Launch the web server (do it every time you run InvokeAI):
-
-    ```terminal
-    invokeai-web
-    ```
-
-8. Point your browser to http://localhost:9090 to bring up the web interface.
-
-9. Type `banana sushi` in the box on the top left and click `Invoke`.
-
-Be sure to activate the virtual environment each time before re-launching InvokeAI,
-using `source .venv/bin/activate` or `.venv\Scripts\activate`.
-
-## Detailed Installation Instructions
-
-This fork is supported across Linux, Windows and Macintosh. Linux
-users can use either an Nvidia-based card (with CUDA support) or an
-AMD card (using the ROCm driver). For full installation and upgrade
-instructions, please see:
-[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
-
-<a name="migrating-to-3"></a>
-### Migrating a v2.3 InvokeAI root directory
-
-The InvokeAI root directory is where the InvokeAI startup file,
-installed models, and generated images are stored. It is ordinarily
-named `invokeai` and located in your home directory. The contents and
-layout of this directory has changed between versions 2.3 and 3.0 and
-cannot be used directly.
-
-We currently recommend that you use the installer to create a new root
-directory named differently from the 2.3 one, e.g. `invokeai-3` and
-then use a migration script to copy your 2.3 models into the new
-location. However, if you choose, you can upgrade this directory in
-place.  This section gives both recipes.
-
-#### Creating a new root directory and migrating old models
-
-This is the safer recipe because it leaves your old root directory in
-place to fall back on.
-
-1. Follow the instructions above to create and install InvokeAI in a
-directory that has a different name from the 2.3 invokeai directory.
-In this example, we will use "invokeai-3"
-
-2. When you are prompted to select models to install, select a minimal
-set of models, such as stable-diffusion-v1.5 only.
-
-3. After installation is complete launch `invokeai.sh` (Linux/Mac) or
-`invokeai.bat` and select option 8 "Open the developers console". This
-will take you to the command line.
-
-4. Issue the command `invokeai-migrate3 --from /path/to/v2.3-root --to
-/path/to/invokeai-3-root`. Provide the correct `--from` and `--to`
-paths for your v2.3 and v3.0 root directories respectively.
-
-This will copy and convert your old models from 2.3 format to 3.0
-format and create a new `models` directory in the 3.0 directory. The
-old models directory (which contains the models selected at install
-time) will be renamed `models.orig` and can be deleted once you have
-confirmed that the migration was successful.
-
- If you wish, you can pass the 2.3 root directory to both `--from` and
-`--to` in order to update in place. Warning: this directory will no
-longer be usable with InvokeAI 2.3.
-
-#### Migrating in place
-
-For the adventurous, you may do an in-place upgrade from 2.3 to 3.0
-without touching the command line. ***This recipe does not work on
-Windows platforms due to a bug in the Windows version of the 2.3
-upgrade script.** See the next section for a Windows recipe.
-
-##### For Mac and Linux Users:
-
-1. Launch the InvokeAI launcher script in your current v2.3 root directory.
-
-2. Select option [9] "Update InvokeAI" to bring up the updater dialog.
-
-3. Select option [1] to upgrade to the latest release.
-
-4. Once the upgrade is finished you will be returned to the launcher
-menu. Select option [6] "Re-run the configure script to fix a broken
-install or to complete a major upgrade".
-
-This will run the configure script against the v2.3 directory and
-update it to the 3.0 format. The following files will be replaced:
-
-  - The invokeai.init file, replaced by invokeai.yaml
-  - The models directory
-  - The configs/models.yaml model index
-  
-The original versions of these files will be saved with the suffix
-".orig" appended to the end. Once you have confirmed that the upgrade
-worked, you can safely remove these files. Alternatively you can
-restore a working v2.3 directory by removing the new files and
-restoring the ".orig" files' original names.
-
-##### For Windows Users:
-
-Windows Users can upgrade with the
-
-1. Enter the 2.3 root directory you wish to upgrade
-2. Launch `invoke.sh` or `invoke.bat`
-3. Select the "Developer's console" option [8]
-4. Type the following commands
-
-```
-pip install "invokeai @ https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v3.0.0" --use-pep517 --upgrade
-invokeai-configure --root .
-```
-(Replace `v3.0.0` with the current release number if this document is out of date).
-
-The first command will install and upgrade new software to run
-InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
-You may now launch the WebUI in the usual way, by selecting option [1]
-from the launcher script
-
-#### Migrating Images
-
-The migration script will migrate your invokeai settings and models,
-including textual inversion models, LoRAs and merges that you may have
-installed previously. However it does **not** migrate the generated
-images stored in your 2.3-format outputs directory. To do this, you 
-need to run an additional step:
-
-1. From a working InvokeAI 3.0 root directory, start the launcher and
-enter menu option [8] to open the "developer's console".
-
-2. At the developer's console command line, type the command:
-
-```bash
-invokeai-import-images
-```
-
-3. This will lead you through the process of confirming the desired
-   source and destination for the imported images. The images will
-   appear in the gallery board of your choice, and contain the
-   original prompt, model name, and other parameters used to generate
-   the image.
-   
-(Many kudos to **techjedi** for contributing this script.)
-
-## Hardware Requirements
-
-InvokeAI is supported across Linux, Windows and macOS. Linux
-users can use either an Nvidia-based card (with CUDA support) or an
-AMD card (using the ROCm driver).
-
-### System
-
-You will need one of the following:
-
-- An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB
-  of VRAM is highly recommended for rendering using the Stable
-  Diffusion XL models
-- An Apple computer with an M1 chip.
-- An AMD-based graphics card with 4GB or more VRAM memory (Linux
-  only), 6-8 GB for XL rendering.
-
-We do not recommend the GTX 1650 or 1660 series video cards. They are
-unable to run in half-precision mode and do not have sufficient VRAM
-to render 512x512 images.
-
-**Memory** - At least 12 GB Main Memory RAM.
-
-**Disk** - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
-
-## Features
-
-Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
-
-### *Web Server & UI*
-
-InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
-
-### *Unified Canvas*
-
-The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
-
-### *Workflows & Nodes*
-
-InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
-
-### *Board & Gallery Management*
-
-Invoke AI provides an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow. 
-
-### Other features
-
-- *Support for both ckpt and diffusers models*
-- *SD 2.0, 2.1, XL support*
-- *Upscaling Tools*
-- *Embedding Manager & Support*
-- *Model Manager & Support*
-- *Workflow creation & management*
-- *Node-Based Architecture*
-
-
-### Latest Changes
-
-For our latest changes, view our [Release
-Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
-[CHANGELOG](docs/CHANGELOG.md).
-
-### Troubleshooting / FAQ
-
-Please check out our **[FAQ](https://invoke-ai.github.io/InvokeAI/help/FAQ/)** to get solutions for common installation
-problems and other issues. For more help, please join our [Discord][discord link]
-
-## Contributing
-
-Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
-cleanup, testing, or code reviews, is very much encouraged to do so.
-
-Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
-
-If you are unfamiliar with how
-to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing: 
-[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
-
-We hope you enjoy using our software as much as we enjoy creating it,
-and we hope that some of those of you who are reading this will elect
-to become part of our community.
-
-Welcome to InvokeAI!
-
-### Contributors
-
-This fork is a combined effort of various people from across the world.
-[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
-their time, hard work and effort.
-
-### Support
-
-For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
-
-Original portions of the software are Copyright (c) 2023 by respective contributors.
-

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.
 
-##### Collection and Scalar Fields
+##### Single and Collection Fields
 
-Field types have a name and two flags which may identify it as a **collection** or **collection or scalar** field.
+Field types have a name and cardinality property which may identify it as a **SINGLE**, **COLLECTION** or **SINGLE_OR_COLLECTION** field.
 
-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).
+- 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).
 
 ## Implementation
 
@@ -173,8 +173,7 @@ Field types are represented as structured objects:
 ```ts
 type FieldType = {
   name: string;
-  isCollection: boolean;
-  isCollectionOrScalar: boolean;
+  cardinality: 'SINGLE' | 'COLLECTION' | 'SINGLE_OR_COLLECTION';
 };
 ```
 
@@ -186,7 +185,7 @@ 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`).
+We create a field type name from this `type` string (e.g. `string` -> `StringField`). The cardinality is `"SINGLE"`.
 
 ##### Complex Types
 
@@ -200,13 +199,13 @@ 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, adding `isCollection: true` to the field type.
+We use the item type for field type name. The cardinality is `"COLLECTION"`.
 
-##### Collection or Scalar Types
+##### Single or Collection 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, adding `isCollectionOrScalar: true` to the field type.
+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"`.
 
 ##### Optional Fields
 

docs/features/CONFIGURATION.md

@@ -51,13 +51,11 @@ 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/CONTROLNET.md

@@ -165,7 +165,7 @@ Additionally, each  section can be expanded with the "Show Advanced" button in o
 There are several ways to install IP-Adapter models with an existing InvokeAI installation:
 
 1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
-2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
+2. Through the Model Manager UI with models from the *Tools* section of [models.invoke.ai](https://models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.  
 3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. 
 
 #### Using IP-Adapter

docs/features/TRAINING.md

@@ -4,278 +4,6 @@ title: Training
 
 # :material-file-document: Training
 
-# Textual Inversion Training
-## **Personalizing Text-to-Image Generation**
+Invoke Training has moved to its own repository, with a dedicated UI for accessing common scripts like Textual Inversion and LoRA 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
+You can find more by visiting the repo at https://github.com/invoke-ai/invoke-training

docs/installation/010_INSTALL_AUTOMATED.md

@@ -1,8 +1,10 @@
-# Automatic Install
+# Automatic Install & Updates
 
-The installer is used for both new installs and updates.
+**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.
 
-Both release and pre-release versions can be installed using it. It also supports install a wheel if needed.
+Both release and pre-release versions can be installed using the installer. It also supports install through a wheel if needed.
 
 Be sure to review the [installation requirements] and ensure your system has everything it needs to install Invoke.
 
@@ -96,7 +98,7 @@ 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]: INSTALLATION.md#installation-requirements
+[installation requirements]: INSTALL_REQUIREMENTS.md
 [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].
+Before you start, go through the [installation requirements](./INSTALL_REQUIREMENTS.md).
 
 ### Installation Walkthrough
 
@@ -79,7 +79,7 @@ Before you start, go through the [installation requirements].
 
 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]. 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.
+    - 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.
 
         !!! example "Install with an extra index URL"
 
@@ -116,4 +116,4 @@ Before you start, go through the [installation requirements].
 
     !!! warning
 
-        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.
+        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.

docs/installation/INSTALLATION.md

@@ -1,4 +1,4 @@
-# Installation Overview
+# Installation and Updating Overview
 
 Before installing, review the [installation requirements] to ensure your system is set up properly.
 
@@ -6,14 +6,21 @@ See the [FAQ] for frequently-encountered installation issues.
 
 If you need more help, join our [discord] or [create an issue].
 
-<h2>Automatic Install</h2>
+<h2>Automatic Install & Updates </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. Linux only.
+        Any GPU with at least 8GB VRAM.
         ```
 
     === "AMD"
 
         ```
-        Any GPU with at least 16GB VRAM.
+        Any GPU with at least 16GB VRAM. Linux only.
         ```
 
     === "Mac"

installer/templates/invoke.bat.in

@@ -10,8 +10,7 @@ set INVOKEAI_ROOT=.
 echo Desired action:
 echo 1. Generate images with the browser-based interface
 echo 2. Open the developer console
-echo 3. Run the InvokeAI image database maintenance script
-echo 4. Command-line help
+echo 3. Command-line help
 echo Q - Quit
 echo.
 echo To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.
@@ -34,9 +33,6 @@ IF /I "%choice%" == "1" (
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
 ) ELSE IF /I "%choice%" == "3" (
-   echo Running the db maintenance script...
-   python .venv\Scripts\invokeai-db-maintenance.exe
-) ELSE IF /I "%choice%" == "4" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai-web.exe --help %*
     pause

installer/templates/invoke.sh.in

@@ -47,11 +47,6 @@ do_choice() {
         bash --init-file "$file_name"
         ;;
     3)
-        clear
-        printf "Running the db maintenance script\n"
-        invokeai-db-maintenance --root ${INVOKEAI_ROOT}
-        ;;
-    4)
         clear
         printf "Command-line help\n"
         invokeai-web --help
@@ -71,8 +66,7 @@ do_line_input() {
     printf "What would you like to do?\n"
     printf "1: Generate images using the browser-based interface\n"
     printf "2: Open the developer console\n"
-    printf "3: Run the InvokeAI image database maintenance script\n"
-    printf "4: Command-line help\n"
+    printf "3: Command-line help\n"
     printf "Q: Quit\n\n"
     printf "To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.\n\n"
     read -p "Please enter 1-4, Q: [1] " yn

invokeai/app/api/routers/app_info.py

@@ -13,7 +13,6 @@ 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__
 
@@ -109,9 +108,7 @@ async def get_config() -> AppConfig:
         upscaling_models.append(str(Path(model).stem))
     upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
 
-    nsfw_methods = []
-    if SafetyChecker.safety_checker_available():
-        nsfw_methods.append("nsfw_checker")
+    nsfw_methods = ["nsfw_checker"]
 
     watermarking_methods = ["invisible_watermark"]
 

invokeai/app/api/routers/images.py

@@ -6,13 +6,12 @@ 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, ValidationError
+from pydantic import BaseModel, Field, JsonValue
 
-from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
+from invokeai.app.invocations.fields import MetadataField
 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
 
@@ -42,13 +41,17 @@ 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
+    _metadata = None
+    _workflow = None
+    _graph = None
 
     contents = await file.read()
     try:
@@ -62,22 +65,28 @@ async def upload_image(
 
     # TODO: retain non-invokeai metadata on upload?
     # attempt to parse metadata from image
-    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
+    metadata_raw = metadata if isinstance(metadata, str) else pil_image.info.get("invokeai_metadata", None)
+    if isinstance(metadata_raw, str):
+        _metadata = metadata_raw
+    else:
+        ApiDependencies.invoker.services.logger.debug("Failed to parse metadata for uploaded image")
+        pass
 
     # attempt to parse workflow from image
     workflow_raw = pil_image.info.get("invokeai_workflow", None)
-    if 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
+    if isinstance(workflow_raw, str):
+        _workflow = workflow_raw
+    else:
+        ApiDependencies.invoker.services.logger.debug("Failed to parse workflow for uploaded image")
+        pass
+
+    # attempt to extract graph from image
+    graph_raw = pil_image.info.get("invokeai_graph", None)
+    if isinstance(graph_raw, str):
+        _graph = graph_raw
+    else:
+        ApiDependencies.invoker.services.logger.debug("Failed to parse graph for uploaded image")
+        pass
 
     try:
         image_dto = ApiDependencies.invoker.services.images.create(
@@ -86,8 +95,9 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
-            metadata=metadata,
-            workflow=workflow,
+            metadata=_metadata,
+            workflow=_workflow,
+            graph=_graph,
             is_intermediate=is_intermediate,
         )
 
@@ -185,14 +195,21 @@ 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=Optional[WorkflowWithoutID]
+    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=WorkflowAndGraphResponse
 )
 async def get_image_workflow(
     image_name: str = Path(description="The name of image whose workflow to get"),
-) -> Optional[WorkflowWithoutID]:
+) -> WorkflowAndGraphResponse:
     try:
-        return ApiDependencies.invoker.services.images.get_workflow(image_name)
+        workflow = ApiDependencies.invoker.services.images.get_workflow(image_name)
+        graph = ApiDependencies.invoker.services.images.get_graph(image_name)
+        return WorkflowAndGraphResponse(workflow=workflow, graph=graph)
     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
+from typing import Any, Dict, List, Optional, Type
 
 from fastapi import Body, Path, Query, Response, UploadFile
 from fastapi.responses import FileResponse
@@ -16,6 +16,7 @@ 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,
@@ -52,6 +53,13 @@ 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.
@@ -118,8 +126,7 @@ 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:
-        cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
-        model.cover_image = cover_image
+        model = add_cover_image_to_model_config(model, ApiDependencies)
     return ModelsList(models=found_models)
 
 
@@ -160,12 +167,9 @@ 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: AnyModelConfig = record_store.get_model(key)
-        cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
-        config.cover_image = cover_image
-        return config
+        config = ApiDependencies.invoker.services.model_manager.store.get_model(key)
+        return add_cover_image_to_model_config(config, ApiDependencies)
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
 
@@ -294,14 +298,15 @@ async def update_model_record(
     installer = ApiDependencies.invoker.services.model_manager.install
     try:
         record_store.update_model(key, changes=changes)
-        model_response: AnyModelConfig = installer.sync_model_path(key)
+        config = installer.sync_model_path(key)
+        config = add_cover_image_to_model_config(config, ApiDependencies)
         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 model_response
+    return config
 
 
 @model_manager_router.get(
@@ -648,6 +653,14 @@ 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)
 
@@ -655,7 +668,8 @@ async def convert_model(
     shutil.rmtree(cache_path)
 
     # return the config record for the new diffusers directory
-    new_config: AnyModelConfig = store.get_model(new_key)
+    new_config = store.get_model(new_key)
+    new_config = add_cover_image_to_model_config(new_config, ApiDependencies)
     return new_config
 
 

invokeai/app/api_app.py

@@ -28,7 +28,7 @@ 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.session_processor.session_processor_common import ProgressImage
-from invokeai.backend.util.devices import get_torch_device_name
+from invokeai.backend.util.devices import TorchDevice
 
 from ..backend.util.logging import InvokeAILogger
 from .api.dependencies import ApiDependencies
@@ -63,7 +63,7 @@ logger = InvokeAILogger.get_logger(config=app_config)
 mimetypes.add_type("application/javascript", ".js")
 mimetypes.add_type("text/css", ".css")
 
-torch_device_name = get_torch_device_name()
+torch_device_name = TorchDevice.get_torch_device_name()
 logger.info(f"Using torch device: {torch_device_name}")
 
 
@@ -164,6 +164,12 @@ def custom_openapi() -> dict[str, Any]:
     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
@@ -172,6 +178,8 @@ def custom_openapi() -> dict[str, Any]:
         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?

invokeai/app/invocations/compel.py

@@ -24,7 +24,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningFieldData,
     SDXLConditioningInfo,
 )
-from invokeai.backend.util.devices import torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .model import CLIPField
@@ -99,7 +99,7 @@ class CompelInvocation(BaseInvocation):
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
-                dtype_for_device_getter=torch_dtype,
+                dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
             )
 
@@ -193,7 +193,7 @@ class SDXLPromptInvocationBase:
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
-                dtype_for_device_getter=torch_dtype,
+                dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,  # TODO:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,

invokeai/app/invocations/controlnet_image_processors.py

@@ -24,7 +24,6 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
-    Input,
     InputField,
     OutputField,
     UIType,
@@ -35,22 +34,16 @@ 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.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, invocation, invocation_output
-
-CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
-CONTROLNET_RESIZE_VALUES = Literal[
-    "just_resize",
-    "crop_resize",
-    "fill_resize",
-    "just_resize_simple",
-]
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, Classification, invocation, invocation_output
 
 
 class ControlField(BaseModel):
@@ -86,13 +79,13 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.1")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
 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, input=Input.Direct, ui_type=UIType.ControlNetModel
+        description=FieldDescriptions.controlnet_model, 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"
@@ -171,13 +164,13 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.3.2",
+    version="1.3.3",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, 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)"
     )
@@ -205,13 +198,13 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, 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)
@@ -234,13 +227,13 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     coarse: bool = InputField(default=False, description="Whether to use coarse mode")
 
     def run_processor(self, image: Image.Image) -> Image.Image:
@@ -256,13 +249,13 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image) -> Image.Image:
         processor = LineartAnimeProcessor()
@@ -279,15 +272,15 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.4",
 )
 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=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, 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")
 
@@ -310,13 +303,13 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
@@ -327,13 +320,13 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.2"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.3"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, 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`")
 
@@ -350,13 +343,13 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.2"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.3"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
 
@@ -377,13 +370,13 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, 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")
@@ -407,7 +400,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -423,15 +416,15 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.4",
 )
 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=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         mediapipe_face_processor = MediapipeFaceDetector()
@@ -450,7 +443,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -458,8 +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=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
@@ -479,7 +472,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -519,13 +512,13 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.4",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
@@ -566,12 +559,12 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""
 
-    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
+    color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
 
     def run_processor(self, image: Image.Image):
         np_image = np.array(image, dtype=np.uint8)
@@ -598,7 +591,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
     title="Depth Anything Processor",
     tags=["controlnet", "depth", "depth anything"],
     category="controlnet",
-    version="1.1.1",
+    version="1.1.2",
 )
 class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a depth map based on the Depth Anything algorithm"""
@@ -606,7 +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=64, multiple_of=64, description=FieldDescriptions.image_res)
+    resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
@@ -621,7 +614,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     title="DW Openpose Image Processor",
     tags=["controlnet", "dwpose", "openpose"],
     category="controlnet",
-    version="1.1.0",
+    version="1.1.1",
 )
 class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Generates an openpose pose from an image using DWPose"""
@@ -629,7 +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=0, description=FieldDescriptions.image_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         dw_openpose = DWOpenposeDetector()
@@ -641,3 +634,27 @@ 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

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from pathlib import Path
 from typing import Literal, Optional
 
 import cv2
@@ -504,7 +503,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Blur NSFW Image",
     tags=["image", "nsfw"],
     category="image",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add blur to NSFW-flagged images"""
@@ -516,23 +515,12 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         logger = context.logger
         logger.debug("Running NSFW checker")
-        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 = SafetyChecker.blur_if_nsfw(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

@@ -4,20 +4,8 @@ from typing import List, Literal, Optional, Union
 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,
-    Input,
-    InputField,
-    OutputField,
-    TensorField,
-    UIType,
-)
+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.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
@@ -36,6 +24,7 @@ class IPAdapterField(BaseModel):
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
     image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the IP-Adapter.")
+    target_blocks: List[str] = Field(default=[], description="The IP Adapter blocks to apply")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -69,7 +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="1.3.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -78,7 +67,6 @@ 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,
     )
@@ -90,6 +78,9 @@ class IPAdapterInvocation(BaseInvocation):
     weight: Union[float, List[float]] = InputField(
         default=1, description="The weight given to the IP-Adapter", title="Weight"
     )
+    method: Literal["full", "style", "composition"] = InputField(
+        default="full", description="The method to apply the IP-Adapter"
+    )
     begin_step_percent: float = InputField(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -124,12 +115,32 @@ class IPAdapterInvocation(BaseInvocation):
 
         image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
 
+        if self.method == "style":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["up_blocks.1"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = ["up_blocks.0.attentions.1"]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "composition":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["down_blocks.2", "mid_block"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = ["down_blocks.2.attentions.1"]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "full":
+            target_blocks = ["block"]
+        else:
+            raise ValueError(f"Unexpected IP-Adapter method: '{self.method}'.")
+
         return IPAdapterOutput(
             ip_adapter=IPAdapterField(
                 image=self.image,
                 ip_adapter_model=self.ip_adapter_model,
                 image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
                 weight=self.weight,
+                target_blocks=target_blocks,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,
                 mask=self.mask,

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, Iterator, List, Literal, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
 
 import einops
 import numpy as np
@@ -11,7 +11,6 @@ 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
@@ -21,9 +20,12 @@ 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 import DPMSolverSDEScheduler
-from diffusers.schedulers import SchedulerMixin as Scheduler
+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 PIL import Image, ImageFilter
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
@@ -51,6 +53,7 @@ from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
+from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
@@ -72,15 +75,12 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     image_resized_to_grid_as_tensor,
 )
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_precision, choose_torch_device
+from ...backend.util.devices import TorchDevice
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .controlnet_image_processors import ControlField
 from .model import ModelIdentifierField, UNetField, VAEField
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
-DEFAULT_PRECISION = choose_precision(choose_torch_device())
+DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()
 
 
 @invocation_output("scheduler_output")
@@ -188,7 +188,7 @@ class GradientMaskOutput(BaseInvocationOutput):
     title="Create Gradient Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.0.0",
+    version="1.1.0",
 )
 class CreateGradientMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
@@ -201,6 +201,32 @@ class CreateGradientMaskInvocation(BaseInvocation):
     minimum_denoise: float = InputField(
         default=0.0, ge=0, le=1, description="Minimum denoise level for the coherence region", ui_order=4
     )
+    image: Optional[ImageField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] Image",
+        ui_order=6,
+    )
+    unet: Optional[UNetField] = InputField(
+        description="OPTIONAL: If the Unet is a specialized Inpainting model, masked_latents will be generated from the image with the VAE",
+        default=None,
+        input=Input.Connection,
+        title="[OPTIONAL] UNet",
+        ui_order=5,
+    )
+    vae: Optional[VAEField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] VAE",
+        input=Input.Connection,
+        ui_order=7,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == "float32",
+        description=FieldDescriptions.fp32,
+        ui_order=9,
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:
@@ -236,8 +262,27 @@ class CreateGradientMaskInvocation(BaseInvocation):
         expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
         expanded_image_dto = context.images.save(expanded_mask_image)
 
+        masked_latents_name = None
+        if self.unet is not None and self.vae is not None and self.image is not None:
+            # all three fields must be present at the same time
+            main_model_config = context.models.get_config(self.unet.unet.key)
+            assert isinstance(main_model_config, MainConfigBase)
+            if main_model_config.variant is ModelVariantType.Inpaint:
+                mask = blur_tensor
+                vae_info: LoadedModel = context.models.load(self.vae.vae)
+                image = context.images.get_pil(self.image.image_name)
+                image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+                if image_tensor.dim() == 3:
+                    image_tensor = image_tensor.unsqueeze(0)
+                img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+                masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+                masked_latents = ImageToLatentsInvocation.vae_encode(
+                    vae_info, self.fp32, self.tiled, masked_image.clone()
+                )
+                masked_latents_name = context.tensors.save(tensor=masked_latents)
+
         return GradientMaskOutput(
-            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=None, gradient=True),
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=True),
             expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
         )
 
@@ -298,7 +343,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
     cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
+        default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale"
     )
     denoising_start: float = InputField(
         default=0.0,
@@ -478,9 +523,10 @@ 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(
@@ -520,6 +566,11 @@ 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,
@@ -535,13 +586,6 @@ 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:
@@ -682,6 +726,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 IPAdapterData(
                     ip_adapter_model=ip_adapter_model,
                     weight=single_ip_adapter.weight,
+                    target_blocks=single_ip_adapter.target_blocks,
                     begin_step_percent=single_ip_adapter.begin_step_percent,
                     end_step_percent=single_ip_adapter.end_step_percent,
                     ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
@@ -776,7 +821,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         denoising_start: float,
         denoising_end: float,
         seed: int,
-    ) -> Tuple[int, List[int], int]:
+    ) -> Tuple[int, List[int], int, Dict[str, Any]]:
         assert isinstance(scheduler, ConfigMixin)
         if scheduler.config.get("cpu_only", False):
             scheduler.set_timesteps(steps, device="cpu")
@@ -804,13 +849,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
         timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
         num_inference_steps = len(timesteps) // scheduler.order
 
-        scheduler_step_kwargs = {}
+        scheduler_step_kwargs: Dict[str, Any] = {}
         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 = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
+            scheduler_step_kwargs.update({"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)})
+        if isinstance(scheduler, TCDScheduler):
+            scheduler_step_kwargs.update({"eta": 1.0})
 
         return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
 
@@ -959,9 +1006,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
             result_latents = result_latents.to("cpu")
-            torch.cuda.empty_cache()
-            if choose_torch_device() == torch.device("mps"):
-                mps.empty_cache()
+            TorchDevice.empty_cache()
 
             name = context.tensors.save(tensor=result_latents)
         return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)
@@ -1028,9 +1073,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.disable_tiling()
 
             # clear memory as vae decode can request a lot
-            torch.cuda.empty_cache()
-            if choose_torch_device() == torch.device("mps"):
-                mps.empty_cache()
+            TorchDevice.empty_cache()
 
             with torch.inference_mode():
                 # copied from diffusers pipeline
@@ -1042,9 +1085,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
         image_dto = context.images.save(image=image)
 
@@ -1083,9 +1124,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.tensors.load(self.latents.latents_name)
-
-        # TODO:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
@@ -1096,9 +1135,8 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
-        torch.cuda.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
+
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=resized_latents)
         return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@@ -1125,8 +1163,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        # TODO:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
         # resizing
         resized_latents = torch.nn.functional.interpolate(
@@ -1138,9 +1175,7 @@ class ScaleLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
-        torch.cuda.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=resized_latents)
         return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@@ -1272,8 +1307,7 @@ class BlendLatentsInvocation(BaseInvocation):
         if latents_a.shape != latents_b.shape:
             raise Exception("Latents to blend must be the same size.")
 
-        # TODO:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
         def slerp(
             t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
@@ -1326,9 +1360,8 @@ class BlendLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
-        torch.cuda.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
+
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=blended_latents)
         return LatentsOutput.build(latents_name=name, latents=blended_latents)

invokeai/app/invocations/mask.py

@@ -1,7 +1,8 @@
+import numpy as np
 import torch
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext, invocation
-from invokeai.app.invocations.fields import InputField, TensorField, WithMetadata
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
+from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithMetadata
 from invokeai.app.invocations.primitives import MaskOutput
 
 
@@ -34,3 +35,86 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
             width=self.width,
             height=self.height,
         )
+
+
+@invocation(
+    "alpha_mask_to_tensor",
+    title="Alpha Mask to Tensor",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
+class AlphaMaskToTensorInvocation(BaseInvocation):
+    """Convert a mask image to a tensor. Opaque regions are 1 and transparent regions are 0."""
+
+    image: ImageField = InputField(description="The mask image to convert.")
+    invert: bool = InputField(default=False, description="Whether to invert the mask.")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        image = context.images.get_pil(self.image.image_name)
+        mask = torch.zeros((1, image.height, image.width), dtype=torch.bool)
+        if self.invert:
+            mask[0] = torch.tensor(np.array(image)[:, :, 3] == 0, dtype=torch.bool)
+        else:
+            mask[0] = torch.tensor(np.array(image)[:, :, 3] > 0, dtype=torch.bool)
+
+        return MaskOutput(
+            mask=TensorField(tensor_name=context.tensors.save(mask)),
+            height=mask.shape[1],
+            width=mask.shape[2],
+        )
+
+
+@invocation(
+    "invert_tensor_mask",
+    title="Invert Tensor Mask",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
+class InvertTensorMaskInvocation(BaseInvocation):
+    """Inverts a tensor mask."""
+
+    mask: TensorField = InputField(description="The tensor mask to convert.")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        mask = context.tensors.load(self.mask.tensor_name)
+        inverted = ~mask
+
+        return MaskOutput(
+            mask=TensorField(tensor_name=context.tensors.save(inverted)),
+            height=inverted.shape[1],
+            width=inverted.shape[2],
+        )
+
+
+@invocation(
+    "image_mask_to_tensor",
+    title="Image Mask to Tensor",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+)
+class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
+    """Convert a mask image to a tensor. Converts the image to grayscale and uses thresholding at the specified value."""
+
+    image: ImageField = InputField(description="The mask image to convert.")
+    cutoff: int = InputField(ge=0, le=255, description="Cutoff (<)", default=128)
+    invert: bool = InputField(default=False, description="Whether to invert the mask.")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        image = context.images.get_pil(self.image.image_name, mode="L")
+
+        mask = torch.zeros((1, image.height, image.width), dtype=torch.bool)
+        if self.invert:
+            mask[0] = torch.tensor(np.array(image)[:, :] >= self.cutoff, dtype=torch.bool)
+        else:
+            mask[0] = torch.tensor(np.array(image)[:, :] < self.cutoff, dtype=torch.bool)
+
+        return MaskOutput(
+            mask=TensorField(tensor_name=context.tensors.save(mask)),
+            height=mask.shape[1],
+            width=mask.shape[2],
+        )

invokeai/app/invocations/metadata.py

@@ -3,7 +3,6 @@ from typing import Any, Literal, Optional, Union
 from pydantic import BaseModel, ConfigDict, Field
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -14,6 +13,7 @@ from invokeai.app.invocations.fields import (
 )
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
 
 from ...version import __version__
 
@@ -36,6 +36,7 @@ class IPAdapterMetadataField(BaseModel):
     image: ImageField = Field(description="The IP-Adapter image prompt.")
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
     clip_vision_model: Literal["ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
+    method: Literal["full", "style", "composition"] = Field(description="Method to apply IP Weights with")
     weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
     begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
     end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")

invokeai/app/invocations/model.py

@@ -11,6 +11,7 @@ from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType,
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Classification,
     invocation,
     invocation_output,
 )
@@ -93,19 +94,46 @@ 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="1.0.2",
+    version="1.0.3",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.main_model, input=Input.Direct, ui_type=UIType.MainModel
-    )
+    model: ModelIdentifierField = InputField(description=FieldDescriptions.main_model, ui_type=UIType.MainModel)
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
@@ -134,12 +162,12 @@ class LoRALoaderOutput(BaseInvocationOutput):
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.2")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.3")
 class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -190,6 +218,75 @@ 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"""
@@ -204,13 +301,13 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.2",
+    version="1.0.3",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
+        description=FieldDescriptions.lora_model, title="LoRA", ui_type=UIType.LoRAModel
     )
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = InputField(
@@ -279,12 +376,78 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
         return output
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
+@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")
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
     vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, input=Input.Direct, title="VAE", ui_type=UIType.VAEModel
+        description=FieldDescriptions.vae_model, title="VAE", ui_type=UIType.VAEModel
     )
 
     def invoke(self, context: InvocationContext) -> VAEOutput:

invokeai/app/invocations/noise.py

@@ -9,7 +9,7 @@ from invokeai.app.invocations.fields import FieldDescriptions, InputField, Laten
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import SEED_MAX
 
-from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.util.devices import TorchDevice
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -46,7 +46,7 @@ def get_noise(
             height // downsampling_factor,
             width // downsampling_factor,
         ],
-        dtype=torch_dtype(device),
+        dtype=TorchDevice.choose_torch_dtype(device=device),
         device=noise_device_type,
         generator=generator,
     ).to("cpu")
@@ -111,14 +111,14 @@ class NoiseInvocation(BaseInvocation):
 
     @field_validator("seed", mode="before")
     def modulo_seed(cls, v):
-        """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
+        """Return the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
         return v % (SEED_MAX + 1)
 
     def invoke(self, context: InvocationContext) -> NoiseOutput:
         noise = get_noise(
             width=self.width,
             height=self.height,
-            device=choose_torch_device(),
+            device=TorchDevice.choose_torch_device(),
             seed=self.seed,
             use_cpu=self.use_cpu,
         )

invokeai/app/invocations/sdxl.py

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

invokeai/app/invocations/t2i_adapter.py

@@ -8,11 +8,11 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, 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
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES
 
 
 class T2IAdapterField(BaseModel):
@@ -45,7 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.2"
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""
@@ -55,7 +55,6 @@ 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/invocations/upscale.py

@@ -4,7 +4,6 @@ from typing import Literal
 
 import cv2
 import numpy as np
-import torch
 from PIL import Image
 from pydantic import ConfigDict
 
@@ -14,7 +13,7 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, WithBoard, WithMetadata
@@ -35,9 +34,6 @@ ESRGAN_MODEL_URLS: dict[str, str] = {
     "RealESRGAN_x2plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
 }
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
 
 @invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.2")
 class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -120,9 +116,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
         upscaled_image = upscaler.upscale(cv2_image)
         pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
         image_dto = context.images.save(image=pil_image)
 

invokeai/app/services/config/config_default.py

@@ -27,12 +27,12 @@ DEFAULT_RAM_CACHE = 10.0
 DEFAULT_VRAM_CACHE = 0.25
 DEFAULT_CONVERT_CACHE = 20.0
 DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
-PRECISION = Literal["auto", "float16", "bfloat16", "float32", "autocast"]
+PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
 LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
 LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
-CONFIG_SCHEMA_VERSION = "4.0.0"
+CONFIG_SCHEMA_VERSION = "4.0.1"
 
 
 def get_default_ram_cache_size() -> float:
@@ -105,7 +105,7 @@ class InvokeAIAppConfig(BaseSettings):
         lazy_offload: Keep models in VRAM until their space is needed.
         log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
-        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`, `autocast`
+        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
         attention_slice_size: Slice size, valid when attention_type=="sliced".<br>Valid values: `auto`, `balanced`, `max`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`
@@ -370,6 +370,9 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             # `max_vram_cache_size` was renamed to `vram` some time in v3, but both names were used
             if k == "max_vram_cache_size" and "vram" not in category_dict:
                 parsed_config_dict["vram"] = v
+            # autocast was removed in v4.0.1
+            if k == "precision" and v == "autocast":
+                parsed_config_dict["precision"] = "auto"
             if k == "conf_path":
                 parsed_config_dict["legacy_models_yaml_path"] = v
             if k == "legacy_conf_dir":
@@ -392,6 +395,28 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
     return config
 
 
+def migrate_v4_0_0_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
+    """Migrate v4.0.0 config dictionary to a current config object.
+
+    Args:
+        config_dict: A dictionary of settings from a v4.0.0 config file.
+
+    Returns:
+        An instance of `InvokeAIAppConfig` with the migrated settings.
+    """
+    parsed_config_dict: dict[str, Any] = {}
+    for k, v in config_dict.items():
+        # autocast was removed from precision in v4.0.1
+        if k == "precision" and v == "autocast":
+            parsed_config_dict["precision"] = "auto"
+        else:
+            parsed_config_dict[k] = v
+        if k == "schema_version":
+            parsed_config_dict[k] = CONFIG_SCHEMA_VERSION
+    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
+    return config
+
+
 def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     """Load and migrate a config file to the latest version.
 
@@ -418,17 +443,21 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
             raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
         migrated_config.write_file(config_path)
         return migrated_config
-    else:
-        # Attempt to load as a v4 config file
-        try:
-            # Meta is not included in the model fields, so we need to validate it separately
-            config = InvokeAIAppConfig.model_validate(loaded_config_dict)
-            assert (
-                config.schema_version == CONFIG_SCHEMA_VERSION
-            ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
-            return config
-        except Exception as e:
-            raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
+
+    if loaded_config_dict["schema_version"] == "4.0.0":
+        loaded_config_dict = migrate_v4_0_0_config_dict(loaded_config_dict)
+        loaded_config_dict.write_file(config_path)
+
+    # Attempt to load as a v4 config file
+    try:
+        # Meta is not included in the model fields, so we need to validate it separately
+        config = InvokeAIAppConfig.model_validate(loaded_config_dict)
+        assert (
+            config.schema_version == CONFIG_SCHEMA_VERSION
+        ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        return config
+    except Exception as e:
+        raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
 
 
 @lru_cache(maxsize=1)

invokeai/app/services/download/download_default.py

@@ -318,10 +318,8 @@ class DownloadQueueService(DownloadQueueServiceBase):
         in_progress_path.rename(job.download_path)
 
     def _validate_filename(self, directory: str, filename: str) -> bool:
-        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
+        pc_name_max = get_pc_name_max(directory)
+        pc_path_max = get_pc_path_max(directory)
         if "/" in filename:
             return False
         if filename.startswith(".."):
@@ -419,6 +417,26 @@ 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

@@ -122,6 +122,8 @@ 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(
@@ -135,6 +137,8 @@ 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

@@ -4,9 +4,6 @@ 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."""
@@ -33,8 +30,9 @@ class ImageFileStorageBase(ABC):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = 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."""
@@ -46,6 +44,11 @@ class ImageFileStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
+    def get_workflow(self, image_name: str) -> Optional[str]:
         """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

@@ -7,9 +7,7 @@ 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
@@ -56,8 +54,9 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
@@ -68,13 +67,14 @@ class DiskImageFileStorage(ImageFileStorageBase):
             info_dict = {}
 
             if metadata is not None:
-                metadata_json = metadata.model_dump_json()
-                info_dict["invokeai_metadata"] = metadata_json
-                pnginfo.add_text("invokeai_metadata", metadata_json)
+                info_dict["invokeai_metadata"] = metadata
+                pnginfo.add_text("invokeai_metadata", metadata)
             if workflow is not None:
-                workflow_json = workflow.model_dump_json()
-                info_dict["invokeai_workflow"] = workflow_json
-                pnginfo.add_text("invokeai_workflow", workflow_json)
+                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)
 
             # When saving the image, the image object's info field is not populated. We need to set it
             image.info = info_dict
@@ -129,11 +129,18 @@ class DiskImageFileStorage(ImageFileStorageBase):
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
 
-    def get_workflow(self, image_name: str) -> WorkflowWithoutID | None:
+    def get_workflow(self, image_name: str) -> str | None:
         image = self.get(image_name)
         workflow = image.info.get("invokeai_workflow", None)
-        if workflow is not None:
-            return WorkflowWithoutID.model_validate_json(workflow)
+        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
         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[MetadataField] = None,
+        metadata: Optional[str] = None,
     ) -> datetime:
         """Saves an image record."""
         pass

invokeai/app/services/image_records/image_records_sqlite.py

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

invokeai/app/services/images/images_base.py

@@ -12,7 +12,6 @@ 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):
@@ -51,8 +50,9 @@ class ImageServiceABC(ABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
-        metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -87,7 +87,12 @@ class ImageServiceABC(ABC):
         pass
 
     @abstractmethod
-    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
+    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]:
         """Gets an image's workflow."""
         pass
 

invokeai/app/services/images/images_default.py

@@ -5,7 +5,6 @@ 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,
@@ -42,8 +41,9 @@ class ImageService(ImageServiceABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
-        metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowWithoutID] = None,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = 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,
+                has_workflow=workflow is not None or graph 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
+                image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
             )
             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[WorkflowWithoutID]:
+    def get_workflow(self, image_name: str) -> Optional[str]:
         try:
             return self.__invoker.services.image_files.get_workflow(image_name)
         except ImageFileNotFoundException:
@@ -167,6 +167,16 @@ 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/model_install/model_install_default.py

@@ -3,7 +3,6 @@
 import locale
 import os
 import re
-import signal
 import threading
 import time
 from hashlib import sha256
@@ -13,6 +12,7 @@ from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import Any, Dict, List, Optional, Union
 
+import torch
 import yaml
 from huggingface_hub import HfFolder
 from pydantic.networks import AnyHttpUrl
@@ -42,7 +42,8 @@ from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMet
 from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import InvokeAILogger
-from invokeai.backend.util.devices import choose_precision, choose_torch_device
+from invokeai.backend.util.catch_sigint import catch_sigint
+from invokeai.backend.util.devices import TorchDevice
 
 from .model_install_base import (
     MODEL_SOURCE_TO_TYPE_MAP,
@@ -111,17 +112,6 @@ class ModelInstallService(ModelInstallServiceBase):
     def start(self, invoker: Optional[Invoker] = None) -> None:
         """Start the installer thread."""
 
-        # Yes, this is weird. When the installer thread is running, the
-        # thread masks the ^C signal. When we receive a
-        # sigINT, we stop the thread, reset sigINT, and send a new
-        # sigINT to the parent process.
-        def sigint_handler(signum, frame):
-            self.stop()
-            signal.signal(signal.SIGINT, signal.SIG_DFL)
-            signal.raise_signal(signal.SIGINT)
-
-        signal.signal(signal.SIGINT, sigint_handler)
-
         with self._lock:
             if self._running:
                 raise Exception("Attempt to start the installer service twice")
@@ -131,7 +121,8 @@ class ModelInstallService(ModelInstallServiceBase):
             # In normal use, we do not want to scan the models directory - it should never have orphaned models.
             # We should only do the scan when the flag is set (which should only be set when testing).
             if self.app_config.scan_models_on_startup:
-                self._register_orphaned_models()
+                with catch_sigint():
+                    self._register_orphaned_models()
 
             # Check all models' paths and confirm they exist. A model could be missing if it was installed on a volume
             # that isn't currently mounted. In this case, we don't want to delete the model from the database, but we do
@@ -634,11 +625,10 @@ class ModelInstallService(ModelInstallServiceBase):
             self._next_job_id += 1
         return id
 
-    @staticmethod
-    def _guess_variant() -> Optional[ModelRepoVariant]:
+    def _guess_variant(self) -> Optional[ModelRepoVariant]:
         """Guess the best HuggingFace variant type to download."""
-        precision = choose_precision(choose_torch_device())
-        return ModelRepoVariant.FP16 if precision == "float16" else None
+        precision = TorchDevice.choose_torch_dtype()
+        return ModelRepoVariant.FP16 if precision == torch.float16 else None
 
     def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         return ModelInstallJob(
@@ -754,6 +744,8 @@ class ModelInstallService(ModelInstallServiceBase):
             self._download_cache[download_job.source] = install_job  # matches a download job to an install job
             install_job.download_parts.add(download_job)
 
+        # only start the jobs once install_job.download_parts is fully populated
+        for download_job in install_job.download_parts:
             self._download_queue.submit_download_job(
                 download_job,
                 on_start=self._download_started_callback,
@@ -762,6 +754,7 @@ class ModelInstallService(ModelInstallServiceBase):
                 on_error=self._download_error_callback,
                 on_cancelled=self._download_cancelled_callback,
             )
+
         return install_job
 
     def _stat_size(self, path: Path) -> int:

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,12 +1,14 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
+from typing import Optional
+
 import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from ..config import InvokeAIAppConfig
@@ -67,7 +69,7 @@ class ModelManagerService(ModelManagerServiceBase):
         model_record_service: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
         events: EventServiceBase,
-        execution_device: torch.device = choose_torch_device(),
+        execution_device: Optional[torch.device] = None,
     ) -> Self:
         """
         Construct the model manager service instance.
@@ -82,7 +84,7 @@ class ModelManagerService(ModelManagerServiceBase):
             max_vram_cache_size=app_config.vram,
             lazy_offloading=app_config.lazy_offload,
             logger=logger,
-            execution_device=execution_device,
+            execution_device=execution_device or TorchDevice.choose_torch_device(),
         )
         convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
         loader = ModelLoadService(

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -1,6 +1,6 @@
+import shutil
 import tempfile
 import typing
-from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Optional, TypeVar
 
@@ -17,12 +17,6 @@ if TYPE_CHECKING:
 T = TypeVar("T")
 
 
-@dataclass
-class DeleteAllResult:
-    deleted_count: int
-    freed_space_bytes: float
-
-
 class ObjectSerializerDisk(ObjectSerializerBase[T]):
     """Disk-backed storage for arbitrary python objects. Serialization is handled by `torch.save` and `torch.load`.
 
@@ -35,6 +29,12 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
         self._ephemeral = ephemeral
         self._base_output_dir = output_dir
         self._base_output_dir.mkdir(parents=True, exist_ok=True)
+
+        if self._ephemeral:
+            # Remove dangling tempdirs that might have been left over from an earlier unplanned shutdown.
+            for temp_dir in filter(Path.is_dir, self._base_output_dir.glob("tmp*")):
+                shutil.rmtree(temp_dir)
+
         # Must specify `ignore_cleanup_errors` to avoid fatal errors during cleanup on Windows
         self._tempdir = (
             tempfile.TemporaryDirectory(dir=self._base_output_dir, ignore_cleanup_errors=True) if ephemeral else None

invokeai/app/services/session_processor/session_processor_default.py

@@ -237,6 +237,8 @@ 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
-        elif isinstance(self._data.invocation, WithMetadata):
-            metadata_ = self._data.invocation.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()
 
         # If `board_id` is provided directly, use that. Else, use the board provided by `WithBoard`, falling back to None.
         board_id_ = None
@@ -191,6 +191,14 @@ 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,
@@ -198,7 +206,8 @@ class ImagesInterface(InvocationContextInterface):
             board_id=board_id_,
             metadata=metadata_,
             image_origin=ResourceOrigin.INTERNAL,
-            workflow=self._data.queue_item.workflow,
+            workflow=workflow_,
+            graph=graph_,
             session_id=self._data.queue_item.session_id,
             node_id=self._data.invocation.id,
         )

invokeai/app/util/controlnet_utils.py

@@ -1,13 +1,21 @@
-from typing import Union
+from typing import Any, Literal, Union
 
 import cv2
 import numpy as np
 import torch
-from controlnet_aux.util import HWC3
-from diffusers.utils import PIL_INTERPOLATION
 from einops import rearrange
 from PIL import Image
 
+from invokeai.backend.image_util.util import nms, normalize_image_channel_count
+
+CONTROLNET_RESIZE_VALUES = Literal[
+    "just_resize",
+    "crop_resize",
+    "fill_resize",
+    "just_resize_simple",
+]
+CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
+
 ###################################################################
 # Copy of scripts/lvminthin.py from Mikubill/sd-webui-controlnet
 ###################################################################
@@ -68,17 +76,6 @@ def lvmin_thin(x, prunings=True):
     return y
 
 
-def nake_nms(x):
-    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
-    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
-    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
-    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
-    y = np.zeros_like(x)
-    for f in [f1, f2, f3, f4]:
-        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
-    return y
-
-
 ################################################################################
 # copied from Mikubill/sd-webui-controlnet external_code.py and modified for InvokeAI
 ################################################################################
@@ -134,98 +131,122 @@ def pixel_perfect_resolution(
     return int(np.round(estimation))
 
 
+def clone_contiguous(x: np.ndarray[Any, Any]) -> np.ndarray[Any, Any]:
+    """Get a memory-contiguous clone of the given numpy array, as a safety measure and to improve computation efficiency."""
+    return np.ascontiguousarray(x).copy()
+
+
+def np_img_to_torch(np_img: np.ndarray[Any, Any], device: torch.device) -> torch.Tensor:
+    """Convert a numpy image to a PyTorch tensor. The image is normalized to 0-1, rearranged to BCHW format and sent to
+    the specified device."""
+
+    torch_img = torch.from_numpy(np_img)
+    normalized = torch_img.float() / 255.0
+    bchw = rearrange(normalized, "h w c -> 1 c h w")
+    on_device = bchw.to(device)
+    return on_device.clone()
+
+
+def heuristic_resize(np_img: np.ndarray[Any, Any], size: tuple[int, int]) -> np.ndarray[Any, Any]:
+    """Resizes an image using a heuristic to choose the best resizing strategy.
+
+    - If the image appears to be an edge map, special handling will be applied to ensure the edges are not distorted.
+    - Single-pixel edge maps use NMS and thinning to keep the edges as single-pixel lines.
+    - Low-color-count images are resized with nearest-neighbor to preserve color information (for e.g. segmentation maps).
+    - The alpha channel is handled separately to ensure it is resized correctly.
+
+    Args:
+        np_img (np.ndarray): The input image.
+        size (tuple[int, int]): The target size for the image.
+
+    Returns:
+        np.ndarray: The resized image.
+
+    Adapted from https://github.com/Mikubill/sd-webui-controlnet.
+    """
+
+    # Return early if the image is already at the requested size
+    if np_img.shape[0] == size[1] and np_img.shape[1] == size[0]:
+        return np_img
+
+    # If the image has an alpha channel, separate it for special handling later.
+    inpaint_mask = None
+    if np_img.ndim == 3 and np_img.shape[2] == 4:
+        inpaint_mask = np_img[:, :, 3]
+        np_img = np_img[:, :, 0:3]
+
+    new_size_is_smaller = (size[0] * size[1]) < (np_img.shape[0] * np_img.shape[1])
+    new_size_is_bigger = (size[0] * size[1]) > (np_img.shape[0] * np_img.shape[1])
+    unique_color_count = np.unique(np_img.reshape(-1, np_img.shape[2]), axis=0).shape[0]
+    is_one_pixel_edge = False
+    is_binary = False
+
+    if unique_color_count == 2:
+        # If the image has only two colors, it is likely binary. Check if the image has one-pixel edges.
+        is_binary = np.min(np_img) < 16 and np.max(np_img) > 240
+        if is_binary:
+            eroded = cv2.erode(np_img, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
+            dilated = cv2.dilate(eroded, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
+            one_pixel_edge_count = np.where(dilated < np_img)[0].shape[0]
+            all_edge_count = np.where(np_img > 127)[0].shape[0]
+            is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
+
+    if 2 < unique_color_count < 200:
+        # With a low color count, we assume this is a map where exact colors are important. Near-neighbor preserves
+        # the colors as needed.
+        interpolation = cv2.INTER_NEAREST
+    elif new_size_is_smaller:
+        # This works best for downscaling
+        interpolation = cv2.INTER_AREA
+    else:
+        # Fall back for other cases
+        interpolation = cv2.INTER_CUBIC  # Must be CUBIC because we now use nms. NEVER CHANGE THIS
+
+    # This may be further transformed depending on the binary nature of the image.
+    resized = cv2.resize(np_img, size, interpolation=interpolation)
+
+    if inpaint_mask is not None:
+        # Resize the inpaint mask to match the resized image using the same interpolation method.
+        inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
+
+    # If the image is binary, we will perform some additional processing to ensure the edges are preserved.
+    if is_binary:
+        resized = np.mean(resized.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
+        if is_one_pixel_edge:
+            # Use NMS and thinning to keep the edges as single-pixel lines.
+            resized = nms(resized)
+            _, resized = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+            resized = lvmin_thin(resized, prunings=new_size_is_bigger)
+        else:
+            _, resized = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        resized = np.stack([resized] * 3, axis=2)
+
+    # Restore the alpha channel if it was present.
+    if inpaint_mask is not None:
+        inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
+        inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
+        resized = np.concatenate([resized, inpaint_mask], axis=2)
+
+    return resized
+
+
 ###########################################################################
 # Copied from detectmap_proc method in scripts/detectmap_proc.py in Mikubill/sd-webui-controlnet
 #    modified for InvokeAI
 ###########################################################################
-# def detectmap_proc(detected_map, module, resize_mode, h, w):
-def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device: torch.device = torch.device("cpu")):
-    # if 'inpaint' in module:
-    #     np_img = np_img.astype(np.float32)
-    # else:
-    #     np_img = HWC3(np_img)
-    np_img = HWC3(np_img)
+def np_img_resize(
+    np_img: np.ndarray,
+    resize_mode: CONTROLNET_RESIZE_VALUES,
+    h: int,
+    w: int,
+    device: torch.device = torch.device("cpu"),
+) -> tuple[torch.Tensor, np.ndarray[Any, Any]]:
+    np_img = normalize_image_channel_count(np_img)
 
-    def safe_numpy(x):
-        # A very safe method to make sure that Apple/Mac works
-        y = x
-
-        # below is very boring but do not change these. If you change these Apple or Mac may fail.
-        y = y.copy()
-        y = np.ascontiguousarray(y)
-        y = y.copy()
-        return y
-
-    def get_pytorch_control(x):
-        # A very safe method to make sure that Apple/Mac works
-        y = x
-
-        # below is very boring but do not change these. If you change these Apple or Mac may fail.
-        y = torch.from_numpy(y)
-        y = y.float() / 255.0
-        y = rearrange(y, "h w c -> 1 c h w")
-        y = y.clone()
-        # y = y.to(devices.get_device_for("controlnet"))
-        y = y.to(device)
-        y = y.clone()
-        return y
-
-    def high_quality_resize(x: np.ndarray, size):
-        # Written by lvmin
-        # Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
-        inpaint_mask = None
-        if x.ndim == 3 and x.shape[2] == 4:
-            inpaint_mask = x[:, :, 3]
-            x = x[:, :, 0:3]
-
-        new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
-        new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
-        unique_color_count = np.unique(x.reshape(-1, x.shape[2]), axis=0).shape[0]
-        is_one_pixel_edge = False
-        is_binary = False
-        if unique_color_count == 2:
-            is_binary = np.min(x) < 16 and np.max(x) > 240
-            if is_binary:
-                xc = x
-                xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-                xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-                one_pixel_edge_count = np.where(xc < x)[0].shape[0]
-                all_edge_count = np.where(x > 127)[0].shape[0]
-                is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
-
-        if 2 < unique_color_count < 200:
-            interpolation = cv2.INTER_NEAREST
-        elif new_size_is_smaller:
-            interpolation = cv2.INTER_AREA
-        else:
-            interpolation = cv2.INTER_CUBIC  # Must be CUBIC because we now use nms. NEVER CHANGE THIS
-
-        y = cv2.resize(x, size, interpolation=interpolation)
-        if inpaint_mask is not None:
-            inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
-
-        if is_binary:
-            y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
-            if is_one_pixel_edge:
-                y = nake_nms(y)
-                _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-                y = lvmin_thin(y, prunings=new_size_is_bigger)
-            else:
-                _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-            y = np.stack([y] * 3, axis=2)
-
-        if inpaint_mask is not None:
-            inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
-            inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
-            y = np.concatenate([y, inpaint_mask], axis=2)
-
-        return y
-
-    # if resize_mode == external_code.ResizeMode.RESIZE:
     if resize_mode == "just_resize":  # RESIZE
-        np_img = high_quality_resize(np_img, (w, h))
-        np_img = safe_numpy(np_img)
-        return get_pytorch_control(np_img), np_img
+        np_img = heuristic_resize(np_img, (w, h))
+        np_img = clone_contiguous(np_img)
+        return np_img_to_torch(np_img, device), np_img
 
     old_h, old_w, _ = np_img.shape
     old_w = float(old_w)
@@ -236,7 +257,6 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
     def safeint(x: Union[int, float]) -> int:
         return int(np.round(x))
 
-    # if resize_mode == external_code.ResizeMode.OUTER_FIT:
     if resize_mode == "fill_resize":  # OUTER_FIT
         k = min(k0, k1)
         borders = np.concatenate([np_img[0, :, :], np_img[-1, :, :], np_img[:, 0, :], np_img[:, -1, :]], axis=0)
@@ -245,23 +265,23 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
             # Inpaint hijack
             high_quality_border_color[3] = 255
         high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
-        np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
+        np_img = heuristic_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
         new_h, new_w, _ = np_img.shape
         pad_h = max(0, (h - new_h) // 2)
         pad_w = max(0, (w - new_w) // 2)
         high_quality_background[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = np_img
         np_img = high_quality_background
-        np_img = safe_numpy(np_img)
-        return get_pytorch_control(np_img), np_img
+        np_img = clone_contiguous(np_img)
+        return np_img_to_torch(np_img, device), np_img
     else:  # resize_mode == "crop_resize"  (INNER_FIT)
         k = max(k0, k1)
-        np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
+        np_img = heuristic_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
         new_h, new_w, _ = np_img.shape
         pad_h = max(0, (new_h - h) // 2)
         pad_w = max(0, (new_w - w) // 2)
         np_img = np_img[pad_h : pad_h + h, pad_w : pad_w + w]
-        np_img = safe_numpy(np_img)
-        return get_pytorch_control(np_img), np_img
+        np_img = clone_contiguous(np_img)
+        return np_img_to_torch(np_img, device), np_img
 
 
 def prepare_control_image(
@@ -269,12 +289,12 @@ def prepare_control_image(
     width: int,
     height: int,
     num_channels: int = 3,
-    device="cuda",
-    dtype=torch.float16,
-    do_classifier_free_guidance=True,
-    control_mode="balanced",
-    resize_mode="just_resize_simple",
-):
+    device: str = "cuda",
+    dtype: torch.dtype = torch.float16,
+    control_mode: CONTROLNET_MODE_VALUES = "balanced",
+    resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
+    do_classifier_free_guidance: bool = True,
+) -> torch.Tensor:
     """Pre-process images for ControlNets or T2I-Adapters.
 
     Args:
@@ -292,26 +312,15 @@ def prepare_control_image(
         resize_mode (str, optional): Defaults to "just_resize_simple".
 
     Raises:
-        NotImplementedError: If resize_mode == "crop_resize_simple".
-        NotImplementedError: If resize_mode == "fill_resize_simple".
         ValueError: If `resize_mode` is not recognized.
         ValueError: If `num_channels` is out of range.
 
     Returns:
         torch.Tensor: The pre-processed input tensor.
     """
-    if (
-        resize_mode == "just_resize_simple"
-        or resize_mode == "crop_resize_simple"
-        or resize_mode == "fill_resize_simple"
-    ):
+    if resize_mode == "just_resize_simple":
         image = image.convert("RGB")
-        if resize_mode == "just_resize_simple":
-            image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-        elif resize_mode == "crop_resize_simple":
-            raise NotImplementedError(f"prepare_control_image is not implemented for resize_mode='{resize_mode}'.")
-        elif resize_mode == "fill_resize_simple":
-            raise NotImplementedError(f"prepare_control_image is not implemented for resize_mode='{resize_mode}'.")
+        image = image.resize((width, height), resample=Image.LANCZOS)
         nimage = np.array(image)
         nimage = nimage[None, :]
         nimage = np.concatenate([nimage], axis=0)
@@ -328,8 +337,7 @@ def prepare_control_image(
             resize_mode=resize_mode,
             h=height,
             w=width,
-            # device=torch.device('cpu')
-            device=device,
+            device=torch.device(device),
         )
     else:
         raise ValueError(f"Unsupported resize_mode: '{resize_mode}'.")

invokeai/backend/image_util/__init__.py

@@ -4,5 +4,4 @@ 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/depth_anything/__init__.py

@@ -13,7 +13,7 @@ from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
 from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 config = get_config()
@@ -56,7 +56,7 @@ class DepthAnythingDetector:
     def __init__(self) -> None:
         self.model = None
         self.model_size: Union[Literal["large", "base", "small"], None] = None
-        self.device = choose_torch_device()
+        self.device = TorchDevice.choose_torch_device()
 
     def load_model(self, model_size: Literal["large", "base", "small"] = "small"):
         DEPTH_ANYTHING_MODEL_PATH = config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"]
@@ -81,7 +81,7 @@ class DepthAnythingDetector:
             self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
             self.model.eval()
 
-        self.model.to(choose_torch_device())
+        self.model.to(self.device)
         return self.model
 
     def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
@@ -94,7 +94,7 @@ class DepthAnythingDetector:
 
         image_height, image_width = np_image.shape[:2]
         np_image = transform({"image": np_image})["image"]
-        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(choose_torch_device())
+        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
 
         with torch.no_grad():
             depth = self.model(tensor_image)

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -7,7 +7,7 @@ import onnxruntime as ort
 
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.download_with_progress import download_with_progress_bar
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .onnxdet import inference_detector
 from .onnxpose import inference_pose
@@ -28,9 +28,9 @@ config = get_config()
 
 class Wholebody:
     def __init__(self):
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
-        providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
+        providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
 
         DET_MODEL_PATH = config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"]
         download_with_progress_bar("yolox_l.onnx", DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)

invokeai/backend/image_util/hed.py

@@ -8,7 +8,7 @@ from huggingface_hub import hf_hub_download
 from PIL import Image
 
 from invokeai.backend.image_util.util import (
-    non_maximum_suppression,
+    nms,
     normalize_image_channel_count,
     np_to_pil,
     pil_to_np,
@@ -134,7 +134,7 @@ class HEDProcessor:
         detected_map = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)
 
         if scribble:
-            detected_map = non_maximum_suppression(detected_map, 127, 3.0)
+            detected_map = nms(detected_map, 127, 3.0)
             detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
             detected_map[detected_map > 4] = 255
             detected_map[detected_map < 255] = 0

invokeai/backend/image_util/infill_methods/lama.py

@@ -8,7 +8,7 @@ from PIL import Image
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.download_with_progress import download_with_progress_bar
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 
 def norm_img(np_img):
@@ -29,7 +29,7 @@ def load_jit_model(url_or_path, device):
 
 class LaMA:
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
         model_location = get_config().models_path / "core/misc/lama/lama.pt"
 
         if not model_location.exists():

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -11,7 +11,7 @@ from cv2.typing import MatLike
 from tqdm import tqdm
 
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 """
 Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
@@ -65,7 +65,7 @@ class RealESRGAN:
         self.pre_pad = pre_pad
         self.mod_scale: Optional[int] = None
         self.half = half
-        self.device = choose_torch_device()
+        self.device = TorchDevice.choose_torch_device()
 
         loadnet = torch.load(model_path, map_location=torch.device("cpu"))
 

invokeai/backend/image_util/safety_checker.py

@@ -8,14 +8,15 @@ from pathlib import Path
 
 import numpy as np
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from PIL import Image
+from PIL import Image, ImageFilter
 from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.util.devices import choose_torch_device
+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"
 
 
@@ -24,34 +25,34 @@ class SafetyChecker:
     Wrapper around SafetyChecker model.
     """
 
-    safety_checker = None
     feature_extractor = None
-    tried_load: bool = False
+    safety_checker = None
 
     @classmethod
     def _load_safety_checker(cls):
-        if cls.tried_load:
+        if cls.safety_checker is not None and cls.feature_extractor is not None:
             return
 
         try:
-            cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(get_config().models_path / CHECKER_PATH)
-            cls.feature_extractor = AutoFeatureExtractor.from_pretrained(get_config().models_path / CHECKER_PATH)
+            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)
         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
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
         features = cls.feature_extractor([image], return_tensors="pt")
         features.to(device)
         cls.safety_checker.to(device)
@@ -60,3 +61,24 @@ 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

@@ -1,52 +0,0 @@
-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

@@ -1,4 +1,5 @@
 from math import ceil, floor, sqrt
+from typing import Optional
 
 import cv2
 import numpy as np
@@ -143,20 +144,21 @@ 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 *= scaling_factor
-    w *= scaling_factor
-    h = int(np.round(h / 64.0)) * 64
-    w = int(np.round(w / 64.0)) * 64
+    h = int(h * scaling_factor)
+    w = int(w * scaling_factor)
     if scaling_factor > 1:
         return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_LANCZOS4)
     else:
         return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_AREA)
 
 
-def non_maximum_suppression(image: np.ndarray, threshold: int, sigma: float):
+def nms(np_img: np.ndarray, threshold: Optional[int] = None, sigma: Optional[float] = None) -> np.ndarray:
     """
     Apply non-maximum suppression to an image.
 
+    If both threshold and sigma are provided, the image will blurred before the suppression and thresholded afterwards,
+    resulting in a binary output image.
+
     This function is adapted from https://github.com/lllyasviel/ControlNet.
 
     Args:
@@ -166,23 +168,36 @@ def non_maximum_suppression(image: np.ndarray, threshold: int, sigma: float):
 
     Returns:
         The image after non-maximum suppression.
+
+    Raises:
+        ValueError: If only one of threshold and sigma provided.
     """
 
-    image = cv2.GaussianBlur(image.astype(np.float32), (0, 0), sigma)
+    # Raise a value error if only one of threshold and sigma is provided
+    if (threshold is None) != (sigma is None):
+        raise ValueError("Both threshold and sigma must be provided if one is provided.")
+
+    if sigma is not None and threshold is not None:
+        # Blurring the image can help to thin out features
+        np_img = cv2.GaussianBlur(np_img.astype(np.float32), (0, 0), sigma)
 
     filter_1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
     filter_2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
     filter_3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
     filter_4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
 
-    y = np.zeros_like(image)
+    nms_img = np.zeros_like(np_img)
 
     for f in [filter_1, filter_2, filter_3, filter_4]:
-        np.putmask(y, cv2.dilate(image, kernel=f) == image, image)
+        np.putmask(nms_img, cv2.dilate(np_img, kernel=f) == np_img, np_img)
 
-    z = np.zeros_like(y, dtype=np.uint8)
-    z[y > threshold] = 255
-    return z
+    if sigma is not None and threshold is not None:
+        # We blurred - now threshold to get a binary image
+        thresholded = np.zeros_like(nms_img, dtype=np.uint8)
+        thresholded[nms_img > threshold] = 255
+        return thresholded
+
+    return nms_img
 
 
 def safe_step(x: np.ndarray, step: int = 2) -> np.ndarray:

invokeai/backend/model_manager/config.py

@@ -301,12 +301,12 @@ class MainConfigBase(ModelConfigBase):
     default_settings: Optional[MainModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
+    variant: ModelVariantType = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
     """Model config for main checkpoint models."""
 
-    variant: ModelVariantType = ModelVariantType.Normal
     prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
     upcast_attention: bool = False
 

invokeai/backend/model_manager/load/load_default.py

@@ -18,7 +18,7 @@ from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoad
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data, calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 
 # TO DO: The loader is not thread safe!
@@ -37,7 +37,7 @@ class ModelLoader(ModelLoaderBase):
         self._logger = logger
         self._ram_cache = ram_cache
         self._convert_cache = convert_cache
-        self._torch_dtype = torch_dtype(choose_torch_device())
+        self._torch_dtype = TorchDevice.choose_torch_dtype()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """

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

@@ -30,15 +30,12 @@ import torch
 
 from invokeai.backend.model_manager import AnyModel, SubModelType
 from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from .model_cache_base import CacheRecord, CacheStats, ModelCacheBase, ModelLockerBase
 from .model_locker import ModelLocker
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
 # Maximum size of the cache, in gigs
 # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
 DEFAULT_MAX_CACHE_SIZE = 6.0
@@ -244,9 +241,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
                 )
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
     def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
         """Move model into the indicated device.
@@ -416,10 +411,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 self.stats.cleared = models_cleared
             gc.collect()
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
-
+        TorchDevice.empty_cache()
         self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
 
     def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:

invokeai/backend/model_manager/merge.py

@@ -17,7 +17,7 @@ from diffusers.utils import logging as dlogging
 
 from invokeai.app.services.model_install import ModelInstallServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 from . import (
     AnyModelConfig,
@@ -43,6 +43,7 @@ class ModelMerger(object):
         Initialize a ModelMerger object with the model installer.
         """
         self._installer = installer
+        self._dtype = TorchDevice.choose_torch_dtype()
 
     def merge_diffusion_models(
         self,
@@ -68,7 +69,7 @@ class ModelMerger(object):
             warnings.simplefilter("ignore")
             verbosity = dlogging.get_verbosity()
             dlogging.set_verbosity_error()
-            dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
+            dtype = torch.float16 if variant == "fp16" else self._dtype
 
             # Note that checkpoint_merger will not work with downloaded HuggingFace fp16 models
             # until upstream https://github.com/huggingface/diffusers/pull/6670 is merged and released.
@@ -151,7 +152,7 @@ class ModelMerger(object):
         dump_path.mkdir(parents=True, exist_ok=True)
         dump_path = dump_path / merged_model_name
 
-        dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
+        dtype = torch.float16 if variant == "fp16" else self._dtype
         merged_pipe.save_pretrained(dump_path.as_posix(), safe_serialization=True, torch_dtype=dtype, variant=variant)
 
         # register model and get its unique key

invokeai/backend/model_manager/probe.py

@@ -51,6 +51,7 @@ 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/model_manager/starter_models.py

@@ -155,7 +155,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter",
         base=BaseModelType.StableDiffusion1,
-        source="InvokeAI/ip_adapter_sd15",
+        source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
         description="IP-Adapter for SD 1.5 models",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sd_image_encoder],
@@ -163,7 +163,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter Plus",
         base=BaseModelType.StableDiffusion1,
-        source="InvokeAI/ip_adapter_plus_sd15",
+        source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
         description="Refined IP-Adapter for SD 1.5 models",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sd_image_encoder],
@@ -171,7 +171,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter Plus Face",
         base=BaseModelType.StableDiffusion1,
-        source="InvokeAI/ip_adapter_plus_face_sd15",
+        source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
         description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sd_image_encoder],
@@ -179,7 +179,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter SDXL",
         base=BaseModelType.StableDiffusionXL,
-        source="InvokeAI/ip_adapter_sdxl",
+        source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
         description="IP-Adapter for SDXL models",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sdxl_image_encoder],

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -21,14 +21,11 @@ from pydantic import Field
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    IPAdapterData,
-    TextConditioningData,
-)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import IPAdapterData, TextConditioningData
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
+from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher, UNetIPAdapterData
 from invokeai.backend.util.attention import auto_detect_slice_size
-from invokeai.backend.util.devices import normalize_device
+from invokeai.backend.util.devices import TorchDevice
 
 
 @dataclass
@@ -258,7 +255,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free
         elif self.unet.device.type == "cuda":
-            mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
+            mem_free, _ = torch.cuda.mem_get_info(TorchDevice.normalize(self.unet.device))
         else:
             raise ValueError(f"unrecognized device {self.unet.device}")
         # input tensor of [1, 4, h/8, w/8]
@@ -394,8 +391,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         unet_attention_patcher = None
         self.use_ip_adapter = use_ip_adapter
         attn_ctx = nullcontext()
+
         if use_ip_adapter or use_regional_prompting:
-            ip_adapters = [ipa.ip_adapter_model for ipa in ip_adapter_data] if use_ip_adapter else None
+            ip_adapters: Optional[List[UNetIPAdapterData]] = (
+                [{"ip_adapter": ipa.ip_adapter_model, "target_blocks": ipa.target_blocks} for ipa in ip_adapter_data]
+                if use_ip_adapter
+                else None
+            )
             unet_attention_patcher = UNetAttentionPatcher(ip_adapters)
             attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
 

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -53,6 +53,7 @@ class IPAdapterData:
     ip_adapter_model: IPAdapter
     ip_adapter_conditioning: IPAdapterConditioningInfo
     mask: torch.Tensor
+    target_blocks: List[str]
 
     # Either a single weight applied to all steps, or a list of weights for each step.
     weight: Union[float, List[float]] = 1.0

invokeai/backend/stable_diffusion/diffusion/custom_atttention.py

@@ -1,4 +1,5 @@
-from typing import Optional
+from dataclasses import dataclass
+from typing import List, Optional, cast
 
 import torch
 import torch.nn.functional as F
@@ -9,6 +10,12 @@ from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import Regiona
 from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
 
 
+@dataclass
+class IPAdapterAttentionWeights:
+    ip_adapter_weights: IPAttentionProcessorWeights
+    skip: bool
+
+
 class CustomAttnProcessor2_0(AttnProcessor2_0):
     """A custom implementation of AttnProcessor2_0 that supports additional Invoke features.
     This implementation is based on
@@ -20,7 +27,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
     def __init__(
         self,
-        ip_adapter_weights: Optional[list[IPAttentionProcessorWeights]] = None,
+        ip_adapter_attention_weights: Optional[List[IPAdapterAttentionWeights]] = None,
     ):
         """Initialize a CustomAttnProcessor2_0.
         Note: Arguments that are the same for all attention layers are passed to __call__(). Arguments that are
@@ -30,23 +37,22 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                 for the i'th IP-Adapter.
         """
         super().__init__()
-        self._ip_adapter_weights = ip_adapter_weights
-
-    def _is_ip_adapter_enabled(self) -> bool:
-        return self._ip_adapter_weights is not None
+        self._ip_adapter_attention_weights = ip_adapter_attention_weights
 
     def __call__(
         self,
         attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        temb: Optional[torch.FloatTensor] = None,
-        # For regional prompting:
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        # For Regional Prompting:
         regional_prompt_data: Optional[RegionalPromptData] = None,
-        percent_through: Optional[torch.FloatTensor] = None,
+        percent_through: Optional[torch.Tensor] = None,
         # For IP-Adapter:
         regional_ip_data: Optional[RegionalIPData] = None,
+        *args,
+        **kwargs,
     ) -> torch.FloatTensor:
         """Apply attention.
         Args:
@@ -130,17 +136,19 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
         # Apply IP-Adapter conditioning.
         if is_cross_attention:
-            if self._is_ip_adapter_enabled():
+            if self._ip_adapter_attention_weights:
                 assert regional_ip_data is not None
                 ip_masks = regional_ip_data.get_masks(query_seq_len=query_seq_len)
+
                 assert (
                     len(regional_ip_data.image_prompt_embeds)
-                    == len(self._ip_adapter_weights)
+                    == len(self._ip_adapter_attention_weights)
                     == len(regional_ip_data.scales)
                     == ip_masks.shape[1]
                 )
+
                 for ipa_index, ipa_embed in enumerate(regional_ip_data.image_prompt_embeds):
-                    ipa_weights = self._ip_adapter_weights[ipa_index]
+                    ipa_weights = self._ip_adapter_attention_weights[ipa_index].ip_adapter_weights
                     ipa_scale = regional_ip_data.scales[ipa_index]
                     ip_mask = ip_masks[0, ipa_index, ...]
 
@@ -153,29 +161,33 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
                     # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
-                    ip_key = ipa_weights.to_k_ip(ip_hidden_states)
-                    ip_value = ipa_weights.to_v_ip(ip_hidden_states)
+                    if not self._ip_adapter_attention_weights[ipa_index].skip:
+                        ip_key = ipa_weights.to_k_ip(ip_hidden_states)
+                        ip_value = ipa_weights.to_v_ip(ip_hidden_states)
 
-                    # Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
+                        # Expected ip_key and ip_value shape:
+                        # (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
 
-                    ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-                    ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+                        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+                        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
-                    # Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
+                        # Expected ip_key and ip_value shape:
+                        # (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
 
-                    # TODO: add support for attn.scale when we move to Torch 2.1
-                    ip_hidden_states = F.scaled_dot_product_attention(
-                        query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
-                    )
+                        # TODO: add support for attn.scale when we move to Torch 2.1
+                        ip_hidden_states = F.scaled_dot_product_attention(
+                            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+                        )
 
-                    # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
+                        # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
+                        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(
+                            batch_size, -1, attn.heads * head_dim
+                        )
 
-                    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-                    ip_hidden_states = ip_hidden_states.to(query.dtype)
+                        ip_hidden_states = ip_hidden_states.to(query.dtype)
 
-                    # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
-
-                    hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
+                        # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
+                        hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
             else:
                 # If IP-Adapter is not enabled, then regional_ip_data should not be passed in.
                 assert regional_ip_data is None
@@ -188,11 +200,15 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         hidden_states = attn.to_out[1](hidden_states)
 
         if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
             hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
 
         if attn.residual_connection:
             hidden_states = hidden_states + residual
 
         hidden_states = hidden_states / attn.rescale_output_factor
+        # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        # End of unmodified block from AttnProcessor2_0
 
-        return hidden_states
+        # casting torch.Tensor to torch.FloatTensor to avoid type issues
+        return cast(torch.FloatTensor, hidden_states)

invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py

@@ -1,17 +1,25 @@
 from contextlib import contextmanager
-from typing import Optional
+from typing import List, Optional, TypedDict
 
 from diffusers.models import UNet2DConditionModel
 
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.stable_diffusion.diffusion.custom_atttention import CustomAttnProcessor2_0
+from invokeai.backend.stable_diffusion.diffusion.custom_atttention import (
+    CustomAttnProcessor2_0,
+    IPAdapterAttentionWeights,
+)
+
+
+class UNetIPAdapterData(TypedDict):
+    ip_adapter: IPAdapter
+    target_blocks: List[str]
 
 
 class UNetAttentionPatcher:
     """A class for patching a UNet with CustomAttnProcessor2_0 attention layers."""
 
-    def __init__(self, ip_adapters: Optional[list[IPAdapter]]):
-        self._ip_adapters = ip_adapters
+    def __init__(self, ip_adapter_data: Optional[List[UNetIPAdapterData]]):
+        self._ip_adapters = ip_adapter_data
 
     def _prepare_attention_processors(self, unet: UNet2DConditionModel):
         """Prepare a dict of attention processors that can be injected into a unet, and load the IP-Adapter attention
@@ -26,9 +34,22 @@ class UNetAttentionPatcher:
                 attn_procs[name] = CustomAttnProcessor2_0()
             else:
                 # Collect the weights from each IP Adapter for the idx'th attention processor.
-                attn_procs[name] = CustomAttnProcessor2_0(
-                    [ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in self._ip_adapters],
-                )
+                ip_adapter_attention_weights_collection: list[IPAdapterAttentionWeights] = []
+
+                for ip_adapter in self._ip_adapters:
+                    ip_adapter_weights = ip_adapter["ip_adapter"].attn_weights.get_attention_processor_weights(idx)
+                    skip = True
+                    for block in ip_adapter["target_blocks"]:
+                        if block in name:
+                            skip = False
+                            break
+                    ip_adapter_attention_weights: IPAdapterAttentionWeights = IPAdapterAttentionWeights(
+                        ip_adapter_weights=ip_adapter_weights, skip=skip
+                    )
+                    ip_adapter_attention_weights_collection.append(ip_adapter_attention_weights)
+
+                attn_procs[name] = CustomAttnProcessor2_0(ip_adapter_attention_weights_collection)
+
         return attn_procs
 
     @contextmanager

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -13,6 +13,7 @@ from diffusers import (
     LCMScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
+    TCDScheduler,
     UniPCMultistepScheduler,
 )
 
@@ -40,4 +41,5 @@ 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

@@ -1,89 +1,51 @@
-from __future__ import annotations
-
 from contextlib import contextmanager
-from typing import Callable, List, Union
+from typing import Callable, List, Optional, Tuple, 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
 
-    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
-    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
+    # 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]] = []
+
     try:
-        # 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
+        x_mode = "circular" if "x" in seamless_axes else "constant"
+        y_mode = "circular" if "y" in seamless_axes else "constant"
 
-            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)
+        conv_layers: List[torch.nn.Conv2d] = []
 
-                if block_num >= len(model.down_blocks) - skipped_layers:
-                    continue
+        for module in model.modules():
+            if isinstance(module, torch.nn.Conv2d):
+                conv_layers.append(module)
 
-                # 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)
+        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)
 
         yield
 
     finally:
-        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
+        for layer, orig_conv_forward in original_layers:
+            layer._conv_forward = orig_conv_forward

invokeai/backend/util/__init__.py

@@ -2,7 +2,6 @@
 Initialization file for invokeai.backend.util
 """
 
-from .devices import choose_precision, choose_torch_device
 from .logging import InvokeAILogger
 from .util import GIG, Chdir, directory_size
 
@@ -11,6 +10,4 @@ __all__ = [
     "directory_size",
     "Chdir",
     "InvokeAILogger",
-    "choose_precision",
-    "choose_torch_device",
 ]

invokeai/backend/util/catch_sigint.py

@@ -0,0 +1,29 @@
+"""
+This module defines a context manager `catch_sigint()` which temporarily replaces
+the sigINT handler defined by the ASGI in order to allow the user to ^C the application
+and shut it down immediately. This was implemented in order to allow the user to interrupt
+slow model hashing during startup.
+
+Use like this:
+
+  from invokeai.backend.util.catch_sigint import catch_sigint
+  with catch_sigint():
+      run_some_hard_to_interrupt_process()
+"""
+
+import signal
+from contextlib import contextmanager
+from typing import Generator
+
+
+def sigint_handler(signum, frame):  # type: ignore
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+    signal.raise_signal(signal.SIGINT)
+
+
+@contextmanager
+def catch_sigint() -> Generator[None, None, None]:
+    original_handler = signal.getsignal(signal.SIGINT)
+    signal.signal(signal.SIGINT, sigint_handler)
+    yield
+    signal.signal(signal.SIGINT, original_handler)

invokeai/backend/util/devices.py

@@ -1,89 +1,110 @@
-from __future__ import annotations
-
-from contextlib import nullcontext
-from typing import Literal, Optional, Union
+from typing import Dict, Literal, Optional, Union
 
 import torch
-from torch import autocast
+from deprecated import deprecated
 
-from invokeai.app.services.config.config_default import PRECISION, get_config
+from invokeai.app.services.config.config_default import get_config
 
+# legacy APIs
+TorchPrecisionNames = Literal["float32", "float16", "bfloat16"]
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
 
 
+@deprecated("Use TorchDevice.choose_torch_dtype() instead.")  # type: ignore
+def choose_precision(device: torch.device) -> TorchPrecisionNames:
+    """Return the string representation of the recommended torch device."""
+    torch_dtype = TorchDevice.choose_torch_dtype(device)
+    return PRECISION_TO_NAME[torch_dtype]
+
+
+@deprecated("Use TorchDevice.choose_torch_device() instead.")  # type: ignore
 def choose_torch_device() -> torch.device:
-    """Convenience routine for guessing which GPU device to run model on"""
-    config = get_config()
-    if config.device == "auto":
-        if torch.cuda.is_available():
-            return torch.device("cuda")
-        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            return torch.device("mps")
+    """Return the torch.device to use for accelerated inference."""
+    return TorchDevice.choose_torch_device()
+
+
+@deprecated("Use TorchDevice.choose_torch_dtype() instead.")  # type: ignore
+def torch_dtype(device: torch.device) -> torch.dtype:
+    """Return the torch precision for the recommended torch device."""
+    return TorchDevice.choose_torch_dtype(device)
+
+
+NAME_TO_PRECISION: Dict[TorchPrecisionNames, torch.dtype] = {
+    "float32": torch.float32,
+    "float16": torch.float16,
+    "bfloat16": torch.bfloat16,
+}
+PRECISION_TO_NAME: Dict[torch.dtype, TorchPrecisionNames] = {v: k for k, v in NAME_TO_PRECISION.items()}
+
+
+class TorchDevice:
+    """Abstraction layer for torch devices."""
+
+    @classmethod
+    def choose_torch_device(cls) -> torch.device:
+        """Return the torch.device to use for accelerated inference."""
+        app_config = get_config()
+        if app_config.device != "auto":
+            device = torch.device(app_config.device)
+        elif torch.cuda.is_available():
+            device = CUDA_DEVICE
+        elif torch.backends.mps.is_available():
+            device = MPS_DEVICE
         else:
-            return CPU_DEVICE
-    else:
-        return torch.device(config.device)
+            device = CPU_DEVICE
+        return cls.normalize(device)
 
+    @classmethod
+    def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
+        """Return the precision to use for accelerated inference."""
+        device = device or cls.choose_torch_device()
+        config = get_config()
+        if device.type == "cuda" and torch.cuda.is_available():
+            device_name = torch.cuda.get_device_name(device)
+            if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
+                # These GPUs have limited support for float16
+                return cls._to_dtype("float32")
+            elif config.precision == "auto":
+                # Default to float16 for CUDA devices
+                return cls._to_dtype("float16")
+            else:
+                # Use the user-defined precision
+                return cls._to_dtype(config.precision)
 
-def get_torch_device_name() -> str:
-    device = choose_torch_device()
-    return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
+        elif device.type == "mps" and torch.backends.mps.is_available():
+            if config.precision == "auto":
+                # Default to float16 for MPS devices
+                return cls._to_dtype("float16")
+            else:
+                # Use the user-defined precision
+                return cls._to_dtype(config.precision)
+        # CPU / safe fallback
+        return cls._to_dtype("float32")
 
+    @classmethod
+    def get_torch_device_name(cls) -> str:
+        """Return the device name for the current torch device."""
+        device = cls.choose_torch_device()
+        return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
 
-def choose_precision(device: torch.device) -> Literal["float32", "float16", "bfloat16"]:
-    """Return an appropriate precision for the given torch device."""
-    app_config = get_config()
-    if device.type == "cuda":
-        device_name = torch.cuda.get_device_name(device)
-        if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
-            # These GPUs have limited support for float16
-            return "float32"
-        elif app_config.precision == "auto" or app_config.precision == "autocast":
-            # Default to float16 for CUDA devices
-            return "float16"
-        else:
-            # Use the user-defined precision
-            return app_config.precision
-    elif device.type == "mps":
-        if app_config.precision == "auto" or app_config.precision == "autocast":
-            # Default to float16 for MPS devices
-            return "float16"
-        else:
-            # Use the user-defined precision
-            return app_config.precision
-    # CPU / safe fallback
-    return "float32"
-
-
-def torch_dtype(device: Optional[torch.device] = None) -> torch.dtype:
-    device = device or choose_torch_device()
-    precision = choose_precision(device)
-    if precision == "float16":
-        return torch.float16
-    if precision == "bfloat16":
-        return torch.bfloat16
-    else:
-        # "auto", "autocast", "float32"
-        return torch.float32
-
-
-def choose_autocast(precision: PRECISION):
-    """Returns an autocast context or nullcontext for the given precision string"""
-    # float16 currently requires autocast to avoid errors like:
-    # 'expected scalar type Half but found Float'
-    if precision == "autocast" or precision == "float16":
-        return autocast
-    return nullcontext
-
-
-def normalize_device(device: Union[str, torch.device]) -> torch.device:
-    """Ensure device has a device index defined, if appropriate."""
-    device = torch.device(device)
-    if device.index is None:
-        # cuda might be the only torch backend that currently uses the device index?
-        # I don't see anything like `current_device` for cpu or mps.
-        if device.type == "cuda":
+    @classmethod
+    def normalize(cls, device: Union[str, torch.device]) -> torch.device:
+        """Add the device index to CUDA devices."""
+        device = torch.device(device)
+        if device.index is None and device.type == "cuda" and torch.cuda.is_available():
             device = torch.device(device.type, torch.cuda.current_device())
-    return device
+        return device
+
+    @classmethod
+    def empty_cache(cls) -> None:
+        """Clear the GPU device cache."""
+        if torch.backends.mps.is_available():
+            torch.mps.empty_cache()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    @classmethod
+    def _to_dtype(cls, precision_name: TorchPrecisionNames) -> torch.dtype:
+        return NAME_TO_PRECISION[precision_name]

invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml

@@ -0,0 +1,98 @@
+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

@@ -10,6 +10,8 @@ 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

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

invokeai/frontend/web/.storybook/preview.tsx

@@ -11,6 +11,7 @@ import { createStore } from '../src/app/store/store';
 // @ts-ignore
 import translationEN from '../public/locales/en.json';
 import { ReduxInit } from './ReduxInit';
+import { $store } from 'app/store/nanostores/store';
 
 i18n.use(initReactI18next).init({
   lng: 'en',
@@ -25,6 +26,7 @@ i18n.use(initReactI18next).init({
 });
 
 const store = createStore(undefined, false);
+$store.set(store);
 $baseUrl.set('http://localhost:9090');
 
 const preview: Preview = {

invokeai/frontend/web/package.json

@@ -35,6 +35,7 @@
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "test": "vitest",
+    "test:ui": "vitest --coverage --ui",
     "test:no-watch": "vitest --no-watch"
   },
   "madge": {
@@ -52,58 +53,61 @@
   },
   "dependencies": {
     "@chakra-ui/react-use-size": "^2.1.0",
-    "@dagrejs/dagre": "^1.1.1",
-    "@dagrejs/graphlib": "^2.2.1",
+    "@dagrejs/dagre": "^1.1.2",
+    "@dagrejs/graphlib": "^2.2.2",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
-    "@fontsource-variable/inter": "^5.0.17",
-    "@invoke-ai/ui-library": "^0.0.21",
+    "@fontsource-variable/inter": "^5.0.18",
+    "@invoke-ai/ui-library": "^0.0.25",
     "@nanostores/react": "^0.7.2",
-    "@reduxjs/toolkit": "2.2.2",
+    "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
     "chakra-react-select": "^4.7.6",
     "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
-    "framer-motion": "^11.0.22",
-    "i18next": "^23.10.1",
-    "i18next-http-backend": "^2.5.0",
+    "fracturedjsonjs": "^4.0.1",
+    "framer-motion": "^11.1.8",
+    "i18next": "^23.11.3",
+    "i18next-http-backend": "^2.5.1",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.6",
     "lodash-es": "^4.17.21",
-    "nanostores": "^0.10.0",
+    "nanostores": "^0.10.3",
     "new-github-issue-url": "^1.0.0",
-    "overlayscrollbars": "^2.6.1",
-    "overlayscrollbars-react": "^0.5.5",
+    "overlayscrollbars": "^2.7.3",
+    "overlayscrollbars-react": "^0.5.6",
     "query-string": "^9.0.0",
-    "react": "^18.2.0",
+    "react": "^18.3.1",
     "react-colorful": "^5.6.1",
-    "react-dom": "^18.2.0",
+    "react-dom": "^18.3.1",
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.51.2",
+    "react-hook-form": "^7.51.4",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^14.1.0",
-    "react-icons": "^5.0.1",
+    "react-i18next": "^14.1.1",
+    "react-icons": "^5.2.0",
     "react-konva": "^18.2.10",
-    "react-redux": "9.1.0",
-    "react-resizable-panels": "^2.0.16",
+    "react-redux": "9.1.2",
+    "react-resizable-panels": "^2.0.19",
     "react-select": "5.8.0",
     "react-use": "^17.5.0",
-    "react-virtuoso": "^4.7.5",
-    "reactflow": "^11.10.4",
+    "react-virtuoso": "^4.7.10",
+    "reactflow": "^11.11.3",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
+    "redux-undo": "^1.1.0",
     "rfdc": "^1.3.1",
     "roarr": "^7.21.1",
     "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": "^3.22.4",
-    "zod-validation-error": "^3.0.3"
+    "zod": "^3.23.6",
+    "zod-validation-error": "^3.2.0"
   },
   "peerDependencies": {
     "@chakra-ui/react": "^2.8.2",
@@ -114,40 +118,42 @@
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@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",
+    "@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",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.11.30",
-    "@types/react": "^18.2.73",
-    "@types/react-dom": "^18.2.22",
+    "@types/node": "^20.12.10",
+    "@types/react": "^18.3.1",
+    "@types/react-dom": "^18.3.0",
     "@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": "^5.6.1",
+    "knip": "^5.12.3",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.7.5",
     "prettier": "^3.2.5",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.0.4",
+    "storybook": "^8.0.10",
     "ts-toolbelt": "^9.6.0",
     "tsafe": "^1.6.6",
-    "typescript": "^5.4.3",
-    "vite": "^5.2.6",
-    "vite-plugin-css-injected-by-js": "^3.5.0",
-    "vite-plugin-dts": "^3.8.0",
+    "typescript": "^5.4.5",
+    "vite": "^5.2.11",
+    "vite-plugin-css-injected-by-js": "^3.5.1",
+    "vite-plugin-dts": "^3.9.1",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.3.2",
-    "vitest": "^1.4.0"
+    "vitest": "^1.6.0"
   }
 }

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

@@ -76,7 +76,9 @@
         "aboutHeading": "Nutzen Sie Ihre kreative Energie",
         "toResolve": "Lösen",
         "add": "Hinzufügen",
-        "loglevel": "Protokoll Stufe"
+        "loglevel": "Protokoll Stufe",
+        "selected": "Ausgewählt",
+        "beta": "Beta"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -85,7 +87,8 @@
         "loadMore": "Mehr laden",
         "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
-        "deleteImage": "Lösche Bild",
+        "deleteImage_one": "Lösche Bild",
+        "deleteImage_other": "Lösche {{count}} Bilder",
         "copy": "Kopieren",
         "download": "Runterladen",
         "setCurrentImage": "Setze aktuelle Bild",
@@ -396,7 +399,14 @@
         "cancel": "Stornieren",
         "defaultSettingsSaved": "Standardeinstellungen gespeichert",
         "addModels": "Model hinzufügen",
-        "deleteModelImage": "Lösche Model Bild"
+        "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"
     },
     "parameters": {
         "images": "Bilder",
@@ -685,7 +695,11 @@
         "hands": "Hände",
         "dwOpenpose": "DW Openpose",
         "dwOpenposeDescription": "Posenschätzung mit DW Openpose",
-        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus"
+        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus",
+        "ipAdapterMethod": "Methode",
+        "composition": "Nur Komposition",
+        "full": "Voll",
+        "style": "Nur Style"
     },
     "queue": {
         "status": "Status",
@@ -716,7 +730,6 @@
         "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

@@ -2,6 +2,7 @@
     "accessibility": {
         "about": "About",
         "createIssue": "Create Issue",
+        "submitSupportTicket": "Submit Support Ticket",
         "invokeProgressBar": "Invoke progress bar",
         "menu": "Menu",
         "mode": "Mode",
@@ -69,6 +70,7 @@
         "auto": "Auto",
         "back": "Back",
         "batch": "Batch Manager",
+        "beta": "Beta",
         "cancel": "Cancel",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
@@ -83,13 +85,17 @@
         "direction": "Direction",
         "ipAdapter": "IP Adapter",
         "t2iAdapter": "T2I Adapter",
+        "positivePrompt": "Positive Prompt",
+        "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",
@@ -135,7 +141,15 @@
         "red": "Red",
         "green": "Green",
         "blue": "Blue",
-        "alpha": "Alpha"
+        "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",
+        "enabled": "Enabled",
+        "disabled": "Disabled"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -151,6 +165,7 @@
         "balanced": "Balanced",
         "base": "Base",
         "beginEndStepPercent": "Begin / End Step Percentage",
+        "beginEndStepPercentShort": "Begin/End %",
         "bgth": "bg_th",
         "canny": "Canny",
         "cannyDescription": "Canny edge detection",
@@ -213,12 +228,17 @@
         "resize": "Resize",
         "resizeSimple": "Resize (Simple)",
         "resizeMode": "Resize Mode",
+        "ipAdapterMethod": "Method",
+        "full": "Full",
+        "style": "Style Only",
+        "composition": "Composition Only",
         "safe": "Safe",
         "saveControlImage": "Save Control Image",
-        "scribble": "scribble",
+        "scribble": "Scribble",
         "selectModel": "Select a model",
         "selectCLIPVisionModel": "Select a CLIP Vision model",
-        "setControlImageDimensions": "Set Control Image Dimensions To W/H",
+        "setControlImageDimensions": "Copy size to W/H (optimize for model)",
+        "setControlImageDimensionsForce": "Copy size to W/H (ignore model)",
         "showAdvanced": "Show Advanced",
         "small": "Small",
         "toggleControlNet": "Toggle this ControlNet",
@@ -244,7 +264,6 @@
         "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",
@@ -297,7 +316,13 @@
         "batchFailedToQueue": "Failed to Queue Batch",
         "graphQueued": "Graph queued",
         "graphFailedToQueue": "Failed to queue graph",
-        "openQueue": "Open Queue"
+        "openQueue": "Open Queue",
+        "prompts_one": "Prompt",
+        "prompts_other": "Prompts",
+        "iterations_one": "Iteration",
+        "iterations_other": "Iterations",
+        "generations_one": "Generation",
+        "generations_other": "Generations"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -573,6 +598,10 @@
         "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": {
@@ -748,10 +777,15 @@
         "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",
-        "collectionFieldType": "{{name}} Collection",
-        "collectionOrScalarFieldType": "{{name}} Collection|Scalar",
+        "singleFieldType": "{{name}} (Single)",
+        "collectionFieldType": "{{name}} (Collection)",
+        "collectionOrScalarFieldType": "{{name}} (Single or Collection)",
         "colorCodeEdges": "Color-Code Edges",
         "colorCodeEdgesHelp": "Color-code edges according to their connected fields",
         "connectionWouldCreateCycle": "Connection would create a cycle",
@@ -770,6 +804,8 @@
         "float": "Float",
         "fullyContainNodes": "Fully Contain Nodes to Select",
         "fullyContainNodesHelp": "Nodes must be fully inside the selection box to be selected",
+        "showEdgeLabels": "Show Edge Labels",
+        "showEdgeLabelsHelp": "Show labels on edges, indicating the connected nodes",
         "hideLegendNodes": "Hide Field Type Legend",
         "hideMinimapnodes": "Hide MiniMap",
         "inputMayOnlyHaveOneConnection": "Input may only have one connection",
@@ -851,6 +887,7 @@
         "versionUnknown": " Version Unknown",
         "workflow": "Workflow",
         "graph": "Graph",
+        "noGraph": "No Graph",
         "workflowAuthor": "Author",
         "workflowContact": "Contact",
         "workflowDescription": "Short Description",
@@ -863,7 +900,10 @@
         "zoomInNodes": "Zoom In",
         "zoomOutNodes": "Zoom Out",
         "betaDesc": "This invocation is in beta. Until it is stable, it may have breaking changes during app updates. We plan to support this invocation long-term.",
-        "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time."
+        "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time.",
+        "imageAccessError": "Unable to find image {{image_name}}, resetting to default",
+        "boardAccessError": "Unable to find board {{board_id}}, resetting to default",
+        "modelAccessError": "Unable to find model {{key}}, resetting to default"
     },
     "parameters": {
         "aspect": "Aspect",
@@ -886,6 +926,7 @@
         "denoisingStrength": "Denoising Strength",
         "downloadImage": "Download Image",
         "general": "General",
+        "globalSettings": "Global Settings",
         "height": "Height",
         "imageFit": "Fit Initial Image To Output Size",
         "images": "Images",
@@ -903,23 +944,37 @@
             "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"
+            "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"
+            }
         },
         "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",
@@ -1021,8 +1076,9 @@
     },
     "toast": {
         "addedToBoard": "Added to board",
-        "baseModelChangedCleared_one": "Base model changed, cleared or disabled {{count}} incompatible submodel",
-        "baseModelChangedCleared_other": "Base model changed, cleared or disabled {{count}} incompatible submodels",
+        "baseModelChanged": "Base Model Changed",
+        "baseModelChangedCleared_one": "Cleared or disabled {{count}} incompatible submodel",
+        "baseModelChangedCleared_other": "Cleared or disabled {{count}} incompatible submodels",
         "canceled": "Processing Canceled",
         "canvasCopiedClipboard": "Canvas Copied to Clipboard",
         "canvasDownloaded": "Canvas Downloaded",
@@ -1043,10 +1099,17 @@
         "metadataLoadFailed": "Failed to load metadata",
         "modelAddedSimple": "Model Added to Queue",
         "modelImportCanceled": "Model Import Canceled",
+        "outOfMemoryError": "Out of Memory Error",
+        "outOfMemoryErrorDesc": "Your current generation settings exceed system capacity. Please adjust your settings and try again.",
         "parameters": "Parameters",
-        "parameterNotSet": "{{parameter}} not set",
-        "parameterSet": "{{parameter}} set",
-        "parametersNotSet": "Parameters Not Set",
+        "parameterSet": "Parameter Recalled",
+        "parameterSetDesc": "Recalled {{parameter}}",
+        "parameterNotSet": "Parameter Recalled",
+        "parameterNotSetDesc": "Unable to recall {{parameter}}",
+        "parameterNotSetDescWithMessage": "Unable to recall {{parameter}}: {{message}}",
+        "parametersSet": "Parameters Recalled",
+        "parametersNotSet": "Parameters Not Recalled",
+        "errorCopied": "Error Copied",
         "problemCopyingCanvas": "Problem Copying Canvas",
         "problemCopyingCanvasDesc": "Unable to export base layer",
         "problemCopyingImage": "Unable to Copy Image",
@@ -1066,11 +1129,13 @@
         "sentToImageToImage": "Sent To Image To Image",
         "sentToUnifiedCanvas": "Sent to Unified Canvas",
         "serverError": "Server Error",
+        "sessionRef": "Session: {{sessionId}}",
         "setAsCanvasInitialImage": "Set as canvas initial image",
         "setCanvasInitialImage": "Set canvas initial image",
         "setControlImage": "Set as control image",
         "setInitialImage": "Set as initial image",
         "setNodeField": "Set as node field",
+        "somethingWentWrong": "Something Went Wrong",
         "uploadFailed": "Upload failed",
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "uploadInitialImage": "Upload Initial Image",
@@ -1176,6 +1241,10 @@
             "heading": "Resize Mode",
             "paragraphs": ["Method to fit Control Adapter's input image size to the output generation size."]
         },
+        "ipAdapterMethod": {
+            "heading": "Method",
+            "paragraphs": ["Method by which to apply the current IP Adapter."]
+        },
         "controlNetWeight": {
             "heading": "Weight",
             "paragraphs": [
@@ -1494,5 +1563,54 @@
     },
     "app": {
         "storeNotInitialized": "Store is not initialized"
+    },
+    "controlLayers": {
+        "deleteAll": "Delete All",
+        "addLayer": "Add Layer",
+        "moveToFront": "Move to Front",
+        "moveToBack": "Move to Back",
+        "moveForward": "Move Forward",
+        "moveBackward": "Move Backward",
+        "brushSize": "Brush Size",
+        "controlLayers": "Control Layers",
+        "globalMaskOpacity": "Global Mask Opacity",
+        "autoNegative": "Auto Negative",
+        "deletePrompt": "Delete Prompt",
+        "resetRegion": "Reset Region",
+        "debugLayers": "Debug Layers",
+        "rectangle": "Rectangle",
+        "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)"
+        }
     }
 }

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

@@ -25,7 +25,24 @@
         "areYouSure": "¿Estas seguro?",
         "batch": "Administrador de lotes",
         "modelManager": "Administrador de modelos",
-        "communityLabel": "Comunidad"
+        "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:"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -33,7 +50,9 @@
         "autoSwitchNewImages": "Auto seleccionar Imágenes nuevas",
         "loadMore": "Cargar más",
         "noImagesInGallery": "No hay imágenes para mostrar",
-        "deleteImage": "Eliminar Imagen",
+        "deleteImage_one": "Eliminar Imagen",
+        "deleteImage_many": "",
+        "deleteImage_other": "",
         "deleteImageBin": "Las imágenes eliminadas se enviarán a la papelera de tu sistema operativo.",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",
@@ -363,7 +382,7 @@
         "canvasMerged": "Lienzo consolidado",
         "sentToImageToImage": "Enviar hacia Imagen a Imagen",
         "sentToUnifiedCanvas": "Enviar hacia Lienzo Consolidado",
-        "parametersNotSet": "Parámetros no establecidos",
+        "parametersNotSet": "Parámetros no recuperados",
         "metadataLoadFailed": "Error al cargar metadatos",
         "serverError": "Error en el servidor",
         "canceled": "Procesando la cancelación",
@@ -371,7 +390,8 @@
         "uploadFailedInvalidUploadDesc": "Debe ser una sola imagen PNG o JPEG",
         "parameterSet": "Conjunto de parámetros",
         "parameterNotSet": "Parámetro no configurado",
-        "problemCopyingImage": "No se puede copiar la imagen"
+        "problemCopyingImage": "No se puede copiar la imagen",
+        "errorCopied": "Error al copiar"
     },
     "tooltip": {
         "feature": {
@@ -441,7 +461,13 @@
         "previousImage": "Imagen anterior",
         "nextImage": "Siguiente imagen",
         "showOptionsPanel": "Mostrar el panel lateral",
-        "menu": "Menú"
+        "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"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -454,5 +480,68 @@
         "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 unificata",
+        "unifiedCanvas": "Tela",
         "nodes": "Flussi di lavoro",
         "upload": "Caricamento",
         "load": "Carica",
@@ -74,7 +74,18 @@
         "file": "File",
         "toResolve": "Da risolvere",
         "add": "Aggiungi",
-        "loglevel": "Livello di log"
+        "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"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -82,7 +93,9 @@
         "autoSwitchNewImages": "Passaggio automatico a nuove immagini",
         "loadMore": "Carica altro",
         "noImagesInGallery": "Nessuna immagine da visualizzare",
-        "deleteImage": "Elimina l'immagine",
+        "deleteImage_one": "Elimina l'immagine",
+        "deleteImage_many": "Elimina {{count}} immagini",
+        "deleteImage_other": "Elimina {{count}} immagini",
         "deleteImagePermanent": "Le immagini eliminate non possono essere ripristinate.",
         "deleteImageBin": "Le immagini eliminate verranno spostate nel cestino del tuo sistema operativo.",
         "assets": "Risorse",
@@ -178,8 +191,8 @@
             "desc": "Mostra le informazioni sui metadati dell'immagine corrente"
         },
         "sendToImageToImage": {
-            "title": "Invia a Immagine a Immagine",
-            "desc": "Invia l'immagine corrente a da Immagine a Immagine"
+            "title": "Invia a Generazione da immagine",
+            "desc": "Invia l'immagine corrente a Generazione da immagine"
         },
         "deleteImage": {
             "title": "Elimina immagine",
@@ -332,6 +345,10 @@
         "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": {
@@ -469,8 +486,8 @@
         "scaledHeight": "Altezza ridimensionata",
         "infillMethod": "Metodo di riempimento",
         "tileSize": "Dimensione piastrella",
-        "sendToImg2Img": "Invia a Immagine a Immagine",
-        "sendToUnifiedCanvas": "Invia a Tela Unificata",
+        "sendToImg2Img": "Invia a Generazione da immagine",
+        "sendToUnifiedCanvas": "Invia alla Tela",
         "downloadImage": "Scarica l'immagine",
         "usePrompt": "Usa Prompt",
         "useSeed": "Usa Seme",
@@ -506,13 +523,24 @@
             "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"
+            "missingFieldTemplate": "Modello di campo mancante",
+            "imageNotProcessedForControlAdapter": "L'immagine dell'adattatore di controllo #{{number}} non è stata elaborata",
+            "layer": {
+                "initialImageNoImageSelected": "Nessuna immagine iniziale selezionata",
+                "t2iAdapterIncompatibleDimensions": "L'adattatore T2I richiede che la dimensione dell'immagine sia un multiplo di {{multiple}}",
+                "controlAdapterNoModelSelected": "Nessun modello di Adattatore di Controllo selezionato",
+                "controlAdapterIncompatibleBaseModel": "Il modello base dell'adattatore di controllo non è compatibile",
+                "controlAdapterNoImageSelected": "Nessuna immagine dell'adattatore di controllo selezionata",
+                "controlAdapterImageNotProcessed": "Immagine dell'adattatore di controllo non elaborata",
+                "ipAdapterNoModelSelected": "Nessun adattatore IP selezionato",
+                "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
+                "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
+                "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
+                "rgNoRegion": "Nessuna regione selezionata"
+            }
         },
         "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"
@@ -532,7 +560,10 @@
         "infillMosaicMinColor": "Colore minimo",
         "infillMosaicMaxColor": "Colore massimo",
         "infillMosaicTileHeight": "Altezza piastrella",
-        "infillColorValue": "Colore di riempimento"
+        "infillColorValue": "Colore di riempimento",
+        "globalSettings": "Impostazioni globali",
+        "globalPositivePromptPlaceholder": "Prompt positivo globale",
+        "globalNegativePromptPlaceholder": "Prompt negativo globale"
     },
     "settings": {
         "models": "Modelli",
@@ -557,7 +588,7 @@
         "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 di Tela Unificata e ControlNet.",
+        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato della Tela e degli Adattatori di Controllo.",
         "intermediatesClearedFailed": "Problema con la cancellazione delle immagini intermedie",
         "clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
         "clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
@@ -573,8 +604,8 @@
         "imageCopied": "Immagine copiata",
         "imageNotLoadedDesc": "Impossibile trovare l'immagine",
         "canvasMerged": "Tela unita",
-        "sentToImageToImage": "Inviato a Immagine a Immagine",
-        "sentToUnifiedCanvas": "Inviato a Tela Unificata",
+        "sentToImageToImage": "Inviato a Generazione da immagine",
+        "sentToUnifiedCanvas": "Inviato alla Tela",
         "parametersNotSet": "Parametri non impostati",
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "serverError": "Errore del Server",
@@ -793,7 +824,7 @@
         "float": "In virgola mobile",
         "currentImageDescription": "Visualizza l'immagine corrente nell'editor dei nodi",
         "fieldTypesMustMatch": "I tipi di campo devono corrispondere",
-        "edge": "Bordo",
+        "edge": "Collegamento",
         "currentImage": "Immagine corrente",
         "integer": "Numero Intero",
         "inputMayOnlyHaveOneConnection": "L'ingresso può avere solo una connessione",
@@ -806,8 +837,8 @@
         "unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi",
         "addLinearView": "Aggiungi alla vista Lineare",
         "unknownErrorValidatingWorkflow": "Errore sconosciuto durante la convalida del flusso di lavoro",
-        "collectionFieldType": "{{name}} Raccolta",
-        "collectionOrScalarFieldType": "{{name}} Raccolta|Scalare",
+        "collectionFieldType": "{{name}} (Raccolta)",
+        "collectionOrScalarFieldType": "{{name}} (Singola o Raccolta)",
         "nodeVersion": "Versione Nodo",
         "inputFieldTypeParseError": "Impossibile analizzare il tipo di campo di input {{node}}.{{field}} ({{message}})",
         "unsupportedArrayItemType": "Tipo di elemento dell'array non supportato \"{{type}}\"",
@@ -843,7 +874,15 @@
         "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"
+        "graph": "Grafico",
+        "showEdgeLabelsHelp": "Mostra etichette sui collegamenti, che indicano i nodi collegati",
+        "showEdgeLabels": "Mostra le etichette del collegamento",
+        "cannotMixAndMatchCollectionItemTypes": "Impossibile combinare e abbinare i tipi di elementi della raccolta",
+        "noGraph": "Nessun grafico",
+        "missingNode": "Nodo di invocazione mancante",
+        "missingInvocationTemplate": "Modello di invocazione mancante",
+        "missingFieldTemplate": "Modello di campo mancante",
+        "singleFieldType": "{{name}} (Singola)"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -920,7 +959,7 @@
         "colorMapTileSize": "Dimensione piastrella",
         "mediapipeFaceDescription": "Rilevamento dei volti tramite Mediapipe",
         "hedDescription": "Rilevamento dei bordi nidificati olisticamente",
-        "setControlImageDimensions": "Imposta le dimensioni dell'immagine di controllo su L/A",
+        "setControlImageDimensions": "Copia le dimensioni in L/A (ottimizza per il modello)",
         "maxFaces": "Numero massimo di volti",
         "addT2IAdapter": "Aggiungi $t(common.t2iAdapter)",
         "addControlNet": "Aggiungi $t(common.controlNet)",
@@ -949,12 +988,17 @@
         "mediapipeFace": "Mediapipe Volto",
         "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
         "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
-        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision"
+        "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)"
     },
     "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",
@@ -991,7 +1035,7 @@
         "cancelBatchSucceeded": "Lotto annullato",
         "clearTooltip": "Annulla e cancella tutti gli elementi",
         "current": "Attuale",
-        "pauseTooltip": "Sospende l'elaborazione",
+        "pauseTooltip": "Sospendi l'elaborazione",
         "failed": "Falliti",
         "cancelItem": "Annulla l'elemento",
         "next": "Prossimo",
@@ -1009,7 +1053,16 @@
         "graphFailedToQueue": "Impossibile mettere in coda il grafico",
         "batchFieldValues": "Valori Campi Lotto",
         "time": "Tempo",
-        "openQueue": "Apri coda"
+        "openQueue": "Apri coda",
+        "iterations_one": "Iterazione",
+        "iterations_many": "Iterazioni",
+        "iterations_other": "Iterazioni",
+        "prompts_one": "Prompt",
+        "prompts_many": "Prompt",
+        "prompts_other": "Prompt",
+        "generations_one": "Generazione",
+        "generations_many": "Generazioni",
+        "generations_other": "Generazioni"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1392,6 +1445,12 @@
             "paragraphs": [
                 "La dimensione del bordo del passaggio di coerenza."
             ]
+        },
+        "ipAdapterMethod": {
+            "heading": "Metodo",
+            "paragraphs": [
+                "Metodo con cui applicare l'adattatore IP corrente."
+            ]
         }
     },
     "sdxl": {
@@ -1520,5 +1579,55 @@
         "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",
+        "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

@@ -90,7 +90,7 @@
         "problemDeletingImages": "画像の削除中に問題が発生",
         "drop": "ドロップ",
         "dropOrUpload": "$t(gallery.drop) またはアップロード",
-        "deleteImage": "画像を削除",
+        "deleteImage_other": "画像を削除",
         "deleteImageBin": "削除された画像はOSのゴミ箱に送られます。",
         "deleteImagePermanent": "削除された画像は復元できません。",
         "download": "ダウンロード",
@@ -570,7 +570,6 @@
         "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

@@ -82,7 +82,7 @@
         "drop": "드랍",
         "problemDeletingImages": "이미지 삭제 중 발생한 문제",
         "downloadSelection": "선택 항목 다운로드",
-        "deleteImage": "이미지 삭제",
+        "deleteImage_other": "이미지 삭제",
         "currentlyInUse": "이 이미지는 현재 다음 기능에서 사용되고 있습니다:",
         "dropOrUpload": "$t(gallery.drop) 또는 업로드",
         "copy": "복사",
@@ -505,7 +505,6 @@
         "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

@@ -6,7 +6,7 @@
         "settingsLabel": "Instellingen",
         "img2img": "Afbeelding naar afbeelding",
         "unifiedCanvas": "Centraal canvas",
-        "nodes": "Werkstroom-editor",
+        "nodes": "Werkstromen",
         "upload": "Upload",
         "load": "Laad",
         "statusDisconnected": "Niet verbonden",
@@ -34,7 +34,60 @@
         "controlNet": "ControlNet",
         "imageFailedToLoad": "Kan afbeelding niet laden",
         "learnMore": "Meer informatie",
-        "advanced": "Uitgebreid"
+        "advanced": "Uitgebreid",
+        "file": "Bestand",
+        "installed": "Geïnstalleerd",
+        "notInstalled": "Niet $t(common.installed)",
+        "simple": "Eenvoudig",
+        "somethingWentWrong": "Er ging iets mis",
+        "add": "Voeg toe",
+        "checkpoint": "Checkpoint",
+        "details": "Details",
+        "outputs": "Uitvoeren",
+        "save": "Bewaar",
+        "nextPage": "Volgende pagina",
+        "blue": "Blauw",
+        "alpha": "Alfa",
+        "red": "Rood",
+        "editor": "Editor",
+        "folder": "Map",
+        "format": "structuur",
+        "goTo": "Ga naar",
+        "template": "Sjabloon",
+        "input": "Invoer",
+        "loglevel": "Logboekniveau",
+        "safetensors": "Safetensors",
+        "saveAs": "Bewaar als",
+        "created": "Gemaakt",
+        "green": "Groen",
+        "tab": "Tab",
+        "positivePrompt": "Positieve prompt",
+        "negativePrompt": "Negatieve prompt",
+        "selected": "Geselecteerd",
+        "orderBy": "Sorteer op",
+        "prevPage": "Vorige pagina",
+        "beta": "Bèta",
+        "copyError": "$t(gallery.copy) Fout",
+        "toResolve": "Op te lossen",
+        "aboutDesc": "Gebruik je Invoke voor het werk? Kijk dan naar:",
+        "aboutHeading": "Creatieve macht voor jou",
+        "copy": "Kopieer",
+        "data": "Gegevens",
+        "or": "of",
+        "updated": "Bijgewerkt",
+        "outpaint": "outpainten",
+        "viewing": "Bekijken",
+        "viewingDesc": "Beoordeel afbeelding in een grote galerijweergave",
+        "editing": "Bewerken",
+        "editingDesc": "Bewerk op het canvas Stuurlagen",
+        "ai": "ai",
+        "inpaint": "inpainten",
+        "unknown": "Onbekend",
+        "delete": "Verwijder",
+        "direction": "Richting",
+        "error": "Fout",
+        "localSystem": "Lokaal systeem",
+        "unknownError": "Onbekende fout"
     },
     "gallery": {
         "galleryImageSize": "Afbeeldingsgrootte",
@@ -42,7 +95,8 @@
         "autoSwitchNewImages": "Wissel autom. naar nieuwe afbeeldingen",
         "loadMore": "Laad meer",
         "noImagesInGallery": "Geen afbeeldingen om te tonen",
-        "deleteImage": "Verwijder afbeelding",
+        "deleteImage_one": "Verwijder afbeelding",
+        "deleteImage_other": "",
         "deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
         "deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
         "assets": "Eigen onderdelen",
@@ -309,10 +363,41 @@
         "modelSyncFailed": "Synchronisatie modellen mislukt",
         "modelDeleteFailed": "Model kon niet verwijderd worden",
         "convertingModelBegin": "Model aan het converteren. Even geduld.",
-        "predictionType": "Soort voorspelling (voor Stable Diffusion 2.x-modellen en incidentele Stable Diffusion 1.x-modellen)",
+        "predictionType": "Soort voorspelling",
         "advanced": "Uitgebreid",
         "modelType": "Soort model",
-        "vaePrecision": "Nauwkeurigheid VAE"
+        "vaePrecision": "Nauwkeurigheid VAE",
+        "loraTriggerPhrases": "LoRA-triggerzinnen",
+        "urlOrLocalPathHelper": "URL's zouden moeten wijzen naar een los bestand. Lokale paden kunnen wijzen naar een los bestand of map voor een individueel Diffusers-model.",
+        "modelName": "Modelnaam",
+        "path": "Pad",
+        "triggerPhrases": "Triggerzinnen",
+        "typePhraseHere": "Typ zin hier in",
+        "useDefaultSettings": "Gebruik standaardinstellingen",
+        "modelImageDeleteFailed": "Fout bij verwijderen modelafbeelding",
+        "modelImageUpdated": "Modelafbeelding bijgewerkt",
+        "modelImageUpdateFailed": "Fout bij bijwerken modelafbeelding",
+        "noMatchingModels": "Geen overeenkomende modellen",
+        "scanPlaceholder": "Pad naar een lokale map",
+        "noModelsInstalled": "Geen modellen geïnstalleerd",
+        "noModelsInstalledDesc1": "Installeer modellen met de",
+        "noModelSelected": "Geen model geselecteerd",
+        "starterModels": "Beginnermodellen",
+        "textualInversions": "Tekstuele omkeringen",
+        "upcastAttention": "Upcast-aandacht",
+        "uploadImage": "Upload afbeelding",
+        "mainModelTriggerPhrases": "Triggerzinnen hoofdmodel",
+        "urlOrLocalPath": "URL of lokaal pad",
+        "scanFolderHelper": "De map zal recursief worden ingelezen voor modellen. Dit kan enige tijd in beslag nemen voor erg grote mappen.",
+        "simpleModelPlaceholder": "URL of pad naar een lokaal pad of Diffusers-map",
+        "modelSettings": "Modelinstellingen",
+        "pathToConfig": "Pad naar configuratie",
+        "prune": "Snoei",
+        "pruneTooltip": "Snoei voltooide importeringen uit wachtrij",
+        "repoVariant": "Repovariant",
+        "scanFolder": "Lees map in",
+        "scanResults": "Resultaten inlezen",
+        "source": "Bron"
     },
     "parameters": {
         "images": "Afbeeldingen",
@@ -352,13 +437,13 @@
         "copyImage": "Kopieer afbeelding",
         "denoisingStrength": "Sterkte ontruisen",
         "scheduler": "Planner",
-        "seamlessXAxis": "X-as",
-        "seamlessYAxis": "Y-as",
+        "seamlessXAxis": "Naadloze tegels in x-as",
+        "seamlessYAxis": "Naadloze tegels in y-as",
         "clipSkip": "Overslaan CLIP",
         "negativePromptPlaceholder": "Negatieve prompt",
         "controlNetControlMode": "Aansturingsmodus",
         "positivePromptPlaceholder": "Positieve prompt",
-        "maskBlur": "Vervaag",
+        "maskBlur": "Vervaging van masker",
         "invoke": {
             "noNodesInGraph": "Geen knooppunten in graaf",
             "noModelSelected": "Geen model ingesteld",
@@ -368,11 +453,25 @@
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} invoer ontbreekt",
             "noControlImageForControlAdapter": "Controle-adapter #{{number}} heeft geen controle-afbeelding",
             "noModelForControlAdapter": "Control-adapter #{{number}} heeft geen model ingesteld staan.",
-            "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is ongeldig in combinatie met het hoofdmodel.",
+            "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is niet compatibel met het hoofdmodel.",
             "systemDisconnected": "Systeem is niet verbonden",
             "missingNodeTemplate": "Knooppuntsjabloon ontbreekt",
             "missingFieldTemplate": "Veldsjabloon ontbreekt",
-            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan"
+            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan",
+            "layer": {
+                "initialImageNoImageSelected": "geen initiële afbeelding geselecteerd",
+                "controlAdapterNoModelSelected": "geen controle-adaptermodel geselecteerd",
+                "controlAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor controle-adapter",
+                "controlAdapterNoImageSelected": "geen afbeelding voor controle-adapter geselecteerd",
+                "controlAdapterImageNotProcessed": "Afbeelding voor controle-adapter niet verwerkt",
+                "ipAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor IP-adapter",
+                "ipAdapterNoImageSelected": "geen afbeelding voor IP-adapter geselecteerd",
+                "rgNoRegion": "geen gebied geselecteerd",
+                "rgNoPromptsOrIPAdapters": "geen tekstprompts of IP-adapters",
+                "t2iAdapterIncompatibleDimensions": "T2I-adapter vereist een afbeelding met afmetingen met een veelvoud van 64",
+                "ipAdapterNoModelSelected": "geen IP-adapter geselecteerd"
+            },
+            "imageNotProcessedForControlAdapter": "De afbeelding van controle-adapter #{{number}} is niet verwerkt"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Afbeelding is te groot om te vergroten met het x4-model. Gebruik hiervoor het x2-model",
@@ -382,9 +481,26 @@
         "useCpuNoise": "Gebruik CPU-ruis",
         "imageActions": "Afbeeldingshandeling",
         "iterations": "Iteraties",
-        "iterationsWithCount_one": "{{count}} iteratie",
-        "iterationsWithCount_other": "{{count}} iteraties",
-        "coherenceMode": "Modus"
+        "coherenceMode": "Modus",
+        "infillColorValue": "Vulkleur",
+        "remixImage": "Meng afbeelding opnieuw",
+        "setToOptimalSize": "Optimaliseer grootte voor het model",
+        "setToOptimalSizeTooSmall": "$t(parameters.setToOptimalSize) (is mogelijk te klein)",
+        "aspect": "Beeldverhouding",
+        "infillMosaicTileWidth": "Breedte tegel",
+        "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (is mogelijk te groot)",
+        "lockAspectRatio": "Zet beeldverhouding vast",
+        "infillMosaicTileHeight": "Hoogte tegel",
+        "globalNegativePromptPlaceholder": "Globale negatieve prompt",
+        "globalPositivePromptPlaceholder": "Globale positieve prompt",
+        "useSize": "Gebruik grootte",
+        "swapDimensions": "Wissel afmetingen om",
+        "globalSettings": "Globale instellingen",
+        "coherenceEdgeSize": "Randgrootte",
+        "coherenceMinDenoise": "Min. ontruising",
+        "infillMosaicMinColor": "Min. kleur",
+        "infillMosaicMaxColor": "Max. kleur",
+        "cfgRescaleMultiplier": "Vermenigvuldiger voor CFG-herschaling"
     },
     "settings": {
         "models": "Modellen",
@@ -411,7 +527,12 @@
         "intermediatesCleared_one": "{{count}} tussentijdse afbeelding gewist",
         "intermediatesCleared_other": "{{count}} tussentijdse afbeeldingen gewist",
         "clearIntermediatesDesc1": "Als je tussentijdse afbeeldingen wist, dan wordt de staat hersteld van je canvas en van ControlNet.",
-        "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen"
+        "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen",
+        "clearIntermediatesDisabled": "Wachtrij moet leeg zijn om tussentijdse afbeeldingen te kunnen leegmaken",
+        "enableInformationalPopovers": "Schakel informatieve hulpballonnen in",
+        "enableInvisibleWatermark": "Schakel onzichtbaar watermerk in",
+        "enableNSFWChecker": "Schakel NSFW-controle in",
+        "reloadingIn": "Opnieuw laden na"
     },
     "toast": {
         "uploadFailed": "Upload mislukt",
@@ -426,8 +547,8 @@
         "connected": "Verbonden met server",
         "canceled": "Verwerking geannuleerd",
         "uploadFailedInvalidUploadDesc": "Moet een enkele PNG- of JPEG-afbeelding zijn",
-        "parameterNotSet": "Parameter niet ingesteld",
-        "parameterSet": "Instellen parameters",
+        "parameterNotSet": "{{parameter}} niet ingesteld",
+        "parameterSet": "{{parameter}} ingesteld",
         "problemCopyingImage": "Kan Afbeelding Niet Kopiëren",
         "baseModelChangedCleared_one": "Basismodel is gewijzigd: {{count}} niet-compatibel submodel weggehaald of uitgeschakeld",
         "baseModelChangedCleared_other": "Basismodel is gewijzigd: {{count}} niet-compatibele submodellen weggehaald of uitgeschakeld",
@@ -444,11 +565,11 @@
         "maskSavedAssets": "Masker bewaard in Assets",
         "problemDownloadingCanvas": "Fout bij downloaden van canvas",
         "problemMergingCanvas": "Fout bij samenvoegen canvas",
-        "setCanvasInitialImage": "Ingesteld als initiële canvasafbeelding",
+        "setCanvasInitialImage": "Initiële canvasafbeelding ingesteld",
         "imageUploaded": "Afbeelding geüpload",
         "addedToBoard": "Toegevoegd aan bord",
         "workflowLoaded": "Werkstroom geladen",
-        "modelAddedSimple": "Model toegevoegd",
+        "modelAddedSimple": "Model toegevoegd aan wachtrij",
         "problemImportingMaskDesc": "Kan masker niet exporteren",
         "problemCopyingCanvas": "Fout bij kopiëren canvas",
         "problemSavingCanvas": "Fout bij bewaren canvas",
@@ -460,7 +581,18 @@
         "maskSentControlnetAssets": "Masker gestuurd naar ControlNet en Assets",
         "canvasSavedGallery": "Canvas bewaard in galerij",
         "imageUploadFailed": "Fout bij uploaden afbeelding",
-        "problemImportingMask": "Fout bij importeren masker"
+        "problemImportingMask": "Fout bij importeren masker",
+        "workflowDeleted": "Werkstroom verwijderd",
+        "invalidUpload": "Ongeldige upload",
+        "uploadInitialImage": "Initiële afbeelding uploaden",
+        "setAsCanvasInitialImage": "Ingesteld als initiële afbeelding voor canvas",
+        "problemRetrievingWorkflow": "Fout bij ophalen van werkstroom",
+        "parameters": "Parameters",
+        "modelImportCanceled": "Importeren model geannuleerd",
+        "problemDeletingWorkflow": "Fout bij verwijderen van werkstroom",
+        "prunedQueue": "Wachtrij gesnoeid",
+        "problemDownloadingImage": "Fout bij downloaden afbeelding",
+        "resetInitialImage": "Initiële afbeelding hersteld"
     },
     "tooltip": {
         "feature": {
@@ -534,7 +666,11 @@
         "showOptionsPanel": "Toon zijscherm",
         "menu": "Menu",
         "showGalleryPanel": "Toon deelscherm Galerij",
-        "loadMore": "Laad meer"
+        "loadMore": "Laad meer",
+        "about": "Over",
+        "mode": "Modus",
+        "resetUI": "$t(accessibility.reset) UI",
+        "createIssue": "Maak probleem aan"
     },
     "nodes": {
         "zoomOutNodes": "Uitzoomen",
@@ -548,7 +684,7 @@
         "loadWorkflow": "Laad werkstroom",
         "downloadWorkflow": "Download JSON van werkstroom",
         "scheduler": "Planner",
-        "missingTemplate": "Ontbrekende sjabloon",
+        "missingTemplate": "Ongeldig knooppunt: knooppunt {{node}} van het soort {{type}} heeft een ontbrekend sjabloon (niet geïnstalleerd?)",
         "workflowDescription": "Korte beschrijving",
         "versionUnknown": " Versie onbekend",
         "noNodeSelected": "Geen knooppunt gekozen",
@@ -564,7 +700,7 @@
         "integer": "Geheel getal",
         "nodeTemplate": "Sjabloon knooppunt",
         "nodeOpacity": "Dekking knooppunt",
-        "unableToLoadWorkflow": "Kan werkstroom niet valideren",
+        "unableToLoadWorkflow": "Fout bij laden werkstroom",
         "snapToGrid": "Lijn uit op raster",
         "noFieldsLinearview": "Geen velden toegevoegd aan lineaire weergave",
         "nodeSearch": "Zoek naar knooppunten",
@@ -615,11 +751,56 @@
         "unknownField": "Onbekend veld",
         "colorCodeEdges": "Kleurgecodeerde randen",
         "unknownNode": "Onbekend knooppunt",
-        "mismatchedVersion": "Heeft niet-overeenkomende versie",
+        "mismatchedVersion": "Ongeldig knooppunt: knooppunt {{node}} van het soort {{type}} heeft een niet-overeenkomende versie (probeer het bij te werken?)",
         "addNodeToolTip": "Voeg knooppunt toe (Shift+A, spatie)",
         "loadingNodes": "Bezig met laden van knooppunten...",
         "snapToGridHelp": "Lijn knooppunten uit op raster bij verplaatsing",
-        "workflowSettings": "Instellingen werkstroomeditor"
+        "workflowSettings": "Instellingen werkstroomeditor",
+        "addLinearView": "Voeg toe aan lineaire weergave",
+        "nodePack": "Knooppuntpakket",
+        "unknownInput": "Onbekende invoer: {{name}}",
+        "sourceNodeFieldDoesNotExist": "Ongeldige rand: bron-/uitvoerveld {{node}}.{{field}} bestaat niet",
+        "collectionFieldType": "Verzameling {{name}}",
+        "deletedInvalidEdge": "Ongeldige hoek {{source}} -> {{target}} verwijderd",
+        "graph": "Grafiek",
+        "targetNodeDoesNotExist": "Ongeldige rand: doel-/invoerknooppunt {{node}} bestaat niet",
+        "resetToDefaultValue": "Herstel naar standaardwaarden",
+        "editMode": "Bewerk in Werkstroom-editor",
+        "showEdgeLabels": "Toon randlabels",
+        "showEdgeLabelsHelp": "Toon labels aan randen, waarmee de verbonden knooppunten mee worden aangegeven",
+        "clearWorkflowDesc2": "Je huidige werkstroom heeft niet-bewaarde wijzigingen.",
+        "unableToParseFieldType": "fout bij bepalen soort veld",
+        "sourceNodeDoesNotExist": "Ongeldige rand: bron-/uitvoerknooppunt {{node}} bestaat niet",
+        "unsupportedArrayItemType": "niet-ondersteunde soort van het array-onderdeel \"{{type}}\"",
+        "targetNodeFieldDoesNotExist": "Ongeldige rand: doel-/invoerveld {{node}}.{{field}} bestaat niet",
+        "reorderLinearView": "Herorden lineaire weergave",
+        "newWorkflowDesc": "Een nieuwe werkstroom aanmaken?",
+        "collectionOrScalarFieldType": "Verzameling|scalair {{name}}",
+        "newWorkflow": "Nieuwe werkstroom",
+        "unknownErrorValidatingWorkflow": "Onbekende fout bij valideren werkstroom",
+        "unsupportedAnyOfLength": "te veel union-leden ({{count}})",
+        "unknownOutput": "Onbekende uitvoer: {{name}}",
+        "viewMode": "Gebruik in lineaire weergave",
+        "unableToExtractSchemaNameFromRef": "fout bij het extraheren van de schemanaam via de ref",
+        "unsupportedMismatchedUnion": "niet-overeenkomende soort CollectionOrScalar met basissoorten {{firstType}} en {{secondType}}",
+        "unknownNodeType": "Onbekend soort knooppunt",
+        "edit": "Bewerk",
+        "updateAllNodes": "Werk knooppunten bij",
+        "allNodesUpdated": "Alle knooppunten bijgewerkt",
+        "nodeVersion": "Knooppuntversie",
+        "newWorkflowDesc2": "Je huidige werkstroom heeft niet-bewaarde wijzigingen.",
+        "clearWorkflow": "Maak werkstroom leeg",
+        "clearWorkflowDesc": "Deze werkstroom leegmaken en met een nieuwe beginnen?",
+        "inputFieldTypeParseError": "Fout bij bepalen van het soort invoerveld {{node}}.{{field}} ({{message}})",
+        "outputFieldTypeParseError": "Fout bij het bepalen van het soort uitvoerveld {{node}}.{{field}} ({{message}})",
+        "unableToExtractEnumOptions": "fout bij extraheren enumeratie-opties",
+        "unknownFieldType": "Soort $t(nodes.unknownField): {{type}}",
+        "unableToGetWorkflowVersion": "Fout bij ophalen schemaversie van werkstroom",
+        "betaDesc": "Deze uitvoering is in bèta. Totdat deze stabiel is kunnen er wijzigingen voorkomen gedurende app-updates die zaken kapotmaken. We zijn van plan om deze uitvoering op lange termijn te gaan ondersteunen.",
+        "prototypeDesc": "Deze uitvoering is een prototype. Er kunnen wijzigingen voorkomen gedurende app-updates die zaken kapotmaken. Deze kunnen op een willekeurig moment verwijderd worden.",
+        "noFieldsViewMode": "Deze werkstroom heeft geen geselecteerde velden om te tonen. Bekijk de volledige werkstroom om de waarden te configureren.",
+        "unableToUpdateNodes_one": "Fout bij bijwerken van {{count}} knooppunt",
+        "unableToUpdateNodes_other": "Fout bij bijwerken van {{count}} knooppunten"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -692,9 +873,28 @@
         "canny": "Canny",
         "depthZoeDescription": "Genereer diepteblad via Zoe",
         "hedDescription": "Herkenning van holistisch-geneste randen",
-        "setControlImageDimensions": "Stel afmetingen controle-afbeelding in op B/H",
+        "setControlImageDimensions": "Kopieer grootte naar B/H (optimaliseer voor model)",
         "scribble": "Krabbel",
-        "maxFaces": "Max. gezichten"
+        "maxFaces": "Max. gezichten",
+        "dwOpenpose": "DW Openpose",
+        "depthAnything": "Depth Anything",
+        "base": "Basis",
+        "hands": "Handen",
+        "selectCLIPVisionModel": "Selecteer een CLIP Vision-model",
+        "modelSize": "Modelgrootte",
+        "small": "Klein",
+        "large": "Groot",
+        "resizeSimple": "Wijzig grootte (eenvoudig)",
+        "beginEndStepPercentShort": "Begin-/eind-%",
+        "depthAnythingDescription": "Genereren dieptekaart d.m.v. de techniek Depth Anything",
+        "face": "Gezicht",
+        "body": "Lichaam",
+        "dwOpenposeDescription": "Schatting menselijke pose d.m.v. DW Openpose",
+        "ipAdapterMethod": "Methode",
+        "full": "Volledig",
+        "style": "Alleen stijl",
+        "composition": "Alleen samenstelling",
+        "setControlImageDimensionsForce": "Kopieer grootte naar B/H (negeer model)"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -707,7 +907,10 @@
         "maxPrompts": "Max. prompts",
         "promptsWithCount_one": "{{count}} prompt",
         "promptsWithCount_other": "{{count}} prompts",
-        "dynamicPrompts": "Dynamische prompts"
+        "dynamicPrompts": "Dynamische prompts",
+        "showDynamicPrompts": "Toon dynamische prompts",
+        "loading": "Genereren van dynamische prompts...",
+        "promptsPreview": "Voorvertoning prompts"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -720,7 +923,7 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "De planner bepaalt hoe ruis per iteratie wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model."
+                "De planner gebruikt gedurende het genereringsproces."
             ],
             "heading": "Planner"
         },
@@ -807,8 +1010,8 @@
         },
         "clipSkip": {
             "paragraphs": [
-                "Kies hoeveel CLIP-modellagen je wilt overslaan.",
-                "Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen."
+                "Aantal over te slaan CLIP-modellagen.",
+                "Bepaalde modellen zijn beter geschikt met bepaalde Overslaan CLIP-instellingen."
             ],
             "heading": "Overslaan CLIP"
         },
@@ -939,7 +1142,6 @@
         "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",
@@ -993,17 +1195,26 @@
         "denoisingStrength": "Sterkte ontruising",
         "refinermodel": "Verfijningsmodel",
         "posAestheticScore": "Positieve esthetische score",
-        "concatPromptStyle": "Plak prompt- en stijltekst aan elkaar",
+        "concatPromptStyle": "Koppelen van prompt en stijl",
         "loading": "Bezig met laden...",
         "steps": "Stappen",
-        "posStylePrompt": "Positieve-stijlprompt"
+        "posStylePrompt": "Positieve-stijlprompt",
+        "freePromptStyle": "Handmatige stijlprompt",
+        "refinerSteps": "Aantal stappen verfijner"
     },
     "models": {
         "noMatchingModels": "Geen overeenkomend modellen",
         "loading": "bezig met laden",
         "noMatchingLoRAs": "Geen overeenkomende LoRA's",
         "noModelsAvailable": "Geen modellen beschikbaar",
-        "selectModel": "Kies een model"
+        "selectModel": "Kies een model",
+        "noLoRAsInstalled": "Geen LoRA's geïnstalleerd",
+        "noRefinerModelsInstalled": "Geen SDXL-verfijningsmodellen geïnstalleerd",
+        "defaultVAE": "Standaard-VAE",
+        "lora": "LoRA",
+        "esrganModel": "ESRGAN-model",
+        "addLora": "Voeg LoRA toe",
+        "concepts": "Concepten"
     },
     "boards": {
         "autoAddBoard": "Voeg automatisch bord toe",
@@ -1021,7 +1232,13 @@
         "downloadBoard": "Download bord",
         "changeBoard": "Wijzig bord",
         "loading": "Bezig met laden...",
-        "clearSearch": "Maak zoekopdracht leeg"
+        "clearSearch": "Maak zoekopdracht leeg",
+        "deleteBoard": "Verwijder bord",
+        "deleteBoardAndImages": "Verwijder bord en afbeeldingen",
+        "deleteBoardOnly": "Verwijder alleen bord",
+        "deletedBoardsCannotbeRestored": "Verwijderde borden kunnen niet worden hersteld",
+        "movingImagesToBoard_one": "Verplaatsen van {{count}} afbeelding naar bord:",
+        "movingImagesToBoard_other": "Verplaatsen van {{count}} afbeeldingen naar bord:"
     },
     "invocationCache": {
         "disable": "Schakel uit",
@@ -1038,5 +1255,39 @@
         "clear": "Wis",
         "maxCacheSize": "Max. grootte cache",
         "cacheSize": "Grootte cache"
+    },
+    "accordions": {
+        "generation": {
+            "title": "Genereren"
+        },
+        "image": {
+            "title": "Afbeelding"
+        },
+        "advanced": {
+            "title": "Geavanceerd",
+            "options": "$t(accordions.advanced.title) Opties"
+        },
+        "control": {
+            "title": "Besturing"
+        },
+        "compositing": {
+            "title": "Samenstellen",
+            "coherenceTab": "Coherentiefase",
+            "infillTab": "Invullen"
+        }
+    },
+    "hrf": {
+        "upscaleMethod": "Opschaalmethode",
+        "metadata": {
+            "strength": "Sterkte oplossing voor hoge resolutie",
+            "method": "Methode oplossing voor hoge resolutie",
+            "enabled": "Oplossing voor hoge resolutie ingeschakeld"
+        },
+        "hrf": "Oplossing voor hoge resolutie",
+        "enableHrf": "Schakel oplossing in voor hoge resolutie"
+    },
+    "prompt": {
+        "addPromptTrigger": "Voeg prompttrigger toe",
+        "compatibleEmbeddings": "Compatibele embeddings"
     }
 }

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

@@ -76,7 +76,18 @@
         "localSystem": "Локальная система",
         "aboutDesc": "Используя Invoke для работы? Проверьте это:",
         "add": "Добавить",
-        "loglevel": "Уровень логов"
+        "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"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -86,7 +97,9 @@
         "noImagesInGallery": "Изображений нет",
         "deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
         "deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
-        "deleteImage": "Удалить изображение",
+        "deleteImage_one": "Удалить изображение",
+        "deleteImage_few": "Удалить {{count}} изображения",
+        "deleteImage_many": "Удалить {{count}} изображений",
         "assets": "Ресурсы",
         "autoAssignBoardOnClick": "Авто-назначение доски по клику",
         "deleteSelection": "Удалить выделенное",
@@ -334,6 +347,10 @@
         "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": {
@@ -510,7 +527,8 @@
             "missingNodeTemplate": "Отсутствует шаблон узла",
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
-            "invoke": "Создать"
+            "invoke": "Создать",
+            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -521,9 +539,6 @@
         "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",
@@ -539,7 +554,10 @@
         "infillMosaicTileHeight": "Высота плиток",
         "infillMosaicMinColor": "Мин цвет",
         "infillMosaicMaxColor": "Макс цвет",
-        "infillColorValue": "Цвет заливки"
+        "infillColorValue": "Цвет заливки",
+        "globalSettings": "Глобальные настройки",
+        "globalNegativePromptPlaceholder": "Глобальный негативный запрос",
+        "globalPositivePromptPlaceholder": "Глобальный запрос"
     },
     "settings": {
         "models": "Модели",
@@ -704,7 +722,9 @@
         "coherenceModeBoxBlur": "коробчатое размытие",
         "discardCurrent": "Отбросить текущее",
         "invertBrushSizeScrollDirection": "Инвертировать прокрутку для размера кисти",
-        "initialFitImageSize": "Подогнать размер изображения при перебросе"
+        "initialFitImageSize": "Подогнать размер изображения при перебросе",
+        "hideBoundingBox": "Скрыть ограничительную рамку",
+        "showBoundingBox": "Показать ограничительную рамку"
     },
     "accessibility": {
         "uploadImage": "Загрузить изображение",
@@ -847,7 +867,10 @@
         "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."
+        "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"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -915,8 +938,8 @@
         "lineartAnime": "Контурный рисунок в стиле аниме",
         "mediapipeFaceDescription": "Обнаружение лиц с помощью Mediapipe",
         "hedDescription": "Целостное обнаружение границ",
-        "setControlImageDimensions": "Установите размеры контрольного изображения на Ш/В",
-        "scribble": "каракули",
+        "setControlImageDimensions": "Скопируйте размер в Ш/В (оптимизируйте для модели)",
+        "scribble": "Штрихи",
         "maxFaces": "Макс Лица",
         "mlsdDescription": "Минималистичный детектор отрезков линии",
         "resizeSimple": "Изменить размер (простой)",
@@ -931,7 +954,18 @@
         "small": "Маленький",
         "body": "Тело",
         "hands": "Руки",
-        "selectCLIPVisionModel": "Выбрать модель CLIP Vision"
+        "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)"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1310,6 +1344,12 @@
             "paragraphs": [
                 "Плавно укладывайте изображение вдоль вертикальной оси."
             ]
+        },
+        "ipAdapterMethod": {
+            "heading": "Метод",
+            "paragraphs": [
+                "Метод, с помощью которого применяется текущий IP-адаптер."
+            ]
         }
     },
     "metadata": {
@@ -1357,7 +1397,6 @@
         "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",
@@ -1473,7 +1512,11 @@
         "projectWorkflows": "Рабочие процессы проекта",
         "defaultWorkflows": "Стандартные рабочие процессы",
         "name": "Имя",
-        "noRecentWorkflows": "Нет последних рабочих процессов"
+        "noRecentWorkflows": "Нет последних рабочих процессов",
+        "loadWorkflow": "Рабочий процесс $t(common.load)",
+        "convertGraph": "Конвертировать график",
+        "loadFromGraph": "Загрузка рабочего процесса из графика",
+        "autoLayout": "Автоматическое расположение"
     },
     "hrf": {
         "enableHrf": "Включить исправление высокого разрешения",
@@ -1526,5 +1569,55 @@
         "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",
+        "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/tr.json

@@ -298,7 +298,8 @@
         "noImagesInGallery": "Gösterilecek Görsel Yok",
         "autoSwitchNewImages": "Yeni Görseli Biter Bitmez Gör",
         "currentlyInUse": "Bu görsel şurada kullanımda:",
-        "deleteImage": "Görseli Sil",
+        "deleteImage_one": "Görseli Sil",
+        "deleteImage_other": "",
         "loadMore": "Daha Getir",
         "setCurrentImage": "Çalışma Görseli Yap",
         "unableToLoad": "Galeri Yüklenemedi",

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？查看：",
+        "aboutDesc": "使用 Invoke 工作？来看看：",
         "add": "添加",
         "loglevel": "日志级别",
         "copy": "复制",
@@ -78,7 +78,7 @@
         "autoSwitchNewImages": "自动切换到新图像",
         "loadMore": "加载更多",
         "noImagesInGallery": "无图像可用于显示",
-        "deleteImage": "删除图片",
+        "deleteImage_other": "删除图片",
         "deleteImageBin": "被删除的图片会发送到你操作系统的回收站。",
         "deleteImagePermanent": "删除的图片无法被恢复。",
         "assets": "素材",
@@ -445,7 +445,6 @@
             "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",
@@ -853,7 +852,6 @@
         "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

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

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

@@ -21,10 +21,10 @@ 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';
-import Toaster from './Toaster';
 
 const DEFAULT_CONFIG = {};
 
@@ -46,6 +46,7 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
   useSocketIO();
   useGlobalModifiersInit();
   useGlobalHotkeys();
+  useGetOpenAPISchemaQuery();
 
   const { dropzone, isHandlingUpload, setIsHandlingUpload } = useFullscreenDropzone();
 
@@ -94,7 +95,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
       <DeleteImageModal />
       <ChangeBoardModal />
       <DynamicPromptsModal />
-      <Toaster />
       <PreselectedImage selectedImage={selectedImage} />
     </ErrorBoundary>
   );

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

@@ -1,5 +1,8 @@
-import { Button, Flex, Heading, Link, Text, useToast } from '@invoke-ai/ui-library';
+import { Button, Flex, Heading, Image, Link, Text } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { toast } from 'features/toast/toast';
 import newGithubIssueUrl from 'new-github-issue-url';
+import InvokeLogoYellow from 'public/assets/images/invoke-symbol-ylw-lrg.svg';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold, PiArrowSquareOutBold, PiCopyBold } from 'react-icons/pi';
@@ -11,31 +14,39 @@ type Props = {
 };
 
 const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
-  const toast = useToast();
   const { t } = useTranslation();
+  const isLocal = useAppSelector((s) => s.config.isLocal);
 
   const handleCopy = useCallback(() => {
     const text = JSON.stringify(serializeError(error), null, 2);
     navigator.clipboard.writeText(`\`\`\`\n${text}\n\`\`\``);
     toast({
-      title: 'Error Copied',
+      id: 'ERROR_COPIED',
+      title: t('toast.errorCopied'),
     });
-  }, [error, toast]);
+  }, [error, t]);
 
-  const url = useMemo(
-    () =>
-      newGithubIssueUrl({
+  const url = useMemo(() => {
+    if (isLocal) {
+      return newGithubIssueUrl({
         user: 'invoke-ai',
         repo: 'InvokeAI',
         template: 'BUG_REPORT.yml',
         title: `[bug]: ${error.name}: ${error.message}`,
-      }),
-    [error.message, error.name]
-  );
+      });
+    } else {
+      return 'https://support.invoke.ai/support/tickets/new';
+    }
+  }, [error.message, error.name, isLocal]);
+
   return (
     <Flex layerStyle="body" w="100vw" h="100vh" alignItems="center" justifyContent="center" p={4}>
       <Flex layerStyle="first" flexDir="column" borderRadius="base" justifyContent="center" gap={8} p={16}>
-        <Heading>{t('common.somethingWentWrong')}</Heading>
+        <Flex alignItems="center" gap="2">
+          <Image src={InvokeLogoYellow} alt="invoke-logo" w="24px" h="24px" minW="24px" minH="24px" userSelect="none" />
+          <Heading fontSize="2xl">{t('common.somethingWentWrong')}</Heading>
+        </Flex>
+
         <Flex
           layerStyle="second"
           px={8}
@@ -57,7 +68,9 @@ const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
             {t('common.copyError')}
           </Button>
           <Link href={url} isExternal>
-            <Button leftIcon={<PiArrowSquareOutBold />}>{t('accessibility.createIssue')}</Button>
+            <Button leftIcon={<PiArrowSquareOutBold />}>
+              {isLocal ? t('accessibility.createIssue') : t('accessibility.submitSupportTicket')}
+            </Button>
           </Link>
         </Flex>
       </Flex>

invokeai/frontend/web/src/app/components/Toaster.ts

@@ -1,44 +0,0 @@
-import { useToast } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
-import type { MakeToastArg } from 'features/system/util/makeToast';
-import { makeToast } from 'features/system/util/makeToast';
-import { memo, useCallback, useEffect } from 'react';
-
-/**
- * Logical component. Watches the toast queue and makes toasts when the queue is not empty.
- * @returns null
- */
-const Toaster = () => {
-  const dispatch = useAppDispatch();
-  const toastQueue = useAppSelector((s) => s.system.toastQueue);
-  const toast = useToast();
-  useEffect(() => {
-    toastQueue.forEach((t) => {
-      toast(t);
-    });
-    toastQueue.length > 0 && dispatch(clearToastQueue());
-  }, [dispatch, toast, toastQueue]);
-
-  return null;
-};
-
-/**
- * Returns a function that can be used to make a toast.
- * @example
- * const toaster = useAppToaster();
- * toaster('Hello world!');
- * toaster({ title: 'Hello world!', status: 'success' });
- * @returns A function that can be used to make a toast.
- * @see makeToast
- * @see MakeToastArg
- * @see UseToastOptions
- */
-export const useAppToaster = () => {
-  const dispatch = useAppDispatch();
-  const toaster = useCallback((arg: MakeToastArg) => dispatch(addToast(makeToast(arg))), [dispatch]);
-
-  return toaster;
-};
-
-export default memo(Toaster);

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

@@ -67,6 +67,8 @@ 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);
     }
 
@@ -75,6 +77,8 @@ 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 +20,14 @@ export type LoggerNamespace =
   | 'models'
   | 'config'
   | 'canvas'
-  | 'txt2img'
-  | 'img2img'
+  | 'generation'
   | 'nodes'
   | 'system'
   | 'socketio'
   | 'session'
   | 'queue'
-  | 'dnd';
+  | 'dnd'
+  | 'controlLayers';
 
 export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 

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

@@ -1,3 +1,6 @@
+/* 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

@@ -1,7 +1,6 @@
 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';
@@ -25,13 +24,6 @@ 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

@@ -16,6 +16,7 @@ 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';
@@ -31,7 +32,6 @@ 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';
@@ -41,12 +41,10 @@ import { addGeneratorProgressEventListener } from 'app/store/middleware/listener
 import { addGraphExecutionStateCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGraphExecutionStateComplete';
 import { addInvocationCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete';
 import { addInvocationErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError';
-import { addInvocationRetrievalErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationRetrievalError';
 import { addInvocationStartedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted';
 import { addModelInstallEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelInstall';
 import { addModelLoadEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelLoad';
 import { addSocketQueueItemStatusChangedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketQueueItemStatusChanged';
-import { addSessionRetrievalErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketSessionRetrievalError';
 import { addSocketSubscribedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketSubscribed';
 import { addSocketUnsubscribedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketUnsubscribed';
 import { addStagingAreaImageSavedListener } from 'app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved';
@@ -72,9 +70,6 @@ const startAppListening = listenerMiddleware.startListening as AppStartListening
 // Image uploaded
 addImageUploadedFulfilledListener(startAppListening);
 
-// Image selected
-addInitialImageSelectedListener(startAppListening);
-
 // Image deleted
 addRequestedSingleImageDeletionListener(startAppListening);
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
@@ -117,8 +112,6 @@ addSocketSubscribedEventListener(startAppListening);
 addSocketUnsubscribedEventListener(startAppListening);
 addModelLoadEventListener(startAppListening);
 addModelInstallEventListener(startAppListening);
-addSessionRetrievalErrorEventListener(startAppListening);
-addInvocationRetrievalErrorEventListener(startAppListening);
 addSocketQueueItemStatusChangedEventListener(startAppListening);
 addBulkDownloadListeners(startAppListening);
 
@@ -157,3 +150,4 @@ addUpscaleRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
+addControlAdapterPreprocessor(startAppListening);

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

@@ -8,7 +8,7 @@ import {
   resetCanvas,
   setInitialCanvasImage,
 } from 'features/canvas/store/canvasSlice';
-import { addToast } from 'features/system/store/systemSlice';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 
@@ -30,22 +30,20 @@ export const addCommitStagingAreaImageListener = (startAppListening: AppStartLis
         req.reset();
         if (canceled > 0) {
           log.debug(`Canceled ${canceled} canvas batches`);
-          dispatch(
-            addToast({
-              title: t('queue.cancelBatchSucceeded'),
-              status: 'success',
-            })
-          );
+          toast({
+            id: 'CANCEL_BATCH_SUCCEEDED',
+            title: t('queue.cancelBatchSucceeded'),
+            status: 'success',
+          });
         }
         dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
-        dispatch(
-          addToast({
-            title: t('queue.cancelBatchFailed'),
-            status: 'error',
-          })
-        );
+        toast({
+          id: 'CANCEL_BATCH_FAILED',
+          title: t('queue.cancelBatchFailed'),
+          status: 'error',
+        });
       }
     },
   });

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

@@ -1,8 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
-import { toast } from 'common/util/toast';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -16,18 +16,15 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
       const arg = action.meta.arg.originalArgs;
       logger('queue').debug({ enqueueResult: parseify(response) }, 'Batch enqueued');
 
-      if (!toast.isActive('batch-queued')) {
-        toast({
-          id: 'batch-queued',
-          title: t('queue.batchQueued'),
-          description: t('queue.batchQueuedDesc', {
-            count: response.enqueued,
-            direction: arg.prepend ? t('queue.front') : t('queue.back'),
-          }),
-          duration: 1000,
-          status: 'success',
-        });
-      }
+      toast({
+        id: 'QUEUE_BATCH_SUCCEEDED',
+        title: t('queue.batchQueued'),
+        status: 'success',
+        description: t('queue.batchQueuedDesc', {
+          count: response.enqueued,
+          direction: arg.prepend ? t('queue.front') : t('queue.back'),
+        }),
+      });
     },
   });
 
@@ -40,9 +37,10 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
 
       if (!response) {
         toast({
+          id: 'QUEUE_BATCH_FAILED',
           title: t('queue.batchFailedToQueue'),
           status: 'error',
-          description: 'Unknown Error',
+          description: t('common.unknownError'),
         });
         logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
         return;
@@ -52,7 +50,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
       if (result.success) {
         result.data.data.detail.map((e) => {
           toast({
-            id: 'batch-failed-to-queue',
+            id: 'QUEUE_BATCH_FAILED',
             title: truncate(upperFirst(e.msg), { length: 128 }),
             status: 'error',
             description: truncate(
@@ -64,9 +62,10 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
         });
       } else if (response.status !== 403) {
         toast({
+          id: 'QUEUE_BATCH_FAILED',
           title: t('queue.batchFailedToQueue'),
-          description: t('common.unknownError'),
           status: 'error',
+          description: t('common.unknownError'),
         });
       }
       logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));

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) => {
@@ -14,19 +14,14 @@ 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 { generation, canvas, nodes, controlAdapters } = getState();
+      const { canvas, nodes, controlAdapters, controlLayers } = getState();
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(generation, canvas, nodes, controlAdapters, image_name);
-
-        if (imageUsage.isInitialImage && !wasInitialImageReset) {
-          dispatch(clearInitialImage());
-          wasInitialImageReset = true;
-        }
+        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
 
         if (imageUsage.isCanvasImage && !wasCanvasReset) {
           dispatch(resetCanvas());
@@ -42,6 +37,11 @@ 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/bulkDownload.tsx

@@ -1,8 +1,7 @@
-import type { UseToastOptions } from '@invoke-ai/ui-library';
 import { ExternalLink } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { toast } from 'common/util/toast';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 import {
@@ -28,7 +27,6 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
         // Show the response message if it exists, otherwise show the default message
         description: action.payload.response || t('gallery.bulkDownloadRequestedDesc'),
         duration: null,
-        isClosable: true,
       });
     },
   });
@@ -40,9 +38,9 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
       // There isn't any toast to update if we get this event.
       toast({
+        id: 'BULK_DOWNLOAD_REQUEST_FAILED',
         title: t('gallery.bulkDownloadRequestFailed'),
-        status: 'success',
-        isClosable: true,
+        status: 'error',
       });
     },
   });
@@ -65,7 +63,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
       // TODO(psyche): This URL may break in in some environments (e.g. Nvidia workbench) but we need to test it first
       const url = `/api/v1/images/download/${bulk_download_item_name}`;
 
-      const toastOptions: UseToastOptions = {
+      toast({
         id: bulk_download_item_name,
         title: t('gallery.bulkDownloadReady', 'Download ready'),
         status: 'success',
@@ -77,14 +75,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
           />
         ),
         duration: null,
-        isClosable: true,
-      };
-
-      if (toast.isActive(bulk_download_item_name)) {
-        toast.update(bulk_download_item_name, toastOptions);
-      } else {
-        toast(toastOptions);
-      }
+      });
     },
   });
 
@@ -95,20 +86,13 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
       const { bulk_download_item_name } = action.payload.data;
 
-      const toastOptions: UseToastOptions = {
+      toast({
         id: bulk_download_item_name,
         title: t('gallery.bulkDownloadFailed'),
         status: 'error',
         description: action.payload.data.error,
         duration: null,
-        isClosable: true,
-      };
-
-      if (toast.isActive(bulk_download_item_name)) {
-        toast.update(bulk_download_item_name, toastOptions);
-      } else {
-        toast(toastOptions);
-      }
+      });
     },
   });
 };

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

@@ -2,14 +2,14 @@ import { $logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { addToast } from 'features/system/store/systemSlice';
 import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 
 export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: canvasCopiedToClipboard,
-    effect: async (action, { dispatch, getState }) => {
+    effect: async (action, { getState }) => {
       const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
       const state = getState();
 
@@ -19,22 +19,20 @@ export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartLi
         copyBlobToClipboard(blob);
       } catch (err) {
         moduleLog.error(String(err));
-        dispatch(
-          addToast({
-            title: t('toast.problemCopyingCanvas'),
-            description: t('toast.problemCopyingCanvasDesc'),
-            status: 'error',
-          })
-        );
+        toast({
+          id: 'CANVAS_COPY_FAILED',
+          title: t('toast.problemCopyingCanvas'),
+          description: t('toast.problemCopyingCanvasDesc'),
+          status: 'error',
+        });
         return;
       }
 
-      dispatch(
-        addToast({
-          title: t('toast.canvasCopiedClipboard'),
-          status: 'success',
-        })
-      );
+      toast({
+        id: 'CANVAS_COPY_SUCCEEDED',
+        title: t('toast.canvasCopiedClipboard'),
+        status: 'success',
+      });
     },
   });
 };

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

@@ -3,13 +3,13 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
 import { downloadBlob } from 'features/canvas/util/downloadBlob';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
-import { addToast } from 'features/system/store/systemSlice';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 
 export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: canvasDownloadedAsImage,
-    effect: async (action, { dispatch, getState }) => {
+    effect: async (action, { getState }) => {
       const moduleLog = $logger.get().child({ namespace: 'canvasSavedToGalleryListener' });
       const state = getState();
 
@@ -18,18 +18,17 @@ export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartLi
         blob = await getBaseLayerBlob(state);
       } catch (err) {
         moduleLog.error(String(err));
-        dispatch(
-          addToast({
-            title: t('toast.problemDownloadingCanvas'),
-            description: t('toast.problemDownloadingCanvasDesc'),
-            status: 'error',
-          })
-        );
+        toast({
+          id: 'CANVAS_DOWNLOAD_FAILED',
+          title: t('toast.problemDownloadingCanvas'),
+          description: t('toast.problemDownloadingCanvasDesc'),
+          status: 'error',
+        });
         return;
       }
 
       downloadBlob(blob, 'canvas.png');
-      dispatch(addToast({ title: t('toast.canvasDownloaded'), status: 'success' }));
+      toast({ id: 'CANVAS_DOWNLOAD_SUCCEEDED', title: t('toast.canvasDownloaded'), status: 'success' });
     },
   });
 };

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

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { canvasImageToControlAdapter } from 'features/canvas/store/actions';
 import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
 import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
-import { addToast } from 'features/system/store/systemSlice';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -20,13 +20,12 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
         blob = await getBaseLayerBlob(state, true);
       } catch (err) {
         log.error(String(err));
-        dispatch(
-          addToast({
-            title: t('toast.problemSavingCanvas'),
-            description: t('toast.problemSavingCanvasDesc'),
-            status: 'error',
-          })
-        );
+        toast({
+          id: 'PROBLEM_SAVING_CANVAS',
+          title: t('toast.problemSavingCanvas'),
+          description: t('toast.problemSavingCanvasDesc'),
+          status: 'error',
+        });
         return;
       }
 
@@ -43,7 +42,7 @@ export const addCanvasImageToControlNetListener = (startAppListening: AppStartLi
           crop_visible: false,
           postUploadAction: {
             type: 'TOAST',
-            toastOptions: { title: t('toast.canvasSentControlnetAssets') },
+            title: t('toast.canvasSentControlnetAssets'),
           },
         })
       ).unwrap();

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

@@ -2,7 +2,7 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { canvasMaskSavedToGallery } from 'features/canvas/store/actions';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
-import { addToast } from 'features/system/store/systemSlice';
+import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -29,13 +29,12 @@ export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartL
 
       if (!maskBlob) {
         log.error('Problem getting mask layer blob');
-        dispatch(
-          addToast({
-            title: t('toast.problemSavingMask'),
-            description: t('toast.problemSavingMaskDesc'),
-            status: 'error',
-          })
-        );
+        toast({
+          id: 'PROBLEM_SAVING_MASK',
+          title: t('toast.problemSavingMask'),
+          description: t('toast.problemSavingMaskDesc'),
+          status: 'error',
+        });
         return;
       }
 
@@ -52,7 +51,7 @@ export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartL
           crop_visible: true,
           postUploadAction: {
             type: 'TOAST',
-            toastOptions: { title: t('toast.maskSavedAssets') },
+            title: t('toast.maskSavedAssets'),
           },
         })
       );