fix(ui): restore missing galleryImageClicked reducer

These have moved to the viewer components

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=2.1.2
-ARG TORCHVISION_VERSION=0.16.2
+ARG TORCH_VERSION=2.1.0
+ARG TORCHVISION_VERSION=0.16
 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 [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
@@ -54,7 +54,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
         pip install -e ".[xformers]"; \
     else \
-        pip install $extra_index_url_arg -e "."; \
+        pip install -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 $service_name
+  docker compose build $build_args
   unset build_args
 
   printf "%s\n" "starting service $service_name"

docs/features/CONTROLNET.md

@@ -94,8 +94,6 @@ 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

@@ -32,7 +32,6 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
     + [Image Picker](#image-picker)
     + [Image Resize Plus](#image-resize-plus)
-    + [Latent Upscale](#latent-upscale)
     + [Load Video Frame](#load-video-frame)
     + [Make 3D](#make-3d)
     + [Mask Operations](#mask-operations)
@@ -43,7 +42,6 @@ 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)
@@ -291,13 +289,6 @@ View:
 </br><img src="https://raw.githubusercontent.com/VeyDlin/image-resize-plus-node/master/.readme/node.png" width="500" />
 
 
---------------------------------
-### Latent Upscale
-
-**Description:** This node uses a small (~2.4mb) model to upscale the latents used in a Stable Diffusion 1.5 or Stable Diffusion XL image generation, rather than the typical interpolation method, avoiding the traditional downsides of the latent upscale technique.
-
-**Node Link:** [https://github.com/gogurtenjoyer/latent-upscale](https://github.com/gogurtenjoyer/latent-upscale)
-
 --------------------------------
 ### Load Video Frame
 
@@ -443,17 +434,6 @@ 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.                                                                                                                         |
-| DW Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
+| 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

@@ -14,19 +14,11 @@ function is_bin_in_path {
 }
 
 function git_show {
-    git show -s --format=oneline --abbrev-commit "$1" | cat
+    git show -s --format='%h %s' $1
 }
 
-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..."
@@ -40,6 +32,13 @@ 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=$(
     cd ..
     python -c "from invokeai.version import __version__ as version; print(version)"
@@ -48,9 +47,38 @@ PATCH=""
 VERSION="v${VERSION}${PATCH}"
 
 echo -e "${BGREEN}HEAD${RESET}:"
-git_show HEAD
+git_show
 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
@@ -69,13 +97,16 @@ 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
-cp install.bat.in InvokeAI-Installer/install.bat
+perl -p -e "s/^set INVOKEAI_VERSION=.*/set INVOKEAI_VERSION=$VERSION/" install.bat.in >InvokeAI-Installer/install.bat
 cp WinLongPathsEnabled.reg InvokeAI-Installer/
 
 # Zip everything up

installer/install.bat.in

@@ -15,6 +15,7 @@ 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 Optional, Tuple
+from typing import Union
 
 SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
 INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
@@ -21,20 +21,40 @@ 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()
-        self.available_releases = get_github_releases()
+
+    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
 
     def mktemp_venv(self) -> TemporaryDirectory:
         """
@@ -58,9 +78,12 @@ class Installer:
 
         return venv_dir
 
-    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory | None:
+    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory:
         """
         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...")
@@ -72,27 +95,39 @@ class Installer:
         cmd.extend(self.reqs)
 
         try:
-            # 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, venv_parent) -> Path:
+    def app_venv(self, path: str = None):
         """
         Create a virtualenv for the InvokeAI installation
         """
 
-        venv_dir = venv_parent / ".venv"
+        # 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"
 
         # Prefer to copy python executables
         # so that updates to system python don't break InvokeAI
@@ -106,7 +141,7 @@ class Installer:
         return venv_dir
 
     def install(
-        self, version=None, root: str = "~/invokeai", yes_to_all=False, find_links: Optional[Path] = None
+        self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
     ) -> None:
         """
         Install the InvokeAI application into the given runtime path
@@ -123,20 +158,15 @@ class Installer:
 
         import messages
 
-        messages.welcome(self.available_releases)
+        messages.welcome()
 
-        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)
+        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)
 
         # create the venv for the app
-        self.venv = self.app_venv(venv_parent=destination)
+        self.venv = self.app_venv()
 
-        self.instance = InvokeAiInstance(runtime=destination, venv=self.venv, version=version)
+        self.instance = InvokeAiInstance(runtime=self.dest, 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)
@@ -160,7 +190,7 @@ 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 = "stable") -> None:
+    def __init__(self, runtime: Path, venv: Path, version: str) -> None:
         self.runtime = runtime
         self.venv = venv
         self.pip = get_pip_from_venv(venv)
@@ -169,7 +199,6 @@ 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]:
         """
@@ -183,7 +212,54 @@ class InvokeAiInstance:
 
     def install(self, extra_index_url=None, optional_modules=None, find_links=None):
         """
-        Install the package from PyPi.
+        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.
 
         :param extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
         :type extra_index_url: str
@@ -195,52 +271,53 @@ class InvokeAiInstance:
         :type find_links: Path
         """
 
-        import messages
-
-        # not currently used, but may be useful for "install most recent version" option
-        if self.version == "prerelease":
+        ## this only applies to pypi installs; TODO actually use this
+        if self.version == "pre":
             version = None
-            pre_flag = "--pre"
-        elif self.version == "stable":
-            version = None
-            pre_flag = None
+            pre = "--pre"
         else:
             version = self.version
-            pre_flag = None
+            pre = None
 
-        src = "invokeai"
-        if optional_modules:
-            src += optional_modules
-        if version:
-            src += f"=={version}"
+        ## 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.")
 
-        messages.simple_banner("Installing the InvokeAI Application :art:")
+        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"
 
-        from plumbum import FG, ProcessExecutionError, local  # type: ignore
+        from plumbum import FG, local
 
         pip = local[self.pip]
 
-        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)
+        (
+            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
+        )
 
     def configure(self):
         """
@@ -296,6 +373,7 @@ class InvokeAiInstance:
 
         ext = "bat" if OS == "Windows" else "sh"
 
+        # scripts = ['invoke', 'update']
         scripts = ["invoke"]
 
         for script in scripts:
@@ -330,23 +408,6 @@ 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,
@@ -370,43 +431,7 @@ def set_sys_path(venv_path: Path) -> None:
     sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
 
 
-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]:
+def get_torch_source() -> (Union[str, None], str):
     """
     Determine the extra index URL for pip to use for torch installation.
     This depends on the OS and the graphics accelerator in use.
@@ -421,26 +446,25 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     :rtype: list
     """
 
-    from messages import select_gpu
+    from messages import graphical_accelerator
 
-    # device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
-    device = select_gpu()
+    # device can be one of: "cuda", "rocm", "cpu", "idk"
+    device = graphical_accelerator()
 
     url = None
     optional_modules = "[onnx]"
     if OS == "Linux":
-        if device.value == "rocm":
+        if device == "rocm":
             url = "https://download.pytorch.org/whl/rocm5.6"
-        elif device.value == "cpu":
+        elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 
-    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]"
+    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]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 

installer/lib/messages.py

@@ -5,11 +5,10 @@ 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 FuzzyWordCompleter, PathCompleter
+from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
 from rich.console import Console, Group, group
@@ -36,26 +35,16 @@ else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
 
-def welcome(available_releases: tuple | None = None) -> None:
+def welcome():
     @group()
     def text():
-        if (platform_specific := _platform_specific_help()) is not None:
+        if (platform_specific := _platform_specific_help()) != "":
             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(
@@ -72,30 +61,19 @@ def welcome(available_releases: tuple | None = None) -> None:
     console.line()
 
 
-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.")
-
+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)
     console.line()
 
-    return "stable" if response == "" else response
+    return dest_confirmed
 
 
 def user_wants_auto_configuration() -> bool:
@@ -131,23 +109,7 @@ def user_wants_auto_configuration() -> bool:
     return choice.lower().startswith("a")
 
 
-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:
+def dest_path(dest=None) -> Path:
     """
     Prompt the user for the destination path and create the path
 
@@ -162,21 +124,25 @@ def dest_path(dest=None) -> Path | None:
     else:
         dest = Path.cwd().expanduser().resolve()
     prev_dest = init_path = dest
-    dest_confirmed = False
+
+    dest_confirmed = confirm_install(dest)
 
     while not dest_confirmed:
-        browse_start = (dest or Path.cwd()).expanduser().resolve()
+        # 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()
 
         path_completer = PathCompleter(
             only_directories=True,
             expanduser=True,
-            get_paths=lambda: [str(browse_start)],  # noqa: B023
+            get_paths=lambda: [browse_start],  # noqa: B023
             # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
         )
 
         console.line()
-
-        console.print(f":grey_question: [orange3]Please select the install destination:[/] \\[{browse_start}]: ")
+        console.print(f"[orange3]Please select the destination directory for the installation:[/] \\[{browse_start}]: ")
         selected = prompt(
             ">>> ",
             complete_in_thread=True,
@@ -189,7 +155,6 @@ def dest_path(dest=None) -> Path | None:
         )
         prev_dest = dest
         dest = Path(selected)
-
         console.line()
 
         dest_confirmed = confirm_install(dest.expanduser().resolve())
@@ -217,45 +182,41 @@ def dest_path(dest=None) -> Path | None:
         console.rule("Goodbye!")
 
 
-class GpuType(Enum):
-    CUDA = "cuda"
-    CUDA_AND_DML = "cuda_and_dml"
-    ROCM = "rocm"
-    CPU = "cpu"
-    AUTODETECT = "autodetect"
-
-
-def select_gpu() -> GpuType:
+def graphical_accelerator():
     """
-    Prompt the user to select the GPU driver
+    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.
     """
 
     if ARCH == "arm64" and OS != "Darwin":
         print(f"Only CPU acceleration is available on {ARCH} architecture. Proceeding with that.")
-        return GpuType.CPU
+        return "cpu"
 
     nvidia = (
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
-        GpuType.CUDA,
+        "cuda",
     )
     nvidia_with_dml = (
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
-        GpuType.CUDA_AND_DML,
+        "cuda_and_dml",
     )
     amd = (
         "an [gold1 b]AMD[/] GPU (using ROCm™)",
-        GpuType.ROCM,
+        "rocm",
     )
     cpu = (
-        "Do not install any GPU support, use CPU for generation (slow)",
-        GpuType.CPU,
+        "no compatible GPU, or specifically prefer to use the CPU",
+        "cpu",
     )
-    autodetect = (
+    idk = (
         "I'm not sure what to choose",
-        GpuType.AUTODETECT,
+        "idk",
     )
 
-    options = []
     if OS == "Windows":
         options = [nvidia, nvidia_with_dml, cpu]
     if OS == "Linux":
@@ -269,7 +230,7 @@ def select_gpu() -> GpuType:
         return options[0][1]
 
     # "I don't know" is always added the last option
-    options.append(autodetect)  # type: ignore
+    options.append(idk)
 
     options = {str(i): opt for i, opt in enumerate(options, 1)}
 
@@ -304,9 +265,9 @@ def select_gpu() -> GpuType:
         ),
     )
 
-    if options[choice][1] is GpuType.AUTODETECT:
+    if options[choice][1] == "idk":
         console.print(
-            "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."
+            "No problem. We will try to install a version that [i]should[/i] be compatible. :crossed_fingers:"
         )
 
     return options[choice][1]
@@ -330,7 +291,7 @@ 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, lexer="regedit")
+        syntax = Syntax(code.read(), line_numbers=True)
 
     console.print(
         Panel(
@@ -340,7 +301,7 @@ 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:",
-                        str(syntax),
+                        syntax,
                     ]
                 )
             ),
@@ -379,7 +340,7 @@ def introduction() -> None:
     console.line(2)
 
 
-def _platform_specific_help() -> Text | None:
+def _platform_specific_help() -> str:
     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/[/]."""
@@ -393,5 +354,5 @@ def _platform_specific_help() -> Text | None:
      [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
         )
     else:
-        return
+        text = ""
     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 (DEPRECATED - please use the installer)
+echo 8. Update InvokeAI
 echo 9. Run the InvokeAI image database maintenance script
 echo 10. Command-line help
 echo Q - Quit
@@ -52,10 +52,8 @@ 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 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
+   echo Running invokeai-update...
+   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
@@ -79,3 +77,4 @@ pause
 
 :ending
 exit /b
+

installer/templates/invoke.sh.in

@@ -90,9 +90,7 @@ do_choice() {
         ;;
     8)
         clear
-        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
+        printf "Update InvokeAI\n"
         python -m invokeai.frontend.install.invokeai_update
         ;;
     9)
@@ -124,7 +122,7 @@ 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 (DEPRECATED - please use the installer)"
+        8 "Update InvokeAI"
 	9 "Run the InvokeAI image database maintenance script"
 	10 "Command-line help"
     )

installer/templates/update.bat.in

@@ -0,0 +1,72 @@
+@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

@@ -0,0 +1,58 @@
+#!/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="/ws/socket.io")
+        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
         app.mount("/ws", self.__app)
 
         self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)

invokeai/app/invocations/controlnet_image_processors.py

@@ -17,6 +17,7 @@ from controlnet_aux import (
     MidasDetector,
     MLSDdetector,
     NormalBaeDetector,
+    OpenposeDetector,
     PidiNetDetector,
     SamDetector,
     ZoeDetector,
@@ -30,7 +31,6 @@ 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 (
@@ -276,6 +276,31 @@ 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",
@@ -599,7 +624,7 @@ 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: Image.Image):
+    def run_processor(self, image):
         depth_anything_detector = DepthAnythingDetector()
         depth_anything_detector.load_model(model_size=self.model_size)
 
@@ -608,30 +633,3 @@ 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/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 metadata")
+            self.__invoker.services.logger.error("Problem getting image DTO")
             raise e
 
     def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -54,17 +54,6 @@ 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()
@@ -167,7 +156,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     pass
 
                 except CanceledException:
-                    stats_cleanup(graph_execution_state.id)
+                    with suppress(GESStatsNotFoundError):
+                        self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 except Exception as e:
@@ -192,6 +182,8 @@ 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
@@ -223,13 +215,21 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             error=traceback.format_exc(),
                         )
                 elif is_complete:
-                    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)
+                    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)
 
         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:
-            raise GESStatsNotFoundError(
-                f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
+            msg = f"Attempted to clear statistics for unknown graph {graph_execution_state_id}: {e}."
+            logger.error(msg)
+            raise GESStatsNotFoundError(msg) 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:
-            raise GESStatsNotFoundError(
-                f"Attempted to get model cache statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from 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
 
         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:
-            raise GESStatsNotFoundError(
-                f"Attempted to get graph statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
+            msg = f"Attempted to get graph statistics for unknown graph {graph_execution_state_id}: {e}."
+            logger.error(msg)
+            raise GESStatsNotFoundError(msg) 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:
-            raise GESStatsNotFoundError(
-                f"Attempted to get node statistics for unknown graph {graph_execution_state_id}: {e}."
-            ) from e
+            msg = f"Attempted to get node statistics for unknown graph {graph_execution_state_id}: {e}."
+            logger.error(msg)
+            raise GESStatsNotFoundError(msg) 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, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, ConfigDict, field_validator, model_validator
@@ -141,16 +141,6 @@ 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
 
@@ -540,7 +530,7 @@ class Graph(BaseModel):
         except NodeNotFoundError:
             return False
 
-    def get_node(self, node_path: str) -> BaseInvocation:
+    def get_node(self, node_path: str) -> InvocationsUnion:
         """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)
@@ -891,7 +881,7 @@ 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: BaseInvocationOutput) -> None:
+    def complete(self, node_id: str, output: InvocationOutputsUnion):
         """Marks a node as complete"""
 
         if node_id not in self.execution_graph.nodes:
@@ -1128,22 +1118,17 @@ 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 = [
-                copydeep(getattr(self.results[edge.source.node_id], edge.source.field))
+                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:
-                setattr(
-                    node,
-                    edge.destination.field,
-                    copydeep(getattr(self.results[edge.source.node_id], edge.source.field)),
-                )
+                output_value = getattr(self.results[edge.source.node_id], edge.source.field)
+                setattr(node, edge.destination.field, output_value)
 
     # 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

@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-
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-      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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-      any Contribution intentionally submitted for inclusion in the Work
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-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
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-      result of this License or out of the use or inability to use the
-      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
-      identification within third-party archives.
-
-   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

@@ -1,18 +0,0 @@
-"""
-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

@@ -1,75 +0,0 @@
-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

@@ -1,125 +0,0 @@
-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

@@ -1,81 +0,0 @@
-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

@@ -1,128 +0,0 @@
-# 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

@@ -1,361 +0,0 @@
-# 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

@@ -1,155 +0,0 @@
-# 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

@@ -1,67 +0,0 @@
-# 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/model_cache.py

@@ -287,14 +287,6 @@ class ModelCache(object):
         if torch.device(source_device).type == torch.device(target_device).type:
             return
 
-        if target_device.type == "cuda":
-            vram_device = (
-                target_device if target_device.index is not None else torch.device(str(target_device), index=0)
-            )
-            free_mem, _ = torch.cuda.mem_get_info(torch.device(vram_device))
-            if cache_entry.size > free_mem:
-                raise torch.cuda.OutOfMemoryError
-
         start_model_to_time = time.time()
         snapshot_before = self._capture_memory_snapshot()
         cache_entry.model.to(target_device)
@@ -364,10 +356,6 @@ class ModelCache(object):
                     self.cache.logger.debug(f"Locking {self.key} in {self.cache.execution_device}")
                     self.cache._print_cuda_stats()
 
-                except torch.cuda.OutOfMemoryError:
-                    self.cache.logger.warning("Out of GPU memory encountered.")
-                    self.cache_entry.unlock()
-                    raise
                 except Exception:
                     self.cache_entry.unlock()
                     raise
@@ -536,6 +524,7 @@ class ModelCache(object):
                 break
             if not cache_entry.locked and cache_entry.loaded:
                 self._move_model_to_device(model_key, self.storage_device)
+
                 vram_in_use = torch.cuda.memory_allocated()
                 self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
 

invokeai/backend/model_management/seamless.py

@@ -1,11 +1,10 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import Callable, List, Union
+from typing import List, Union
 
 import torch.nn as nn
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
 
 
 def _conv_forward_asymmetric(self, input, weight, bias):
@@ -27,51 +26,70 @@ 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:
-        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
-        skipped_layers = 1
+        to_restore = []
+
         for m_name, m in model.named_modules():
-            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:
+            if isinstance(model, UNet2DConditionModel):
+                if ".attentions." in m_name:
                     continue
 
-                # Skip the second resnet (could be configurable)
-                if resnet_num > 0:
+                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:
                     continue
 
-                # Skip Conv2d layers (could be configurable)
-                if submodule_name == "conv2":
+                if False and ".downsamplers." 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 ".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
 
-            to_restore.append((m, m._conv_forward))
-            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+                    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)
 
         yield
 

invokeai/frontend/web/docs/STATE_MGMT.md

@@ -4,7 +4,23 @@ The app makes heavy use of Redux Toolkit, its Query library, and `nanostores`.
 
 ## Redux
 
-TODO
+We use [Redux Toolkit] + RTK Query extensively.
+
+### Persistence
+
+The usual persistence layer for redux is [redux-persist]. Unfortunately, it is abandoned, and not possible to fork. Past releases of it depend on a malicious package that was removed from npm, so it's very difficult (impossible?) to build. The current state of the repo is also non-functional, as it was abandoned mid-rewrite.
+
+We had a need to debounce our persistence, and patched redux-persist's build directly to do so. This didn't feel great. We've since moved to [redux-remember], a well-designed, minimal, and actively maintained library.
+
+#### Slice migration
+
+When rehydrating state, we sometimes need to migrate data. This is handled by the `unserialize` function in [store.ts], which is used by redux-remember to rehydrate persisted state. This function uses some lodash utils to strip out unknown keys, and merge any new keys into the rehydrated state.
+
+Sometimes the shape of state changes, but it keeps the same property name in the slice. In that case, we need to _transform_ incoming data.
+
+To handle this, each persisted slice must have a `SliceConfig`, which includes its latest initial value, and a migrate function. The migrate function, defined in the slice, does does any transformations and updates the version.
+
+The version of the slice is currently only incremented when we need to run _transform_ migrations. If keys are added or removed from state, the version is not bumped.
 
 ## `nanostores`
 
@@ -36,3 +52,7 @@ const myStringOption = useStore($myStringOption);
 - For performance-critical code and in callbacks, redux selectors can be problematic due to the declarative reactivity system. Consider refactoring to use `nanostores` if there's a **measurable** performance issue.
 
 [nanostores]: https://github.com/nanostores/nanostores/
+[Redux Toolkit]: https://redux-toolkit.js.org/
+[redux-persist]: https://github.com/rt2zz/redux-persist
+[redux-remember]: https://github.com/zewish/redux-remember
+[store.ts]: invokeai/frontend/web/src/app/store/store.ts

invokeai/frontend/web/package.json

@@ -52,10 +52,9 @@
     "@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",
+    "@invoke-ai/ui-library": "^0.0.20",
     "@mantine/form": "6.0.21",
     "@nanostores/react": "^0.7.1",
     "@reduxjs/toolkit": "2.0.1",

invokeai/frontend/web/pnpm-lock.yaml

@@ -22,9 +22,6 @@ 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)
@@ -32,8 +29,8 @@ dependencies:
     specifier: ^5.0.16
     version: 5.0.16
   '@invoke-ai/ui-library':
-    specifier: ^0.0.18
-    version: 0.0.18(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.16)(@internationalized/date@3.5.1)(@types/react@18.2.48)(i18next@23.7.16)(react-dom@18.2.0)(react@18.2.0)
+    specifier: ^0.0.20
+    version: 0.0.20(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.16)(@internationalized/date@3.5.1)(@types/react@18.2.48)(i18next@23.7.16)(react-dom@18.2.0)(react@18.2.0)
   '@mantine/form':
     specifier: 6.0.21
     version: 6.0.21(react@18.2.0)
@@ -1703,6 +1700,13 @@ packages:
     dependencies:
       regenerator-runtime: 0.14.1
 
+  /@babel/runtime@7.23.9:
+    resolution: {integrity: sha512-0CX6F+BI2s9dkUqr08KFrAIZgNFj75rdBU/DjCyYLIaV/quFjkk6T+EJ2LkZHyZTbEV4L5p97mNkUsHl2wLFAw==}
+    engines: {node: '>=6.9.0'}
+    dependencies:
+      regenerator-runtime: 0.14.1
+    dev: false
+
   /@babel/template@7.22.15:
     resolution: {integrity: sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==}
     engines: {node: '>=6.9.0'}
@@ -1963,7 +1967,7 @@ packages:
     dependencies:
       '@chakra-ui/dom-utils': 2.1.0
       react: 18.2.0
-      react-focus-lock: 2.9.6(@types/react@18.2.48)(react@18.2.0)
+      react-focus-lock: 2.9.7(@types/react@18.2.48)(react@18.2.0)
     transitivePeerDependencies:
       - '@types/react'
     dev: false
@@ -2887,18 +2891,6 @@ 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:
@@ -3007,7 +2999,7 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.23.8
+      '@babel/runtime': 7.23.9
       '@emotion/babel-plugin': 11.11.0
       '@emotion/is-prop-valid': 1.2.1
       '@emotion/react': 11.11.3(@types/react@18.2.48)(react@18.2.0)
@@ -3602,8 +3594,8 @@ packages:
       prettier: 3.2.4
     dev: true
 
-  /@invoke-ai/ui-library@0.0.18(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.16)(@internationalized/date@3.5.1)(@types/react@18.2.48)(i18next@23.7.16)(react-dom@18.2.0)(react@18.2.0):
-    resolution: {integrity: sha512-Yme+2+pzYy3TPb7ZT0hYmBwahH29ZRSVIxLKSexh3BsbJXbTzGssRQU78QvK6Ymxemgbso3P8Rs+IW0zNhQKjQ==}
+  /@invoke-ai/ui-library@0.0.20(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.16)(@internationalized/date@3.5.1)(@types/react@18.2.48)(i18next@23.7.16)(react-dom@18.2.0)(react@18.2.0):
+    resolution: {integrity: sha512-8BDL9LWbmpAZHTJB0B+zMJ0kBq7vQF0tem6q9WB03CPqdE307FiDmZ++NZF7BP8Rp4Sivdi6OagaXL7WV6e0Pw==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
@@ -3625,8 +3617,8 @@ packages:
       framer-motion: 10.18.0(react-dom@18.2.0)(react@18.2.0)
       lodash-es: 4.17.21
       nanostores: 0.9.5
-      overlayscrollbars: 2.4.7
-      overlayscrollbars-react: 0.5.4(overlayscrollbars@2.4.7)(react@18.2.0)
+      overlayscrollbars: 2.5.0
+      overlayscrollbars-react: 0.5.4(overlayscrollbars@2.5.0)(react@18.2.0)
       react: 18.2.0
       react-dom: 18.2.0(react@18.2.0)
       react-i18next: 14.0.1(i18next@23.7.16)(react-dom@18.2.0)(react@18.2.0)
@@ -11348,13 +11340,13 @@ packages:
       react: 18.2.0
     dev: false
 
-  /overlayscrollbars-react@0.5.4(overlayscrollbars@2.4.7)(react@18.2.0):
+  /overlayscrollbars-react@0.5.4(overlayscrollbars@2.5.0)(react@18.2.0):
     resolution: {integrity: sha512-FPKx9XnXovTnI4+2JXig5uEaTLSEJ6svOwPzIfBBXTHBRNsz2+WhYUmfM0K/BNYxjgDEwuPm+NQhEoOA0RoG1g==}
     peerDependencies:
       overlayscrollbars: ^2.0.0
       react: '>=16.8.0'
     dependencies:
-      overlayscrollbars: 2.4.7
+      overlayscrollbars: 2.5.0
       react: 18.2.0
     dev: false
 
@@ -11362,8 +11354,8 @@ packages:
     resolution: {integrity: sha512-C7tmhetwMv9frEvIT/RfkAVEgbjRNz/Gh2zE8BVmN+jl35GRaAnz73rlGQCMRoC2arpACAXyMNnJkzHb7GBrcA==}
     dev: false
 
-  /overlayscrollbars@2.4.7:
-    resolution: {integrity: sha512-02X2/nHno35dzebCx+EO2tRDaKAOltZqUKdUqvq3Pt8htCuhJbYi+mjr0CYerVeGRRoZ2Uo6/8XrNg//DJJ+GA==}
+  /overlayscrollbars@2.5.0:
+    resolution: {integrity: sha512-CWVC2dwS07XZfLHDm5GmZN1iYggiJ8Vufnvzwt0gwR9Yz1hVckKeTxg7VILZeYVGhDYJHZ1Xc8Xfys5dWZ1qiA==}
     dev: false
 
   /p-limit@2.3.0:
@@ -11851,7 +11843,7 @@ packages:
     peerDependencies:
       react: ^15.3.0 || ^16.0.0 || ^17.0.0 || ^18.0.0
     dependencies:
-      '@babel/runtime': 7.23.8
+      '@babel/runtime': 7.23.9
       react: 18.2.0
     dev: false
 
@@ -11937,8 +11929,8 @@ packages:
     resolution: {integrity: sha512-nsO+KSNgo1SbJqJEYRE9ERzo7YtYbou/OqjSQKxV7jcKox7+usiUVZOAC+XnDOABXggQTno0Y1CpVnuWEc1boQ==}
     dev: false
 
-  /react-focus-lock@2.9.6(@types/react@18.2.48)(react@18.2.0):
-    resolution: {integrity: sha512-B7gYnCjHNrNYwY2juS71dHbf0+UpXXojt02svxybj8N5bxceAkzPChKEncHuratjUHkIFNCn06k2qj1DRlzTug==}
+  /react-focus-lock@2.9.7(@types/react@18.2.48)(react@18.2.0):
+    resolution: {integrity: sha512-EfhX040SELLqnQ9JftqsmQCG49iByg8F5X5m19Er+n371OaETZ35dlNPZrLOOTlnnwD4c2Zv0KDgabDTc7dPHw==}
     peerDependencies:
       '@types/react': ^16.8.0 || ^17.0.0 || ^18.0.0
       react: ^16.8.0 || ^17.0.0 || ^18.0.0
@@ -11946,7 +11938,7 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.23.8
+      '@babel/runtime': 7.23.9
       '@types/react': 18.2.48
       focus-lock: 1.0.0
       prop-types: 15.8.1
@@ -12008,7 +12000,7 @@ packages:
       react-native:
         optional: true
     dependencies:
-      '@babel/runtime': 7.23.8
+      '@babel/runtime': 7.23.9
       html-parse-stringify: 3.0.1
       i18next: 23.7.16
       react: 18.2.0

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

@@ -175,7 +175,6 @@
         "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.",
@@ -236,9 +235,6 @@
         "fill": "Fill",
         "h": "H",
         "handAndFace": "Hand and Face",
-        "face": "Face",
-        "body": "Body",
-        "hands": "Hands",
         "hed": "HED",
         "hedDescription": "Holistically-Nested Edge Detection",
         "hideAdvanced": "Hide Advanced",
@@ -265,8 +261,8 @@
         "noneDescription": "No processing applied",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE processing",
-        "dwOpenpose": "DW Openpose",
-        "dwOpenposeDescription": "Human pose estimation using DW Openpose",
+        "openPose": "Openpose",
+        "openPoseDescription": "Human pose estimation using Openpose",
         "pidi": "PIDI",
         "pidiDescription": "PIDI image processing",
         "processor": "Processor",
@@ -901,7 +897,6 @@
         "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",
@@ -997,10 +992,8 @@
         "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.",
@@ -1071,7 +1064,6 @@
         "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",
@@ -1424,8 +1416,9 @@
         "clipSkip": {
             "heading": "CLIP Skip",
             "paragraphs": [
-                "How many layers of the CLIP model to skip.",
-                "Certain models are better suited to be used with CLIP Skip."
+                "Choose how many layers of the CLIP model to skip.",
+                "Some models work better with certain CLIP Skip settings.",
+                "A higher value typically results in a less detailed image."
             ]
         },
         "paramNegativeConditioning": {
@@ -1445,8 +1438,7 @@
         "paramScheduler": {
             "heading": "Scheduler",
             "paragraphs": [
-                "Scheduler used during the generation process.",
-                "Each scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output."
+                "Scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output."
             ]
         },
         "compositingBlur": {
@@ -1463,52 +1455,47 @@
         },
         "compositingCoherenceMode": {
             "heading": "Mode",
-            "paragraphs": ["Method used to create a coherent image with the newly generated masked area."]
+            "paragraphs": ["The mode of the Coherence Pass."]
         },
         "compositingCoherenceSteps": {
             "heading": "Steps",
-            "paragraphs": ["Number of steps in the Coherence Pass.", "Similar to Generation Steps."]
+            "paragraphs": ["Number of denoising steps used in the Coherence Pass.", "Same as the main Steps parameter."]
         },
         "compositingStrength": {
             "heading": "Strength",
-            "paragraphs": ["Amount of noise added for the Coherence Pass.", "Similar to Denoising Strength."]
+            "paragraphs": [
+                "Denoising strength for the Coherence Pass.",
+                "Same as the Image to Image Denoising Strength parameter."
+            ]
         },
         "compositingMaskAdjustments": {
             "heading": "Mask Adjustments",
             "paragraphs": ["Adjust the mask."]
         },
+        "controlNetBeginEnd": {
+            "heading": "Begin / End Step Percentage",
+            "paragraphs": [
+                "Which steps of the denoising process will have the ControlNet applied.",
+                "ControlNets applied at the beginning of the process guide composition, and ControlNets applied at the end guide details."
+            ]
+        },
+        "controlNetControlMode": {
+            "heading": "Control Mode",
+            "paragraphs": ["Lends more weight to either the prompt or ControlNet."]
+        },
+        "controlNetResizeMode": {
+            "heading": "Resize Mode",
+            "paragraphs": ["How the ControlNet image will be fit to the image output size."]
+        },
         "controlNet": {
             "heading": "ControlNet",
             "paragraphs": [
                 "ControlNets provide guidance to the generation process, helping create images with controlled composition, structure, or style, depending on the model selected."
             ]
         },
-        "controlNetBeginEnd": {
-            "heading": "Begin / End Step Percentage",
-            "paragraphs": [
-                "The part of the of the denoising process that will have the Control Adapter applied.",
-                "Generally, Control Adapters applied at the start of the process guide composition, and Control Adapters applied at the end guide details."
-            ]
-        },
-        "controlNetControlMode": {
-            "heading": "Control Mode",
-            "paragraphs": ["Lend more weight to either the prompt or ControlNet."]
-        },
-        "controlNetProcessor": {
-            "heading": "Processor",
-            "paragraphs": [
-                "Method of processing the input image to guide the generation process. Different processors will providedifferent effects or styles in your generated images."
-            ]
-        },
-        "controlNetResizeMode": {
-            "heading": "Resize Mode",
-            "paragraphs": ["Method to fit Control Adapter's input image size to the output generation size."]
-        },
         "controlNetWeight": {
             "heading": "Weight",
-            "paragraphs": [
-                "Weight of the Control Adapter. Higher weight will lead to larger impacts on the final image."
-            ]
+            "paragraphs": ["How strongly the ControlNet will impact the generated image."]
         },
         "dynamicPrompts": {
             "heading": "Dynamic Prompts",
@@ -1531,23 +1518,13 @@
                 "Per Image will use a unique seed for each image. This provides more variation."
             ]
         },
-        "imageFit": {
-            "heading": "Fit Initial Image to Output Size",
-            "paragraphs": [
-                "Resizes the initial image to the width and height of the output image. Recommended to enable."
-            ]
-        },
         "infillMethod": {
             "heading": "Infill Method",
-            "paragraphs": ["Method of infilling during the Outpainting or Inpainting process."]
+            "paragraphs": ["Method to infill the selected area."]
         },
         "lora": {
-            "heading": "LoRA",
-            "paragraphs": ["Lightweight models that are used in conjunction with base models."]
-        },
-        "loraWeight": {
-            "heading": "Weight",
-            "paragraphs": ["Weight of the LoRA. Higher weight will lead to larger impacts on the final image."]
+            "heading": "LoRA Weight",
+            "paragraphs": ["Higher LoRA weight will lead to larger impacts on the final image."]
         },
         "noiseUseCPU": {
             "heading": "Use CPU Noise",
@@ -1557,25 +1534,14 @@
                 "There is no performance impact to enabling CPU Noise."
             ]
         },
-        "paramAspect": {
-            "heading": "Aspect",
-            "paragraphs": [
-                "Aspect ratio of the generated image. Changing the ratio will update the Width and Height accordingly.",
-                "“Optimize” will set the Width and Height to optimal dimensions for the chosen model."
-            ]
-        },
         "paramCFGScale": {
             "heading": "CFG Scale",
-            "paragraphs": [
-                "Controls how much the prompt influences the generation process.",
-                "High CFG Scale values can result in over-saturation and distorted generation results. "
-            ]
+            "paragraphs": ["Controls how much your prompt influences the generation process."]
         },
         "paramCFGRescaleMultiplier": {
             "heading": "CFG Rescale Multiplier",
             "paragraphs": [
-                "Rescale multiplier for CFG guidance, used for models trained using zero-terminal SNR (ztsnr).",
-                "Suggested value of 0.7 for these models."
+                "Rescale multiplier for CFG guidance, used for models trained using zero-terminal SNR (ztsnr). Suggested value 0.7."
             ]
         },
         "paramDenoisingStrength": {
@@ -1585,16 +1551,6 @@
                 "0 will result in an identical image, while 1 will result in a completely new image."
             ]
         },
-        "paramHeight": {
-            "heading": "Height",
-            "paragraphs": ["Height of the generated image. Must be a multiple of 8."]
-        },
-        "paramHrf": {
-            "heading": "Enable High Resolution Fix",
-            "paragraphs": [
-                "Generate high quality images at a larger resolution than optimal for the model. Generally used to prevent duplication in the generated image."
-            ]
-        },
         "paramIterations": {
             "heading": "Iterations",
             "paragraphs": [
@@ -1605,7 +1561,8 @@
         "paramModel": {
             "heading": "Model",
             "paragraphs": [
-                "Model used for generation. Different models are trained to specialize in producing different aesthetic results and content."
+                "Model used for the denoising steps.",
+                "Different models are typically trained to specialize in producing particular aesthetic results and content."
             ]
         },
         "paramRatio": {
@@ -1619,7 +1576,7 @@
             "heading": "Seed",
             "paragraphs": [
                 "Controls the starting noise used for generation.",
-                "Disable the “Random” option to produce identical results with the same generation settings."
+                "Disable “Random Seed” to produce identical results with the same generation settings."
             ]
         },
         "paramSteps": {
@@ -1629,10 +1586,6 @@
                 "Higher step counts will typically create better images but will require more generation time."
             ]
         },
-        "paramUpscaleMethod": {
-            "heading": "Upscale Method",
-            "paragraphs": ["Method used to upscale the image for High Resolution Fix."]
-        },
         "paramVAE": {
             "heading": "VAE",
             "paragraphs": ["Model used for translating AI output into the final image."]
@@ -1640,82 +1593,14 @@
         "paramVAEPrecision": {
             "heading": "VAE Precision",
             "paragraphs": [
-                "The precision used during VAE encoding and decoding.",
-                "Fp16/Half precision is more efficient, at the expense of minor image variations."
-            ]
-        },
-        "paramWidth": {
-            "heading": "Width",
-            "paragraphs": ["Width of the generated image. Must be a multiple of 8."]
-        },
-        "patchmatchDownScaleSize": {
-            "heading": "Downscale",
-            "paragraphs": [
-                "How much downscaling occurs before infilling.",
-                "Higher downscaling will improve performance and reduce quality."
-            ]
-        },
-        "refinerModel": {
-            "heading": "Refiner Model",
-            "paragraphs": [
-                "Model used during the refiner portion of the generation process.",
-                "Similar to the Generation Model."
-            ]
-        },
-        "refinerPositiveAestheticScore": {
-            "heading": "Positive Aesthetic Score",
-            "paragraphs": [
-                "Weight generations to be more similar to images with a high aesthetic score, based on the training data."
-            ]
-        },
-        "refinerNegativeAestheticScore": {
-            "heading": "Negative Aesthetic Score",
-            "paragraphs": [
-                "Weight generations to be more similar to images with a low aesthetic score, based on the training data."
-            ]
-        },
-        "refinerScheduler": {
-            "heading": "Scheduler",
-            "paragraphs": [
-                "Scheduler used during the refiner portion of the generation process.",
-                "Similar to the Generation Scheduler."
-            ]
-        },
-        "refinerStart": {
-            "heading": "Refiner Start",
-            "paragraphs": [
-                "Where in the generation process the refiner will start to be used.",
-                "0 means the refiner will be used for the entire generation process, 0.8 means the refiner will be used for the last 20% of the generation process."
-            ]
-        },
-        "refinerSteps": {
-            "heading": "Steps",
-            "paragraphs": [
-                "Number of steps that will be performed during the refiner portion of the generation process.",
-                "Similar to the Generation Steps."
-            ]
-        },
-        "refinerCfgScale": {
-            "heading": "CFG Scale",
-            "paragraphs": [
-                "Controls how much the prompt influences the generation process.",
-                "Similar to the Generation CFG Scale."
+                "The precision used during VAE encoding and decoding. FP16/half precision is more efficient, at the expense of minor image variations."
             ]
         },
         "scaleBeforeProcessing": {
             "heading": "Scale Before Processing",
             "paragraphs": [
-                "“Auto” scales the selected area to the size best suited for the model before the image generation process.",
-                "“Manual” allows you to choose the width and height the selected area will be scaled to before the image generation process."
+                "Scales the selected area to the size best suited for the model before the image generation process."
             ]
-        },
-        "seamlessTilingXAxis": {
-            "heading": "Seamless Tiling X Axis",
-            "paragraphs": ["Seamlessly tile an image along the horizontal axis."]
-        },
-        "seamlessTilingYAxis": {
-            "heading": "Seamless Tiling Y Axis",
-            "paragraphs": ["Seamlessly tile an image along the vertical axis."]
         }
     },
     "ui": {
@@ -1724,6 +1609,13 @@
         "showProgressImages": "Show Progress Images",
         "swapSizes": "Swap Sizes"
     },
+    "viewer": {
+        "viewerModeImage": "Image",
+        "viewerModeInfo": "Info",
+        "viewerModeProgress": "Progress",
+        "dropLabel": "View Image",
+        "noProgress": "Nothing in Progress"
+    },
     "unifiedCanvas": {
         "accept": "Accept",
         "activeLayer": "Active Layer",

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

@@ -795,8 +795,7 @@
         "workflowDeleted": "Flusso di lavoro eliminato",
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
-        "uploadInitialImage": "Carica l'immagine iniziale",
-        "problemDownloadingImage": "Impossibile scaricare l'immagine"
+        "uploadInitialImage": "Carica l'immagine iniziale"
     },
     "tooltip": {
         "feature": {
@@ -1135,14 +1134,7 @@
         "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}})",
-        "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.",
-        "viewMode": "Utilizzare nella vista lineare",
-        "reorderLinearView": "Riordina la vista lineare",
-        "editMode": "Modifica nell'editor del flusso di lavoro",
-        "resetToDefaultValue": "Ripristina il valore predefinito"
+        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1199,6 +1191,7 @@
         "f": "F",
         "h": "A",
         "prompt": "Prompt",
+        "openPoseDescription": "Stima della posa umana utilizzando Openpose",
         "resizeMode": "Ridimensionamento",
         "weight": "Peso",
         "selectModel": "Seleziona un modello",
@@ -1245,11 +1238,7 @@
         "large": "Grande",
         "small": "Piccolo",
         "depthAnythingDescription": "Generazione di mappe di profondità utilizzando la tecnica Depth Anything",
-        "modelSize": "Dimensioni del modello",
-        "dwOpenposeDescription": "Stima della posa umana utilizzando DW Openpose",
-        "face": "Viso",
-        "body": "Corpo",
-        "hands": "Mani"
+        "modelSize": "Dimensioni del modello"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1379,8 +1368,7 @@
     "popovers": {
         "paramScheduler": {
             "paragraphs": [
-                "Il campionatore utilizzato durante il processo di generazione.",
-                "Ciascun campionatore definisce come aggiungere in modo iterativo il rumore a un'immagine o come aggiornare un campione in base all'output di un modello."
+                "Il campionatore definisce come aggiungere in modo iterativo il rumore a un'immagine o come aggiornare un campione in base all'output di un modello."
             ],
             "heading": "Campionatore"
         },
@@ -1393,8 +1381,8 @@
         "compositingCoherenceSteps": {
             "heading": "Passi",
             "paragraphs": [
-                "Numero di passi utilizzati nel Passaggio di Coerenza.",
-                "Simile ai passi di generazione."
+                "Numero di passi di riduzione del rumore utilizzati nel Passaggio di Coerenza.",
+                "Uguale al parametro principale Passi."
             ]
         },
         "compositingBlur": {
@@ -1406,13 +1394,14 @@
         "compositingCoherenceMode": {
             "heading": "Modalità",
             "paragraphs": [
-                "Metodo utilizzato per creare un'immagine coerente con l'area mascherata appena generata."
+                "La modalità del Passaggio di Coerenza."
             ]
         },
         "clipSkip": {
             "paragraphs": [
                 "Scegli quanti livelli del modello CLIP saltare.",
-                "Alcuni modelli funzionano meglio con determinate impostazioni di CLIP Skip."
+                "Alcuni modelli funzionano meglio con determinate impostazioni di CLIP Skip.",
+                "Un valore più alto in genere produce un'immagine meno dettagliata."
             ]
         },
         "compositingCoherencePass": {
@@ -1424,8 +1413,8 @@
         "compositingStrength": {
             "heading": "Forza",
             "paragraphs": [
-                "Quantità di rumore aggiunta per il Passaggio di Coerenza.",
-                "Simile alla forza di riduzione del rumore."
+                "Intensità di riduzione del rumore per il passaggio di coerenza.",
+                "Uguale al parametro intensità di riduzione del rumore da immagine a immagine."
             ]
         },
         "paramNegativeConditioning": {
@@ -1451,8 +1440,8 @@
         "controlNetBeginEnd": {
             "heading": "Percentuale passi Inizio / Fine",
             "paragraphs": [
-                "La parte del processo di rimozione del rumore in cui verrà applicato l'adattatore di controllo.",
-                "In genere, gli adattatori di controllo applicati all'inizio del processo guidano la composizione, mentre quelli applicati alla fine guidano i dettagli."
+                "A quali passi del processo di rimozione del rumore verrà applicato ControlNet.",
+                "I ControlNet applicati all'inizio del processo guidano la composizione, mentre i ControlNet applicati alla fine guidano i dettagli."
             ]
         },
         "noiseUseCPU": {
@@ -1465,7 +1454,7 @@
         },
         "scaleBeforeProcessing": {
             "paragraphs": [
-                "\"Auto\" ridimensiona l'area selezionata alla dimensione più adatta al modello prima del processo di generazione dell'immagine."
+                "Ridimensiona l'area selezionata alla dimensione più adatta al modello prima del processo di generazione dell'immagine."
             ],
             "heading": "Scala prima dell'elaborazione"
         },
@@ -1500,21 +1489,20 @@
         "paramVAEPrecision": {
             "heading": "Precisione VAE",
             "paragraphs": [
-                "La precisione utilizzata durante la codifica e decodifica VAE.",
-                "Fp16/Mezza precisione è più efficiente, a scapito di minori variazioni dell'immagine."
+                "La precisione utilizzata durante la codifica e decodifica VAE. FP16/mezza precisione è più efficiente, a scapito di minori variazioni dell'immagine."
             ]
         },
         "paramSeed": {
             "paragraphs": [
                 "Controlla il rumore iniziale utilizzato per la generazione.",
-                "Disabilita l'opzione \"Casuale\" per produrre risultati identici con le stesse impostazioni di generazione."
+                "Disabilita seme \"Casuale\" per produrre risultati identici con le stesse impostazioni di generazione."
             ],
             "heading": "Seme"
         },
         "controlNetResizeMode": {
             "heading": "Modalità ridimensionamento",
             "paragraphs": [
-                "Metodo per adattare le dimensioni dell'immagine in ingresso dell'adattatore di controllo alle dimensioni della generazione di output."
+                "Come l'immagine ControlNet verrà adattata alle dimensioni di output dell'immagine."
             ]
         },
         "dynamicPromptsSeedBehaviour": {
@@ -1529,7 +1517,8 @@
         "paramModel": {
             "heading": "Modello",
             "paragraphs": [
-                "Modello utilizzato per la generazione. Diversi modelli vengono addestrati per specializzarsi nella produzione di risultati e contenuti estetici diversi."
+                "Modello utilizzato per i passaggi di riduzione del rumore.",
+                "Diversi modelli sono generalmente addestrati per specializzarsi nella produzione di particolari risultati e contenuti estetici."
             ]
         },
         "paramDenoisingStrength": {
@@ -1547,26 +1536,25 @@
         },
         "infillMethod": {
             "paragraphs": [
-                "Metodo di riempimento durante il processo di Outpainting o Inpainting."
+                "Metodo per riempire l'area selezionata."
             ],
             "heading": "Metodo di riempimento"
         },
         "controlNetWeight": {
             "heading": "Peso",
             "paragraphs": [
-                "Peso dell'adattatore di controllo. Un peso maggiore porterà a impatti maggiori sull'immagine finale."
+                "Quanto forte sarà l'impatto di ControlNet sull'immagine generata."
             ]
         },
         "paramCFGScale": {
             "heading": "Scala CFG",
             "paragraphs": [
-                "Controlla quanto il prompt influenza il processo di generazione.",
-                "Valori elevati della scala CFG possono provocare una saturazione eccessiva e distorsioni nei risultati della generazione. "
+                "Controlla quanto il tuo prompt influenza il processo di generazione."
             ]
         },
         "controlNetControlMode": {
             "paragraphs": [
-                "Attribuisce più peso al prompt oppure a ControlNet."
+                "Attribuisce più peso al prompt o a ControlNet."
             ],
             "heading": "Modalità di controllo"
         },
@@ -1578,9 +1566,9 @@
             ]
         },
         "lora": {
-            "heading": "LoRA",
+            "heading": "Peso LoRA",
             "paragraphs": [
-                "Modelli leggeri utilizzati insieme ai modelli base."
+                "Un peso LoRA più elevato porterà a impatti maggiori sull'immagine finale."
             ]
         },
         "controlNet": {
@@ -1592,65 +1580,8 @@
         "paramCFGRescaleMultiplier": {
             "heading": "Moltiplicatore di riscala CFG",
             "paragraphs": [
-                "Moltiplicatore di riscala per la guida CFG, utilizzato per modelli addestrati utilizzando SNR a terminale zero (ztsnr).",
-                "Valore suggerito di 0.7 per questi modelli."
+                "Moltiplicatore di riscala per la guida CFG, utilizzato per modelli addestrati utilizzando SNR a terminale zero (ztsnr). Valore suggerito 0.7."
             ]
-        },
-        "controlNetProcessor": {
-            "heading": "Processore",
-            "paragraphs": [
-                "Metodo di elaborazione dell'immagine di input per guidare il processo di generazione. Processori diversi forniranno effetti o stili diversi nelle immagini generate."
-            ]
-        },
-        "imageFit": {
-            "heading": "Adatta l'immagine iniziale alle dimensioni di output",
-            "paragraphs": [
-                "Ridimensiona l'immagine iniziale in base alla larghezza e all'altezza dell'immagine di output. Si consiglia di abilitarlo."
-            ]
-        },
-        "loraWeight": {
-            "heading": "Peso",
-            "paragraphs": [
-                "Peso del LoRA. Un peso maggiore comporterà un impatto maggiore sull'immagine finale."
-            ]
-        },
-        "paramAspect": {
-            "heading": "Aspetto",
-            "paragraphs": [
-                "Proporzioni dell'immagine generata. La modifica del rapporto aggiornerà di conseguenza la larghezza e l'altezza.",
-                "\"Ottimizza\" imposterà la larghezza e l'altezza alle dimensioni ottimali per il modello scelto."
-            ]
-        },
-        "paramHeight": {
-            "heading": "Altezza",
-            "paragraphs": [
-                "Altezza dell'immagine generata. Deve essere un multiplo di 8."
-            ]
-        },
-        "paramHrf": {
-            "heading": "Abilita correzione alta risoluzione",
-            "paragraphs": [
-                "Genera immagini di alta qualità con una risoluzione maggiore di quella ottimale per il modello. Generalmente utilizzato per impedire la duplicazione nell'immagine generata."
-            ]
-        },
-        "paramUpscaleMethod": {
-            "heading": "Metodo di ampliamento",
-            "paragraphs": [
-                "Metodo utilizzato per eseguire l'ampliamento dell'immagine per la correzione ad alta risoluzione."
-            ]
-        },
-        "patchmatchDownScaleSize": {
-            "heading": "Ridimensiona",
-            "paragraphs": [
-                "Quanto ridimensionamento avviene prima del riempimento.",
-                "Un ridimensionamento più elevato migliorerà le prestazioni e ridurrà la qualità."
-            ]
-        },
-        "paramWidth": {
-            "paragraphs": [
-                "Larghezza dell'immagine generata. Deve essere un multiplo di 8."
-            ],
-            "heading": "Larghezza"
         }
     },
     "sdxl": {
@@ -1741,9 +1672,7 @@
         "downloadWorkflow": "Salva su file",
         "uploadWorkflow": "Carica da file",
         "projectWorkflows": "Flussi di lavoro del progetto",
-        "noWorkflows": "Nessun flusso di lavoro",
-        "workflowCleared": "Flusso di lavoro cancellato",
-        "saveWorkflowToProject": "Salva flusso di lavoro nel progetto"
+        "noWorkflows": "Nessun flusso di lavoro"
     },
     "app": {
         "storeNotInitialized": "Il negozio non è inizializzato"

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

@@ -555,6 +555,7 @@
         "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

@@ -333,6 +333,7 @@
         "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",
@@ -369,6 +370,7 @@
         "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

@@ -1033,6 +1033,7 @@
         "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",
@@ -1071,6 +1072,7 @@
         "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",
@@ -1217,14 +1219,16 @@
         "clipSkip": {
             "paragraphs": [
                 "Kies hoeveel CLIP-modellagen je wilt overslaan.",
-                "Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen."
+                "Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen.",
+                "Een hogere waarde geeft meestal een minder gedetailleerde afbeelding."
             ],
             "heading": "Overslaan CLIP"
         },
         "paramModel": {
             "heading": "Model",
             "paragraphs": [
-                "Model gebruikt voor de ontruisingsstappen."
+                "Model gebruikt voor de ontruisingsstappen.",
+                "Verschillende modellen zijn meestal getraind om zich te specialiseren in het maken van bepaalde esthetische resultaten en materiaal."
             ]
         },
         "compositingCoherencePass": {

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

@@ -1155,6 +1155,7 @@
         "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",
@@ -1353,14 +1354,16 @@
         "clipSkip": {
             "paragraphs": [
                 "Выберите, сколько слоев модели CLIP нужно пропустить.",
-                "Некоторые модели работают лучше с определенными настройками пропуска CLIP."
+                "Некоторые модели работают лучше с определенными настройками пропуска CLIP.",
+                "Более высокое значение обычно приводит к менее детализированному изображению."
             ],
             "heading": "CLIP пропуск"
         },
         "paramModel": {
             "heading": "Модель",
             "paragraphs": [
-                "Модель, используемая для шагов шумоподавления."
+                "Модель, используемая для шагов шумоподавления.",
+                "Различные модели обычно обучаются, чтобы специализироваться на достижении определенных эстетических результатов и содержания."
             ]
         },
         "compositingCoherencePass": {

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

@@ -259,6 +259,7 @@
         "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",
@@ -272,6 +273,7 @@
         "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

@@ -1143,6 +1143,7 @@
         "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",
@@ -1206,6 +1207,7 @@
         "megaControl": "Mega Control (超级控制)",
         "depthZoe": "Depth (Zoe)",
         "colorMap": "Color",
+        "openPose": "Openpose",
         "controlAdapter_other": "Control Adapters",
         "lineartAnime": "Lineart Anime",
         "canny": "Canny",
@@ -1444,14 +1446,16 @@
         "clipSkip": {
             "paragraphs": [
                 "选择要跳过 CLIP 模型多少层。",
-                "部分模型跳过特定数值的层时效果会更好。"
+                "部分模型跳过特定数值的层时效果会更好。",
+                "较高的数值通常会导致图像细节更少。"
             ],
             "heading": "CLIP 跳过层"
         },
         "paramModel": {
             "heading": "模型",
             "paragraphs": [
-                "用于去噪过程的模型。"
+                "用于去噪过程的模型。",
+                "不同的模型一般会通过接受训练来专门产生特定的美学内容和结果。"
             ]
         },
         "paramIterations": {

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 { appInfoApi } from 'services/api/endpoints/appInfo';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 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 (appInfoApi.endpoints.getOpenAPISchema.matchFulfilled(action)) {
+  if (receivedOpenAPISchema.fulfilled.match(action)) {
     return {
       ...action,
       payload: '<OpenAPI schema omitted>',

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

@@ -23,7 +23,6 @@ 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,
@@ -48,6 +47,7 @@ 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
-addGetOpenAPISchemaListener();
+addReceivedOpenAPISchemaListener();
 
 // Workflows
 addWorkflowLoadRequestedListener();

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

@@ -1,25 +1,25 @@
-import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
-import { canvasBatchIdsReset, commitStagingAreaImage, discardStagedImages } from 'features/canvas/store/canvasSlice';
+import { matchAnyStagingAreaDismissed } from 'features/canvas/store/canvasSlice';
 import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 
 import { startAppListening } from '..';
 
-const matcher = isAnyOf(commitStagingAreaImage, discardStagedImages);
-
 export const addCommitStagingAreaImageListener = () => {
   startAppListening({
-    matcher,
+    matcher: matchAnyStagingAreaDismissed,
     effect: async (_, { dispatch, getState }) => {
       const log = logger('canvas');
       const state = getState();
-      const { batchIds } = state.canvas;
+      const { canvasBatchIds } = state.progress;
 
       try {
         const req = dispatch(
-          queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: batchIds }, { fixedCacheKey: 'cancelByBatchIds' })
+          queueApi.endpoints.cancelByBatchIds.initiate(
+            { batch_ids: canvasBatchIds },
+            { fixedCacheKey: 'cancelByBatchIds' }
+          )
         );
         const { canceled } = await req.unwrap();
         req.reset();
@@ -32,7 +32,6 @@ export const addCommitStagingAreaImageListener = () => {
             })
           );
         }
-        dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
         dispatch(

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

@@ -1,5 +1,5 @@
 import { createAction } from '@reduxjs/toolkit';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageCache } from 'services/api/types';
@@ -28,7 +28,7 @@ export const addFirstListImagesListener = () => {
       if (data.ids.length > 0) {
         // Select the first image
         const firstImage = imagesSelectors.selectAll(data)[0];
-        dispatch(imageSelected(firstImage ?? null));
+        dispatch(imageSelectionChanged(firstImage ?? null));
       }
     },
   });

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

@@ -1,5 +1,5 @@
 import { isAnyOf } from '@reduxjs/toolkit';
-import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
+import { boardIdSelected, galleryViewChanged, imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { ASSETS_CATEGORIES, IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { imagesApi } from 'services/api/endpoints/images';
 import { imagesSelectors } from 'services/api/util';
@@ -37,17 +37,17 @@ export const addBoardIdSelectedListener = () => {
 
         if (boardImagesData && boardIdSelected.match(action) && action.payload.selectedImageName) {
           const selectedImage = imagesSelectors.selectById(boardImagesData, action.payload.selectedImageName);
-          dispatch(imageSelected(selectedImage || null));
+          dispatch(imageSelectionChanged(selectedImage ?? null));
         } else if (boardImagesData) {
           const firstImage = imagesSelectors.selectAll(boardImagesData)[0];
-          dispatch(imageSelected(firstImage || null));
+          dispatch(imageSelectionChanged(firstImage ?? null));
         } else {
           // board has no images - deselect
-          dispatch(imageSelected(null));
+          dispatch(imageSelectionChanged(null));
         }
       } else {
         // fallback - deselect
-        dispatch(imageSelected(null));
+        dispatch(imageSelectionChanged(null));
       }
     },
   });

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

@@ -2,13 +2,14 @@ import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
 import { parseify } from 'common/util/serialize';
-import { canvasBatchIdAdded, stagingAreaInitialized } from 'features/canvas/store/canvasSlice';
+import { stagingAreaInitialized } from 'features/canvas/store/canvasSlice';
 import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
 import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
 import { canvasGraphBuilt } from 'features/nodes/store/actions';
 import { buildCanvasGraph } from 'features/nodes/util/graph/buildCanvasGraph';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
+import { canvasBatchEnqueued } from 'features/progress/store/progressSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
@@ -121,8 +122,6 @@ export const addEnqueueRequestedCanvasListener = () => {
         const enqueueResult = await req.unwrap();
         req.reset();
 
-        const batchId = enqueueResult.batch.batch_id as string; // we know the is a string, backend provides it
-
         // Prep the canvas staging area if it is not yet initialized
         if (!state.canvas.layerState.stagingArea.boundingBox) {
           dispatch(
@@ -135,8 +134,9 @@ export const addEnqueueRequestedCanvasListener = () => {
           );
         }
 
-        // Associate the session with the canvas session ID
-        dispatch(canvasBatchIdAdded(batchId));
+        if (enqueueResult.batch.batch_id) {
+          dispatch(canvasBatchEnqueued(enqueueResult.batch.batch_id));
+        }
       } catch {
         // no-op
       }

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

@@ -1,19 +1,10 @@
-import { createAction } from '@reduxjs/toolkit';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { selectionChanged } from 'features/gallery/store/gallerySlice';
+import { galleryImageClicked, imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
-import type { ImageDTO } from 'services/api/types';
 import { imagesSelectors } from 'services/api/util';
 
 import { startAppListening } from '..';
 
-export const galleryImageClicked = createAction<{
-  imageDTO: ImageDTO;
-  shiftKey: boolean;
-  ctrlKey: boolean;
-  metaKey: boolean;
-}>('gallery/imageClicked');
-
 /**
  * This listener handles the logic for selecting images in the gallery.
  *
@@ -52,16 +43,16 @@ export const addGalleryImageClickedListener = () => {
           const start = Math.min(lastClickedIndex, currentClickedIndex);
           const end = Math.max(lastClickedIndex, currentClickedIndex);
           const imagesToSelect = imageDTOs.slice(start, end + 1);
-          dispatch(selectionChanged(selection.concat(imagesToSelect)));
+          dispatch(imageSelectionChanged(selection.concat(imagesToSelect)));
         }
       } else if (ctrlKey || metaKey) {
         if (selection.some((i) => i.image_name === imageDTO.image_name) && selection.length > 1) {
-          dispatch(selectionChanged(selection.filter((n) => n.image_name !== imageDTO.image_name)));
+          dispatch(imageSelectionChanged(selection.filter((n) => n.image_name !== imageDTO.image_name)));
         } else {
-          dispatch(selectionChanged(selection.concat(imageDTO)));
+          dispatch(imageSelectionChanged(selection.concat(imageDTO)));
         }
       } else {
-        dispatch(selectionChanged([imageDTO]));
+        dispatch(imageSelectionChanged(imageDTO));
       }
     },
   });

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

@@ -9,7 +9,7 @@ import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { isImageFieldInputInstance } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
@@ -62,9 +62,9 @@ export const addRequestedSingleImageDeletionListener = () => {
         const newSelectedImageDTO = filteredImageDTOs[newSelectedImageIndex];
 
         if (newSelectedImageDTO) {
-          dispatch(imageSelected(newSelectedImageDTO));
+          dispatch(imageSelectionChanged(newSelectedImageDTO));
         } else {
-          dispatch(imageSelected(null));
+          dispatch(imageSelectionChanged(null));
         }
       }
 
@@ -160,9 +160,9 @@ export const addRequestedMultipleImageDeletionListener = () => {
         const newSelectedImageDTO = data ? imagesSelectors.selectAll(data)[0] : undefined;
 
         if (newSelectedImageDTO) {
-          dispatch(imageSelected(newSelectedImageDTO));
+          dispatch(imageSelectionChanged(newSelectedImageDTO));
         } else {
-          dispatch(imageSelected(null));
+          dispatch(imageSelectionChanged(null));
         }
 
         dispatch(isModalOpenChanged(false));

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

@@ -7,7 +7,7 @@ import {
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -37,14 +37,14 @@ export const addImageDroppedListener = () => {
       }
 
       /**
-       * Image dropped on current image
+       * Image dropped on viewer
        */
       if (
-        overData.actionType === 'SET_CURRENT_IMAGE' &&
+        overData.actionType === 'SET_VIEWER_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        dispatch(imageSelected(activeData.payload.imageDTO));
+        dispatch(imageSelectionChanged(activeData.payload.imageDTO));
         return;
       }
 

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

@@ -1,4 +1,4 @@
-import { selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
@@ -25,7 +25,7 @@ export const addImagesStarredListener = () => {
           updatedSelection.push(selectedImageDTO);
         }
       });
-      dispatch(selectionChanged(updatedSelection));
+      dispatch(imageSelectionChanged(updatedSelection));
     },
   });
 };

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

@@ -1,4 +1,4 @@
-import { selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
@@ -25,7 +25,7 @@ export const addImagesUnstarredListener = () => {
           updatedSelection.push(selectedImageDTO);
         }
       });
-      dispatch(selectionChanged(updatedSelection));
+      dispatch(imageSelectionChanged(updatedSelection));
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/receivedOpenAPISchema.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 { appInfoApi } from 'services/api/endpoints/appInfo';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 
 import { startAppListening } from '..';
 
-export const addGetOpenAPISchemaListener = () => {
+export const addReceivedOpenAPISchemaListener = () => {
   startAppListening({
-    matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
+    actionCreator: receivedOpenAPISchema.fulfilled,
     effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
+      log.debug({ schemaJSON }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
@@ -26,14 +26,10 @@ export const addGetOpenAPISchemaListener = () => {
   });
 
   startAppListening({
-    matcher: appInfoApi.endpoints.getOpenAPISchema.matchRejected,
+    actionCreator: receivedOpenAPISchema.rejected,
     effect: (action) => {
-      // 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');
-      }
+      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

@@ -1,9 +1,10 @@
 import { logger } from 'app/logging/logger';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
-import { isEqual } from 'lodash-es';
+import { isEqual, size } 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 '../..';
@@ -76,4 +77,17 @@ 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/socketio/socketInvocationComplete.ts

@@ -1,10 +1,11 @@
 import { logger } from 'app/logging/logger';
 import { parseify } from 'common/util/serialize';
 import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
-import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
+import { boardIdSelected, galleryViewChanged, imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { isImageOutput } from 'features/nodes/types/common';
 import { LINEAR_UI_OUTPUT, nodeIDDenyList } from 'features/nodes/util/graph/constants';
+import { imageInvocationComplete } from 'features/progress/store/progressSlice';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 import { imagesAdapter } from 'services/api/util';
@@ -29,7 +30,7 @@ export const addInvocationCompleteEventListener = () => {
       // This complete event has an associated image output
       if (isImageOutput(result) && !nodeTypeDenylist.includes(node.type) && !nodeIDDenyList.includes(source_node_id)) {
         const { image_name } = result.image;
-        const { canvas, gallery } = getState();
+        const { gallery, progress } = getState();
 
         // This populates the `getImageDTO` cache
         const imageDTORequest = dispatch(
@@ -41,8 +42,10 @@ export const addInvocationCompleteEventListener = () => {
         const imageDTO = await imageDTORequest.unwrap();
         imageDTORequest.unsubscribe();
 
+        dispatch(imageInvocationComplete({ data, imageDTO }));
+
         // Add canvas images to the staging area
-        if (canvas.batchIds.includes(queue_batch_id) && [LINEAR_UI_OUTPUT].includes(data.source_node_id)) {
+        if (progress.canvasBatchIds.includes(queue_batch_id) && [LINEAR_UI_OUTPUT].includes(data.source_node_id)) {
           dispatch(addImageToStagingArea(imageDTO));
         }
 
@@ -102,7 +105,7 @@ export const addInvocationCompleteEventListener = () => {
               );
             }
 
-            dispatch(imageSelected(imageDTO));
+            dispatch(imageSelectionChanged(imageDTO));
           }
         }
       }

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

@@ -6,6 +6,7 @@ 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';
@@ -52,6 +53,7 @@ export const addWorkflowLoadRequestedListener = () => {
           });
         }
 
+        dispatch(setActiveTab('nodes'));
         requestAnimationFrame(() => {
           $flow.get()?.fitView();
         });

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

@@ -20,6 +20,7 @@ import { nodesTemplatesSlice } from 'features/nodes/store/nodeTemplatesSlice';
 import { workflowPersistConfig, workflowSlice } from 'features/nodes/store/workflowSlice';
 import { generationPersistConfig, generationSlice } from 'features/parameters/store/generationSlice';
 import { postprocessingPersistConfig, postprocessingSlice } from 'features/parameters/store/postprocessingSlice';
+import { progressPersistConfig, progressSlice } from 'features/progress/store/progressSlice';
 import { queueSlice } from 'features/queue/store/queueSlice';
 import { sdxlPersistConfig, sdxlSlice } from 'features/sdxl/store/sdxlSlice';
 import { configSlice } from 'features/system/store/configSlice';
@@ -61,6 +62,7 @@ const allReducers = {
   [queueSlice.name]: queueSlice.reducer,
   [workflowSlice.name]: workflowSlice.reducer,
   [hrfSlice.name]: hrfSlice.reducer,
+  [progressSlice.name]: progressSlice.reducer,
   [api.reducerPath]: api.reducer,
 };
 
@@ -105,6 +107,7 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [loraPersistConfig.name]: loraPersistConfig,
   [modelManagerPersistConfig.name]: modelManagerPersistConfig,
   [hrfPersistConfig.name]: hrfPersistConfig,
+  [progressPersistConfig.name]: progressPersistConfig,
 };
 
 const unserialize: UnserializeFunction = (data, key) => {

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

@@ -37,17 +37,18 @@ type IAIDndImageProps = FlexProps & {
   isSelected?: boolean;
   thumbnail?: boolean;
   noContentFallback?: ReactElement;
-  useThumbailFallback?: boolean;
   withHoverOverlay?: boolean;
   children?: JSX.Element;
   uploadElement?: ReactNode;
   dataTestId?: string;
+  fallbackSrc?: string;
 };
 
 const IAIDndImage = (props: IAIDndImageProps) => {
   const {
     imageDTO,
     onError,
+    onLoad,
     onClick,
     withMetadataOverlay = false,
     isDropDisabled = false,
@@ -64,7 +65,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
     thumbnail = false,
     noContentFallback = defaultNoContentFallback,
     uploadElement = defaultUploadElement,
-    useThumbailFallback,
+    fallbackSrc,
     withHoverOverlay = false,
     children,
     onMouseOver,
@@ -150,8 +151,8 @@ const IAIDndImage = (props: IAIDndImageProps) => {
               <Image
                 src={thumbnail ? imageDTO.thumbnail_url : imageDTO.image_url}
                 fallbackStrategy="beforeLoadOrError"
-                fallbackSrc={useThumbailFallback ? imageDTO.thumbnail_url : undefined}
-                fallback={useThumbailFallback ? undefined : <IAILoadingImageFallback image={imageDTO} />}
+                fallbackSrc={fallbackSrc ?? imageDTO.thumbnail_url}
+                fallback={fallbackSrc ? undefined : <IAILoadingImageFallback image={imageDTO} />}
                 onError={onError}
                 draggable={false}
                 w={imageDTO.width}
@@ -161,6 +162,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
                 borderRadius="base"
                 sx={imageSx}
                 data-testid={dataTestId}
+                onLoad={onLoad}
               />
               {withMetadataOverlay && <ImageMetadataOverlay imageDTO={imageDTO} />}
               <SelectionOverlay isSelected={isSelected} isHovered={withHoverOverlay ? isHovered : false} />
@@ -175,11 +177,13 @@ const IAIDndImage = (props: IAIDndImageProps) => {
             </>
           )}
           {!imageDTO && isUploadDisabled && noContentFallback}
-          {imageDTO && !isDragDisabled && (
+          {imageDTO && !isDragDisabled && draggableData && (
             <IAIDraggable data={draggableData} disabled={isDragDisabled || !imageDTO} onClick={onClick} />
           )}
           {children}
-          {!isDropDisabled && <IAIDroppable data={droppableData} disabled={isDropDisabled} dropLabel={dropLabel} />}
+          {!isDropDisabled && droppableData && (
+            <IAIDroppable data={droppableData} disabled={isDropDisabled} dropLabel={dropLabel} />
+          )}
         </Flex>
       )}
     </ImageContextMenu>

invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts

@@ -13,46 +13,28 @@ export type Feature =
   | 'compositingCoherenceSteps'
   | 'compositingStrength'
   | 'compositingMaskAdjustments'
-  | 'controlNet'
   | 'controlNetBeginEnd'
   | 'controlNetControlMode'
-  | 'controlNetProcessor'
   | 'controlNetResizeMode'
+  | 'controlNet'
   | 'controlNetWeight'
   | 'dynamicPrompts'
   | 'dynamicPromptsMaxPrompts'
   | 'dynamicPromptsSeedBehaviour'
-  | 'imageFit'
   | 'infillMethod'
   | 'lora'
-  | 'loraWeight'
   | 'noiseUseCPU'
-  | 'paramAspect'
   | 'paramCFGScale'
   | 'paramCFGRescaleMultiplier'
   | 'paramDenoisingStrength'
-  | 'paramHeight'
-  | 'paramHrf'
   | 'paramIterations'
   | 'paramModel'
   | 'paramRatio'
   | 'paramSeed'
   | 'paramSteps'
-  | 'paramUpscaleMethod'
   | 'paramVAE'
   | 'paramVAEPrecision'
-  | 'paramWidth'
-  | 'patchmatchDownScaleSize'
-  | 'refinerModel'
-  | 'refinerNegativeAestheticScore'
-  | 'refinerPositiveAestheticScore'
-  | 'refinerScheduler'
-  | 'refinerStart'
-  | 'refinerSteps'
-  | 'refinerCfgScale'
-  | 'scaleBeforeProcessing'
-  | 'seamlessTilingXAxis'
-  | 'seamlessTilingYAxis';
+  | 'scaleBeforeProcessing';
 
 export type PopoverData = PopoverProps & {
   image?: string;
@@ -64,57 +46,21 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   paramNegativeConditioning: {
     placement: 'right',
   },
-  clipSkip: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
-  },
   controlNet: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000105880',
   },
-  controlNetBeginEnd: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178148',
-  },
-  controlNetWeight: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178148',
-  },
   lora: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000159072',
   },
-  loraWeight: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000159072-concepts-low-rank-adaptations-loras-',
-  },
-  compositingBlur: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
-  },
-  compositingBlurMethod: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
-  },
   compositingCoherenceMode: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
-  },
-  compositingCoherenceSteps: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
-  },
-  compositingStrength: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158838',
   },
   infillMethod: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158841-infill-and-scaling',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158841',
   },
   scaleBeforeProcessing: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000158841',
   },
-  paramCFGScale: {
-    href: 'https://www.youtube.com/watch?v=1OeHEJrsTpI',
-  },
-  paramCFGRescaleMultiplier: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
-  },
-  paramDenoisingStrength: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000094998-image-to-image',
-  },
-  paramHrf: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000096700-how-can-i-get-larger-images-what-does-upscaling-do-',
-  },
   paramIterations: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000159073',
   },
@@ -124,10 +70,7 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   },
   paramScheduler: {
     placement: 'right',
-    href: 'https://www.youtube.com/watch?v=1OeHEJrsTpI',
-  },
-  paramSeed: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000096684-what-is-a-seed-how-do-i-use-it-to-recreate-the-same-image-',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000159073',
   },
   paramModel: {
     placement: 'right',
@@ -138,53 +81,15 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   },
   controlNetControlMode: {
     placement: 'right',
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178148',
-  },
-  controlNetProcessor: {
-    placement: 'right',
-    href: 'https://support.invoke.ai/support/solutions/articles/151000105880-using-controlnet',
   },
   controlNetResizeMode: {
     placement: 'right',
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178148',
   },
   paramVAE: {
     placement: 'right',
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
   },
   paramVAEPrecision: {
     placement: 'right',
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
-  },
-  paramUpscaleMethod: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000096700-how-can-i-get-larger-images-what-does-upscaling-do-',
-  },
-  refinerModel: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  refinerNegativeAestheticScore: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  refinerPositiveAestheticScore: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  refinerScheduler: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  refinerStart: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  refinerSteps: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  refinerCfgScale: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178333-using-the-refiner',
-  },
-  seamlessTilingXAxis: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
-  },
-  seamlessTilingYAxis: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
   },
 } as const;
 

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

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

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

@@ -28,7 +28,6 @@ import { Layer, Stage } from 'react-konva';
 
 import IAICanvasBoundingBoxOverlay from './IAICanvasBoundingBoxOverlay';
 import IAICanvasGrid from './IAICanvasGrid';
-import IAICanvasIntermediateImage from './IAICanvasIntermediateImage';
 import IAICanvasMaskCompositer from './IAICanvasMaskCompositer';
 import IAICanvasMaskLines from './IAICanvasMaskLines';
 import IAICanvasObjectRenderer from './IAICanvasObjectRenderer';
@@ -55,7 +54,7 @@ const IAICanvas = () => {
   const shouldShowBoundingBox = useAppSelector((s) => s.canvas.shouldShowBoundingBox);
   const shouldShowGrid = useAppSelector((s) => s.canvas.shouldShowGrid);
   const stageScale = useAppSelector((s) => s.canvas.stageScale);
-  const shouldShowIntermediates = useAppSelector((s) => s.canvas.shouldShowIntermediates);
+  // const shouldShowIntermediates = useAppSelector((s) => s.canvas.shouldShowIntermediates);
   const shouldAntialias = useAppSelector((s) => s.canvas.shouldAntialias);
   const shouldRestrictStrokesToBox = useAppSelector((s) => s.canvas.shouldRestrictStrokesToBox);
   const { stageCoordinates, stageDimensions } = useAppSelector(selector);
@@ -184,7 +183,7 @@ const IAICanvas = () => {
           <Layer id="preview" imageSmoothingEnabled={shouldAntialias}>
             {!isStaging && <IAICanvasToolPreview visible={tool !== 'move'} listening={false} />}
             <IAICanvasStagingArea listening={false} visible={isStaging} />
-            {shouldShowIntermediates && <IAICanvasIntermediateImage />}
+            {/* {shouldShowIntermediates && <IAICanvasIntermediateImage />} */}
             <IAICanvasBoundingBox visible={shouldShowBoundingBox && !isStaging} />
           </Layer>
         </ChakraStage>

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

@@ -1,20 +1,33 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectCanvasSlice } from 'features/canvas/store/canvasSlice';
-import { selectSystemSlice } from 'features/system/store/systemSlice';
+import { selectProgressSlice } from 'features/progress/store/progressSlice';
 import { memo, useEffect, useState } from 'react';
 import { Image as KonvaImage } from 'react-konva';
 
-const progressImageSelector = createMemoizedSelector([selectSystemSlice, selectCanvasSlice], (system, canvas) => {
-  const { denoiseProgress } = system;
-  const { batchIds } = canvas;
+export const progressImageSelector = createMemoizedSelector(
+  [selectProgressSlice, selectCanvasSlice],
+  (progress, canvas) => {
+    const isLatestProgressFromCanvas =
+      progress.latestDenoiseProgress && progress.canvasBatchIds.includes(progress.latestDenoiseProgress.queue_batch_id);
 
-  return {
-    progressImage:
-      denoiseProgress && batchIds.includes(denoiseProgress.batch_id) ? denoiseProgress.progress_image : undefined,
-    boundingBox: canvas.layerState.stagingArea.boundingBox,
-  };
-});
+    const { selectedImageIndex, images } = canvas.layerState.stagingArea;
+    const _currentStagingAreaImage =
+      images.length > 0 && selectedImageIndex !== undefined ? images[selectedImageIndex] : undefined;
+
+    const isProgressImageIncomplete =
+      progress.latestDenoiseProgress?.graph_execution_state_id !==
+      progress.latestImageOutputEvent?.graph_execution_state_id;
+
+    return {
+      progressImage:
+        progress.latestDenoiseProgress && isLatestProgressFromCanvas && isProgressImageIncomplete
+          ? progress.latestDenoiseProgress.progress_image
+          : undefined,
+      boundingBox: canvas.layerState.stagingArea.boundingBox,
+    };
+  }
+);
 
 const IAICanvasIntermediateImage = () => {
   const { progressImage, boundingBox } = useAppSelector(progressImageSelector);

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

@@ -5,7 +5,7 @@ import type { GroupConfig } from 'konva/lib/Group';
 import { memo } from 'react';
 import { Group, Rect } from 'react-konva';
 
-import IAICanvasImage from './IAICanvasImage';
+import { IAICanvasStagingAreaImage } from './IAICanvasStagingAreaImage';
 
 const dash = [4, 4];
 
@@ -37,12 +37,11 @@ const selector = createMemoizedSelector(selectCanvasSlice, (canvas) => {
 type Props = GroupConfig;
 
 const IAICanvasStagingArea = (props: Props) => {
-  const { currentStagingAreaImage, shouldShowStagingImage, shouldShowStagingOutline, x, y, width, height } =
-    useAppSelector(selector);
+  const { shouldShowStagingOutline, x, y, width, height } = useAppSelector(selector);
 
   return (
     <Group {...props}>
-      {shouldShowStagingImage && currentStagingAreaImage && <IAICanvasImage canvasImage={currentStagingAreaImage} />}
+      <IAICanvasStagingAreaImage />
       {shouldShowStagingOutline && (
         <Group listening={false}>
           <Rect

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

@@ -0,0 +1,71 @@
+import { skipToken } from '@reduxjs/toolkit/query';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { $authToken } from 'app/store/nanostores/authToken';
+import { useAppSelector } from 'app/store/storeHooks';
+import IAICanvasIntermediateImage, {
+  progressImageSelector,
+} from 'features/canvas/components/IAICanvasIntermediateImage';
+import { selectCanvasSlice } from 'features/canvas/store/canvasSlice';
+import { selectProgressSlice } from 'features/progress/store/progressSlice';
+import { memo } from 'react';
+import { Image } from 'react-konva';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import useImage from 'use-image';
+
+import IAICanvasImageErrorFallback from './IAICanvasImageErrorFallback';
+
+const selector = createMemoizedSelector([selectProgressSlice, selectCanvasSlice], (progress, canvas) => {
+  const { selectedImageIndex, images } = canvas.layerState.stagingArea;
+
+  const currentStagingAreaImage =
+    images.length > 0 && selectedImageIndex !== undefined ? images[selectedImageIndex] : undefined;
+
+  const progressImage =
+    progress.latestDenoiseProgress && progress.canvasBatchIds.includes(progress.latestDenoiseProgress.queue_batch_id)
+      ? progress.latestDenoiseProgress.progress_image
+      : undefined;
+
+  const boundingBox = canvas.layerState.stagingArea.boundingBox ?? {
+    ...canvas.boundingBoxCoordinates,
+    ...canvas.boundingBoxDimensions,
+  };
+
+  return {
+    currentStagingAreaImage,
+    progressImage,
+    boundingBox,
+  };
+});
+
+export const IAICanvasStagingAreaImage = memo(() => {
+  const { currentStagingAreaImage, boundingBox } = useAppSelector(selector);
+  const { progressImage } = useAppSelector(progressImageSelector);
+  const { currentData: imageDTO, isError } = useGetImageDTOQuery(currentStagingAreaImage?.imageName ?? skipToken);
+  const [stagedImageEl, stagedImageElStatus] = useImage(
+    imageDTO?.image_url ?? '',
+    $authToken.get() ? 'use-credentials' : 'anonymous'
+  );
+
+  if (currentStagingAreaImage && (isError || stagedImageElStatus === 'failed')) {
+    return <IAICanvasImageErrorFallback canvasImage={currentStagingAreaImage} />;
+  }
+
+  if (progressImage) {
+    return <IAICanvasIntermediateImage />;
+  }
+
+  if (stagedImageEl) {
+    return (
+      <Image
+        x={boundingBox.x}
+        y={boundingBox.y}
+        width={boundingBox.width}
+        height={boundingBox.height}
+        image={stagedImageEl}
+        listening={false}
+      />
+    );
+  }
+});
+
+IAICanvasStagingAreaImage.displayName = 'IAICanvasStagingAreaImage';

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

@@ -1,12 +1,14 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { selectProgressSlice } from 'features/progress/store/progressSlice';
 
 import { selectCanvasSlice } from './canvasSlice';
 import { isCanvasBaseImage } from './canvasTypes';
 
 export const isStagingSelector = createSelector(
+  selectProgressSlice,
   selectCanvasSlice,
-  (canvas) => canvas.batchIds.length > 0 || canvas.layerState.stagingArea.images.length > 0
+  (progress, canvas) => progress.canvasBatchIds.length > 0 || canvas.layerState.stagingArea.images.length > 0
 );
 
 export const initialCanvasImageSelector = createMemoizedSelector(selectCanvasSlice, (canvas) =>

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

@@ -1,5 +1,5 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
-import { createSlice } from '@reduxjs/toolkit';
+import { createSlice, isAnyOf } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { roundDownToMultiple, roundToMultiple } from 'common/util/roundDownToMultiple';
 import calculateCoordinates from 'features/canvas/util/calculateCoordinates';
@@ -15,9 +15,7 @@ import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/
 import type { IRect, Vector2d } from 'konva/lib/types';
 import { clamp, cloneDeep } from 'lodash-es';
 import type { RgbaColor } from 'react-colorful';
-import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
-import { socketQueueItemStatusChanged } from 'services/events/actions';
 
 import type {
   BoundingBoxScaleMethod,
@@ -79,7 +77,6 @@ export const initialCanvasState: CanvasState = {
   stageCoordinates: { x: 0, y: 0 },
   stageDimensions: { width: 0, height: 0 },
   stageScale: 1,
-  batchIds: [],
   aspectRatio: {
     id: '1:1',
     value: 1,
@@ -180,7 +177,6 @@ export const canvasSlice = createSlice({
           ],
         };
         state.futureLayerStates = [];
-        state.batchIds = [];
 
         const newScale = calculateScale(
           stageDimensions.width,
@@ -237,12 +233,6 @@ export const canvasSlice = createSlice({
     setShouldShowBoundingBox: (state, action: PayloadAction<boolean>) => {
       state.shouldShowBoundingBox = action.payload;
     },
-    canvasBatchIdAdded: (state, action: PayloadAction<string>) => {
-      state.batchIds.push(action.payload);
-    },
-    canvasBatchIdsReset: (state) => {
-      state.batchIds = [];
-    },
     stagingAreaInitialized: (
       state,
       action: PayloadAction<{
@@ -293,7 +283,6 @@ export const canvasSlice = createSlice({
       state.futureLayerStates = [];
       state.shouldShowStagingOutline = true;
       state.shouldShowStagingImage = true;
-      state.batchIds = [];
     },
     addFillRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions, brushColor } = state;
@@ -426,7 +415,6 @@ export const canvasSlice = createSlice({
       state.pastLayerStates.push(cloneDeep(state.layerState));
       state.layerState = cloneDeep(initialLayerState);
       state.futureLayerStates = [];
-      state.batchIds = [];
       state.boundingBoxCoordinates = {
         ...initialCanvasState.boundingBoxCoordinates,
       };
@@ -536,7 +524,6 @@ export const canvasSlice = createSlice({
       state.futureLayerStates = [];
       state.shouldShowStagingOutline = true;
       state.shouldShowStagingImage = true;
-      state.batchIds = [];
     },
     setBoundingBoxScaleMethod: {
       reducer: (state, action: PayloadActionWithOptimalDimension<BoundingBoxScaleMethod>) => {
@@ -644,23 +631,6 @@ export const canvasSlice = createSlice({
         optimalDimension
       );
     });
-
-    builder.addCase(socketQueueItemStatusChanged, (state, action) => {
-      const batch_status = action.payload.data.batch_status;
-      if (!state.batchIds.includes(batch_status.batch_id)) {
-        return;
-      }
-
-      if (batch_status.in_progress === 0 && batch_status.pending === 0) {
-        state.batchIds = state.batchIds.filter((id) => id !== batch_status.batch_id);
-      }
-    });
-    builder.addMatcher(queueApi.endpoints.clearQueue.matchFulfilled, (state) => {
-      state.batchIds = [];
-    });
-    builder.addMatcher(queueApi.endpoints.cancelByBatchIds.matchFulfilled, (state, action) => {
-      state.batchIds = state.batchIds.filter((id) => !action.meta.arg.originalArgs.batch_ids.includes(id));
-    });
   },
 });
 
@@ -713,8 +683,6 @@ export const {
   stagingAreaInitialized,
   setShouldAntialias,
   canvasResized,
-  canvasBatchIdAdded,
-  canvasBatchIdsReset,
   aspectRatioChanged,
   scaledBoundingBoxDimensionsReset,
 } = canvasSlice.actions;
@@ -736,3 +704,5 @@ export const canvasPersistConfig: PersistConfig<CanvasState> = {
   migrate: migrateCanvasState,
   persistDenylist: [],
 };
+
+export const matchAnyStagingAreaDismissed = isAnyOf(commitStagingAreaImage, discardStagedImages);

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

@@ -142,7 +142,6 @@ export interface CanvasState {
   stageDimensions: Dimensions;
   stageScale: number;
   generationMode?: GenerationMode;
-  batchIds: string[];
   aspectRatio: AspectRatioState;
 }
 

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

@@ -5,7 +5,6 @@ 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';
@@ -92,14 +91,19 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
       return;
     }
 
-    const width = roundToMultiple(controlImage.width, 8);
-    const height = roundToMultiple(controlImage.height, 8);
-
     if (activeTabName === 'unifiedCanvas') {
-      dispatch(setBoundingBoxDimensions({ width, height }, optimalDimension));
+      dispatch(
+        setBoundingBoxDimensions(
+          {
+            width: controlImage.width,
+            height: controlImage.height,
+          },
+          optimalDimension
+        )
+      );
     } else {
-      dispatch(widthChanged(width));
-      dispatch(heightChanged(height));
+      dispatch(widthChanged(controlImage.width));
+      dispatch(heightChanged(controlImage.height));
     }
   }, [controlImage, activeTabName, dispatch, optimalDimension]);
 

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

@@ -6,7 +6,6 @@ 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';
@@ -14,6 +13,7 @@ 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 === 'dw_openpose_image_processor') {
-    return <DWOpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
+  if (processorNode.type === 'openpose_image_processor') {
+    return <OpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
   }
 
   if (processorNode.type === 'pidi_image_processor') {

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

@@ -1,6 +1,5 @@
 import { CompositeRangeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import { useControlAdapterBeginEndStepPct } from 'features/controlAdapters/hooks/useControlAdapterBeginEndStepPct';
 import { useControlAdapterIsEnabled } from 'features/controlAdapters/hooks/useControlAdapterIsEnabled';
 import {
@@ -62,10 +61,12 @@ export const ParamControlAdapterBeginEnd = memo(({ id }: Props) => {
   }
 
   return (
-    <FormControl isDisabled={!isEnabled} orientation="vertical">
-      <InformationalPopover feature="controlNetBeginEnd">
-        <FormLabel>{t('controlnet.beginEndStepPercent')}</FormLabel>
-      </InformationalPopover>
+    <FormControl
+      isDisabled={!isEnabled}
+      // feature="controlNetBeginEnd"
+      orientation="vertical"
+    >
+      <FormLabel>{t('controlnet.beginEndStepPercent')}</FormLabel>
       <CompositeRangeSlider
         aria-label={ariaLabel}
         value={value}

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

@@ -2,7 +2,6 @@ import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import { useControlAdapterIsEnabled } from 'features/controlAdapters/hooks/useControlAdapterIsEnabled';
 import { useControlAdapterProcessorNode } from 'features/controlAdapters/hooks/useControlAdapterProcessorNode';
 import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
@@ -59,9 +58,7 @@ const ParamControlAdapterProcessorSelect = ({ id }: Props) => {
   }
   return (
     <FormControl isDisabled={!isEnabled}>
-      <InformationalPopover feature="controlNetProcessor">
-        <FormLabel>{t('controlnet.processor')}</FormLabel>
-      </InformationalPopover>
+      <FormLabel>{t('controlnet.processor')}</FormLabel>
       <Combobox value={value} options={options} onChange={onChange} />
     </FormControl>
   );

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

@@ -1,92 +0,0 @@
-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

@@ -0,0 +1,92 @@
+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

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

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

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

@@ -1,23 +0,0 @@
-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/dnd/types/index.ts

@@ -12,14 +12,19 @@ import type {
 } from '@dnd-kit/core';
 import type { BoardId } from 'features/gallery/store/types';
 import type { FieldInputInstance, FieldInputTemplate } from 'features/nodes/types/field';
+import type { ViewerMode } from 'features/ui/store/uiTypes';
 import type { ImageDTO } from 'services/api/types';
 
 type BaseDropData = {
   id: string;
 };
 
-export type CurrentImageDropData = BaseDropData & {
-  actionType: 'SET_CURRENT_IMAGE';
+export type ViewerImageDropData = BaseDropData & {
+  actionType: 'SET_VIEWER_IMAGE';
+  context: {
+    viewerMode: ViewerMode;
+    currentImageName?: string | null;
+  };
 };
 
 export type InitialImageDropData = BaseDropData & {
@@ -59,13 +64,13 @@ export type RemoveFromBoardDropData = BaseDropData & {
 };
 
 export type TypesafeDroppableData =
-  | CurrentImageDropData
   | InitialImageDropData
   | ControlAdapterDropData
   | CanvasInitialImageDropData
   | NodesImageDropData
   | AddToBoardDropData
-  | RemoveFromBoardDropData;
+  | RemoveFromBoardDropData
+  | ViewerImageDropData;
 
 type BaseDragData = {
   id: string;

invokeai/frontend/web/src/features/dnd/util/isValidDrop.ts

@@ -13,8 +13,11 @@ export const isValidDrop = (overData: TypesafeDroppableData | undefined, active:
   }
 
   switch (actionType) {
-    case 'SET_CURRENT_IMAGE':
-      return payloadType === 'IMAGE_DTO';
+    case 'SET_VIEWER_IMAGE':
+      return (
+        payloadType === 'IMAGE_DTO' &&
+        overData.context.currentImageName !== active.data.current.payload.imageDTO.image_name
+      );
     case 'SET_INITIAL_IMAGE':
       return payloadType === 'IMAGE_DTO';
     case 'SET_CONTROL_ADAPTER_IMAGE':

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -1,295 +0,0 @@
-import { ButtonGroup, Flex, IconButton, Menu, MenuButton, MenuList } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
-import { skipToken } from '@reduxjs/toolkit/query';
-import { useAppToaster } from 'app/components/Toaster';
-import { upscaleRequested } from 'app/store/middleware/listenerMiddleware/listeners/upscaleRequested';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { DeleteImageButton } from 'features/deleteImageModal/components/DeleteImageButton';
-import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice';
-import SingleSelectionMenuItems from 'features/gallery/components/ImageContextMenu/SingleSelectionMenuItems';
-import { sentImageToImg2Img } from 'features/gallery/store/actions';
-import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
-import { selectGallerySlice } from 'features/gallery/store/gallerySlice';
-import ParamUpscalePopover from 'features/parameters/components/Upscale/ParamUpscaleSettings';
-import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters';
-import { initialImageSelected } from 'features/parameters/store/actions';
-import { useIsQueueMutationInProgress } from 'features/queue/hooks/useIsQueueMutationInProgress';
-import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
-import { selectSystemSlice } from 'features/system/store/systemSlice';
-import { setShouldShowImageDetails, setShouldShowProgressInViewer } from 'features/ui/store/uiSlice';
-import { useGetAndLoadEmbeddedWorkflow } from 'features/workflowLibrary/hooks/useGetAndLoadEmbeddedWorkflow';
-import { memo, useCallback } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
-import { useTranslation } from 'react-i18next';
-import {
-  PiArrowsCounterClockwiseBold,
-  PiAsteriskBold,
-  PiDotsThreeOutlineFill,
-  PiFlowArrowBold,
-  PiHourglassHighBold,
-  PiInfoBold,
-  PiPlantBold,
-  PiQuotesBold,
-  PiRulerBold,
-} from 'react-icons/pi';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
-import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata';
-
-const selectShouldDisableToolbarButtons = createSelector(
-  selectSystemSlice,
-  selectGallerySlice,
-  selectLastSelectedImage,
-  (system, gallery, lastSelectedImage) => {
-    const hasProgressImage = Boolean(system.denoiseProgress?.progress_image);
-    return hasProgressImage || !lastSelectedImage;
-  }
-);
-
-const CurrentImageButtons = () => {
-  const dispatch = useAppDispatch();
-  const isConnected = useAppSelector((s) => s.system.isConnected);
-  const shouldShowImageDetails = useAppSelector((s) => s.ui.shouldShowImageDetails);
-  const shouldShowProgressInViewer = useAppSelector((s) => s.ui.shouldShowProgressInViewer);
-  const lastSelectedImage = useAppSelector(selectLastSelectedImage);
-  const shouldDisableToolbarButtons = useAppSelector(selectShouldDisableToolbarButtons);
-
-  const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
-  const isQueueMutationInProgress = useIsQueueMutationInProgress();
-  const toaster = useAppToaster();
-  const { t } = useTranslation();
-
-  const { recallBothPrompts, recallSeed, recallWidthAndHeight, recallAllParameters } = useRecallParameters();
-
-  const { currentData: imageDTO } = useGetImageDTOQuery(lastSelectedImage?.image_name ?? skipToken);
-
-  const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata(lastSelectedImage?.image_name);
-
-  const { getAndLoadEmbeddedWorkflow, getAndLoadEmbeddedWorkflowResult } = useGetAndLoadEmbeddedWorkflow({});
-
-  const handleLoadWorkflow = useCallback(() => {
-    if (!lastSelectedImage || !lastSelectedImage.has_workflow) {
-      return;
-    }
-    getAndLoadEmbeddedWorkflow(lastSelectedImage.image_name);
-  }, [getAndLoadEmbeddedWorkflow, lastSelectedImage]);
-
-  useHotkeys('w', handleLoadWorkflow, [lastSelectedImage]);
-
-  const handleClickUseAllParameters = useCallback(() => {
-    recallAllParameters(metadata);
-  }, [metadata, recallAllParameters]);
-
-  useHotkeys('a', handleClickUseAllParameters, [metadata]);
-
-  const handleUseSeed = useCallback(() => {
-    recallSeed(metadata?.seed);
-  }, [metadata?.seed, recallSeed]);
-
-  useHotkeys('s', handleUseSeed, [metadata]);
-
-  const handleUsePrompt = useCallback(() => {
-    recallBothPrompts(
-      metadata?.positive_prompt,
-      metadata?.negative_prompt,
-      metadata?.positive_style_prompt,
-      metadata?.negative_style_prompt
-    );
-  }, [
-    metadata?.negative_prompt,
-    metadata?.positive_prompt,
-    metadata?.positive_style_prompt,
-    metadata?.negative_style_prompt,
-    recallBothPrompts,
-  ]);
-
-  useHotkeys('p', handleUsePrompt, [metadata]);
-
-  const handleRemixImage = useCallback(() => {
-    // Recalls all metadata parameters except seed
-    recallAllParameters({
-      ...metadata,
-      seed: undefined,
-    });
-  }, [metadata, recallAllParameters]);
-
-  useHotkeys('r', handleRemixImage, [metadata]);
-
-  const handleUseSize = useCallback(() => {
-    recallWidthAndHeight(metadata?.width, metadata?.height);
-  }, [metadata?.width, metadata?.height, recallWidthAndHeight]);
-
-  useHotkeys('d', handleUseSize, [metadata]);
-
-  const handleSendToImageToImage = useCallback(() => {
-    dispatch(sentImageToImg2Img());
-    dispatch(initialImageSelected(imageDTO));
-  }, [dispatch, imageDTO]);
-
-  useHotkeys('shift+i', handleSendToImageToImage, [imageDTO]);
-
-  const handleClickUpscale = useCallback(() => {
-    if (!imageDTO) {
-      return;
-    }
-    dispatch(upscaleRequested({ imageDTO }));
-  }, [dispatch, imageDTO]);
-
-  const handleDelete = useCallback(() => {
-    if (!imageDTO) {
-      return;
-    }
-    dispatch(imagesToDeleteSelected([imageDTO]));
-  }, [dispatch, imageDTO]);
-
-  useHotkeys(
-    'Shift+U',
-    () => {
-      handleClickUpscale();
-    },
-    {
-      enabled: () => Boolean(isUpscalingEnabled && !shouldDisableToolbarButtons && isConnected),
-    },
-    [isUpscalingEnabled, imageDTO, shouldDisableToolbarButtons, isConnected]
-  );
-
-  const handleClickShowImageDetails = useCallback(
-    () => dispatch(setShouldShowImageDetails(!shouldShowImageDetails)),
-    [dispatch, shouldShowImageDetails]
-  );
-
-  useHotkeys(
-    'i',
-    () => {
-      if (imageDTO) {
-        handleClickShowImageDetails();
-      } else {
-        toaster({
-          title: t('toast.metadataLoadFailed'),
-          status: 'error',
-          duration: 2500,
-          isClosable: true,
-        });
-      }
-    },
-    [imageDTO, shouldShowImageDetails, toaster]
-  );
-
-  useHotkeys(
-    'delete',
-    () => {
-      handleDelete();
-    },
-    [dispatch, imageDTO]
-  );
-
-  const handleClickProgressImagesToggle = useCallback(() => {
-    dispatch(setShouldShowProgressInViewer(!shouldShowProgressInViewer));
-  }, [dispatch, shouldShowProgressInViewer]);
-
-  return (
-    <>
-      <Flex flexWrap="wrap" justifyContent="center" alignItems="center" gap={2}>
-        <ButtonGroup isDisabled={shouldDisableToolbarButtons}>
-          <Menu isLazy>
-            <MenuButton
-              as={IconButton}
-              aria-label={t('parameters.imageActions')}
-              tooltip={t('parameters.imageActions')}
-              isDisabled={!imageDTO}
-              icon={<PiDotsThreeOutlineFill />}
-            />
-            <MenuList>{imageDTO && <SingleSelectionMenuItems imageDTO={imageDTO} />}</MenuList>
-          </Menu>
-        </ButtonGroup>
-
-        <ButtonGroup isDisabled={shouldDisableToolbarButtons}>
-          <IconButton
-            icon={<PiFlowArrowBold />}
-            tooltip={`${t('nodes.loadWorkflow')} (W)`}
-            aria-label={`${t('nodes.loadWorkflow')} (W)`}
-            isDisabled={!imageDTO?.has_workflow}
-            onClick={handleLoadWorkflow}
-            isLoading={getAndLoadEmbeddedWorkflowResult.isLoading}
-          />
-          <IconButton
-            isLoading={isLoadingMetadata}
-            icon={<PiArrowsCounterClockwiseBold />}
-            tooltip={`${t('parameters.remixImage')} (R)`}
-            aria-label={`${t('parameters.remixImage')} (R)`}
-            isDisabled={!metadata?.positive_prompt}
-            onClick={handleRemixImage}
-          />
-          <IconButton
-            isLoading={isLoadingMetadata}
-            icon={<PiQuotesBold />}
-            tooltip={`${t('parameters.usePrompt')} (P)`}
-            aria-label={`${t('parameters.usePrompt')} (P)`}
-            isDisabled={!metadata?.positive_prompt}
-            onClick={handleUsePrompt}
-          />
-          <IconButton
-            isLoading={isLoadingMetadata}
-            icon={<PiPlantBold />}
-            tooltip={`${t('parameters.useSeed')} (S)`}
-            aria-label={`${t('parameters.useSeed')} (S)`}
-            isDisabled={metadata?.seed === null || metadata?.seed === undefined}
-            onClick={handleUseSeed}
-          />
-          <IconButton
-            isLoading={isLoadingMetadata}
-            icon={<PiRulerBold />}
-            tooltip={`${t('parameters.useSize')} (D)`}
-            aria-label={`${t('parameters.useSize')} (D)`}
-            isDisabled={
-              metadata?.height === null ||
-              metadata?.height === undefined ||
-              metadata?.width === null ||
-              metadata?.width === undefined
-            }
-            onClick={handleUseSize}
-          />
-          <IconButton
-            isLoading={isLoadingMetadata}
-            icon={<PiAsteriskBold />}
-            tooltip={`${t('parameters.useAll')} (A)`}
-            aria-label={`${t('parameters.useAll')} (A)`}
-            isDisabled={!metadata}
-            onClick={handleClickUseAllParameters}
-          />
-        </ButtonGroup>
-
-        {isUpscalingEnabled && (
-          <ButtonGroup isDisabled={isQueueMutationInProgress}>
-            {isUpscalingEnabled && <ParamUpscalePopover imageDTO={imageDTO} />}
-          </ButtonGroup>
-        )}
-
-        <ButtonGroup>
-          <IconButton
-            icon={<PiInfoBold />}
-            tooltip={`${t('parameters.info')} (I)`}
-            aria-label={`${t('parameters.info')} (I)`}
-            isChecked={shouldShowImageDetails}
-            onClick={handleClickShowImageDetails}
-          />
-        </ButtonGroup>
-
-        <ButtonGroup>
-          <IconButton
-            aria-label={t('settings.displayInProgress')}
-            tooltip={t('settings.displayInProgress')}
-            icon={<PiHourglassHighBold />}
-            isChecked={shouldShowProgressInViewer}
-            onClick={handleClickProgressImagesToggle}
-          />
-        </ButtonGroup>
-
-        <ButtonGroup>
-          <DeleteImageButton onClick={handleDelete} />
-        </ButtonGroup>
-      </Flex>
-    </>
-  );
-};
-
-export default memo(CurrentImageButtons);

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageDisplay.tsx

@@ -1,24 +0,0 @@
-import { Flex } from '@invoke-ai/ui-library';
-import { memo } from 'react';
-
-import CurrentImageButtons from './CurrentImageButtons';
-import CurrentImagePreview from './CurrentImagePreview';
-
-const CurrentImageDisplay = () => {
-  return (
-    <Flex
-      position="relative"
-      flexDirection="column"
-      height="100%"
-      width="100%"
-      rowGap={4}
-      alignItems="center"
-      justifyContent="center"
-    >
-      <CurrentImageButtons />
-      <CurrentImagePreview />
-    </Flex>
-  );
-};
-
-export default memo(CurrentImageDisplay);

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImagePreview.tsx

@@ -1,129 +0,0 @@
-import { Box, Flex } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
-import { skipToken } from '@reduxjs/toolkit/query';
-import { useAppSelector } from 'app/store/storeHooks';
-import IAIDndImage from 'common/components/IAIDndImage';
-import { IAINoContentFallback } from 'common/components/IAIImageFallback';
-import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import ProgressImage from 'features/gallery/components/CurrentImage/ProgressImage';
-import ImageMetadataViewer from 'features/gallery/components/ImageMetadataViewer/ImageMetadataViewer';
-import NextPrevImageButtons from 'features/gallery/components/NextPrevImageButtons';
-import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
-import type { AnimationProps } from 'framer-motion';
-import { AnimatePresence, motion } from 'framer-motion';
-import type { CSSProperties } from 'react';
-import { memo, useCallback, useMemo, useRef, useState } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiImageBold } from 'react-icons/pi';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
-
-const selectLastSelectedImageName = createSelector(
-  selectLastSelectedImage,
-  (lastSelectedImage) => lastSelectedImage?.image_name
-);
-
-const CurrentImagePreview = () => {
-  const shouldShowImageDetails = useAppSelector((s) => s.ui.shouldShowImageDetails);
-  const imageName = useAppSelector(selectLastSelectedImageName);
-  const hasDenoiseProgress = useAppSelector((s) => Boolean(s.system.denoiseProgress));
-  const shouldShowProgressInViewer = useAppSelector((s) => s.ui.shouldShowProgressInViewer);
-
-  const { currentData: imageDTO } = useGetImageDTOQuery(imageName ?? skipToken);
-
-  const draggableData = useMemo<TypesafeDraggableData | undefined>(() => {
-    if (imageDTO) {
-      return {
-        id: 'current-image',
-        payloadType: 'IMAGE_DTO',
-        payload: { imageDTO },
-      };
-    }
-  }, [imageDTO]);
-
-  const droppableData = useMemo<TypesafeDroppableData | undefined>(
-    () => ({
-      id: 'current-image',
-      actionType: 'SET_CURRENT_IMAGE',
-    }),
-    []
-  );
-
-  // Show and hide the next/prev buttons on mouse move
-  const [shouldShowNextPrevButtons, setShouldShowNextPrevButtons] = useState<boolean>(false);
-
-  const timeoutId = useRef(0);
-
-  const { t } = useTranslation();
-
-  const handleMouseOver = useCallback(() => {
-    setShouldShowNextPrevButtons(true);
-    window.clearTimeout(timeoutId.current);
-  }, []);
-
-  const handleMouseOut = useCallback(() => {
-    timeoutId.current = window.setTimeout(() => {
-      setShouldShowNextPrevButtons(false);
-    }, 500);
-  }, []);
-
-  return (
-    <Flex
-      onMouseOver={handleMouseOver}
-      onMouseOut={handleMouseOut}
-      width="full"
-      height="full"
-      alignItems="center"
-      justifyContent="center"
-      position="relative"
-    >
-      {hasDenoiseProgress && shouldShowProgressInViewer ? (
-        <ProgressImage />
-      ) : (
-        <IAIDndImage
-          imageDTO={imageDTO}
-          droppableData={droppableData}
-          draggableData={draggableData}
-          isUploadDisabled={true}
-          fitContainer
-          useThumbailFallback
-          dropLabel={t('gallery.setCurrentImage')}
-          noContentFallback={<IAINoContentFallback icon={PiImageBold} label={t('gallery.noImageSelected')} />}
-          dataTestId="image-preview"
-        />
-      )}
-      {shouldShowImageDetails && imageDTO && (
-        <Box position="absolute" top="0" width="full" height="full" borderRadius="base">
-          <ImageMetadataViewer image={imageDTO} />
-        </Box>
-      )}
-      <AnimatePresence>
-        {!shouldShowImageDetails && imageDTO && shouldShowNextPrevButtons && (
-          <motion.div key="nextPrevButtons" initial={initial} animate={animate} exit={exit} style={motionStyles}>
-            <NextPrevImageButtons />
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </Flex>
-  );
-};
-
-export default memo(CurrentImagePreview);
-
-const initial: AnimationProps['initial'] = {
-  opacity: 0,
-};
-const animate: AnimationProps['animate'] = {
-  opacity: 1,
-  transition: { duration: 0.1 },
-};
-const exit: AnimationProps['exit'] = {
-  opacity: 0,
-  transition: { duration: 0.1 },
-};
-const motionStyles: CSSProperties = {
-  position: 'absolute',
-  top: '0',
-  width: '100%',
-  height: '100%',
-  pointerEvents: 'none',
-};

invokeai/frontend/web/src/features/gallery/components/CurrentImage/ProgressImage.tsx

@@ -1,38 +0,0 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
-import { Image } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { memo, useMemo } from 'react';
-
-const CurrentImagePreview = () => {
-  const progress_image = useAppSelector((s) => s.system.denoiseProgress?.progress_image);
-  const shouldAntialiasProgressImage = useAppSelector((s) => s.system.shouldAntialiasProgressImage);
-
-  const sx = useMemo<SystemStyleObject>(
-    () => ({
-      imageRendering: shouldAntialiasProgressImage ? 'auto' : 'pixelated',
-    }),
-    [shouldAntialiasProgressImage]
-  );
-
-  if (!progress_image) {
-    return null;
-  }
-
-  return (
-    <Image
-      src={progress_image.dataURL}
-      width={progress_image.width}
-      height={progress_image.height}
-      draggable={false}
-      data-testid="progress-image"
-      objectFit="contain"
-      maxWidth="full"
-      maxHeight="full"
-      position="absolute"
-      borderRadius="base"
-      sx={sx}
-    />
-  );
-};
-
-export default memo(CurrentImagePreview);

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx

@@ -28,12 +28,11 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
     <Flex
       layerStyle="first"
       padding={4}
-      gap={1}
+      gap={4}
       flexDirection="column"
       width="full"
       height="full"
       borderRadius="base"
-      position="absolute"
       overflow="hidden"
     >
       <ExternalLink href={image.image_url} label={image.image_name} />

invokeai/frontend/web/src/features/gallery/hooks/useGalleryNavigation.ts

@@ -5,7 +5,7 @@ import { imageListContainerTestId } from 'features/gallery/components/ImageGrid/
 import { virtuosoGridRefs } from 'features/gallery/components/ImageGrid/types';
 import { useGalleryImages } from 'features/gallery/hooks/useGalleryImages';
 import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageSelectionChanged } from 'features/gallery/store/gallerySlice';
 import { getIsVisible } from 'features/gallery/util/getIsVisible';
 import { getScrollToIndexAlign } from 'features/gallery/util/getScrollToIndexAlign';
 import { clamp } from 'lodash-es';
@@ -144,7 +144,7 @@ export const useGalleryNavigation = (): UseGalleryNavigationReturn => {
       if (!image || index === lastSelectedImageIndex) {
         return;
       }
-      dispatch(imageSelected(image));
+      dispatch(imageSelectionChanged(image));
       scrollToImage(image.image_name, index);
     },
     [dispatch, lastSelectedImageIndex, data]

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts

@@ -1,7 +1,6 @@
 import { createSelector } from '@reduxjs/toolkit';
-import { galleryImageClicked } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { selectGallerySlice, selectionChanged } from 'features/gallery/store/gallerySlice';
+import { galleryImageClicked, imageSelectionChanged, selectGallerySlice } from 'features/gallery/store/gallerySlice';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import type { MouseEvent } from 'react';
 import { useCallback, useMemo } from 'react';
@@ -26,7 +25,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
         return;
       }
       if (!isMultiSelectEnabled) {
-        dispatch(selectionChanged([imageDTO]));
+        dispatch(imageSelectionChanged(imageDTO));
         return;
       }
 

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

@@ -14,5 +14,3 @@ 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/gallery/store/gallerySlice.ts

@@ -1,5 +1,5 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
-import { createSlice, isAnyOf } from '@reduxjs/toolkit';
+import { createAction, createSlice, isAnyOf } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { uniqBy } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
@@ -26,11 +26,16 @@ export const gallerySlice = createSlice({
   name: 'gallery',
   initialState: initialGalleryState,
   reducers: {
-    imageSelected: (state, action: PayloadAction<ImageDTO | null>) => {
-      state.selection = action.payload ? [action.payload] : [];
-    },
-    selectionChanged: (state, action: PayloadAction<ImageDTO[]>) => {
-      state.selection = uniqBy(action.payload, (i) => i.image_name);
+    imageSelectionChanged: (state, action: PayloadAction<ImageDTO[] | ImageDTO | null>) => {
+      if (!action.payload) {
+        state.selection = [];
+        return;
+      }
+      if (Array.isArray(action.payload)) {
+        state.selection = uniqBy(action.payload, (i) => i.image_name);
+        return;
+      }
+      state.selection = [action.payload];
     },
     shouldAutoSwitchChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldAutoSwitch = action.payload;
@@ -97,14 +102,13 @@ export const gallerySlice = createSlice({
 });
 
 export const {
-  imageSelected,
   shouldAutoSwitchChanged,
   autoAssignBoardOnClickChanged,
   setGalleryImageMinimumWidth,
   boardIdSelected,
   autoAddBoardIdChanged,
   galleryViewChanged,
-  selectionChanged,
+  imageSelectionChanged,
   boardSearchTextChanged,
   moreImagesLoaded,
 } = gallerySlice.actions;
@@ -130,3 +134,10 @@ export const galleryPersistConfig: PersistConfig<GalleryState> = {
   migrate: migrateGalleryState,
   persistDenylist: ['selection', 'selectedBoardId', 'galleryView', 'offset', 'limit'],
 };
+
+export const galleryImageClicked = createAction<{
+  imageDTO: ImageDTO;
+  shiftKey: boolean;
+  ctrlKey: boolean;
+  metaKey: boolean;
+}>(`${gallerySlice.name}/galleryImageClicked`);

invokeai/frontend/web/src/features/hrf/components/ParamHrfMethod.tsx

@@ -1,7 +1,6 @@
 import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import { setHrfMethod } from 'features/hrf/store/hrfSlice';
 import { isParameterHRFMethod } from 'features/parameters/types/parameterSchemas';
 import { memo, useCallback, useMemo } from 'react';
@@ -31,9 +30,7 @@ const ParamHrfMethodSelect = () => {
 
   return (
     <FormControl>
-      <InformationalPopover feature="paramUpscaleMethod">
-        <FormLabel>{t('hrf.upscaleMethod')}</FormLabel>
-      </InformationalPopover>
+      <FormLabel>{t('hrf.upscaleMethod')}</FormLabel>
       <Combobox value={value} options={options} onChange={onChange} />
     </FormControl>
   );

invokeai/frontend/web/src/features/hrf/components/ParamHrfStrength.tsx

@@ -1,6 +1,5 @@
 import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import { setHrfStrength } from 'features/hrf/store/hrfSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -26,9 +25,7 @@ const ParamHrfStrength = () => {
 
   return (
     <FormControl>
-      <InformationalPopover feature="paramDenoisingStrength">
-        <FormLabel>{`${t('parameters.denoisingStrength')}`}</FormLabel>
-      </InformationalPopover>
+      <FormLabel>{t('parameters.denoisingStrength')}</FormLabel>
       <CompositeSlider
         min={sliderMin}
         max={sliderMax}

invokeai/frontend/web/src/features/hrf/components/ParamHrfToggle.tsx

@@ -1,6 +1,5 @@
 import { FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import { setHrfEnabled } from 'features/hrf/store/hrfSlice';
 import type { ChangeEvent } from 'react';
 import { memo, useCallback } from 'react';
@@ -19,9 +18,7 @@ const ParamHrfToggle = () => {
 
   return (
     <FormControl w="full">
-      <InformationalPopover feature="paramHrf">
-        <FormLabel flexGrow={1}>{t('hrf.enableHrf')}</FormLabel>
-      </InformationalPopover>
+      <FormLabel flexGrow={1}>{t('hrf.enableHrf')}</FormLabel>
       <Switch isChecked={hrfEnabled} onChange={handleHrfEnabled} />
     </FormControl>
   );

invokeai/frontend/web/src/features/lora/components/LoRACard.tsx

@@ -10,7 +10,6 @@ import {
   Text,
 } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import { loraIsEnabledChanged, loraRemoved, loraWeightChanged } from 'features/lora/store/loraSlice';
 import { memo, useCallback } from 'react';
@@ -58,31 +57,29 @@ export const LoRACard = memo((props: LoRACardProps) => {
           </Flex>
         </Flex>
       </CardHeader>
-      <InformationalPopover feature="loraWeight">
-        <CardBody>
-          <CompositeSlider
-            value={lora.weight}
-            onChange={handleChange}
-            min={-1}
-            max={2}
-            step={0.01}
-            marks={marks}
-            defaultValue={0.75}
-            isDisabled={!lora.isEnabled}
-          />
-          <CompositeNumberInput
-            value={lora.weight}
-            onChange={handleChange}
-            min={-5}
-            max={5}
-            step={0.01}
-            w={20}
-            flexShrink={0}
-            defaultValue={0.75}
-            isDisabled={!lora.isEnabled}
-          />
-        </CardBody>
-      </InformationalPopover>
+      <CardBody>
+        <CompositeSlider
+          value={lora.weight}
+          onChange={handleChange}
+          min={-1}
+          max={2}
+          step={0.01}
+          marks={marks}
+          defaultValue={0.75}
+          isDisabled={!lora.isEnabled}
+        />
+        <CompositeNumberInput
+          value={lora.weight}
+          onChange={handleChange}
+          min={-5}
+          max={5}
+          step={0.01}
+          w={20}
+          flexShrink={0}
+          defaultValue={0.75}
+          isDisabled={!lora.isEnabled}
+        />
+      </CardBody>
     </Card>
   );
 });

invokeai/frontend/web/src/features/lora/components/LoRASelect.tsx

@@ -2,7 +2,6 @@ import type { ChakraProps } from '@invoke-ai/ui-library';
 import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
 import { loraAdded, selectLoraSlice } from 'features/lora/store/loraSlice';
 import { memo, useCallback, useMemo } from 'react';
@@ -58,9 +57,7 @@ const LoRASelect = () => {
 
   return (
     <FormControl isDisabled={!options.length}>
-      <InformationalPopover feature="lora">
-        <FormLabel>{t('models.lora')} </FormLabel>
-      </InformationalPopover>
+      <FormLabel>{t('models.lora')} </FormLabel>
       <Combobox
         placeholder={placeholder}
         value={null}

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, isEnabled: true };
+      state.loras[id] = { id, model_name, base_model, weight };
     },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;

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

@@ -1,6 +1,7 @@
 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';
@@ -10,7 +11,6 @@ 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 { data, isLoading } = useGetOpenAPISchemaQuery();
+  const isReady = useAppSelector((s) => s.nodes.isReady);
   const { t } = useTranslation();
   return (
     <Flex
@@ -53,7 +53,7 @@ const NodeEditor = () => {
       justifyContent="center"
     >
       <AnimatePresence>
-        {data && (
+        {isReady && (
           <motion.div initial={initial} animate={animate} exit={exit} style={isReadyMotionStyles}>
             <Flow />
             <AddNodePopover />
@@ -65,7 +65,7 @@ const NodeEditor = () => {
         )}
       </AnimatePresence>
       <AnimatePresence>
-        {isLoading && (
+        {!isReady && (
           <motion.div initial={initial} animate={animate} exit={exit} style={notIsReadyMotionStyles}>
             <Flex
               layerStyle="first"