diff --git a/README.md b/README.md
index ff06db8d21..f540e7be75 100644
--- a/README.md
+++ b/README.md
@@ -2,21 +2,102 @@
-# 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]
+
-[![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][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
+
+
+
+
+
+
+## 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 .
+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/
-
-
-
-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/)] [Discord Server] [Documentation and
- Tutorials]
- [Bug Reports]
- [Discussion,
- Ideas & Q&A]
- [Contributing]
-
-
-
-
-
-
-
-
-
-## 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/)
-
-
-### 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.
-
diff --git a/docs/features/CONFIGURATION.md b/docs/features/CONFIGURATION.md
index 41f7a3ced3..d6bfe44901 100644
--- a/docs/features/CONFIGURATION.md
+++ b/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.
diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py
index a49c910eeb..354a736a74 100644
--- a/invokeai/app/invocations/controlnet_image_processors.py
+++ b/invokeai/app/invocations/controlnet_image_processors.py
@@ -35,6 +35,7 @@ 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
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
@@ -44,14 +45,6 @@ from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
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",
-]
-
class ControlField(BaseModel):
image: ImageField = Field(description="The control image")
diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py
index a02d0a57ef..9c7264a9bb 100644
--- a/invokeai/app/invocations/metadata.py
+++ b/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__
diff --git a/invokeai/app/invocations/t2i_adapter.py b/invokeai/app/invocations/t2i_adapter.py
index e550a7b313..b22a089d3f 100644
--- a/invokeai/app/invocations/t2i_adapter.py
+++ b/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.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):
diff --git a/invokeai/app/util/controlnet_utils.py b/invokeai/app/util/controlnet_utils.py
index b3e2560211..fde8d52ee6 100644
--- a/invokeai/app/util/controlnet_utils.py
+++ b/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}'.")
diff --git a/invokeai/backend/image_util/hed.py b/invokeai/backend/image_util/hed.py
index 378e3b96e9..97706df8b9 100644
--- a/invokeai/backend/image_util/hed.py
+++ b/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
diff --git a/invokeai/backend/image_util/util.py b/invokeai/backend/image_util/util.py
index 7cfe0ad1a5..f704f068e3 100644
--- a/invokeai/backend/image_util/util.py
+++ b/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
@@ -153,10 +154,13 @@ def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.n
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 +170,36 @@ 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:
diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py
index bf21a7fe7b..8f33e4b49f 100644
--- a/invokeai/backend/model_manager/probe.py
+++ b/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",
diff --git a/invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml b/invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml
new file mode 100644
index 0000000000..eea5c15a49
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/invokeai/version/invokeai_version.py b/invokeai/version/invokeai_version.py
index caf00b543f..0c1d77bc3d 100644
--- a/invokeai/version/invokeai_version.py
+++ b/invokeai/version/invokeai_version.py
@@ -1 +1 @@
-__version__ = "4.2.0a2"
+__version__ = "4.2.0a3"
diff --git a/tests/app/util/test_controlnet_utils.py b/tests/app/util/test_controlnet_utils.py
index 21662cce8d..9806fe7806 100644
--- a/tests/app/util/test_controlnet_utils.py
+++ b/tests/app/util/test_controlnet_utils.py
@@ -3,6 +3,7 @@ import pytest
from PIL import Image
from invokeai.app.util.controlnet_utils import prepare_control_image
+from invokeai.backend.image_util.util import nms
@pytest.mark.parametrize("num_channels", [1, 2, 3])
@@ -40,3 +41,10 @@ def test_prepare_control_image_num_channels_too_large(num_channels):
device="cpu",
do_classifier_free_guidance=False,
)
+
+
+@pytest.mark.parametrize("threshold,sigma", [(None, 1.0), (1, None)])
+def test_nms_invalid_options(threshold: None | int, sigma: None | float):
+ """Test that an exception is raised in nms(...) if only one of the `threshold` or `sigma` parameters are provided."""
+ with pytest.raises(ValueError):
+ nms(np.zeros((256, 256, 3), dtype=np.uint8), threshold, sigma)