Bug fix to Metadata To Model & Metadata To SDXL Model for when no metadata found.

- Added `Metadata to Loras`, `Metadata To SDXL LoRAs`.
- Added unet, clip and vae outputs to `Metadata To Model` & `Metadata To SDXL Model`
- Added default vae as an input to `Metadata to VAE`

docker/Dockerfile

@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.0
-ARG TORCHVISION_VERSION=0.16
+ARG TORCH_VERSION=2.1.2
+ARG TORCHVISION_VERSION=0.16.2
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
@@ -54,7 +54,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
         pip install -e ".[xformers]"; \
     else \
-        pip install -e "."; \
+        pip install $extra_index_url_arg -e "."; \
     fi
 
 # #### Build the Web UI ------------------------------------

docker/README.md

@@ -28,7 +28,7 @@ This is done via Docker Desktop preferences
 
 ### Configure Invoke environment
 
-1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
+1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
     a. the desired location of the InvokeAI runtime directory, or
     b. an existing, v3.0.0 compatible runtime directory.
 1. Execute `run.sh`

docker/run.sh

@@ -21,7 +21,7 @@ run() {
     printf "%s\n" "$build_args"
   fi
 
-  docker compose build $build_args
+  docker compose build $build_args $service_name
   unset build_args
 
   printf "%s\n" "starting service $service_name"

docs/features/CONTROLNET.md

@@ -94,6 +94,8 @@ A model that helps generate creative QR codes that still scan. Can also be used
 **Openpose**: 
 The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
 
+*Note:* The DWPose Processor has replaced the OpenPose processor in Invoke. Workflows and generations that relied on the OpenPose Processor will need to be updated to use the DWPose Processor instead.
+
 **Mediapipe Face**:
 
 The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.

docs/installation/020_INSTALL_MANUAL.md

@@ -230,13 +230,13 @@ manager, please follow these steps:
         === "local Webserver"
 
             ```bash
-            invokeai --web
+            invokeai-web
             ```
 
         === "Public Webserver"
 
             ```bash
-            invokeai --web --host 0.0.0.0
+            invokeai-web --host 0.0.0.0
             ```
 
         === "CLI"
@@ -402,4 +402,4 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
 Note that if you run into problems with the Conda installation, the InvokeAI
 staff will **not** be able to help you out. Caveat Emptor!
 
-[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
+[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939

docs/installation/040_INSTALL_DOCKER.md

@@ -69,7 +69,7 @@ a token and copy it, since you will need in for the next step.
 
 ### Setup
 
-Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
+Set up your environmnent variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
 
 Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
 

docs/nodes/communityNodes.md

@@ -42,6 +42,7 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
     + [Remote Image](#remote-image)
+    + [BriaAI Background Remove](#briaai-remove-background)
     + [Remove Background](#remove-background)    
     + [Retroize](#retroize)
     + [Size Stepper Nodes](#size-stepper-nodes)
@@ -434,6 +435,17 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
 
+--------------------------------
+
+### BriaAI Remove Background
+
+**Description**: Implements one click background removal with BriaAI's new version 1.4 model which seems to be be producing better results than any other previous background removal tool.
+
+**Node Link:** https://github.com/blessedcoolant/invoke_bria_rmbg
+
+**View**
+<img src="https://raw.githubusercontent.com/blessedcoolant/invoke_bria_rmbg/main/assets/preview.jpg" />
+
 --------------------------------
 ### Remove Background
 

docs/nodes/defaultNodes.md

@@ -81,7 +81,7 @@ their descriptions.
 | ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
 | ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
 | OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
-| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
+| DW Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
 | PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
 | Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
 | Random Integer                                                | Outputs a single random integer.                                                                                                                     |

installer/create_installer.sh

@@ -14,11 +14,19 @@ function is_bin_in_path {
 }
 
 function git_show {
-    git show -s --format='%h %s' $1
+    git show -s --format=oneline --abbrev-commit "$1" | cat
 }
 
+if [[ -v "VIRTUAL_ENV" ]]; then
+    # we can't just call 'deactivate' because this function is not exported
+    # to the environment of this script from the bash process that runs the script
+    echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
+    exit -1
+fi
+
 cd "$(dirname "$0")"
 
+echo
 echo -e "${BYELLOW}This script must be run from the installer directory!${RESET}"
 echo "The current working directory is $(pwd)"
 read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
@@ -32,13 +40,6 @@ if ! is_bin_in_path python && is_bin_in_path python3; then
     }
 fi
 
-if [[ -v "VIRTUAL_ENV" ]]; then
-    # we can't just call 'deactivate' because this function is not exported
-    # to the environment of this script from the bash process that runs the script
-    echo -e "${BRED}A virtual environment is activated. Please deactivate it before proceeding.${RESET}"
-    exit -1
-fi
-
 VERSION=$(
     cd ..
     python -c "from invokeai.version import __version__ as version; print(version)"
@@ -47,38 +48,9 @@ PATCH=""
 VERSION="v${VERSION}${PATCH}"
 
 echo -e "${BGREEN}HEAD${RESET}:"
-git_show
+git_show HEAD
 echo
 
-# ---------------------- FRONTEND ----------------------
-
-pushd ../invokeai/frontend/web >/dev/null
-echo
-echo "Installing frontend dependencies..."
-echo
-pnpm i --frozen-lockfile
-echo
-echo "Building frontend..."
-echo
-pnpm build
-popd
-
-# ---------------------- BACKEND ----------------------
-
-echo
-echo "Building wheel..."
-echo
-
-# install the 'build' package in the user site packages, if needed
-# could be improved by using a temporary venv, but it's tiny and harmless
-if [[ $(python -c 'from importlib.util import find_spec; print(find_spec("build") is None)') == "True" ]]; then
-    pip install --user build
-fi
-
-rm -rf ../build
-
-python -m build --wheel --outdir dist/ ../.
-
 # ----------------------
 
 echo
@@ -97,16 +69,13 @@ done
 mkdir InvokeAI-Installer/lib
 cp lib/*.py InvokeAI-Installer/lib
 
-# Move the wheel
-mv dist/*.whl InvokeAI-Installer/lib/
-
 # Install scripts
 # Mac/Linux
 cp install.sh.in InvokeAI-Installer/install.sh
 chmod a+x InvokeAI-Installer/install.sh
 
 # Windows
-perl -p -e "s/^set INVOKEAI_VERSION=.*/set INVOKEAI_VERSION=$VERSION/" install.bat.in >InvokeAI-Installer/install.bat
+cp install.bat.in InvokeAI-Installer/install.bat
 cp WinLongPathsEnabled.reg InvokeAI-Installer/
 
 # Zip everything up

installer/install.bat.in

@@ -15,7 +15,6 @@ if "%1" == "use-cache" (
 @rem Config
 @rem The version in the next line is replaced by an up to date release number
 @rem when create_installer.sh is run. Change the release number there.
-set INVOKEAI_VERSION=latest
 set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
 set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting
 set PYTHON_URL=https://www.python.org/downloads/windows/

installer/lib/installer.py

@@ -11,7 +11,7 @@ import sys
 import venv
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Union
+from typing import Optional, Tuple
 
 SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
 INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
@@ -21,40 +21,20 @@ OS = platform.uname().system
 ARCH = platform.uname().machine
 VERSION = "latest"
 
-### Feature flags
-# Install the virtualenv into the runtime dir
-FF_VENV_IN_RUNTIME = True
-
-# Install the wheel packaged with the installer
-FF_USE_LOCAL_WHEEL = True
-
 
 class Installer:
     """
     Deploys an InvokeAI installation into a given path
     """
 
+    reqs: list[str] = INSTALLER_REQS
+
     def __init__(self) -> None:
-        self.reqs = INSTALLER_REQS
-        self.preflight()
         if os.getenv("VIRTUAL_ENV") is not None:
             print("A virtual environment is already activated. Please 'deactivate' before installation.")
             sys.exit(-1)
         self.bootstrap()
-
-    def preflight(self) -> None:
-        """
-        Preflight checks
-        """
-
-        # TODO
-        # verify python version
-        # on macOS verify XCode tools are present
-        # verify libmesa, libglx on linux
-        # check that the system arch is not i386 (?)
-        # check that the system has a GPU, and the type of GPU
-
-        pass
+        self.available_releases = get_github_releases()
 
     def mktemp_venv(self) -> TemporaryDirectory:
         """
@@ -78,12 +58,9 @@ class Installer:
 
         return venv_dir
 
-    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory:
+    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory | None:
         """
         Bootstrap the installer venv with packages required at install time
-
-        :return: path to the virtual environment directory that was bootstrapped
-        :rtype: TemporaryDirectory
         """
 
         print("Initializing the installer. This may take a minute - please wait...")
@@ -95,39 +72,27 @@ class Installer:
         cmd.extend(self.reqs)
 
         try:
-            res = subprocess.check_output(cmd).decode()
+            # upgrade pip to the latest version to avoid a confusing message
+            res = upgrade_pip(Path(venv_dir.name))
             if verbose:
                 print(res)
+
+            # run the install prerequisites installation
+            res = subprocess.check_output(cmd).decode()
+
+            if verbose:
+                print(res)
+
             return venv_dir
         except subprocess.CalledProcessError as e:
             print(e)
 
-    def app_venv(self, path: str = None):
+    def app_venv(self, venv_parent) -> Path:
         """
         Create a virtualenv for the InvokeAI installation
         """
 
-        # explicit venv location
-        # currently unused in normal operation
-        # useful for testing or special cases
-        if path is not None:
-            venv_dir = Path(path)
-
-        # experimental / testing
-        elif not FF_VENV_IN_RUNTIME:
-            if OS == "Windows":
-                venv_dir_parent = os.getenv("APPDATA", "~/AppData/Roaming")
-            elif OS == "Darwin":
-                # there is no environment variable on macOS to find this
-                # TODO: confirm this is working as expected
-                venv_dir_parent = "~/Library/Application Support"
-            elif OS == "Linux":
-                venv_dir_parent = os.getenv("XDG_DATA_DIR", "~/.local/share")
-            venv_dir = Path(venv_dir_parent).expanduser().resolve() / f"InvokeAI/{VERSION}/venv"
-
-        # stable / current
-        else:
-            venv_dir = self.dest / ".venv"
+        venv_dir = venv_parent / ".venv"
 
         # Prefer to copy python executables
         # so that updates to system python don't break InvokeAI
@@ -141,7 +106,7 @@ class Installer:
         return venv_dir
 
     def install(
-        self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
+        self, version=None, root: str = "~/invokeai", yes_to_all=False, find_links: Optional[Path] = None
     ) -> None:
         """
         Install the InvokeAI application into the given runtime path
@@ -158,15 +123,20 @@ class Installer:
 
         import messages
 
-        messages.welcome()
+        messages.welcome(self.available_releases)
 
-        default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
-        self.dest = default_path if yes_to_all else messages.dest_path(root)
+        version = messages.choose_version(self.available_releases)
+
+        auto_dest = Path(os.environ.get("INVOKEAI_ROOT", root)).expanduser().resolve()
+        destination = auto_dest if yes_to_all else messages.dest_path(root)
+        if destination is None:
+            print("Could not find or create the destination directory. Installation cancelled.")
+            sys.exit(0)
 
         # create the venv for the app
-        self.venv = self.app_venv()
+        self.venv = self.app_venv(venv_parent=destination)
 
-        self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
+        self.instance = InvokeAiInstance(runtime=destination, venv=self.venv, version=version)
 
         # install dependencies and the InvokeAI application
         (extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
@@ -190,7 +160,7 @@ class InvokeAiInstance:
     A single runtime directory *may* be shared by multiple virtual environments, though this isn't currently tested or supported.
     """
 
-    def __init__(self, runtime: Path, venv: Path, version: str) -> None:
+    def __init__(self, runtime: Path, venv: Path, version: str = "stable") -> None:
         self.runtime = runtime
         self.venv = venv
         self.pip = get_pip_from_venv(venv)
@@ -199,6 +169,7 @@ class InvokeAiInstance:
         set_sys_path(venv)
         os.environ["INVOKEAI_ROOT"] = str(self.runtime.expanduser().resolve())
         os.environ["VIRTUAL_ENV"] = str(self.venv.expanduser().resolve())
+        upgrade_pip(venv)
 
     def get(self) -> tuple[Path, Path]:
         """
@@ -212,54 +183,7 @@ class InvokeAiInstance:
 
     def install(self, extra_index_url=None, optional_modules=None, find_links=None):
         """
-        Install this instance, including dependencies and the app itself
-
-        :param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
-        :type extra_index_url: str
-        """
-
-        import messages
-
-        # install torch first to ensure the correct version gets installed.
-        # works with either source or wheel install with negligible impact on installation times.
-        messages.simple_banner("Installing PyTorch :fire:")
-        self.install_torch(extra_index_url, find_links)
-
-        messages.simple_banner("Installing the InvokeAI Application :art:")
-        self.install_app(extra_index_url, optional_modules, find_links)
-
-    def install_torch(self, extra_index_url=None, find_links=None):
-        """
-        Install PyTorch
-        """
-
-        from plumbum import FG, local
-
-        pip = local[self.pip]
-
-        (
-            pip[
-                "install",
-                "--require-virtualenv",
-                "numpy==1.26.3",  # choose versions that won't be uninstalled during phase 2
-                "urllib3~=1.26.0",
-                "requests~=2.28.0",
-                "torch==2.1.2",
-                "torchmetrics==0.11.4",
-                "torchvision==0.16.2",
-                "--force-reinstall",
-                "--find-links" if find_links is not None else None,
-                find_links,
-                "--extra-index-url" if extra_index_url is not None else None,
-                extra_index_url,
-            ]
-            & FG
-        )
-
-    def install_app(self, extra_index_url=None, optional_modules=None, find_links=None):
-        """
-        Install the application with pip.
-        Supports installation from PyPi or from a local source directory.
+        Install the package from PyPi.
 
         :param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
         :type extra_index_url: str
@@ -271,53 +195,52 @@ class InvokeAiInstance:
         :type find_links: Path
         """
 
-        ## this only applies to pypi installs; TODO actually use this
-        if self.version == "pre":
+        import messages
+
+        # not currently used, but may be useful for "install most recent version" option
+        if self.version == "prerelease":
             version = None
-            pre = "--pre"
+            pre_flag = "--pre"
+        elif self.version == "stable":
+            version = None
+            pre_flag = None
         else:
             version = self.version
-            pre = None
+            pre_flag = None
 
-        ## TODO: only local wheel will be installed as of now; support for --version arg is TODO
-        if FF_USE_LOCAL_WHEEL:
-            # if no wheel, try to do a source install before giving up
-            try:
-                src = str(next(Path(__file__).parent.glob("InvokeAI-*.whl")))
-            except StopIteration:
-                try:
-                    src = Path(__file__).parents[1].expanduser().resolve()
-                    # if the above directory contains one of these files, we'll do a source install
-                    next(src.glob("pyproject.toml"))
-                    next(src.glob("invokeai"))
-                except StopIteration:
-                    print("Unable to find a wheel or perform a source install. Giving up.")
+        src = "invokeai"
+        if optional_modules:
+            src += optional_modules
+        if version:
+            src += f"=={version}"
 
-        elif version == "source":
-            # this makes an assumption about the location of the installer package in the source tree
-            src = Path(__file__).parents[1].expanduser().resolve()
-        else:
-            # will install from PyPi
-            src = f"invokeai=={version}" if version is not None else "invokeai"
+        messages.simple_banner("Installing the InvokeAI Application :art:")
 
-        from plumbum import FG, local
+        from plumbum import FG, ProcessExecutionError, local  # type: ignore
 
         pip = local[self.pip]
 
-        (
-            pip[
-                "install",
-                "--require-virtualenv",
-                "--use-pep517",
-                str(src) + (optional_modules if optional_modules else ""),
-                "--find-links" if find_links is not None else None,
-                find_links,
-                "--extra-index-url" if extra_index_url is not None else None,
-                extra_index_url,
-                pre,
-            ]
-            & FG
-        )
+        pipeline = pip[
+            "install",
+            "--require-virtualenv",
+            "--force-reinstall",
+            "--use-pep517",
+            str(src),
+            "--find-links" if find_links is not None else None,
+            find_links,
+            "--extra-index-url" if extra_index_url is not None else None,
+            extra_index_url,
+            pre_flag,
+        ]
+
+        try:
+            _ = pipeline & FG
+        except ProcessExecutionError as e:
+            print(f"Error: {e}")
+            print(
+                "Could not install InvokeAI. Please try downloading the latest version of the installer and install again."
+            )
+            sys.exit(1)
 
     def configure(self):
         """
@@ -373,7 +296,6 @@ class InvokeAiInstance:
 
         ext = "bat" if OS == "Windows" else "sh"
 
-        # scripts = ['invoke', 'update']
         scripts = ["invoke"]
 
         for script in scripts:
@@ -408,6 +330,23 @@ def get_pip_from_venv(venv_path: Path) -> str:
     return str(venv_path.expanduser().resolve() / pip)
 
 
+def upgrade_pip(venv_path: Path) -> str | None:
+    """
+    Upgrade the pip executable in the given virtual environment
+    """
+
+    python = "Scripts\\python.exe" if OS == "Windows" else "bin/python"
+    python = str(venv_path.expanduser().resolve() / python)
+
+    try:
+        result = subprocess.check_output([python, "-m", "pip", "install", "--upgrade", "pip"]).decode()
+    except subprocess.CalledProcessError as e:
+        print(e)
+        result = None
+
+    return result
+
+
 def set_sys_path(venv_path: Path) -> None:
     """
     Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,
@@ -431,7 +370,43 @@ def set_sys_path(venv_path: Path) -> None:
     sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
 
 
-def get_torch_source() -> (Union[str, None], str):
+def get_github_releases() -> tuple[list, list] | None:
+    """
+    Query Github for published (pre-)release versions.
+    Return a tuple where the first element is a list of stable releases and the second element is a list of pre-releases.
+    Return None if the query fails for any reason.
+    """
+
+    import requests
+
+    ## get latest releases using github api
+    url = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+    releases, pre_releases = [], []
+    try:
+        res = requests.get(url)
+        res.raise_for_status()
+        tag_info = res.json()
+        for tag in tag_info:
+            if not tag["prerelease"]:
+                releases.append(tag["tag_name"].lstrip("v"))
+            else:
+                pre_releases.append(tag["tag_name"].lstrip("v"))
+    except requests.HTTPError as e:
+        print(f"Error: {e}")
+        print("Could not fetch version information from GitHub. Please check your network connection and try again.")
+        return
+    except Exception as e:
+        print(f"Error: {e}")
+        print("An unexpected error occurred while trying to fetch version information from GitHub. Please try again.")
+        return
+
+    releases.sort(reverse=True)
+    pre_releases.sort(reverse=True)
+
+    return releases, pre_releases
+
+
+def get_torch_source() -> Tuple[str | None, str | None]:
     """
     Determine the extra index URL for pip to use for torch installation.
     This depends on the OS and the graphics accelerator in use.
@@ -446,25 +421,26 @@ def get_torch_source() -> (Union[str, None], str):
     :rtype: list
     """
 
-    from messages import graphical_accelerator
+    from messages import select_gpu
 
-    # device can be one of: "cuda", "rocm", "cpu", "idk"
-    device = graphical_accelerator()
+    # device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
+    device = select_gpu()
 
     url = None
     optional_modules = "[onnx]"
     if OS == "Linux":
-        if device == "rocm":
+        if device.value == "rocm":
             url = "https://download.pytorch.org/whl/rocm5.6"
-        elif device == "cpu":
+        elif device.value == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 
-    if device == "cuda":
-        url = "https://download.pytorch.org/whl/cu121"
-        optional_modules = "[xformers,onnx-cuda]"
-    if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu121"
-        optional_modules = "[xformers,onnx-directml]"
+    elif OS == "Windows":
+        if device.value == "cuda":
+            url = "https://download.pytorch.org/whl/cu121"
+            optional_modules = "[xformers,onnx-cuda]"
+        if device.value == "cuda_and_dml":
+            url = "https://download.pytorch.org/whl/cu121"
+            optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 

installer/lib/messages.py

@@ -5,10 +5,11 @@ Installer user interaction
 
 import os
 import platform
+from enum import Enum
 from pathlib import Path
 
 from prompt_toolkit import HTML, prompt
-from prompt_toolkit.completion import PathCompleter
+from prompt_toolkit.completion import FuzzyWordCompleter, PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
 from rich.console import Console, Group, group
@@ -35,16 +36,26 @@ else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
 
-def welcome():
+def welcome(available_releases: tuple | None = None) -> None:
     @group()
     def text():
-        if (platform_specific := _platform_specific_help()) != "":
+        if (platform_specific := _platform_specific_help()) is not None:
             yield platform_specific
             yield ""
         yield Text.from_markup(
             "Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
             justify="center",
         )
+        if available_releases is not None:
+            latest_stable = available_releases[0][0]
+            last_pre = available_releases[1][0]
+            yield ""
+            yield Text.from_markup(
+                f"[red3]🠶[/] Latest stable release (recommended): [b bright_white]{latest_stable}", justify="center"
+            )
+            yield Text.from_markup(
+                f"[red3]🠶[/] Last published pre-release version: [b bright_white]{last_pre}", justify="center"
+            )
 
     console.rule()
     print(
@@ -61,19 +72,30 @@ def welcome():
     console.line()
 
 
-def confirm_install(dest: Path) -> bool:
-    if dest.exists():
-        print(f":exclamation: Directory {dest} already exists :exclamation:")
-        dest_confirmed = Confirm.ask(
-            ":stop_sign: (re)install in this location?",
-            default=False,
-        )
-    else:
-        print(f"InvokeAI will be installed in {dest}")
-        dest_confirmed = Confirm.ask("Use this location?", default=True)
+def choose_version(available_releases: tuple | None = None) -> str:
+    """
+    Prompt the user to choose an Invoke version to install
+    """
+
+    # short circuit if we couldn't get a version list
+    # still try to install the latest stable version
+    if available_releases is None:
+        return "stable"
+
+    console.print(":grey_question: [orange3]Please choose an Invoke version to install.")
+
+    choices = available_releases[0] + available_releases[1]
+
+    response = prompt(
+        message=f"   <Enter> to install the recommended release ({choices[0]}). <Tab> or type to pick a version: ",
+        complete_while_typing=True,
+        completer=FuzzyWordCompleter(choices),
+    )
+    console.print(f"   Version {choices[0] if response == '' else response} will be installed.")
+
     console.line()
 
-    return dest_confirmed
+    return "stable" if response == "" else response
 
 
 def user_wants_auto_configuration() -> bool:
@@ -109,7 +131,23 @@ def user_wants_auto_configuration() -> bool:
     return choice.lower().startswith("a")
 
 
-def dest_path(dest=None) -> Path:
+def confirm_install(dest: Path) -> bool:
+    if dest.exists():
+        print(f":stop_sign: Directory {dest} already exists!")
+        print("   Is this location correct?")
+        default = False
+    else:
+        print(f":file_folder: InvokeAI will be installed in {dest}")
+        default = True
+
+    dest_confirmed = Confirm.ask("   Please confirm:", default=default)
+
+    console.line()
+
+    return dest_confirmed
+
+
+def dest_path(dest=None) -> Path | None:
     """
     Prompt the user for the destination path and create the path
 
@@ -124,25 +162,21 @@ def dest_path(dest=None) -> Path:
     else:
         dest = Path.cwd().expanduser().resolve()
     prev_dest = init_path = dest
-
-    dest_confirmed = confirm_install(dest)
+    dest_confirmed = False
 
     while not dest_confirmed:
-        # if the given destination already exists, the starting point for browsing is its parent directory.
-        # the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
-        # if the destination dir does NOT exist, then the user must have changed their mind about the selection.
-        # since we can't read their mind, start browsing at Path.cwd().
-        browse_start = (prev_dest.parent if prev_dest.exists() else Path.cwd()).expanduser().resolve()
+        browse_start = (dest or Path.cwd()).expanduser().resolve()
 
         path_completer = PathCompleter(
             only_directories=True,
             expanduser=True,
-            get_paths=lambda: [browse_start],  # noqa: B023
+            get_paths=lambda: [str(browse_start)],  # noqa: B023
             # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
         )
 
         console.line()
-        console.print(f"[orange3]Please select the destination directory for the installation:[/] \\[{browse_start}]: ")
+
+        console.print(f":grey_question: [orange3]Please select the install destination:[/] \\[{browse_start}]: ")
         selected = prompt(
             ">>> ",
             complete_in_thread=True,
@@ -155,6 +189,7 @@ def dest_path(dest=None) -> Path:
         )
         prev_dest = dest
         dest = Path(selected)
+
         console.line()
 
         dest_confirmed = confirm_install(dest.expanduser().resolve())
@@ -182,41 +217,45 @@ def dest_path(dest=None) -> Path:
         console.rule("Goodbye!")
 
 
-def graphical_accelerator():
+class GpuType(Enum):
+    CUDA = "cuda"
+    CUDA_AND_DML = "cuda_and_dml"
+    ROCM = "rocm"
+    CPU = "cpu"
+    AUTODETECT = "autodetect"
+
+
+def select_gpu() -> GpuType:
     """
-    Prompt the user to select the graphical accelerator in their system
-    This does not validate user's choices (yet), but only offers choices
-    valid for the platform.
-    CUDA is the fallback.
-    We may be able to detect the GPU driver by shelling out to `modprobe` or `lspci`,
-    but this is not yet supported or reliable. Also, some users may have exotic preferences.
+    Prompt the user to select the GPU driver
     """
 
     if ARCH == "arm64" and OS != "Darwin":
         print(f"Only CPU acceleration is available on {ARCH} architecture. Proceeding with that.")
-        return "cpu"
+        return GpuType.CPU
 
     nvidia = (
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
-        "cuda",
+        GpuType.CUDA,
     )
     nvidia_with_dml = (
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
-        "cuda_and_dml",
+        GpuType.CUDA_AND_DML,
     )
     amd = (
         "an [gold1 b]AMD[/] GPU (using ROCm™)",
-        "rocm",
+        GpuType.ROCM,
     )
     cpu = (
-        "no compatible GPU, or specifically prefer to use the CPU",
-        "cpu",
+        "Do not install any GPU support, use CPU for generation (slow)",
+        GpuType.CPU,
     )
-    idk = (
+    autodetect = (
         "I'm not sure what to choose",
-        "idk",
+        GpuType.AUTODETECT,
     )
 
+    options = []
     if OS == "Windows":
         options = [nvidia, nvidia_with_dml, cpu]
     if OS == "Linux":
@@ -230,7 +269,7 @@ def graphical_accelerator():
         return options[0][1]
 
     # "I don't know" is always added the last option
-    options.append(idk)
+    options.append(autodetect)  # type: ignore
 
     options = {str(i): opt for i, opt in enumerate(options, 1)}
 
@@ -265,9 +304,9 @@ def graphical_accelerator():
         ),
     )
 
-    if options[choice][1] == "idk":
+    if options[choice][1] is GpuType.AUTODETECT:
         console.print(
-            "No problem. We will try to install a version that [i]should[/i] be compatible. :crossed_fingers:"
+            "No problem. We will install CUDA support first :crossed_fingers: If Invoke does not detect a GPU, please re-run the installer and select one of the other GPU types."
         )
 
     return options[choice][1]
@@ -291,7 +330,7 @@ def windows_long_paths_registry() -> None:
     """
 
     with open(str(Path(__file__).parent / "WinLongPathsEnabled.reg"), "r", encoding="utf-16le") as code:
-        syntax = Syntax(code.read(), line_numbers=True)
+        syntax = Syntax(code.read(), line_numbers=True, lexer="regedit")
 
     console.print(
         Panel(
@@ -301,7 +340,7 @@ def windows_long_paths_registry() -> None:
                         "We will now apply a registry fix to enable long paths on Windows. InvokeAI needs this to function correctly. We are asking your permission to modify the Windows Registry on your behalf.",
                         "",
                         "This is the change that will be applied:",
-                        syntax,
+                        str(syntax),
                     ]
                 )
             ),
@@ -340,7 +379,7 @@ def introduction() -> None:
     console.line(2)
 
 
-def _platform_specific_help() -> str:
+def _platform_specific_help() -> Text | None:
     if OS == "Darwin":
         text = Text.from_markup(
             """[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
@@ -354,5 +393,5 @@ def _platform_specific_help() -> str:
      [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
         )
     else:
-        text = ""
+        return
     return text

installer/templates/invoke.bat.in

@@ -15,7 +15,7 @@ echo 4. Download and install models
 echo 5. Change InvokeAI startup options
 echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
 echo 7. Open the developer console
-echo 8. Update InvokeAI
+echo 8. Update InvokeAI (DEPRECATED - please use the installer)
 echo 9. Run the InvokeAI image database maintenance script
 echo 10. Command-line help
 echo Q - Quit
@@ -52,8 +52,10 @@ IF /I "%choice%" == "1" (
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
 ) ELSE IF /I "%choice%" == "8" (
-   echo Running invokeai-update...
-   python -m invokeai.frontend.install.invokeai_update
+    echo UPDATING FROM WITHIN THE APP IS BEING DEPRECATED.
+    echo Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.
+    timeout 4
+    python -m invokeai.frontend.install.invokeai_update
 ) ELSE IF /I "%choice%" == "9" (
    echo Running the db maintenance script...
    python .venv\Scripts\invokeai-db-maintenance.exe
@@ -77,4 +79,3 @@ pause
 
 :ending
 exit /b
-

installer/templates/invoke.sh.in

@@ -90,7 +90,9 @@ do_choice() {
         ;;
     8)
         clear
-        printf "Update InvokeAI\n"
+        printf "UPDATING FROM WITHIN THE APP IS BEING DEPRECATED\n"
+        printf "Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.\n"
+        sleep 4
         python -m invokeai.frontend.install.invokeai_update
         ;;
     9)
@@ -122,7 +124,7 @@ do_dialog() {
         5 "Change InvokeAI startup options"
         6 "Re-run the configure script to fix a broken install or to complete a major upgrade"
         7 "Open the developer console"
-        8 "Update InvokeAI"
+        8 "Update InvokeAI (DEPRECATED - please use the installer)"
 	9 "Run the InvokeAI image database maintenance script"
 	10 "Command-line help"
     )

installer/templates/update.bat.in

@@ -1,72 +0,0 @@
-@echo off
-setlocal EnableExtensions EnableDelayedExpansion
-
-PUSHD "%~dp0"
-
-set INVOKE_AI_VERSION=latest
-set arg=%1
-if "%arg%" neq "" (
-   if "%arg:~0,2%" equ "/?" (
-       echo Usage: update.bat ^<release name or branch^>
-       echo Updates InvokeAI to use the indicated version of the code base.
-       echo Find the version or branch for the release you want, and pass it as the argument.
-       echo For example '.\update.bat v2.2.5' for release 2.2.5.
-       echo             '.\update.bat main' for the latest development version
-       echo.
-       echo If no argument provided then will install the most recent release, equivalent to
-       echo '.\update.bat latest'
-       exit /b
-   ) else (
-       set INVOKE_AI_VERSION=%arg%
-   )
-)
-
-set INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/!INVOKE_AI_VERSION!.zip"
-set INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/!INVOKE_AI_VERSION!/environments-and-requirements/requirements-base.txt
-set INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
-
-call curl -I "%INVOKE_AI_DEP%" -fs >.tmp.out
-if %errorlevel% neq 0 (
-    echo '!INVOKE_AI_VERSION!' is not a known branch name or tag. Please check the version and try again.
-    echo "Press any key to continue"
-    pause
-    exit /b
-)
-del .tmp.out
-
-echo This script will update InvokeAI and all its dependencies to !INVOKE_AI_SRC!.
-echo If you do not want to do this, press control-C now!
-pause
-
-call curl -L "%INVOKE_AI_DEP%" > environments-and-requirements/requirements-base.txt
-call curl -L "%INVOKE_AI_MODELS%" > configs/INITIAL_MODELS.yaml
-
-
-call .venv\Scripts\activate.bat
-call .venv\Scripts\python -mpip install -r requirements.txt
-if %errorlevel% neq 0 (
-   echo Installation of requirements failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
-   pause
-   exit /b
-)
-
-call .venv\Scripts\python -mpip install !INVOKE_AI_SRC!
-if %errorlevel% neq 0 (
-   echo Installation of InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
-   pause
-   exit /b
-)
-
-@rem call .venv\Scripts\invokeai-configure --root=.
-
-@rem if %errorlevel% neq 0 (
-@rem    echo Configuration InvokeAI failed. See https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting for suggestions.
-@rem    pause
-@rem    exit /b
-@rem )
-
-echo InvokeAI has been updated to '%INVOKE_AI_VERSION%'
-
-echo "Press any key to continue"
-pause
-endlocal

installer/templates/update.sh.in

@@ -1,58 +0,0 @@
-#!/usr/bin/env bash
-
-set -eu
-
-if [ $# -ge 1 ] && [ "${1:0:2}" == "-h" ]; then
-    echo "Usage: update.sh <release>"
-    echo "Updates InvokeAI to use the indicated version of the code base."
-    echo "Find the version or branch for the release you want, and pass it as the argument."
-    echo "For example: update.sh v2.2.5 for release 2.2.5."
-    echo "             update.sh main for the current development version."
-    echo ""
-    echo "If no argument provided then will install the version tagged with 'latest', equivalent to"
-    echo "update.sh latest"
-    exit -1
-fi
-
-INVOKE_AI_VERSION=${1:-latest}
-
-INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/$INVOKE_AI_VERSION.zip"
-INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/environments-and-requirements/requirements-base.txt
-INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
-
-# ensure we're in the correct folder in case user's CWD is somewhere else
-scriptdir=$(dirname "$0")
-cd "$scriptdir"
-
-function _err_exit {
-    if test "$1" -ne 0
-    then
-	echo "Something went wrong while installing InvokeAI and/or its requirements."
-	echo "Update cannot continue. Please report this error to https://github.com/invoke-ai/InvokeAI/issues"
-        echo -e "Error code $1; Error caught was '$2'"
-        read -p "Press any key to exit..."
-        exit
-    fi
-}
-
-if ! curl -I "$INVOKE_AI_DEP" -fs >/dev/null; then
-    echo \'$INVOKE_AI_VERSION\' is not a known branch name or tag. Please check the version and try again.
-    exit
-fi
-
-echo This script will update InvokeAI and all its dependencies to version \'$INVOKE_AI_VERSION\'.
-echo If you do not want to do this, press control-C now!
-read -p "Press any key to continue, or CTRL-C to exit..."
-
-curl -L "$INVOKE_AI_DEP" > environments-and-requirements/requirements-base.txt
-curl -L "$INVOKE_AI_MODELS" > configs/INITIAL_MODELS.yaml
-
-. .venv/bin/activate
-
-./.venv/bin/python -mpip install -r requirements.txt
-_err_exit $? "The pip program failed to install InvokeAI's requirements."
-
-./.venv/bin/python -mpip install $INVOKE_AI_SRC
-_err_exit $? "The pip program failed to install InvokeAI."
-
-echo InvokeAI updated to \'$INVOKE_AI_VERSION\'

invokeai/app/api/sockets.py

@@ -14,7 +14,7 @@ class SocketIO:
 
     def __init__(self, app: FastAPI):
         self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
-        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
+        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="/ws/socket.io")
         app.mount("/ws", self.__app)
 
         self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)

invokeai/app/invocations/adapters_linked.py

@@ -0,0 +1,131 @@
+from typing import Optional, Union
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.controlnet_image_processors import ControlField, ControlNetInvocation
+from invokeai.app.invocations.ip_adapter import IPAdapterField, IPAdapterInvocation
+from invokeai.app.invocations.t2i_adapter import T2IAdapterField, T2IAdapterInvocation
+from invokeai.app.shared.fields import FieldDescriptions
+
+
+def append_list(new_item, items, item_cls):
+    """Add an item to an exiting item or list of items then output as a list of items."""
+
+    result = []
+    if items is None or (isinstance(items, list) and len(items) == 0):
+        pass
+    elif isinstance(items, item_cls):
+        result.append(items)
+    elif isinstance(items, list) and all(isinstance(i, item_cls) for i in items):
+        result.extend(items)
+    else:
+        raise ValueError(f"Invalid adapter list format: {items}")
+
+    result.append(new_item)
+    return result
+
+
+@invocation_output("control_list_output")
+class ControlListOutput(BaseInvocationOutput):
+    # Outputs
+    control_list: list[ControlField] = OutputField(description=FieldDescriptions.control)
+
+
+@invocation(
+    "controlnet-linked",
+    title="ControlNet-Linked",
+    tags=["controlnet"],
+    category="controlnet",
+    version="1.1.0",
+)
+class ControlNetLinkedInvocation(ControlNetInvocation):
+    """Collects ControlNet info to pass to other nodes."""
+
+    control_list: Optional[Union[ControlField, list[ControlField]]] = InputField(
+        default=None,
+        title="ControlNet-List",
+        input=Input.Connection,
+        ui_order=0,
+    )
+
+    def invoke(self, context: InvocationContext) -> ControlListOutput:
+        # Call parent
+        output = super().invoke(context).control
+        # Append the control output to the input list
+        control_list = append_list(output, self.control_list, ControlField)
+        return ControlListOutput(control_list=control_list)
+
+
+@invocation_output("ip_adapter_list_output")
+class IPAdapterListOutput(BaseInvocationOutput):
+    # Outputs
+    ip_adapter_list: list[IPAdapterField] = OutputField(
+        description=FieldDescriptions.ip_adapter, title="IP-Adapter-List"
+    )
+
+
+@invocation(
+    "ip_adapter_linked",
+    title="IP-Adapter-Linked",
+    tags=["ip_adapter", "control"],
+    category="ip_adapter",
+    version="1.1.0",
+)
+class IPAdapterLinkedInvocation(IPAdapterInvocation):
+    """Collects IP-Adapter info to pass to other nodes."""
+
+    ip_adapter_list: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
+        description=FieldDescriptions.ip_adapter,
+        title="IP-Adapter-List",
+        default=None,
+        input=Input.Connection,
+        ui_order=0,
+    )
+
+    def invoke(self, context: InvocationContext) -> IPAdapterListOutput:
+        # Call parent
+        output = super().invoke(context).ip_adapter
+        # Append the control output to the input list
+        result = append_list(output, self.ip_adapter_list, IPAdapterField)
+        return IPAdapterListOutput(ip_adapter_list=result)
+
+
+@invocation_output("ip_adapters_output")
+class T2IAdapterListOutput(BaseInvocationOutput):
+    # Outputs
+    t2i_adapter_list: list[T2IAdapterField] = OutputField(
+        description=FieldDescriptions.t2i_adapter, title="T2I Adapter-List"
+    )
+
+
+@invocation(
+    "t2i_adapter_linked",
+    title="T2I-Adapter-Linked",
+    tags=["t2i_adapter", "control"],
+    category="t2i_adapter",
+    version="1.0.0",
+)
+class T2IAdapterLinkedInvocation(T2IAdapterInvocation):
+    """Collects T2I-Adapter info to pass to other nodes."""
+
+    t2i_adapter_list: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField(
+        description=FieldDescriptions.ip_adapter,
+        title="T2I-Adapter",
+        default=None,
+        input=Input.Connection,
+        ui_order=0,
+    )
+
+    def invoke(self, context: InvocationContext) -> T2IAdapterListOutput:
+        # Call parent
+        output = super().invoke(context).t2i_adapter
+        # Append the control output to the input list
+        t2i_adapter_list = append_list(output, self.t2i_adapter_list, T2IAdapterField)
+        return T2IAdapterListOutput(t2i_adapter_list=t2i_adapter_list)

invokeai/app/invocations/controlnet_image_processors.py

@@ -17,7 +17,6 @@ from controlnet_aux import (
     MidasDetector,
     MLSDdetector,
     NormalBaeDetector,
-    OpenposeDetector,
     PidiNetDetector,
     SamDetector,
     ZoeDetector,
@@ -31,6 +30,7 @@ from invokeai.app.invocations.util import validate_begin_end_step, validate_weig
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
+from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
@@ -276,31 +276,6 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
         return processed_image
 
 
-@invocation(
-    "openpose_image_processor",
-    title="Openpose Processor",
-    tags=["controlnet", "openpose", "pose"],
-    category="controlnet",
-    version="1.2.0",
-)
-class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
-    """Applies Openpose processing to image"""
-
-    hand_and_face: bool = InputField(default=False, description="Whether to use hands and face 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)
-
-    def run_processor(self, image):
-        openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = openpose_processor(
-            image,
-            detect_resolution=self.detect_resolution,
-            image_resolution=self.image_resolution,
-            hand_and_face=self.hand_and_face,
-        )
-        return processed_image
-
-
 @invocation(
     "midas_depth_image_processor",
     title="Midas Depth Processor",
@@ -624,7 +599,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
     offload: bool = InputField(default=False)
 
-    def run_processor(self, image):
+    def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
         depth_anything_detector.load_model(model_size=self.model_size)
 
@@ -633,3 +608,30 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
 
         processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
         return processed_image
+
+
+@invocation(
+    "dw_openpose_image_processor",
+    title="DW Openpose Image Processor",
+    tags=["controlnet", "dwpose", "openpose"],
+    category="controlnet",
+    version="1.0.0",
+)
+class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates an openpose pose from an image using DWPose"""
+
+    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)
+
+    def run_processor(self, image):
+        dw_openpose = DWOpenposeDetector()
+        processed_image = dw_openpose(
+            image,
+            draw_face=self.draw_face,
+            draw_hands=self.draw_hands,
+            draw_body=self.draw_body,
+            resolution=self.image_resolution,
+        )
+        return processed_image

invokeai/app/invocations/latent.py

@@ -860,9 +860,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
                 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()
+            torch.cuda.empty_cache()
+            if choose_torch_device() == torch.device("mps"):
+                mps.empty_cache()
 
             with torch.inference_mode():
                 # copied from diffusers pipeline
@@ -874,9 +874,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata):
 
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
-        # torch.cuda.empty_cache()
-        # if choose_torch_device() == torch.device("mps"):
-        #     mps.empty_cache()
+        torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
 
         image_dto = context.services.images.create(
             image=image,

invokeai/app/invocations/upscale.py

@@ -5,12 +5,12 @@ from typing import Literal
 import cv2
 import numpy as np
 import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from pydantic import ConfigDict
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+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
 

invokeai/app/services/images/images_default.py

@@ -154,7 +154,7 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Image record not found")
             raise
         except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image DTO")
+            self.__invoker.services.logger.error("Problem getting image metadata")
             raise e
 
     def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -54,6 +54,17 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 else None
             )
 
+            def stats_cleanup(graph_execution_state_id: str) -> None:
+                if profiler:
+                    profile_path = profiler.stop()
+                    stats_path = profile_path.with_suffix(".json")
+                    self.__invoker.services.performance_statistics.dump_stats(
+                        graph_execution_state_id=graph_execution_state_id, output_path=stats_path
+                    )
+                with suppress(GESStatsNotFoundError):
+                    self.__invoker.services.performance_statistics.log_stats(graph_execution_state_id)
+                    self.__invoker.services.performance_statistics.reset_stats(graph_execution_state_id)
+
             while not stop_event.is_set():
                 try:
                     queue_item = self.__invoker.services.queue.get()
@@ -156,8 +167,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     pass
 
                 except CanceledException:
-                    with suppress(GESStatsNotFoundError):
-                        self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
+                    stats_cleanup(graph_execution_state.id)
                     pass
 
                 except Exception as e:
@@ -182,8 +192,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         error_type=e.__class__.__name__,
                         error=error,
                     )
-                    with suppress(GESStatsNotFoundError):
-                        self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 # Check queue to see if this is canceled, and skip if so
@@ -215,21 +223,13 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             error=traceback.format_exc(),
                         )
                 elif is_complete:
-                    with suppress(GESStatsNotFoundError):
-                        self.__invoker.services.performance_statistics.log_stats(graph_execution_state.id)
-                        self.__invoker.services.events.emit_graph_execution_complete(
-                            queue_batch_id=queue_item.session_queue_batch_id,
-                            queue_item_id=queue_item.session_queue_item_id,
-                            queue_id=queue_item.session_queue_id,
-                            graph_execution_state_id=graph_execution_state.id,
-                        )
-                        if profiler:
-                            profile_path = profiler.stop()
-                            stats_path = profile_path.with_suffix(".json")
-                            self.__invoker.services.performance_statistics.dump_stats(
-                                graph_execution_state_id=graph_execution_state.id, output_path=stats_path
-                            )
-                        self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
+                    self.__invoker.services.events.emit_graph_execution_complete(
+                        queue_batch_id=queue_item.session_queue_batch_id,
+                        queue_item_id=queue_item.session_queue_item_id,
+                        queue_id=queue_item.session_queue_id,
+                        graph_execution_state_id=graph_execution_state.id,
+                    )
+                    stats_cleanup(graph_execution_state.id)
 
         except KeyboardInterrupt:
             pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -106,9 +106,9 @@ class InvocationStatsService(InvocationStatsServiceBase):
             del self._stats[graph_execution_state_id]
             del self._cache_stats[graph_execution_state_id]
         except KeyError as e:
-            msg = f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}."
-            logger.error(msg)
-            raise GESStatsNotFoundError(msg) from e
+            raise GESStatsNotFoundError(
+                f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}."
+            ) from e
 
     def get_stats(self, graph_execution_state_id: str) -> InvocationStatsSummary:
         graph_stats_summary = self._get_graph_summary(graph_execution_state_id)
@@ -136,9 +136,9 @@ class InvocationStatsService(InvocationStatsServiceBase):
         try:
             cache_stats = self._cache_stats[graph_execution_state_id]
         except KeyError as e:
-            msg = f"Attempted to get model cache statistics for unknown graph {graph_execution_state_id}: {e}."
-            logger.error(msg)
-            raise GESStatsNotFoundError(msg) from e
+            raise GESStatsNotFoundError(
+                f"Attempted to get model cache statistics for unknown graph {graph_execution_state_id}: {e}."
+            ) from e
 
         return ModelCacheStatsSummary(
             cache_hits=cache_stats.hits,
@@ -154,9 +154,9 @@ class InvocationStatsService(InvocationStatsServiceBase):
         try:
             graph_stats = self._stats[graph_execution_state_id]
         except KeyError as e:
-            msg = f"Attempted to get graph statistics for unknown graph {graph_execution_state_id}: {e}."
-            logger.error(msg)
-            raise GESStatsNotFoundError(msg) from e
+            raise GESStatsNotFoundError(
+                f"Attempted to get graph statistics for unknown graph {graph_execution_state_id}: {e}."
+            ) from e
 
         return graph_stats.get_graph_stats_summary(graph_execution_state_id)
 
@@ -164,8 +164,8 @@ class InvocationStatsService(InvocationStatsServiceBase):
         try:
             graph_stats = self._stats[graph_execution_state_id]
         except KeyError as e:
-            msg = f"Attempted to get node statistics for unknown graph {graph_execution_state_id}: {e}."
-            logger.error(msg)
-            raise GESStatsNotFoundError(msg) from e
+            raise GESStatsNotFoundError(
+                f"Attempted to get node statistics for unknown graph {graph_execution_state_id}: {e}."
+            ) from e
 
         return graph_stats.get_node_stats_summaries()

invokeai/app/services/shared/graph.py

@@ -2,7 +2,7 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, ConfigDict, field_validator, model_validator
@@ -141,6 +141,16 @@ def are_connections_compatible(
     return are_connection_types_compatible(from_node_field, to_node_field)
 
 
+T = TypeVar("T")
+
+
+def copydeep(obj: T) -> T:
+    """Deep-copies an object. If it is a pydantic model, use the model's copy method."""
+    if isinstance(obj, BaseModel):
+        return obj.model_copy(deep=True)
+    return copy.deepcopy(obj)
+
+
 class NodeAlreadyInGraphError(ValueError):
     pass
 
@@ -530,7 +540,7 @@ class Graph(BaseModel):
         except NodeNotFoundError:
             return False
 
-    def get_node(self, node_path: str) -> InvocationsUnion:
+    def get_node(self, node_path: str) -> BaseInvocation:
         """Gets a node from the graph using a node path."""
         # Materialized graphs may have nodes at the top level
         graph, node_id = self._get_graph_and_node(node_path)
@@ -881,7 +891,7 @@ class GraphExecutionState(BaseModel):
         # If next is still none, there's no next node, return None
         return next_node
 
-    def complete(self, node_id: str, output: InvocationOutputsUnion):
+    def complete(self, node_id: str, output: BaseInvocationOutput) -> None:
         """Marks a node as complete"""
 
         if node_id not in self.execution_graph.nodes:
@@ -1118,17 +1128,22 @@ class GraphExecutionState(BaseModel):
 
     def _prepare_inputs(self, node: BaseInvocation):
         input_edges = [e for e in self.execution_graph.edges if e.destination.node_id == node.id]
+        # Inputs must be deep-copied, else if a node mutates the object, other nodes that get the same input
+        # will see the mutation.
         if isinstance(node, CollectInvocation):
             output_collection = [
-                getattr(self.results[edge.source.node_id], edge.source.field)
+                copydeep(getattr(self.results[edge.source.node_id], edge.source.field))
                 for edge in input_edges
                 if edge.destination.field == "item"
             ]
             node.collection = output_collection
         else:
             for edge in input_edges:
-                output_value = getattr(self.results[edge.source.node_id], edge.source.field)
-                setattr(node, edge.destination.field, output_value)
+                setattr(
+                    node,
+                    edge.destination.field,
+                    copydeep(getattr(self.results[edge.source.node_id], edge.source.field)),
+                )
 
     # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
     def _is_edge_valid(self, edge: Edge) -> bool:

invokeai/backend/image_util/basicsr/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
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+
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+
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+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
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+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+
+   7. Disclaimer of Warranty. Unless required by applicable law or
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+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
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+
+   Copyright 2018-2022 BasicSR Authors
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

invokeai/backend/image_util/basicsr/__init__.py

@@ -0,0 +1,18 @@
+"""
+Adapted from https://github.com/XPixelGroup/BasicSR
+License: Apache-2.0
+
+As of Feb 2024, `basicsr` appears to be unmaintained. It imports a function from `torchvision` that is removed in
+`torchvision` 0.17. Here is the deprecation warning:
+
+    UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in
+    0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in
+    torchvision.transforms.v2.functional.
+
+As a result, a dependency on `basicsr` means we cannot keep our `torchvision` dependency up to date.
+
+Because we only rely on a single class `RRDBNet` from `basicsr`, we've copied the relevant code here and removed the
+dependency on `basicsr`.
+
+The code is almost unchanged, only a few type annotations have been added. The license is also copied.
+"""

invokeai/backend/image_util/basicsr/arch_util.py

@@ -0,0 +1,75 @@
+from typing import Type
+
+import torch
+from torch import nn as nn
+from torch.nn import init as init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+@torch.no_grad()
+def default_init_weights(
+    module_list: list[nn.Module] | nn.Module, scale: float = 1, bias_fill: float = 0, **kwargs
+) -> None:
+    """Initialize network weights.
+
+    Args:
+        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
+        scale (float): Scale initialized weights, especially for residual
+            blocks. Default: 1.
+        bias_fill (float): The value to fill bias. Default: 0
+        kwargs (dict): Other arguments for initialization function.
+    """
+    if not isinstance(module_list, list):
+        module_list = [module_list]
+    for module in module_list:
+        for m in module.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, _BatchNorm):
+                init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+
+
+def make_layer(basic_block: Type[nn.Module], num_basic_block: int, **kwarg) -> nn.Sequential:
+    """Make layers by stacking the same blocks.
+
+    Args:
+        basic_block (Type[nn.Module]): nn.Module class for basic block.
+        num_basic_block (int): number of blocks.
+
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+# TODO: may write a cpp file
+def pixel_unshuffle(x: torch.Tensor, scale: int) -> torch.Tensor:
+    """Pixel unshuffle.
+
+    Args:
+        x (Tensor): Input feature with shape (b, c, hh, hw).
+        scale (int): Downsample ratio.
+
+    Returns:
+        Tensor: the pixel unshuffled feature.
+    """
+    b, c, hh, hw = x.size()
+    out_channel = c * (scale**2)
+    assert hh % scale == 0 and hw % scale == 0
+    h = hh // scale
+    w = hw // scale
+    x_view = x.view(b, c, h, scale, w, scale)
+    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)

invokeai/backend/image_util/basicsr/rrdbnet_arch.py

@@ -0,0 +1,125 @@
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+from .arch_util import default_init_weights, make_layer, pixel_unshuffle
+
+
+class ResidualDenseBlock(nn.Module):
+    """Residual Dense Block.
+
+    Used in RRDB block in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat: int = 64, num_grow_ch: int = 32) -> None:
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        # Empirically, we use 0.2 to scale the residual for better performance
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    """Residual in Residual Dense Block.
+
+    Used in RRDB-Net in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat: int, num_grow_ch: int = 32) -> None:
+        super(RRDB, self).__init__()
+        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.rdb1(x)
+        out = self.rdb2(out)
+        out = self.rdb3(out)
+        # Empirically, we use 0.2 to scale the residual for better performance
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    """Networks consisting of Residual in Residual Dense Block, which is used
+    in ESRGAN.
+
+    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
+
+    We extend ESRGAN for scale x2 and scale x1.
+    Note: This is one option for scale 1, scale 2 in RRDBNet.
+    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
+    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64
+        num_block (int): Block number in the trunk network. Defaults: 23
+        num_grow_ch (int): Channels for each growth. Default: 32.
+    """
+
+    def __init__(
+        self,
+        num_in_ch: int,
+        num_out_ch: int,
+        scale: int = 4,
+        num_feat: int = 64,
+        num_block: int = 23,
+        num_grow_ch: int = 32,
+    ) -> None:
+        super(RRDBNet, self).__init__()
+        self.scale = scale
+        if scale == 2:
+            num_in_ch = num_in_ch * 4
+        elif scale == 1:
+            num_in_ch = num_in_ch * 16
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
+        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        # upsample
+        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.scale == 2:
+            feat = pixel_unshuffle(x, scale=2)
+        elif self.scale == 1:
+            feat = pixel_unshuffle(x, scale=4)
+        else:
+            feat = x
+        feat = self.conv_first(feat)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        # upsample
+        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")))
+        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out

invokeai/backend/image_util/dw_openpose/__init__.py

@@ -0,0 +1,81 @@
+import numpy as np
+import torch
+from controlnet_aux.util import resize_image
+from PIL import Image
+
+from invokeai.backend.image_util.dw_openpose.utils import draw_bodypose, draw_facepose, draw_handpose
+from invokeai.backend.image_util.dw_openpose.wholebody import Wholebody
+
+
+def draw_pose(pose, H, W, draw_face=True, draw_body=True, draw_hands=True, resolution=512):
+    bodies = pose["bodies"]
+    faces = pose["faces"]
+    hands = pose["hands"]
+    candidate = bodies["candidate"]
+    subset = bodies["subset"]
+    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
+
+    if draw_body:
+        canvas = draw_bodypose(canvas, candidate, subset)
+
+    if draw_hands:
+        canvas = draw_handpose(canvas, hands)
+
+    if draw_face:
+        canvas = draw_facepose(canvas, faces)
+
+    dwpose_image = resize_image(
+        canvas,
+        resolution,
+    )
+    dwpose_image = Image.fromarray(dwpose_image)
+
+    return dwpose_image
+
+
+class DWOpenposeDetector:
+    """
+    Code from the original implementation of the DW Openpose Detector.
+    Credits: https://github.com/IDEA-Research/DWPose
+    """
+
+    def __init__(self) -> None:
+        self.pose_estimation = Wholebody()
+
+    def __call__(
+        self, image: Image.Image, draw_face=False, draw_body=True, draw_hands=False, resolution=512
+    ) -> Image.Image:
+        np_image = np.array(image)
+        H, W, C = np_image.shape
+
+        with torch.no_grad():
+            candidate, subset = self.pose_estimation(np_image)
+            nums, keys, locs = candidate.shape
+            candidate[..., 0] /= float(W)
+            candidate[..., 1] /= float(H)
+            body = candidate[:, :18].copy()
+            body = body.reshape(nums * 18, locs)
+            score = subset[:, :18]
+            for i in range(len(score)):
+                for j in range(len(score[i])):
+                    if score[i][j] > 0.3:
+                        score[i][j] = int(18 * i + j)
+                    else:
+                        score[i][j] = -1
+
+            un_visible = subset < 0.3
+            candidate[un_visible] = -1
+
+            # foot = candidate[:, 18:24]
+
+            faces = candidate[:, 24:92]
+
+            hands = candidate[:, 92:113]
+            hands = np.vstack([hands, candidate[:, 113:]])
+
+            bodies = {"candidate": body, "subset": score}
+            pose = {"bodies": bodies, "hands": hands, "faces": faces}
+
+            return draw_pose(
+                pose, H, W, draw_face=draw_face, draw_hands=draw_hands, draw_body=draw_body, resolution=resolution
+            )

invokeai/backend/image_util/dw_openpose/onnxdet.py

@@ -0,0 +1,128 @@
+# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
+
+import cv2
+import numpy as np
+
+
+def nms(boxes, scores, nms_thr):
+    """Single class NMS implemented in Numpy."""
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= nms_thr)[0]
+        order = order[inds + 1]
+
+    return keep
+
+
+def multiclass_nms(boxes, scores, nms_thr, score_thr):
+    """Multiclass NMS implemented in Numpy. Class-aware version."""
+    final_dets = []
+    num_classes = scores.shape[1]
+    for cls_ind in range(num_classes):
+        cls_scores = scores[:, cls_ind]
+        valid_score_mask = cls_scores > score_thr
+        if valid_score_mask.sum() == 0:
+            continue
+        else:
+            valid_scores = cls_scores[valid_score_mask]
+            valid_boxes = boxes[valid_score_mask]
+            keep = nms(valid_boxes, valid_scores, nms_thr)
+            if len(keep) > 0:
+                cls_inds = np.ones((len(keep), 1)) * cls_ind
+                dets = np.concatenate([valid_boxes[keep], valid_scores[keep, None], cls_inds], 1)
+                final_dets.append(dets)
+    if len(final_dets) == 0:
+        return None
+    return np.concatenate(final_dets, 0)
+
+
+def demo_postprocess(outputs, img_size, p6=False):
+    grids = []
+    expanded_strides = []
+    strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
+
+    hsizes = [img_size[0] // stride for stride in strides]
+    wsizes = [img_size[1] // stride for stride in strides]
+
+    for hsize, wsize, stride in zip(hsizes, wsizes, strides, strict=False):
+        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
+        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+        grids.append(grid)
+        shape = grid.shape[:2]
+        expanded_strides.append(np.full((*shape, 1), stride))
+
+    grids = np.concatenate(grids, 1)
+    expanded_strides = np.concatenate(expanded_strides, 1)
+    outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
+    outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
+
+    return outputs
+
+
+def preprocess(img, input_size, swap=(2, 0, 1)):
+    if len(img.shape) == 3:
+        padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
+    else:
+        padded_img = np.ones(input_size, dtype=np.uint8) * 114
+
+    r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
+    resized_img = cv2.resize(
+        img,
+        (int(img.shape[1] * r), int(img.shape[0] * r)),
+        interpolation=cv2.INTER_LINEAR,
+    ).astype(np.uint8)
+    padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
+
+    padded_img = padded_img.transpose(swap)
+    padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
+    return padded_img, r
+
+
+def inference_detector(session, oriImg):
+    input_shape = (640, 640)
+    img, ratio = preprocess(oriImg, input_shape)
+
+    ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
+    output = session.run(None, ort_inputs)
+    predictions = demo_postprocess(output[0], input_shape)[0]
+
+    boxes = predictions[:, :4]
+    scores = predictions[:, 4:5] * predictions[:, 5:]
+
+    boxes_xyxy = np.ones_like(boxes)
+    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
+    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
+    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
+    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
+    boxes_xyxy /= ratio
+    dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
+    if dets is not None:
+        final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
+        isscore = final_scores > 0.3
+        iscat = final_cls_inds == 0
+        isbbox = [i and j for (i, j) in zip(isscore, iscat, strict=False)]
+        final_boxes = final_boxes[isbbox]
+    else:
+        final_boxes = np.array([])
+
+    return final_boxes

invokeai/backend/image_util/dw_openpose/onnxpose.py

@@ -0,0 +1,361 @@
+# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
+
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+
+def preprocess(
+    img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256)
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Do preprocessing for RTMPose model inference.
+
+    Args:
+        img (np.ndarray): Input image in shape.
+        input_size (tuple): Input image size in shape (w, h).
+
+    Returns:
+        tuple:
+        - resized_img (np.ndarray): Preprocessed image.
+        - center (np.ndarray): Center of image.
+        - scale (np.ndarray): Scale of image.
+    """
+    # get shape of image
+    img_shape = img.shape[:2]
+    out_img, out_center, out_scale = [], [], []
+    if len(out_bbox) == 0:
+        out_bbox = [[0, 0, img_shape[1], img_shape[0]]]
+    for i in range(len(out_bbox)):
+        x0 = out_bbox[i][0]
+        y0 = out_bbox[i][1]
+        x1 = out_bbox[i][2]
+        y1 = out_bbox[i][3]
+        bbox = np.array([x0, y0, x1, y1])
+
+        # get center and scale
+        center, scale = bbox_xyxy2cs(bbox, padding=1.25)
+
+        # do affine transformation
+        resized_img, scale = top_down_affine(input_size, scale, center, img)
+
+        # normalize image
+        mean = np.array([123.675, 116.28, 103.53])
+        std = np.array([58.395, 57.12, 57.375])
+        resized_img = (resized_img - mean) / std
+
+        out_img.append(resized_img)
+        out_center.append(center)
+        out_scale.append(scale)
+
+    return out_img, out_center, out_scale
+
+
+def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray:
+    """Inference RTMPose model.
+
+    Args:
+        sess (ort.InferenceSession): ONNXRuntime session.
+        img (np.ndarray): Input image in shape.
+
+    Returns:
+        outputs (np.ndarray): Output of RTMPose model.
+    """
+    all_out = []
+    # build input
+    for i in range(len(img)):
+        input = [img[i].transpose(2, 0, 1)]
+
+        # build output
+        sess_input = {sess.get_inputs()[0].name: input}
+        sess_output = []
+        for out in sess.get_outputs():
+            sess_output.append(out.name)
+
+        # run model
+        outputs = sess.run(sess_output, sess_input)
+        all_out.append(outputs)
+
+    return all_out
+
+
+def postprocess(
+    outputs: List[np.ndarray],
+    model_input_size: Tuple[int, int],
+    center: Tuple[int, int],
+    scale: Tuple[int, int],
+    simcc_split_ratio: float = 2.0,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Postprocess for RTMPose model output.
+
+    Args:
+        outputs (np.ndarray): Output of RTMPose model.
+        model_input_size (tuple): RTMPose model Input image size.
+        center (tuple): Center of bbox in shape (x, y).
+        scale (tuple): Scale of bbox in shape (w, h).
+        simcc_split_ratio (float): Split ratio of simcc.
+
+    Returns:
+        tuple:
+        - keypoints (np.ndarray): Rescaled keypoints.
+        - scores (np.ndarray): Model predict scores.
+    """
+    all_key = []
+    all_score = []
+    for i in range(len(outputs)):
+        # use simcc to decode
+        simcc_x, simcc_y = outputs[i]
+        keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio)
+
+        # rescale keypoints
+        keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2
+        all_key.append(keypoints[0])
+        all_score.append(scores[0])
+
+    return np.array(all_key), np.array(all_score)
+
+
+def bbox_xyxy2cs(bbox: np.ndarray, padding: float = 1.0) -> Tuple[np.ndarray, np.ndarray]:
+    """Transform the bbox format from (x,y,w,h) into (center, scale)
+
+    Args:
+        bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
+            as (left, top, right, bottom)
+        padding (float): BBox padding factor that will be multilied to scale.
+            Default: 1.0
+
+    Returns:
+        tuple: A tuple containing center and scale.
+        - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
+            (n, 2)
+        - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
+            (n, 2)
+    """
+    # convert single bbox from (4, ) to (1, 4)
+    dim = bbox.ndim
+    if dim == 1:
+        bbox = bbox[None, :]
+
+    # get bbox center and scale
+    x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
+    center = np.hstack([x1 + x2, y1 + y2]) * 0.5
+    scale = np.hstack([x2 - x1, y2 - y1]) * padding
+
+    if dim == 1:
+        center = center[0]
+        scale = scale[0]
+
+    return center, scale
+
+
+def _fix_aspect_ratio(bbox_scale: np.ndarray, aspect_ratio: float) -> np.ndarray:
+    """Extend the scale to match the given aspect ratio.
+
+    Args:
+        scale (np.ndarray): The image scale (w, h) in shape (2, )
+        aspect_ratio (float): The ratio of ``w/h``
+
+    Returns:
+        np.ndarray: The reshaped image scale in (2, )
+    """
+    w, h = np.hsplit(bbox_scale, [1])
+    bbox_scale = np.where(w > h * aspect_ratio, np.hstack([w, w / aspect_ratio]), np.hstack([h * aspect_ratio, h]))
+    return bbox_scale
+
+
+def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray:
+    """Rotate a point by an angle.
+
+    Args:
+        pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
+        angle_rad (float): rotation angle in radian
+
+    Returns:
+        np.ndarray: Rotated point in shape (2, )
+    """
+    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
+    rot_mat = np.array([[cs, -sn], [sn, cs]])
+    return rot_mat @ pt
+
+
+def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+    """To calculate the affine matrix, three pairs of points are required. This
+    function is used to get the 3rd point, given 2D points a & b.
+
+    The 3rd point is defined by rotating vector `a - b` by 90 degrees
+    anticlockwise, using b as the rotation center.
+
+    Args:
+        a (np.ndarray): The 1st point (x,y) in shape (2, )
+        b (np.ndarray): The 2nd point (x,y) in shape (2, )
+
+    Returns:
+        np.ndarray: The 3rd point.
+    """
+    direction = a - b
+    c = b + np.r_[-direction[1], direction[0]]
+    return c
+
+
+def get_warp_matrix(
+    center: np.ndarray,
+    scale: np.ndarray,
+    rot: float,
+    output_size: Tuple[int, int],
+    shift: Tuple[float, float] = (0.0, 0.0),
+    inv: bool = False,
+) -> np.ndarray:
+    """Calculate the affine transformation matrix that can warp the bbox area
+    in the input image to the output size.
+
+    Args:
+        center (np.ndarray[2, ]): Center of the bounding box (x, y).
+        scale (np.ndarray[2, ]): Scale of the bounding box
+            wrt [width, height].
+        rot (float): Rotation angle (degree).
+        output_size (np.ndarray[2, ] | list(2,)): Size of the
+            destination heatmaps.
+        shift (0-100%): Shift translation ratio wrt the width/height.
+            Default (0., 0.).
+        inv (bool): Option to inverse the affine transform direction.
+            (inv=False: src->dst or inv=True: dst->src)
+
+    Returns:
+        np.ndarray: A 2x3 transformation matrix
+    """
+    shift = np.array(shift)
+    src_w = scale[0]
+    dst_w = output_size[0]
+    dst_h = output_size[1]
+
+    # compute transformation matrix
+    rot_rad = np.deg2rad(rot)
+    src_dir = _rotate_point(np.array([0.0, src_w * -0.5]), rot_rad)
+    dst_dir = np.array([0.0, dst_w * -0.5])
+
+    # get four corners of the src rectangle in the original image
+    src = np.zeros((3, 2), dtype=np.float32)
+    src[0, :] = center + scale * shift
+    src[1, :] = center + src_dir + scale * shift
+    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
+
+    # get four corners of the dst rectangle in the input image
+    dst = np.zeros((3, 2), dtype=np.float32)
+    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
+    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
+    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
+
+    if inv:
+        warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
+    else:
+        warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    return warp_mat
+
+
+def top_down_affine(
+    input_size: dict, bbox_scale: dict, bbox_center: dict, img: np.ndarray
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Get the bbox image as the model input by affine transform.
+
+    Args:
+        input_size (dict): The input size of the model.
+        bbox_scale (dict): The bbox scale of the img.
+        bbox_center (dict): The bbox center of the img.
+        img (np.ndarray): The original image.
+
+    Returns:
+        tuple: A tuple containing center and scale.
+        - np.ndarray[float32]: img after affine transform.
+        - np.ndarray[float32]: bbox scale after affine transform.
+    """
+    w, h = input_size
+    warp_size = (int(w), int(h))
+
+    # reshape bbox to fixed aspect ratio
+    bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h)
+
+    # get the affine matrix
+    center = bbox_center
+    scale = bbox_scale
+    rot = 0
+    warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h))
+
+    # do affine transform
+    img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR)
+
+    return img, bbox_scale
+
+
+def get_simcc_maximum(simcc_x: np.ndarray, simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Get maximum response location and value from simcc representations.
+
+    Note:
+        instance number: N
+        num_keypoints: K
+        heatmap height: H
+        heatmap width: W
+
+    Args:
+        simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx)
+        simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy)
+
+    Returns:
+        tuple:
+        - locs (np.ndarray): locations of maximum heatmap responses in shape
+            (K, 2) or (N, K, 2)
+        - vals (np.ndarray): values of maximum heatmap responses in shape
+            (K,) or (N, K)
+    """
+    N, K, Wx = simcc_x.shape
+    simcc_x = simcc_x.reshape(N * K, -1)
+    simcc_y = simcc_y.reshape(N * K, -1)
+
+    # get maximum value locations
+    x_locs = np.argmax(simcc_x, axis=1)
+    y_locs = np.argmax(simcc_y, axis=1)
+    locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32)
+    max_val_x = np.amax(simcc_x, axis=1)
+    max_val_y = np.amax(simcc_y, axis=1)
+
+    # get maximum value across x and y axis
+    mask = max_val_x > max_val_y
+    max_val_x[mask] = max_val_y[mask]
+    vals = max_val_x
+    locs[vals <= 0.0] = -1
+
+    # reshape
+    locs = locs.reshape(N, K, 2)
+    vals = vals.reshape(N, K)
+
+    return locs, vals
+
+
+def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]:
+    """Modulate simcc distribution with Gaussian.
+
+    Args:
+        simcc_x (np.ndarray[K, Wx]): model predicted simcc in x.
+        simcc_y (np.ndarray[K, Wy]): model predicted simcc in y.
+        simcc_split_ratio (int): The split ratio of simcc.
+
+    Returns:
+        tuple: A tuple containing center and scale.
+        - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2)
+        - np.ndarray[float32]: scores in shape (K,) or (n, K)
+    """
+    keypoints, scores = get_simcc_maximum(simcc_x, simcc_y)
+    keypoints /= simcc_split_ratio
+
+    return keypoints, scores
+
+
+def inference_pose(session, out_bbox, oriImg):
+    h, w = session.get_inputs()[0].shape[2:]
+    model_input_size = (w, h)
+    resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size)
+    outputs = inference(session, resized_img)
+    keypoints, scores = postprocess(outputs, model_input_size, center, scale)
+
+    return keypoints, scores

invokeai/backend/image_util/dw_openpose/utils.py

@@ -0,0 +1,155 @@
+# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
+
+import math
+
+import cv2
+import matplotlib
+import numpy as np
+
+eps = 0.01
+
+
+def draw_bodypose(canvas, candidate, subset):
+    H, W, C = canvas.shape
+    candidate = np.array(candidate)
+    subset = np.array(subset)
+
+    stickwidth = 4
+
+    limbSeq = [
+        [2, 3],
+        [2, 6],
+        [3, 4],
+        [4, 5],
+        [6, 7],
+        [7, 8],
+        [2, 9],
+        [9, 10],
+        [10, 11],
+        [2, 12],
+        [12, 13],
+        [13, 14],
+        [2, 1],
+        [1, 15],
+        [15, 17],
+        [1, 16],
+        [16, 18],
+        [3, 17],
+        [6, 18],
+    ]
+
+    colors = [
+        [255, 0, 0],
+        [255, 85, 0],
+        [255, 170, 0],
+        [255, 255, 0],
+        [170, 255, 0],
+        [85, 255, 0],
+        [0, 255, 0],
+        [0, 255, 85],
+        [0, 255, 170],
+        [0, 255, 255],
+        [0, 170, 255],
+        [0, 85, 255],
+        [0, 0, 255],
+        [85, 0, 255],
+        [170, 0, 255],
+        [255, 0, 255],
+        [255, 0, 170],
+        [255, 0, 85],
+    ]
+
+    for i in range(17):
+        for n in range(len(subset)):
+            index = subset[n][np.array(limbSeq[i]) - 1]
+            if -1 in index:
+                continue
+            Y = candidate[index.astype(int), 0] * float(W)
+            X = candidate[index.astype(int), 1] * float(H)
+            mX = np.mean(X)
+            mY = np.mean(Y)
+            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+            cv2.fillConvexPoly(canvas, polygon, colors[i])
+
+    canvas = (canvas * 0.6).astype(np.uint8)
+
+    for i in range(18):
+        for n in range(len(subset)):
+            index = int(subset[n][i])
+            if index == -1:
+                continue
+            x, y = candidate[index][0:2]
+            x = int(x * W)
+            y = int(y * H)
+            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
+
+    return canvas
+
+
+def draw_handpose(canvas, all_hand_peaks):
+    H, W, C = canvas.shape
+
+    edges = [
+        [0, 1],
+        [1, 2],
+        [2, 3],
+        [3, 4],
+        [0, 5],
+        [5, 6],
+        [6, 7],
+        [7, 8],
+        [0, 9],
+        [9, 10],
+        [10, 11],
+        [11, 12],
+        [0, 13],
+        [13, 14],
+        [14, 15],
+        [15, 16],
+        [0, 17],
+        [17, 18],
+        [18, 19],
+        [19, 20],
+    ]
+
+    for peaks in all_hand_peaks:
+        peaks = np.array(peaks)
+
+        for ie, e in enumerate(edges):
+            x1, y1 = peaks[e[0]]
+            x2, y2 = peaks[e[1]]
+            x1 = int(x1 * W)
+            y1 = int(y1 * H)
+            x2 = int(x2 * W)
+            y2 = int(y2 * H)
+            if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+                cv2.line(
+                    canvas,
+                    (x1, y1),
+                    (x2, y2),
+                    matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
+                    thickness=2,
+                )
+
+        for _, keyponit in enumerate(peaks):
+            x, y = keyponit
+            x = int(x * W)
+            y = int(y * H)
+            if x > eps and y > eps:
+                cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+    return canvas
+
+
+def draw_facepose(canvas, all_lmks):
+    H, W, C = canvas.shape
+    for lmks in all_lmks:
+        lmks = np.array(lmks)
+        for lmk in lmks:
+            x, y = lmk
+            x = int(x * W)
+            y = int(y * H)
+            if x > eps and y > eps:
+                cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
+    return canvas

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -0,0 +1,67 @@
+# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
+# Modified pathing to suit Invoke
+
+import pathlib
+
+import numpy as np
+import onnxruntime as ort
+
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.util import download_with_progress_bar
+
+from .onnxdet import inference_detector
+from .onnxpose import inference_pose
+
+DWPOSE_MODELS = {
+    "yolox_l.onnx": {
+        "local": "any/annotators/dwpose/yolox_l.onnx",
+        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true",
+    },
+    "dw-ll_ucoco_384.onnx": {
+        "local": "any/annotators/dwpose/dw-ll_ucoco_384.onnx",
+        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true",
+    },
+}
+
+config = InvokeAIAppConfig.get_config()
+
+
+class Wholebody:
+    def __init__(self):
+        device = choose_torch_device()
+
+        providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
+
+        DET_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"])
+        if not DET_MODEL_PATH.exists():
+            download_with_progress_bar(DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)
+
+        POSE_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["local"])
+        if not POSE_MODEL_PATH.exists():
+            download_with_progress_bar(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["url"], POSE_MODEL_PATH)
+
+        onnx_det = DET_MODEL_PATH
+        onnx_pose = POSE_MODEL_PATH
+
+        self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
+        self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
+
+    def __call__(self, oriImg):
+        det_result = inference_detector(self.session_det, oriImg)
+        keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
+
+        keypoints_info = np.concatenate((keypoints, scores[..., None]), axis=-1)
+        # compute neck joint
+        neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
+        # neck score when visualizing pred
+        neck[:, 2:4] = np.logical_and(keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3).astype(int)
+        new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1)
+        mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3]
+        openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17]
+        new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx]
+        keypoints_info = new_keypoints_info
+
+        keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2]
+
+        return keypoints, scores

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -7,10 +7,10 @@ import cv2
 import numpy as np
 import numpy.typing as npt
 import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
 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
 
 """

invokeai/backend/model_management/seamless.py

@@ -1,10 +1,11 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import List, Union
+from typing import Callable, List, Union
 
 import torch.nn as nn
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 
 
 def _conv_forward_asymmetric(self, input, weight, bias):
@@ -26,70 +27,51 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
+    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
+    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:
-        to_restore = []
-
+        # 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 isinstance(model, UNet2DConditionModel):
-                if ".attentions." in m_name:
+            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                continue
+
+            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)
+
+                if block_num >= len(model.down_blocks) - skipped_layers:
                     continue
 
-                if ".resnets." in m_name:
-                    if ".conv2" in m_name:
-                        continue
-                    if ".conv_shortcut" in m_name:
-                        continue
-
-            """
-            if isinstance(model, UNet2DConditionModel):
-                if False and ".upsamplers." in m_name:
+                # Skip the second resnet (could be configurable)
+                if resnet_num > 0:
                     continue
 
-                if False and ".downsamplers." in m_name:
+                # Skip Conv2d layers (could be configurable)
+                if submodule_name == "conv2":
                     continue
 
-                if True and ".resnets." in m_name:
-                    if True and ".conv1" in m_name:
-                        if False and "down_blocks" in m_name:
-                            continue
-                        if False and "mid_block" in m_name:
-                            continue
-                        if False and "up_blocks" in m_name:
-                            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],
+            )
 
-                    if True and ".conv2" in m_name:
-                        continue
-
-                    if True and ".conv_shortcut" in m_name:
-                        continue
-
-                if True and ".attentions." in m_name:
-                    continue
-
-                if False and m_name in ["conv_in", "conv_out"]:
-                    continue
-            """
-
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                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)
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

invokeai/frontend/web/package.json

@@ -52,6 +52,7 @@
     "@chakra-ui/react-use-size": "^2.1.0",
     "@dagrejs/graphlib": "^2.1.13",
     "@dnd-kit/core": "^6.1.0",
+    "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.16",
     "@invoke-ai/ui-library": "^0.0.18",

invokeai/frontend/web/pnpm-lock.yaml

@@ -22,6 +22,9 @@ dependencies:
   '@dnd-kit/core':
     specifier: ^6.1.0
     version: 6.1.0(react-dom@18.2.0)(react@18.2.0)
+  '@dnd-kit/sortable':
+    specifier: ^8.0.0
+    version: 8.0.0(@dnd-kit/core@6.1.0)(react@18.2.0)
   '@dnd-kit/utilities':
     specifier: ^3.2.2
     version: 3.2.2(react@18.2.0)
@@ -2884,6 +2887,18 @@ packages:
       tslib: 2.6.2
     dev: false
 
+  /@dnd-kit/sortable@8.0.0(@dnd-kit/core@6.1.0)(react@18.2.0):
+    resolution: {integrity: sha512-U3jk5ebVXe1Lr7c2wU7SBZjcWdQP+j7peHJfCspnA81enlu88Mgd7CC8Q+pub9ubP7eKVETzJW+IBAhsqbSu/g==}
+    peerDependencies:
+      '@dnd-kit/core': ^6.1.0
+      react: '>=16.8.0'
+    dependencies:
+      '@dnd-kit/core': 6.1.0(react-dom@18.2.0)(react@18.2.0)
+      '@dnd-kit/utilities': 3.2.2(react@18.2.0)
+      react: 18.2.0
+      tslib: 2.6.2
+    dev: false
+
   /@dnd-kit/utilities@3.2.2(react@18.2.0):
     resolution: {integrity: sha512-+MKAJEOfaBe5SmV6t34p80MMKhjvUz0vRrvVJbPT0WElzaOJ/1xs+D+KDv+tD/NE5ujfrChEcshd4fLn0wpiqg==}
     peerDependencies:

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

@@ -56,7 +56,7 @@
         "nodeEditor": "Knoten Editor",
         "statusMergingModels": "Modelle zusammenführen",
         "ipAdapter": "IP Adapter",
-        "controlAdapter": "Control Adapter",
+        "controlAdapter": "Control-Adapter",
         "auto": "Automatisch",
         "controlNet": "ControlNet",
         "imageFailedToLoad": "Kann Bild nicht laden",
@@ -69,18 +69,18 @@
         "random": "Zufall",
         "batch": "Stapel-Manager",
         "advanced": "Erweitert",
-        "unifiedCanvas": "Einheitliche Leinwand",
+        "unifiedCanvas": "Leinwand",
         "openInNewTab": "In einem neuem Tab öffnen",
         "statusProcessing": "wird bearbeitet",
         "linear": "Linear",
         "imagePrompt": "Bild Prompt",
         "checkpoint": "Checkpoint",
-        "inpaint": "inpaint",
+        "inpaint": "Inpaint",
         "simple": "Einfach",
         "template": "Vorlage",
         "outputs": "Ausgabe",
         "data": "Daten",
-        "safetensors": "Safetensors",
+        "safetensors": "Safe-Tensors",
         "outpaint": "Ausmalen",
         "details": "Details",
         "format": "Format",
@@ -115,7 +115,8 @@
         "orderBy": "Ordnen nach",
         "saveAs": "Speicher als",
         "updated": "Aktualisiert",
-        "copy": "Kopieren"
+        "copy": "Kopieren",
+        "aboutHeading": "Nutzen Sie Ihre kreative Energie"
     },
     "gallery": {
         "generations": "Erzeugungen",
@@ -126,7 +127,7 @@
         "galleryImageResetSize": "Größe zurücksetzen",
         "gallerySettings": "Galerie-Einstellungen",
         "maintainAspectRatio": "Seitenverhältnis beibehalten",
-        "autoSwitchNewImages": "Automatisch zu neuen Bildern wechseln",
+        "autoSwitchNewImages": "Auto-Wechsel zu neuen Bildern",
         "singleColumnLayout": "Einspaltiges Layout",
         "allImagesLoaded": "Alle Bilder geladen",
         "loadMore": "Mehr laden",
@@ -146,26 +147,30 @@
         "deleteImagePermanent": "Gelöschte Bilder können nicht wiederhergestellt werden.",
         "autoAssignBoardOnClick": "Board per Klick automatisch zuweisen",
         "noImageSelected": "Kein Bild ausgewählt",
-        "problemDeletingImagesDesc": "Eins oder mehr Bilder könnten nicht gelöscht werden",
+        "problemDeletingImagesDesc": "Ein oder mehrere Bilder konnten nicht gelöscht werden",
         "starImage": "Bild markieren",
         "assets": "Ressourcen",
         "unstarImage": "Markierung Entfernen",
         "image": "Bild",
-        "deleteSelection": "Lösche markierte"
+        "deleteSelection": "Lösche markierte",
+        "dropToUpload": "$t(gallery.drop) zum hochladen",
+        "dropOrUpload": "$t(gallery.drop) oder hochladen",
+        "drop": "Ablegen",
+        "problemDeletingImages": "Problem beim Löschen der Bilder"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
         "appHotkeys": "App-Tastenkombinationen",
-        "generalHotkeys": "Allgemeine Tastenkürzel",
-        "galleryHotkeys": "Galerie Tastenkürzel",
-        "unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel",
+        "generalHotkeys": "Allgemein",
+        "galleryHotkeys": "Galerie",
+        "unifiedCanvasHotkeys": "Leinwand",
         "invoke": {
             "desc": "Ein Bild erzeugen",
             "title": "Invoke"
         },
         "cancel": {
             "title": "Abbrechen",
-            "desc": "Bilderzeugung abbrechen"
+            "desc": "Aktuelle Bilderzeugung abbrechen"
         },
         "focusPrompt": {
             "title": "Fokussiere Prompt",
@@ -221,7 +226,7 @@
         },
         "sendToImageToImage": {
             "title": "An Bild zu Bild senden",
-            "desc": "Aktuelles Bild an Bild zu Bild senden"
+            "desc": "Aktuelles Bild an Bild-zu-Bild senden"
         },
         "deleteImage": {
             "title": "Bild löschen",
@@ -253,7 +258,7 @@
         },
         "selectEraser": {
             "title": "Radiergummi auswählen",
-            "desc": "Wählt den Radiergummi für die Leinwand aus"
+            "desc": "Wählt den Radiergummi aus"
         },
         "decreaseBrushSize": {
             "title": "Pinselgröße verkleinern",
@@ -325,7 +330,7 @@
         },
         "downloadImage": {
             "title": "Bild herunterladen",
-            "desc": "Aktuelle Leinwand herunterladen"
+            "desc": "Aktuelles Bild herunterladen"
         },
         "undoStroke": {
             "title": "Pinselstrich rückgängig machen",
@@ -351,42 +356,55 @@
             "title": "Staging-Bild akzeptieren",
             "desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs"
         },
-        "nodesHotkeys": "Knoten Tastenkürzel",
+        "nodesHotkeys": "Knoten",
         "addNodes": {
             "title": "Knotenpunkt hinzufügen",
             "desc": "Öffnet das Menü zum Hinzufügen von Knoten"
         },
         "cancelAndClear": {
-            "title": "Abbruch und leeren"
+            "title": "Abbruch und leeren",
+            "desc": "Aktuelle Berechnung abbrechen und alle wartenden löschen"
         },
         "noHotkeysFound": "Kein Hotkey gefunden",
         "searchHotkeys": "Hotkeys durchsuchen",
-        "clearSearch": "Suche leeren"
+        "clearSearch": "Suche leeren",
+        "resetOptionsAndGallery": {
+            "desc": "Optionen und Galerie-Panels zurücksetzen",
+            "title": "Optionen und Galerie zurücksetzen"
+        },
+        "remixImage": {
+            "desc": "Alle Parameter außer Seed vom aktuellen Bild verwenden",
+            "title": "Remix des Bilds erstellen"
+        },
+        "toggleOptionsAndGallery": {
+            "title": "Optionen und Galerie umschalten",
+            "desc": "Optionen und Galerie-Panels öffnen und schließen"
+        }
     },
     "modelManager": {
         "modelAdded": "Model hinzugefügt",
         "modelUpdated": "Model aktualisiert",
         "modelEntryDeleted": "Modelleintrag gelöscht",
         "cannotUseSpaces": "Leerzeichen können nicht verwendet werden",
-        "addNew": "Neue hinzufügen",
-        "addNewModel": "Neues Model hinzufügen",
+        "addNew": "Neu hinzufügen",
+        "addNewModel": "Neues Modell hinzufügen",
         "addManually": "Manuell hinzufügen",
         "nameValidationMsg": "Geben Sie einen Namen für Ihr Model ein",
         "description": "Beschreibung",
         "descriptionValidationMsg": "Fügen Sie eine Beschreibung für Ihr Model hinzu",
         "config": "Konfiguration",
-        "configValidationMsg": "Pfad zur Konfigurationsdatei Ihres Models.",
+        "configValidationMsg": "Pfad zur Konfigurationsdatei Ihres Modells.",
         "modelLocation": "Ort des Models",
         "modelLocationValidationMsg": "Pfad zum Speicherort Ihres Models",
         "vaeLocation": "VAE Ort",
         "vaeLocationValidationMsg": "Pfad zum Speicherort Ihres VAE.",
         "width": "Breite",
-        "widthValidationMsg": "Standardbreite Ihres Models.",
+        "widthValidationMsg": "Standardbreite Ihres Modells.",
         "height": "Höhe",
         "heightValidationMsg": "Standardbhöhe Ihres Models.",
-        "addModel": "Model hinzufügen",
+        "addModel": "Modell hinzufügen",
         "updateModel": "Model aktualisieren",
-        "availableModels": "Verfügbare Models",
+        "availableModels": "Verfügbare Modelle",
         "search": "Suche",
         "load": "Laden",
         "active": "Aktiv",
@@ -483,7 +501,7 @@
         "quickAdd": "Schnell hinzufügen",
         "simpleModelDesc": "Geben Sie einen Pfad zu einem lokalen Diffusers-Modell, einem lokalen Checkpoint-/Safetensors-Modell, einer HuggingFace-Repo-ID oder einer Checkpoint-/Diffusers-Modell-URL an.",
         "modelDeleted": "Modell gelöscht",
-        "inpainting": "v1 Inpainting",
+        "inpainting": "V1-Inpainting",
         "modelUpdateFailed": "Modellaktualisierung fehlgeschlagen",
         "useCustomConfig": "Benutzerdefinierte Konfiguration verwenden",
         "settings": "Einstellungen",
@@ -500,12 +518,14 @@
         "interpolationType": "Interpolationstyp",
         "oliveModels": "Olives",
         "variant": "Variante",
-        "loraModels": "LoRAs",
+        "loraModels": "\"LoRAs\"",
         "modelDeleteFailed": "Modell konnte nicht gelöscht werden",
         "mergedModelName": "Zusammengeführter Modellname",
         "checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
         "formMessageDiffusersModelLocation": "Diffusers Modell Speicherort",
-        "noModelSelected": "Kein Modell ausgewählt"
+        "noModelSelected": "Kein Modell ausgewählt",
+        "conversionNotSupported": "Umwandlung nicht unterstützt",
+        "configFile": "Konfigurationsdatei"
     },
     "parameters": {
         "images": "Bilder",
@@ -544,8 +564,8 @@
         "img2imgStrength": "Bild-zu-Bild-Stärke",
         "toggleLoopback": "Loopback umschalten",
         "sendTo": "Senden an",
-        "sendToImg2Img": "Senden an Bild zu Bild",
-        "sendToUnifiedCanvas": "Senden an Unified Canvas",
+        "sendToImg2Img": "Senden an Bild-zu-Bild",
+        "sendToUnifiedCanvas": "Senden an Leinwand",
         "copyImageToLink": "Bild-Link kopieren",
         "downloadImage": "Bild herunterladen",
         "openInViewer": "Im Viewer öffnen",
@@ -583,7 +603,10 @@
         "resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.",
         "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
         "models": "Modelle",
-        "useSlidersForAll": "Schieberegler für alle Optionen verwenden"
+        "useSlidersForAll": "Schieberegler für alle Optionen verwenden",
+        "showAdvancedOptions": "Erweiterte Optionen anzeigen",
+        "alternateCanvasLayout": "Alternatives Leinwand-Layout",
+        "clearIntermediatesDesc1": "Das Löschen der Zwischenprodukte setzt Leinwand und ControlNet zurück."
     },
     "toast": {
         "tempFoldersEmptied": "Temp-Ordner geleert",
@@ -597,7 +620,7 @@
         "imageSavedToGallery": "Bild in die Galerie gespeichert",
         "canvasMerged": "Leinwand zusammengeführt",
         "sentToImageToImage": "Gesendet an Bild zu Bild",
-        "sentToUnifiedCanvas": "Gesendet an Unified Canvas",
+        "sentToUnifiedCanvas": "Gesendet an Leinwand",
         "parametersSet": "Parameter festlegen",
         "parametersNotSet": "Parameter nicht festgelegt",
         "parametersNotSetDesc": "Keine Metadaten für dieses Bild gefunden.",
@@ -614,7 +637,21 @@
         "metadataLoadFailed": "Metadaten konnten nicht geladen werden",
         "initialImageSet": "Ausgangsbild festgelegt",
         "initialImageNotSet": "Ausgangsbild nicht festgelegt",
-        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden"
+        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden",
+        "setCanvasInitialImage": "Ausgangsbild setzen",
+        "problemMergingCanvas": "Problem bei Verschmelzung der Leinwand",
+        "canvasCopiedClipboard": "Leinwand in Zwischenablage kopiert",
+        "canvasSentControlnetAssets": "Leinwand an ControlNet & Sammlung geschickt",
+        "problemDownloadingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "canvasDownloaded": "Leinwand heruntergeladen",
+        "problemSavingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "canvasSavedGallery": "Leinwand in Galerie gespeichert",
+        "problemMergingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "problemSavingCanvas": "Problem beim Speichern der Leinwand",
+        "problemCopyingCanvas": "Problem beim Kopieren der Leinwand",
+        "problemCopyingCanvasDesc": "Kann Basis-Layer nicht exportieren",
+        "problemDownloadingCanvas": "Problem beim Herunterladen der Leinwand",
+        "setAsCanvasInitialImage": "Als Ausgangsbild gesetzt"
     },
     "tooltip": {
         "feature": {
@@ -626,8 +663,8 @@
             "upscale": "Verwenden Sie ESRGAN, um das Bild unmittelbar nach der Erzeugung zu vergrößern.",
             "faceCorrection": "Gesichtskorrektur mit GFPGAN oder Codeformer: Der Algorithmus erkennt Gesichter im Bild und korrigiert alle Fehler. Ein hoher Wert verändert das Bild stärker, was zu attraktiveren Gesichtern führt. Codeformer mit einer höheren Genauigkeit bewahrt das Originalbild auf Kosten einer stärkeren Gesichtskorrektur.",
             "imageToImage": "Bild zu Bild lädt ein beliebiges Bild als Ausgangsbild, aus dem dann zusammen mit dem Prompt ein neues Bild erzeugt wird. Je höher der Wert ist, desto stärker wird das Ergebnisbild verändert. Werte von 0,0 bis 1,0 sind möglich, der empfohlene Bereich ist .25-.75",
-            "boundingBox": "Der Begrenzungsrahmen ist derselbe wie die Einstellungen für Breite und Höhe bei Text zu Bild oder Bild zu Bild. Es wird nur der Bereich innerhalb des Rahmens verarbeitet.",
-            "seamCorrection": "Steuert die Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auf der Leinwand auftreten.",
+            "boundingBox": "Der Begrenzungsrahmen ist derselbe wie die Einstellungen für Breite und Höhe bei Text-zu-Bild oder Bild-zu-Bild. Es wird nur der Bereich innerhalb des Rahmens verarbeitet.",
+            "seamCorrection": "Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auftreten.",
             "infillAndScaling": "Verwalten Sie Infill-Methoden (für maskierte oder gelöschte Bereiche der Leinwand) und Skalierung (nützlich für kleine Begrenzungsrahmengrößen)."
         }
     },
@@ -638,17 +675,17 @@
         "maskingOptions": "Maskierungsoptionen",
         "enableMask": "Maske aktivieren",
         "preserveMaskedArea": "Maskierten Bereich bewahren",
-        "clearMask": "Maske löschen",
+        "clearMask": "Maske löschen (Shift+C)",
         "brush": "Pinsel",
         "eraser": "Radierer",
         "fillBoundingBox": "Begrenzungsrahmen füllen",
         "eraseBoundingBox": "Begrenzungsrahmen löschen",
-        "colorPicker": "Farbpipette",
+        "colorPicker": "Pipette",
         "brushOptions": "Pinseloptionen",
         "brushSize": "Größe",
         "move": "Bewegen",
         "resetView": "Ansicht zurücksetzen",
-        "mergeVisible": "Sichtbare Zusammenführen",
+        "mergeVisible": "Sichtbare zusammenführen",
         "saveToGallery": "In Galerie speichern",
         "copyToClipboard": "In Zwischenablage kopieren",
         "downloadAsImage": "Als Bild herunterladen",
@@ -656,21 +693,21 @@
         "redo": "Wiederherstellen",
         "clearCanvas": "Leinwand löschen",
         "canvasSettings": "Leinwand-Einstellungen",
-        "showIntermediates": "Zwischenprodukte anzeigen",
+        "showIntermediates": "Zwischenbilder anzeigen",
         "showGrid": "Gitternetz anzeigen",
         "snapToGrid": "Am Gitternetz einrasten",
         "darkenOutsideSelection": "Außerhalb der Auswahl verdunkeln",
         "autoSaveToGallery": "Automatisch in Galerie speichern",
         "saveBoxRegionOnly": "Nur Auswahlbox speichern",
-        "limitStrokesToBox": "Striche auf Box beschränken",
-        "showCanvasDebugInfo": "Zusätzliche Informationen zur Leinwand anzeigen",
+        "limitStrokesToBox": "Striche auf Auswahl beschränken",
+        "showCanvasDebugInfo": "Zusätzliche Informationen anzeigen",
         "clearCanvasHistory": "Leinwand-Verlauf löschen",
         "clearHistory": "Verlauf löschen",
-        "clearCanvasHistoryMessage": "Wenn Sie den Verlauf der Leinwand löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
-        "clearCanvasHistoryConfirm": "Sind Sie sicher, dass Sie den Verlauf der Leinwand löschen möchten?",
+        "clearCanvasHistoryMessage": "Wenn Sie den Verlauf löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
+        "clearCanvasHistoryConfirm": "Sind Sie sicher, dass Sie den Verlauf löschen möchten?",
         "emptyTempImageFolder": "Temp-Image Ordner leeren",
         "emptyFolder": "Leerer Ordner",
-        "emptyTempImagesFolderMessage": "Wenn Sie den Ordner für temporäre Bilder leeren, wird auch der Unified Canvas vollständig zurückgesetzt. Dies umfasst den gesamten Verlauf der Rückgängig-/Wiederherstellungsvorgänge, die Bilder im Bereitstellungsbereich und die Leinwand-Basisebene.",
+        "emptyTempImagesFolderMessage": "Wenn Sie den Ordner für temporäre Bilder leeren, wird die Leinwand zurückgesetzt. Dies umfasst den gesamten Verlauf der Rückgängig-/Wiederherstellungsvorgänge, die Bilder im Bereitstellungsbereich und die Leinwand-Basisebene.",
         "emptyTempImagesFolderConfirm": "Sind Sie sicher, dass Sie den temporären Ordner leeren wollen?",
         "activeLayer": "Aktive Ebene",
         "canvasScale": "Leinwand Maßstab",
@@ -687,14 +724,14 @@
         "discardAll": "Alles verwerfen",
         "betaClear": "Löschen",
         "betaDarkenOutside": "Außen abdunkeln",
-        "betaLimitToBox": "Begrenzung auf das Feld",
+        "betaLimitToBox": "Auf Auswahl begrenzen",
         "betaPreserveMasked": "Maskiertes bewahren",
         "antialiasing": "Kantenglättung",
         "showResultsOn": "Zeige Ergebnisse (An)",
         "showResultsOff": "Zeige Ergebnisse (Aus)"
     },
     "accessibility": {
-        "modelSelect": "Model Auswahl",
+        "modelSelect": "Modell-Auswahl",
         "uploadImage": "Bild hochladen",
         "previousImage": "Voriges Bild",
         "useThisParameter": "Benutze diesen Parameter",
@@ -706,11 +743,11 @@
         "modifyConfig": "Optionen einstellen",
         "toggleAutoscroll": "Auroscroll ein/ausschalten",
         "toggleLogViewer": "Log Betrachter ein/ausschalten",
-        "showOptionsPanel": "Zeige Optionen",
+        "showOptionsPanel": "Seitenpanel anzeigen",
         "reset": "Zurücksetzten",
         "nextImage": "Nächstes Bild",
         "zoomOut": "Verkleinern",
-        "rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen",
+        "rotateCounterClockwise": "Gegen den Uhrzeigersinn drehen",
         "showGalleryPanel": "Galeriefenster anzeigen",
         "exitViewer": "Betrachten beenden",
         "menu": "Menü",
@@ -725,14 +762,14 @@
         "autoAddBoard": "Automatisches Hinzufügen zum Ordner",
         "topMessage": "Dieser Ordner enthält Bilder die in den folgenden Funktionen verwendet werden:",
         "move": "Bewegen",
-        "menuItemAutoAdd": "Automatisches Hinzufügen zu diesem Ordner",
+        "menuItemAutoAdd": "Auto-Hinzufügen zu diesem Ordner",
         "myBoard": "Meine Ordner",
         "searchBoard": "Ordner durchsuchen...",
         "noMatching": "Keine passenden Ordner",
         "selectBoard": "Ordner aussuchen",
         "cancel": "Abbrechen",
         "addBoard": "Ordner hinzufügen",
-        "uncategorized": "Nicht kategorisiert",
+        "uncategorized": "Ohne Kategorie",
         "downloadBoard": "Ordner runterladen",
         "changeBoard": "Ordner wechseln",
         "loading": "Laden...",
@@ -742,15 +779,15 @@
         "deleteBoard": "Löschen Ordner",
         "deleteBoardAndImages": "Löschen Ordner und Bilder",
         "deletedBoardsCannotbeRestored": "Gelöschte Ordner könnte nicht wiederhergestellt werden",
-        "movingImagesToBoard_one": "Verschiebe {{count}} Bild zu Ordner",
-        "movingImagesToBoard_other": "Verschiebe {{count}} Bilder in Ordner"
+        "movingImagesToBoard_one": "Verschiebe {{count}} Bild zu Ordner:",
+        "movingImagesToBoard_other": "Verschiebe {{count}} Bilder in Ordner:"
     },
     "controlnet": {
         "showAdvanced": "Zeige Erweitert",
         "contentShuffleDescription": "Mischt den Inhalt von einem Bild",
         "addT2IAdapter": "$t(common.t2iAdapter) hinzufügen",
-        "importImageFromCanvas": "Importieren Bild von Zeichenfläche",
-        "lineartDescription": "Konvertiere Bild zu Lineart",
+        "importImageFromCanvas": "Bild von Zeichenfläche importieren",
+        "lineartDescription": "Konvertiere Bild in Strichzeichnung",
         "importMaskFromCanvas": "Importiere Maske von Zeichenfläche",
         "hed": "HED",
         "hideAdvanced": "Verstecke Erweitert",
@@ -764,7 +801,7 @@
         "depthMidasDescription": "Tiefenmap erstellen mit Midas",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert",
-        "weight": "Breite",
+        "weight": "Einfluss",
         "selectModel": "Wähle ein Modell",
         "depthMidas": "Tiefe (Midas)",
         "w": "W",
@@ -786,17 +823,17 @@
         "toggleControlNet": "Schalten ControlNet um",
         "delete": "Löschen",
         "controlAdapter_one": "Control Adapter",
-        "controlAdapter_other": "Control Adapters",
-        "colorMapTileSize": "Tile Größe",
+        "controlAdapter_other": "Control Adapter",
+        "colorMapTileSize": "Kachelgröße",
         "depthZoeDescription": "Tiefenmap erstellen mit Zoe",
-        "setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe",
+        "setControlImageDimensions": "Setze Control-Bild Auflösung auf Breite/Höhe",
         "handAndFace": "Hand und Gesicht",
         "enableIPAdapter": "Aktiviere IP Adapter",
         "resize": "Größe ändern",
         "resetControlImage": "Zurücksetzen vom Referenz Bild",
         "balanced": "Ausgewogen",
         "prompt": "Prompt",
-        "resizeMode": "Größenänderungsmodus",
+        "resizeMode": "Größe",
         "processor": "Prozessor",
         "saveControlImage": "Speichere Referenz Bild",
         "safe": "Speichern",
@@ -805,35 +842,33 @@
         "pidi": "PIDI",
         "normalBae": "Normales BAE",
         "mlsdDescription": "Minimalistischer Liniensegmentdetektor",
-        "openPoseDescription": "Schätzung der menschlichen Pose mit Openpose",
         "control": "Kontrolle",
-        "coarse": "Coarse",
+        "coarse": "Grob",
         "crop": "Zuschneiden",
         "pidiDescription": "PIDI-Bildverarbeitung",
         "mediapipeFace": "Mediapipe Gesichter",
         "mlsd": "M-LSD",
         "controlMode": "Steuermodus",
-        "cannyDescription": "Canny Ecken Erkennung",
-        "lineart": "Lineart",
+        "cannyDescription": "Canny Umrisserkennung",
+        "lineart": "Linienzeichnung",
         "lineartAnimeDescription": "Lineart-Verarbeitung im Anime-Stil",
         "minConfidence": "Minimales Vertrauen",
         "megaControl": "Mega-Kontrolle",
-        "autoConfigure": "Prozessor automatisch konfigurieren",
+        "autoConfigure": "Prozessor Auto-konfig",
         "normalBaeDescription": "Normale BAE-Verarbeitung",
         "noneDescription": "Es wurde keine Verarbeitung angewendet",
-        "openPose": "Openpose",
-        "lineartAnime": "Lineart Anime",
+        "lineartAnime": "Lineart Anime / \"Strichzeichnung Anime\"",
         "mediapipeFaceDescription": "Gesichtserkennung mit Mediapipe",
-        "canny": "Canny",
+        "canny": "\"Canny\"",
         "hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
         "scribble": "Scribble",
-        "maxFaces": "Maximal Anzahl Gesichter",
+        "maxFaces": "Maximale Anzahl Gesichter",
         "resizeSimple": "Größe ändern (einfach)",
         "large": "Groß",
         "modelSize": "Modell Größe",
         "small": "Klein",
         "base": "Basis",
-        "depthAnything": "Depth Anything",
+        "depthAnything": "Depth Anything / \"Tiefe irgendwas\"",
         "depthAnythingDescription": "Erstellung einer Tiefenkarte mit der Depth Anything-Technik"
     },
     "queue": {
@@ -876,7 +911,7 @@
         "enqueueing": "Stapel in der Warteschlange",
         "queueMaxExceeded": "Maximum von {{max_queue_size}} Elementen erreicht, würde {{skip}} Elemente überspringen",
         "cancelBatchFailed": "Problem beim Abbruch vom Stapel",
-        "clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?",
+        "clearQueueAlertDialog2": "Warteschlange wirklich leeren?",
         "pruneSucceeded": "{{item_count}} abgeschlossene Elemente aus der Warteschlange entfernt",
         "pauseSucceeded": "Prozessor angehalten",
         "cancelFailed": "Problem beim Stornieren des Auftrags",
@@ -890,78 +925,189 @@
         "resumeSucceeded": "Prozessor wieder aufgenommen",
         "resumeTooltip": "Prozessor wieder aufnehmen",
         "time": "Zeit",
-        "batchQueuedDesc_one": "{{count}} Eintrage ans {{direction}} der Wartschlange hinzugefügt",
-        "batchQueuedDesc_other": "{{count}} Einträge ans {{direction}} der Wartschlange hinzugefügt"
+        "batchQueuedDesc_one": "{{count}} Eintrag ans {{direction}} der Wartschlange hinzugefügt",
+        "batchQueuedDesc_other": "{{count}} Einträge ans {{direction}} der Wartschlange hinzugefügt",
+        "openQueue": "Warteschlange öffnen",
+        "batchFailedToQueue": "Fehler beim Einreihen in die Stapelverarbeitung",
+        "batchFieldValues": "Stapelverarbeitungswerte",
+        "batchQueued": "Stapelverarbeitung eingereiht",
+        "graphQueued": "Graph eingereiht",
+        "graphFailedToQueue": "Fehler beim Einreihen des Graphen"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
-        "metadata": "Meta-Data",
-        "strength": "Bild zu Bild stärke",
+        "metadata": "Meta-Daten",
+        "strength": "Bild zu Bild Stärke",
         "imageDetails": "Bild Details",
         "model": "Modell",
         "noImageDetails": "Keine Bild Details gefunden",
         "cfgScale": "CFG-Skala",
-        "fit": "Bild zu Bild passen",
+        "fit": "Bild zu Bild anpassen",
         "height": "Höhe",
-        "noMetaData": "Keine Meta-Data gefunden",
+        "noMetaData": "Keine Meta-Daten gefunden",
         "width": "Breite",
         "createdBy": "Erstellt von",
         "steps": "Schritte",
         "seamless": "Nahtlos",
         "positivePrompt": "Positiver Prompt",
         "generationMode": "Generierungsmodus",
-        "Threshold": "Noise Schwelle",
-        "seed": "Samen",
-        "perlin": "Perlin Noise",
+        "Threshold": "Rauschen-Schwelle",
+        "seed": "Seed",
+        "perlin": "Perlin-Rauschen",
         "hiresFix": "Optimierung für hohe Auflösungen",
         "initImage": "Erstes Bild",
-        "variations": "Samengewichtspaare",
+        "variations": "Seed-Gewichtungs-Paare",
         "vae": "VAE",
-        "workflow": "Arbeitsablauf",
-        "scheduler": "Scheduler",
+        "workflow": "Workflow",
+        "scheduler": "Planer",
         "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
-        "recallParameters": "Recall Parameters"
+        "recallParameters": "Parameter wiederherstellen"
     },
     "popovers": {
         "noiseUseCPU": {
-            "heading": "Nutze Prozessor rauschen"
+            "heading": "Nutze Prozessor rauschen",
+            "paragraphs": [
+                "Entscheidet, ob auf der CPU oder GPU Rauschen erzeugt wird.",
+                "Mit aktiviertem CPU-Rauschen wird ein bestimmter Seedwert das gleiche Bild auf jeder Maschine erzeugen.",
+                "CPU-Rauschen einzuschalten beeinflusst nicht die Systemleistung."
+            ]
         },
         "paramModel": {
-            "heading": "Modell"
+            "heading": "Modell",
+            "paragraphs": [
+                "Modell für die Entrauschungsschritte.",
+                "Verschiedene Modelle werden in der Regel so trainiert, dass sie sich auf die Erzeugung bestimmter Ästhetik und/oder Inhalte spezialisiert."
+            ]
         },
         "paramIterations": {
-            "heading": "Iterationen"
+            "heading": "Iterationen",
+            "paragraphs": [
+                "Die Anzahl der Bilder, die erzeugt werden sollen.",
+                "Wenn \"Dynamische Prompts\" aktiviert ist, wird jeder einzelne Prompt so oft generiert."
+            ]
         },
         "paramCFGScale": {
-            "heading": "CFG-Skala"
+            "heading": "CFG-Skala",
+            "paragraphs": [
+                "Bestimmt, wie viel Ihr Prompt den Erzeugungsprozess beeinflusst."
+            ]
         },
         "paramSteps": {
-            "heading": "Schritte"
+            "heading": "Schritte",
+            "paragraphs": [
+                "Anzahl der Schritte, die bei jeder Generierung durchgeführt werden.",
+                "Höhere Schrittzahlen werden in der Regel bessere Bilder ergeben, aber mehr Zeit benötigen."
+            ]
         },
         "lora": {
-            "heading": "LoRA Gewichte"
+            "heading": "LoRA Gewichte",
+            "paragraphs": [
+                "Höhere LoRA-Wichtungen führen zu größeren Auswirkungen auf das endgültige Bild."
+            ]
         },
         "infillMethod": {
-            "heading": "Füllmethode"
+            "heading": "Füllmethode",
+            "paragraphs": [
+                "Infill-Methode für den ausgewählten Bereich."
+            ]
         },
         "paramVAE": {
-            "heading": "VAE"
+            "heading": "VAE",
+            "paragraphs": [
+                "Verwendetes Modell, um den KI-Ausgang in das endgültige Bild zu übersetzen."
+            ]
+        },
+        "paramRatio": {
+            "heading": "Seitenverhältnis",
+            "paragraphs": [
+                "Das Seitenverhältnis des erzeugten Bildes.",
+                "Für SD1.5-Modelle wird eine Bildgröße von 512x512 Pixel empfohlen, für SDXL-Modelle sind es 1024x1024 Pixel."
+            ]
+        },
+        "paramDenoisingStrength": {
+            "paragraphs": [
+                "Wie viel Rauschen dem Eingabebild hinzugefügt wird.",
+                "0 wird zu einem identischen Bild führen, während 1 zu einem völlig neuen Bild führt."
+            ],
+            "heading": "Stärke der Entrauschung"
+        },
+        "paramVAEPrecision": {
+            "heading": "VAE-Präzision",
+            "paragraphs": [
+                "Die bei der VAE-Kodierung und Dekodierung verwendete Präzision. FP16/Halbpräzision ist effizienter, aber auf Kosten kleiner Bildvariationen."
+            ]
+        },
+        "paramCFGRescaleMultiplier": {
+            "heading": "CFG Rescale Multiplikator",
+            "paragraphs": [
+                "Rescale-Multiplikator für die CFG-Lenkung, der für Modelle verwendet wird, die mit dem zero-terminal SNR (ztsnr) trainiert wurden. Empfohlener Wert: 0,7."
+            ]
+        },
+        "scaleBeforeProcessing": {
+            "paragraphs": [
+                "Skaliert den ausgewählten Bereich auf die Größe, die für das Modell am besten geeignet ist."
+            ],
+            "heading": "Skalieren vor der Verarbeitung"
+        },
+        "paramSeed": {
+            "paragraphs": [
+                "Kontrolliert das für die Erzeugung verwendete Startrauschen.",
+                "Deaktivieren Sie “Random Seed”, um identische Ergebnisse mit den gleichen Generierungseinstellungen zu erzeugen."
+            ],
+            "heading": "Seed"
+        },
+        "dynamicPromptsMaxPrompts": {
+            "paragraphs": [
+                "Beschränkt die Anzahl der Prompts, die von \"Dynamic Prompts\" generiert werden können."
+            ],
+            "heading": "Maximale Prompts"
+        },
+        "dynamicPromptsSeedBehaviour": {
+            "paragraphs": [
+                "Bestimmt, wie der Seed-Wert beim Erzeugen von Prompts verwendet wird.",
+                "Verwenden Sie dies, um schnelle Variationen eines einzigen Seeds zu erkunden.",
+                "Wenn Sie z. B. 5 Prompts haben, wird jedes Bild den selben Seed-Wert verwenden.",
+                "\"Per Bild\" wird einen einzigartigen Seed-Wert für jedes Bild verwenden. Dies bietet mehr Variationen."
+            ],
+            "heading": "Seed-Verhalten"
+        },
+        "dynamicPrompts": {
+            "paragraphs": [
+                "\"Dynamische Prompts\" übersetzt einen Prompt in mehrere.",
+                "Die Ausgangs-Syntax ist \"ein {roter|grüner|blauer} ball\". Das generiert 3 Prompts: \"ein roter ball\", \"ein grüner ball\" und \"ein blauer ball\".",
+                "Sie können die Syntax so oft verwenden, wie Sie in einem einzigen Prompt möchten, aber stellen Sie sicher, dass die Anzahl der Prompts zur Einstellung von \"Max Prompts\" passt."
+            ],
+            "heading": "Dynamische Prompts"
+        },
+        "controlNetWeight": {
+            "paragraphs": [
+                "Wie stark wird das ControlNet das generierte Bild beeinflussen wird."
+            ],
+            "heading": "Einfluss"
         }
     },
     "ui": {
         "lockRatio": "Verhältnis sperren",
         "hideProgressImages": "Verstecke Prozess Bild",
-        "showProgressImages": "Zeige Prozess Bild"
+        "showProgressImages": "Zeige Prozess Bild",
+        "swapSizes": "Tausche Größen"
     },
     "invocationCache": {
         "disable": "Deaktivieren",
-        "misses": "Cache Nötig",
+        "misses": "Cache nicht genutzt",
         "hits": "Cache Treffer",
         "enable": "Aktivieren",
         "clear": "Leeren",
         "maxCacheSize": "Maximale Cache Größe",
         "cacheSize": "Cache Größe",
-        "useCache": "Benutze Cache"
+        "useCache": "Benutze Cache",
+        "enableFailed": "Problem beim Aktivieren des Zwischenspeichers",
+        "disableFailed": "Problem bei Deaktivierung des Cache",
+        "enableSucceeded": "Zwischenspeicher aktiviert",
+        "disableSucceeded": "Invocation-Cache deaktiviert",
+        "clearSucceeded": "Zwischenspeicher gelöscht",
+        "invocationCache": "Zwischenspeicher",
+        "clearFailed": "Problem beim Löschen des Zwischenspeichers"
     },
     "embedding": {
         "noMatchingEmbedding": "Keine passenden Embeddings",
@@ -1000,10 +1146,151 @@
         "colorCodeEdges": "Farbkodierte Kanten",
         "addNodeToolTip": "Knoten hinzufügen (Umschalt+A, Leertaste)",
         "boardField": "Ordner",
-        "boardFieldDescription": "Ein Galerie Ordner"
+        "boardFieldDescription": "Ein Galerie Ordner",
+        "collectionFieldType": "{{name}} Sammlung",
+        "controlCollectionDescription": "Kontrollinformationen zwischen Knotenpunkten weitergegeben.",
+        "connectionWouldCreateCycle": "Verbindung würde einen Kreislauf/cycle schaffen",
+        "ipAdapterDescription": "Ein Adapter für die Bildabfrage (IP-Adapter) / Bildprompt-Adapter.",
+        "controlField": "Kontrolle",
+        "inputFields": "Eingabefelder",
+        "imageField": "Bild",
+        "inputMayOnlyHaveOneConnection": "Eingang darf nur eine Verbindung haben",
+        "integerCollectionDescription": "Eine Sammlung ganzer Zahlen.",
+        "integerDescription": "\"Integer\" sind ganze Zahlen ohne Dezimalpunkt.",
+        "conditioningPolymorphic": "Konditionierung polymorphisch",
+        "conditioningPolymorphicDescription": "Die Konditionierung kann zwischen den Knoten weitergegeben werden.",
+        "invalidOutputSchema": "Ungültiges Ausgabeschema",
+        "ipAdapterModel": "IP-Adapter Modell",
+        "conditioningFieldDescription": "Die Konditionierung kann zwischen den Knotenpunkten weitergegeben werden.",
+        "ipAdapterCollectionDescription": "Eine Sammlung von IP-Adaptern.",
+        "collectionDescription": "Zu erledigen",
+        "imageFieldDescription": "Bilder können zwischen Knoten weitergegeben werden.",
+        "imagePolymorphic": "Bild Polymorphie",
+        "imagePolymorphicDescription": "Eine Bildersammlung.",
+        "inputField": "Eingabefeld",
+        "hideLegendNodes": "Feldtyp-Legende ausblenden",
+        "collectionItemDescription": "Zu erledigen",
+        "inputNode": "Eingangsknoten",
+        "integer": "Ganze Zahl",
+        "integerCollection": "Ganzzahlige Sammlung",
+        "addLinearView": "Zur linearen Ansicht hinzufügen",
+        "currentImageDescription": "Zeigt das aktuelle Bild im Node-Editor an",
+        "ipAdapter": "IP-Adapter",
+        "hideMinimapnodes": "Miniatur-Kartenansicht ausblenden",
+        "imageCollection": "Bildersammlung",
+        "imageCollectionDescription": "Eine Sammlung von Bildern.",
+        "denoiseMaskField": "Entrauschen-Maske",
+        "ipAdapterCollection": "IP-Adapter Sammlung",
+        "newWorkflowDesc2": "Ihr aktueller Arbeitsablauf hat ungespeicherte Änderungen.",
+        "problemSettingTitle": "Problem beim Einstellen des Titels",
+        "noConnectionData": "Keine Verbindungsdaten",
+        "outputField": "Ausgabefeld",
+        "outputFieldInInput": "Ausgabefeld im Eingang",
+        "problemReadingWorkflow": "Problem beim Lesen des Arbeitsablaufs vom Bild",
+        "reloadNodeTemplates": "Knoten-Vorlagen neu laden",
+        "newWorkflow": "Neuer Arbeitsablauf / Workflow",
+        "newWorkflowDesc": "Einen neuen Arbeitsablauf erstellen?",
+        "noFieldsLinearview": "Keine Felder zur linearen Ansicht hinzugefügt",
+        "clearWorkflow": "Workflow löschen",
+        "clearWorkflowDesc": "Diesen Arbeitsablauf löschen und neu starten?",
+        "noConnectionInProgress": "Es besteht keine Verbindung",
+        "notes": "Anmerkungen",
+        "nodeVersion": "Knoten Version",
+        "noOutputSchemaName": "Kein Name des Ausgabeschemas im ref-Objekt gefunden",
+        "node": "Knoten",
+        "nodeSearch": "Knoten suchen",
+        "removeLinearView": "Entfernen aus Linear View",
+        "nodeOutputs": "Knoten-Ausgänge",
+        "nodeTemplate": "Knoten-Vorlage",
+        "nodeType": "Knotentyp",
+        "noFieldType": "Kein Feldtyp",
+        "oNNXModelField": "ONNX-Modell",
+        "noMatchingNodes": "Keine passenden Knoten",
+        "noNodeSelected": "Kein Knoten gewählt",
+        "noImageFoundState": "Kein Anfangsbild im Status gefunden",
+        "nodeOpacity": "Knoten-Deckkraft",
+        "noOutputRecorded": "Keine Ausgänge aufgezeichnet",
+        "outputSchemaNotFound": "Ausgabeschema nicht gefunden",
+        "oNNXModelFieldDescription": "ONNX-Modellfeld.",
+        "outputNode": "Ausgabeknoten",
+        "pickOne": "Eins auswählen",
+        "problemReadingMetadata": "Problem beim Lesen von Metadaten aus dem Bild",
+        "notesDescription": "Anmerkungen zum Arbeitsablauf hinzufügen",
+        "outputFields": "Ausgabefelder",
+        "sDXLRefinerModelField": "Refiner-Modell",
+        "sDXLMainModelFieldDescription": "SDXL Modellfeld.",
+        "clearWorkflowDesc2": "Ihr aktueller Arbeitsablauf hat ungespeicherte Änderungen.",
+        "skipped": "Übersprungen",
+        "schedulerDescription": "Zu erledigen",
+        "scheduler": "Planer",
+        "showGraphNodes": "Graph Overlay anzeigen",
+        "showMinimapnodes": "MiniMap anzeigen",
+        "sDXLMainModelField": "SDXL Modell",
+        "skippedReservedInput": "Reserviertes Eingabefeld übersprungen",
+        "sDXLRefinerModelFieldDescription": "Zu erledigen",
+        "showLegendNodes": "Feldtyp-Legende anzeigen",
+        "skippedReservedOutput": "Reserviertes Ausgangsfeld übersprungen",
+        "skippingInputNoTemplate": "Überspringe Eingabefeld ohne Vorlage",
+        "executionStateCompleted": "Erledigt",
+        "denoiseMaskFieldDescription": "Denoise Maske kann zwischen Knoten weitergegeben werden",
+        "downloadWorkflow": "Workflow JSON herunterladen",
+        "executionStateInProgress": "In Bearbeitung",
+        "snapToGridHelp": "Knoten am Gitternetz einrasten bei Bewegung",
+        "controlCollection": "Control-Sammlung",
+        "controlFieldDescription": "Control-Informationen zwischen Knotenpunkten weitergegeben.",
+        "latentsField": "Latents",
+        "mainModelFieldDescription": "Zu erledigen",
+        "missingTemplate": "Ungültiger Knoten: Knoten {{node}} vom Typ {{type}} fehlt Vorlage (nicht installiert?)",
+        "skippingUnknownInputType": "Überspringe unbekannten Eingabe-Feldtyp",
+        "stringCollectionDescription": "Eine Sammlung von Zeichenfolgen.",
+        "string": "Zeichenfolge",
+        "stringCollection": "Sammlung von Zeichenfolgen",
+        "stringDescription": "Zeichenfolgen (Strings) sind Text.",
+        "fieldTypesMustMatch": "Feldtypen müssen übereinstimmen",
+        "fitViewportNodes": "An Ansichtsgröße anpassen",
+        "missingCanvaInitMaskImages": "Fehlende Startbilder und Masken auf der Leinwand",
+        "missingCanvaInitImage": "Fehlendes Startbild auf der Leinwand",
+        "ipAdapterModelDescription": "IP-Adapter-Modellfeld",
+        "latentsPolymorphicDescription": "Zwischen Nodes können Latents weitergegeben werden.",
+        "loadingNodes": "Lade Nodes...",
+        "latentsCollectionDescription": "Zwischen Knoten können Latents weitergegeben werden.",
+        "mismatchedVersion": "Ungültiger Knoten: Knoten {{node}} vom Typ {{type}} hat keine passende Version (Update versuchen?)",
+        "colorCollectionDescription": "Zu erledigen",
+        "ipAdapterPolymorphicDescription": "Eine Sammlung von IP-Adaptern.",
+        "fullyContainNodesHelp": "Nodes müssen vollständig innerhalb der Auswahlbox sein, um ausgewählt werden zu können",
+        "latentsFieldDescription": "Zwischen Nodes können Latents weitergegeben werden.",
+        "noWorkflow": "Kein Workflow",
+        "hideGraphNodes": "Graph Overlay verbergen",
+        "sourceNode": "Quellknoten",
+        "executionStateError": "Fehler",
+        "latentsCollection": "Latents Sammlung",
+        "maybeIncompatible": "Möglicherweise inkompatibel mit installierten",
+        "nodePack": "Knoten-Pack",
+        "skippingUnknownOutputType": "Überspringe unbekannten Ausgabe-Feldtyp",
+        "loadWorkflow": "Lade Workflow",
+        "snapToGrid": "Am Gitternetz einrasten",
+        "skippingReservedFieldType": "Überspringe reservierten Feldtyp",
+        "loRAModelField": "LoRA",
+        "loRAModelFieldDescription": "Zu erledigen",
+        "mainModelField": "Modell",
+        "doesNotExist": "existiert nicht",
+        "vaeField": "VAE",
+        "unknownOutput": "Unbekannte Ausgabe: {{name}}",
+        "updateNode": "Knoten updaten",
+        "edge": "Rand / Kante",
+        "sourceNodeDoesNotExist": "Ungültiger Rand: Quell- / Ausgabe-Knoten {{node}} existiert nicht",
+        "updateAllNodes": "Update Knoten",
+        "allNodesUpdated": "Alle Knoten aktualisiert",
+        "unknownTemplate": "Unbekannte Vorlage",
+        "floatDescription": "Floats sind Zahlen mit einem Dezimalpunkt.",
+        "updateApp": "Update App",
+        "vaeFieldDescription": "VAE Submodell.",
+        "unknownInput": "Unbekannte Eingabe: {{name}}",
+        "unknownNodeType": "Unbekannter Knotentyp",
+        "float": "Kommazahlen"
     },
     "hrf": {
-        "enableHrf": "Aktivieren Sie die Korrektur für hohe Auflösungen",
+        "enableHrf": "Korrektur für hohe Auflösungen",
         "upscaleMethod": "Vergrößerungsmethoden",
         "enableHrfTooltip": "Generieren Sie mit einer niedrigeren Anfangsauflösung, skalieren Sie auf die Basisauflösung hoch und führen Sie dann Image-to-Image aus.",
         "metadata": {
@@ -1026,7 +1313,14 @@
         "noLoRAsInstalled": "Keine LoRAs installiert",
         "selectLoRA": "Wählen ein LoRA aus",
         "esrganModel": "ESRGAN Modell",
-        "addLora": "LoRA hinzufügen"
+        "addLora": "LoRA hinzufügen",
+        "defaultVAE": "Standard VAE",
+        "noLoRAsLoaded": "Keine LoRAs geladen",
+        "lora": "LoRA",
+        "allLoRAsAdded": "Alle LoRAs hinzugefügt",
+        "incompatibleBaseModel": "Inkompatibles Basismodell",
+        "noMainModelSelected": "Kein Hauptmodell ausgewählt",
+        "loraAlreadyAdded": "LoRA bereits hinzugefügt"
     },
     "accordions": {
         "generation": {
@@ -1050,5 +1344,40 @@
             "infillTab": "Füllung",
             "title": "Compositing"
         }
+    },
+    "workflows": {
+        "workflows": "Arbeitsabläufe",
+        "noSystemWorkflows": "Keine System-Arbeitsabläufe",
+        "workflowName": "Arbeitsablauf-Name",
+        "workflowIsOpen": "Arbeitsablauf ist geöffnet",
+        "saveWorkflowAs": "Arbeitsablauf speichern als",
+        "searchWorkflows": "Suche Arbeitsabläufe",
+        "newWorkflowCreated": "Neuer Arbeitsablauf erstellt",
+        "problemSavingWorkflow": "Problem beim Speichern des Arbeitsablaufs",
+        "noRecentWorkflows": "Keine kürzlichen Arbeitsabläufe",
+        "problemLoading": "Problem beim Laden von Arbeitsabläufen",
+        "downloadWorkflow": "Speichern als",
+        "savingWorkflow": "Speichere Arbeitsablauf...",
+        "saveWorkflow": "Arbeitsablauf speichern",
+        "noWorkflows": "Keine Arbeitsabläufe",
+        "workflowLibrary": "Bibliothek",
+        "defaultWorkflows": "Standard-Arbeitsabläufe",
+        "unnamedWorkflow": "Unbenannter Arbeitsablauf",
+        "noDescription": "Keine Beschreibung",
+        "clearWorkflowSearchFilter": "Suchfilter zurücksetzen",
+        "workflowEditorMenu": "Arbeitsablauf-Editor Menü",
+        "deleteWorkflow": "Arbeitsablauf löschen",
+        "userWorkflows": "Meine Arbeitsabläufe",
+        "workflowSaved": "Arbeitsablauf gespeichert",
+        "uploadWorkflow": "Aus Datei laden",
+        "projectWorkflows": "Projekt-Arbeitsabläufe",
+        "openWorkflow": "Arbeitsablauf öffnen",
+        "noUserWorkflows": "Keine Benutzer-Arbeitsabläufe",
+        "saveWorkflowToProject": "Arbeitsablauf in Projekt speichern",
+        "workflowCleared": "Arbeitsablauf gelöscht",
+        "loading": "Lade Arbeitsabläufe"
+    },
+    "app": {
+        "storeNotInitialized": "App-Store ist nicht initialisiert"
     }
 }

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

@@ -175,6 +175,7 @@
         "statusUpscaling": "Upscaling",
         "statusUpscalingESRGAN": "Upscaling (ESRGAN)",
         "template": "Template",
+        "toResolve": "To resolve",
         "training": "Training",
         "trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.",
         "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
@@ -235,6 +236,9 @@
         "fill": "Fill",
         "h": "H",
         "handAndFace": "Hand and Face",
+        "face": "Face",
+        "body": "Body",
+        "hands": "Hands",
         "hed": "HED",
         "hedDescription": "Holistically-Nested Edge Detection",
         "hideAdvanced": "Hide Advanced",
@@ -261,8 +265,8 @@
         "noneDescription": "No processing applied",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE processing",
-        "openPose": "Openpose",
-        "openPoseDescription": "Human pose estimation using Openpose",
+        "dwOpenpose": "DW Openpose",
+        "dwOpenposeDescription": "Human pose estimation using DW Openpose",
         "pidi": "PIDI",
         "pidiDescription": "PIDI image processing",
         "processor": "Processor",
@@ -897,6 +901,7 @@
         "doesNotExist": "does not exist",
         "downloadWorkflow": "Download Workflow JSON",
         "edge": "Edge",
+        "editMode": "Edit in Workflow Editor",
         "enum": "Enum",
         "enumDescription": "Enums are values that may be one of a number of options.",
         "executionStateCompleted": "Completed",
@@ -992,8 +997,10 @@
         "problemReadingMetadata": "Problem reading metadata from image",
         "problemReadingWorkflow": "Problem reading workflow from image",
         "problemSettingTitle": "Problem Setting Title",
+        "resetToDefaultValue": "Reset to default value",
         "reloadNodeTemplates": "Reload Node Templates",
         "removeLinearView": "Remove from Linear View",
+        "reorderLinearView": "Reorder Linear View",
         "newWorkflow": "New Workflow",
         "newWorkflowDesc": "Create a new workflow?",
         "newWorkflowDesc2": "Your current workflow has unsaved changes.",
@@ -1064,6 +1071,7 @@
         "vaeModelFieldDescription": "TODO",
         "validateConnections": "Validate Connections and Graph",
         "validateConnectionsHelp": "Prevent invalid connections from being made, and invalid graphs from being invoked",
+        "viewMode": "Use in Linear View",
         "unableToGetWorkflowVersion": "Unable to get workflow schema version",
         "unrecognizedWorkflowVersion": "Unrecognized workflow schema version {{version}}",
         "version": "Version",

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

@@ -795,7 +795,8 @@
         "workflowDeleted": "Flusso di lavoro eliminato",
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
-        "uploadInitialImage": "Carica l'immagine iniziale"
+        "uploadInitialImage": "Carica l'immagine iniziale",
+        "problemDownloadingImage": "Impossibile scaricare l'immagine"
     },
     "tooltip": {
         "feature": {
@@ -1134,7 +1135,10 @@
         "newWorkflow": "Nuovo flusso di lavoro",
         "newWorkflowDesc": "Creare un nuovo flusso di lavoro?",
         "newWorkflowDesc2": "Il flusso di lavoro attuale presenta modifiche non salvate.",
-        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})"
+        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})",
+        "clearWorkflowDesc": "Cancellare questo flusso di lavoro e avviarne uno nuovo?",
+        "clearWorkflow": "Cancella il flusso di lavoro",
+        "clearWorkflowDesc2": "Il tuo flusso di lavoro attuale presenta modifiche non salvate."
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1191,7 +1195,6 @@
         "f": "F",
         "h": "A",
         "prompt": "Prompt",
-        "openPoseDescription": "Stima della posa umana utilizzando Openpose",
         "resizeMode": "Ridimensionamento",
         "weight": "Peso",
         "selectModel": "Seleziona un modello",
@@ -1672,7 +1675,9 @@
         "downloadWorkflow": "Salva su file",
         "uploadWorkflow": "Carica da file",
         "projectWorkflows": "Flussi di lavoro del progetto",
-        "noWorkflows": "Nessun flusso di lavoro"
+        "noWorkflows": "Nessun flusso di lavoro",
+        "workflowCleared": "Flusso di lavoro cancellato",
+        "saveWorkflowToProject": "Salva flusso di lavoro nel progetto"
     },
     "app": {
         "storeNotInitialized": "Il negozio non è inizializzato"

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

@@ -555,7 +555,6 @@
         "balanced": "バランス",
         "prompt": "プロンプト",
         "depthMidasDescription": "Midasを使用して深度マップを生成",
-        "openPoseDescription": "Openposeを使用してポーズを推定",
         "control": "コントロール",
         "resizeMode": "リサイズモード",
         "weight": "重み",

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

@@ -333,7 +333,6 @@
         "h": "H",
         "prompt": "프롬프트",
         "depthMidasDescription": "Midas를 사용하여 Depth map 생성하기",
-        "openPoseDescription": "Openpose를 이용한 사람 포즈 추정",
         "control": "Control",
         "resizeMode": "크기 조정 모드",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) 사용 가능,$t(common.controlNet) 사용 불가능",
@@ -370,7 +369,6 @@
         "normalBaeDescription": "Normal BAE 처리",
         "noneDescription": "처리되지 않음",
         "saveControlImage": "Control Image 저장",
-        "openPose": "Openpose",
         "toggleControlNet": "해당 ControlNet으로 전환",
         "delete": "삭제",
         "controlAdapter_other": "Control Adapter(s)",

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

@@ -1033,7 +1033,6 @@
         "prompt": "Prompt",
         "depthMidasDescription": "Genereer diepteblad via Midas",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
-        "openPoseDescription": "Menselijke pose-benadering via Openpose",
         "control": "Controle",
         "resizeMode": "Modus schaling",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ingeschakeld, $t(common.controlNet)s uitgeschakeld",
@@ -1072,7 +1071,6 @@
         "normalBaeDescription": "Normale BAE-verwerking",
         "noneDescription": "Geen verwerking toegepast",
         "saveControlImage": "Bewaar controle-afbeelding",
-        "openPose": "Openpose",
         "toggleControlNet": "Zet deze ControlNet aan/uit",
         "delete": "Verwijder",
         "controlAdapter_one": "Control-adapter",

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

@@ -1155,7 +1155,6 @@
         "resetControlImage": "Сбросить контрольное изображение",
         "prompt": "Запрос",
         "controlnet": "$t(controlnet.controlAdapter_one) №{{number}} $t(common.controlNet)",
-        "openPoseDescription": "Оценка позы человека с помощью Openpose",
         "resizeMode": "Режим изменения размера",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) включен, $t(common.controlNet)s отключен",
         "weight": "Вес",

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

@@ -259,7 +259,6 @@
         "mediapipeFace": "Mediapipe Yüz",
         "megaControl": "Aşırı Yönetim",
         "mlsd": "M-LSD",
-        "openPoseDescription": "Openpose kullanarak poz belirleme",
         "setControlImageDimensions": "Yönetim Görseli Boyutlarını En/Boydan Al",
         "pidi": "PIDI",
         "scribble": "çiziktirme",
@@ -273,7 +272,6 @@
         "mlsdDescription": "Minimalist Line Segment Detector (Kolay Çizgi Parçası Algılama)",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE işleme",
-        "openPose": "Openpose",
         "resetControlImage": "Yönetim Görselini Kaldır",
         "enableIPAdapter": "IP Aracını Etkinleştir",
         "lineart": "Çizim",

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

@@ -1143,7 +1143,6 @@
         "balanced": "平衡",
         "prompt": "Prompt (提示词控制)",
         "depthMidasDescription": "使用 Midas 生成深度图",
-        "openPoseDescription": "使用 Openpose 进行人体姿态估计",
         "resizeMode": "缩放模式",
         "weight": "权重",
         "selectModel": "选择一个模型",
@@ -1207,7 +1206,6 @@
         "megaControl": "Mega Control (超级控制)",
         "depthZoe": "Depth (Zoe)",
         "colorMap": "Color",
-        "openPose": "Openpose",
         "controlAdapter_other": "Control Adapters",
         "lineartAnime": "Lineart Anime",
         "canny": "Canny",

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

@@ -2,7 +2,7 @@ import type { UnknownAction } from '@reduxjs/toolkit';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { cloneDeep } from 'lodash-es';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
 
@@ -18,7 +18,7 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     }
   }
 
-  if (receivedOpenAPISchema.fulfilled.match(action)) {
+  if (appInfoApi.endpoints.getOpenAPISchema.matchFulfilled(action)) {
     return {
       ...action,
       payload: '<OpenAPI schema omitted>',

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

@@ -23,6 +23,7 @@ import { addControlNetImageProcessedListener } from './listeners/controlNetImage
 import { addEnqueueRequestedCanvasListener } from './listeners/enqueueRequestedCanvas';
 import { addEnqueueRequestedLinear } from './listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from './listeners/enqueueRequestedNodes';
+import { addGetOpenAPISchemaListener } from './listeners/getOpenAPISchema';
 import {
   addImageAddedToBoardFulfilledListener,
   addImageAddedToBoardRejectedListener,
@@ -47,7 +48,6 @@ import { addInitialImageSelectedListener } from './listeners/initialImageSelecte
 import { addModelSelectedListener } from './listeners/modelSelected';
 import { addModelsLoadedListener } from './listeners/modelsLoaded';
 import { addDynamicPromptsListener } from './listeners/promptChanged';
-import { addReceivedOpenAPISchemaListener } from './listeners/receivedOpenAPISchema';
 import { addSocketConnectedEventListener as addSocketConnectedListener } from './listeners/socketio/socketConnected';
 import { addSocketDisconnectedEventListener as addSocketDisconnectedListener } from './listeners/socketio/socketDisconnected';
 import { addGeneratorProgressEventListener as addGeneratorProgressListener } from './listeners/socketio/socketGeneratorProgress';
@@ -150,7 +150,7 @@ addImageRemovedFromBoardRejectedListener();
 addBoardIdSelectedListener();
 
 // Node schemas
-addReceivedOpenAPISchemaListener();
+addGetOpenAPISchemaListener();
 
 // Workflows
 addWorkflowLoadRequestedListener();

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

@@ -3,18 +3,18 @@ import { parseify } from 'common/util/serialize';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 
 import { startAppListening } from '..';
 
-export const addReceivedOpenAPISchemaListener = () => {
+export const addGetOpenAPISchemaListener = () => {
   startAppListening({
-    actionCreator: receivedOpenAPISchema.fulfilled,
+    matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
     effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON }, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
@@ -26,10 +26,14 @@ export const addReceivedOpenAPISchemaListener = () => {
   });
 
   startAppListening({
-    actionCreator: receivedOpenAPISchema.rejected,
+    matcher: appInfoApi.endpoints.getOpenAPISchema.matchRejected,
     effect: (action) => {
-      const log = logger('system');
-      log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
+      // If action.meta.condition === true, the request was canceled/skipped because another request was in flight or
+      // the value was already in the cache. We don't want to log these errors.
+      if (!action.meta.condition) {
+        const log = logger('system');
+        log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
+      }
     },
   });
 };

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

@@ -1,10 +1,9 @@
 import { logger } from 'app/logging/logger';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
-import { isEqual, size } from 'lodash-es';
+import { isEqual } from 'lodash-es';
 import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { socketConnected } from 'services/events/actions';
 
 import { startAppListening } from '../..';
@@ -77,17 +76,4 @@ export const addSocketConnectedEventListener = () => {
       }
     },
   });
-
-  startAppListening({
-    actionCreator: socketConnected,
-    effect: async (action, { dispatch, getState }) => {
-      const { nodeTemplates, config } = getState();
-      // We only want to re-fetch the schema if we don't have any node templates
-      if (!size(nodeTemplates.templates) && !config.disabledTabs.includes('nodes')) {
-        // This request is a createAsyncThunk - resetting API state as in the above listener
-        // will not trigger this request, so we need to manually do it.
-        dispatch(receivedOpenAPISchema());
-      }
-    },
-  });
 };

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

@@ -6,7 +6,6 @@ import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/typ
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
 import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
-import { setActiveTab } from 'features/ui/store/uiSlice';
 import { t } from 'i18next';
 import { z } from 'zod';
 import { fromZodError } from 'zod-validation-error';
@@ -53,7 +52,6 @@ export const addWorkflowLoadRequestedListener = () => {
           });
         }
 
-        dispatch(setActiveTab('nodes'));
         requestAnimationFrame(() => {
           $flow.get()?.fitView();
         });

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

@@ -1,19 +1,28 @@
+import { useStore } from '@nanostores/react';
 import { useAppToaster } from 'app/components/Toaster';
+import { $authToken } from 'app/store/nanostores/authToken';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { imageDownloaded } from 'features/gallery/store/actions';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-import { useImageUrlToBlob } from './useImageUrlToBlob';
-
 export const useDownloadImage = () => {
   const toaster = useAppToaster();
   const { t } = useTranslation();
-  const imageUrlToBlob = useImageUrlToBlob();
+  const dispatch = useAppDispatch();
+  const authToken = useStore($authToken);
 
   const downloadImage = useCallback(
     async (image_url: string, image_name: string) => {
       try {
-        const blob = await imageUrlToBlob(image_url);
-
+        const requestOpts = authToken
+          ? {
+              headers: {
+                Authorization: `Bearer ${authToken}`,
+              },
+            }
+          : {};
+        const blob = await fetch(image_url, requestOpts).then((resp) => resp.blob());
         if (!blob) {
           throw new Error('Unable to create Blob');
         }
@@ -26,6 +35,7 @@ export const useDownloadImage = () => {
         document.body.appendChild(a);
         a.click();
         window.URL.revokeObjectURL(url);
+        dispatch(imageDownloaded());
       } catch (err) {
         toaster({
           title: t('toast.problemDownloadingImage'),
@@ -36,7 +46,7 @@ export const useDownloadImage = () => {
         });
       }
     },
-    [t, toaster, imageUrlToBlob]
+    [t, toaster, dispatch, authToken]
   );
 
   return { downloadImage };

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

@@ -5,6 +5,7 @@ import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIDndImage from 'common/components/IAIDndImage';
 import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
+import { roundToMultiple } from 'common/util/roundDownToMultiple';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
 import { useControlAdapterControlImage } from 'features/controlAdapters/hooks/useControlAdapterControlImage';
 import { useControlAdapterProcessedControlImage } from 'features/controlAdapters/hooks/useControlAdapterProcessedControlImage';
@@ -91,19 +92,14 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
       return;
     }
 
+    const width = roundToMultiple(controlImage.width, 8);
+    const height = roundToMultiple(controlImage.height, 8);
+
     if (activeTabName === 'unifiedCanvas') {
-      dispatch(
-        setBoundingBoxDimensions(
-          {
-            width: controlImage.width,
-            height: controlImage.height,
-          },
-          optimalDimension
-        )
-      );
+      dispatch(setBoundingBoxDimensions({ width, height }, optimalDimension));
     } else {
-      dispatch(widthChanged(controlImage.width));
-      dispatch(heightChanged(controlImage.height));
+      dispatch(widthChanged(width));
+      dispatch(heightChanged(height));
     }
   }, [controlImage, activeTabName, dispatch, optimalDimension]);
 

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

@@ -6,6 +6,7 @@ import CannyProcessor from './processors/CannyProcessor';
 import ColorMapProcessor from './processors/ColorMapProcessor';
 import ContentShuffleProcessor from './processors/ContentShuffleProcessor';
 import DepthAnyThingProcessor from './processors/DepthAnyThingProcessor';
+import DWOpenposeProcessor from './processors/DWOpenposeProcessor';
 import HedProcessor from './processors/HedProcessor';
 import LineartAnimeProcessor from './processors/LineartAnimeProcessor';
 import LineartProcessor from './processors/LineartProcessor';
@@ -13,7 +14,6 @@ import MediapipeFaceProcessor from './processors/MediapipeFaceProcessor';
 import MidasDepthProcessor from './processors/MidasDepthProcessor';
 import MlsdImageProcessor from './processors/MlsdImageProcessor';
 import NormalBaeProcessor from './processors/NormalBaeProcessor';
-import OpenposeProcessor from './processors/OpenposeProcessor';
 import PidiProcessor from './processors/PidiProcessor';
 import ZoeDepthProcessor from './processors/ZoeDepthProcessor';
 
@@ -73,8 +73,8 @@ const ControlAdapterProcessorComponent = ({ id }: Props) => {
     return <NormalBaeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
   }
 
-  if (processorNode.type === 'openpose_image_processor') {
-    return <OpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
+  if (processorNode.type === 'dw_openpose_image_processor') {
+    return <DWOpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
   }
 
   if (processorNode.type === 'pidi_image_processor') {

invokeai/frontend/web/src/features/controlAdapters/components/processors/DWOpenposeProcessor.tsx

@@ -0,0 +1,92 @@
+import { CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
+import { useProcessorNodeChanged } from 'features/controlAdapters/components/hooks/useProcessorNodeChanged';
+import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
+import type { RequiredDWOpenposeImageProcessorInvocation } from 'features/controlAdapters/store/types';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.dw_openpose_image_processor
+  .default as RequiredDWOpenposeImageProcessorInvocation;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredDWOpenposeImageProcessorInvocation;
+  isEnabled: boolean;
+};
+
+const DWOpenposeProcessor = (props: Props) => {
+  const { controlNetId, processorNode, isEnabled } = props;
+  const { image_resolution, draw_body, draw_face, draw_hands } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+  const { t } = useTranslation();
+
+  const handleDrawBodyChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { draw_body: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDrawFaceChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { draw_face: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleDrawHandsChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { draw_hands: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <Flex sx={{ flexDir: 'row', gap: 6 }}>
+        <FormControl isDisabled={!isEnabled} w="max-content">
+          <FormLabel>{t('controlnet.body')}</FormLabel>
+          <Switch defaultChecked={DEFAULTS.draw_body} isChecked={draw_body} onChange={handleDrawBodyChanged} />
+        </FormControl>
+        <FormControl isDisabled={!isEnabled} w="max-content">
+          <FormLabel>{t('controlnet.face')}</FormLabel>
+          <Switch defaultChecked={DEFAULTS.draw_face} isChecked={draw_face} onChange={handleDrawFaceChanged} />
+        </FormControl>
+        <FormControl isDisabled={!isEnabled} w="max-content">
+          <FormLabel>{t('controlnet.hands')}</FormLabel>
+          <Switch defaultChecked={DEFAULTS.draw_hands} isChecked={draw_hands} onChange={handleDrawHandsChanged} />
+        </FormControl>
+      </Flex>
+      <FormControl isDisabled={!isEnabled}>
+        <FormLabel>{t('controlnet.imageResolution')}</FormLabel>
+        <CompositeSlider
+          value={image_resolution}
+          onChange={handleImageResolutionChanged}
+          defaultValue={DEFAULTS.image_resolution}
+          min={0}
+          max={4096}
+          marks
+        />
+        <CompositeNumberInput
+          value={image_resolution}
+          onChange={handleImageResolutionChanged}
+          defaultValue={DEFAULTS.image_resolution}
+          min={0}
+          max={4096}
+        />
+      </FormControl>
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(DWOpenposeProcessor);

invokeai/frontend/web/src/features/controlAdapters/components/processors/OpenposeProcessor.tsx

@@ -1,92 +0,0 @@
-import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
-import { useProcessorNodeChanged } from 'features/controlAdapters/components/hooks/useProcessorNodeChanged';
-import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
-import type { RequiredOpenposeImageProcessorInvocation } from 'features/controlAdapters/store/types';
-import type { ChangeEvent } from 'react';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-import ProcessorWrapper from './common/ProcessorWrapper';
-
-const DEFAULTS = CONTROLNET_PROCESSORS.openpose_image_processor.default as RequiredOpenposeImageProcessorInvocation;
-
-type Props = {
-  controlNetId: string;
-  processorNode: RequiredOpenposeImageProcessorInvocation;
-  isEnabled: boolean;
-};
-
-const OpenposeProcessor = (props: Props) => {
-  const { controlNetId, processorNode, isEnabled } = props;
-  const { image_resolution, detect_resolution, hand_and_face } = processorNode;
-  const processorChanged = useProcessorNodeChanged();
-  const { t } = useTranslation();
-
-  const handleDetectResolutionChanged = useCallback(
-    (v: number) => {
-      processorChanged(controlNetId, { detect_resolution: v });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  const handleImageResolutionChanged = useCallback(
-    (v: number) => {
-      processorChanged(controlNetId, { image_resolution: v });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  const handleHandAndFaceChanged = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      processorChanged(controlNetId, { hand_and_face: e.target.checked });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  return (
-    <ProcessorWrapper>
-      <FormControl isDisabled={!isEnabled}>
-        <FormLabel>{t('controlnet.detectResolution')}</FormLabel>
-        <CompositeSlider
-          value={detect_resolution}
-          onChange={handleDetectResolutionChanged}
-          defaultValue={DEFAULTS.detect_resolution}
-          min={0}
-          max={4096}
-          marks
-        />
-        <CompositeNumberInput
-          value={detect_resolution}
-          onChange={handleDetectResolutionChanged}
-          defaultValue={DEFAULTS.detect_resolution}
-          min={0}
-          max={4096}
-        />
-      </FormControl>
-      <FormControl isDisabled={!isEnabled}>
-        <FormLabel>{t('controlnet.imageResolution')}</FormLabel>
-        <CompositeSlider
-          value={image_resolution}
-          onChange={handleImageResolutionChanged}
-          defaultValue={DEFAULTS.image_resolution}
-          min={0}
-          max={4096}
-          marks
-        />
-        <CompositeNumberInput
-          value={image_resolution}
-          onChange={handleImageResolutionChanged}
-          defaultValue={DEFAULTS.image_resolution}
-          min={0}
-          max={4096}
-        />
-      </FormControl>
-      <FormControl isDisabled={!isEnabled}>
-        <FormLabel>{t('controlnet.handAndFace')}</FormLabel>
-        <Switch isChecked={hand_and_face} onChange={handleHandAndFaceChanged} />
-      </FormControl>
-    </ProcessorWrapper>
-  );
-};
-
-export default memo(OpenposeProcessor);

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

@@ -205,20 +205,21 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
       image_resolution: 512,
     },
   },
-  openpose_image_processor: {
-    type: 'openpose_image_processor',
+  dw_openpose_image_processor: {
+    type: 'dw_openpose_image_processor',
     get label() {
-      return i18n.t('controlnet.openPose');
+      return i18n.t('controlnet.dwOpenpose');
     },
     get description() {
-      return i18n.t('controlnet.openPoseDescription');
+      return i18n.t('controlnet.dwOpenposeDescription');
     },
     default: {
-      id: 'openpose_image_processor',
-      type: 'openpose_image_processor',
-      detect_resolution: 512,
+      id: 'dw_openpose_image_processor',
+      type: 'dw_openpose_image_processor',
       image_resolution: 512,
-      hand_and_face: false,
+      draw_body: true,
+      draw_face: false,
+      draw_hands: false,
     },
   },
   pidi_image_processor: {
@@ -266,7 +267,7 @@ export const CONTROLNET_MODEL_DEFAULT_PROCESSORS: {
   lineart_anime: 'lineart_anime_image_processor',
   softedge: 'hed_image_processor',
   shuffle: 'content_shuffle_image_processor',
-  openpose: 'openpose_image_processor',
+  openpose: 'dw_openpose_image_processor',
   mediapipe: 'mediapipe_face_processor',
   pidi: 'pidi_image_processor',
   zoe: 'zoe_depth_image_processor',

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

@@ -11,6 +11,7 @@ import type {
   ColorMapImageProcessorInvocation,
   ContentShuffleImageProcessorInvocation,
   DepthAnythingImageProcessorInvocation,
+  DWOpenposeImageProcessorInvocation,
   HedImageProcessorInvocation,
   LineartAnimeImageProcessorInvocation,
   LineartImageProcessorInvocation,
@@ -18,7 +19,6 @@ import type {
   MidasDepthImageProcessorInvocation,
   MlsdImageProcessorInvocation,
   NormalbaeImageProcessorInvocation,
-  OpenposeImageProcessorInvocation,
   PidiImageProcessorInvocation,
   ZoeDepthImageProcessorInvocation,
 } from 'services/api/types';
@@ -40,7 +40,7 @@ export type ControlAdapterProcessorNode =
   | MidasDepthImageProcessorInvocation
   | MlsdImageProcessorInvocation
   | NormalbaeImageProcessorInvocation
-  | OpenposeImageProcessorInvocation
+  | DWOpenposeImageProcessorInvocation
   | PidiImageProcessorInvocation
   | ZoeDepthImageProcessorInvocation;
 
@@ -143,11 +143,11 @@ export type RequiredNormalbaeImageProcessorInvocation = O.Required<
 >;
 
 /**
- * The Openpose processor node, with parameters flagged as required
+ * The DW Openpose processor node, with parameters flagged as required
  */
-export type RequiredOpenposeImageProcessorInvocation = O.Required<
-  OpenposeImageProcessorInvocation,
-  'type' | 'detect_resolution' | 'image_resolution' | 'hand_and_face'
+export type RequiredDWOpenposeImageProcessorInvocation = O.Required<
+  DWOpenposeImageProcessorInvocation,
+  'type' | 'image_resolution' | 'draw_body' | 'draw_face' | 'draw_hands'
 >;
 
 /**
@@ -179,7 +179,7 @@ export type RequiredControlAdapterProcessorNode =
       | RequiredMidasDepthImageProcessorInvocation
       | RequiredMlsdImageProcessorInvocation
       | RequiredNormalbaeImageProcessorInvocation
-      | RequiredOpenposeImageProcessorInvocation
+      | RequiredDWOpenposeImageProcessorInvocation
       | RequiredPidiImageProcessorInvocation
       | RequiredZoeDepthImageProcessorInvocation,
       'id'
@@ -299,10 +299,10 @@ export const isNormalbaeImageProcessorInvocation = (obj: unknown): obj is Normal
 };
 
 /**
- * Type guard for OpenposeImageProcessorInvocation
+ * Type guard for DWOpenposeImageProcessorInvocation
  */
-export const isOpenposeImageProcessorInvocation = (obj: unknown): obj is OpenposeImageProcessorInvocation => {
-  if (isObject(obj) && 'type' in obj && obj.type === 'openpose_image_processor') {
+export const isDWOpenposeImageProcessorInvocation = (obj: unknown): obj is DWOpenposeImageProcessorInvocation => {
+  if (isObject(obj) && 'type' in obj && obj.type === 'dw_openpose_image_processor') {
     return true;
   }
   return false;

invokeai/frontend/web/src/features/dnd/components/DndSortable.tsx

@@ -0,0 +1,23 @@
+import type { DragEndEvent } from '@dnd-kit/core';
+import { SortableContext, verticalListSortingStrategy } from '@dnd-kit/sortable';
+import type { PropsWithChildren } from 'react';
+import { memo } from 'react';
+
+import { DndContextTypesafe } from './DndContextTypesafe';
+
+type Props = PropsWithChildren & {
+  items: string[];
+  onDragEnd(event: DragEndEvent): void;
+};
+
+const DndSortable = (props: Props) => {
+  return (
+    <DndContextTypesafe onDragEnd={props.onDragEnd}>
+      <SortableContext items={props.items} strategy={verticalListSortingStrategy}>
+        {props.children}
+      </SortableContext>
+    </DndContextTypesafe>
+  );
+};
+
+export default memo(DndSortable);

invokeai/frontend/web/src/features/gallery/store/actions.ts

@@ -14,3 +14,5 @@ export const requestedBoardImagesDeletion = createAction<RequestedBoardImagesDel
 export const sentImageToCanvas = createAction('gallery/sentImageToCanvas');
 
 export const sentImageToImg2Img = createAction('gallery/sentImageToImg2Img');
+
+export const imageDownloaded = createAction('gallery/imageDownloaded');

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -35,7 +35,7 @@ export const loraSlice = createSlice({
     },
     loraRecalled: (state, action: PayloadAction<LoRAModelConfigEntity & { weight: number }>) => {
       const { model_name, id, base_model, weight } = action.payload;
-      state.loras[id] = { id, model_name, base_model, weight };
+      state.loras[id] = { id, model_name, base_model, weight, isEnabled: true };
     },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;

invokeai/frontend/web/src/features/nodes/components/NodeEditor.tsx

@@ -1,7 +1,6 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import TopPanel from 'features/nodes/components/flow/panels/TopPanel/TopPanel';
 import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/SaveWorkflowAsDialog';
@@ -11,6 +10,7 @@ import type { CSSProperties } from 'react';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { MdDeviceHub } from 'react-icons/md';
+import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 import AddNodePopover from './flow/AddNodePopover/AddNodePopover';
 import { Flow } from './flow/Flow';
@@ -40,7 +40,7 @@ const exit: AnimationProps['exit'] = {
 };
 
 const NodeEditor = () => {
-  const isReady = useAppSelector((s) => s.nodes.isReady);
+  const { data, isLoading } = useGetOpenAPISchemaQuery();
   const { t } = useTranslation();
   return (
     <Flex
@@ -53,7 +53,7 @@ const NodeEditor = () => {
       justifyContent="center"
     >
       <AnimatePresence>
-        {isReady && (
+        {data && (
           <motion.div initial={initial} animate={animate} exit={exit} style={isReadyMotionStyles}>
             <Flow />
             <AddNodePopover />
@@ -65,7 +65,7 @@ const NodeEditor = () => {
         )}
       </AnimatePresence>
       <AnimatePresence>
-        {!isReady && (
+        {isLoading && (
           <motion.div initial={initial} animate={animate} exit={exit} style={notIsReadyMotionStyles}>
             <Flex
               layerStyle="first"

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/FieldContextMenu.tsx

@@ -1,84 +0,0 @@
-import type { ContextMenuProps } from '@invoke-ai/ui-library';
-import { ContextMenu, MenuGroup, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useFieldInputKind } from 'features/nodes/hooks/useFieldInputKind';
-import { useFieldLabel } from 'features/nodes/hooks/useFieldLabel';
-import { useFieldTemplateTitle } from 'features/nodes/hooks/useFieldTemplateTitle';
-import {
-  selectWorkflowSlice,
-  workflowExposedFieldAdded,
-  workflowExposedFieldRemoved,
-} from 'features/nodes/store/workflowSlice';
-import type { ReactNode } from 'react';
-import { memo, useCallback, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiMinusBold, PiPlusBold } from 'react-icons/pi';
-
-type Props = {
-  nodeId: string;
-  fieldName: string;
-  kind: 'input' | 'output';
-  children: ContextMenuProps<HTMLDivElement>['children'];
-};
-
-const FieldContextMenu = ({ nodeId, fieldName, kind, children }: Props) => {
-  const dispatch = useAppDispatch();
-  const label = useFieldLabel(nodeId, fieldName);
-  const fieldTemplateTitle = useFieldTemplateTitle(nodeId, fieldName, kind);
-  const input = useFieldInputKind(nodeId, fieldName);
-  const { t } = useTranslation();
-
-  const selectIsExposed = useMemo(
-    () =>
-      createSelector(selectWorkflowSlice, (workflow) => {
-        return Boolean(workflow.exposedFields.find((f) => f.nodeId === nodeId && f.fieldName === fieldName));
-      }),
-    [fieldName, nodeId]
-  );
-
-  const mayExpose = useMemo(() => input && ['any', 'direct'].includes(input), [input]);
-
-  const isExposed = useAppSelector(selectIsExposed);
-
-  const handleExposeField = useCallback(() => {
-    dispatch(workflowExposedFieldAdded({ nodeId, fieldName }));
-  }, [dispatch, fieldName, nodeId]);
-
-  const handleUnexposeField = useCallback(() => {
-    dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
-  }, [dispatch, fieldName, nodeId]);
-
-  const menuItems = useMemo(() => {
-    const menuItems: ReactNode[] = [];
-    if (mayExpose && !isExposed) {
-      menuItems.push(
-        <MenuItem key={`${nodeId}.${fieldName}.expose-field`} icon={<PiPlusBold />} onClick={handleExposeField}>
-          {t('nodes.addLinearView')}
-        </MenuItem>
-      );
-    }
-    if (mayExpose && isExposed) {
-      menuItems.push(
-        <MenuItem key={`${nodeId}.${fieldName}.unexpose-field`} icon={<PiMinusBold />} onClick={handleUnexposeField}>
-          {t('nodes.removeLinearView')}
-        </MenuItem>
-      );
-    }
-    return menuItems;
-  }, [fieldName, handleExposeField, handleUnexposeField, isExposed, mayExpose, nodeId, t]);
-
-  const renderMenuFunc = useCallback(
-    () =>
-      !menuItems.length ? null : (
-        <MenuList visibility="visible">
-          <MenuGroup title={label || fieldTemplateTitle || t('nodes.unknownField')}>{menuItems}</MenuGroup>
-        </MenuList>
-      ),
-    [fieldTemplateTitle, label, menuItems, t]
-  );
-
-  return <ContextMenu renderMenu={renderMenuFunc}>{children}</ContextMenu>;
-};
-
-export default memo(FieldContextMenu);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/FieldLinearViewToggle.tsx

@@ -0,0 +1,68 @@
+import { IconButton } from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useFieldValue } from 'features/nodes/hooks/useFieldValue';
+import {
+  selectWorkflowSlice,
+  workflowExposedFieldAdded,
+  workflowExposedFieldRemoved,
+} from 'features/nodes/store/workflowSlice';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiMinusBold, PiPlusBold } from 'react-icons/pi';
+
+type Props = {
+  nodeId: string;
+  fieldName: string;
+};
+
+const FieldLinearViewToggle = ({ nodeId, fieldName }: Props) => {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const value = useFieldValue(nodeId, fieldName);
+  const selectIsExposed = useMemo(
+    () =>
+      createSelector(selectWorkflowSlice, (workflow) => {
+        return Boolean(workflow.exposedFields.find((f) => f.nodeId === nodeId && f.fieldName === fieldName));
+      }),
+    [fieldName, nodeId]
+  );
+
+  const isExposed = useAppSelector(selectIsExposed);
+
+  const handleExposeField = useCallback(() => {
+    dispatch(workflowExposedFieldAdded({ nodeId, fieldName, value }));
+  }, [dispatch, fieldName, nodeId, value]);
+
+  const handleUnexposeField = useCallback(() => {
+    dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
+  }, [dispatch, fieldName, nodeId]);
+
+  if (!isExposed) {
+    return (
+      <IconButton
+        variant="ghost"
+        tooltip={t('nodes.addLinearView')}
+        aria-label={t('nodes.addLinearView')}
+        icon={<PiPlusBold />}
+        onClick={handleExposeField}
+        pointerEvents="auto"
+        size="xs"
+      />
+    );
+  } else {
+    return (
+      <IconButton
+        variant="ghost"
+        tooltip={t('nodes.removeLinearView')}
+        aria-label={t('nodes.removeLinearView')}
+        icon={<PiMinusBold />}
+        onClick={handleUnexposeField}
+        pointerEvents="auto"
+        size="xs"
+      />
+    );
+  }
+};
+
+export default memo(FieldLinearViewToggle);

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/InputField.tsx

@@ -4,12 +4,12 @@ import { useDoesInputHaveValue } from 'features/nodes/hooks/useDoesInputHaveValu
 import { useFieldInputInstance } from 'features/nodes/hooks/useFieldInputInstance';
 import { useFieldInputTemplate } from 'features/nodes/hooks/useFieldInputTemplate';
 import type { PropsWithChildren } from 'react';
-import { memo, useMemo } from 'react';
+import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 
 import EditableFieldTitle from './EditableFieldTitle';
-import FieldContextMenu from './FieldContextMenu';
 import FieldHandle from './FieldHandle';
+import FieldLinearViewToggle from './FieldLinearViewToggle';
 import InputFieldRenderer from './InputFieldRenderer';
 
 interface Props {
@@ -22,6 +22,7 @@ const InputField = ({ nodeId, fieldName }: Props) => {
   const fieldTemplate = useFieldInputTemplate(nodeId, fieldName);
   const fieldInstance = useFieldInputInstance(nodeId, fieldName);
   const doesFieldHaveValue = useDoesInputHaveValue(nodeId, fieldName);
+  const [isHovered, setIsHovered] = useState(false);
 
   const { isConnected, isConnectionInProgress, isConnectionStartField, connectionError, shouldDim } =
     useConnectionState({ nodeId, fieldName, kind: 'input' });
@@ -46,6 +47,14 @@ const InputField = ({ nodeId, fieldName }: Props) => {
     return false;
   }, [fieldTemplate, isConnected, doesFieldHaveValue]);
 
+  const onMouseEnter = useCallback(() => {
+    setIsHovered(true);
+  }, []);
+
+  const onMouseLeave = useCallback(() => {
+    setIsHovered(false);
+  }, []);
+
   if (!fieldTemplate || !fieldInstance) {
     return (
       <InputFieldWrapper shouldDim={shouldDim}>
@@ -87,19 +96,17 @@ const InputField = ({ nodeId, fieldName }: Props) => {
   return (
     <InputFieldWrapper shouldDim={shouldDim}>
       <FormControl isInvalid={isMissingInput} isDisabled={isConnected} orientation="vertical" px={2}>
-        <Flex flexDir="column" w="full" gap={1}>
-          <FieldContextMenu nodeId={nodeId} fieldName={fieldName} kind="input">
-            {(ref) => (
-              <EditableFieldTitle
-                ref={ref}
-                nodeId={nodeId}
-                fieldName={fieldName}
-                kind="input"
-                isMissingInput={isMissingInput}
-                withTooltip
-              />
-            )}
-          </FieldContextMenu>
+        <Flex flexDir="column" w="full" gap={1} onMouseEnter={onMouseEnter} onMouseLeave={onMouseLeave}>
+          <Flex>
+            <EditableFieldTitle
+              nodeId={nodeId}
+              fieldName={fieldName}
+              kind="input"
+              isMissingInput={isMissingInput}
+              withTooltip
+            />
+            {isHovered && <FieldLinearViewToggle nodeId={nodeId} fieldName={fieldName} />}
+          </Flex>
           <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
         </Flex>
       </FormControl>

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/fields/LinearViewField.tsx

@@ -1,12 +1,15 @@
+import { useSortable } from '@dnd-kit/sortable';
+import { CSS } from '@dnd-kit/utilities';
 import { Flex, Icon, IconButton, Spacer, Tooltip } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import NodeSelectionOverlay from 'common/components/NodeSelectionOverlay';
+import { useFieldOriginalValue } from 'features/nodes/hooks/useFieldOriginalValue';
 import { useMouseOverNode } from 'features/nodes/hooks/useMouseOverNode';
 import { workflowExposedFieldRemoved } from 'features/nodes/store/workflowSlice';
 import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiInfoBold, PiTrashSimpleBold } from 'react-icons/pi';
+import { PiArrowCounterClockwiseBold, PiDotsSixVerticalBold, PiInfoBold, PiTrashSimpleBold } from 'react-icons/pi';
 
 import EditableFieldTitle from './EditableFieldTitle';
 import FieldTooltipContent from './FieldTooltipContent';
@@ -19,46 +22,79 @@ type Props = {
 
 const LinearViewField = ({ nodeId, fieldName }: Props) => {
   const dispatch = useAppDispatch();
+  const { isValueChanged, onReset } = useFieldOriginalValue(nodeId, fieldName);
   const { isMouseOverNode, handleMouseOut, handleMouseOver } = useMouseOverNode(nodeId);
   const { t } = useTranslation();
+
   const handleRemoveField = useCallback(() => {
     dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
   }, [dispatch, fieldName, nodeId]);
 
+  const { attributes, listeners, setNodeRef, transform, transition } = useSortable({ id: `${nodeId}.${fieldName}` });
+
+  const style = {
+    transform: CSS.Translate.toString(transform),
+    transition,
+  };
+
   return (
     <Flex
       onMouseEnter={handleMouseOver}
       onMouseLeave={handleMouseOut}
       layerStyle="second"
+      alignItems="center"
       position="relative"
       borderRadius="base"
       w="full"
       p={4}
-      flexDir="column"
+      paddingLeft={0}
+      ref={setNodeRef}
+      style={style}
     >
-      <Flex>
-        <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
-        <Spacer />
-        <Tooltip
-          label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
-          openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
-          placement="top"
-        >
-          <Flex h="full" alignItems="center">
-            <Icon fontSize="sm" color="base.300" as={PiInfoBold} />
-          </Flex>
-        </Tooltip>
-        <IconButton
-          aria-label={t('nodes.removeLinearView')}
-          tooltip={t('nodes.removeLinearView')}
-          variant="ghost"
-          size="sm"
-          onClick={handleRemoveField}
-          icon={<PiTrashSimpleBold />}
-        />
+      <IconButton
+        aria-label={t('nodes.reorderLinearView')}
+        variant="ghost"
+        icon={<PiDotsSixVerticalBold />}
+        {...listeners}
+        {...attributes}
+        mx={2}
+        height="full"
+      />
+      <Flex flexDir="column" w="full">
+        <Flex alignItems="center">
+          <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
+          <Spacer />
+          {isValueChanged && (
+            <IconButton
+              aria-label={t('nodes.resetToDefaultValue')}
+              tooltip={t('nodes.resetToDefaultValue')}
+              variant="ghost"
+              size="sm"
+              onClick={onReset}
+              icon={<PiArrowCounterClockwiseBold />}
+            />
+          )}
+          <Tooltip
+            label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
+            openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
+            placement="top"
+          >
+            <Flex h="full" alignItems="center">
+              <Icon fontSize="sm" color="base.300" as={PiInfoBold} />
+            </Flex>
+          </Tooltip>
+          <IconButton
+            aria-label={t('nodes.removeLinearView')}
+            tooltip={t('nodes.removeLinearView')}
+            variant="ghost"
+            size="sm"
+            onClick={handleRemoveField}
+            icon={<PiTrashSimpleBold />}
+          />
+        </Flex>
+        <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
+        <NodeSelectionOverlay isSelected={false} isHovered={isMouseOverNode} />
       </Flex>
-      <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
-      <NodeSelectionOverlay isSelected={false} isHovered={isMouseOverNode} />
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/TopPanel.tsx

@@ -1,25 +1,23 @@
 import { Flex, Spacer } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import AddNodeButton from 'features/nodes/components/flow/panels/TopPanel/AddNodeButton';
 import ClearFlowButton from 'features/nodes/components/flow/panels/TopPanel/ClearFlowButton';
 import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
 import UpdateNodesButton from 'features/nodes/components/flow/panels/TopPanel/UpdateNodesButton';
-import WorkflowName from 'features/nodes/components/flow/panels/TopPanel/WorkflowName';
-import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
+import { WorkflowName } from 'features/nodes/components/sidePanel/WorkflowName';
 import WorkflowLibraryMenu from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
 import { memo } from 'react';
 
 const TopCenterPanel = () => {
+  const name = useAppSelector((s) => s.workflow.name);
   return (
     <Flex gap={2} top={2} left={2} right={2} position="absolute" alignItems="flex-start" pointerEvents="none">
-      <Flex flexDir="column" gap="2">
-        <Flex gap="2">
-          <AddNodeButton />
-          <WorkflowLibraryButton />
-        </Flex>
+      <Flex gap="2">
+        <AddNodeButton />
         <UpdateNodesButton />
       </Flex>
       <Spacer />
-      <WorkflowName />
+      {!!name.length && <WorkflowName />}
       <Spacer />
       <ClearFlowButton />
       <SaveWorkflowButton />

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/UpdateNodesButton.tsx

@@ -25,6 +25,7 @@ const UpdateNodesButton = () => {
       icon={<PiWarningBold />}
       onClick={handleClickUpdateNodes}
       pointerEvents="auto"
+      colorScheme="warning"
     />
   );
 };

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/WorkflowName.tsx

@@ -1,15 +0,0 @@
-import { Text } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { memo } from 'react';
-
-const TopCenterPanel = () => {
-  const name = useAppSelector((s) => s.workflow.name);
-
-  return (
-    <Text m={2} fontSize="lg" userSelect="none" noOfLines={1} wordBreak="break-all" fontWeight="semibold" opacity={0.8}>
-      {name}
-    </Text>
-  );
-};
-
-export default memo(TopCenterPanel);

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopRightPanel/ReloadSchemaButton.tsx

@@ -1,17 +1,16 @@
 import { Button } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowsClockwiseBold } from 'react-icons/pi';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { useLazyGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 const ReloadNodeTemplatesButton = () => {
   const { t } = useTranslation();
-  const dispatch = useAppDispatch();
+  const [_getOpenAPISchema] = useLazyGetOpenAPISchemaQuery();
 
   const handleReloadSchema = useCallback(() => {
-    dispatch(receivedOpenAPISchema());
-  }, [dispatch]);
+    _getOpenAPISchema();
+  }, [_getOpenAPISchema]);
 
   return (
     <Button

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopRightPanel/TopRightPanel.tsx

@@ -1,15 +0,0 @@
-import { Flex } from '@invoke-ai/ui-library';
-import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
-import WorkflowLibraryMenu from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
-import { memo } from 'react';
-
-const TopRightPanel = () => {
-  return (
-    <Flex gap={2} position="absolute" top={2} insetInlineEnd={2}>
-      <WorkflowLibraryButton />
-      <WorkflowLibraryMenu />
-    </Flex>
-  );
-};
-
-export default memo(TopRightPanel);

invokeai/frontend/web/src/features/nodes/components/sidePanel/ModeToggle.tsx

@@ -0,0 +1,43 @@
+import { Flex, IconButton } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { workflowModeChanged } from 'features/nodes/store/workflowSlice';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiEyeBold, PiPencilBold } from 'react-icons/pi';
+
+export const ModeToggle = () => {
+  const dispatch = useAppDispatch();
+  const mode = useAppSelector((s) => s.workflow.mode);
+  const { t } = useTranslation();
+
+  const onClickEdit = useCallback(() => {
+    dispatch(workflowModeChanged('edit'));
+  }, [dispatch]);
+
+  const onClickView = useCallback(() => {
+    dispatch(workflowModeChanged('view'));
+  }, [dispatch]);
+
+  return (
+    <Flex justifyContent="flex-end">
+      {mode === 'view' && (
+        <IconButton
+          aria-label={t('nodes.editMode')}
+          tooltip={t('nodes.editMode')}
+          onClick={onClickEdit}
+          icon={<PiPencilBold />}
+          colorScheme="invokeBlue"
+        />
+      )}
+      {mode === 'edit' && (
+        <IconButton
+          aria-label={t('nodes.viewMode')}
+          tooltip={t('nodes.viewMode')}
+          onClick={onClickView}
+          icon={<PiEyeBold />}
+          colorScheme="invokeBlue"
+        />
+      )}
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/NodeEditorPanelGroup.tsx

@@ -1,22 +1,37 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
 import QueueControls from 'features/queue/components/QueueControls';
 import ResizeHandle from 'features/ui/components/tabs/ResizeHandle';
 import { usePanelStorage } from 'features/ui/hooks/usePanelStorage';
+import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
 import type { CSSProperties } from 'react';
 import { memo, useCallback, useRef } from 'react';
 import type { ImperativePanelGroupHandle } from 'react-resizable-panels';
 import { Panel, PanelGroup } from 'react-resizable-panels';
 
 import InspectorPanel from './inspector/InspectorPanel';
+import { WorkflowViewMode } from './viewMode/WorkflowViewMode';
 import WorkflowPanel from './workflow/WorkflowPanel';
+import { WorkflowMenu } from './WorkflowMenu';
+import { WorkflowName } from './WorkflowName';
 
 const panelGroupStyles: CSSProperties = { height: '100%', width: '100%' };
 
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    mode: workflow.mode,
+  };
+});
+
 const NodeEditorPanelGroup = () => {
+  const { mode } = useAppSelector(selector);
   const panelGroupRef = useRef<ImperativePanelGroupHandle>(null);
   const panelStorage = usePanelStorage();
+
   const handleDoubleClickHandle = useCallback(() => {
     if (!panelGroupRef.current) {
       return;
@@ -27,22 +42,33 @@ const NodeEditorPanelGroup = () => {
   return (
     <Flex w="full" h="full" gap={2} flexDir="column">
       <QueueControls />
-      <PanelGroup
-        ref={panelGroupRef}
-        id="workflow-panel-group"
-        autoSaveId="workflow-panel-group"
-        direction="vertical"
-        style={panelGroupStyles}
-        storage={panelStorage}
-      >
-        <Panel id="workflow" collapsible minSize={25}>
-          <WorkflowPanel />
-        </Panel>
-        <ResizeHandle orientation="horizontal" onDoubleClick={handleDoubleClickHandle} />
-        <Panel id="inspector" collapsible minSize={25}>
-          <InspectorPanel />
-        </Panel>
-      </PanelGroup>
+      <Flex w="full" justifyContent="space-between" alignItems="center" gap="4" padding={1}>
+        <Flex justifyContent="space-between" alignItems="center" gap="4">
+          <WorkflowLibraryButton />
+          <WorkflowName />
+        </Flex>
+        <WorkflowMenu />
+      </Flex>
+
+      {mode === 'view' && <WorkflowViewMode />}
+      {mode === 'edit' && (
+        <PanelGroup
+          ref={panelGroupRef}
+          id="workflow-panel-group"
+          autoSaveId="workflow-panel-group"
+          direction="vertical"
+          style={panelGroupStyles}
+          storage={panelStorage}
+        >
+          <Panel id="workflow" collapsible minSize={25}>
+            <WorkflowPanel />
+          </Panel>
+          <ResizeHandle orientation="horizontal" onDoubleClick={handleDoubleClickHandle} />
+          <Panel id="inspector" collapsible minSize={25}>
+            <InspectorPanel />
+          </Panel>
+        </PanelGroup>
+      )}
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowMenu.tsx

@@ -0,0 +1,26 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { NewWorkflowButton } from 'features/workflowLibrary/components/NewWorkflowButton';
+
+import { ModeToggle } from './ModeToggle';
+
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    mode: workflow.mode,
+  };
+});
+
+export const WorkflowMenu = () => {
+  const { mode } = useAppSelector(selector);
+
+  return (
+    <Flex gap="2" alignItems="center">
+      {mode === 'edit' && <SaveWorkflowButton />}
+      <NewWorkflowButton />
+      <ModeToggle />
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowName.tsx

@@ -0,0 +1,37 @@
+import { Flex, Icon, Text, Tooltip } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useTranslation } from 'react-i18next';
+import { PiDotOutlineFill } from 'react-icons/pi';
+
+import WorkflowInfoTooltipContent from './viewMode/WorkflowInfoTooltipContent';
+import { WorkflowWarning } from './viewMode/WorkflowWarning';
+
+export const WorkflowName = () => {
+  const { name, isTouched, mode } = useAppSelector((s) => s.workflow);
+  const { t } = useTranslation();
+
+  return (
+    <Flex gap="1" alignItems="center">
+      {name.length ? (
+        <Tooltip label={<WorkflowInfoTooltipContent />} placement="top">
+          <Text fontSize="lg" userSelect="none" noOfLines={1} wordBreak="break-all" fontWeight="semibold">
+            {name}
+          </Text>
+        </Tooltip>
+      ) : (
+        <Text fontSize="lg" fontStyle="italic" fontWeight="semibold">
+          {t('workflows.unnamedWorkflow')}
+        </Text>
+      )}
+
+      {isTouched && mode === 'edit' && (
+        <Tooltip label="Workflow has unsaved changes">
+          <Flex>
+            <Icon as={PiDotOutlineFill} boxSize="20px" sx={{ color: 'invokeYellow.500' }} />
+          </Flex>
+        </Tooltip>
+      )}
+      <WorkflowWarning />
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowField.tsx

@@ -0,0 +1,53 @@
+import { Flex, FormLabel, Icon, IconButton, Spacer, Tooltip } from '@invoke-ai/ui-library';
+import FieldTooltipContent from 'features/nodes/components/flow/nodes/Invocation/fields/FieldTooltipContent';
+import InputFieldRenderer from 'features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer';
+import { useFieldLabel } from 'features/nodes/hooks/useFieldLabel';
+import { useFieldOriginalValue } from 'features/nodes/hooks/useFieldOriginalValue';
+import { useFieldTemplateTitle } from 'features/nodes/hooks/useFieldTemplateTitle';
+import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
+import { t } from 'i18next';
+import { memo } from 'react';
+import { PiArrowCounterClockwiseBold, PiInfoBold } from 'react-icons/pi';
+
+type Props = {
+  nodeId: string;
+  fieldName: string;
+};
+
+const WorkflowField = ({ nodeId, fieldName }: Props) => {
+  const label = useFieldLabel(nodeId, fieldName);
+  const fieldTemplateTitle = useFieldTemplateTitle(nodeId, fieldName, 'input');
+  const { isValueChanged, onReset } = useFieldOriginalValue(nodeId, fieldName);
+
+  return (
+    <Flex layerStyle="second" position="relative" borderRadius="base" w="full" p={4} gap="2" flexDir="column">
+      <Flex alignItems="center">
+        <FormLabel fontSize="sm">{label || fieldTemplateTitle}</FormLabel>
+
+        <Spacer />
+        {isValueChanged && (
+          <IconButton
+            aria-label={t('nodes.resetToDefaultValue')}
+            tooltip={t('nodes.resetToDefaultValue')}
+            variant="ghost"
+            size="sm"
+            onClick={onReset}
+            icon={<PiArrowCounterClockwiseBold />}
+          />
+        )}
+        <Tooltip
+          label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
+          openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
+          placement="top"
+        >
+          <Flex h="24px" alignItems="center">
+            <Icon fontSize="md" color="base.300" as={PiInfoBold} />
+          </Flex>
+        </Tooltip>
+      </Flex>
+      <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
+    </Flex>
+  );
+};
+
+export default memo(WorkflowField);

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowInfoTooltipContent.tsx

@@ -0,0 +1,68 @@
+import { Box, Flex, Text } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    name: workflow.name,
+    description: workflow.description,
+    notes: workflow.notes,
+    author: workflow.author,
+    tags: workflow.tags,
+  };
+});
+
+const WorkflowInfoTooltipContent = () => {
+  const { name, description, notes, author, tags } = useAppSelector(selector);
+  const { t } = useTranslation();
+
+  return (
+    <Flex flexDir="column" gap="2">
+      {!!name.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowName')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {name}
+          </Text>
+        </Box>
+      )}
+      {!!author.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowAuthor')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {author}
+          </Text>
+        </Box>
+      )}
+      {!!tags.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowTags')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {tags}
+          </Text>
+        </Box>
+      )}
+      {!!description.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowDescription')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {description}
+          </Text>
+        </Box>
+      )}
+      {!!notes.length && (
+        <Box>
+          <Text fontWeight="semibold">{t('nodes.workflowNotes')}</Text>
+          <Text opacity={0.7} fontStyle="oblique 5deg">
+            {notes}
+          </Text>
+        </Box>
+      )}
+    </Flex>
+  );
+};
+
+export default memo(WorkflowInfoTooltipContent);

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowViewMode.tsx

@@ -0,0 +1,39 @@
+import { Box, Flex } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { IAINoContentFallback } from 'common/components/IAIImageFallback';
+import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { t } from 'i18next';
+import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
+
+import WorkflowField from './WorkflowField';
+
+const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
+  return {
+    fields: workflow.exposedFields,
+    name: workflow.name,
+  };
+});
+
+export const WorkflowViewMode = () => {
+  const { isLoading } = useGetOpenAPISchemaQuery();
+  const { fields } = useAppSelector(selector);
+  return (
+    <Box position="relative" w="full" h="full">
+      <ScrollableContent>
+        <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
+          {isLoading ? (
+            <IAINoContentFallback label={t('nodes.loadingNodes')} icon={null} />
+          ) : fields.length ? (
+            fields.map(({ nodeId, fieldName }) => (
+              <WorkflowField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
+            ))
+          ) : (
+            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
+          )}
+        </Flex>
+      </ScrollableContent>
+    </Box>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowWarning.tsx

@@ -0,0 +1,21 @@
+import { Flex, Icon, Tooltip } from '@invoke-ai/ui-library';
+import { useGetNodesNeedUpdate } from 'features/nodes/hooks/useGetNodesNeedUpdate';
+import { PiWarningBold } from 'react-icons/pi';
+
+import { WorkflowWarningTooltip } from './WorkflowWarningTooltip';
+
+export const WorkflowWarning = () => {
+  const nodesNeedUpdate = useGetNodesNeedUpdate();
+
+  if (!nodesNeedUpdate) {
+    return <></>;
+  }
+
+  return (
+    <Tooltip label={<WorkflowWarningTooltip />}>
+      <Flex h="full" alignItems="center" gap="2">
+        <Icon color="warning.400" as={PiWarningBold} />
+      </Flex>
+    </Tooltip>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowWarningTooltip.tsx

@@ -0,0 +1,20 @@
+import { Flex, Text } from '@invoke-ai/ui-library';
+import { useTranslation } from 'react-i18next';
+
+export const WorkflowWarningTooltip = () => {
+  const { t } = useTranslation();
+
+  return (
+    <Flex flexDir="column" gap="2">
+      <Flex flexDir="column" gap="2">
+        <Text fontWeight="semibold">{t('toast.loadedWithWarnings')}</Text>
+        <Flex flexDir="column">
+          <Text>{t('common.toResolve')}:</Text>
+          <Text>
+            {t('nodes.editMode')} &gt;&gt; {t('nodes.updateAllNodes')} &gt;&gt; {t('common.save')}
+          </Text>
+        </Flex>
+      </Flex>
+    </Flex>
+  );
+};

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/WorkflowLinearTab.tsx

@@ -1,31 +1,61 @@
+import { arrayMove } from '@dnd-kit/sortable';
 import { Box, Flex } from '@invoke-ai/ui-library';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
+import DndSortable from 'features/dnd/components/DndSortable';
+import type { DragEndEvent } from 'features/dnd/types';
 import LinearViewField from 'features/nodes/components/flow/nodes/Invocation/fields/LinearViewField';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
-import { memo } from 'react';
+import { selectWorkflowSlice, workflowExposedFieldsReordered } from 'features/nodes/store/workflowSlice';
+import type { FieldIdentifier } from 'features/nodes/types/field';
+import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
+import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => workflow.exposedFields);
 
 const WorkflowLinearTab = () => {
   const fields = useAppSelector(selector);
+  const { isLoading } = useGetOpenAPISchemaQuery();
   const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+
+  const handleDragEnd = useCallback(
+    (event: DragEndEvent) => {
+      const { active, over } = event;
+      const fieldsStrings = fields.map((field) => `${field.nodeId}.${field.fieldName}`);
+
+      if (over && active.id !== over.id) {
+        const oldIndex = fieldsStrings.indexOf(active.id as string);
+        const newIndex = fieldsStrings.indexOf(over.id as string);
+
+        const newFields = arrayMove(fieldsStrings, oldIndex, newIndex)
+          .map((field) => fields.find((obj) => `${obj.nodeId}.${obj.fieldName}` === field))
+          .filter((field) => field) as FieldIdentifier[];
+
+        dispatch(workflowExposedFieldsReordered(newFields));
+      }
+    },
+    [dispatch, fields]
+  );
 
   return (
     <Box position="relative" w="full" h="full">
       <ScrollableContent>
-        <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
-          {fields.length ? (
-            fields.map(({ nodeId, fieldName }) => (
-              <LinearViewField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
-            ))
-          ) : (
-            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
-          )}
-        </Flex>
+        <DndSortable onDragEnd={handleDragEnd} items={fields.map((field) => `${field.nodeId}.${field.fieldName}`)}>
+          <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
+            {isLoading ? (
+              <IAINoContentFallback label={t('nodes.loadingNodes')} icon={null} />
+            ) : fields.length ? (
+              fields.map(({ nodeId, fieldName }) => (
+                <LinearViewField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
+              ))
+            ) : (
+              <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
+            )}
+          </Flex>
+        </DndSortable>
       </ScrollableContent>
     </Box>
   );

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/WorkflowPanel.tsx

@@ -12,17 +12,17 @@ const WorkflowPanel = () => {
     <Flex layerStyle="first" flexDir="column" w="full" h="full" borderRadius="base" p={2} gap={2}>
       <Tabs variant="line" display="flex" w="full" h="full" flexDir="column">
         <TabList>
-          <Tab>{t('common.linear')}</Tab>
           <Tab>{t('common.details')}</Tab>
+          <Tab>{t('common.linear')}</Tab>
           <Tab>JSON</Tab>
         </TabList>
 
         <TabPanels>
           <TabPanel>
-            <WorkflowLinearTab />
+            <WorkflowGeneralTab />
           </TabPanel>
           <TabPanel>
-            <WorkflowGeneralTab />
+            <WorkflowLinearTab />
           </TabPanel>
           <TabPanel>
             <WorkflowJSONTab />

invokeai/frontend/web/src/features/nodes/hooks/useFieldOriginalValue.ts

@@ -0,0 +1,28 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useFieldValue } from 'features/nodes/hooks/useFieldValue';
+import { fieldValueReset } from 'features/nodes/store/nodesSlice';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { isEqual } from 'lodash-es';
+import { useCallback, useMemo } from 'react';
+
+export const useFieldOriginalValue = (nodeId: string, fieldName: string) => {
+  const dispatch = useAppDispatch();
+  const selectOriginalExposedFieldValues = useMemo(
+    () =>
+      createSelector(
+        selectWorkflowSlice,
+        (workflow) =>
+          workflow.originalExposedFieldValues.find((v) => v.nodeId === nodeId && v.fieldName === fieldName)?.value
+      ),
+    [nodeId, fieldName]
+  );
+  const originalValue = useAppSelector(selectOriginalExposedFieldValues);
+  const value = useFieldValue(nodeId, fieldName);
+  const isValueChanged = useMemo(() => !isEqual(value, originalValue), [value, originalValue]);
+  const onReset = useCallback(() => {
+    dispatch(fieldValueReset({ nodeId, fieldName, value: originalValue }));
+  }, [dispatch, fieldName, nodeId, originalValue]);
+
+  return { originalValue, isValueChanged, onReset };
+};

invokeai/frontend/web/src/features/nodes/hooks/useFieldValue.ts

@@ -0,0 +1,23 @@
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
+import { isInvocationNode } from 'features/nodes/types/invocation';
+import { useMemo } from 'react';
+
+export const useFieldValue = (nodeId: string, fieldName: string) => {
+  const selector = useMemo(
+    () =>
+      createMemoizedSelector(selectNodesSlice, (nodes) => {
+        const node = nodes.nodes.find((node) => node.id === nodeId);
+        if (!isInvocationNode(node)) {
+          return;
+        }
+        return node?.data.inputs[fieldName]?.value;
+      }),
+    [fieldName, nodeId]
+  );
+
+  const value = useAppSelector(selector);
+
+  return value;
+};

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -2,7 +2,6 @@ import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice, isAnyOf } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { workflowLoaded } from 'features/nodes/store/actions';
-import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { SHARED_NODE_PROPERTIES } from 'features/nodes/types/constants';
 import type {
   BoardFieldValue,
@@ -19,6 +18,7 @@ import type {
   MainModelFieldValue,
   SchedulerFieldValue,
   SDXLRefinerModelFieldValue,
+  StatefulFieldValue,
   StringFieldValue,
   T2IAdapterModelFieldValue,
   VAEModelFieldValue,
@@ -37,6 +37,7 @@ import {
   zMainModelFieldValue,
   zSchedulerFieldValue,
   zSDXLRefinerModelFieldValue,
+  zStatefulFieldValue,
   zStringFieldValue,
   zT2IAdapterModelFieldValue,
   zVAEModelFieldValue,
@@ -65,7 +66,6 @@ import {
   SelectionMode,
   updateEdge,
 } from 'reactflow';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import {
   socketGeneratorProgress,
   socketInvocationComplete,
@@ -92,7 +92,6 @@ export const initialNodesState: NodesState = {
   _version: 1,
   nodes: [],
   edges: [],
-  isReady: false,
   connectionStartParams: null,
   connectionStartFieldType: null,
   connectionMade: false,
@@ -481,6 +480,9 @@ export const nodesSlice = createSlice({
     selectedEdgesChanged: (state, action: PayloadAction<string[]>) => {
       state.selectedEdges = action.payload;
     },
+    fieldValueReset: (state, action: FieldValueAction<StatefulFieldValue>) => {
+      fieldValueReducer(state, action, zStatefulFieldValue);
+    },
     fieldStringValueChanged: (state, action: FieldValueAction<StringFieldValue>) => {
       fieldValueReducer(state, action, zStringFieldValue);
     },
@@ -677,10 +679,6 @@ export const nodesSlice = createSlice({
     },
   },
   extraReducers: (builder) => {
-    builder.addCase(receivedOpenAPISchema.pending, (state) => {
-      state.isReady = false;
-    });
-
     builder.addCase(workflowLoaded, (state, action) => {
       const { nodes, edges } = action.payload;
       state.nodes = applyNodeChanges(
@@ -752,9 +750,6 @@ export const nodesSlice = createSlice({
         });
       }
     });
-    builder.addCase(nodeTemplatesBuilt, (state) => {
-      state.isReady = true;
-    });
   },
 });
 
@@ -770,6 +765,7 @@ export const {
   edgesChanged,
   edgesDeleted,
   edgeUpdated,
+  fieldValueReset,
   fieldBoardValueChanged,
   fieldBooleanValueChanged,
   fieldColorValueChanged,
@@ -844,7 +840,6 @@ export const isAnyNodeOrEdgeMutation = isAnyOf(
   nodeIsOpenChanged,
   nodeLabelChanged,
   nodeNotesChanged,
-  nodesChanged,
   nodesDeleted,
   nodeUseCacheChanged,
   notesNodeValueChanged,
@@ -871,7 +866,6 @@ export const nodesPersistConfig: PersistConfig<NodesState> = {
     'connectionStartFieldType',
     'selectedNodes',
     'selectedEdges',
-    'isReady',
     'nodesToCopy',
     'edgesToCopy',
     'connectionMade',

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

@@ -1,4 +1,4 @@
-import type { FieldType } from 'features/nodes/types/field';
+import type { FieldIdentifier, FieldType, StatefulFieldValue } from 'features/nodes/types/field';
 import type {
   AnyNode,
   InvocationNodeEdge,
@@ -26,7 +26,6 @@ export type NodesState = {
   selectedEdges: string[];
   nodeExecutionStates: Record<string, NodeExecutionState>;
   viewport: Viewport;
-  isReady: boolean;
   nodesToCopy: AnyNode[];
   edgesToCopy: InvocationNodeEdge[];
   isAddNodePopoverOpen: boolean;
@@ -34,9 +33,16 @@ export type NodesState = {
   selectionMode: SelectionMode;
 };
 
+export type WorkflowMode = 'edit' | 'view';
+export type FieldIdentifierWithValue = FieldIdentifier & {
+  value: StatefulFieldValue;
+};
+
 export type WorkflowsState = Omit<WorkflowV2, 'nodes' | 'edges'> & {
   _version: 1;
   isTouched: boolean;
+  mode: WorkflowMode;
+  originalExposedFieldValues: FieldIdentifierWithValue[];
 };
 
 export type NodeTemplatesState = {

invokeai/frontend/web/src/features/nodes/store/workflowSlice.ts

@@ -2,11 +2,16 @@ import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { workflowLoaded } from 'features/nodes/store/actions';
-import { isAnyNodeOrEdgeMutation, nodeEditorReset, nodesDeleted } from 'features/nodes/store/nodesSlice';
-import type { WorkflowsState as WorkflowState } from 'features/nodes/store/types';
+import { isAnyNodeOrEdgeMutation, nodeEditorReset, nodesChanged, nodesDeleted } from 'features/nodes/store/nodesSlice';
+import type {
+  FieldIdentifierWithValue,
+  WorkflowMode,
+  WorkflowsState as WorkflowState,
+} from 'features/nodes/store/types';
 import type { FieldIdentifier } from 'features/nodes/types/field';
+import { isInvocationNode } from 'features/nodes/types/invocation';
 import type { WorkflowCategory, WorkflowV2 } from 'features/nodes/types/workflow';
-import { cloneDeep, isEqual, uniqBy } from 'lodash-es';
+import { cloneDeep, isEqual, omit, uniqBy } from 'lodash-es';
 
 export const blankWorkflow: Omit<WorkflowV2, 'nodes' | 'edges'> = {
   name: '',
@@ -23,7 +28,9 @@ export const blankWorkflow: Omit<WorkflowV2, 'nodes' | 'edges'> = {
 
 export const initialWorkflowState: WorkflowState = {
   _version: 1,
-  isTouched: true,
+  isTouched: false,
+  mode: 'view',
+  originalExposedFieldValues: [],
   ...blankWorkflow,
 };
 
@@ -31,15 +38,29 @@ export const workflowSlice = createSlice({
   name: 'workflow',
   initialState: initialWorkflowState,
   reducers: {
-    workflowExposedFieldAdded: (state, action: PayloadAction<FieldIdentifier>) => {
+    workflowModeChanged: (state, action: PayloadAction<WorkflowMode>) => {
+      state.mode = action.payload;
+    },
+    workflowExposedFieldAdded: (state, action: PayloadAction<FieldIdentifierWithValue>) => {
       state.exposedFields = uniqBy(
-        state.exposedFields.concat(action.payload),
+        state.exposedFields.concat(omit(action.payload, 'value')),
+        (field) => `${field.nodeId}-${field.fieldName}`
+      );
+      state.originalExposedFieldValues = uniqBy(
+        state.originalExposedFieldValues.concat(action.payload),
         (field) => `${field.nodeId}-${field.fieldName}`
       );
       state.isTouched = true;
     },
     workflowExposedFieldRemoved: (state, action: PayloadAction<FieldIdentifier>) => {
       state.exposedFields = state.exposedFields.filter((field) => !isEqual(field, action.payload));
+      state.originalExposedFieldValues = state.originalExposedFieldValues.filter(
+        (field) => !isEqual(omit(field, 'value'), action.payload)
+      );
+      state.isTouched = true;
+    },
+    workflowExposedFieldsReordered: (state, action: PayloadAction<FieldIdentifier[]>) => {
+      state.exposedFields = action.payload;
       state.isTouched = true;
     },
     workflowNameChanged: (state, action: PayloadAction<string>) => {
@@ -78,15 +99,43 @@ export const workflowSlice = createSlice({
     workflowIDChanged: (state, action: PayloadAction<string>) => {
       state.id = action.payload;
     },
-    workflowReset: () => cloneDeep(initialWorkflowState),
     workflowSaved: (state) => {
       state.isTouched = false;
     },
   },
   extraReducers: (builder) => {
     builder.addCase(workflowLoaded, (state, action) => {
-      const { nodes: _nodes, edges: _edges, ...workflowExtra } = action.payload;
-      return { ...initialWorkflowState, ...cloneDeep(workflowExtra) };
+      const { nodes, edges: _edges, ...workflowExtra } = action.payload;
+
+      const originalExposedFieldValues: FieldIdentifierWithValue[] = [];
+
+      workflowExtra.exposedFields.forEach((field) => {
+        const node = nodes.find((n) => n.id === field.nodeId);
+
+        if (!isInvocationNode(node)) {
+          return;
+        }
+
+        const input = node.data.inputs[field.fieldName];
+
+        if (!input) {
+          return;
+        }
+
+        const originalExposedFieldValue = {
+          nodeId: field.nodeId,
+          fieldName: field.fieldName,
+          value: input.value,
+        };
+        originalExposedFieldValues.push(originalExposedFieldValue);
+      });
+
+      return {
+        ...cloneDeep(initialWorkflowState),
+        ...cloneDeep(workflowExtra),
+        originalExposedFieldValues,
+        mode: state.mode,
+      };
     });
 
     builder.addCase(nodesDeleted, (state, action) => {
@@ -97,6 +146,29 @@ export const workflowSlice = createSlice({
 
     builder.addCase(nodeEditorReset, () => cloneDeep(initialWorkflowState));
 
+    builder.addCase(nodesChanged, (state, action) => {
+      // Not all changes to nodes should result in the workflow being marked touched
+      const filteredChanges = action.payload.filter((change) => {
+        // We always want to mark the workflow as touched if a node is added, removed, or reset
+        if (['add', 'remove', 'reset'].includes(change.type)) {
+          return true;
+        }
+
+        // Position changes can change the position and the dragging status of the node - ignore if the change doesn't
+        // affect the position
+        if (change.type === 'position' && (change.position || change.positionAbsolute)) {
+          return true;
+        }
+
+        // This change isn't relevant
+        return false;
+      });
+
+      if (filteredChanges.length > 0) {
+        state.isTouched = true;
+      }
+    });
+
     builder.addMatcher(isAnyNodeOrEdgeMutation, (state) => {
       state.isTouched = true;
     });
@@ -104,8 +176,10 @@ export const workflowSlice = createSlice({
 });
 
 export const {
+  workflowModeChanged,
   workflowExposedFieldAdded,
   workflowExposedFieldRemoved,
+  workflowExposedFieldsReordered,
   workflowNameChanged,
   workflowCategoryChanged,
   workflowDescriptionChanged,
@@ -115,7 +189,6 @@ export const {
   workflowVersionChanged,
   workflowContactChanged,
   workflowIDChanged,
-  workflowReset,
   workflowSaved,
 } = workflowSlice.actions;
 

invokeai/frontend/web/src/features/nodes/util/graph/addHrfToGraph.ts

@@ -23,6 +23,7 @@ import {
   NOISE,
   NOISE_HRF,
   RESIZE_HRF,
+  SEAMLESS,
   VAE_LOADER,
 } from './constants';
 import { setMetadataReceivingNode, upsertMetadata } from './metadata';
@@ -30,7 +31,6 @@ import { setMetadataReceivingNode, upsertMetadata } from './metadata';
 // Copy certain connections from previous DENOISE_LATENTS to new DENOISE_LATENTS_HRF.
 function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void {
   const destinationFields = [
-    'vae',
     'control',
     'ip_adapter',
     'metadata',
@@ -107,9 +107,10 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
   }
   const log = logger('txt2img');
 
-  const { vae } = state.generation;
+  const { vae, seamlessXAxis, seamlessYAxis } = state.generation;
   const { hrfStrength, hrfEnabled, hrfMethod } = state.hrf;
   const isAutoVae = !vae;
+  const isSeamlessEnabled = seamlessXAxis || seamlessYAxis;
   const width = state.generation.width;
   const height = state.generation.height;
   const optimalDimension = selectOptimalDimension(state);
@@ -158,7 +159,7 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
     },
     {
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -259,7 +260,7 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
   graph.edges.push(
     {
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -322,7 +323,7 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
   graph.edges.push(
     {
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
         field: 'vae',
       },
       destination: {

invokeai/frontend/web/src/features/nodes/util/graph/addSDXLLoRAstoGraph.ts

@@ -1,7 +1,7 @@
 import type { RootState } from 'app/store/store';
 import type { LoRAMetadataItem } from 'features/nodes/types/metadata';
 import { zLoRAMetadataItem } from 'features/nodes/types/metadata';
-import { forEach, size } from 'lodash-es';
+import { filter, size } from 'lodash-es';
 import type { NonNullableGraph, SDXLLoraLoaderInvocation } from 'services/api/types';
 
 import {
@@ -31,8 +31,8 @@ export const addSDXLLoRAsToGraph = (
    * So we need to inject a LoRA chain into the graph.
    */
 
-  const { loras } = state.lora;
-  const loraCount = size(loras);
+  const enabledLoRAs = filter(state.lora.loras, (l) => l.isEnabled ?? false);
+  const loraCount = size(enabledLoRAs);
 
   if (loraCount === 0) {
     return;
@@ -59,7 +59,7 @@ export const addSDXLLoRAsToGraph = (
   let lastLoraNodeId = '';
   let currentLoraIndex = 0;
 
-  forEach(loras, (lora) => {
+  enabledLoRAs.forEach((lora) => {
     const { model_name, base_model, weight } = lora;
     const currentLoraNodeId = `${LORA_LOADER}_${model_name.replace('.', '_')}`;
 

invokeai/frontend/web/src/features/nodes/util/graph/addSeamlessToLinearGraph.ts

@@ -14,6 +14,7 @@ import {
   SDXL_IMAGE_TO_IMAGE_GRAPH,
   SDXL_TEXT_TO_IMAGE_GRAPH,
   SEAMLESS,
+  VAE_LOADER,
 } from './constants';
 import { upsertMetadata } from './metadata';
 
@@ -23,7 +24,8 @@ export const addSeamlessToLinearGraph = (
   modelLoaderNodeId: string
 ): void => {
   // Remove Existing UNet Connections
-  const { seamlessXAxis, seamlessYAxis } = state.generation;
+  const { seamlessXAxis, seamlessYAxis, vae } = state.generation;
+  const isAutoVae = !vae;
 
   graph.nodes[SEAMLESS] = {
     id: SEAMLESS,
@@ -32,6 +34,15 @@ export const addSeamlessToLinearGraph = (
     seamless_y: seamlessYAxis,
   } as SeamlessModeInvocation;
 
+  if (!isAutoVae) {
+    graph.nodes[VAE_LOADER] = {
+      type: 'vae_loader',
+      id: VAE_LOADER,
+      is_intermediate: true,
+      vae_model: vae,
+    };
+  }
+
   if (seamlessXAxis) {
     upsertMetadata(graph, {
       seamless_x: seamlessXAxis,
@@ -75,7 +86,7 @@ export const addSeamlessToLinearGraph = (
     },
     {
       source: {
-        node_id: modelLoaderNodeId,
+        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {

invokeai/frontend/web/src/features/nodes/util/graph/addVAEToGraph.ts

@@ -21,6 +21,7 @@ import {
   SDXL_IMAGE_TO_IMAGE_GRAPH,
   SDXL_REFINER_INPAINT_CREATE_MASK,
   SDXL_TEXT_TO_IMAGE_GRAPH,
+  SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
@@ -31,15 +32,16 @@ export const addVAEToGraph = (
   graph: NonNullableGraph,
   modelLoaderNodeId: string = MAIN_MODEL_LOADER
 ): void => {
-  const { vae, canvasCoherenceMode } = state.generation;
+  const { vae, canvasCoherenceMode, seamlessXAxis, seamlessYAxis } = state.generation;
   const { boundingBoxScaleMethod } = state.canvas;
   const { refinerModel } = state.sdxl;
 
   const isUsingScaledDimensions = ['auto', 'manual'].includes(boundingBoxScaleMethod);
 
   const isAutoVae = !vae;
+  const isSeamlessEnabled = seamlessXAxis || seamlessYAxis;
 
-  if (!isAutoVae) {
+  if (!isAutoVae && !isSeamlessEnabled) {
     graph.nodes[VAE_LOADER] = {
       type: 'vae_loader',
       id: VAE_LOADER,
@@ -56,7 +58,7 @@ export const addVAEToGraph = (
   ) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -74,7 +76,7 @@ export const addVAEToGraph = (
   ) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -92,7 +94,7 @@ export const addVAEToGraph = (
   ) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -111,7 +113,7 @@ export const addVAEToGraph = (
     graph.edges.push(
       {
         source: {
-          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -121,7 +123,7 @@ export const addVAEToGraph = (
       },
       {
         source: {
-          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -131,7 +133,7 @@ export const addVAEToGraph = (
       },
       {
         source: {
-          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -145,7 +147,7 @@ export const addVAEToGraph = (
     if (canvasCoherenceMode !== 'unmasked') {
       graph.edges.push({
         source: {
-          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -160,7 +162,7 @@ export const addVAEToGraph = (
     if (graph.id === SDXL_CANVAS_INPAINT_GRAPH || graph.id === SDXL_CANVAS_OUTPAINT_GRAPH) {
       graph.edges.push({
         source: {
-          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasImageToImageGraph.ts

@@ -123,6 +123,7 @@ export const buildCanvasImageToImageGraph = (state: RootState, initialImage: Ima
         id: DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
+        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 1 - strength,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasInpaintGraph.ts

@@ -58,6 +58,7 @@ export const buildCanvasInpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     img2imgStrength: strength,
@@ -152,6 +153,7 @@ export const buildCanvasInpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - strength,
         denoising_end: 1,
@@ -175,6 +177,7 @@ export const buildCanvasInpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasOutpaintGraph.ts

@@ -60,6 +60,7 @@ export const buildCanvasOutpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     img2imgStrength: strength,
@@ -161,6 +162,7 @@ export const buildCanvasOutpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - strength,
         denoising_end: 1,
@@ -184,6 +186,7 @@ export const buildCanvasOutpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLImageToImageGraph.ts

@@ -124,6 +124,7 @@ export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage:
         id: SDXL_DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
+        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLInpaintGraph.ts

@@ -60,6 +60,7 @@ export const buildCanvasSDXLInpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     seed,
@@ -151,6 +152,7 @@ export const buildCanvasSDXLInpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,
         denoising_end: refinerModel ? refinerStart : 1,
@@ -174,6 +176,7 @@ export const buildCanvasSDXLInpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLOutpaintGraph.ts

@@ -62,6 +62,7 @@ export const buildCanvasSDXLOutpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
+    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     seed,
@@ -160,6 +161,7 @@ export const buildCanvasSDXLOutpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,
         denoising_end: refinerModel ? refinerStart : 1,
@@ -183,6 +185,7 @@ export const buildCanvasSDXLOutpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
+        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLTextToImageGraph.ts

@@ -117,6 +117,7 @@ export const buildCanvasSDXLTextToImageGraph = (state: RootState): NonNullableGr
         id: SDXL_DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
+        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 0,