diff --git a/pull_request_template.md b/.github/pull_request_template.md similarity index 100% rename from pull_request_template.md rename to .github/pull_request_template.md diff --git a/.github/workflows/style-checks.yml b/.github/workflows/style-checks.yml index 8aceb6469e..d29b489418 100644 --- a/.github/workflows/style-checks.yml +++ b/.github/workflows/style-checks.yml @@ -1,13 +1,14 @@ -name: Black # TODO: add isort and flake8 later +name: style checks +# just formatting for now +# TODO: add isort and flake8 later on: - pull_request: {} + pull_request: push: - branches: master - tags: "*" + branches: main jobs: - test: + black: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 @@ -19,8 +20,7 @@ jobs: - name: Install dependencies with pip run: | - pip install --upgrade pip wheel - pip install .[test] + pip install black # - run: isort --check-only . - run: black --check . diff --git a/.github/workflows/test-invoke-pip-skip.yml b/.github/workflows/test-invoke-pip-skip.yml deleted file mode 100644 index 004b46d5a8..0000000000 --- a/.github/workflows/test-invoke-pip-skip.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Test invoke.py pip - -# This is a dummy stand-in for the actual tests -# we don't need to run python tests on non-Python changes -# But PRs require passing tests to be mergeable - -on: - pull_request: - paths: - - '**' - - '!pyproject.toml' - - '!invokeai/**' - - '!tests/**' - - 'invokeai/frontend/web/**' - merge_group: - workflow_dispatch: - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} - cancel-in-progress: true - -jobs: - matrix: - if: github.event.pull_request.draft == false - strategy: - matrix: - python-version: - - '3.10' - pytorch: - - linux-cuda-11_7 - - linux-rocm-5_2 - - linux-cpu - - macos-default - - windows-cpu - include: - - pytorch: linux-cuda-11_7 - os: ubuntu-22.04 - - pytorch: linux-rocm-5_2 - os: ubuntu-22.04 - - pytorch: linux-cpu - os: ubuntu-22.04 - - pytorch: macos-default - os: macOS-12 - - pytorch: windows-cpu - os: windows-2022 - name: ${{ matrix.pytorch }} on ${{ matrix.python-version }} - runs-on: ${{ matrix.os }} - steps: - - name: skip - run: echo "no build required" diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml index 40be0a529e..6086d10069 100644 --- a/.github/workflows/test-invoke-pip.yml +++ b/.github/workflows/test-invoke-pip.yml @@ -3,16 +3,7 @@ on: push: branches: - 'main' - paths: - - 'pyproject.toml' - - 'invokeai/**' - - '!invokeai/frontend/web/**' pull_request: - paths: - - 'pyproject.toml' - - 'invokeai/**' - - 'tests/**' - - '!invokeai/frontend/web/**' types: - 'ready_for_review' - 'opened' @@ -65,10 +56,23 @@ jobs: id: checkout-sources uses: actions/checkout@v3 + - name: Check for changed python files + id: changed-files + uses: tj-actions/changed-files@v37 + with: + files_yaml: | + python: + - 'pyproject.toml' + - 'invokeai/**' + - '!invokeai/frontend/web/**' + - 'tests/**' + - name: set test prompt to main branch validation + if: steps.changed-files.outputs.python_any_changed == 'true' run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }} - name: setup python + if: steps.changed-files.outputs.python_any_changed == 'true' uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} @@ -76,6 +80,7 @@ jobs: cache-dependency-path: pyproject.toml - name: install invokeai + if: steps.changed-files.outputs.python_any_changed == 'true' env: PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }} run: > @@ -83,6 +88,7 @@ jobs: --editable=".[test]" - name: run pytest + if: steps.changed-files.outputs.python_any_changed == 'true' id: run-pytest run: pytest diff --git a/README.md b/README.md index 3ca1ca1f26..f10c372545 100644 --- a/README.md +++ b/README.md @@ -161,7 +161,7 @@ the command `npm install -g yarn` if needed) _For Windows/Linux with an NVIDIA GPU:_ ```terminal - pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117 + pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118 ``` _For Linux with an AMD GPU:_ @@ -184,8 +184,9 @@ the command `npm install -g yarn` if needed) 6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once): ```terminal - invokeai-configure + invokeai-configure --root . ``` + Don't miss the dot at the end! 7. Launch the web server (do it every time you run InvokeAI): @@ -193,15 +194,9 @@ the command `npm install -g yarn` if needed) invokeai-web ``` -8. Build Node.js assets +8. Point your browser to http://localhost:9090 to bring up the web interface. - ```terminal - cd invokeai/frontend/web/ - yarn vite build - ``` - -9. Point your browser to http://localhost:9090 to bring up the web interface. -10. Type `banana sushi` in the box on the top left and click `Invoke`. +9. Type `banana sushi` in the box on the top left and click `Invoke`. Be sure to activate the virtual environment each time before re-launching InvokeAI, using `source .venv/bin/activate` or `.venv\Scripts\activate`. @@ -311,13 +306,30 @@ InvokeAI. The second will prepare the 2.3 directory for use with 3.0. You may now launch the WebUI in the usual way, by selecting option [1] from the launcher script -#### Migration Caveats +#### Migrating Images The migration script will migrate your invokeai settings and models, including textual inversion models, LoRAs and merges that you may have installed previously. However it does **not** migrate the generated -images stored in your 2.3-format outputs directory. You will need to -manually import selected images into the 3.0 gallery via drag-and-drop. +images stored in your 2.3-format outputs directory. To do this, you +need to run an additional step: + +1. From a working InvokeAI 3.0 root directory, start the launcher and +enter menu option [8] to open the "developer's console". + +2. At the developer's console command line, type the command: + +```bash +invokeai-import-images +``` + +3. This will lead you through the process of confirming the desired + source and destination for the imported images. The images will + appear in the gallery board of your choice, and contain the + original prompt, model name, and other parameters used to generate + the image. + +(Many kudos to **techjedi** for contributing this script.) ## Hardware Requirements diff --git a/docker/docker-entrypoint.sh b/docker/docker-entrypoint.sh index d45ed3d7ef..6d776feb0e 100755 --- a/docker/docker-entrypoint.sh +++ b/docker/docker-entrypoint.sh @@ -29,8 +29,8 @@ configure() { echo "To reconfigure InvokeAI, delete the above file." echo "======================================================================" else - mkdir -p ${INVOKEAI_ROOT} - chown --recursive ${USER} ${INVOKEAI_ROOT} + mkdir -p "${INVOKEAI_ROOT}" + chown --recursive ${USER} "${INVOKEAI_ROOT}" gosu ${USER} invokeai-configure --yes --default_only fi } @@ -50,16 +50,16 @@ fi if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then apt-get update apt-get install -y openssh-server - pushd $HOME + pushd "$HOME" mkdir -p .ssh - echo ${PUBLIC_KEY} > .ssh/authorized_keys + echo "${PUBLIC_KEY}" > .ssh/authorized_keys chmod -R 700 .ssh popd service ssh start fi -cd ${INVOKEAI_ROOT} +cd "${INVOKEAI_ROOT}" # Run the CMD as the Container User (not root). exec gosu ${USER} "$@" diff --git a/docs/assets/features/upscale-dialog.png b/docs/assets/features/upscale-dialog.png index 3ba0136545..937bda9db3 100644 Binary files a/docs/assets/features/upscale-dialog.png and b/docs/assets/features/upscale-dialog.png differ diff --git a/docs/features/POSTPROCESS.md b/docs/features/POSTPROCESS.md index 22ef51e109..33a2d19412 100644 --- a/docs/features/POSTPROCESS.md +++ b/docs/features/POSTPROCESS.md @@ -4,35 +4,13 @@ title: Postprocessing # :material-image-edit: Postprocessing -## Intro - -This extension provides the ability to restore faces and upscale images. +This sections details the ability to improve faces and upscale images. ## Face Fixing -The default face restoration module is GFPGAN. The default upscale is -Real-ESRGAN. For an alternative face restoration module, see -[CodeFormer Support](#codeformer-support) below. +As of InvokeAI 3.0, the easiest way to improve faces created during image generation is through the Inpainting functionality of the Unified Canvas. Simply add the image containing the faces that you would like to improve to the canvas, mask the face to be improved and run the invocation. For best results, make sure to use an inpainting specific model; these are usually identified by the "-inpainting" term in the model name. -As of version 1.14, environment.yaml will install the Real-ESRGAN package into -the standard install location for python packages, and will put GFPGAN into a -subdirectory of "src" in the InvokeAI directory. Upscaling with Real-ESRGAN -should "just work" without further intervention. Simply indicate the desired scale on -the popup in the Web GUI. - -**GFPGAN** requires a series of downloadable model files to work. These are -loaded when you run `invokeai-configure`. If GFPAN is failing with an -error, please run the following from the InvokeAI directory: - -```bash -invokeai-configure -``` - -If you do not run this script in advance, the GFPGAN module will attempt to -download the models files the first time you try to perform facial -reconstruction. - -### Upscaling +## Upscaling Open the upscaling dialog by clicking on the "expand" icon located above the image display area in the Web UI: @@ -41,82 +19,23 @@ above the image display area in the Web UI: ![upscale1](../assets/features/upscale-dialog.png) -There are three different upscaling parameters that you can -adjust. The first is the scale itself, either 2x or 4x. +The default upscaling option is Real-ESRGAN x2 Plus, which will scale your image by a factor of two. This means upscaling a 512x512 image will result in a new 1024x1024 image. -The second is the "Denoising Strength." Higher values will smooth out -the image and remove digital chatter, but may lose fine detail at -higher values. +Other options are the x4 upscalers, which will scale your image by a factor of 4. -Third, "Upscale Strength" allows you to adjust how the You can set the -scaling stength between `0` and `1.0` to control the intensity of the -scaling. AI upscalers generally tend to smooth out texture details. If -you wish to retain some of those for natural looking results, we -recommend using values between `0.5 to 0.8`. - -[This figure](../assets/features/upscaling-montage.png) illustrates -the effects of denoising and strength. The original image was 512x512, -4x scaled to 2048x2048. The "original" version on the upper left was -scaled using simple pixel averaging. The remainder use the ESRGAN -upscaling algorithm at different levels of denoising and strength. - -
-![upscaling](../assets/features/upscaling-montage.png){ width=720 } -
- -Both denoising and strength default to 0.75. - -### Face Restoration - -InvokeAI offers alternative two face restoration algorithms, -[GFPGAN](https://github.com/TencentARC/GFPGAN) and -[CodeFormer](https://huggingface.co/spaces/sczhou/CodeFormer). These -algorithms improve the appearance of faces, particularly eyes and -mouths. Issues with faces are less common with the latest set of -Stable Diffusion models than with the original 1.4 release, but the -restoration algorithms can still make a noticeable improvement in -certain cases. You can also apply restoration to old photographs you -upload. - -To access face restoration, click the "smiley face" icon in the -toolbar above the InvokeAI image panel. You will be presented with a -dialog that offers a choice between the two algorithm and sliders that -allow you to adjust their parameters. Alternatively, you may open the -left-hand accordion panel labeled "Face Restoration" and have the -restoration algorithm of your choice applied to generated images -automatically. - - -Like upscaling, there are a number of parameters that adjust the face -restoration output. GFPGAN has a single parameter, `strength`, which -controls how much the algorithm is allowed to adjust the -image. CodeFormer has two parameters, `strength`, and `fidelity`, -which together control the quality of the output image as described in -the [CodeFormer project -page](https://shangchenzhou.com/projects/CodeFormer/). Default values -are 0.75 for both parameters, which achieves a reasonable balance -between changing the image too much and not enough. - -[This figure](../assets/features/restoration-montage.png) illustrates -the effects of adjusting GFPGAN and CodeFormer parameters. - -
-![upscaling](../assets/features/restoration-montage.png){ width=720 } -
!!! note - GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads + Real-ESRGAN is memory intensive. In order to avoid crashes and memory overloads during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed its work. In single image generations, you will see the output right away but when you are using multiple - iterations, the images will first be generated and then upscaled and face restored after that + iterations, the images will first be generated and then upscaled after that process is complete. While the image generation is taking place, you will still be able to preview the base images. ## How to disable -If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries, -you can disable them on the invoke.py command line with the `--no_restore` and -`--no_esrgan` options, respectively. +If, for some reason, you do not wish to load the ESRGAN libraries, +you can disable them on the invoke.py command line with the `--no_esrgan` options. diff --git a/docs/installation/010_INSTALL_AUTOMATED.md b/docs/installation/010_INSTALL_AUTOMATED.md index 9b66fcbd83..0937c07bca 100644 --- a/docs/installation/010_INSTALL_AUTOMATED.md +++ b/docs/installation/010_INSTALL_AUTOMATED.md @@ -264,7 +264,7 @@ experimental versions later. you can create several levels of subfolders and drop your models into whichever ones you want. - - ***Autoimport FolderLICENSE*** + - ***LICENSE*** At the bottom of the screen you will see a checkbox for accepting the CreativeML Responsible AI Licenses. You need to accept the license @@ -471,7 +471,7 @@ Then type the following commands: === "NVIDIA System" ```bash - pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117 + pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu118 pip install xformers ``` diff --git a/docs/installation/020_INSTALL_MANUAL.md b/docs/installation/020_INSTALL_MANUAL.md index c0fb4c046f..a68e5b1414 100644 --- a/docs/installation/020_INSTALL_MANUAL.md +++ b/docs/installation/020_INSTALL_MANUAL.md @@ -148,7 +148,7 @@ manager, please follow these steps: === "CUDA (NVidia)" ```bash - pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117 + pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118 ``` === "ROCm (AMD)" @@ -192,8 +192,10 @@ manager, please follow these steps: your outputs. ```terminal - invokeai-configure + invokeai-configure --root . ``` + + Don't miss the dot at the end of the command! The script `invokeai-configure` will interactively guide you through the process of downloading and installing the weights files needed for InvokeAI. @@ -225,12 +227,6 @@ manager, please follow these steps: !!! warning "Make sure that the virtual environment is activated, which should create `(.venv)` in front of your prompt!" - === "CLI" - - ```bash - invokeai - ``` - === "local Webserver" ```bash @@ -243,6 +239,12 @@ manager, please follow these steps: invokeai --web --host 0.0.0.0 ``` + === "CLI" + + ```bash + invokeai + ``` + If you choose the run the web interface, point your browser at http://localhost:9090 in order to load the GUI. @@ -310,7 +312,7 @@ installation protocol (important!) === "CUDA (NVidia)" ```bash - pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117 + pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118 ``` === "ROCm (AMD)" @@ -354,7 +356,7 @@ you can do so using this unsupported recipe: mkdir ~/invokeai conda create -n invokeai python=3.10 conda activate invokeai -pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117 +pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118 invokeai-configure --root ~/invokeai invokeai --root ~/invokeai --web ``` diff --git a/docs/installation/030_INSTALL_CUDA_AND_ROCM.md b/docs/installation/030_INSTALL_CUDA_AND_ROCM.md index 3d3445e3a0..314043f538 100644 --- a/docs/installation/030_INSTALL_CUDA_AND_ROCM.md +++ b/docs/installation/030_INSTALL_CUDA_AND_ROCM.md @@ -34,11 +34,11 @@ directly from NVIDIA. **Do not try to install Ubuntu's nvidia-cuda-toolkit package. It is out of date and will cause conflicts among the NVIDIA driver and binaries.** -Go to [CUDA Toolkit 11.7 -Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive), -and use the target selection wizard to choose your operating system, -hardware platform, and preferred installation method (e.g. "local" -versus "network"). +Go to [CUDA Toolkit +Downloads](https://developer.nvidia.com/cuda-downloads), and use the +target selection wizard to choose your operating system, hardware +platform, and preferred installation method (e.g. "local" versus +"network"). This will provide you with a downloadable install file or, depending on your choices, a recipe for downloading and running a install shell @@ -61,7 +61,7 @@ Runtime Site](https://developer.nvidia.com/nvidia-container-runtime) When installing torch and torchvision manually with `pip`, remember to provide the argument `--extra-index-url -https://download.pytorch.org/whl/cu117` as described in the [Manual +https://download.pytorch.org/whl/cu118` as described in the [Manual Installation Guide](020_INSTALL_MANUAL.md). ## :simple-amd: ROCm diff --git a/docs/installation/050_INSTALLING_MODELS.md b/docs/installation/050_INSTALLING_MODELS.md index ce2004e841..d1c0cfacca 100644 --- a/docs/installation/050_INSTALLING_MODELS.md +++ b/docs/installation/050_INSTALLING_MODELS.md @@ -124,7 +124,7 @@ installation. Examples: invokeai-model-install --list controlnet # (install the model at the indicated URL) -invokeai-model-install --add http://civitai.com/2860 +invokeai-model-install --add https://civitai.com/api/download/models/128713 # (delete the named model) invokeai-model-install --delete sd-1/main/analog-diffusion @@ -170,4 +170,4 @@ elsewhere on disk and they will be autoimported. You can also create subfolders and organize them as you wish. The location of the autoimport directories are controlled by settings -in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md). \ No newline at end of file +in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md). diff --git a/docs/installation/070_INSTALL_XFORMERS.md b/docs/installation/070_INSTALL_XFORMERS.md index be54a3ee86..849f9d1ddc 100644 --- a/docs/installation/070_INSTALL_XFORMERS.md +++ b/docs/installation/070_INSTALL_XFORMERS.md @@ -28,18 +28,21 @@ command line, then just be sure to activate it's virtual environment. Then run the following three commands: ```sh -pip install xformers==0.0.16rc425 -pip install triton +pip install xformers~=0.0.19 +pip install triton # WON'T WORK ON WINDOWS python -m xformers.info output ``` The first command installs `xformers`, the second installs the `triton` training accelerator, and the third prints out the `xformers` -installation status. If all goes well, you'll see a report like the +installation status. On Windows, please omit the `triton` package, +which is not available on that platform. + +If all goes well, you'll see a report like the following: ```sh -xFormers 0.0.16rc425 +xFormers 0.0.20 memory_efficient_attention.cutlassF: available memory_efficient_attention.cutlassB: available memory_efficient_attention.flshattF: available @@ -48,22 +51,28 @@ memory_efficient_attention.smallkF: available memory_efficient_attention.smallkB: available memory_efficient_attention.tritonflashattF: available memory_efficient_attention.tritonflashattB: available +indexing.scaled_index_addF: available +indexing.scaled_index_addB: available +indexing.index_select: available +swiglu.dual_gemm_silu: available +swiglu.gemm_fused_operand_sum: available swiglu.fused.p.cpp: available is_triton_available: True is_functorch_available: False -pytorch.version: 1.13.1+cu117 +pytorch.version: 2.0.1+cu118 pytorch.cuda: available -gpu.compute_capability: 8.6 -gpu.name: NVIDIA RTX A2000 12GB +gpu.compute_capability: 8.9 +gpu.name: NVIDIA GeForce RTX 4070 build.info: available -build.cuda_version: 1107 -build.python_version: 3.10.9 -build.torch_version: 1.13.1+cu117 +build.cuda_version: 1108 +build.python_version: 3.10.11 +build.torch_version: 2.0.1+cu118 build.env.TORCH_CUDA_ARCH_LIST: 5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6 build.env.XFORMERS_BUILD_TYPE: Release build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS: None build.env.NVCC_FLAGS: None -build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.16rc425 +build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.20 +build.nvcc_version: 11.8.89 source.privacy: open source ``` @@ -83,14 +92,14 @@ installed from source. These instructions were written for a system running Ubuntu 22.04, but other Linux distributions should be able to adapt this recipe. -#### 1. Install CUDA Toolkit 11.7 +#### 1. Install CUDA Toolkit 11.8 You will need the CUDA developer's toolkit in order to compile and install xFormers. **Do not try to install Ubuntu's nvidia-cuda-toolkit package.** It is out of date and will cause conflicts among the NVIDIA driver and binaries. Instead install the CUDA Toolkit package provided -by NVIDIA itself. Go to [CUDA Toolkit 11.7 -Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive) +by NVIDIA itself. Go to [CUDA Toolkit 11.8 +Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive) and use the target selection wizard to choose your platform and Linux distribution. Select an installer type of "runfile (local)" at the last step. @@ -101,17 +110,17 @@ example, the install script recipe for Ubuntu 22.04 running on a x86_64 system is: ``` -wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run -sudo sh cuda_11.7.0_515.43.04_linux.run +wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run +sudo sh cuda_11.8.0_520.61.05_linux.run ``` Rather than cut-and-paste this example, We recommend that you walk through the toolkit wizard in order to get the most up to date installer for your system. -#### 2. Confirm/Install pyTorch 1.13 with CUDA 11.7 support +#### 2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support -If you are using InvokeAI 2.3 or higher, these will already be +If you are using InvokeAI 3.0.2 or higher, these will already be installed. If not, you can check whether you have the needed libraries using a quick command. Activate the invokeai virtual environment, either by entering the "developer's console", or manually with a @@ -124,7 +133,7 @@ Then run the command: python -c 'exec("import torch\nprint(torch.__version__)")' ``` -If it prints __1.13.1+cu117__ you're good. If not, you can install the +If it prints __1.13.1+cu118__ you're good. If not, you can install the most up to date libraries with this command: ```sh diff --git a/installer/lib/installer.py b/installer/lib/installer.py index 545d99ab0b..cc694c2934 100644 --- a/installer/lib/installer.py +++ b/installer/lib/installer.py @@ -348,7 +348,7 @@ class InvokeAiInstance: introduction() - from invokeai.frontend.install import invokeai_configure + from invokeai.frontend.install.invokeai_configure import invokeai_configure # NOTE: currently the config script does its own arg parsing! this means the command-line switches # from the installer will also automatically propagate down to the config script. @@ -463,10 +463,10 @@ def get_torch_source() -> (Union[str, None], str): url = "https://download.pytorch.org/whl/cpu" if device == "cuda": - url = "https://download.pytorch.org/whl/cu117" + url = "https://download.pytorch.org/whl/cu118" optional_modules = "[xformers,onnx-cuda]" if device == "cuda_and_dml": - url = "https://download.pytorch.org/whl/cu117" + url = "https://download.pytorch.org/whl/cu118" optional_modules = "[xformers,onnx-directml]" # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13 diff --git a/installer/readme.txt b/installer/readme.txt index 5138d0cd52..b9a97e2093 100644 --- a/installer/readme.txt +++ b/installer/readme.txt @@ -8,16 +8,13 @@ Preparations: to work. Instructions are given here: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/ - NOTE: At this time we do not recommend Python 3.11. We recommend - Version 3.10.9, which has been extensively tested with InvokeAI. - Before you start the installer, please open up your system's command line window (Terminal or Command) and type the commands: python --version If all is well, it will print "Python 3.X.X", where the version number - is at least 3.9.1, and less than 3.11. + is at least 3.9.*, and not higher than 3.11.*. If this works, check the version of the Python package manager, pip: diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py index 83fc8d9e11..df51ca8581 100644 --- a/invokeai/app/api/dependencies.py +++ b/invokeai/app/api/dependencies.py @@ -2,7 +2,6 @@ from typing import Optional from logging import Logger -import os from invokeai.app.services.board_image_record_storage import ( SqliteBoardImageRecordStorage, ) @@ -32,6 +31,7 @@ from ..services.sqlite import SqliteItemStorage from ..services.model_manager_service import ModelManagerService from ..services.batch_manager import BatchManager from ..services.batch_manager_storage import SqliteBatchProcessStorage +from ..services.invocation_stats import InvocationStatsService from .events import FastAPIEventService @@ -57,7 +57,7 @@ logger = InvokeAILogger.getLogger() class ApiDependencies: """Contains and initializes all dependencies for the API""" - invoker: Optional[Invoker] = None + invoker: Invoker @staticmethod def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger): @@ -70,8 +70,9 @@ class ApiDependencies: output_folder = config.output_path # TODO: build a file/path manager? - db_location = config.db_path - db_location.parent.mkdir(parents=True, exist_ok=True) + db_path = config.db_path + db_path.parent.mkdir(parents=True, exist_ok=True) + db_location = str(db_path) graph_execution_manager = SqliteItemStorage[GraphExecutionState]( filename=db_location, table_name="graph_executions" @@ -134,6 +135,7 @@ class ApiDependencies: graph_execution_manager=graph_execution_manager, processor=DefaultInvocationProcessor(), configuration=config, + performance_statistics=InvocationStatsService(graph_execution_manager), logger=logger, ) diff --git a/invokeai/app/api/routers/board_images.py b/invokeai/app/api/routers/board_images.py index 6cb073ca7c..73607ecb7d 100644 --- a/invokeai/app/api/routers/board_images.py +++ b/invokeai/app/api/routers/board_images.py @@ -1,24 +1,30 @@ -from fastapi import Body, HTTPException, Path, Query +from fastapi import Body, HTTPException from fastapi.routing import APIRouter -from invokeai.app.services.board_record_storage import BoardRecord, BoardChanges -from invokeai.app.services.image_record_storage import OffsetPaginatedResults -from invokeai.app.services.models.board_record import BoardDTO -from invokeai.app.services.models.image_record import ImageDTO +from pydantic import BaseModel, Field from ..dependencies import ApiDependencies board_images_router = APIRouter(prefix="/v1/board_images", tags=["boards"]) +class AddImagesToBoardResult(BaseModel): + board_id: str = Field(description="The id of the board the images were added to") + added_image_names: list[str] = Field(description="The image names that were added to the board") + + +class RemoveImagesFromBoardResult(BaseModel): + removed_image_names: list[str] = Field(description="The image names that were removed from their board") + + @board_images_router.post( "/", - operation_id="create_board_image", + operation_id="add_image_to_board", responses={ 201: {"description": "The image was added to a board successfully"}, }, status_code=201, ) -async def create_board_image( +async def add_image_to_board( board_id: str = Body(description="The id of the board to add to"), image_name: str = Body(description="The name of the image to add"), ): @@ -29,26 +35,78 @@ async def create_board_image( ) return result except Exception as e: - raise HTTPException(status_code=500, detail="Failed to add to board") + raise HTTPException(status_code=500, detail="Failed to add image to board") @board_images_router.delete( "/", - operation_id="remove_board_image", + operation_id="remove_image_from_board", responses={ 201: {"description": "The image was removed from the board successfully"}, }, status_code=201, ) -async def remove_board_image( - board_id: str = Body(description="The id of the board"), - image_name: str = Body(description="The name of the image to remove"), +async def remove_image_from_board( + image_name: str = Body(description="The name of the image to remove", embed=True), ): - """Deletes a board_image""" + """Removes an image from its board, if it had one""" try: - result = ApiDependencies.invoker.services.board_images.remove_image_from_board( - board_id=board_id, image_name=image_name - ) + result = ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name) return result except Exception as e: - raise HTTPException(status_code=500, detail="Failed to update board") + raise HTTPException(status_code=500, detail="Failed to remove image from board") + + +@board_images_router.post( + "/batch", + operation_id="add_images_to_board", + responses={ + 201: {"description": "Images were added to board successfully"}, + }, + status_code=201, + response_model=AddImagesToBoardResult, +) +async def add_images_to_board( + board_id: str = Body(description="The id of the board to add to"), + image_names: list[str] = Body(description="The names of the images to add", embed=True), +) -> AddImagesToBoardResult: + """Adds a list of images to a board""" + try: + added_image_names: list[str] = [] + for image_name in image_names: + try: + ApiDependencies.invoker.services.board_images.add_image_to_board( + board_id=board_id, image_name=image_name + ) + added_image_names.append(image_name) + except: + pass + return AddImagesToBoardResult(board_id=board_id, added_image_names=added_image_names) + except Exception as e: + raise HTTPException(status_code=500, detail="Failed to add images to board") + + +@board_images_router.post( + "/batch/delete", + operation_id="remove_images_from_board", + responses={ + 201: {"description": "Images were removed from board successfully"}, + }, + status_code=201, + response_model=RemoveImagesFromBoardResult, +) +async def remove_images_from_board( + image_names: list[str] = Body(description="The names of the images to remove", embed=True), +) -> RemoveImagesFromBoardResult: + """Removes a list of images from their board, if they had one""" + try: + removed_image_names: list[str] = [] + for image_name in image_names: + try: + ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name) + removed_image_names.append(image_name) + except: + pass + return RemoveImagesFromBoardResult(removed_image_names=removed_image_names) + except Exception as e: + raise HTTPException(status_code=500, detail="Failed to remove images from board") diff --git a/invokeai/app/api/routers/images.py b/invokeai/app/api/routers/images.py index 498a1139e4..21fd09e051 100644 --- a/invokeai/app/api/routers/images.py +++ b/invokeai/app/api/routers/images.py @@ -1,21 +1,20 @@ import io from typing import Optional +from PIL import Image from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile from fastapi.responses import FileResponse from fastapi.routing import APIRouter -from PIL import Image +from pydantic import BaseModel from invokeai.app.invocations.metadata import ImageMetadata from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.services.image_record_storage import OffsetPaginatedResults -from invokeai.app.services.item_storage import PaginatedResults from invokeai.app.services.models.image_record import ( ImageDTO, ImageRecordChanges, ImageUrlsDTO, ) - from ..dependencies import ApiDependencies images_router = APIRouter(prefix="/v1/images", tags=["images"]) @@ -25,7 +24,7 @@ IMAGE_MAX_AGE = 31536000 @images_router.post( - "/", + "/upload", operation_id="upload_image", responses={ 201: {"description": "The image was uploaded successfully"}, @@ -77,7 +76,7 @@ async def upload_image( raise HTTPException(status_code=500, detail="Failed to create image") -@images_router.delete("/{image_name}", operation_id="delete_image") +@images_router.delete("/i/{image_name}", operation_id="delete_image") async def delete_image( image_name: str = Path(description="The name of the image to delete"), ) -> None: @@ -103,7 +102,7 @@ async def clear_intermediates() -> int: @images_router.patch( - "/{image_name}", + "/i/{image_name}", operation_id="update_image", response_model=ImageDTO, ) @@ -120,7 +119,7 @@ async def update_image( @images_router.get( - "/{image_name}", + "/i/{image_name}", operation_id="get_image_dto", response_model=ImageDTO, ) @@ -136,7 +135,7 @@ async def get_image_dto( @images_router.get( - "/{image_name}/metadata", + "/i/{image_name}/metadata", operation_id="get_image_metadata", response_model=ImageMetadata, ) @@ -151,8 +150,9 @@ async def get_image_metadata( raise HTTPException(status_code=404) -@images_router.get( - "/{image_name}/full", +@images_router.api_route( + "/i/{image_name}/full", + methods=["GET", "HEAD"], operation_id="get_image_full", response_class=Response, responses={ @@ -187,7 +187,7 @@ async def get_image_full( @images_router.get( - "/{image_name}/thumbnail", + "/i/{image_name}/thumbnail", operation_id="get_image_thumbnail", response_class=Response, responses={ @@ -216,7 +216,7 @@ async def get_image_thumbnail( @images_router.get( - "/{image_name}/urls", + "/i/{image_name}/urls", operation_id="get_image_urls", response_model=ImageUrlsDTO, ) @@ -265,3 +265,24 @@ async def list_image_dtos( ) return image_dtos + + +class DeleteImagesFromListResult(BaseModel): + deleted_images: list[str] + + +@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesFromListResult) +async def delete_images_from_list( + image_names: list[str] = Body(description="The list of names of images to delete", embed=True), +) -> DeleteImagesFromListResult: + try: + deleted_images: list[str] = [] + for image_name in image_names: + try: + ApiDependencies.invoker.services.images.delete(image_name) + deleted_images.append(image_name) + except: + pass + return DeleteImagesFromListResult(deleted_images=deleted_images) + except Exception as e: + raise HTTPException(status_code=500, detail="Failed to delete images") diff --git a/invokeai/app/api/routers/models.py b/invokeai/app/api/routers/models.py index 2f59f1dd0f..b6c1edbbe1 100644 --- a/invokeai/app/api/routers/models.py +++ b/invokeai/app/api/routers/models.py @@ -104,8 +104,12 @@ async def update_model( ): # model manager moved model path during rename - don't overwrite it info.path = new_info.get("path") + # replace empty string values with None/null to avoid phenomenon of vae: '' + info_dict = info.dict() + info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()} + ApiDependencies.invoker.services.model_manager.update_model( - model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info.dict() + model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict ) model_raw = ApiDependencies.invoker.services.model_manager.list_model( diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py index 92c255eb45..a0b681e2d4 100644 --- a/invokeai/app/cli_app.py +++ b/invokeai/app/cli_app.py @@ -39,6 +39,7 @@ from invokeai.app.services.resource_name import SimpleNameService from invokeai.app.services.urls import LocalUrlService from invokeai.app.services.batch_manager import BatchManager from invokeai.app.services.batch_manager_storage import SqliteBatchProcessStorage +from invokeai.app.services.invocation_stats import InvocationStatsService from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage @@ -317,6 +318,7 @@ def invoke_cli(): graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"), graph_execution_manager=graph_execution_manager, processor=DefaultInvocationProcessor(), + performance_statistics=InvocationStatsService(graph_execution_manager), logger=logger, configuration=config, ) diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index ada7a06a57..86565366d9 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -16,7 +16,7 @@ from ...backend.util.devices import torch_dtype from ...backend.model_management import ModelType from ...backend.model_management.models import ModelNotFoundException from ...backend.model_management.lora import ModelPatcher -from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent +from ...backend.stable_diffusion import InvokeAIDiffuserComponent, BasicConditioningInfo, SDXLConditioningInfo from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext from .model import ClipField from dataclasses import dataclass @@ -29,28 +29,9 @@ class ConditioningField(BaseModel): schema_extra = {"required": ["conditioning_name"]} -@dataclass -class BasicConditioningInfo: - # type: Literal["basic_conditioning"] = "basic_conditioning" - embeds: torch.Tensor - extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] - # weight: float - # mode: ConditioningAlgo - - -@dataclass -class SDXLConditioningInfo(BasicConditioningInfo): - # type: Literal["sdxl_conditioning"] = "sdxl_conditioning" - pooled_embeds: torch.Tensor - add_time_ids: torch.Tensor - - -ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")] - - @dataclass class ConditioningFieldData: - conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]] + conditionings: List[BasicConditioningInfo] # unconditioned: Optional[torch.Tensor] @@ -109,12 +90,15 @@ class CompelInvocation(BaseInvocation): name = trigger[1:-1] try: ti_list.append( - context.services.model_manager.get_model( - model_name=name, - base_model=self.clip.text_encoder.base_model, - model_type=ModelType.TextualInversion, - context=context, - ).context.model + ( + name, + context.services.model_manager.get_model( + model_name=name, + base_model=self.clip.text_encoder.base_model, + model_type=ModelType.TextualInversion, + context=context, + ).context.model, + ) ) except ModelNotFoundException: # print(e) @@ -173,7 +157,15 @@ class CompelInvocation(BaseInvocation): class SDXLPromptInvocationBase: - def run_clip_raw(self, context, clip_field, prompt, get_pooled): + def run_clip_compel( + self, + context: InvocationContext, + clip_field: ClipField, + prompt: str, + get_pooled: bool, + lora_prefix: str, + zero_on_empty: bool, + ): tokenizer_info = context.services.model_manager.get_model( **clip_field.tokenizer.dict(), context=context, @@ -183,79 +175,21 @@ class SDXLPromptInvocationBase: context=context, ) - def _lora_loader(): - for lora in clip_field.loras: - lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context) - yield (lora_info.context.model, lora.weight) - del lora_info - return - - # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras] - - ti_list = [] - for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt): - name = trigger[1:-1] - try: - ti_list.append( - context.services.model_manager.get_model( - model_name=name, - base_model=clip_field.text_encoder.base_model, - model_type=ModelType.TextualInversion, - context=context, - ).context.model - ) - except ModelNotFoundException: - # print(e) - # import traceback - # print(traceback.format_exc()) - print(f'Warn: trigger: "{trigger}" not found') - - with ModelPatcher.apply_lora_text_encoder( - text_encoder_info.context.model, _lora_loader() - ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as ( - tokenizer, - ti_manager, - ), ModelPatcher.apply_clip_skip( - text_encoder_info.context.model, clip_field.skipped_layers - ), text_encoder_info as text_encoder: - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - prompt_embeds = text_encoder( - text_input_ids.to(text_encoder.device), - output_hidden_states=True, + # return zero on empty + if prompt == "" and zero_on_empty: + cpu_text_encoder = text_encoder_info.context.model + c = torch.zeros( + (1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size), + dtype=text_encoder_info.context.cache.precision, ) if get_pooled: - c_pooled = prompt_embeds[0] + c_pooled = torch.zeros( + (1, cpu_text_encoder.config.hidden_size), + dtype=c.dtype, + ) else: c_pooled = None - c = prompt_embeds.hidden_states[-2] - - del tokenizer - del text_encoder - del tokenizer_info - del text_encoder_info - - c = c.detach().to("cpu") - if c_pooled is not None: - c_pooled = c_pooled.detach().to("cpu") - - return c, c_pooled, None - - def run_clip_compel(self, context, clip_field, prompt, get_pooled): - tokenizer_info = context.services.model_manager.get_model( - **clip_field.tokenizer.dict(), - context=context, - ) - text_encoder_info = context.services.model_manager.get_model( - **clip_field.text_encoder.dict(), - context=context, - ) + return c, c_pooled, None def _lora_loader(): for lora in clip_field.loras: @@ -271,12 +205,15 @@ class SDXLPromptInvocationBase: name = trigger[1:-1] try: ti_list.append( - context.services.model_manager.get_model( - model_name=name, - base_model=clip_field.text_encoder.base_model, - model_type=ModelType.TextualInversion, - context=context, - ).context.model + ( + name, + context.services.model_manager.get_model( + model_name=name, + base_model=clip_field.text_encoder.base_model, + model_type=ModelType.TextualInversion, + context=context, + ).context.model, + ) ) except ModelNotFoundException: # print(e) @@ -284,8 +221,8 @@ class SDXLPromptInvocationBase: # print(traceback.format_exc()) print(f'Warn: trigger: "{trigger}" not found') - with ModelPatcher.apply_lora_text_encoder( - text_encoder_info.context.model, _lora_loader() + with ModelPatcher.apply_lora( + text_encoder_info.context.model, _lora_loader(), lora_prefix ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as ( tokenizer, ti_manager, @@ -357,11 +294,17 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): @torch.no_grad() def invoke(self, context: InvocationContext) -> CompelOutput: - c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False) + c1, c1_pooled, ec1 = self.run_clip_compel( + context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True + ) if self.style.strip() == "": - c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True) + c2, c2_pooled, ec2 = self.run_clip_compel( + context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True + ) else: - c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True) + c2, c2_pooled, ec2 = self.run_clip_compel( + context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True + ) original_size = (self.original_height, self.original_width) crop_coords = (self.crop_top, self.crop_left) @@ -415,117 +358,8 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase @torch.no_grad() def invoke(self, context: InvocationContext) -> CompelOutput: - c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True) - - original_size = (self.original_height, self.original_width) - crop_coords = (self.crop_top, self.crop_left) - - add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)]) - - conditioning_data = ConditioningFieldData( - conditionings=[ - SDXLConditioningInfo( - embeds=c2, - pooled_embeds=c2_pooled, - add_time_ids=add_time_ids, - extra_conditioning=ec2, # or None - ) - ] - ) - - conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning" - context.services.latents.save(conditioning_name, conditioning_data) - - return CompelOutput( - conditioning=ConditioningField( - conditioning_name=conditioning_name, - ), - ) - - -class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): - """Pass unmodified prompt to conditioning without compel processing.""" - - type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt" - - prompt: str = Field(default="", description="Prompt") - style: str = Field(default="", description="Style prompt") - original_width: int = Field(1024, description="") - original_height: int = Field(1024, description="") - crop_top: int = Field(0, description="") - crop_left: int = Field(0, description="") - target_width: int = Field(1024, description="") - target_height: int = Field(1024, description="") - clip: ClipField = Field(None, description="Clip to use") - clip2: ClipField = Field(None, description="Clip2 to use") - - # Schema customisation - class Config(InvocationConfig): - schema_extra = { - "ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}}, - } - - @torch.no_grad() - def invoke(self, context: InvocationContext) -> CompelOutput: - c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False) - if self.style.strip() == "": - c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True) - else: - c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True) - - original_size = (self.original_height, self.original_width) - crop_coords = (self.crop_top, self.crop_left) - target_size = (self.target_height, self.target_width) - - add_time_ids = torch.tensor([original_size + crop_coords + target_size]) - - conditioning_data = ConditioningFieldData( - conditionings=[ - SDXLConditioningInfo( - embeds=torch.cat([c1, c2], dim=-1), - pooled_embeds=c2_pooled, - add_time_ids=add_time_ids, - extra_conditioning=ec1, - ) - ] - ) - - conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning" - context.services.latents.save(conditioning_name, conditioning_data) - - return CompelOutput( - conditioning=ConditioningField( - conditioning_name=conditioning_name, - ), - ) - - -class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): - """Parse prompt using compel package to conditioning.""" - - type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt" - - style: str = Field(default="", description="Style prompt") # TODO: ? - original_width: int = Field(1024, description="") - original_height: int = Field(1024, description="") - crop_top: int = Field(0, description="") - crop_left: int = Field(0, description="") - aesthetic_score: float = Field(6.0, description="") - clip2: ClipField = Field(None, description="Clip to use") - - # Schema customisation - class Config(InvocationConfig): - schema_extra = { - "ui": { - "title": "SDXL Refiner Prompt (Raw)", - "tags": ["prompt", "compel"], - "type_hints": {"model": "model"}, - }, - } - - @torch.no_grad() - def invoke(self, context: InvocationContext) -> CompelOutput: - c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True) + # TODO: if there will appear lora for refiner - write proper prefix + c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "", zero_on_empty=False) original_size = (self.original_height, self.original_width) crop_coords = (self.crop_top, self.crop_left) diff --git a/invokeai/app/invocations/generate.py b/invokeai/app/invocations/generate.py deleted file mode 100644 index d48c9f922e..0000000000 --- a/invokeai/app/invocations/generate.py +++ /dev/null @@ -1,251 +0,0 @@ -# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) - -from functools import partial -from typing import Literal, Optional, get_args - -import torch -from pydantic import Field - -from invokeai.app.models.image import ColorField, ImageCategory, ImageField, ResourceOrigin -from invokeai.app.util.misc import SEED_MAX, get_random_seed -from invokeai.backend.generator.inpaint import infill_methods - -from ...backend.generator import Inpaint, InvokeAIGenerator -from ...backend.stable_diffusion import PipelineIntermediateState -from ..util.step_callback import stable_diffusion_step_callback -from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext -from .image import ImageOutput - -from ...backend.model_management.lora import ModelPatcher -from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline -from .model import UNetField, VaeField -from .compel import ConditioningField -from contextlib import contextmanager, ExitStack, ContextDecorator - -SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())] -INFILL_METHODS = Literal[tuple(infill_methods())] -DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile" - - -from .latent import get_scheduler - - -class OldModelContext(ContextDecorator): - model: StableDiffusionGeneratorPipeline - - def __init__(self, model): - self.model = model - - def __enter__(self): - return self.model - - def __exit__(self, *exc): - return False - - -class OldModelInfo: - name: str - hash: str - context: OldModelContext - - def __init__(self, name: str, hash: str, model: StableDiffusionGeneratorPipeline): - self.name = name - self.hash = hash - self.context = OldModelContext( - model=model, - ) - - -class InpaintInvocation(BaseInvocation): - """Generates an image using inpaint.""" - - type: Literal["inpaint"] = "inpaint" - - positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation") - negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation") - seed: int = Field( - ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed - ) - steps: int = Field(default=30, gt=0, description="The number of steps to use to generate the image") - width: int = Field( - default=512, - multiple_of=8, - gt=0, - description="The width of the resulting image", - ) - height: int = Field( - default=512, - multiple_of=8, - gt=0, - description="The height of the resulting image", - ) - cfg_scale: float = Field( - default=7.5, - ge=1, - description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", - ) - scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use") - unet: UNetField = Field(default=None, description="UNet model") - vae: VaeField = Field(default=None, description="Vae model") - - # Inputs - image: Optional[ImageField] = Field(description="The input image") - strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the original image") - fit: bool = Field( - default=True, - description="Whether or not the result should be fit to the aspect ratio of the input image", - ) - - # Inputs - mask: Optional[ImageField] = Field(description="The mask") - seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)") - seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)") - seam_strength: float = Field(default=0.75, gt=0, le=1, description="The seam inpaint strength") - seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint") - tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)") - infill_method: INFILL_METHODS = Field( - default=DEFAULT_INFILL_METHOD, - description="The method used to infill empty regions (px)", - ) - inpaint_width: Optional[int] = Field( - default=None, - multiple_of=8, - gt=0, - description="The width of the inpaint region (px)", - ) - inpaint_height: Optional[int] = Field( - default=None, - multiple_of=8, - gt=0, - description="The height of the inpaint region (px)", - ) - inpaint_fill: Optional[ColorField] = Field( - default=ColorField(r=127, g=127, b=127, a=255), - description="The solid infill method color", - ) - inpaint_replace: float = Field( - default=0.0, - ge=0.0, - le=1.0, - description="The amount by which to replace masked areas with latent noise", - ) - - # Schema customisation - class Config(InvocationConfig): - schema_extra = { - "ui": {"tags": ["stable-diffusion", "image"], "title": "Inpaint"}, - } - - def dispatch_progress( - self, - context: InvocationContext, - source_node_id: str, - intermediate_state: PipelineIntermediateState, - ) -> None: - stable_diffusion_step_callback( - context=context, - intermediate_state=intermediate_state, - node=self.dict(), - source_node_id=source_node_id, - ) - - def get_conditioning(self, context, unet): - positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name) - c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype) - extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning - - negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name) - uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype) - - return (uc, c, extra_conditioning_info) - - @contextmanager - def load_model_old_way(self, context, scheduler): - def _lora_loader(): - for lora in self.unet.loras: - lora_info = context.services.model_manager.get_model( - **lora.dict(exclude={"weight"}), - context=context, - ) - yield (lora_info.context.model, lora.weight) - del lora_info - return - - unet_info = context.services.model_manager.get_model( - **self.unet.unet.dict(), - context=context, - ) - vae_info = context.services.model_manager.get_model( - **self.vae.vae.dict(), - context=context, - ) - - with vae_info as vae, ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet: - device = context.services.model_manager.mgr.cache.execution_device - dtype = context.services.model_manager.mgr.cache.precision - - pipeline = StableDiffusionGeneratorPipeline( - vae=vae, - text_encoder=None, - tokenizer=None, - unet=unet, - scheduler=scheduler, - safety_checker=None, - feature_extractor=None, - requires_safety_checker=False, - precision="float16" if dtype == torch.float16 else "float32", - execution_device=device, - ) - - yield OldModelInfo( - name=self.unet.unet.model_name, - hash="", - model=pipeline, - ) - - def invoke(self, context: InvocationContext) -> ImageOutput: - image = None if self.image is None else context.services.images.get_pil_image(self.image.image_name) - mask = None if self.mask is None else context.services.images.get_pil_image(self.mask.image_name) - - # Get the source node id (we are invoking the prepared node) - graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) - source_node_id = graph_execution_state.prepared_source_mapping[self.id] - - scheduler = get_scheduler( - context=context, - scheduler_info=self.unet.scheduler, - scheduler_name=self.scheduler, - ) - - with self.load_model_old_way(context, scheduler) as model: - conditioning = self.get_conditioning(context, model.context.model.unet) - - outputs = Inpaint(model).generate( - conditioning=conditioning, - scheduler=scheduler, - init_image=image, - mask_image=mask, - step_callback=partial(self.dispatch_progress, context, source_node_id), - **self.dict( - exclude={"positive_conditioning", "negative_conditioning", "scheduler", "image", "mask"} - ), # Shorthand for passing all of the parameters above manually - ) - - # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object - # each time it is called. We only need the first one. - generator_output = next(outputs) - - image_dto = context.services.images.create( - image=generator_output.image, - image_origin=ResourceOrigin.INTERNAL, - image_category=ImageCategory.GENERAL, - session_id=context.graph_execution_state_id, - node_id=self.id, - is_intermediate=self.is_intermediate, - ) - - return ImageOutput( - image=ImageField(image_name=image_dto.image_name), - width=image_dto.width, - height=image_dto.height, - ) diff --git a/invokeai/app/invocations/image.py b/invokeai/app/invocations/image.py index 3f40ea3cbe..2c47020207 100644 --- a/invokeai/app/invocations/image.py +++ b/invokeai/app/invocations/image.py @@ -1,28 +1,19 @@ # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) -from typing import Literal, Optional - -import numpy -from PIL import Image, ImageFilter, ImageOps, ImageChops -from pydantic import Field from pathlib import Path -from typing import Union +from typing import Literal, Optional, Union + +import cv2 +import numpy +from PIL import Image, ImageChops, ImageFilter, ImageOps +from pydantic import Field + from invokeai.app.invocations.metadata import CoreMetadata -from ..models.image import ( - ImageCategory, - ImageField, - ResourceOrigin, - PILInvocationConfig, - ImageOutput, - MaskOutput, -) -from .baseinvocation import ( - BaseInvocation, - InvocationContext, - InvocationConfig, -) -from invokeai.backend.image_util.safety_checker import SafetyChecker from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark +from invokeai.backend.image_util.safety_checker import SafetyChecker + +from ..models.image import ImageCategory, ImageField, ImageOutput, MaskOutput, PILInvocationConfig, ResourceOrigin +from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext class LoadImageInvocation(BaseInvocation): @@ -142,9 +133,10 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig): def invoke(self, context: InvocationContext) -> ImageOutput: base_image = context.services.images.get_pil_image(self.base_image.image_name) image = context.services.images.get_pil_image(self.image.image_name) - mask = ( - None if self.mask is None else ImageOps.invert(context.services.images.get_pil_image(self.mask.image_name)) - ) + mask = None + if self.mask is not None: + mask = context.services.images.get_pil_image(self.mask.image_name) + mask = ImageOps.invert(mask.convert("L")) # TODO: probably shouldn't invert mask here... should user be required to do it? min_x = min(0, self.x) @@ -500,7 +492,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig): image = context.services.images.get_pil_image(self.image.image_name) image_arr = numpy.asarray(image, dtype=numpy.float32) / 255 - image_arr = image_arr * (self.max - self.min) + self.max + image_arr = image_arr * (self.max - self.min) + self.min lerp_image = Image.fromarray(numpy.uint8(image_arr)) @@ -650,3 +642,332 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig): width=image_dto.width, height=image_dto.height, ) + + +class MaskEdgeInvocation(BaseInvocation, PILInvocationConfig): + """Applies an edge mask to an image""" + + # fmt: off + type: Literal["mask_edge"] = "mask_edge" + + # Inputs + image: Optional[ImageField] = Field(default=None, description="The image to apply the mask to") + edge_size: int = Field(description="The size of the edge") + edge_blur: int = Field(description="The amount of blur on the edge") + low_threshold: int = Field(description="First threshold for the hysteresis procedure in Canny edge detection") + high_threshold: int = Field(description="Second threshold for the hysteresis procedure in Canny edge detection") + # fmt: on + + def invoke(self, context: InvocationContext) -> MaskOutput: + mask = context.services.images.get_pil_image(self.image.image_name) + + npimg = numpy.asarray(mask, dtype=numpy.uint8) + npgradient = numpy.uint8(255 * (1.0 - numpy.floor(numpy.abs(0.5 - numpy.float32(npimg) / 255.0) * 2.0))) + npedge = cv2.Canny(npimg, threshold1=self.low_threshold, threshold2=self.high_threshold) + npmask = npgradient + npedge + npmask = cv2.dilate(npmask, numpy.ones((3, 3), numpy.uint8), iterations=int(self.edge_size / 2)) + + new_mask = Image.fromarray(npmask) + + if self.edge_blur > 0: + new_mask = new_mask.filter(ImageFilter.BoxBlur(self.edge_blur)) + + new_mask = ImageOps.invert(new_mask) + + image_dto = context.services.images.create( + image=new_mask, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.MASK, + node_id=self.id, + session_id=context.graph_execution_state_id, + is_intermediate=self.is_intermediate, + ) + + return MaskOutput( + mask=ImageField(image_name=image_dto.image_name), + width=image_dto.width, + height=image_dto.height, + ) + + +class MaskCombineInvocation(BaseInvocation, PILInvocationConfig): + """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.""" + + # fmt: off + type: Literal["mask_combine"] = "mask_combine" + + # Inputs + mask1: ImageField = Field(default=None, description="The first mask to combine") + mask2: ImageField = Field(default=None, description="The second image to combine") + # fmt: on + + class Config(InvocationConfig): + schema_extra = { + "ui": {"title": "Mask Combine", "tags": ["mask", "combine"]}, + } + + def invoke(self, context: InvocationContext) -> ImageOutput: + mask1 = context.services.images.get_pil_image(self.mask1.image_name).convert("L") + mask2 = context.services.images.get_pil_image(self.mask2.image_name).convert("L") + + combined_mask = ImageChops.multiply(mask1, mask2) + + image_dto = context.services.images.create( + image=combined_mask, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + session_id=context.graph_execution_state_id, + is_intermediate=self.is_intermediate, + ) + + return ImageOutput( + image=ImageField(image_name=image_dto.image_name), + width=image_dto.width, + height=image_dto.height, + ) + + +class ColorCorrectInvocation(BaseInvocation, PILInvocationConfig): + """ + Shifts the colors of a target image to match the reference image, optionally + using a mask to only color-correct certain regions of the target image. + """ + + type: Literal["color_correct"] = "color_correct" + + image: Optional[ImageField] = Field(default=None, description="The image to color-correct") + reference: Optional[ImageField] = Field(default=None, description="Reference image for color-correction") + mask: Optional[ImageField] = Field(default=None, description="Mask to use when applying color-correction") + mask_blur_radius: float = Field(default=8, description="Mask blur radius") + + def invoke(self, context: InvocationContext) -> ImageOutput: + pil_init_mask = None + if self.mask is not None: + pil_init_mask = context.services.images.get_pil_image(self.mask.image_name).convert("L") + + init_image = context.services.images.get_pil_image(self.reference.image_name) + + result = context.services.images.get_pil_image(self.image.image_name).convert("RGBA") + + # if init_image is None or init_mask is None: + # return result + + # Get the original alpha channel of the mask if there is one. + # Otherwise it is some other black/white image format ('1', 'L' or 'RGB') + # pil_init_mask = ( + # init_mask.getchannel("A") + # if init_mask.mode == "RGBA" + # else init_mask.convert("L") + # ) + pil_init_image = init_image.convert("RGBA") # Add an alpha channel if one doesn't exist + + # Build an image with only visible pixels from source to use as reference for color-matching. + init_rgb_pixels = numpy.asarray(init_image.convert("RGB"), dtype=numpy.uint8) + init_a_pixels = numpy.asarray(pil_init_image.getchannel("A"), dtype=numpy.uint8) + init_mask_pixels = numpy.asarray(pil_init_mask, dtype=numpy.uint8) + + # Get numpy version of result + np_image = numpy.asarray(result.convert("RGB"), dtype=numpy.uint8) + + # Mask and calculate mean and standard deviation + mask_pixels = init_a_pixels * init_mask_pixels > 0 + np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :] + np_image_masked = np_image[mask_pixels, :] + + if np_init_rgb_pixels_masked.size > 0: + init_means = np_init_rgb_pixels_masked.mean(axis=0) + init_std = np_init_rgb_pixels_masked.std(axis=0) + gen_means = np_image_masked.mean(axis=0) + gen_std = np_image_masked.std(axis=0) + + # Color correct + np_matched_result = np_image.copy() + np_matched_result[:, :, :] = ( + ( + ( + (np_matched_result[:, :, :].astype(numpy.float32) - gen_means[None, None, :]) + / gen_std[None, None, :] + ) + * init_std[None, None, :] + + init_means[None, None, :] + ) + .clip(0, 255) + .astype(numpy.uint8) + ) + matched_result = Image.fromarray(np_matched_result, mode="RGB") + else: + matched_result = Image.fromarray(np_image, mode="RGB") + + # Blur the mask out (into init image) by specified amount + if self.mask_blur_radius > 0: + nm = numpy.asarray(pil_init_mask, dtype=numpy.uint8) + nmd = cv2.erode( + nm, + kernel=numpy.ones((3, 3), dtype=numpy.uint8), + iterations=int(self.mask_blur_radius / 2), + ) + pmd = Image.fromarray(nmd, mode="L") + blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(self.mask_blur_radius)) + else: + blurred_init_mask = pil_init_mask + + multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, result.split()[-1]) + + # Paste original on color-corrected generation (using blurred mask) + matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask) + + image_dto = context.services.images.create( + image=matched_result, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + session_id=context.graph_execution_state_id, + is_intermediate=self.is_intermediate, + ) + + return ImageOutput( + image=ImageField(image_name=image_dto.image_name), + width=image_dto.width, + height=image_dto.height, + ) + + +class ImageHueAdjustmentInvocation(BaseInvocation): + """Adjusts the Hue of an image.""" + + # fmt: off + type: Literal["img_hue_adjust"] = "img_hue_adjust" + + # Inputs + image: ImageField = Field(default=None, description="The image to adjust") + hue: int = Field(default=0, description="The degrees by which to rotate the hue, 0-360") + # fmt: on + + def invoke(self, context: InvocationContext) -> ImageOutput: + pil_image = context.services.images.get_pil_image(self.image.image_name) + + # Convert image to HSV color space + hsv_image = numpy.array(pil_image.convert("HSV")) + + # Convert hue from 0..360 to 0..256 + hue = int(256 * ((self.hue % 360) / 360)) + + # Increment each hue and wrap around at 255 + hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue) % 256 + + # Convert back to PIL format and to original color mode + pil_image = Image.fromarray(hsv_image, mode="HSV").convert("RGBA") + + image_dto = context.services.images.create( + image=pil_image, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + is_intermediate=self.is_intermediate, + session_id=context.graph_execution_state_id, + ) + + return ImageOutput( + image=ImageField( + image_name=image_dto.image_name, + ), + width=image_dto.width, + height=image_dto.height, + ) + + +class ImageLuminosityAdjustmentInvocation(BaseInvocation): + """Adjusts the Luminosity (Value) of an image.""" + + # fmt: off + type: Literal["img_luminosity_adjust"] = "img_luminosity_adjust" + + # Inputs + image: ImageField = Field(default=None, description="The image to adjust") + luminosity: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)") + # fmt: on + + def invoke(self, context: InvocationContext) -> ImageOutput: + pil_image = context.services.images.get_pil_image(self.image.image_name) + + # Convert PIL image to OpenCV format (numpy array), note color channel + # ordering is changed from RGB to BGR + image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1] + + # Convert image to HSV color space + hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # Adjust the luminosity (value) + hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255) + + # Convert image back to BGR color space + image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR) + + # Convert back to PIL format and to original color mode + pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA") + + image_dto = context.services.images.create( + image=pil_image, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + is_intermediate=self.is_intermediate, + session_id=context.graph_execution_state_id, + ) + + return ImageOutput( + image=ImageField( + image_name=image_dto.image_name, + ), + width=image_dto.width, + height=image_dto.height, + ) + + +class ImageSaturationAdjustmentInvocation(BaseInvocation): + """Adjusts the Saturation of an image.""" + + # fmt: off + type: Literal["img_saturation_adjust"] = "img_saturation_adjust" + + # Inputs + image: ImageField = Field(default=None, description="The image to adjust") + saturation: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation") + # fmt: on + + def invoke(self, context: InvocationContext) -> ImageOutput: + pil_image = context.services.images.get_pil_image(self.image.image_name) + + # Convert PIL image to OpenCV format (numpy array), note color channel + # ordering is changed from RGB to BGR + image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1] + + # Convert image to HSV color space + hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # Adjust the saturation + hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255) + + # Convert image back to BGR color space + image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR) + + # Convert back to PIL format and to original color mode + pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA") + + image_dto = context.services.images.create( + image=pil_image, + image_origin=ResourceOrigin.INTERNAL, + image_category=ImageCategory.GENERAL, + node_id=self.id, + is_intermediate=self.is_intermediate, + session_id=context.graph_execution_state_id, + ) + + return ImageOutput( + image=ImageField( + image_name=image_dto.image_name, + ), + width=image_dto.width, + height=image_dto.height, + ) diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 3edbe86150..c66c9c6214 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -5,16 +5,24 @@ from typing import List, Literal, Optional, Union import einops import torch -from diffusers import ControlNetModel +import torchvision.transforms as T from diffusers.image_processor import VaeImageProcessor -from diffusers.schedulers import SchedulerMixin as Scheduler +from diffusers.models.attention_processor import ( + AttnProcessor2_0, + LoRAAttnProcessor2_0, + LoRAXFormersAttnProcessor, + XFormersAttnProcessor, +) +from diffusers.schedulers import DPMSolverSDEScheduler, SchedulerMixin as Scheduler from pydantic import BaseModel, Field, validator +from torchvision.transforms.functional import resize as tv_resize from invokeai.app.invocations.metadata import CoreMetadata +from invokeai.app.util.controlnet_utils import prepare_control_image from invokeai.app.util.step_callback import stable_diffusion_step_callback from invokeai.backend.model_management.models import ModelType, SilenceWarnings -from ...backend.model_management.lora import ModelPatcher +from ...backend.model_management import BaseModelType, ModelPatcher from ...backend.stable_diffusion import PipelineIntermediateState from ...backend.stable_diffusion.diffusers_pipeline import ( ConditioningData, @@ -24,23 +32,13 @@ from ...backend.stable_diffusion.diffusers_pipeline import ( ) from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP -from ...backend.model_management import ModelPatcher -from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision +from ...backend.util.devices import choose_precision, choose_torch_device, torch_dtype from ..models.image import ImageCategory, ImageField, ResourceOrigin from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext from .compel import ConditioningField from .controlnet_image_processors import ControlField from .image import ImageOutput from .model import ModelInfo, UNetField, VaeField -from invokeai.app.util.controlnet_utils import prepare_control_image - -from diffusers.models.attention_processor import ( - AttnProcessor2_0, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - XFormersAttnProcessor, -) - DEFAULT_PRECISION = choose_precision(choose_torch_device()) @@ -49,6 +47,7 @@ class LatentsField(BaseModel): """A latents field used for passing latents between invocations""" latents_name: Optional[str] = Field(default=None, description="The name of the latents") + seed: Optional[int] = Field(description="Seed used to generate this latents") class Config: schema_extra = {"required": ["latents_name"]} @@ -67,9 +66,9 @@ class LatentsOutput(BaseInvocationOutput): # fmt: on -def build_latents_output(latents_name: str, latents: torch.Tensor): +def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int]): return LatentsOutput( - latents=LatentsField(latents_name=latents_name), + latents=LatentsField(latents_name=latents_name, seed=seed), width=latents.size()[3] * 8, height=latents.size()[2] * 8, ) @@ -82,6 +81,7 @@ def get_scheduler( context: InvocationContext, scheduler_info: ModelInfo, scheduler_name: str, + seed: int, ) -> Scheduler: scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"]) orig_scheduler_info = context.services.model_manager.get_model( @@ -98,6 +98,11 @@ def get_scheduler( **scheduler_extra_config, "_backup": scheduler_config, } + + # make dpmpp_sde reproducable(seed can be passed only in initializer) + if scheduler_class is DPMSolverSDEScheduler: + scheduler_config["noise_sampler_seed"] = seed + scheduler = scheduler_class.from_config(scheduler_config) # hack copied over from generate.py @@ -106,25 +111,31 @@ def get_scheduler( return scheduler -# Text to image -class TextToLatentsInvocation(BaseInvocation): - """Generates latents from conditionings.""" +class DenoiseLatentsInvocation(BaseInvocation): + """Denoises noisy latents to decodable images""" - type: Literal["t2l"] = "t2l" + type: Literal["denoise_latents"] = "denoise_latents" # Inputs - # fmt: off positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation") negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation") noise: Optional[LatentsField] = Field(description="The noise to use") - steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image") - cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", ) - scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" ) + steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image") + cfg_scale: Union[float, List[float]] = Field( + default=7.5, + ge=1, + description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", + ) + denoising_start: float = Field(default=0.0, ge=0, le=1, description="") + denoising_end: float = Field(default=1.0, ge=0, le=1, description="") + scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use") unet: UNetField = Field(default=None, description="UNet submodel") control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use") - # seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", ) - # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") - # fmt: on + latents: Optional[LatentsField] = Field(description="The latents to use as a base image") + mask: Optional[ImageField] = Field( + None, + description="Mask", + ) @validator("cfg_scale") def ge_one(cls, v): @@ -142,12 +153,11 @@ class TextToLatentsInvocation(BaseInvocation): class Config(InvocationConfig): schema_extra = { "ui": { - "title": "Text To Latents", - "tags": ["latents"], + "title": "Denoise Latents", + "tags": ["denoise", "latents"], "type_hints": { "model": "model", "control": "control", - # "cfg_scale": "float", "cfg_scale": "number", }, }, @@ -159,12 +169,14 @@ class TextToLatentsInvocation(BaseInvocation): context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState, + base_model: BaseModelType, ) -> None: stable_diffusion_step_callback( context=context, intermediate_state=intermediate_state, node=self.dict(), source_node_id=source_node_id, + base_model=base_model, ) def get_conditioning_data( @@ -172,13 +184,14 @@ class TextToLatentsInvocation(BaseInvocation): context: InvocationContext, scheduler, unet, + seed, ) -> ConditioningData: positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name) - c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype) - extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning + c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype) + extra_conditioning_info = c.extra_conditioning negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name) - uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype) + uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype) conditioning_data = ConditioningData( unconditioned_embeddings=uc, @@ -198,7 +211,8 @@ class TextToLatentsInvocation(BaseInvocation): # for ddim scheduler eta=0.0, # ddim_eta # for ancestral and sde schedulers - generator=torch.Generator(device=unet.device).manual_seed(0), + # flip all bits to have noise different from initial + generator=torch.Generator(device=unet.device).manual_seed(seed ^ 0xFFFFFFFF), ) return conditioning_data @@ -231,7 +245,6 @@ class TextToLatentsInvocation(BaseInvocation): safety_checker=None, feature_extractor=None, requires_safety_checker=False, - precision="float16" if unet.dtype == torch.float16 else "float32", ) def prep_control_data( @@ -310,110 +323,83 @@ class TextToLatentsInvocation(BaseInvocation): # MultiControlNetModel has been refactored out, just need list[ControlNetData] return control_data - @torch.no_grad() - def invoke(self, context: InvocationContext) -> LatentsOutput: - with SilenceWarnings(): - noise = context.services.latents.get(self.noise.latents_name) + # original idea by https://github.com/AmericanPresidentJimmyCarter + # TODO: research more for second order schedulers timesteps + def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end): + num_inference_steps = steps + if scheduler.config.get("cpu_only", False): + scheduler.set_timesteps(num_inference_steps, device="cpu") + timesteps = scheduler.timesteps.to(device=device) + else: + scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = scheduler.timesteps - # Get the source node id (we are invoking the prepared node) - graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) - source_node_id = graph_execution_state.prepared_source_mapping[self.id] + # apply denoising_start + t_start_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_start))) + t_start_idx = len(list(filter(lambda ts: ts >= t_start_val, timesteps))) + timesteps = timesteps[t_start_idx:] + if scheduler.order == 2 and t_start_idx > 0: + timesteps = timesteps[1:] - def step_callback(state: PipelineIntermediateState): - self.dispatch_progress(context, source_node_id, state) + # save start timestep to apply noise + init_timestep = timesteps[:1] - def _lora_loader(): - for lora in self.unet.loras: - lora_info = context.services.model_manager.get_model( - **lora.dict(exclude={"weight"}), - context=context, - ) - yield (lora_info.context.model, lora.weight) - del lora_info - return + # apply denoising_end + t_end_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_end))) + t_end_idx = len(list(filter(lambda ts: ts >= t_end_val, timesteps))) + if scheduler.order == 2 and t_end_idx > 0: + t_end_idx += 1 + timesteps = timesteps[:t_end_idx] - unet_info = context.services.model_manager.get_model( - **self.unet.unet.dict(), - context=context, - ) - with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet( - unet_info.context.model, _lora_loader() - ), unet_info as unet: - noise = noise.to(device=unet.device, dtype=unet.dtype) + # calculate step count based on scheduler order + num_inference_steps = len(timesteps) + if scheduler.order == 2: + num_inference_steps += num_inference_steps % 2 + num_inference_steps = num_inference_steps // 2 - scheduler = get_scheduler( - context=context, - scheduler_info=self.unet.scheduler, - scheduler_name=self.scheduler, - ) + return num_inference_steps, timesteps, init_timestep - pipeline = self.create_pipeline(unet, scheduler) - conditioning_data = self.get_conditioning_data(context, scheduler, unet) + def prep_mask_tensor(self, mask, context, lantents): + if mask is None: + return None - control_data = self.prep_control_data( - model=pipeline, - context=context, - control_input=self.control, - latents_shape=noise.shape, - # do_classifier_free_guidance=(self.cfg_scale >= 1.0)) - do_classifier_free_guidance=True, - exit_stack=exit_stack, - ) - - # TODO: Verify the noise is the right size - result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( - latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)), - noise=noise, - num_inference_steps=self.steps, - conditioning_data=conditioning_data, - control_data=control_data, # list[ControlNetData] - callback=step_callback, - ) - - # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 - result_latents = result_latents.to("cpu") - torch.cuda.empty_cache() - - name = f"{context.graph_execution_state_id}__{self.id}" - context.services.latents.save(name, result_latents) - return build_latents_output(latents_name=name, latents=result_latents) - - -class LatentsToLatentsInvocation(TextToLatentsInvocation): - """Generates latents using latents as base image.""" - - type: Literal["l2l"] = "l2l" - - # Inputs - latents: Optional[LatentsField] = Field(description="The latents to use as a base image") - strength: float = Field(default=0.7, ge=0, le=1, description="The strength of the latents to use") - - # Schema customisation - class Config(InvocationConfig): - schema_extra = { - "ui": { - "title": "Latent To Latents", - "tags": ["latents"], - "type_hints": { - "model": "model", - "control": "control", - "cfg_scale": "number", - }, - }, - } + mask_image = context.services.images.get_pil_image(mask.image_name) + if mask_image.mode != "L": + # FIXME: why do we get passed an RGB image here? We can only use single-channel. + mask_image = mask_image.convert("L") + mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False) + if mask_tensor.dim() == 3: + mask_tensor = mask_tensor.unsqueeze(0) + mask_tensor = tv_resize(mask_tensor, lantents.shape[-2:], T.InterpolationMode.BILINEAR) + return 1 - mask_tensor @torch.no_grad() def invoke(self, context: InvocationContext) -> LatentsOutput: with SilenceWarnings(): # this quenches NSFW nag from diffusers - noise = context.services.latents.get(self.noise.latents_name) - latent = context.services.latents.get(self.latents.latents_name) + seed = None + noise = None + if self.noise is not None: + noise = context.services.latents.get(self.noise.latents_name) + seed = self.noise.seed + + if self.latents is not None: + latents = context.services.latents.get(self.latents.latents_name) + if seed is None: + seed = self.latents.seed + else: + latents = torch.zeros_like(noise) + + if seed is None: + seed = 0 + + mask = self.prep_mask_tensor(self.mask, context, latents) # Get the source node id (we are invoking the prepared node) graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) source_node_id = graph_execution_state.prepared_source_mapping[self.id] def step_callback(state: PipelineIntermediateState): - self.dispatch_progress(context, source_node_id, state) + self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model) def _lora_loader(): for lora in self.unet.loras: @@ -432,44 +418,48 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation): with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet( unet_info.context.model, _lora_loader() ), unet_info as unet: - noise = noise.to(device=unet.device, dtype=unet.dtype) - latent = latent.to(device=unet.device, dtype=unet.dtype) + latents = latents.to(device=unet.device, dtype=unet.dtype) + if noise is not None: + noise = noise.to(device=unet.device, dtype=unet.dtype) + if mask is not None: + mask = mask.to(device=unet.device, dtype=unet.dtype) scheduler = get_scheduler( context=context, scheduler_info=self.unet.scheduler, scheduler_name=self.scheduler, + seed=seed, ) pipeline = self.create_pipeline(unet, scheduler) - conditioning_data = self.get_conditioning_data(context, scheduler, unet) + conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed) control_data = self.prep_control_data( model=pipeline, context=context, control_input=self.control, - latents_shape=noise.shape, + latents_shape=latents.shape, # do_classifier_free_guidance=(self.cfg_scale >= 1.0)) do_classifier_free_guidance=True, exit_stack=exit_stack, ) - # TODO: Verify the noise is the right size - initial_latents = ( - latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype) - ) - - timesteps, _ = pipeline.get_img2img_timesteps( - self.steps, - self.strength, + num_inference_steps, timesteps, init_timestep = self.init_scheduler( + scheduler, device=unet.device, + steps=self.steps, + denoising_start=self.denoising_start, + denoising_end=self.denoising_end, ) result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( - latents=initial_latents, + latents=latents, timesteps=timesteps, + init_timestep=init_timestep, noise=noise, - num_inference_steps=self.steps, + seed=seed, + mask=mask, + num_inference_steps=num_inference_steps, conditioning_data=conditioning_data, control_data=control_data, # list[ControlNetData] callback=step_callback, @@ -481,7 +471,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation): name = f"{context.graph_execution_state_id}__{self.id}" context.services.latents.save(name, result_latents) - return build_latents_output(latents_name=name, latents=result_latents) + return build_latents_output(latents_name=name, latents=result_latents, seed=seed) # Latent to image @@ -623,7 +613,7 @@ class ResizeLatentsInvocation(BaseInvocation): name = f"{context.graph_execution_state_id}__{self.id}" # context.services.latents.set(name, resized_latents) context.services.latents.save(name, resized_latents) - return build_latents_output(latents_name=name, latents=resized_latents) + return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed) class ScaleLatentsInvocation(BaseInvocation): @@ -665,7 +655,7 @@ class ScaleLatentsInvocation(BaseInvocation): name = f"{context.graph_execution_state_id}__{self.id}" # context.services.latents.set(name, resized_latents) context.services.latents.save(name, resized_latents) - return build_latents_output(latents_name=name, latents=resized_latents) + return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed) class ImageToLatentsInvocation(BaseInvocation): @@ -746,4 +736,4 @@ class ImageToLatentsInvocation(BaseInvocation): name = f"{context.graph_execution_state_id}__{self.id}" latents = latents.to("cpu") context.services.latents.save(name, latents) - return build_latents_output(latents_name=name, latents=latents) + return build_latents_output(latents_name=name, latents=latents, seed=None) diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py index 3588ef4ebe..d0549f8539 100644 --- a/invokeai/app/invocations/metadata.py +++ b/invokeai/app/invocations/metadata.py @@ -1,7 +1,8 @@ from typing import Literal, Optional, Union -from pydantic import BaseModel, Field +from pydantic import Field +from ...version import __version__ from invokeai.app.invocations.baseinvocation import ( BaseInvocation, BaseInvocationOutput, @@ -10,18 +11,20 @@ from invokeai.app.invocations.baseinvocation import ( ) from invokeai.app.invocations.controlnet_image_processors import ControlField from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField +from invokeai.app.util.model_exclude_null import BaseModelExcludeNull -class LoRAMetadataField(BaseModel): +class LoRAMetadataField(BaseModelExcludeNull): """LoRA metadata for an image generated in InvokeAI.""" lora: LoRAModelField = Field(description="The LoRA model") weight: float = Field(description="The weight of the LoRA model") -class CoreMetadata(BaseModel): +class CoreMetadata(BaseModelExcludeNull): """Core generation metadata for an image generated in InvokeAI.""" + app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image") generation_mode: str = Field( description="The generation mode that output this image", ) @@ -64,13 +67,16 @@ class CoreMetadata(BaseModel): ) refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner") refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner") - refiner_aesthetic_store: Union[float, None] = Field( + refiner_positive_aesthetic_store: Union[float, None] = Field( + default=None, description="The aesthetic score used for the refiner" + ) + refiner_negative_aesthetic_store: Union[float, None] = Field( default=None, description="The aesthetic score used for the refiner" ) refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising") -class ImageMetadata(BaseModel): +class ImageMetadata(BaseModelExcludeNull): """An image's generation metadata""" metadata: Optional[dict] = Field( @@ -133,7 +139,10 @@ class MetadataAccumulatorInvocation(BaseInvocation): ) refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner") refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner") - refiner_aesthetic_store: Union[float, None] = Field( + refiner_positive_aesthetic_score: Union[float, None] = Field( + default=None, description="The aesthetic score used for the refiner" + ) + refiner_negative_aesthetic_score: Union[float, None] = Field( default=None, description="The aesthetic score used for the refiner" ) refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising") diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py index c19e5c5c9a..0d21f8f0ce 100644 --- a/invokeai/app/invocations/model.py +++ b/invokeai/app/invocations/model.py @@ -262,6 +262,103 @@ class LoraLoaderInvocation(BaseInvocation): return output +class SDXLLoraLoaderOutput(BaseInvocationOutput): + """Model loader output""" + + # fmt: off + type: Literal["sdxl_lora_loader_output"] = "sdxl_lora_loader_output" + + unet: Optional[UNetField] = Field(default=None, description="UNet submodel") + clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels") + clip2: Optional[ClipField] = Field(default=None, description="Tokenizer2 and text_encoder2 submodels") + # fmt: on + + +class SDXLLoraLoaderInvocation(BaseInvocation): + """Apply selected lora to unet and text_encoder.""" + + type: Literal["sdxl_lora_loader"] = "sdxl_lora_loader" + + lora: Union[LoRAModelField, None] = Field(default=None, description="Lora model name") + weight: float = Field(default=0.75, description="With what weight to apply lora") + + unet: Optional[UNetField] = Field(description="UNet model for applying lora") + clip: Optional[ClipField] = Field(description="Clip model for applying lora") + clip2: Optional[ClipField] = Field(description="Clip2 model for applying lora") + + class Config(InvocationConfig): + schema_extra = { + "ui": { + "title": "SDXL Lora Loader", + "tags": ["lora", "loader"], + "type_hints": {"lora": "lora_model"}, + }, + } + + def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput: + if self.lora is None: + raise Exception("No LoRA provided") + + base_model = self.lora.base_model + lora_name = self.lora.model_name + + if not context.services.model_manager.model_exists( + base_model=base_model, + model_name=lora_name, + model_type=ModelType.Lora, + ): + raise Exception(f"Unknown lora name: {lora_name}!") + + if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras): + raise Exception(f'Lora "{lora_name}" already applied to unet') + + if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras): + raise Exception(f'Lora "{lora_name}" already applied to clip') + + if self.clip2 is not None and any(lora.model_name == lora_name for lora in self.clip2.loras): + raise Exception(f'Lora "{lora_name}" already applied to clip2') + + output = SDXLLoraLoaderOutput() + + if self.unet is not None: + output.unet = copy.deepcopy(self.unet) + output.unet.loras.append( + LoraInfo( + base_model=base_model, + model_name=lora_name, + model_type=ModelType.Lora, + submodel=None, + weight=self.weight, + ) + ) + + if self.clip is not None: + output.clip = copy.deepcopy(self.clip) + output.clip.loras.append( + LoraInfo( + base_model=base_model, + model_name=lora_name, + model_type=ModelType.Lora, + submodel=None, + weight=self.weight, + ) + ) + + if self.clip2 is not None: + output.clip2 = copy.deepcopy(self.clip2) + output.clip2.loras.append( + LoraInfo( + base_model=base_model, + model_name=lora_name, + model_type=ModelType.Lora, + submodel=None, + weight=self.weight, + ) + ) + + return output + + class VAEModelField(BaseModel): """Vae model field""" diff --git a/invokeai/app/invocations/noise.py b/invokeai/app/invocations/noise.py index fff0f29f14..db64e5b6e5 100644 --- a/invokeai/app/invocations/noise.py +++ b/invokeai/app/invocations/noise.py @@ -71,9 +71,9 @@ class NoiseOutput(BaseInvocationOutput): # fmt: on -def build_noise_output(latents_name: str, latents: torch.Tensor): +def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int): return NoiseOutput( - noise=LatentsField(latents_name=latents_name), + noise=LatentsField(latents_name=latents_name, seed=seed), width=latents.size()[3] * 8, height=latents.size()[2] * 8, ) @@ -132,4 +132,4 @@ class NoiseInvocation(BaseInvocation): ) name = f"{context.graph_execution_state_id}__{self.id}" context.services.latents.save(name, noise) - return build_noise_output(latents_name=name, latents=noise) + return build_noise_output(latents_name=name, latents=noise, seed=self.seed) diff --git a/invokeai/app/invocations/onnx.py b/invokeai/app/invocations/onnx.py index 2bec128b87..4f04a4f023 100644 --- a/invokeai/app/invocations/onnx.py +++ b/invokeai/app/invocations/onnx.py @@ -65,7 +65,6 @@ class ONNXPromptInvocation(BaseInvocation): **self.clip.text_encoder.dict(), ) with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder, ExitStack() as stack: - # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.clip.loras] loras = [ (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras @@ -76,18 +75,14 @@ class ONNXPromptInvocation(BaseInvocation): name = trigger[1:-1] try: ti_list.append( - # stack.enter_context( - # context.services.model_manager.get_model( - # model_name=name, - # base_model=self.clip.text_encoder.base_model, - # model_type=ModelType.TextualInversion, - # ) - # ) - context.services.model_manager.get_model( - model_name=name, - base_model=self.clip.text_encoder.base_model, - model_type=ModelType.TextualInversion, - ).context.model + ( + name, + context.services.model_manager.get_model( + model_name=name, + base_model=self.clip.text_encoder.base_model, + model_type=ModelType.TextualInversion, + ).context.model, + ) ) except Exception: # print(e) @@ -217,6 +212,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation): context=context, scheduler_info=self.unet.scheduler, scheduler_name=self.scheduler, + seed=0, # TODO: refactor this node ) def torch2numpy(latent: torch.Tensor): diff --git a/invokeai/app/invocations/sdxl.py b/invokeai/app/invocations/sdxl.py index 7dfceba853..a5a1c2c641 100644 --- a/invokeai/app/invocations/sdxl.py +++ b/invokeai/app/invocations/sdxl.py @@ -1,17 +1,10 @@ import torch -import inspect -from tqdm import tqdm -from typing import List, Literal, Optional, Union - -from pydantic import Field, validator +from typing import Literal +from pydantic import Field from ...backend.model_management import ModelType, SubModelType -from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext - from .model import UNetField, ClipField, VaeField, MainModelField, ModelInfo -from .compel import ConditioningField -from .latent import LatentsField, SAMPLER_NAME_VALUES, LatentsOutput, get_scheduler, build_latents_output class SDXLModelLoaderOutput(BaseInvocationOutput): @@ -201,506 +194,3 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation): ), ), ) - - -# Text to image -class SDXLTextToLatentsInvocation(BaseInvocation): - """Generates latents from conditionings.""" - - type: Literal["t2l_sdxl"] = "t2l_sdxl" - - # Inputs - # fmt: off - positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation") - negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation") - noise: Optional[LatentsField] = Field(description="The noise to use") - steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image") - cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", ) - scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" ) - unet: UNetField = Field(default=None, description="UNet submodel") - denoising_end: float = Field(default=1.0, gt=0, le=1, description="") - # control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use") - # seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", ) - # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") - # fmt: on - - @validator("cfg_scale") - def ge_one(cls, v): - """validate that all cfg_scale values are >= 1""" - if isinstance(v, list): - for i in v: - if i < 1: - raise ValueError("cfg_scale must be greater than 1") - else: - if v < 1: - raise ValueError("cfg_scale must be greater than 1") - return v - - # Schema customisation - class Config(InvocationConfig): - schema_extra = { - "ui": { - "title": "SDXL Text To Latents", - "tags": ["latents"], - "type_hints": { - "model": "model", - # "cfg_scale": "float", - "cfg_scale": "number", - }, - }, - } - - def dispatch_progress( - self, - context: InvocationContext, - source_node_id: str, - sample, - step, - total_steps, - ) -> None: - stable_diffusion_xl_step_callback( - context=context, - node=self.dict(), - source_node_id=source_node_id, - sample=sample, - step=step, - total_steps=total_steps, - ) - - # based on - # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375 - @torch.no_grad() - def invoke(self, context: InvocationContext) -> LatentsOutput: - graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) - source_node_id = graph_execution_state.prepared_source_mapping[self.id] - latents = context.services.latents.get(self.noise.latents_name) - - positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name) - prompt_embeds = positive_cond_data.conditionings[0].embeds - pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds - add_time_ids = positive_cond_data.conditionings[0].add_time_ids - - negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name) - negative_prompt_embeds = negative_cond_data.conditionings[0].embeds - negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds - add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids - - scheduler = get_scheduler( - context=context, - scheduler_info=self.unet.scheduler, - scheduler_name=self.scheduler, - ) - - num_inference_steps = self.steps - - unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context) - do_classifier_free_guidance = True - cross_attention_kwargs = None - with unet_info as unet: - scheduler.set_timesteps(num_inference_steps, device=unet.device) - timesteps = scheduler.timesteps - - latents = latents.to(device=unet.device, dtype=unet.dtype) * scheduler.init_noise_sigma - - extra_step_kwargs = dict() - if "eta" in set(inspect.signature(scheduler.step).parameters.keys()): - extra_step_kwargs.update( - eta=0.0, - ) - if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): - extra_step_kwargs.update( - generator=torch.Generator(device=unet.device).manual_seed(0), - ) - - num_warmup_steps = len(timesteps) - self.steps * scheduler.order - - # apply denoising_end - skipped_final_steps = int(round((1 - self.denoising_end) * self.steps)) - num_inference_steps = num_inference_steps - skipped_final_steps - timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps] - - if not context.services.configuration.sequential_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) - add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype) - add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype) - add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype) - latents = latents.to(device=unet.device, dtype=unet.dtype) - - with tqdm(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - - latent_model_input = scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - noise_pred = unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - cross_attention_kwargs=cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond) - # del noise_pred_uncond - # del noise_pred_text - - # if do_classifier_free_guidance and guidance_rescale > 0.0: - # # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - # noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): - progress_bar.update() - self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps) - # if callback is not None and i % callback_steps == 0: - # callback(i, t, latents) - else: - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype) - negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype) - add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype) - pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype) - prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype) - add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype) - latents = latents.to(device=unet.device, dtype=unet.dtype) - - with tqdm(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - # latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - - latent_model_input = scheduler.scale_model_input(latents, t) - - # import gc - # gc.collect() - # torch.cuda.empty_cache() - - # predict the noise residual - - added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_neg_time_ids} - noise_pred_uncond = unet( - latent_model_input, - t, - encoder_hidden_states=negative_prompt_embeds, - cross_attention_kwargs=cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids} - noise_pred_text = unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - cross_attention_kwargs=cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond) - - # del noise_pred_text - # del noise_pred_uncond - # import gc - # gc.collect() - # torch.cuda.empty_cache() - - # if do_classifier_free_guidance and guidance_rescale > 0.0: - # # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - # noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - # del noise_pred - # import gc - # gc.collect() - # torch.cuda.empty_cache() - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): - progress_bar.update() - self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps) - # if callback is not None and i % callback_steps == 0: - # callback(i, t, latents) - - ################# - - latents = latents.to("cpu") - torch.cuda.empty_cache() - - name = f"{context.graph_execution_state_id}__{self.id}" - context.services.latents.save(name, latents) - return build_latents_output(latents_name=name, latents=latents) - - -class SDXLLatentsToLatentsInvocation(BaseInvocation): - """Generates latents from conditionings.""" - - type: Literal["l2l_sdxl"] = "l2l_sdxl" - - # Inputs - # fmt: off - positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation") - negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation") - noise: Optional[LatentsField] = Field(description="The noise to use") - steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image") - cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", ) - scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" ) - unet: UNetField = Field(default=None, description="UNet submodel") - latents: Optional[LatentsField] = Field(description="Initial latents") - - denoising_start: float = Field(default=0.0, ge=0, le=1, description="") - denoising_end: float = Field(default=1.0, ge=0, le=1, description="") - - # control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use") - # seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", ) - # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'") - # fmt: on - - @validator("cfg_scale") - def ge_one(cls, v): - """validate that all cfg_scale values are >= 1""" - if isinstance(v, list): - for i in v: - if i < 1: - raise ValueError("cfg_scale must be greater than 1") - else: - if v < 1: - raise ValueError("cfg_scale must be greater than 1") - return v - - # Schema customisation - class Config(InvocationConfig): - schema_extra = { - "ui": { - "title": "SDXL Latents to Latents", - "tags": ["latents"], - "type_hints": { - "model": "model", - # "cfg_scale": "float", - "cfg_scale": "number", - }, - }, - } - - def dispatch_progress( - self, - context: InvocationContext, - source_node_id: str, - sample, - step, - total_steps, - ) -> None: - stable_diffusion_xl_step_callback( - context=context, - node=self.dict(), - source_node_id=source_node_id, - sample=sample, - step=step, - total_steps=total_steps, - ) - - # based on - # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375 - @torch.no_grad() - def invoke(self, context: InvocationContext) -> LatentsOutput: - graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id) - source_node_id = graph_execution_state.prepared_source_mapping[self.id] - latents = context.services.latents.get(self.latents.latents_name) - - positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name) - prompt_embeds = positive_cond_data.conditionings[0].embeds - pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds - add_time_ids = positive_cond_data.conditionings[0].add_time_ids - - negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name) - negative_prompt_embeds = negative_cond_data.conditionings[0].embeds - negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds - add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids - - scheduler = get_scheduler( - context=context, - scheduler_info=self.unet.scheduler, - scheduler_name=self.scheduler, - ) - - unet_info = context.services.model_manager.get_model( - **self.unet.unet.dict(), - context=context, - ) - - do_classifier_free_guidance = True - cross_attention_kwargs = None - with unet_info as unet: - # apply denoising_start - num_inference_steps = self.steps - scheduler.set_timesteps(num_inference_steps, device=unet.device) - - t_start = int(round(self.denoising_start * num_inference_steps)) - timesteps = scheduler.timesteps[t_start * scheduler.order :] - num_inference_steps = num_inference_steps - t_start - - # apply noise(if provided) - if self.noise is not None and timesteps.shape[0] > 0: - noise = context.services.latents.get(self.noise.latents_name) - latents = scheduler.add_noise(latents, noise, timesteps[:1]) - del noise - - # apply scheduler extra args - extra_step_kwargs = dict() - if "eta" in set(inspect.signature(scheduler.step).parameters.keys()): - extra_step_kwargs.update( - eta=0.0, - ) - if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): - extra_step_kwargs.update( - generator=torch.Generator(device=unet.device).manual_seed(0), - ) - - num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0) - - # apply denoising_end - skipped_final_steps = int(round((1 - self.denoising_end) * self.steps)) - num_inference_steps = num_inference_steps - skipped_final_steps - timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps] - - if not context.services.configuration.sequential_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0) - add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0) - - prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype) - add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype) - add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype) - latents = latents.to(device=unet.device, dtype=unet.dtype) - - with tqdm(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - - latent_model_input = scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} - noise_pred = unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - cross_attention_kwargs=cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond) - # del noise_pred_uncond - # del noise_pred_text - - # if do_classifier_free_guidance and guidance_rescale > 0.0: - # # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - # noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): - progress_bar.update() - self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps) - # if callback is not None and i % callback_steps == 0: - # callback(i, t, latents) - else: - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype) - negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype) - add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype) - pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype) - prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype) - add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype) - latents = latents.to(device=unet.device, dtype=unet.dtype) - - with tqdm(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - # latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - - latent_model_input = scheduler.scale_model_input(latents, t) - - # import gc - # gc.collect() - # torch.cuda.empty_cache() - - # predict the noise residual - - added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_time_ids} - noise_pred_uncond = unet( - latent_model_input, - t, - encoder_hidden_states=negative_prompt_embeds, - cross_attention_kwargs=cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids} - noise_pred_text = unet( - latent_model_input, - t, - encoder_hidden_states=prompt_embeds, - cross_attention_kwargs=cross_attention_kwargs, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - - # perform guidance - noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond) - - # del noise_pred_text - # del noise_pred_uncond - # import gc - # gc.collect() - # torch.cuda.empty_cache() - - # if do_classifier_free_guidance and guidance_rescale > 0.0: - # # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - # noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - - # del noise_pred - # import gc - # gc.collect() - # torch.cuda.empty_cache() - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): - progress_bar.update() - self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps) - # if callback is not None and i % callback_steps == 0: - # callback(i, t, latents) - - ################# - - latents = latents.to("cpu") - torch.cuda.empty_cache() - - name = f"{context.graph_execution_state_id}__{self.id}" - context.services.latents.save(name, latents) - return build_latents_output(latents_name=name, latents=latents) diff --git a/invokeai/app/services/board_image_record_storage.py b/invokeai/app/services/board_image_record_storage.py index f0007c8cef..03badf9866 100644 --- a/invokeai/app/services/board_image_record_storage.py +++ b/invokeai/app/services/board_image_record_storage.py @@ -25,7 +25,6 @@ class BoardImageRecordStorageBase(ABC): @abstractmethod def remove_image_from_board( self, - board_id: str, image_name: str, ) -> None: """Removes an image from a board.""" @@ -154,7 +153,6 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase): def remove_image_from_board( self, - board_id: str, image_name: str, ) -> None: try: @@ -162,9 +160,9 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase): self._cursor.execute( """--sql DELETE FROM board_images - WHERE board_id = ? AND image_name = ?; + WHERE image_name = ?; """, - (board_id, image_name), + (image_name,), ) self._conn.commit() except sqlite3.Error as e: diff --git a/invokeai/app/services/board_images.py b/invokeai/app/services/board_images.py index 22332d6c29..f41526bfa7 100644 --- a/invokeai/app/services/board_images.py +++ b/invokeai/app/services/board_images.py @@ -31,7 +31,6 @@ class BoardImagesServiceABC(ABC): @abstractmethod def remove_image_from_board( self, - board_id: str, image_name: str, ) -> None: """Removes an image from a board.""" @@ -93,10 +92,9 @@ class BoardImagesService(BoardImagesServiceABC): def remove_image_from_board( self, - board_id: str, image_name: str, ) -> None: - self._services.board_image_records.remove_image_from_board(board_id, image_name) + self._services.board_image_records.remove_image_from_board(image_name) def get_all_board_image_names_for_board( self, diff --git a/invokeai/app/services/config.py b/invokeai/app/services/config.py index a9a4a64f75..9ff43467c5 100644 --- a/invokeai/app/services/config.py +++ b/invokeai/app/services/config.py @@ -24,11 +24,10 @@ InvokeAI: sequential_guidance: false precision: float16 max_cache_size: 6 - max_vram_cache_size: 2.7 + max_vram_cache_size: 0.5 always_use_cpu: false free_gpu_mem: false Features: - restore: true esrgan: true patchmatch: true internet_available: true @@ -165,7 +164,7 @@ import pydoc import os import sys from argparse import ArgumentParser -from omegaconf import OmegaConf, DictConfig +from omegaconf import OmegaConf, DictConfig, ListConfig from pathlib import Path from pydantic import BaseSettings, Field, parse_obj_as from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args @@ -173,6 +172,7 @@ from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_ty INIT_FILE = Path("invokeai.yaml") DB_FILE = Path("invokeai.db") LEGACY_INIT_FILE = Path("invokeai.init") +DEFAULT_MAX_VRAM = 0.5 class InvokeAISettings(BaseSettings): @@ -189,7 +189,12 @@ class InvokeAISettings(BaseSettings): opt = parser.parse_args(argv) for name in self.__fields__: if name not in self._excluded(): - setattr(self, name, getattr(opt, name)) + value = getattr(opt, name) + if isinstance(value, ListConfig): + value = list(value) + elif isinstance(value, DictConfig): + value = dict(value) + setattr(self, name, value) def to_yaml(self) -> str: """ @@ -274,7 +279,7 @@ class InvokeAISettings(BaseSettings): @classmethod def _excluded(self) -> List[str]: # internal fields that shouldn't be exposed as command line options - return ["type", "initconf", "cached_root"] + return ["type", "initconf"] @classmethod def _excluded_from_yaml(self) -> List[str]: @@ -282,15 +287,10 @@ class InvokeAISettings(BaseSettings): return [ "type", "initconf", - "gpu_mem_reserved", - "max_loaded_models", "version", "from_file", "model", - "restore", "root", - "nsfw_checker", - "cached_root", ] class Config: @@ -356,7 +356,7 @@ class InvokeAISettings(BaseSettings): def _find_root() -> Path: venv = Path(os.environ.get("VIRTUAL_ENV") or ".") if os.environ.get("INVOKEAI_ROOT"): - root = Path(os.environ.get("INVOKEAI_ROOT")).resolve() + root = Path(os.environ["INVOKEAI_ROOT"]) elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]): root = (venv.parent).resolve() else: @@ -389,21 +389,17 @@ class InvokeAIAppConfig(InvokeAISettings): internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features') log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features') patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features') - restore : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED') always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance') free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance') - max_loaded_models : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='DEPRECATED') max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance') max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance') - gpu_mem_reserved : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED') - nsfw_checker : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED') precision : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance') sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance') xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance') tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance') - root : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths') + root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths') autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths') lora_dir : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths') embedding_dir : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths') @@ -415,8 +411,7 @@ class InvokeAIAppConfig(InvokeAISettings): outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths') from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths') use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths') - - model : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models') + ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features') log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=", "syslog=path|address:host:port", "http="', category="Logging") # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues @@ -424,9 +419,11 @@ class InvokeAIAppConfig(InvokeAISettings): log_level : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging") version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other") - cached_root : Path = Field(default=None, description="internal use only", category="DEPRECATED") # fmt: on + class Config: + validate_assignment = True + def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False): """ Update settings with contents of init file, environment, and @@ -472,15 +469,12 @@ class InvokeAIAppConfig(InvokeAISettings): """ Path to the runtime root directory """ - # we cache value of root to protect against it being '.' and the cwd changing - if self.cached_root: - root = self.cached_root - elif self.root: + if self.root: root = Path(self.root).expanduser().absolute() else: - root = self.find_root() - self.cached_root = root - return self.cached_root + root = self.find_root().expanduser().absolute() + self.root = root # insulate ourselves from relative paths that may change + return root @property def root_dir(self) -> Path: diff --git a/invokeai/app/services/default_graphs.py b/invokeai/app/services/default_graphs.py index cafb6f0339..caee5b631e 100644 --- a/invokeai/app/services/default_graphs.py +++ b/invokeai/app/services/default_graphs.py @@ -1,4 +1,4 @@ -from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation +from ..invocations.latent import LatentsToImageInvocation, DenoiseLatentsInvocation from ..invocations.image import ImageNSFWBlurInvocation from ..invocations.noise import NoiseInvocation from ..invocations.compel import CompelInvocation @@ -23,7 +23,7 @@ def create_text_to_image() -> LibraryGraph: "3": NoiseInvocation(id="3"), "4": CompelInvocation(id="4"), "5": CompelInvocation(id="5"), - "6": TextToLatentsInvocation(id="6"), + "6": DenoiseLatentsInvocation(id="6"), "7": LatentsToImageInvocation(id="7"), "8": ImageNSFWBlurInvocation(id="8"), }, diff --git a/invokeai/app/services/events.py b/invokeai/app/services/events.py index 30fa89bd29..a266fe4f18 100644 --- a/invokeai/app/services/events.py +++ b/invokeai/app/services/events.py @@ -35,6 +35,7 @@ class EventServiceBase: source_node_id: str, progress_image: Optional[ProgressImage], step: int, + order: int, total_steps: int, ) -> None: """Emitted when there is generation progress""" @@ -46,6 +47,7 @@ class EventServiceBase: source_node_id=source_node_id, progress_image=progress_image.dict() if progress_image is not None else None, step=step, + order=order, total_steps=total_steps, ), ) diff --git a/invokeai/app/services/images.py b/invokeai/app/services/images.py index f8376eb626..2240846dac 100644 --- a/invokeai/app/services/images.py +++ b/invokeai/app/services/images.py @@ -289,9 +289,10 @@ class ImageService(ImageServiceABC): def get_metadata(self, image_name: str) -> Optional[ImageMetadata]: try: image_record = self._services.image_records.get(image_name) + metadata = self._services.image_records.get_metadata(image_name) if not image_record.session_id: - return ImageMetadata() + return ImageMetadata(metadata=metadata) session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id) graph = None @@ -303,7 +304,6 @@ class ImageService(ImageServiceABC): self._services.logger.warn(f"Failed to parse session graph: {e}") graph = None - metadata = self._services.image_records.get_metadata(image_name) return ImageMetadata(graph=graph, metadata=metadata) except ImageRecordNotFoundException: self._services.logger.error("Image record not found") diff --git a/invokeai/app/services/invocation_services.py b/invokeai/app/services/invocation_services.py index 839df11469..9b890d1d69 100644 --- a/invokeai/app/services/invocation_services.py +++ b/invokeai/app/services/invocation_services.py @@ -34,6 +34,7 @@ class InvocationServices: logger: "Logger" model_manager: "ModelManagerServiceBase" processor: "InvocationProcessorABC" + performance_statistics: "InvocationStatsServiceBase" queue: "InvocationQueueABC" def __init__( @@ -50,6 +51,7 @@ class InvocationServices: logger: "Logger", model_manager: "ModelManagerServiceBase", processor: "InvocationProcessorABC", + performance_statistics: "InvocationStatsServiceBase", queue: "InvocationQueueABC", ): self.batch_manager = batch_manager @@ -65,4 +67,5 @@ class InvocationServices: self.logger = logger self.model_manager = model_manager self.processor = processor + self.performance_statistics = performance_statistics self.queue = queue diff --git a/invokeai/app/services/invocation_stats.py b/invokeai/app/services/invocation_stats.py new file mode 100644 index 0000000000..50320a6611 --- /dev/null +++ b/invokeai/app/services/invocation_stats.py @@ -0,0 +1,223 @@ +# Copyright 2023 Lincoln D. Stein +"""Utility to collect execution time and GPU usage stats on invocations in flight""" + +""" +Usage: + +statistics = InvocationStatsService(graph_execution_manager) +with statistics.collect_stats(invocation, graph_execution_state.id): + ... execute graphs... +statistics.log_stats() + +Typical output: +[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3 +[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used +[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s +[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G + +The abstract base class for this class is InvocationStatsServiceBase. An implementing class which +writes to the system log is stored in InvocationServices.performance_statistics. +""" + +import time +from abc import ABC, abstractmethod +from contextlib import AbstractContextManager +from dataclasses import dataclass, field +from typing import Dict + +import torch + +import invokeai.backend.util.logging as logger + +from ..invocations.baseinvocation import BaseInvocation +from .graph import GraphExecutionState +from .item_storage import ItemStorageABC + + +class InvocationStatsServiceBase(ABC): + "Abstract base class for recording node memory/time performance statistics" + + @abstractmethod + def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]): + """ + Initialize the InvocationStatsService and reset counters to zero + :param graph_execution_manager: Graph execution manager for this session + """ + pass + + @abstractmethod + def collect_stats( + self, + invocation: BaseInvocation, + graph_execution_state_id: str, + ) -> AbstractContextManager: + """ + Return a context object that will capture the statistics on the execution + of invocaation. Use with: to place around the part of the code that executes the invocation. + :param invocation: BaseInvocation object from the current graph. + :param graph_execution_state: GraphExecutionState object from the current session. + """ + pass + + @abstractmethod + def reset_stats(self, graph_execution_state_id: str): + """ + Reset all statistics for the indicated graph + :param graph_execution_state_id + """ + pass + + @abstractmethod + def reset_all_stats(self): + """Zero all statistics""" + pass + + @abstractmethod + def update_invocation_stats( + self, + graph_id: str, + invocation_type: str, + time_used: float, + vram_used: float, + ): + """ + Add timing information on execution of a node. Usually + used internally. + :param graph_id: ID of the graph that is currently executing + :param invocation_type: String literal type of the node + :param time_used: Time used by node's exection (sec) + :param vram_used: Maximum VRAM used during exection (GB) + """ + pass + + @abstractmethod + def log_stats(self): + """ + Write out the accumulated statistics to the log or somewhere else. + """ + pass + + +@dataclass +class NodeStats: + """Class for tracking execution stats of an invocation node""" + + calls: int = 0 + time_used: float = 0.0 # seconds + max_vram: float = 0.0 # GB + + +@dataclass +class NodeLog: + """Class for tracking node usage""" + + # {node_type => NodeStats} + nodes: Dict[str, NodeStats] = field(default_factory=dict) + + +class InvocationStatsService(InvocationStatsServiceBase): + """Accumulate performance information about a running graph. Collects time spent in each node, + as well as the maximum and current VRAM utilisation for CUDA systems""" + + def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]): + self.graph_execution_manager = graph_execution_manager + # {graph_id => NodeLog} + self._stats: Dict[str, NodeLog] = {} + + class StatsContext: + def __init__(self, invocation: BaseInvocation, graph_id: str, collector: "InvocationStatsServiceBase"): + self.invocation = invocation + self.collector = collector + self.graph_id = graph_id + self.start_time = 0 + + def __enter__(self): + self.start_time = time.time() + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats() + + def __exit__(self, *args): + self.collector.update_invocation_stats( + self.graph_id, + self.invocation.type, + time.time() - self.start_time, + torch.cuda.max_memory_allocated() / 1e9 if torch.cuda.is_available() else 0.0, + ) + + def collect_stats( + self, + invocation: BaseInvocation, + graph_execution_state_id: str, + ) -> StatsContext: + """ + Return a context object that will capture the statistics. + :param invocation: BaseInvocation object from the current graph. + :param graph_execution_state: GraphExecutionState object from the current session. + """ + if not self._stats.get(graph_execution_state_id): # first time we're seeing this + self._stats[graph_execution_state_id] = NodeLog() + return self.StatsContext(invocation, graph_execution_state_id, self) + + def reset_all_stats(self): + """Zero all statistics""" + self._stats = {} + + def reset_stats(self, graph_execution_id: str): + """Zero the statistics for the indicated graph.""" + try: + self._stats.pop(graph_execution_id) + except KeyError: + logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_id}") + + def update_invocation_stats(self, graph_id: str, invocation_type: str, time_used: float, vram_used: float): + """ + Add timing information on execution of a node. Usually + used internally. + :param graph_id: ID of the graph that is currently executing + :param invocation_type: String literal type of the node + :param time_used: Floating point seconds used by node's exection + """ + if not self._stats[graph_id].nodes.get(invocation_type): + self._stats[graph_id].nodes[invocation_type] = NodeStats() + stats = self._stats[graph_id].nodes[invocation_type] + stats.calls += 1 + stats.time_used += time_used + stats.max_vram = max(stats.max_vram, vram_used) + + def log_stats(self): + """ + Send the statistics to the system logger at the info level. + Stats will only be printed if when the execution of the graph + is complete. + """ + completed = set() + for graph_id, node_log in self._stats.items(): + current_graph_state = self.graph_execution_manager.get(graph_id) + if not current_graph_state.is_complete(): + continue + + total_time = 0 + logger.info(f"Graph stats: {graph_id}") + logger.info("Node Calls Seconds VRAM Used") + for node_type, stats in self._stats[graph_id].nodes.items(): + logger.info(f"{node_type:<20} {stats.calls:>5} {stats.time_used:7.3f}s {stats.max_vram:4.2f}G") + total_time += stats.time_used + + logger.info(f"TOTAL GRAPH EXECUTION TIME: {total_time:7.3f}s") + if torch.cuda.is_available(): + logger.info("Current VRAM utilization " + "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)) + + completed.add(graph_id) + + for graph_id in completed: + del self._stats[graph_id] diff --git a/invokeai/app/services/model_manager_service.py b/invokeai/app/services/model_manager_service.py index c84cc3d189..fd14e26364 100644 --- a/invokeai/app/services/model_manager_service.py +++ b/invokeai/app/services/model_manager_service.py @@ -3,9 +3,10 @@ from __future__ import annotations from abc import ABC, abstractmethod +from logging import Logger from pathlib import Path from pydantic import Field -from typing import Optional, Union, Callable, List, Tuple, TYPE_CHECKING +from typing import Literal, Optional, Union, Callable, List, Tuple, TYPE_CHECKING from types import ModuleType from invokeai.backend.model_management import ( @@ -193,7 +194,7 @@ class ModelManagerServiceBase(ABC): self, model_name: str, base_model: BaseModelType, - model_type: Union[ModelType.Main, ModelType.Vae], + model_type: Literal[ModelType.Main, ModelType.Vae], ) -> AddModelResult: """ Convert a checkpoint file into a diffusers folder, deleting the cached @@ -292,7 +293,7 @@ class ModelManagerService(ModelManagerServiceBase): def __init__( self, config: InvokeAIAppConfig, - logger: ModuleType, + logger: Logger, ): """ Initialize with the path to the models.yaml config file. @@ -396,7 +397,7 @@ class ModelManagerService(ModelManagerServiceBase): model_type, ) - def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict: + def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]: """ Given a model name returns a dict-like (OmegaConf) object describing it. """ @@ -416,7 +417,7 @@ class ModelManagerService(ModelManagerServiceBase): """ return self.mgr.list_models(base_model, model_type) - def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict: + def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]: """ Return information about the model using the same format as list_models() """ @@ -429,7 +430,7 @@ class ModelManagerService(ModelManagerServiceBase): model_type: ModelType, model_attributes: dict, clobber: bool = False, - ) -> None: + ) -> AddModelResult: """ Update the named model with a dictionary of attributes. Will fail with an assertion error if the name already exists. Pass clobber=True to overwrite. @@ -478,7 +479,7 @@ class ModelManagerService(ModelManagerServiceBase): self, model_name: str, base_model: BaseModelType, - model_type: Union[ModelType.Main, ModelType.Vae], + model_type: Literal[ModelType.Main, ModelType.Vae], convert_dest_directory: Optional[Path] = Field( default=None, description="Optional directory location for merged model" ), @@ -573,9 +574,9 @@ class ModelManagerService(ModelManagerServiceBase): default=None, description="Base model shared by all models to be merged" ), merged_model_name: str = Field(default=None, description="Name of destination model after merging"), - alpha: Optional[float] = 0.5, + alpha: float = 0.5, interp: Optional[MergeInterpolationMethod] = None, - force: Optional[bool] = False, + force: bool = False, merge_dest_directory: Optional[Path] = Field( default=None, description="Optional directory location for merged model" ), @@ -633,8 +634,8 @@ class ModelManagerService(ModelManagerServiceBase): model_name: str, base_model: BaseModelType, model_type: ModelType, - new_name: str = None, - new_base: BaseModelType = None, + new_name: Optional[str] = None, + new_base: Optional[BaseModelType] = None, ): """ Rename the indicated model. Can provide a new name and/or a new base. diff --git a/invokeai/app/services/models/board_image.py b/invokeai/app/services/models/board_image.py new file mode 100644 index 0000000000..fe585215f3 --- /dev/null +++ b/invokeai/app/services/models/board_image.py @@ -0,0 +1,8 @@ +from pydantic import Field + +from invokeai.app.util.model_exclude_null import BaseModelExcludeNull + + +class BoardImage(BaseModelExcludeNull): + board_id: str = Field(description="The id of the board") + image_name: str = Field(description="The name of the image") diff --git a/invokeai/app/services/models/board_record.py b/invokeai/app/services/models/board_record.py index 658698e794..53fa299faf 100644 --- a/invokeai/app/services/models/board_record.py +++ b/invokeai/app/services/models/board_record.py @@ -1,10 +1,11 @@ from typing import Optional, Union from datetime import datetime -from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr +from pydantic import Field from invokeai.app.util.misc import get_iso_timestamp +from invokeai.app.util.model_exclude_null import BaseModelExcludeNull -class BoardRecord(BaseModel): +class BoardRecord(BaseModelExcludeNull): """Deserialized board record.""" board_id: str = Field(description="The unique ID of the board.") diff --git a/invokeai/app/services/models/image_record.py b/invokeai/app/services/models/image_record.py index a105d03ba8..294b760630 100644 --- a/invokeai/app/services/models/image_record.py +++ b/invokeai/app/services/models/image_record.py @@ -1,13 +1,14 @@ import datetime from typing import Optional, Union -from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr +from pydantic import Extra, Field, StrictBool, StrictStr from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.util.misc import get_iso_timestamp +from invokeai.app.util.model_exclude_null import BaseModelExcludeNull -class ImageRecord(BaseModel): +class ImageRecord(BaseModelExcludeNull): """Deserialized image record without metadata.""" image_name: str = Field(description="The unique name of the image.") @@ -40,7 +41,7 @@ class ImageRecord(BaseModel): """The node ID that generated this image, if it is a generated image.""" -class ImageRecordChanges(BaseModel, extra=Extra.forbid): +class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid): """A set of changes to apply to an image record. Only limited changes are valid: @@ -60,7 +61,7 @@ class ImageRecordChanges(BaseModel, extra=Extra.forbid): """The image's new `is_intermediate` flag.""" -class ImageUrlsDTO(BaseModel): +class ImageUrlsDTO(BaseModelExcludeNull): """The URLs for an image and its thumbnail.""" image_name: str = Field(description="The unique name of the image.") @@ -76,11 +77,15 @@ class ImageDTO(ImageRecord, ImageUrlsDTO): board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.") """The id of the board the image belongs to, if one exists.""" + pass def image_record_to_dto( - image_record: ImageRecord, image_url: str, thumbnail_url: str, board_id: Optional[str] + image_record: ImageRecord, + image_url: str, + thumbnail_url: str, + board_id: Optional[str], ) -> ImageDTO: """Converts an image record to an image DTO.""" return ImageDTO( diff --git a/invokeai/app/services/processor.py b/invokeai/app/services/processor.py index 50fe217e05..41170a304b 100644 --- a/invokeai/app/services/processor.py +++ b/invokeai/app/services/processor.py @@ -1,14 +1,15 @@ import time import traceback -from threading import Event, Thread, BoundedSemaphore - -from ..invocations.baseinvocation import InvocationContext -from .invocation_queue import InvocationQueueItem -from .invoker import InvocationProcessorABC, Invoker -from ..models.exceptions import CanceledException +from threading import BoundedSemaphore, Event, Thread import invokeai.backend.util.logging as logger +from ..invocations.baseinvocation import InvocationContext +from ..models.exceptions import CanceledException +from .invocation_queue import InvocationQueueItem +from .invocation_stats import InvocationStatsServiceBase +from .invoker import InvocationProcessorABC, Invoker + class DefaultInvocationProcessor(InvocationProcessorABC): __invoker_thread: Thread @@ -35,6 +36,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC): def __process(self, stop_event: Event): try: self.__threadLimit.acquire() + statistics: InvocationStatsServiceBase = self.__invoker.services.performance_statistics + while not stop_event.is_set(): try: queue_item: InvocationQueueItem = self.__invoker.services.queue.get() @@ -83,35 +86,38 @@ class DefaultInvocationProcessor(InvocationProcessorABC): # Invoke try: - outputs = invocation.invoke( - InvocationContext( - services=self.__invoker.services, - graph_execution_state_id=graph_execution_state.id, + with statistics.collect_stats(invocation, graph_execution_state.id): + outputs = invocation.invoke( + InvocationContext( + services=self.__invoker.services, + graph_execution_state_id=graph_execution_state.id, + ) ) - ) - # Check queue to see if this is canceled, and skip if so - if self.__invoker.services.queue.is_canceled(graph_execution_state.id): - continue + # Check queue to see if this is canceled, and skip if so + if self.__invoker.services.queue.is_canceled(graph_execution_state.id): + continue - # Save outputs and history - graph_execution_state.complete(invocation.id, outputs) + # Save outputs and history + graph_execution_state.complete(invocation.id, outputs) - # Save the state changes - self.__invoker.services.graph_execution_manager.set(graph_execution_state) + # Save the state changes + self.__invoker.services.graph_execution_manager.set(graph_execution_state) - # Send complete event - self.__invoker.services.events.emit_invocation_complete( - graph_execution_state_id=graph_execution_state.id, - node=invocation.dict(), - source_node_id=source_node_id, - result=outputs.dict(), - ) + # Send complete event + self.__invoker.services.events.emit_invocation_complete( + graph_execution_state_id=graph_execution_state.id, + node=invocation.dict(), + source_node_id=source_node_id, + result=outputs.dict(), + ) + statistics.log_stats() except KeyboardInterrupt: pass except CanceledException: + statistics.reset_stats(graph_execution_state.id) pass except Exception as e: @@ -133,7 +139,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC): error_type=e.__class__.__name__, error=error, ) - + statistics.reset_stats(graph_execution_state.id) pass # Check queue to see if this is canceled, and skip if so diff --git a/invokeai/app/services/urls.py b/invokeai/app/services/urls.py index 73d8ddadf4..7688b3bdd3 100644 --- a/invokeai/app/services/urls.py +++ b/invokeai/app/services/urls.py @@ -20,6 +20,6 @@ class LocalUrlService(UrlServiceBase): # These paths are determined by the routes in invokeai/app/api/routers/images.py if thumbnail: - return f"{self._base_url}/images/{image_basename}/thumbnail" + return f"{self._base_url}/images/i/{image_basename}/thumbnail" - return f"{self._base_url}/images/{image_basename}/full" + return f"{self._base_url}/images/i/{image_basename}/full" diff --git a/invokeai/app/util/misc.py b/invokeai/app/util/misc.py index 503f3af4c8..8702d86c67 100644 --- a/invokeai/app/util/misc.py +++ b/invokeai/app/util/misc.py @@ -18,5 +18,5 @@ SEED_MAX = np.iinfo(np.uint32).max def get_random_seed(): - rng = np.random.default_rng(seed=0) + rng = np.random.default_rng(seed=None) return int(rng.integers(0, SEED_MAX)) diff --git a/invokeai/app/util/model_exclude_null.py b/invokeai/app/util/model_exclude_null.py new file mode 100644 index 0000000000..d864b8fab8 --- /dev/null +++ b/invokeai/app/util/model_exclude_null.py @@ -0,0 +1,23 @@ +from typing import Any +from pydantic import BaseModel + + +""" +We want to exclude null values from objects that make their way to the client. + +Unfortunately there is no built-in way to do this in pydantic, so we need to override the default +dict method to do this. + +From https://github.com/tiangolo/fastapi/discussions/8882#discussioncomment-5154541 +""" + + +class BaseModelExcludeNull(BaseModel): + def dict(self, *args, **kwargs) -> dict[str, Any]: + """ + Override the default dict method to exclude None values in the response + """ + kwargs.pop("exclude_none", None) + return super().dict(*args, exclude_none=True, **kwargs) + + pass diff --git a/invokeai/app/util/step_callback.py b/invokeai/app/util/step_callback.py index 994d83e705..3cadfa1c12 100644 --- a/invokeai/app/util/step_callback.py +++ b/invokeai/app/util/step_callback.py @@ -4,9 +4,9 @@ from invokeai.app.models.exceptions import CanceledException from invokeai.app.models.image import ProgressImage from ..invocations.baseinvocation import InvocationContext from ...backend.util.util import image_to_dataURL -from ...backend.generator.base import Generator from ...backend.stable_diffusion import PipelineIntermediateState from invokeai.app.services.config import InvokeAIAppConfig +from ...backend.model_management.models import BaseModelType def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None): @@ -29,6 +29,7 @@ def stable_diffusion_step_callback( intermediate_state: PipelineIntermediateState, node: dict, source_node_id: str, + base_model: BaseModelType, ): if context.services.queue.is_canceled(context.graph_execution_state_id): raise CanceledException @@ -56,23 +57,50 @@ def stable_diffusion_step_callback( # TODO: only output a preview image when requested - # origingally adapted from code by @erucipe and @keturn here: - # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7 + if base_model in [BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner]: + # fast latents preview matrix for sdxl + # generated by @StAlKeR7779 + sdxl_latent_rgb_factors = torch.tensor( + [ + # R G B + [0.3816, 0.4930, 0.5320], + [-0.3753, 0.1631, 0.1739], + [0.1770, 0.3588, -0.2048], + [-0.4350, -0.2644, -0.4289], + ], + dtype=sample.dtype, + device=sample.device, + ) - # these updated numbers for v1.5 are from @torridgristle - v1_5_latent_rgb_factors = torch.tensor( - [ - # R G B - [0.3444, 0.1385, 0.0670], # L1 - [0.1247, 0.4027, 0.1494], # L2 - [-0.3192, 0.2513, 0.2103], # L3 - [-0.1307, -0.1874, -0.7445], # L4 - ], - dtype=sample.dtype, - device=sample.device, - ) + sdxl_smooth_matrix = torch.tensor( + [ + [0.0358, 0.0964, 0.0358], + [0.0964, 0.4711, 0.0964], + [0.0358, 0.0964, 0.0358], + ], + dtype=sample.dtype, + device=sample.device, + ) - image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors) + image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix) + else: + # origingally adapted from code by @erucipe and @keturn here: + # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7 + + # these updated numbers for v1.5 are from @torridgristle + v1_5_latent_rgb_factors = torch.tensor( + [ + # R G B + [0.3444, 0.1385, 0.0670], # L1 + [0.1247, 0.4027, 0.1494], # L2 + [-0.3192, 0.2513, 0.2103], # L3 + [-0.1307, -0.1874, -0.7445], # L4 + ], + dtype=sample.dtype, + device=sample.device, + ) + + image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors) (width, height) = image.size width *= 8 @@ -86,59 +114,6 @@ def stable_diffusion_step_callback( source_node_id=source_node_id, progress_image=ProgressImage(width=width, height=height, dataURL=dataURL), step=intermediate_state.step, - total_steps=node["steps"], - ) - - -def stable_diffusion_xl_step_callback( - context: InvocationContext, - node: dict, - source_node_id: str, - sample, - step, - total_steps, -): - if context.services.queue.is_canceled(context.graph_execution_state_id): - raise CanceledException - - sdxl_latent_rgb_factors = torch.tensor( - [ - # R G B - [0.3816, 0.4930, 0.5320], - [-0.3753, 0.1631, 0.1739], - [0.1770, 0.3588, -0.2048], - [-0.4350, -0.2644, -0.4289], - ], - dtype=sample.dtype, - device=sample.device, - ) - - sdxl_smooth_matrix = torch.tensor( - [ - # [ 0.0478, 0.1285, 0.0478], - # [ 0.1285, 0.2948, 0.1285], - # [ 0.0478, 0.1285, 0.0478], - [0.0358, 0.0964, 0.0358], - [0.0964, 0.4711, 0.0964], - [0.0358, 0.0964, 0.0358], - ], - dtype=sample.dtype, - device=sample.device, - ) - - image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix) - - (width, height) = image.size - width *= 8 - height *= 8 - - dataURL = image_to_dataURL(image, image_format="JPEG") - - context.services.events.emit_generator_progress( - graph_execution_state_id=context.graph_execution_state_id, - node=node, - source_node_id=source_node_id, - progress_image=ProgressImage(width=width, height=height, dataURL=dataURL), - step=step, - total_steps=total_steps, + order=intermediate_state.order, + total_steps=intermediate_state.total_steps, ) diff --git a/invokeai/backend/__init__.py b/invokeai/backend/__init__.py index aa2a1f1ca6..a4e6f180ae 100644 --- a/invokeai/backend/__init__.py +++ b/invokeai/backend/__init__.py @@ -1,6 +1,5 @@ """ Initialization file for invokeai.backend """ -from .generator import InvokeAIGeneratorBasicParams, InvokeAIGenerator, InvokeAIGeneratorOutput, Img2Img, Inpaint from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo from .model_management.models import SilenceWarnings diff --git a/invokeai/backend/generator/__init__.py b/invokeai/backend/generator/__init__.py deleted file mode 100644 index 8a7f1c9167..0000000000 --- a/invokeai/backend/generator/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -Initialization file for the invokeai.generator package -""" -from .base import ( - InvokeAIGenerator, - InvokeAIGeneratorBasicParams, - InvokeAIGeneratorOutput, - Img2Img, - Inpaint, - Generator, -) -from .inpaint import infill_methods diff --git a/invokeai/backend/generator/base.py b/invokeai/backend/generator/base.py deleted file mode 100644 index af3231a7d1..0000000000 --- a/invokeai/backend/generator/base.py +++ /dev/null @@ -1,559 +0,0 @@ -""" -Base class for invokeai.backend.generator.* -including img2img, txt2img, and inpaint -""" -from __future__ import annotations - -import itertools -import dataclasses -import diffusers -import os -import random -import traceback -from abc import ABCMeta -from argparse import Namespace -from contextlib import nullcontext - -import cv2 -import numpy as np -import torch -from PIL import Image, ImageChops, ImageFilter -from accelerate.utils import set_seed -from diffusers import DiffusionPipeline -from tqdm import trange -from typing import Callable, List, Iterator, Optional, Type, Union -from dataclasses import dataclass, field -from diffusers.schedulers import SchedulerMixin as Scheduler - -import invokeai.backend.util.logging as logger -from ..image_util import configure_model_padding -from ..util.util import rand_perlin_2d -from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline -from ..stable_diffusion.schedulers import SCHEDULER_MAP - -downsampling = 8 - - -@dataclass -class InvokeAIGeneratorBasicParams: - seed: Optional[int] = None - width: int = 512 - height: int = 512 - cfg_scale: float = 7.5 - steps: int = 20 - ddim_eta: float = 0.0 - scheduler: str = "ddim" - precision: str = "float16" - perlin: float = 0.0 - threshold: float = 0.0 - seamless: bool = False - seamless_axes: List[str] = field(default_factory=lambda: ["x", "y"]) - h_symmetry_time_pct: Optional[float] = None - v_symmetry_time_pct: Optional[float] = None - variation_amount: float = 0.0 - with_variations: list = field(default_factory=list) - - -@dataclass -class InvokeAIGeneratorOutput: - """ - InvokeAIGeneratorOutput is a dataclass that contains the outputs of a generation - operation, including the image, its seed, the model name used to generate the image - and the model hash, as well as all the generate() parameters that went into - generating the image (in .params, also available as attributes) - """ - - image: Image.Image - seed: int - model_hash: str - attention_maps_images: List[Image.Image] - params: Namespace - - -# we are interposing a wrapper around the original Generator classes so that -# old code that calls Generate will continue to work. -class InvokeAIGenerator(metaclass=ABCMeta): - def __init__( - self, - model_info: dict, - params: InvokeAIGeneratorBasicParams = InvokeAIGeneratorBasicParams(), - **kwargs, - ): - self.model_info = model_info - self.params = params - self.kwargs = kwargs - - def generate( - self, - conditioning: tuple, - scheduler, - callback: Optional[Callable] = None, - step_callback: Optional[Callable] = None, - iterations: int = 1, - **keyword_args, - ) -> Iterator[InvokeAIGeneratorOutput]: - """ - Return an iterator across the indicated number of generations. - Each time the iterator is called it will return an InvokeAIGeneratorOutput - object. Use like this: - - outputs = txt2img.generate(prompt='banana sushi', iterations=5) - for result in outputs: - print(result.image, result.seed) - - In the typical case of wanting to get just a single image, iterations - defaults to 1 and do: - - output = next(txt2img.generate(prompt='banana sushi') - - Pass None to get an infinite iterator. - - outputs = txt2img.generate(prompt='banana sushi', iterations=None) - for o in outputs: - print(o.image, o.seed) - - """ - generator_args = dataclasses.asdict(self.params) - generator_args.update(keyword_args) - - model_info = self.model_info - model_name = model_info.name - model_hash = model_info.hash - with model_info.context as model: - gen_class = self._generator_class() - generator = gen_class(model, self.params.precision, **self.kwargs) - if self.params.variation_amount > 0: - generator.set_variation( - generator_args.get("seed"), - generator_args.get("variation_amount"), - generator_args.get("with_variations"), - ) - - if isinstance(model, DiffusionPipeline): - for component in [model.unet, model.vae]: - configure_model_padding( - component, generator_args.get("seamless", False), generator_args.get("seamless_axes") - ) - else: - configure_model_padding( - model, generator_args.get("seamless", False), generator_args.get("seamless_axes") - ) - - iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1) - for i in iteration_count: - results = generator.generate( - conditioning=conditioning, - step_callback=step_callback, - sampler=scheduler, - **generator_args, - ) - output = InvokeAIGeneratorOutput( - image=results[0][0], - seed=results[0][1], - attention_maps_images=results[0][2], - model_hash=model_hash, - params=Namespace(model_name=model_name, **generator_args), - ) - if callback: - callback(output) - yield output - - @classmethod - def schedulers(self) -> List[str]: - """ - Return list of all the schedulers that we currently handle. - """ - return list(SCHEDULER_MAP.keys()) - - def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]): - return generator_class(model, self.params.precision) - - @classmethod - def _generator_class(cls) -> Type[Generator]: - """ - In derived classes return the name of the generator to apply. - If you don't override will return the name of the derived - class, which nicely parallels the generator class names. - """ - return Generator - - -# ------------------------------------ -class Img2Img(InvokeAIGenerator): - def generate( - self, init_image: Union[Image.Image, torch.FloatTensor], strength: float = 0.75, **keyword_args - ) -> Iterator[InvokeAIGeneratorOutput]: - return super().generate(init_image=init_image, strength=strength, **keyword_args) - - @classmethod - def _generator_class(cls): - from .img2img import Img2Img - - return Img2Img - - -# ------------------------------------ -# Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff -class Inpaint(Img2Img): - def generate( - self, - mask_image: Union[Image.Image, torch.FloatTensor], - # Seam settings - when 0, doesn't fill seam - seam_size: int = 96, - seam_blur: int = 16, - seam_strength: float = 0.7, - seam_steps: int = 30, - tile_size: int = 32, - inpaint_replace=False, - infill_method=None, - inpaint_width=None, - inpaint_height=None, - inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF), - **keyword_args, - ) -> Iterator[InvokeAIGeneratorOutput]: - return super().generate( - mask_image=mask_image, - seam_size=seam_size, - seam_blur=seam_blur, - seam_strength=seam_strength, - seam_steps=seam_steps, - tile_size=tile_size, - inpaint_replace=inpaint_replace, - infill_method=infill_method, - inpaint_width=inpaint_width, - inpaint_height=inpaint_height, - inpaint_fill=inpaint_fill, - **keyword_args, - ) - - @classmethod - def _generator_class(cls): - from .inpaint import Inpaint - - return Inpaint - - -class Generator: - downsampling_factor: int - latent_channels: int - precision: str - model: DiffusionPipeline - - def __init__(self, model: DiffusionPipeline, precision: str, **kwargs): - self.model = model - self.precision = precision - self.seed = None - self.latent_channels = model.unet.config.in_channels - self.downsampling_factor = downsampling # BUG: should come from model or config - self.perlin = 0.0 - self.threshold = 0 - self.variation_amount = 0 - self.with_variations = [] - self.use_mps_noise = False - self.free_gpu_mem = None - - # this is going to be overridden in img2img.py, txt2img.py and inpaint.py - def get_make_image(self, **kwargs): - """ - Returns a function returning an image derived from the prompt and the initial image - Return value depends on the seed at the time you call it - """ - raise NotImplementedError("image_iterator() must be implemented in a descendent class") - - def set_variation(self, seed, variation_amount, with_variations): - self.seed = seed - self.variation_amount = variation_amount - self.with_variations = with_variations - - def generate( - self, - width, - height, - sampler, - init_image=None, - iterations=1, - seed=None, - image_callback=None, - step_callback=None, - threshold=0.0, - perlin=0.0, - h_symmetry_time_pct=None, - v_symmetry_time_pct=None, - free_gpu_mem: bool = False, - **kwargs, - ): - scope = nullcontext - self.free_gpu_mem = free_gpu_mem - attention_maps_images = [] - attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image()) - make_image = self.get_make_image( - sampler=sampler, - init_image=init_image, - width=width, - height=height, - step_callback=step_callback, - threshold=threshold, - perlin=perlin, - h_symmetry_time_pct=h_symmetry_time_pct, - v_symmetry_time_pct=v_symmetry_time_pct, - attention_maps_callback=attention_maps_callback, - **kwargs, - ) - results = [] - seed = seed if seed is not None and seed >= 0 else self.new_seed() - first_seed = seed - seed, initial_noise = self.generate_initial_noise(seed, width, height) - - # There used to be an additional self.model.ema_scope() here, but it breaks - # the inpaint-1.5 model. Not sure what it did.... ? - with scope(self.model.device.type): - for n in trange(iterations, desc="Generating"): - x_T = None - if self.variation_amount > 0: - set_seed(seed) - target_noise = self.get_noise(width, height) - x_T = self.slerp(self.variation_amount, initial_noise, target_noise) - elif initial_noise is not None: - # i.e. we specified particular variations - x_T = initial_noise - else: - set_seed(seed) - try: - x_T = self.get_noise(width, height) - except: - logger.error("An error occurred while getting initial noise") - print(traceback.format_exc()) - - # Pass on the seed in case a layer beneath us needs to generate noise on its own. - image = make_image(x_T, seed) - - results.append([image, seed, attention_maps_images]) - - if image_callback is not None: - attention_maps_image = None if len(attention_maps_images) == 0 else attention_maps_images[-1] - image_callback( - image, - seed, - first_seed=first_seed, - attention_maps_image=attention_maps_image, - ) - - seed = self.new_seed() - - # Free up memory from the last generation. - clear_cuda_cache = kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None - if clear_cuda_cache is not None: - clear_cuda_cache() - - return results - - def sample_to_image(self, samples) -> Image.Image: - """ - Given samples returned from a sampler, converts - it into a PIL Image - """ - with torch.inference_mode(): - image = self.model.decode_latents(samples) - return self.model.numpy_to_pil(image)[0] - - def repaste_and_color_correct( - self, - result: Image.Image, - init_image: Image.Image, - init_mask: Image.Image, - mask_blur_radius: int = 8, - ) -> Image.Image: - if init_image is None or init_mask is None: - return result - - # Get the original alpha channel of the mask if there is one. - # Otherwise it is some other black/white image format ('1', 'L' or 'RGB') - pil_init_mask = init_mask.getchannel("A") if init_mask.mode == "RGBA" else init_mask.convert("L") - pil_init_image = init_image.convert("RGBA") # Add an alpha channel if one doesn't exist - - # Build an image with only visible pixels from source to use as reference for color-matching. - init_rgb_pixels = np.asarray(init_image.convert("RGB"), dtype=np.uint8) - init_a_pixels = np.asarray(pil_init_image.getchannel("A"), dtype=np.uint8) - init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8) - - # Get numpy version of result - np_image = np.asarray(result, dtype=np.uint8) - - # Mask and calculate mean and standard deviation - mask_pixels = init_a_pixels * init_mask_pixels > 0 - np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :] - np_image_masked = np_image[mask_pixels, :] - - if np_init_rgb_pixels_masked.size > 0: - init_means = np_init_rgb_pixels_masked.mean(axis=0) - init_std = np_init_rgb_pixels_masked.std(axis=0) - gen_means = np_image_masked.mean(axis=0) - gen_std = np_image_masked.std(axis=0) - - # Color correct - np_matched_result = np_image.copy() - np_matched_result[:, :, :] = ( - ( - ( - (np_matched_result[:, :, :].astype(np.float32) - gen_means[None, None, :]) - / gen_std[None, None, :] - ) - * init_std[None, None, :] - + init_means[None, None, :] - ) - .clip(0, 255) - .astype(np.uint8) - ) - matched_result = Image.fromarray(np_matched_result, mode="RGB") - else: - matched_result = Image.fromarray(np_image, mode="RGB") - - # Blur the mask out (into init image) by specified amount - if mask_blur_radius > 0: - nm = np.asarray(pil_init_mask, dtype=np.uint8) - nmd = cv2.erode( - nm, - kernel=np.ones((3, 3), dtype=np.uint8), - iterations=int(mask_blur_radius / 2), - ) - pmd = Image.fromarray(nmd, mode="L") - blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius)) - else: - blurred_init_mask = pil_init_mask - - multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1]) - - # Paste original on color-corrected generation (using blurred mask) - matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask) - return matched_result - - @staticmethod - def sample_to_lowres_estimated_image(samples): - # origingally adapted from code by @erucipe and @keturn here: - # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7 - - # these updated numbers for v1.5 are from @torridgristle - v1_5_latent_rgb_factors = torch.tensor( - [ - # R G B - [0.3444, 0.1385, 0.0670], # L1 - [0.1247, 0.4027, 0.1494], # L2 - [-0.3192, 0.2513, 0.2103], # L3 - [-0.1307, -0.1874, -0.7445], # L4 - ], - dtype=samples.dtype, - device=samples.device, - ) - - latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors - latents_ubyte = ( - ((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte() # change scale from -1..1 to 0..1 # to 0..255 - ).cpu() - - return Image.fromarray(latents_ubyte.numpy()) - - def generate_initial_noise(self, seed, width, height): - initial_noise = None - if self.variation_amount > 0 or len(self.with_variations) > 0: - # use fixed initial noise plus random noise per iteration - set_seed(seed) - initial_noise = self.get_noise(width, height) - for v_seed, v_weight in self.with_variations: - seed = v_seed - set_seed(seed) - next_noise = self.get_noise(width, height) - initial_noise = self.slerp(v_weight, initial_noise, next_noise) - if self.variation_amount > 0: - random.seed() # reset RNG to an actually random state, so we can get a random seed for variations - seed = random.randrange(0, np.iinfo(np.uint32).max) - return (seed, initial_noise) - - def get_perlin_noise(self, width, height): - fixdevice = "cpu" if (self.model.device.type == "mps") else self.model.device - # limit noise to only the diffusion image channels, not the mask channels - input_channels = min(self.latent_channels, 4) - # round up to the nearest block of 8 - temp_width = int((width + 7) / 8) * 8 - temp_height = int((height + 7) / 8) * 8 - noise = torch.stack( - [ - rand_perlin_2d((temp_height, temp_width), (8, 8), device=self.model.device).to(fixdevice) - for _ in range(input_channels) - ], - dim=0, - ).to(self.model.device) - return noise[0:4, 0:height, 0:width] - - def new_seed(self): - self.seed = random.randrange(0, np.iinfo(np.uint32).max) - return self.seed - - def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995): - """ - Spherical linear interpolation - Args: - t (float/np.ndarray): Float value between 0.0 and 1.0 - v0 (np.ndarray): Starting vector - v1 (np.ndarray): Final vector - DOT_THRESHOLD (float): Threshold for considering the two vectors as - colineal. Not recommended to alter this. - Returns: - v2 (np.ndarray): Interpolation vector between v0 and v1 - """ - inputs_are_torch = False - if not isinstance(v0, np.ndarray): - inputs_are_torch = True - v0 = v0.detach().cpu().numpy() - if not isinstance(v1, np.ndarray): - inputs_are_torch = True - v1 = v1.detach().cpu().numpy() - - dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1))) - if np.abs(dot) > DOT_THRESHOLD: - v2 = (1 - t) * v0 + t * v1 - else: - theta_0 = np.arccos(dot) - sin_theta_0 = np.sin(theta_0) - theta_t = theta_0 * t - sin_theta_t = np.sin(theta_t) - s0 = np.sin(theta_0 - theta_t) / sin_theta_0 - s1 = sin_theta_t / sin_theta_0 - v2 = s0 * v0 + s1 * v1 - - if inputs_are_torch: - v2 = torch.from_numpy(v2).to(self.model.device) - - return v2 - - # this is a handy routine for debugging use. Given a generated sample, - # convert it into a PNG image and store it at the indicated path - def save_sample(self, sample, filepath): - image = self.sample_to_image(sample) - dirname = os.path.dirname(filepath) or "." - if not os.path.exists(dirname): - logger.info(f"creating directory {dirname}") - os.makedirs(dirname, exist_ok=True) - image.save(filepath, "PNG") - - def torch_dtype(self) -> torch.dtype: - return torch.float16 if self.precision == "float16" else torch.float32 - - # returns a tensor filled with random numbers from a normal distribution - def get_noise(self, width, height): - device = self.model.device - # limit noise to only the diffusion image channels, not the mask channels - input_channels = min(self.latent_channels, 4) - x = torch.randn( - [ - 1, - input_channels, - height // self.downsampling_factor, - width // self.downsampling_factor, - ], - dtype=self.torch_dtype(), - device=device, - ) - if self.perlin > 0.0: - perlin_noise = self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor) - x = (1 - self.perlin) * x + self.perlin * perlin_noise - return x diff --git a/invokeai/backend/generator/img2img.py b/invokeai/backend/generator/img2img.py deleted file mode 100644 index 5490b2325c..0000000000 --- a/invokeai/backend/generator/img2img.py +++ /dev/null @@ -1,92 +0,0 @@ -""" -invokeai.backend.generator.img2img descends from .generator -""" -from typing import Optional - -import torch -from accelerate.utils import set_seed -from diffusers import logging - -from ..stable_diffusion import ( - ConditioningData, - PostprocessingSettings, - StableDiffusionGeneratorPipeline, -) -from .base import Generator - - -class Img2Img(Generator): - def __init__(self, model, precision): - super().__init__(model, precision) - self.init_latent = None # by get_noise() - - def get_make_image( - self, - sampler, - steps, - cfg_scale, - ddim_eta, - conditioning, - init_image, - strength, - step_callback=None, - threshold=0.0, - warmup=0.2, - perlin=0.0, - h_symmetry_time_pct=None, - v_symmetry_time_pct=None, - attention_maps_callback=None, - **kwargs, - ): - """ - Returns a function returning an image derived from the prompt and the initial image - Return value depends on the seed at the time you call it. - """ - self.perlin = perlin - - # noinspection PyTypeChecker - pipeline: StableDiffusionGeneratorPipeline = self.model - pipeline.scheduler = sampler - - uc, c, extra_conditioning_info = conditioning - conditioning_data = ConditioningData( - uc, - c, - cfg_scale, - extra_conditioning_info, - postprocessing_settings=PostprocessingSettings( - threshold=threshold, - warmup=warmup, - h_symmetry_time_pct=h_symmetry_time_pct, - v_symmetry_time_pct=v_symmetry_time_pct, - ), - ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta) - - def make_image(x_T: torch.Tensor, seed: int): - # FIXME: use x_T for initial seeded noise - # We're not at the moment because the pipeline automatically resizes init_image if - # necessary, which the x_T input might not match. - # In the meantime, reset the seed prior to generating pipeline output so we at least get the same result. - logging.set_verbosity_error() # quench safety check warnings - pipeline_output = pipeline.img2img_from_embeddings( - init_image, - strength, - steps, - conditioning_data, - noise_func=self.get_noise_like, - callback=step_callback, - seed=seed, - ) - if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None: - attention_maps_callback(pipeline_output.attention_map_saver) - return pipeline.numpy_to_pil(pipeline_output.images)[0] - - return make_image - - def get_noise_like(self, like: torch.Tensor): - device = like.device - x = torch.randn_like(like, device=device) - if self.perlin > 0.0: - shape = like.shape - x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(shape[3], shape[2]) - return x diff --git a/invokeai/backend/generator/inpaint.py b/invokeai/backend/generator/inpaint.py deleted file mode 100644 index 7aeb3d4809..0000000000 --- a/invokeai/backend/generator/inpaint.py +++ /dev/null @@ -1,379 +0,0 @@ -""" -invokeai.backend.generator.inpaint descends from .generator -""" -from __future__ import annotations - -import math -from typing import Tuple, Union, Optional - -import cv2 -import numpy as np -import torch -from PIL import Image, ImageChops, ImageFilter, ImageOps - -from ..image_util import PatchMatch, debug_image -from ..stable_diffusion.diffusers_pipeline import ( - ConditioningData, - StableDiffusionGeneratorPipeline, - image_resized_to_grid_as_tensor, -) -from .img2img import Img2Img - - -def infill_methods() -> list[str]: - methods = [ - "tile", - "solid", - ] - if PatchMatch.patchmatch_available(): - methods.insert(0, "patchmatch") - return methods - - -class Inpaint(Img2Img): - def __init__(self, model, precision): - self.inpaint_height = 0 - self.inpaint_width = 0 - self.enable_image_debugging = False - self.init_latent = None - self.pil_image = None - self.pil_mask = None - self.mask_blur_radius = 0 - self.infill_method = None - super().__init__(model, precision) - - # Outpaint support code - def get_tile_images(self, image: np.ndarray, width=8, height=8): - _nrows, _ncols, depth = image.shape - _strides = image.strides - - nrows, _m = divmod(_nrows, height) - ncols, _n = divmod(_ncols, width) - if _m != 0 or _n != 0: - return None - - return np.lib.stride_tricks.as_strided( - np.ravel(image), - shape=(nrows, ncols, height, width, depth), - strides=(height * _strides[0], width * _strides[1], *_strides), - writeable=False, - ) - - def infill_patchmatch(self, im: Image.Image) -> Image.Image: - if im.mode != "RGBA": - return im - - # Skip patchmatch if patchmatch isn't available - if not PatchMatch.patchmatch_available(): - return im - - # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though) - im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3) - im_patched = Image.fromarray(im_patched_np, mode="RGB") - return im_patched - - def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image: - # Only fill if there's an alpha layer - if im.mode != "RGBA": - return im - - a = np.asarray(im, dtype=np.uint8) - - tile_size_tuple = (tile_size, tile_size) - - # Get the image as tiles of a specified size - tiles = self.get_tile_images(a, *tile_size_tuple).copy() - - # Get the mask as tiles - tiles_mask = tiles[:, :, :, :, 3] - - # Find any mask tiles with any fully transparent pixels (we will be replacing these later) - tmask_shape = tiles_mask.shape - tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape)) - n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:]) - tiles_mask = tiles_mask > 0 - tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1) - - # Get RGB tiles in single array and filter by the mask - tshape = tiles.shape - tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:])) - filtered_tiles = tiles_all[tiles_mask] - - if len(filtered_tiles) == 0: - return im - - # Find all invalid tiles and replace with a random valid tile - replace_count = (tiles_mask == False).sum() - rng = np.random.default_rng(seed=seed) - tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[ - rng.choice(filtered_tiles.shape[0], replace_count), :, :, : - ] - - # Convert back to an image - tiles_all = tiles_all.reshape(tshape) - tiles_all = tiles_all.swapaxes(1, 2) - st = tiles_all.reshape( - ( - math.prod(tiles_all.shape[0:2]), - math.prod(tiles_all.shape[2:4]), - tiles_all.shape[4], - ) - ) - si = Image.fromarray(st, mode="RGBA") - - return si - - def mask_edge(self, mask: Image.Image, edge_size: int, edge_blur: int) -> Image.Image: - npimg = np.asarray(mask, dtype=np.uint8) - - # Detect any partially transparent regions - npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0))) - - # Detect hard edges - npedge = cv2.Canny(npimg, threshold1=100, threshold2=200) - - # Combine - npmask = npgradient + npedge - - # Expand - npmask = cv2.dilate(npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2)) - - new_mask = Image.fromarray(npmask) - - if edge_blur > 0: - new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur)) - - return ImageOps.invert(new_mask) - - def seam_paint( - self, - im: Image.Image, - seam_size: int, - seam_blur: int, - seed, - steps, - cfg_scale, - ddim_eta, - conditioning, - strength, - noise, - infill_method, - step_callback, - ) -> Image.Image: - hard_mask = self.pil_image.split()[-1].copy() - mask = self.mask_edge(hard_mask, seam_size, seam_blur) - - make_image = self.get_make_image( - steps, - cfg_scale, - ddim_eta, - conditioning, - init_image=im.copy().convert("RGBA"), - mask_image=mask, - strength=strength, - mask_blur_radius=0, - seam_size=0, - step_callback=step_callback, - inpaint_width=im.width, - inpaint_height=im.height, - infill_method=infill_method, - ) - - seam_noise = self.get_noise(im.width, im.height) - - result = make_image(seam_noise, seed=None) - - return result - - @torch.no_grad() - def get_make_image( - self, - steps, - cfg_scale, - ddim_eta, - conditioning, - init_image: Union[Image.Image, torch.FloatTensor], - mask_image: Union[Image.Image, torch.FloatTensor], - strength: float, - mask_blur_radius: int = 8, - # Seam settings - when 0, doesn't fill seam - seam_size: int = 96, - seam_blur: int = 16, - seam_strength: float = 0.7, - seam_steps: int = 30, - tile_size: int = 32, - step_callback=None, - inpaint_replace=False, - enable_image_debugging=False, - infill_method=None, - inpaint_width=None, - inpaint_height=None, - inpaint_fill: Tuple[int, int, int, int] = (0x7F, 0x7F, 0x7F, 0xFF), - attention_maps_callback=None, - **kwargs, - ): - """ - Returns a function returning an image derived from the prompt and - the initial image + mask. Return value depends on the seed at - the time you call it. kwargs are 'init_latent' and 'strength' - """ - - self.enable_image_debugging = enable_image_debugging - infill_method = infill_method or infill_methods()[0] - self.infill_method = infill_method - - self.inpaint_width = inpaint_width - self.inpaint_height = inpaint_height - - if isinstance(init_image, Image.Image): - self.pil_image = init_image.copy() - - # Do infill - if infill_method == "patchmatch" and PatchMatch.patchmatch_available(): - init_filled = self.infill_patchmatch(self.pil_image.copy()) - elif infill_method == "tile": - init_filled = self.tile_fill_missing(self.pil_image.copy(), seed=self.seed, tile_size=tile_size) - elif infill_method == "solid": - solid_bg = Image.new("RGBA", init_image.size, inpaint_fill) - init_filled = Image.alpha_composite(solid_bg, init_image) - else: - raise ValueError(f"Non-supported infill type {infill_method}", infill_method) - init_filled.paste(init_image, (0, 0), init_image.split()[-1]) - - # Resize if requested for inpainting - if inpaint_width and inpaint_height: - init_filled = init_filled.resize((inpaint_width, inpaint_height)) - - debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging) - - # Create init tensor - init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB")) - - if isinstance(mask_image, Image.Image): - self.pil_mask = mask_image.copy() - debug_image( - mask_image, - "mask_image BEFORE multiply with pil_image", - debug_status=self.enable_image_debugging, - ) - - init_alpha = self.pil_image.getchannel("A") - if mask_image.mode != "L": - # FIXME: why do we get passed an RGB image here? We can only use single-channel. - mask_image = mask_image.convert("L") - mask_image = ImageChops.multiply(mask_image, init_alpha) - self.pil_mask = mask_image - - # Resize if requested for inpainting - if inpaint_width and inpaint_height: - mask_image = mask_image.resize((inpaint_width, inpaint_height)) - - debug_image( - mask_image, - "mask_image AFTER multiply with pil_image", - debug_status=self.enable_image_debugging, - ) - mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False) - else: - mask: torch.FloatTensor = mask_image - - self.mask_blur_radius = mask_blur_radius - - # noinspection PyTypeChecker - pipeline: StableDiffusionGeneratorPipeline = self.model - - # todo: support cross-attention control - uc, c, _ = conditioning - conditioning_data = ConditioningData(uc, c, cfg_scale).add_scheduler_args_if_applicable( - pipeline.scheduler, eta=ddim_eta - ) - - def make_image(x_T: torch.Tensor, seed: int): - pipeline_output = pipeline.inpaint_from_embeddings( - init_image=init_image, - mask=1 - mask, # expects white means "paint here." - strength=strength, - num_inference_steps=steps, - conditioning_data=conditioning_data, - noise_func=self.get_noise_like, - callback=step_callback, - seed=seed, - ) - - if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None: - attention_maps_callback(pipeline_output.attention_map_saver) - - result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0]) - - # Seam paint if this is our first pass (seam_size set to 0 during seam painting) - if seam_size > 0: - old_image = self.pil_image or init_image - old_mask = self.pil_mask or mask_image - - result = self.seam_paint( - result, - seam_size, - seam_blur, - seed, - seam_steps, - cfg_scale, - ddim_eta, - conditioning, - seam_strength, - x_T, - infill_method, - step_callback, - ) - - # Restore original settings - self.get_make_image( - steps, - cfg_scale, - ddim_eta, - conditioning, - old_image, - old_mask, - strength, - mask_blur_radius, - seam_size, - seam_blur, - seam_strength, - seam_steps, - tile_size, - step_callback, - inpaint_replace, - enable_image_debugging, - inpaint_width=inpaint_width, - inpaint_height=inpaint_height, - infill_method=infill_method, - **kwargs, - ) - - return result - - return make_image - - def sample_to_image(self, samples) -> Image.Image: - gen_result = super().sample_to_image(samples).convert("RGB") - return self.postprocess_size_and_mask(gen_result) - - def postprocess_size_and_mask(self, gen_result: Image.Image) -> Image.Image: - debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging) - - # Resize if necessary - if self.inpaint_width and self.inpaint_height: - gen_result = gen_result.resize(self.pil_image.size) - - if self.pil_image is None or self.pil_mask is None: - return gen_result - - corrected_result = self.repaste_and_color_correct( - gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius - ) - debug_image( - corrected_result, - "corrected_result", - debug_status=self.enable_image_debugging, - ) - - return corrected_result diff --git a/invokeai/backend/install/check_root.py b/invokeai/backend/install/check_root.py index ded9e66635..2104c3a4c7 100644 --- a/invokeai/backend/install/check_root.py +++ b/invokeai/backend/install/check_root.py @@ -12,16 +12,17 @@ def check_invokeai_root(config: InvokeAIAppConfig): assert config.model_conf_path.exists(), f"{config.model_conf_path} not found" assert config.db_path.parent.exists(), f"{config.db_path.parent} not found" assert config.models_path.exists(), f"{config.models_path} not found" - for model in [ - "CLIP-ViT-bigG-14-laion2B-39B-b160k", - "bert-base-uncased", - "clip-vit-large-patch14", - "sd-vae-ft-mse", - "stable-diffusion-2-clip", - "stable-diffusion-safety-checker", - ]: - path = config.models_path / f"core/convert/{model}" - assert path.exists(), f"{path} is missing" + if not config.ignore_missing_core_models: + for model in [ + "CLIP-ViT-bigG-14-laion2B-39B-b160k", + "bert-base-uncased", + "clip-vit-large-patch14", + "sd-vae-ft-mse", + "stable-diffusion-2-clip", + "stable-diffusion-safety-checker", + ]: + path = config.models_path / f"core/convert/{model}" + assert path.exists(), f"{path} is missing" except Exception as e: print() print(f"An exception has occurred: {str(e)}") @@ -32,5 +33,10 @@ def check_invokeai_root(config: InvokeAIAppConfig): print( '** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **' ) + print( + '** (To skip this check completely, add "--ignore_missing_core_models" to your CLI args. Not installing ' + "these core models will prevent the loading of some or all .safetensors and .ckpt files. However, you can " + "always come back and install these core models in the future.)" + ) input("Press any key to continue...") sys.exit(0) diff --git a/invokeai/backend/install/invokeai_configure.py b/invokeai/backend/install/invokeai_configure.py index 4bf2a484a1..6996b913c2 100755 --- a/invokeai/backend/install/invokeai_configure.py +++ b/invokeai/backend/install/invokeai_configure.py @@ -10,15 +10,17 @@ import sys import argparse import io import os +import psutil import shutil import textwrap +import torch import traceback import yaml import warnings from argparse import Namespace +from enum import Enum from pathlib import Path from shutil import get_terminal_size -from typing import get_type_hints from urllib import request import npyscreen @@ -44,6 +46,8 @@ from invokeai.app.services.config import ( ) from invokeai.backend.util.logging import InvokeAILogger from invokeai.frontend.install.model_install import addModelsForm, process_and_execute + +# TO DO - Move all the frontend code into invokeai.frontend.install from invokeai.frontend.install.widgets import ( SingleSelectColumns, CenteredButtonPress, @@ -53,6 +57,7 @@ from invokeai.frontend.install.widgets import ( CyclingForm, MIN_COLS, MIN_LINES, + WindowTooSmallException, ) from invokeai.backend.install.legacy_arg_parsing import legacy_parser from invokeai.backend.install.model_install_backend import ( @@ -61,6 +66,7 @@ from invokeai.backend.install.model_install_backend import ( ModelInstall, ) from invokeai.backend.model_management.model_probe import ModelType, BaseModelType +from pydantic.error_wrappers import ValidationError warnings.filterwarnings("ignore") transformers.logging.set_verbosity_error() @@ -76,6 +82,13 @@ Default_config_file = config.model_conf_path SD_Configs = config.legacy_conf_path PRECISION_CHOICES = ["auto", "float16", "float32"] +GB = 1073741824 # GB in bytes +HAS_CUDA = torch.cuda.is_available() +_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0) + + +MAX_VRAM /= GB +MAX_RAM = psutil.virtual_memory().total / GB INIT_FILE_PREAMBLE = """# InvokeAI initialization file # This is the InvokeAI initialization file, which contains command-line default values. @@ -86,6 +99,12 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file logger = InvokeAILogger.getLogger() +class DummyWidgetValue(Enum): + zero = 0 + true = True + false = False + + # -------------------------------------------- def postscript(errors: None): if not any(errors): @@ -376,15 +395,47 @@ Use cursor arrows to make a checkbox selection, and space to toggle. max_width=80, scroll_exit=True, ) - self.max_cache_size = self.add_widget_intelligent( - IntTitleSlider, - name="Size of the RAM cache used for fast model switching (GB)", - value=old_opts.max_cache_size, - out_of=20, - lowest=3, - begin_entry_at=6, + self.nextrely += 1 + self.add_widget_intelligent( + npyscreen.TitleFixedText, + name="RAM cache size (GB). Make this at least large enough to hold a single full model.", + begin_entry_at=0, + editable=False, + color="CONTROL", scroll_exit=True, ) + self.nextrely -= 1 + self.max_cache_size = self.add_widget_intelligent( + npyscreen.Slider, + value=clip(old_opts.max_cache_size, range=(3.0, MAX_RAM), step=0.5), + out_of=round(MAX_RAM), + lowest=0.0, + step=0.5, + relx=8, + scroll_exit=True, + ) + if HAS_CUDA: + self.nextrely += 1 + self.add_widget_intelligent( + npyscreen.TitleFixedText, + name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.", + begin_entry_at=0, + editable=False, + color="CONTROL", + scroll_exit=True, + ) + self.nextrely -= 1 + self.max_vram_cache_size = self.add_widget_intelligent( + npyscreen.Slider, + value=clip(old_opts.max_vram_cache_size, range=(0, MAX_VRAM), step=0.25), + out_of=round(MAX_VRAM * 2) / 2, + lowest=0.0, + relx=8, + step=0.25, + scroll_exit=True, + ) + else: + self.max_vram_cache_size = DummyWidgetValue.zero self.nextrely += 1 self.outdir = self.add_widget_intelligent( FileBox, @@ -401,7 +452,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle. self.autoimport_dirs = {} self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent( FileBox, - name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models", + name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models", value=str(config.root_path / config.autoimport_dir), select_dir=True, must_exist=False, @@ -476,6 +527,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS "outdir", "free_gpu_mem", "max_cache_size", + "max_vram_cache_size", "xformers_enabled", "always_use_cpu", ]: @@ -553,6 +605,16 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections: ) +# ------------------------------------- +def clip(value: float, range: tuple[float, float], step: float) -> float: + minimum, maximum = range + if value < minimum: + value = minimum + if value > maximum: + value = maximum + return round(value / step) * step + + # ------------------------------------- def initialize_rootdir(root: Path, yes_to_all: bool = False): logger.info("Initializing InvokeAI runtime directory") @@ -592,13 +654,13 @@ def maybe_create_models_yaml(root: Path): # ------------------------------------- def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace): - # parse_args() will read from init file if present invokeai_opts = default_startup_options(initfile) invokeai_opts.root = program_opts.root - # The third argument is needed in the Windows 11 environment to - # launch a console window running this program. - set_min_terminal_size(MIN_COLS, MIN_LINES) + if not set_min_terminal_size(MIN_COLS, MIN_LINES): + raise WindowTooSmallException( + "Could not increase terminal size. Try running again with a larger window or smaller font size." + ) # the install-models application spawns a subprocess to install # models, and will crash unless this is set before running. @@ -654,10 +716,13 @@ def migrate_init_file(legacy_format: Path): old = legacy_parser.parse_args([f"@{str(legacy_format)}"]) new = InvokeAIAppConfig.get_config() - fields = list(get_type_hints(InvokeAIAppConfig).keys()) + fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"] for attr in fields: if hasattr(old, attr): - setattr(new, attr, getattr(old, attr)) + try: + setattr(new, attr, getattr(old, attr)) + except ValidationError as e: + print(f"* Ignoring incompatible value for field {attr}:\n {str(e)}") # a few places where the field names have changed and we have to # manually add in the new names/values @@ -777,6 +842,7 @@ def main(): models_to_download = default_user_selections(opt) new_init_file = config.root_path / "invokeai.yaml" + if opt.yes_to_all: write_default_options(opt, new_init_file) init_options = Namespace(precision="float32" if opt.full_precision else "float16") @@ -802,6 +868,8 @@ def main(): postscript(errors=errors) if not opt.yes_to_all: input("Press any key to continue...") + except WindowTooSmallException as e: + logger.error(str(e)) except KeyboardInterrupt: print("\nGoodbye! Come back soon.") diff --git a/invokeai/backend/install/migrate_to_3.py b/invokeai/backend/install/migrate_to_3.py index 9152e46951..3f360f291d 100644 --- a/invokeai/backend/install/migrate_to_3.py +++ b/invokeai/backend/install/migrate_to_3.py @@ -591,7 +591,6 @@ script, which will perform a full upgrade in place.""", # TODO: revisit - don't rely on invokeai.yaml to exist yet! dest_is_setup = (dest_root / "models/core").exists() and (dest_root / "databases").exists() if not dest_is_setup: - import invokeai.frontend.install.invokeai_configure from invokeai.backend.install.invokeai_configure import initialize_rootdir initialize_rootdir(dest_root, True) diff --git a/invokeai/backend/install/model_install_backend.py b/invokeai/backend/install/model_install_backend.py index c0a7244367..3a6d879bee 100644 --- a/invokeai/backend/install/model_install_backend.py +++ b/invokeai/backend/install/model_install_backend.py @@ -13,6 +13,7 @@ import requests from diffusers import DiffusionPipeline from diffusers import logging as dlogging import onnx +import torch from huggingface_hub import hf_hub_url, HfFolder, HfApi from omegaconf import OmegaConf from tqdm import tqdm @@ -23,6 +24,7 @@ from invokeai.app.services.config import InvokeAIAppConfig from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo from invokeai.backend.util import download_with_resume +from invokeai.backend.util.devices import torch_dtype, choose_torch_device from ..util.logging import InvokeAILogger warnings.filterwarnings("ignore") @@ -99,9 +101,9 @@ class ModelInstall(object): def __init__( self, config: InvokeAIAppConfig, - prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None, - model_manager: ModelManager = None, - access_token: str = None, + prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None, + model_manager: Optional[ModelManager] = None, + access_token: Optional[str] = None, ): self.config = config self.mgr = model_manager or ModelManager(config.model_conf_path) @@ -303,7 +305,7 @@ class ModelInstall(object): with TemporaryDirectory(dir=self.config.models_path) as staging: staging = Path(staging) - if "model_index.json" in files and "unet/model.onnx" not in files: + if "model_index.json" in files: location = self._download_hf_pipeline(repo_id, staging) # pipeline elif "unet/model.onnx" in files: location = self._download_hf_model(repo_id, files, staging) @@ -416,15 +418,25 @@ class ModelInstall(object): does a save_pretrained() to the indicated staging area. """ _, name = repo_id.split("/") - revisions = ["fp16", "main"] if self.config.precision == "float16" else ["main"] + precision = torch_dtype(choose_torch_device()) + variants = ["fp16", None] if precision == torch.float16 else [None, "fp16"] + model = None - for revision in revisions: + for variant in variants: try: - model = DiffusionPipeline.from_pretrained(repo_id, revision=revision, safety_checker=None) - except: # most errors are due to fp16 not being present. Fix this to catch other errors - pass + model = DiffusionPipeline.from_pretrained( + repo_id, + variant=variant, + torch_dtype=precision, + safety_checker=None, + ) + except Exception as e: # most errors are due to fp16 not being present. Fix this to catch other errors + if "fp16" not in str(e): + print(e) + if model: break + if not model: logger.error(f"Diffusers model {repo_id} could not be downloaded. Skipping.") return None diff --git a/invokeai/backend/model_management/__init__.py b/invokeai/backend/model_management/__init__.py index cf057f3a89..8e083c1045 100644 --- a/invokeai/backend/model_management/__init__.py +++ b/invokeai/backend/model_management/__init__.py @@ -13,3 +13,4 @@ from .models import ( DuplicateModelException, ) from .model_merge import ModelMerger, MergeInterpolationMethod +from .lora import ModelPatcher diff --git a/invokeai/backend/model_management/lora.py b/invokeai/backend/model_management/lora.py index 4287072a65..3da27988dc 100644 --- a/invokeai/backend/model_management/lora.py +++ b/invokeai/backend/model_management/lora.py @@ -20,424 +20,6 @@ from diffusers.models import UNet2DConditionModel from safetensors.torch import load_file from transformers import CLIPTextModel, CLIPTokenizer -# TODO: rename and split this file - - -class LoRALayerBase: - # rank: Optional[int] - # alpha: Optional[float] - # bias: Optional[torch.Tensor] - # layer_key: str - - # @property - # def scale(self): - # return self.alpha / self.rank if (self.alpha and self.rank) else 1.0 - - def __init__( - self, - layer_key: str, - values: dict, - ): - if "alpha" in values: - self.alpha = values["alpha"].item() - else: - self.alpha = None - - if "bias_indices" in values and "bias_values" in values and "bias_size" in values: - self.bias = torch.sparse_coo_tensor( - values["bias_indices"], - values["bias_values"], - tuple(values["bias_size"]), - ) - - else: - self.bias = None - - self.rank = None # set in layer implementation - self.layer_key = layer_key - - def forward( - self, - module: torch.nn.Module, - input_h: Any, # for real looks like Tuple[torch.nn.Tensor] but not sure - multiplier: float, - ): - if type(module) == torch.nn.Conv2d: - op = torch.nn.functional.conv2d - extra_args = dict( - stride=module.stride, - padding=module.padding, - dilation=module.dilation, - groups=module.groups, - ) - - else: - op = torch.nn.functional.linear - extra_args = {} - - weight = self.get_weight() - - bias = self.bias if self.bias is not None else 0 - scale = self.alpha / self.rank if (self.alpha and self.rank) else 1.0 - return ( - op( - *input_h, - (weight + bias).view(module.weight.shape), - None, - **extra_args, - ) - * multiplier - * scale - ) - - def get_weight(self): - raise NotImplementedError() - - def calc_size(self) -> int: - model_size = 0 - for val in [self.bias]: - if val is not None: - model_size += val.nelement() * val.element_size() - return model_size - - def to( - self, - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, - ): - if self.bias is not None: - self.bias = self.bias.to(device=device, dtype=dtype) - - -# TODO: find and debug lora/locon with bias -class LoRALayer(LoRALayerBase): - # up: torch.Tensor - # mid: Optional[torch.Tensor] - # down: torch.Tensor - - def __init__( - self, - layer_key: str, - values: dict, - ): - super().__init__(layer_key, values) - - self.up = values["lora_up.weight"] - self.down = values["lora_down.weight"] - if "lora_mid.weight" in values: - self.mid = values["lora_mid.weight"] - else: - self.mid = None - - self.rank = self.down.shape[0] - - def get_weight(self): - if self.mid is not None: - up = self.up.reshape(self.up.shape[0], self.up.shape[1]) - down = self.down.reshape(self.down.shape[0], self.down.shape[1]) - weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down) - else: - weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1) - - return weight - - def calc_size(self) -> int: - model_size = super().calc_size() - for val in [self.up, self.mid, self.down]: - if val is not None: - model_size += val.nelement() * val.element_size() - return model_size - - def to( - self, - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, - ): - super().to(device=device, dtype=dtype) - - self.up = self.up.to(device=device, dtype=dtype) - self.down = self.down.to(device=device, dtype=dtype) - - if self.mid is not None: - self.mid = self.mid.to(device=device, dtype=dtype) - - -class LoHALayer(LoRALayerBase): - # w1_a: torch.Tensor - # w1_b: torch.Tensor - # w2_a: torch.Tensor - # w2_b: torch.Tensor - # t1: Optional[torch.Tensor] = None - # t2: Optional[torch.Tensor] = None - - def __init__( - self, - layer_key: str, - values: dict, - ): - super().__init__(layer_key, values) - - self.w1_a = values["hada_w1_a"] - self.w1_b = values["hada_w1_b"] - self.w2_a = values["hada_w2_a"] - self.w2_b = values["hada_w2_b"] - - if "hada_t1" in values: - self.t1 = values["hada_t1"] - else: - self.t1 = None - - if "hada_t2" in values: - self.t2 = values["hada_t2"] - else: - self.t2 = None - - self.rank = self.w1_b.shape[0] - - def get_weight(self): - if self.t1 is None: - weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b) - - else: - rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a) - rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a) - weight = rebuild1 * rebuild2 - - return weight - - def calc_size(self) -> int: - model_size = super().calc_size() - for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]: - if val is not None: - model_size += val.nelement() * val.element_size() - return model_size - - def to( - self, - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, - ): - super().to(device=device, dtype=dtype) - - self.w1_a = self.w1_a.to(device=device, dtype=dtype) - self.w1_b = self.w1_b.to(device=device, dtype=dtype) - if self.t1 is not None: - self.t1 = self.t1.to(device=device, dtype=dtype) - - self.w2_a = self.w2_a.to(device=device, dtype=dtype) - self.w2_b = self.w2_b.to(device=device, dtype=dtype) - if self.t2 is not None: - self.t2 = self.t2.to(device=device, dtype=dtype) - - -class LoKRLayer(LoRALayerBase): - # w1: Optional[torch.Tensor] = None - # w1_a: Optional[torch.Tensor] = None - # w1_b: Optional[torch.Tensor] = None - # w2: Optional[torch.Tensor] = None - # w2_a: Optional[torch.Tensor] = None - # w2_b: Optional[torch.Tensor] = None - # t2: Optional[torch.Tensor] = None - - def __init__( - self, - layer_key: str, - values: dict, - ): - super().__init__(layer_key, values) - - if "lokr_w1" in values: - self.w1 = values["lokr_w1"] - self.w1_a = None - self.w1_b = None - else: - self.w1 = None - self.w1_a = values["lokr_w1_a"] - self.w1_b = values["lokr_w1_b"] - - if "lokr_w2" in values: - self.w2 = values["lokr_w2"] - self.w2_a = None - self.w2_b = None - else: - self.w2 = None - self.w2_a = values["lokr_w2_a"] - self.w2_b = values["lokr_w2_b"] - - if "lokr_t2" in values: - self.t2 = values["lokr_t2"] - else: - self.t2 = None - - if "lokr_w1_b" in values: - self.rank = values["lokr_w1_b"].shape[0] - elif "lokr_w2_b" in values: - self.rank = values["lokr_w2_b"].shape[0] - else: - self.rank = None # unscaled - - def get_weight(self): - w1 = self.w1 - if w1 is None: - w1 = self.w1_a @ self.w1_b - - w2 = self.w2 - if w2 is None: - if self.t2 is None: - w2 = self.w2_a @ self.w2_b - else: - w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b) - - if len(w2.shape) == 4: - w1 = w1.unsqueeze(2).unsqueeze(2) - w2 = w2.contiguous() - weight = torch.kron(w1, w2) - - return weight - - def calc_size(self) -> int: - model_size = super().calc_size() - for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]: - if val is not None: - model_size += val.nelement() * val.element_size() - return model_size - - def to( - self, - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, - ): - super().to(device=device, dtype=dtype) - - if self.w1 is not None: - self.w1 = self.w1.to(device=device, dtype=dtype) - else: - self.w1_a = self.w1_a.to(device=device, dtype=dtype) - self.w1_b = self.w1_b.to(device=device, dtype=dtype) - - if self.w2 is not None: - self.w2 = self.w2.to(device=device, dtype=dtype) - else: - self.w2_a = self.w2_a.to(device=device, dtype=dtype) - self.w2_b = self.w2_b.to(device=device, dtype=dtype) - - if self.t2 is not None: - self.t2 = self.t2.to(device=device, dtype=dtype) - - -class LoRAModel: # (torch.nn.Module): - _name: str - layers: Dict[str, LoRALayer] - _device: torch.device - _dtype: torch.dtype - - def __init__( - self, - name: str, - layers: Dict[str, LoRALayer], - device: torch.device, - dtype: torch.dtype, - ): - self._name = name - self._device = device or torch.cpu - self._dtype = dtype or torch.float32 - self.layers = layers - - @property - def name(self): - return self._name - - @property - def device(self): - return self._device - - @property - def dtype(self): - return self._dtype - - def to( - self, - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, - ) -> LoRAModel: - # TODO: try revert if exception? - for key, layer in self.layers.items(): - layer.to(device=device, dtype=dtype) - self._device = device - self._dtype = dtype - - def calc_size(self) -> int: - model_size = 0 - for _, layer in self.layers.items(): - model_size += layer.calc_size() - return model_size - - @classmethod - def from_checkpoint( - cls, - file_path: Union[str, Path], - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, - ): - device = device or torch.device("cpu") - dtype = dtype or torch.float32 - - if isinstance(file_path, str): - file_path = Path(file_path) - - model = cls( - device=device, - dtype=dtype, - name=file_path.stem, # TODO: - layers=dict(), - ) - - if file_path.suffix == ".safetensors": - state_dict = load_file(file_path.absolute().as_posix(), device="cpu") - else: - state_dict = torch.load(file_path, map_location="cpu") - - state_dict = cls._group_state(state_dict) - - for layer_key, values in state_dict.items(): - # lora and locon - if "lora_down.weight" in values: - layer = LoRALayer(layer_key, values) - - # loha - elif "hada_w1_b" in values: - layer = LoHALayer(layer_key, values) - - # lokr - elif "lokr_w1_b" in values or "lokr_w1" in values: - layer = LoKRLayer(layer_key, values) - - else: - # TODO: diff/ia3/... format - print(f">> Encountered unknown lora layer module in {model.name}: {layer_key}") - return - - # lower memory consumption by removing already parsed layer values - state_dict[layer_key].clear() - - layer.to(device=device, dtype=dtype) - model.layers[layer_key] = layer - - return model - - @staticmethod - def _group_state(state_dict: dict): - state_dict_groupped = dict() - - for key, value in state_dict.items(): - stem, leaf = key.split(".", 1) - if stem not in state_dict_groupped: - state_dict_groupped[stem] = dict() - state_dict_groupped[stem][leaf] = value - - return state_dict_groupped - - """ loras = [ (lora_model1, 0.7), @@ -516,6 +98,26 @@ class ModelPatcher: with cls.apply_lora(text_encoder, loras, "lora_te_"): yield + @classmethod + @contextmanager + def apply_sdxl_lora_text_encoder( + cls, + text_encoder: CLIPTextModel, + loras: List[Tuple[LoRAModel, float]], + ): + with cls.apply_lora(text_encoder, loras, "lora_te1_"): + yield + + @classmethod + @contextmanager + def apply_sdxl_lora_text_encoder2( + cls, + text_encoder: CLIPTextModel, + loras: List[Tuple[LoRAModel, float]], + ): + with cls.apply_lora(text_encoder, loras, "lora_te2_"): + yield + @classmethod @contextmanager def apply_lora( @@ -541,7 +143,7 @@ class ModelPatcher: # with torch.autocast(device_type="cpu"): layer.to(dtype=torch.float32) layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0 - layer_weight = layer.get_weight() * lora_weight * layer_scale + layer_weight = layer.get_weight(original_weights[module_key]) * lora_weight * layer_scale if module.weight.shape != layer_weight.shape: # TODO: debug on lycoris @@ -562,7 +164,7 @@ class ModelPatcher: cls, tokenizer: CLIPTokenizer, text_encoder: CLIPTextModel, - ti_list: List[Any], + ti_list: List[Tuple[str, Any]], ) -> Tuple[CLIPTokenizer, TextualInversionManager]: init_tokens_count = None new_tokens_added = None @@ -572,27 +174,27 @@ class ModelPatcher: ti_manager = TextualInversionManager(ti_tokenizer) init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings - def _get_trigger(ti, index): - trigger = ti.name + def _get_trigger(ti_name, index): + trigger = ti_name if index > 0: trigger += f"-!pad-{i}" return f"<{trigger}>" # modify tokenizer new_tokens_added = 0 - for ti in ti_list: + for ti_name, ti in ti_list: for i in range(ti.embedding.shape[0]): - new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti, i)) + new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i)) # modify text_encoder text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added) model_embeddings = text_encoder.get_input_embeddings() - for ti in ti_list: + for ti_name, ti in ti_list: ti_tokens = [] for i in range(ti.embedding.shape[0]): embedding = ti.embedding[i] - trigger = _get_trigger(ti, i) + trigger = _get_trigger(ti_name, i) token_id = ti_tokenizer.convert_tokens_to_ids(trigger) if token_id == ti_tokenizer.unk_token_id: @@ -637,7 +239,6 @@ class ModelPatcher: class TextualInversionModel: - name: str embedding: torch.Tensor # [n, 768]|[n, 1280] @classmethod @@ -651,7 +252,6 @@ class TextualInversionModel: file_path = Path(file_path) result = cls() # TODO: - result.name = file_path.stem # TODO: if file_path.suffix == ".safetensors": state_dict = load_file(file_path.absolute().as_posix(), device="cpu") @@ -761,7 +361,8 @@ class ONNXModelPatcher: layer.to(dtype=torch.float32) layer_key = layer_key.replace(prefix, "") - layer_weight = layer.get_weight().detach().cpu().numpy() * lora_weight + # TODO: rewrite to pass original tensor weight(required by ia3) + layer_weight = layer.get_weight(None).detach().cpu().numpy() * lora_weight if layer_key is blended_loras: blended_loras[layer_key] += layer_weight else: @@ -828,7 +429,7 @@ class ONNXModelPatcher: cls, tokenizer: CLIPTokenizer, text_encoder: IAIOnnxRuntimeModel, - ti_list: List[Any], + ti_list: List[Tuple[str, Any]], ) -> Tuple[CLIPTokenizer, TextualInversionManager]: from .models.base import IAIOnnxRuntimeModel @@ -841,17 +442,17 @@ class ONNXModelPatcher: ti_tokenizer = copy.deepcopy(tokenizer) ti_manager = TextualInversionManager(ti_tokenizer) - def _get_trigger(ti, index): - trigger = ti.name + def _get_trigger(ti_name, index): + trigger = ti_name if index > 0: trigger += f"-!pad-{i}" return f"<{trigger}>" # modify tokenizer new_tokens_added = 0 - for ti in ti_list: + for ti_name, ti in ti_list: for i in range(ti.embedding.shape[0]): - new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti, i)) + new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i)) # modify text_encoder orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"] @@ -861,11 +462,11 @@ class ONNXModelPatcher: axis=0, ) - for ti in ti_list: + for ti_name, ti in ti_list: ti_tokens = [] for i in range(ti.embedding.shape[0]): embedding = ti.embedding[i].detach().numpy() - trigger = _get_trigger(ti, i) + trigger = _get_trigger(ti_name, i) token_id = ti_tokenizer.convert_tokens_to_ids(trigger) if token_id == ti_tokenizer.unk_token_id: diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index b4c3e48a48..2b8d020269 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -28,8 +28,6 @@ import torch import logging import invokeai.backend.util.logging as logger -from invokeai.app.services.config import get_invokeai_config -from .lora import LoRAModel, TextualInversionModel from .models import BaseModelType, ModelType, SubModelType, ModelBase # Maximum size of the cache, in gigs @@ -188,7 +186,7 @@ class ModelCache(object): cache_entry = self._cached_models.get(key, None) if cache_entry is None: self.logger.info( - f"Loading model {model_path}, type {base_model.value}:{model_type.value}:{submodel.value if submodel else ''}" + f"Loading model {model_path}, type {base_model.value}:{model_type.value}{':'+submodel.value if submodel else ''}" ) # this will remove older cached models until diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 832a96e18f..adc3aaa661 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -228,19 +228,19 @@ the root is the InvokeAI ROOTDIR. """ from __future__ import annotations -import os import hashlib +import os import textwrap -import yaml +import types from dataclasses import dataclass from pathlib import Path -from typing import Optional, List, Tuple, Union, Dict, Set, Callable, types from shutil import rmtree, move +from typing import Optional, List, Literal, Tuple, Union, Dict, Set, Callable import torch +import yaml from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig - from pydantic import BaseModel, Field import invokeai.backend.util.logging as logger @@ -259,6 +259,7 @@ from .models import ( ModelNotFoundException, InvalidModelException, DuplicateModelException, + ModelBase, ) # We are only starting to number the config file with release 3. @@ -361,7 +362,7 @@ class ModelManager(object): if model_key.startswith("_"): continue model_name, base_model, model_type = self.parse_key(model_key) - model_class = MODEL_CLASSES[base_model][model_type] + model_class = self._get_implementation(base_model, model_type) # alias for config file model_config["model_format"] = model_config.pop("format") self.models[model_key] = model_class.create_config(**model_config) @@ -381,18 +382,24 @@ class ModelManager(object): # causing otherwise unreferenced models to be removed from memory self._read_models() - def model_exists( - self, - model_name: str, - base_model: BaseModelType, - model_type: ModelType, - ) -> bool: + def model_exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType, *, rescan=False) -> bool: """ - Given a model name, returns True if it is a valid - identifier. + Given a model name, returns True if it is a valid identifier. + + :param model_name: symbolic name of the model in models.yaml + :param model_type: ModelType enum indicating the type of model to return + :param base_model: BaseModelType enum indicating the base model used by this model + :param rescan: if True, scan_models_directory """ model_key = self.create_key(model_name, base_model, model_type) - return model_key in self.models + exists = model_key in self.models + + # if model not found try to find it (maybe file just pasted) + if rescan and not exists: + self.scan_models_directory(base_model=base_model, model_type=model_type) + exists = self.model_exists(model_name, base_model, model_type, rescan=False) + + return exists @classmethod def create_key( @@ -443,39 +450,32 @@ class ModelManager(object): :param model_name: symbolic name of the model in models.yaml :param model_type: ModelType enum indicating the type of model to return :param base_model: BaseModelType enum indicating the base model used by this model - :param submode_typel: an ModelType enum indicating the portion of + :param submodel_type: an ModelType enum indicating the portion of the model to retrieve (e.g. ModelType.Vae) """ - model_class = MODEL_CLASSES[base_model][model_type] model_key = self.create_key(model_name, base_model, model_type) - # if model not found try to find it (maybe file just pasted) - if model_key not in self.models: - self.scan_models_directory(base_model=base_model, model_type=model_type) - if model_key not in self.models: - raise ModelNotFoundException(f"Model not found - {model_key}") + if not self.model_exists(model_name, base_model, model_type, rescan=True): + raise ModelNotFoundException(f"Model not found - {model_key}") - model_config = self.models[model_key] - model_path = self.resolve_model_path(model_config.path) + model_config = self._get_model_config(base_model, model_name, model_type) + + model_path, is_submodel_override = self._get_model_path(model_config, submodel_type) + + if is_submodel_override: + model_type = submodel_type + submodel_type = None + + model_class = self._get_implementation(base_model, model_type) if not model_path.exists(): if model_class.save_to_config: self.models[model_key].error = ModelError.NotFound - raise Exception(f'Files for model "{model_key}" not found') + raise Exception(f'Files for model "{model_key}" not found at {model_path}') else: self.models.pop(model_key, None) - raise ModelNotFoundException(f"Model not found - {model_key}") - - # vae/movq override - # TODO: - if submodel_type is not None and hasattr(model_config, submodel_type): - override_path = getattr(model_config, submodel_type) - if override_path: - model_path = self.app_config.root_path / override_path - model_type = submodel_type - submodel_type = None - model_class = MODEL_CLASSES[base_model][model_type] + raise ModelNotFoundException(f'Files for model "{model_key}" not found at {model_path}') # TODO: path # TODO: is it accurate to use path as id @@ -513,12 +513,61 @@ class ModelManager(object): _cache=self.cache, ) + def _get_model_path( + self, model_config: ModelConfigBase, submodel_type: Optional[SubModelType] = None + ) -> (Path, bool): + """Extract a model's filesystem path from its config. + + :return: The fully qualified Path of the module (or submodule). + """ + model_path = model_config.path + is_submodel_override = False + + # Does the config explicitly override the submodel? + if submodel_type is not None and hasattr(model_config, submodel_type): + submodel_path = getattr(model_config, submodel_type) + if submodel_path is not None and len(submodel_path) > 0: + model_path = getattr(model_config, submodel_type) + is_submodel_override = True + + model_path = self.resolve_model_path(model_path) + return model_path, is_submodel_override + + def _get_model_config(self, base_model: BaseModelType, model_name: str, model_type: ModelType) -> ModelConfigBase: + """Get a model's config object.""" + model_key = self.create_key(model_name, base_model, model_type) + try: + model_config = self.models[model_key] + except KeyError: + raise ModelNotFoundException(f"Model not found - {model_key}") + return model_config + + def _get_implementation(self, base_model: BaseModelType, model_type: ModelType) -> type[ModelBase]: + """Get the concrete implementation class for a specific model type.""" + model_class = MODEL_CLASSES[base_model][model_type] + return model_class + + def _instantiate( + self, + model_name: str, + base_model: BaseModelType, + model_type: ModelType, + submodel_type: Optional[SubModelType] = None, + ) -> ModelBase: + """Make a new instance of this model, without loading it.""" + model_config = self._get_model_config(base_model, model_name, model_type) + model_path, is_submodel_override = self._get_model_path(model_config, submodel_type) + # FIXME: do non-overriden submodels get the right class? + constructor = self._get_implementation(base_model, model_type) + instance = constructor(model_path, base_model, model_type) + return instance + def model_info( self, model_name: str, base_model: BaseModelType, model_type: ModelType, - ) -> dict: + ) -> Union[dict, None]: """ Given a model name returns the OmegaConf (dict-like) object describing it. """ @@ -540,13 +589,16 @@ class ModelManager(object): model_name: str, base_model: BaseModelType, model_type: ModelType, - ) -> dict: + ) -> Union[dict, None]: """ Returns a dict describing one installed model, using the combined format of the list_models() method. """ models = self.list_models(base_model, model_type, model_name) - return models[0] if models else None + if len(models) >= 1: + return models[0] + else: + return None def list_models( self, @@ -560,7 +612,7 @@ class ModelManager(object): model_keys = ( [self.create_key(model_name, base_model, model_type)] - if model_name + if model_name and base_model and model_type else sorted(self.models, key=str.casefold) ) models = [] @@ -596,7 +648,7 @@ class ModelManager(object): Print a table of models and their descriptions. This needs to be redone """ # TODO: redo - for model_type, model_dict in self.list_models().items(): + for model_dict in self.list_models(): for model_name, model_info in model_dict.items(): line = f'{model_info["name"]:25s} {model_info["type"]:10s} {model_info["description"]}' print(line) @@ -658,7 +710,7 @@ class ModelManager(object): if path := model_attributes.get("path"): model_attributes["path"] = str(self.relative_model_path(Path(path))) - model_class = MODEL_CLASSES[base_model][model_type] + model_class = self._get_implementation(base_model, model_type) model_config = model_class.create_config(**model_attributes) model_key = self.create_key(model_name, base_model, model_type) @@ -670,7 +722,7 @@ class ModelManager(object): # TODO: if path changed and old_model.path inside models folder should we delete this too? # remove conversion cache as config changed - old_model_path = self.app_config.root_path / old_model.path + old_model_path = self.resolve_model_path(old_model.path) old_model_cache = self._get_model_cache_path(old_model_path) if old_model_cache.exists(): if old_model_cache.is_dir(): @@ -699,8 +751,8 @@ class ModelManager(object): model_name: str, base_model: BaseModelType, model_type: ModelType, - new_name: str = None, - new_base: BaseModelType = None, + new_name: Optional[str] = None, + new_base: Optional[BaseModelType] = None, ): """ Rename or rebase a model. @@ -753,7 +805,7 @@ class ModelManager(object): self, model_name: str, base_model: BaseModelType, - model_type: Union[ModelType.Main, ModelType.Vae], + model_type: Literal[ModelType.Main, ModelType.Vae], dest_directory: Optional[Path] = None, ) -> AddModelResult: """ @@ -767,6 +819,10 @@ class ModelManager(object): This will raise a ValueError unless the model is a checkpoint. """ info = self.model_info(model_name, base_model, model_type) + + if info is None: + raise FileNotFoundError(f"model not found: {model_name}") + if info["model_format"] != "checkpoint": raise ValueError(f"not a checkpoint format model: {model_name}") @@ -780,7 +836,7 @@ class ModelManager(object): model_type, **submodel, ) - checkpoint_path = self.app_config.root_path / info["path"] + checkpoint_path = self.resolve_model_path(info["path"]) old_diffusers_path = self.resolve_model_path(model.location) new_diffusers_path = ( dest_directory or self.app_config.models_path / base_model.value / model_type.value @@ -836,7 +892,7 @@ class ModelManager(object): return search_folder, found_models - def commit(self, conf_file: Path = None) -> None: + def commit(self, conf_file: Optional[Path] = None) -> None: """ Write current configuration out to the indicated file. """ @@ -845,7 +901,7 @@ class ModelManager(object): for model_key, model_config in self.models.items(): model_name, base_model, model_type = self.parse_key(model_key) - model_class = MODEL_CLASSES[base_model][model_type] + model_class = self._get_implementation(base_model, model_type) if model_class.save_to_config: # TODO: or exclude_unset better fits here? data_to_save[model_key] = model_config.dict(exclude_defaults=True, exclude={"error"}) @@ -903,7 +959,7 @@ class ModelManager(object): model_path = self.resolve_model_path(model_config.path).absolute() if not model_path.exists(): - model_class = MODEL_CLASSES[cur_base_model][cur_model_type] + model_class = self._get_implementation(cur_base_model, cur_model_type) if model_class.save_to_config: model_config.error = ModelError.NotFound self.models.pop(model_key, None) @@ -919,7 +975,7 @@ class ModelManager(object): for cur_model_type in ModelType: if model_type is not None and cur_model_type != model_type: continue - model_class = MODEL_CLASSES[cur_base_model][cur_model_type] + model_class = self._get_implementation(cur_base_model, cur_model_type) models_dir = self.resolve_model_path(Path(cur_base_model.value, cur_model_type.value)) if not models_dir.exists(): @@ -935,7 +991,9 @@ class ModelManager(object): raise DuplicateModelException(f"Model with key {model_key} added twice") model_path = self.relative_model_path(model_path) - model_config: ModelConfigBase = model_class.probe_config(str(model_path)) + model_config: ModelConfigBase = model_class.probe_config( + str(model_path), model_base=cur_base_model + ) self.models[model_key] = model_config new_models_found = True except DuplicateModelException as e: @@ -983,7 +1041,7 @@ class ModelManager(object): # LS: hacky # Patch in the SD VAE from core so that it is available for use by the UI try: - self.heuristic_import({self.resolve_model_path("core/convert/sd-vae-ft-mse")}) + self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))}) except: pass @@ -992,7 +1050,7 @@ class ModelManager(object): model_manager=self, prediction_type_helper=ask_user_for_prediction_type, ) - known_paths = {config.root_path / x["path"] for x in self.list_models()} + known_paths = {self.resolve_model_path(x["path"]) for x in self.list_models()} directories = { config.root_path / x for x in [ @@ -1011,7 +1069,7 @@ class ModelManager(object): def heuristic_import( self, items_to_import: Set[str], - prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None, + prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None, ) -> Dict[str, AddModelResult]: """Import a list of paths, repo_ids or URLs. Returns the set of successfully imported items. diff --git a/invokeai/backend/model_management/model_merge.py b/invokeai/backend/model_management/model_merge.py index 8cf3ce4ad0..74419a4433 100644 --- a/invokeai/backend/model_management/model_merge.py +++ b/invokeai/backend/model_management/model_merge.py @@ -33,7 +33,7 @@ class ModelMerger(object): self, model_paths: List[Path], alpha: float = 0.5, - interp: MergeInterpolationMethod = None, + interp: Optional[MergeInterpolationMethod] = None, force: bool = False, **kwargs, ) -> DiffusionPipeline: @@ -73,7 +73,7 @@ class ModelMerger(object): base_model: Union[BaseModelType, str], merged_model_name: str, alpha: float = 0.5, - interp: MergeInterpolationMethod = None, + interp: Optional[MergeInterpolationMethod] = None, force: bool = False, merge_dest_directory: Optional[Path] = None, **kwargs, @@ -122,7 +122,7 @@ class ModelMerger(object): dump_path.mkdir(parents=True, exist_ok=True) dump_path = dump_path / merged_model_name - merged_pipe.save_pretrained(dump_path, safe_serialization=1) + merged_pipe.save_pretrained(dump_path, safe_serialization=True) attributes = dict( path=str(dump_path), description=f"Merge of models {', '.join(model_names)}", diff --git a/invokeai/backend/model_management/model_probe.py b/invokeai/backend/model_management/model_probe.py index c3964d760c..145c56c273 100644 --- a/invokeai/backend/model_management/model_probe.py +++ b/invokeai/backend/model_management/model_probe.py @@ -17,6 +17,7 @@ from .models import ( SilenceWarnings, InvalidModelException, ) +from .util import lora_token_vector_length from .models.base import read_checkpoint_meta @@ -315,21 +316,16 @@ class LoRACheckpointProbe(CheckpointProbeBase): def get_base_type(self) -> BaseModelType: checkpoint = self.checkpoint - key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight" - key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a" - lora_token_vector_length = ( - checkpoint[key1].shape[1] - if key1 in checkpoint - else checkpoint[key2].shape[0] - if key2 in checkpoint - else 768 - ) - if lora_token_vector_length == 768: + token_vector_length = lora_token_vector_length(checkpoint) + + if token_vector_length == 768: return BaseModelType.StableDiffusion1 - elif lora_token_vector_length == 1024: + elif token_vector_length == 1024: return BaseModelType.StableDiffusion2 + elif token_vector_length == 2048: + return BaseModelType.StableDiffusionXL else: - return None + raise InvalidModelException(f"Unknown LoRA type: {self.checkpoint_path}") class TextualInversionCheckpointProbe(CheckpointProbeBase): diff --git a/invokeai/backend/model_management/models/base.py b/invokeai/backend/model_management/models/base.py index e6a20e79ec..d335b645c8 100644 --- a/invokeai/backend/model_management/models/base.py +++ b/invokeai/backend/model_management/models/base.py @@ -292,8 +292,9 @@ class DiffusersModel(ModelBase): ) break except Exception as e: - # print("====ERR LOAD====") - # print(f"{variant}: {e}") + if not str(e).startswith("Error no file"): + print("====ERR LOAD====") + print(f"{variant}: {e}") pass else: raise Exception(f"Failed to load {self.base_model}:{self.model_type}:{child_type} model") diff --git a/invokeai/backend/model_management/models/lora.py b/invokeai/backend/model_management/models/lora.py index 642f8bbeec..b6f321d60b 100644 --- a/invokeai/backend/model_management/models/lora.py +++ b/invokeai/backend/model_management/models/lora.py @@ -1,21 +1,23 @@ +import bisect import os -import torch from enum import Enum -from typing import Optional, Union, Literal +from pathlib import Path +from typing import Dict, Optional, Union + +import torch +from safetensors.torch import load_file + from .base import ( + BaseModelType, + InvalidModelException, ModelBase, ModelConfigBase, - BaseModelType, + ModelNotFoundException, ModelType, SubModelType, classproperty, - InvalidModelException, - ModelNotFoundException, ) -# TODO: naming -from ..lora import LoRAModel as LoRAModelRaw - class LoRAModelFormat(str, Enum): LyCORIS = "lycoris" @@ -50,6 +52,7 @@ class LoRAModel(ModelBase): model = LoRAModelRaw.from_checkpoint( file_path=self.model_path, dtype=torch_dtype, + base_model=self.base_model, ) self.model_size = model.calc_size() @@ -87,3 +90,622 @@ class LoRAModel(ModelBase): raise NotImplementedError("Diffusers lora not supported") else: return model_path + + +class LoRALayerBase: + # rank: Optional[int] + # alpha: Optional[float] + # bias: Optional[torch.Tensor] + # layer_key: str + + # @property + # def scale(self): + # return self.alpha / self.rank if (self.alpha and self.rank) else 1.0 + + def __init__( + self, + layer_key: str, + values: dict, + ): + if "alpha" in values: + self.alpha = values["alpha"].item() + else: + self.alpha = None + + if "bias_indices" in values and "bias_values" in values and "bias_size" in values: + self.bias = torch.sparse_coo_tensor( + values["bias_indices"], + values["bias_values"], + tuple(values["bias_size"]), + ) + + else: + self.bias = None + + self.rank = None # set in layer implementation + self.layer_key = layer_key + + def get_weight(self, orig_weight: torch.Tensor): + raise NotImplementedError() + + def calc_size(self) -> int: + model_size = 0 + for val in [self.bias]: + if val is not None: + model_size += val.nelement() * val.element_size() + return model_size + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + if self.bias is not None: + self.bias = self.bias.to(device=device, dtype=dtype) + + +# TODO: find and debug lora/locon with bias +class LoRALayer(LoRALayerBase): + # up: torch.Tensor + # mid: Optional[torch.Tensor] + # down: torch.Tensor + + def __init__( + self, + layer_key: str, + values: dict, + ): + super().__init__(layer_key, values) + + self.up = values["lora_up.weight"] + self.down = values["lora_down.weight"] + if "lora_mid.weight" in values: + self.mid = values["lora_mid.weight"] + else: + self.mid = None + + self.rank = self.down.shape[0] + + def get_weight(self, orig_weight: torch.Tensor): + if self.mid is not None: + up = self.up.reshape(self.up.shape[0], self.up.shape[1]) + down = self.down.reshape(self.down.shape[0], self.down.shape[1]) + weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down) + else: + weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1) + + return weight + + def calc_size(self) -> int: + model_size = super().calc_size() + for val in [self.up, self.mid, self.down]: + if val is not None: + model_size += val.nelement() * val.element_size() + return model_size + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + super().to(device=device, dtype=dtype) + + self.up = self.up.to(device=device, dtype=dtype) + self.down = self.down.to(device=device, dtype=dtype) + + if self.mid is not None: + self.mid = self.mid.to(device=device, dtype=dtype) + + +class LoHALayer(LoRALayerBase): + # w1_a: torch.Tensor + # w1_b: torch.Tensor + # w2_a: torch.Tensor + # w2_b: torch.Tensor + # t1: Optional[torch.Tensor] = None + # t2: Optional[torch.Tensor] = None + + def __init__( + self, + layer_key: str, + values: dict, + ): + super().__init__(layer_key, values) + + self.w1_a = values["hada_w1_a"] + self.w1_b = values["hada_w1_b"] + self.w2_a = values["hada_w2_a"] + self.w2_b = values["hada_w2_b"] + + if "hada_t1" in values: + self.t1 = values["hada_t1"] + else: + self.t1 = None + + if "hada_t2" in values: + self.t2 = values["hada_t2"] + else: + self.t2 = None + + self.rank = self.w1_b.shape[0] + + def get_weight(self, orig_weight: torch.Tensor): + if self.t1 is None: + weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b) + + else: + rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a) + rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a) + weight = rebuild1 * rebuild2 + + return weight + + def calc_size(self) -> int: + model_size = super().calc_size() + for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]: + if val is not None: + model_size += val.nelement() * val.element_size() + return model_size + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + super().to(device=device, dtype=dtype) + + self.w1_a = self.w1_a.to(device=device, dtype=dtype) + self.w1_b = self.w1_b.to(device=device, dtype=dtype) + if self.t1 is not None: + self.t1 = self.t1.to(device=device, dtype=dtype) + + self.w2_a = self.w2_a.to(device=device, dtype=dtype) + self.w2_b = self.w2_b.to(device=device, dtype=dtype) + if self.t2 is not None: + self.t2 = self.t2.to(device=device, dtype=dtype) + + +class LoKRLayer(LoRALayerBase): + # w1: Optional[torch.Tensor] = None + # w1_a: Optional[torch.Tensor] = None + # w1_b: Optional[torch.Tensor] = None + # w2: Optional[torch.Tensor] = None + # w2_a: Optional[torch.Tensor] = None + # w2_b: Optional[torch.Tensor] = None + # t2: Optional[torch.Tensor] = None + + def __init__( + self, + layer_key: str, + values: dict, + ): + super().__init__(layer_key, values) + + if "lokr_w1" in values: + self.w1 = values["lokr_w1"] + self.w1_a = None + self.w1_b = None + else: + self.w1 = None + self.w1_a = values["lokr_w1_a"] + self.w1_b = values["lokr_w1_b"] + + if "lokr_w2" in values: + self.w2 = values["lokr_w2"] + self.w2_a = None + self.w2_b = None + else: + self.w2 = None + self.w2_a = values["lokr_w2_a"] + self.w2_b = values["lokr_w2_b"] + + if "lokr_t2" in values: + self.t2 = values["lokr_t2"] + else: + self.t2 = None + + if "lokr_w1_b" in values: + self.rank = values["lokr_w1_b"].shape[0] + elif "lokr_w2_b" in values: + self.rank = values["lokr_w2_b"].shape[0] + else: + self.rank = None # unscaled + + def get_weight(self, orig_weight: torch.Tensor): + w1 = self.w1 + if w1 is None: + w1 = self.w1_a @ self.w1_b + + w2 = self.w2 + if w2 is None: + if self.t2 is None: + w2 = self.w2_a @ self.w2_b + else: + w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + w2 = w2.contiguous() + weight = torch.kron(w1, w2) + + return weight + + def calc_size(self) -> int: + model_size = super().calc_size() + for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]: + if val is not None: + model_size += val.nelement() * val.element_size() + return model_size + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + super().to(device=device, dtype=dtype) + + if self.w1 is not None: + self.w1 = self.w1.to(device=device, dtype=dtype) + else: + self.w1_a = self.w1_a.to(device=device, dtype=dtype) + self.w1_b = self.w1_b.to(device=device, dtype=dtype) + + if self.w2 is not None: + self.w2 = self.w2.to(device=device, dtype=dtype) + else: + self.w2_a = self.w2_a.to(device=device, dtype=dtype) + self.w2_b = self.w2_b.to(device=device, dtype=dtype) + + if self.t2 is not None: + self.t2 = self.t2.to(device=device, dtype=dtype) + + +class FullLayer(LoRALayerBase): + # weight: torch.Tensor + + def __init__( + self, + layer_key: str, + values: dict, + ): + super().__init__(layer_key, values) + + self.weight = values["diff"] + + if len(values.keys()) > 1: + _keys = list(values.keys()) + _keys.remove("diff") + raise NotImplementedError(f"Unexpected keys in lora diff layer: {_keys}") + + self.rank = None # unscaled + + def get_weight(self, orig_weight: torch.Tensor): + return self.weight + + def calc_size(self) -> int: + model_size = super().calc_size() + model_size += self.weight.nelement() * self.weight.element_size() + return model_size + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + super().to(device=device, dtype=dtype) + + self.weight = self.weight.to(device=device, dtype=dtype) + + +class IA3Layer(LoRALayerBase): + # weight: torch.Tensor + # on_input: torch.Tensor + + def __init__( + self, + layer_key: str, + values: dict, + ): + super().__init__(layer_key, values) + + self.weight = values["weight"] + self.on_input = values["on_input"] + + self.rank = None # unscaled + + def get_weight(self, orig_weight: torch.Tensor): + weight = self.weight + if not self.on_input: + weight = weight.reshape(-1, 1) + return orig_weight * weight + + def calc_size(self) -> int: + model_size = super().calc_size() + model_size += self.weight.nelement() * self.weight.element_size() + model_size += self.on_input.nelement() * self.on_input.element_size() + return model_size + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + super().to(device=device, dtype=dtype) + + self.weight = self.weight.to(device=device, dtype=dtype) + self.on_input = self.on_input.to(device=device, dtype=dtype) + + +# TODO: rename all methods used in model logic with Info postfix and remove here Raw postfix +class LoRAModelRaw: # (torch.nn.Module): + _name: str + layers: Dict[str, LoRALayer] + _device: torch.device + _dtype: torch.dtype + + def __init__( + self, + name: str, + layers: Dict[str, LoRALayer], + device: torch.device, + dtype: torch.dtype, + ): + self._name = name + self._device = device or torch.cpu + self._dtype = dtype or torch.float32 + self.layers = layers + + @property + def name(self): + return self._name + + @property + def device(self): + return self._device + + @property + def dtype(self): + return self._dtype + + def to( + self, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + # TODO: try revert if exception? + for key, layer in self.layers.items(): + layer.to(device=device, dtype=dtype) + self._device = device + self._dtype = dtype + + def calc_size(self) -> int: + model_size = 0 + for _, layer in self.layers.items(): + model_size += layer.calc_size() + return model_size + + @classmethod + def _convert_sdxl_keys_to_diffusers_format(cls, state_dict): + """Convert the keys of an SDXL LoRA state_dict to diffusers format. + + The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in + diffusers format, then this function will have no effect. + + This function is adapted from: + https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409 + + Args: + state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict. + + Raises: + ValueError: If state_dict contains an unrecognized key, or not all keys could be converted. + + Returns: + Dict[str, Tensor]: The diffusers-format state_dict. + """ + converted_count = 0 # The number of Stability AI keys converted to diffusers format. + not_converted_count = 0 # The number of keys that were not converted. + + # Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes. + # For example, we want to efficiently find `input_blocks_4_1` in the list when searching for + # `input_blocks_4_1_proj_in`. + stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP) + stability_unet_keys.sort() + + new_state_dict = dict() + for full_key, value in state_dict.items(): + if full_key.startswith("lora_unet_"): + search_key = full_key.replace("lora_unet_", "") + # Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix. + position = bisect.bisect_right(stability_unet_keys, search_key) + map_key = stability_unet_keys[position - 1] + # Now, check if the map_key *actually* matches the search_key. + if search_key.startswith(map_key): + new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key]) + new_state_dict[new_key] = value + converted_count += 1 + else: + new_state_dict[full_key] = value + not_converted_count += 1 + elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"): + # The CLIP text encoders have the same keys in both Stability AI and diffusers formats. + new_state_dict[full_key] = value + continue + else: + raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.") + + if converted_count > 0 and not_converted_count > 0: + raise ValueError( + f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count}," + f" not_converted={not_converted_count}" + ) + + return new_state_dict + + @classmethod + def from_checkpoint( + cls, + file_path: Union[str, Path], + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + base_model: Optional[BaseModelType] = None, + ): + device = device or torch.device("cpu") + dtype = dtype or torch.float32 + + if isinstance(file_path, str): + file_path = Path(file_path) + + model = cls( + device=device, + dtype=dtype, + name=file_path.stem, # TODO: + layers=dict(), + ) + + if file_path.suffix == ".safetensors": + state_dict = load_file(file_path.absolute().as_posix(), device="cpu") + else: + state_dict = torch.load(file_path, map_location="cpu") + + state_dict = cls._group_state(state_dict) + + if base_model == BaseModelType.StableDiffusionXL: + state_dict = cls._convert_sdxl_keys_to_diffusers_format(state_dict) + + for layer_key, values in state_dict.items(): + # lora and locon + if "lora_down.weight" in values: + layer = LoRALayer(layer_key, values) + + # loha + elif "hada_w1_b" in values: + layer = LoHALayer(layer_key, values) + + # lokr + elif "lokr_w1_b" in values or "lokr_w1" in values: + layer = LoKRLayer(layer_key, values) + + # diff + elif "diff" in values: + layer = FullLayer(layer_key, values) + + # ia3 + elif "weight" in values and "on_input" in values: + layer = IA3Layer(layer_key, values) + + else: + print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}") + raise Exception("Unknown lora format!") + + # lower memory consumption by removing already parsed layer values + state_dict[layer_key].clear() + + layer.to(device=device, dtype=dtype) + model.layers[layer_key] = layer + + return model + + @staticmethod + def _group_state(state_dict: dict): + state_dict_groupped = dict() + + for key, value in state_dict.items(): + stem, leaf = key.split(".", 1) + if stem not in state_dict_groupped: + state_dict_groupped[stem] = dict() + state_dict_groupped[stem][leaf] = value + + return state_dict_groupped + + +# code from +# https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32 +def make_sdxl_unet_conversion_map(): + """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format.""" + unet_conversion_map_layer = [] + + for i in range(3): # num_blocks is 3 in sdxl + # loop over downblocks/upblocks + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3*i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + # if i > 0: commentout for sdxl + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}." # change for sdxl + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + + hf_mid_atn_prefix = "mid_block.attentions.0." + sd_mid_atn_prefix = "middle_block.1." + unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + + for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2*j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0.", "norm1."), + ("in_layers.2.", "conv1."), + ("out_layers.0.", "norm2."), + ("out_layers.3.", "conv2."), + ("emb_layers.1.", "time_emb_proj."), + ("skip_connection.", "conv_shortcut."), + ] + + unet_conversion_map = [] + for sd, hf in unet_conversion_map_layer: + if "resnets" in hf: + for sd_res, hf_res in unet_conversion_map_resnet: + unet_conversion_map.append((sd + sd_res, hf + hf_res)) + else: + unet_conversion_map.append((sd, hf)) + + for j in range(2): + hf_time_embed_prefix = f"time_embedding.linear_{j+1}." + sd_time_embed_prefix = f"time_embed.{j*2}." + unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix)) + + for j in range(2): + hf_label_embed_prefix = f"add_embedding.linear_{j+1}." + sd_label_embed_prefix = f"label_emb.0.{j*2}." + unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix)) + + unet_conversion_map.append(("input_blocks.0.0.", "conv_in.")) + unet_conversion_map.append(("out.0.", "conv_norm_out.")) + unet_conversion_map.append(("out.2.", "conv_out.")) + + return unet_conversion_map + + +SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = { + sd.rstrip(".").replace(".", "_"): hf.rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map() +} diff --git a/invokeai/backend/model_management/models/sdxl.py b/invokeai/backend/model_management/models/sdxl.py index 7fc3efb77c..5bbe05be98 100644 --- a/invokeai/backend/model_management/models/sdxl.py +++ b/invokeai/backend/model_management/models/sdxl.py @@ -1,6 +1,5 @@ import os import json -import invokeai.backend.util.logging as logger from enum import Enum from pydantic import Field from typing import Literal, Optional @@ -12,6 +11,7 @@ from .base import ( DiffusersModel, read_checkpoint_meta, classproperty, + InvalidModelException, ) from omegaconf import OmegaConf @@ -65,7 +65,7 @@ class StableDiffusionXLModel(DiffusersModel): in_channels = unet_config["in_channels"] else: - raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)") + raise InvalidModelException(f"{path} is not a recognized Stable Diffusion diffusers model") else: raise NotImplementedError(f"Unknown stable diffusion 2.* format: {model_format}") @@ -80,8 +80,10 @@ class StableDiffusionXLModel(DiffusersModel): raise Exception("Unkown stable diffusion 2.* model format") if ckpt_config_path is None: - # TO DO: implement picking - pass + # avoid circular import + from .stable_diffusion import _select_ckpt_config + + ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant) return cls.create_config( path=path, diff --git a/invokeai/backend/model_management/models/stable_diffusion.py b/invokeai/backend/model_management/models/stable_diffusion.py index d81b0150e5..a112e8bc96 100644 --- a/invokeai/backend/model_management/models/stable_diffusion.py +++ b/invokeai/backend/model_management/models/stable_diffusion.py @@ -4,6 +4,7 @@ from enum import Enum from pydantic import Field from pathlib import Path from typing import Literal, Optional, Union +from diffusers import StableDiffusionInpaintPipeline, StableDiffusionPipeline from .base import ( ModelConfigBase, BaseModelType, @@ -263,6 +264,8 @@ def _convert_ckpt_and_cache( weights = app_config.models_path / model_config.path config_file = app_config.root_path / model_config.config output_path = Path(output_path) + variant = model_config.variant + pipeline_class = StableDiffusionInpaintPipeline if variant == "inpaint" else StableDiffusionPipeline # return cached version if it exists if output_path.exists(): @@ -289,6 +292,7 @@ def _convert_ckpt_and_cache( original_config_file=config_file, extract_ema=True, scan_needed=True, + pipeline_class=pipeline_class, from_safetensors=weights.suffix == ".safetensors", precision=torch_dtype(choose_torch_device()), **kwargs, diff --git a/invokeai/backend/model_management/models/vae.py b/invokeai/backend/model_management/models/vae.py index b15844bcf8..957a102ffb 100644 --- a/invokeai/backend/model_management/models/vae.py +++ b/invokeai/backend/model_management/models/vae.py @@ -1,9 +1,14 @@ import os -import torch -import safetensors from enum import Enum from pathlib import Path -from typing import Optional, Union, Literal +from typing import Optional + +import safetensors +import torch +from diffusers.utils import is_safetensors_available +from omegaconf import OmegaConf + +from invokeai.app.services.config import InvokeAIAppConfig from .base import ( ModelBase, ModelConfigBase, @@ -18,9 +23,6 @@ from .base import ( InvalidModelException, ModelNotFoundException, ) -from invokeai.app.services.config import InvokeAIAppConfig -from diffusers.utils import is_safetensors_available -from omegaconf import OmegaConf class VaeModelFormat(str, Enum): @@ -80,7 +82,7 @@ class VaeModel(ModelBase): @classmethod def detect_format(cls, path: str): if not os.path.exists(path): - raise ModelNotFoundException() + raise ModelNotFoundException(f"Does not exist as local file: {path}") if os.path.isdir(path): if os.path.exists(os.path.join(path, "config.json")): diff --git a/invokeai/backend/model_management/util.py b/invokeai/backend/model_management/util.py new file mode 100644 index 0000000000..6d70107c93 --- /dev/null +++ b/invokeai/backend/model_management/util.py @@ -0,0 +1,75 @@ +# Copyright (c) 2023 The InvokeAI Development Team +"""Utilities used by the Model Manager""" + + +def lora_token_vector_length(checkpoint: dict) -> int: + """ + Given a checkpoint in memory, return the lora token vector length + + :param checkpoint: The checkpoint + """ + + def _get_shape_1(key, tensor, checkpoint): + lora_token_vector_length = None + + if "." not in key: + return lora_token_vector_length # wrong key format + model_key, lora_key = key.split(".", 1) + + # check lora/locon + if lora_key == "lora_down.weight": + lora_token_vector_length = tensor.shape[1] + + # check loha (don't worry about hada_t1/hada_t2 as it used only in 4d shapes) + elif lora_key in ["hada_w1_b", "hada_w2_b"]: + lora_token_vector_length = tensor.shape[1] + + # check lokr (don't worry about lokr_t2 as it used only in 4d shapes) + elif "lokr_" in lora_key: + if model_key + ".lokr_w1" in checkpoint: + _lokr_w1 = checkpoint[model_key + ".lokr_w1"] + elif model_key + "lokr_w1_b" in checkpoint: + _lokr_w1 = checkpoint[model_key + ".lokr_w1_b"] + else: + return lora_token_vector_length # unknown format + + if model_key + ".lokr_w2" in checkpoint: + _lokr_w2 = checkpoint[model_key + ".lokr_w2"] + elif model_key + "lokr_w2_b" in checkpoint: + _lokr_w2 = checkpoint[model_key + ".lokr_w2_b"] + else: + return lora_token_vector_length # unknown format + + lora_token_vector_length = _lokr_w1.shape[1] * _lokr_w2.shape[1] + + elif lora_key == "diff": + lora_token_vector_length = tensor.shape[1] + + # ia3 can be detected only by shape[0] in text encoder + elif lora_key == "weight" and "lora_unet_" not in model_key: + lora_token_vector_length = tensor.shape[0] + + return lora_token_vector_length + + lora_token_vector_length = None + lora_te1_length = None + lora_te2_length = None + for key, tensor in checkpoint.items(): + if key.startswith("lora_unet_") and ("_attn2_to_k." in key or "_attn2_to_v." in key): + lora_token_vector_length = _get_shape_1(key, tensor, checkpoint) + elif key.startswith("lora_te") and "_self_attn_" in key: + tmp_length = _get_shape_1(key, tensor, checkpoint) + if key.startswith("lora_te_"): + lora_token_vector_length = tmp_length + elif key.startswith("lora_te1_"): + lora_te1_length = tmp_length + elif key.startswith("lora_te2_"): + lora_te2_length = tmp_length + + if lora_te1_length is not None and lora_te2_length is not None: + lora_token_vector_length = lora_te1_length + lora_te2_length + + if lora_token_vector_length is not None: + break + + return lora_token_vector_length diff --git a/invokeai/backend/stable_diffusion/__init__.py b/invokeai/backend/stable_diffusion/__init__.py index 37024ccace..21273c6201 100644 --- a/invokeai/backend/stable_diffusion/__init__.py +++ b/invokeai/backend/stable_diffusion/__init__.py @@ -8,4 +8,4 @@ from .diffusers_pipeline import ( ) from .diffusion import InvokeAIDiffuserComponent from .diffusion.cross_attention_map_saving import AttentionMapSaver -from .diffusion.shared_invokeai_diffusion import PostprocessingSettings +from .diffusion.shared_invokeai_diffusion import PostprocessingSettings, BasicConditioningInfo, SDXLConditioningInfo diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py index 624d47ff64..aa809bc3ec 100644 --- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py +++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py @@ -4,15 +4,11 @@ import dataclasses import inspect import math import secrets -from collections.abc import Sequence from dataclasses import dataclass, field -from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union -from pydantic import Field +from typing import Any, Callable, Generic, List, Optional, Type, Union -import einops import PIL.Image -import numpy as np -from accelerate.utils import set_seed +import einops import psutil import torch import torchvision.transforms as T @@ -22,36 +18,31 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import ( StableDiffusionPipeline, ) - -from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import ( - StableDiffusionImg2ImgPipeline, -) from diffusers.pipelines.stable_diffusion.safety_checker import ( StableDiffusionSafetyChecker, ) from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput -from diffusers.utils import PIL_INTERPOLATION from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.outputs import BaseOutput -from torchvision.transforms.functional import resize as tv_resize +from pydantic import Field from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer -from typing_extensions import ParamSpec from invokeai.app.services.config import InvokeAIAppConfig -from ..util import CPU_DEVICE, normalize_device from .diffusion import ( AttentionMapSaver, InvokeAIDiffuserComponent, PostprocessingSettings, + BasicConditioningInfo, ) -from .offloading import FullyLoadedModelGroup, ModelGroup +from ..util import normalize_device @dataclass class PipelineIntermediateState: - run_id: str step: int + order: int + total_steps: int timestep: int latents: torch.Tensor predicted_original: Optional[torch.Tensor] = None @@ -102,7 +93,6 @@ class AddsMaskGuidance: mask_latents: torch.FloatTensor scheduler: SchedulerMixin noise: torch.Tensor - _debug: Optional[Callable] = None def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput: output_class = step_output.__class__ # We'll create a new one with masked data. @@ -139,8 +129,6 @@ class AddsMaskGuidance: # mask_latents = self.scheduler.scale_model_input(mask_latents, t) mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size) masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype)) - if self._debug: - self._debug(masked_input, f"t={t} lerped") return masked_input @@ -172,33 +160,6 @@ def is_inpainting_model(unet: UNet2DConditionModel): return unet.conv_in.in_channels == 9 -CallbackType = TypeVar("CallbackType") -ReturnType = TypeVar("ReturnType") -ParamType = ParamSpec("ParamType") - - -@dataclass(frozen=True) -class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]): - """Convert a generator to a function with a callback and a return value.""" - - generator_method: Callable[ParamType, ReturnType] - callback_arg_type: Type[CallbackType] - - def __call__( - self, - *args: ParamType.args, - callback: Callable[[CallbackType], Any] = None, - **kwargs: ParamType.kwargs, - ) -> ReturnType: - result = None - for result in self.generator_method(*args, **kwargs): - if callback is not None and isinstance(result, self.callback_arg_type): - callback(result) - if result is None: - raise AssertionError("why was that an empty generator?") - return result - - @dataclass class ControlNetData: model: ControlNetModel = Field(default=None) @@ -212,8 +173,8 @@ class ControlNetData: @dataclass class ConditioningData: - unconditioned_embeddings: torch.Tensor - text_embeddings: torch.Tensor + unconditioned_embeddings: BasicConditioningInfo + text_embeddings: BasicConditioningInfo guidance_scale: Union[float, List[float]] """ Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). @@ -289,9 +250,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ - _model_group: ModelGroup - - ID_LENGTH = 8 def __init__( self, @@ -303,9 +261,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): safety_checker: Optional[StableDiffusionSafetyChecker], feature_extractor: Optional[CLIPFeatureExtractor], requires_safety_checker: bool = False, - precision: str = "float32", control_model: ControlNetModel = None, - execution_device: Optional[torch.device] = None, ): super().__init__( vae, @@ -330,9 +286,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): # control_model=control_model, ) self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward) - - self._model_group = FullyLoadedModelGroup(execution_device or self.unet.device) - self._model_group.install(*self._submodels) self.control_model = control_model def _adjust_memory_efficient_attention(self, latents: torch.Tensor): @@ -340,99 +293,41 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): if xformers is available, use it, otherwise use sliced attention. """ config = InvokeAIAppConfig.get_config() - if torch.cuda.is_available() and is_xformers_available() and not config.disable_xformers: - self.enable_xformers_memory_efficient_attention() + if self.unet.device.type == "cuda": + if is_xformers_available() and not config.disable_xformers: + self.enable_xformers_memory_efficient_attention() + return + elif hasattr(torch.nn.functional, "scaled_dot_product_attention"): + # diffusers enable sdp automatically + return + + if self.unet.device.type == "cpu" or self.unet.device.type == "mps": + mem_free = psutil.virtual_memory().free + elif self.unet.device.type == "cuda": + mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device)) else: - if self.device.type == "cpu" or self.device.type == "mps": - mem_free = psutil.virtual_memory().free - elif self.device.type == "cuda": - mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.device)) - else: - raise ValueError(f"unrecognized device {self.device}") - # input tensor of [1, 4, h/8, w/8] - # output tensor of [16, (h/8 * w/8), (h/8 * w/8)] - bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4 - max_size_required_for_baddbmm = ( - 16 - * latents.size(dim=2) - * latents.size(dim=3) - * latents.size(dim=2) - * latents.size(dim=3) - * bytes_per_element_needed_for_baddbmm_duplication - ) - if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0): # 3.3 / 4.0 is from old Invoke code - self.enable_attention_slicing(slice_size="max") - elif torch.backends.mps.is_available(): - # diffusers recommends always enabling for mps - self.enable_attention_slicing(slice_size="max") - else: - self.disable_attention_slicing() + raise ValueError(f"unrecognized device {self.unet.device}") + # input tensor of [1, 4, h/8, w/8] + # output tensor of [16, (h/8 * w/8), (h/8 * w/8)] + bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4 + max_size_required_for_baddbmm = ( + 16 + * latents.size(dim=2) + * latents.size(dim=3) + * latents.size(dim=2) + * latents.size(dim=3) + * bytes_per_element_needed_for_baddbmm_duplication + ) + if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0): # 3.3 / 4.0 is from old Invoke code + self.enable_attention_slicing(slice_size="max") + elif torch.backends.mps.is_available(): + # diffusers recommends always enabling for mps + self.enable_attention_slicing(slice_size="max") + else: + self.disable_attention_slicing() def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False): - # overridden method; types match the superclass. - if torch_device is None: - return self - self._model_group.set_device(torch.device(torch_device)) - self._model_group.ready() - - @property - def device(self) -> torch.device: - return self._model_group.execution_device - - @property - def _submodels(self) -> Sequence[torch.nn.Module]: - module_names, _, _ = self.extract_init_dict(dict(self.config)) - submodels = [] - for name in module_names.keys(): - if hasattr(self, name): - value = getattr(self, name) - else: - value = getattr(self.config, name) - if isinstance(value, torch.nn.Module): - submodels.append(value) - return submodels - - def image_from_embeddings( - self, - latents: torch.Tensor, - num_inference_steps: int, - conditioning_data: ConditioningData, - *, - noise: torch.Tensor, - callback: Callable[[PipelineIntermediateState], None] = None, - run_id=None, - ) -> InvokeAIStableDiffusionPipelineOutput: - r""" - Function invoked when calling the pipeline for generation. - - :param conditioning_data: - :param latents: Pre-generated un-noised latents, to be used as inputs for - image generation. Can be used to tweak the same generation with different prompts. - :param num_inference_steps: The number of denoising steps. More denoising steps usually lead to a higher quality - image at the expense of slower inference. - :param noise: Noise to add to the latents, sampled from a Gaussian distribution. - :param callback: - :param run_id: - """ - result_latents, result_attention_map_saver = self.latents_from_embeddings( - latents, - num_inference_steps, - conditioning_data, - noise=noise, - run_id=run_id, - callback=callback, - ) - # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 - torch.cuda.empty_cache() - - with torch.inference_mode(): - image = self.decode_latents(result_latents) - output = InvokeAIStableDiffusionPipelineOutput( - images=image, - nsfw_content_detected=[], - attention_map_saver=result_attention_map_saver, - ) - return self.check_for_safety(output, dtype=conditioning_data.dtype) + raise Exception("Should not be called") def latents_from_embeddings( self, @@ -440,35 +335,72 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): num_inference_steps: int, conditioning_data: ConditioningData, *, - noise: torch.Tensor, - timesteps=None, + noise: Optional[torch.Tensor], + timesteps: torch.Tensor, + init_timestep: torch.Tensor, additional_guidance: List[Callable] = None, - run_id=None, callback: Callable[[PipelineIntermediateState], None] = None, control_data: List[ControlNetData] = None, + mask: Optional[torch.Tensor] = None, + seed: Optional[int] = None, ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]: - if self.scheduler.config.get("cpu_only", False): - scheduler_device = torch.device("cpu") - else: - scheduler_device = self._model_group.device_for(self.unet) + if init_timestep.shape[0] == 0: + return latents, None - if timesteps is None: - self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device) - timesteps = self.scheduler.timesteps - infer_latents_from_embeddings = GeneratorToCallbackinator( - self.generate_latents_from_embeddings, PipelineIntermediateState - ) - result: PipelineIntermediateState = infer_latents_from_embeddings( - latents, - timesteps, - conditioning_data, - noise=noise, - run_id=run_id, - additional_guidance=additional_guidance, - control_data=control_data, - callback=callback, - ) - return result.latents, result.attention_map_saver + if additional_guidance is None: + additional_guidance = [] + + orig_latents = latents.clone() + + batch_size = latents.shape[0] + batched_t = init_timestep.expand(batch_size) + + if noise is not None: + # latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers + latents = self.scheduler.add_noise(latents, noise, batched_t) + + if mask is not None: + if is_inpainting_model(self.unet): + # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint + # (that's why there's a mask!) but it seems to really want that blanked out. + # masked_latents = latents * torch.where(mask < 0.5, 1, 0) TODO: inpaint/outpaint/infill + + # TODO: we should probably pass this in so we don't have to try/finally around setting it. + self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(self._unet_forward, mask, orig_latents) + else: + # if no noise provided, noisify unmasked area based on seed(or 0 as fallback) + if noise is None: + noise = torch.randn( + orig_latents.shape, + dtype=torch.float32, + device="cpu", + generator=torch.Generator(device="cpu").manual_seed(seed or 0), + ).to(device=orig_latents.device, dtype=orig_latents.dtype) + + latents = self.scheduler.add_noise(latents, noise, batched_t) + latents = torch.lerp( + orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype) + ) + + additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise)) + + try: + latents, attention_map_saver = self.generate_latents_from_embeddings( + latents, + timesteps, + conditioning_data, + additional_guidance=additional_guidance, + control_data=control_data, + callback=callback, + ) + finally: + self.invokeai_diffuser.model_forward_callback = self._unet_forward + + # restore unmasked part + if mask is not None: + latents = torch.lerp(orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)) + + return latents, attention_map_saver def generate_latents_from_embeddings( self, @@ -476,42 +408,40 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): timesteps, conditioning_data: ConditioningData, *, - noise: torch.Tensor, - run_id: str = None, additional_guidance: List[Callable] = None, control_data: List[ControlNetData] = None, + callback: Callable[[PipelineIntermediateState], None] = None, ): self._adjust_memory_efficient_attention(latents) - if run_id is None: - run_id = secrets.token_urlsafe(self.ID_LENGTH) if additional_guidance is None: additional_guidance = [] + + batch_size = latents.shape[0] + attention_map_saver: Optional[AttentionMapSaver] = None + + if timesteps.shape[0] == 0: + return latents, attention_map_saver + extra_conditioning_info = conditioning_data.extra with self.invokeai_diffuser.custom_attention_context( self.invokeai_diffuser.model, extra_conditioning_info=extra_conditioning_info, step_count=len(self.scheduler.timesteps), ): - yield PipelineIntermediateState( - run_id=run_id, - step=-1, - timestep=self.scheduler.config.num_train_timesteps, - latents=latents, - ) + if callback is not None: + callback( + PipelineIntermediateState( + step=-1, + order=self.scheduler.order, + total_steps=len(timesteps), + timestep=self.scheduler.config.num_train_timesteps, + latents=latents, + ) + ) - batch_size = latents.shape[0] - batched_t = torch.full( - (batch_size,), - timesteps[0], - dtype=timesteps.dtype, - device=self._model_group.device_for(self.unet), - ) - latents = self.scheduler.add_noise(latents, noise, batched_t) - - attention_map_saver: Optional[AttentionMapSaver] = None # print("timesteps:", timesteps) for i, t in enumerate(self.progress_bar(timesteps)): - batched_t.fill_(t) + batched_t = t.expand(batch_size) step_output = self.step( batched_t, latents, @@ -540,14 +470,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): # attention_map_saver = AttentionMapSaver(token_ids=attention_map_token_ids, latents_shape=latents.shape[-2:]) # self.invokeai_diffuser.setup_attention_map_saving(attention_map_saver) - yield PipelineIntermediateState( - run_id=run_id, - step=i, - timestep=int(t), - latents=latents, - predicted_original=predicted_original, - attention_map_saver=attention_map_saver, - ) + if callback is not None: + callback( + PipelineIntermediateState( + step=i, + order=self.scheduler.order, + total_steps=len(timesteps), + timestep=int(t), + latents=latents, + predicted_original=predicted_original, + attention_map_saver=attention_map_saver, + ) + ) return latents, attention_map_saver @@ -569,95 +503,39 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): # TODO: should this scaling happen here or inside self._unet_forward? # i.e. before or after passing it to InvokeAIDiffuserComponent - unet_latent_input = self.scheduler.scale_model_input(latents, timestep) + latent_model_input = self.scheduler.scale_model_input(latents, timestep) # default is no controlnet, so set controlnet processing output to None - down_block_res_samples, mid_block_res_sample = None, None - + controlnet_down_block_samples, controlnet_mid_block_sample = None, None if control_data is not None: - # control_data should be type List[ControlNetData] - # this loop covers both ControlNet (one ControlNetData in list) - # and MultiControlNet (multiple ControlNetData in list) - for i, control_datum in enumerate(control_data): - control_mode = control_datum.control_mode - # soft_injection and cfg_injection are the two ControlNet control_mode booleans - # that are combined at higher level to make control_mode enum - # soft_injection determines whether to do per-layer re-weighting adjustment (if True) - # or default weighting (if False) - soft_injection = control_mode == "more_prompt" or control_mode == "more_control" - # cfg_injection = determines whether to apply ControlNet to only the conditional (if True) - # or the default both conditional and unconditional (if False) - cfg_injection = control_mode == "more_control" or control_mode == "unbalanced" + controlnet_down_block_samples, controlnet_mid_block_sample = self.invokeai_diffuser.do_controlnet_step( + control_data=control_data, + sample=latent_model_input, + timestep=timestep, + step_index=step_index, + total_step_count=total_step_count, + conditioning_data=conditioning_data, + ) - first_control_step = math.floor(control_datum.begin_step_percent * total_step_count) - last_control_step = math.ceil(control_datum.end_step_percent * total_step_count) - # only apply controlnet if current step is within the controlnet's begin/end step range - if step_index >= first_control_step and step_index <= last_control_step: - if cfg_injection: - control_latent_input = unet_latent_input - else: - # expand the latents input to control model if doing classifier free guidance - # (which I think for now is always true, there is conditional elsewhere that stops execution if - # classifier_free_guidance is <= 1.0 ?) - control_latent_input = torch.cat([unet_latent_input] * 2) - - if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned - encoder_hidden_states = conditioning_data.text_embeddings - encoder_attention_mask = None - else: - ( - encoder_hidden_states, - encoder_attention_mask, - ) = self.invokeai_diffuser._concat_conditionings_for_batch( - conditioning_data.unconditioned_embeddings, - conditioning_data.text_embeddings, - ) - if isinstance(control_datum.weight, list): - # if controlnet has multiple weights, use the weight for the current step - controlnet_weight = control_datum.weight[step_index] - else: - # if controlnet has a single weight, use it for all steps - controlnet_weight = control_datum.weight - - # controlnet(s) inference - down_samples, mid_sample = control_datum.model( - sample=control_latent_input, - timestep=timestep, - encoder_hidden_states=encoder_hidden_states, - controlnet_cond=control_datum.image_tensor, - conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale - encoder_attention_mask=encoder_attention_mask, - guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel - return_dict=False, - ) - if cfg_injection: - # Inferred ControlNet only for the conditional batch. - # To apply the output of ControlNet to both the unconditional and conditional batches, - # prepend zeros for unconditional batch - down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples] - mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample]) - - if down_block_res_samples is None and mid_block_res_sample is None: - down_block_res_samples, mid_block_res_sample = down_samples, mid_sample - else: - # add controlnet outputs together if have multiple controlnets - down_block_res_samples = [ - samples_prev + samples_curr - for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) - ] - mid_block_res_sample += mid_sample - - # predict the noise residual - noise_pred = self.invokeai_diffuser.do_diffusion_step( - x=unet_latent_input, - sigma=t, - unconditioning=conditioning_data.unconditioned_embeddings, - conditioning=conditioning_data.text_embeddings, - unconditional_guidance_scale=conditioning_data.guidance_scale, + uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step( + sample=latent_model_input, + timestep=t, # TODO: debug how handled batched and non batched timesteps step_index=step_index, total_step_count=total_step_count, - down_block_additional_residuals=down_block_res_samples, # from controlnet(s) - mid_block_additional_residual=mid_block_res_sample, # from controlnet(s) + conditioning_data=conditioning_data, + # extra: + down_block_additional_residuals=controlnet_down_block_samples, # from controlnet(s) + mid_block_additional_residual=controlnet_mid_block_sample, # from controlnet(s) + ) + + guidance_scale = conditioning_data.guidance_scale + if isinstance(guidance_scale, list): + guidance_scale = guidance_scale[step_index] + + noise_pred = self.invokeai_diffuser._combine( + uc_noise_pred, + c_noise_pred, + guidance_scale, ) # compute the previous noisy sample x_t -> x_t-1 @@ -699,224 +577,3 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): cross_attention_kwargs=cross_attention_kwargs, **kwargs, ).sample - - def img2img_from_embeddings( - self, - init_image: Union[torch.FloatTensor, PIL.Image.Image], - strength: float, - num_inference_steps: int, - conditioning_data: ConditioningData, - *, - callback: Callable[[PipelineIntermediateState], None] = None, - run_id=None, - noise_func=None, - seed=None, - ) -> InvokeAIStableDiffusionPipelineOutput: - if isinstance(init_image, PIL.Image.Image): - init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB")) - - if init_image.dim() == 3: - init_image = einops.rearrange(init_image, "c h w -> 1 c h w") - - # 6. Prepare latent variables - initial_latents = self.non_noised_latents_from_image( - init_image, - device=self._model_group.device_for(self.unet), - dtype=self.unet.dtype, - ) - if seed is not None: - set_seed(seed) - noise = noise_func(initial_latents) - - return self.img2img_from_latents_and_embeddings( - initial_latents, - num_inference_steps, - conditioning_data, - strength, - noise, - run_id, - callback, - ) - - def img2img_from_latents_and_embeddings( - self, - initial_latents, - num_inference_steps, - conditioning_data: ConditioningData, - strength, - noise: torch.Tensor, - run_id=None, - callback=None, - ) -> InvokeAIStableDiffusionPipelineOutput: - timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength) - result_latents, result_attention_maps = self.latents_from_embeddings( - latents=initial_latents - if strength < 1.0 - else torch.zeros_like(initial_latents, device=initial_latents.device, dtype=initial_latents.dtype), - num_inference_steps=num_inference_steps, - conditioning_data=conditioning_data, - timesteps=timesteps, - noise=noise, - run_id=run_id, - callback=callback, - ) - - # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 - torch.cuda.empty_cache() - - with torch.inference_mode(): - image = self.decode_latents(result_latents) - output = InvokeAIStableDiffusionPipelineOutput( - images=image, - nsfw_content_detected=[], - attention_map_saver=result_attention_maps, - ) - return self.check_for_safety(output, dtype=conditioning_data.dtype) - - def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device=None) -> (torch.Tensor, int): - img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components) - assert img2img_pipeline.scheduler is self.scheduler - - if self.scheduler.config.get("cpu_only", False): - scheduler_device = torch.device("cpu") - else: - scheduler_device = self._model_group.device_for(self.unet) - - img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device) - timesteps, adjusted_steps = img2img_pipeline.get_timesteps( - num_inference_steps, strength, device=scheduler_device - ) - # Workaround for low strength resulting in zero timesteps. - # TODO: submit upstream fix for zero-step img2img - if timesteps.numel() == 0: - timesteps = self.scheduler.timesteps[-1:] - adjusted_steps = timesteps.numel() - return timesteps, adjusted_steps - - def inpaint_from_embeddings( - self, - init_image: torch.FloatTensor, - mask: torch.FloatTensor, - strength: float, - num_inference_steps: int, - conditioning_data: ConditioningData, - *, - callback: Callable[[PipelineIntermediateState], None] = None, - run_id=None, - noise_func=None, - seed=None, - ) -> InvokeAIStableDiffusionPipelineOutput: - device = self._model_group.device_for(self.unet) - latents_dtype = self.unet.dtype - - if isinstance(init_image, PIL.Image.Image): - init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB")) - - init_image = init_image.to(device=device, dtype=latents_dtype) - mask = mask.to(device=device, dtype=latents_dtype) - - if init_image.dim() == 3: - init_image = init_image.unsqueeze(0) - - timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength) - - # 6. Prepare latent variables - # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents - # because we have our own noise function - init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype) - if seed is not None: - set_seed(seed) - noise = noise_func(init_image_latents) - - if mask.dim() == 3: - mask = mask.unsqueeze(0) - latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR).to( - device=device, dtype=latents_dtype - ) - - guidance: List[Callable] = [] - - if is_inpainting_model(self.unet): - # You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint - # (that's why there's a mask!) but it seems to really want that blanked out. - masked_init_image = init_image * torch.where(mask < 0.5, 1, 0) - masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype) - - # TODO: we should probably pass this in so we don't have to try/finally around setting it. - self.invokeai_diffuser.model_forward_callback = AddsMaskLatents( - self._unet_forward, latent_mask, masked_latents - ) - else: - guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise)) - - try: - result_latents, result_attention_maps = self.latents_from_embeddings( - latents=init_image_latents - if strength < 1.0 - else torch.zeros_like( - init_image_latents, device=init_image_latents.device, dtype=init_image_latents.dtype - ), - num_inference_steps=num_inference_steps, - conditioning_data=conditioning_data, - noise=noise, - timesteps=timesteps, - additional_guidance=guidance, - run_id=run_id, - callback=callback, - ) - finally: - self.invokeai_diffuser.model_forward_callback = self._unet_forward - - # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699 - torch.cuda.empty_cache() - - with torch.inference_mode(): - image = self.decode_latents(result_latents) - output = InvokeAIStableDiffusionPipelineOutput( - images=image, - nsfw_content_detected=[], - attention_map_saver=result_attention_maps, - ) - return self.check_for_safety(output, dtype=conditioning_data.dtype) - - def non_noised_latents_from_image(self, init_image, *, device: torch.device, dtype): - init_image = init_image.to(device=device, dtype=dtype) - with torch.inference_mode(): - self._model_group.load(self.vae) - init_latent_dist = self.vae.encode(init_image).latent_dist - init_latents = init_latent_dist.sample().to(dtype=dtype) # FIXME: uses torch.randn. make reproducible! - - init_latents = 0.18215 * init_latents - return init_latents - - def check_for_safety(self, output, dtype): - with torch.inference_mode(): - screened_images, has_nsfw_concept = self.run_safety_checker(output.images, dtype=dtype) - screened_attention_map_saver = None - if has_nsfw_concept is None or not has_nsfw_concept: - screened_attention_map_saver = output.attention_map_saver - return InvokeAIStableDiffusionPipelineOutput( - screened_images, - has_nsfw_concept, - # block the attention maps if NSFW content is detected - attention_map_saver=screened_attention_map_saver, - ) - - def run_safety_checker(self, image, device=None, dtype=None): - # overriding to use the model group for device info instead of requiring the caller to know. - if self.safety_checker is not None: - device = self._model_group.device_for(self.safety_checker) - return super().run_safety_checker(image, device, dtype) - - def decode_latents(self, latents): - # Explicit call to get the vae loaded, since `decode` isn't the forward method. - self._model_group.load(self.vae) - return super().decode_latents(latents) - - def debug_latents(self, latents, msg): - from invokeai.backend.image_util import debug_image - - with torch.inference_mode(): - decoded = self.numpy_to_pil(self.decode_latents(latents)) - for i, img in enumerate(decoded): - debug_image(img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True) diff --git a/invokeai/backend/stable_diffusion/diffusion/__init__.py b/invokeai/backend/stable_diffusion/diffusion/__init__.py index 6dd2817f29..76f338aeaf 100644 --- a/invokeai/backend/stable_diffusion/diffusion/__init__.py +++ b/invokeai/backend/stable_diffusion/diffusion/__init__.py @@ -3,4 +3,9 @@ Initialization file for invokeai.models.diffusion """ from .cross_attention_control import InvokeAICrossAttentionMixin from .cross_attention_map_saving import AttentionMapSaver -from .shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings +from .shared_invokeai_diffusion import ( + InvokeAIDiffuserComponent, + PostprocessingSettings, + BasicConditioningInfo, + SDXLConditioningInfo, +) diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py index 272518e928..e739855b9e 100644 --- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py +++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py @@ -1,6 +1,8 @@ +from __future__ import annotations + from contextlib import contextmanager from dataclasses import dataclass -from math import ceil +import math from typing import Any, Callable, Dict, Optional, Union, List import numpy as np @@ -32,6 +34,29 @@ ModelForwardCallback: TypeAlias = Union[ ] +@dataclass +class BasicConditioningInfo: + embeds: torch.Tensor + extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] + # weight: float + # mode: ConditioningAlgo + + def to(self, device, dtype=None): + self.embeds = self.embeds.to(device=device, dtype=dtype) + return self + + +@dataclass +class SDXLConditioningInfo(BasicConditioningInfo): + pooled_embeds: torch.Tensor + add_time_ids: torch.Tensor + + def to(self, device, dtype=None): + self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype) + self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype) + return super().to(device=device, dtype=dtype) + + @dataclass(frozen=True) class PostprocessingSettings: threshold: float @@ -78,10 +103,9 @@ class InvokeAIDiffuserComponent: self.cross_attention_control_context = None self.sequential_guidance = config.sequential_guidance - @classmethod @contextmanager def custom_attention_context( - cls, + self, unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int, @@ -91,18 +115,19 @@ class InvokeAIDiffuserComponent: old_attn_processors = unet.attn_processors # Load lora conditions into the model if extra_conditioning_info.wants_cross_attention_control: - cross_attention_control_context = Context( + self.cross_attention_control_context = Context( arguments=extra_conditioning_info.cross_attention_control_args, step_count=step_count, ) setup_cross_attention_control_attention_processors( unet, - cross_attention_control_context, + self.cross_attention_control_context, ) try: yield None finally: + self.cross_attention_control_context = None if old_attn_processors is not None: unet.set_attn_processor(old_attn_processors) # TODO resuscitate attention map saving @@ -127,33 +152,125 @@ class InvokeAIDiffuserComponent: for _, module in tokens_cross_attention_modules: module.set_attention_slice_calculated_callback(None) - def do_diffusion_step( + def do_controlnet_step( self, - x: torch.Tensor, - sigma: torch.Tensor, - unconditioning: Union[torch.Tensor, dict], - conditioning: Union[torch.Tensor, dict], - # unconditional_guidance_scale: float, - unconditional_guidance_scale: Union[float, List[float]], - step_index: Optional[int] = None, - total_step_count: Optional[int] = None, + control_data, + sample: torch.Tensor, + timestep: torch.Tensor, + step_index: int, + total_step_count: int, + conditioning_data, + ): + down_block_res_samples, mid_block_res_sample = None, None + + # control_data should be type List[ControlNetData] + # this loop covers both ControlNet (one ControlNetData in list) + # and MultiControlNet (multiple ControlNetData in list) + for i, control_datum in enumerate(control_data): + control_mode = control_datum.control_mode + # soft_injection and cfg_injection are the two ControlNet control_mode booleans + # that are combined at higher level to make control_mode enum + # soft_injection determines whether to do per-layer re-weighting adjustment (if True) + # or default weighting (if False) + soft_injection = control_mode == "more_prompt" or control_mode == "more_control" + # cfg_injection = determines whether to apply ControlNet to only the conditional (if True) + # or the default both conditional and unconditional (if False) + cfg_injection = control_mode == "more_control" or control_mode == "unbalanced" + + first_control_step = math.floor(control_datum.begin_step_percent * total_step_count) + last_control_step = math.ceil(control_datum.end_step_percent * total_step_count) + # only apply controlnet if current step is within the controlnet's begin/end step range + if step_index >= first_control_step and step_index <= last_control_step: + if cfg_injection: + sample_model_input = sample + else: + # expand the latents input to control model if doing classifier free guidance + # (which I think for now is always true, there is conditional elsewhere that stops execution if + # classifier_free_guidance is <= 1.0 ?) + sample_model_input = torch.cat([sample] * 2) + + added_cond_kwargs = None + + if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned + if type(conditioning_data.text_embeddings) is SDXLConditioningInfo: + added_cond_kwargs = { + "text_embeds": conditioning_data.text_embeddings.pooled_embeds, + "time_ids": conditioning_data.text_embeddings.add_time_ids, + } + encoder_hidden_states = conditioning_data.text_embeddings.embeds + encoder_attention_mask = None + else: + if type(conditioning_data.text_embeddings) is SDXLConditioningInfo: + added_cond_kwargs = { + "text_embeds": torch.cat( + [ + # TODO: how to pad? just by zeros? or even truncate? + conditioning_data.unconditioned_embeddings.pooled_embeds, + conditioning_data.text_embeddings.pooled_embeds, + ], + dim=0, + ), + "time_ids": torch.cat( + [ + conditioning_data.unconditioned_embeddings.add_time_ids, + conditioning_data.text_embeddings.add_time_ids, + ], + dim=0, + ), + } + ( + encoder_hidden_states, + encoder_attention_mask, + ) = self._concat_conditionings_for_batch( + conditioning_data.unconditioned_embeddings.embeds, + conditioning_data.text_embeddings.embeds, + ) + if isinstance(control_datum.weight, list): + # if controlnet has multiple weights, use the weight for the current step + controlnet_weight = control_datum.weight[step_index] + else: + # if controlnet has a single weight, use it for all steps + controlnet_weight = control_datum.weight + + # controlnet(s) inference + down_samples, mid_sample = control_datum.model( + sample=sample_model_input, + timestep=timestep, + encoder_hidden_states=encoder_hidden_states, + controlnet_cond=control_datum.image_tensor, + conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale + encoder_attention_mask=encoder_attention_mask, + guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel + return_dict=False, + ) + if cfg_injection: + # Inferred ControlNet only for the conditional batch. + # To apply the output of ControlNet to both the unconditional and conditional batches, + # prepend zeros for unconditional batch + down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples] + mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample]) + + if down_block_res_samples is None and mid_block_res_sample is None: + down_block_res_samples, mid_block_res_sample = down_samples, mid_sample + else: + # add controlnet outputs together if have multiple controlnets + down_block_res_samples = [ + samples_prev + samples_curr + for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) + ] + mid_block_res_sample += mid_sample + + return down_block_res_samples, mid_block_res_sample + + def do_unet_step( + self, + sample: torch.Tensor, + timestep: torch.Tensor, + conditioning_data, # TODO: type + step_index: int, + total_step_count: int, **kwargs, ): - """ - :param x: current latents - :param sigma: aka t, passed to the internal model to control how much denoising will occur - :param unconditioning: embeddings for unconditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768] - :param conditioning: embeddings for conditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768] - :param unconditional_guidance_scale: aka CFG scale, controls how much effect the conditioning tensor has - :param step_index: counts upwards from 0 to (step_count-1) (as passed to setup_cross_attention_control, if using). May be called multiple times for a single step, therefore do not assume that its value will monotically increase. If None, will be estimated by comparing sigma against self.model.sigmas . - :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning. - """ - - if isinstance(unconditional_guidance_scale, list): - guidance_scale = unconditional_guidance_scale[step_index] - else: - guidance_scale = unconditional_guidance_scale - cross_attention_control_types_to_do = [] context: Context = self.cross_attention_control_context if self.cross_attention_control_context is not None: @@ -163,25 +280,15 @@ class InvokeAIDiffuserComponent: ) wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0 - wants_hybrid_conditioning = isinstance(conditioning, dict) - if wants_hybrid_conditioning: - unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning( - x, - sigma, - unconditioning, - conditioning, - **kwargs, - ) - elif wants_cross_attention_control: + if wants_cross_attention_control: ( unconditioned_next_x, conditioned_next_x, ) = self._apply_cross_attention_controlled_conditioning( - x, - sigma, - unconditioning, - conditioning, + sample, + timestep, + conditioning_data, cross_attention_control_types_to_do, **kwargs, ) @@ -190,10 +297,9 @@ class InvokeAIDiffuserComponent: unconditioned_next_x, conditioned_next_x, ) = self._apply_standard_conditioning_sequentially( - x, - sigma, - unconditioning, - conditioning, + sample, + timestep, + conditioning_data, **kwargs, ) @@ -202,21 +308,13 @@ class InvokeAIDiffuserComponent: unconditioned_next_x, conditioned_next_x, ) = self._apply_standard_conditioning( - x, - sigma, - unconditioning, - conditioning, + sample, + timestep, + conditioning_data, **kwargs, ) - combined_next_x = self._combine( - # unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale - unconditioned_next_x, - conditioned_next_x, - guidance_scale, - ) - - return combined_next_x + return unconditioned_next_x, conditioned_next_x def do_latent_postprocessing( self, @@ -228,7 +326,6 @@ class InvokeAIDiffuserComponent: ) -> torch.Tensor: if postprocessing_settings is not None: percent_through = step_index / total_step_count - latents = self.apply_threshold(postprocessing_settings, latents, percent_through) latents = self.apply_symmetry(postprocessing_settings, latents, percent_through) return latents @@ -281,17 +378,40 @@ class InvokeAIDiffuserComponent: # methods below are called from do_diffusion_step and should be considered private to this class. - def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs): + def _apply_standard_conditioning(self, x, sigma, conditioning_data, **kwargs): # fast batched path x_twice = torch.cat([x] * 2) sigma_twice = torch.cat([sigma] * 2) - both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(unconditioning, conditioning) + added_cond_kwargs = None + if type(conditioning_data.text_embeddings) is SDXLConditioningInfo: + added_cond_kwargs = { + "text_embeds": torch.cat( + [ + # TODO: how to pad? just by zeros? or even truncate? + conditioning_data.unconditioned_embeddings.pooled_embeds, + conditioning_data.text_embeddings.pooled_embeds, + ], + dim=0, + ), + "time_ids": torch.cat( + [ + conditioning_data.unconditioned_embeddings.add_time_ids, + conditioning_data.text_embeddings.add_time_ids, + ], + dim=0, + ), + } + + both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch( + conditioning_data.unconditioned_embeddings.embeds, conditioning_data.text_embeddings.embeds + ) both_results = self.model_forward_callback( x_twice, sigma_twice, both_conditionings, encoder_attention_mask=encoder_attention_mask, + added_cond_kwargs=added_cond_kwargs, **kwargs, ) unconditioned_next_x, conditioned_next_x = both_results.chunk(2) @@ -301,8 +421,7 @@ class InvokeAIDiffuserComponent: self, x: torch.Tensor, sigma, - unconditioning: torch.Tensor, - conditioning: torch.Tensor, + conditioning_data, **kwargs, ): # low-memory sequential path @@ -320,52 +439,46 @@ class InvokeAIDiffuserComponent: if mid_block_additional_residual is not None: uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2) + added_cond_kwargs = None + is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo + if is_sdxl: + added_cond_kwargs = { + "text_embeds": conditioning_data.unconditioned_embeddings.pooled_embeds, + "time_ids": conditioning_data.unconditioned_embeddings.add_time_ids, + } + unconditioned_next_x = self.model_forward_callback( x, sigma, - unconditioning, + conditioning_data.unconditioned_embeddings.embeds, down_block_additional_residuals=uncond_down_block, mid_block_additional_residual=uncond_mid_block, + added_cond_kwargs=added_cond_kwargs, **kwargs, ) + + if is_sdxl: + added_cond_kwargs = { + "text_embeds": conditioning_data.text_embeddings.pooled_embeds, + "time_ids": conditioning_data.text_embeddings.add_time_ids, + } + conditioned_next_x = self.model_forward_callback( x, sigma, - conditioning, + conditioning_data.text_embeddings.embeds, down_block_additional_residuals=cond_down_block, mid_block_additional_residual=cond_mid_block, + added_cond_kwargs=added_cond_kwargs, **kwargs, ) return unconditioned_next_x, conditioned_next_x - # TODO: looks unused - def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs): - assert isinstance(conditioning, dict) - assert isinstance(unconditioning, dict) - x_twice = torch.cat([x] * 2) - sigma_twice = torch.cat([sigma] * 2) - both_conditionings = dict() - for k in conditioning: - if isinstance(conditioning[k], list): - both_conditionings[k] = [ - torch.cat([unconditioning[k][i], conditioning[k][i]]) for i in range(len(conditioning[k])) - ] - else: - both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]]) - unconditioned_next_x, conditioned_next_x = self.model_forward_callback( - x_twice, - sigma_twice, - both_conditionings, - **kwargs, - ).chunk(2) - return unconditioned_next_x, conditioned_next_x - def _apply_cross_attention_controlled_conditioning( self, x: torch.Tensor, sigma, - unconditioning, - conditioning, + conditioning_data, cross_attention_control_types_to_do, **kwargs, ): @@ -391,26 +504,43 @@ class InvokeAIDiffuserComponent: mask=context.cross_attention_mask, cross_attention_types_to_do=[], ) + + added_cond_kwargs = None + is_sdxl = type(conditioning_data.text_embeddings) is SDXLConditioningInfo + if is_sdxl: + added_cond_kwargs = { + "text_embeds": conditioning_data.unconditioned_embeddings.pooled_embeds, + "time_ids": conditioning_data.unconditioned_embeddings.add_time_ids, + } + # no cross attention for unconditioning (negative prompt) unconditioned_next_x = self.model_forward_callback( x, sigma, - unconditioning, + conditioning_data.unconditioned_embeddings.embeds, {"swap_cross_attn_context": cross_attn_processor_context}, down_block_additional_residuals=uncond_down_block, mid_block_additional_residual=uncond_mid_block, + added_cond_kwargs=added_cond_kwargs, **kwargs, ) + if is_sdxl: + added_cond_kwargs = { + "text_embeds": conditioning_data.text_embeddings.pooled_embeds, + "time_ids": conditioning_data.text_embeddings.add_time_ids, + } + # do requested cross attention types for conditioning (positive prompt) cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do conditioned_next_x = self.model_forward_callback( x, sigma, - conditioning, + conditioning_data.text_embeddings.embeds, {"swap_cross_attn_context": cross_attn_processor_context}, down_block_additional_residuals=cond_down_block, mid_block_additional_residual=cond_mid_block, + added_cond_kwargs=added_cond_kwargs, **kwargs, ) return unconditioned_next_x, conditioned_next_x @@ -421,63 +551,6 @@ class InvokeAIDiffuserComponent: combined_next_x = unconditioned_next_x + scaled_delta return combined_next_x - def apply_threshold( - self, - postprocessing_settings: PostprocessingSettings, - latents: torch.Tensor, - percent_through: float, - ) -> torch.Tensor: - if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0: - return latents - - threshold = postprocessing_settings.threshold - warmup = postprocessing_settings.warmup - - if percent_through < warmup: - current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup)) - else: - current_threshold = threshold - - if current_threshold <= 0: - return latents - - maxval = latents.max().item() - minval = latents.min().item() - - scale = 0.7 # default value from #395 - - if self.debug_thresholding: - std, mean = [i.item() for i in torch.std_mean(latents)] - outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold)) - logger.info(f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})") - logger.debug(f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}") - logger.debug(f"{outside / latents.numel() * 100:.2f}% values outside threshold") - - if maxval < current_threshold and minval > -current_threshold: - return latents - - num_altered = 0 - - # MPS torch.rand_like is fine because torch.rand_like is wrapped in generate.py! - - if maxval > current_threshold: - latents = torch.clone(latents) - maxval = np.clip(maxval * scale, 1, current_threshold) - num_altered += torch.count_nonzero(latents > maxval) - latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval - - if minval < -current_threshold: - latents = torch.clone(latents) - minval = np.clip(minval * scale, -current_threshold, -1) - num_altered += torch.count_nonzero(latents < minval) - latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval - - if self.debug_thresholding: - logger.debug(f"min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})") - logger.debug(f"{num_altered / latents.numel() * 100:.2f}% values altered") - - return latents - def apply_symmetry( self, postprocessing_settings: PostprocessingSettings, @@ -539,18 +612,6 @@ class InvokeAIDiffuserComponent: self.last_percent_through = percent_through return latents.to(device=dev) - def estimate_percent_through(self, step_index, sigma): - if step_index is not None and self.cross_attention_control_context is not None: - # percent_through will never reach 1.0 (but this is intended) - return float(step_index) / float(self.cross_attention_control_context.step_count) - # find the best possible index of the current sigma in the sigma sequence - smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma) - sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0 - # flip because sigmas[0] is for the fully denoised image - # percent_through must be <1 - return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0]) - # print('estimated percent_through', percent_through, 'from sigma', sigma.item()) - # todo: make this work @classmethod def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale): @@ -564,7 +625,7 @@ class InvokeAIDiffuserComponent: # below is fugly omg conditionings = [uc] + [c for c, weight in weighted_cond_list] weights = [1] + [weight for c, weight in weighted_cond_list] - chunk_count = ceil(len(conditionings) / 2) + chunk_count = math.ceil(len(conditionings) / 2) deltas = None for chunk_index in range(chunk_count): offset = chunk_index * 2 diff --git a/invokeai/backend/stable_diffusion/offloading.py b/invokeai/backend/stable_diffusion/offloading.py deleted file mode 100644 index aa2426d514..0000000000 --- a/invokeai/backend/stable_diffusion/offloading.py +++ /dev/null @@ -1,253 +0,0 @@ -from __future__ import annotations - -import warnings -import weakref -from abc import ABCMeta, abstractmethod -from collections.abc import MutableMapping -from typing import Callable, Union - -import torch -from accelerate.utils import send_to_device -from torch.utils.hooks import RemovableHandle - -OFFLOAD_DEVICE = torch.device("cpu") - - -class _NoModel: - """Symbol that indicates no model is loaded. - - (We can't weakref.ref(None), so this was my best idea at the time to come up with something - type-checkable.) - """ - - def __bool__(self): - return False - - def to(self, device: torch.device): - pass - - def __repr__(self): - return "" - - -NO_MODEL = _NoModel() - - -class ModelGroup(metaclass=ABCMeta): - """ - A group of models. - - The use case I had in mind when writing this is the sub-models used by a DiffusionPipeline, - e.g. its text encoder, U-net, VAE, etc. - - Those models are :py:class:`diffusers.ModelMixin`, but "model" is interchangeable with - :py:class:`torch.nn.Module` here. - """ - - def __init__(self, execution_device: torch.device): - self.execution_device = execution_device - - @abstractmethod - def install(self, *models: torch.nn.Module): - """Add models to this group.""" - pass - - @abstractmethod - def uninstall(self, models: torch.nn.Module): - """Remove models from this group.""" - pass - - @abstractmethod - def uninstall_all(self): - """Remove all models from this group.""" - - @abstractmethod - def load(self, model: torch.nn.Module): - """Load this model to the execution device.""" - pass - - @abstractmethod - def offload_current(self): - """Offload the current model(s) from the execution device.""" - pass - - @abstractmethod - def ready(self): - """Ready this group for use.""" - pass - - @abstractmethod - def set_device(self, device: torch.device): - """Change which device models from this group will execute on.""" - pass - - @abstractmethod - def device_for(self, model) -> torch.device: - """Get the device the given model will execute on. - - The model should already be a member of this group. - """ - pass - - @abstractmethod - def __contains__(self, model): - """Check if the model is a member of this group.""" - pass - - def __repr__(self) -> str: - return f"<{self.__class__.__name__} object at {id(self):x}: " f"device={self.execution_device} >" - - -class LazilyLoadedModelGroup(ModelGroup): - """ - Only one model from this group is loaded on the GPU at a time. - - Running the forward method of a model will displace the previously-loaded model, - offloading it to CPU. - - If you call other methods on the model, e.g. ``model.encode(x)`` instead of ``model(x)``, - you will need to explicitly load it with :py:method:`.load(model)`. - - This implementation relies on pytorch forward-pre-hooks, and it will copy forward arguments - to the appropriate execution device, as long as they are positional arguments and not keyword - arguments. (I didn't make the rules; that's the way the pytorch 1.13 API works for hooks.) - """ - - _hooks: MutableMapping[torch.nn.Module, RemovableHandle] - _current_model_ref: Callable[[], Union[torch.nn.Module, _NoModel]] - - def __init__(self, execution_device: torch.device): - super().__init__(execution_device) - self._hooks = weakref.WeakKeyDictionary() - self._current_model_ref = weakref.ref(NO_MODEL) - - def install(self, *models: torch.nn.Module): - for model in models: - self._hooks[model] = model.register_forward_pre_hook(self._pre_hook) - - def uninstall(self, *models: torch.nn.Module): - for model in models: - hook = self._hooks.pop(model) - hook.remove() - if self.is_current_model(model): - # no longer hooked by this object, so don't claim to manage it - self.clear_current_model() - - def uninstall_all(self): - self.uninstall(*self._hooks.keys()) - - def _pre_hook(self, module: torch.nn.Module, forward_input): - self.load(module) - if len(forward_input) == 0: - warnings.warn( - f"Hook for {module.__class__.__name__} got no input. " f"Inputs must be positional, not keywords.", - stacklevel=3, - ) - return send_to_device(forward_input, self.execution_device) - - def load(self, module): - if not self.is_current_model(module): - self.offload_current() - self._load(module) - - def offload_current(self): - module = self._current_model_ref() - if module is not NO_MODEL: - module.to(OFFLOAD_DEVICE) - self.clear_current_model() - - def _load(self, module: torch.nn.Module) -> torch.nn.Module: - assert self.is_empty(), f"A model is already loaded: {self._current_model_ref()}" - module = module.to(self.execution_device) - self.set_current_model(module) - return module - - def is_current_model(self, model: torch.nn.Module) -> bool: - """Is the given model the one currently loaded on the execution device?""" - return self._current_model_ref() is model - - def is_empty(self): - """Are none of this group's models loaded on the execution device?""" - return self._current_model_ref() is NO_MODEL - - def set_current_model(self, value): - self._current_model_ref = weakref.ref(value) - - def clear_current_model(self): - self._current_model_ref = weakref.ref(NO_MODEL) - - def set_device(self, device: torch.device): - if device == self.execution_device: - return - self.execution_device = device - current = self._current_model_ref() - if current is not NO_MODEL: - current.to(device) - - def device_for(self, model): - if model not in self: - raise KeyError(f"This does not manage this model {type(model).__name__}", model) - return self.execution_device # this implementation only dispatches to one device - - def ready(self): - pass # always ready to load on-demand - - def __contains__(self, model): - return model in self._hooks - - def __repr__(self) -> str: - return ( - f"<{self.__class__.__name__} object at {id(self):x}: " - f"current_model={type(self._current_model_ref()).__name__} >" - ) - - -class FullyLoadedModelGroup(ModelGroup): - """ - A group of models without any implicit loading or unloading. - - :py:meth:`.ready` loads _all_ the models to the execution device at once. - """ - - _models: weakref.WeakSet - - def __init__(self, execution_device: torch.device): - super().__init__(execution_device) - self._models = weakref.WeakSet() - - def install(self, *models: torch.nn.Module): - for model in models: - self._models.add(model) - model.to(self.execution_device) - - def uninstall(self, *models: torch.nn.Module): - for model in models: - self._models.remove(model) - - def uninstall_all(self): - self.uninstall(*self._models) - - def load(self, model): - model.to(self.execution_device) - - def offload_current(self): - for model in self._models: - model.to(OFFLOAD_DEVICE) - - def ready(self): - for model in self._models: - self.load(model) - - def set_device(self, device: torch.device): - self.execution_device = device - for model in self._models: - if model.device != OFFLOAD_DEVICE: - model.to(device) - - def device_for(self, model): - if model not in self: - raise KeyError("This does not manage this model f{type(model).__name__}", model) - return self.execution_device # this implementation only dispatches to one device - - def __contains__(self, model): - return model in self._models diff --git a/invokeai/backend/util/devices.py b/invokeai/backend/util/devices.py index eeabcc35db..1827f295e4 100644 --- a/invokeai/backend/util/devices.py +++ b/invokeai/backend/util/devices.py @@ -1,6 +1,8 @@ from __future__ import annotations from contextlib import nullcontext +from packaging import version +import platform import torch from torch import autocast @@ -30,7 +32,7 @@ def choose_precision(device: torch.device) -> str: device_name = torch.cuda.get_device_name(device) if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name): return "float16" - elif device.type == "mps": + elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"): return "float16" return "float32" diff --git a/invokeai/frontend/install/__init__.py b/invokeai/frontend/install/__init__.py index fb8cdff1b3..2a248eb49f 100644 --- a/invokeai/frontend/install/__init__.py +++ b/invokeai/frontend/install/__init__.py @@ -1,6 +1,3 @@ """ Initialization file for invokeai.frontend.config """ -from .invokeai_configure import main as invokeai_configure -from .invokeai_update import main as invokeai_update -from .model_install import main as invokeai_model_install diff --git a/invokeai/frontend/install/import_images.py b/invokeai/frontend/install/import_images.py new file mode 100644 index 0000000000..1f68f23607 --- /dev/null +++ b/invokeai/frontend/install/import_images.py @@ -0,0 +1,795 @@ +# Copyright (c) 2023 - The InvokeAI Team +# Primary Author: David Lovell (github @f412design, discord @techjedi) +# co-author, minor tweaks - Lincoln Stein + +# pylint: disable=line-too-long +# pylint: disable=broad-exception-caught +"""Script to import images into the new database system for 3.0.0""" + +import os +import datetime +import shutil +import locale +import sqlite3 +import json +import glob +import re +import uuid +import yaml +import PIL +import PIL.ImageOps +import PIL.PngImagePlugin + +from pathlib import Path +from prompt_toolkit import prompt +from prompt_toolkit.shortcuts import message_dialog +from prompt_toolkit.completion import PathCompleter +from prompt_toolkit.key_binding import KeyBindings + +from invokeai.app.services.config import InvokeAIAppConfig + +app_config = InvokeAIAppConfig.get_config() + +bindings = KeyBindings() + + +@bindings.add("c-c") +def _(event): + raise KeyboardInterrupt + + +# release notes +# "Use All" with size dimensions not selectable in the UI will not load dimensions + + +class Config: + """Configuration loader.""" + + def __init__(self): + pass + + TIMESTAMP_STRING = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ") + + INVOKE_DIRNAME = "invokeai" + YAML_FILENAME = "invokeai.yaml" + DATABASE_FILENAME = "invokeai.db" + + database_path = None + database_backup_dir = None + outputs_path = None + thumbnail_path = None + + def find_and_load(self): + """find the yaml config file and load""" + root = app_config.root_path + if not self.confirm_and_load(os.path.abspath(root)): + print("\r\nSpecify custom database and outputs paths:") + self.confirm_and_load_from_user() + + self.database_backup_dir = os.path.join(os.path.dirname(self.database_path), "backup") + self.thumbnail_path = os.path.join(self.outputs_path, "thumbnails") + + def confirm_and_load(self, invoke_root): + """Validates a yaml path exists, confirms the user wants to use it and loads config.""" + yaml_path = os.path.join(invoke_root, self.YAML_FILENAME) + if os.path.exists(yaml_path): + db_dir, outdir = self.load_paths_from_yaml(yaml_path) + if os.path.isabs(db_dir): + database_path = os.path.join(db_dir, self.DATABASE_FILENAME) + else: + database_path = os.path.join(invoke_root, db_dir, self.DATABASE_FILENAME) + + if os.path.isabs(outdir): + outputs_path = os.path.join(outdir, "images") + else: + outputs_path = os.path.join(invoke_root, outdir, "images") + + db_exists = os.path.exists(database_path) + outdir_exists = os.path.exists(outputs_path) + + text = f"Found {self.YAML_FILENAME} file at {yaml_path}:" + text += f"\n Database : {database_path}" + text += f"\n Outputs : {outputs_path}" + text += "\n\nUse these paths for import (yes) or choose different ones (no) [Yn]: " + + if db_exists and outdir_exists: + if (prompt(text).strip() or "Y").upper().startswith("Y"): + self.database_path = database_path + self.outputs_path = outputs_path + return True + else: + return False + else: + print(" Invalid: One or more paths in this config did not exist and cannot be used.") + + else: + message_dialog( + title="Path not found", + text=f"Auto-discovery of configuration failed! Could not find ({yaml_path}), Custom paths can be specified.", + ).run() + return False + + def confirm_and_load_from_user(self): + default = "" + while True: + database_path = os.path.expanduser( + prompt( + "Database: Specify absolute path to the database to import into: ", + completer=PathCompleter( + expanduser=True, file_filter=lambda x: Path(x).is_dir() or x.endswith((".db")) + ), + default=default, + ) + ) + if database_path.endswith(".db") and os.path.isabs(database_path) and os.path.exists(database_path): + break + default = database_path + "/" if Path(database_path).is_dir() else database_path + + default = "" + while True: + outputs_path = os.path.expanduser( + prompt( + "Outputs: Specify absolute path to outputs/images directory to import into: ", + completer=PathCompleter(expanduser=True, only_directories=True), + default=default, + ) + ) + + if outputs_path.endswith("images") and os.path.isabs(outputs_path) and os.path.exists(outputs_path): + break + default = outputs_path + "/" if Path(outputs_path).is_dir() else outputs_path + + self.database_path = database_path + self.outputs_path = outputs_path + + return + + def load_paths_from_yaml(self, yaml_path): + """Load an Invoke AI yaml file and get the database and outputs paths.""" + try: + with open(yaml_path, "rt", encoding=locale.getpreferredencoding()) as file: + yamlinfo = yaml.safe_load(file) + db_dir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("db_dir", None) + outdir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("outdir", None) + return db_dir, outdir + except Exception: + print(f"Failed to load paths from yaml file! {yaml_path}!") + return None, None + + +class ImportStats: + """DTO for tracking work progress.""" + + def __init__(self): + pass + + time_start = datetime.datetime.utcnow() + count_source_files = 0 + count_skipped_file_exists = 0 + count_skipped_db_exists = 0 + count_imported = 0 + count_imported_by_version = {} + count_file_errors = 0 + + @staticmethod + def get_elapsed_time_string(): + """Get a friendly time string for the time elapsed since processing start.""" + time_now = datetime.datetime.utcnow() + total_seconds = (time_now - ImportStats.time_start).total_seconds() + hours = int((total_seconds) / 3600) + minutes = int(((total_seconds) % 3600) / 60) + seconds = total_seconds % 60 + out_str = f"{hours} hour(s) -" if hours > 0 else "" + out_str += f"{minutes} minute(s) -" if minutes > 0 else "" + out_str += f"{seconds:.2f} second(s)" + return out_str + + +class InvokeAIMetadata: + """DTO for core Invoke AI generation properties parsed from metadata.""" + + def __init__(self): + pass + + def __str__(self): + formatted_str = f"{self.generation_mode}~{self.steps}~{self.cfg_scale}~{self.model_name}~{self.scheduler}~{self.seed}~{self.width}~{self.height}~{self.rand_device}~{self.strength}~{self.init_image}" + formatted_str += f"\r\npositive_prompt: {self.positive_prompt}" + formatted_str += f"\r\nnegative_prompt: {self.negative_prompt}" + return formatted_str + + generation_mode = None + steps = None + cfg_scale = None + model_name = None + scheduler = None + seed = None + width = None + height = None + rand_device = None + strength = None + init_image = None + positive_prompt = None + negative_prompt = None + imported_app_version = None + + def to_json(self): + """Convert the active instance to json format.""" + prop_dict = {} + prop_dict["generation_mode"] = self.generation_mode + # dont render prompt nodes if neither are set to avoid the ui thinking it can set them + # if at least one exists, render them both, but use empty string instead of None if one of them is empty + # this allows the field that is empty to actually be cleared byt he UI instead of leaving the previous value + if self.positive_prompt or self.negative_prompt: + prop_dict["positive_prompt"] = "" if self.positive_prompt is None else self.positive_prompt + prop_dict["negative_prompt"] = "" if self.negative_prompt is None else self.negative_prompt + prop_dict["width"] = self.width + prop_dict["height"] = self.height + # only render seed if it has a value to avoid ui thinking it can set this and then error + if self.seed: + prop_dict["seed"] = self.seed + prop_dict["rand_device"] = self.rand_device + prop_dict["cfg_scale"] = self.cfg_scale + prop_dict["steps"] = self.steps + prop_dict["scheduler"] = self.scheduler + prop_dict["clip_skip"] = 0 + prop_dict["model"] = {} + prop_dict["model"]["model_name"] = self.model_name + prop_dict["model"]["base_model"] = None + prop_dict["controlnets"] = [] + prop_dict["loras"] = [] + prop_dict["vae"] = None + prop_dict["strength"] = self.strength + prop_dict["init_image"] = self.init_image + prop_dict["positive_style_prompt"] = None + prop_dict["negative_style_prompt"] = None + prop_dict["refiner_model"] = None + prop_dict["refiner_cfg_scale"] = None + prop_dict["refiner_steps"] = None + prop_dict["refiner_scheduler"] = None + prop_dict["refiner_aesthetic_store"] = None + prop_dict["refiner_start"] = None + prop_dict["imported_app_version"] = self.imported_app_version + + return json.dumps(prop_dict) + + +class InvokeAIMetadataParser: + """Parses strings with json data to find Invoke AI core metadata properties.""" + + def __init__(self): + pass + + def parse_meta_tag_dream(self, dream_string): + """Take as input an png metadata json node for the 'dream' field variant from prior to 1.15""" + props = InvokeAIMetadata() + + props.imported_app_version = "pre1.15" + seed_match = re.search("-S\\s*(\\d+)", dream_string) + if seed_match is not None: + try: + props.seed = int(seed_match[1]) + except ValueError: + props.seed = None + raw_prompt = re.sub("(-S\\s*\\d+)", "", dream_string) + else: + raw_prompt = dream_string + + pos_prompt, neg_prompt = self.split_prompt(raw_prompt) + + props.positive_prompt = pos_prompt + props.negative_prompt = neg_prompt + + return props + + def parse_meta_tag_sd_metadata(self, tag_value): + """Take as input an png metadata json node for the 'sd-metadata' field variant from 1.15 through 2.3.5 post 2""" + props = InvokeAIMetadata() + + props.imported_app_version = tag_value.get("app_version") + props.model_name = tag_value.get("model_weights") + img_node = tag_value.get("image") + if img_node is not None: + props.generation_mode = img_node.get("type") + props.width = img_node.get("width") + props.height = img_node.get("height") + props.seed = img_node.get("seed") + props.rand_device = "cuda" # hardcoded since all generations pre 3.0 used cuda random noise instead of cpu + props.cfg_scale = img_node.get("cfg_scale") + props.steps = img_node.get("steps") + props.scheduler = self.map_scheduler(img_node.get("sampler")) + props.strength = img_node.get("strength") + if props.strength is None: + props.strength = img_node.get("strength_steps") # try second name for this property + props.init_image = img_node.get("init_image_path") + if props.init_image is None: # try second name for this property + props.init_image = img_node.get("init_img") + # remove the path info from init_image so if we move the init image, it will be correctly relative in the new location + if props.init_image is not None: + props.init_image = os.path.basename(props.init_image) + raw_prompt = img_node.get("prompt") + if isinstance(raw_prompt, list): + raw_prompt = raw_prompt[0].get("prompt") + + props.positive_prompt, props.negative_prompt = self.split_prompt(raw_prompt) + + return props + + def parse_meta_tag_invokeai(self, tag_value): + """Take as input an png metadata json node for the 'invokeai' field variant from 3.0.0 beta 1 through 5""" + props = InvokeAIMetadata() + + props.imported_app_version = "3.0.0 or later" + props.generation_mode = tag_value.get("type") + if props.generation_mode is not None: + props.generation_mode = props.generation_mode.replace("t2l", "txt2img").replace("l2l", "img2img") + + props.width = tag_value.get("width") + props.height = tag_value.get("height") + props.seed = tag_value.get("seed") + props.cfg_scale = tag_value.get("cfg_scale") + props.steps = tag_value.get("steps") + props.scheduler = tag_value.get("scheduler") + props.strength = tag_value.get("strength") + props.positive_prompt = tag_value.get("positive_conditioning") + props.negative_prompt = tag_value.get("negative_conditioning") + + return props + + def map_scheduler(self, old_scheduler): + """Convert the legacy sampler names to matching 3.0 schedulers""" + if old_scheduler is None: + return None + + match (old_scheduler): + case "ddim": + return "ddim" + case "plms": + return "pnmd" + case "k_lms": + return "lms" + case "k_dpm_2": + return "kdpm_2" + case "k_dpm_2_a": + return "kdpm_2_a" + case "dpmpp_2": + return "dpmpp_2s" + case "k_dpmpp_2": + return "dpmpp_2m" + case "k_dpmpp_2_a": + return None # invalid, in 2.3.x, selecting this sample would just fallback to last run or plms if new session + case "k_euler": + return "euler" + case "k_euler_a": + return "euler_a" + case "k_heun": + return "heun" + return None + + def split_prompt(self, raw_prompt: str): + """Split the unified prompt strings by extracting all negative prompt blocks out into the negative prompt.""" + if raw_prompt is None: + return "", "" + raw_prompt_search = raw_prompt.replace("\r", "").replace("\n", "") + matches = re.findall(r"\[(.+?)\]", raw_prompt_search) + if len(matches) > 0: + negative_prompt = "" + if len(matches) == 1: + negative_prompt = matches[0].strip().strip(",") + else: + for match in matches: + negative_prompt += f"({match.strip().strip(',')})" + positive_prompt = re.sub(r"(\[.+?\])", "", raw_prompt_search).strip() + else: + positive_prompt = raw_prompt_search.strip() + negative_prompt = "" + + return positive_prompt, negative_prompt + + +class DatabaseMapper: + """Class to abstract database functionality.""" + + def __init__(self, database_path, database_backup_dir): + self.database_path = database_path + self.database_backup_dir = database_backup_dir + self.connection = None + self.cursor = None + + def connect(self): + """Open connection to the database.""" + self.connection = sqlite3.connect(self.database_path) + self.cursor = self.connection.cursor() + + def get_board_names(self): + """Get a list of the current board names from the database.""" + sql_get_board_name = "SELECT board_name FROM boards" + self.cursor.execute(sql_get_board_name) + rows = self.cursor.fetchall() + return [row[0] for row in rows] + + def does_image_exist(self, image_name): + """Check database if a image name already exists and return a boolean.""" + sql_get_image_by_name = f"SELECT image_name FROM images WHERE image_name='{image_name}'" + self.cursor.execute(sql_get_image_by_name) + rows = self.cursor.fetchall() + return True if len(rows) > 0 else False + + def add_new_image_to_database(self, filename, width, height, metadata, modified_date_string): + """Add an image to the database.""" + sql_add_image = f"""INSERT INTO images (image_name, image_origin, image_category, width, height, session_id, node_id, metadata, is_intermediate, created_at, updated_at) +VALUES ('{filename}', 'internal', 'general', {width}, {height}, null, null, '{metadata}', 0, '{modified_date_string}', '{modified_date_string}')""" + self.cursor.execute(sql_add_image) + self.connection.commit() + + def get_board_id_with_create(self, board_name): + """Get the board id for supplied name, and create the board if one does not exist.""" + sql_find_board = f"SELECT board_id FROM boards WHERE board_name='{board_name}' COLLATE NOCASE" + self.cursor.execute(sql_find_board) + rows = self.cursor.fetchall() + if len(rows) > 0: + return rows[0][0] + else: + board_date_string = datetime.datetime.utcnow().date().isoformat() + new_board_id = str(uuid.uuid4()) + sql_insert_board = f"INSERT INTO boards (board_id, board_name, created_at, updated_at) VALUES ('{new_board_id}', '{board_name}', '{board_date_string}', '{board_date_string}')" + self.cursor.execute(sql_insert_board) + self.connection.commit() + return new_board_id + + def add_image_to_board(self, filename, board_id): + """Add an image mapping to a board.""" + add_datetime_str = datetime.datetime.utcnow().isoformat() + sql_add_image_to_board = f"""INSERT INTO board_images (board_id, image_name, created_at, updated_at) + VALUES ('{board_id}', '{filename}', '{add_datetime_str}', '{add_datetime_str}')""" + self.cursor.execute(sql_add_image_to_board) + self.connection.commit() + + def disconnect(self): + """Disconnect from the db, cleaning up connections and cursors.""" + if self.cursor is not None: + self.cursor.close() + if self.connection is not None: + self.connection.close() + + def backup(self, timestamp_string): + """Take a backup of the database.""" + if not os.path.exists(self.database_backup_dir): + print(f"Database backup directory {self.database_backup_dir} does not exist -> creating...", end="") + os.makedirs(self.database_backup_dir) + print("Done!") + database_backup_path = os.path.join(self.database_backup_dir, f"backup-{timestamp_string}-invokeai.db") + print(f"Making DB Backup at {database_backup_path}...", end="") + shutil.copy2(self.database_path, database_backup_path) + print("Done!") + + +class MediaImportProcessor: + """Containing class for script functionality.""" + + def __init__(self): + pass + + board_name_id_map = {} + + def get_import_file_list(self): + """Ask the user for the import folder and scan for the list of files to return.""" + while True: + default = "" + while True: + import_dir = os.path.expanduser( + prompt( + "Inputs: Specify absolute path containing InvokeAI .png images to import: ", + completer=PathCompleter(expanduser=True, only_directories=True), + default=default, + ) + ) + if len(import_dir) > 0 and Path(import_dir).is_dir(): + break + default = import_dir + + recurse_directories = ( + (prompt("Include files from subfolders recursively [yN]? ").strip() or "N").upper().startswith("N") + ) + if recurse_directories: + is_recurse = False + matching_file_list = glob.glob(import_dir + "/*.png", recursive=False) + else: + is_recurse = True + matching_file_list = glob.glob(import_dir + "/**/*.png", recursive=True) + + if len(matching_file_list) > 0: + return import_dir, is_recurse, matching_file_list + else: + print(f"The specific path {import_dir} exists, but does not contain .png files!") + + def get_file_details(self, filepath): + """Retrieve the embedded metedata fields and dimensions from an image file.""" + with PIL.Image.open(filepath) as img: + img.load() + png_width, png_height = img.size + img_info = img.info + return img_info, png_width, png_height + + def select_board_option(self, board_names, timestamp_string): + """Allow the user to choose how a board is selected for imported files.""" + while True: + print("\r\nOptions for board selection for imported images:") + print(f"1) Select an existing board name. (found {len(board_names)})") + print("2) Specify a board name to create/add to.") + print("3) Create/add to board named 'IMPORT'.") + print( + f"4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_{timestamp_string})." + ) + print( + "5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5)." + ) + input_option = input("Specify desired board option: ") + match (input_option): + case "1": + if len(board_names) < 1: + print("\r\nThere are no existing board names to choose from. Select another option!") + continue + board_name = self.select_item_from_list( + board_names, "board name", True, "Cancel, go back and choose a different board option." + ) + if board_name is not None: + return board_name + case "2": + while True: + board_name = input("Specify new/existing board name: ") + if board_name: + return board_name + case "3": + return "IMPORT" + case "4": + return f"IMPORT_{timestamp_string}" + case "5": + return "IMPORT_APPVERSION" + + def select_item_from_list(self, items, entity_name, allow_cancel, cancel_string): + """A general function to render a list of items to select in the console, prompt the user for a selection and ensure a valid entry is selected.""" + print(f"Select a {entity_name.lower()} from the following list:") + index = 1 + for item in items: + print(f"{index}) {item}") + index += 1 + if allow_cancel: + print(f"{index}) {cancel_string}") + while True: + try: + option_number = int(input("Specify number of selection: ")) + except ValueError: + continue + if allow_cancel and option_number == index: + return None + if option_number >= 1 and option_number <= len(items): + return items[option_number - 1] + + def import_image(self, filepath: str, board_name_option: str, db_mapper: DatabaseMapper, config: Config): + """Import a single file by its path""" + parser = InvokeAIMetadataParser() + file_name = os.path.basename(filepath) + file_destination_path = os.path.join(config.outputs_path, file_name) + + print("===============================================================================") + print(f"Importing {filepath}") + + # check destination to see if the file was previously imported + if os.path.exists(file_destination_path): + print("File already exists in the destination, skipping!") + ImportStats.count_skipped_file_exists += 1 + return + + # check if file name is already referenced in the database + if db_mapper.does_image_exist(file_name): + print("A reference to a file with this name already exists in the database, skipping!") + ImportStats.count_skipped_db_exists += 1 + return + + # load image info and dimensions + img_info, png_width, png_height = self.get_file_details(filepath) + + # parse metadata + destination_needs_meta_update = True + log_version_note = "(Unknown)" + if "invokeai_metadata" in img_info: + # for the latest, we will just re-emit the same json, no need to parse/modify + converted_field = None + latest_json_string = img_info.get("invokeai_metadata") + log_version_note = "3.0.0+" + destination_needs_meta_update = False + else: + if "sd-metadata" in img_info: + converted_field = parser.parse_meta_tag_sd_metadata(json.loads(img_info.get("sd-metadata"))) + elif "invokeai" in img_info: + converted_field = parser.parse_meta_tag_invokeai(json.loads(img_info.get("invokeai"))) + elif "dream" in img_info: + converted_field = parser.parse_meta_tag_dream(img_info.get("dream")) + elif "Dream" in img_info: + converted_field = parser.parse_meta_tag_dream(img_info.get("Dream")) + else: + converted_field = InvokeAIMetadata() + destination_needs_meta_update = False + print("File does not have metadata from known Invoke AI versions, add only, no update!") + + # use the loaded img dimensions if the metadata didnt have them + if converted_field.width is None: + converted_field.width = png_width + if converted_field.height is None: + converted_field.height = png_height + + log_version_note = converted_field.imported_app_version if converted_field else "NoVersion" + log_version_note = log_version_note or "NoVersion" + + latest_json_string = converted_field.to_json() + + print(f"From Invoke AI Version {log_version_note} with dimensions {png_width} x {png_height}.") + + # if metadata needs update, then update metdata and copy in one shot + if destination_needs_meta_update: + print("Updating metadata while copying...", end="") + self.update_file_metadata_while_copying( + filepath, file_destination_path, "invokeai_metadata", latest_json_string + ) + print("Done!") + else: + print("No metadata update necessary, copying only...", end="") + shutil.copy2(filepath, file_destination_path) + print("Done!") + + # create thumbnail + print("Creating thumbnail...", end="") + thumbnail_path = os.path.join(config.thumbnail_path, os.path.splitext(file_name)[0]) + ".webp" + thumbnail_size = 256, 256 + with PIL.Image.open(filepath) as source_image: + source_image.thumbnail(thumbnail_size) + source_image.save(thumbnail_path, "webp") + print("Done!") + + # finalize the dynamic board name if there is an APPVERSION token in it. + if converted_field is not None: + board_name = board_name_option.replace("APPVERSION", converted_field.imported_app_version or "NoVersion") + else: + board_name = board_name_option.replace("APPVERSION", "Latest") + + # maintain a map of alrady created/looked up ids to avoid DB queries + print("Finding/Creating board...", end="") + if board_name in self.board_name_id_map: + board_id = self.board_name_id_map[board_name] + else: + board_id = db_mapper.get_board_id_with_create(board_name) + self.board_name_id_map[board_name] = board_id + print("Done!") + + # add image to db + print("Adding image to database......", end="") + modified_time = datetime.datetime.utcfromtimestamp(os.path.getmtime(filepath)) + db_mapper.add_new_image_to_database(file_name, png_width, png_height, latest_json_string, modified_time) + print("Done!") + + # add image to board + print("Adding image to board......", end="") + db_mapper.add_image_to_board(file_name, board_id) + print("Done!") + + ImportStats.count_imported += 1 + if log_version_note in ImportStats.count_imported_by_version: + ImportStats.count_imported_by_version[log_version_note] += 1 + else: + ImportStats.count_imported_by_version[log_version_note] = 1 + + def update_file_metadata_while_copying(self, filepath, file_destination_path, tag_name, tag_value): + """Perform a metadata update with save to a new destination which accomplishes a copy while updating metadata.""" + with PIL.Image.open(filepath) as target_image: + existing_img_info = target_image.info + metadata = PIL.PngImagePlugin.PngInfo() + # re-add any existing invoke ai tags unless they are the one we are trying to add + for key in existing_img_info: + if key != tag_name and key in ("dream", "Dream", "sd-metadata", "invokeai", "invokeai_metadata"): + metadata.add_text(key, existing_img_info[key]) + metadata.add_text(tag_name, tag_value) + target_image.save(file_destination_path, pnginfo=metadata) + + def process(self): + """Begin main processing.""" + + print("===============================================================================") + print("This script will import images generated by earlier versions of") + print("InvokeAI into the currently installed root directory:") + print(f" {app_config.root_path}") + print("If this is not what you want to do, type ctrl-C now to cancel.") + + # load config + print("===============================================================================") + print("= Configuration & Settings") + + config = Config() + config.find_and_load() + db_mapper = DatabaseMapper(config.database_path, config.database_backup_dir) + db_mapper.connect() + + import_dir, is_recurse, import_file_list = self.get_import_file_list() + ImportStats.count_source_files = len(import_file_list) + + board_names = db_mapper.get_board_names() + board_name_option = self.select_board_option(board_names, config.TIMESTAMP_STRING) + + print("\r\n===============================================================================") + print("= Import Settings Confirmation") + + print() + print(f"Database File Path : {config.database_path}") + print(f"Outputs/Images Directory : {config.outputs_path}") + print(f"Import Image Source Directory : {import_dir}") + print(f" Recurse Source SubDirectories : {'Yes' if is_recurse else 'No'}") + print(f"Count of .png file(s) found : {len(import_file_list)}") + print(f"Board name option specified : {board_name_option}") + print(f"Database backup will be taken at : {config.database_backup_dir}") + + print("\r\nNotes about the import process:") + print("- Source image files will not be modified, only copied to the outputs directory.") + print("- If the same file name already exists in the destination, the file will be skipped.") + print("- If the same file name already has a record in the database, the file will be skipped.") + print("- Invoke AI metadata tags will be updated/written into the imported copy only.") + print( + "- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)" + ) + print( + "- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer." + ) + print( + "- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder." + ) + + while True: + should_continue = prompt("\nDo you wish to continue with the import [Yn] ? ").lower() or "y" + if should_continue == "n": + print("\r\nCancelling Import") + return + elif should_continue == "y": + print() + break + + db_mapper.backup(config.TIMESTAMP_STRING) + + print() + ImportStats.time_start = datetime.datetime.utcnow() + + for filepath in import_file_list: + try: + self.import_image(filepath, board_name_option, db_mapper, config) + except sqlite3.Error as sql_ex: + print(f"A database related exception was found processing {filepath}, will continue to next file. ") + print("Exception detail:") + print(sql_ex) + ImportStats.count_file_errors += 1 + except Exception as ex: + print(f"Exception processing {filepath}, will continue to next file. ") + print("Exception detail:") + print(ex) + ImportStats.count_file_errors += 1 + + print("\r\n===============================================================================") + print(f"= Import Complete - Elpased Time: {ImportStats.get_elapsed_time_string()}") + print() + print(f"Source File(s) : {ImportStats.count_source_files}") + print(f"Total Imported : {ImportStats.count_imported}") + print(f"Skipped b/c file already exists on disk : {ImportStats.count_skipped_file_exists}") + print(f"Skipped b/c file already exists in db : {ImportStats.count_skipped_db_exists}") + print(f"Errors during import : {ImportStats.count_file_errors}") + if ImportStats.count_imported > 0: + print("\r\nBreakdown of imported files by version:") + for key, version in ImportStats.count_imported_by_version.items(): + print(f" {key:20} : {version}") + + +def main(): + try: + processor = MediaImportProcessor() + processor.process() + except KeyboardInterrupt: + print("\r\n\r\nUser cancelled execution.") + + +if __name__ == "__main__": + main() diff --git a/invokeai/frontend/install/invokeai_configure.py b/invokeai/frontend/install/invokeai_configure.py index e087e4f40f..157d57fafc 100644 --- a/invokeai/frontend/install/invokeai_configure.py +++ b/invokeai/frontend/install/invokeai_configure.py @@ -1,4 +1,4 @@ """ Wrapper for invokeai.backend.configure.invokeai_configure """ -from ...backend.install.invokeai_configure import main +from ...backend.install.invokeai_configure import main as invokeai_configure diff --git a/invokeai/frontend/install/model_install.py b/invokeai/frontend/install/model_install.py index 0633553a3d..ade3043d96 100644 --- a/invokeai/frontend/install/model_install.py +++ b/invokeai/frontend/install/model_install.py @@ -28,7 +28,6 @@ from npyscreen import widget from invokeai.backend.util.logging import InvokeAILogger from invokeai.backend.install.model_install_backend import ( - ModelInstallList, InstallSelections, ModelInstall, SchedulerPredictionType, @@ -41,12 +40,12 @@ from invokeai.frontend.install.widgets import ( SingleSelectColumns, TextBox, BufferBox, - FileBox, set_min_terminal_size, select_stable_diffusion_config_file, CyclingForm, MIN_COLS, MIN_LINES, + WindowTooSmallException, ) from invokeai.app.services.config import InvokeAIAppConfig @@ -156,7 +155,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage): BufferBox, name="Log Messages", editable=False, - max_height=15, + max_height=6, ) self.nextrely += 1 @@ -693,7 +692,11 @@ def select_and_download_models(opt: Namespace): # needed to support the probe() method running under a subprocess torch.multiprocessing.set_start_method("spawn") - set_min_terminal_size(MIN_COLS, MIN_LINES) + if not set_min_terminal_size(MIN_COLS, MIN_LINES): + raise WindowTooSmallException( + "Could not increase terminal size. Try running again with a larger window or smaller font size." + ) + installApp = AddModelApplication(opt) try: installApp.run() @@ -772,7 +775,7 @@ def main(): if not config.model_conf_path.exists(): logger.info("Your InvokeAI root directory is not set up. Calling invokeai-configure.") - from invokeai.frontend.install import invokeai_configure + from invokeai.frontend.install.invokeai_configure import invokeai_configure invokeai_configure() sys.exit(0) @@ -787,6 +790,8 @@ def main(): curses.echo() curses.endwin() logger.info("Goodbye! Come back soon.") + except WindowTooSmallException as e: + logger.error(str(e)) except widget.NotEnoughSpaceForWidget as e: if str(e).startswith("Height of 1 allocated"): logger.error("Insufficient vertical space for the interface. Please make your window taller and try again") diff --git a/invokeai/frontend/install/widgets.py b/invokeai/frontend/install/widgets.py index 10da15bf13..156503f0b8 100644 --- a/invokeai/frontend/install/widgets.py +++ b/invokeai/frontend/install/widgets.py @@ -21,31 +21,40 @@ MIN_COLS = 130 MIN_LINES = 38 +class WindowTooSmallException(Exception): + pass + + # ------------------------------------- -def set_terminal_size(columns: int, lines: int): - ts = get_terminal_size() - width = max(columns, ts.columns) - height = max(lines, ts.lines) - +def set_terminal_size(columns: int, lines: int) -> bool: OS = platform.uname().system - if OS == "Windows": - pass - # not working reliably - ask user to adjust the window - # _set_terminal_size_powershell(width,height) - elif OS in ["Darwin", "Linux"]: - _set_terminal_size_unix(width, height) + screen_ok = False + while not screen_ok: + ts = get_terminal_size() + width = max(columns, ts.columns) + height = max(lines, ts.lines) - # check whether it worked.... - ts = get_terminal_size() - pause = False - if ts.columns < columns: - print("\033[1mThis window is too narrow for the user interface.\033[0m") - pause = True - if ts.lines < lines: - print("\033[1mThis window is too short for the user interface.\033[0m") - pause = True - if pause: - input("Maximize the window then press any key to continue..") + if OS == "Windows": + pass + # not working reliably - ask user to adjust the window + # _set_terminal_size_powershell(width,height) + elif OS in ["Darwin", "Linux"]: + _set_terminal_size_unix(width, height) + + # check whether it worked.... + ts = get_terminal_size() + if ts.columns < columns or ts.lines < lines: + print( + f"\033[1mThis window is too small for the interface. InvokeAI requires {columns}x{lines} (w x h) characters, but window is {ts.columns}x{ts.lines}\033[0m" + ) + resp = input( + "Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.." + ) + if resp.upper().startswith("Q"): + break + else: + screen_ok = True + return screen_ok def _set_terminal_size_powershell(width: int, height: int): @@ -80,14 +89,14 @@ def _set_terminal_size_unix(width: int, height: int): sys.stdout.flush() -def set_min_terminal_size(min_cols: int, min_lines: int): +def set_min_terminal_size(min_cols: int, min_lines: int) -> bool: # make sure there's enough room for the ui term_cols, term_lines = get_terminal_size() if term_cols >= min_cols and term_lines >= min_lines: - return + return True cols = max(term_cols, min_cols) lines = max(term_lines, min_lines) - set_terminal_size(cols, lines) + return set_terminal_size(cols, lines) class IntSlider(npyscreen.Slider): @@ -164,7 +173,7 @@ class FloatSlider(npyscreen.Slider): class FloatTitleSlider(npyscreen.TitleText): - _entry_type = FloatSlider + _entry_type = npyscreen.Slider class SelectColumnBase: diff --git a/invokeai/frontend/merge/merge_diffusers.py b/invokeai/frontend/merge/merge_diffusers.py index db493ec17f..f2e211a1b4 100644 --- a/invokeai/frontend/merge/merge_diffusers.py +++ b/invokeai/frontend/merge/merge_diffusers.py @@ -382,7 +382,8 @@ def run_cli(args: Namespace): def main(): args = _parse_args() - config.parse_args(["--root", str(args.root_dir)]) + if args.root_dir: + config.parse_args(["--root", str(args.root_dir)]) try: if args.front_end: diff --git a/invokeai/frontend/web/dist/assets/App-0a099278.js b/invokeai/frontend/web/dist/assets/App-0a099278.js new file mode 100644 index 0000000000..6ce732787f --- /dev/null +++ b/invokeai/frontend/web/dist/assets/App-0a099278.js @@ -0,0 +1,169 @@ +import{t as Cv,r as dR,i as kv,a as Tc,b as P_,S as j_,c as I_,d as E_,e as _v,f as O_,g as Pv,h as fR,j as pR,k as hR,l as mR,m as R_,n as gR,o as vR,p as bR,q as M_,s as yR,u as xR,v as wR,w as SR,x as CR,y as kR,z as _R,A as f,B as i,C as cm,D as Lp,E as PR,F as D_,G as A_,H as jR,P as vd,I as X1,J as IR,K as ER,L as OR,M as RR,N 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n=this,r=this.props.size,o=function(c){if(typeof n.state[c]>"u"||n.state[c]==="auto")return"auto";if(n.propsSize&&n.propsSize[c]&&n.propsSize[c].toString().endsWith("%")){if(n.state[c].toString().endsWith("%"))return n.state[c].toString();var d=n.getParentSize(),p=Number(n.state[c].toString().replace("px","")),h=p/d[c]*100;return h+"%"}return ev(n.state[c])},s=r&&typeof r.width<"u"&&!this.state.isResizing?ev(r.width):o("width"),a=r&&typeof r.height<"u"&&!this.state.isResizing?ev(r.height):o("height");return{width:s,height:a}},enumerable:!1,configurable:!0}),t.prototype.getParentSize=function(){if(!this.parentNode)return this.window?{width:this.window.innerWidth,height:this.window.innerHeight}:{width:0,height:0};var n=this.appendBase();if(!n)return{width:0,height:0};var r=!1,o=this.parentNode.style.flexWrap;o!=="wrap"&&(r=!0,this.parentNode.style.flexWrap="wrap"),n.style.position="relative",n.style.minWidth="100%",n.style.minHeight="100%";var s={width:n.offsetWidth,height:n.offsetHeight};return r&&(this.parentNode.style.flexWrap=o),this.removeBase(n),s},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(!(!this.resizable||!this.window)){var n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var o=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof o>"u"||o==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var o=this.props.boundsByDirection,s=this.state.direction,a=o&&Ul("left",s),c=o&&Ul("top",s),d,p;if(this.props.bounds==="parent"){var h=this.parentNode;h&&(d=a?this.resizableRight-this.parentLeft:h.offsetWidth+(this.parentLeft-this.resizableLeft),p=c?this.resizableBottom-this.parentTop:h.offsetHeight+(this.parentTop-this.resizableTop))}else this.props.bounds==="window"?this.window&&(d=a?this.resizableRight:this.window.innerWidth-this.resizableLeft,p=c?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(d=a?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),p=c?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return d&&Number.isFinite(d)&&(n=n&&n"u"?10:s.width,m=typeof o.width>"u"||o.width<0?n:o.width,v=typeof s.height>"u"?10:s.height,b=typeof o.height>"u"||o.height<0?r:o.height,w=d||0,y=p||0;if(c){var S=(v-w)*this.ratio+y,k=(b-w)*this.ratio+y,_=(h-y)/this.ratio+w,P=(m-y)/this.ratio+w,I=Math.max(h,S),E=Math.min(m,k),O=Math.max(v,_),R=Math.min(b,P);n=up(n,I,E),r=up(r,O,R)}else n=up(n,h,m),r=up(r,v,b);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var o=this.props.bounds.getBoundingClientRect();this.targetLeft=o.left,this.targetTop=o.top}if(this.resizable){var s=this.resizable.getBoundingClientRect(),a=s.left,c=s.top,d=s.right,p=s.bottom;this.resizableLeft=a,this.resizableRight=d,this.resizableTop=c,this.resizableBottom=p}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var o=0,s=0;if(n.nativeEvent&&pY(n.nativeEvent)?(o=n.nativeEvent.clientX,s=n.nativeEvent.clientY):n.nativeEvent&&dp(n.nativeEvent)&&(o=n.nativeEvent.touches[0].clientX,s=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var a=this.props.onResizeStart(n,r,this.resizable);if(a===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var c,d=this.window.getComputedStyle(this.resizable);if(d.flexBasis!=="auto"){var p=this.parentNode;if(p){var h=this.window.getComputedStyle(p).flexDirection;this.flexDir=h.startsWith("row")?"row":"column",c=d.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var m={original:{x:o,y:s,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:qs(qs({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:c};this.setState(m)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&dp(n))try{n.preventDefault(),n.stopPropagation()}catch{}var o=this.props,s=o.maxWidth,a=o.maxHeight,c=o.minWidth,d=o.minHeight,p=dp(n)?n.touches[0].clientX:n.clientX,h=dp(n)?n.touches[0].clientY:n.clientY,m=this.state,v=m.direction,b=m.original,w=m.width,y=m.height,S=this.getParentSize(),k=hY(S,this.window.innerWidth,this.window.innerHeight,s,a,c,d);s=k.maxWidth,a=k.maxHeight,c=k.minWidth,d=k.minHeight;var _=this.calculateNewSizeFromDirection(p,h),P=_.newHeight,I=_.newWidth,E=this.calculateNewMaxFromBoundary(s,a);this.props.snap&&this.props.snap.x&&(I=M4(I,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(P=M4(P,this.props.snap.y,this.props.snapGap));var O=this.calculateNewSizeFromAspectRatio(I,P,{width:E.maxWidth,height:E.maxHeight},{width:c,height:d});if(I=O.newWidth,P=O.newHeight,this.props.grid){var R=R4(I,this.props.grid[0]),M=R4(P,this.props.grid[1]),D=this.props.snapGap||0;I=D===0||Math.abs(R-I)<=D?R:I,P=D===0||Math.abs(M-P)<=D?M:P}var A={width:I-b.width,height:P-b.height};if(w&&typeof w=="string"){if(w.endsWith("%")){var L=I/S.width*100;I=L+"%"}else if(w.endsWith("vw")){var Q=I/this.window.innerWidth*100;I=Q+"vw"}else if(w.endsWith("vh")){var F=I/this.window.innerHeight*100;I=F+"vh"}}if(y&&typeof y=="string"){if(y.endsWith("%")){var L=P/S.height*100;P=L+"%"}else if(y.endsWith("vw")){var Q=P/this.window.innerWidth*100;P=Q+"vw"}else if(y.endsWith("vh")){var F=P/this.window.innerHeight*100;P=F+"vh"}}var V={width:this.createSizeForCssProperty(I,"width"),height:this.createSizeForCssProperty(P,"height")};this.flexDir==="row"?V.flexBasis=V.width:this.flexDir==="column"&&(V.flexBasis=V.height),_i.flushSync(function(){r.setState(V)}),this.props.onResize&&this.props.onResize(n,v,this.resizable,A)}},t.prototype.onMouseUp=function(n){var r=this.state,o=r.isResizing,s=r.direction,a=r.original;if(!(!o||!this.resizable)){var 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i.jsxs(W,{onMouseEnter:w,onMouseLeave:y,sx:{position:"relative",w:"full",h:t,alignItems:"center",justifyContent:"center",pointerEvents:s?"auto":"none",opacity:s?1:.5},children:[i.jsx(fl,{draggableData:S,droppableData:k,imageDTO:m,isDropDisabled:P||!s,onClickReset:b,postUploadAction:_,resetTooltip:"Reset Control Image",withResetIcon:!!m}),i.jsx(Re,{sx:{position:"absolute",top:0,insetInlineStart:0,w:"full",h:"full",opacity:P?1:0,transitionProperty:"common",transitionDuration:"normal",pointerEvents:"none"},children:i.jsx(fl,{draggableData:S,droppableData:k,imageDTO:v,isUploadDisabled:!0,isDropDisabled:!s,onClickReset:b,resetTooltip:"Reset Control Image",withResetIcon:!!m})}),d.includes(a)&&i.jsx(W,{sx:{position:"absolute",top:0,insetInlineStart:0,w:"full",h:"full",alignItems:"center",justifyContent:"center",opacity:.8,borderRadius:"base",bg:"base.400",_dark:{bg:"base.900"}},children:i.jsx(hl,{size:"xl",sx:{color:"base.100",_dark:{color:"base.400"}}})})]})},Tk=f.memo(Yre),Ts=()=>{const e=te();return f.useCallback((n,r)=>{e(bM({controlNetId:n,changes:r}))},[e])};function Ns(e){return i.jsx(W,{sx:{flexDirection:"column",gap:2},children:e.children})}const Nk=ls.canny_image_processor.default,Qre=e=>{const{controlNetId:t,processorNode:n,isEnabled:r}=e,{low_threshold:o,high_threshold:s}=n,a=B(kr),c=Ts(),d=f.useCallback(v=>{c(t,{low_threshold:v})},[t,c]),p=f.useCallback(()=>{c(t,{low_threshold:Nk.low_threshold})},[t,c]),h=f.useCallback(v=>{c(t,{high_threshold:v})},[t,c]),m=f.useCallback(()=>{c(t,{high_threshold:Nk.high_threshold})},[t,c]);return i.jsxs(Ns,{children:[i.jsx(_t,{isDisabled:a||!r,label:"Low Threshold",value:o,onChange:d,handleReset:p,withReset:!0,min:0,max:255,withInput:!0,withSliderMarks:!0}),i.jsx(_t,{isDisabled:a||!r,label:"High 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i.jsxs(Ns,{children:[i.jsx(_t,{label:"Detect Resolution",value:n,onChange:d,handleReset:m,withReset:!0,min:0,max:4096,withInput:!0,withSliderMarks:!0,isDisabled:a||!s}),i.jsx(_t,{label:"Image Resolution",value:r,onChange:p,handleReset:v,withReset:!0,min:0,max:4096,withInput:!0,withSliderMarks:!0,isDisabled:a||!s}),i.jsx(yr,{label:"Scribble",isChecked:o,onChange:h,isDisabled:a||!s})]})},noe=f.memo(toe),Lk=ls.lineart_anime_image_processor.default,roe=e=>{const{controlNetId:t,processorNode:n,isEnabled:r}=e,{image_resolution:o,detect_resolution:s}=n,a=Ts(),c=B(kr),d=f.useCallback(v=>{a(t,{detect_resolution:v})},[t,a]),p=f.useCallback(v=>{a(t,{image_resolution:v})},[t,a]),h=f.useCallback(()=>{a(t,{detect_resolution:Lk.detect_resolution})},[t,a]),m=f.useCallback(()=>{a(t,{image_resolution:Lk.image_resolution})},[t,a]);return i.jsxs(Ns,{children:[i.jsx(_t,{label:"Detect 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i.jsxs(Mo,{label:e("parameters.seamCorrectionHeader"),children:[i.jsx(fde,{}),i.jsx(dde,{}),i.jsx(hde,{}),i.jsx(pde,{})]})},gde=f.memo(mde),vde=be([lt,lr],({canvas:e,generation:t},n)=>{const{boundingBoxDimensions:r}=e,{aspectRatio:o}=t;return{boundingBoxDimensions:r,isStaging:n,aspectRatio:o}},Je),bde=()=>{const e=te(),{boundingBoxDimensions:t,isStaging:n,aspectRatio:r}=B(vde),{t:o}=ye(),s=c=>{if(e(Js({...t,height:Math.floor(c)})),r){const d=ws(c*r,64);e(Js({width:d,height:Math.floor(c)}))}},a=()=>{if(e(Js({...t,height:Math.floor(512)})),r){const c=ws(512*r,64);e(Js({width:c,height:Math.floor(512)}))}};return 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i.jsxs(W,{sx:{gap:2,p:4,borderRadius:4,flexDirection:"column",w:"full",bg:"base.150",_dark:{bg:"base.750"}},children:[i.jsxs(W,{alignItems:"center",gap:2,children:[i.jsx(Qe,{sx:{fontSize:"sm",width:"full",color:"base.700",_dark:{color:"base.300"}},children:t("parameters.aspectRatio")}),i.jsx(ml,{}),i.jsx(zO,{}),i.jsx(ze,{tooltip:t("ui.swapSizes"),"aria-label":t("ui.swapSizes"),size:"sm",icon:i.jsx(vO,{}),fontSize:20,onClick:()=>e(GD())})]}),i.jsx(Sde,{}),i.jsx(yde,{})]})}const Cde=be(lt,({ui:e,generation:t})=>{const{shouldUseSliders:n}=e,{shouldRandomizeSeed:r}=t;return{shouldUseSliders:n,activeLabel:r?void 0:"Manual Seed"}},Je),kde=()=>{const{shouldUseSliders:e,activeLabel:t}=B(Cde);return i.jsx(Mo,{label:"General",activeLabel:t,defaultIsOpen:!0,children:i.jsxs(W,{sx:{flexDirection:"column",gap:3},children:[e?i.jsxs(i.Fragment,{children:[i.jsx(wi,{}),i.jsx(Ci,{}),i.jsx(xi,{}),i.jsx(Si,{}),i.jsx(Re,{pt:2,children:i.jsx(ki,{})}),i.jsx(__,{})]}):i.jsxs(i.Fragment,{children:[i.jsxs(W,{gap:3,children:[i.jsx(wi,{}),i.jsx(Ci,{}),i.jsx(xi,{})]}),i.jsx(Si,{}),i.jsx(Re,{pt:2,children:i.jsx(ki,{})}),i.jsx(__,{})]}),i.jsx(KO,{})]})})},_de=f.memo(kde),R8=()=>i.jsxs(i.Fragment,{children:[i.jsx(ix,{}),i.jsx(Yd,{}),i.jsx(_de,{}),i.jsx(sx,{}),i.jsx(Jd,{}),i.jsx(Kd,{}),i.jsx(ax,{}),i.jsx(gde,{}),i.jsx(ude,{}),i.jsx(ox,{})]}),Pde=()=>i.jsxs(W,{sx:{gap:4,w:"full",h:"full"},children:[i.jsx(rx,{children:i.jsx(R8,{})}),i.jsx(que,{})]}),jde=f.memo(Pde),Ide=[{id:"txt2img",translationKey:"common.txt2img",icon:i.jsx(fo,{as:WY,sx:{boxSize:6,pointerEvents:"none"}}),content:i.jsx(ile,{})},{id:"img2img",translationKey:"common.img2img",icon:i.jsx(fo,{as:Oc,sx:{boxSize:6,pointerEvents:"none"}}),content:i.jsx(ase,{})},{id:"unifiedCanvas",translationKey:"common.unifiedCanvas",icon:i.jsx(fo,{as:Zee,sx:{boxSize:6,pointerEvents:"none"}}),content:i.jsx(jde,{})},{id:"nodes",translationKey:"common.nodes",icon:i.jsx(fo,{as:Jee,sx:{boxSize:6,pointerEvents:"none"}}),content:i.jsx(rle,{})},{id:"modelManager",translationKey:"modelManager.modelManager",icon:i.jsx(fo,{as:$Y,sx:{boxSize:6,pointerEvents:"none"}}),content:i.jsx(aae,{})}],Ede=be([bO,vo],(e,t)=>{const{disabledTabs:n}=e,{isNodesEnabled:r}=t;return 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i.jsxs(Ld,{defaultIndex:e,index:e,onChange:w,sx:{flexGrow:1,gap:4},isLazy:!0,children:[i.jsxs(zd,{sx:{pt:2,gap:4,flexDir:"column"},children:[h,i.jsx(ml,{}),i.jsx(ote,{})]}),i.jsxs(Jy,{id:"app",autoSaveId:"app",direction:"horizontal",style:{height:"100%",width:"100%"},children:[i.jsx(md,{id:"main",children:i.jsx($m,{style:{height:"100%",width:"100%"},children:m})}),r&&s&&!M8.includes(t)&&i.jsxs(i.Fragment,{children:[i.jsx(WO,{}),i.jsx(md,{ref:v,onResize:d,id:"gallery",order:3,defaultSize:b>wv&&b<100?b:wv,minSize:b,maxSize:50,children:i.jsx(hO,{})})]})]})]})},Mde=f.memo(Rde),Dde=be([Kn,Ba],(e,t)=>{const{shouldPinGallery:n,shouldShowGallery:r}=t;return{shouldPinGallery:n,shouldShowGalleryButton:M8.includes(e)?!1:!r}},{memoizeOptions:{resultEqualityCheck:Zt}}),Ade=()=>{const{t:e}=ye(),{shouldPinGallery:t,shouldShowGalleryButton:n}=B(Dde),r=te(),o=()=>{r(Av(!0)),t&&r(ko())};return n?i.jsx(ze,{tooltip:"Show Gallery 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t?null:i.jsx(GI,{direction:"left",isResizable:!1,isOpen:n,onClose:o,children:i.jsxs(W,{sx:{flexDir:"column",h:"full",w:tx,gap:2,position:"relative",flexShrink:0,overflowY:"auto"},children:[i.jsxs(W,{paddingBottom:4,justifyContent:"space-between",alignItems:"center",children:[i.jsx(gO,{}),i.jsx(HO,{})]}),i.jsx(W,{sx:{gap:2,flexDirection:"column",h:"full",w:"full"},children:a})]})})},Fde=f.memo(Bde),Hde=be([e=>e.hotkeys,e=>e.ui],(e,t)=>{const{shift:n}=e,{shouldPinParametersPanel:r,shouldPinGallery:o}=t;return{shift:n,shouldPinGallery:o,shouldPinParametersPanel:r}},{memoizeOptions:{resultEqualityCheck:Zt}}),Wde=()=>{const e=te(),{shift:t,shouldPinParametersPanel:n,shouldPinGallery:r}=B(Hde),o=B(Kn);return tt("*",()=>{mP("shift")?!t&&e(jo(!0)):t&&e(jo(!1))},{keyup:!0,keydown:!0},[t]),tt("o",()=>{e(YD()),o==="unifiedCanvas"&&n&&e(ko())}),tt(["shift+o"],()=>{e(QD()),o==="unifiedCanvas"&&e(ko())}),tt("g",()=>{e(JD()),o==="unifiedCanvas"&&r&&e(ko())}),tt(["shift+g"],()=>{e(G_()),o==="unifiedCanvas"&&e(ko())}),tt("1",()=>{e(Yl("txt2img"))}),tt("2",()=>{e(Yl("img2img"))}),tt("3",()=>{e(Yl("unifiedCanvas"))}),tt("4",()=>{e(Yl("nodes"))}),null},Vde=f.memo(Wde),Ude={},Gde=({config:e=Ude,headerComponent:t})=>{const n=B(rP),r=OF(),o=te();return f.useEffect(()=>{Bn.changeLanguage(n)},[n]),f.useEffect(()=>{o5(e)&&(r.info({namespace:"App",config:e},"Received config"),o(ZD(e)))},[o,e,r]),f.useEffect(()=>{o(e9())},[o]),i.jsxs(i.Fragment,{children:[i.jsxs(sl,{w:"100vw",h:"100vh",position:"relative",overflow:"hidden",children:[i.jsx(FH,{children:i.jsxs(sl,{sx:{gap:4,p:4,gridAutoRows:"min-content 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r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var o=this.props.bounds.getBoundingClientRect();this.targetLeft=o.left,this.targetTop=o.top}if(this.resizable){var s=this.resizable.getBoundingClientRect(),a=s.left,c=s.top,d=s.right,p=s.bottom;this.resizableLeft=a,this.resizableRight=d,this.resizableTop=c,this.resizableBottom=p}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var o=0,s=0;if(n.nativeEvent&&TH(n.nativeEvent)?(o=n.nativeEvent.clientX,s=n.nativeEvent.clientY):n.nativeEvent&&qf(n.nativeEvent)&&(o=n.nativeEvent.touches[0].clientX,s=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var a=this.props.onResizeStart(n,r,this.resizable);if(a===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var c,d=this.window.getComputedStyle(this.resizable);if(d.flexBasis!=="auto"){var p=this.parentNode;if(p){var h=this.window.getComputedStyle(p).flexDirection;this.flexDir=h.startsWith("row")?"row":"column",c=d.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var m={original:{x:o,y:s,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:qs(qs({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:c};this.setState(m)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&qf(n))try{n.preventDefault(),n.stopPropagation()}catch{}var o=this.props,s=o.maxWidth,a=o.maxHeight,c=o.minWidth,d=o.minHeight,p=qf(n)?n.touches[0].clientX:n.clientX,h=qf(n)?n.touches[0].clientY:n.clientY,m=this.state,v=m.direction,b=m.original,w=m.width,y=m.height,S=this.getParentSize(),_=NH(S,this.window.innerWidth,this.window.innerHeight,s,a,c,d);s=_.maxWidth,a=_.maxHeight,c=_.minWidth,d=_.minHeight;var k=this.calculateNewSizeFromDirection(p,h),j=k.newHeight,I=k.newWidth,E=this.calculateNewMaxFromBoundary(s,a);this.props.snap&&this.props.snap.x&&(I=jS(I,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(j=jS(j,this.props.snap.y,this.props.snapGap));var O=this.calculateNewSizeFromAspectRatio(I,j,{width:E.maxWidth,height:E.maxHeight},{width:c,height:d});if(I=O.newWidth,j=O.newHeight,this.props.grid){var R=PS(I,this.props.grid[0]),M=PS(j,this.props.grid[1]),A=this.props.snapGap||0;I=A===0||Math.abs(R-I)<=A?R:I,j=A===0||Math.abs(M-j)<=A?M:j}var T={width:I-b.width,height:j-b.height};if(w&&typeof w=="string"){if(w.endsWith("%")){var $=I/S.width*100;I=$+"%"}else if(w.endsWith("vw")){var Q=I/this.window.innerWidth*100;I=Q+"vw"}else if(w.endsWith("vh")){var B=I/this.window.innerHeight*100;I=B+"vh"}}if(y&&typeof y=="string"){if(y.endsWith("%")){var $=j/S.height*100;j=$+"%"}else if(y.endsWith("vw")){var Q=j/this.window.innerWidth*100;j=Q+"vw"}else if(y.endsWith("vh")){var B=j/this.window.innerHeight*100;j=B+"vh"}}var V={width:this.createSizeForCssProperty(I,"width"),height:this.createSizeForCssProperty(j,"height")};this.flexDir==="row"?V.flexBasis=V.width:this.flexDir==="column"&&(V.flexBasis=V.height),_i.flushSync(function(){r.setState(V)}),this.props.onResize&&this.props.onResize(n,v,this.resizable,T)}},t.prototype.onMouseUp=function(n){var r=this.state,o=r.isResizing,s=r.direction,a=r.original;if(!(!o||!this.resizable)){var c={width:this.size.width-a.width,height:this.size.height-a.height};this.props.onResizeStop&&this.props.onResizeStop(n,s,this.resizable,c),this.props.size&&this.setState(this.props.size),this.unbindEvents(),this.setState({isResizing:!1,backgroundStyle:qs(qs({},this.state.backgroundStyle),{cursor:"auto"})})}},t.prototype.updateSize=function(n){this.setState({width:n.width,height:n.height})},t.prototype.renderResizer=function(){var n=this,r=this.props,o=r.enable,s=r.handleStyles,a=r.handleClasses,c=r.handleWrapperStyle,d=r.handleWrapperClass,p=r.handleComponent;if(!o)return null;var h=Object.keys(o).map(function(m){return o[m]!==!1?f.createElement(MH,{key:m,direction:m,onResizeStart:n.onResizeStart,replaceStyles:s&&s[m],className:a&&a[m]},p&&p[m]?p[m]:null):null});return f.createElement("div",{className:d,style:c},h)},t.prototype.render=function(){var n=this,r=Object.keys(this.props).reduce(function(a,c){return 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i.jsxs(F,{flexDirection:"column",rowGap:4,width:"100%",children:[i.jsxs(F,{flexDirection:"column",children:[i.jsx(qe,{fontSize:"lg",fontWeight:"bold",children:n.model_name}),i.jsxs(qe,{fontSize:"sm",color:"base.400",children:[Qn[n.base_model]," Model"]})]}),i.jsx(Pi,{}),i.jsx("form",{onSubmit:c.onSubmit(p=>d(p)),children:i.jsxs(F,{flexDirection:"column",overflowY:"scroll",gap:4,children:[i.jsx(br,{label:a("modelManager.name"),...c.getInputProps("model_name")}),i.jsx(br,{label:a("modelManager.description"),...c.getInputProps("description")}),i.jsx(Xd,{required:!0,...c.getInputProps("base_model")}),i.jsx(lg,{required:!0,...c.getInputProps("variant")}),i.jsx(br,{required:!0,label:a("modelManager.modelLocation"),...c.getInputProps("path")}),i.jsx(br,{label:a("modelManager.vaeLocation"),...c.getInputProps("vae")}),i.jsx(Jt,{type:"submit",isDisabled:t||o,isLoading:o,children:a("modelManager.updateModel")})]})})]})}function Mse(e){const t=z(Cr),{model:n}=e,[r,{isLoading:o}]=EM(),s=te(),{t:a}=ye(),c=wl({initialValues:{model_name:n.model_name?n.model_name:"",base_model:n.base_model,model_type:"lora",path:n.path?n.path:"",description:n.description?n.description:"",model_format:n.model_format},validate:{path:p=>p.trim().length===0?"Must provide a path":null}}),d=f.useCallback(p=>{const h={base_model:n.base_model,model_name:n.model_name,body:p};r(h).unwrap().then(m=>{c.setValues(m),s(On(Mn({title:a("modelManager.modelUpdated"),status:"success"})))}).catch(m=>{c.reset(),s(On(Mn({title:a("modelManager.modelUpdateFailed"),status:"error"})))})},[s,c,n.base_model,n.model_name,a,r]);return i.jsxs(F,{flexDirection:"column",rowGap:4,width:"100%",children:[i.jsxs(F,{flexDirection:"column",children:[i.jsx(qe,{fontSize:"lg",fontWeight:"bold",children:n.model_name}),i.jsxs(qe,{fontSize:"sm",color:"base.400",children:[Qn[n.base_model]," Model ⋅"," ",OM[n.model_format]," 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i.jsxs(F,{sx:{gap:2,alignItems:"center",w:"full"},children:[i.jsx(F,{as:Jt,isChecked:c,sx:{justifyContent:"start",p:2,borderRadius:"base",w:"full",alignItems:"center",bg:c?"accent.400":"base.100",color:c?"base.50":"base.800",_hover:{bg:c?"accent.500":"base.300",color:c?"base.50":"base.800"},_dark:{color:c?"base.50":"base.100",bg:c?"accent.600":"base.850",_hover:{color:c?"base.50":"base.100",bg:c?"accent.550":"base.700"}}},onClick:p,children:i.jsxs(F,{gap:4,alignItems:"center",children:[i.jsx(ml,{minWidth:14,p:.5,fontSize:"sm",variant:"solid",children:DM[a.base_model]}),i.jsx(wn,{label:a.description,hasArrow:!0,placement:"bottom",children:i.jsx(qe,{sx:{fontWeight:500},children:a.model_name})})]})}),i.jsx(ix,{title:n("modelManager.deleteModel"),acceptCallback:h,acceptButtonText:n("modelManager.delete"),triggerComponent:i.jsx(Le,{icon:i.jsx(oU,{}),"aria-label":n("modelManager.deleteConfig"),isDisabled:t,colorScheme:"error"}),children:i.jsxs(F,{rowGap:4,flexDirection:"column",children:[i.jsx("p",{style:{fontWeight:"bold"},children:n("modelManager.deleteMsg1")}),i.jsx("p",{children:n("modelManager.deleteMsg2")})]})})]})}const Dse=e=>{const{selectedModelId:t,setSelectedModelId:n}=e,{t:r}=ye(),[o,s]=f.useState(""),[a,c]=f.useState("images"),{filteredDiffusersModels:d}=na(qi,{selectFromResult:({data:w})=>({filteredDiffusersModels:Cu(w,"main","diffusers",o)})}),{filteredCheckpointModels:p}=na(qi,{selectFromResult:({data:w})=>({filteredCheckpointModels:Cu(w,"main","checkpoint",o)})}),{filteredLoraModels:h}=cm(void 0,{selectFromResult:({data:w})=>({filteredLoraModels:Cu(w,"lora",void 0,o)})}),{filteredOnnxModels:m}=Fp(qi,{selectFromResult:({data:w})=>({filteredOnnxModels:Cu(w,"onnx","onnx",o)})}),{filteredOliveModels:v}=Fp(qi,{selectFromResult:({data:w})=>({filteredOliveModels:Cu(w,"onnx","olive",o)})}),b=f.useCallback(w=>{s(w.target.value)},[]);return 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pi({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]}),this.toUpperCase=()=>new pi({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}_parse(t){if(this._def.coerce&&(t.data=String(t.data)),this._getType(t)!==re.string){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.string,received:o.parsedType}),be}const r=new jn;let i;for(const o of this._def.checks)if(o.kind==="min")t.data.lengtho.value&&(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="length"){const s=t.data.length>o.value,a=t.data.length"u"?null:t==null?void 0:t.precision,offset:(n=t==null?void 0:t.offset)!==null&&n!==void 0?n:!1,...ge.errToObj(t==null?void 0:t.message)})}regex(t,n){return this._addCheck({kind:"regex",regex:t,...ge.errToObj(n)})}includes(t,n){return this._addCheck({kind:"includes",value:t,position:n==null?void 0:n.position,...ge.errToObj(n==null?void 0:n.message)})}startsWith(t,n){return this._addCheck({kind:"startsWith",value:t,...ge.errToObj(n)})}endsWith(t,n){return this._addCheck({kind:"endsWith",value:t,...ge.errToObj(n)})}min(t,n){return this._addCheck({kind:"min",value:t,...ge.errToObj(n)})}max(t,n){return this._addCheck({kind:"max",value:t,...ge.errToObj(n)})}length(t,n){return this._addCheck({kind:"length",value:t,...ge.errToObj(n)})}get isDatetime(){return!!this._def.checks.find(t=>t.kind==="datetime")}get isEmail(){return!!this._def.checks.find(t=>t.kind==="email")}get isURL(){return!!this._def.checks.find(t=>t.kind==="url")}get isEmoji(){return!!this._def.checks.find(t=>t.kind==="emoji")}get isUUID(){return!!this._def.checks.find(t=>t.kind==="uuid")}get isCUID(){return!!this._def.checks.find(t=>t.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(t=>t.kind==="cuid2")}get isULID(){return!!this._def.checks.find(t=>t.kind==="ulid")}get isIP(){return!!this._def.checks.find(t=>t.kind==="ip")}get minLength(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxLength(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value{var t;return new pi({checks:[],typeName:ye.ZodString,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...we(e)})};function xY(e,t){const n=(e.toString().split(".")[1]||"").length,r=(t.toString().split(".")[1]||"").length,i=n>r?n:r,o=parseInt(e.toFixed(i).replace(".","")),s=parseInt(t.toFixed(i).replace(".",""));return o%s/Math.pow(10,i)}class zs extends Ce{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(t){if(this._def.coerce&&(t.data=Number(t.data)),this._getType(t)!==re.number){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.number,received:o.parsedType}),be}let r;const i=new jn;for(const o of this._def.checks)o.kind==="int"?Ve.isInteger(t.data)||(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.invalid_type,expected:"integer",received:"float",message:o.message}),i.dirty()):o.kind==="min"?(o.inclusive?t.datao.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.too_big,maximum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="multipleOf"?xY(t.data,o.value)!==0&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):o.kind==="finite"?Number.isFinite(t.data)||(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.not_finite,message:o.message}),i.dirty()):Ve.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,ge.toString(n))}gt(t,n){return this.setLimit("min",t,!1,ge.toString(n))}lte(t,n){return this.setLimit("max",t,!0,ge.toString(n))}lt(t,n){return this.setLimit("max",t,!1,ge.toString(n))}setLimit(t,n,r,i){return new zs({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:ge.toString(i)}]})}_addCheck(t){return new zs({...this._def,checks:[...this._def.checks,t]})}int(t){return this._addCheck({kind:"int",message:ge.toString(t)})}positive(t){return this._addCheck({kind:"min",value:0,inclusive:!1,message:ge.toString(t)})}negative(t){return this._addCheck({kind:"max",value:0,inclusive:!1,message:ge.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:0,inclusive:!0,message:ge.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:0,inclusive:!0,message:ge.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:ge.toString(n)})}finite(t){return this._addCheck({kind:"finite",message:ge.toString(t)})}safe(t){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:ge.toString(t)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:ge.toString(t)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.valuet.kind==="int"||t.kind==="multipleOf"&&Ve.isInteger(t.value))}get isFinite(){let t=null,n=null;for(const r of this._def.checks){if(r.kind==="finite"||r.kind==="int"||r.kind==="multipleOf")return!0;r.kind==="min"?(n===null||r.value>n)&&(n=r.value):r.kind==="max"&&(t===null||r.valuenew zs({checks:[],typeName:ye.ZodNumber,coerce:(e==null?void 0:e.coerce)||!1,...we(e)});class Us extends Ce{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(t){if(this._def.coerce&&(t.data=BigInt(t.data)),this._getType(t)!==re.bigint){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.bigint,received:o.parsedType}),be}let r;const i=new jn;for(const o of this._def.checks)o.kind==="min"?(o.inclusive?t.datao.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.too_big,type:"bigint",maximum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="multipleOf"?t.data%o.value!==BigInt(0)&&(r=this._getOrReturnCtx(t,r),oe(r,{code:ee.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):Ve.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,ge.toString(n))}gt(t,n){return this.setLimit("min",t,!1,ge.toString(n))}lte(t,n){return this.setLimit("max",t,!0,ge.toString(n))}lt(t,n){return this.setLimit("max",t,!1,ge.toString(n))}setLimit(t,n,r,i){return new Us({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:ge.toString(i)}]})}_addCheck(t){return new Us({...this._def,checks:[...this._def.checks,t]})}positive(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:ge.toString(t)})}negative(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:ge.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:ge.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:ge.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:ge.toString(n)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value{var t;return new Us({checks:[],typeName:ye.ZodBigInt,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...we(e)})};class hf extends Ce{_parse(t){if(this._def.coerce&&(t.data=!!t.data),this._getType(t)!==re.boolean){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.boolean,received:r.parsedType}),be}return tr(t.data)}}hf.create=e=>new hf({typeName:ye.ZodBoolean,coerce:(e==null?void 0:e.coerce)||!1,...we(e)});class Ja extends Ce{_parse(t){if(this._def.coerce&&(t.data=new Date(t.data)),this._getType(t)!==re.date){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_type,expected:re.date,received:o.parsedType}),be}if(isNaN(t.data.getTime())){const o=this._getOrReturnCtx(t);return oe(o,{code:ee.invalid_date}),be}const r=new jn;let i;for(const o of this._def.checks)o.kind==="min"?t.data.getTime()o.value&&(i=this._getOrReturnCtx(t,i),oe(i,{code:ee.too_big,message:o.message,inclusive:!0,exact:!1,maximum:o.value,type:"date"}),r.dirty()):Ve.assertNever(o);return{status:r.value,value:new Date(t.data.getTime())}}_addCheck(t){return new Ja({...this._def,checks:[...this._def.checks,t]})}min(t,n){return this._addCheck({kind:"min",value:t.getTime(),message:ge.toString(n)})}max(t,n){return this._addCheck({kind:"max",value:t.getTime(),message:ge.toString(n)})}get minDate(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t!=null?new Date(t):null}get maxDate(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.valuenew Ja({checks:[],coerce:(e==null?void 0:e.coerce)||!1,typeName:ye.ZodDate,...we(e)});class bm extends Ce{_parse(t){if(this._getType(t)!==re.symbol){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.symbol,received:r.parsedType}),be}return tr(t.data)}}bm.create=e=>new bm({typeName:ye.ZodSymbol,...we(e)});class pf extends Ce{_parse(t){if(this._getType(t)!==re.undefined){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.undefined,received:r.parsedType}),be}return tr(t.data)}}pf.create=e=>new pf({typeName:ye.ZodUndefined,...we(e)});class gf extends Ce{_parse(t){if(this._getType(t)!==re.null){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.null,received:r.parsedType}),be}return tr(t.data)}}gf.create=e=>new gf({typeName:ye.ZodNull,...we(e)});class Ju extends Ce{constructor(){super(...arguments),this._any=!0}_parse(t){return tr(t.data)}}Ju.create=e=>new Ju({typeName:ye.ZodAny,...we(e)});class ja extends Ce{constructor(){super(...arguments),this._unknown=!0}_parse(t){return tr(t.data)}}ja.create=e=>new ja({typeName:ye.ZodUnknown,...we(e)});class Vo extends Ce{_parse(t){const n=this._getOrReturnCtx(t);return oe(n,{code:ee.invalid_type,expected:re.never,received:n.parsedType}),be}}Vo.create=e=>new Vo({typeName:ye.ZodNever,...we(e)});class Sm extends Ce{_parse(t){if(this._getType(t)!==re.undefined){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.void,received:r.parsedType}),be}return tr(t.data)}}Sm.create=e=>new Sm({typeName:ye.ZodVoid,...we(e)});class Si extends Ce{_parse(t){const{ctx:n,status:r}=this._processInputParams(t),i=this._def;if(n.parsedType!==re.array)return oe(n,{code:ee.invalid_type,expected:re.array,received:n.parsedType}),be;if(i.exactLength!==null){const s=n.data.length>i.exactLength.value,a=n.data.lengthi.maxLength.value&&(oe(n,{code:ee.too_big,maximum:i.maxLength.value,type:"array",inclusive:!0,exact:!1,message:i.maxLength.message}),r.dirty()),n.common.async)return Promise.all([...n.data].map((s,a)=>i.type._parseAsync(new eo(n,s,n.path,a)))).then(s=>jn.mergeArray(r,s));const o=[...n.data].map((s,a)=>i.type._parseSync(new eo(n,s,n.path,a)));return jn.mergeArray(r,o)}get element(){return this._def.type}min(t,n){return new Si({...this._def,minLength:{value:t,message:ge.toString(n)}})}max(t,n){return new Si({...this._def,maxLength:{value:t,message:ge.toString(n)}})}length(t,n){return new Si({...this._def,exactLength:{value:t,message:ge.toString(n)}})}nonempty(t){return this.min(1,t)}}Si.create=(e,t)=>new Si({type:e,minLength:null,maxLength:null,exactLength:null,typeName:ye.ZodArray,...we(t)});function Kl(e){if(e instanceof Tt){const t={};for(const n in e.shape){const r=e.shape[n];t[n]=Ro.create(Kl(r))}return new Tt({...e._def,shape:()=>t})}else return e instanceof Si?new Si({...e._def,type:Kl(e.element)}):e instanceof Ro?Ro.create(Kl(e.unwrap())):e instanceof tl?tl.create(Kl(e.unwrap())):e instanceof to?to.create(e.items.map(t=>Kl(t))):e}class Tt extends Ce{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const t=this._def.shape(),n=Ve.objectKeys(t);return this._cached={shape:t,keys:n}}_parse(t){if(this._getType(t)!==re.object){const u=this._getOrReturnCtx(t);return oe(u,{code:ee.invalid_type,expected:re.object,received:u.parsedType}),be}const{status:r,ctx:i}=this._processInputParams(t),{shape:o,keys:s}=this._getCached(),a=[];if(!(this._def.catchall instanceof Vo&&this._def.unknownKeys==="strip"))for(const u in i.data)s.includes(u)||a.push(u);const l=[];for(const u of s){const c=o[u],d=i.data[u];l.push({key:{status:"valid",value:u},value:c._parse(new eo(i,d,i.path,u)),alwaysSet:u in i.data})}if(this._def.catchall instanceof Vo){const u=this._def.unknownKeys;if(u==="passthrough")for(const c of a)l.push({key:{status:"valid",value:c},value:{status:"valid",value:i.data[c]}});else if(u==="strict")a.length>0&&(oe(i,{code:ee.unrecognized_keys,keys:a}),r.dirty());else if(u!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const u=this._def.catchall;for(const c of a){const d=i.data[c];l.push({key:{status:"valid",value:c},value:u._parse(new eo(i,d,i.path,c)),alwaysSet:c in i.data})}}return i.common.async?Promise.resolve().then(async()=>{const u=[];for(const c of l){const d=await c.key;u.push({key:d,value:await c.value,alwaysSet:c.alwaysSet})}return u}).then(u=>jn.mergeObjectSync(r,u)):jn.mergeObjectSync(r,l)}get shape(){return this._def.shape()}strict(t){return ge.errToObj,new Tt({...this._def,unknownKeys:"strict",...t!==void 0?{errorMap:(n,r)=>{var i,o,s,a;const l=(s=(o=(i=this._def).errorMap)===null||o===void 0?void 0:o.call(i,n,r).message)!==null&&s!==void 0?s:r.defaultError;return n.code==="unrecognized_keys"?{message:(a=ge.errToObj(t).message)!==null&&a!==void 0?a:l}:{message:l}}}:{}})}strip(){return new Tt({...this._def,unknownKeys:"strip"})}passthrough(){return new Tt({...this._def,unknownKeys:"passthrough"})}extend(t){return new Tt({...this._def,shape:()=>({...this._def.shape(),...t})})}merge(t){return new Tt({unknownKeys:t._def.unknownKeys,catchall:t._def.catchall,shape:()=>({...this._def.shape(),...t._def.shape()}),typeName:ye.ZodObject})}setKey(t,n){return this.augment({[t]:n})}catchall(t){return new Tt({...this._def,catchall:t})}pick(t){const n={};return Ve.objectKeys(t).forEach(r=>{t[r]&&this.shape[r]&&(n[r]=this.shape[r])}),new Tt({...this._def,shape:()=>n})}omit(t){const n={};return Ve.objectKeys(this.shape).forEach(r=>{t[r]||(n[r]=this.shape[r])}),new Tt({...this._def,shape:()=>n})}deepPartial(){return Kl(this)}partial(t){const n={};return Ve.objectKeys(this.shape).forEach(r=>{const i=this.shape[r];t&&!t[r]?n[r]=i:n[r]=i.optional()}),new Tt({...this._def,shape:()=>n})}required(t){const n={};return Ve.objectKeys(this.shape).forEach(r=>{if(t&&!t[r])n[r]=this.shape[r];else{let o=this.shape[r];for(;o instanceof Ro;)o=o._def.innerType;n[r]=o}}),new Tt({...this._def,shape:()=>n})}keyof(){return _O(Ve.objectKeys(this.shape))}}Tt.create=(e,t)=>new Tt({shape:()=>e,unknownKeys:"strip",catchall:Vo.create(),typeName:ye.ZodObject,...we(t)});Tt.strictCreate=(e,t)=>new Tt({shape:()=>e,unknownKeys:"strict",catchall:Vo.create(),typeName:ye.ZodObject,...we(t)});Tt.lazycreate=(e,t)=>new Tt({shape:e,unknownKeys:"strip",catchall:Vo.create(),typeName:ye.ZodObject,...we(t)});class mf extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t),r=this._def.options;function i(o){for(const a of o)if(a.result.status==="valid")return a.result;for(const a of o)if(a.result.status==="dirty")return n.common.issues.push(...a.ctx.common.issues),a.result;const s=o.map(a=>new bi(a.ctx.common.issues));return oe(n,{code:ee.invalid_union,unionErrors:s}),be}if(n.common.async)return Promise.all(r.map(async o=>{const s={...n,common:{...n.common,issues:[]},parent:null};return{result:await o._parseAsync({data:n.data,path:n.path,parent:s}),ctx:s}})).then(i);{let o;const s=[];for(const l of r){const u={...n,common:{...n.common,issues:[]},parent:null},c=l._parseSync({data:n.data,path:n.path,parent:u});if(c.status==="valid")return c;c.status==="dirty"&&!o&&(o={result:c,ctx:u}),u.common.issues.length&&s.push(u.common.issues)}if(o)return n.common.issues.push(...o.ctx.common.issues),o.result;const a=s.map(l=>new bi(l));return oe(n,{code:ee.invalid_union,unionErrors:a}),be}}get options(){return this._def.options}}mf.create=(e,t)=>new mf({options:e,typeName:ye.ZodUnion,...we(t)});const dg=e=>e instanceof bf?dg(e.schema):e instanceof Ti?dg(e.innerType()):e instanceof Sf?[e.value]:e instanceof Gs?e.options:e instanceof _f?Object.keys(e.enum):e instanceof wf?dg(e._def.innerType):e instanceof pf?[void 0]:e instanceof gf?[null]:null;class x0 extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==re.object)return oe(n,{code:ee.invalid_type,expected:re.object,received:n.parsedType}),be;const r=this.discriminator,i=n.data[r],o=this.optionsMap.get(i);return o?n.common.async?o._parseAsync({data:n.data,path:n.path,parent:n}):o._parseSync({data:n.data,path:n.path,parent:n}):(oe(n,{code:ee.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[r]}),be)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(t,n,r){const i=new Map;for(const o of n){const s=dg(o.shape[t]);if(!s)throw new Error(`A discriminator value for key \`${t}\` could not be extracted from all schema options`);for(const a of s){if(i.has(a))throw new Error(`Discriminator property ${String(t)} has duplicate value ${String(a)}`);i.set(a,o)}}return new x0({typeName:ye.ZodDiscriminatedUnion,discriminator:t,options:n,optionsMap:i,...we(r)})}}function $_(e,t){const n=ms(e),r=ms(t);if(e===t)return{valid:!0,data:e};if(n===re.object&&r===re.object){const i=Ve.objectKeys(t),o=Ve.objectKeys(e).filter(a=>i.indexOf(a)!==-1),s={...e,...t};for(const a of o){const l=$_(e[a],t[a]);if(!l.valid)return{valid:!1};s[a]=l.data}return{valid:!0,data:s}}else if(n===re.array&&r===re.array){if(e.length!==t.length)return{valid:!1};const i=[];for(let o=0;o{if(D_(o)||D_(s))return be;const a=$_(o.value,s.value);return a.valid?((L_(o)||L_(s))&&n.dirty(),{status:n.value,value:a.data}):(oe(r,{code:ee.invalid_intersection_types}),be)};return r.common.async?Promise.all([this._def.left._parseAsync({data:r.data,path:r.path,parent:r}),this._def.right._parseAsync({data:r.data,path:r.path,parent:r})]).then(([o,s])=>i(o,s)):i(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}yf.create=(e,t,n)=>new yf({left:e,right:t,typeName:ye.ZodIntersection,...we(n)});class to extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.array)return oe(r,{code:ee.invalid_type,expected:re.array,received:r.parsedType}),be;if(r.data.lengththis._def.items.length&&(oe(r,{code:ee.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),n.dirty());const o=[...r.data].map((s,a)=>{const l=this._def.items[a]||this._def.rest;return l?l._parse(new eo(r,s,r.path,a)):null}).filter(s=>!!s);return r.common.async?Promise.all(o).then(s=>jn.mergeArray(n,s)):jn.mergeArray(n,o)}get items(){return this._def.items}rest(t){return new to({...this._def,rest:t})}}to.create=(e,t)=>{if(!Array.isArray(e))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new to({items:e,typeName:ye.ZodTuple,rest:null,...we(t)})};class vf extends Ce{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.object)return oe(r,{code:ee.invalid_type,expected:re.object,received:r.parsedType}),be;const i=[],o=this._def.keyType,s=this._def.valueType;for(const a in r.data)i.push({key:o._parse(new eo(r,a,r.path,a)),value:s._parse(new eo(r,r.data[a],r.path,a))});return r.common.async?jn.mergeObjectAsync(n,i):jn.mergeObjectSync(n,i)}get element(){return this._def.valueType}static create(t,n,r){return n instanceof Ce?new vf({keyType:t,valueType:n,typeName:ye.ZodRecord,...we(r)}):new vf({keyType:pi.create(),valueType:t,typeName:ye.ZodRecord,...we(n)})}}class _m extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.map)return oe(r,{code:ee.invalid_type,expected:re.map,received:r.parsedType}),be;const i=this._def.keyType,o=this._def.valueType,s=[...r.data.entries()].map(([a,l],u)=>({key:i._parse(new eo(r,a,r.path,[u,"key"])),value:o._parse(new eo(r,l,r.path,[u,"value"]))}));if(r.common.async){const a=new Map;return Promise.resolve().then(async()=>{for(const l of s){const u=await l.key,c=await l.value;if(u.status==="aborted"||c.status==="aborted")return be;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}})}else{const a=new Map;for(const l of s){const u=l.key,c=l.value;if(u.status==="aborted"||c.status==="aborted")return be;(u.status==="dirty"||c.status==="dirty")&&n.dirty(),a.set(u.value,c.value)}return{status:n.value,value:a}}}}_m.create=(e,t,n)=>new _m({valueType:t,keyType:e,typeName:ye.ZodMap,...we(n)});class el extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==re.set)return oe(r,{code:ee.invalid_type,expected:re.set,received:r.parsedType}),be;const i=this._def;i.minSize!==null&&r.data.sizei.maxSize.value&&(oe(r,{code:ee.too_big,maximum:i.maxSize.value,type:"set",inclusive:!0,exact:!1,message:i.maxSize.message}),n.dirty());const o=this._def.valueType;function s(l){const u=new Set;for(const c of l){if(c.status==="aborted")return be;c.status==="dirty"&&n.dirty(),u.add(c.value)}return{status:n.value,value:u}}const a=[...r.data.values()].map((l,u)=>o._parse(new eo(r,l,r.path,u)));return r.common.async?Promise.all(a).then(l=>s(l)):s(a)}min(t,n){return new el({...this._def,minSize:{value:t,message:ge.toString(n)}})}max(t,n){return new el({...this._def,maxSize:{value:t,message:ge.toString(n)}})}size(t,n){return this.min(t,n).max(t,n)}nonempty(t){return this.min(1,t)}}el.create=(e,t)=>new el({valueType:e,minSize:null,maxSize:null,typeName:ye.ZodSet,...we(t)});class Ru extends Ce{constructor(){super(...arguments),this.validate=this.implement}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==re.function)return oe(n,{code:ee.invalid_type,expected:re.function,received:n.parsedType}),be;function r(a,l){return mm({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,gm(),ff].filter(u=>!!u),issueData:{code:ee.invalid_arguments,argumentsError:l}})}function i(a,l){return mm({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,gm(),ff].filter(u=>!!u),issueData:{code:ee.invalid_return_type,returnTypeError:l}})}const o={errorMap:n.common.contextualErrorMap},s=n.data;return this._def.returns instanceof ec?tr(async(...a)=>{const l=new bi([]),u=await this._def.args.parseAsync(a,o).catch(f=>{throw l.addIssue(r(a,f)),l}),c=await s(...u);return await this._def.returns._def.type.parseAsync(c,o).catch(f=>{throw l.addIssue(i(c,f)),l})}):tr((...a)=>{const l=this._def.args.safeParse(a,o);if(!l.success)throw new bi([r(a,l.error)]);const u=s(...l.data),c=this._def.returns.safeParse(u,o);if(!c.success)throw new bi([i(u,c.error)]);return c.data})}parameters(){return this._def.args}returnType(){return this._def.returns}args(...t){return new Ru({...this._def,args:to.create(t).rest(ja.create())})}returns(t){return new Ru({...this._def,returns:t})}implement(t){return this.parse(t)}strictImplement(t){return this.parse(t)}static create(t,n,r){return new Ru({args:t||to.create([]).rest(ja.create()),returns:n||ja.create(),typeName:ye.ZodFunction,...we(r)})}}class bf extends Ce{get schema(){return this._def.getter()}_parse(t){const{ctx:n}=this._processInputParams(t);return this._def.getter()._parse({data:n.data,path:n.path,parent:n})}}bf.create=(e,t)=>new bf({getter:e,typeName:ye.ZodLazy,...we(t)});class Sf extends Ce{_parse(t){if(t.data!==this._def.value){const n=this._getOrReturnCtx(t);return oe(n,{received:n.data,code:ee.invalid_literal,expected:this._def.value}),be}return{status:"valid",value:t.data}}get value(){return this._def.value}}Sf.create=(e,t)=>new Sf({value:e,typeName:ye.ZodLiteral,...we(t)});function _O(e,t){return new Gs({values:e,typeName:ye.ZodEnum,...we(t)})}class Gs extends Ce{_parse(t){if(typeof t.data!="string"){const n=this._getOrReturnCtx(t),r=this._def.values;return oe(n,{expected:Ve.joinValues(r),received:n.parsedType,code:ee.invalid_type}),be}if(this._def.values.indexOf(t.data)===-1){const n=this._getOrReturnCtx(t),r=this._def.values;return oe(n,{received:n.data,code:ee.invalid_enum_value,options:r}),be}return tr(t.data)}get options(){return this._def.values}get enum(){const t={};for(const n of this._def.values)t[n]=n;return t}get Values(){const t={};for(const n of this._def.values)t[n]=n;return t}get Enum(){const t={};for(const n of this._def.values)t[n]=n;return t}extract(t){return Gs.create(t)}exclude(t){return Gs.create(this.options.filter(n=>!t.includes(n)))}}Gs.create=_O;class _f extends Ce{_parse(t){const n=Ve.getValidEnumValues(this._def.values),r=this._getOrReturnCtx(t);if(r.parsedType!==re.string&&r.parsedType!==re.number){const i=Ve.objectValues(n);return oe(r,{expected:Ve.joinValues(i),received:r.parsedType,code:ee.invalid_type}),be}if(n.indexOf(t.data)===-1){const i=Ve.objectValues(n);return oe(r,{received:r.data,code:ee.invalid_enum_value,options:i}),be}return tr(t.data)}get enum(){return this._def.values}}_f.create=(e,t)=>new _f({values:e,typeName:ye.ZodNativeEnum,...we(t)});class ec extends Ce{unwrap(){return this._def.type}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==re.promise&&n.common.async===!1)return oe(n,{code:ee.invalid_type,expected:re.promise,received:n.parsedType}),be;const r=n.parsedType===re.promise?n.data:Promise.resolve(n.data);return tr(r.then(i=>this._def.type.parseAsync(i,{path:n.path,errorMap:n.common.contextualErrorMap})))}}ec.create=(e,t)=>new ec({type:e,typeName:ye.ZodPromise,...we(t)});class Ti extends Ce{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===ye.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=this._def.effect||null;if(i.type==="preprocess"){const s=i.transform(r.data);return r.common.async?Promise.resolve(s).then(a=>this._def.schema._parseAsync({data:a,path:r.path,parent:r})):this._def.schema._parseSync({data:s,path:r.path,parent:r})}const o={addIssue:s=>{oe(r,s),s.fatal?n.abort():n.dirty()},get path(){return r.path}};if(o.addIssue=o.addIssue.bind(o),i.type==="refinement"){const s=a=>{const l=i.refinement(a,o);if(r.common.async)return Promise.resolve(l);if(l instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return a};if(r.common.async===!1){const a=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});return a.status==="aborted"?be:(a.status==="dirty"&&n.dirty(),s(a.value),{status:n.value,value:a.value})}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(a=>a.status==="aborted"?be:(a.status==="dirty"&&n.dirty(),s(a.value).then(()=>({status:n.value,value:a.value}))))}if(i.type==="transform")if(r.common.async===!1){const s=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});if(!ym(s))return s;const a=i.transform(s.value,o);if(a instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:n.value,value:a}}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(s=>ym(s)?Promise.resolve(i.transform(s.value,o)).then(a=>({status:n.value,value:a})):s);Ve.assertNever(i)}}Ti.create=(e,t,n)=>new Ti({schema:e,typeName:ye.ZodEffects,effect:t,...we(n)});Ti.createWithPreprocess=(e,t,n)=>new Ti({schema:t,effect:{type:"preprocess",transform:e},typeName:ye.ZodEffects,...we(n)});class Ro extends Ce{_parse(t){return this._getType(t)===re.undefined?tr(void 0):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}Ro.create=(e,t)=>new Ro({innerType:e,typeName:ye.ZodOptional,...we(t)});class tl extends Ce{_parse(t){return this._getType(t)===re.null?tr(null):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}tl.create=(e,t)=>new tl({innerType:e,typeName:ye.ZodNullable,...we(t)});class wf extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t);let r=n.data;return n.parsedType===re.undefined&&(r=this._def.defaultValue()),this._def.innerType._parse({data:r,path:n.path,parent:n})}removeDefault(){return this._def.innerType}}wf.create=(e,t)=>new wf({innerType:e,typeName:ye.ZodDefault,defaultValue:typeof t.default=="function"?t.default:()=>t.default,...we(t)});class wm extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t),r={...n,common:{...n.common,issues:[]}},i=this._def.innerType._parse({data:r.data,path:r.path,parent:{...r}});return vm(i)?i.then(o=>({status:"valid",value:o.status==="valid"?o.value:this._def.catchValue({get error(){return new bi(r.common.issues)},input:r.data})})):{status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new bi(r.common.issues)},input:r.data})}}removeCatch(){return this._def.innerType}}wm.create=(e,t)=>new wm({innerType:e,typeName:ye.ZodCatch,catchValue:typeof t.catch=="function"?t.catch:()=>t.catch,...we(t)});class xm extends Ce{_parse(t){if(this._getType(t)!==re.nan){const r=this._getOrReturnCtx(t);return oe(r,{code:ee.invalid_type,expected:re.nan,received:r.parsedType}),be}return{status:"valid",value:t.data}}}xm.create=e=>new xm({typeName:ye.ZodNaN,...we(e)});const CY=Symbol("zod_brand");class wO extends Ce{_parse(t){const{ctx:n}=this._processInputParams(t),r=n.data;return this._def.type._parse({data:r,path:n.path,parent:n})}unwrap(){return this._def.type}}class mh extends Ce{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.common.async)return(async()=>{const o=await this._def.in._parseAsync({data:r.data,path:r.path,parent:r});return o.status==="aborted"?be:o.status==="dirty"?(n.dirty(),SO(o.value)):this._def.out._parseAsync({data:o.value,path:r.path,parent:r})})();{const i=this._def.in._parseSync({data:r.data,path:r.path,parent:r});return i.status==="aborted"?be:i.status==="dirty"?(n.dirty(),{status:"dirty",value:i.value}):this._def.out._parseSync({data:i.value,path:r.path,parent:r})}}static create(t,n){return new mh({in:t,out:n,typeName:ye.ZodPipeline})}}const xO=(e,t={},n)=>e?Ju.create().superRefine((r,i)=>{var o,s;if(!e(r)){const a=typeof t=="function"?t(r):typeof t=="string"?{message:t}:t,l=(s=(o=a.fatal)!==null&&o!==void 0?o:n)!==null&&s!==void 0?s:!0,u=typeof a=="string"?{message:a}:a;i.addIssue({code:"custom",...u,fatal:l})}}):Ju.create(),TY={object:Tt.lazycreate};var ye;(function(e){e.ZodString="ZodString",e.ZodNumber="ZodNumber",e.ZodNaN="ZodNaN",e.ZodBigInt="ZodBigInt",e.ZodBoolean="ZodBoolean",e.ZodDate="ZodDate",e.ZodSymbol="ZodSymbol",e.ZodUndefined="ZodUndefined",e.ZodNull="ZodNull",e.ZodAny="ZodAny",e.ZodUnknown="ZodUnknown",e.ZodNever="ZodNever",e.ZodVoid="ZodVoid",e.ZodArray="ZodArray",e.ZodObject="ZodObject",e.ZodUnion="ZodUnion",e.ZodDiscriminatedUnion="ZodDiscriminatedUnion",e.ZodIntersection="ZodIntersection",e.ZodTuple="ZodTuple",e.ZodRecord="ZodRecord",e.ZodMap="ZodMap",e.ZodSet="ZodSet",e.ZodFunction="ZodFunction",e.ZodLazy="ZodLazy",e.ZodLiteral="ZodLiteral",e.ZodEnum="ZodEnum",e.ZodEffects="ZodEffects",e.ZodNativeEnum="ZodNativeEnum",e.ZodOptional="ZodOptional",e.ZodNullable="ZodNullable",e.ZodDefault="ZodDefault",e.ZodCatch="ZodCatch",e.ZodPromise="ZodPromise",e.ZodBranded="ZodBranded",e.ZodPipeline="ZodPipeline"})(ye||(ye={}));const EY=(e,t={message:`Input not instance of ${e.name}`})=>xO(n=>n instanceof e,t),CO=pi.create,TO=zs.create,PY=xm.create,AY=Us.create,EO=hf.create,kY=Ja.create,RY=bm.create,OY=pf.create,MY=gf.create,IY=Ju.create,NY=ja.create,DY=Vo.create,LY=Sm.create,$Y=Si.create,FY=Tt.create,BY=Tt.strictCreate,jY=mf.create,VY=x0.create,zY=yf.create,UY=to.create,GY=vf.create,HY=_m.create,qY=el.create,WY=Ru.create,KY=bf.create,XY=Sf.create,YY=Gs.create,QY=_f.create,ZY=ec.create,K4=Ti.create,JY=Ro.create,eQ=tl.create,tQ=Ti.createWithPreprocess,nQ=mh.create,rQ=()=>CO().optional(),iQ=()=>TO().optional(),oQ=()=>EO().optional(),sQ={string:e=>pi.create({...e,coerce:!0}),number:e=>zs.create({...e,coerce:!0}),boolean:e=>hf.create({...e,coerce:!0}),bigint:e=>Us.create({...e,coerce:!0}),date:e=>Ja.create({...e,coerce:!0})},aQ=be;var _t=Object.freeze({__proto__:null,defaultErrorMap:ff,setErrorMap:dY,getErrorMap:gm,makeIssue:mm,EMPTY_PATH:fY,addIssueToContext:oe,ParseStatus:jn,INVALID:be,DIRTY:SO,OK:tr,isAborted:D_,isDirty:L_,isValid:ym,isAsync:vm,get util(){return Ve},get objectUtil(){return N_},ZodParsedType:re,getParsedType:ms,ZodType:Ce,ZodString:pi,ZodNumber:zs,ZodBigInt:Us,ZodBoolean:hf,ZodDate:Ja,ZodSymbol:bm,ZodUndefined:pf,ZodNull:gf,ZodAny:Ju,ZodUnknown:ja,ZodNever:Vo,ZodVoid:Sm,ZodArray:Si,ZodObject:Tt,ZodUnion:mf,ZodDiscriminatedUnion:x0,ZodIntersection:yf,ZodTuple:to,ZodRecord:vf,ZodMap:_m,ZodSet:el,ZodFunction:Ru,ZodLazy:bf,ZodLiteral:Sf,ZodEnum:Gs,ZodNativeEnum:_f,ZodPromise:ec,ZodEffects:Ti,ZodTransformer:Ti,ZodOptional:Ro,ZodNullable:tl,ZodDefault:wf,ZodCatch:wm,ZodNaN:xm,BRAND:CY,ZodBranded:wO,ZodPipeline:mh,custom:xO,Schema:Ce,ZodSchema:Ce,late:TY,get ZodFirstPartyTypeKind(){return ye},coerce:sQ,any:IY,array:$Y,bigint:AY,boolean:EO,date:kY,discriminatedUnion:VY,effect:K4,enum:YY,function:WY,instanceof:EY,intersection:zY,lazy:KY,literal:XY,map:HY,nan:PY,nativeEnum:QY,never:DY,null:MY,nullable:eQ,number:TO,object:FY,oboolean:oQ,onumber:iQ,optional:JY,ostring:rQ,pipeline:nQ,preprocess:tQ,promise:ZY,record:GY,set:qY,strictObject:BY,string:CO,symbol:RY,transformer:K4,tuple:UY,undefined:OY,union:jY,unknown:NY,void:LY,NEVER:aQ,ZodIssueCode:ee,quotelessJson:cY,ZodError:bi});const lQ=_t.string(),Twe=e=>lQ.safeParse(e).success,uQ=_t.string(),Ewe=e=>uQ.safeParse(e).success,cQ=_t.string(),Pwe=e=>cQ.safeParse(e).success,dQ=_t.string(),Awe=e=>dQ.safeParse(e).success,fQ=_t.number().int().min(1),kwe=e=>fQ.safeParse(e).success,hQ=_t.number().min(1),Rwe=e=>hQ.safeParse(e).success,pQ=_t.enum(["euler","deis","ddim","ddpm","dpmpp_2s","dpmpp_2m","dpmpp_2m_sde","dpmpp_sde","heun","kdpm_2","lms","pndm","unipc","euler_k","dpmpp_2s_k","dpmpp_2m_k","dpmpp_2m_sde_k","dpmpp_sde_k","heun_k","lms_k","euler_a","kdpm_2_a"]),Owe=e=>pQ.safeParse(e).success,Mwe={euler:"Euler",deis:"DEIS",ddim:"DDIM",ddpm:"DDPM",dpmpp_sde:"DPM++ SDE",dpmpp_2s:"DPM++ 2S",dpmpp_2m:"DPM++ 2M",dpmpp_2m_sde:"DPM++ 2M SDE",heun:"Heun",kdpm_2:"KDPM 2",lms:"LMS",pndm:"PNDM",unipc:"UniPC",euler_k:"Euler Karras",dpmpp_sde_k:"DPM++ SDE Karras",dpmpp_2s_k:"DPM++ 2S Karras",dpmpp_2m_k:"DPM++ 2M Karras",dpmpp_2m_sde_k:"DPM++ 2M SDE Karras",heun_k:"Heun Karras",lms_k:"LMS Karras",euler_a:"Euler Ancestral",kdpm_2_a:"KDPM 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Um{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.init(t,n)}init(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.prefix=n.prefix||"i18next:",this.logger=t||iae,this.options=n,this.debug=n.debug}log(){for(var t=arguments.length,n=new Array(t),r=0;r{this.observers[r]=this.observers[r]||[],this.observers[r].push(n)}),this}off(t,n){if(this.observers[t]){if(!n){delete this.observers[t];return}this.observers[t]=this.observers[t].filter(r=>r!==n)}}emit(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),i=1;i{s(...r)}),this.observers["*"]&&[].concat(this.observers["*"]).forEach(s=>{s.apply(s,[t,...r])})}}function Yc(){let e,t;const n=new Promise((r,i)=>{e=r,t=i});return n.resolve=e,n.reject=t,n}function CE(e){return e==null?"":""+e}function oae(e,t,n){e.forEach(r=>{t[r]&&(n[r]=t[r])})}function hC(e,t,n){function r(s){return s&&s.indexOf("###")>-1?s.replace(/###/g,"."):s}function i(){return!e||typeof e=="string"}const o=typeof 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0&&(this.options.keySeparator="."),this.options.ignoreJSONStructure===void 0&&(this.options.ignoreJSONStructure=!0)}addNamespaces(t){this.options.ns.indexOf(t)<0&&this.options.ns.push(t)}removeNamespaces(t){const n=this.options.ns.indexOf(t);n>-1&&this.options.ns.splice(n,1)}getResource(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};const o=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,s=i.ignoreJSONStructure!==void 0?i.ignoreJSONStructure:this.options.ignoreJSONStructure;let a=[t,n];r&&typeof r!="string"&&(a=a.concat(r)),r&&typeof r=="string"&&(a=a.concat(o?r.split(o):r)),t.indexOf(".")>-1&&(a=t.split("."));const l=Gm(this.data,a);return l||!s||typeof r!="string"?l:Hm(this.data&&this.data[t]&&this.data[t][n],r,o)}addResource(t,n,r,i){let o=arguments.length>4&&arguments[4]!==void 0?arguments[4]:{silent:!1};const s=o.keySeparator!==void 0?o.keySeparator:this.options.keySeparator;let a=[t,n];r&&(a=a.concat(s?r.split(s):r)),t.indexOf(".")>-1&&(a=t.split("."),i=n,n=a[1]),this.addNamespaces(n),TE(this.data,a,i),o.silent||this.emit("added",t,n,r,i)}addResources(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{silent:!1};for(const o in r)(typeof r[o]=="string"||Object.prototype.toString.apply(r[o])==="[object Array]")&&this.addResource(t,n,o,r[o],{silent:!0});i.silent||this.emit("added",t,n,r)}addResourceBundle(t,n,r,i,o){let s=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{silent:!1},a=[t,n];t.indexOf(".")>-1&&(a=t.split("."),i=r,r=n,n=a[1]),this.addNamespaces(n);let l=Gm(this.data,a)||{};i?$I(l,r,o):l={...l,...r},TE(this.data,a,l),s.silent||this.emit("added",t,n,r)}removeResourceBundle(t,n){this.hasResourceBundle(t,n)&&delete this.data[t][n],this.removeNamespaces(n),this.emit("removed",t,n)}hasResourceBundle(t,n){return this.getResource(t,n)!==void 0}getResourceBundle(t,n){return n||(n=this.options.defaultNS),this.options.compatibilityAPI==="v1"?{...this.getResource(t,n)}:this.getResource(t,n)}getDataByLanguage(t){return this.data[t]}hasLanguageSomeTranslations(t){const n=this.getDataByLanguage(t);return!!(n&&Object.keys(n)||[]).find(i=>n[i]&&Object.keys(n[i]).length>0)}toJSON(){return this.data}}var FI={processors:{},addPostProcessor(e){this.processors[e.name]=e},handle(e,t,n,r,i){return e.forEach(o=>{this.processors[o]&&(t=this.processors[o].process(t,n,r,i))}),t}};const PE={};class Wm extends ev{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};super(),oae(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],t,this),this.options=n,this.options.keySeparator===void 0&&(this.options.keySeparator="."),this.logger=qi.create("translator")}changeLanguage(t){t&&(this.language=t)}exists(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(t==null)return!1;const r=this.resolve(t,n);return r&&r.res!==void 0}extractFromKey(t,n){let r=n.nsSeparator!==void 0?n.nsSeparator:this.options.nsSeparator;r===void 0&&(r=":");const i=n.keySeparator!==void 0?n.keySeparator:this.options.keySeparator;let o=n.ns||this.options.defaultNS||[];const s=r&&t.indexOf(r)>-1,a=!this.options.userDefinedKeySeparator&&!n.keySeparator&&!this.options.userDefinedNsSeparator&&!n.nsSeparator&&!dae(t,r,i);if(s&&!a){const l=t.match(this.interpolator.nestingRegexp);if(l&&l.length>0)return{key:t,namespaces:o};const u=t.split(r);(r!==i||r===i&&this.options.ns.indexOf(u[0])>-1)&&(o=u.shift()),t=u.join(i)}return typeof o=="string"&&(o=[o]),{key:t,namespaces:o}}translate(t,n,r){if(typeof n!="object"&&this.options.overloadTranslationOptionHandler&&(n=this.options.overloadTranslationOptionHandler(arguments)),typeof n=="object"&&(n={...n}),n||(n={}),t==null)return"";Array.isArray(t)||(t=[String(t)]);const i=n.returnDetails!==void 0?n.returnDetails:this.options.returnDetails,o=n.keySeparator!==void 0?n.keySeparator:this.options.keySeparator,{key:s,namespaces:a}=this.extractFromKey(t[t.length-1],n),l=a[a.length-1],u=n.lng||this.language,c=n.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(u&&u.toLowerCase()==="cimode"){if(c){const b=n.nsSeparator||this.options.nsSeparator;return i?{res:`${l}${b}${s}`,usedKey:s,exactUsedKey:s,usedLng:u,usedNS:l}:`${l}${b}${s}`}return i?{res:s,usedKey:s,exactUsedKey:s,usedLng:u,usedNS:l}:s}const d=this.resolve(t,n);let f=d&&d.res;const h=d&&d.usedKey||s,p=d&&d.exactUsedKey||s,m=Object.prototype.toString.apply(f),S=["[object Number]","[object Function]","[object RegExp]"],y=n.joinArrays!==void 0?n.joinArrays:this.options.joinArrays,v=!this.i18nFormat||this.i18nFormat.handleAsObject;if(v&&f&&(typeof f!="string"&&typeof f!="boolean"&&typeof f!="number")&&S.indexOf(m)<0&&!(typeof y=="string"&&m==="[object Array]")){if(!n.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");const b=this.options.returnedObjectHandler?this.options.returnedObjectHandler(h,f,{...n,ns:a}):`key '${s} (${this.language})' returned an object instead of string.`;return i?(d.res=b,d):b}if(o){const b=m==="[object Array]",_=b?[]:{},w=b?p:h;for(const x in f)if(Object.prototype.hasOwnProperty.call(f,x)){const T=`${w}${o}${x}`;_[x]=this.translate(T,{...n,joinArrays:!1,ns:a}),_[x]===T&&(_[x]=f[x])}f=_}}else if(v&&typeof y=="string"&&m==="[object Array]")f=f.join(y),f&&(f=this.extendTranslation(f,t,n,r));else{let b=!1,_=!1;const w=n.count!==void 0&&typeof n.count!="string",x=Wm.hasDefaultValue(n),T=w?this.pluralResolver.getSuffix(u,n.count,n):"",P=n.ordinal&&w?this.pluralResolver.getSuffix(u,n.count,{ordinal:!1}):"",E=n[`defaultValue${T}`]||n[`defaultValue${P}`]||n.defaultValue;!this.isValidLookup(f)&&x&&(b=!0,f=E),this.isValidLookup(f)||(_=!0,f=s);const $=(n.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey)&&_?void 0:f,I=x&&E!==f&&this.options.updateMissing;if(_||b||I){if(this.logger.log(I?"updateKey":"missingKey",u,l,s,I?E:f),o){const N=this.resolve(s,{...n,keySeparator:!1});N&&N.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}let C=[];const R=this.languageUtils.getFallbackCodes(this.options.fallbackLng,n.lng||this.language);if(this.options.saveMissingTo==="fallback"&&R&&R[0])for(let N=0;N{const L=x&&D!==f?D:$;this.options.missingKeyHandler?this.options.missingKeyHandler(N,l,O,L,I,n):this.backendConnector&&this.backendConnector.saveMissing&&this.backendConnector.saveMissing(N,l,O,L,I,n),this.emit("missingKey",N,l,O,f)};this.options.saveMissing&&(this.options.saveMissingPlurals&&w?C.forEach(N=>{this.pluralResolver.getSuffixes(N,n).forEach(O=>{M([N],s+O,n[`defaultValue${O}`]||E)})}):M(C,s,E))}f=this.extendTranslation(f,t,n,d,r),_&&f===s&&this.options.appendNamespaceToMissingKey&&(f=`${l}:${s}`),(_||b)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?f=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${l}:${s}`:s,b?f:void 0):f=this.options.parseMissingKeyHandler(f))}return i?(d.res=f,d):f}extendTranslation(t,n,r,i,o){var s=this;if(this.i18nFormat&&this.i18nFormat.parse)t=this.i18nFormat.parse(t,{...this.options.interpolation.defaultVariables,...r},i.usedLng,i.usedNS,i.usedKey,{resolved:i});else if(!r.skipInterpolation){r.interpolation&&this.interpolator.init({...r,interpolation:{...this.options.interpolation,...r.interpolation}});const u=typeof t=="string"&&(r&&r.interpolation&&r.interpolation.skipOnVariables!==void 0?r.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);let c;if(u){const f=t.match(this.interpolator.nestingRegexp);c=f&&f.length}let d=r.replace&&typeof r.replace!="string"?r.replace:r;if(this.options.interpolation.defaultVariables&&(d={...this.options.interpolation.defaultVariables,...d}),t=this.interpolator.interpolate(t,d,r.lng||this.language,r),u){const f=t.match(this.interpolator.nestingRegexp),h=f&&f.length;c1&&arguments[1]!==void 0?arguments[1]:{},r,i,o,s,a;return typeof t=="string"&&(t=[t]),t.forEach(l=>{if(this.isValidLookup(r))return;const u=this.extractFromKey(l,n),c=u.key;i=c;let d=u.namespaces;this.options.fallbackNS&&(d=d.concat(this.options.fallbackNS));const f=n.count!==void 0&&typeof n.count!="string",h=f&&!n.ordinal&&n.count===0&&this.pluralResolver.shouldUseIntlApi(),p=n.context!==void 0&&(typeof n.context=="string"||typeof n.context=="number")&&n.context!=="",m=n.lngs?n.lngs:this.languageUtils.toResolveHierarchy(n.lng||this.language,n.fallbackLng);d.forEach(S=>{this.isValidLookup(r)||(a=S,!PE[`${m[0]}-${S}`]&&this.utils&&this.utils.hasLoadedNamespace&&!this.utils.hasLoadedNamespace(a)&&(PE[`${m[0]}-${S}`]=!0,this.logger.warn(`key "${i}" for languages "${m.join(", ")}" won't get resolved as namespace "${a}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(y=>{if(this.isValidLookup(r))return;s=y;const v=[c];if(this.i18nFormat&&this.i18nFormat.addLookupKeys)this.i18nFormat.addLookupKeys(v,c,y,S,n);else{let b;f&&(b=this.pluralResolver.getSuffix(y,n.count,n));const _=`${this.options.pluralSeparator}zero`,w=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(f&&(v.push(c+b),n.ordinal&&b.indexOf(w)===0&&v.push(c+b.replace(w,this.options.pluralSeparator)),h&&v.push(c+_)),p){const x=`${c}${this.options.contextSeparator}${n.context}`;v.push(x),f&&(v.push(x+b),n.ordinal&&b.indexOf(w)===0&&v.push(x+b.replace(w,this.options.pluralSeparator)),h&&v.push(x+_))}}let g;for(;g=v.pop();)this.isValidLookup(r)||(o=g,r=this.getResource(y,S,g,n))}))})}),{res:r,usedKey:i,exactUsedKey:o,usedLng:s,usedNS:a}}isValidLookup(t){return t!==void 0&&!(!this.options.returnNull&&t===null)&&!(!this.options.returnEmptyString&&t==="")}getResource(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(t,n,r,i):this.resourceStore.getResource(t,n,r,i)}static hasDefaultValue(t){const n="defaultValue";for(const r in t)if(Object.prototype.hasOwnProperty.call(t,r)&&n===r.substring(0,n.length)&&t[r]!==void 0)return!0;return!1}}function Bb(e){return e.charAt(0).toUpperCase()+e.slice(1)}class AE{constructor(t){this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=qi.create("languageUtils")}getScriptPartFromCode(t){if(t=qm(t),!t||t.indexOf("-")<0)return null;const n=t.split("-");return n.length===2||(n.pop(),n[n.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(n.join("-"))}getLanguagePartFromCode(t){if(t=qm(t),!t||t.indexOf("-")<0)return t;const n=t.split("-");return this.formatLanguageCode(n[0])}formatLanguageCode(t){if(typeof t=="string"&&t.indexOf("-")>-1){const n=["hans","hant","latn","cyrl","cans","mong","arab"];let r=t.split("-");return this.options.lowerCaseLng?r=r.map(i=>i.toLowerCase()):r.length===2?(r[0]=r[0].toLowerCase(),r[1]=r[1].toUpperCase(),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=Bb(r[1].toLowerCase()))):r.length===3&&(r[0]=r[0].toLowerCase(),r[1].length===2&&(r[1]=r[1].toUpperCase()),r[0]!=="sgn"&&r[2].length===2&&(r[2]=r[2].toUpperCase()),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=Bb(r[1].toLowerCase())),n.indexOf(r[2].toLowerCase())>-1&&(r[2]=Bb(r[2].toLowerCase()))),r.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?t.toLowerCase():t}isSupportedCode(t){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(t=this.getLanguagePartFromCode(t)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(t)>-1}getBestMatchFromCodes(t){if(!t)return null;let n;return t.forEach(r=>{if(n)return;const i=this.formatLanguageCode(r);(!this.options.supportedLngs||this.isSupportedCode(i))&&(n=i)}),!n&&this.options.supportedLngs&&t.forEach(r=>{if(n)return;const i=this.getLanguagePartFromCode(r);if(this.isSupportedCode(i))return n=i;n=this.options.supportedLngs.find(o=>{if(o===i)return o;if(!(o.indexOf("-")<0&&i.indexOf("-")<0)&&o.indexOf(i)===0)return o})}),n||(n=this.getFallbackCodes(this.options.fallbackLng)[0]),n}getFallbackCodes(t,n){if(!t)return[];if(typeof t=="function"&&(t=t(n)),typeof t=="string"&&(t=[t]),Object.prototype.toString.apply(t)==="[object Array]")return t;if(!n)return t.default||[];let r=t[n];return r||(r=t[this.getScriptPartFromCode(n)]),r||(r=t[this.formatLanguageCode(n)]),r||(r=t[this.getLanguagePartFromCode(n)]),r||(r=t.default),r||[]}toResolveHierarchy(t,n){const r=this.getFallbackCodes(n||this.options.fallbackLng||[],t),i=[],o=s=>{s&&(this.isSupportedCode(s)?i.push(s):this.logger.warn(`rejecting language code not found in supportedLngs: ${s}`))};return typeof t=="string"&&(t.indexOf("-")>-1||t.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&o(this.formatLanguageCode(t)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&o(this.getScriptPartFromCode(t)),this.options.load!=="currentOnly"&&o(this.getLanguagePartFromCode(t))):typeof t=="string"&&o(this.formatLanguageCode(t)),r.forEach(s=>{i.indexOf(s)<0&&o(this.formatLanguageCode(s))}),i}}let fae=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],hae={1:function(e){return+(e>1)},2:function(e){return+(e!=1)},3:function(e){return 0},4:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},5:function(e){return e==0?0:e==1?1:e==2?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5},6:function(e){return e==1?0:e>=2&&e<=4?1:2},7:function(e){return e==1?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},8:function(e){return e==1?0:e==2?1:e!=8&&e!=11?2:3},9:function(e){return+(e>=2)},10:function(e){return e==1?0:e==2?1:e<7?2:e<11?3:4},11:function(e){return e==1||e==11?0:e==2||e==12?1:e>2&&e<20?2:3},12:function(e){return+(e%10!=1||e%100==11)},13:function(e){return+(e!==0)},14:function(e){return e==1?0:e==2?1:e==3?2:3},15:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2},16:function(e){return e%10==1&&e%100!=11?0:e!==0?1:2},17:function(e){return e==1||e%10==1&&e%100!=11?0:1},18:function(e){return e==0?0:e==1?1:2},19:function(e){return e==1?0:e==0||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3},20:function(e){return e==1?0:e==0||e%100>0&&e%100<20?1:2},21:function(e){return e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0},22:function(e){return e==1?0:e==2?1:(e<0||e>10)&&e%10==0?2:3}};const pae=["v1","v2","v3"],gae=["v4"],kE={zero:0,one:1,two:2,few:3,many:4,other:5};function mae(){const e={};return fae.forEach(t=>{t.lngs.forEach(n=>{e[n]={numbers:t.nr,plurals:hae[t.fc]}})}),e}class yae{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.languageUtils=t,this.options=n,this.logger=qi.create("pluralResolver"),(!this.options.compatibilityJSON||gae.includes(this.options.compatibilityJSON))&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=mae()}addRule(t,n){this.rules[t]=n}getRule(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(qm(t),{type:n.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[t]||this.rules[this.languageUtils.getLanguagePartFromCode(t)]}needsPlural(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return this.shouldUseIntlApi()?r&&r.resolvedOptions().pluralCategories.length>1:r&&r.numbers.length>1}getPluralFormsOfKey(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(t,r).map(i=>`${n}${i}`)}getSuffixes(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};const r=this.getRule(t,n);return r?this.shouldUseIntlApi()?r.resolvedOptions().pluralCategories.sort((i,o)=>kE[i]-kE[o]).map(i=>`${this.options.prepend}${n.ordinal?`ordinal${this.options.prepend}`:""}${i}`):r.numbers.map(i=>this.getSuffix(t,i,n)):[]}getSuffix(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};const i=this.getRule(t,r);return i?this.shouldUseIntlApi()?`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${i.select(n)}`:this.getSuffixRetroCompatible(i,n):(this.logger.warn(`no plural rule found for: ${t}`),"")}getSuffixRetroCompatible(t,n){const r=t.noAbs?t.plurals(n):t.plurals(Math.abs(n));let i=t.numbers[r];this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1&&(i===2?i="plural":i===1&&(i=""));const o=()=>this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString();return this.options.compatibilityJSON==="v1"?i===1?"":typeof i=="number"?`_plural_${i.toString()}`:o():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1?o():this.options.prepend&&r.toString()?this.options.prepend+r.toString():r.toString()}shouldUseIntlApi(){return!pae.includes(this.options.compatibilityJSON)}}function RE(e,t,n){let r=arguments.length>3&&arguments[3]!==void 0?arguments[3]:".",i=arguments.length>4&&arguments[4]!==void 0?arguments[4]:!0,o=aae(e,t,n);return!o&&i&&typeof n=="string"&&(o=Hm(e,n,r),o===void 0&&(o=Hm(t,n,r))),o}class vae{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=qi.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||(n=>n),this.init(t)}init(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};t.interpolation||(t.interpolation={escapeValue:!0});const n=t.interpolation;this.escape=n.escape!==void 0?n.escape:uae,this.escapeValue=n.escapeValue!==void 0?n.escapeValue:!0,this.useRawValueToEscape=n.useRawValueToEscape!==void 0?n.useRawValueToEscape:!1,this.prefix=n.prefix?jl(n.prefix):n.prefixEscaped||"{{",this.suffix=n.suffix?jl(n.suffix):n.suffixEscaped||"}}",this.formatSeparator=n.formatSeparator?n.formatSeparator:n.formatSeparator||",",this.unescapePrefix=n.unescapeSuffix?"":n.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":n.unescapeSuffix||"",this.nestingPrefix=n.nestingPrefix?jl(n.nestingPrefix):n.nestingPrefixEscaped||jl("$t("),this.nestingSuffix=n.nestingSuffix?jl(n.nestingSuffix):n.nestingSuffixEscaped||jl(")"),this.nestingOptionsSeparator=n.nestingOptionsSeparator?n.nestingOptionsSeparator:n.nestingOptionsSeparator||",",this.maxReplaces=n.maxReplaces?n.maxReplaces:1e3,this.alwaysFormat=n.alwaysFormat!==void 0?n.alwaysFormat:!1,this.resetRegExp()}reset(){this.options&&this.init(this.options)}resetRegExp(){const t=`${this.prefix}(.+?)${this.suffix}`;this.regexp=new RegExp(t,"g");const n=`${this.prefix}${this.unescapePrefix}(.+?)${this.unescapeSuffix}${this.suffix}`;this.regexpUnescape=new RegExp(n,"g");const r=`${this.nestingPrefix}(.+?)${this.nestingSuffix}`;this.nestingRegexp=new RegExp(r,"g")}interpolate(t,n,r,i){let o,s,a;const l=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function u(p){return p.replace(/\$/g,"$$$$")}const c=p=>{if(p.indexOf(this.formatSeparator)<0){const v=RE(n,l,p,this.options.keySeparator,this.options.ignoreJSONStructure);return this.alwaysFormat?this.format(v,void 0,r,{...i,...n,interpolationkey:p}):v}const m=p.split(this.formatSeparator),S=m.shift().trim(),y=m.join(this.formatSeparator).trim();return this.format(RE(n,l,S,this.options.keySeparator,this.options.ignoreJSONStructure),y,r,{...i,...n,interpolationkey:S})};this.resetRegExp();const d=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,f=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 0?i.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables;return[{regex:this.regexpUnescape,safeValue:p=>u(p)},{regex:this.regexp,safeValue:p=>this.escapeValue?u(this.escape(p)):u(p)}].forEach(p=>{for(a=0;o=p.regex.exec(t);){const m=o[1].trim();if(s=c(m),s===void 0)if(typeof d=="function"){const y=d(t,o,i);s=typeof y=="string"?y:""}else if(i&&Object.prototype.hasOwnProperty.call(i,m))s="";else if(f){s=o[0];continue}else this.logger.warn(`missed to pass in variable ${m} for interpolating ${t}`),s="";else typeof s!="string"&&!this.useRawValueToEscape&&(s=CE(s));const S=p.safeValue(s);if(t=t.replace(o[0],S),f?(p.regex.lastIndex+=s.length,p.regex.lastIndex-=o[0].length):p.regex.lastIndex=0,a++,a>=this.maxReplaces)break}}),t}nest(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i,o,s;function a(l,u){const c=this.nestingOptionsSeparator;if(l.indexOf(c)<0)return l;const d=l.split(new RegExp(`${c}[ ]*{`));let f=`{${d[1]}`;l=d[0],f=this.interpolate(f,s);const h=f.match(/'/g),p=f.match(/"/g);(h&&h.length%2===0&&!p||p.length%2!==0)&&(f=f.replace(/'/g,'"'));try{s=JSON.parse(f),u&&(s={...u,...s})}catch(m){return this.logger.warn(`failed parsing options string in nesting for key ${l}`,m),`${l}${c}${f}`}return delete s.defaultValue,l}for(;i=this.nestingRegexp.exec(t);){let l=[];s={...r},s=s.replace&&typeof s.replace!="string"?s.replace:s,s.applyPostProcessor=!1,delete s.defaultValue;let u=!1;if(i[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(i[1])){const c=i[1].split(this.formatSeparator).map(d=>d.trim());i[1]=c.shift(),l=c,u=!0}if(o=n(a.call(this,i[1].trim(),s),s),o&&i[0]===t&&typeof o!="string")return o;typeof o!="string"&&(o=CE(o)),o||(this.logger.warn(`missed to resolve ${i[1]} for nesting ${t}`),o=""),u&&(o=l.reduce((c,d)=>this.format(c,d,r.lng,{...r,interpolationkey:i[1].trim()}),o.trim())),t=t.replace(i[0],o),this.regexp.lastIndex=0}return t}}function bae(e){let t=e.toLowerCase().trim();const n={};if(e.indexOf("(")>-1){const r=e.split("(");t=r[0].toLowerCase().trim();const i=r[1].substring(0,r[1].length-1);t==="currency"&&i.indexOf(":")<0?n.currency||(n.currency=i.trim()):t==="relativetime"&&i.indexOf(":")<0?n.range||(n.range=i.trim()):i.split(";").forEach(s=>{if(!s)return;const[a,...l]=s.split(":"),u=l.join(":").trim().replace(/^'+|'+$/g,"");n[a.trim()]||(n[a.trim()]=u),u==="false"&&(n[a.trim()]=!1),u==="true"&&(n[a.trim()]=!0),isNaN(u)||(n[a.trim()]=parseInt(u,10))})}return{formatName:t,formatOptions:n}}function Vl(e){const t={};return function(r,i,o){const s=i+JSON.stringify(o);let a=t[s];return a||(a=e(qm(i),o),t[s]=a),a(r)}}class Sae{constructor(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this.logger=qi.create("formatter"),this.options=t,this.formats={number:Vl((n,r)=>{const i=new Intl.NumberFormat(n,{...r});return o=>i.format(o)}),currency:Vl((n,r)=>{const i=new Intl.NumberFormat(n,{...r,style:"currency"});return o=>i.format(o)}),datetime:Vl((n,r)=>{const i=new Intl.DateTimeFormat(n,{...r});return o=>i.format(o)}),relativetime:Vl((n,r)=>{const i=new Intl.RelativeTimeFormat(n,{...r});return o=>i.format(o,r.range||"day")}),list:Vl((n,r)=>{const i=new Intl.ListFormat(n,{...r});return o=>i.format(o)})},this.init(t)}init(t){const r=(arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}}).interpolation;this.formatSeparator=r.formatSeparator?r.formatSeparator:r.formatSeparator||","}add(t,n){this.formats[t.toLowerCase().trim()]=n}addCached(t,n){this.formats[t.toLowerCase().trim()]=Vl(n)}format(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return n.split(this.formatSeparator).reduce((a,l)=>{const{formatName:u,formatOptions:c}=bae(l);if(this.formats[u]){let d=a;try{const f=i&&i.formatParams&&i.formatParams[i.interpolationkey]||{},h=f.locale||f.lng||i.locale||i.lng||r;d=this.formats[u](a,h,{...c,...i,...f})}catch(f){this.logger.warn(f)}return d}else this.logger.warn(`there was no format function for ${u}`);return a},t)}}function _ae(e,t){e.pending[t]!==void 0&&(delete e.pending[t],e.pendingCount--)}class wae extends ev{constructor(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};super(),this.backend=t,this.store=n,this.services=r,this.languageUtils=r.languageUtils,this.options=i,this.logger=qi.create("backendConnector"),this.waitingReads=[],this.maxParallelReads=i.maxParallelReads||10,this.readingCalls=0,this.maxRetries=i.maxRetries>=0?i.maxRetries:5,this.retryTimeout=i.retryTimeout>=1?i.retryTimeout:350,this.state={},this.queue=[],this.backend&&this.backend.init&&this.backend.init(r,i.backend,i)}queueLoad(t,n,r,i){const o={},s={},a={},l={};return t.forEach(u=>{let c=!0;n.forEach(d=>{const f=`${u}|${d}`;!r.reload&&this.store.hasResourceBundle(u,d)?this.state[f]=2:this.state[f]<0||(this.state[f]===1?s[f]===void 0&&(s[f]=!0):(this.state[f]=1,c=!1,s[f]===void 0&&(s[f]=!0),o[f]===void 0&&(o[f]=!0),l[d]===void 0&&(l[d]=!0)))}),c||(a[u]=!0)}),(Object.keys(o).length||Object.keys(s).length)&&this.queue.push({pending:s,pendingCount:Object.keys(s).length,loaded:{},errors:[],callback:i}),{toLoad:Object.keys(o),pending:Object.keys(s),toLoadLanguages:Object.keys(a),toLoadNamespaces:Object.keys(l)}}loaded(t,n,r){const i=t.split("|"),o=i[0],s=i[1];n&&this.emit("failedLoading",o,s,n),r&&this.store.addResourceBundle(o,s,r),this.state[t]=n?-1:2;const a={};this.queue.forEach(l=>{sae(l.loaded,[o],s),_ae(l,t),n&&l.errors.push(n),l.pendingCount===0&&!l.done&&(Object.keys(l.loaded).forEach(u=>{a[u]||(a[u]={});const c=l.loaded[u];c.length&&c.forEach(d=>{a[u][d]===void 0&&(a[u][d]=!0)})}),l.done=!0,l.errors.length?l.callback(l.errors):l.callback())}),this.emit("loaded",a),this.queue=this.queue.filter(l=>!l.done)}read(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,o=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,s=arguments.length>5?arguments[5]:void 0;if(!t.length)return s(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:t,ns:n,fcName:r,tried:i,wait:o,callback:s});return}this.readingCalls++;const a=(u,c)=>{if(this.readingCalls--,this.waitingReads.length>0){const d=this.waitingReads.shift();this.read(d.lng,d.ns,d.fcName,d.tried,d.wait,d.callback)}if(u&&c&&i{this.read.call(this,t,n,r,i+1,o*2,s)},o);return}s(u,c)},l=this.backend[r].bind(this.backend);if(l.length===2){try{const u=l(t,n);u&&typeof u.then=="function"?u.then(c=>a(null,c)).catch(a):a(null,u)}catch(u){a(u)}return}return l(t,n,a)}prepareLoading(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. 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You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. 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Caching is skipped.");return}i+=u*2+1,o+=u*2+1,a-=u,l-=u;var f=new Dp.SceneCanvas({pixelRatio:s,width:i,height:o}),h=new Dp.SceneCanvas({pixelRatio:s,width:0,height:0,willReadFrequently:!0}),p=new Dp.HitCanvas({pixelRatio:d,width:i,height:o}),m=f.getContext(),S=p.getContext();return p.isCache=!0,f.isCache=!0,this._cache.delete(ba),this._filterUpToDate=!1,n.imageSmoothingEnabled===!1&&(f.getContext()._context.imageSmoothingEnabled=!1,h.getContext()._context.imageSmoothingEnabled=!1),m.save(),S.save(),m.translate(-a,-l),S.translate(-a,-l),this._isUnderCache=!0,this._clearSelfAndDescendantCache(Sg),this._clearSelfAndDescendantCache(e6),this.drawScene(f,this),this.drawHit(p,this),this._isUnderCache=!1,m.restore(),S.restore(),c&&(m.save(),m.beginPath(),m.rect(0,0,i,o),m.closePath(),m.setAttr("strokeStyle","red"),m.setAttr("lineWidth",5),m.stroke(),m.restore()),this._cache.set(ba,{scene:f,filter:h,hit:p,x:a,y:l}),this._requestDraw(),this}isCached(){return this._cache.has(ba)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,s,a,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var c=l.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),{x:i,y:o,width:s-i,height:a-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),s,a,l,u;if(t){if(!this._filterUpToDate){var c=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(s=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/c,r.getHeight()/c),a=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l{var n,r;if(!t)return this;for(n in t)n!==Jce&&(r=r6+ke.Util._capitalize(n),ke.Util._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(m2,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(y2,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;Br.DD._dragElements.forEach(s=>{s.dragStatus==="dragging"&&(s.node.nodeType==="Stage"||s.node.getLayer()===r)&&(i=!0)});var o=!n&&!va.Konva.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,s,a;function l(u){for(i=[],o=u.length,s=0;s0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==tde&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(ss),this._clearSelfAndDescendantCache(bo)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new ke.Transform,s=this.offset();return o.m=i.slice(),o.translate(s.x,s.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(ss);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(ss),this._clearSelfAndDescendantCache(bo),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,s;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,s=0;s0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return ke.Util.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return ke.Util.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&ke.Util.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(Sg,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,s,a;t.attrs={};for(r in n)i=n[r],a=ke.Util.isObject(i)&&!ke.Util._isPlainObject(i)&&!ke.Util._isArray(i),!a&&(o=typeof this[r]=="function"&&this[r],delete n[r],s=o?o.call(this):null,n[r]=i,s!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),ke.Util._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i{try{const i=t==null?void 0:t.callback;i&&delete t.callback,ke.Util._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():va.Konva.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,s,a;for(o=0;o=0;if(r&&!this.isDragging()){var i=!1;Br.DD._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=Br.DD._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&Br.DD._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return ke.Util.haveIntersection(r,this.getClientRect())}static create(t,n){return ke.Util._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=v2.prototype.getClassName.call(t),i=t.children,o,s,a;n&&(t.attrs.container=n),va.Konva[r]||(ke.Util.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=va.Konva[r];if(o=new l(t.attrs),i)for(s=i.length,a=0;a0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(t.length===0)return this;if(t.length>1){for(var n=0;n0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=Hb.Node.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=Hb.Node.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(a){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.hit;if(a){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),s=this.clipWidth(),a=this.clipHeight(),l=this.clipFunc(),u=s&&a||l;const c=r===this;if(u){o.save();var d=this.getAbsoluteTransform(r),f=d.getMatrix();o.transform(f[0],f[1],f[2],f[3],f[4],f[5]),o.beginPath();let S;if(l)S=l.call(this,o,this);else{var h=this.clipX(),p=this.clipY();o.rect(h,p,s,a)}o.clip.apply(o,S),f=d.copy().invert().getMatrix(),o.transform(f[0],f[1],f[2],f[3],f[4],f[5])}var m=!c&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";m&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(S){S[t](n,r)}),m&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,s,a,l,u={x:1/0,y:1/0,width:0,height:0},c=this;(n=this.children)===null||n===void 0||n.forEach(function(m){if(m.visible()){var S=m.getClientRect({relativeTo:c,skipShadow:t.skipShadow,skipStroke:t.skipStroke});S.width===0&&S.height===0||(o===void 0?(o=S.x,s=S.y,a=S.x+S.width,l=S.y+S.height):(o=Math.min(o,S.x),s=Math.min(s,S.y),a=Math.max(a,S.x+S.width),l=Math.max(l,S.y+S.height)))}});for(var d=this.find("Shape"),f=!1,h=0;hY.indexOf("pointer")>=0?"pointer":Y.indexOf("touch")>=0?"touch":"mouse",U=Y=>{const B=j(Y);if(B==="pointer")return i.Konva.pointerEventsEnabled&&L.pointer;if(B==="touch")return L.touch;if(B==="mouse")return L.mouse};function G(Y={}){return(Y.clipFunc||Y.clipWidth||Y.clipHeight)&&t.Util.warn("Stage does not support clipping. Please use clip for Layers or Groups."),Y}const W="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);";e.stages=[];class X extends r.Container{constructor(B){super(G(B)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),e.stages.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{G(this.attrs)}),this._checkVisibility()}_validateAdd(B){const H=B.getType()==="Layer",Q=B.getType()==="FastLayer";H||Q||t.Util.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const B=this.visible()?"":"none";this.content.style.display=B}setContainer(B){if(typeof B===c){if(B.charAt(0)==="."){var H=B.slice(1);B=document.getElementsByClassName(H)[0]}else{var Q;B.charAt(0)!=="#"?Q=B:Q=B.slice(1),B=document.getElementById(Q)}if(!B)throw"Can not find container in document with id "+Q}return this._setAttr("container",B),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),B.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var B=this.children,H=B.length,Q;for(Q=0;Q-1&&e.stages.splice(H,1),t.Util.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const B=this._pointerPositions[0]||this._changedPointerPositions[0];return B?{x:B.x,y:B.y}:(t.Util.warn(W),null)}_getPointerById(B){return this._pointerPositions.find(H=>H.id===B)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(B){B=B||{},B.x=B.x||0,B.y=B.y||0,B.width=B.width||this.width(),B.height=B.height||this.height();var H=new o.SceneCanvas({width:B.width,height:B.height,pixelRatio:B.pixelRatio||1}),Q=H.getContext()._context,J=this.children;return(B.x||B.y)&&Q.translate(-1*B.x,-1*B.y),J.forEach(function(ne){if(ne.isVisible()){var te=ne._toKonvaCanvas(B);Q.drawImage(te._canvas,B.x,B.y,te.getWidth()/te.getPixelRatio(),te.getHeight()/te.getPixelRatio())}}),H}getIntersection(B){if(!B)return null;var H=this.children,Q=H.length,J=Q-1,ne;for(ne=J;ne>=0;ne--){const te=H[ne].getIntersection(B);if(te)return te}return null}_resizeDOM(){var B=this.width(),H=this.height();this.content&&(this.content.style.width=B+d,this.content.style.height=H+d),this.bufferCanvas.setSize(B,H),this.bufferHitCanvas.setSize(B,H),this.children.forEach(Q=>{Q.setSize({width:B,height:H}),Q.draw()})}add(B,...H){if(arguments.length>1){for(var Q=0;QO&&t.Util.warn("The stage has "+J+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),B.setSize({width:this.width(),height:this.height()}),B.draw(),i.Konva.isBrowser&&this.content.appendChild(B.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(B){return l.hasPointerCapture(B,this)}setPointerCapture(B){l.setPointerCapture(B,this)}releaseCapture(B){l.releaseCapture(B,this)}getLayers(){return this.children}_bindContentEvents(){i.Konva.isBrowser&&D.forEach(([B,H])=>{this.content.addEventListener(B,Q=>{this[H](Q)},{passive:!1})})}_pointerenter(B){this.setPointersPositions(B);const H=U(B.type);this._fire(H.pointerenter,{evt:B,target:this,currentTarget:this})}_pointerover(B){this.setPointersPositions(B);const H=U(B.type);this._fire(H.pointerover,{evt:B,target:this,currentTarget:this})}_getTargetShape(B){let H=this[B+"targetShape"];return H&&!H.getStage()&&(H=null),H}_pointerleave(B){const H=U(B.type),Q=j(B.type);if(H){this.setPointersPositions(B);var J=this._getTargetShape(Q),ne=!s.DD.isDragging||i.Konva.hitOnDragEnabled;J&&ne?(J._fireAndBubble(H.pointerout,{evt:B}),J._fireAndBubble(H.pointerleave,{evt:B}),this._fire(H.pointerleave,{evt:B,target:this,currentTarget:this}),this[Q+"targetShape"]=null):ne&&(this._fire(H.pointerleave,{evt:B,target:this,currentTarget:this}),this._fire(H.pointerout,{evt:B,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(B){const H=U(B.type),Q=j(B.type);if(H){this.setPointersPositions(B);var J=!1;this._changedPointerPositions.forEach(ne=>{var te=this.getIntersection(ne);if(s.DD.justDragged=!1,i.Konva["_"+Q+"ListenClick"]=!0,!(te&&te.isListening()))return;i.Konva.capturePointerEventsEnabled&&te.setPointerCapture(ne.id),this[Q+"ClickStartShape"]=te,te._fireAndBubble(H.pointerdown,{evt:B,pointerId:ne.id}),J=!0;const ve=B.type.indexOf("touch")>=0;te.preventDefault()&&B.cancelable&&ve&&B.preventDefault()}),J||this._fire(H.pointerdown,{evt:B,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(B){const H=U(B.type),Q=j(B.type);if(!H)return;s.DD.isDragging&&s.DD.node.preventDefault()&&B.cancelable&&B.preventDefault(),this.setPointersPositions(B);var J=!s.DD.isDragging||i.Konva.hitOnDragEnabled;if(!J)return;var ne={};let te=!1;var xe=this._getTargetShape(Q);this._changedPointerPositions.forEach(ve=>{const ce=l.getCapturedShape(ve.id)||this.getIntersection(ve),De=ve.id,se={evt:B,pointerId:De};var pt=xe!==ce;if(pt&&xe&&(xe._fireAndBubble(H.pointerout,Object.assign({},se),ce),xe._fireAndBubble(H.pointerleave,Object.assign({},se),ce)),ce){if(ne[ce._id])return;ne[ce._id]=!0}ce&&ce.isListening()?(te=!0,pt&&(ce._fireAndBubble(H.pointerover,Object.assign({},se),xe),ce._fireAndBubble(H.pointerenter,Object.assign({},se),xe),this[Q+"targetShape"]=ce),ce._fireAndBubble(H.pointermove,Object.assign({},se))):xe&&(this._fire(H.pointerover,{evt:B,target:this,currentTarget:this,pointerId:De}),this[Q+"targetShape"]=null)}),te||this._fire(H.pointermove,{evt:B,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(B){const H=U(B.type),Q=j(B.type);if(!H)return;this.setPointersPositions(B);const J=this[Q+"ClickStartShape"],ne=this[Q+"ClickEndShape"];var te={};let xe=!1;this._changedPointerPositions.forEach(ve=>{const ce=l.getCapturedShape(ve.id)||this.getIntersection(ve);if(ce){if(ce.releaseCapture(ve.id),te[ce._id])return;te[ce._id]=!0}const De=ve.id,se={evt:B,pointerId:De};let pt=!1;i.Konva["_"+Q+"InDblClickWindow"]?(pt=!0,clearTimeout(this[Q+"DblTimeout"])):s.DD.justDragged||(i.Konva["_"+Q+"InDblClickWindow"]=!0,clearTimeout(this[Q+"DblTimeout"])),this[Q+"DblTimeout"]=setTimeout(function(){i.Konva["_"+Q+"InDblClickWindow"]=!1},i.Konva.dblClickWindow),ce&&ce.isListening()?(xe=!0,this[Q+"ClickEndShape"]=ce,ce._fireAndBubble(H.pointerup,Object.assign({},se)),i.Konva["_"+Q+"ListenClick"]&&J&&J===ce&&(ce._fireAndBubble(H.pointerclick,Object.assign({},se)),pt&&ne&&ne===ce&&ce._fireAndBubble(H.pointerdblclick,Object.assign({},se)))):(this[Q+"ClickEndShape"]=null,i.Konva["_"+Q+"ListenClick"]&&this._fire(H.pointerclick,{evt:B,target:this,currentTarget:this,pointerId:De}),pt&&this._fire(H.pointerdblclick,{evt:B,target:this,currentTarget:this,pointerId:De}))}),xe||this._fire(H.pointerup,{evt:B,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),i.Konva["_"+Q+"ListenClick"]=!1,B.cancelable&&Q!=="touch"&&B.preventDefault()}_contextmenu(B){this.setPointersPositions(B);var H=this.getIntersection(this.getPointerPosition());H&&H.isListening()?H._fireAndBubble($,{evt:B}):this._fire($,{evt:B,target:this,currentTarget:this})}_wheel(B){this.setPointersPositions(B);var H=this.getIntersection(this.getPointerPosition());H&&H.isListening()?H._fireAndBubble(N,{evt:B}):this._fire(N,{evt:B,target:this,currentTarget:this})}_pointercancel(B){this.setPointersPositions(B);const H=l.getCapturedShape(B.pointerId)||this.getIntersection(this.getPointerPosition());H&&H._fireAndBubble(_,l.createEvent(B)),l.releaseCapture(B.pointerId)}_lostpointercapture(B){l.releaseCapture(B.pointerId)}setPointersPositions(B){var H=this._getContentPosition(),Q=null,J=null;B=B||window.event,B.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(B.touches,ne=>{this._pointerPositions.push({id:ne.identifier,x:(ne.clientX-H.left)/H.scaleX,y:(ne.clientY-H.top)/H.scaleY})}),Array.prototype.forEach.call(B.changedTouches||B.touches,ne=>{this._changedPointerPositions.push({id:ne.identifier,x:(ne.clientX-H.left)/H.scaleX,y:(ne.clientY-H.top)/H.scaleY})})):(Q=(B.clientX-H.left)/H.scaleX,J=(B.clientY-H.top)/H.scaleY,this.pointerPos={x:Q,y:J},this._pointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(B)}],this._changedPointerPositions=[{x:Q,y:J,id:t.Util._getFirstPointerId(B)}])}_setPointerPosition(B){t.Util.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(B)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var B=this.content.getBoundingClientRect();return{top:B.top,left:B.left,scaleX:B.width/this.content.clientWidth||1,scaleY:B.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new o.SceneCanvas({width:this.width(),height:this.height()}),this.bufferHitCanvas=new o.HitCanvas({pixelRatio:1,width:this.width(),height:this.height()}),!!i.Konva.isBrowser){var B=this.container();if(!B)throw"Stage has no container. A container is required.";B.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),B.appendChild(this.content),this._resizeDOM()}}cache(){return t.Util.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(B){B.batchDraw()}),this}}e.Stage=X,X.prototype.nodeType=u,(0,a._registerNode)(X),n.Factory.addGetterSetter(X,"container")})(dN);var kh={},on={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Shape=e.shapes=void 0;const t=Pe,n=Ot,r=Te,i=wt,o=de,s=Pe,a=Tr;var l="hasShadow",u="shadowRGBA",c="patternImage",d="linearGradient",f="radialGradient";let h;function p(){return h||(h=n.Util.createCanvasElement().getContext("2d"),h)}e.shapes={};function m(P){const E=this.attrs.fillRule;E?P.fill(E):P.fill()}function S(P){P.stroke()}function y(P){P.fill()}function v(P){P.stroke()}function g(){this._clearCache(l)}function b(){this._clearCache(u)}function _(){this._clearCache(c)}function w(){this._clearCache(d)}function x(){this._clearCache(f)}class T extends i.Node{constructor(E){super(E);let A;for(;A=n.Util.getRandomColor(),!(A&&!(A in e.shapes)););this.colorKey=A,e.shapes[A]=this}getContext(){return n.Util.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return n.Util.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(l,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(c,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var E=p();const A=E.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(A&&A.setTransform){const $=new n.Transform;$.translate(this.fillPatternX(),this.fillPatternY()),$.rotate(t.Konva.getAngle(this.fillPatternRotation())),$.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),$.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const I=$.getMatrix(),C=typeof DOMMatrix>"u"?{a:I[0],b:I[1],c:I[2],d:I[3],e:I[4],f:I[5]}:new DOMMatrix(I);A.setTransform(C)}return A}}_getLinearGradient(){return this._getCache(d,this.__getLinearGradient)}__getLinearGradient(){var E=this.fillLinearGradientColorStops();if(E){for(var A=p(),$=this.fillLinearGradientStartPoint(),I=this.fillLinearGradientEndPoint(),C=A.createLinearGradient($.x,$.y,I.x,I.y),R=0;Rthis.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const E=this.hitStrokeWidth();return E==="auto"?this.hasStroke():this.strokeEnabled()&&!!E}intersects(E){var A=this.getStage(),$=A.bufferHitCanvas,I;return $.getContext().clear(),this.drawHit($,null,!0),I=$.context.getImageData(Math.round(E.x),Math.round(E.y),1,1).data,I[3]>0}destroy(){return i.Node.prototype.destroy.call(this),delete e.shapes[this.colorKey],delete this.colorKey,this}_useBufferCanvas(E){var A;if(!this.getStage()||!((A=this.attrs.perfectDrawEnabled)!==null&&A!==void 0?A:!0))return!1;const I=E||this.hasFill(),C=this.hasStroke(),R=this.getAbsoluteOpacity()!==1;if(I&&C&&R)return!0;const M=this.hasShadow(),N=this.shadowForStrokeEnabled();return!!(I&&C&&M&&N)}setStrokeHitEnabled(E){n.Util.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),E?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var E=this.size();return{x:this._centroid?-E.width/2:0,y:this._centroid?-E.height/2:0,width:E.width,height:E.height}}getClientRect(E={}){const A=E.skipTransform,$=E.relativeTo,I=this.getSelfRect(),R=!E.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,M=I.width+R,N=I.height+R,O=!E.skipShadow&&this.hasShadow(),D=O?this.shadowOffsetX():0,L=O?this.shadowOffsetY():0,j=M+Math.abs(D),U=N+Math.abs(L),G=O&&this.shadowBlur()||0,W=j+G*2,X=U+G*2,Y={width:W,height:X,x:-(R/2+G)+Math.min(D,0)+I.x,y:-(R/2+G)+Math.min(L,0)+I.y};return A?Y:this._transformedRect(Y,$)}drawScene(E,A){var $=this.getLayer(),I=E||$.getCanvas(),C=I.getContext(),R=this._getCanvasCache(),M=this.getSceneFunc(),N=this.hasShadow(),O,D,L,j=I.isCache,U=A===this;if(!this.isVisible()&&!U)return this;if(R){C.save();var G=this.getAbsoluteTransform(A).getMatrix();return C.transform(G[0],G[1],G[2],G[3],G[4],G[5]),this._drawCachedSceneCanvas(C),C.restore(),this}if(!M)return this;if(C.save(),this._useBufferCanvas()&&!j){O=this.getStage(),D=O.bufferCanvas,L=D.getContext(),L.clear(),L.save(),L._applyLineJoin(this);var W=this.getAbsoluteTransform(A).getMatrix();L.transform(W[0],W[1],W[2],W[3],W[4],W[5]),M.call(this,L,this),L.restore();var X=D.pixelRatio;N&&C._applyShadow(this),C._applyOpacity(this),C._applyGlobalCompositeOperation(this),C.drawImage(D._canvas,0,0,D.width/X,D.height/X)}else{if(C._applyLineJoin(this),!U){var W=this.getAbsoluteTransform(A).getMatrix();C.transform(W[0],W[1],W[2],W[3],W[4],W[5]),C._applyOpacity(this),C._applyGlobalCompositeOperation(this)}N&&C._applyShadow(this),M.call(this,C,this)}return C.restore(),this}drawHit(E,A,$=!1){if(!this.shouldDrawHit(A,$))return this;var I=this.getLayer(),C=E||I.hitCanvas,R=C&&C.getContext(),M=this.hitFunc()||this.sceneFunc(),N=this._getCanvasCache(),O=N&&N.hit;if(this.colorKey||n.Util.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),O){R.save();var D=this.getAbsoluteTransform(A).getMatrix();return R.transform(D[0],D[1],D[2],D[3],D[4],D[5]),this._drawCachedHitCanvas(R),R.restore(),this}if(!M)return this;if(R.save(),R._applyLineJoin(this),!(this===A)){var j=this.getAbsoluteTransform(A).getMatrix();R.transform(j[0],j[1],j[2],j[3],j[4],j[5])}return M.call(this,R,this),R.restore(),this}drawHitFromCache(E=0){var A=this._getCanvasCache(),$=this._getCachedSceneCanvas(),I=A.hit,C=I.getContext(),R=I.getWidth(),M=I.getHeight(),N,O,D,L,j,U;C.clear(),C.drawImage($._canvas,0,0,R,M);try{for(N=C.getImageData(0,0,R,M),O=N.data,D=O.length,L=n.Util._hexToRgb(this.colorKey),j=0;jE?(O[j]=L.r,O[j+1]=L.g,O[j+2]=L.b,O[j+3]=255):O[j+3]=0;C.putImageData(N,0,0)}catch(G){n.Util.error("Unable to draw hit graph from cached scene canvas. "+G.message)}return this}hasPointerCapture(E){return a.hasPointerCapture(E,this)}setPointerCapture(E){a.setPointerCapture(E,this)}releaseCapture(E){a.releaseCapture(E,this)}}e.Shape=T,T.prototype._fillFunc=m,T.prototype._strokeFunc=S,T.prototype._fillFuncHit=y,T.prototype._strokeFuncHit=v,T.prototype._centroid=!1,T.prototype.nodeType="Shape",(0,s._registerNode)(T),T.prototype.eventListeners={},T.prototype.on.call(T.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",g),T.prototype.on.call(T.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",b),T.prototype.on.call(T.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",_),T.prototype.on.call(T.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",w),T.prototype.on.call(T.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",x),r.Factory.addGetterSetter(T,"stroke",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(T,"strokeWidth",2,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillAfterStrokeEnabled",!1),r.Factory.addGetterSetter(T,"hitStrokeWidth","auto",(0,o.getNumberOrAutoValidator)()),r.Factory.addGetterSetter(T,"strokeHitEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(T,"perfectDrawEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(T,"shadowForStrokeEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(T,"lineJoin"),r.Factory.addGetterSetter(T,"lineCap"),r.Factory.addGetterSetter(T,"sceneFunc"),r.Factory.addGetterSetter(T,"hitFunc"),r.Factory.addGetterSetter(T,"dash"),r.Factory.addGetterSetter(T,"dashOffset",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"shadowColor",void 0,(0,o.getStringValidator)()),r.Factory.addGetterSetter(T,"shadowBlur",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"shadowOpacity",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(T,"shadowOffset",["x","y"]),r.Factory.addGetterSetter(T,"shadowOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"shadowOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternImage"),r.Factory.addGetterSetter(T,"fill",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(T,"fillPatternX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillLinearGradientColorStops"),r.Factory.addGetterSetter(T,"strokeLinearGradientColorStops"),r.Factory.addGetterSetter(T,"fillRadialGradientStartRadius",0),r.Factory.addGetterSetter(T,"fillRadialGradientEndRadius",0),r.Factory.addGetterSetter(T,"fillRadialGradientColorStops"),r.Factory.addGetterSetter(T,"fillPatternRepeat","repeat"),r.Factory.addGetterSetter(T,"fillEnabled",!0),r.Factory.addGetterSetter(T,"strokeEnabled",!0),r.Factory.addGetterSetter(T,"shadowEnabled",!0),r.Factory.addGetterSetter(T,"dashEnabled",!0),r.Factory.addGetterSetter(T,"strokeScaleEnabled",!0),r.Factory.addGetterSetter(T,"fillPriority","color"),r.Factory.addComponentsGetterSetter(T,"fillPatternOffset",["x","y"]),r.Factory.addGetterSetter(T,"fillPatternOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(T,"fillPatternScale",["x","y"]),r.Factory.addGetterSetter(T,"fillPatternScaleX",1,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(T,"fillPatternScaleY",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(T,"fillLinearGradientStartPoint",["x","y"]),r.Factory.addComponentsGetterSetter(T,"strokeLinearGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillLinearGradientStartPointX",0),r.Factory.addGetterSetter(T,"strokeLinearGradientStartPointX",0),r.Factory.addGetterSetter(T,"fillLinearGradientStartPointY",0),r.Factory.addGetterSetter(T,"strokeLinearGradientStartPointY",0),r.Factory.addComponentsGetterSetter(T,"fillLinearGradientEndPoint",["x","y"]),r.Factory.addComponentsGetterSetter(T,"strokeLinearGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillLinearGradientEndPointX",0),r.Factory.addGetterSetter(T,"strokeLinearGradientEndPointX",0),r.Factory.addGetterSetter(T,"fillLinearGradientEndPointY",0),r.Factory.addGetterSetter(T,"strokeLinearGradientEndPointY",0),r.Factory.addComponentsGetterSetter(T,"fillRadialGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillRadialGradientStartPointX",0),r.Factory.addGetterSetter(T,"fillRadialGradientStartPointY",0),r.Factory.addComponentsGetterSetter(T,"fillRadialGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(T,"fillRadialGradientEndPointX",0),r.Factory.addGetterSetter(T,"fillRadialGradientEndPointY",0),r.Factory.addGetterSetter(T,"fillPatternRotation",0),r.Factory.addGetterSetter(T,"fillRule",void 0,(0,o.getStringValidator)()),r.Factory.backCompat(T,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"})})(on);Object.defineProperty(kh,"__esModule",{value:!0});kh.Layer=void 0;const go=Ot,qb=gl,Gl=wt,xC=Te,s6=_i,lde=de,ude=on,cde=Pe;var dde="#",fde="beforeDraw",hde="draw",pN=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],pde=pN.length;class bc extends qb.Container{constructor(t){super(t),this.canvas=new s6.SceneCanvas,this.hitCanvas=new s6.HitCanvas({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(fde,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),qb.Container.prototype.drawScene.call(this,i,n),this._fire(hde,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),qb.Container.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){go.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return go.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return go.Util.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}kh.Layer=bc;bc.prototype.nodeType="Layer";(0,cde._registerNode)(bc);xC.Factory.addGetterSetter(bc,"imageSmoothingEnabled",!0);xC.Factory.addGetterSetter(bc,"clearBeforeDraw",!0);xC.Factory.addGetterSetter(bc,"hitGraphEnabled",!0,(0,lde.getBooleanValidator)());var uv={};Object.defineProperty(uv,"__esModule",{value:!0});uv.FastLayer=void 0;const gde=Ot,mde=kh,yde=Pe;class CC extends mde.Layer{constructor(t){super(t),this.listening(!1),gde.Util.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}uv.FastLayer=CC;CC.prototype.nodeType="FastLayer";(0,yde._registerNode)(CC);var Sc={};Object.defineProperty(Sc,"__esModule",{value:!0});Sc.Group=void 0;const vde=Ot,bde=gl,Sde=Pe;class TC extends bde.Container{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&vde.Util.throw("You may only add groups and shapes to groups.")}}Sc.Group=TC;TC.prototype.nodeType="Group";(0,Sde._registerNode)(TC);var _c={};Object.defineProperty(_c,"__esModule",{value:!0});_c.Animation=void 0;const Wb=Pe,a6=Ot;var Kb=function(){return Wb.glob.performance&&Wb.glob.performance.now?function(){return Wb.glob.performance.now()}:function(){return new Date().getTime()}}();class Gi{constructor(t,n){this.id=Gi.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:Kb(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;ithis.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():p<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=p,this.update())}getTime(){return this._time}setPosition(p){this.prevPos=this._pos,this.propFunc(p),this._pos=p}getPosition(p){return p===void 0&&(p=this._time),this.func(p,this.begin,this._change,this.duration)}play(){this.state=a,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=l,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(p){this.pause(),this._time=p,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var p=this.getTimer()-this._startTime;this.state===a?this.setTime(p):this.state===l&&this.setTime(this.duration-p)}pause(){this.state=s,this.fire("onPause")}getTimer(){return new Date().getTime()}}class f{constructor(p){var m=this,S=p.node,y=S._id,v,g=p.easing||e.Easings.Linear,b=!!p.yoyo,_;typeof p.duration>"u"?v=.3:p.duration===0?v=.001:v=p.duration,this.node=S,this._id=u++;var w=S.getLayer()||(S instanceof i.Konva.Stage?S.getLayers():null);w||t.Util.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new n.Animation(function(){m.tween.onEnterFrame()},w),this.tween=new d(_,function(x){m._tweenFunc(x)},g,0,1,v*1e3,b),this._addListeners(),f.attrs[y]||(f.attrs[y]={}),f.attrs[y][this._id]||(f.attrs[y][this._id]={}),f.tweens[y]||(f.tweens[y]={});for(_ in p)o[_]===void 0&&this._addAttr(_,p[_]);this.reset(),this.onFinish=p.onFinish,this.onReset=p.onReset,this.onUpdate=p.onUpdate}_addAttr(p,m){var S=this.node,y=S._id,v,g,b,_,w,x,T,P;if(b=f.tweens[y][p],b&&delete f.attrs[y][b][p],v=S.getAttr(p),t.Util._isArray(m))if(g=[],w=Math.max(m.length,v.length),p==="points"&&m.length!==v.length&&(m.length>v.length?(T=v,v=t.Util._prepareArrayForTween(v,m,S.closed())):(x=m,m=t.Util._prepareArrayForTween(m,v,S.closed()))),p.indexOf("fill")===0)for(_=0;_{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueEnd&&p.setAttr("points",m.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var p=this.node,m=f.attrs[p._id][this._id];m.points&&m.points.trueStart&&p.points(m.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(p){return this.tween.seek(p*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var p=this.node._id,m=this._id,S=f.tweens[p],y;this.pause();for(y in S)delete f.tweens[p][y];delete f.attrs[p][m]}}e.Tween=f,f.attrs={},f.tweens={},r.Node.prototype.to=function(h){var p=h.onFinish;h.node=this,h.onFinish=function(){this.destroy(),p&&p()};var m=new f(h);m.play()},e.Easings={BackEaseIn(h,p,m,S){var y=1.70158;return m*(h/=S)*h*((y+1)*h-y)+p},BackEaseOut(h,p,m,S){var y=1.70158;return m*((h=h/S-1)*h*((y+1)*h+y)+1)+p},BackEaseInOut(h,p,m,S){var y=1.70158;return(h/=S/2)<1?m/2*(h*h*(((y*=1.525)+1)*h-y))+p:m/2*((h-=2)*h*(((y*=1.525)+1)*h+y)+2)+p},ElasticEaseIn(h,p,m,S,y,v){var g=0;return h===0?p:(h/=S)===1?p+m:(v||(v=S*.3),!y||y0?t:n),c=s*n,d=a*(a>0?t:n),f=l*(l>0?n:t);return{x:u,y:r?-1*f:d,width:c-u,height:f-d}}}cv.Arc=Yo;Yo.prototype._centroid=!0;Yo.prototype.className="Arc";Yo.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,wde._registerNode)(Yo);dv.Factory.addGetterSetter(Yo,"innerRadius",0,(0,fv.getNumberValidator)());dv.Factory.addGetterSetter(Yo,"outerRadius",0,(0,fv.getNumberValidator)());dv.Factory.addGetterSetter(Yo,"angle",0,(0,fv.getNumberValidator)());dv.Factory.addGetterSetter(Yo,"clockwise",!1,(0,fv.getBooleanValidator)());var hv={},Rh={};Object.defineProperty(Rh,"__esModule",{value:!0});Rh.Line=void 0;const pv=Te,xde=on,mN=de,Cde=Pe;function b2(e,t,n,r,i,o,s){var a=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),u=s*a/(a+l),c=s*l/(a+l),d=n-u*(i-e),f=r-u*(o-t),h=n+c*(i-e),p=r+c*(o-t);return[d,f,h,p]}function u6(e,t){var n=e.length,r=[],i,o;for(i=2;i4){for(a=this.getTensionPoints(),l=a.length,u=o?0:4,o||t.quadraticCurveTo(a[0],a[1],a[2],a[3]);u{let u,c,d;u=l/2,c=0;for(let h=0;h<20;h++)d=u*e.tValues[20][h]+u,c+=e.cValues[20][h]*r(s,a,d);return u*c};e.getCubicArcLength=t;const n=(s,a,l)=>{l===void 0&&(l=1);const u=s[0]-2*s[1]+s[2],c=a[0]-2*a[1]+a[2],d=2*s[1]-2*s[0],f=2*a[1]-2*a[0],h=4*(u*u+c*c),p=4*(u*d+c*f),m=d*d+f*f;if(h===0)return l*Math.sqrt(Math.pow(s[2]-s[0],2)+Math.pow(a[2]-a[0],2));const S=p/(2*h),y=m/h,v=l+S,g=y-S*S,b=v*v+g>0?Math.sqrt(v*v+g):0,_=S*S+g>0?Math.sqrt(S*S+g):0,w=S+Math.sqrt(S*S+g)!==0?g*Math.log(Math.abs((v+b)/(S+_))):0;return Math.sqrt(h)/2*(v*b-S*_+w)};e.getQuadraticArcLength=n;function r(s,a,l){const u=i(1,l,s),c=i(1,l,a),d=u*u+c*c;return Math.sqrt(d)}const i=(s,a,l)=>{const u=l.length-1;let c,d;if(u===0)return 0;if(s===0){d=0;for(let f=0;f<=u;f++)d+=e.binomialCoefficients[u][f]*Math.pow(1-a,u-f)*Math.pow(a,f)*l[f];return d}else{c=new Array(u);for(let f=0;f{let u=1,c=s/a,d=(s-l(c))/a,f=0;for(;u>.001;){const h=l(c+d),p=Math.abs(s-h)/a;if(p500)break}return c};e.t2length=o})(yN);Object.defineProperty(wc,"__esModule",{value:!0});wc.Path=void 0;const Tde=Te,Ede=on,Pde=Pe,Hl=yN;class Zt extends Ede.Shape{constructor(t){super(t),this.dataArray=[],this.pathLength=0,this._readDataAttribute(),this.on("dataChange.konva",function(){this._readDataAttribute()})}_readDataAttribute(){this.dataArray=Zt.parsePathData(this.data()),this.pathLength=Zt.getPathLength(this.dataArray)}_sceneFunc(t){var n=this.dataArray;t.beginPath();for(var r=!1,i=0;ic?u:c,S=u>c?1:u/c,y=u>c?c/u:1;t.translate(a,l),t.rotate(h),t.scale(S,y),t.arc(0,0,m,d,d+f,1-p),t.scale(1/S,1/y),t.rotate(-h),t.translate(-a,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(u){if(u.command==="A"){var c=u.points[4],d=u.points[5],f=u.points[4]+d,h=Math.PI/180;if(Math.abs(c-f)f;p-=h){const m=Zt.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],p,0);t.push(m.x,m.y)}else for(let p=c+h;pn[i].pathLength;)t-=n[i].pathLength,++i;if(i===o)return r=n[i-1].points.slice(-2),{x:r[0],y:r[1]};if(t<.01)return r=n[i].points.slice(0,2),{x:r[0],y:r[1]};var s=n[i],a=s.points;switch(s.command){case"L":return Zt.getPointOnLine(t,s.start.x,s.start.y,a[0],a[1]);case"C":return Zt.getPointOnCubicBezier((0,Hl.t2length)(t,Zt.getPathLength(n),m=>(0,Hl.getCubicArcLength)([s.start.x,a[0],a[2],a[4]],[s.start.y,a[1],a[3],a[5]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return Zt.getPointOnQuadraticBezier((0,Hl.t2length)(t,Zt.getPathLength(n),m=>(0,Hl.getQuadraticArcLength)([s.start.x,a[0],a[2]],[s.start.y,a[1],a[3]],m)),s.start.x,s.start.y,a[0],a[1],a[2],a[3]);case"A":var l=a[0],u=a[1],c=a[2],d=a[3],f=a[4],h=a[5],p=a[6];return f+=h*t/s.pathLength,Zt.getPointOnEllipticalArc(l,u,c,d,f,p)}return null}static getPointOnLine(t,n,r,i,o,s,a){s===void 0&&(s=n),a===void 0&&(a=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i0&&!isNaN(p[0]);){var v=null,g=[],b=l,_=u,w,x,T,P,E,A,$,I,C,R;switch(h){case"l":l+=p.shift(),u+=p.shift(),v="L",g.push(l,u);break;case"L":l=p.shift(),u=p.shift(),g.push(l,u);break;case"m":var M=p.shift(),N=p.shift();if(l+=M,u+=N,v="M",s.length>2&&s[s.length-1].command==="z"){for(var O=s.length-2;O>=0;O--)if(s[O].command==="M"){l=s[O].points[0]+M,u=s[O].points[1]+N;break}}g.push(l,u),h="l";break;case"M":l=p.shift(),u=p.shift(),v="M",g.push(l,u),h="L";break;case"h":l+=p.shift(),v="L",g.push(l,u);break;case"H":l=p.shift(),v="L",g.push(l,u);break;case"v":u+=p.shift(),v="L",g.push(l,u);break;case"V":u=p.shift(),v="L",g.push(l,u);break;case"C":g.push(p.shift(),p.shift(),p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"c":g.push(l+p.shift(),u+p.shift(),l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),v="C",g.push(l,u);break;case"S":x=l,T=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),T=u+(u-w.points[3])),g.push(x,T,p.shift(),p.shift()),l=p.shift(),u=p.shift(),v="C",g.push(l,u);break;case"s":x=l,T=u,w=s[s.length-1],w.command==="C"&&(x=l+(l-w.points[2]),T=u+(u-w.points[3])),g.push(x,T,l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),v="C",g.push(l,u);break;case"Q":g.push(p.shift(),p.shift()),l=p.shift(),u=p.shift(),g.push(l,u);break;case"q":g.push(l+p.shift(),u+p.shift()),l+=p.shift(),u+=p.shift(),v="Q",g.push(l,u);break;case"T":x=l,T=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),T=u+(u-w.points[1])),l=p.shift(),u=p.shift(),v="Q",g.push(x,T,l,u);break;case"t":x=l,T=u,w=s[s.length-1],w.command==="Q"&&(x=l+(l-w.points[0]),T=u+(u-w.points[1])),l+=p.shift(),u+=p.shift(),v="Q",g.push(x,T,l,u);break;case"A":P=p.shift(),E=p.shift(),A=p.shift(),$=p.shift(),I=p.shift(),C=l,R=u,l=p.shift(),u=p.shift(),v="A",g=this.convertEndpointToCenterParameterization(C,R,l,u,$,I,P,E,A);break;case"a":P=p.shift(),E=p.shift(),A=p.shift(),$=p.shift(),I=p.shift(),C=l,R=u,l+=p.shift(),u+=p.shift(),v="A",g=this.convertEndpointToCenterParameterization(C,R,l,u,$,I,P,E,A);break}s.push({command:v||h,points:g,start:{x:b,y:_},pathLength:this.calcLength(b,_,v||h,g)})}(h==="z"||h==="Z")&&s.push({command:"z",points:[],start:void 0,pathLength:0})}return s}static calcLength(t,n,r,i){var o,s,a,l,u=Zt;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":return(0,Hl.getCubicArcLength)([t,i[0],i[2],i[4]],[n,i[1],i[3],i[5]],1);case"Q":return(0,Hl.getQuadraticArcLength)([t,i[0],i[2]],[n,i[1],i[3]],1);case"A":o=0;var c=i[4],d=i[5],f=i[4]+d,h=Math.PI/180;if(Math.abs(c-f)f;l-=h)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;else for(l=c+h;l1&&(a*=Math.sqrt(h),l*=Math.sqrt(h));var p=Math.sqrt((a*a*(l*l)-a*a*(f*f)-l*l*(d*d))/(a*a*(f*f)+l*l*(d*d)));o===s&&(p*=-1),isNaN(p)&&(p=0);var m=p*a*f/l,S=p*-l*d/a,y=(t+r)/2+Math.cos(c)*m-Math.sin(c)*S,v=(n+i)/2+Math.sin(c)*m+Math.cos(c)*S,g=function(E){return Math.sqrt(E[0]*E[0]+E[1]*E[1])},b=function(E,A){return(E[0]*A[0]+E[1]*A[1])/(g(E)*g(A))},_=function(E,A){return(E[0]*A[1]=1&&(P=0),s===0&&P>0&&(P=P-2*Math.PI),s===1&&P<0&&(P=P+2*Math.PI),[y,v,a,l,w,P,c,s]}}wc.Path=Zt;Zt.prototype.className="Path";Zt.prototype._attrsAffectingSize=["data"];(0,Pde._registerNode)(Zt);Tde.Factory.addGetterSetter(Zt,"data");Object.defineProperty(hv,"__esModule",{value:!0});hv.Arrow=void 0;const gv=Te,Ade=Rh,vN=de,kde=Pe,c6=wc;class yl extends Ade.Line{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var s=this.pointerLength(),a=r.length,l,u;if(o){const f=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[a-2],r[a-1]],h=c6.Path.calcLength(i[i.length-4],i[i.length-3],"C",f),p=c6.Path.getPointOnQuadraticBezier(Math.min(1,1-s/h),f[0],f[1],f[2],f[3],f[4],f[5]);l=r[a-2]-p.x,u=r[a-1]-p.y}else l=r[a-2]-r[a-4],u=r[a-1]-r[a-3];var c=(Math.atan2(u,l)+n)%n,d=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[a-2],r[a-1]),t.rotate(c),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-s,d/2),t.lineTo(-s,-d/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}hv.Arrow=yl;yl.prototype.className="Arrow";(0,kde._registerNode)(yl);gv.Factory.addGetterSetter(yl,"pointerLength",10,(0,vN.getNumberValidator)());gv.Factory.addGetterSetter(yl,"pointerWidth",10,(0,vN.getNumberValidator)());gv.Factory.addGetterSetter(yl,"pointerAtBeginning",!1);gv.Factory.addGetterSetter(yl,"pointerAtEnding",!0);var mv={};Object.defineProperty(mv,"__esModule",{value:!0});mv.Circle=void 0;const Rde=Te,Ode=on,Mde=de,Ide=Pe;let xc=class extends Ode.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}};mv.Circle=xc;xc.prototype._centroid=!0;xc.prototype.className="Circle";xc.prototype._attrsAffectingSize=["radius"];(0,Ide._registerNode)(xc);Rde.Factory.addGetterSetter(xc,"radius",0,(0,Mde.getNumberValidator)());var yv={};Object.defineProperty(yv,"__esModule",{value:!0});yv.Ellipse=void 0;const EC=Te,Nde=on,bN=de,Dde=Pe;class sa extends Nde.Shape{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}yv.Ellipse=sa;sa.prototype.className="Ellipse";sa.prototype._centroid=!0;sa.prototype._attrsAffectingSize=["radiusX","radiusY"];(0,Dde._registerNode)(sa);EC.Factory.addComponentsGetterSetter(sa,"radius",["x","y"]);EC.Factory.addGetterSetter(sa,"radiusX",0,(0,bN.getNumberValidator)());EC.Factory.addGetterSetter(sa,"radiusY",0,(0,bN.getNumberValidator)());var vv={};Object.defineProperty(vv,"__esModule",{value:!0});vv.Image=void 0;const Xb=Ot,vl=Te,Lde=on,$de=Pe,Oh=de;let ao=class SN extends Lde.Shape{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.cornerRadius(),o=this.attrs.image;let s;if(o){const a=this.attrs.cropWidth,l=this.attrs.cropHeight;a&&l?s=[o,this.cropX(),this.cropY(),a,l,0,0,n,r]:s=[o,0,0,n,r]}(this.hasFill()||this.hasStroke()||i)&&(t.beginPath(),i?Xb.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),o&&(i&&t.clip(),t.drawImage.apply(t,s))}_hitFunc(t){var n=this.width(),r=this.height(),i=this.cornerRadius();t.beginPath(),i?Xb.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=Xb.Util.createImageElement();i.onload=function(){var o=new SN({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}};vv.Image=ao;ao.prototype.className="Image";(0,$de._registerNode)(ao);vl.Factory.addGetterSetter(ao,"cornerRadius",0,(0,Oh.getNumberOrArrayOfNumbersValidator)(4));vl.Factory.addGetterSetter(ao,"image");vl.Factory.addComponentsGetterSetter(ao,"crop",["x","y","width","height"]);vl.Factory.addGetterSetter(ao,"cropX",0,(0,Oh.getNumberValidator)());vl.Factory.addGetterSetter(ao,"cropY",0,(0,Oh.getNumberValidator)());vl.Factory.addGetterSetter(ao,"cropWidth",0,(0,Oh.getNumberValidator)());vl.Factory.addGetterSetter(ao,"cropHeight",0,(0,Oh.getNumberValidator)());var sc={};Object.defineProperty(sc,"__esModule",{value:!0});sc.Tag=sc.Label=void 0;const bv=Te,Fde=on,Bde=Sc,PC=de,_N=Pe;var wN=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],jde="Change.konva",Vde="none",S2="up",_2="right",w2="down",x2="left",zde=wN.length;class AC extends Bde.Group{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r{n=Math.min(n,s.x),r=Math.max(r,s.x),i=Math.min(i,s.y),o=Math.max(o,s.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}_v.RegularPolygon=Sl;Sl.prototype.className="RegularPolygon";Sl.prototype._centroid=!0;Sl.prototype._attrsAffectingSize=["radius"];(0,Xde._registerNode)(Sl);xN.Factory.addGetterSetter(Sl,"radius",0,(0,CN.getNumberValidator)());xN.Factory.addGetterSetter(Sl,"sides",0,(0,CN.getNumberValidator)());var wv={};Object.defineProperty(wv,"__esModule",{value:!0});wv.Ring=void 0;const TN=Te,Yde=on,EN=de,Qde=Pe;var d6=Math.PI*2;class _l extends Yde.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,d6,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),d6,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}wv.Ring=_l;_l.prototype.className="Ring";_l.prototype._centroid=!0;_l.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,Qde._registerNode)(_l);TN.Factory.addGetterSetter(_l,"innerRadius",0,(0,EN.getNumberValidator)());TN.Factory.addGetterSetter(_l,"outerRadius",0,(0,EN.getNumberValidator)());var xv={};Object.defineProperty(xv,"__esModule",{value:!0});xv.Sprite=void 0;const wl=Te,Zde=on,Jde=_c,PN=de,efe=Pe;class lo extends Zde.Shape{constructor(t){super(t),this._updated=!0,this.anim=new Jde.Animation(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+0],l=o[i+1],u=o[i+2],c=o[i+3],d=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,c),t.closePath(),t.fillStrokeShape(this)),d)if(s){var f=s[n],h=r*2;t.drawImage(d,a,l,u,c,f[h+0],f[h+1],u,c)}else t.drawImage(d,a,l,u,c,0,0,u,c)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+2],l=o[i+3];if(t.beginPath(),s){var u=s[n],c=r*2;t.rect(u[c+0],u[c+1],a,l)}else t.rect(0,0,a,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Fp;function Qb(){return Fp||(Fp=C2.Util.createCanvasElement().getContext(afe),Fp)}function vfe(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function bfe(e){e.setAttr("miterLimit",2),e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function Sfe(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class zt extends rfe.Shape{constructor(t){super(Sfe(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n1&&(y+=s)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=C2.Util._isString(t)?t:t==null?"":t+"";return this._setAttr(lfe,n),this}getWidth(){var t=this.attrs.width===ql||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===ql||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return C2.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=Qb(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+$p+this.fontVariant()+$p+(this.fontSize()+ffe)+yfe(this.fontFamily())}_addTextLine(t){this.align()===Jc&&(t=t.trim());var r=this._getTextWidth(t);return this.textArr.push({text:t,width:r,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return Qb().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(` -`),n=+this.fontSize(),r=0,i=this.lineHeight()*n,o=this.attrs.width,s=this.attrs.height,a=o!==ql&&o!==void 0,l=s!==ql&&s!==void 0,u=this.padding(),c=o-u*2,d=s-u*2,f=0,h=this.wrap(),p=h!==p6,m=h!==gfe&&p,S=this.ellipsis();this.textArr=[],Qb().font=this._getContextFont();for(var y=S?this._getTextWidth(Yb):0,v=0,g=t.length;vc)for(;b.length>0;){for(var w=0,x=b.length,T="",P=0;w>>1,A=b.slice(0,E+1),$=this._getTextWidth(A)+y;$<=c?(w=E+1,T=A,P=$):x=E}if(T){if(m){var I,C=b[T.length],R=C===$p||C===f6;R&&P<=c?I=T.length:I=Math.max(T.lastIndexOf($p),T.lastIndexOf(f6))+1,I>0&&(w=I,T=T.slice(0,w),P=this._getTextWidth(T))}T=T.trimRight(),this._addTextLine(T),r=Math.max(r,P),f+=i;var M=this._shouldHandleEllipsis(f);if(M){this._tryToAddEllipsisToLastLine();break}if(b=b.slice(w),b=b.trimLeft(),b.length>0&&(_=this._getTextWidth(b),_<=c)){this._addTextLine(b),f+=i,r=Math.max(r,_);break}}else break}else this._addTextLine(b),f+=i,r=Math.max(r,_),this._shouldHandleEllipsis(f)&&vd)break}this.textHeight=n,this.textWidth=r}_shouldHandleEllipsis(t){var n=+this.fontSize(),r=this.lineHeight()*n,i=this.attrs.height,o=i!==ql&&i!==void 0,s=this.padding(),a=i-s*2,l=this.wrap(),u=l!==p6;return!u||o&&t+r>a}_tryToAddEllipsisToLastLine(){var t=this.attrs.width,n=t!==ql&&t!==void 0,r=this.padding(),i=t-r*2,o=this.ellipsis(),s=this.textArr[this.textArr.length-1];if(!(!s||!o)){if(n){var a=this._getTextWidth(s.text+Yb)n?null:ed.Path.getPointAtLengthOfDataArray(t,this.dataArray)}_readDataAttribute(){this.dataArray=ed.Path.parsePathData(this.attrs.data),this.pathLength=this._getTextPathLength()}_sceneFunc(t){t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",this.textBaseline()),t.setAttr("textAlign","left"),t.save();var n=this.textDecoration(),r=this.fill(),i=this.fontSize(),o=this.glyphInfo;n==="underline"&&t.beginPath();for(var s=0;s=1){var r=n[0].p0;t.moveTo(r.x,r.y)}for(var i=0;ie+`.${LN}`).join(" "),y6="nodesRect",Afe=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],kfe={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const Rfe="ontouchstart"in si.Konva._global;function Ofe(e,t){if(e==="rotater")return"crosshair";t+=Je.Util.degToRad(kfe[e]||0);var n=(Je.Util.radToDeg(t)%360+360)%360;return Je.Util._inRange(n,315+22.5,360)||Je.Util._inRange(n,0,22.5)?"ns-resize":Je.Util._inRange(n,45-22.5,45+22.5)?"nesw-resize":Je.Util._inRange(n,90-22.5,90+22.5)?"ew-resize":Je.Util._inRange(n,135-22.5,135+22.5)?"nwse-resize":Je.Util._inRange(n,180-22.5,180+22.5)?"ns-resize":Je.Util._inRange(n,225-22.5,225+22.5)?"nesw-resize":Je.Util._inRange(n,270-22.5,270+22.5)?"ew-resize":Je.Util._inRange(n,315-22.5,315+22.5)?"nwse-resize":(Je.Util.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var Zm=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],v6=1e8;function Mfe(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function $N(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function Ife(e,t){const n=Mfe(e);return $N(e,t,n)}function Nfe(e,t,n){let r=t;for(let i=0;ii.isAncestorOf(this)?(Je.Util.error("Konva.Transformer cannot be an a child of the node you are trying to attach"),!1):!0);this._nodes=t=n,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(i=>{const o=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},s=i._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");i.on(s,o),i.on(Afe.map(a=>a+`.${this._getEventNamespace()}`).join(" "),o),i.on(`absoluteTransformChange.${this._getEventNamespace()}`,o),this._proxyDrag(i)}),this._resetTransformCache();var r=!!this.findOne(".top-left");return r&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,s=i.y-n.y;this.nodes().forEach(a=>{if(a===t||a.isDragging())return;const l=a.getAbsolutePosition();a.setAbsolutePosition({x:l.x+o,y:l.y+s}),a.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(y6),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(y6,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),s=t.getAbsolutePosition(r),a=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=(si.Konva.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),c={x:s.x+a*Math.cos(u)+l*Math.sin(-u),y:s.y+l*Math.cos(u)+a*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return $N(c,-si.Konva.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-v6,y:-v6,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const c=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var d=[{x:c.x,y:c.y},{x:c.x+c.width,y:c.y},{x:c.x+c.width,y:c.y+c.height},{x:c.x,y:c.y+c.height}],f=u.getAbsoluteTransform();d.forEach(function(h){var p=f.point(h);n.push(p)})});const r=new Je.Transform;r.rotate(-si.Konva.getAngle(this.rotation()));var i,o,s,a;n.forEach(function(u){var c=r.point(u);i===void 0&&(i=s=c.x,o=a=c.y),i=Math.min(i,c.x),o=Math.min(o,c.y),s=Math.max(s,c.x),a=Math.max(a,c.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:s-i,height:a-o,rotation:si.Konva.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),Zm.forEach((function(t){this._createAnchor(t)}).bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new Tfe.Rect({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:Rfe?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=si.Konva.getAngle(this.rotation()),o=Ofe(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new Cfe.Shape({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*Je.Util._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var s=t.target.getAbsolutePosition(),a=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-s.x,y:a.y-s.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),s=o.getStage();s.setPointersPositions(t);const a=s.getPointerPosition();let l={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const c=o.getAbsolutePosition();if(!(u.x===c.x&&u.y===c.y)){if(this._movingAnchorName==="rotater"){var d=this._getNodeRect();n=o.x()-d.width/2,r=-o.y()+d.height/2;let I=Math.atan2(-r,n)+Math.PI/2;d.height<0&&(I-=Math.PI);var f=si.Konva.getAngle(this.rotation());const C=f+I,R=si.Konva.getAngle(this.rotationSnapTolerance()),N=Nfe(this.rotationSnaps(),C,R)-d.rotation,O=Ife(d,N);this._fitNodesInto(O,t);return}var h=this.shiftBehavior(),p;h==="inverted"?p=this.keepRatio()&&!t.shiftKey:h==="none"?p=this.keepRatio():p=this.keepRatio()||t.shiftKey;var g=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-left").x()>m.x?-1:1,y=this.findOne(".top-left").y()>m.y?-1:1;n=i*this.cos*S,r=i*this.sin*y,this.findOne(".top-left").x(m.x-n),this.findOne(".top-left").y(m.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-m.x,2)+Math.pow(m.y-o.y(),2));var S=this.findOne(".top-right").x()m.y?-1:1;n=i*this.cos*S,r=i*this.sin*y,this.findOne(".top-right").x(m.x+n),this.findOne(".top-right").y(m.y-r)}var v=o.position();this.findOne(".top-left").y(v.y),this.findOne(".bottom-right").x(v.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(p){var m=g?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(m.x-o.x(),2)+Math.pow(o.y()-m.y,2));var S=m.x{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(Je.Util._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(Je.Util._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var s=new Je.Transform;if(s.rotate(si.Konva.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const d=s.point({x:-this.padding()*2,y:0});if(t.x+=d.x,t.y+=d.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const d=s.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const d=s.point({x:0,y:-this.padding()*2});if(t.x+=d.x,t.y+=d.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const d=s.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=d.x,this._anchorDragOffset.y-=d.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const d=this.boundBoxFunc()(r,t);d?t=d:Je.Util.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const a=1e7,l=new Je.Transform;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/a,r.height/a);const u=new Je.Transform;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/a,t.height/a);const c=u.multiply(l.invert());this._nodes.forEach(d=>{var f;const h=d.getParent().getAbsoluteTransform(),p=d.getTransform().copy();p.translate(d.offsetX(),d.offsetY());const m=new Je.Transform;m.multiply(h.copy().invert()).multiply(c).multiply(h).multiply(p);const S=m.decompose();d.setAttrs(S),this._fire("transform",{evt:n,target:d}),d._fire("transform",{evt:n,target:d}),(f=d.getLayer())===null||f===void 0||f.batchDraw()}),this.rotation(Je.Util._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(Je.Util._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),s=this.resizeEnabled(),a=this.padding(),l=this.anchorSize();const u=this.find("._anchor");u.forEach(d=>{d.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+a,offsetY:l/2+a,visible:s&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+a,visible:s&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-a,offsetY:l/2+a,visible:s&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+a,visible:s&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-a,visible:s&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-a,visible:s&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*Je.Util._sign(i)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0});const c=this.anchorStyleFunc();c&&u.forEach(d=>{c(d)}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),m6.Group.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return g6.Node.prototype.toObject.call(this)}clone(t){var n=g6.Node.prototype.clone.call(this,t);return n}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}Ev.Transformer=ze;function Dfe(e){return e instanceof Array||Je.Util.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){Zm.indexOf(t)===-1&&Je.Util.warn("Unknown anchor name: "+t+". 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255)");Ze.Factory.addGetterSetter(ze,"anchorStrokeWidth",1,(0,ua.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"anchorFill","white");Ze.Factory.addGetterSetter(ze,"anchorCornerRadius",0,(0,ua.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"borderStroke","rgb(0, 161, 255)");Ze.Factory.addGetterSetter(ze,"borderStrokeWidth",1,(0,ua.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"borderDash");Ze.Factory.addGetterSetter(ze,"keepRatio",!0);Ze.Factory.addGetterSetter(ze,"shiftBehavior","default");Ze.Factory.addGetterSetter(ze,"centeredScaling",!1);Ze.Factory.addGetterSetter(ze,"ignoreStroke",!1);Ze.Factory.addGetterSetter(ze,"padding",0,(0,ua.getNumberValidator)());Ze.Factory.addGetterSetter(ze,"node");Ze.Factory.addGetterSetter(ze,"nodes");Ze.Factory.addGetterSetter(ze,"boundBoxFunc");Ze.Factory.addGetterSetter(ze,"anchorDragBoundFunc");Ze.Factory.addGetterSetter(ze,"anchorStyleFunc");Ze.Factory.addGetterSetter(ze,"shouldOverdrawWholeArea",!1);Ze.Factory.addGetterSetter(ze,"useSingleNodeRotation",!0);Ze.Factory.backCompat(ze,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});var Pv={};Object.defineProperty(Pv,"__esModule",{value:!0});Pv.Wedge=void 0;const Av=Te,Lfe=on,$fe=Pe,FN=de,Ffe=Pe;class Qo extends Lfe.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,$fe.Konva.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}Pv.Wedge=Qo;Qo.prototype.className="Wedge";Qo.prototype._centroid=!0;Qo.prototype._attrsAffectingSize=["radius"];(0,Ffe._registerNode)(Qo);Av.Factory.addGetterSetter(Qo,"radius",0,(0,FN.getNumberValidator)());Av.Factory.addGetterSetter(Qo,"angle",0,(0,FN.getNumberValidator)());Av.Factory.addGetterSetter(Qo,"clockwise",!1);Av.Factory.backCompat(Qo,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});var kv={};Object.defineProperty(kv,"__esModule",{value:!0});kv.Blur=void 0;const b6=Te,Bfe=wt,jfe=de;function S6(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var Vfe=[512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,289,287,285,282,280,278,275,273,271,269,267,265,263,261,259],zfe=[9,11,12,13,13,14,14,15,15,15,15,16,16,16,16,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24];function Ufe(e,t){var n=e.data,r=e.width,i=e.height,o,s,a,l,u,c,d,f,h,p,m,S,y,v,g,b,_,w,x,T,P,E,A,$,I=t+t+1,C=r-1,R=i-1,M=t+1,N=M*(M+1)/2,O=new S6,D=null,L=O,j=null,U=null,G=Vfe[t],W=zfe[t];for(a=1;a>W,A!==0?(A=255/A,n[c]=(f*G>>W)*A,n[c+1]=(h*G>>W)*A,n[c+2]=(p*G>>W)*A):n[c]=n[c+1]=n[c+2]=0,f-=S,h-=y,p-=v,m-=g,S-=j.r,y-=j.g,v-=j.b,g-=j.a,l=d+((l=o+t+1)>W,A>0?(A=255/A,n[l]=(f*G>>W)*A,n[l+1]=(h*G>>W)*A,n[l+2]=(p*G>>W)*A):n[l]=n[l+1]=n[l+2]=0,f-=S,h-=y,p-=v,m-=g,S-=j.r,y-=j.g,v-=j.b,g-=j.a,l=o+((l=s+M)0&&Ufe(t,n)};kv.Blur=Gfe;b6.Factory.addGetterSetter(Bfe.Node,"blurRadius",0,(0,jfe.getNumberValidator)(),b6.Factory.afterSetFilter);var Rv={};Object.defineProperty(Rv,"__esModule",{value:!0});Rv.Brighten=void 0;const _6=Te,Hfe=wt,qfe=de,Wfe=function(e){var t=this.brightness()*255,n=e.data,r=n.length,i;for(i=0;i255?255:i,o=o<0?0:o>255?255:o,s=s<0?0:s>255?255:s,n[a]=i,n[a+1]=o,n[a+2]=s};Ov.Contrast=Yfe;w6.Factory.addGetterSetter(Kfe.Node,"contrast",0,(0,Xfe.getNumberValidator)(),w6.Factory.afterSetFilter);var Mv={};Object.defineProperty(Mv,"__esModule",{value:!0});Mv.Emboss=void 0;const Ws=Te,Iv=wt,Qfe=Ot,BN=de,Zfe=function(e){var t=this.embossStrength()*10,n=this.embossWhiteLevel()*255,r=this.embossDirection(),i=this.embossBlend(),o=0,s=0,a=e.data,l=e.width,u=e.height,c=l*4,d=u;switch(r){case"top-left":o=-1,s=-1;break;case"top":o=-1,s=0;break;case"top-right":o=-1,s=1;break;case"right":o=0,s=1;break;case"bottom-right":o=1,s=1;break;case"bottom":o=1,s=0;break;case"bottom-left":o=1,s=-1;break;case"left":o=0,s=-1;break;default:Qfe.Util.error("Unknown emboss direction: "+r)}do{var f=(d-1)*c,h=o;d+h<1&&(h=0),d+h>u&&(h=0);var p=(d-1+h)*l*4,m=l;do{var S=f+(m-1)*4,y=s;m+y<1&&(y=0),m+y>l&&(y=0);var v=p+(m-1+y)*4,g=a[S]-a[v],b=a[S+1]-a[v+1],_=a[S+2]-a[v+2],w=g,x=w>0?w:-w,T=b>0?b:-b,P=_>0?_:-_;if(T>x&&(w=b),P>x&&(w=_),w*=t,i){var E=a[S]+w,A=a[S+1]+w,$=a[S+2]+w;a[S]=E>255?255:E<0?0:E,a[S+1]=A>255?255:A<0?0:A,a[S+2]=$>255?255:$<0?0:$}else{var I=n-w;I<0?I=0:I>255&&(I=255),a[S]=a[S+1]=a[S+2]=I}}while(--m)}while(--d)};Mv.Emboss=Zfe;Ws.Factory.addGetterSetter(Iv.Node,"embossStrength",.5,(0,BN.getNumberValidator)(),Ws.Factory.afterSetFilter);Ws.Factory.addGetterSetter(Iv.Node,"embossWhiteLevel",.5,(0,BN.getNumberValidator)(),Ws.Factory.afterSetFilter);Ws.Factory.addGetterSetter(Iv.Node,"embossDirection","top-left",null,Ws.Factory.afterSetFilter);Ws.Factory.addGetterSetter(Iv.Node,"embossBlend",!1,null,Ws.Factory.afterSetFilter);var Nv={};Object.defineProperty(Nv,"__esModule",{value:!0});Nv.Enhance=void 0;const x6=Te,Jfe=wt,ehe=de;function eS(e,t,n,r,i){var o=n-t,s=i-r,a;return o===0?r+s/2:s===0?r:(a=(e-t)/o,a=s*a+r,a)}const the=function(e){var 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r=[.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111,.1111111111111111],i=Math.round(Math.sqrt(r.length)),o=Math.floor(i/2),s=[],a=0;a=0&&h=0&&p=n))for(o=m;o=r||(s=(n*o+i)*4,a+=b[s+0],l+=b[s+1],u+=b[s+2],c+=b[s+3],g+=1);for(a=a/g,l=l/g,u=u/g,c=c/g,i=h;i=n))for(o=m;o=r||(s=(n*o+i)*4,b[s+0]=a,b[s+1]=l,b[s+2]=u,b[s+3]=c)}};zv.Pixelate=Che;P6.Factory.addGetterSetter(whe.Node,"pixelSize",8,(0,xhe.getNumberValidator)(),P6.Factory.afterSetFilter);var Uv={};Object.defineProperty(Uv,"__esModule",{value:!0});Uv.Posterize=void 0;const A6=Te,The=wt,Ehe=de,Phe=function(e){var t=Math.round(this.levels()*254)+1,n=e.data,r=n.length,i=255/t,o;for(o=0;o255?255:e<0?0:Math.round(e)});ey.Factory.addGetterSetter(DC.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});ey.Factory.addGetterSetter(DC.Node,"blue",0,Ahe.RGBComponent,ey.Factory.afterSetFilter);var Hv={};Object.defineProperty(Hv,"__esModule",{value:!0});Hv.RGBA=void 0;const Bf=Te,qv=wt,Rhe=de,Ohe=function(e){var t=e.data,n=t.length,r=this.red(),i=this.green(),o=this.blue(),s=this.alpha(),a,l;for(a=0;a255?255:e<0?0:Math.round(e)});Bf.Factory.addGetterSetter(qv.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Bf.Factory.addGetterSetter(qv.Node,"blue",0,Rhe.RGBComponent,Bf.Factory.afterSetFilter);Bf.Factory.addGetterSetter(qv.Node,"alpha",1,function(e){return this._filterUpToDate=!1,e>1?1:e<0?0:e});var Wv={};Object.defineProperty(Wv,"__esModule",{value:!0});Wv.Sepia=void 0;const Mhe=function(e){var t=e.data,n=t.length,r,i,o,s;for(r=0;r127&&(u=255-u),c>127&&(c=255-c),d>127&&(d=255-d),t[l]=u,t[l+1]=c,t[l+2]=d}while(--a)}while(--o)};Kv.Solarize=Ihe;var Xv={};Object.defineProperty(Xv,"__esModule",{value:!0});Xv.Threshold=void 0;const k6=Te,Nhe=wt,Dhe=de,Lhe=function(e){var t=this.threshold()*255,n=e.data,r=n.length,i;for(i=0;i{const{width:r,height:i}=t,o=document.createElement("div"),s=new nd.Stage({container:o,width:r,height:i}),a=new nd.Layer,l=new nd.Layer;return a.add(new nd.Rect({...t,fill:n?"black":"white"})),e.forEach(u=>l.add(new nd.Line({points:u.points,stroke:n?"white":"black",strokeWidth:u.strokeWidth*2,tension:0,lineCap:"round",lineJoin:"round",shadowForStrokeEnabled:!1,globalCompositeOperation:u.tool==="brush"?"source-over":"destination-out"}))),s.add(a),s.add(l),o.remove(),s},wpe=async(e,t,n)=>new Promise((r,i)=>{const o=document.createElement("canvas");o.width=t,o.height=n;const s=o.getContext("2d"),a=new Image;if(!s){o.remove(),i("Unable to get context");return}a.onload=function(){s.drawImage(a,0,0),o.remove(),r(s.getImageData(0,0,t,n))},a.src=e}),M6=async(e,t)=>{const n=e.toDataURL(t);return await wpe(n,t.width,t.height)},xpe=async(e,t,n,r,i)=>{const o=fe("canvas"),s=nv(),a=Nle();if(!s||!a){o.error("Unable to find canvas / 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p=c?u:Wo.shouldUseCpuNoise,m=i.model_type.includes("onnx"),S=m?rv:Pn,y={id:SC,nodes:{[Be]:{type:m?"prompt_onnx":"compel",id:Be,is_intermediate:!0,prompt:n},[qe]:{type:m?"prompt_onnx":"compel",id:qe,is_intermediate:!0,prompt:r},[$e]:{type:"noise",id:$e,is_intermediate:!0,width:d,height:f,use_cpu:p},[cn]:{type:m?"t2l_onnx":"t2l",id:cn,is_intermediate:!0,cfg_scale:o,scheduler:s,steps:a},[S]:{type:S,id:S,is_intermediate:!0,model:i},[it]:{type:"clip_skip",id:it,is_intermediate:!0,skipped_layers:l},[We]:{type:m?"l2i_onnx":"l2i",id:We,is_intermediate:!h}},edges:[{source:{node_id:qe,field:"conditioning"},destination:{node_id:cn,field:"negative_conditioning"}},{source:{node_id:Be,field:"conditioning"},destination:{node_id:cn,field:"positive_conditioning"}},{source:{node_id:S,field:"clip"},destination:{node_id:it,field:"clip"}},{source:{node_id:it,field:"clip"},destination:{node_id:Be,field:"clip"}},{source:{node_id:it,field:"clip"},destination:{node_id:qe,field:"clip"}},{source:{node_id:S,field:"unet"},destination:{node_id:cn,field:"unet"}},{source:{node_id:cn,field:"latents"},destination:{node_id:We,field:"latents"}},{source:{node_id:$e,field:"noise"},destination:{node_id:cn,field:"noise"}}]};return y.nodes[Ke]={id:Ke,type:"metadata_accumulator",generation_mode:"txt2img",cfg_scale:o,height:f,width:d,positive_prompt:"",negative_prompt:r,model:i,seed:0,steps:a,rand_device:p?"cpu":"cuda",scheduler:s,vae:void 0,controlnets:[],loras:[],clip_skip:l},y.edges.push({source:{node_id:Ke,field:"metadata"},destination:{node_id:We,field:"metadata"}}),Ih(e,y,cn,S),Nh(e,y,S),Cc(e,y),Yv(e,y,cn),e.system.shouldUseNSFWChecker&&xl(e,y),e.system.shouldUseWatermarker&&Cl(e,y),y},Ope=(e,t,n,r)=>{let i;if(t==="txt2img")i=Rpe(e);else if(t==="img2img"){if(!n)throw new Error("Missing canvas init image");i=Ape(e,n)}else{if(!n||!r)throw new Error("Missing canvas init and mask images");i=kpe(e,n,r)}return i},Mpe=()=>{le({predicate:e=>Ph.match(e)&&e.payload==="unifiedCanvas",effect:async(e,{getState:t,dispatch:n,take:r})=>{const i=fe("session"),o=t(),{layerState:s,boundingBoxCoordinates:a,boundingBoxDimensions:l,isMaskEnabled:u,shouldPreserveMaskedArea:c}=o.canvas,d=await xpe(s,a,l,u,c);if(!d){i.error("Unable 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y=m?p:Wo.shouldUseCpuNoise,v={id:Ym,nodes:{[Pn]:{type:"main_model_loader",id:Pn,model:i},[it]:{type:"clip_skip",id:it,skipped_layers:h},[Be]:{type:"compel",id:Be,prompt:n},[qe]:{type:"compel",id:qe,prompt:r},[$e]:{type:"noise",id:$e,use_cpu:y},[We]:{type:"l2i",id:We,fp32:S==="fp32"},[Wt]:{type:"l2l",id:Wt,cfg_scale:o,scheduler:s,steps:a,strength:u},[vt]:{type:"i2l",id:vt,fp32:S==="fp32"}},edges:[{source:{node_id:Pn,field:"unet"},destination:{node_id:Wt,field:"unet"}},{source:{node_id:Pn,field:"clip"},destination:{node_id:it,field:"clip"}},{source:{node_id:it,field:"clip"},destination:{node_id:Be,field:"clip"}},{source:{node_id:it,field:"clip"},destination:{node_id:qe,field:"clip"}},{source:{node_id:Wt,field:"latents"},destination:{node_id:We,field:"latents"}},{source:{node_id:vt,field:"latents"},destination:{node_id:Wt,field:"latents"}},{source:{node_id:$e,field:"noise"},destination:{node_id:Wt,field:"noise"}},{source:{node_id:qe,field:"conditioning"},destination:{node_id:Wt,field:"negative_conditioning"}},{source:{node_id:Be,field:"conditioning"},destination:{node_id:Wt,field:"positive_conditioning"}}]};if(c&&(l.width!==d||l.height!==f)){const 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s=this.originPoint[t]+r[t];this.constraints&&this.constraints[t]&&(s=_1e(s,this.constraints[t],this.elastic[t])),o.set(s)}resolveConstraints(){const{dragConstraints:t,dragElastic:n}=this.getProps(),{layout:r}=this.visualElement.projection||{},i=this.constraints;t&&pu(t)?this.constraints||(this.constraints=this.resolveRefConstraints()):t&&r?this.constraints=w1e(r.layoutBox,t):this.constraints=!1,this.elastic=E1e(n),i!==this.constraints&&r&&this.constraints&&!this.hasMutatedConstraints&&Li(o=>{this.getAxisMotionValue(o)&&(this.constraints[o]=T1e(r.layoutBox[o],this.constraints[o]))})}resolveRefConstraints(){const{dragConstraints:t,onMeasureDragConstraints:n}=this.getProps();if(!t||!pu(t))return!1;const r=t.current,{projection:i}=this.visualElement;if(!i||!i.layout)return!1;const o=M1e(r,i.root,this.visualElement.getTransformPagePoint());let s=x1e(i.layout.layoutBox,o);if(n){const a=n(P1e(s));this.hasMutatedConstraints=!!a,a&&(s=xL(a))}return s}startAnimation(t){const{drag:n,dragMomentum:r,dragElastic:i,dragTransition:o,dragSnapToOrigin:s,onDragTransitionEnd:a}=this.getProps(),l=this.constraints||{},u=Li(c=>{if(!qp(c,n,this.currentDirection))return;let d=l&&l[c]||{};s&&(d={min:0,max:0});const f=i?200:1e6,h=i?40:1e7,p={type:"inertia",velocity:r?t[c]:0,bounceStiffness:f,bounceDamping:h,timeConstant:750,restDelta:1,restSpeed:10,...o,...d};return this.startAxisValueAnimation(c,p)});return Promise.all(u).then(a)}startAxisValueAnimation(t,n){const r=this.getAxisMotionValue(t);return r.start(s3(t,r,0,n))}stopAnimation(){Li(t=>this.getAxisMotionValue(t).stop())}getAxisMotionValue(t){const n="_drag"+t.toUpperCase(),r=this.visualElement.getProps(),i=r[n];return i||this.visualElement.getValue(t,(r.initial?r.initial[t]:void 0)||0)}snapToCursor(t){Li(n=>{const{drag:r}=this.getProps();if(!qp(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:s,max:a}=i.layout.layoutBox[n];o.set(t[n]-At(s,a,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!pu(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};Li(s=>{const a=this.getAxisMotionValue(s);if(a){const l=a.get();i[s]=C1e({min:l,max:l},this.constraints[s])}});const{transformTemplate:o}=this.visualElement.getProps();this.visualElement.current.style.transform=o?o({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),Li(s=>{if(!qp(s,t,null))return;const a=this.getAxisMotionValue(s),{min:l,max:u}=this.constraints[s];a.set(At(l,u,i[s]))})}addListeners(){if(!this.visualElement.current)return;I1e.set(this.visualElement,this);const t=this.visualElement.current,n=Io(t,"pointerdown",l=>{const{drag:u,dragListener:c=!0}=this.getProps();u&&c&&this.start(l)}),r=()=>{const{dragConstraints:l}=this.getProps();pu(l)&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,o=i.addEventListener("measure",r);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),r();const s=Ao(window,"resize",()=>this.scalePositionWithinConstraints()),a=i.addEventListener("didUpdate",({delta:l,hasLayoutChanged:u})=>{this.isDragging&&u&&(Li(c=>{const d=this.getAxisMotionValue(c);d&&(this.originPoint[c]+=l[c].translate,d.set(d.get()+l[c].translate))}),this.visualElement.render())});return()=>{s(),n(),o(),a&&a()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:o=!1,dragElastic:s=z2,dragMomentum:a=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:o,dragElastic:s,dragMomentum:a}}}function qp(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function D1e(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class L1e extends ca{constructor(t){super(t),this.removeGroupControls=Yt,this.removeListeners=Yt,this.controls=new N1e(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||Yt}unmount(){this.removeGroupControls(),this.removeListeners()}}const MP=e=>(t,n)=>{e&&St.update(()=>e(t,n))};class $1e extends ca{constructor(){super(...arguments),this.removePointerDownListener=Yt}onPointerDown(t){this.session=new _L(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:MP(t),onStart:MP(n),onMove:r,onEnd:(o,s)=>{delete this.session,i&&St.update(()=>i(o,s))}}}mount(){this.removePointerDownListener=Io(this.node.current,"pointerdown",t=>this.onPointerDown(t))}update(){this.session&&this.session.updateHandlers(this.createPanHandlers())}unmount(){this.removePointerDownListener(),this.session&&this.session.end()}}function F1e(){const e=k.useContext(Lh);if(e===null)return[!0,null];const{isPresent:t,onExitComplete:n,register:r}=e,i=k.useId();return k.useEffect(()=>r(i),[]),!t&&n?[!1,()=>n&&n(i)]:[!0]}function j4e(){return B1e(k.useContext(Lh))}function B1e(e){return e===null?!0:e.isPresent}const Ag={hasAnimatedSinceResize:!0,hasEverUpdated:!1};function IP(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const od={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(me.test(e))e=parseFloat(e);else return e;const n=IP(e,t.target.x),r=IP(e,t.target.y);return`${n}% ${r}%`}},j1e={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=Xs.parse(e);if(i.length>5)return r;const o=Xs.createTransformer(e),s=typeof i[0]!="number"?1:0,a=n.x.scale*t.x,l=n.y.scale*t.y;i[0+s]/=a,i[1+s]/=l;const u=At(a,l,.5);return typeof i[2+s]=="number"&&(i[2+s]/=u),typeof i[3+s]=="number"&&(i[3+s]/=u),o(i)}};class V1e extends Xe.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;Kye(z1e),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),Ag.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,s=r.projection;return s&&(s.isPresent=o,i||t.layoutDependency!==n||n===void 0?s.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?s.promote():s.relegate()||St.postRender(()=>{const a=s.getStack();(!a||!a.members.length)&&this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),queueMicrotask(()=>{!t.currentAnimation&&t.isLead()&&this.safeToRemove()}))}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n&&n.group&&n.group.remove(i),r&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t&&t()}render(){return null}}function PL(e){const[t,n]=F1e(),r=k.useContext(qC);return Xe.createElement(V1e,{...e,layoutGroup:r,switchLayoutGroup:k.useContext(wD),isPresent:t,safeToRemove:n})}const z1e={borderRadius:{...od,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:od,borderTopRightRadius:od,borderBottomLeftRadius:od,borderBottomRightRadius:od,boxShadow:j1e},AL=["TopLeft","TopRight","BottomLeft","BottomRight"],U1e=AL.length,NP=e=>typeof e=="string"?parseFloat(e):e,DP=e=>typeof e=="number"||me.test(e);function G1e(e,t,n,r,i,o){i?(e.opacity=At(0,n.opacity!==void 0?n.opacity:1,H1e(r)),e.opacityExit=At(t.opacity!==void 0?t.opacity:1,0,q1e(r))):o&&(e.opacity=At(t.opacity!==void 0?t.opacity:1,n.opacity!==void 0?n.opacity:1,r));for(let s=0;srt?1:n(qf(e,t,r))}function $P(e,t){e.min=t.min,e.max=t.max}function Vr(e,t){$P(e.x,t.x),$P(e.y,t.y)}function FP(e,t,n,r,i){return e-=t,e=hy(e,1/n,r),i!==void 0&&(e=hy(e,1/i,r)),e}function W1e(e,t=0,n=1,r=.5,i,o=e,s=e){if(Ji.test(t)&&(t=parseFloat(t),t=At(s.min,s.max,t/100)-s.min),typeof t!="number")return;let a=At(o.min,o.max,r);e===o&&(a-=t),e.min=FP(e.min,t,n,a,i),e.max=FP(e.max,t,n,a,i)}function BP(e,t,[n,r,i],o,s){W1e(e,t[n],t[r],t[i],t.scale,o,s)}const K1e=["x","scaleX","originX"],X1e=["y","scaleY","originY"];function jP(e,t,n,r){BP(e.x,t,K1e,n?n.x:void 0,r?r.x:void 0),BP(e.y,t,X1e,n?n.y:void 0,r?r.y:void 0)}function VP(e){return e.translate===0&&e.scale===1}function RL(e){return VP(e.x)&&VP(e.y)}function H2(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function zP(e){return Mr(e.x)/Mr(e.y)}class Y1e{constructor(){this.members=[]}add(t){a3(this.members,t),t.scheduleRender()}remove(t){if(l3(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function UP(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:c}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),c&&(r+=`rotateY(${c}deg) `)}const s=e.x.scale*t.x,a=e.y.scale*t.y;return(s!==1||a!==1)&&(r+=`scale(${s}, ${a})`),r||"none"}const Q1e=(e,t)=>e.depth-t.depth;class Z1e{constructor(){this.children=[],this.isDirty=!1}add(t){a3(this.children,t),this.isDirty=!0}remove(t){l3(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(Q1e),this.isDirty=!1,this.children.forEach(t)}}function J1e(e,t){const n=performance.now(),r=({timestamp:i})=>{const o=i-n;o>=t&&(Uo(r),e(o-t))};return St.read(r,!0),()=>Uo(r)}function ebe(e){window.MotionDebug&&window.MotionDebug.record(e)}function tbe(e){return e instanceof SVGElement&&e.tagName!=="svg"}function nbe(e,t,n){const r=_r(e)?e:cc(e);return r.start(s3("",r,t,n)),r.animation}const GP=["","X","Y","Z"],HP=1e3;let rbe=0;const Ea={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function OL({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(s={},a=t==null?void 0:t()){this.id=rbe++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{Ea.totalNodes=Ea.resolvedTargetDeltas=Ea.recalculatedProjection=0,this.nodes.forEach(sbe),this.nodes.forEach(dbe),this.nodes.forEach(fbe),this.nodes.forEach(abe),ebe(Ea)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=s,this.root=a?a.root||a:this,this.path=a?[...a.path,a]:[],this.parent=a,this.depth=a?a.depth+1:0;for(let l=0;lthis.root.updateBlockedByResize=!1;e(s,()=>{this.root.updateBlockedByResize=!0,d&&d(),d=J1e(f,250),Ag.hasAnimatedSinceResize&&(Ag.hasAnimatedSinceResize=!1,this.nodes.forEach(WP))})}l&&this.root.registerSharedNode(l,this),this.options.animate!==!1&&c&&(l||u)&&this.addEventListener("didUpdate",({delta:d,hasLayoutChanged:f,hasRelativeTargetChanged:h,layout:p})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const m=this.options.transition||c.getDefaultTransition()||ybe,{onLayoutAnimationStart:S,onLayoutAnimationComplete:y}=c.getProps(),v=!this.targetLayout||!H2(this.targetLayout,p)||h,g=!f&&h;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||g||f&&(v||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(d,g);const b={...yL(m,"layout"),onPlay:S,onComplete:y};(c.shouldReduceMotion||this.options.layoutRoot)&&(b.delay=0,b.type=!1),this.startAnimation(b)}else f||WP(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=p})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const s=this.getStack();s&&s.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,Uo(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(hbe),this.animationId++)}getTransformTemplate(){const{visualElement:s}=this.options;return s&&s.getProps().transformTemplate}willUpdate(s=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let c=0;cthis.update()))}clearAllSnapshots(){this.nodes.forEach(lbe),this.sharedNodes.forEach(pbe)}scheduleUpdateProjection(){St.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){St.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l{const _=b/1e3;KP(d.x,s.x,_),KP(d.y,s.y,_),this.setTargetDelta(d),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(Nd(f,this.layout.layoutBox,this.relativeParent.layout.layoutBox),gbe(this.relativeTarget,this.relativeTargetOrigin,f,_),g&&H2(this.relativeTarget,g)&&(this.isProjectionDirty=!1),g||(g=qt()),Vr(g,this.relativeTarget)),m&&(this.animationValues=c,G1e(c,u,this.latestValues,_,v,y)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=_},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(s){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(Uo(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=St.update(()=>{Ag.hasAnimatedSinceResize=!0,this.currentAnimation=nbe(0,HP,{...s,onUpdate:a=>{this.mixTargetDelta(a),s.onUpdate&&s.onUpdate(a)},onComplete:()=>{s.onComplete&&s.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const s=this.getStack();s&&s.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(HP),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const s=this.getLead();let{targetWithTransforms:a,target:l,layout:u,latestValues:c}=s;if(!(!a||!l||!u)){if(this!==s&&this.layout&&u&&ML(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||qt();const d=Mr(this.layout.layoutBox.x);l.x.min=s.target.x.min,l.x.max=l.x.min+d;const f=Mr(this.layout.layoutBox.y);l.y.min=s.target.y.min,l.y.max=l.y.min+f}Vr(a,l),yu(a,c),Id(this.projectionDeltaWithTransform,this.layoutCorrected,a,c)}}registerSharedNode(s,a){this.sharedNodes.has(s)||this.sharedNodes.set(s,new Y1e),this.sharedNodes.get(s).add(a);const u=a.options.initialPromotionConfig;a.promote({transition:u?u.transition:void 0,preserveFollowOpacity:u&&u.shouldPreserveFollowOpacity?u.shouldPreserveFollowOpacity(a):void 0})}isLead(){const s=this.getStack();return s?s.lead===this:!0}getLead(){var s;const{layoutId:a}=this.options;return a?((s=this.getStack())===null||s===void 0?void 0:s.lead)||this:this}getPrevLead(){var s;const{layoutId:a}=this.options;return a?(s=this.getStack())===null||s===void 0?void 0:s.prevLead:void 0}getStack(){const{layoutId:s}=this.options;if(s)return this.root.sharedNodes.get(s)}promote({needsReset:s,transition:a,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),s&&(this.projectionDelta=void 0,this.needsReset=!0),a&&this.setOptions({transition:a})}relegate(){const s=this.getStack();return s?s.relegate(this):!1}resetRotation(){const{visualElement:s}=this.options;if(!s)return;let a=!1;const{latestValues:l}=s;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(a=!0),!a)return;const u={};for(let c=0;c{var a;return(a=s.currentAnimation)===null||a===void 0?void 0:a.stop()}),this.root.nodes.forEach(qP),this.root.sharedNodes.clear()}}}function ibe(e){e.updateLayout()}function obe(e){var t;const n=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&n&&e.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:i}=e.layout,{animationType:o}=e.options,s=n.source!==e.layout.source;o==="size"?Li(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Mr(f);f.min=r[d].min,f.max=f.min+h}):ML(o,n.layoutBox,r)&&Li(d=>{const f=s?n.measuredBox[d]:n.layoutBox[d],h=Mr(r[d]);f.max=f.min+h,e.relativeTarget&&!e.currentAnimation&&(e.isProjectionDirty=!0,e.relativeTarget[d].max=e.relativeTarget[d].min+h)});const a=mu();Id(a,r,n.layoutBox);const l=mu();s?Id(l,e.applyTransform(i,!0),n.measuredBox):Id(l,r,n.layoutBox);const u=!RL(a);let c=!1;if(!e.resumeFrom){const d=e.getClosestProjectingParent();if(d&&!d.resumeFrom){const{snapshot:f,layout:h}=d;if(f&&h){const p=qt();Nd(p,n.layoutBox,f.layoutBox);const m=qt();Nd(m,r,h.layoutBox),H2(p,m)||(c=!0),d.options.layoutRoot&&(e.relativeTarget=m,e.relativeTargetOrigin=p,e.relativeParent=d)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:l,layoutDelta:a,hasLayoutChanged:u,hasRelativeTargetChanged:c})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function sbe(e){Ea.totalNodes++,e.parent&&(e.isProjecting()||(e.isProjectionDirty=e.parent.isProjectionDirty),e.isSharedProjectionDirty||(e.isSharedProjectionDirty=!!(e.isProjectionDirty||e.parent.isProjectionDirty||e.parent.isSharedProjectionDirty)),e.isTransformDirty||(e.isTransformDirty=e.parent.isTransformDirty))}function abe(e){e.isProjectionDirty=e.isSharedProjectionDirty=e.isTransformDirty=!1}function lbe(e){e.clearSnapshot()}function qP(e){e.clearMeasurements()}function ube(e){e.isLayoutDirty=!1}function cbe(e){const{visualElement:t}=e.options;t&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function 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this.opts.query=="string"&&(this.opts.query=KSe(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=JL,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port},this.opts.transportOptions[t]);return new o_e[t](r)}open(){let t;if(this.opts.rememberUpgrade&&Jl.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch{this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;Jl.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",d=>{if(!r)if(d.type==="pong"&&d.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;Jl.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(c(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const f=new Error("probe error");f.transport=n.name,this.emitReserved("upgradeError",f)}}))};function o(){r||(r=!0,c(),n.close(),n=null)}const s=d=>{const f=new Error("probe error: "+d);f.transport=n.name,o(),this.emitReserved("upgradeError",f)};function a(){s("transport closed")}function l(){s("socket closed")}function u(d){n&&d.name!==n.name&&o()}const 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n=[];let r=0;const i=t.length;for(;rtypeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,s$=Object.prototype.toString,h_e=typeof Blob=="function"||typeof Blob<"u"&&s$.call(Blob)==="[object BlobConstructor]",p_e=typeof File=="function"||typeof File<"u"&&s$.call(File)==="[object FileConstructor]";function y3(e){return d_e&&(e instanceof ArrayBuffer||f_e(e))||h_e&&e instanceof Blob||p_e&&e instanceof File}function Og(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n=0&&e.num{delete this.acks[t];for(let s=0;s{this.io.clearTimeoutFn(o),n.apply(this,[null,...s])}}emitWithAck(t,...n){const r=this.flags.timeout!==void 0||this._opts.ackTimeout!==void 0;return new Promise((i,o)=>{n.push((s,a)=>r?s?o(s):i(a):i(s)),this.emit(t,...n)})}_addToQueue(t){let n;typeof t[t.length-1]=="function"&&(n=t.pop());const r={id:this._queueSeq++,tryCount:0,pending:!1,args:t,flags:Object.assign({fromQueue:!0},this.flags)};t.push((i,...o)=>r!==this._queue[0]?void 0:(i!==null?r.tryCount>this._opts.retries&&(this._queue.shift(),n&&n(i)):(this._queue.shift(),n&&n(null,...o)),r.pending=!1,this._drainQueue())),this._queue.push(r),this._drainQueue()}_drainQueue(t=!1){if(!this.connected||this._queue.length===0)return;const n=this._queue[0];n.pending&&!t||(n.pending=!0,n.tryCount++,this.flags=n.flags,this.emit.apply(this,n.args))}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this._sendConnectPacket(t)}):this._sendConnectPacket(this.auth)}_sendConnectPacket(t){this.packet({type:Ne.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},t):t})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case 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0&&(t=Uh);const[n]=e,r=C$(n?or(n):null),[i,o]=k.useReducer(a,CS),s=h1({callback:o});return e.length>0&&i===CS&&o(),io(()=>{e.length?e.forEach(l=>s==null?void 0:s.observe(l)):(s==null||s.disconnect(),o())},[e]),i;function a(){return e.length?e.map(l=>g$(l)?r:new x3(t(l),l)):CS}}function T$(e){if(!e)return null;if(e.children.length>1)return e;const t=e.children[0];return Vh(t)?t:e}function k2e(e){let{measure:t}=e;const[n,r]=k.useState(null),i=k.useCallback(u=>{for(const{target:c}of u)if(Vh(c)){r(d=>{const f=t(c);return d?{...d,width:f.width,height:f.height}:f});break}},[t]),o=h1({callback:i}),s=k.useCallback(u=>{const c=T$(u);o==null||o.disconnect(),c&&(o==null||o.observe(c)),r(c?t(c):null)},[t,o]),[a,l]=my(s);return k.useMemo(()=>({nodeRef:a,rect:n,setRef:l}),[n,a,l])}const R2e=[{sensor:_$,options:{}},{sensor:S$,options:{}}],O2e={current:{}},Ig={draggable:{measure:_8},droppable:{measure:_8,strategy:Zf.WhileDragging,frequency:lw.Optimized},dragOverlay:{measure:Uh}};class $d extends 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tn.UnregisterDroppable:{const{id:n,key:r}=t,i=e.droppable.containers.get(n);if(!i||r!==i.key)return e;const o=new $d(e.droppable.containers);return o.delete(n),{...e,droppable:{...e.droppable,containers:o}}}default:return e}}function D2e(e){let{disabled:t}=e;const{active:n,activatorEvent:r,draggableNodes:i}=k.useContext(Gh),o=yy(r),s=yy(n==null?void 0:n.id);return k.useEffect(()=>{if(!t&&!r&&o&&s!=null){if(!_3(o)||document.activeElement===o.target)return;const a=i.get(s);if(!a)return;const{activatorNode:l,node:u}=a;if(!l.current&&!u.current)return;requestAnimationFrame(()=>{for(const c of[l.current,u.current]){if(!c)continue;const d=P_e(c);if(d){d.focus();break}}})}},[r,t,i,s,o]),null}function A$(e,t){let{transform:n,...r}=t;return e!=null&&e.length?e.reduce((i,o)=>o({transform:i,...r}),n):n}function L2e(e){return k.useMemo(()=>({draggable:{...Ig.draggable,...e==null?void 0:e.draggable},droppable:{...Ig.droppable,...e==null?void 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J=E8(Y,U.draggable.measure,Q),ne=E8(Y?Y.parentElement:null),te=k.useRef({activatorEvent:null,active:null,activeNode:Y,collisionRect:null,collisions:null,droppableRects:G,draggableNodes:T,draggingNode:null,draggingNodeRect:null,droppableContainers:E,over:null,scrollableAncestors:[],scrollAdjustedTranslate:null}),xe=E.getNodeFor((n=te.current.over)==null?void 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Sn({active:C.current,activeNode:Mn,event:Gn,options:an,context:te,onStart(Hn){const _n=C.current;if(_n==null)return;const co=T.get(_n);if(!co)return;const{onDragStart:Zo}=D.current,Jo={active:{id:_n,data:co.data,rect:$}};zi.unstable_batchedUpdates(()=>{Zo==null||Zo(Jo),_(hs.Initializing),y({type:tn.DragStart,initialCoordinates:Hn,active:_n}),v({type:"onDragStart",event:Jo})})},onMove(Hn){y({type:tn.DragMove,coordinates:Hn})},onEnd:Ri(tn.DragEnd),onCancel:Ri(tn.DragCancel)});zi.unstable_batchedUpdates(()=>{M(ar),O(Ct.nativeEvent)});function Ri(Hn){return async function(){const{active:co,collisions:Zo,over:Jo,scrollAdjustedTranslate:Al}=te.current;let fo=null;if(co&&Al){const{cancelDrop:qn}=D.current;fo={activatorEvent:Gn,active:co,collisions:Zo,delta:Al,over:Jo},Hn===tn.DragEnd&&typeof qn=="function"&&await Promise.resolve(qn(fo))&&(Hn=tn.DragCancel)}C.current=null,zi.unstable_batchedUpdates(()=>{y({type:Hn}),_(hs.Uninitialized),wr(null),M(null),O(null);const qn=Hn===tn.DragEnd?"onDragEnd":"onDragCancel";if(fo){const ha=D.current[qn];ha==null||ha(fo),v({type:qn,event:fo})}})}}},[T]),bn=k.useCallback((Ct,nt)=>(Sn,an)=>{const Mn=Sn.nativeEvent,Gn=T.get(an);if(C.current!==null||!Gn||Mn.dndKit||Mn.defaultPrevented)return;const ar={active:Gn};Ct(Sn,nt.options,ar)===!0&&(Mn.dndKit={capturedBy:nt.sensor},C.current=an,uo(Sn,nt))},[T,uo]),ii=v2e(c,bn);E2e(c),io(()=>{J&&b===hs.Initializing&&_(hs.Initialized)},[J,b]),k.useEffect(()=>{const{onDragMove:Ct}=D.current,{active:nt,activatorEvent:Sn,collisions:an,over:Mn}=te.current;if(!nt||!Sn)return;const Gn={active:nt,activatorEvent:Sn,collisions:an,delta:{x:On.x,y:On.y},over:Mn};zi.unstable_batchedUpdates(()=>{Ct==null||Ct(Gn),v({type:"onDragMove",event:Gn})})},[On.x,On.y]),k.useEffect(()=>{const{active:Ct,activatorEvent:nt,collisions:Sn,droppableContainers:an,scrollAdjustedTranslate:Mn}=te.current;if(!Ct||C.current==null||!nt||!Mn)return;const{onDragOver:Gn}=D.current,ar=an.get(Gt),Ri=ar&&ar.rect.current?{id:ar.id,rect:ar.rect.current,data:ar.data,disabled:ar.disabled}:null,Hn={active:Ct,activatorEvent:nt,collisions:Sn,delta:{x:Mn.x,y:Mn.y},over:Ri};zi.unstable_batchedUpdates(()=>{wr(Ri),Gn==null||Gn(Hn),v({type:"onDragOver",event:Hn})})},[Gt]),io(()=>{te.current={activatorEvent:N,active:I,activeNode:Y,collisionRect:vn,collisions:Un,droppableRects:G,draggableNodes:T,draggingNode:ce,draggingNodeRect:De,droppableContainers:E,over:xt,scrollableAncestors:Mt,scrollAdjustedTranslate:On},$.current={initial:De,translated:vn}},[I,Y,Un,vn,T,ce,De,G,E,xt,Mt,On]),p2e({...H,delta:P,draggingRect:vn,pointerCoordinates:Ut,scrollableAncestors:Mt,scrollableAncestorRects:ut});const da=k.useMemo(()=>({active:I,activeNode:Y,activeNodeRect:J,activatorEvent:N,collisions:Un,containerNodeRect:ne,dragOverlay:ve,draggableNodes:T,droppableContainers:E,droppableRects:G,over:xt,measureDroppableContainers:W,scrollableAncestors:Mt,scrollableAncestorRects:ut,measuringConfiguration:U,measuringScheduled:X,windowRect:yn}),[I,Y,J,N,Un,ne,ve,T,E,G,xt,W,Mt,ut,U,X,yn]),ki=k.useMemo(()=>({activatorEvent:N,activators:ii,active:I,activeNodeRect:J,ariaDescribedById:{draggable:L},dispatch:y,draggableNodes:T,over:xt,measureDroppableContainers:W}),[N,ii,I,J,y,L,T,xt,W]);return Xe.createElement(u$.Provider,{value:g},Xe.createElement(Gh.Provider,{value:ki},Xe.createElement(P$.Provider,{value:da},Xe.createElement(p1.Provider,{value:ri},u)),Xe.createElement(D2e,{disabled:(a==null?void 0:a.restoreFocus)===!1})),Xe.createElement($_e,{...a,hiddenTextDescribedById:L}));function fa(){const Ct=(R==null?void 0:R.autoScrollEnabled)===!1,nt=typeof l=="object"?l.enabled===!1:l===!1,Sn=w&&!Ct&&!nt;return typeof l=="object"?{...l,enabled:Sn}:{enabled:Sn}}}),B2e=k.createContext(null),k8="button",j2e="Droppable";function V2e(e){let{id:t,data:n,disabled:r=!1,attributes:i}=e;const o=f1(j2e),{activators:s,activatorEvent:a,active:l,activeNodeRect:u,ariaDescribedById:c,draggableNodes:d,over:f}=k.useContext(Gh),{role:h=k8,roleDescription:p="draggable",tabIndex:m=0}=i??{},S=(l==null?void 0:l.id)===t,y=k.useContext(S?p1:B2e),[v,g]=my(),[b,_]=my(),w=P2e(s,t),x=Xf(n);io(()=>(d.set(t,{id:t,key:o,node:v,activatorNode:b,data:x}),()=>{const P=d.get(t);P&&P.key===o&&d.delete(t)}),[d,t]);const T=k.useMemo(()=>({role:h,tabIndex:m,"aria-disabled":r,"aria-pressed":S&&h===k8?!0:void 0,"aria-roledescription":p,"aria-describedby":c.draggable}),[r,h,m,S,p,c.draggable]);return{active:l,activatorEvent:a,activeNodeRect:u,attributes:T,isDragging:S,listeners:r?void 0:w,node:v,over:f,setNodeRef:g,setActivatorNodeRef:_,transform:y}}function z2e(){return k.useContext(P$)}const U2e="Droppable",G2e={timeout:25};function H2e(e){let{data:t,disabled:n=!1,id:r,resizeObserverConfig:i}=e;const o=f1(U2e),{active:s,dispatch:a,over:l,measureDroppableContainers:u}=k.useContext(Gh),c=k.useRef({disabled:n}),d=k.useRef(!1),f=k.useRef(null),h=k.useRef(null),{disabled:p,updateMeasurementsFor:m,timeout:S}={...G2e,...i},y=Xf(m??r),v=k.useCallback(()=>{if(!d.current){d.current=!0;return}h.current!=null&&clearTimeout(h.current),h.current=setTimeout(()=>{u(Array.isArray(y.current)?y.current:[y.current]),h.current=null},S)},[S]),g=h1({callback:v,disabled:p||!s}),b=k.useCallback((T,P)=>{g&&(P&&(g.unobserve(P),d.current=!1),T&&g.observe(T))},[g]),[_,w]=my(b),x=Xf(t);return k.useEffect(()=>{!g||!_.current||(g.disconnect(),d.current=!1,g.observe(_.current))},[_,g]),io(()=>(a({type:tn.RegisterDroppable,element:{id:r,key:o,disabled:n,node:_,rect:f,data:x}}),()=>a({type:tn.UnregisterDroppable,key:o,id:r})),[r]),k.useEffect(()=>{n!==c.current.disabled&&(a({type:tn.SetDroppableDisabled,id:r,key:o,disabled:n}),c.current.disabled=n)},[r,o,n,a]),{active:s,rect:f,isOver:(l==null?void 0:l.id)===r,node:_,over:l,setNodeRef:w}}function q2e(e){let{animation:t,children:n}=e;const[r,i]=k.useState(null),[o,s]=k.useState(null),a=yy(n);return!n&&!r&&a&&i(a),io(()=>{if(!o)return;const l=r==null?void 0:r.key,u=r==null?void 0:r.props.id;if(l==null||u==null){i(null);return}Promise.resolve(t(u,o)).then(()=>{i(null)})},[t,r,o]),Xe.createElement(Xe.Fragment,null,n,r?k.cloneElement(r,{ref:s}):null)}const W2e={x:0,y:0,scaleX:1,scaleY:1};function K2e(e){let{children:t}=e;return Xe.createElement(Gh.Provider,{value:E$},Xe.createElement(p1.Provider,{value:W2e},t))}const X2e={position:"fixed",touchAction:"none"},Y2e=e=>_3(e)?"transform 250ms ease":void 0,Q2e=k.forwardRef((e,t)=>{let{as:n,activatorEvent:r,adjustScale:i,children:o,className:s,rect:a,style:l,transform:u,transition:c=Y2e}=e;if(!a)return null;const d=i?u:{...u,scaleX:1,scaleY:1},f={...X2e,width:a.width,height:a.height,top:a.top,left:a.left,transform:Qf.Transform.toString(d),transformOrigin:i&&r?j_e(r,a):void 0,transition:typeof c=="function"?c(r):c,...l};return Xe.createElement(n,{className:s,style:f,ref:t},o)}),Z2e=e=>t=>{let{active:n,dragOverlay:r}=t;const i={},{styles:o,className:s}=e;if(o!=null&&o.active)for(const[a,l]of Object.entries(o.active))l!==void 0&&(i[a]=n.node.style.getPropertyValue(a),n.node.style.setProperty(a,l));if(o!=null&&o.dragOverlay)for(const[a,l]of Object.entries(o.dragOverlay))l!==void 0&&r.node.style.setProperty(a,l);return s!=null&&s.active&&n.node.classList.add(s.active),s!=null&&s.dragOverlay&&r.node.classList.add(s.dragOverlay),function(){for(const[l,u]of 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r=[];for(const i of n){if(i.status==="aborted")return je;i.status==="dirty"&&t.dirty(),r.push(i.value)}return{status:t.value,value:r}}static async mergeObjectAsync(t,n){const r=[];for(const i of n)r.push({key:await i.key,value:await i.value});return Wr.mergeObjectSync(t,r)}static mergeObjectSync(t,n){const r={};for(const i of n){const{key:o,value:s}=i;if(o.status==="aborted"||s.status==="aborted")return je;o.status==="dirty"&&t.dirty(),s.status==="dirty"&&t.dirty(),(typeof s.value<"u"||i.alwaysSet)&&(r[o.value]=s.value)}return{status:t.value,value:r}}}const je=Object.freeze({status:"aborted"}),jD=e=>({status:"dirty",value:e}),ui=e=>({status:"valid",value:e}),T3=e=>e.status==="aborted",E3=e=>e.status==="dirty",p1=e=>e.status==="valid",g1=e=>typeof Promise<"u"&&e instanceof Promise;var Le;(function(e){e.errToObj=t=>typeof t=="string"?{message:t}:t||{},e.toString=t=>typeof t=="string"?t:t==null?void 0:t.message})(Le||(Le={}));class 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tt{constructor(t){this.spa=this.safeParseAsync,this._def=t,this.parse=this.parse.bind(this),this.safeParse=this.safeParse.bind(this),this.parseAsync=this.parseAsync.bind(this),this.safeParseAsync=this.safeParseAsync.bind(this),this.spa=this.spa.bind(this),this.refine=this.refine.bind(this),this.refinement=this.refinement.bind(this),this.superRefine=this.superRefine.bind(this),this.optional=this.optional.bind(this),this.nullable=this.nullable.bind(this),this.nullish=this.nullish.bind(this),this.array=this.array.bind(this),this.promise=this.promise.bind(this),this.or=this.or.bind(this),this.and=this.and.bind(this),this.transform=this.transform.bind(this),this.brand=this.brand.bind(this),this.default=this.default.bind(this),this.catch=this.catch.bind(this),this.describe=this.describe.bind(this),this.pipe=this.pipe.bind(this),this.isNullable=this.isNullable.bind(this),this.isOptional=this.isOptional.bind(this)}get description(){return this._def.description}_getType(t){return 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RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}(([+-]\\d{2}(:?\\d{2})?)|Z)$`):new RegExp(`^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{${e.precision}}Z$`):e.precision===0?e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"):e.offset?new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?(([+-]\\d{2}(:?\\d{2})?)|Z)$"):new RegExp("^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?Z$");function Sle(e,t){return!!((t==="v4"||!t)&&vle.test(e)||(t==="v6"||!t)&&_le.test(e))}class Ho extends tt{constructor(){super(...arguments),this._regex=(t,n,r)=>this.refinement(i=>t.test(i),{validation:n,code:me.invalid_string,...Le.errToObj(r)}),this.nonempty=t=>this.min(1,Le.errToObj(t)),this.trim=()=>new Ho({...this._def,checks:[...this._def.checks,{kind:"trim"}]}),this.toLowerCase=()=>new Ho({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]}),this.toUpperCase=()=>new Ho({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}_parse(t){if(this._def.coerce&&(t.data=String(t.data)),this._getType(t)!==xe.string){const o=this._getOrReturnCtx(t);return Te(o,{code:me.invalid_type,expected:xe.string,received:o.parsedType}),je}const r=new Wr;let i;for(const o of this._def.checks)if(o.kind==="min")t.data.lengtho.value&&(i=this._getOrReturnCtx(t,i),Te(i,{code:me.too_big,maximum:o.value,type:"string",inclusive:!0,exact:!1,message:o.message}),r.dirty());else if(o.kind==="length"){const s=t.data.length>o.value,a=t.data.length"u"?null:t==null?void 0:t.precision,offset:(n=t==null?void 0:t.offset)!==null&&n!==void 0?n:!1,...Le.errToObj(t==null?void 0:t.message)})}regex(t,n){return this._addCheck({kind:"regex",regex:t,...Le.errToObj(n)})}includes(t,n){return this._addCheck({kind:"includes",value:t,position:n==null?void 0:n.position,...Le.errToObj(n==null?void 0:n.message)})}startsWith(t,n){return this._addCheck({kind:"startsWith",value:t,...Le.errToObj(n)})}endsWith(t,n){return this._addCheck({kind:"endsWith",value:t,...Le.errToObj(n)})}min(t,n){return this._addCheck({kind:"min",value:t,...Le.errToObj(n)})}max(t,n){return this._addCheck({kind:"max",value:t,...Le.errToObj(n)})}length(t,n){return this._addCheck({kind:"length",value:t,...Le.errToObj(n)})}get isDatetime(){return!!this._def.checks.find(t=>t.kind==="datetime")}get isEmail(){return!!this._def.checks.find(t=>t.kind==="email")}get isURL(){return!!this._def.checks.find(t=>t.kind==="url")}get isEmoji(){return!!this._def.checks.find(t=>t.kind==="emoji")}get isUUID(){return!!this._def.checks.find(t=>t.kind==="uuid")}get isCUID(){return!!this._def.checks.find(t=>t.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(t=>t.kind==="cuid2")}get isULID(){return!!this._def.checks.find(t=>t.kind==="ulid")}get isIP(){return!!this._def.checks.find(t=>t.kind==="ip")}get minLength(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxLength(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value{var t;return new Ho({checks:[],typeName:Ue.ZodString,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Qe(e)})};function wle(e,t){const n=(e.toString().split(".")[1]||"").length,r=(t.toString().split(".")[1]||"").length,i=n>r?n:r,o=parseInt(e.toFixed(i).replace(".","")),s=parseInt(t.toFixed(i).replace(".",""));return o%s/Math.pow(10,i)}class zl extends tt{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(t){if(this._def.coerce&&(t.data=Number(t.data)),this._getType(t)!==xe.number){const o=this._getOrReturnCtx(t);return Te(o,{code:me.invalid_type,expected:xe.number,received:o.parsedType}),je}let r;const i=new Wr;for(const o of this._def.checks)o.kind==="int"?wt.isInteger(t.data)||(r=this._getOrReturnCtx(t,r),Te(r,{code:me.invalid_type,expected:"integer",received:"float",message:o.message}),i.dirty()):o.kind==="min"?(o.inclusive?t.datao.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),Te(r,{code:me.too_big,maximum:o.value,type:"number",inclusive:o.inclusive,exact:!1,message:o.message}),i.dirty()):o.kind==="multipleOf"?wle(t.data,o.value)!==0&&(r=this._getOrReturnCtx(t,r),Te(r,{code:me.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):o.kind==="finite"?Number.isFinite(t.data)||(r=this._getOrReturnCtx(t,r),Te(r,{code:me.not_finite,message:o.message}),i.dirty()):wt.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,Le.toString(n))}gt(t,n){return this.setLimit("min",t,!1,Le.toString(n))}lte(t,n){return this.setLimit("max",t,!0,Le.toString(n))}lt(t,n){return this.setLimit("max",t,!1,Le.toString(n))}setLimit(t,n,r,i){return new zl({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:Le.toString(i)}]})}_addCheck(t){return new zl({...this._def,checks:[...this._def.checks,t]})}int(t){return this._addCheck({kind:"int",message:Le.toString(t)})}positive(t){return this._addCheck({kind:"min",value:0,inclusive:!1,message:Le.toString(t)})}negative(t){return this._addCheck({kind:"max",value:0,inclusive:!1,message:Le.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:0,inclusive:!0,message:Le.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:0,inclusive:!0,message:Le.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:Le.toString(n)})}finite(t){return this._addCheck({kind:"finite",message:Le.toString(t)})}safe(t){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:Le.toString(t)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:Le.toString(t)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.valuet.kind==="int"||t.kind==="multipleOf"&&wt.isInteger(t.value))}get isFinite(){let t=null,n=null;for(const r of this._def.checks){if(r.kind==="finite"||r.kind==="int"||r.kind==="multipleOf")return!0;r.kind==="min"?(n===null||r.value>n)&&(n=r.value):r.kind==="max"&&(t===null||r.valuenew zl({checks:[],typeName:Ue.ZodNumber,coerce:(e==null?void 0:e.coerce)||!1,...Qe(e)});class Vl extends tt{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(t){if(this._def.coerce&&(t.data=BigInt(t.data)),this._getType(t)!==xe.bigint){const o=this._getOrReturnCtx(t);return Te(o,{code:me.invalid_type,expected:xe.bigint,received:o.parsedType}),je}let r;const i=new Wr;for(const o of this._def.checks)o.kind==="min"?(o.inclusive?t.datao.value:t.data>=o.value)&&(r=this._getOrReturnCtx(t,r),Te(r,{code:me.too_big,type:"bigint",maximum:o.value,inclusive:o.inclusive,message:o.message}),i.dirty()):o.kind==="multipleOf"?t.data%o.value!==BigInt(0)&&(r=this._getOrReturnCtx(t,r),Te(r,{code:me.not_multiple_of,multipleOf:o.value,message:o.message}),i.dirty()):wt.assertNever(o);return{status:i.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,Le.toString(n))}gt(t,n){return this.setLimit("min",t,!1,Le.toString(n))}lte(t,n){return this.setLimit("max",t,!0,Le.toString(n))}lt(t,n){return this.setLimit("max",t,!1,Le.toString(n))}setLimit(t,n,r,i){return new Vl({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:Le.toString(i)}]})}_addCheck(t){return new Vl({...this._def,checks:[...this._def.checks,t]})}positive(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:Le.toString(t)})}negative(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:Le.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:Le.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:Le.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:Le.toString(n)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value{var t;return new Vl({checks:[],typeName:Ue.ZodBigInt,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Qe(e)})};class dg extends tt{_parse(t){if(this._def.coerce&&(t.data=!!t.data),this._getType(t)!==xe.boolean){const r=this._getOrReturnCtx(t);return Te(r,{code:me.invalid_type,expected:xe.boolean,received:r.parsedType}),je}return ui(t.data)}}dg.create=e=>new dg({typeName:Ue.ZodBoolean,coerce:(e==null?void 0:e.coerce)||!1,...Qe(e)});class nc extends tt{_parse(t){if(this._def.coerce&&(t.data=new Date(t.data)),this._getType(t)!==xe.date){const o=this._getOrReturnCtx(t);return Te(o,{code:me.invalid_type,expected:xe.date,received:o.parsedType}),je}if(isNaN(t.data.getTime())){const o=this._getOrReturnCtx(t);return Te(o,{code:me.invalid_date}),je}const r=new Wr;let i;for(const o of this._def.checks)o.kind==="min"?t.data.getTime()o.value&&(i=this._getOrReturnCtx(t,i),Te(i,{code:me.too_big,message:o.message,inclusive:!0,exact:!1,maximum:o.value,type:"date"}),r.dirty()):wt.assertNever(o);return{status:r.value,value:new Date(t.data.getTime())}}_addCheck(t){return new nc({...this._def,checks:[...this._def.checks,t]})}min(t,n){return this._addCheck({kind:"min",value:t.getTime(),message:Le.toString(n)})}max(t,n){return this._addCheck({kind:"max",value:t.getTime(),message:Le.toString(n)})}get minDate(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t!=null?new Date(t):null}get maxDate(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.valuenew nc({checks:[],coerce:(e==null?void 0:e.coerce)||!1,typeName:Ue.ZodDate,...Qe(e)});class m1 extends tt{_parse(t){if(this._getType(t)!==xe.symbol){const r=this._getOrReturnCtx(t);return Te(r,{code:me.invalid_type,expected:xe.symbol,received:r.parsedType}),je}return ui(t.data)}}m1.create=e=>new m1({typeName:Ue.ZodSymbol,...Qe(e)});class fg extends tt{_parse(t){if(this._getType(t)!==xe.undefined){const r=this._getOrReturnCtx(t);return Te(r,{code:me.invalid_type,expected:xe.undefined,received:r.parsedType}),je}return ui(t.data)}}fg.create=e=>new fg({typeName:Ue.ZodUndefined,...Qe(e)});class hg extends tt{_parse(t){if(this._getType(t)!==xe.null){const r=this._getOrReturnCtx(t);return Te(r,{code:me.invalid_type,expected:xe.null,received:r.parsedType}),je}return ui(t.data)}}hg.create=e=>new hg({typeName:Ue.ZodNull,...Qe(e)});class wf extends tt{constructor(){super(...arguments),this._any=!0}_parse(t){return ui(t.data)}}wf.create=e=>new wf({typeName:Ue.ZodAny,...Qe(e)});class Gu extends tt{constructor(){super(...arguments),this._unknown=!0}_parse(t){return ui(t.data)}}Gu.create=e=>new Gu({typeName:Ue.ZodUnknown,...Qe(e)});class Oa extends tt{_parse(t){const n=this._getOrReturnCtx(t);return Te(n,{code:me.invalid_type,expected:xe.never,received:n.parsedType}),je}}Oa.create=e=>new Oa({typeName:Ue.ZodNever,...Qe(e)});class y1 extends tt{_parse(t){if(this._getType(t)!==xe.undefined){const r=this._getOrReturnCtx(t);return Te(r,{code:me.invalid_type,expected:xe.void,received:r.parsedType}),je}return ui(t.data)}}y1.create=e=>new y1({typeName:Ue.ZodVoid,...Qe(e)});class Yo extends tt{_parse(t){const{ctx:n,status:r}=this._processInputParams(t),i=this._def;if(n.parsedType!==xe.array)return Te(n,{code:me.invalid_type,expected:xe.array,received:n.parsedType}),je;if(i.exactLength!==null){const s=n.data.length>i.exactLength.value,a=n.data.lengthi.maxLength.value&&(Te(n,{code:me.too_big,maximum:i.maxLength.value,type:"array",inclusive:!0,exact:!1,message:i.maxLength.message}),r.dirty()),n.common.async)return Promise.all([...n.data].map((s,a)=>i.type._parseAsync(new Ms(n,s,n.path,a)))).then(s=>Wr.mergeArray(r,s));const o=[...n.data].map((s,a)=>i.type._parseSync(new Ms(n,s,n.path,a)));return Wr.mergeArray(r,o)}get element(){return this._def.type}min(t,n){return new Yo({...this._def,minLength:{value:t,message:Le.toString(n)}})}max(t,n){return new Yo({...this._def,maxLength:{value:t,message:Le.toString(n)}})}length(t,n){return new Yo({...this._def,exactLength:{value:t,message:Le.toString(n)}})}nonempty(t){return this.min(1,t)}}Yo.create=(e,t)=>new Yo({type:e,minLength:null,maxLength:null,exactLength:null,typeName:Ue.ZodArray,...Qe(t)});function yd(e){if(e instanceof gn){const t={};for(const n in e.shape){const r=e.shape[n];t[n]=_a.create(yd(r))}return new gn({...e._def,shape:()=>t})}else return e instanceof Yo?new Yo({...e._def,type:yd(e.element)}):e instanceof _a?_a.create(yd(e.unwrap())):e instanceof ic?ic.create(yd(e.unwrap())):e instanceof Ns?Ns.create(e.items.map(t=>yd(t))):e}class gn extends tt{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const t=this._def.shape(),n=wt.objectKeys(t);return this._cached={shape:t,keys:n}}_parse(t){if(this._getType(t)!==xe.object){const u=this._getOrReturnCtx(t);return Te(u,{code:me.invalid_type,expected:xe.object,received:u.parsedType}),je}const{status:r,ctx:i}=this._processInputParams(t),{shape:o,keys:s}=this._getCached(),a=[];if(!(this._def.catchall instanceof Oa&&this._def.unknownKeys==="strip"))for(const u in i.data)s.includes(u)||a.push(u);const l=[];for(const u of s){const d=o[u],f=i.data[u];l.push({key:{status:"valid",value:u},value:d._parse(new Ms(i,f,i.path,u)),alwaysSet:u in i.data})}if(this._def.catchall instanceof Oa){const u=this._def.unknownKeys;if(u==="passthrough")for(const d of a)l.push({key:{status:"valid",value:d},value:{status:"valid",value:i.data[d]}});else if(u==="strict")a.length>0&&(Te(i,{code:me.unrecognized_keys,keys:a}),r.dirty());else if(u!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const u=this._def.catchall;for(const d of a){const f=i.data[d];l.push({key:{status:"valid",value:d},value:u._parse(new Ms(i,f,i.path,d)),alwaysSet:d in i.data})}}return i.common.async?Promise.resolve().then(async()=>{const u=[];for(const d of l){const f=await d.key;u.push({key:f,value:await d.value,alwaysSet:d.alwaysSet})}return u}).then(u=>Wr.mergeObjectSync(r,u)):Wr.mergeObjectSync(r,l)}get shape(){return this._def.shape()}strict(t){return Le.errToObj,new gn({...this._def,unknownKeys:"strict",...t!==void 0?{errorMap:(n,r)=>{var i,o,s,a;const l=(s=(o=(i=this._def).errorMap)===null||o===void 0?void 0:o.call(i,n,r).message)!==null&&s!==void 0?s:r.defaultError;return n.code==="unrecognized_keys"?{message:(a=Le.errToObj(t).message)!==null&&a!==void 0?a:l}:{message:l}}}:{}})}strip(){return new gn({...this._def,unknownKeys:"strip"})}passthrough(){return new gn({...this._def,unknownKeys:"passthrough"})}extend(t){return new gn({...this._def,shape:()=>({...this._def.shape(),...t})})}merge(t){return new gn({unknownKeys:t._def.unknownKeys,catchall:t._def.catchall,shape:()=>({...this._def.shape(),...t._def.shape()}),typeName:Ue.ZodObject})}setKey(t,n){return this.augment({[t]:n})}catchall(t){return new gn({...this._def,catchall:t})}pick(t){const n={};return wt.objectKeys(t).forEach(r=>{t[r]&&this.shape[r]&&(n[r]=this.shape[r])}),new gn({...this._def,shape:()=>n})}omit(t){const n={};return wt.objectKeys(this.shape).forEach(r=>{t[r]||(n[r]=this.shape[r])}),new gn({...this._def,shape:()=>n})}deepPartial(){return yd(this)}partial(t){const n={};return wt.objectKeys(this.shape).forEach(r=>{const i=this.shape[r];t&&!t[r]?n[r]=i:n[r]=i.optional()}),new gn({...this._def,shape:()=>n})}required(t){const n={};return wt.objectKeys(this.shape).forEach(r=>{if(t&&!t[r])n[r]=this.shape[r];else{let o=this.shape[r];for(;o instanceof _a;)o=o._def.innerType;n[r]=o}}),new gn({...this._def,shape:()=>n})}keyof(){return GD(wt.objectKeys(this.shape))}}gn.create=(e,t)=>new gn({shape:()=>e,unknownKeys:"strip",catchall:Oa.create(),typeName:Ue.ZodObject,...Qe(t)});gn.strictCreate=(e,t)=>new gn({shape:()=>e,unknownKeys:"strict",catchall:Oa.create(),typeName:Ue.ZodObject,...Qe(t)});gn.lazycreate=(e,t)=>new gn({shape:e,unknownKeys:"strip",catchall:Oa.create(),typeName:Ue.ZodObject,...Qe(t)});class pg extends tt{_parse(t){const{ctx:n}=this._processInputParams(t),r=this._def.options;function i(o){for(const a of o)if(a.result.status==="valid")return a.result;for(const a of o)if(a.result.status==="dirty")return n.common.issues.push(...a.ctx.common.issues),a.result;const s=o.map(a=>new Xo(a.ctx.common.issues));return Te(n,{code:me.invalid_union,unionErrors:s}),je}if(n.common.async)return Promise.all(r.map(async o=>{const s={...n,common:{...n.common,issues:[]},parent:null};return{result:await o._parseAsync({data:n.data,path:n.path,parent:s}),ctx:s}})).then(i);{let o;const s=[];for(const l of r){const u={...n,common:{...n.common,issues:[]},parent:null},d=l._parseSync({data:n.data,path:n.path,parent:u});if(d.status==="valid")return d;d.status==="dirty"&&!o&&(o={result:d,ctx:u}),u.common.issues.length&&s.push(u.common.issues)}if(o)return n.common.issues.push(...o.ctx.common.issues),o.result;const a=s.map(l=>new Xo(l));return Te(n,{code:me.invalid_union,unionErrors:a}),je}}get options(){return this._def.options}}pg.create=(e,t)=>new pg({options:e,typeName:Ue.ZodUnion,...Qe(t)});const cv=e=>e instanceof yg?cv(e.schema):e instanceof ts?cv(e.innerType()):e instanceof vg?[e.value]:e instanceof jl?e.options:e instanceof _g?Object.keys(e.enum):e instanceof bg?cv(e._def.innerType):e instanceof fg?[void 0]:e instanceof hg?[null]:null;class ib extends tt{_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==xe.object)return Te(n,{code:me.invalid_type,expected:xe.object,received:n.parsedType}),je;const r=this.discriminator,i=n.data[r],o=this.optionsMap.get(i);return o?n.common.async?o._parseAsync({data:n.data,path:n.path,parent:n}):o._parseSync({data:n.data,path:n.path,parent:n}):(Te(n,{code:me.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[r]}),je)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(t,n,r){const i=new Map;for(const o of n){const s=cv(o.shape[t]);if(!s)throw new Error(`A discriminator value for key \`${t}\` could not be extracted from all schema options`);for(const a of s){if(i.has(a))throw new Error(`Discriminator property ${String(t)} has duplicate value ${String(a)}`);i.set(a,o)}}return new ib({typeName:Ue.ZodDiscriminatedUnion,discriminator:t,options:n,optionsMap:i,...Qe(r)})}}function A3(e,t){const n=ml(e),r=ml(t);if(e===t)return{valid:!0,data:e};if(n===xe.object&&r===xe.object){const i=wt.objectKeys(t),o=wt.objectKeys(e).filter(a=>i.indexOf(a)!==-1),s={...e,...t};for(const a of o){const l=A3(e[a],t[a]);if(!l.valid)return{valid:!1};s[a]=l.data}return{valid:!0,data:s}}else if(n===xe.array&&r===xe.array){if(e.length!==t.length)return{valid:!1};const i=[];for(let o=0;o{if(T3(o)||T3(s))return je;const a=A3(o.value,s.value);return a.valid?((E3(o)||E3(s))&&n.dirty(),{status:n.value,value:a.data}):(Te(r,{code:me.invalid_intersection_types}),je)};return r.common.async?Promise.all([this._def.left._parseAsync({data:r.data,path:r.path,parent:r}),this._def.right._parseAsync({data:r.data,path:r.path,parent:r})]).then(([o,s])=>i(o,s)):i(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}gg.create=(e,t,n)=>new gg({left:e,right:t,typeName:Ue.ZodIntersection,...Qe(n)});class Ns extends tt{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==xe.array)return Te(r,{code:me.invalid_type,expected:xe.array,received:r.parsedType}),je;if(r.data.lengththis._def.items.length&&(Te(r,{code:me.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),n.dirty());const o=[...r.data].map((s,a)=>{const l=this._def.items[a]||this._def.rest;return l?l._parse(new Ms(r,s,r.path,a)):null}).filter(s=>!!s);return r.common.async?Promise.all(o).then(s=>Wr.mergeArray(n,s)):Wr.mergeArray(n,o)}get items(){return this._def.items}rest(t){return new Ns({...this._def,rest:t})}}Ns.create=(e,t)=>{if(!Array.isArray(e))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new Ns({items:e,typeName:Ue.ZodTuple,rest:null,...Qe(t)})};class mg extends tt{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==xe.object)return Te(r,{code:me.invalid_type,expected:xe.object,received:r.parsedType}),je;const i=[],o=this._def.keyType,s=this._def.valueType;for(const a in r.data)i.push({key:o._parse(new Ms(r,a,r.path,a)),value:s._parse(new Ms(r,r.data[a],r.path,a))});return r.common.async?Wr.mergeObjectAsync(n,i):Wr.mergeObjectSync(n,i)}get element(){return this._def.valueType}static create(t,n,r){return n instanceof tt?new mg({keyType:t,valueType:n,typeName:Ue.ZodRecord,...Qe(r)}):new mg({keyType:Ho.create(),valueType:t,typeName:Ue.ZodRecord,...Qe(n)})}}class v1 extends tt{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==xe.map)return Te(r,{code:me.invalid_type,expected:xe.map,received:r.parsedType}),je;const i=this._def.keyType,o=this._def.valueType,s=[...r.data.entries()].map(([a,l],u)=>({key:i._parse(new Ms(r,a,r.path,[u,"key"])),value:o._parse(new Ms(r,l,r.path,[u,"value"]))}));if(r.common.async){const a=new Map;return Promise.resolve().then(async()=>{for(const l of s){const u=await l.key,d=await l.value;if(u.status==="aborted"||d.status==="aborted")return je;(u.status==="dirty"||d.status==="dirty")&&n.dirty(),a.set(u.value,d.value)}return{status:n.value,value:a}})}else{const a=new Map;for(const l of s){const u=l.key,d=l.value;if(u.status==="aborted"||d.status==="aborted")return je;(u.status==="dirty"||d.status==="dirty")&&n.dirty(),a.set(u.value,d.value)}return{status:n.value,value:a}}}}v1.create=(e,t,n)=>new v1({valueType:t,keyType:e,typeName:Ue.ZodMap,...Qe(n)});class rc extends tt{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==xe.set)return Te(r,{code:me.invalid_type,expected:xe.set,received:r.parsedType}),je;const i=this._def;i.minSize!==null&&r.data.sizei.maxSize.value&&(Te(r,{code:me.too_big,maximum:i.maxSize.value,type:"set",inclusive:!0,exact:!1,message:i.maxSize.message}),n.dirty());const o=this._def.valueType;function s(l){const u=new Set;for(const d of l){if(d.status==="aborted")return je;d.status==="dirty"&&n.dirty(),u.add(d.value)}return{status:n.value,value:u}}const a=[...r.data.values()].map((l,u)=>o._parse(new Ms(r,l,r.path,u)));return r.common.async?Promise.all(a).then(l=>s(l)):s(a)}min(t,n){return new rc({...this._def,minSize:{value:t,message:Le.toString(n)}})}max(t,n){return new rc({...this._def,maxSize:{value:t,message:Le.toString(n)}})}size(t,n){return this.min(t,n).max(t,n)}nonempty(t){return this.min(1,t)}}rc.create=(e,t)=>new rc({valueType:e,minSize:null,maxSize:null,typeName:Ue.ZodSet,...Qe(t)});class Jd extends tt{constructor(){super(...arguments),this.validate=this.implement}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==xe.function)return Te(n,{code:me.invalid_type,expected:xe.function,received:n.parsedType}),je;function r(a,l){return h1({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,f1(),cg].filter(u=>!!u),issueData:{code:me.invalid_arguments,argumentsError:l}})}function i(a,l){return h1({data:a,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,f1(),cg].filter(u=>!!u),issueData:{code:me.invalid_return_type,returnTypeError:l}})}const o={errorMap:n.common.contextualErrorMap},s=n.data;return this._def.returns instanceof xf?ui(async(...a)=>{const l=new Xo([]),u=await this._def.args.parseAsync(a,o).catch(p=>{throw l.addIssue(r(a,p)),l}),d=await s(...u);return await this._def.returns._def.type.parseAsync(d,o).catch(p=>{throw l.addIssue(i(d,p)),l})}):ui((...a)=>{const l=this._def.args.safeParse(a,o);if(!l.success)throw new Xo([r(a,l.error)]);const u=s(...l.data),d=this._def.returns.safeParse(u,o);if(!d.success)throw new Xo([i(u,d.error)]);return d.data})}parameters(){return this._def.args}returnType(){return this._def.returns}args(...t){return new Jd({...this._def,args:Ns.create(t).rest(Gu.create())})}returns(t){return new Jd({...this._def,returns:t})}implement(t){return this.parse(t)}strictImplement(t){return this.parse(t)}static create(t,n,r){return new Jd({args:t||Ns.create([]).rest(Gu.create()),returns:n||Gu.create(),typeName:Ue.ZodFunction,...Qe(r)})}}class yg extends tt{get schema(){return this._def.getter()}_parse(t){const{ctx:n}=this._processInputParams(t);return this._def.getter()._parse({data:n.data,path:n.path,parent:n})}}yg.create=(e,t)=>new yg({getter:e,typeName:Ue.ZodLazy,...Qe(t)});class vg extends tt{_parse(t){if(t.data!==this._def.value){const n=this._getOrReturnCtx(t);return Te(n,{received:n.data,code:me.invalid_literal,expected:this._def.value}),je}return{status:"valid",value:t.data}}get value(){return this._def.value}}vg.create=(e,t)=>new vg({value:e,typeName:Ue.ZodLiteral,...Qe(t)});function GD(e,t){return new jl({values:e,typeName:Ue.ZodEnum,...Qe(t)})}class jl extends tt{_parse(t){if(typeof t.data!="string"){const n=this._getOrReturnCtx(t),r=this._def.values;return Te(n,{expected:wt.joinValues(r),received:n.parsedType,code:me.invalid_type}),je}if(this._def.values.indexOf(t.data)===-1){const n=this._getOrReturnCtx(t),r=this._def.values;return Te(n,{received:n.data,code:me.invalid_enum_value,options:r}),je}return ui(t.data)}get options(){return this._def.values}get enum(){const t={};for(const n of this._def.values)t[n]=n;return t}get Values(){const t={};for(const n of this._def.values)t[n]=n;return t}get Enum(){const t={};for(const n of this._def.values)t[n]=n;return t}extract(t){return jl.create(t)}exclude(t){return jl.create(this.options.filter(n=>!t.includes(n)))}}jl.create=GD;class _g extends tt{_parse(t){const n=wt.getValidEnumValues(this._def.values),r=this._getOrReturnCtx(t);if(r.parsedType!==xe.string&&r.parsedType!==xe.number){const i=wt.objectValues(n);return Te(r,{expected:wt.joinValues(i),received:r.parsedType,code:me.invalid_type}),je}if(n.indexOf(t.data)===-1){const i=wt.objectValues(n);return Te(r,{received:r.data,code:me.invalid_enum_value,options:i}),je}return ui(t.data)}get enum(){return this._def.values}}_g.create=(e,t)=>new _g({values:e,typeName:Ue.ZodNativeEnum,...Qe(t)});class xf extends tt{unwrap(){return this._def.type}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==xe.promise&&n.common.async===!1)return Te(n,{code:me.invalid_type,expected:xe.promise,received:n.parsedType}),je;const r=n.parsedType===xe.promise?n.data:Promise.resolve(n.data);return ui(r.then(i=>this._def.type.parseAsync(i,{path:n.path,errorMap:n.common.contextualErrorMap})))}}xf.create=(e,t)=>new xf({type:e,typeName:Ue.ZodPromise,...Qe(t)});class ts extends tt{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===Ue.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(t){const{status:n,ctx:r}=this._processInputParams(t),i=this._def.effect||null;if(i.type==="preprocess"){const s=i.transform(r.data);return r.common.async?Promise.resolve(s).then(a=>this._def.schema._parseAsync({data:a,path:r.path,parent:r})):this._def.schema._parseSync({data:s,path:r.path,parent:r})}const o={addIssue:s=>{Te(r,s),s.fatal?n.abort():n.dirty()},get path(){return r.path}};if(o.addIssue=o.addIssue.bind(o),i.type==="refinement"){const s=a=>{const l=i.refinement(a,o);if(r.common.async)return Promise.resolve(l);if(l instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return a};if(r.common.async===!1){const a=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});return a.status==="aborted"?je:(a.status==="dirty"&&n.dirty(),s(a.value),{status:n.value,value:a.value})}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(a=>a.status==="aborted"?je:(a.status==="dirty"&&n.dirty(),s(a.value).then(()=>({status:n.value,value:a.value}))))}if(i.type==="transform")if(r.common.async===!1){const s=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});if(!p1(s))return s;const a=i.transform(s.value,o);if(a instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:n.value,value:a}}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(s=>p1(s)?Promise.resolve(i.transform(s.value,o)).then(a=>({status:n.value,value:a})):s);wt.assertNever(i)}}ts.create=(e,t,n)=>new ts({schema:e,typeName:Ue.ZodEffects,effect:t,...Qe(n)});ts.createWithPreprocess=(e,t,n)=>new ts({schema:t,effect:{type:"preprocess",transform:e},typeName:Ue.ZodEffects,...Qe(n)});class _a extends tt{_parse(t){return this._getType(t)===xe.undefined?ui(void 0):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}_a.create=(e,t)=>new _a({innerType:e,typeName:Ue.ZodOptional,...Qe(t)});class ic extends tt{_parse(t){return this._getType(t)===xe.null?ui(null):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}ic.create=(e,t)=>new ic({innerType:e,typeName:Ue.ZodNullable,...Qe(t)});class bg extends tt{_parse(t){const{ctx:n}=this._processInputParams(t);let r=n.data;return n.parsedType===xe.undefined&&(r=this._def.defaultValue()),this._def.innerType._parse({data:r,path:n.path,parent:n})}removeDefault(){return this._def.innerType}}bg.create=(e,t)=>new bg({innerType:e,typeName:Ue.ZodDefault,defaultValue:typeof t.default=="function"?t.default:()=>t.default,...Qe(t)});class _1 extends tt{_parse(t){const{ctx:n}=this._processInputParams(t),r={...n,common:{...n.common,issues:[]}},i=this._def.innerType._parse({data:r.data,path:r.path,parent:{...r}});return g1(i)?i.then(o=>({status:"valid",value:o.status==="valid"?o.value:this._def.catchValue({get error(){return new Xo(r.common.issues)},input:r.data})})):{status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new Xo(r.common.issues)},input:r.data})}}removeCatch(){return this._def.innerType}}_1.create=(e,t)=>new _1({innerType:e,typeName:Ue.ZodCatch,catchValue:typeof t.catch=="function"?t.catch:()=>t.catch,...Qe(t)});class b1 extends tt{_parse(t){if(this._getType(t)!==xe.nan){const r=this._getOrReturnCtx(t);return Te(r,{code:me.invalid_type,expected:xe.nan,received:r.parsedType}),je}return{status:"valid",value:t.data}}}b1.create=e=>new b1({typeName:Ue.ZodNaN,...Qe(e)});const xle=Symbol("zod_brand");class HD extends tt{_parse(t){const{ctx:n}=this._processInputParams(t),r=n.data;return this._def.type._parse({data:r,path:n.path,parent:n})}unwrap(){return this._def.type}}class hm extends tt{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.common.async)return(async()=>{const o=await this._def.in._parseAsync({data:r.data,path:r.path,parent:r});return o.status==="aborted"?je:o.status==="dirty"?(n.dirty(),jD(o.value)):this._def.out._parseAsync({data:o.value,path:r.path,parent:r})})();{const i=this._def.in._parseSync({data:r.data,path:r.path,parent:r});return i.status==="aborted"?je:i.status==="dirty"?(n.dirty(),{status:"dirty",value:i.value}):this._def.out._parseSync({data:i.value,path:r.path,parent:r})}}static create(t,n){return new hm({in:t,out:n,typeName:Ue.ZodPipeline})}}const WD=(e,t={},n)=>e?wf.create().superRefine((r,i)=>{var o,s;if(!e(r)){const a=typeof t=="function"?t(r):typeof t=="string"?{message:t}:t,l=(s=(o=a.fatal)!==null&&o!==void 0?o:n)!==null&&s!==void 0?s:!0,u=typeof a=="string"?{message:a}:a;i.addIssue({code:"custom",...u,fatal:l})}}):wf.create(),Cle={object:gn.lazycreate};var Ue;(function(e){e.ZodString="ZodString",e.ZodNumber="ZodNumber",e.ZodNaN="ZodNaN",e.ZodBigInt="ZodBigInt",e.ZodBoolean="ZodBoolean",e.ZodDate="ZodDate",e.ZodSymbol="ZodSymbol",e.ZodUndefined="ZodUndefined",e.ZodNull="ZodNull",e.ZodAny="ZodAny",e.ZodUnknown="ZodUnknown",e.ZodNever="ZodNever",e.ZodVoid="ZodVoid",e.ZodArray="ZodArray",e.ZodObject="ZodObject",e.ZodUnion="ZodUnion",e.ZodDiscriminatedUnion="ZodDiscriminatedUnion",e.ZodIntersection="ZodIntersection",e.ZodTuple="ZodTuple",e.ZodRecord="ZodRecord",e.ZodMap="ZodMap",e.ZodSet="ZodSet",e.ZodFunction="ZodFunction",e.ZodLazy="ZodLazy",e.ZodLiteral="ZodLiteral",e.ZodEnum="ZodEnum",e.ZodEffects="ZodEffects",e.ZodNativeEnum="ZodNativeEnum",e.ZodOptional="ZodOptional",e.ZodNullable="ZodNullable",e.ZodDefault="ZodDefault",e.ZodCatch="ZodCatch",e.ZodPromise="ZodPromise",e.ZodBranded="ZodBranded",e.ZodPipeline="ZodPipeline"})(Ue||(Ue={}));const Tle=(e,t={message:`Input not instance of ${e.name}`})=>WD(n=>n instanceof e,t),qD=Ho.create,KD=zl.create,Ele=b1.create,Ale=Vl.create,XD=dg.create,Ple=nc.create,Rle=m1.create,Ole=fg.create,kle=hg.create,Ile=wf.create,Mle=Gu.create,Nle=Oa.create,Lle=y1.create,Dle=Yo.create,$le=gn.create,Fle=gn.strictCreate,Ble=pg.create,Ule=ib.create,zle=gg.create,Vle=Ns.create,jle=mg.create,Gle=v1.create,Hle=rc.create,Wle=Jd.create,qle=yg.create,Kle=vg.create,Xle=jl.create,Yle=_g.create,Qle=xf.create,N9=ts.create,Zle=_a.create,Jle=ic.create,eue=ts.createWithPreprocess,tue=hm.create,nue=()=>qD().optional(),rue=()=>KD().optional(),iue=()=>XD().optional(),oue={string:e=>Ho.create({...e,coerce:!0}),number:e=>zl.create({...e,coerce:!0}),boolean:e=>dg.create({...e,coerce:!0}),bigint:e=>Vl.create({...e,coerce:!0}),date:e=>nc.create({...e,coerce:!0})},sue=je;var xt=Object.freeze({__proto__:null,defaultErrorMap:cg,setErrorMap:cle,getErrorMap:f1,makeIssue:h1,EMPTY_PATH:dle,addIssueToContext:Te,ParseStatus:Wr,INVALID:je,DIRTY:jD,OK:ui,isAborted:T3,isDirty:E3,isValid:p1,isAsync:g1,get util(){return wt},get objectUtil(){return C3},ZodParsedType:xe,getParsedType:ml,ZodType:tt,ZodString:Ho,ZodNumber:zl,ZodBigInt:Vl,ZodBoolean:dg,ZodDate:nc,ZodSymbol:m1,ZodUndefined:fg,ZodNull:hg,ZodAny:wf,ZodUnknown:Gu,ZodNever:Oa,ZodVoid:y1,ZodArray:Yo,ZodObject:gn,ZodUnion:pg,ZodDiscriminatedUnion:ib,ZodIntersection:gg,ZodTuple:Ns,ZodRecord:mg,ZodMap:v1,ZodSet:rc,ZodFunction:Jd,ZodLazy:yg,ZodLiteral:vg,ZodEnum:jl,ZodNativeEnum:_g,ZodPromise:xf,ZodEffects:ts,ZodTransformer:ts,ZodOptional:_a,ZodNullable:ic,ZodDefault:bg,ZodCatch:_1,ZodNaN:b1,BRAND:xle,ZodBranded:HD,ZodPipeline:hm,custom:WD,Schema:tt,ZodSchema:tt,late:Cle,get ZodFirstPartyTypeKind(){return Ue},coerce:oue,any:Ile,array:Dle,bigint:Ale,boolean:XD,date:Ple,discriminatedUnion:Ule,effect:N9,enum:Xle,function:Wle,instanceof:Tle,intersection:zle,lazy:qle,literal:Kle,map:Gle,nan:Ele,nativeEnum:Yle,never:Nle,null:kle,nullable:Jle,number:KD,object:$le,oboolean:iue,onumber:rue,optional:Zle,ostring:nue,pipeline:tue,preprocess:eue,promise:Qle,record:jle,set:Hle,strictObject:Fle,string:qD,symbol:Rle,transformer:N9,tuple:Vle,undefined:Ole,union:Ble,unknown:Mle,void:Lle,NEVER:sue,ZodIssueCode:me,quotelessJson:ule,ZodError:Xo});const aue=xt.string(),oRe=e=>aue.safeParse(e).success,lue=xt.string(),sRe=e=>lue.safeParse(e).success,uue=xt.string(),aRe=e=>uue.safeParse(e).success,cue=xt.string(),lRe=e=>cue.safeParse(e).success,due=xt.number().int().min(1),uRe=e=>due.safeParse(e).success,fue=xt.number().min(1),cRe=e=>fue.safeParse(e).success,hue=xt.enum(["euler","deis","ddim","ddpm","dpmpp_2s","dpmpp_2m","dpmpp_2m_sde","dpmpp_sde","heun","kdpm_2","lms","pndm","unipc","euler_k","dpmpp_2s_k","dpmpp_2m_k","dpmpp_2m_sde_k","dpmpp_sde_k","heun_k","lms_k","euler_a","kdpm_2_a"]),dRe=e=>hue.safeParse(e).success,fRe={euler:"Euler",deis:"DEIS",ddim:"DDIM",ddpm:"DDPM",dpmpp_sde:"DPM++ SDE",dpmpp_2s:"DPM++ 2S",dpmpp_2m:"DPM++ 2M",dpmpp_2m_sde:"DPM++ 2M SDE",heun:"Heun",kdpm_2:"KDPM 2",lms:"LMS",pndm:"PNDM",unipc:"UniPC",euler_k:"Euler Karras",dpmpp_sde_k:"DPM++ SDE Karras",dpmpp_2s_k:"DPM++ 2S Karras",dpmpp_2m_k:"DPM++ 2M Karras",dpmpp_2m_sde_k:"DPM++ 2M SDE Karras",heun_k:"Heun Karras",lms_k:"LMS Karras",euler_a:"Euler Ancestral",kdpm_2_a:"KDPM 2 Ancestral"},pue=xt.number().int().min(0).max(lle),hRe=e=>pue.safeParse(e).success,gue=xt.number().multipleOf(8).min(64),pRe=e=>gue.safeParse(e).success,mue=xt.number().multipleOf(8).min(64),gRe=e=>mue.safeParse(e).success,pm=xt.enum(["sd-1","sd-2","sdxl","sdxl-refiner"]),ST=xt.object({model_name:xt.string().min(1),base_model:pm,model_type:xt.literal("main")}),mRe=e=>ST.safeParse(e).success,YD=xt.object({model_name:xt.string().min(1),base_model:xt.literal("sdxl-refiner"),model_type:xt.literal("main")}),yRe=e=>YD.safeParse(e).success,yue=xt.object({model_name:xt.string().min(1),base_model:pm,model_type:xt.literal("onnx")}),QD=xt.union([ST,yue]),vue=xt.object({model_name:xt.string().min(1),base_model:pm}),vRe=xt.object({model_name:xt.string().min(1),base_model:pm}),_Re=xt.object({model_name:xt.string().min(1),base_model:pm}),_ue=xt.number().min(0).max(1),bRe=e=>_ue.safeParse(e).success;xt.enum(["fp16","fp32"]);const bue=xt.number().min(1).max(10),SRe=e=>bue.safeParse(e).success,Sue=xt.number().min(0).max(1),wRe=e=>Sue.safeParse(e).success,Da={cfgScale:7.5,height:512,img2imgStrength:.75,infillMethod:"patchmatch",iterations:1,perlin:0,positivePrompt:"",negativePrompt:"",scheduler:"euler",seamBlur:16,seamSize:96,seamSteps:30,seamStrength:.7,seed:0,seedWeights:"",shouldFitToWidthHeight:!0,shouldGenerateVariations:!1,shouldRandomizeSeed:!0,shouldUseNoiseSettings:!1,steps:50,threshold:0,tileSize:32,variationAmount:.1,width:512,shouldUseSymmetry:!1,horizontalSymmetrySteps:0,verticalSymmetrySteps:0,model:null,vae:null,vaePrecision:"fp32",seamlessXAxis:!1,seamlessYAxis:!1,clipSkip:0,shouldUseCpuNoise:!0,shouldShowAdvancedOptions:!1,aspectRatio:null},wue=Da,ZD=yn({name:"generation",initialState:wue,reducers:{setPositivePrompt:(e,t)=>{e.positivePrompt=t.payload},setNegativePrompt:(e,t)=>{e.negativePrompt=t.payload},setIterations:(e,t)=>{e.iterations=t.payload},setSteps:(e,t)=>{e.steps=t.payload},clampSymmetrySteps:e=>{e.horizontalSymmetrySteps=bl(e.horizontalSymmetrySteps,0,e.steps),e.verticalSymmetrySteps=bl(e.verticalSymmetrySteps,0,e.steps)},setCfgScale:(e,t)=>{e.cfgScale=t.payload},setThreshold:(e,t)=>{e.threshold=t.payload},setPerlin:(e,t)=>{e.perlin=t.payload},setHeight:(e,t)=>{e.height=t.payload},setWidth:(e,t)=>{e.width=t.payload},toggleSize:e=>{const[t,n]=[e.width,e.height];e.width=n,e.height=t},setScheduler:(e,t)=>{e.scheduler=t.payload},setSeed:(e,t)=>{e.seed=t.payload,e.shouldRandomizeSeed=!1},setImg2imgStrength:(e,t)=>{e.img2imgStrength=t.payload},setSeamlessXAxis:(e,t)=>{e.seamlessXAxis=t.payload},setSeamlessYAxis:(e,t)=>{e.seamlessYAxis=t.payload},setShouldFitToWidthHeight:(e,t)=>{e.shouldFitToWidthHeight=t.payload},resetSeed:e=>{e.seed=-1},setShouldGenerateVariations:(e,t)=>{e.shouldGenerateVariations=t.payload},setVariationAmount:(e,t)=>{e.variationAmount=t.payload},setSeedWeights:(e,t)=>{e.seedWeights=t.payload,e.shouldGenerateVariations=!0,e.variationAmount=0},resetParametersState:e=>({...e,...Da}),setShouldRandomizeSeed:(e,t)=>{e.shouldRandomizeSeed=t.payload},clearInitialImage:e=>{e.initialImage=void 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a=[t,n];r&&(a=a.concat(s?r.split(s):r)),t.indexOf(".")>-1&&(a=t.split("."),i=n,n=a[1]),this.addNamespaces(n),mO(this.data,a,i),o.silent||this.emit("added",t,n,r,i)}addResources(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{silent:!1};for(const o in r)(typeof r[o]=="string"||Object.prototype.toString.apply(r[o])==="[object Array]")&&this.addResource(t,n,o,r[o],{silent:!0});i.silent||this.emit("added",t,n,r)}addResourceBundle(t,n,r,i,o){let s=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{silent:!1},a=[t,n];t.indexOf(".")>-1&&(a=t.split("."),i=r,r=n,n=a[1]),this.addNamespaces(n);let l=W1(this.data,a)||{};i?SU(l,r,o):l={...l,...r},mO(this.data,a,l),s.silent||this.emit("added",t,n,r)}removeResourceBundle(t,n){this.hasResourceBundle(t,n)&&delete this.data[t][n],this.removeNamespaces(n),this.emit("removed",t,n)}hasResourceBundle(t,n){return this.getResource(t,n)!==void 0}getResourceBundle(t,n){return n||(n=this.options.defaultNS),this.options.compatibilityAPI==="v1"?{...this.getResource(t,n)}:this.getResource(t,n)}getDataByLanguage(t){return this.data[t]}hasLanguageSomeTranslations(t){const n=this.getDataByLanguage(t);return!!(n&&Object.keys(n)||[]).find(i=>n[i]&&Object.keys(n[i]).length>0)}toJSON(){return this.data}}var wU={processors:{},addPostProcessor(e){this.processors[e.name]=e},handle(e,t,n,r,i){return e.forEach(o=>{this.processors[o]&&(t=this.processors[o].process(t,n,r,i))}),t}};const vO={};class X1 extends eS{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};super(),Xve(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],t,this),this.options=n,this.options.keySeparator===void 0&&(this.options.keySeparator="."),this.logger=Cs.create("translator")}changeLanguage(t){t&&(this.language=t)}exists(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(t==null)return!1;const r=this.resolve(t,n);return r&&r.res!==void 0}extractFromKey(t,n){let r=n.nsSeparator!==void 0?n.nsSeparator:this.options.nsSeparator;r===void 0&&(r=":");const i=n.keySeparator!==void 0?n.keySeparator:this.options.keySeparator;let o=n.ns||this.options.defaultNS||[];const s=r&&t.indexOf(r)>-1,a=!this.options.userDefinedKeySeparator&&!n.keySeparator&&!this.options.userDefinedNsSeparator&&!n.nsSeparator&&!t1e(t,r,i);if(s&&!a){const l=t.match(this.interpolator.nestingRegexp);if(l&&l.length>0)return{key:t,namespaces:o};const u=t.split(r);(r!==i||r===i&&this.options.ns.indexOf(u[0])>-1)&&(o=u.shift()),t=u.join(i)}return typeof o=="string"&&(o=[o]),{key:t,namespaces:o}}translate(t,n,r){if(typeof n!="object"&&this.options.overloadTranslationOptionHandler&&(n=this.options.overloadTranslationOptionHandler(arguments)),typeof n=="object"&&(n={...n}),n||(n={}),t==null)return"";Array.isArray(t)||(t=[String(t)]);const i=n.returnDetails!==void 0?n.returnDetails:this.options.returnDetails,o=n.keySeparator!==void 0?n.keySeparator:this.options.keySeparator,{key:s,namespaces:a}=this.extractFromKey(t[t.length-1],n),l=a[a.length-1],u=n.lng||this.language,d=n.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(u&&u.toLowerCase()==="cimode"){if(d){const S=n.nsSeparator||this.options.nsSeparator;return i?{res:`${l}${S}${s}`,usedKey:s,exactUsedKey:s,usedLng:u,usedNS:l}:`${l}${S}${s}`}return i?{res:s,usedKey:s,exactUsedKey:s,usedLng:u,usedNS:l}:s}const f=this.resolve(t,n);let p=f&&f.res;const g=f&&f.usedKey||s,m=f&&f.exactUsedKey||s,v=Object.prototype.toString.apply(p),x=["[object Number]","[object Function]","[object RegExp]"],_=n.joinArrays!==void 0?n.joinArrays:this.options.joinArrays,b=!this.i18nFormat||this.i18nFormat.handleAsObject;if(b&&p&&(typeof p!="string"&&typeof p!="boolean"&&typeof p!="number")&&x.indexOf(v)<0&&!(typeof _=="string"&&v==="[object Array]")){if(!n.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");const S=this.options.returnedObjectHandler?this.options.returnedObjectHandler(g,p,{...n,ns:a}):`key '${s} (${this.language})' returned an object instead of string.`;return i?(f.res=S,f):S}if(o){const S=v==="[object Array]",C=S?[]:{},T=S?m:g;for(const E in p)if(Object.prototype.hasOwnProperty.call(p,E)){const P=`${T}${o}${E}`;C[E]=this.translate(P,{...n,joinArrays:!1,ns:a}),C[E]===P&&(C[E]=p[E])}p=C}}else if(b&&typeof _=="string"&&v==="[object Array]")p=p.join(_),p&&(p=this.extendTranslation(p,t,n,r));else{let S=!1,C=!1;const T=n.count!==void 0&&typeof n.count!="string",E=X1.hasDefaultValue(n),P=T?this.pluralResolver.getSuffix(u,n.count,n):"",k=n.ordinal&&T?this.pluralResolver.getSuffix(u,n.count,{ordinal:!1}):"",O=n[`defaultValue${P}`]||n[`defaultValue${k}`]||n.defaultValue;!this.isValidLookup(p)&&E&&(S=!0,p=O),this.isValidLookup(p)||(C=!0,p=s);const G=(n.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey)&&C?void 0:p,U=E&&O!==p&&this.options.updateMissing;if(C||S||U){if(this.logger.log(U?"updateKey":"missingKey",u,l,s,U?O:p),o){const B=this.resolve(s,{...n,keySeparator:!1});B&&B.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}let A=[];const N=this.languageUtils.getFallbackCodes(this.options.fallbackLng,n.lng||this.language);if(this.options.saveMissingTo==="fallback"&&N&&N[0])for(let B=0;B{const j=E&&V!==p?V:G;this.options.missingKeyHandler?this.options.missingKeyHandler(B,l,D,j,U,n):this.backendConnector&&this.backendConnector.saveMissing&&this.backendConnector.saveMissing(B,l,D,j,U,n),this.emit("missingKey",B,l,D,p)};this.options.saveMissing&&(this.options.saveMissingPlurals&&T?A.forEach(B=>{this.pluralResolver.getSuffixes(B,n).forEach(D=>{F([B],s+D,n[`defaultValue${D}`]||O)})}):F(A,s,O))}p=this.extendTranslation(p,t,n,f,r),C&&p===s&&this.options.appendNamespaceToMissingKey&&(p=`${l}:${s}`),(C||S)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?p=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${l}:${s}`:s,S?p:void 0):p=this.options.parseMissingKeyHandler(p))}return i?(f.res=p,f):p}extendTranslation(t,n,r,i,o){var s=this;if(this.i18nFormat&&this.i18nFormat.parse)t=this.i18nFormat.parse(t,{...this.options.interpolation.defaultVariables,...r},i.usedLng,i.usedNS,i.usedKey,{resolved:i});else if(!r.skipInterpolation){r.interpolation&&this.interpolator.init({...r,interpolation:{...this.options.interpolation,...r.interpolation}});const u=typeof t=="string"&&(r&&r.interpolation&&r.interpolation.skipOnVariables!==void 0?r.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);let d;if(u){const p=t.match(this.interpolator.nestingRegexp);d=p&&p.length}let f=r.replace&&typeof r.replace!="string"?r.replace:r;if(this.options.interpolation.defaultVariables&&(f={...this.options.interpolation.defaultVariables,...f}),t=this.interpolator.interpolate(t,f,r.lng||this.language,r),u){const p=t.match(this.interpolator.nestingRegexp),g=p&&p.length;d1&&arguments[1]!==void 0?arguments[1]:{},r,i,o,s,a;return typeof t=="string"&&(t=[t]),t.forEach(l=>{if(this.isValidLookup(r))return;const u=this.extractFromKey(l,n),d=u.key;i=d;let f=u.namespaces;this.options.fallbackNS&&(f=f.concat(this.options.fallbackNS));const p=n.count!==void 0&&typeof n.count!="string",g=p&&!n.ordinal&&n.count===0&&this.pluralResolver.shouldUseIntlApi(),m=n.context!==void 0&&(typeof n.context=="string"||typeof n.context=="number")&&n.context!=="",v=n.lngs?n.lngs:this.languageUtils.toResolveHierarchy(n.lng||this.language,n.fallbackLng);f.forEach(x=>{this.isValidLookup(r)||(a=x,!vO[`${v[0]}-${x}`]&&this.utils&&this.utils.hasLoadedNamespace&&!this.utils.hasLoadedNamespace(a)&&(vO[`${v[0]}-${x}`]=!0,this.logger.warn(`key "${i}" for languages "${v.join(", ")}" won't get resolved as namespace "${a}" was not yet loaded`,"This means something IS WRONG in your setup. 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Wait for the callback or Promise to resolve before accessing it!!!")),v.forEach(_=>{if(this.isValidLookup(r))return;s=_;const b=[d];if(this.i18nFormat&&this.i18nFormat.addLookupKeys)this.i18nFormat.addLookupKeys(b,d,_,x,n);else{let S;p&&(S=this.pluralResolver.getSuffix(_,n.count,n));const C=`${this.options.pluralSeparator}zero`,T=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(p&&(b.push(d+S),n.ordinal&&S.indexOf(T)===0&&b.push(d+S.replace(T,this.options.pluralSeparator)),g&&b.push(d+C)),m){const E=`${d}${this.options.contextSeparator}${n.context}`;b.push(E),p&&(b.push(E+S),n.ordinal&&S.indexOf(T)===0&&b.push(E+S.replace(T,this.options.pluralSeparator)),g&&b.push(E+C))}}let y;for(;y=b.pop();)this.isValidLookup(r)||(o=y,r=this.getResource(_,x,y,n))}))})}),{res:r,usedKey:i,exactUsedKey:o,usedLng:s,usedNS:a}}isValidLookup(t){return t!==void 0&&!(!this.options.returnNull&&t===null)&&!(!this.options.returnEmptyString&&t==="")}getResource(t,n,r){let i=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(t,n,r,i):this.resourceStore.getResource(t,n,r,i)}static hasDefaultValue(t){const n="defaultValue";for(const r in t)if(Object.prototype.hasOwnProperty.call(t,r)&&n===r.substring(0,n.length)&&t[r]!==void 0)return!0;return!1}}function Rx(e){return e.charAt(0).toUpperCase()+e.slice(1)}class _O{constructor(t){this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=Cs.create("languageUtils")}getScriptPartFromCode(t){if(t=K1(t),!t||t.indexOf("-")<0)return null;const n=t.split("-");return n.length===2||(n.pop(),n[n.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(n.join("-"))}getLanguagePartFromCode(t){if(t=K1(t),!t||t.indexOf("-")<0)return t;const n=t.split("-");return this.formatLanguageCode(n[0])}formatLanguageCode(t){if(typeof t=="string"&&t.indexOf("-")>-1){const n=["hans","hant","latn","cyrl","cans","mong","arab"];let r=t.split("-");return this.options.lowerCaseLng?r=r.map(i=>i.toLowerCase()):r.length===2?(r[0]=r[0].toLowerCase(),r[1]=r[1].toUpperCase(),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=Rx(r[1].toLowerCase()))):r.length===3&&(r[0]=r[0].toLowerCase(),r[1].length===2&&(r[1]=r[1].toUpperCase()),r[0]!=="sgn"&&r[2].length===2&&(r[2]=r[2].toUpperCase()),n.indexOf(r[1].toLowerCase())>-1&&(r[1]=Rx(r[1].toLowerCase())),n.indexOf(r[2].toLowerCase())>-1&&(r[2]=Rx(r[2].toLowerCase()))),r.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?t.toLowerCase():t}isSupportedCode(t){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(t=this.getLanguagePartFromCode(t)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(t)>-1}getBestMatchFromCodes(t){if(!t)return null;let n;return t.forEach(r=>{if(n)return;const i=this.formatLanguageCode(r);(!this.options.supportedLngs||this.isSupportedCode(i))&&(n=i)}),!n&&this.options.supportedLngs&&t.forEach(r=>{if(n)return;const i=this.getLanguagePartFromCode(r);if(this.isSupportedCode(i))return n=i;n=this.options.supportedLngs.find(o=>{if(o===i)return o;if(!(o.indexOf("-")<0&&i.indexOf("-")<0)&&o.indexOf(i)===0)return o})}),n||(n=this.getFallbackCodes(this.options.fallbackLng)[0]),n}getFallbackCodes(t,n){if(!t)return[];if(typeof t=="function"&&(t=t(n)),typeof t=="string"&&(t=[t]),Object.prototype.toString.apply(t)==="[object Array]")return t;if(!n)return t.default||[];let r=t[n];return r||(r=t[this.getScriptPartFromCode(n)]),r||(r=t[this.formatLanguageCode(n)]),r||(r=t[this.getLanguagePartFromCode(n)]),r||(r=t.default),r||[]}toResolveHierarchy(t,n){const r=this.getFallbackCodes(n||this.options.fallbackLng||[],t),i=[],o=s=>{s&&(this.isSupportedCode(s)?i.push(s):this.logger.warn(`rejecting language code not found in supportedLngs: ${s}`))};return typeof t=="string"&&(t.indexOf("-")>-1||t.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&o(this.formatLanguageCode(t)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&o(this.getScriptPartFromCode(t)),this.options.load!=="currentOnly"&&o(this.getLanguagePartFromCode(t))):typeof t=="string"&&o(this.formatLanguageCode(t)),r.forEach(s=>{i.indexOf(s)<0&&o(this.formatLanguageCode(s))}),i}}let n1e=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],r1e={1:function(e){return+(e>1)},2:function(e){return+(e!=1)},3:function(e){return 0},4:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},5:function(e){return e==0?0:e==1?1:e==2?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5},6:function(e){return e==1?0:e>=2&&e<=4?1:2},7:function(e){return e==1?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2},8:function(e){return e==1?0:e==2?1:e!=8&&e!=11?2:3},9:function(e){return+(e>=2)},10:function(e){return e==1?0:e==2?1:e<7?2:e<11?3:4},11:function(e){return e==1||e==11?0:e==2||e==12?1:e>2&&e<20?2:3},12:function(e){return+(e%10!=1||e%100==11)},13:function(e){return+(e!==0)},14:function(e){return e==1?0:e==2?1:e==3?2:3},15:function(e){return e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2},16:function(e){return e%10==1&&e%100!=11?0:e!==0?1:2},17:function(e){return e==1||e%10==1&&e%100!=11?0:1},18:function(e){return e==0?0:e==1?1:2},19:function(e){return e==1?0:e==0||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3},20:function(e){return e==1?0:e==0||e%100>0&&e%100<20?1:2},21:function(e){return e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0},22:function(e){return e==1?0:e==2?1:(e<0||e>10)&&e%10==0?2:3}};const i1e=["v1","v2","v3"],o1e=["v4"],bO={zero:0,one:1,two:2,few:3,many:4,other:5};function s1e(){const e={};return n1e.forEach(t=>{t.lngs.forEach(n=>{e[n]={numbers:t.nr,plurals:r1e[t.fc]}})}),e}class a1e{constructor(t){let n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.languageUtils=t,this.options=n,this.logger=Cs.create("pluralResolver"),(!this.options.compatibilityJSON||o1e.includes(this.options.compatibilityJSON))&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. 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r?this.shouldUseIntlApi()?r.resolvedOptions().pluralCategories.sort((i,o)=>bO[i]-bO[o]).map(i=>`${this.options.prepend}${n.ordinal?`ordinal${this.options.prepend}`:""}${i}`):r.numbers.map(i=>this.getSuffix(t,i,n)):[]}getSuffix(t,n){let r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};const i=this.getRule(t,r);return i?this.shouldUseIntlApi()?`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${i.select(n)}`:this.getSuffixRetroCompatible(i,n):(this.logger.warn(`no plural rule found for: ${t}`),"")}getSuffixRetroCompatible(t,n){const r=t.noAbs?t.plurals(n):t.plurals(Math.abs(n));let i=t.numbers[r];this.options.simplifyPluralSuffix&&t.numbers.length===2&&t.numbers[0]===1&&(i===2?i="plural":i===1&&(i=""));const o=()=>this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString();return this.options.compatibilityJSON==="v1"?i===1?"":typeof 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",KO="stage",sl="transform",GSe="Stage",s5="visible",HSe=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(bv);let WSe=1;class ze{constructor(t){this._id=WSe++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(t),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(t){(t===sl||t===la)&&this._cache.get(t)?this._cache.get(t).dirty=!0:t?this._cache.delete(t):this._cache.clear()}_getCache(t,n){var r=this._cache.get(t),i=t===sl||t===la,o=r===void 0||i&&r.dirty===!0;return o&&(r=n.call(this),this._cache.set(t,r)),r}_calculate(t,n,r){if(!this._attachedDepsListeners.get(t)){const i=n.map(o=>o+"Change.konva").join(bv);this.on(i,()=>{this._clearCache(t)}),this._attachedDepsListeners.set(t,!0)}return this._getCache(t,r)}_getCanvasCache(){return this._cache.get(xu)}_clearSelfAndDescendantCache(t){this._clearCache(t),t===la&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(xu)){const{scene:t,filter:n,hit:r}=this._cache.get(xu);ct.Util.releaseCanvas(t,n,r),this._cache.delete(xu)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(t){var n=t||{},r={};(n.x===void 0||n.y===void 0||n.width===void 0||n.height===void 0)&&(r=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var i=Math.ceil(n.width||r.width),o=Math.ceil(n.height||r.height),s=n.pixelRatio,a=n.x===void 0?Math.floor(r.x):n.x,l=n.y===void 0?Math.floor(r.y):n.y,u=n.offset||0,d=n.drawBorder||!1,f=n.hitCanvasPixelRatio||1;if(!i||!o){ct.Util.error("Can not cache the node. Width or height of the node equals 0. Caching is skipped.");return}i+=u*2+1,o+=u*2+1,a-=u,l-=u;var p=new N0.SceneCanvas({pixelRatio:s,width:i,height:o}),g=new N0.SceneCanvas({pixelRatio:s,width:0,height:0,willReadFrequently:!0}),m=new N0.HitCanvas({pixelRatio:f,width:i,height:o}),v=p.getContext(),x=m.getContext();return m.isCache=!0,p.isCache=!0,this._cache.delete(xu),this._filterUpToDate=!1,n.imageSmoothingEnabled===!1&&(p.getContext()._context.imageSmoothingEnabled=!1,g.getContext()._context.imageSmoothingEnabled=!1),v.save(),x.save(),v.translate(-a,-l),x.translate(-a,-l),this._isUnderCache=!0,this._clearSelfAndDescendantCache(_v),this._clearSelfAndDescendantCache(jO),this.drawScene(p,this),this.drawHit(m,this),this._isUnderCache=!1,v.restore(),x.restore(),d&&(v.save(),v.beginPath(),v.rect(0,0,i,o),v.closePath(),v.setAttr("strokeStyle","red"),v.setAttr("lineWidth",5),v.stroke(),v.restore()),this._cache.set(xu,{scene:p,filter:g,hit:m,x:a,y:l}),this._requestDraw(),this}isCached(){return this._cache.has(xu)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,s,a,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var d=l.point(u);i===void 0&&(i=s=d.x,o=a=d.y),i=Math.min(i,d.x),o=Math.min(o,d.y),s=Math.max(s,d.x),a=Math.max(a,d.y)}),{x:i,y:o,width:s-i,height:a-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),s,a,l,u;if(t){if(!this._filterUpToDate){var d=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(s=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/d,r.getHeight()/d),a=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l{var n,r;if(!t)return this;for(n in t)n!==VSe&&(r=WO+ct.Util._capitalize(n),ct.Util._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(o5,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(s5,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;uo.DD._dragElements.forEach(s=>{s.dragStatus==="dragging"&&(s.node.nodeType==="Stage"||s.node.getLayer()===r)&&(i=!0)});var o=!n&&!wu.Konva.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,s,a;function l(u){for(i=[],o=u.length,s=0;s0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==GSe&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(sl),this._clearSelfAndDescendantCache(la)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new ct.Transform,s=this.offset();return o.m=i.slice(),o.translate(s.x,s.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(sl);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(sl),this._clearSelfAndDescendantCache(la),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,s;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,s=0;s0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return ct.Util.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return ct.Util.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&ct.Util.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(_v,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,s,a;t.attrs={};for(r in n)i=n[r],a=ct.Util.isObject(i)&&!ct.Util._isPlainObject(i)&&!ct.Util._isArray(i),!a&&(o=typeof this[r]=="function"&&this[r],delete n[r],s=o?o.call(this):null,n[r]=i,s!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),ct.Util._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i{try{const i=t==null?void 0:t.callback;i&&delete t.callback,ct.Util._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():wu.Konva.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,s,a;for(o=0;o=0;if(r&&!this.isDragging()){var i=!1;uo.DD._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=uo.DD._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&uo.DD._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return ct.Util.haveIntersection(r,this.getClientRect())}static create(t,n){return ct.Util._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=ze.prototype.getClassName.call(t),i=t.children,o,s,a;n&&(t.attrs.container=n),wu.Konva[r]||(ct.Util.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=wu.Konva[r];if(o=new l(t.attrs),i)for(s=i.length,a=0;a0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(t.length===0)return this;if(t.length>1){for(var n=0;n0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=Lx.Node.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=Lx.Node.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(a){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),s=this._getCanvasCache(),a=s&&s.hit;if(a){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),s=this.clipWidth(),a=this.clipHeight(),l=this.clipFunc(),u=s&&a||l;const d=r===this;if(u){o.save();var f=this.getAbsoluteTransform(r),p=f.getMatrix();o.transform(p[0],p[1],p[2],p[3],p[4],p[5]),o.beginPath();let x;if(l)x=l.call(this,o,this);else{var g=this.clipX(),m=this.clipY();o.rect(g,m,s,a)}o.clip.apply(o,x),p=f.copy().invert().getMatrix(),o.transform(p[0],p[1],p[2],p[3],p[4],p[5])}var v=!d&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";v&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(x){x[t](n,r)}),v&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,s,a,l,u={x:1/0,y:1/0,width:0,height:0},d=this;(n=this.children)===null||n===void 0||n.forEach(function(v){if(v.visible()){var x=v.getClientRect({relativeTo:d,skipShadow:t.skipShadow,skipStroke:t.skipStroke});x.width===0&&x.height===0||(o===void 0?(o=x.x,s=x.y,a=x.x+x.width,l=x.y+x.height):(o=Math.min(o,x.x),s=Math.min(s,x.y),a=Math.max(a,x.x+x.width),l=Math.max(l,x.y+x.height)))}});for(var f=this.find("Shape"),p=!1,g=0;gle.indexOf("pointer")>=0?"pointer":le.indexOf("touch")>=0?"touch":"mouse",Z=le=>{const W=q(le);if(W==="pointer")return i.Konva.pointerEventsEnabled&&j.pointer;if(W==="touch")return j.touch;if(W==="mouse")return j.mouse};function ee(le={}){return(le.clipFunc||le.clipWidth||le.clipHeight)&&t.Util.warn("Stage does not support clipping. Please use clip for Layers or Groups."),le}const ie="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);";e.stages=[];class se extends r.Container{constructor(W){super(ee(W)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),e.stages.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{ee(this.attrs)}),this._checkVisibility()}_validateAdd(W){const ne=W.getType()==="Layer",fe=W.getType()==="FastLayer";ne||fe||t.Util.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const W=this.visible()?"":"none";this.content.style.display=W}setContainer(W){if(typeof W===d){if(W.charAt(0)==="."){var ne=W.slice(1);W=document.getElementsByClassName(ne)[0]}else{var fe;W.charAt(0)!=="#"?fe=W:fe=W.slice(1),W=document.getElementById(fe)}if(!W)throw"Can not find container in document with id "+fe}return this._setAttr("container",W),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),W.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var W=this.children,ne=W.length,fe;for(fe=0;fe-1&&e.stages.splice(ne,1),t.Util.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const W=this._pointerPositions[0]||this._changedPointerPositions[0];return W?{x:W.x,y:W.y}:(t.Util.warn(ie),null)}_getPointerById(W){return this._pointerPositions.find(ne=>ne.id===W)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(W){W=W||{},W.x=W.x||0,W.y=W.y||0,W.width=W.width||this.width(),W.height=W.height||this.height();var ne=new o.SceneCanvas({width:W.width,height:W.height,pixelRatio:W.pixelRatio||1}),fe=ne.getContext()._context,pe=this.children;return(W.x||W.y)&&fe.translate(-1*W.x,-1*W.y),pe.forEach(function(ve){if(ve.isVisible()){var ye=ve._toKonvaCanvas(W);fe.drawImage(ye._canvas,W.x,W.y,ye.getWidth()/ye.getPixelRatio(),ye.getHeight()/ye.getPixelRatio())}}),ne}getIntersection(W){if(!W)return null;var ne=this.children,fe=ne.length,pe=fe-1,ve;for(ve=pe;ve>=0;ve--){const ye=ne[ve].getIntersection(W);if(ye)return ye}return null}_resizeDOM(){var W=this.width(),ne=this.height();this.content&&(this.content.style.width=W+f,this.content.style.height=ne+f),this.bufferCanvas.setSize(W,ne),this.bufferHitCanvas.setSize(W,ne),this.children.forEach(fe=>{fe.setSize({width:W,height:ne}),fe.draw()})}add(W,...ne){if(arguments.length>1){for(var fe=0;feD&&t.Util.warn("The stage has "+pe+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),W.setSize({width:this.width(),height:this.height()}),W.draw(),i.Konva.isBrowser&&this.content.appendChild(W.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(W){return l.hasPointerCapture(W,this)}setPointerCapture(W){l.setPointerCapture(W,this)}releaseCapture(W){l.releaseCapture(W,this)}getLayers(){return this.children}_bindContentEvents(){i.Konva.isBrowser&&V.forEach(([W,ne])=>{this.content.addEventListener(W,fe=>{this[ne](fe)},{passive:!1})})}_pointerenter(W){this.setPointersPositions(W);const ne=Z(W.type);this._fire(ne.pointerenter,{evt:W,target:this,currentTarget:this})}_pointerover(W){this.setPointersPositions(W);const ne=Z(W.type);this._fire(ne.pointerover,{evt:W,target:this,currentTarget:this})}_getTargetShape(W){let ne=this[W+"targetShape"];return ne&&!ne.getStage()&&(ne=null),ne}_pointerleave(W){const ne=Z(W.type),fe=q(W.type);if(ne){this.setPointersPositions(W);var pe=this._getTargetShape(fe),ve=!s.DD.isDragging||i.Konva.hitOnDragEnabled;pe&&ve?(pe._fireAndBubble(ne.pointerout,{evt:W}),pe._fireAndBubble(ne.pointerleave,{evt:W}),this._fire(ne.pointerleave,{evt:W,target:this,currentTarget:this}),this[fe+"targetShape"]=null):ve&&(this._fire(ne.pointerleave,{evt:W,target:this,currentTarget:this}),this._fire(ne.pointerout,{evt:W,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(W){const ne=Z(W.type),fe=q(W.type);if(ne){this.setPointersPositions(W);var pe=!1;this._changedPointerPositions.forEach(ve=>{var ye=this.getIntersection(ve);if(s.DD.justDragged=!1,i.Konva["_"+fe+"ListenClick"]=!0,!(ye&&ye.isListening()))return;i.Konva.capturePointerEventsEnabled&&ye.setPointerCapture(ve.id),this[fe+"ClickStartShape"]=ye,ye._fireAndBubble(ne.pointerdown,{evt:W,pointerId:ve.id}),pe=!0;const Me=W.type.indexOf("touch")>=0;ye.preventDefault()&&W.cancelable&&Me&&W.preventDefault()}),pe||this._fire(ne.pointerdown,{evt:W,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(W){const ne=Z(W.type),fe=q(W.type);if(!ne)return;s.DD.isDragging&&s.DD.node.preventDefault()&&W.cancelable&&W.preventDefault(),this.setPointersPositions(W);var pe=!s.DD.isDragging||i.Konva.hitOnDragEnabled;if(!pe)return;var ve={};let ye=!1;var We=this._getTargetShape(fe);this._changedPointerPositions.forEach(Me=>{const Ee=l.getCapturedShape(Me.id)||this.getIntersection(Me),lt=Me.id,_e={evt:W,pointerId:lt};var jt=We!==Ee;if(jt&&We&&(We._fireAndBubble(ne.pointerout,Object.assign({},_e),Ee),We._fireAndBubble(ne.pointerleave,Object.assign({},_e),Ee)),Ee){if(ve[Ee._id])return;ve[Ee._id]=!0}Ee&&Ee.isListening()?(ye=!0,jt&&(Ee._fireAndBubble(ne.pointerover,Object.assign({},_e),We),Ee._fireAndBubble(ne.pointerenter,Object.assign({},_e),We),this[fe+"targetShape"]=Ee),Ee._fireAndBubble(ne.pointermove,Object.assign({},_e))):We&&(this._fire(ne.pointerover,{evt:W,target:this,currentTarget:this,pointerId:lt}),this[fe+"targetShape"]=null)}),ye||this._fire(ne.pointermove,{evt:W,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(W){const ne=Z(W.type),fe=q(W.type);if(!ne)return;this.setPointersPositions(W);const pe=this[fe+"ClickStartShape"],ve=this[fe+"ClickEndShape"];var ye={};let We=!1;this._changedPointerPositions.forEach(Me=>{const Ee=l.getCapturedShape(Me.id)||this.getIntersection(Me);if(Ee){if(Ee.releaseCapture(Me.id),ye[Ee._id])return;ye[Ee._id]=!0}const lt=Me.id,_e={evt:W,pointerId:lt};let jt=!1;i.Konva["_"+fe+"InDblClickWindow"]?(jt=!0,clearTimeout(this[fe+"DblTimeout"])):s.DD.justDragged||(i.Konva["_"+fe+"InDblClickWindow"]=!0,clearTimeout(this[fe+"DblTimeout"])),this[fe+"DblTimeout"]=setTimeout(function(){i.Konva["_"+fe+"InDblClickWindow"]=!1},i.Konva.dblClickWindow),Ee&&Ee.isListening()?(We=!0,this[fe+"ClickEndShape"]=Ee,Ee._fireAndBubble(ne.pointerup,Object.assign({},_e)),i.Konva["_"+fe+"ListenClick"]&&pe&&pe===Ee&&(Ee._fireAndBubble(ne.pointerclick,Object.assign({},_e)),jt&&ve&&ve===Ee&&Ee._fireAndBubble(ne.pointerdblclick,Object.assign({},_e)))):(this[fe+"ClickEndShape"]=null,i.Konva["_"+fe+"ListenClick"]&&this._fire(ne.pointerclick,{evt:W,target:this,currentTarget:this,pointerId:lt}),jt&&this._fire(ne.pointerdblclick,{evt:W,target:this,currentTarget:this,pointerId:lt}))}),We||this._fire(ne.pointerup,{evt:W,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),i.Konva["_"+fe+"ListenClick"]=!1,W.cancelable&&fe!=="touch"&&W.preventDefault()}_contextmenu(W){this.setPointersPositions(W);var ne=this.getIntersection(this.getPointerPosition());ne&&ne.isListening()?ne._fireAndBubble(G,{evt:W}):this._fire(G,{evt:W,target:this,currentTarget:this})}_wheel(W){this.setPointersPositions(W);var ne=this.getIntersection(this.getPointerPosition());ne&&ne.isListening()?ne._fireAndBubble(B,{evt:W}):this._fire(B,{evt:W,target:this,currentTarget:this})}_pointercancel(W){this.setPointersPositions(W);const ne=l.getCapturedShape(W.pointerId)||this.getIntersection(this.getPointerPosition());ne&&ne._fireAndBubble(C,l.createEvent(W)),l.releaseCapture(W.pointerId)}_lostpointercapture(W){l.releaseCapture(W.pointerId)}setPointersPositions(W){var ne=this._getContentPosition(),fe=null,pe=null;W=W||window.event,W.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(W.touches,ve=>{this._pointerPositions.push({id:ve.identifier,x:(ve.clientX-ne.left)/ne.scaleX,y:(ve.clientY-ne.top)/ne.scaleY})}),Array.prototype.forEach.call(W.changedTouches||W.touches,ve=>{this._changedPointerPositions.push({id:ve.identifier,x:(ve.clientX-ne.left)/ne.scaleX,y:(ve.clientY-ne.top)/ne.scaleY})})):(fe=(W.clientX-ne.left)/ne.scaleX,pe=(W.clientY-ne.top)/ne.scaleY,this.pointerPos={x:fe,y:pe},this._pointerPositions=[{x:fe,y:pe,id:t.Util._getFirstPointerId(W)}],this._changedPointerPositions=[{x:fe,y:pe,id:t.Util._getFirstPointerId(W)}])}_setPointerPosition(W){t.Util.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(W)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var W=this.content.getBoundingClientRect();return{top:W.top,left:W.left,scaleX:W.width/this.content.clientWidth||1,scaleY:W.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new o.SceneCanvas({width:this.width(),height:this.height()}),this.bufferHitCanvas=new o.HitCanvas({pixelRatio:1,width:this.width(),height:this.height()}),!!i.Konva.isBrowser){var W=this.container();if(!W)throw"Stage has no container. A container is required.";W.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),W.appendChild(this.content),this._resizeDOM()}}cache(){return t.Util.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(W){W.batchDraw()}),this}}e.Stage=se,se.prototype.nodeType=u,(0,a._registerNode)(se),n.Factory.addGetterSetter(se,"container")})(YU);var Pm={},rr={};(function(e){Object.defineProperty(e,"__esModule",{value:!0}),e.Shape=e.shapes=void 0;const t=rt,n=Sn,r=nt,i=fn,o=ke,s=rt,a=Vi;var l="hasShadow",u="shadowRGBA",d="patternImage",f="linearGradient",p="radialGradient";let g;function m(){return g||(g=n.Util.createCanvasElement().getContext("2d"),g)}e.shapes={};function v(k){const O=this.attrs.fillRule;O?k.fill(O):k.fill()}function x(k){k.stroke()}function _(k){k.fill()}function b(k){k.stroke()}function y(){this._clearCache(l)}function S(){this._clearCache(u)}function C(){this._clearCache(d)}function T(){this._clearCache(f)}function E(){this._clearCache(p)}class P extends i.Node{constructor(O){super(O);let M;for(;M=n.Util.getRandomColor(),!(M&&!(M in e.shapes)););this.colorKey=M,e.shapes[M]=this}getContext(){return n.Util.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return n.Util.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(l,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(d,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var O=m();const M=O.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(M&&M.setTransform){const G=new n.Transform;G.translate(this.fillPatternX(),this.fillPatternY()),G.rotate(t.Konva.getAngle(this.fillPatternRotation())),G.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),G.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const U=G.getMatrix(),A=typeof DOMMatrix>"u"?{a:U[0],b:U[1],c:U[2],d:U[3],e:U[4],f:U[5]}:new DOMMatrix(U);M.setTransform(A)}return M}}_getLinearGradient(){return this._getCache(f,this.__getLinearGradient)}__getLinearGradient(){var O=this.fillLinearGradientColorStops();if(O){for(var M=m(),G=this.fillLinearGradientStartPoint(),U=this.fillLinearGradientEndPoint(),A=M.createLinearGradient(G.x,G.y,U.x,U.y),N=0;Nthis.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const O=this.hitStrokeWidth();return O==="auto"?this.hasStroke():this.strokeEnabled()&&!!O}intersects(O){var M=this.getStage(),G=M.bufferHitCanvas,U;return G.getContext().clear(),this.drawHit(G,null,!0),U=G.context.getImageData(Math.round(O.x),Math.round(O.y),1,1).data,U[3]>0}destroy(){return i.Node.prototype.destroy.call(this),delete e.shapes[this.colorKey],delete this.colorKey,this}_useBufferCanvas(O){var M;if(!this.getStage()||!((M=this.attrs.perfectDrawEnabled)!==null&&M!==void 0?M:!0))return!1;const U=O||this.hasFill(),A=this.hasStroke(),N=this.getAbsoluteOpacity()!==1;if(U&&A&&N)return!0;const F=this.hasShadow(),B=this.shadowForStrokeEnabled();return!!(U&&A&&F&&B)}setStrokeHitEnabled(O){n.Util.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),O?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var O=this.size();return{x:this._centroid?-O.width/2:0,y:this._centroid?-O.height/2:0,width:O.width,height:O.height}}getClientRect(O={}){const M=O.skipTransform,G=O.relativeTo,U=this.getSelfRect(),N=!O.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,F=U.width+N,B=U.height+N,D=!O.skipShadow&&this.hasShadow(),V=D?this.shadowOffsetX():0,j=D?this.shadowOffsetY():0,q=F+Math.abs(V),Z=B+Math.abs(j),ee=D&&this.shadowBlur()||0,ie=q+ee*2,se=Z+ee*2,le={width:ie,height:se,x:-(N/2+ee)+Math.min(V,0)+U.x,y:-(N/2+ee)+Math.min(j,0)+U.y};return M?le:this._transformedRect(le,G)}drawScene(O,M){var G=this.getLayer(),U=O||G.getCanvas(),A=U.getContext(),N=this._getCanvasCache(),F=this.getSceneFunc(),B=this.hasShadow(),D,V,j,q=U.isCache,Z=M===this;if(!this.isVisible()&&!Z)return this;if(N){A.save();var ee=this.getAbsoluteTransform(M).getMatrix();return A.transform(ee[0],ee[1],ee[2],ee[3],ee[4],ee[5]),this._drawCachedSceneCanvas(A),A.restore(),this}if(!F)return this;if(A.save(),this._useBufferCanvas()&&!q){D=this.getStage(),V=D.bufferCanvas,j=V.getContext(),j.clear(),j.save(),j._applyLineJoin(this);var ie=this.getAbsoluteTransform(M).getMatrix();j.transform(ie[0],ie[1],ie[2],ie[3],ie[4],ie[5]),F.call(this,j,this),j.restore();var se=V.pixelRatio;B&&A._applyShadow(this),A._applyOpacity(this),A._applyGlobalCompositeOperation(this),A.drawImage(V._canvas,0,0,V.width/se,V.height/se)}else{if(A._applyLineJoin(this),!Z){var ie=this.getAbsoluteTransform(M).getMatrix();A.transform(ie[0],ie[1],ie[2],ie[3],ie[4],ie[5]),A._applyOpacity(this),A._applyGlobalCompositeOperation(this)}B&&A._applyShadow(this),F.call(this,A,this)}return A.restore(),this}drawHit(O,M,G=!1){if(!this.shouldDrawHit(M,G))return this;var U=this.getLayer(),A=O||U.hitCanvas,N=A&&A.getContext(),F=this.hitFunc()||this.sceneFunc(),B=this._getCanvasCache(),D=B&&B.hit;if(this.colorKey||n.Util.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),D){N.save();var V=this.getAbsoluteTransform(M).getMatrix();return N.transform(V[0],V[1],V[2],V[3],V[4],V[5]),this._drawCachedHitCanvas(N),N.restore(),this}if(!F)return this;if(N.save(),N._applyLineJoin(this),!(this===M)){var q=this.getAbsoluteTransform(M).getMatrix();N.transform(q[0],q[1],q[2],q[3],q[4],q[5])}return F.call(this,N,this),N.restore(),this}drawHitFromCache(O=0){var M=this._getCanvasCache(),G=this._getCachedSceneCanvas(),U=M.hit,A=U.getContext(),N=U.getWidth(),F=U.getHeight(),B,D,V,j,q,Z;A.clear(),A.drawImage(G._canvas,0,0,N,F);try{for(B=A.getImageData(0,0,N,F),D=B.data,V=D.length,j=n.Util._hexToRgb(this.colorKey),q=0;qO?(D[q]=j.r,D[q+1]=j.g,D[q+2]=j.b,D[q+3]=255):D[q+3]=0;A.putImageData(B,0,0)}catch(ee){n.Util.error("Unable to draw hit graph from cached scene canvas. "+ee.message)}return this}hasPointerCapture(O){return a.hasPointerCapture(O,this)}setPointerCapture(O){a.setPointerCapture(O,this)}releaseCapture(O){a.releaseCapture(O,this)}}e.Shape=P,P.prototype._fillFunc=v,P.prototype._strokeFunc=x,P.prototype._fillFuncHit=_,P.prototype._strokeFuncHit=b,P.prototype._centroid=!1,P.prototype.nodeType="Shape",(0,s._registerNode)(P),P.prototype.eventListeners={},P.prototype.on.call(P.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",y),P.prototype.on.call(P.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",S),P.prototype.on.call(P.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",C),P.prototype.on.call(P.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",T),P.prototype.on.call(P.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",E),r.Factory.addGetterSetter(P,"stroke",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(P,"strokeWidth",2,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"fillAfterStrokeEnabled",!1),r.Factory.addGetterSetter(P,"hitStrokeWidth","auto",(0,o.getNumberOrAutoValidator)()),r.Factory.addGetterSetter(P,"strokeHitEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(P,"perfectDrawEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(P,"shadowForStrokeEnabled",!0,(0,o.getBooleanValidator)()),r.Factory.addGetterSetter(P,"lineJoin"),r.Factory.addGetterSetter(P,"lineCap"),r.Factory.addGetterSetter(P,"sceneFunc"),r.Factory.addGetterSetter(P,"hitFunc"),r.Factory.addGetterSetter(P,"dash"),r.Factory.addGetterSetter(P,"dashOffset",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"shadowColor",void 0,(0,o.getStringValidator)()),r.Factory.addGetterSetter(P,"shadowBlur",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"shadowOpacity",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(P,"shadowOffset",["x","y"]),r.Factory.addGetterSetter(P,"shadowOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"shadowOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"fillPatternImage"),r.Factory.addGetterSetter(P,"fill",void 0,(0,o.getStringOrGradientValidator)()),r.Factory.addGetterSetter(P,"fillPatternX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"fillPatternY",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"fillLinearGradientColorStops"),r.Factory.addGetterSetter(P,"strokeLinearGradientColorStops"),r.Factory.addGetterSetter(P,"fillRadialGradientStartRadius",0),r.Factory.addGetterSetter(P,"fillRadialGradientEndRadius",0),r.Factory.addGetterSetter(P,"fillRadialGradientColorStops"),r.Factory.addGetterSetter(P,"fillPatternRepeat","repeat"),r.Factory.addGetterSetter(P,"fillEnabled",!0),r.Factory.addGetterSetter(P,"strokeEnabled",!0),r.Factory.addGetterSetter(P,"shadowEnabled",!0),r.Factory.addGetterSetter(P,"dashEnabled",!0),r.Factory.addGetterSetter(P,"strokeScaleEnabled",!0),r.Factory.addGetterSetter(P,"fillPriority","color"),r.Factory.addComponentsGetterSetter(P,"fillPatternOffset",["x","y"]),r.Factory.addGetterSetter(P,"fillPatternOffsetX",0,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"fillPatternOffsetY",0,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(P,"fillPatternScale",["x","y"]),r.Factory.addGetterSetter(P,"fillPatternScaleX",1,(0,o.getNumberValidator)()),r.Factory.addGetterSetter(P,"fillPatternScaleY",1,(0,o.getNumberValidator)()),r.Factory.addComponentsGetterSetter(P,"fillLinearGradientStartPoint",["x","y"]),r.Factory.addComponentsGetterSetter(P,"strokeLinearGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(P,"fillLinearGradientStartPointX",0),r.Factory.addGetterSetter(P,"strokeLinearGradientStartPointX",0),r.Factory.addGetterSetter(P,"fillLinearGradientStartPointY",0),r.Factory.addGetterSetter(P,"strokeLinearGradientStartPointY",0),r.Factory.addComponentsGetterSetter(P,"fillLinearGradientEndPoint",["x","y"]),r.Factory.addComponentsGetterSetter(P,"strokeLinearGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(P,"fillLinearGradientEndPointX",0),r.Factory.addGetterSetter(P,"strokeLinearGradientEndPointX",0),r.Factory.addGetterSetter(P,"fillLinearGradientEndPointY",0),r.Factory.addGetterSetter(P,"strokeLinearGradientEndPointY",0),r.Factory.addComponentsGetterSetter(P,"fillRadialGradientStartPoint",["x","y"]),r.Factory.addGetterSetter(P,"fillRadialGradientStartPointX",0),r.Factory.addGetterSetter(P,"fillRadialGradientStartPointY",0),r.Factory.addComponentsGetterSetter(P,"fillRadialGradientEndPoint",["x","y"]),r.Factory.addGetterSetter(P,"fillRadialGradientEndPointX",0),r.Factory.addGetterSetter(P,"fillRadialGradientEndPointY",0),r.Factory.addGetterSetter(P,"fillPatternRotation",0),r.Factory.addGetterSetter(P,"fillRule",void 0,(0,o.getStringValidator)()),r.Factory.backCompat(P,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"})})(rr);Object.defineProperty(Pm,"__esModule",{value:!0});Pm.Layer=void 0;const ia=Sn,Dx=vc,hd=fn,bE=nt,XO=Qo,QSe=ke,ZSe=rr,JSe=rt;var e2e="#",t2e="beforeDraw",n2e="draw",JU=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],r2e=JU.length;class Vf extends Dx.Container{constructor(t){super(t),this.canvas=new XO.SceneCanvas,this.hitCanvas=new XO.HitCanvas({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(t2e,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),Dx.Container.prototype.drawScene.call(this,i,n),this._fire(n2e,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),Dx.Container.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){ia.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return ia.Util.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return ia.Util.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}Pm.Layer=Vf;Vf.prototype.nodeType="Layer";(0,JSe._registerNode)(Vf);bE.Factory.addGetterSetter(Vf,"imageSmoothingEnabled",!0);bE.Factory.addGetterSetter(Vf,"clearBeforeDraw",!0);bE.Factory.addGetterSetter(Vf,"hitGraphEnabled",!0,(0,QSe.getBooleanValidator)());var lS={};Object.defineProperty(lS,"__esModule",{value:!0});lS.FastLayer=void 0;const i2e=Sn,o2e=Pm,s2e=rt;class SE extends o2e.Layer{constructor(t){super(t),this.listening(!1),i2e.Util.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}lS.FastLayer=SE;SE.prototype.nodeType="FastLayer";(0,s2e._registerNode)(SE);var jf={};Object.defineProperty(jf,"__esModule",{value:!0});jf.Group=void 0;const a2e=Sn,l2e=vc,u2e=rt;class wE extends l2e.Container{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&a2e.Util.throw("You may only add groups and shapes to groups.")}}jf.Group=wE;wE.prototype.nodeType="Group";(0,u2e._registerNode)(wE);var Gf={};Object.defineProperty(Gf,"__esModule",{value:!0});Gf.Animation=void 0;const $x=rt,YO=Sn;var Fx=function(){return $x.glob.performance&&$x.glob.performance.now?function(){return $x.glob.performance.now()}:function(){return new Date().getTime()}}();class ws{constructor(t,n){this.id=ws.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:Fx(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;ithis.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():m<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=m,this.update())}getTime(){return this._time}setPosition(m){this.prevPos=this._pos,this.propFunc(m),this._pos=m}getPosition(m){return m===void 0&&(m=this._time),this.func(m,this.begin,this._change,this.duration)}play(){this.state=a,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=l,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(m){this.pause(),this._time=m,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var m=this.getTimer()-this._startTime;this.state===a?this.setTime(m):this.state===l&&this.setTime(this.duration-m)}pause(){this.state=s,this.fire("onPause")}getTimer(){return new Date().getTime()}}class p{constructor(m){var v=this,x=m.node,_=x._id,b,y=m.easing||e.Easings.Linear,S=!!m.yoyo,C;typeof m.duration>"u"?b=.3:m.duration===0?b=.001:b=m.duration,this.node=x,this._id=u++;var T=x.getLayer()||(x instanceof i.Konva.Stage?x.getLayers():null);T||t.Util.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new n.Animation(function(){v.tween.onEnterFrame()},T),this.tween=new f(C,function(E){v._tweenFunc(E)},y,0,1,b*1e3,S),this._addListeners(),p.attrs[_]||(p.attrs[_]={}),p.attrs[_][this._id]||(p.attrs[_][this._id]={}),p.tweens[_]||(p.tweens[_]={});for(C in m)o[C]===void 0&&this._addAttr(C,m[C]);this.reset(),this.onFinish=m.onFinish,this.onReset=m.onReset,this.onUpdate=m.onUpdate}_addAttr(m,v){var x=this.node,_=x._id,b,y,S,C,T,E,P,k;if(S=p.tweens[_][m],S&&delete p.attrs[_][S][m],b=x.getAttr(m),t.Util._isArray(v))if(y=[],T=Math.max(v.length,b.length),m==="points"&&v.length!==b.length&&(v.length>b.length?(P=b,b=t.Util._prepareArrayForTween(b,v,x.closed())):(E=v,v=t.Util._prepareArrayForTween(v,b,x.closed()))),m.indexOf("fill")===0)for(C=0;C{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var m=this.node,v=p.attrs[m._id][this._id];v.points&&v.points.trueEnd&&m.setAttr("points",v.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var m=this.node,v=p.attrs[m._id][this._id];v.points&&v.points.trueStart&&m.points(v.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(m){return this.tween.seek(m*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var m=this.node._id,v=this._id,x=p.tweens[m],_;this.pause();for(_ in x)delete p.tweens[m][_];delete p.attrs[m][v]}}e.Tween=p,p.attrs={},p.tweens={},r.Node.prototype.to=function(g){var m=g.onFinish;g.node=this,g.onFinish=function(){this.destroy(),m&&m()};var v=new p(g);v.play()},e.Easings={BackEaseIn(g,m,v,x){var _=1.70158;return v*(g/=x)*g*((_+1)*g-_)+m},BackEaseOut(g,m,v,x){var _=1.70158;return v*((g=g/x-1)*g*((_+1)*g+_)+1)+m},BackEaseInOut(g,m,v,x){var _=1.70158;return(g/=x/2)<1?v/2*(g*g*(((_*=1.525)+1)*g-_))+m:v/2*((g-=2)*g*(((_*=1.525)+1)*g+_)+2)+m},ElasticEaseIn(g,m,v,x,_,b){var y=0;return g===0?m:(g/=x)===1?m+v:(b||(b=x*.3),!_||_0?t:n),d=s*n,f=a*(a>0?t:n),p=l*(l>0?n:t);return{x:u,y:r?-1*p:f,width:d-u,height:p-f}}}uS.Arc=Ua;Ua.prototype._centroid=!0;Ua.prototype.className="Arc";Ua.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,d2e._registerNode)(Ua);cS.Factory.addGetterSetter(Ua,"innerRadius",0,(0,dS.getNumberValidator)());cS.Factory.addGetterSetter(Ua,"outerRadius",0,(0,dS.getNumberValidator)());cS.Factory.addGetterSetter(Ua,"angle",0,(0,dS.getNumberValidator)());cS.Factory.addGetterSetter(Ua,"clockwise",!1,(0,dS.getBooleanValidator)());var fS={},Rm={};Object.defineProperty(Rm,"__esModule",{value:!0});Rm.Line=void 0;const hS=nt,f2e=rr,tz=ke,h2e=rt;function a5(e,t,n,r,i,o,s){var a=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),u=s*a/(a+l),d=s*l/(a+l),f=n-u*(i-e),p=r-u*(o-t),g=n+d*(i-e),m=r+d*(o-t);return[f,p,g,m]}function ZO(e,t){var n=e.length,r=[],i,o;for(i=2;i4){for(a=this.getTensionPoints(),l=a.length,u=o?0:4,o||t.quadraticCurveTo(a[0],a[1],a[2],a[3]);u{let u,d,f;u=l/2,d=0;for(let g=0;g<20;g++)f=u*e.tValues[20][g]+u,d+=e.cValues[20][g]*r(s,a,f);return u*d};e.getCubicArcLength=t;const n=(s,a,l)=>{l===void 0&&(l=1);const u=s[0]-2*s[1]+s[2],d=a[0]-2*a[1]+a[2],f=2*s[1]-2*s[0],p=2*a[1]-2*a[0],g=4*(u*u+d*d),m=4*(u*f+d*p),v=f*f+p*p;if(g===0)return l*Math.sqrt(Math.pow(s[2]-s[0],2)+Math.pow(a[2]-a[0],2));const x=m/(2*g),_=v/g,b=l+x,y=_-x*x,S=b*b+y>0?Math.sqrt(b*b+y):0,C=x*x+y>0?Math.sqrt(x*x+y):0,T=x+Math.sqrt(x*x+y)!==0?y*Math.log(Math.abs((b+S)/(x+C))):0;return Math.sqrt(g)/2*(b*S-x*C+T)};e.getQuadraticArcLength=n;function r(s,a,l){const u=i(1,l,s),d=i(1,l,a),f=u*u+d*d;return Math.sqrt(f)}const i=(s,a,l)=>{const u=l.length-1;let d,f;if(u===0)return 0;if(s===0){f=0;for(let p=0;p<=u;p++)f+=e.binomialCoefficients[u][p]*Math.pow(1-a,u-p)*Math.pow(a,p)*l[p];return f}else{d=new Array(u);for(let p=0;p{let u=1,d=s/a,f=(s-l(d))/a,p=0;for(;u>.001;){const g=l(d+f),m=Math.abs(s-g)/a;if(m500)break}return d};e.t2length=o})(nz);Object.defineProperty(Hf,"__esModule",{value:!0});Hf.Path=void 0;const p2e=nt,g2e=rr,m2e=rt,pd=nz;class Zn extends g2e.Shape{constructor(t){super(t),this.dataArray=[],this.pathLength=0,this._readDataAttribute(),this.on("dataChange.konva",function(){this._readDataAttribute()})}_readDataAttribute(){this.dataArray=Zn.parsePathData(this.data()),this.pathLength=Zn.getPathLength(this.dataArray)}_sceneFunc(t){var n=this.dataArray;t.beginPath();for(var r=!1,i=0;id?u:d,x=u>d?1:u/d,_=u>d?d/u:1;t.translate(a,l),t.rotate(g),t.scale(x,_),t.arc(0,0,v,f,f+p,1-m),t.scale(1/x,1/_),t.rotate(-g),t.translate(-a,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(u){if(u.command==="A"){var d=u.points[4],f=u.points[5],p=u.points[4]+f,g=Math.PI/180;if(Math.abs(d-p)p;m-=g){const v=Zn.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],m,0);t.push(v.x,v.y)}else for(let m=d+g;mn[i].pathLength;)t-=n[i].pathLength,++i;if(i===o)return r=n[i-1].points.slice(-2),{x:r[0],y:r[1]};if(t<.01)return r=n[i].points.slice(0,2),{x:r[0],y:r[1]};var s=n[i],a=s.points;switch(s.command){case"L":return Zn.getPointOnLine(t,s.start.x,s.start.y,a[0],a[1]);case"C":return Zn.getPointOnCubicBezier((0,pd.t2length)(t,Zn.getPathLength(n),v=>(0,pd.getCubicArcLength)([s.start.x,a[0],a[2],a[4]],[s.start.y,a[1],a[3],a[5]],v)),s.start.x,s.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return Zn.getPointOnQuadraticBezier((0,pd.t2length)(t,Zn.getPathLength(n),v=>(0,pd.getQuadraticArcLength)([s.start.x,a[0],a[2]],[s.start.y,a[1],a[3]],v)),s.start.x,s.start.y,a[0],a[1],a[2],a[3]);case"A":var l=a[0],u=a[1],d=a[2],f=a[3],p=a[4],g=a[5],m=a[6];return p+=g*t/s.pathLength,Zn.getPointOnEllipticalArc(l,u,d,f,p,m)}return null}static getPointOnLine(t,n,r,i,o,s,a){s===void 0&&(s=n),a===void 0&&(a=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i0&&!isNaN(m[0]);){var b=null,y=[],S=l,C=u,T,E,P,k,O,M,G,U,A,N;switch(g){case"l":l+=m.shift(),u+=m.shift(),b="L",y.push(l,u);break;case"L":l=m.shift(),u=m.shift(),y.push(l,u);break;case"m":var F=m.shift(),B=m.shift();if(l+=F,u+=B,b="M",s.length>2&&s[s.length-1].command==="z"){for(var D=s.length-2;D>=0;D--)if(s[D].command==="M"){l=s[D].points[0]+F,u=s[D].points[1]+B;break}}y.push(l,u),g="l";break;case"M":l=m.shift(),u=m.shift(),b="M",y.push(l,u),g="L";break;case"h":l+=m.shift(),b="L",y.push(l,u);break;case"H":l=m.shift(),b="L",y.push(l,u);break;case"v":u+=m.shift(),b="L",y.push(l,u);break;case"V":u=m.shift(),b="L",y.push(l,u);break;case"C":y.push(m.shift(),m.shift(),m.shift(),m.shift()),l=m.shift(),u=m.shift(),y.push(l,u);break;case"c":y.push(l+m.shift(),u+m.shift(),l+m.shift(),u+m.shift()),l+=m.shift(),u+=m.shift(),b="C",y.push(l,u);break;case"S":E=l,P=u,T=s[s.length-1],T.command==="C"&&(E=l+(l-T.points[2]),P=u+(u-T.points[3])),y.push(E,P,m.shift(),m.shift()),l=m.shift(),u=m.shift(),b="C",y.push(l,u);break;case"s":E=l,P=u,T=s[s.length-1],T.command==="C"&&(E=l+(l-T.points[2]),P=u+(u-T.points[3])),y.push(E,P,l+m.shift(),u+m.shift()),l+=m.shift(),u+=m.shift(),b="C",y.push(l,u);break;case"Q":y.push(m.shift(),m.shift()),l=m.shift(),u=m.shift(),y.push(l,u);break;case"q":y.push(l+m.shift(),u+m.shift()),l+=m.shift(),u+=m.shift(),b="Q",y.push(l,u);break;case"T":E=l,P=u,T=s[s.length-1],T.command==="Q"&&(E=l+(l-T.points[0]),P=u+(u-T.points[1])),l=m.shift(),u=m.shift(),b="Q",y.push(E,P,l,u);break;case"t":E=l,P=u,T=s[s.length-1],T.command==="Q"&&(E=l+(l-T.points[0]),P=u+(u-T.points[1])),l+=m.shift(),u+=m.shift(),b="Q",y.push(E,P,l,u);break;case"A":k=m.shift(),O=m.shift(),M=m.shift(),G=m.shift(),U=m.shift(),A=l,N=u,l=m.shift(),u=m.shift(),b="A",y=this.convertEndpointToCenterParameterization(A,N,l,u,G,U,k,O,M);break;case"a":k=m.shift(),O=m.shift(),M=m.shift(),G=m.shift(),U=m.shift(),A=l,N=u,l+=m.shift(),u+=m.shift(),b="A",y=this.convertEndpointToCenterParameterization(A,N,l,u,G,U,k,O,M);break}s.push({command:b||g,points:y,start:{x:S,y:C},pathLength:this.calcLength(S,C,b||g,y)})}(g==="z"||g==="Z")&&s.push({command:"z",points:[],start:void 0,pathLength:0})}return s}static calcLength(t,n,r,i){var o,s,a,l,u=Zn;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":return(0,pd.getCubicArcLength)([t,i[0],i[2],i[4]],[n,i[1],i[3],i[5]],1);case"Q":return(0,pd.getQuadraticArcLength)([t,i[0],i[2]],[n,i[1],i[3]],1);case"A":o=0;var d=i[4],f=i[5],p=i[4]+f,g=Math.PI/180;if(Math.abs(d-p)p;l-=g)a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(s.x,s.y,a.x,a.y),s=a;else for(l=d+g;l1&&(a*=Math.sqrt(g),l*=Math.sqrt(g));var m=Math.sqrt((a*a*(l*l)-a*a*(p*p)-l*l*(f*f))/(a*a*(p*p)+l*l*(f*f)));o===s&&(m*=-1),isNaN(m)&&(m=0);var v=m*a*p/l,x=m*-l*f/a,_=(t+r)/2+Math.cos(d)*v-Math.sin(d)*x,b=(n+i)/2+Math.sin(d)*v+Math.cos(d)*x,y=function(O){return Math.sqrt(O[0]*O[0]+O[1]*O[1])},S=function(O,M){return(O[0]*M[0]+O[1]*M[1])/(y(O)*y(M))},C=function(O,M){return(O[0]*M[1]=1&&(k=0),s===0&&k>0&&(k=k-2*Math.PI),s===1&&k<0&&(k=k+2*Math.PI),[_,b,a,l,T,k,d,s]}}Hf.Path=Zn;Zn.prototype.className="Path";Zn.prototype._attrsAffectingSize=["data"];(0,m2e._registerNode)(Zn);p2e.Factory.addGetterSetter(Zn,"data");Object.defineProperty(fS,"__esModule",{value:!0});fS.Arrow=void 0;const pS=nt,y2e=Rm,rz=ke,v2e=rt,JO=Hf;class bc extends y2e.Line{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var s=this.pointerLength(),a=r.length,l,u;if(o){const p=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[a-2],r[a-1]],g=JO.Path.calcLength(i[i.length-4],i[i.length-3],"C",p),m=JO.Path.getPointOnQuadraticBezier(Math.min(1,1-s/g),p[0],p[1],p[2],p[3],p[4],p[5]);l=r[a-2]-m.x,u=r[a-1]-m.y}else l=r[a-2]-r[a-4],u=r[a-1]-r[a-3];var d=(Math.atan2(u,l)+n)%n,f=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[a-2],r[a-1]),t.rotate(d),t.moveTo(0,0),t.lineTo(-s,f/2),t.lineTo(-s,-f/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-s,f/2),t.lineTo(-s,-f/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}fS.Arrow=bc;bc.prototype.className="Arrow";(0,v2e._registerNode)(bc);pS.Factory.addGetterSetter(bc,"pointerLength",10,(0,rz.getNumberValidator)());pS.Factory.addGetterSetter(bc,"pointerWidth",10,(0,rz.getNumberValidator)());pS.Factory.addGetterSetter(bc,"pointerAtBeginning",!1);pS.Factory.addGetterSetter(bc,"pointerAtEnding",!0);var gS={};Object.defineProperty(gS,"__esModule",{value:!0});gS.Circle=void 0;const _2e=nt,b2e=rr,S2e=ke,w2e=rt;let Wf=class extends b2e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}};gS.Circle=Wf;Wf.prototype._centroid=!0;Wf.prototype.className="Circle";Wf.prototype._attrsAffectingSize=["radius"];(0,w2e._registerNode)(Wf);_2e.Factory.addGetterSetter(Wf,"radius",0,(0,S2e.getNumberValidator)());var mS={};Object.defineProperty(mS,"__esModule",{value:!0});mS.Ellipse=void 0;const xE=nt,x2e=rr,iz=ke,C2e=rt;class ou extends x2e.Shape{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}mS.Ellipse=ou;ou.prototype.className="Ellipse";ou.prototype._centroid=!0;ou.prototype._attrsAffectingSize=["radiusX","radiusY"];(0,C2e._registerNode)(ou);xE.Factory.addComponentsGetterSetter(ou,"radius",["x","y"]);xE.Factory.addGetterSetter(ou,"radiusX",0,(0,iz.getNumberValidator)());xE.Factory.addGetterSetter(ou,"radiusY",0,(0,iz.getNumberValidator)());var yS={};Object.defineProperty(yS,"__esModule",{value:!0});yS.Image=void 0;const Bx=Sn,Sc=nt,T2e=rr,E2e=rt,Om=ke;let Us=class oz extends T2e.Shape{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.cornerRadius(),o=this.attrs.image;let s;if(o){const a=this.attrs.cropWidth,l=this.attrs.cropHeight;a&&l?s=[o,this.cropX(),this.cropY(),a,l,0,0,n,r]:s=[o,0,0,n,r]}(this.hasFill()||this.hasStroke()||i)&&(t.beginPath(),i?Bx.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),o&&(i&&t.clip(),t.drawImage.apply(t,s))}_hitFunc(t){var n=this.width(),r=this.height(),i=this.cornerRadius();t.beginPath(),i?Bx.Util.drawRoundedRectPath(t,n,r,i):t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=Bx.Util.createImageElement();i.onload=function(){var o=new oz({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}};yS.Image=Us;Us.prototype.className="Image";(0,E2e._registerNode)(Us);Sc.Factory.addGetterSetter(Us,"cornerRadius",0,(0,Om.getNumberOrArrayOfNumbersValidator)(4));Sc.Factory.addGetterSetter(Us,"image");Sc.Factory.addComponentsGetterSetter(Us,"crop",["x","y","width","height"]);Sc.Factory.addGetterSetter(Us,"cropX",0,(0,Om.getNumberValidator)());Sc.Factory.addGetterSetter(Us,"cropY",0,(0,Om.getNumberValidator)());Sc.Factory.addGetterSetter(Us,"cropWidth",0,(0,Om.getNumberValidator)());Sc.Factory.addGetterSetter(Us,"cropHeight",0,(0,Om.getNumberValidator)());var Rf={};Object.defineProperty(Rf,"__esModule",{value:!0});Rf.Tag=Rf.Label=void 0;const vS=nt,A2e=rr,P2e=jf,CE=ke,sz=rt;var az=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],R2e="Change.konva",O2e="none",l5="up",u5="right",c5="down",d5="left",k2e=az.length;class TE extends P2e.Group{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r{n=Math.min(n,s.x),r=Math.max(r,s.x),i=Math.min(i,s.y),o=Math.max(o,s.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}bS.RegularPolygon=xc;xc.prototype.className="RegularPolygon";xc.prototype._centroid=!0;xc.prototype._attrsAffectingSize=["radius"];(0,F2e._registerNode)(xc);lz.Factory.addGetterSetter(xc,"radius",0,(0,uz.getNumberValidator)());lz.Factory.addGetterSetter(xc,"sides",0,(0,uz.getNumberValidator)());var SS={};Object.defineProperty(SS,"__esModule",{value:!0});SS.Ring=void 0;const cz=nt,B2e=rr,dz=ke,U2e=rt;var ek=Math.PI*2;class Cc extends B2e.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,ek,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),ek,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}SS.Ring=Cc;Cc.prototype.className="Ring";Cc.prototype._centroid=!0;Cc.prototype._attrsAffectingSize=["innerRadius","outerRadius"];(0,U2e._registerNode)(Cc);cz.Factory.addGetterSetter(Cc,"innerRadius",0,(0,dz.getNumberValidator)());cz.Factory.addGetterSetter(Cc,"outerRadius",0,(0,dz.getNumberValidator)());var wS={};Object.defineProperty(wS,"__esModule",{value:!0});wS.Sprite=void 0;const Tc=nt,z2e=rr,V2e=Gf,fz=ke,j2e=rt;class zs extends z2e.Shape{constructor(t){super(t),this._updated=!0,this.anim=new V2e.Animation(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+0],l=o[i+1],u=o[i+2],d=o[i+3],f=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,d),t.closePath(),t.fillStrokeShape(this)),f)if(s){var p=s[n],g=r*2;t.drawImage(f,a,l,u,d,p[g+0],p[g+1],u,d)}else t.drawImage(f,a,l,u,d,0,0,u,d)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],s=this.frameOffsets(),a=o[i+2],l=o[i+3];if(t.beginPath(),s){var u=s[n],d=r*2;t.rect(u[d+0],u[d+1],a,l)}else t.rect(0,0,a,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var $0;function zx(){return $0||($0=f5.Util.createCanvasElement().getContext(Y2e),$0)}function awe(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function lwe(e){e.setAttr("miterLimit",2),e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function uwe(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class Dn extends W2e.Shape{constructor(t){super(uwe(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n1&&(_+=s)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=f5.Util._isString(t)?t:t==null?"":t+"";return this._setAttr(Q2e,n),this}getWidth(){var t=this.attrs.width===gd||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===gd||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return f5.Util.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=zx(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+D0+this.fontVariant()+D0+(this.fontSize()+twe)+swe(this.fontFamily())}_addTextLine(t){this.align()===Zh&&(t=t.trim());var r=this._getTextWidth(t);return this.textArr.push({text:t,width:r,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return zx().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(` +`),n=+this.fontSize(),r=0,i=this.lineHeight()*n,o=this.attrs.width,s=this.attrs.height,a=o!==gd&&o!==void 0,l=s!==gd&&s!==void 0,u=this.padding(),d=o-u*2,f=s-u*2,p=0,g=this.wrap(),m=g!==rk,v=g!==iwe&&m,x=this.ellipsis();this.textArr=[],zx().font=this._getContextFont();for(var _=x?this._getTextWidth(Ux):0,b=0,y=t.length;bd)for(;S.length>0;){for(var T=0,E=S.length,P="",k=0;T>>1,M=S.slice(0,O+1),G=this._getTextWidth(M)+_;G<=d?(T=O+1,P=M,k=G):E=O}if(P){if(v){var U,A=S[P.length],N=A===D0||A===tk;N&&k<=d?U=P.length:U=Math.max(P.lastIndexOf(D0),P.lastIndexOf(tk))+1,U>0&&(T=U,P=P.slice(0,T),k=this._getTextWidth(P))}P=P.trimRight(),this._addTextLine(P),r=Math.max(r,k),p+=i;var F=this._shouldHandleEllipsis(p);if(F){this._tryToAddEllipsisToLastLine();break}if(S=S.slice(T),S=S.trimLeft(),S.length>0&&(C=this._getTextWidth(S),C<=d)){this._addTextLine(S),p+=i,r=Math.max(r,C);break}}else break}else this._addTextLine(S),p+=i,r=Math.max(r,C),this._shouldHandleEllipsis(p)&&bf)break}this.textHeight=n,this.textWidth=r}_shouldHandleEllipsis(t){var n=+this.fontSize(),r=this.lineHeight()*n,i=this.attrs.height,o=i!==gd&&i!==void 0,s=this.padding(),a=i-s*2,l=this.wrap(),u=l!==rk;return!u||o&&t+r>a}_tryToAddEllipsisToLastLine(){var t=this.attrs.width,n=t!==gd&&t!==void 0,r=this.padding(),i=t-r*2,o=this.ellipsis(),s=this.textArr[this.textArr.length-1];if(!(!s||!o)){if(n){var a=this._getTextWidth(s.text+Ux)n?null:Jh.Path.getPointAtLengthOfDataArray(t,this.dataArray)}_readDataAttribute(){this.dataArray=Jh.Path.parsePathData(this.attrs.data),this.pathLength=this._getTextPathLength()}_sceneFunc(t){t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",this.textBaseline()),t.setAttr("textAlign","left"),t.save();var n=this.textDecoration(),r=this.fill(),i=this.fontSize(),o=this.glyphInfo;n==="underline"&&t.beginPath();for(var s=0;s=1){var r=n[0].p0;t.moveTo(r.x,r.y)}for(var i=0;ie+`.${Sz}`).join(" "),sk="nodesRect",ywe=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],vwe={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const _we="ontouchstart"in $o.Konva._global;function bwe(e,t){if(e==="rotater")return"crosshair";t+=zt.Util.degToRad(vwe[e]||0);var n=(zt.Util.radToDeg(t)%360+360)%360;return zt.Util._inRange(n,315+22.5,360)||zt.Util._inRange(n,0,22.5)?"ns-resize":zt.Util._inRange(n,45-22.5,45+22.5)?"nesw-resize":zt.Util._inRange(n,90-22.5,90+22.5)?"ew-resize":zt.Util._inRange(n,135-22.5,135+22.5)?"nwse-resize":zt.Util._inRange(n,180-22.5,180+22.5)?"ns-resize":zt.Util._inRange(n,225-22.5,225+22.5)?"nesw-resize":zt.Util._inRange(n,270-22.5,270+22.5)?"ew-resize":zt.Util._inRange(n,315-22.5,315+22.5)?"nwse-resize":(zt.Util.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var e_=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],ak=1e8;function Swe(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function wz(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function wwe(e,t){const n=Swe(e);return wz(e,t,n)}function xwe(e,t,n){let r=t;for(let i=0;ii.isAncestorOf(this)?(zt.Util.error("Konva.Transformer cannot be an a child of the node you are trying to attach"),!1):!0);this._nodes=t=n,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(i=>{const o=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},s=i._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");i.on(s,o),i.on(ywe.map(a=>a+`.${this._getEventNamespace()}`).join(" "),o),i.on(`absoluteTransformChange.${this._getEventNamespace()}`,o),this._proxyDrag(i)}),this._resetTransformCache();var r=!!this.findOne(".top-left");return r&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,s=i.y-n.y;this.nodes().forEach(a=>{if(a===t||a.isDragging())return;const l=a.getAbsolutePosition();a.setAbsolutePosition({x:l.x+o,y:l.y+s}),a.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(sk),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(sk,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),s=t.getAbsolutePosition(r),a=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=($o.Konva.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),d={x:s.x+a*Math.cos(u)+l*Math.sin(-u),y:s.y+l*Math.cos(u)+a*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return wz(d,-$o.Konva.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-ak,y:-ak,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const d=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var f=[{x:d.x,y:d.y},{x:d.x+d.width,y:d.y},{x:d.x+d.width,y:d.y+d.height},{x:d.x,y:d.y+d.height}],p=u.getAbsoluteTransform();f.forEach(function(g){var m=p.point(g);n.push(m)})});const r=new zt.Transform;r.rotate(-$o.Konva.getAngle(this.rotation()));var i,o,s,a;n.forEach(function(u){var d=r.point(u);i===void 0&&(i=s=d.x,o=a=d.y),i=Math.min(i,d.x),o=Math.min(o,d.y),s=Math.max(s,d.x),a=Math.max(a,d.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:s-i,height:a-o,rotation:$o.Konva.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),e_.forEach((function(t){this._createAnchor(t)}).bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new pwe.Rect({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:_we?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=$o.Konva.getAngle(this.rotation()),o=bwe(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new hwe.Shape({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*zt.Util._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var s=t.target.getAbsolutePosition(),a=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-s.x,y:a.y-s.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),s=o.getStage();s.setPointersPositions(t);const a=s.getPointerPosition();let l={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const d=o.getAbsolutePosition();if(!(u.x===d.x&&u.y===d.y)){if(this._movingAnchorName==="rotater"){var f=this._getNodeRect();n=o.x()-f.width/2,r=-o.y()+f.height/2;let U=Math.atan2(-r,n)+Math.PI/2;f.height<0&&(U-=Math.PI);var p=$o.Konva.getAngle(this.rotation());const A=p+U,N=$o.Konva.getAngle(this.rotationSnapTolerance()),B=xwe(this.rotationSnaps(),A,N)-f.rotation,D=wwe(f,B);this._fitNodesInto(D,t);return}var g=this.shiftBehavior(),m;g==="inverted"?m=this.keepRatio()&&!t.shiftKey:g==="none"?m=this.keepRatio():m=this.keepRatio()||t.shiftKey;var y=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(m){var v=y?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(v.x-o.x(),2)+Math.pow(v.y-o.y(),2));var x=this.findOne(".top-left").x()>v.x?-1:1,_=this.findOne(".top-left").y()>v.y?-1:1;n=i*this.cos*x,r=i*this.sin*_,this.findOne(".top-left").x(v.x-n),this.findOne(".top-left").y(v.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(m){var v=y?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-v.x,2)+Math.pow(v.y-o.y(),2));var x=this.findOne(".top-right").x()v.y?-1:1;n=i*this.cos*x,r=i*this.sin*_,this.findOne(".top-right").x(v.x+n),this.findOne(".top-right").y(v.y-r)}var b=o.position();this.findOne(".top-left").y(b.y),this.findOne(".bottom-right").x(b.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(m){var v=y?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(v.x-o.x(),2)+Math.pow(o.y()-v.y,2));var x=v.x{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(zt.Util._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(zt.Util._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var s=new zt.Transform;if(s.rotate($o.Konva.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const f=s.point({x:-this.padding()*2,y:0});if(t.x+=f.x,t.y+=f.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=f.x,this._anchorDragOffset.y-=f.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const f=s.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=f.x,this._anchorDragOffset.y-=f.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const f=s.point({x:0,y:-this.padding()*2});if(t.x+=f.x,t.y+=f.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=f.x,this._anchorDragOffset.y-=f.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const f=s.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=f.x,this._anchorDragOffset.y-=f.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const f=this.boundBoxFunc()(r,t);f?t=f:zt.Util.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const a=1e7,l=new zt.Transform;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/a,r.height/a);const u=new zt.Transform;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/a,t.height/a);const d=u.multiply(l.invert());this._nodes.forEach(f=>{var p;const g=f.getParent().getAbsoluteTransform(),m=f.getTransform().copy();m.translate(f.offsetX(),f.offsetY());const v=new zt.Transform;v.multiply(g.copy().invert()).multiply(d).multiply(g).multiply(m);const x=v.decompose();f.setAttrs(x),this._fire("transform",{evt:n,target:f}),f._fire("transform",{evt:n,target:f}),(p=f.getLayer())===null||p===void 0||p.batchDraw()}),this.rotation(zt.Util._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(zt.Util._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),s=this.resizeEnabled(),a=this.padding(),l=this.anchorSize();const u=this.find("._anchor");u.forEach(f=>{f.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+a,offsetY:l/2+a,visible:s&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+a,visible:s&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-a,offsetY:l/2+a,visible:s&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+a,visible:s&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-a,visible:s&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-a,visible:s&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-a,offsetY:l/2-a,visible:s&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*zt.Util._sign(i)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0});const d=this.anchorStyleFunc();d&&u.forEach(f=>{d(f)}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),ok.Group.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return ik.Node.prototype.toObject.call(this)}clone(t){var n=ik.Node.prototype.clone.call(this,t);return n}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}TS.Transformer=Ct;function Cwe(e){return e instanceof Array||zt.Util.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){e_.indexOf(t)===-1&&zt.Util.warn("Unknown anchor name: "+t+". Available names are: "+e_.join(", "))}),e||[]}Ct.prototype.className="Transformer";(0,gwe._registerNode)(Ct);Ft.Factory.addGetterSetter(Ct,"enabledAnchors",e_,Cwe);Ft.Factory.addGetterSetter(Ct,"flipEnabled",!0,(0,lu.getBooleanValidator)());Ft.Factory.addGetterSetter(Ct,"resizeEnabled",!0);Ft.Factory.addGetterSetter(Ct,"anchorSize",10,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"rotateEnabled",!0);Ft.Factory.addGetterSetter(Ct,"rotationSnaps",[]);Ft.Factory.addGetterSetter(Ct,"rotateAnchorOffset",50,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"rotationSnapTolerance",5,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"borderEnabled",!0);Ft.Factory.addGetterSetter(Ct,"anchorStroke","rgb(0, 161, 255)");Ft.Factory.addGetterSetter(Ct,"anchorStrokeWidth",1,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"anchorFill","white");Ft.Factory.addGetterSetter(Ct,"anchorCornerRadius",0,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"borderStroke","rgb(0, 161, 255)");Ft.Factory.addGetterSetter(Ct,"borderStrokeWidth",1,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"borderDash");Ft.Factory.addGetterSetter(Ct,"keepRatio",!0);Ft.Factory.addGetterSetter(Ct,"shiftBehavior","default");Ft.Factory.addGetterSetter(Ct,"centeredScaling",!1);Ft.Factory.addGetterSetter(Ct,"ignoreStroke",!1);Ft.Factory.addGetterSetter(Ct,"padding",0,(0,lu.getNumberValidator)());Ft.Factory.addGetterSetter(Ct,"node");Ft.Factory.addGetterSetter(Ct,"nodes");Ft.Factory.addGetterSetter(Ct,"boundBoxFunc");Ft.Factory.addGetterSetter(Ct,"anchorDragBoundFunc");Ft.Factory.addGetterSetter(Ct,"anchorStyleFunc");Ft.Factory.addGetterSetter(Ct,"shouldOverdrawWholeArea",!1);Ft.Factory.addGetterSetter(Ct,"useSingleNodeRotation",!0);Ft.Factory.backCompat(Ct,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});var ES={};Object.defineProperty(ES,"__esModule",{value:!0});ES.Wedge=void 0;const AS=nt,Twe=rr,Ewe=rt,xz=ke,Awe=rt;class za extends Twe.Shape{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,Ewe.Konva.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}ES.Wedge=za;za.prototype.className="Wedge";za.prototype._centroid=!0;za.prototype._attrsAffectingSize=["radius"];(0,Awe._registerNode)(za);AS.Factory.addGetterSetter(za,"radius",0,(0,xz.getNumberValidator)());AS.Factory.addGetterSetter(za,"angle",0,(0,xz.getNumberValidator)());AS.Factory.addGetterSetter(za,"clockwise",!1);AS.Factory.backCompat(za,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});var PS={};Object.defineProperty(PS,"__esModule",{value:!0});PS.Blur=void 0;const lk=nt,Pwe=fn,Rwe=ke;function uk(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var Owe=[512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,289,287,285,282,280,278,275,273,271,269,267,265,263,261,259],kwe=[9,11,12,13,13,14,14,15,15,15,15,16,16,16,16,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24];function Iwe(e,t){var n=e.data,r=e.width,i=e.height,o,s,a,l,u,d,f,p,g,m,v,x,_,b,y,S,C,T,E,P,k,O,M,G,U=t+t+1,A=r-1,N=i-1,F=t+1,B=F*(F+1)/2,D=new uk,V=null,j=D,q=null,Z=null,ee=Owe[t],ie=kwe[t];for(a=1;a>ie,M!==0?(M=255/M,n[d]=(p*ee>>ie)*M,n[d+1]=(g*ee>>ie)*M,n[d+2]=(m*ee>>ie)*M):n[d]=n[d+1]=n[d+2]=0,p-=x,g-=_,m-=b,v-=y,x-=q.r,_-=q.g,b-=q.b,y-=q.a,l=f+((l=o+t+1)>ie,M>0?(M=255/M,n[l]=(p*ee>>ie)*M,n[l+1]=(g*ee>>ie)*M,n[l+2]=(m*ee>>ie)*M):n[l]=n[l+1]=n[l+2]=0,p-=x,g-=_,m-=b,v-=y,x-=q.r,_-=q.g,b-=q.b,y-=q.a,l=o+((l=s+F)0&&Iwe(t,n)};PS.Blur=Mwe;lk.Factory.addGetterSetter(Pwe.Node,"blurRadius",0,(0,Rwe.getNumberValidator)(),lk.Factory.afterSetFilter);var RS={};Object.defineProperty(RS,"__esModule",{value:!0});RS.Brighten=void 0;const ck=nt,Nwe=fn,Lwe=ke,Dwe=function(e){var t=this.brightness()*255,n=e.data,r=n.length,i;for(i=0;i255?255:i,o=o<0?0:o>255?255:o,s=s<0?0:s>255?255:s,n[a]=i,n[a+1]=o,n[a+2]=s};OS.Contrast=Bwe;dk.Factory.addGetterSetter($we.Node,"contrast",0,(0,Fwe.getNumberValidator)(),dk.Factory.afterSetFilter);var kS={};Object.defineProperty(kS,"__esModule",{value:!0});kS.Emboss=void 0;const ql=nt,IS=fn,Uwe=Sn,Cz=ke,zwe=function(e){var t=this.embossStrength()*10,n=this.embossWhiteLevel()*255,r=this.embossDirection(),i=this.embossBlend(),o=0,s=0,a=e.data,l=e.width,u=e.height,d=l*4,f=u;switch(r){case"top-left":o=-1,s=-1;break;case"top":o=-1,s=0;break;case"top-right":o=-1,s=1;break;case"right":o=0,s=1;break;case"bottom-right":o=1,s=1;break;case"bottom":o=1,s=0;break;case"bottom-left":o=1,s=-1;break;case"left":o=0,s=-1;break;default:Uwe.Util.error("Unknown emboss direction: "+r)}do{var p=(f-1)*d,g=o;f+g<1&&(g=0),f+g>u&&(g=0);var m=(f-1+g)*l*4,v=l;do{var x=p+(v-1)*4,_=s;v+_<1&&(_=0),v+_>l&&(_=0);var b=m+(v-1+_)*4,y=a[x]-a[b],S=a[x+1]-a[b+1],C=a[x+2]-a[b+2],T=y,E=T>0?T:-T,P=S>0?S:-S,k=C>0?C:-C;if(P>E&&(T=S),k>E&&(T=C),T*=t,i){var O=a[x]+T,M=a[x+1]+T,G=a[x+2]+T;a[x]=O>255?255:O<0?0:O,a[x+1]=M>255?255:M<0?0:M,a[x+2]=G>255?255:G<0?0:G}else{var U=n-T;U<0?U=0:U>255&&(U=255),a[x]=a[x+1]=a[x+2]=U}}while(--v)}while(--f)};kS.Emboss=zwe;ql.Factory.addGetterSetter(IS.Node,"embossStrength",.5,(0,Cz.getNumberValidator)(),ql.Factory.afterSetFilter);ql.Factory.addGetterSetter(IS.Node,"embossWhiteLevel",.5,(0,Cz.getNumberValidator)(),ql.Factory.afterSetFilter);ql.Factory.addGetterSetter(IS.Node,"embossDirection","top-left",null,ql.Factory.afterSetFilter);ql.Factory.addGetterSetter(IS.Node,"embossBlend",!1,null,ql.Factory.afterSetFilter);var MS={};Object.defineProperty(MS,"__esModule",{value:!0});MS.Enhance=void 0;const fk=nt,Vwe=fn,jwe=ke;function Gx(e,t,n,r,i){var o=n-t,s=i-r,a;return o===0?r+s/2:s===0?r:(a=(e-t)/o,a=s*a+r,a)}const Gwe=function(e){var t=e.data,n=t.length,r=t[0],i=r,o,s=t[1],a=s,l,u=t[2],d=u,f,p,g=this.enhance();if(g!==0){for(p=0;pi&&(i=o),l=t[p+1],la&&(a=l),f=t[p+2],fd&&(d=f);i===r&&(i=255,r=0),a===s&&(a=255,s=0),d===u&&(d=255,u=0);var m,v,x,_,b,y,S,C,T;for(g>0?(v=i+g*(255-i),x=r-g*(r-0),b=a+g*(255-a),y=s-g*(s-0),C=d+g*(255-d),T=u-g*(u-0)):(m=(i+r)*.5,v=i+g*(i-m),x=r+g*(r-m),_=(a+s)*.5,b=a+g*(a-_),y=s+g*(s-_),S=(d+u)*.5,C=d+g*(d-S),T=u+g*(u-S)),p=0;p_?x:_;var b=s,y=o,S,C,T=360/y*Math.PI/180,E,P;for(C=0;Cy?b:y;var S=s,C=o,T,E,P=n.polarRotation||0,k,O;for(d=0;dt&&(S=y,C=0,T=-1),i=0;i=0&&g=0&&m=0&&g=0&&m=255*4?255:0}return s}function oxe(e,t,n){for(var 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GS={};Object.defineProperty(GS,"__esModule",{value:!0});GS.RGBA=void 0;const Dg=nt,HS=fn,_xe=ke,bxe=function(e){var t=e.data,n=t.length,r=this.red(),i=this.green(),o=this.blue(),s=this.alpha(),a,l;for(a=0;a255?255:e<0?0:Math.round(e)});Dg.Factory.addGetterSetter(HS.Node,"green",0,function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)});Dg.Factory.addGetterSetter(HS.Node,"blue",0,_xe.RGBComponent,Dg.Factory.afterSetFilter);Dg.Factory.addGetterSetter(HS.Node,"alpha",1,function(e){return this._filterUpToDate=!1,e>1?1:e<0?0:e});var WS={};Object.defineProperty(WS,"__esModule",{value:!0});WS.Sepia=void 0;const Sxe=function(e){var t=e.data,n=t.length,r,i,o,s;for(r=0;r127&&(u=255-u),d>127&&(d=255-d),f>127&&(f=255-f),t[l]=u,t[l+1]=d,t[l+2]=f}while(--a)}while(--o)};qS.Solarize=wxe;var KS={};Object.defineProperty(KS,"__esModule",{value:!0});KS.Threshold=void 0;const vk=nt,xxe=fn,Cxe=ke,Txe=function(e){var 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0)||0)}snapToCursor(t){ps(n=>{const{drag:r}=this.getProps();if(!q0(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:s,max:a}=i.layout.layoutBox[n];o.set(t[n]-vn(s,a,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!$d(n)||!r||!this.constraints)return;this.stopAnimation();const i={x:0,y:0};ps(s=>{const a=this.getAxisMotionValue(s);if(a){const l=a.get();i[s]=eAe({min:l,max:l},this.constraints[s])}});const{transformTemplate:o}=this.visualElement.getProps();this.visualElement.current.style.transform=o?o({},""):"none",r.root&&r.root.updateScroll(),r.updateLayout(),this.resolveConstraints(),ps(s=>{if(!q0(s,t,null))return;const a=this.getAxisMotionValue(s),{min:l,max:u}=this.constraints[s];a.set(vn(l,u,i[s]))})}addListeners(){if(!this.visualElement.current)return;uAe.set(this.visualElement,this);const t=this.visualElement.current,n=wa(t,"pointerdown",l=>{const{drag:u,dragListener:d=!0}=this.getProps();u&&d&&this.start(l)}),r=()=>{const{dragConstraints:l}=this.getProps();$d(l)&&(this.constraints=this.resolveRefConstraints())},{projection:i}=this.visualElement,o=i.addEventListener("measure",r);i&&!i.layout&&(i.root&&i.root.updateScroll(),i.updateLayout()),r();const s=ya(window,"resize",()=>this.scalePositionWithinConstraints()),a=i.addEventListener("didUpdate",({delta:l,hasLayoutChanged:u})=>{this.isDragging&&u&&(ps(d=>{const f=this.getAxisMotionValue(d);f&&(this.originPoint[d]+=l[d].translate,f.set(f.get()+l[d].translate))}),this.visualElement.render())});return()=>{s(),n(),o(),a&&a()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:o=!1,dragElastic:s=L5,dragMomentum:a=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:o,dragElastic:s,dragMomentum:a}}}function q0(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function dAe(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}class fAe extends uu{constructor(t){super(t),this.removeGroupControls=Gn,this.removeListeners=Gn,this.controls=new cAe(t)}mount(){const{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||Gn}unmount(){this.removeGroupControls(),this.removeListeners()}}const II=e=>(t,n)=>{e&&dn.update(()=>e(t,n))};class hAe extends uu{constructor(){super(...arguments),this.removePointerDownListener=Gn}onPointerDown(t){this.session=new Sj(t,this.createPanHandlers(),{transformPagePoint:this.node.getTransformPagePoint()})}createPanHandlers(){const{onPanSessionStart:t,onPanStart:n,onPan:r,onPanEnd:i}=this.node.getProps();return{onSessionStart:II(t),onStart:II(n),onMove:r,onEnd:(o,s)=>{delete 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o=Xl.createTransformer(e),s=typeof i[0]!="number"?1:0,a=n.x.scale*t.x,l=n.y.scale*t.y;i[0+s]/=a,i[1+s]/=l;const u=vn(a,l,.5);return typeof i[2+s]=="number"&&(i[2+s]/=u),typeof i[3+s]=="number"&&(i[3+s]/=u),o(i)}};class yAe extends Lt.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;CTe(vAe),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),kv.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,s=r.projection;return s&&(s.isPresent=o,i||t.layoutDependency!==n||n===void 0?s.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?s.promote():s.relegate()||dn.postRender(()=>{const a=s.getStack();(!a||!a.members.length)&&this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),queueMicrotask(()=>{!t.currentAnimation&&t.isLead()&&this.safeToRemove()}))}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n&&n.group&&n.group.remove(i),r&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t&&t()}render(){return null}}function Aj(e){const[t,n]=pAe(),r=L.useContext(XE);return Lt.createElement(yAe,{...e,layoutGroup:r,switchLayoutGroup:L.useContext(wV),isPresent:t,safeToRemove:n})}const vAe={borderRadius:{...op,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:op,borderTopRightRadius:op,borderBottomLeftRadius:op,borderBottomRightRadius:op,boxShadow:mAe},Pj=["TopLeft","TopRight","BottomLeft","BottomRight"],_Ae=Pj.length,NI=e=>typeof e=="string"?parseFloat(e):e,LI=e=>typeof e=="number"||Be.test(e);function bAe(e,t,n,r,i,o){i?(e.opacity=vn(0,n.opacity!==void 0?n.opacity:1,SAe(r)),e.opacityExit=vn(t.opacity!==void 0?t.opacity:1,0,wAe(r))):o&&(e.opacity=vn(t.opacity!==void 0?t.opacity:1,n.opacity!==void 0?n.opacity:1,r));for(let s=0;s<_Ae;s++){const a=`border${Pj[s]}Radius`;let l=DI(t,a),u=DI(n,a);if(l===void 0&&u===void 0)continue;l||(l=0),u||(u=0),l===0||u===0||LI(l)===LI(u)?(e[a]=Math.max(vn(NI(l),NI(u),r),0),(Is.test(u)||Is.test(l))&&(e[a]+="%")):e[a]=u}(t.rotate||n.rotate)&&(e.rotate=vn(t.rotate||0,n.rotate||0,r))}function DI(e,t){return e[t]!==void 0?e[t]:e.borderRadius}const SAe=Rj(0,.5,o6),wAe=Rj(.5,.95,Gn);function Rj(e,t,n){return r=>rt?1:n(jg(e,t,r))}function $I(e,t){e.min=t.min,e.max=t.max}function fo(e,t){$I(e.x,t.x),$I(e.y,t.y)}function FI(e,t,n,r,i){return e-=t,e=g_(e,1/n,r),i!==void 0&&(e=g_(e,1/i,r)),e}function xAe(e,t=0,n=1,r=.5,i,o=e,s=e){if(Is.test(t)&&(t=parseFloat(t),t=vn(s.min,s.max,t/100)-s.min),typeof t!="number")return;let a=vn(o.min,o.max,r);e===o&&(a-=t),e.min=FI(e.min,t,n,a,i),e.max=FI(e.max,t,n,a,i)}function BI(e,t,[n,r,i],o,s){xAe(e,t[n],t[r],t[i],t.scale,o,s)}const CAe=["x","scaleX","originX"],TAe=["y","scaleY","originY"];function UI(e,t,n,r){BI(e.x,t,CAe,n?n.x:void 0,r?r.x:void 0),BI(e.y,t,TAe,n?n.y:void 0,r?r.y:void 0)}function zI(e){return e.translate===0&&e.scale===1}function Oj(e){return zI(e.x)&&zI(e.y)}function F5(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function VI(e){return Xi(e.x)/Xi(e.y)}class EAe{constructor(){this.members=[]}add(t){c6(this.members,t),t.scheduleRender()}remove(t){if(d6(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){const r=this.lead;if(t!==r&&(this.prevLead=r,this.lead=t,t.show(),r)){r.instance&&r.scheduleRender(),t.scheduleRender(),t.resumeFrom=r,n&&(t.resumeFrom.preserveOpacity=!0),r.snapshot&&(t.snapshot=r.snapshot,t.snapshot.latestValues=r.animationValues||r.latestValues),t.root&&t.root.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:i}=t.options;i===!1&&r.hide()}}exitAnimationComplete(){this.members.forEach(t=>{const{options:n,resumingFrom:r}=t;n.onExitComplete&&n.onExitComplete(),r&&r.options.onExitComplete&&r.options.onExitComplete()})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function jI(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:d}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),d&&(r+=`rotateY(${d}deg) `)}const s=e.x.scale*t.x,a=e.y.scale*t.y;return(s!==1||a!==1)&&(r+=`scale(${s}, ${a})`),r||"none"}const AAe=(e,t)=>e.depth-t.depth;class PAe{constructor(){this.children=[],this.isDirty=!1}add(t){c6(this.children,t),this.isDirty=!0}remove(t){d6(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(AAe),this.isDirty=!1,this.children.forEach(t)}}function RAe(e,t){const n=performance.now(),r=({timestamp:i})=>{const o=i-n;o>=t&&(Ia(r),e(o-t))};return dn.read(r,!0),()=>Ia(r)}function OAe(e){window.MotionDebug&&window.MotionDebug.record(e)}function kAe(e){return e instanceof SVGElement&&e.tagName!=="svg"}function IAe(e,t,n){const r=ki(e)?e:Mf(e);return r.start(u6("",r,t,n)),r.animation}const GI=["","X","Y","Z"],HI=1e3;let MAe=0;const Ou={type:"projectionFrame",totalNodes:0,resolvedTargetDeltas:0,recalculatedProjection:0};function kj({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(s={},a=t==null?void 0:t()){this.id=MAe++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.isSharedProjectionDirty=!1,this.isTransformDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.hasTreeAnimated=!1,this.updateScheduled=!1,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{Ou.totalNodes=Ou.resolvedTargetDeltas=Ou.recalculatedProjection=0,this.nodes.forEach(DAe),this.nodes.forEach(zAe),this.nodes.forEach(VAe),this.nodes.forEach($Ae),OAe(Ou)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.latestValues=s,this.root=a?a.root||a:this,this.path=a?[...a.path,a]:[],this.parent=a,this.depth=a?a.depth+1:0;for(let l=0;lthis.root.updateBlockedByResize=!1;e(s,()=>{this.root.updateBlockedByResize=!0,f&&f(),f=RAe(p,250),kv.hasAnimatedSinceResize&&(kv.hasAnimatedSinceResize=!1,this.nodes.forEach(qI))})}l&&this.root.registerSharedNode(l,this),this.options.animate!==!1&&d&&(l||u)&&this.addEventListener("didUpdate",({delta:f,hasLayoutChanged:p,hasRelativeTargetChanged:g,layout:m})=>{if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const v=this.options.transition||d.getDefaultTransition()||qAe,{onLayoutAnimationStart:x,onLayoutAnimationComplete:_}=d.getProps(),b=!this.targetLayout||!F5(this.targetLayout,m)||g,y=!p&&g;if(this.options.layoutRoot||this.resumeFrom&&this.resumeFrom.instance||y||p&&(b||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(f,y);const S={...yj(v,"layout"),onPlay:x,onComplete:_};(d.shouldReduceMotion||this.options.layoutRoot)&&(S.delay=0,S.type=!1),this.startAnimation(S)}else p||qI(this),this.isLead()&&this.options.onExitComplete&&this.options.onExitComplete();this.targetLayout=m})}unmount(){this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this);const s=this.getStack();s&&s.remove(this),this.parent&&this.parent.children.delete(this),this.instance=void 0,Ia(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){return this.isAnimationBlocked||this.parent&&this.parent.isTreeAnimationBlocked()||!1}startUpdate(){this.isUpdateBlocked()||(this.isUpdating=!0,this.nodes&&this.nodes.forEach(jAe),this.animationId++)}getTransformTemplate(){const{visualElement:s}=this.options;return s&&s.getProps().transformTemplate}willUpdate(s=!0){if(this.root.hasTreeAnimated=!0,this.root.isUpdateBlocked()){this.options.onExitComplete&&this.options.onExitComplete();return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let d=0;dthis.update()))}clearAllSnapshots(){this.nodes.forEach(FAe),this.sharedNodes.forEach(GAe)}scheduleUpdateProjection(){dn.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){dn.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l{const C=S/1e3;KI(f.x,s.x,C),KI(f.y,s.y,C),this.setTargetDelta(f),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&this.relativeParent&&this.relativeParent.layout&&(Mp(p,this.layout.layoutBox,this.relativeParent.layout.layoutBox),HAe(this.relativeTarget,this.relativeTargetOrigin,p,C),y&&F5(this.relativeTarget,y)&&(this.isProjectionDirty=!1),y||(y=Bn()),fo(y,this.relativeTarget)),v&&(this.animationValues=d,bAe(d,u,this.latestValues,C,b,_)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=C},this.mixTargetDelta(this.options.layoutRoot?1e3:0)}startAnimation(s){this.notifyListeners("animationStart"),this.currentAnimation&&this.currentAnimation.stop(),this.resumingFrom&&this.resumingFrom.currentAnimation&&this.resumingFrom.currentAnimation.stop(),this.pendingAnimation&&(Ia(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=dn.update(()=>{kv.hasAnimatedSinceResize=!0,this.currentAnimation=IAe(0,HI,{...s,onUpdate:a=>{this.mixTargetDelta(a),s.onUpdate&&s.onUpdate(a)},onComplete:()=>{s.onComplete&&s.onComplete(),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0);const s=this.getStack();s&&s.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(HI),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const s=this.getLead();let{targetWithTransforms:a,target:l,layout:u,latestValues:d}=s;if(!(!a||!l||!u)){if(this!==s&&this.layout&&u&&Ij(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||Bn();const f=Xi(this.layout.layoutBox.x);l.x.min=s.target.x.min,l.x.max=l.x.min+f;const p=Xi(this.layout.layoutBox.y);l.y.min=s.target.y.min,l.y.max=l.y.min+p}fo(a,l),Ud(a,d),Ip(this.projectionDeltaWithTransform,this.layoutCorrected,a,d)}}registerSharedNode(s,a){this.sharedNodes.has(s)||this.sharedNodes.set(s,new EAe),this.sharedNodes.get(s).add(a);const u=a.options.initialPromotionConfig;a.promote({transition:u?u.transition:void 0,preserveFollowOpacity:u&&u.shouldPreserveFollowOpacity?u.shouldPreserveFollowOpacity(a):void 0})}isLead(){const s=this.getStack();return s?s.lead===this:!0}getLead(){var s;const{layoutId:a}=this.options;return a?((s=this.getStack())===null||s===void 0?void 0:s.lead)||this:this}getPrevLead(){var s;const{layoutId:a}=this.options;return a?(s=this.getStack())===null||s===void 0?void 0:s.prevLead:void 0}getStack(){const{layoutId:s}=this.options;if(s)return this.root.sharedNodes.get(s)}promote({needsReset:s,transition:a,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),s&&(this.projectionDelta=void 0,this.needsReset=!0),a&&this.setOptions({transition:a})}relegate(){const s=this.getStack();return s?s.relegate(this):!1}resetRotation(){const{visualElement:s}=this.options;if(!s)return;let a=!1;const{latestValues:l}=s;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(a=!0),!a)return;const u={};for(let d=0;d{var a;return(a=s.currentAnimation)===null||a===void 0?void 0:a.stop()}),this.root.nodes.forEach(WI),this.root.sharedNodes.clear()}}}function NAe(e){e.updateLayout()}function LAe(e){var t;const n=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&n&&e.hasListeners("didUpdate")){const{layoutBox:r,measuredBox:i}=e.layout,{animationType:o}=e.options,s=n.source!==e.layout.source;o==="size"?ps(f=>{const p=s?n.measuredBox[f]:n.layoutBox[f],g=Xi(p);p.min=r[f].min,p.max=p.min+g}):Ij(o,n.layoutBox,r)&&ps(f=>{const p=s?n.measuredBox[f]:n.layoutBox[f],g=Xi(r[f]);p.max=p.min+g,e.relativeTarget&&!e.currentAnimation&&(e.isProjectionDirty=!0,e.relativeTarget[f].max=e.relativeTarget[f].min+g)});const a=Bd();Ip(a,r,n.layoutBox);const l=Bd();s?Ip(l,e.applyTransform(i,!0),n.measuredBox):Ip(l,r,n.layoutBox);const u=!Oj(a);let d=!1;if(!e.resumeFrom){const f=e.getClosestProjectingParent();if(f&&!f.resumeFrom){const{snapshot:p,layout:g}=f;if(p&&g){const m=Bn();Mp(m,n.layoutBox,p.layoutBox);const v=Bn();Mp(v,r,g.layoutBox),F5(m,v)||(d=!0),f.options.layoutRoot&&(e.relativeTarget=v,e.relativeTargetOrigin=m,e.relativeParent=f)}}}e.notifyListeners("didUpdate",{layout:r,snapshot:n,delta:l,layoutDelta:a,hasLayoutChanged:u,hasRelativeTargetChanged:d})}else if(e.isLead()){const{onExitComplete:r}=e.options;r&&r()}e.options.transition=void 0}function DAe(e){Ou.totalNodes++,e.parent&&(e.isProjecting()||(e.isProjectionDirty=e.parent.isProjectionDirty),e.isSharedProjectionDirty||(e.isSharedProjectionDirty=!!(e.isProjectionDirty||e.parent.isProjectionDirty||e.parent.isSharedProjectionDirty)),e.isTransformDirty||(e.isTransformDirty=e.parent.isTransformDirty))}function $Ae(e){e.isProjectionDirty=e.isSharedProjectionDirty=e.isTransformDirty=!1}function FAe(e){e.clearSnapshot()}function WI(e){e.clearMeasurements()}function BAe(e){e.isLayoutDirty=!1}function UAe(e){const{visualElement:t}=e.options;t&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function qI(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0,e.isProjectionDirty=!0}function zAe(e){e.resolveTargetDelta()}function VAe(e){e.calcProjection()}function jAe(e){e.resetRotation()}function GAe(e){e.removeLeadSnapshot()}function KI(e,t,n){e.translate=vn(t.translate,0,n),e.scale=vn(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function XI(e,t,n,r){e.min=vn(t.min,n.min,r),e.max=vn(t.max,n.max,r)}function HAe(e,t,n,r){XI(e.x,t.x,n.x,r),XI(e.y,t.y,n.y,r)}function WAe(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const qAe={duration:.45,ease:[.4,0,.1,1]};function YI(e){e.min=Math.round(e.min*2)/2,e.max=Math.round(e.max*2)/2}function KAe(e){YI(e.x),YI(e.y)}function Ij(e,t,n){return e==="position"||e==="preserve-aspect"&&!N5(VI(t),VI(n),.2)}const 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er;(function(e){e.DragStart="dragStart",e.DragMove="dragMove",e.DragEnd="dragEnd",e.DragCancel="dragCancel",e.DragOver="dragOver",e.RegisterDroppable="registerDroppable",e.SetDroppableDisabled="setDroppableDisabled",e.UnregisterDroppable="unregisterDroppable"})(er||(er={}));function S_(){}function l7(e,t){return L.useMemo(()=>({sensor:e,options:t??{}}),[e,t])}function _8e(){for(var e=arguments.length,t=new Array(e),n=0;n[...t].filter(r=>r!=null),[...t])}const ns=Object.freeze({x:0,y:0});function b8e(e,t){return Math.sqrt(Math.pow(e.x-t.x,2)+Math.pow(e.y-t.y,2))}function S8e(e,t){const n=qg(e);if(!n)return"0 0";const r={x:(n.x-t.left)/t.width*100,y:(n.y-t.top)/t.height*100};return r.x+"% "+r.y+"%"}function w8e(e,t){let{data:{value:n}}=e,{data:{value:r}}=t;return n-r}function x8e(e,t){let{data:{value:n}}=e,{data:{value:r}}=t;return r-n}function C8e(e){let{left:t,top:n,height:r,width:i}=e;return[{x:t,y:n},{x:t+i,y:n},{x:t,y:n+r},{x:t+i,y:n+r}]}function T8e(e,t){if(!e||e.length===0)return null;const[n]=e;return t?n[t]:n}function E8e(e,t){const n=Math.max(t.top,e.top),r=Math.max(t.left,e.left),i=Math.min(t.left+t.width,e.left+e.width),o=Math.min(t.top+t.height,e.top+e.height),s=i-r,a=o-n;if(r{let{collisionRect:t,droppableRects:n,droppableContainers:r}=e;const i=[];for(const o of r){const{id:s}=o,a=n.get(s);if(a){const l=E8e(a,t);l>0&&i.push({id:s,data:{droppableContainer:o,value:l}})}}return i.sort(x8e)};function P8e(e,t){const{top:n,left:r,bottom:i,right:o}=t;return n<=e.y&&e.y<=i&&r<=e.x&&e.x<=o}const R8e=e=>{let{droppableContainers:t,droppableRects:n,pointerCoordinates:r}=e;if(!r)return[];const i=[];for(const o of t){const{id:s}=o,a=n.get(s);if(a&&P8e(r,a)){const u=C8e(a).reduce((f,p)=>f+b8e(r,p),0),d=Number((u/4).toFixed(4));i.push({id:s,data:{droppableContainer:o,value:d}})}}return i.sort(w8e)};function O8e(e,t,n){return{...e,scaleX:t&&n?t.width/n.width:1,scaleY:t&&n?t.height/n.height:1}}function Yj(e,t){return e&&t?{x:e.left-t.left,y:e.top-t.top}:ns}function 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n=e.getBoundingClientRect();if(t.ignoreTransform){const{transform:u,transformOrigin:d}=hi(e).getComputedStyle(e);u&&(n=M8e(n,u,d))}const{top:r,left:i,width:o,height:s,bottom:a,right:l}=n;return{top:r,left:i,width:o,height:s,bottom:a,right:l}}function u7(e){return Vm(e,{ignoreTransform:!0})}function L8e(e){const t=e.innerWidth,n=e.innerHeight;return{top:0,left:0,right:t,bottom:n,width:t,height:n}}function D8e(e,t){return t===void 0&&(t=hi(e).getComputedStyle(e)),t.position==="fixed"}function $8e(e,t){t===void 0&&(t=hi(e).getComputedStyle(e));const n=/(auto|scroll|overlay)/;return["overflow","overflowX","overflowY"].some(i=>{const o=t[i];return typeof o=="string"?n.test(o):!1})}function _6(e,t){const n=[];function r(i){if(t!=null&&n.length>=t||!i)return n;if(y6(i)&&i.scrollingElement!=null&&!n.includes(i.scrollingElement))return n.push(i.scrollingElement),n;if(!Um(i)||i8e(i)||n.includes(i))return n;const o=hi(e).getComputedStyle(i);return i!==e&&$8e(i,o)&&n.push(i),D8e(i,o)?n:r(i.parentNode)}return e?r(e):n}function Zj(e){const[t]=_6(e,1);return t??null}function dC(e){return!u2||!e?null:Yf(e)?e:m6(e)?y6(e)||e===Qf(e).scrollingElement?window:Um(e)?e:null:null}function Jj(e){return Yf(e)?e.scrollX:e.scrollLeft}function eG(e){return Yf(e)?e.scrollY:e.scrollTop}function K5(e){return{x:Jj(e),y:eG(e)}}var fr;(function(e){e[e.Forward=1]="Forward",e[e.Backward=-1]="Backward"})(fr||(fr={}));function tG(e){return!u2||!e?!1:e===document.scrollingElement}function nG(e){const t={x:0,y:0},n=tG(e)?{height:window.innerHeight,width:window.innerWidth}:{height:e.clientHeight,width:e.clientWidth},r={x:e.scrollWidth-n.width,y:e.scrollHeight-n.height},i=e.scrollTop<=t.y,o=e.scrollLeft<=t.x,s=e.scrollTop>=r.y,a=e.scrollLeft>=r.x;return{isTop:i,isLeft:o,isBottom:s,isRight:a,maxScroll:r,minScroll:t}}const F8e={x:.2,y:.2};function B8e(e,t,n,r,i){let{top:o,left:s,right:a,bottom:l}=n;r===void 0&&(r=10),i===void 0&&(i=F8e);const{isTop:u,isBottom:d,isLeft:f,isRight:p}=nG(e),g={x:0,y:0},m={x:0,y:0},v={height:t.height*i.y,width:t.width*i.x};return!u&&o<=t.top+v.height?(g.y=fr.Backward,m.y=r*Math.abs((t.top+v.height-o)/v.height)):!d&&l>=t.bottom-v.height&&(g.y=fr.Forward,m.y=r*Math.abs((t.bottom-v.height-l)/v.height)),!p&&a>=t.right-v.width?(g.x=fr.Forward,m.x=r*Math.abs((t.right-v.width-a)/v.width)):!f&&s<=t.left+v.width&&(g.x=fr.Backward,m.x=r*Math.abs((t.left+v.width-s)/v.width)),{direction:g,speed:m}}function U8e(e){if(e===document.scrollingElement){const{innerWidth:o,innerHeight:s}=window;return{top:0,left:0,right:o,bottom:s,width:o,height:s}}const{top:t,left:n,right:r,bottom:i}=e.getBoundingClientRect();return{top:t,left:n,right:r,bottom:i,width:e.clientWidth,height:e.clientHeight}}function rG(e){return e.reduce((t,n)=>uf(t,K5(n)),ns)}function z8e(e){return e.reduce((t,n)=>t+Jj(n),0)}function V8e(e){return e.reduce((t,n)=>t+eG(n),0)}function iG(e,t){if(t===void 0&&(t=Vm),!e)return;const{top:n,left:r,bottom:i,right:o}=t(e);Zj(e)&&(i<=0||o<=0||n>=window.innerHeight||r>=window.innerWidth)&&e.scrollIntoView({block:"center",inline:"center"})}const j8e=[["x",["left","right"],z8e],["y",["top","bottom"],V8e]];class b6{constructor(t,n){this.rect=void 0,this.width=void 0,this.height=void 0,this.top=void 0,this.bottom=void 0,this.right=void 0,this.left=void 0;const r=_6(n),i=rG(r);this.rect={...t},this.width=t.width,this.height=t.height;for(const[o,s,a]of j8e)for(const l of s)Object.defineProperty(this,l,{get:()=>{const u=a(r),d=i[o]-u;return this.rect[l]+d},enumerable:!0});Object.defineProperty(this,"rect",{enumerable:!1})}}class Np{constructor(t){this.target=void 0,this.listeners=[],this.removeAll=()=>{this.listeners.forEach(n=>{var r;return(r=this.target)==null?void 0:r.removeEventListener(...n)})},this.target=t}add(t,n,r){var i;(i=this.target)==null||i.addEventListener(t,n,r),this.listeners.push([t,n,r])}}function G8e(e){const{EventTarget:t}=hi(e);return e instanceof t?e:Qf(e)}function fC(e,t){const n=Math.abs(e.x),r=Math.abs(e.y);return typeof t=="number"?Math.sqrt(n**2+r**2)>t:"x"in t&&"y"in t?n>t.x&&r>t.y:"x"in t?n>t.x:"y"in t?r>t.y:!1}var po;(function(e){e.Click="click",e.DragStart="dragstart",e.Keydown="keydown",e.ContextMenu="contextmenu",e.Resize="resize",e.SelectionChange="selectionchange",e.VisibilityChange="visibilitychange"})(po||(po={}));function c7(e){e.preventDefault()}function H8e(e){e.stopPropagation()}var Qt;(function(e){e.Space="Space",e.Down="ArrowDown",e.Right="ArrowRight",e.Left="ArrowLeft",e.Up="ArrowUp",e.Esc="Escape",e.Enter="Enter"})(Qt||(Qt={}));const oG={start:[Qt.Space,Qt.Enter],cancel:[Qt.Esc],end:[Qt.Space,Qt.Enter]},W8e=(e,t)=>{let{currentCoordinates:n}=t;switch(e.code){case Qt.Right:return{...n,x:n.x+25};case Qt.Left:return{...n,x:n.x-25};case Qt.Down:return{...n,y:n.y+25};case Qt.Up:return{...n,y:n.y-25}}};class sG{constructor(t){this.props=void 0,this.autoScrollEnabled=!1,this.referenceCoordinates=void 0,this.listeners=void 0,this.windowListeners=void 0,this.props=t;const{event:{target:n}}=t;this.props=t,this.listeners=new Np(Qf(n)),this.windowListeners=new Np(hi(n)),this.handleKeyDown=this.handleKeyDown.bind(this),this.handleCancel=this.handleCancel.bind(this),this.attach()}attach(){this.handleStart(),this.windowListeners.add(po.Resize,this.handleCancel),this.windowListeners.add(po.VisibilityChange,this.handleCancel),setTimeout(()=>this.listeners.add(po.Keydown,this.handleKeyDown))}handleStart(){const{activeNode:t,onStart:n}=this.props,r=t.node.current;r&&iG(r),n(ns)}handleKeyDown(t){if(v6(t)){const{active:n,context:r,options:i}=this.props,{keyboardCodes:o=oG,coordinateGetter:s=W8e,scrollBehavior:a="smooth"}=i,{code:l}=t;if(o.end.includes(l)){this.handleEnd(t);return}if(o.cancel.includes(l)){this.handleCancel(t);return}const{collisionRect:u}=r.current,d=u?{x:u.left,y:u.top}:ns;this.referenceCoordinates||(this.referenceCoordinates=d);const f=s(t,{active:n,context:r.current,currentCoordinates:d});if(f){const p=b_(f,d),g={x:0,y:0},{scrollableAncestors:m}=r.current;for(const v of m){const x=t.code,{isTop:_,isRight:b,isLeft:y,isBottom:S,maxScroll:C,minScroll:T}=nG(v),E=U8e(v),P={x:Math.min(x===Qt.Right?E.right-E.width/2:E.right,Math.max(x===Qt.Right?E.left:E.left+E.width/2,f.x)),y:Math.min(x===Qt.Down?E.bottom-E.height/2:E.bottom,Math.max(x===Qt.Down?E.top:E.top+E.height/2,f.y))},k=x===Qt.Right&&!b||x===Qt.Left&&!y,O=x===Qt.Down&&!S||x===Qt.Up&&!_;if(k&&P.x!==f.x){const M=v.scrollLeft+p.x,G=x===Qt.Right&&M<=C.x||x===Qt.Left&&M>=T.x;if(G&&!p.y){v.scrollTo({left:M,behavior:a});return}G?g.x=v.scrollLeft-M:g.x=x===Qt.Right?v.scrollLeft-C.x:v.scrollLeft-T.x,g.x&&v.scrollBy({left:-g.x,behavior:a});break}else if(O&&P.y!==f.y){const M=v.scrollTop+p.y,G=x===Qt.Down&&M<=C.y||x===Qt.Up&&M>=T.y;if(G&&!p.x){v.scrollTo({top:M,behavior:a});return}G?g.y=v.scrollTop-M:g.y=x===Qt.Down?v.scrollTop-C.y:v.scrollTop-T.y,g.y&&v.scrollBy({top:-g.y,behavior:a});break}}this.handleMove(t,uf(b_(f,this.referenceCoordinates),g))}}}handleMove(t,n){const{onMove:r}=this.props;t.preventDefault(),r(n)}handleEnd(t){const{onEnd:n}=this.props;t.preventDefault(),this.detach(),n()}handleCancel(t){const{onCancel:n}=this.props;t.preventDefault(),this.detach(),n()}detach(){this.listeners.removeAll(),this.windowListeners.removeAll()}}sG.activators=[{eventName:"onKeyDown",handler:(e,t,n)=>{let{keyboardCodes:r=oG,onActivation:i}=t,{active:o}=n;const{code:s}=e.nativeEvent;if(r.start.includes(s)){const a=o.activatorNode.current;return a&&e.target!==a?!1:(e.preventDefault(),i==null||i({event:e.nativeEvent}),!0)}return!1}}];function d7(e){return!!(e&&"distance"in e)}function f7(e){return!!(e&&"delay"in e)}class S6{constructor(t,n,r){var i;r===void 0&&(r=G8e(t.event.target)),this.props=void 0,this.events=void 0,this.autoScrollEnabled=!0,this.document=void 0,this.activated=!1,this.initialCoordinates=void 0,this.timeoutId=null,this.listeners=void 0,this.documentListeners=void 0,this.windowListeners=void 0,this.props=t,this.events=n;const{event:o}=t,{target:s}=o;this.props=t,this.events=n,this.document=Qf(s),this.documentListeners=new Np(this.document),this.listeners=new Np(r),this.windowListeners=new Np(hi(s)),this.initialCoordinates=(i=qg(o))!=null?i:ns,this.handleStart=this.handleStart.bind(this),this.handleMove=this.handleMove.bind(this),this.handleEnd=this.handleEnd.bind(this),this.handleCancel=this.handleCancel.bind(this),this.handleKeydown=this.handleKeydown.bind(this),this.removeTextSelection=this.removeTextSelection.bind(this),this.attach()}attach(){const{events:t,props:{options:{activationConstraint:n}}}=this;if(this.listeners.add(t.move.name,this.handleMove,{passive:!1}),this.listeners.add(t.end.name,this.handleEnd),this.windowListeners.add(po.Resize,this.handleCancel),this.windowListeners.add(po.DragStart,c7),this.windowListeners.add(po.VisibilityChange,this.handleCancel),this.windowListeners.add(po.ContextMenu,c7),this.documentListeners.add(po.Keydown,this.handleKeydown),n){if(d7(n))return;if(f7(n)){this.timeoutId=setTimeout(this.handleStart,n.delay);return}}this.handleStart()}detach(){this.listeners.removeAll(),this.windowListeners.removeAll(),setTimeout(this.documentListeners.removeAll,50),this.timeoutId!==null&&(clearTimeout(this.timeoutId),this.timeoutId=null)}handleStart(){const{initialCoordinates:t}=this,{onStart:n}=this.props;t&&(this.activated=!0,this.documentListeners.add(po.Click,H8e,{capture:!0}),this.removeTextSelection(),this.documentListeners.add(po.SelectionChange,this.removeTextSelection),n(t))}handleMove(t){var n;const{activated:r,initialCoordinates:i,props:o}=this,{onMove:s,options:{activationConstraint:a}}=o;if(!i)return;const l=(n=qg(t))!=null?n:ns,u=b_(i,l);if(!r&&a){if(f7(a))return fC(u,a.tolerance)?this.handleCancel():void 0;if(d7(a))return a.tolerance!=null&&fC(u,a.tolerance)?this.handleCancel():fC(u,a.distance)?this.handleStart():void 0}t.cancelable&&t.preventDefault(),s(l)}handleEnd(){const{onEnd:t}=this.props;this.detach(),t()}handleCancel(){const{onCancel:t}=this.props;this.detach(),t()}handleKeydown(t){t.code===Qt.Esc&&this.handleCancel()}removeTextSelection(){var t;(t=this.document.getSelection())==null||t.removeAllRanges()}}const q8e={move:{name:"pointermove"},end:{name:"pointerup"}};class aG extends S6{constructor(t){const{event:n}=t,r=Qf(n.target);super(t,q8e,r)}}aG.activators=[{eventName:"onPointerDown",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;return!n.isPrimary||n.button!==0?!1:(r==null||r({event:n}),!0)}}];const K8e={move:{name:"mousemove"},end:{name:"mouseup"}};var X5;(function(e){e[e.RightClick=2]="RightClick"})(X5||(X5={}));class lG extends S6{constructor(t){super(t,K8e,Qf(t.event.target))}}lG.activators=[{eventName:"onMouseDown",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;return n.button===X5.RightClick?!1:(r==null||r({event:n}),!0)}}];const hC={move:{name:"touchmove"},end:{name:"touchend"}};class uG extends S6{constructor(t){super(t,hC)}static setup(){return window.addEventListener(hC.move.name,t,{capture:!1,passive:!1}),function(){window.removeEventListener(hC.move.name,t)};function t(){}}}uG.activators=[{eventName:"onTouchStart",handler:(e,t)=>{let{nativeEvent:n}=e,{onActivation:r}=t;const{touches:i}=n;return i.length>1?!1:(r==null||r({event:n}),!0)}}];var Lp;(function(e){e[e.Pointer=0]="Pointer",e[e.DraggableRect=1]="DraggableRect"})(Lp||(Lp={}));var w_;(function(e){e[e.TreeOrder=0]="TreeOrder",e[e.ReversedTreeOrder=1]="ReversedTreeOrder"})(w_||(w_={}));function X8e(e){let{acceleration:t,activator:n=Lp.Pointer,canScroll:r,draggingRect:i,enabled:o,interval:s=5,order:a=w_.TreeOrder,pointerCoordinates:l,scrollableAncestors:u,scrollableAncestorRects:d,delta:f,threshold:p}=e;const g=Q8e({delta:f,disabled:!o}),[m,v]=o8e(),x=L.useRef({x:0,y:0}),_=L.useRef({x:0,y:0}),b=L.useMemo(()=>{switch(n){case Lp.Pointer:return l?{top:l.y,bottom:l.y,left:l.x,right:l.x}:null;case Lp.DraggableRect:return i}},[n,i,l]),y=L.useRef(null),S=L.useCallback(()=>{const T=y.current;if(!T)return;const E=x.current.x*_.current.x,P=x.current.y*_.current.y;T.scrollBy(E,P)},[]),C=L.useMemo(()=>a===w_.TreeOrder?[...u].reverse():u,[a,u]);L.useEffect(()=>{if(!o||!u.length||!b){v();return}for(const T of C){if((r==null?void 0:r(T))===!1)continue;const E=u.indexOf(T),P=d[E];if(!P)continue;const{direction:k,speed:O}=B8e(T,P,b,t,p);for(const M 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Vs=L.useMemo(()=>({active:U,activeNode:le,activeNodeRect:pe,activatorEvent:B,collisions:qn,containerNodeRect:ve,dragOverlay:Me,draggableNodes:P,droppableContainers:O,droppableRects:ee,over:Gt,measureDroppableContainers:ie,scrollableAncestors:Bt,scrollableAncestorRects:it,measuringConfiguration:Z,measuringScheduled:se,windowRect:Wn}),[U,le,pe,B,qn,ve,Me,P,O,ee,Gt,ie,Bt,it,Z,se,Wn]),Ji=L.useMemo(()=>({activatorEvent:B,activators:Yr,active:U,activeNodeRect:pe,ariaDescribedById:{draggable:j},dispatch:_,draggableNodes:P,over:Gt,measureDroppableContainers:ie}),[B,Yr,U,pe,_,j,P,Gt,ie]);return Lt.createElement(Xj.Provider,{value:y},Lt.createElement(jm.Provider,{value:Ji},Lt.createElement(hG.Provider,{value:Vs},Lt.createElement(h2.Provider,{value:Xr},u)),Lt.createElement(m9e,{disabled:(a==null?void 0:a.restoreFocus)===!1})),Lt.createElement(v8e,{...a,hiddenTextDescribedById:j}));function js(){const Xt=(N==null?void 0:N.autoScrollEnabled)===!1,kt=typeof 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as F,Vr as G,Zr as H,L as I,Fr as J,Hr as K,Wr as L,Br as M,Mr as N,nn as O,G as P,Or as Q,zr as R,Rr as S,Qe as T,sn as U,cn as V,mn as W,Er as X,M as a,ct as b,an as c,ne as d,un as e,fn as f,He as g,dn as h,Cr as i,W as j,Le as k,pn as l,gn as m,P as n,tn as o,rn as p,Dr as q,E as r,Qr as s,en as t,ln as u,qr as v,Lr as w,Xr as x,Jr as y,Kr as z}; diff --git a/invokeai/frontend/web/dist/index.html b/invokeai/frontend/web/dist/index.html index d4024bc0c8..32d631cc03 100644 --- a/invokeai/frontend/web/dist/index.html +++ b/invokeai/frontend/web/dist/index.html @@ -12,7 +12,7 @@ margin: 0; } - + diff --git a/invokeai/frontend/web/dist/locales/en.json b/invokeai/frontend/web/dist/locales/en.json index cf84e4d773..63380a19fa 100644 --- a/invokeai/frontend/web/dist/locales/en.json +++ b/invokeai/frontend/web/dist/locales/en.json @@ -124,7 +124,8 @@ "deleteImageBin": "Deleted images will be sent to your operating system's Bin.", "deleteImagePermanent": "Deleted images cannot be restored.", "images": "Images", - "assets": "Assets" + "assets": "Assets", + "autoAssignBoardOnClick": "Auto-Assign Board on Click" }, "hotkeys": { "keyboardShortcuts": "Keyboard Shortcuts", diff --git a/invokeai/frontend/web/package.json b/invokeai/frontend/web/package.json index a76c2ecc02..8cc2c158be 100644 --- a/invokeai/frontend/web/package.json +++ b/invokeai/frontend/web/package.json @@ -23,7 +23,7 @@ "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"", "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"", "build": "yarn run lint && vite build", - "typegen": "npx ts-node scripts/typegen.ts", + "typegen": "node scripts/typegen.js", "preview": "vite preview", "lint:madge": "madge --circular src/main.tsx", "lint:eslint": "eslint --max-warnings=0 .", diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index cf84e4d773..fbae5b4a30 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -124,7 +124,8 @@ "deleteImageBin": "Deleted images will be sent to your operating system's Bin.", "deleteImagePermanent": "Deleted images cannot be restored.", "images": "Images", - "assets": "Assets" + "assets": "Assets", + "autoAssignBoardOnClick": "Auto-Assign Board on Click" }, "hotkeys": { "keyboardShortcuts": "Keyboard Shortcuts", @@ -502,6 +503,9 @@ "hiresStrength": "High Res Strength", "imageFit": "Fit Initial Image To Output Size", "codeformerFidelity": "Fidelity", + "maskAdjustmentsHeader": "Mask Adjustments", + "maskBlur": "Mask Blur", + "maskBlurMethod": "Mask Blur Method", "seamSize": "Seam Size", "seamBlur": "Seam Blur", "seamStrength": "Seam Strength", diff --git a/invokeai/frontend/web/scripts/typegen.ts b/invokeai/frontend/web/scripts/typegen.js similarity index 91% rename from invokeai/frontend/web/scripts/typegen.ts rename to invokeai/frontend/web/scripts/typegen.js index 015ae918ab..ec67c48f2d 100644 --- a/invokeai/frontend/web/scripts/typegen.ts +++ b/invokeai/frontend/web/scripts/typegen.js @@ -1,7 +1,7 @@ import fs from 'node:fs'; import openapiTS from 'openapi-typescript'; -const OPENAPI_URL = 'http://localhost:9090/openapi.json'; +const OPENAPI_URL = 'http://127.0.0.1:9090/openapi.json'; const OUTPUT_FILE = 'src/services/api/schema.d.ts'; async function main() { diff --git a/invokeai/frontend/web/src/app/components/App.tsx b/invokeai/frontend/web/src/app/components/App.tsx index 963d285f72..fa45ae93cd 100644 --- a/invokeai/frontend/web/src/app/components/App.tsx +++ b/invokeai/frontend/web/src/app/components/App.tsx @@ -4,8 +4,9 @@ import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/ap import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { PartialAppConfig } from 'app/types/invokeai'; import ImageUploader from 'common/components/ImageUploader'; +import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal'; +import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal'; import GalleryDrawer from 'features/gallery/components/GalleryPanel'; -import DeleteImageModal from 'features/imageDeletion/components/DeleteImageModal'; import SiteHeader from 'features/system/components/SiteHeader'; import { configChanged } from 'features/system/store/configSlice'; import { languageSelector } from 'features/system/store/systemSelectors'; @@ -16,7 +17,6 @@ import ParametersDrawer from 'features/ui/components/ParametersDrawer'; import i18n from 'i18n'; import { size } from 'lodash-es'; import { ReactNode, memo, useEffect } from 'react'; -import UpdateImageBoardModal from '../../features/gallery/components/Boards/UpdateImageBoardModal'; import GlobalHotkeys from './GlobalHotkeys'; import Toaster from './Toaster'; @@ -84,7 +84,7 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => { - + diff --git a/invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx b/invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx index 82526900ad..c97778ffcd 100644 --- a/invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx +++ b/invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx @@ -58,7 +58,7 @@ const DragPreview = (props: OverlayDragImageProps) => { ); } - if (props.dragData.payloadType === 'IMAGE_NAMES') { + if (props.dragData.payloadType === 'IMAGE_DTOS') { return ( { ...STYLES, }} > - {props.dragData.payload.image_names.length} + {props.dragData.payload.imageDTOs.length} Images ); diff --git a/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx b/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx index 24bdceac3a..56eeb9b5db 100644 --- a/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx +++ b/invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx @@ -18,27 +18,32 @@ import { DragStartEvent, TypesafeDraggableData, } from './typesafeDnd'; +import { logger } from 'app/logging/logger'; type ImageDndContextProps = PropsWithChildren; const ImageDndContext = (props: ImageDndContextProps) => { const [activeDragData, setActiveDragData] = useState(null); + const log = logger('images'); const dispatch = useAppDispatch(); - const handleDragStart = useCallback((event: DragStartEvent) => { - console.log('dragStart', event.active.data.current); - const activeData = event.active.data.current; - if (!activeData) { - return; - } - setActiveDragData(activeData); - }, []); + const handleDragStart = useCallback( + (event: DragStartEvent) => { + log.trace({ dragData: event.active.data.current }, 'Drag started'); + const activeData = event.active.data.current; + if (!activeData) { + return; + } + setActiveDragData(activeData); + }, + [log] + ); const handleDragEnd = useCallback( (event: DragEndEvent) => { - console.log('dragEnd', event.active.data.current); + log.trace({ dragData: event.active.data.current }, 'Drag ended'); const overData = event.over?.data.current; if (!activeDragData || !overData) { return; @@ -46,7 +51,7 @@ const ImageDndContext = (props: ImageDndContextProps) => { dispatch(dndDropped({ overData, activeData: activeDragData })); setActiveDragData(null); }, - [activeDragData, dispatch] + [activeDragData, dispatch, log] ); const mouseSensor = useSensor(MouseSensor, { diff --git a/invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx b/invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx index 5f08466710..6f24302070 100644 --- a/invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx +++ b/invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx @@ -11,7 +11,6 @@ import { useDraggable as useOriginalDraggable, useDroppable as useOriginalDroppable, } from '@dnd-kit/core'; -import { BoardId } from 'features/gallery/store/types'; import { ImageDTO } from 'services/api/types'; type BaseDropData = { @@ -54,9 +53,13 @@ export type AddToBatchDropData = BaseDropData & { actionType: 'ADD_TO_BATCH'; }; -export type MoveBoardDropData = BaseDropData & { - actionType: 'MOVE_BOARD'; - context: { boardId: BoardId }; +export type AddToBoardDropData = BaseDropData & { + actionType: 'ADD_TO_BOARD'; + context: { boardId: string }; +}; + +export type RemoveFromBoardDropData = BaseDropData & { + actionType: 'REMOVE_FROM_BOARD'; }; export type TypesafeDroppableData = @@ -67,7 +70,8 @@ export type TypesafeDroppableData = | NodesImageDropData | AddToBatchDropData | NodesMultiImageDropData - | MoveBoardDropData; + | AddToBoardDropData + | RemoveFromBoardDropData; type BaseDragData = { id: string; @@ -78,14 +82,12 @@ export type ImageDraggableData = BaseDragData & { payload: { imageDTO: ImageDTO }; }; -export type ImageNamesDraggableData = BaseDragData & { - payloadType: 'IMAGE_NAMES'; - payload: { image_names: string[] }; +export type ImageDTOsDraggableData = BaseDragData & { + payloadType: 'IMAGE_DTOS'; + payload: { imageDTOs: ImageDTO[] }; }; -export type TypesafeDraggableData = - | ImageDraggableData - | ImageNamesDraggableData; +export type TypesafeDraggableData = ImageDraggableData | ImageDTOsDraggableData; interface UseDroppableTypesafeArguments extends Omit { @@ -156,14 +158,39 @@ export const isValidDrop = ( case 'SET_NODES_IMAGE': return payloadType === 'IMAGE_DTO'; case 'SET_MULTI_NODES_IMAGE': - return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES'; + return payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS'; case 'ADD_TO_BATCH': - return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES'; - case 'MOVE_BOARD': { + return payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS'; + case 'ADD_TO_BOARD': { // If the board is the same, don't allow the drop // Check the payload types - const isPayloadValid = payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES'; + const isPayloadValid = payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS'; + if (!isPayloadValid) { + return false; + } + + // Check if the image's board is the board we are dragging onto + if (payloadType === 'IMAGE_DTO') { + const { imageDTO } = active.data.current.payload; + const currentBoard = imageDTO.board_id ?? 'none'; + const destinationBoard = overData.context.boardId; + + return currentBoard !== destinationBoard; + } + + if (payloadType === 'IMAGE_DTOS') { + // TODO (multi-select) + return true; + } + + return false; + } + case 'REMOVE_FROM_BOARD': { + // If the board is the same, don't allow the drop + + // Check the payload types + const isPayloadValid = payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS'; if (!isPayloadValid) { return false; } @@ -172,20 +199,16 @@ export const isValidDrop = ( if (payloadType === 'IMAGE_DTO') { const { imageDTO } = active.data.current.payload; const currentBoard = imageDTO.board_id; - const destinationBoard = overData.context.boardId; - const isSameBoard = currentBoard === destinationBoard; - const isDestinationValid = !currentBoard ? destinationBoard : true; - - return !isSameBoard && isDestinationValid; + return currentBoard !== 'none'; } - if (payloadType === 'IMAGE_NAMES') { + if (payloadType === 'IMAGE_DTOS') { // TODO (multi-select) - return false; + return true; } - return true; + return false; } default: return false; diff --git a/invokeai/frontend/web/src/app/components/InvokeAIUI.tsx b/invokeai/frontend/web/src/app/components/InvokeAIUI.tsx index 7df390bce6..93b7825db7 100644 --- a/invokeai/frontend/web/src/app/components/InvokeAIUI.tsx +++ b/invokeai/frontend/web/src/app/components/InvokeAIUI.tsx @@ -1,4 +1,6 @@ +import { Middleware } from '@reduxjs/toolkit'; import { store } from 'app/store/store'; +import { PartialAppConfig } from 'app/types/invokeai'; import React, { lazy, memo, @@ -7,16 +9,11 @@ import React, { useEffect, } from 'react'; import { Provider } from 'react-redux'; - -import { PartialAppConfig } from 'app/types/invokeai'; import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares'; -import Loading from '../../common/components/Loading/Loading'; - -import { Middleware } from '@reduxjs/toolkit'; -import { $authToken, $baseUrl } from 'services/api/client'; +import { $authToken, $baseUrl, $projectId } from 'services/api/client'; import { socketMiddleware } from 'services/events/middleware'; +import Loading from '../../common/components/Loading/Loading'; import '../../i18n'; -import { AddImageToBoardContextProvider } from '../contexts/AddImageToBoardContext'; import ImageDndContext from './ImageDnd/ImageDndContext'; const App = lazy(() => import('./App')); @@ -37,6 +34,7 @@ const InvokeAIUI = ({ config, headerComponent, middleware, + projectId, }: Props) => { useEffect(() => { // configure API client token @@ -49,6 +47,11 @@ const InvokeAIUI = ({ $baseUrl.set(apiUrl); } + // configure API client project header + if (projectId) { + $projectId.set(projectId); + } + // reset dynamically added middlewares resetMiddlewares(); @@ -68,8 +71,9 @@ const InvokeAIUI = ({ // Reset the API client token and base url on unmount $baseUrl.set(undefined); $authToken.set(undefined); + $projectId.set(undefined); }; - }, [apiUrl, token, middleware]); + }, [apiUrl, token, middleware, projectId]); return ( @@ -77,9 +81,7 @@ const InvokeAIUI = ({ }> - - - + diff --git a/invokeai/frontend/web/src/app/contexts/AddImageToBoardContext.tsx b/invokeai/frontend/web/src/app/contexts/AddImageToBoardContext.tsx deleted file mode 100644 index d5b3b746f1..0000000000 --- a/invokeai/frontend/web/src/app/contexts/AddImageToBoardContext.tsx +++ /dev/null @@ -1,91 +0,0 @@ -import { useDisclosure } from '@chakra-ui/react'; -import { PropsWithChildren, createContext, useCallback, useState } from 'react'; -import { ImageDTO } from 'services/api/types'; -import { imagesApi } from 'services/api/endpoints/images'; -import { useAppDispatch } from '../store/storeHooks'; - -export type ImageUsage = { - isInitialImage: boolean; - isCanvasImage: boolean; - isNodesImage: boolean; - isControlNetImage: boolean; -}; - -type AddImageToBoardContextValue = { - /** - * Whether the move image dialog is open. - */ - isOpen: boolean; - /** - * Closes the move image dialog. - */ - onClose: () => void; - /** - * The image pending movement - */ - image?: ImageDTO; - onClickAddToBoard: (image: ImageDTO) => void; - handleAddToBoard: (boardId: string) => void; -}; - -export const AddImageToBoardContext = - createContext({ - isOpen: false, - onClose: () => undefined, - onClickAddToBoard: () => undefined, - handleAddToBoard: () => undefined, - }); - -type Props = PropsWithChildren; - -export const AddImageToBoardContextProvider = (props: Props) => { - const [imageToMove, setImageToMove] = useState(); - const { isOpen, onOpen, onClose } = useDisclosure(); - const dispatch = useAppDispatch(); - - // Clean up after deleting or dismissing the modal - const closeAndClearImageToDelete = useCallback(() => { - setImageToMove(undefined); - onClose(); - }, [onClose]); - - const onClickAddToBoard = useCallback( - (image?: ImageDTO) => { - if (!image) { - return; - } - setImageToMove(image); - onOpen(); - }, - [setImageToMove, onOpen] - ); - - const handleAddToBoard = useCallback( - (boardId: string) => { - if (imageToMove) { - dispatch( - imagesApi.endpoints.addImageToBoard.initiate({ - imageDTO: imageToMove, - board_id: boardId, - }) - ); - closeAndClearImageToDelete(); - } - }, - [dispatch, closeAndClearImageToDelete, imageToMove] - ); - - return ( - - {props.children} - - ); -}; diff --git a/invokeai/frontend/web/src/app/contexts/ImageUploaderTriggerContext.ts b/invokeai/frontend/web/src/app/contexts/ImageUploaderTriggerContext.ts deleted file mode 100644 index 804e124625..0000000000 --- a/invokeai/frontend/web/src/app/contexts/ImageUploaderTriggerContext.ts +++ /dev/null @@ -1,8 +0,0 @@ -import { createContext } from 'react'; - -type VoidFunc = () => void; - -type ImageUploaderTriggerContextType = VoidFunc | null; - -export const ImageUploaderTriggerContext = - createContext(null); diff --git a/invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts b/invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts index 3407b3f7de..1b21770aa0 100644 --- a/invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts +++ b/invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts @@ -23,6 +23,6 @@ const serializationDenylist: { }; export const serialize: SerializeFunction = (data, key) => { - const result = omit(data, serializationDenylist[key]); + const result = omit(data, serializationDenylist[key] ?? []); return JSON.stringify(result); }; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts index f06c324bc6..c15b072a07 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts @@ -27,7 +27,8 @@ import { addImageDeletedFulfilledListener, addImageDeletedPendingListener, addImageDeletedRejectedListener, - addRequestedImageDeletionListener, + addRequestedSingleImageDeletionListener, + addRequestedMultipleImageDeletionListener, } from './listeners/imageDeleted'; import { addImageDroppedListener } from './listeners/imageDropped'; import { @@ -111,7 +112,8 @@ addImageUploadedRejectedListener(); addInitialImageSelectedListener(); // Image deleted -addRequestedImageDeletionListener(); +addRequestedSingleImageDeletionListener(); +addRequestedMultipleImageDeletionListener(); addImageDeletedPendingListener(); addImageDeletedFulfilledListener(); addImageDeletedRejectedListener(); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/addFirstListImagesListener.ts.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/addFirstListImagesListener.ts.ts index ee12f39a12..15e7d48708 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/addFirstListImagesListener.ts.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/addFirstListImagesListener.ts.ts @@ -1,12 +1,10 @@ import { createAction } from '@reduxjs/toolkit'; import { imageSelected } from 'features/gallery/store/gallerySlice'; import { IMAGE_CATEGORIES } from 'features/gallery/store/types'; -import { - ImageCache, - getListImagesUrl, - imagesApi, -} from 'services/api/endpoints/images'; +import { imagesApi } from 'services/api/endpoints/images'; import { startAppListening } from '..'; +import { getListImagesUrl, imagesAdapter } from 'services/api/util'; +import { ImageCache } from 'services/api/types'; export const appStarted = createAction('app/appStarted'); @@ -34,7 +32,8 @@ export const addFirstListImagesListener = () => { if (data.ids.length > 0) { // Select the first image - dispatch(imageSelected(data.ids[0] as string)); + const firstImage = imagesAdapter.getSelectors().selectAll(data)[0]; + dispatch(imageSelected(firstImage ?? null)); } }, }); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts index 2d0ece3595..700b4e7626 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appConfigReceived.ts @@ -18,7 +18,9 @@ export const addAppConfigReceivedListener = () => { const infillMethod = getState().generation.infillMethod; if (!infill_methods.includes(infillMethod)) { - dispatch(setInfillMethod(infill_methods[0])); + // if there is no infill method, set it to the first one + // if there is no first one... god help us + dispatch(setInfillMethod(infill_methods[0] as string)); } if (!nsfw_methods.includes('nsfw_checker')) { diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted.ts index f0af52ced6..d4a36d64dc 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted.ts @@ -1,14 +1,14 @@ import { resetCanvas } from 'features/canvas/store/canvasSlice'; import { controlNetReset } from 'features/controlNet/store/controlNetSlice'; -import { getImageUsage } from 'features/imageDeletion/store/imageDeletionSelectors'; +import { getImageUsage } from 'features/deleteImageModal/store/selectors'; import { nodeEditorReset } from 'features/nodes/store/nodesSlice'; import { clearInitialImage } from 'features/parameters/store/generationSlice'; +import { imagesApi } from 'services/api/endpoints/images'; import { startAppListening } from '..'; -import { boardsApi } from '../../../../../services/api/endpoints/boards'; export const addDeleteBoardAndImagesFulfilledListener = () => { startAppListening({ - matcher: boardsApi.endpoints.deleteBoardAndImages.matchFulfilled, + matcher: imagesApi.endpoints.deleteBoardAndImages.matchFulfilled, effect: async (action, { dispatch, getState }) => { const { deleted_images } = action.payload; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardIdSelected.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardIdSelected.ts index f9c856d6cb..1b13181911 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardIdSelected.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardIdSelected.ts @@ -10,6 +10,7 @@ import { } from 'features/gallery/store/types'; import { imagesApi } from 'services/api/endpoints/images'; import { startAppListening } from '..'; +import { imagesSelectors } from 'services/api/util'; export const addBoardIdSelectedListener = () => { startAppListening({ @@ -52,8 +53,9 @@ export const addBoardIdSelectedListener = () => { queryArgs )(getState()); - if (boardImagesData?.ids.length) { - dispatch(imageSelected((boardImagesData.ids[0] as string) ?? null)); + if (boardImagesData) { + const firstImage = imagesSelectors.selectAll(boardImagesData)[0]; + dispatch(imageSelected(firstImage ?? null)); } else { // board has no images - deselect dispatch(imageSelected(null)); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts index 47f7aded27..dbadb72a52 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery.ts @@ -26,6 +26,8 @@ export const addCanvasSavedToGalleryListener = () => { return; } + const { autoAddBoardId } = state.gallery; + dispatch( imagesApi.endpoints.uploadImage.initiate({ file: new File([blob], 'savedCanvas.png', { @@ -33,7 +35,7 @@ export const addCanvasSavedToGalleryListener = () => { }), image_category: 'general', is_intermediate: false, - board_id: state.gallery.autoAddBoardId, + board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId, crop_visible: true, postUploadAction: { type: 'TOAST', diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess.ts index 4a47e8d64e..61bcf28833 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess.ts @@ -31,15 +31,20 @@ const predicate: AnyListenerPredicate = ( // do not process if the user just disabled auto-config if ( prevState.controlNet.controlNets[action.payload.controlNetId] - .shouldAutoConfig === true + ?.shouldAutoConfig === true ) { return false; } } - const { controlImage, processorType, shouldAutoConfig } = - state.controlNet.controlNets[action.payload.controlNetId]; + const cn = state.controlNet.controlNets[action.payload.controlNetId]; + if (!cn) { + // something is wrong, the controlNet should exist + return false; + } + + const { controlImage, processorType, shouldAutoConfig } = cn; if (controlNetModelChanged.match(action) && !shouldAutoConfig) { // do not process if the action is a model change but the processor settings are dirty return false; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts index 313b2a02d8..fa915ef21b 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed.ts @@ -17,7 +17,7 @@ export const addControlNetImageProcessedListener = () => { const { controlNetId } = action.payload; const controlNet = getState().controlNet.controlNets[controlNetId]; - if (!controlNet.controlImage) { + if (!controlNet?.controlImage) { log.error('Unable to process ControlNet image'); return; } diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts index 428ce53219..cdfae0095e 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDeleted.ts @@ -1,57 +1,72 @@ import { logger } from 'app/logging/logger'; import { resetCanvas } from 'features/canvas/store/canvasSlice'; import { controlNetReset } from 'features/controlNet/store/controlNetSlice'; +import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions'; +import { isModalOpenChanged } from 'features/deleteImageModal/store/slice'; import { selectListImagesBaseQueryArgs } from 'features/gallery/store/gallerySelectors'; import { imageSelected } from 'features/gallery/store/gallerySlice'; -import { imageDeletionConfirmed } from 'features/imageDeletion/store/actions'; -import { isModalOpenChanged } from 'features/imageDeletion/store/imageDeletionSlice'; import { nodeEditorReset } from 'features/nodes/store/nodesSlice'; import { clearInitialImage } from 'features/parameters/store/generationSlice'; import { clamp } from 'lodash-es'; import { api } from 'services/api'; import { imagesApi } from 'services/api/endpoints/images'; +import { imagesAdapter } from 'services/api/util'; import { startAppListening } from '..'; -/** - * Called when the user requests an image deletion - */ -export const addRequestedImageDeletionListener = () => { +export const addRequestedSingleImageDeletionListener = () => { startAppListening({ actionCreator: imageDeletionConfirmed, effect: async (action, { dispatch, getState, condition }) => { - const { imageDTO, imageUsage } = action.payload; + const { imageDTOs, imagesUsage } = action.payload; + + if (imageDTOs.length !== 1 || imagesUsage.length !== 1) { + // handle multiples in separate listener + return; + } + + const imageDTO = imageDTOs[0]; + const imageUsage = imagesUsage[0]; + + if (!imageDTO || !imageUsage) { + // satisfy noUncheckedIndexedAccess + return; + } dispatch(isModalOpenChanged(false)); - const { image_name } = imageDTO; - const state = getState(); const lastSelectedImage = - state.gallery.selection[state.gallery.selection.length - 1]; + state.gallery.selection[state.gallery.selection.length - 1]?.image_name; + + if (imageDTO && imageDTO?.image_name === lastSelectedImage) { + const { image_name } = imageDTO; - if (lastSelectedImage === image_name) { const baseQueryArgs = selectListImagesBaseQueryArgs(state); const { data } = imagesApi.endpoints.listImages.select(baseQueryArgs)(state); - const ids = data?.ids ?? []; + const cachedImageDTOs = data + ? imagesAdapter.getSelectors().selectAll(data) + : []; - const deletedImageIndex = ids.findIndex( - (result) => result.toString() === image_name + const deletedImageIndex = cachedImageDTOs.findIndex( + (i) => i.image_name === image_name ); - const filteredIds = ids.filter((id) => id.toString() !== image_name); + const filteredImageDTOs = cachedImageDTOs.filter( + (i) => i.image_name !== image_name + ); const newSelectedImageIndex = clamp( deletedImageIndex, 0, - filteredIds.length - 1 + filteredImageDTOs.length - 1 ); - const newSelectedImageId = filteredIds[newSelectedImageIndex]; + const newSelectedImageDTO = filteredImageDTOs[newSelectedImageIndex]; - if (newSelectedImageId) { - dispatch(imageSelected(newSelectedImageId as string)); + if (newSelectedImageDTO) { + dispatch(imageSelected(newSelectedImageDTO)); } else { dispatch(imageSelected(null)); } @@ -97,6 +112,66 @@ export const addRequestedImageDeletionListener = () => { }); }; +/** + * Called when the user requests an image deletion + */ +export const addRequestedMultipleImageDeletionListener = () => { + startAppListening({ + actionCreator: imageDeletionConfirmed, + effect: async (action, { dispatch, getState }) => { + const { imageDTOs, imagesUsage } = action.payload; + + if (imageDTOs.length < 1 || imagesUsage.length < 1) { + // handle singles in separate listener + return; + } + + try { + // Delete from server + await dispatch( + imagesApi.endpoints.deleteImages.initiate({ imageDTOs }) + ).unwrap(); + const state = getState(); + const baseQueryArgs = selectListImagesBaseQueryArgs(state); + const { data } = + imagesApi.endpoints.listImages.select(baseQueryArgs)(state); + + const newSelectedImageDTO = data + ? imagesAdapter.getSelectors().selectAll(data)[0] + : undefined; + + if (newSelectedImageDTO) { + dispatch(imageSelected(newSelectedImageDTO)); + } else { + dispatch(imageSelected(null)); + } + + dispatch(isModalOpenChanged(false)); + + // We need to reset the features where the image is in use - none of these work if their image(s) don't exist + + if (imagesUsage.some((i) => i.isCanvasImage)) { + dispatch(resetCanvas()); + } + + if (imagesUsage.some((i) => i.isControlNetImage)) { + dispatch(controlNetReset()); + } + + if (imagesUsage.some((i) => i.isInitialImage)) { + dispatch(clearInitialImage()); + } + + if (imagesUsage.some((i) => i.isNodesImage)) { + dispatch(nodeEditorReset()); + } + } catch { + // no-op + } + }, + }); +}; + /** * Called when the actual delete request is sent to the server */ diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts index fdf0849a12..043105cb66 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageDropped.ts @@ -6,10 +6,7 @@ import { import { logger } from 'app/logging/logger'; import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice'; import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice'; -import { - imageSelected, - imagesAddedToBatch, -} from 'features/gallery/store/gallerySlice'; +import { imageSelected } from 'features/gallery/store/gallerySlice'; import { fieldValueChanged } from 'features/nodes/store/nodesSlice'; import { initialImageChanged } from 'features/parameters/store/generationSlice'; import { imagesApi } from 'services/api/endpoints/images'; @@ -27,19 +24,32 @@ export const addImageDroppedListener = () => { const log = logger('images'); const { activeData, overData } = action.payload; - log.debug({ activeData, overData }, 'Image or selection dropped'); + if (activeData.payloadType === 'IMAGE_DTO') { + log.debug({ activeData, overData }, 'Image dropped'); + } else if (activeData.payloadType === 'IMAGE_DTOS') { + log.debug( + { activeData, overData }, + `Images (${activeData.payload.imageDTOs.length}) dropped` + ); + } else { + log.debug({ activeData, overData }, `Unknown payload dropped`); + } - // set current image + /** + * Image dropped on current image + */ if ( overData.actionType === 'SET_CURRENT_IMAGE' && activeData.payloadType === 'IMAGE_DTO' && activeData.payload.imageDTO ) { - dispatch(imageSelected(activeData.payload.imageDTO.image_name)); + dispatch(imageSelected(activeData.payload.imageDTO)); return; } - // set initial image + /** + * Image dropped on initial image + */ if ( overData.actionType === 'SET_INITIAL_IMAGE' && activeData.payloadType === 'IMAGE_DTO' && @@ -49,27 +59,9 @@ export const addImageDroppedListener = () => { return; } - // add image to batch - if ( - overData.actionType === 'ADD_TO_BATCH' && - activeData.payloadType === 'IMAGE_DTO' && - activeData.payload.imageDTO - ) { - dispatch(imagesAddedToBatch([activeData.payload.imageDTO.image_name])); - return; - } - - // add multiple images to batch - if ( - overData.actionType === 'ADD_TO_BATCH' && - activeData.payloadType === 'IMAGE_NAMES' - ) { - dispatch(imagesAddedToBatch(activeData.payload.image_names)); - - return; - } - - // set control image + /** + * Image dropped on ControlNet + */ if ( overData.actionType === 'SET_CONTROLNET_IMAGE' && activeData.payloadType === 'IMAGE_DTO' && @@ -85,7 +77,9 @@ export const addImageDroppedListener = () => { return; } - // set canvas image + /** + * Image dropped on Canvas + */ if ( overData.actionType === 'SET_CANVAS_INITIAL_IMAGE' && activeData.payloadType === 'IMAGE_DTO' && @@ -95,7 +89,9 @@ export const addImageDroppedListener = () => { return; } - // set nodes image + /** + * Image dropped on node image field + */ if ( overData.actionType === 'SET_NODES_IMAGE' && activeData.payloadType === 'IMAGE_DTO' && @@ -112,61 +108,36 @@ export const addImageDroppedListener = () => { return; } - // set multiple nodes images (single image handler) - if ( - overData.actionType === 'SET_MULTI_NODES_IMAGE' && - activeData.payloadType === 'IMAGE_DTO' && - activeData.payload.imageDTO - ) { - const { fieldName, nodeId } = overData.context; - dispatch( - fieldValueChanged({ - nodeId, - fieldName, - value: [activeData.payload.imageDTO], - }) - ); - return; - } - - // // set multiple nodes images (multiple images handler) + /** + * TODO + * Image selection dropped on node image collection field + */ // if ( // overData.actionType === 'SET_MULTI_NODES_IMAGE' && - // activeData.payloadType === 'IMAGE_NAMES' + // activeData.payloadType === 'IMAGE_DTO' && + // activeData.payload.imageDTO // ) { // const { fieldName, nodeId } = overData.context; // dispatch( - // imageCollectionFieldValueChanged({ + // fieldValueChanged({ // nodeId, // fieldName, - // value: activeData.payload.image_names.map((image_name) => ({ - // image_name, - // })), + // value: [activeData.payload.imageDTO], // }) // ); // return; // } - // add image to board + /** + * Image dropped on user board + */ if ( - overData.actionType === 'MOVE_BOARD' && + overData.actionType === 'ADD_TO_BOARD' && activeData.payloadType === 'IMAGE_DTO' && activeData.payload.imageDTO ) { const { imageDTO } = activeData.payload; const { boardId } = overData.context; - - // image was droppe on the "NoBoardBoard" - if (!boardId) { - dispatch( - imagesApi.endpoints.removeImageFromBoard.initiate({ - imageDTO, - }) - ); - return; - } - - // image was dropped on a user board dispatch( imagesApi.endpoints.addImageToBoard.initiate({ imageDTO, @@ -176,67 +147,58 @@ export const addImageDroppedListener = () => { return; } - // // add gallery selection to board - // if ( - // overData.actionType === 'MOVE_BOARD' && - // activeData.payloadType === 'IMAGE_NAMES' && - // overData.context.boardId - // ) { - // console.log('adding gallery selection to board'); - // const board_id = overData.context.boardId; - // dispatch( - // boardImagesApi.endpoints.addManyBoardImages.initiate({ - // board_id, - // image_names: activeData.payload.image_names, - // }) - // ); - // return; - // } + /** + * Image dropped on 'none' board + */ + if ( + overData.actionType === 'REMOVE_FROM_BOARD' && + activeData.payloadType === 'IMAGE_DTO' && + activeData.payload.imageDTO + ) { + const { imageDTO } = activeData.payload; + dispatch( + imagesApi.endpoints.removeImageFromBoard.initiate({ + imageDTO, + }) + ); + return; + } - // // remove gallery selection from board - // if ( - // overData.actionType === 'MOVE_BOARD' && - // activeData.payloadType === 'IMAGE_NAMES' && - // overData.context.boardId === null - // ) { - // console.log('removing gallery selection to board'); - // dispatch( - // boardImagesApi.endpoints.deleteManyBoardImages.initiate({ - // image_names: activeData.payload.image_names, - // }) - // ); - // return; - // } + /** + * Multiple images dropped on user board + */ + if ( + overData.actionType === 'ADD_TO_BOARD' && + activeData.payloadType === 'IMAGE_DTOS' && + activeData.payload.imageDTOs + ) { + const { imageDTOs } = activeData.payload; + const { boardId } = overData.context; + dispatch( + imagesApi.endpoints.addImagesToBoard.initiate({ + imageDTOs, + board_id: boardId, + }) + ); + return; + } - // // add batch selection to board - // if ( - // overData.actionType === 'MOVE_BOARD' && - // activeData.payloadType === 'IMAGE_NAMES' && - // overData.context.boardId - // ) { - // const board_id = overData.context.boardId; - // dispatch( - // boardImagesApi.endpoints.addManyBoardImages.initiate({ - // board_id, - // image_names: activeData.payload.image_names, - // }) - // ); - // return; - // } - - // // remove batch selection from board - // if ( - // overData.actionType === 'MOVE_BOARD' && - // activeData.payloadType === 'IMAGE_NAMES' && - // overData.context.boardId === null - // ) { - // dispatch( - // boardImagesApi.endpoints.deleteManyBoardImages.initiate({ - // image_names: activeData.payload.image_names, - // }) - // ); - // return; - // } + /** + * Multiple images dropped on 'none' board + */ + if ( + overData.actionType === 'REMOVE_FROM_BOARD' && + activeData.payloadType === 'IMAGE_DTOS' && + activeData.payload.imageDTOs + ) { + const { imageDTOs } = activeData.payload; + dispatch( + imagesApi.endpoints.removeImagesFromBoard.initiate({ + imageDTOs, + }) + ); + return; + } }, }); }; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected.ts index 3a5eed95db..88a4e773d5 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected.ts @@ -1,37 +1,32 @@ -import { imageDeletionConfirmed } from 'features/imageDeletion/store/actions'; -import { selectImageUsage } from 'features/imageDeletion/store/imageDeletionSelectors'; +import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions'; +import { selectImageUsage } from 'features/deleteImageModal/store/selectors'; import { - imageToDeleteSelected, + imagesToDeleteSelected, isModalOpenChanged, -} from 'features/imageDeletion/store/imageDeletionSlice'; +} from 'features/deleteImageModal/store/slice'; import { startAppListening } from '..'; export const addImageToDeleteSelectedListener = () => { startAppListening({ - actionCreator: imageToDeleteSelected, + actionCreator: imagesToDeleteSelected, effect: async (action, { dispatch, getState }) => { - const imageDTO = action.payload; + const imageDTOs = action.payload; const state = getState(); const { shouldConfirmOnDelete } = state.system; - const imageUsage = selectImageUsage(getState()); - - if (!imageUsage) { - // should never happen - return; - } + const imagesUsage = selectImageUsage(getState()); const isImageInUse = - imageUsage.isCanvasImage || - imageUsage.isInitialImage || - imageUsage.isControlNetImage || - imageUsage.isNodesImage; + imagesUsage.some((i) => i.isCanvasImage) || + imagesUsage.some((i) => i.isInitialImage) || + imagesUsage.some((i) => i.isControlNetImage) || + imagesUsage.some((i) => i.isNodesImage); if (shouldConfirmOnDelete || isImageInUse) { dispatch(isModalOpenChanged(true)); return; } - dispatch(imageDeletionConfirmed({ imageDTO, imageUsage })); + dispatch(imageDeletionConfirmed({ imageDTOs, imagesUsage })); }, }); }; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts index dd581d893c..6dc2d482a9 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts @@ -2,14 +2,13 @@ import { UseToastOptions } from '@chakra-ui/react'; import { logger } from 'app/logging/logger'; import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice'; import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice'; -import { imagesAddedToBatch } from 'features/gallery/store/gallerySlice'; import { fieldValueChanged } from 'features/nodes/store/nodesSlice'; import { initialImageChanged } from 'features/parameters/store/generationSlice'; import { addToast } from 'features/system/store/systemSlice'; +import { omit } from 'lodash-es'; import { boardsApi } from 'services/api/endpoints/boards'; import { startAppListening } from '..'; import { imagesApi } from '../../../../../services/api/endpoints/images'; -import { omit } from 'lodash-es'; const DEFAULT_UPLOADED_TOAST: UseToastOptions = { title: 'Image Uploaded', @@ -41,7 +40,7 @@ export const addImageUploadedFulfilledListener = () => { // default action - just upload and alert user if (postUploadAction?.type === 'TOAST') { const { toastOptions } = postUploadAction; - if (!autoAddBoardId) { + if (!autoAddBoardId || autoAddBoardId === 'none') { dispatch(addToast({ ...DEFAULT_UPLOADED_TOAST, ...toastOptions })); } else { // Add this image to the board @@ -121,17 +120,6 @@ export const addImageUploadedFulfilledListener = () => { ); return; } - - if (postUploadAction?.type === 'ADD_TO_BATCH') { - dispatch(imagesAddedToBatch([imageDTO.image_name])); - dispatch( - addToast({ - ...DEFAULT_UPLOADED_TOAST, - description: 'Added to batch', - }) - ); - return; - } }, }); }; diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts index 325e843900..436a58aa8e 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts @@ -15,7 +15,7 @@ import { setShouldUseSDXLRefiner, } from 'features/sdxl/store/sdxlSlice'; import { forEach, some } from 'lodash-es'; -import { modelsApi } from 'services/api/endpoints/models'; +import { modelsApi, vaeModelsAdapter } from 'services/api/endpoints/models'; import { startAppListening } from '..'; export const addModelsLoadedListener = () => { @@ -144,8 +144,9 @@ export const addModelsLoadedListener = () => { return; } - const firstModelId = action.payload.ids[0]; - const firstModel = action.payload.entities[firstModelId]; + const firstModel = vaeModelsAdapter + .getSelectors() + .selectAll(action.payload)[0]; if (!firstModel) { // No custom VAEs loaded at all; use the default diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts index e36c49be63..5b3b9424b6 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete.ts @@ -7,10 +7,12 @@ import { imageSelected, } from 'features/gallery/store/gallerySlice'; import { IMAGE_CATEGORIES } from 'features/gallery/store/types'; +import { CANVAS_OUTPUT } from 'features/nodes/util/graphBuilders/constants'; import { progressImageSet } from 'features/system/store/systemSlice'; -import { imagesAdapter, imagesApi } from 'services/api/endpoints/images'; +import { imagesApi } from 'services/api/endpoints/images'; import { isImageOutput } from 'services/api/guards'; import { sessionCanceled } from 'services/api/thunks/session'; +import { imagesAdapter } from 'services/api/util'; import { appSocketInvocationComplete, socketInvocationComplete, @@ -51,7 +53,9 @@ export const addInvocationCompleteEventListener = () => { // Add canvas images to the staging area if ( - graph_execution_state_id === canvas.layerState.stagingArea.sessionId + graph_execution_state_id === + canvas.layerState.stagingArea.sessionId && + [CANVAS_OUTPUT].includes(data.source_node_id) ) { dispatch(addImageToStagingArea(imageDTO)); } @@ -67,7 +71,7 @@ export const addInvocationCompleteEventListener = () => { */ const { autoAddBoardId } = gallery; - if (autoAddBoardId) { + if (autoAddBoardId && autoAddBoardId !== 'none') { dispatch( imagesApi.endpoints.addImageToBoard.initiate({ board_id: autoAddBoardId, @@ -83,10 +87,7 @@ export const addInvocationCompleteEventListener = () => { categories: IMAGE_CATEGORIES, }, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.addOne(draft, imageDTO); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.addOne(draft, imageDTO); } ) ); @@ -94,8 +95,8 @@ export const addInvocationCompleteEventListener = () => { dispatch( imagesApi.util.invalidateTags([ - { type: 'BoardImagesTotal', id: autoAddBoardId ?? 'none' }, - { type: 'BoardAssetsTotal', id: autoAddBoardId ?? 'none' }, + { type: 'BoardImagesTotal', id: autoAddBoardId }, + { type: 'BoardAssetsTotal', id: autoAddBoardId }, ]) ); @@ -110,7 +111,7 @@ export const addInvocationCompleteEventListener = () => { } else if (!autoAddBoardId) { dispatch(galleryViewChanged('images')); } - dispatch(imageSelected(imageDTO.image_name)); + dispatch(imageSelected(imageDTO)); } } diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved.ts index a6818acbe0..b14e18ea63 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved.ts @@ -20,7 +20,7 @@ export const addStagingAreaImageSavedListener = () => { // we may need to add it to the autoadd board const { autoAddBoardId } = getState().gallery; - if (autoAddBoardId) { + if (autoAddBoardId && autoAddBoardId !== 'none') { await dispatch( imagesApi.endpoints.addImageToBoard.initiate({ imageDTO: newImageDTO, diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts index 0569827859..6791324fdd 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/tabChanged.ts @@ -1,55 +1,63 @@ import { modelChanged } from 'features/parameters/store/generationSlice'; import { setActiveTab } from 'features/ui/store/uiSlice'; -import { forEach } from 'lodash-es'; import { NON_REFINER_BASE_MODELS } from 'services/api/constants'; -import { - MainModelConfigEntity, - modelsApi, -} from 'services/api/endpoints/models'; +import { mainModelsAdapter, modelsApi } from 'services/api/endpoints/models'; import { startAppListening } from '..'; export const addTabChangedListener = () => { startAppListening({ actionCreator: setActiveTab, - effect: (action, { getState, dispatch }) => { + effect: async (action, { getState, dispatch }) => { const activeTabName = action.payload; if (activeTabName === 'unifiedCanvas') { - // grab the models from RTK Query cache - const { data } = modelsApi.endpoints.getMainModels.select( - NON_REFINER_BASE_MODELS - )(getState()); + const currentBaseModel = getState().generation.model?.base_model; - if (!data) { - // no models yet, so we can't do anything - dispatch(modelChanged(null)); + if ( + currentBaseModel && + ['sd-1', 'sd-2', 'sdxl'].includes(currentBaseModel) + ) { + // if we're already on a valid model, no change needed return; } - // need to filter out all the invalid canvas models (currently, this is just sdxl) - const validCanvasModels: MainModelConfigEntity[] = []; + try { + // just grab fresh models + const modelsRequest = dispatch( + modelsApi.endpoints.getMainModels.initiate(NON_REFINER_BASE_MODELS) + ); + const models = await modelsRequest.unwrap(); + // cancel this cache subscription + modelsRequest.unsubscribe(); - forEach(data.entities, (entity) => { - if (!entity) { + if (!models.ids.length) { + // no valid canvas models + dispatch(modelChanged(null)); return; } - if (['sd-1', 'sd-2'].includes(entity.base_model)) { - validCanvasModels.push(entity); + + // need to filter out all the invalid canvas models (currently sdxl & refiner) + const validCanvasModels = mainModelsAdapter + .getSelectors() + .selectAll(models) + .filter((model) => + ['sd-1', 'sd-2', 'sxdl'].includes(model.base_model) + ); + + const firstValidCanvasModel = validCanvasModels[0]; + + if (!firstValidCanvasModel) { + // no valid canvas models + dispatch(modelChanged(null)); + return; } - }); - // this could still be undefined even tho TS doesn't say so - const firstValidCanvasModel = validCanvasModels[0]; + const { base_model, model_name, model_type } = firstValidCanvasModel; - if (!firstValidCanvasModel) { - // uh oh, we have no models that are valid for canvas + dispatch(modelChanged({ base_model, model_name, model_type })); + } catch { + // network request failed, bail dispatch(modelChanged(null)); - return; } - - // only store the model name and base model in redux - const { base_model, model_name, model_type } = firstValidCanvasModel; - - dispatch(modelChanged({ base_model, model_name, model_type })); } }, }); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts index 39bd742d7d..cd6791cc0b 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/userInvokedCanvas.ts @@ -1,6 +1,7 @@ import { logger } from 'app/logging/logger'; import { userInvoked } from 'app/store/actions'; import openBase64ImageInTab from 'common/util/openBase64ImageInTab'; +import { parseify } from 'common/util/serialize'; import { canvasSessionIdChanged, stagingAreaInitialized, @@ -15,7 +16,6 @@ import { imagesApi } from 'services/api/endpoints/images'; import { sessionCreated } from 'services/api/thunks/session'; import { ImageDTO } from 'services/api/types'; import { startAppListening } from '..'; -import { parseify } from 'common/util/serialize'; /** * This listener is responsible invoking the canvas. This involves a number of steps: diff --git a/invokeai/frontend/web/src/app/store/store.ts b/invokeai/frontend/web/src/app/store/store.ts index d71a147913..6b544252db 100644 --- a/invokeai/frontend/web/src/app/store/store.ts +++ b/invokeai/frontend/web/src/app/store/store.ts @@ -8,9 +8,9 @@ import { import canvasReducer from 'features/canvas/store/canvasSlice'; import controlNetReducer from 'features/controlNet/store/controlNetSlice'; import dynamicPromptsReducer from 'features/dynamicPrompts/store/dynamicPromptsSlice'; -import boardsReducer from 'features/gallery/store/boardSlice'; import galleryReducer from 'features/gallery/store/gallerySlice'; -import imageDeletionReducer from 'features/imageDeletion/store/imageDeletionSlice'; +import deleteImageModalReducer from 'features/deleteImageModal/store/slice'; +import changeBoardModalReducer from 'features/changeBoardModal/store/slice'; import loraReducer from 'features/lora/store/loraSlice'; import nodesReducer from 'features/nodes/store/nodesSlice'; import generationReducer from 'features/parameters/store/generationSlice'; @@ -43,9 +43,9 @@ const allReducers = { ui: uiReducer, hotkeys: hotkeysReducer, controlNet: controlNetReducer, - boards: boardsReducer, dynamicPrompts: dynamicPromptsReducer, - imageDeletion: imageDeletionReducer, + deleteImageModal: deleteImageModalReducer, + changeBoardModal: changeBoardModalReducer, lora: loraReducer, modelmanager: modelmanagerReducer, sdxl: sdxlReducer, diff --git a/invokeai/frontend/web/src/app/types/invokeai.ts b/invokeai/frontend/web/src/app/types/invokeai.ts index b38790e0c9..827424fa7f 100644 --- a/invokeai/frontend/web/src/app/types/invokeai.ts +++ b/invokeai/frontend/web/src/app/types/invokeai.ts @@ -96,7 +96,8 @@ export type AppFeature = | 'consoleLogging' | 'dynamicPrompting' | 'batches' - | 'syncModels'; + | 'syncModels' + | 'multiselect'; /** * A disable-able Stable Diffusion feature diff --git a/invokeai/frontend/web/src/common/components/IAIDropOverlay.tsx b/invokeai/frontend/web/src/common/components/IAIDropOverlay.tsx index 7601758409..f9bb36cc50 100644 --- a/invokeai/frontend/web/src/common/components/IAIDropOverlay.tsx +++ b/invokeai/frontend/web/src/common/components/IAIDropOverlay.tsx @@ -1,4 +1,4 @@ -import { Flex, Text, useColorMode } from '@chakra-ui/react'; +import { Box, Flex, useColorMode } from '@chakra-ui/react'; import { motion } from 'framer-motion'; import { ReactNode, memo, useRef } from 'react'; import { mode } from 'theme/util/mode'; @@ -74,7 +74,7 @@ export const IAIDropOverlay = (props: Props) => { justifyContent: 'center', }} > - { }} > {label} - + diff --git a/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx b/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx index 2c3f5434ad..079421d4e5 100644 --- a/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx +++ b/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx @@ -53,7 +53,9 @@ const IAIMantineSearchableSelect = (props: IAISelectProps) => { // wrap onChange to clear search value on select const handleChange = useCallback( (v: string | null) => { - setSearchValue(''); + // cannot figure out why we were doing this, but it was causing an issue where if you + // select the currently-selected item, it reset the search value to empty + // setSearchValue(''); if (!onChange) { return; diff --git a/invokeai/frontend/web/src/common/components/ImageUploader.tsx b/invokeai/frontend/web/src/common/components/ImageUploader.tsx index de347b8381..c990a9a24e 100644 --- a/invokeai/frontend/web/src/common/components/ImageUploader.tsx +++ b/invokeai/frontend/web/src/common/components/ImageUploader.tsx @@ -78,7 +78,7 @@ const ImageUploader = (props: ImageUploaderProps) => { image_category: 'user', is_intermediate: false, postUploadAction, - board_id: autoAddBoardId, + board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId, }); }, [autoAddBoardId, postUploadAction, uploadImage] diff --git a/invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx b/invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx index c04c0182cd..dcbd81b2dd 100644 --- a/invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx +++ b/invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx @@ -49,7 +49,7 @@ export const useImageUploadButton = ({ image_category: 'user', is_intermediate: false, postUploadAction: postUploadAction ?? { type: 'TOAST' }, - board_id: autoAddBoardId, + board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId, }); }, [autoAddBoardId, postUploadAction, uploadImage] diff --git a/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts b/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts index 1356b24416..64289a1fd3 100644 --- a/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts +++ b/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts @@ -33,6 +33,10 @@ const useColorPicker = () => { 1 ).data; + if (!(a && r && g && b)) { + return; + } + dispatch(setColorPickerColor({ r, g, b, a })); }, commitColorUnderCursor: () => { diff --git a/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts b/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts index 3163e513e9..11f829221a 100644 --- a/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts +++ b/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts @@ -47,7 +47,7 @@ export const initialCanvasState: CanvasState = { boundingBoxCoordinates: { x: 0, y: 0 }, boundingBoxDimensions: { width: 512, height: 512 }, boundingBoxPreviewFill: { r: 0, g: 0, b: 0, a: 0.5 }, - boundingBoxScaleMethod: 'auto', + boundingBoxScaleMethod: 'none', brushColor: { r: 90, g: 90, b: 255, a: 1 }, brushSize: 50, canvasContainerDimensions: { width: 0, height: 0 }, @@ -727,10 +727,13 @@ export const canvasSlice = createSlice({ state.pastLayerStates.shift(); } - state.layerState.objects.push({ - ...images[selectedImageIndex], - }); + const imageToCommit = images[selectedImageIndex]; + if (imageToCommit) { + state.layerState.objects.push({ + ...imageToCommit, + }); + } state.layerState.stagingArea = { ...initialLayerState.stagingArea, }; diff --git a/invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts b/invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts index ba85a7e132..f2ba90b050 100644 --- a/invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts +++ b/invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts @@ -11,9 +11,9 @@ export const LAYER_NAMES = ['base', 'mask'] as const; export type CanvasLayer = (typeof LAYER_NAMES)[number]; export const BOUNDING_BOX_SCALES_DICT = [ + { label: 'None', value: 'none' }, { label: 'Auto', value: 'auto' }, { label: 'Manual', value: 'manual' }, - { label: 'None', value: 'none' }, ]; export const BOUNDING_BOX_SCALES = ['none', 'auto', 'manual'] as const; diff --git a/invokeai/frontend/web/src/features/changeBoardModal/components/ChangeBoardModal.tsx b/invokeai/frontend/web/src/features/changeBoardModal/components/ChangeBoardModal.tsx new file mode 100644 index 0000000000..2443fa6081 --- /dev/null +++ b/invokeai/frontend/web/src/features/changeBoardModal/components/ChangeBoardModal.tsx @@ -0,0 +1,132 @@ +import { + AlertDialog, + AlertDialogBody, + AlertDialogContent, + AlertDialogFooter, + AlertDialogHeader, + AlertDialogOverlay, + Flex, + Text, +} from '@chakra-ui/react'; +import { createSelector } from '@reduxjs/toolkit'; +import { stateSelector } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import IAIButton from 'common/components/IAIButton'; +import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect'; +import { memo, useCallback, useMemo, useRef, useState } from 'react'; +import { useListAllBoardsQuery } from 'services/api/endpoints/boards'; +import { + useAddImagesToBoardMutation, + useRemoveImagesFromBoardMutation, +} from 'services/api/endpoints/images'; +import { changeBoardReset, isModalOpenChanged } from '../store/slice'; + +const selector = createSelector( + [stateSelector], + ({ changeBoardModal }) => { + const { isModalOpen, imagesToChange } = changeBoardModal; + + return { + isModalOpen, + imagesToChange, + }; + }, + defaultSelectorOptions +); + +const ChangeBoardModal = () => { + const dispatch = useAppDispatch(); + const [selectedBoard, setSelectedBoard] = useState(); + const { data: boards, isFetching } = useListAllBoardsQuery(); + const { imagesToChange, isModalOpen } = useAppSelector(selector); + const [addImagesToBoard] = useAddImagesToBoardMutation(); + const [removeImagesFromBoard] = useRemoveImagesFromBoardMutation(); + + const data = useMemo(() => { + const data: { label: string; value: string }[] = [ + { label: 'Uncategorized', value: 'none' }, + ]; + (boards ?? []).forEach((board) => + data.push({ + label: board.board_name, + value: board.board_id, + }) + ); + + return data; + }, [boards]); + + const handleClose = useCallback(() => { + dispatch(changeBoardReset()); + dispatch(isModalOpenChanged(false)); + }, [dispatch]); + + const handleChangeBoard = useCallback(() => { + if (!imagesToChange.length || !selectedBoard) { + return; + } + + if (selectedBoard === 'none') { + removeImagesFromBoard({ imageDTOs: imagesToChange }); + } else { + addImagesToBoard({ + imageDTOs: imagesToChange, + board_id: selectedBoard, + }); + } + setSelectedBoard(null); + dispatch(changeBoardReset()); + }, [ + addImagesToBoard, + dispatch, + imagesToChange, + removeImagesFromBoard, + selectedBoard, + ]); + + const cancelRef = useRef(null); + + return ( + + + + + Change Board + + + + + + Moving {`${imagesToChange.length}`} image + {`${imagesToChange.length > 1 ? 's' : ''}`} to board: + + setSelectedBoard(v)} + value={selectedBoard} + data={data} + /> + + + + + Cancel + + + Move + + + + + + ); +}; + +export default memo(ChangeBoardModal); diff --git a/invokeai/frontend/web/src/features/changeBoardModal/store/initialState.ts b/invokeai/frontend/web/src/features/changeBoardModal/store/initialState.ts new file mode 100644 index 0000000000..d737d0cdcd --- /dev/null +++ b/invokeai/frontend/web/src/features/changeBoardModal/store/initialState.ts @@ -0,0 +1,6 @@ +import { ChangeBoardModalState } from './types'; + +export const initialState: ChangeBoardModalState = { + isModalOpen: false, + imagesToChange: [], +}; diff --git a/invokeai/frontend/web/src/features/changeBoardModal/store/slice.ts b/invokeai/frontend/web/src/features/changeBoardModal/store/slice.ts new file mode 100644 index 0000000000..9855e2d7dd --- /dev/null +++ b/invokeai/frontend/web/src/features/changeBoardModal/store/slice.ts @@ -0,0 +1,25 @@ +import { PayloadAction, createSlice } from '@reduxjs/toolkit'; +import { ImageDTO } from 'services/api/types'; +import { initialState } from './initialState'; + +const changeBoardModal = createSlice({ + name: 'changeBoardModal', + initialState, + reducers: { + isModalOpenChanged: (state, action: PayloadAction) => { + state.isModalOpen = action.payload; + }, + imagesToChangeSelected: (state, action: PayloadAction) => { + state.imagesToChange = action.payload; + }, + changeBoardReset: (state) => { + state.imagesToChange = []; + state.isModalOpen = false; + }, + }, +}); + +export const { isModalOpenChanged, imagesToChangeSelected, changeBoardReset } = + changeBoardModal.actions; + +export default changeBoardModal.reducer; diff --git a/invokeai/frontend/web/src/features/changeBoardModal/store/types.ts b/invokeai/frontend/web/src/features/changeBoardModal/store/types.ts new file mode 100644 index 0000000000..6ce13331d0 --- /dev/null +++ b/invokeai/frontend/web/src/features/changeBoardModal/store/types.ts @@ -0,0 +1,6 @@ +import { ImageDTO } from 'services/api/types'; + +export type ChangeBoardModalState = { + isModalOpen: boolean; + imagesToChange: ImageDTO[]; +}; diff --git a/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx b/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx index d858e46fdb..3252207edc 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx @@ -3,6 +3,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { memo, useCallback } from 'react'; import { FaCopy, FaTrash } from 'react-icons/fa'; import { + ControlNetConfig, controlNetDuplicated, controlNetRemoved, controlNetToggled, @@ -27,18 +28,27 @@ import ParamControlNetProcessorSelect from './parameters/ParamControlNetProcesso import ParamControlNetResizeMode from './parameters/ParamControlNetResizeMode'; type ControlNetProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; const ControlNet = (props: ControlNetProps) => { - const { controlNetId } = props; + const { controlNet } = props; + const { controlNetId } = controlNet; const dispatch = useAppDispatch(); const selector = createSelector( stateSelector, ({ controlNet }) => { - const { isEnabled, shouldAutoConfig } = - controlNet.controlNets[controlNetId]; + const cn = controlNet.controlNets[controlNetId]; + + if (!cn) { + return { + isEnabled: false, + shouldAutoConfig: false, + }; + } + + const { isEnabled, shouldAutoConfig } = cn; return { isEnabled, shouldAutoConfig }; }, @@ -96,7 +106,7 @@ const ControlNet = (props: ControlNetProps) => { transitionDuration: '0.1s', }} > - + { justifyContent: 'space-between', }} > - - + + {!isExpanded && ( { aspectRatio: '1/1', }} > - + )} - - + + - + {isExpanded && ( <> - - - + + + )} diff --git a/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx b/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx index 859495a941..cdab176cd2 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx @@ -12,50 +12,41 @@ import IAIDndImage from 'common/components/IAIDndImage'; import { memo, useCallback, useMemo, useState } from 'react'; import { useGetImageDTOQuery } from 'services/api/endpoints/images'; import { PostUploadAction } from 'services/api/types'; -import { controlNetImageChanged } from '../store/controlNetSlice'; +import { + ControlNetConfig, + controlNetImageChanged, +} from '../store/controlNetSlice'; type Props = { - controlNetId: string; + controlNet: ControlNetConfig; height: SystemStyleObject['h']; }; +const selector = createSelector( + stateSelector, + ({ controlNet }) => { + const { pendingControlImages } = controlNet; + + return { + pendingControlImages, + }; + }, + defaultSelectorOptions +); + const ControlNetImagePreview = (props: Props) => { - const { height, controlNetId } = props; + const { height } = props; + const { + controlImage: controlImageName, + processedControlImage: processedControlImageName, + processorType, + isEnabled, + controlNetId, + } = props.controlNet; + const dispatch = useAppDispatch(); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { pendingControlImages } = controlNet; - const { - controlImage, - processedControlImage, - processorType, - isEnabled, - } = controlNet.controlNets[controlNetId]; - - return { - controlImageName: controlImage, - processedControlImageName: processedControlImage, - processorType, - isEnabled, - pendingControlImages, - }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { - controlImageName, - processedControlImageName, - processorType, - pendingControlImages, - isEnabled, - } = useAppSelector(selector); + const { pendingControlImages } = useAppSelector(selector); const [isMouseOverImage, setIsMouseOverImage] = useState(false); diff --git a/invokeai/frontend/web/src/features/controlNet/components/ControlNetProcessorComponent.tsx b/invokeai/frontend/web/src/features/controlNet/components/ControlNetProcessorComponent.tsx index b7fa329eac..681838ef27 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/ControlNetProcessorComponent.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/ControlNetProcessorComponent.tsx @@ -1,8 +1,5 @@ -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; -import { useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; -import { memo, useMemo } from 'react'; +import { memo } from 'react'; +import { ControlNetConfig } from '../store/controlNetSlice'; import CannyProcessor from './processors/CannyProcessor'; import ContentShuffleProcessor from './processors/ContentShuffleProcessor'; import HedProcessor from './processors/HedProcessor'; @@ -17,28 +14,11 @@ import PidiProcessor from './processors/PidiProcessor'; import ZoeDepthProcessor from './processors/ZoeDepthProcessor'; export type ControlNetProcessorProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; const ControlNetProcessorComponent = (props: ControlNetProcessorProps) => { - const { controlNetId } = props; - - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { isEnabled, processorNode } = - controlNet.controlNets[controlNetId]; - - return { isEnabled, processorNode }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { isEnabled, processorNode } = useAppSelector(selector); + const { controlNetId, isEnabled, processorNode } = props.controlNet; if (processorNode.type === 'canny_image_processor') { return ( diff --git a/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx b/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx index 285fcf7b80..0e044d4575 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/ParamControlNetShouldAutoConfig.tsx @@ -1,34 +1,19 @@ -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAISwitch from 'common/components/IAISwitch'; -import { controlNetAutoConfigToggled } from 'features/controlNet/store/controlNetSlice'; +import { + ControlNetConfig, + controlNetAutoConfigToggled, +} from 'features/controlNet/store/controlNetSlice'; import { selectIsBusy } from 'features/system/store/systemSelectors'; -import { memo, useCallback, useMemo } from 'react'; +import { memo, useCallback } from 'react'; type Props = { - controlNetId: string; + controlNet: ControlNetConfig; }; const ParamControlNetShouldAutoConfig = (props: Props) => { - const { controlNetId } = props; + const { controlNetId, isEnabled, shouldAutoConfig } = props.controlNet; const dispatch = useAppDispatch(); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { isEnabled, shouldAutoConfig } = - controlNet.controlNets[controlNetId]; - return { isEnabled, shouldAutoConfig }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { isEnabled, shouldAutoConfig } = useAppSelector(selector); const isBusy = useAppSelector(selectIsBusy); const handleShouldAutoConfigChanged = useCallback(() => { diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx index 3dd420e7c9..1219239e5d 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetBeginEnd.tsx @@ -9,48 +9,39 @@ import { RangeSliderTrack, Tooltip, } from '@chakra-ui/react'; -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import { useAppDispatch } from 'app/store/storeHooks'; import { + ControlNetConfig, controlNetBeginStepPctChanged, controlNetEndStepPctChanged, } from 'features/controlNet/store/controlNetSlice'; -import { memo, useCallback, useMemo } from 'react'; +import { memo, useCallback } from 'react'; type Props = { - controlNetId: string; + controlNet: ControlNetConfig; }; const formatPct = (v: number) => `${Math.round(v * 100)}%`; const ParamControlNetBeginEnd = (props: Props) => { - const { controlNetId } = props; + const { beginStepPct, endStepPct, isEnabled, controlNetId } = + props.controlNet; const dispatch = useAppDispatch(); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { beginStepPct, endStepPct, isEnabled } = - controlNet.controlNets[controlNetId]; - return { beginStepPct, endStepPct, isEnabled }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { beginStepPct, endStepPct, isEnabled } = useAppSelector(selector); - const handleStepPctChanged = useCallback( (v: number[]) => { dispatch( - controlNetBeginStepPctChanged({ controlNetId, beginStepPct: v[0] }) + controlNetBeginStepPctChanged({ + controlNetId, + beginStepPct: v[0] as number, + }) + ); + dispatch( + controlNetEndStepPctChanged({ + controlNetId, + endStepPct: v[1] as number, + }) ); - dispatch(controlNetEndStepPctChanged({ controlNetId, endStepPct: v[1] })); }, [controlNetId, dispatch] ); diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetControlMode.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetControlMode.tsx index e644e24a02..761edde42b 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetControlMode.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetControlMode.tsx @@ -1,16 +1,14 @@ -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import { useAppDispatch } from 'app/store/storeHooks'; import IAIMantineSelect from 'common/components/IAIMantineSelect'; import { ControlModes, + ControlNetConfig, controlNetControlModeChanged, } from 'features/controlNet/store/controlNetSlice'; -import { useCallback, useMemo } from 'react'; +import { useCallback } from 'react'; type ParamControlNetControlModeProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; const CONTROL_MODE_DATA = [ @@ -23,23 +21,8 @@ const CONTROL_MODE_DATA = [ export default function ParamControlNetControlMode( props: ParamControlNetControlModeProps ) { - const { controlNetId } = props; + const { controlMode, isEnabled, controlNetId } = props.controlNet; const dispatch = useAppDispatch(); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { controlMode, isEnabled } = - controlNet.controlNets[controlNetId]; - return { controlMode, isEnabled }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { controlMode, isEnabled } = useAppSelector(selector); const handleControlModeChange = useCallback( (controlMode: ControlModes) => { diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx index 8392bdd2e3..5d7db854d8 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetModel.tsx @@ -5,7 +5,10 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect'; import IAIMantineSelectItemWithTooltip from 'common/components/IAIMantineSelectItemWithTooltip'; -import { controlNetModelChanged } from 'features/controlNet/store/controlNetSlice'; +import { + ControlNetConfig, + controlNetModelChanged, +} from 'features/controlNet/store/controlNetSlice'; import { MODEL_TYPE_MAP } from 'features/parameters/types/constants'; import { modelIdToControlNetModelParam } from 'features/parameters/util/modelIdToControlNetModelParam'; import { selectIsBusy } from 'features/system/store/systemSelectors'; @@ -14,30 +17,24 @@ import { memo, useCallback, useMemo } from 'react'; import { useGetControlNetModelsQuery } from 'services/api/endpoints/models'; type ParamControlNetModelProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; +const selector = createSelector( + stateSelector, + ({ generation }) => { + const { model } = generation; + return { mainModel: model }; + }, + defaultSelectorOptions +); + const ParamControlNetModel = (props: ParamControlNetModelProps) => { - const { controlNetId } = props; + const { controlNetId, model: controlNetModel, isEnabled } = props.controlNet; const dispatch = useAppDispatch(); const isBusy = useAppSelector(selectIsBusy); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ generation, controlNet }) => { - const { model } = generation; - const controlNetModel = controlNet.controlNets[controlNetId]?.model; - const isEnabled = controlNet.controlNets[controlNetId]?.isEnabled; - return { mainModel: model, controlNetModel, isEnabled }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { mainModel, controlNetModel, isEnabled } = useAppSelector(selector); + const { mainModel } = useAppSelector(selector); const { data: controlNetModels } = useGetControlNetModelsQuery(); diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx index 83c66363ac..190b1bc012 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetProcessorSelect.tsx @@ -1,7 +1,6 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAIMantineSearchableSelect, { IAISelectDataType, @@ -9,13 +8,16 @@ import IAIMantineSearchableSelect, { import { configSelector } from 'features/system/store/configSelectors'; import { selectIsBusy } from 'features/system/store/systemSelectors'; import { map } from 'lodash-es'; -import { memo, useCallback, useMemo } from 'react'; +import { memo, useCallback } from 'react'; import { CONTROLNET_PROCESSORS } from '../../store/constants'; -import { controlNetProcessorTypeChanged } from '../../store/controlNetSlice'; +import { + ControlNetConfig, + controlNetProcessorTypeChanged, +} from '../../store/controlNetSlice'; import { ControlNetProcessorType } from '../../store/types'; type ParamControlNetProcessorSelectProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; const selector = createSelector( @@ -52,23 +54,9 @@ const ParamControlNetProcessorSelect = ( props: ParamControlNetProcessorSelectProps ) => { const dispatch = useAppDispatch(); - const { controlNetId } = props; - const processorNodeSelector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { isEnabled, processorNode } = - controlNet.controlNets[controlNetId]; - return { isEnabled, processorNode }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); + const { controlNetId, isEnabled, processorNode } = props.controlNet; const isBusy = useAppSelector(selectIsBusy); const controlNetProcessors = useAppSelector(selector); - const { isEnabled, processorNode } = useAppSelector(processorNodeSelector); const handleProcessorTypeChanged = useCallback( (v: string | null) => { diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetResizeMode.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetResizeMode.tsx index ee04b8077f..72f15fb178 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetResizeMode.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetResizeMode.tsx @@ -1,16 +1,14 @@ -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import { useAppDispatch } from 'app/store/storeHooks'; import IAIMantineSelect from 'common/components/IAIMantineSelect'; import { + ControlNetConfig, ResizeModes, controlNetResizeModeChanged, } from 'features/controlNet/store/controlNetSlice'; -import { useCallback, useMemo } from 'react'; +import { useCallback } from 'react'; type ParamControlNetResizeModeProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; const RESIZE_MODE_DATA = [ @@ -22,23 +20,8 @@ const RESIZE_MODE_DATA = [ export default function ParamControlNetResizeMode( props: ParamControlNetResizeModeProps ) { - const { controlNetId } = props; + const { resizeMode, isEnabled, controlNetId } = props.controlNet; const dispatch = useAppDispatch(); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { resizeMode, isEnabled } = - controlNet.controlNets[controlNetId]; - return { resizeMode, isEnabled }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { resizeMode, isEnabled } = useAppSelector(selector); const handleResizeModeChange = useCallback( (resizeMode: ResizeModes) => { diff --git a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx index 8643fd7dad..c08283e1f9 100644 --- a/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx +++ b/invokeai/frontend/web/src/features/controlNet/components/parameters/ParamControlNetWeight.tsx @@ -1,32 +1,18 @@ -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import { useAppDispatch } from 'app/store/storeHooks'; import IAISlider from 'common/components/IAISlider'; -import { controlNetWeightChanged } from 'features/controlNet/store/controlNetSlice'; -import { memo, useCallback, useMemo } from 'react'; +import { + ControlNetConfig, + controlNetWeightChanged, +} from 'features/controlNet/store/controlNetSlice'; +import { memo, useCallback } from 'react'; type ParamControlNetWeightProps = { - controlNetId: string; + controlNet: ControlNetConfig; }; const ParamControlNetWeight = (props: ParamControlNetWeightProps) => { - const { controlNetId } = props; + const { weight, isEnabled, controlNetId } = props.controlNet; const dispatch = useAppDispatch(); - const selector = useMemo( - () => - createSelector( - stateSelector, - ({ controlNet }) => { - const { weight, isEnabled } = controlNet.controlNets[controlNetId]; - return { weight, isEnabled }; - }, - defaultSelectorOptions - ), - [controlNetId] - ); - - const { weight, isEnabled } = useAppSelector(selector); const handleWeightChanged = useCallback( (weight: number) => { dispatch(controlNetWeightChanged({ controlNetId, weight })); diff --git a/invokeai/frontend/web/src/features/controlNet/store/constants.ts b/invokeai/frontend/web/src/features/controlNet/store/constants.ts index 00f5377e00..f8f9c38619 100644 --- a/invokeai/frontend/web/src/features/controlNet/store/constants.ts +++ b/invokeai/frontend/web/src/features/controlNet/store/constants.ts @@ -4,7 +4,7 @@ import { } from './types'; type ControlNetProcessorsDict = Record< - string, + ControlNetProcessorType, { type: ControlNetProcessorType | 'none'; label: string; diff --git a/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts b/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts index 0df907d463..8f391521d6 100644 --- a/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts +++ b/invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts @@ -96,8 +96,11 @@ export const controlNetSlice = createSlice({ }> ) => { const { sourceControlNetId, newControlNetId } = action.payload; - - const newControlnet = cloneDeep(state.controlNets[sourceControlNetId]); + const oldControlNet = state.controlNets[sourceControlNetId]; + if (!oldControlNet) { + return; + } + const newControlnet = cloneDeep(oldControlNet); newControlnet.controlNetId = newControlNetId; state.controlNets[newControlNetId] = newControlnet; }, @@ -124,8 +127,11 @@ export const controlNetSlice = createSlice({ action: PayloadAction<{ controlNetId: string }> ) => { const { controlNetId } = action.payload; - state.controlNets[controlNetId].isEnabled = - !state.controlNets[controlNetId].isEnabled; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + cn.isEnabled = !cn.isEnabled; }, controlNetImageChanged: ( state, @@ -135,12 +141,14 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId, controlImage } = action.payload; - state.controlNets[controlNetId].controlImage = controlImage; - state.controlNets[controlNetId].processedControlImage = null; - if ( - controlImage !== null && - state.controlNets[controlNetId].processorType !== 'none' - ) { + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.controlImage = controlImage; + cn.processedControlImage = null; + if (controlImage !== null && cn.processorType !== 'none') { state.pendingControlImages.push(controlNetId); } }, @@ -152,8 +160,12 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId, processedControlImage } = action.payload; - state.controlNets[controlNetId].processedControlImage = - processedControlImage; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.processedControlImage = processedControlImage; state.pendingControlImages = state.pendingControlImages.filter( (id) => id !== controlNetId ); @@ -166,10 +178,15 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId, model } = action.payload; - state.controlNets[controlNetId].model = model; - state.controlNets[controlNetId].processedControlImage = null; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } - if (state.controlNets[controlNetId].shouldAutoConfig) { + cn.model = model; + cn.processedControlImage = null; + + if (cn.shouldAutoConfig) { let processorType: ControlNetProcessorType | undefined = undefined; for (const modelSubstring in CONTROLNET_MODEL_DEFAULT_PROCESSORS) { @@ -180,14 +197,13 @@ export const controlNetSlice = createSlice({ } if (processorType) { - state.controlNets[controlNetId].processorType = processorType; - state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS[ - processorType - ].default as RequiredControlNetProcessorNode; + cn.processorType = processorType; + cn.processorNode = CONTROLNET_PROCESSORS[processorType] + .default as RequiredControlNetProcessorNode; } else { - state.controlNets[controlNetId].processorType = 'none'; - state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS - .none.default as RequiredControlNetProcessorNode; + cn.processorType = 'none'; + cn.processorNode = CONTROLNET_PROCESSORS.none + .default as RequiredControlNetProcessorNode; } } }, @@ -196,28 +212,48 @@ export const controlNetSlice = createSlice({ action: PayloadAction<{ controlNetId: string; weight: number }> ) => { const { controlNetId, weight } = action.payload; - state.controlNets[controlNetId].weight = weight; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.weight = weight; }, controlNetBeginStepPctChanged: ( state, action: PayloadAction<{ controlNetId: string; beginStepPct: number }> ) => { const { controlNetId, beginStepPct } = action.payload; - state.controlNets[controlNetId].beginStepPct = beginStepPct; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.beginStepPct = beginStepPct; }, controlNetEndStepPctChanged: ( state, action: PayloadAction<{ controlNetId: string; endStepPct: number }> ) => { const { controlNetId, endStepPct } = action.payload; - state.controlNets[controlNetId].endStepPct = endStepPct; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.endStepPct = endStepPct; }, controlNetControlModeChanged: ( state, action: PayloadAction<{ controlNetId: string; controlMode: ControlModes }> ) => { const { controlNetId, controlMode } = action.payload; - state.controlNets[controlNetId].controlMode = controlMode; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.controlMode = controlMode; }, controlNetResizeModeChanged: ( state, @@ -227,7 +263,12 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId, resizeMode } = action.payload; - state.controlNets[controlNetId].resizeMode = resizeMode; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.resizeMode = resizeMode; }, controlNetProcessorParamsChanged: ( state, @@ -240,12 +281,17 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId, changes } = action.payload; - const processorNode = state.controlNets[controlNetId].processorNode; - state.controlNets[controlNetId].processorNode = { + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + const processorNode = cn.processorNode; + cn.processorNode = { ...processorNode, ...changes, }; - state.controlNets[controlNetId].shouldAutoConfig = false; + cn.shouldAutoConfig = false; }, controlNetProcessorTypeChanged: ( state, @@ -255,12 +301,16 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId, processorType } = action.payload; - state.controlNets[controlNetId].processedControlImage = null; - state.controlNets[controlNetId].processorType = processorType; - state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS[ - processorType - ].default as RequiredControlNetProcessorNode; - state.controlNets[controlNetId].shouldAutoConfig = false; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + cn.processedControlImage = null; + cn.processorType = processorType; + cn.processorNode = CONTROLNET_PROCESSORS[processorType] + .default as RequiredControlNetProcessorNode; + cn.shouldAutoConfig = false; }, controlNetAutoConfigToggled: ( state, @@ -269,37 +319,36 @@ export const controlNetSlice = createSlice({ }> ) => { const { controlNetId } = action.payload; - const newShouldAutoConfig = - !state.controlNets[controlNetId].shouldAutoConfig; + const cn = state.controlNets[controlNetId]; + if (!cn) { + return; + } + + const newShouldAutoConfig = !cn.shouldAutoConfig; if (newShouldAutoConfig) { // manage the processor for the user let processorType: ControlNetProcessorType | undefined = undefined; for (const modelSubstring in CONTROLNET_MODEL_DEFAULT_PROCESSORS) { - if ( - state.controlNets[controlNetId].model?.model_name.includes( - modelSubstring - ) - ) { + if (cn.model?.model_name.includes(modelSubstring)) { processorType = CONTROLNET_MODEL_DEFAULT_PROCESSORS[modelSubstring]; break; } } if (processorType) { - state.controlNets[controlNetId].processorType = processorType; - state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS[ - processorType - ].default as RequiredControlNetProcessorNode; + cn.processorType = processorType; + cn.processorNode = CONTROLNET_PROCESSORS[processorType] + .default as RequiredControlNetProcessorNode; } else { - state.controlNets[controlNetId].processorType = 'none'; - state.controlNets[controlNetId].processorNode = CONTROLNET_PROCESSORS - .none.default as RequiredControlNetProcessorNode; + cn.processorType = 'none'; + cn.processorNode = CONTROLNET_PROCESSORS.none + .default as RequiredControlNetProcessorNode; } } - state.controlNets[controlNetId].shouldAutoConfig = newShouldAutoConfig; + cn.shouldAutoConfig = newShouldAutoConfig; }, controlNetReset: () => { return { ...initialControlNetState }; @@ -307,9 +356,11 @@ export const controlNetSlice = createSlice({ }, extraReducers: (builder) => { builder.addCase(controlNetImageProcessed, (state, action) => { - if ( - state.controlNets[action.payload.controlNetId].controlImage !== null - ) { + const cn = state.controlNets[action.payload.controlNetId]; + if (!cn) { + return; + } + if (cn.controlImage !== null) { state.pendingControlImages.push(action.payload.controlNetId); } }); diff --git a/invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageButton.tsx b/invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageButton.tsx similarity index 100% rename from invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageButton.tsx rename to invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageButton.tsx diff --git a/invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageModal.tsx b/invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageModal.tsx similarity index 70% rename from invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageModal.tsx rename to invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageModal.tsx index 0e72ea96ad..0d8ecfbae6 100644 --- a/invokeai/frontend/web/src/features/imageDeletion/components/DeleteImageModal.tsx +++ b/invokeai/frontend/web/src/features/deleteImageModal/components/DeleteImageModal.tsx @@ -15,30 +15,42 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAIButton from 'common/components/IAIButton'; import IAISwitch from 'common/components/IAISwitch'; import { setShouldConfirmOnDelete } from 'features/system/store/systemSlice'; - import { stateSelector } from 'app/store/store'; +import { some } from 'lodash-es'; import { ChangeEvent, memo, useCallback, useRef } from 'react'; import { useTranslation } from 'react-i18next'; import { imageDeletionConfirmed } from '../store/actions'; -import { selectImageUsage } from '../store/imageDeletionSelectors'; -import { - imageToDeleteCleared, - isModalOpenChanged, -} from '../store/imageDeletionSlice'; +import { getImageUsage, selectImageUsage } from '../store/selectors'; +import { imageDeletionCanceled, isModalOpenChanged } from '../store/slice'; import ImageUsageMessage from './ImageUsageMessage'; +import { ImageUsage } from '../store/types'; const selector = createSelector( [stateSelector, selectImageUsage], - ({ system, config, imageDeletion }, imageUsage) => { + (state, imagesUsage) => { + const { system, config, deleteImageModal } = state; const { shouldConfirmOnDelete } = system; const { canRestoreDeletedImagesFromBin } = config; - const { imageToDelete, isModalOpen } = imageDeletion; + const { imagesToDelete, isModalOpen } = deleteImageModal; + + const allImageUsage = (imagesToDelete ?? []).map(({ image_name }) => + getImageUsage(state, image_name) + ); + + const imageUsageSummary: ImageUsage = { + isInitialImage: some(allImageUsage, (i) => i.isInitialImage), + isCanvasImage: some(allImageUsage, (i) => i.isCanvasImage), + isNodesImage: some(allImageUsage, (i) => i.isNodesImage), + isControlNetImage: some(allImageUsage, (i) => i.isControlNetImage), + }; + return { shouldConfirmOnDelete, canRestoreDeletedImagesFromBin, - imageToDelete, - imageUsage, + imagesToDelete, + imagesUsage, isModalOpen, + imageUsageSummary, }; }, defaultSelectorOptions @@ -51,9 +63,10 @@ const DeleteImageModal = () => { const { shouldConfirmOnDelete, canRestoreDeletedImagesFromBin, - imageToDelete, - imageUsage, + imagesToDelete, + imagesUsage, isModalOpen, + imageUsageSummary, } = useAppSelector(selector); const handleChangeShouldConfirmOnDelete = useCallback( @@ -63,17 +76,19 @@ const DeleteImageModal = () => { ); const handleClose = useCallback(() => { - dispatch(imageToDeleteCleared()); + dispatch(imageDeletionCanceled()); dispatch(isModalOpenChanged(false)); }, [dispatch]); const handleDelete = useCallback(() => { - if (!imageToDelete || !imageUsage) { + if (!imagesToDelete.length || !imagesUsage.length) { return; } - dispatch(imageToDeleteCleared()); - dispatch(imageDeletionConfirmed({ imageDTO: imageToDelete, imageUsage })); - }, [dispatch, imageToDelete, imageUsage]); + dispatch(imageDeletionCanceled()); + dispatch( + imageDeletionConfirmed({ imageDTOs: imagesToDelete, imagesUsage }) + ); + }, [dispatch, imagesToDelete, imagesUsage]); const cancelRef = useRef(null); @@ -92,7 +107,7 @@ const DeleteImageModal = () => { - + {canRestoreDeletedImagesFromBin diff --git a/invokeai/frontend/web/src/features/imageDeletion/components/ImageUsageMessage.tsx b/invokeai/frontend/web/src/features/deleteImageModal/components/ImageUsageMessage.tsx similarity index 100% rename from invokeai/frontend/web/src/features/imageDeletion/components/ImageUsageMessage.tsx rename to invokeai/frontend/web/src/features/deleteImageModal/components/ImageUsageMessage.tsx diff --git a/invokeai/frontend/web/src/features/imageDeletion/store/actions.ts b/invokeai/frontend/web/src/features/deleteImageModal/store/actions.ts similarity index 65% rename from invokeai/frontend/web/src/features/imageDeletion/store/actions.ts rename to invokeai/frontend/web/src/features/deleteImageModal/store/actions.ts index c67d7d944d..def27c9954 100644 --- a/invokeai/frontend/web/src/features/imageDeletion/store/actions.ts +++ b/invokeai/frontend/web/src/features/deleteImageModal/store/actions.ts @@ -3,6 +3,6 @@ import { ImageDTO } from 'services/api/types'; import { ImageUsage } from './types'; export const imageDeletionConfirmed = createAction<{ - imageDTO: ImageDTO; - imageUsage: ImageUsage; -}>('imageDeletion/imageDeletionConfirmed'); + imageDTOs: ImageDTO[]; + imagesUsage: ImageUsage[]; +}>('deleteImageModal/imageDeletionConfirmed'); diff --git a/invokeai/frontend/web/src/features/deleteImageModal/store/initialState.ts b/invokeai/frontend/web/src/features/deleteImageModal/store/initialState.ts new file mode 100644 index 0000000000..198d4ca51f --- /dev/null +++ b/invokeai/frontend/web/src/features/deleteImageModal/store/initialState.ts @@ -0,0 +1,6 @@ +import { DeleteImageState } from './types'; + +export const initialDeleteImageState: DeleteImageState = { + imagesToDelete: [], + isModalOpen: false, +}; diff --git a/invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSelectors.ts b/invokeai/frontend/web/src/features/deleteImageModal/store/selectors.ts similarity index 84% rename from invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSelectors.ts rename to invokeai/frontend/web/src/features/deleteImageModal/store/selectors.ts index bd8e117496..310521f32a 100644 --- a/invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSelectors.ts +++ b/invokeai/frontend/web/src/features/deleteImageModal/store/selectors.ts @@ -39,17 +39,17 @@ export const getImageUsage = (state: RootState, image_name: string) => { export const selectImageUsage = createSelector( [(state: RootState) => state], (state) => { - const { imageToDelete } = state.imageDeletion; + const { imagesToDelete } = state.deleteImageModal; - if (!imageToDelete) { - return; + if (!imagesToDelete.length) { + return []; } - const { image_name } = imageToDelete; + const imagesUsage = imagesToDelete.map((i) => + getImageUsage(state, i.image_name) + ); - const imageUsage = getImageUsage(state, image_name); - - return imageUsage; + return imagesUsage; }, defaultSelectorOptions ); diff --git a/invokeai/frontend/web/src/features/deleteImageModal/store/slice.ts b/invokeai/frontend/web/src/features/deleteImageModal/store/slice.ts new file mode 100644 index 0000000000..6569009666 --- /dev/null +++ b/invokeai/frontend/web/src/features/deleteImageModal/store/slice.ts @@ -0,0 +1,28 @@ +import { PayloadAction, createSlice } from '@reduxjs/toolkit'; +import { ImageDTO } from 'services/api/types'; +import { initialDeleteImageState } from './initialState'; + +const deleteImageModal = createSlice({ + name: 'deleteImageModal', + initialState: initialDeleteImageState, + reducers: { + isModalOpenChanged: (state, action: PayloadAction) => { + state.isModalOpen = action.payload; + }, + imagesToDeleteSelected: (state, action: PayloadAction) => { + state.imagesToDelete = action.payload; + }, + imageDeletionCanceled: (state) => { + state.imagesToDelete = []; + state.isModalOpen = false; + }, + }, +}); + +export const { + isModalOpenChanged, + imagesToDeleteSelected, + imageDeletionCanceled, +} = deleteImageModal.actions; + +export default deleteImageModal.reducer; diff --git a/invokeai/frontend/web/src/features/deleteImageModal/store/types.ts b/invokeai/frontend/web/src/features/deleteImageModal/store/types.ts new file mode 100644 index 0000000000..2beaa8ca2e --- /dev/null +++ b/invokeai/frontend/web/src/features/deleteImageModal/store/types.ts @@ -0,0 +1,13 @@ +import { ImageDTO } from 'services/api/types'; + +export type DeleteImageState = { + imagesToDelete: ImageDTO[]; + isModalOpen: boolean; +}; + +export type ImageUsage = { + isInitialImage: boolean; + isCanvasImage: boolean; + isNodesImage: boolean; + isControlNetImage: boolean; +}; diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardAutoAddSelect.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardAutoAddSelect.tsx index ad0e5ab80d..96d17b548e 100644 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardAutoAddSelect.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardAutoAddSelect.tsx @@ -11,11 +11,14 @@ import { useListAllBoardsQuery } from 'services/api/endpoints/boards'; const selector = createSelector( [stateSelector], - ({ gallery }) => { - const { autoAddBoardId } = gallery; + ({ gallery, system }) => { + const { autoAddBoardId, autoAssignBoardOnClick } = gallery; + const { isProcessing } = system; return { autoAddBoardId, + autoAssignBoardOnClick, + isProcessing, }; }, defaultSelectorOptions @@ -23,7 +26,8 @@ const selector = createSelector( const BoardAutoAddSelect = () => { const dispatch = useAppDispatch(); - const { autoAddBoardId } = useAppSelector(selector); + const { autoAddBoardId, autoAssignBoardOnClick, isProcessing } = + useAppSelector(selector); const inputRef = useRef(null); const { boards, hasBoards } = useListAllBoardsQuery(undefined, { selectFromResult: ({ data }) => { @@ -52,7 +56,7 @@ const BoardAutoAddSelect = () => { return; } - dispatch(autoAddBoardIdChanged(v === 'none' ? undefined : v)); + dispatch(autoAddBoardIdChanged(v)); }, [dispatch] ); @@ -67,7 +71,7 @@ const BoardAutoAddSelect = () => { data={boards} nothingFound="No matching Boards" itemComponent={IAIMantineSelectItemWithTooltip} - disabled={!hasBoards} + disabled={!hasBoards || autoAssignBoardOnClick || isProcessing} filter={(value, item: SelectItem) => item.label?.toLowerCase().includes(value.toLowerCase().trim()) || item.value.toLowerCase().includes(value.toLowerCase().trim()) diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardContextMenu.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardContextMenu.tsx index 35fcbd87f7..0667c05435 100644 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardContextMenu.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardContextMenu.tsx @@ -11,10 +11,11 @@ import { BoardDTO } from 'services/api/types'; import { menuListMotionProps } from 'theme/components/menu'; import GalleryBoardContextMenuItems from './GalleryBoardContextMenuItems'; import NoBoardContextMenuItems from './NoBoardContextMenuItems'; +import { BoardId } from 'features/gallery/store/types'; type Props = { board?: BoardDTO; - board_id?: string; + board_id: BoardId; children: ContextMenuProps['children']; setBoardToDelete?: (board?: BoardDTO) => void; }; @@ -25,14 +26,17 @@ const BoardContextMenu = memo( const selector = useMemo( () => - createSelector(stateSelector, ({ gallery }) => { + createSelector(stateSelector, ({ gallery, system }) => { const isAutoAdd = gallery.autoAddBoardId === board_id; - return { isAutoAdd }; + const isProcessing = system.isProcessing; + const autoAssignBoardOnClick = gallery.autoAssignBoardOnClick; + return { isAutoAdd, isProcessing, autoAssignBoardOnClick }; }), [board_id] ); - const { isAutoAdd } = useAppSelector(selector); + const { isAutoAdd, isProcessing, autoAssignBoardOnClick } = + useAppSelector(selector); const boardName = useBoardName(board_id); const handleSetAutoAdd = useCallback(() => { @@ -59,7 +63,7 @@ const BoardContextMenu = memo( } - isDisabled={isAutoAdd} + isDisabled={isAutoAdd || isProcessing || autoAssignBoardOnClick} onClick={handleSetAutoAdd} > Auto-add to this Board diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BatchBoard.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BatchBoard.tsx deleted file mode 100644 index a7a3040cce..0000000000 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BatchBoard.tsx +++ /dev/null @@ -1,43 +0,0 @@ -import { createSelector } from '@reduxjs/toolkit'; -import { AddToBatchDropData } from 'app/components/ImageDnd/typesafeDnd'; -import { stateSelector } from 'app/store/store'; -import { useAppSelector } from 'app/store/storeHooks'; -import { boardIdSelected } from 'features/gallery/store/gallerySlice'; -import { useCallback } from 'react'; -import { FaLayerGroup } from 'react-icons/fa'; -import { useDispatch } from 'react-redux'; -import GenericBoard from './GenericBoard'; - -const selector = createSelector(stateSelector, (state) => { - return { - count: state.gallery.batchImageNames.length, - }; -}); - -const BatchBoard = ({ isSelected }: { isSelected: boolean }) => { - const dispatch = useDispatch(); - const { count } = useAppSelector(selector); - - const handleBatchBoardClick = useCallback(() => { - dispatch(boardIdSelected('batch')); - }, [dispatch]); - - const droppableData: AddToBatchDropData = { - id: 'batch-board', - actionType: 'ADD_TO_BATCH', - }; - - return ( - - ); -}; - -export default BatchBoard; diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsList.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsList.tsx index 512fced67c..cb3474f6bd 100644 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsList.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsList.tsx @@ -15,10 +15,9 @@ import NoBoardBoard from './NoBoardBoard'; const selector = createSelector( [stateSelector], - ({ boards, gallery }) => { - const { searchText } = boards; - const { selectedBoardId } = gallery; - return { selectedBoardId, searchText }; + ({ gallery }) => { + const { selectedBoardId, boardSearchText } = gallery; + return { selectedBoardId, boardSearchText }; }, defaultSelectorOptions ); @@ -29,11 +28,11 @@ type Props = { const BoardsList = (props: Props) => { const { isOpen } = props; - const { selectedBoardId, searchText } = useAppSelector(selector); + const { selectedBoardId, boardSearchText } = useAppSelector(selector); const { data: boards } = useListAllBoardsQuery(); - const filteredBoards = searchText + const filteredBoards = boardSearchText ? boards?.filter((board) => - board.board_name.toLowerCase().includes(searchText.toLowerCase()) + board.board_name.toLowerCase().includes(boardSearchText.toLowerCase()) ) : boards; const [boardToDelete, setBoardToDelete] = useState(); @@ -75,7 +74,7 @@ const BoardsList = (props: Props) => { }} > - + {filteredBoards && filteredBoards.map((board) => ( diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsSearch.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsSearch.tsx index 800ffc651f..d7db96a938 100644 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsSearch.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/BoardsSearch.tsx @@ -9,7 +9,7 @@ import { createSelector } from '@reduxjs/toolkit'; import { stateSelector } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; -import { setBoardSearchText } from 'features/gallery/store/boardSlice'; +import { boardSearchTextChanged } from 'features/gallery/store/gallerySlice'; import { ChangeEvent, KeyboardEvent, @@ -21,27 +21,27 @@ import { const selector = createSelector( [stateSelector], - ({ boards }) => { - const { searchText } = boards; - return { searchText }; + ({ gallery }) => { + const { boardSearchText } = gallery; + return { boardSearchText }; }, defaultSelectorOptions ); const BoardsSearch = () => { const dispatch = useAppDispatch(); - const { searchText } = useAppSelector(selector); + const { boardSearchText } = useAppSelector(selector); const inputRef = useRef(null); const handleBoardSearch = useCallback( (searchTerm: string) => { - dispatch(setBoardSearchText(searchTerm)); + dispatch(boardSearchTextChanged(searchTerm)); }, [dispatch] ); const clearBoardSearch = useCallback(() => { - dispatch(setBoardSearchText('')); + dispatch(boardSearchTextChanged('')); }, [dispatch]); const handleKeydown = useCallback( @@ -74,11 +74,11 @@ const BoardsSearch = () => { - {searchText && searchText.length && ( + {boardSearchText && boardSearchText.length && ( createSelector( stateSelector, - ({ gallery }) => { + ({ gallery, system }) => { const isSelectedForAutoAdd = board.board_id === gallery.autoAddBoardId; + const autoAssignBoardOnClick = gallery.autoAssignBoardOnClick; + const isProcessing = system.isProcessing; - return { isSelectedForAutoAdd }; + return { + isSelectedForAutoAdd, + autoAssignBoardOnClick, + isProcessing, + }; }, defaultSelectorOptions ), [board.board_id] ); - const { isSelectedForAutoAdd } = useAppSelector(selector); + const { isSelectedForAutoAdd, autoAssignBoardOnClick, isProcessing } = + useAppSelector(selector); const [isHovered, setIsHovered] = useState(false); const handleMouseOver = useCallback(() => { setIsHovered(true); @@ -57,6 +72,18 @@ const GalleryBoard = memo( const handleMouseOut = useCallback(() => { setIsHovered(false); }, []); + + const { data: imagesTotal } = useGetBoardImagesTotalQuery(board.board_id); + const { data: assetsTotal } = useGetBoardAssetsTotalQuery(board.board_id); + const tooltip = useMemo(() => { + if (!imagesTotal || !assetsTotal) { + return undefined; + } + return `${imagesTotal} image${ + imagesTotal > 1 ? 's' : '' + }, ${assetsTotal} asset${assetsTotal > 1 ? 's' : ''}`; + }, [assetsTotal, imagesTotal]); + const { currentData: coverImage } = useGetImageDTOQuery( board.cover_image_name ?? skipToken ); @@ -66,15 +93,18 @@ const GalleryBoard = memo( const handleSelectBoard = useCallback(() => { dispatch(boardIdSelected(board_id)); - }, [board_id, dispatch]); + if (autoAssignBoardOnClick && !isProcessing) { + dispatch(autoAddBoardIdChanged(board_id)); + } + }, [board_id, autoAssignBoardOnClick, isProcessing, dispatch]); const [updateBoard, { isLoading: isUpdateBoardLoading }] = useUpdateBoardMutation(); - const droppableData: MoveBoardDropData = useMemo( + const droppableData: AddToBoardDropData = useMemo( () => ({ id: board_id, - actionType: 'MOVE_BOARD', + actionType: 'ADD_TO_BOARD', context: { boardId: board_id }, }), [board_id] @@ -135,60 +165,61 @@ const GalleryBoard = memo( setBoardToDelete={setBoardToDelete} > {(ref) => ( - - {coverImage?.thumbnail_url ? ( - - ) : ( - - + + {coverImage?.thumbnail_url ? ( + - - )} - {/* + + + )} + {/* */} - {isSelectedForAutoAdd && } - - - } + + - - + - - + overflow: 'hidden', + textOverflow: 'ellipsis', + }} + noOfLines={1} + /> + + + - Move} - /> - + Move} + /> + + )} diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx index ee1d8f6bea..f1341b1146 100644 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/Boards/BoardsList/NoBoardBoard.tsx @@ -1,50 +1,60 @@ import { Box, Flex, Image, Text } from '@chakra-ui/react'; import { createSelector } from '@reduxjs/toolkit'; -import { MoveBoardDropData } from 'app/components/ImageDnd/typesafeDnd'; +import { RemoveFromBoardDropData } from 'app/components/ImageDnd/typesafeDnd'; import { stateSelector } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import InvokeAILogoImage from 'assets/images/logo.png'; import IAIDroppable from 'common/components/IAIDroppable'; import SelectionOverlay from 'common/components/SelectionOverlay'; -import { boardIdSelected } from 'features/gallery/store/gallerySlice'; +import { + boardIdSelected, + autoAddBoardIdChanged, +} from 'features/gallery/store/gallerySlice'; import { memo, useCallback, useMemo, useState } from 'react'; import { useBoardName } from 'services/api/hooks/useBoardName'; import AutoAddIcon from '../AutoAddIcon'; import BoardContextMenu from '../BoardContextMenu'; + interface Props { isSelected: boolean; } const selector = createSelector( stateSelector, - ({ gallery }) => { - const { autoAddBoardId } = gallery; - return { autoAddBoardId }; + ({ gallery, system }) => { + const { autoAddBoardId, autoAssignBoardOnClick } = gallery; + const { isProcessing } = system; + return { autoAddBoardId, autoAssignBoardOnClick, isProcessing }; }, defaultSelectorOptions ); const NoBoardBoard = memo(({ isSelected }: Props) => { const dispatch = useAppDispatch(); - const { autoAddBoardId } = useAppSelector(selector); - const boardName = useBoardName(undefined); + const { autoAddBoardId, autoAssignBoardOnClick, isProcessing } = + useAppSelector(selector); + const boardName = useBoardName('none'); const handleSelectBoard = useCallback(() => { - dispatch(boardIdSelected(undefined)); - }, [dispatch]); + dispatch(boardIdSelected('none')); + if (autoAssignBoardOnClick && !isProcessing) { + dispatch(autoAddBoardIdChanged('none')); + } + }, [dispatch, autoAssignBoardOnClick, isProcessing]); const [isHovered, setIsHovered] = useState(false); + const handleMouseOver = useCallback(() => { setIsHovered(true); }, []); + const handleMouseOut = useCallback(() => { setIsHovered(false); }, []); - const droppableData: MoveBoardDropData = useMemo( + const droppableData: RemoveFromBoardDropData = useMemo( () => ({ id: 'no_board', - actionType: 'MOVE_BOARD', - context: { boardId: undefined }, + actionType: 'REMOVE_FROM_BOARD', }), [] ); @@ -64,7 +74,7 @@ const NoBoardBoard = memo(({ isSelected }: Props) => { h: 'full', }} > - + {(ref) => ( { alignItems: 'center', }} > - {/* */} invoke-ai-logo { }} /> - {/* - - {totalImages}/{totalAssets} - - */} - {!autoAddBoardId && } + {autoAddBoardId === 'none' && } void; }; -const DeleteImageModal = (props: Props) => { +const DeleteBoardModal = (props: Props) => { const { boardToDelete, setBoardToDelete } = props; const { t } = useTranslation(); const canRestoreDeletedImagesFromBin = useAppSelector( @@ -49,13 +49,10 @@ const DeleteImageModal = (props: Props) => { ); const imageUsageSummary: ImageUsage = { - isInitialImage: some(allImageUsage, (usage) => usage.isInitialImage), - isCanvasImage: some(allImageUsage, (usage) => usage.isCanvasImage), - isNodesImage: some(allImageUsage, (usage) => usage.isNodesImage), - isControlNetImage: some( - allImageUsage, - (usage) => usage.isControlNetImage - ), + isInitialImage: some(allImageUsage, (i) => i.isInitialImage), + isCanvasImage: some(allImageUsage, (i) => i.isCanvasImage), + isNodesImage: some(allImageUsage, (i) => i.isNodesImage), + isControlNetImage: some(allImageUsage, (i) => i.isControlNetImage), }; return { imageUsageSummary }; }), @@ -176,4 +173,4 @@ const DeleteImageModal = (props: Props) => { ); }; -export default memo(DeleteImageModal); +export default memo(DeleteBoardModal); diff --git a/invokeai/frontend/web/src/features/gallery/components/Boards/UpdateImageBoardModal.tsx b/invokeai/frontend/web/src/features/gallery/components/Boards/UpdateImageBoardModal.tsx deleted file mode 100644 index 49eb1502f3..0000000000 --- a/invokeai/frontend/web/src/features/gallery/components/Boards/UpdateImageBoardModal.tsx +++ /dev/null @@ -1,93 +0,0 @@ -import { - AlertDialog, - AlertDialogBody, - AlertDialogContent, - AlertDialogFooter, - AlertDialogHeader, - AlertDialogOverlay, - Box, - Flex, - Spinner, - Text, -} from '@chakra-ui/react'; -import IAIButton from 'common/components/IAIButton'; - -import IAIMantineSearchableSelect from 'common/components/IAIMantineSearchableSelect'; -import { memo, useContext, useRef, useState } from 'react'; -import { useListAllBoardsQuery } from 'services/api/endpoints/boards'; -import { AddImageToBoardContext } from '../../../../app/contexts/AddImageToBoardContext'; - -const UpdateImageBoardModal = () => { - // const boards = useSelector(selectBoardsAll); - const { data: boards, isFetching } = useListAllBoardsQuery(); - const { isOpen, onClose, handleAddToBoard, image } = useContext( - AddImageToBoardContext - ); - const [selectedBoard, setSelectedBoard] = useState(); - - const cancelRef = useRef(null); - - const currentBoard = boards?.find( - (board) => board.board_id === image?.board_id - ); - - return ( - - - - - {currentBoard ? 'Move Image to Board' : 'Add Image to Board'} - - - - - - {currentBoard && ( - - Moving this image from{' '} - {currentBoard.board_name} to - - )} - {isFetching ? ( - - ) : ( - setSelectedBoard(v)} - value={selectedBoard} - data={(boards ?? []).map((board) => ({ - label: board.board_name, - value: board.board_id, - }))} - /> - )} - - - - - Cancel - { - if (selectedBoard) { - handleAddToBoard(selectedBoard); - } - }} - ml={3} - > - {currentBoard ? 'Move' : 'Add'} - - - - - - ); -}; - -export default memo(UpdateImageBoardModal); diff --git a/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx b/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx index 7d25d6bc05..d62027769b 100644 --- a/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx @@ -9,16 +9,14 @@ import { MenuButton, MenuList, } from '@chakra-ui/react'; -// import { runESRGAN, runFacetool } from 'app/socketio/actions'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import IAIIconButton from 'common/components/IAIIconButton'; - import { skipToken } from '@reduxjs/toolkit/dist/query'; import { useAppToaster } from 'app/components/Toaster'; import { upscaleRequested } from 'app/store/middleware/listenerMiddleware/listeners/upscaleRequested'; import { stateSelector } from 'app/store/store'; -import { DeleteImageButton } from 'features/imageDeletion/components/DeleteImageButton'; -import { imageToDeleteSelected } from 'features/imageDeletion/store/imageDeletionSlice'; +import { DeleteImageButton } from 'features/deleteImageModal/components/DeleteImageButton'; +import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice'; import ParamUpscalePopover from 'features/parameters/components/Parameters/Upscale/ParamUpscaleSettings'; import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters'; import { initialImageSelected } from 'features/parameters/store/actions'; @@ -109,13 +107,13 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => { ); const { currentData: imageDTO } = useGetImageDTOQuery( - lastSelectedImage ?? skipToken + lastSelectedImage?.image_name ?? skipToken ); const { currentData: metadataData } = useGetImageMetadataQuery( debounceState.isPending() ? skipToken - : debouncedMetadataQueryArg ?? skipToken + : debouncedMetadataQueryArg?.image_name ?? skipToken ); const metadata = metadataData?.metadata; @@ -173,7 +171,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => { if (!imageDTO) { return; } - dispatch(imageToDeleteSelected(imageDTO)); + dispatch(imagesToDeleteSelected([imageDTO])); }, [dispatch, imageDTO]); useHotkeys( diff --git a/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImagePreview.tsx b/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImagePreview.tsx index fd7eaef46a..f78ee286ef 100644 --- a/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImagePreview.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImagePreview.tsx @@ -32,7 +32,7 @@ export const imagesSelector = createSelector( return { shouldShowImageDetails, shouldHidePreview, - imageName: lastSelectedImage, + imageName: lastSelectedImage?.image_name, progressImage, shouldShowProgressInViewer, shouldAntialiasProgressImage, @@ -57,8 +57,6 @@ const CurrentImagePreview = () => { const { handlePrevImage, handleNextImage, - prevImageId, - nextImageId, isOnLastImage, handleLoadMoreImages, areMoreImagesAvailable, @@ -70,7 +68,7 @@ const CurrentImagePreview = () => { () => { handlePrevImage(); }, - [prevImageId] + [handlePrevImage] ); useHotkeys( @@ -85,11 +83,11 @@ const CurrentImagePreview = () => { } }, [ - nextImageId, isOnLastImage, areMoreImagesAvailable, handleLoadMoreImages, isFetching, + handleNextImage, ] ); diff --git a/invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover.tsx b/invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover.tsx index 21a580d9a9..5c32cc788e 100644 --- a/invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/GallerySettingsPopover.tsx @@ -5,25 +5,35 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAIIconButton from 'common/components/IAIIconButton'; import IAIPopover from 'common/components/IAIPopover'; -import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox'; import IAISlider from 'common/components/IAISlider'; +import IAISwitch from 'common/components/IAISwitch'; import { + autoAssignBoardOnClickChanged, setGalleryImageMinimumWidth, shouldAutoSwitchChanged, + shouldShowDeleteButtonChanged, } from 'features/gallery/store/gallerySlice'; -import { ChangeEvent } from 'react'; +import { ChangeEvent, useCallback } from 'react'; import { useTranslation } from 'react-i18next'; import { FaWrench } from 'react-icons/fa'; import BoardAutoAddSelect from './Boards/BoardAutoAddSelect'; +import IAISimpleCheckbox from 'common/components/IAISimpleCheckbox'; const selector = createSelector( [stateSelector], (state) => { - const { galleryImageMinimumWidth, shouldAutoSwitch } = state.gallery; + const { + galleryImageMinimumWidth, + shouldAutoSwitch, + autoAssignBoardOnClick, + shouldShowDeleteButton, + } = state.gallery; return { galleryImageMinimumWidth, shouldAutoSwitch, + autoAssignBoardOnClick, + shouldShowDeleteButton, }; }, defaultSelectorOptions @@ -33,12 +43,37 @@ const GallerySettingsPopover = () => { const dispatch = useAppDispatch(); const { t } = useTranslation(); - const { galleryImageMinimumWidth, shouldAutoSwitch } = - useAppSelector(selector); + const { + galleryImageMinimumWidth, + shouldAutoSwitch, + autoAssignBoardOnClick, + shouldShowDeleteButton, + } = useAppSelector(selector); - const handleChangeGalleryImageMinimumWidth = (v: number) => { - dispatch(setGalleryImageMinimumWidth(v)); - }; + const handleChangeGalleryImageMinimumWidth = useCallback( + (v: number) => { + dispatch(setGalleryImageMinimumWidth(v)); + }, + [dispatch] + ); + + const handleResetGalleryImageMinimumWidth = useCallback(() => { + dispatch(setGalleryImageMinimumWidth(64)); + }, [dispatch]); + + const handleChangeAutoSwitch = useCallback( + (e: ChangeEvent) => { + dispatch(shouldAutoSwitchChanged(e.target.checked)); + }, + [dispatch] + ); + + const handleChangeShowDeleteButton = useCallback( + (e: ChangeEvent) => { + dispatch(shouldShowDeleteButtonChanged(e.target.checked)); + }, + [dispatch] + ); return ( { /> } > - + { hideTooltip={true} label={t('gallery.galleryImageSize')} withReset - handleReset={() => dispatch(setGalleryImageMinimumWidth(64))} + handleReset={handleResetGalleryImageMinimumWidth} /> - + + ) => - dispatch(shouldAutoSwitchChanged(e.target.checked)) + dispatch(autoAssignBoardOnClickChanged(e.target.checked)) } /> diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/ImageContextMenu.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/ImageContextMenu.tsx index 565012a36f..ce8b4450a6 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/ImageContextMenu.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/ImageContextMenu.tsx @@ -4,28 +4,29 @@ import { MouseEvent, memo, useCallback } from 'react'; import { ImageDTO } from 'services/api/types'; import { menuListMotionProps } from 'theme/components/menu'; import SingleSelectionMenuItems from './SingleSelectionMenuItems'; +import { createSelector } from '@reduxjs/toolkit'; +import { stateSelector } from 'app/store/store'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import { useAppSelector } from 'app/store/storeHooks'; +import MultipleSelectionMenuItems from './MultipleSelectionMenuItems'; type Props = { imageDTO: ImageDTO | undefined; children: ContextMenuProps['children']; }; +const selector = createSelector( + [stateSelector], + ({ gallery }) => { + const selectionCount = gallery.selection.length; + + return { selectionCount }; + }, + defaultSelectorOptions +); + const ImageContextMenu = ({ imageDTO, children }: Props) => { - // const selector = useMemo( - // () => - // createSelector( - // [stateSelector], - // ({ gallery }) => { - // const selectionCount = gallery.selection.length; - - // return { selectionCount }; - // }, - // defaultSelectorOptions - // ), - // [] - // ); - - // const { selectionCount } = useAppSelector(selector); + const { selectionCount } = useAppSelector(selector); const skipEvent = useCallback((e: MouseEvent) => { e.preventDefault(); @@ -38,8 +39,24 @@ const ImageContextMenu = ({ imageDTO, children }: Props) => { bg: 'transparent', _hover: { bg: 'transparent' }, }} - renderMenu={() => - imageDTO ? ( + renderMenu={() => { + if (!imageDTO) { + return null; + } + + if (selectionCount > 1) { + return ( + + + + ); + } + + return ( { > - ) : null - } + ); + }} > {children} diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/MultipleSelectionMenuItems.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/MultipleSelectionMenuItems.tsx index 62d2cb06f4..079fc43a4a 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/MultipleSelectionMenuItems.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/MultipleSelectionMenuItems.tsx @@ -1,30 +1,30 @@ import { MenuItem } from '@chakra-ui/react'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { + imagesToChangeSelected, + isModalOpenChanged, +} from 'features/changeBoardModal/store/slice'; +import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice'; import { useCallback } from 'react'; -import { FaFolder, FaFolderPlus, FaTrash } from 'react-icons/fa'; +import { FaFolder, FaTrash } from 'react-icons/fa'; const MultipleSelectionMenuItems = () => { - const handleAddSelectionToBoard = useCallback(() => { - // TODO: add selection to board - }, []); + const dispatch = useAppDispatch(); + const selection = useAppSelector((state) => state.gallery.selection); + + const handleChangeBoard = useCallback(() => { + dispatch(imagesToChangeSelected(selection)); + dispatch(isModalOpenChanged(true)); + }, [dispatch, selection]); const handleDeleteSelection = useCallback(() => { - // TODO: delete all selected images - }, []); - - const handleAddSelectionToBatch = useCallback(() => { - // TODO: add selection to batch - }, []); + dispatch(imagesToDeleteSelected(selection)); + }, [dispatch, selection]); return ( <> - } onClickCapture={handleAddSelectionToBoard}> - Move Selection to Board - - } - onClickCapture={handleAddSelectionToBatch} - > - Add Selection to Batch + } onClickCapture={handleChangeBoard}> + Change Board { const { imageDTO } = props; - const selector = useMemo( - () => - createSelector( - [stateSelector], - ({ gallery }) => { - const isInBatch = gallery.batchImageNames.includes( - imageDTO.image_name - ); - - return { isInBatch }; - }, - defaultSelectorOptions - ), - [imageDTO.image_name] - ); - - const { isInBatch } = useAppSelector(selector); const dispatch = useAppDispatch(); const { t } = useTranslation(); const toaster = useAppToaster(); const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled; - const isBatchEnabled = useFeatureStatus('batches').isFeatureEnabled; - - const { onClickAddToBoard } = useContext(AddImageToBoardContext); const [debouncedMetadataQueryArg, debounceState] = useDebounce( imageDTO.image_name, @@ -92,14 +68,12 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { if (!imageDTO) { return; } - dispatch(imageToDeleteSelected(imageDTO)); + dispatch(imagesToDeleteSelected([imageDTO])); }, [dispatch, imageDTO]); const { recallBothPrompts, recallSeed, recallAllParameters } = useRecallParameters(); - const [removeFromBoard] = useRemoveImageFromBoardMutation(); - // Recall parameters handlers const handleRecallPrompt = useCallback(() => { recallBothPrompts( @@ -144,20 +118,10 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { recallAllParameters(metadata); }, [metadata, recallAllParameters]); - const handleAddToBoard = useCallback(() => { - onClickAddToBoard(imageDTO); - }, [imageDTO, onClickAddToBoard]); - - const handleRemoveFromBoard = useCallback(() => { - if (!imageDTO.board_id) { - return; - } - removeFromBoard({ imageDTO }); - }, [imageDTO, removeFromBoard]); - - const handleAddToBatch = useCallback(() => { - dispatch(imagesAddedToBatch([imageDTO.image_name])); - }, [dispatch, imageDTO.image_name]); + const handleChangeBoard = useCallback(() => { + dispatch(imagesToChangeSelected([imageDTO])); + dispatch(isModalOpenChanged(true)); + }, [dispatch, imageDTO]); const handleCopyImage = useCallback(() => { copyImageToClipboard(imageDTO.image_url); @@ -229,23 +193,9 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { {t('parameters.sendToUnifiedCanvas')} )} - {isBatchEnabled && ( - } - isDisabled={isInBatch} - onClickCapture={handleAddToBatch} - > - Add to Batch - - )} - } onClickCapture={handleAddToBoard}> - {imageDTO.board_id ? 'Change Board' : 'Add to Board'} + } onClickCapture={handleChangeBoard}> + Change Board - {imageDTO.board_id && ( - } onClickCapture={handleRemoveFromBoard}> - Remove from Board - - )} } diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageGalleryContent.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageGalleryContent.tsx index 5b2072bfc4..f2ff2ad30b 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageGalleryContent.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageGalleryContent.tsx @@ -20,16 +20,14 @@ import BoardsList from './Boards/BoardsList/BoardsList'; import GalleryBoardName from './GalleryBoardName'; import GalleryPinButton from './GalleryPinButton'; import GallerySettingsPopover from './GallerySettingsPopover'; -import BatchImageGrid from './ImageGrid/BatchImageGrid'; import GalleryImageGrid from './ImageGrid/GalleryImageGrid'; const selector = createSelector( [stateSelector], (state) => { - const { selectedBoardId, galleryView } = state.gallery; + const { galleryView } = state.gallery; return { - selectedBoardId, galleryView, }; }, @@ -39,7 +37,7 @@ const selector = createSelector( const ImageGalleryContent = () => { const resizeObserverRef = useRef(null); const galleryGridRef = useRef(null); - const { selectedBoardId, galleryView } = useAppSelector(selector); + const { galleryView } = useAppSelector(selector); const dispatch = useAppDispatch(); const { isOpen: isBoardListOpen, onToggle: onToggleBoardList } = useDisclosure(); @@ -130,12 +128,7 @@ const ImageGalleryContent = () => { - - {selectedBoardId === 'batch' ? ( - - ) : ( - - )} + ); diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageGrid/BatchImage.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageGrid/BatchImage.tsx deleted file mode 100644 index 528e8cc06f..0000000000 --- a/invokeai/frontend/web/src/features/gallery/components/ImageGrid/BatchImage.tsx +++ /dev/null @@ -1,122 +0,0 @@ -import { Box } from '@chakra-ui/react'; -import { createSelector } from '@reduxjs/toolkit'; -import { TypesafeDraggableData } from 'app/components/ImageDnd/typesafeDnd'; -import { stateSelector } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; -import IAIDndImage from 'common/components/IAIDndImage'; -import IAIErrorLoadingImageFallback from 'common/components/IAIErrorLoadingImageFallback'; -import IAIFillSkeleton from 'common/components/IAIFillSkeleton'; -import ImageContextMenu from 'features/gallery/components/ImageContextMenu/ImageContextMenu'; -import { imagesRemovedFromBatch } from 'features/gallery/store/gallerySlice'; -import { memo, useCallback, useMemo } from 'react'; -import { useGetImageDTOQuery } from 'services/api/endpoints/images'; - -const makeSelector = (image_name: string) => - createSelector( - [stateSelector], - (state) => ({ - selectionCount: state.gallery.selection.length, - selection: state.gallery.selection, - isSelected: state.gallery.selection.includes(image_name), - }), - defaultSelectorOptions - ); - -type BatchImageProps = { - imageName: string; -}; - -const BatchImage = (props: BatchImageProps) => { - const dispatch = useAppDispatch(); - const { imageName } = props; - const { - currentData: imageDTO, - isLoading, - isError, - } = useGetImageDTOQuery(imageName); - const selector = useMemo(() => makeSelector(imageName), [imageName]); - - const { isSelected, selectionCount, selection } = useAppSelector(selector); - - const handleClickRemove = useCallback(() => { - dispatch(imagesRemovedFromBatch([imageName])); - }, [dispatch, imageName]); - - // const handleClick = useCallback( - // (e: MouseEvent) => { - // if (e.shiftKey) { - // dispatch(imageRangeEndSelected(imageName)); - // } else if (e.ctrlKey || e.metaKey) { - // dispatch(imageSelectionToggled(imageName)); - // } else { - // dispatch(imageSelected(imageName)); - // } - // }, - // [dispatch, imageName] - // ); - - const draggableData = useMemo(() => { - if (selectionCount > 1) { - return { - id: 'batch', - payloadType: 'IMAGE_NAMES', - payload: { image_names: selection }, - }; - } - - if (imageDTO) { - return { - id: 'batch', - payloadType: 'IMAGE_DTO', - payload: { imageDTO }, - }; - } - }, [imageDTO, selection, selectionCount]); - - if (isLoading) { - return ; - } - - if (isError || !imageDTO) { - return ; - } - - return ( - - - {(ref) => ( - - - - )} - - - ); -}; - -export default memo(BatchImage); diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageGrid/BatchImageGrid.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageGrid/BatchImageGrid.tsx deleted file mode 100644 index feaa47403d..0000000000 --- a/invokeai/frontend/web/src/features/gallery/components/ImageGrid/BatchImageGrid.tsx +++ /dev/null @@ -1,87 +0,0 @@ -import { Box } from '@chakra-ui/react'; -import { useAppSelector } from 'app/store/storeHooks'; -import { useOverlayScrollbars } from 'overlayscrollbars-react'; - -import { memo, useEffect, useRef, useState } from 'react'; -import { useTranslation } from 'react-i18next'; -import { FaImage } from 'react-icons/fa'; - -import { createSelector } from '@reduxjs/toolkit'; -import { stateSelector } from 'app/store/store'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; -import { IAINoContentFallback } from 'common/components/IAIImageFallback'; -import { VirtuosoGrid } from 'react-virtuoso'; -import BatchImage from './BatchImage'; -import ItemContainer from './ImageGridItemContainer'; -import ListContainer from './ImageGridListContainer'; - -const selector = createSelector( - [stateSelector], - (state) => { - return { - imageNames: state.gallery.batchImageNames, - }; - }, - defaultSelectorOptions -); - -const BatchImageGrid = () => { - const { t } = useTranslation(); - const rootRef = useRef(null); - const [scroller, setScroller] = useState(null); - const [initialize, osInstance] = useOverlayScrollbars({ - defer: true, - options: { - scrollbars: { - visibility: 'auto', - autoHide: 'leave', - autoHideDelay: 1300, - theme: 'os-theme-dark', - }, - overflow: { x: 'hidden' }, - }, - }); - - const { imageNames } = useAppSelector(selector); - - useEffect(() => { - const { current: root } = rootRef; - if (scroller && root) { - initialize({ - target: root, - elements: { - viewport: scroller, - }, - }); - } - return () => osInstance()?.destroy(); - }, [scroller, initialize, osInstance]); - - if (imageNames.length) { - return ( - - ( - - )} - /> - - ); - } - - return ( - - ); -}; - -export default memo(BatchImageGrid); diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx index 6a5d28a9ba..c9eee5f1f5 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx @@ -1,27 +1,18 @@ import { Box, Flex } from '@chakra-ui/react'; -import { createSelector } from '@reduxjs/toolkit'; -import { TypesafeDraggableData } from 'app/components/ImageDnd/typesafeDnd'; -import { stateSelector } from 'app/store/store'; +import { + ImageDTOsDraggableData, + ImageDraggableData, + TypesafeDraggableData, +} from 'app/components/ImageDnd/typesafeDnd'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAIDndImage from 'common/components/IAIDndImage'; import IAIFillSkeleton from 'common/components/IAIFillSkeleton'; -import { imageSelected } from 'features/gallery/store/gallerySlice'; -import { imageToDeleteSelected } from 'features/imageDeletion/store/imageDeletionSlice'; +import { useMultiselect } from 'features/gallery/hooks/useMultiselect.ts'; +import { imagesToDeleteSelected } from 'features/deleteImageModal/store/slice'; import { MouseEvent, memo, useCallback, useMemo } from 'react'; +import { FaTrash } from 'react-icons/fa'; import { useGetImageDTOQuery } from 'services/api/endpoints/images'; -export const makeSelector = (image_name: string) => - createSelector( - [stateSelector], - ({ gallery }) => ({ - isSelected: gallery.selection.includes(image_name), - selectionCount: gallery.selection.length, - selection: gallery.selection, - }), - defaultSelectorOptions - ); - interface HoverableImageProps { imageName: string; } @@ -30,22 +21,12 @@ const GalleryImage = (props: HoverableImageProps) => { const dispatch = useAppDispatch(); const { imageName } = props; const { currentData: imageDTO } = useGetImageDTOQuery(imageName); - const localSelector = useMemo(() => makeSelector(imageName), [imageName]); + const shouldShowDeleteButton = useAppSelector( + (state) => state.gallery.shouldShowDeleteButton + ); - const { isSelected, selectionCount, selection } = - useAppSelector(localSelector); - - const handleClick = useCallback(() => { - // disable multiselect for now - // if (e.shiftKey) { - // dispatch(imageRangeEndSelected(imageName)); - // } else if (e.ctrlKey || e.metaKey) { - // dispatch(imageSelectionToggled(imageName)); - // } else { - // dispatch(imageSelected(imageName)); - // } - dispatch(imageSelected(imageName)); - }, [dispatch, imageName]); + const { handleClick, isSelected, selection, selectionCount } = + useMultiselect(imageDTO); const handleDelete = useCallback( (e: MouseEvent) => { @@ -53,26 +34,28 @@ const GalleryImage = (props: HoverableImageProps) => { if (!imageDTO) { return; } - dispatch(imageToDeleteSelected(imageDTO)); + dispatch(imagesToDeleteSelected([imageDTO])); }, [dispatch, imageDTO] ); const draggableData = useMemo(() => { if (selectionCount > 1) { - return { + const data: ImageDTOsDraggableData = { id: 'gallery-image', - payloadType: 'IMAGE_NAMES', - payload: { image_names: selection }, + payloadType: 'IMAGE_DTOS', + payload: { imageDTOs: selection }, }; + return data; } if (imageDTO) { - return { + const data: ImageDraggableData = { id: 'gallery-image', payloadType: 'IMAGE_DTO', payload: { imageDTO }, }; + return data; } }, [imageDTO, selection, selectionCount]); @@ -103,9 +86,9 @@ const GalleryImage = (props: HoverableImageProps) => { isUploadDisabled={true} thumbnail={true} withHoverOverlay - // resetIcon={} - // resetTooltip="Delete image" - // withResetIcon // removed bc it's too easy to accidentally delete images + resetIcon={} + resetTooltip="Delete image" + withResetIcon={shouldShowDeleteButton} // removed bc it's too easy to accidentally delete images /> diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx index df574c860b..c0821c2226 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx @@ -1,6 +1,6 @@ import { useRecallParameters } from 'features/parameters/hooks/useRecallParameters'; import { useCallback } from 'react'; -import { UnsafeImageMetadata } from 'services/api/endpoints/images'; +import { UnsafeImageMetadata } from 'services/api/types'; import ImageMetadataItem from './ImageMetadataItem'; type Props = { diff --git a/invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts.ts b/invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts.ts new file mode 100644 index 0000000000..a162c6788d --- /dev/null +++ b/invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts.ts @@ -0,0 +1,101 @@ +import { createSelector } from '@reduxjs/toolkit'; +import { stateSelector } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import { selectListImagesBaseQueryArgs } from 'features/gallery/store/gallerySelectors'; +import { uniq } from 'lodash-es'; +import { MouseEvent, useCallback, useMemo } from 'react'; +import { useListImagesQuery } from 'services/api/endpoints/images'; +import { ImageDTO } from 'services/api/types'; +import { selectionChanged } from '../store/gallerySlice'; +import { imagesSelectors } from 'services/api/util'; +import { useFeatureStatus } from '../../system/hooks/useFeatureStatus'; + +const selector = createSelector( + [stateSelector, selectListImagesBaseQueryArgs], + ({ gallery }, queryArgs) => { + const selection = gallery.selection; + + return { + queryArgs, + selection, + }; + }, + defaultSelectorOptions +); + +export const useMultiselect = (imageDTO?: ImageDTO) => { + const dispatch = useAppDispatch(); + const { queryArgs, selection } = useAppSelector(selector); + + const { imageDTOs } = useListImagesQuery(queryArgs, { + selectFromResult: (result) => ({ + imageDTOs: result.data ? imagesSelectors.selectAll(result.data) : [], + }), + }); + + const isMultiSelectEnabled = useFeatureStatus('multiselect').isFeatureEnabled; + + const handleClick = useCallback( + (e: MouseEvent) => { + if (!imageDTO) { + return; + } + if (!isMultiSelectEnabled) { + dispatch(selectionChanged([imageDTO])); + return; + } + + if (e.shiftKey) { + const rangeEndImageName = imageDTO.image_name; + const lastSelectedImage = selection[selection.length - 1]?.image_name; + const lastClickedIndex = imageDTOs.findIndex( + (n) => n.image_name === lastSelectedImage + ); + const currentClickedIndex = imageDTOs.findIndex( + (n) => n.image_name === rangeEndImageName + ); + if (lastClickedIndex > -1 && currentClickedIndex > -1) { + // We have a valid range! + const start = Math.min(lastClickedIndex, currentClickedIndex); + const end = Math.max(lastClickedIndex, currentClickedIndex); + const imagesToSelect = imageDTOs.slice(start, end + 1); + dispatch(selectionChanged(uniq(selection.concat(imagesToSelect)))); + } + } else if (e.ctrlKey || e.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) + ) + ); + } else { + dispatch(selectionChanged(uniq(selection.concat(imageDTO)))); + } + } else { + dispatch(selectionChanged([imageDTO])); + } + }, + [dispatch, imageDTO, imageDTOs, selection, isMultiSelectEnabled] + ); + + const isSelected = useMemo( + () => + imageDTO + ? selection.some((i) => i.image_name === imageDTO.image_name) + : false, + [imageDTO, selection] + ); + + const selectionCount = useMemo(() => selection.length, [selection.length]); + + return { + selection, + selectionCount, + isSelected, + handleClick, + }; +}; diff --git a/invokeai/frontend/web/src/features/gallery/hooks/useNextPrevImage.ts b/invokeai/frontend/web/src/features/gallery/hooks/useNextPrevImage.ts index f2572a23b5..670dd7ee9f 100644 --- a/invokeai/frontend/web/src/features/gallery/hooks/useNextPrevImage.ts +++ b/invokeai/frontend/web/src/features/gallery/hooks/useNextPrevImage.ts @@ -4,14 +4,15 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { imageSelected } from 'features/gallery/store/gallerySlice'; import { clamp, isEqual } from 'lodash-es'; import { useCallback } from 'react'; +import { boardsApi } from 'services/api/endpoints/boards'; import { - ListImagesArgs, - imagesAdapter, imagesApi, useLazyListImagesQuery, } from 'services/api/endpoints/images'; import { selectListImagesBaseQueryArgs } from '../store/gallerySelectors'; import { IMAGE_LIMIT } from '../store/types'; +import { ListImagesArgs } from 'services/api/types'; +import { imagesAdapter } from 'services/api/util'; export const nextPrevImageButtonsSelector = createSelector( [stateSelector, selectListImagesBaseQueryArgs], @@ -19,12 +20,21 @@ export const nextPrevImageButtonsSelector = createSelector( const { data, status } = imagesApi.endpoints.listImages.select(baseQueryArgs)(state); + const { data: total } = + state.gallery.galleryView === 'images' + ? boardsApi.endpoints.getBoardImagesTotal.select( + baseQueryArgs.board_id ?? 'none' + )(state) + : boardsApi.endpoints.getBoardAssetsTotal.select( + baseQueryArgs.board_id ?? 'none' + )(state); + const lastSelectedImage = state.gallery.selection[state.gallery.selection.length - 1]; const isFetching = status === 'pending'; - if (!data || !lastSelectedImage || data.total === 0) { + if (!data || !lastSelectedImage || total === 0) { return { isFetching, queryArgs: baseQueryArgs, @@ -44,30 +54,30 @@ export const nextPrevImageButtonsSelector = createSelector( const images = selectors.selectAll(data); const currentImageIndex = images.findIndex( - (i) => i.image_name === lastSelectedImage + (i) => i.image_name === lastSelectedImage.image_name ); const nextImageIndex = clamp(currentImageIndex + 1, 0, images.length - 1); - const prevImageIndex = clamp(currentImageIndex - 1, 0, images.length - 1); const nextImageId = images[nextImageIndex]?.image_name; const prevImageId = images[prevImageIndex]?.image_name; - const nextImage = selectors.selectById(data, nextImageId); - const prevImage = selectors.selectById(data, prevImageId); + const nextImage = nextImageId + ? selectors.selectById(data, nextImageId) + : undefined; + const prevImage = prevImageId + ? selectors.selectById(data, prevImageId) + : undefined; const imagesLength = images.length; return { - isOnFirstImage: currentImageIndex === 0, - isOnLastImage: - !isNaN(currentImageIndex) && currentImageIndex === imagesLength - 1, - areMoreImagesAvailable: (data?.total ?? 0) > imagesLength, + loadedImagesCount: images.length, + currentImageIndex, + areMoreImagesAvailable: (total ?? 0) > imagesLength, isFetching: status === 'pending', nextImage, prevImage, - nextImageId, - prevImageId, queryArgs, }; }, @@ -82,22 +92,22 @@ export const useNextPrevImage = () => { const dispatch = useAppDispatch(); const { - isOnFirstImage, - isOnLastImage, - nextImageId, - prevImageId, + nextImage, + prevImage, areMoreImagesAvailable, isFetching, queryArgs, + loadedImagesCount, + currentImageIndex, } = useAppSelector(nextPrevImageButtonsSelector); const handlePrevImage = useCallback(() => { - prevImageId && dispatch(imageSelected(prevImageId)); - }, [dispatch, prevImageId]); + prevImage && dispatch(imageSelected(prevImage)); + }, [dispatch, prevImage]); const handleNextImage = useCallback(() => { - nextImageId && dispatch(imageSelected(nextImageId)); - }, [dispatch, nextImageId]); + nextImage && dispatch(imageSelected(nextImage)); + }, [dispatch, nextImage]); const [listImages] = useLazyListImagesQuery(); @@ -108,10 +118,12 @@ export const useNextPrevImage = () => { return { handlePrevImage, handleNextImage, - isOnFirstImage, - isOnLastImage, - nextImageId, - prevImageId, + isOnFirstImage: currentImageIndex === 0, + isOnLastImage: + currentImageIndex !== undefined && + currentImageIndex === loadedImagesCount - 1, + nextImage, + prevImage, areMoreImagesAvailable, handleLoadMoreImages, isFetching, diff --git a/invokeai/frontend/web/src/features/gallery/store/actions.ts b/invokeai/frontend/web/src/features/gallery/store/actions.ts index 0e1b1ef2a0..9368fe6cf6 100644 --- a/invokeai/frontend/web/src/features/gallery/store/actions.ts +++ b/invokeai/frontend/web/src/features/gallery/store/actions.ts @@ -1,5 +1,5 @@ import { createAction } from '@reduxjs/toolkit'; -import { ImageUsage } from 'app/contexts/AddImageToBoardContext'; +import { ImageUsage } from 'features/deleteImageModal/store/types'; import { BoardDTO } from 'services/api/types'; export type RequestedBoardImagesDeletionArg = { diff --git a/invokeai/frontend/web/src/features/gallery/store/boardSlice.ts b/invokeai/frontend/web/src/features/gallery/store/boardSlice.ts deleted file mode 100644 index ad43498e51..0000000000 --- a/invokeai/frontend/web/src/features/gallery/store/boardSlice.ts +++ /dev/null @@ -1,29 +0,0 @@ -import { PayloadAction, createSlice } from '@reduxjs/toolkit'; - -type BoardsState = { - searchText: string; - updateBoardModalOpen: boolean; -}; - -export const initialBoardsState: BoardsState = { - updateBoardModalOpen: false, - searchText: '', -}; - -const boardsSlice = createSlice({ - name: 'boards', - initialState: initialBoardsState, - reducers: { - setBoardSearchText: (state, action: PayloadAction) => { - state.searchText = action.payload; - }, - setUpdateBoardModalOpen: (state, action: PayloadAction) => { - state.updateBoardModalOpen = action.payload; - }, - }, -}); - -export const { setBoardSearchText, setUpdateBoardModalOpen } = - boardsSlice.actions; - -export default boardsSlice.reducer; diff --git a/invokeai/frontend/web/src/features/gallery/store/gallerySelectors.ts b/invokeai/frontend/web/src/features/gallery/store/gallerySelectors.ts index b589550157..47e29456a0 100644 --- a/invokeai/frontend/web/src/features/gallery/store/gallerySelectors.ts +++ b/invokeai/frontend/web/src/features/gallery/store/gallerySelectors.ts @@ -1,7 +1,7 @@ import { createSelector } from '@reduxjs/toolkit'; import { RootState } from 'app/store/store'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; -import { ListImagesArgs } from 'services/api/endpoints/images'; +import { ListImagesArgs } from 'services/api/types'; import { ASSETS_CATEGORIES, IMAGE_CATEGORIES, @@ -24,7 +24,7 @@ export const selectListImagesBaseQueryArgs = createSelector( galleryView === 'images' ? IMAGE_CATEGORIES : ASSETS_CATEGORIES; const listImagesBaseQueryArgs: ListImagesArgs = { - board_id: selectedBoardId ?? 'none', + board_id: selectedBoardId, categories, offset: 0, limit: INITIAL_IMAGE_LIMIT, diff --git a/invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts b/invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts index 5eabe5de26..bc7acff6f4 100644 --- a/invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts +++ b/invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts @@ -1,122 +1,71 @@ import type { PayloadAction } from '@reduxjs/toolkit'; import { createSlice, isAnyOf } from '@reduxjs/toolkit'; -import { uniq } from 'lodash-es'; import { boardsApi } from 'services/api/endpoints/boards'; +import { imagesApi } from 'services/api/endpoints/images'; +import { ImageDTO } from 'services/api/types'; import { BoardId, GalleryState, GalleryView } from './types'; export const initialGalleryState: GalleryState = { selection: [], shouldAutoSwitch: true, - autoAddBoardId: undefined, + autoAssignBoardOnClick: true, + autoAddBoardId: 'none', galleryImageMinimumWidth: 96, - selectedBoardId: undefined, + selectedBoardId: 'none', galleryView: 'images', - batchImageNames: [], - isBatchEnabled: false, + shouldShowDeleteButton: false, + boardSearchText: '', }; export const gallerySlice = createSlice({ name: 'gallery', initialState: initialGalleryState, reducers: { - imageRangeEndSelected: () => { - // TODO - }, - // imageRangeEndSelected: (state, action: PayloadAction) => { - // const rangeEndImageName = action.payload; - // const lastSelectedImage = state.selection[state.selection.length - 1]; - // const filteredImages = selectFilteredImagesLocal(state); - // const lastClickedIndex = filteredImages.findIndex( - // (n) => n.image_name === lastSelectedImage - // ); - // const currentClickedIndex = filteredImages.findIndex( - // (n) => n.image_name === rangeEndImageName - // ); - // if (lastClickedIndex > -1 && currentClickedIndex > -1) { - // // We have a valid range! - // const start = Math.min(lastClickedIndex, currentClickedIndex); - // const end = Math.max(lastClickedIndex, currentClickedIndex); - // const imagesToSelect = filteredImages - // .slice(start, end + 1) - // .map((i) => i.image_name); - // state.selection = uniq(state.selection.concat(imagesToSelect)); - // } - // }, - imageSelectionToggled: () => { - // TODO - }, - // imageSelectionToggled: (state, action: PayloadAction) => { - // TODO: multiselect - // if ( - // state.selection.includes(action.payload) && - // state.selection.length > 1 - // ) { - // state.selection = state.selection.filter( - // (imageName) => imageName !== action.payload - // ); - // } else { - // state.selection = uniq(state.selection.concat(action.payload)); - // } - imageSelected: (state, action: PayloadAction) => { + imageSelected: (state, action: PayloadAction) => { state.selection = action.payload ? [action.payload] : []; }, + selectionChanged: (state, action: PayloadAction) => { + state.selection = action.payload; + }, shouldAutoSwitchChanged: (state, action: PayloadAction) => { state.shouldAutoSwitch = action.payload; }, setGalleryImageMinimumWidth: (state, action: PayloadAction) => { state.galleryImageMinimumWidth = action.payload; }, + autoAssignBoardOnClickChanged: (state, action: PayloadAction) => { + state.autoAssignBoardOnClick = action.payload; + }, boardIdSelected: (state, action: PayloadAction) => { state.selectedBoardId = action.payload; state.galleryView = 'images'; }, - isBatchEnabledChanged: (state, action: PayloadAction) => { - state.isBatchEnabled = action.payload; - }, - imagesAddedToBatch: (state, action: PayloadAction) => { - state.batchImageNames = uniq( - state.batchImageNames.concat(action.payload) - ); - }, - imagesRemovedFromBatch: (state, action: PayloadAction) => { - state.batchImageNames = state.batchImageNames.filter( - (imageName) => !action.payload.includes(imageName) - ); - - const newSelection = state.selection.filter( - (imageName) => !action.payload.includes(imageName) - ); - - if (newSelection.length) { - state.selection = newSelection; + autoAddBoardIdChanged: (state, action: PayloadAction) => { + if (!action.payload) { + state.autoAddBoardId = 'none'; return; } - - state.selection = [state.batchImageNames[0]] ?? []; - }, - batchReset: (state) => { - state.batchImageNames = []; - state.selection = []; - }, - autoAddBoardIdChanged: ( - state, - action: PayloadAction - ) => { state.autoAddBoardId = action.payload; }, galleryViewChanged: (state, action: PayloadAction) => { state.galleryView = action.payload; }, + shouldShowDeleteButtonChanged: (state, action: PayloadAction) => { + state.shouldShowDeleteButton = action.payload; + }, + boardSearchTextChanged: (state, action: PayloadAction) => { + state.boardSearchText = action.payload; + }, }, extraReducers: (builder) => { builder.addMatcher(isAnyBoardDeleted, (state, action) => { const deletedBoardId = action.meta.arg.originalArgs; if (deletedBoardId === state.selectedBoardId) { - state.selectedBoardId = undefined; + state.selectedBoardId = 'none'; state.galleryView = 'images'; } if (deletedBoardId === state.autoAddBoardId) { - state.autoAddBoardId = undefined; + state.autoAddBoardId = 'none'; } }); builder.addMatcher( @@ -128,7 +77,7 @@ export const gallerySlice = createSlice({ } if (!boards.map((b) => b.board_id).includes(state.autoAddBoardId)) { - state.autoAddBoardId = undefined; + state.autoAddBoardId = 'none'; } } ); @@ -136,22 +85,21 @@ export const gallerySlice = createSlice({ }); export const { - imageRangeEndSelected, - imageSelectionToggled, imageSelected, shouldAutoSwitchChanged, + autoAssignBoardOnClickChanged, setGalleryImageMinimumWidth, boardIdSelected, - isBatchEnabledChanged, - imagesAddedToBatch, - imagesRemovedFromBatch, autoAddBoardIdChanged, galleryViewChanged, + selectionChanged, + shouldShowDeleteButtonChanged, + boardSearchTextChanged, } = gallerySlice.actions; export default gallerySlice.reducer; const isAnyBoardDeleted = isAnyOf( - boardsApi.endpoints.deleteBoard.matchFulfilled, - boardsApi.endpoints.deleteBoardAndImages.matchFulfilled + imagesApi.endpoints.deleteBoard.matchFulfilled, + imagesApi.endpoints.deleteBoardAndImages.matchFulfilled ); diff --git a/invokeai/frontend/web/src/features/gallery/store/types.ts b/invokeai/frontend/web/src/features/gallery/store/types.ts index d19a6fded3..6860f6ea7b 100644 --- a/invokeai/frontend/web/src/features/gallery/store/types.ts +++ b/invokeai/frontend/web/src/features/gallery/store/types.ts @@ -1,4 +1,4 @@ -import { ImageCategory } from 'services/api/types'; +import { ImageCategory, ImageDTO } from 'services/api/types'; export const IMAGE_CATEGORIES: ImageCategory[] = ['general']; export const ASSETS_CATEGORIES: ImageCategory[] = [ @@ -11,16 +11,16 @@ export const INITIAL_IMAGE_LIMIT = 100; export const IMAGE_LIMIT = 20; export type GalleryView = 'images' | 'assets'; -// export type BoardId = 'no_board' | (string & Record); -export type BoardId = string | undefined; +export type BoardId = 'none' | (string & Record); export type GalleryState = { - selection: string[]; + selection: ImageDTO[]; shouldAutoSwitch: boolean; - autoAddBoardId: string | undefined; + autoAssignBoardOnClick: boolean; + autoAddBoardId: BoardId; galleryImageMinimumWidth: number; selectedBoardId: BoardId; galleryView: GalleryView; - batchImageNames: string[]; - isBatchEnabled: boolean; + shouldShowDeleteButton: boolean; + boardSearchText: string; }; diff --git a/invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSlice.ts b/invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSlice.ts deleted file mode 100644 index 0bfd9a537d..0000000000 --- a/invokeai/frontend/web/src/features/imageDeletion/store/imageDeletionSlice.ts +++ /dev/null @@ -1,37 +0,0 @@ -import { PayloadAction, createSlice } from '@reduxjs/toolkit'; -import { ImageDTO } from 'services/api/types'; - -type DeleteImageState = { - imageToDelete: ImageDTO | null; - isModalOpen: boolean; -}; - -export const initialDeleteImageState: DeleteImageState = { - imageToDelete: null, - isModalOpen: false, -}; - -const imageDeletion = createSlice({ - name: 'imageDeletion', - initialState: initialDeleteImageState, - reducers: { - isModalOpenChanged: (state, action: PayloadAction) => { - state.isModalOpen = action.payload; - }, - imageToDeleteSelected: (state, action: PayloadAction) => { - state.imageToDelete = action.payload; - }, - imageToDeleteCleared: (state) => { - state.imageToDelete = null; - state.isModalOpen = false; - }, - }, -}); - -export const { - isModalOpenChanged, - imageToDeleteSelected, - imageToDeleteCleared, -} = imageDeletion.actions; - -export default imageDeletion.reducer; diff --git a/invokeai/frontend/web/src/features/imageDeletion/store/types.ts b/invokeai/frontend/web/src/features/imageDeletion/store/types.ts deleted file mode 100644 index b3f4dc9c8d..0000000000 --- a/invokeai/frontend/web/src/features/imageDeletion/store/types.ts +++ /dev/null @@ -1,6 +0,0 @@ -export type ImageUsage = { - isInitialImage: boolean; - isCanvasImage: boolean; - isNodesImage: boolean; - isControlNetImage: boolean; -}; diff --git a/invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx b/invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx index e212efbfa2..c2edd94106 100644 --- a/invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx +++ b/invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx @@ -31,7 +31,7 @@ const ParamLoraCollapse = () => { } return ( - + diff --git a/invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx b/invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx index 835c315e5c..5ba4e711ef 100644 --- a/invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx +++ b/invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx @@ -1,3 +1,4 @@ +import { Divider, Flex } from '@chakra-ui/react'; import { createSelector } from '@reduxjs/toolkit'; import { stateSelector } from 'app/store/store'; import { useAppSelector } from 'app/store/storeHooks'; @@ -8,20 +9,21 @@ import ParamLora from './ParamLora'; const selector = createSelector( stateSelector, ({ lora }) => { - const { loras } = lora; - - return { loras }; + return { lorasArray: map(lora.loras) }; }, defaultSelectorOptions ); const ParamLoraList = () => { - const { loras } = useAppSelector(selector); + const { lorasArray } = useAppSelector(selector); return ( <> - {map(loras, (lora) => ( - + {lorasArray.map((lora, i) => ( + + {i > 0 && } + + ))} ); diff --git a/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx b/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx index 631047aaab..2046d36ab2 100644 --- a/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx +++ b/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx @@ -54,12 +54,9 @@ const ParamLoRASelect = () => { }); }); - // Sort Alphabetically - data.sort((a, b) => - a.label && b.label ? (a.label?.localeCompare(b.label) ? 1 : -1) : -1 - ); + data.sort((a, b) => (a.label && !b.label ? 1 : -1)); - return data.sort((a, b) => (a.disabled && !b.disabled ? -1 : 1)); + return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1)); }, [loras, loraModels, currentMainModel?.base_model]); const handleChange = useCallback( diff --git a/invokeai/frontend/web/src/features/lora/store/loraSlice.ts b/invokeai/frontend/web/src/features/lora/store/loraSlice.ts index f0067a85a2..10a1671933 100644 --- a/invokeai/frontend/web/src/features/lora/store/loraSlice.ts +++ b/invokeai/frontend/web/src/features/lora/store/loraSlice.ts @@ -39,11 +39,19 @@ export const loraSlice = createSlice({ action: PayloadAction<{ id: string; weight: number }> ) => { const { id, weight } = action.payload; - state.loras[id].weight = weight; + const lora = state.loras[id]; + if (!lora) { + return; + } + lora.weight = weight; }, loraWeightReset: (state, action: PayloadAction) => { const id = action.payload; - state.loras[id].weight = defaultLoRAConfig.weight; + const lora = state.loras[id]; + if (!lora) { + return; + } + lora.weight = defaultLoRAConfig.weight; }, }, }); diff --git a/invokeai/frontend/web/src/features/nodes/components/search/NodeSearch.tsx b/invokeai/frontend/web/src/features/nodes/components/search/NodeSearch.tsx index 669110fa54..d4a4f8d31f 100644 --- a/invokeai/frontend/web/src/features/nodes/components/search/NodeSearch.tsx +++ b/invokeai/frontend/web/src/features/nodes/components/search/NodeSearch.tsx @@ -170,15 +170,17 @@ const NodeSearch = () => { // } if (key === 'Enter') { - let selectedNodeType: AnyInvocationType; + let selectedNodeType: AnyInvocationType | undefined; if (searchText.length > 0) { - selectedNodeType = filteredNodes[focusedIndex].item.type; + selectedNodeType = filteredNodes[focusedIndex]?.item.type; } else { - selectedNodeType = nodes[focusedIndex].type; + selectedNodeType = nodes[focusedIndex]?.type; } - addNode(selectedNodeType); + if (selectedNodeType) { + addNode(selectedNodeType); + } setShowNodeList(false); } diff --git a/invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts b/invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts index 436396fb38..2e41081e95 100644 --- a/invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts +++ b/invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts @@ -79,9 +79,12 @@ const nodesSlice = createSlice({ ) => { const { nodeId, fieldName, value } = action.payload; const nodeIndex = state.nodes.findIndex((n) => n.id === nodeId); - + const input = state.nodes?.[nodeIndex]?.data?.inputs[fieldName]; + if (!input) { + return; + } if (nodeIndex > -1) { - state.nodes[nodeIndex].data.inputs[fieldName].value = value; + input.value = value; } }, imageCollectionFieldValueChanged: ( @@ -99,16 +102,19 @@ const nodesSlice = createSlice({ return; } - const currentValue = cloneDeep( - state.nodes[nodeIndex].data.inputs[fieldName].value - ); - - if (!currentValue) { - state.nodes[nodeIndex].data.inputs[fieldName].value = value; + const input = state.nodes?.[nodeIndex]?.data?.inputs[fieldName]; + if (!input) { return; } - state.nodes[nodeIndex].data.inputs[fieldName].value = uniqBy( + const currentValue = cloneDeep(input.value); + + if (!currentValue) { + input.value = value; + return; + } + + input.value = uniqBy( (currentValue as ImageField[]).concat(value), 'image_name' ); diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts index 83692533f7..de7d798c69 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts @@ -29,6 +29,8 @@ import { VaeInputFieldTemplate, VaeModelInputFieldTemplate, } from '../types/types'; +import { logger } from 'app/logging/logger'; +import { parseify } from 'common/util/serialize'; export type BaseFieldProperties = 'name' | 'title' | 'description'; @@ -50,7 +52,13 @@ export type BuildInputFieldArg = { */ export const refObjectToFieldType = ( refObject: OpenAPIV3.ReferenceObject -): keyof typeof FIELD_TYPE_MAP => refObject.$ref.split('/').slice(-1)[0]; +): keyof typeof FIELD_TYPE_MAP => { + const name = refObject.$ref.split('/').slice(-1)[0]; + if (!name) { + return 'UNKNOWN FIELD TYPE'; + } + return name; +}; const buildIntegerInputFieldTemplate = ({ schemaObject, @@ -428,7 +436,7 @@ export const getFieldType = ( let rawFieldType = ''; if (typeHints && name in typeHints) { - rawFieldType = typeHints[name]; + rawFieldType = typeHints[name] ?? 'UNKNOWN FIELD TYPE'; } else if (!schemaObject.type) { // if schemaObject has no type, then it should have one of allOf, anyOf, oneOf if (schemaObject.allOf) { @@ -568,10 +576,23 @@ export const buildOutputFieldTemplates = ( // extract output schema name from ref const outputSchemaName = refObject.$ref.split('/').slice(-1)[0]; + if (!outputSchemaName) { + logger('nodes').error( + { refObject: parseify(refObject) }, + 'No output schema name found in ref object' + ); + throw 'No output schema name found in ref object'; + } + // get the output schema itself // eslint-disable-next-line @typescript-eslint/no-non-null-assertion const outputSchema = openAPI.components!.schemas![outputSchemaName]; + if (!outputSchema) { + logger('nodes').error({ outputSchemaName }, 'Output schema not found'); + throw 'Output schema not found'; + } + if (isSchemaObject(outputSchema)) { const outputFields = reduce( outputSchema.properties as OpenAPIV3.SchemaObject, diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts index bc0bfee8fd..cdd91d6e4f 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts @@ -9,7 +9,6 @@ import { CLIP_SKIP, LORA_LOADER, MAIN_MODEL_LOADER, - ONNX_MODEL_LOADER, METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, POSITIVE_CONDITIONING, @@ -36,15 +35,11 @@ export const addLoRAsToGraph = ( | undefined; if (loraCount > 0) { - // Remove MAIN_MODEL_LOADER unet connection to feed it to LoRAs + // Remove modelLoaderNodeId unet connection to feed it to LoRAs graph.edges = graph.edges.filter( (e) => !( - e.source.node_id === MAIN_MODEL_LOADER && - ['unet'].includes(e.source.field) - ) && - !( - e.source.node_id === ONNX_MODEL_LOADER && + e.source.node_id === modelLoaderNodeId && ['unet'].includes(e.source.field) ) ); diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts new file mode 100644 index 0000000000..c0f7f7ca82 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts @@ -0,0 +1,212 @@ +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { forEach, size } from 'lodash-es'; +import { + MetadataAccumulatorInvocation, + SDXLLoraLoaderInvocation, +} from 'services/api/types'; +import { + LORA_LOADER, + METADATA_ACCUMULATOR, + NEGATIVE_CONDITIONING, + POSITIVE_CONDITIONING, + SDXL_MODEL_LOADER, +} from './constants'; + +export const addSDXLLoRAsToGraph = ( + state: RootState, + graph: NonNullableGraph, + baseNodeId: string, + modelLoaderNodeId: string = SDXL_MODEL_LOADER +): void => { + /** + * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them. + * They then output the UNet and CLIP models references on to either the next LoRA in the chain, + * or to the inference/conditioning nodes. + * + * So we need to inject a LoRA chain into the graph. + */ + + const { loras } = state.lora; + const loraCount = size(loras); + const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as + | MetadataAccumulatorInvocation + | undefined; + + if (loraCount > 0) { + // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs + graph.edges = graph.edges.filter( + (e) => + !( + e.source.node_id === modelLoaderNodeId && + ['unet'].includes(e.source.field) + ) && + !( + e.source.node_id === modelLoaderNodeId && + ['clip'].includes(e.source.field) + ) && + !( + e.source.node_id === modelLoaderNodeId && + ['clip2'].includes(e.source.field) + ) + ); + } + + // we need to remember the last lora so we can chain from it + let lastLoraNodeId = ''; + let currentLoraIndex = 0; + + forEach(loras, (lora) => { + const { model_name, base_model, weight } = lora; + const currentLoraNodeId = `${LORA_LOADER}_${model_name.replace('.', '_')}`; + + const loraLoaderNode: SDXLLoraLoaderInvocation = { + type: 'sdxl_lora_loader', + id: currentLoraNodeId, + is_intermediate: true, + lora: { model_name, base_model }, + weight, + }; + + // add the lora to the metadata accumulator + if (metadataAccumulator) { + metadataAccumulator.loras.push({ + lora: { model_name, base_model }, + weight, + }); + } + + // add to graph + graph.nodes[currentLoraNodeId] = loraLoaderNode; + if (currentLoraIndex === 0) { + // first lora = start the lora chain, attach directly to model loader + graph.edges.push({ + source: { + node_id: modelLoaderNodeId, + field: 'unet', + }, + destination: { + node_id: currentLoraNodeId, + field: 'unet', + }, + }); + + graph.edges.push({ + source: { + node_id: modelLoaderNodeId, + field: 'clip', + }, + destination: { + node_id: currentLoraNodeId, + field: 'clip', + }, + }); + + graph.edges.push({ + source: { + node_id: modelLoaderNodeId, + field: 'clip2', + }, + destination: { + node_id: currentLoraNodeId, + field: 'clip2', + }, + }); + } else { + // we are in the middle of the lora chain, instead connect to the previous lora + graph.edges.push({ + source: { + node_id: lastLoraNodeId, + field: 'unet', + }, + destination: { + node_id: currentLoraNodeId, + field: 'unet', + }, + }); + graph.edges.push({ + source: { + node_id: lastLoraNodeId, + field: 'clip', + }, + destination: { + node_id: currentLoraNodeId, + field: 'clip', + }, + }); + + graph.edges.push({ + source: { + node_id: lastLoraNodeId, + field: 'clip2', + }, + destination: { + node_id: currentLoraNodeId, + field: 'clip2', + }, + }); + } + + if (currentLoraIndex === loraCount - 1) { + // final lora, end the lora chain - we need to connect up to inference and conditioning nodes + graph.edges.push({ + source: { + node_id: currentLoraNodeId, + field: 'unet', + }, + destination: { + node_id: baseNodeId, + field: 'unet', + }, + }); + + graph.edges.push({ + source: { + node_id: currentLoraNodeId, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }); + + graph.edges.push({ + source: { + node_id: currentLoraNodeId, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }); + + graph.edges.push({ + source: { + node_id: currentLoraNodeId, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }); + + graph.edges.push({ + source: { + node_id: currentLoraNodeId, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }); + } + + // increment the lora for the next one in the chain + lastLoraNodeId = currentLoraNodeId; + currentLoraIndex += 1; + }); +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts index c47c7be8b4..adce34adf5 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts @@ -2,28 +2,31 @@ import { RootState } from 'app/store/store'; import { MetadataAccumulatorInvocation } from 'services/api/types'; import { NonNullableGraph } from '../../types/types'; import { - IMAGE_TO_LATENTS, + CANVAS_OUTPUT, LATENTS_TO_IMAGE, + MASK_BLUR, METADATA_ACCUMULATOR, - SDXL_LATENTS_TO_LATENTS, + SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH, + SDXL_CANVAS_INPAINT_GRAPH, + SDXL_CANVAS_OUTPAINT_GRAPH, + SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH, SDXL_MODEL_LOADER, - SDXL_REFINER_LATENTS_TO_LATENTS, + SDXL_REFINER_DENOISE_LATENTS, SDXL_REFINER_MODEL_LOADER, SDXL_REFINER_NEGATIVE_CONDITIONING, SDXL_REFINER_POSITIVE_CONDITIONING, } from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; export const addSDXLRefinerToGraph = ( state: RootState, graph: NonNullableGraph, baseNodeId: string ): void => { - const { positivePrompt, negativePrompt } = state.generation; const { refinerModel, - refinerAestheticScore, - positiveStylePrompt, - negativeStylePrompt, + refinerPositiveAestheticScore, + refinerNegativeAestheticScore, refinerSteps, refinerScheduler, refinerCFGScale, @@ -38,13 +41,20 @@ export const addSDXLRefinerToGraph = ( if (metadataAccumulator) { metadataAccumulator.refiner_model = refinerModel; - metadataAccumulator.refiner_aesthetic_store = refinerAestheticScore; + metadataAccumulator.refiner_positive_aesthetic_score = + refinerPositiveAestheticScore; + metadataAccumulator.refiner_negative_aesthetic_score = + refinerNegativeAestheticScore; metadataAccumulator.refiner_cfg_scale = refinerCFGScale; metadataAccumulator.refiner_scheduler = refinerScheduler; metadataAccumulator.refiner_start = refinerStart; metadataAccumulator.refiner_steps = refinerSteps; } + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, true); + // Unplug SDXL Latents Generation To Latents To Image graph.edges = graph.edges.filter( (e) => @@ -59,21 +69,6 @@ export const addSDXLRefinerToGraph = ( ) ); - // connect the VAE back to the i2l, which we just removed in the filter - // but only if we are doing l2l - if (baseNodeId === SDXL_LATENTS_TO_LATENTS) { - graph.edges.push({ - source: { - node_id: SDXL_MODEL_LOADER, - field: 'vae', - }, - destination: { - node_id: IMAGE_TO_LATENTS, - field: 'vae', - }, - }); - } - graph.nodes[SDXL_REFINER_MODEL_LOADER] = { type: 'sdxl_refiner_model_loader', id: SDXL_REFINER_MODEL_LOADER, @@ -82,20 +77,20 @@ export const addSDXLRefinerToGraph = ( graph.nodes[SDXL_REFINER_POSITIVE_CONDITIONING] = { type: 'sdxl_refiner_compel_prompt', id: SDXL_REFINER_POSITIVE_CONDITIONING, - style: `${positivePrompt} ${positiveStylePrompt}`, - aesthetic_score: refinerAestheticScore, + style: craftedPositiveStylePrompt, + aesthetic_score: refinerPositiveAestheticScore, }; graph.nodes[SDXL_REFINER_NEGATIVE_CONDITIONING] = { type: 'sdxl_refiner_compel_prompt', id: SDXL_REFINER_NEGATIVE_CONDITIONING, - style: `${negativePrompt} ${negativeStylePrompt}`, - aesthetic_score: refinerAestheticScore, + style: craftedNegativeStylePrompt, + aesthetic_score: refinerNegativeAestheticScore, }; - graph.nodes[SDXL_REFINER_LATENTS_TO_LATENTS] = { - type: 'l2l_sdxl', - id: SDXL_REFINER_LATENTS_TO_LATENTS, + graph.nodes[SDXL_REFINER_DENOISE_LATENTS] = { + type: 'denoise_latents', + id: SDXL_REFINER_DENOISE_LATENTS, cfg_scale: refinerCFGScale, - steps: refinerSteps / (1 - Math.min(refinerStart, 0.99)), + steps: refinerSteps, scheduler: refinerScheduler, denoising_start: refinerStart, denoising_end: 1, @@ -108,20 +103,10 @@ export const addSDXLRefinerToGraph = ( field: 'unet', }, destination: { - node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + node_id: SDXL_REFINER_DENOISE_LATENTS, field: 'unet', }, }, - { - source: { - node_id: SDXL_REFINER_MODEL_LOADER, - field: 'vae', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'vae', - }, - }, { source: { node_id: SDXL_REFINER_MODEL_LOADER, @@ -148,7 +133,7 @@ export const addSDXLRefinerToGraph = ( field: 'conditioning', }, destination: { - node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + node_id: SDXL_REFINER_DENOISE_LATENTS, field: 'positive_conditioning', }, }, @@ -158,7 +143,7 @@ export const addSDXLRefinerToGraph = ( field: 'conditioning', }, destination: { - node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + node_id: SDXL_REFINER_DENOISE_LATENTS, field: 'negative_conditioning', }, }, @@ -168,19 +153,52 @@ export const addSDXLRefinerToGraph = ( field: 'latents', }, destination: { - node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + node_id: SDXL_REFINER_DENOISE_LATENTS, field: 'latents', }, - }, - { + } + ); + + if ( + graph.id === SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH || + graph.id === SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH + ) { + graph.edges.push({ source: { - node_id: SDXL_REFINER_LATENTS_TO_LATENTS, + node_id: SDXL_REFINER_DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'latents', + }, + }); + } else { + graph.edges.push({ + source: { + node_id: SDXL_REFINER_DENOISE_LATENTS, field: 'latents', }, destination: { node_id: LATENTS_TO_IMAGE, field: 'latents', }, - } - ); + }); + } + + if ( + graph.id === SDXL_CANVAS_INPAINT_GRAPH || + graph.id === SDXL_CANVAS_OUTPAINT_GRAPH + ) { + graph.edges.push({ + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: SDXL_REFINER_DENOISE_LATENTS, + field: 'mask', + }, + }); + } }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts index 1472b3ea3f..360e07062a 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts @@ -2,14 +2,24 @@ import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { MetadataAccumulatorInvocation } from 'services/api/types'; import { + CANVAS_IMAGE_TO_IMAGE_GRAPH, + CANVAS_INPAINT_GRAPH, + CANVAS_OUTPAINT_GRAPH, + CANVAS_OUTPUT, + CANVAS_TEXT_TO_IMAGE_GRAPH, IMAGE_TO_IMAGE_GRAPH, IMAGE_TO_LATENTS, - INPAINT, - INPAINT_GRAPH, + INPAINT_IMAGE, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, METADATA_ACCUMULATOR, ONNX_MODEL_LOADER, + SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH, + SDXL_CANVAS_INPAINT_GRAPH, + SDXL_CANVAS_OUTPAINT_GRAPH, + SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH, + SDXL_IMAGE_TO_IMAGE_GRAPH, + SDXL_TEXT_TO_IMAGE_GRAPH, TEXT_TO_IMAGE_GRAPH, VAE_LOADER, } from './constants'; @@ -35,7 +45,13 @@ export const addVAEToGraph = ( }; } const isOnnxModel = modelLoaderNodeId == ONNX_MODEL_LOADER; - if (graph.id === TEXT_TO_IMAGE_GRAPH || graph.id === IMAGE_TO_IMAGE_GRAPH) { + + if ( + graph.id === TEXT_TO_IMAGE_GRAPH || + graph.id === IMAGE_TO_IMAGE_GRAPH || + graph.id === SDXL_TEXT_TO_IMAGE_GRAPH || + graph.id === SDXL_IMAGE_TO_IMAGE_GRAPH + ) { graph.edges.push({ source: { node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER, @@ -48,7 +64,30 @@ export const addVAEToGraph = ( }); } - if (graph.id === IMAGE_TO_IMAGE_GRAPH) { + if ( + graph.id === CANVAS_TEXT_TO_IMAGE_GRAPH || + graph.id === CANVAS_IMAGE_TO_IMAGE_GRAPH || + graph.id === SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH || + graph.id == SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH + ) { + graph.edges.push({ + source: { + node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER, + field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'vae', + }, + }); + } + + if ( + graph.id === IMAGE_TO_IMAGE_GRAPH || + graph.id === SDXL_IMAGE_TO_IMAGE_GRAPH || + graph.id === CANVAS_IMAGE_TO_IMAGE_GRAPH || + graph.id === SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH + ) { graph.edges.push({ source: { node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER, @@ -61,17 +100,34 @@ export const addVAEToGraph = ( }); } - if (graph.id === INPAINT_GRAPH) { - graph.edges.push({ - source: { - node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER, - field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae', + if ( + graph.id === CANVAS_INPAINT_GRAPH || + graph.id === CANVAS_OUTPAINT_GRAPH || + graph.id === SDXL_CANVAS_INPAINT_GRAPH || + graph.id === SDXL_CANVAS_OUTPAINT_GRAPH + ) { + graph.edges.push( + { + source: { + node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER, + field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae', + }, + destination: { + node_id: INPAINT_IMAGE, + field: 'vae', + }, }, - destination: { - node_id: INPAINT, - field: 'vae', - }, - }); + { + source: { + node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER, + field: isAutoVae && isOnnxModel ? 'vae_decoder' : 'vae', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'vae', + }, + } + ); } if (vae && metadataAccumulator) { diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts index 8a7716071f..d268a3990d 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasGraph.ts @@ -3,6 +3,11 @@ import { NonNullableGraph } from 'features/nodes/types/types'; import { ImageDTO } from 'services/api/types'; import { buildCanvasImageToImageGraph } from './buildCanvasImageToImageGraph'; import { buildCanvasInpaintGraph } from './buildCanvasInpaintGraph'; +import { buildCanvasOutpaintGraph } from './buildCanvasOutpaintGraph'; +import { buildCanvasSDXLImageToImageGraph } from './buildCanvasSDXLImageToImageGraph'; +import { buildCanvasSDXLInpaintGraph } from './buildCanvasSDXLInpaintGraph'; +import { buildCanvasSDXLOutpaintGraph } from './buildCanvasSDXLOutpaintGraph'; +import { buildCanvasSDXLTextToImageGraph } from './buildCanvasSDXLTextToImageGraph'; import { buildCanvasTextToImageGraph } from './buildCanvasTextToImageGraph'; export const buildCanvasGraph = ( @@ -14,17 +19,58 @@ export const buildCanvasGraph = ( let graph: NonNullableGraph; if (generationMode === 'txt2img') { - graph = buildCanvasTextToImageGraph(state); + if ( + state.generation.model && + state.generation.model.base_model === 'sdxl' + ) { + graph = buildCanvasSDXLTextToImageGraph(state); + } else { + graph = buildCanvasTextToImageGraph(state); + } } else if (generationMode === 'img2img') { if (!canvasInitImage) { throw new Error('Missing canvas init image'); } - graph = buildCanvasImageToImageGraph(state, canvasInitImage); - } else { + if ( + state.generation.model && + state.generation.model.base_model === 'sdxl' + ) { + graph = buildCanvasSDXLImageToImageGraph(state, canvasInitImage); + } else { + graph = buildCanvasImageToImageGraph(state, canvasInitImage); + } + } else if (generationMode === 'inpaint') { if (!canvasInitImage || !canvasMaskImage) { throw new Error('Missing canvas init and mask images'); } - graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage); + if ( + state.generation.model && + state.generation.model.base_model === 'sdxl' + ) { + graph = buildCanvasSDXLInpaintGraph( + state, + canvasInitImage, + canvasMaskImage + ); + } else { + graph = buildCanvasInpaintGraph(state, canvasInitImage, canvasMaskImage); + } + } else { + if (!canvasInitImage) { + throw new Error('Missing canvas init image'); + } + if ( + state.generation.model && + state.generation.model.base_model === 'sdxl' + ) { + graph = buildCanvasSDXLOutpaintGraph( + state, + canvasInitImage, + canvasMaskImage + ); + } else { + graph = buildCanvasOutpaintGraph(state, canvasInitImage, canvasMaskImage); + } } return graph; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts index 42f768c107..a68aeef392 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts @@ -14,11 +14,11 @@ import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { + CANVAS_IMAGE_TO_IMAGE_GRAPH, + CANVAS_OUTPUT, CLIP_SKIP, - IMAGE_TO_IMAGE_GRAPH, + DENOISE_LATENTS, IMAGE_TO_LATENTS, - LATENTS_TO_IMAGE, - LATENTS_TO_LATENTS, MAIN_MODEL_LOADER, METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, @@ -73,8 +73,20 @@ export const buildCanvasImageToImageGraph = ( // copy-pasted graph from node editor, filled in with state values & friendly node ids const graph: NonNullableGraph = { - id: IMAGE_TO_IMAGE_GRAPH, + id: CANVAS_IMAGE_TO_IMAGE_GRAPH, nodes: { + [MAIN_MODEL_LOADER]: { + type: 'main_model_loader', + id: MAIN_MODEL_LOADER, + is_intermediate: true, + model, + }, + [CLIP_SKIP]: { + type: 'clip_skip', + id: CLIP_SKIP, + is_intermediate: true, + skipped_layers: clipSkip, + }, [POSITIVE_CONDITIONING]: { type: 'compel', id: POSITIVE_CONDITIONING, @@ -93,27 +105,6 @@ export const buildCanvasImageToImageGraph = ( is_intermediate: true, use_cpu, }, - [MAIN_MODEL_LOADER]: { - type: 'main_model_loader', - id: MAIN_MODEL_LOADER, - is_intermediate: true, - model, - }, - [CLIP_SKIP]: { - type: 'clip_skip', - id: CLIP_SKIP, - is_intermediate: true, - skipped_layers: clipSkip, - }, - [LATENTS_TO_LATENTS]: { - type: 'l2l', - id: LATENTS_TO_LATENTS, - is_intermediate: true, - cfg_scale, - scheduler, - steps, - strength, - }, [IMAGE_TO_LATENTS]: { type: 'i2l', id: IMAGE_TO_LATENTS, @@ -123,13 +114,34 @@ export const buildCanvasImageToImageGraph = ( // image_name: initialImage.image_name, // }, }, - [LATENTS_TO_IMAGE]: { + [DENOISE_LATENTS]: { + type: 'denoise_latents', + id: DENOISE_LATENTS, + is_intermediate: true, + cfg_scale, + scheduler, + steps, + denoising_start: 1 - strength, + denoising_end: 1, + }, + [CANVAS_OUTPUT]: { type: 'l2i', - id: LATENTS_TO_IMAGE, + id: CANVAS_OUTPUT, is_intermediate: !shouldAutoSave, }, }, edges: [ + // Connect Model Loader to CLIP Skip and UNet + { + source: { + node_id: MAIN_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'unet', + }, + }, { source: { node_id: MAIN_MODEL_LOADER, @@ -140,6 +152,7 @@ export const buildCanvasImageToImageGraph = ( field: 'clip', }, }, + // Connect CLIP Skip To Conditioning { source: { node_id: CLIP_SKIP, @@ -160,24 +173,25 @@ export const buildCanvasImageToImageGraph = ( field: 'clip', }, }, + // Connect Everything To Denoise Latents { source: { - node_id: LATENTS_TO_LATENTS, - field: 'latents', + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', }, destination: { - node_id: LATENTS_TO_IMAGE, - field: 'latents', + node_id: DENOISE_LATENTS, + field: 'positive_conditioning', }, }, { source: { - node_id: IMAGE_TO_LATENTS, - field: 'latents', + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', }, destination: { - node_id: LATENTS_TO_LATENTS, - field: 'latents', + node_id: DENOISE_LATENTS, + field: 'negative_conditioning', }, }, { @@ -186,38 +200,29 @@ export const buildCanvasImageToImageGraph = ( field: 'noise', }, destination: { - node_id: LATENTS_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'noise', }, }, { source: { - node_id: MAIN_MODEL_LOADER, - field: 'unet', + node_id: IMAGE_TO_LATENTS, + field: 'latents', }, destination: { - node_id: LATENTS_TO_LATENTS, - field: 'unet', + node_id: DENOISE_LATENTS, + field: 'latents', }, }, + // Decode the denoised latents to an image { source: { - node_id: NEGATIVE_CONDITIONING, - field: 'conditioning', + node_id: DENOISE_LATENTS, + field: 'latents', }, destination: { - node_id: LATENTS_TO_LATENTS, - field: 'negative_conditioning', - }, - }, - { - source: { - node_id: POSITIVE_CONDITIONING, - field: 'conditioning', - }, - destination: { - node_id: LATENTS_TO_LATENTS, - field: 'positive_conditioning', + node_id: CANVAS_OUTPUT, + field: 'latents', }, }, ], @@ -318,32 +323,32 @@ export const buildCanvasImageToImageGraph = ( field: 'metadata', }, destination: { - node_id: LATENTS_TO_IMAGE, + node_id: CANVAS_OUTPUT, field: 'metadata', }, }); // add LoRA support - addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS); + addLoRAsToGraph(state, graph, DENOISE_LATENTS); // optionally add custom VAE - addVAEToGraph(state, graph); + addVAEToGraph(state, graph, MAIN_MODEL_LOADER); // add dynamic prompts - also sets up core iteration and seed addDynamicPromptsToGraph(state, graph); // add controlnet, mutating `graph` - addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS); + addControlNetToLinearGraph(state, graph, DENOISE_LATENTS); // NSFW & watermark - must be last thing added to graph if (state.system.shouldUseNSFWChecker) { // must add before watermarker! - addNSFWCheckerToGraph(state, graph); + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); } if (state.system.shouldUseWatermarker) { // must add after nsfw checker! - addWatermarkerToGraph(state, graph); + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); } return graph; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts index 3cec76757f..6b0da8e197 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts @@ -2,22 +2,35 @@ import { logger } from 'app/logging/logger'; import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { + ImageBlurInvocation, ImageDTO, - InpaintInvocation, + ImageToLatentsInvocation, + NoiseInvocation, RandomIntInvocation, RangeOfSizeInvocation, } from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { + CANVAS_INPAINT_GRAPH, + CANVAS_OUTPUT, CLIP_SKIP, - INPAINT, - INPAINT_GRAPH, + COLOR_CORRECT, + DENOISE_LATENTS, + INPAINT_IMAGE, + INPAINT_IMAGE_RESIZE_DOWN, + INPAINT_IMAGE_RESIZE_UP, ITERATE, + LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, + MASK_BLUR, + MASK_RESIZE_DOWN, + MASK_RESIZE_UP, NEGATIVE_CONDITIONING, + NOISE, POSITIVE_CONDITIONING, RANDOM_INT, RANGE_OF_SIZE, @@ -40,16 +53,14 @@ export const buildCanvasInpaintGraph = ( scheduler, steps, img2imgStrength: strength, - shouldFitToWidthHeight, iterations, seed, shouldRandomizeSeed, - seamSize, - seamBlur, - seamSteps, - seamStrength, - tileSize, - infillMethod, + vaePrecision, + shouldUseNoiseSettings, + shouldUseCpuNoise, + maskBlur, + maskBlurMethod, clipSkip, } = state.generation; @@ -68,40 +79,24 @@ export const buildCanvasInpaintGraph = ( shouldAutoSave, } = state.canvas; + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : shouldUseCpuNoise; + const graph: NonNullableGraph = { - id: INPAINT_GRAPH, + id: CANVAS_INPAINT_GRAPH, nodes: { - [INPAINT]: { - is_intermediate: !shouldAutoSave, - type: 'inpaint', - id: INPAINT, - steps, - width, - height, - cfg_scale, - scheduler, - image: { - image_name: canvasInitImage.image_name, - }, - strength, - fit: shouldFitToWidthHeight, - mask: { - image_name: canvasMaskImage.image_name, - }, - seam_size: seamSize, - seam_blur: seamBlur, - seam_strength: seamStrength, - seam_steps: seamSteps, - tile_size: infillMethod === 'tile' ? tileSize : undefined, - infill_method: infillMethod as InpaintInvocation['infill_method'], - inpaint_width: - boundingBoxScaleMethod !== 'none' - ? scaledBoundingBoxDimensions.width - : undefined, - inpaint_height: - boundingBoxScaleMethod !== 'none' - ? scaledBoundingBoxDimensions.height - : undefined, + [MAIN_MODEL_LOADER]: { + type: 'main_model_loader', + id: MAIN_MODEL_LOADER, + is_intermediate: true, + model, + }, + [CLIP_SKIP]: { + type: 'clip_skip', + id: CLIP_SKIP, + is_intermediate: true, + skipped_layers: clipSkip, }, [POSITIVE_CONDITIONING]: { type: 'compel', @@ -115,17 +110,52 @@ export const buildCanvasInpaintGraph = ( is_intermediate: true, prompt: negativePrompt, }, - [MAIN_MODEL_LOADER]: { - type: 'main_model_loader', - id: MAIN_MODEL_LOADER, + [MASK_BLUR]: { + type: 'img_blur', + id: MASK_BLUR, is_intermediate: true, - model, + radius: maskBlur, + blur_type: maskBlurMethod, }, - [CLIP_SKIP]: { - type: 'clip_skip', - id: CLIP_SKIP, + [INPAINT_IMAGE]: { + type: 'i2l', + id: INPAINT_IMAGE, is_intermediate: true, - skipped_layers: clipSkip, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + use_cpu, + is_intermediate: true, + }, + [DENOISE_LATENTS]: { + type: 'denoise_latents', + id: DENOISE_LATENTS, + is_intermediate: true, + steps: steps, + cfg_scale: cfg_scale, + scheduler: scheduler, + denoising_start: 1 - strength, + denoising_end: 1, + }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [COLOR_CORRECT]: { + type: 'color_correct', + id: COLOR_CORRECT, + is_intermediate: true, + reference: canvasInitImage, + }, + [CANVAS_OUTPUT]: { + type: 'img_paste', + id: CANVAS_OUTPUT, + is_intermediate: !shouldAutoSave, + base_image: canvasInitImage, }, [RANGE_OF_SIZE]: { type: 'range_of_size', @@ -143,13 +173,14 @@ export const buildCanvasInpaintGraph = ( }, }, edges: [ + // Connect Model Loader to CLIP Skip and UNet { source: { node_id: MAIN_MODEL_LOADER, field: 'unet', }, destination: { - node_id: INPAINT, + node_id: DENOISE_LATENTS, field: 'unet', }, }, @@ -163,6 +194,7 @@ export const buildCanvasInpaintGraph = ( field: 'clip', }, }, + // Connect CLIP Skip to Conditioning { source: { node_id: CLIP_SKIP, @@ -183,26 +215,58 @@ export const buildCanvasInpaintGraph = ( field: 'clip', }, }, + // Connect Everything To Inpaint Node + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'positive_conditioning', + }, + }, { source: { node_id: NEGATIVE_CONDITIONING, field: 'conditioning', }, destination: { - node_id: INPAINT, + node_id: DENOISE_LATENTS, field: 'negative_conditioning', }, }, { source: { - node_id: POSITIVE_CONDITIONING, - field: 'conditioning', + node_id: NOISE, + field: 'noise', }, destination: { - node_id: INPAINT, - field: 'positive_conditioning', + node_id: DENOISE_LATENTS, + field: 'noise', }, }, + { + source: { + node_id: INPAINT_IMAGE, + field: 'latents', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'latents', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'mask', + }, + }, + // Iterate { source: { node_id: RANGE_OF_SIZE, @@ -219,19 +283,216 @@ export const buildCanvasInpaintGraph = ( field: 'item', }, destination: { - node_id: INPAINT, + node_id: NOISE, field: 'seed', }, }, + // Decode Inpainted Latents To Image + { + source: { + node_id: DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, ], }; - addLoRAsToGraph(state, graph, INPAINT); + // Handle Scale Before Processing + if (['auto', 'manual'].includes(boundingBoxScaleMethod)) { + const scaledWidth: number = scaledBoundingBoxDimensions.width; + const scaledHeight: number = scaledBoundingBoxDimensions.height; - // Add VAE - addVAEToGraph(state, graph); + // Add Scaling Nodes + graph.nodes[INPAINT_IMAGE_RESIZE_UP] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + image: canvasInitImage, + }; + graph.nodes[MASK_RESIZE_UP] = { + type: 'img_resize', + id: MASK_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + image: canvasMaskImage, + }; + graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + graph.nodes[MASK_RESIZE_DOWN] = { + type: 'img_resize', + id: MASK_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; - // handle seed + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: scaledWidth, + height: scaledHeight, + }; + + // Connect Nodes + graph.edges.push( + // Scale Inpaint Image and Mask + { + source: { + node_id: INPAINT_IMAGE_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: MASK_BLUR, + field: 'image', + }, + }, + // Color Correct The Inpainted Result + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Back Onto Original Image + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } else { + // Add Images To Nodes + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: width, + height: height, + }; + graph.nodes[INPAINT_IMAGE] = { + ...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation), + image: canvasInitImage, + }; + graph.nodes[MASK_BLUR] = { + ...(graph.nodes[MASK_BLUR] as ImageBlurInvocation), + image: canvasMaskImage, + }; + + graph.edges.push( + // Color Correct The Inpainted Result + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Back Onto Original Image + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } + + // Handle Seed if (shouldRandomizeSeed) { // Random int node to generate the starting seed const randomIntNode: RandomIntInvocation = { @@ -251,15 +512,24 @@ export const buildCanvasInpaintGraph = ( (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed; } + // Add VAE + addVAEToGraph(state, graph, MAIN_MODEL_LOADER); + + // add LoRA support + addLoRAsToGraph(state, graph, DENOISE_LATENTS, MAIN_MODEL_LOADER); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, DENOISE_LATENTS); + // NSFW & watermark - must be last thing added to graph if (state.system.shouldUseNSFWChecker) { // must add before watermarker! - addNSFWCheckerToGraph(state, graph, INPAINT); + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); } if (state.system.shouldUseWatermarker) { // must add after nsfw checker! - addWatermarkerToGraph(state, graph, INPAINT); + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); } return graph; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts new file mode 100644 index 0000000000..23f6acb539 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasOutpaintGraph.ts @@ -0,0 +1,677 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { + ImageBlurInvocation, + ImageDTO, + ImageToLatentsInvocation, + InfillPatchmatchInvocation, + InfillTileInvocation, + NoiseInvocation, + RandomIntInvocation, + RangeOfSizeInvocation, +} from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; +import { addLoRAsToGraph } from './addLoRAsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + CANVAS_OUTPAINT_GRAPH, + CANVAS_OUTPUT, + CLIP_SKIP, + COLOR_CORRECT, + DENOISE_LATENTS, + INPAINT_IMAGE, + INPAINT_IMAGE_RESIZE_DOWN, + INPAINT_IMAGE_RESIZE_UP, + INPAINT_INFILL, + INPAINT_INFILL_RESIZE_DOWN, + ITERATE, + LATENTS_TO_IMAGE, + MAIN_MODEL_LOADER, + MASK_BLUR, + MASK_COMBINE, + MASK_FROM_ALPHA, + MASK_RESIZE_DOWN, + MASK_RESIZE_UP, + NEGATIVE_CONDITIONING, + NOISE, + POSITIVE_CONDITIONING, + RANDOM_INT, + RANGE_OF_SIZE, +} from './constants'; + +/** + * Builds the Canvas tab's Outpaint graph. + */ +export const buildCanvasOutpaintGraph = ( + state: RootState, + canvasInitImage: ImageDTO, + canvasMaskImage?: ImageDTO +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + img2imgStrength: strength, + iterations, + seed, + shouldRandomizeSeed, + vaePrecision, + shouldUseNoiseSettings, + shouldUseCpuNoise, + maskBlur, + maskBlurMethod, + tileSize, + infillMethod, + clipSkip, + } = state.generation; + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + // The bounding box determines width and height, not the width and height params + const { width, height } = state.canvas.boundingBoxDimensions; + + // We may need to set the inpaint width and height to scale the image + const { + scaledBoundingBoxDimensions, + boundingBoxScaleMethod, + shouldAutoSave, + } = state.canvas; + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : shouldUseCpuNoise; + + const graph: NonNullableGraph = { + id: CANVAS_OUTPAINT_GRAPH, + nodes: { + [MAIN_MODEL_LOADER]: { + type: 'main_model_loader', + id: MAIN_MODEL_LOADER, + is_intermediate: true, + model, + }, + [CLIP_SKIP]: { + type: 'clip_skip', + id: CLIP_SKIP, + is_intermediate: true, + skipped_layers: clipSkip, + }, + [POSITIVE_CONDITIONING]: { + type: 'compel', + id: POSITIVE_CONDITIONING, + is_intermediate: true, + prompt: positivePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: 'compel', + id: NEGATIVE_CONDITIONING, + is_intermediate: true, + prompt: negativePrompt, + }, + [MASK_FROM_ALPHA]: { + type: 'tomask', + id: MASK_FROM_ALPHA, + is_intermediate: true, + image: canvasInitImage, + }, + [MASK_COMBINE]: { + type: 'mask_combine', + id: MASK_COMBINE, + is_intermediate: true, + mask2: canvasMaskImage, + }, + [MASK_BLUR]: { + type: 'img_blur', + id: MASK_BLUR, + is_intermediate: true, + radius: maskBlur, + blur_type: maskBlurMethod, + }, + [INPAINT_INFILL]: { + type: 'infill_tile', + id: INPAINT_INFILL, + is_intermediate: true, + tile_size: tileSize, + }, + [INPAINT_IMAGE]: { + type: 'i2l', + id: INPAINT_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + use_cpu, + is_intermediate: true, + }, + [DENOISE_LATENTS]: { + type: 'denoise_latents', + id: DENOISE_LATENTS, + is_intermediate: true, + steps: steps, + cfg_scale: cfg_scale, + scheduler: scheduler, + denoising_start: 1 - strength, + denoising_end: 1, + }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [COLOR_CORRECT]: { + type: 'color_correct', + id: COLOR_CORRECT, + is_intermediate: true, + }, + [CANVAS_OUTPUT]: { + type: 'img_paste', + id: CANVAS_OUTPUT, + is_intermediate: !shouldAutoSave, + }, + [RANGE_OF_SIZE]: { + type: 'range_of_size', + id: RANGE_OF_SIZE, + is_intermediate: true, + // seed - must be connected manually + // start: 0, + size: iterations, + step: 1, + }, + [ITERATE]: { + type: 'iterate', + id: ITERATE, + is_intermediate: true, + }, + }, + edges: [ + // Connect Model Loader To UNet & Clip Skip + { + source: { + node_id: MAIN_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: MAIN_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: CLIP_SKIP, + field: 'clip', + }, + }, + // Connect CLIP Skip to Conditioning + { + source: { + node_id: CLIP_SKIP, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: CLIP_SKIP, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + // Connect Infill Result To Inpaint Image + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE, + field: 'image', + }, + }, + // Combine Mask from Init Image with User Painted Mask + { + source: { + node_id: MASK_FROM_ALPHA, + field: 'mask', + }, + destination: { + node_id: MASK_COMBINE, + field: 'mask1', + }, + }, + // Plug Everything Into Inpaint Node + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: INPAINT_IMAGE, + field: 'latents', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'latents', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'mask', + }, + }, + // Iterate + { + source: { + node_id: RANGE_OF_SIZE, + field: 'collection', + }, + destination: { + node_id: ITERATE, + field: 'collection', + }, + }, + { + source: { + node_id: ITERATE, + field: 'item', + }, + destination: { + node_id: NOISE, + field: 'seed', + }, + }, + // Decode the result from Inpaint + { + source: { + node_id: DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, + ], + }; + + // Add Infill Nodes + + if (infillMethod === 'patchmatch') { + graph.nodes[INPAINT_INFILL] = { + type: 'infill_patchmatch', + id: INPAINT_INFILL, + is_intermediate: true, + }; + } + + // Handle Scale Before Processing + if (['auto', 'manual'].includes(boundingBoxScaleMethod)) { + const scaledWidth: number = scaledBoundingBoxDimensions.width; + const scaledHeight: number = scaledBoundingBoxDimensions.height; + + // Add Scaling Nodes + graph.nodes[INPAINT_IMAGE_RESIZE_UP] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + image: canvasInitImage, + }; + graph.nodes[MASK_RESIZE_UP] = { + type: 'img_resize', + id: MASK_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + }; + graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = { + type: 'img_resize', + id: INPAINT_INFILL_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + graph.nodes[MASK_RESIZE_DOWN] = { + type: 'img_resize', + id: MASK_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: scaledWidth, + height: scaledHeight, + }; + + // Connect Nodes + graph.edges.push( + // Scale Inpaint Image + { + source: { + node_id: INPAINT_IMAGE_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: INPAINT_INFILL, + field: 'image', + }, + }, + // Take combined mask and resize and then blur + { + source: { + node_id: MASK_COMBINE, + field: 'image', + }, + destination: { + node_id: MASK_RESIZE_UP, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: MASK_BLUR, + field: 'image', + }, + }, + // Resize Results Down + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: INPAINT_INFILL_RESIZE_DOWN, + field: 'image', + }, + }, + // Color Correct The Inpainted Result + { + source: { + node_id: INPAINT_INFILL_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'reference', + }, + }, + { + source: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Everything Back + { + source: { + node_id: INPAINT_INFILL_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'base_image', + }, + }, + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } else { + // Add Images To Nodes + graph.nodes[INPAINT_INFILL] = { + ...(graph.nodes[INPAINT_INFILL] as + | InfillTileInvocation + | InfillPatchmatchInvocation), + image: canvasInitImage, + }; + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: width, + height: height, + }; + graph.nodes[INPAINT_IMAGE] = { + ...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation), + image: canvasInitImage, + }; + graph.nodes[MASK_BLUR] = { + ...(graph.nodes[MASK_BLUR] as ImageBlurInvocation), + image: canvasMaskImage, + }; + + graph.edges.push( + // Take combined mask and plug it to blur + { + source: { + node_id: MASK_COMBINE, + field: 'image', + }, + destination: { + node_id: MASK_BLUR, + field: 'image', + }, + }, + // Color Correct The Inpainted Result + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'reference', + }, + }, + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Everything Back + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'base_image', + }, + }, + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } + + // Handle Seed + if (shouldRandomizeSeed) { + // Random int node to generate the starting seed + const randomIntNode: RandomIntInvocation = { + id: RANDOM_INT, + type: 'rand_int', + }; + + graph.nodes[RANDOM_INT] = randomIntNode; + + // Connect random int to the start of the range of size so the range starts on the random first seed + graph.edges.push({ + source: { node_id: RANDOM_INT, field: 'a' }, + destination: { node_id: RANGE_OF_SIZE, field: 'start' }, + }); + } else { + // User specified seed, so set the start of the range of size to the seed + (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed; + } + + // Add VAE + addVAEToGraph(state, graph, MAIN_MODEL_LOADER); + + // add LoRA support + addLoRAsToGraph(state, graph, DENOISE_LATENTS, MAIN_MODEL_LOADER); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, DENOISE_LATENTS); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts new file mode 100644 index 0000000000..ef32943bc8 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts @@ -0,0 +1,378 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { + ImageDTO, + ImageResizeInvocation, + ImageToLatentsInvocation, +} from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; +import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph'; +import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + CANVAS_OUTPUT, + IMAGE_TO_LATENTS, + METADATA_ACCUMULATOR, + NEGATIVE_CONDITIONING, + NOISE, + POSITIVE_CONDITIONING, + RESIZE, + SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH, + SDXL_DENOISE_LATENTS, + SDXL_MODEL_LOADER, +} from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; + +/** + * Builds the Canvas tab's Image to Image graph. + */ +export const buildCanvasSDXLImageToImageGraph = ( + state: RootState, + initialImage: ImageDTO +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + vaePrecision, + clipSkip, + shouldUseCpuNoise, + shouldUseNoiseSettings, + } = state.generation; + + const { + shouldUseSDXLRefiner, + refinerStart, + sdxlImg2ImgDenoisingStrength: strength, + shouldConcatSDXLStylePrompt, + } = state.sdxl; + + // The bounding box determines width and height, not the width and height params + const { width, height } = state.canvas.boundingBoxDimensions; + + const { shouldAutoSave } = state.canvas; + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : initialGenerationState.shouldUseCpuNoise; + + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt); + + /** + * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the + * full graph here as a template. Then use the parameters from app state and set friendlier node + * ids. + * + * The only thing we need extra logic for is handling randomized seed, control net, and for img2img, + * the `fit` param. These are added to the graph at the end. + */ + + // copy-pasted graph from node editor, filled in with state values & friendly node ids + const graph: NonNullableGraph = { + id: SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH, + nodes: { + [SDXL_MODEL_LOADER]: { + type: 'sdxl_model_loader', + id: SDXL_MODEL_LOADER, + model, + }, + [POSITIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: POSITIVE_CONDITIONING, + prompt: positivePrompt, + style: craftedPositiveStylePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: NEGATIVE_CONDITIONING, + prompt: negativePrompt, + style: craftedNegativeStylePrompt, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + is_intermediate: true, + use_cpu, + }, + [IMAGE_TO_LATENTS]: { + type: 'i2l', + id: IMAGE_TO_LATENTS, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + // must be set manually later, bc `fit` parameter may require a resize node inserted + // image: { + // image_name: initialImage.image_name, + // }, + }, + [SDXL_DENOISE_LATENTS]: { + type: 'denoise_latents', + id: SDXL_DENOISE_LATENTS, + is_intermediate: true, + cfg_scale, + scheduler, + steps, + denoising_start: shouldUseSDXLRefiner + ? Math.min(refinerStart, 1 - strength) + : 1 - strength, + denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, + }, + [CANVAS_OUTPUT]: { + type: 'l2i', + id: CANVAS_OUTPUT, + is_intermediate: !shouldAutoSave, + fp32: vaePrecision === 'fp32' ? true : false, + }, + }, + edges: [ + // Connect Model Loader To UNet & CLIP + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + // Connect Everything to Denoise Latents + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: IMAGE_TO_LATENTS, + field: 'latents', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + }, + // Decode denoised latents to an image + { + source: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'latents', + }, + }, + ], + }; + + // handle `fit` + if (initialImage.width !== width || initialImage.height !== height) { + // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS` + + // Create a resize node, explicitly setting its image + const resizeNode: ImageResizeInvocation = { + id: RESIZE, + type: 'img_resize', + image: { + image_name: initialImage.image_name, + }, + is_intermediate: true, + width, + height, + }; + + graph.nodes[RESIZE] = resizeNode; + + // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS` + graph.edges.push({ + source: { node_id: RESIZE, field: 'image' }, + destination: { + node_id: IMAGE_TO_LATENTS, + field: 'image', + }, + }); + + // The `RESIZE` node also passes its width and height to `NOISE` + graph.edges.push({ + source: { node_id: RESIZE, field: 'width' }, + destination: { + node_id: NOISE, + field: 'width', + }, + }); + + graph.edges.push({ + source: { node_id: RESIZE, field: 'height' }, + destination: { + node_id: NOISE, + field: 'height', + }, + }); + } else { + // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly + (graph.nodes[IMAGE_TO_LATENTS] as ImageToLatentsInvocation).image = { + image_name: initialImage.image_name, + }; + + // Pass the image's dimensions to the `NOISE` node + graph.edges.push({ + source: { node_id: IMAGE_TO_LATENTS, field: 'width' }, + destination: { + node_id: NOISE, + field: 'width', + }, + }); + graph.edges.push({ + source: { node_id: IMAGE_TO_LATENTS, field: 'height' }, + destination: { + node_id: NOISE, + field: 'height', + }, + }); + } + + // add metadata accumulator, which is only mostly populated - some fields are added later + graph.nodes[METADATA_ACCUMULATOR] = { + id: METADATA_ACCUMULATOR, + type: 'metadata_accumulator', + generation_mode: 'img2img', + cfg_scale, + height, + width, + positive_prompt: '', // set in addDynamicPromptsToGraph + negative_prompt: negativePrompt, + model, + seed: 0, // set in addDynamicPromptsToGraph + steps, + rand_device: use_cpu ? 'cpu' : 'cuda', + scheduler, + vae: undefined, // option; set in addVAEToGraph + controlnets: [], // populated in addControlNetToLinearGraph + loras: [], // populated in addLoRAsToGraph + clip_skip: clipSkip, + strength, + init_image: initialImage.image_name, + }; + + graph.edges.push({ + source: { + node_id: METADATA_ACCUMULATOR, + field: 'metadata', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'metadata', + }, + }); + + // add LoRA support + addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER); + + // Add Refiner if enabled + if (shouldUseSDXLRefiner) { + addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS); + } + + // optionally add custom VAE + addVAEToGraph(state, graph, SDXL_MODEL_LOADER); + + // add dynamic prompts - also sets up core iteration and seed + addDynamicPromptsToGraph(state, graph); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts new file mode 100644 index 0000000000..ba40a70c83 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLInpaintGraph.ts @@ -0,0 +1,555 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { + ImageBlurInvocation, + ImageDTO, + ImageToLatentsInvocation, + NoiseInvocation, + RandomIntInvocation, + RangeOfSizeInvocation, +} from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph'; +import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + CANVAS_OUTPUT, + COLOR_CORRECT, + INPAINT_IMAGE, + INPAINT_IMAGE_RESIZE_DOWN, + INPAINT_IMAGE_RESIZE_UP, + ITERATE, + LATENTS_TO_IMAGE, + MASK_BLUR, + MASK_RESIZE_DOWN, + MASK_RESIZE_UP, + NEGATIVE_CONDITIONING, + NOISE, + POSITIVE_CONDITIONING, + RANDOM_INT, + RANGE_OF_SIZE, + SDXL_CANVAS_INPAINT_GRAPH, + SDXL_DENOISE_LATENTS, + SDXL_MODEL_LOADER, +} from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; + +/** + * Builds the Canvas tab's Inpaint graph. + */ +export const buildCanvasSDXLInpaintGraph = ( + state: RootState, + canvasInitImage: ImageDTO, + canvasMaskImage: ImageDTO +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + iterations, + seed, + shouldRandomizeSeed, + vaePrecision, + shouldUseNoiseSettings, + shouldUseCpuNoise, + maskBlur, + maskBlurMethod, + } = state.generation; + + const { + sdxlImg2ImgDenoisingStrength: strength, + shouldUseSDXLRefiner, + refinerStart, + shouldConcatSDXLStylePrompt, + } = state.sdxl; + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + // The bounding box determines width and height, not the width and height params + const { width, height } = state.canvas.boundingBoxDimensions; + + // We may need to set the inpaint width and height to scale the image + const { + scaledBoundingBoxDimensions, + boundingBoxScaleMethod, + shouldAutoSave, + } = state.canvas; + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : shouldUseCpuNoise; + + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt); + + const graph: NonNullableGraph = { + id: SDXL_CANVAS_INPAINT_GRAPH, + nodes: { + [SDXL_MODEL_LOADER]: { + type: 'sdxl_model_loader', + id: SDXL_MODEL_LOADER, + model, + }, + [POSITIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: POSITIVE_CONDITIONING, + prompt: positivePrompt, + style: craftedPositiveStylePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: NEGATIVE_CONDITIONING, + prompt: negativePrompt, + style: craftedNegativeStylePrompt, + }, + [MASK_BLUR]: { + type: 'img_blur', + id: MASK_BLUR, + is_intermediate: true, + radius: maskBlur, + blur_type: maskBlurMethod, + }, + [INPAINT_IMAGE]: { + type: 'i2l', + id: INPAINT_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + use_cpu, + is_intermediate: true, + }, + [SDXL_DENOISE_LATENTS]: { + type: 'denoise_latents', + id: SDXL_DENOISE_LATENTS, + is_intermediate: true, + steps: steps, + cfg_scale: cfg_scale, + scheduler: scheduler, + denoising_start: shouldUseSDXLRefiner + ? Math.min(refinerStart, 1 - strength) + : 1 - strength, + denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, + }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [COLOR_CORRECT]: { + type: 'color_correct', + id: COLOR_CORRECT, + is_intermediate: true, + reference: canvasInitImage, + }, + [CANVAS_OUTPUT]: { + type: 'img_paste', + id: CANVAS_OUTPUT, + is_intermediate: !shouldAutoSave, + base_image: canvasInitImage, + }, + [RANGE_OF_SIZE]: { + type: 'range_of_size', + id: RANGE_OF_SIZE, + is_intermediate: true, + // seed - must be connected manually + // start: 0, + size: iterations, + step: 1, + }, + [ITERATE]: { + type: 'iterate', + id: ITERATE, + is_intermediate: true, + }, + }, + edges: [ + // Connect Model Loader to UNet and CLIP + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + // Connect everything to Inpaint + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: INPAINT_IMAGE, + field: 'latents', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'mask', + }, + }, + // Iterate + { + source: { + node_id: RANGE_OF_SIZE, + field: 'collection', + }, + destination: { + node_id: ITERATE, + field: 'collection', + }, + }, + { + source: { + node_id: ITERATE, + field: 'item', + }, + destination: { + node_id: NOISE, + field: 'seed', + }, + }, + // Decode inpainted latents to image + { + source: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, + ], + }; + + // Handle Scale Before Processing + if (['auto', 'manual'].includes(boundingBoxScaleMethod)) { + const scaledWidth: number = scaledBoundingBoxDimensions.width; + const scaledHeight: number = scaledBoundingBoxDimensions.height; + + // Add Scaling Nodes + graph.nodes[INPAINT_IMAGE_RESIZE_UP] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + image: canvasInitImage, + }; + graph.nodes[MASK_RESIZE_UP] = { + type: 'img_resize', + id: MASK_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + image: canvasMaskImage, + }; + graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + graph.nodes[MASK_RESIZE_DOWN] = { + type: 'img_resize', + id: MASK_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: scaledWidth, + height: scaledHeight, + }; + + // Connect Nodes + graph.edges.push( + // Scale Inpaint Image and Mask + { + source: { + node_id: INPAINT_IMAGE_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: MASK_BLUR, + field: 'image', + }, + }, + // Color Correct The Inpainted Result + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Back Onto Original Image + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } else { + // Add Images To Nodes + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: width, + height: height, + }; + graph.nodes[INPAINT_IMAGE] = { + ...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation), + image: canvasInitImage, + }; + graph.nodes[MASK_BLUR] = { + ...(graph.nodes[MASK_BLUR] as ImageBlurInvocation), + image: canvasMaskImage, + }; + + graph.edges.push( + // Color Correct The Inpainted Result + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Back Onto Original Image + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } + + // Handle Seed + if (shouldRandomizeSeed) { + // Random int node to generate the starting seed + const randomIntNode: RandomIntInvocation = { + id: RANDOM_INT, + type: 'rand_int', + }; + + graph.nodes[RANDOM_INT] = randomIntNode; + + // Connect random int to the start of the range of size so the range starts on the random first seed + graph.edges.push({ + source: { node_id: RANDOM_INT, field: 'a' }, + destination: { node_id: RANGE_OF_SIZE, field: 'start' }, + }); + } else { + // User specified seed, so set the start of the range of size to the seed + (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed; + } + + // Add Refiner if enabled + if (shouldUseSDXLRefiner) { + addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS); + } + + // optionally add custom VAE + addVAEToGraph(state, graph, SDXL_MODEL_LOADER); + + // add LoRA support + addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts new file mode 100644 index 0000000000..50a773bf50 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLOutpaintGraph.ts @@ -0,0 +1,696 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { + ImageBlurInvocation, + ImageDTO, + ImageToLatentsInvocation, + InfillPatchmatchInvocation, + InfillTileInvocation, + NoiseInvocation, + RandomIntInvocation, + RangeOfSizeInvocation, +} from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph'; +import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + CANVAS_OUTPUT, + COLOR_CORRECT, + INPAINT_IMAGE, + INPAINT_IMAGE_RESIZE_DOWN, + INPAINT_IMAGE_RESIZE_UP, + INPAINT_INFILL, + INPAINT_INFILL_RESIZE_DOWN, + ITERATE, + LATENTS_TO_IMAGE, + MASK_BLUR, + MASK_COMBINE, + MASK_FROM_ALPHA, + MASK_RESIZE_DOWN, + MASK_RESIZE_UP, + NEGATIVE_CONDITIONING, + NOISE, + POSITIVE_CONDITIONING, + RANDOM_INT, + RANGE_OF_SIZE, + SDXL_CANVAS_OUTPAINT_GRAPH, + SDXL_DENOISE_LATENTS, + SDXL_MODEL_LOADER, +} from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; + +/** + * Builds the Canvas tab's Outpaint graph. + */ +export const buildCanvasSDXLOutpaintGraph = ( + state: RootState, + canvasInitImage: ImageDTO, + canvasMaskImage?: ImageDTO +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + iterations, + seed, + shouldRandomizeSeed, + vaePrecision, + shouldUseNoiseSettings, + shouldUseCpuNoise, + maskBlur, + maskBlurMethod, + tileSize, + infillMethod, + } = state.generation; + + const { + sdxlImg2ImgDenoisingStrength: strength, + shouldUseSDXLRefiner, + refinerStart, + shouldConcatSDXLStylePrompt, + } = state.sdxl; + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + // The bounding box determines width and height, not the width and height params + const { width, height } = state.canvas.boundingBoxDimensions; + + // We may need to set the inpaint width and height to scale the image + const { + scaledBoundingBoxDimensions, + boundingBoxScaleMethod, + shouldAutoSave, + } = state.canvas; + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : shouldUseCpuNoise; + + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt); + + const graph: NonNullableGraph = { + id: SDXL_CANVAS_OUTPAINT_GRAPH, + nodes: { + [SDXL_MODEL_LOADER]: { + type: 'sdxl_model_loader', + id: SDXL_MODEL_LOADER, + model, + }, + [POSITIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: POSITIVE_CONDITIONING, + prompt: positivePrompt, + style: craftedPositiveStylePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: 'sdxl_compel_prompt', + id: NEGATIVE_CONDITIONING, + prompt: negativePrompt, + style: craftedNegativeStylePrompt, + }, + [MASK_FROM_ALPHA]: { + type: 'tomask', + id: MASK_FROM_ALPHA, + is_intermediate: true, + image: canvasInitImage, + }, + [MASK_COMBINE]: { + type: 'mask_combine', + id: MASK_COMBINE, + is_intermediate: true, + mask2: canvasMaskImage, + }, + [MASK_BLUR]: { + type: 'img_blur', + id: MASK_BLUR, + is_intermediate: true, + radius: maskBlur, + blur_type: maskBlurMethod, + }, + [INPAINT_INFILL]: { + type: 'infill_tile', + id: INPAINT_INFILL, + is_intermediate: true, + tile_size: tileSize, + }, + [INPAINT_IMAGE]: { + type: 'i2l', + id: INPAINT_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + use_cpu, + is_intermediate: true, + }, + [SDXL_DENOISE_LATENTS]: { + type: 'denoise_latents', + id: SDXL_DENOISE_LATENTS, + is_intermediate: true, + steps: steps, + cfg_scale: cfg_scale, + scheduler: scheduler, + denoising_start: shouldUseSDXLRefiner + ? Math.min(refinerStart, 1 - strength) + : 1 - strength, + denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, + }, + [LATENTS_TO_IMAGE]: { + type: 'l2i', + id: LATENTS_TO_IMAGE, + is_intermediate: true, + fp32: vaePrecision === 'fp32' ? true : false, + }, + [COLOR_CORRECT]: { + type: 'color_correct', + id: COLOR_CORRECT, + is_intermediate: true, + }, + [CANVAS_OUTPUT]: { + type: 'img_paste', + id: CANVAS_OUTPUT, + is_intermediate: !shouldAutoSave, + }, + [RANGE_OF_SIZE]: { + type: 'range_of_size', + id: RANGE_OF_SIZE, + is_intermediate: true, + // seed - must be connected manually + // start: 0, + size: iterations, + step: 1, + }, + [ITERATE]: { + type: 'iterate', + id: ITERATE, + is_intermediate: true, + }, + }, + edges: [ + // Connect Model Loader To UNet and CLIP + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'unet', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: SDXL_MODEL_LOADER, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + // Connect Infill Result To Inpaint Image + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE, + field: 'image', + }, + }, + // Combine Mask from Init Image with User Painted Mask + { + source: { + node_id: MASK_FROM_ALPHA, + field: 'mask', + }, + destination: { + node_id: MASK_COMBINE, + field: 'mask1', + }, + }, + // Connect Everything To Inpaint + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: INPAINT_IMAGE, + field: 'latents', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'mask', + }, + }, + // Iterate + { + source: { + node_id: RANGE_OF_SIZE, + field: 'collection', + }, + destination: { + node_id: ITERATE, + field: 'collection', + }, + }, + { + source: { + node_id: ITERATE, + field: 'item', + }, + destination: { + node_id: NOISE, + field: 'seed', + }, + }, + // Decode inpainted latents to image + { + source: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, + ], + }; + + // Add Infill Nodes + + if (infillMethod === 'patchmatch') { + graph.nodes[INPAINT_INFILL] = { + type: 'infill_patchmatch', + id: INPAINT_INFILL, + is_intermediate: true, + }; + } + + // Handle Scale Before Processing + if (['auto', 'manual'].includes(boundingBoxScaleMethod)) { + const scaledWidth: number = scaledBoundingBoxDimensions.width; + const scaledHeight: number = scaledBoundingBoxDimensions.height; + + // Add Scaling Nodes + graph.nodes[INPAINT_IMAGE_RESIZE_UP] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + image: canvasInitImage, + }; + graph.nodes[MASK_RESIZE_UP] = { + type: 'img_resize', + id: MASK_RESIZE_UP, + is_intermediate: true, + width: scaledWidth, + height: scaledHeight, + }; + graph.nodes[INPAINT_IMAGE_RESIZE_DOWN] = { + type: 'img_resize', + id: INPAINT_IMAGE_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + graph.nodes[INPAINT_INFILL_RESIZE_DOWN] = { + type: 'img_resize', + id: INPAINT_INFILL_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + graph.nodes[MASK_RESIZE_DOWN] = { + type: 'img_resize', + id: MASK_RESIZE_DOWN, + is_intermediate: true, + width: width, + height: height, + }; + + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: scaledWidth, + height: scaledHeight, + }; + + // Connect Nodes + graph.edges.push( + // Scale Inpaint Image + { + source: { + node_id: INPAINT_IMAGE_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: INPAINT_INFILL, + field: 'image', + }, + }, + // Take combined mask and resize and then blur + { + source: { + node_id: MASK_COMBINE, + field: 'image', + }, + destination: { + node_id: MASK_RESIZE_UP, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_UP, + field: 'image', + }, + destination: { + node_id: MASK_BLUR, + field: 'image', + }, + }, + // Resize Results Down + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + }, + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: INPAINT_INFILL_RESIZE_DOWN, + field: 'image', + }, + }, + // Color Correct The Inpainted Result + { + source: { + node_id: INPAINT_INFILL_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'reference', + }, + }, + { + source: { + node_id: INPAINT_IMAGE_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Everything Back + { + source: { + node_id: INPAINT_INFILL_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'base_image', + }, + }, + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_RESIZE_DOWN, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } else { + // Add Images To Nodes + graph.nodes[INPAINT_INFILL] = { + ...(graph.nodes[INPAINT_INFILL] as + | InfillTileInvocation + | InfillPatchmatchInvocation), + image: canvasInitImage, + }; + graph.nodes[NOISE] = { + ...(graph.nodes[NOISE] as NoiseInvocation), + width: width, + height: height, + }; + graph.nodes[INPAINT_IMAGE] = { + ...(graph.nodes[INPAINT_IMAGE] as ImageToLatentsInvocation), + image: canvasInitImage, + }; + graph.nodes[MASK_BLUR] = { + ...(graph.nodes[MASK_BLUR] as ImageBlurInvocation), + image: canvasMaskImage, + }; + + graph.edges.push( + // Take combined mask and plug it to blur + { + source: { + node_id: MASK_COMBINE, + field: 'image', + }, + destination: { + node_id: MASK_BLUR, + field: 'image', + }, + }, + // Color Correct The Inpainted Result + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'reference', + }, + }, + { + source: { + node_id: LATENTS_TO_IMAGE, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: COLOR_CORRECT, + field: 'mask', + }, + }, + // Paste Everything Back + { + source: { + node_id: INPAINT_INFILL, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'base_image', + }, + }, + { + source: { + node_id: COLOR_CORRECT, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'image', + }, + }, + { + source: { + node_id: MASK_BLUR, + field: 'image', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'mask', + }, + } + ); + } + + // Handle seed + if (shouldRandomizeSeed) { + // Random int node to generate the starting seed + const randomIntNode: RandomIntInvocation = { + id: RANDOM_INT, + type: 'rand_int', + }; + + graph.nodes[RANDOM_INT] = randomIntNode; + + // Connect random int to the start of the range of size so the range starts on the random first seed + graph.edges.push({ + source: { node_id: RANDOM_INT, field: 'a' }, + destination: { node_id: RANGE_OF_SIZE, field: 'start' }, + }); + } else { + // User specified seed, so set the start of the range of size to the seed + (graph.nodes[RANGE_OF_SIZE] as RangeOfSizeInvocation).start = seed; + } + + // Add Refiner if enabled + if (shouldUseSDXLRefiner) { + addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS); + } + + // optionally add custom VAE + addVAEToGraph(state, graph, SDXL_MODEL_LOADER); + + // add LoRA support + addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts new file mode 100644 index 0000000000..e79e08ba41 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts @@ -0,0 +1,309 @@ +import { logger } from 'app/logging/logger'; +import { RootState } from 'app/store/store'; +import { NonNullableGraph } from 'features/nodes/types/types'; +import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { + DenoiseLatentsInvocation, + ONNXTextToLatentsInvocation, +} from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; +import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; +import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph'; +import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; +import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { + CANVAS_OUTPUT, + METADATA_ACCUMULATOR, + NEGATIVE_CONDITIONING, + NOISE, + ONNX_MODEL_LOADER, + POSITIVE_CONDITIONING, + SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH, + SDXL_DENOISE_LATENTS, + SDXL_MODEL_LOADER, +} from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; + +/** + * Builds the Canvas tab's Text to Image graph. + */ +export const buildCanvasSDXLTextToImageGraph = ( + state: RootState +): NonNullableGraph => { + const log = logger('nodes'); + const { + positivePrompt, + negativePrompt, + model, + cfgScale: cfg_scale, + scheduler, + steps, + vaePrecision, + clipSkip, + shouldUseCpuNoise, + shouldUseNoiseSettings, + } = state.generation; + + // The bounding box determines width and height, not the width and height params + const { width, height } = state.canvas.boundingBoxDimensions; + + const { shouldAutoSave } = state.canvas; + + const { shouldUseSDXLRefiner, refinerStart, shouldConcatSDXLStylePrompt } = + state.sdxl; + + if (!model) { + log.error('No model found in state'); + throw new Error('No model found in state'); + } + + const use_cpu = shouldUseNoiseSettings + ? shouldUseCpuNoise + : initialGenerationState.shouldUseCpuNoise; + + const isUsingOnnxModel = model.model_type === 'onnx'; + + const modelLoaderNodeId = isUsingOnnxModel + ? ONNX_MODEL_LOADER + : SDXL_MODEL_LOADER; + + const modelLoaderNodeType = isUsingOnnxModel + ? 'onnx_model_loader' + : 'sdxl_model_loader'; + + const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation = + isUsingOnnxModel + ? { + type: 't2l_onnx', + id: SDXL_DENOISE_LATENTS, + is_intermediate: true, + cfg_scale, + scheduler, + steps, + } + : { + type: 'denoise_latents', + id: SDXL_DENOISE_LATENTS, + is_intermediate: true, + cfg_scale, + scheduler, + steps, + denoising_start: 0, + denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, + }; + + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt); + + /** + * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the + * full graph here as a template. Then use the parameters from app state and set friendlier node + * ids. + * + * The only thing we need extra logic for is handling randomized seed, control net, and for img2img, + * the `fit` param. These are added to the graph at the end. + */ + + // copy-pasted graph from node editor, filled in with state values & friendly node ids + // TODO: Actually create the graph correctly for ONNX + const graph: NonNullableGraph = { + id: SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH, + nodes: { + [modelLoaderNodeId]: { + type: modelLoaderNodeType, + id: modelLoaderNodeId, + is_intermediate: true, + model, + }, + [POSITIVE_CONDITIONING]: { + type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt', + id: POSITIVE_CONDITIONING, + is_intermediate: true, + prompt: positivePrompt, + style: craftedPositiveStylePrompt, + }, + [NEGATIVE_CONDITIONING]: { + type: isUsingOnnxModel ? 'prompt_onnx' : 'sdxl_compel_prompt', + id: NEGATIVE_CONDITIONING, + is_intermediate: true, + prompt: negativePrompt, + style: craftedNegativeStylePrompt, + }, + [NOISE]: { + type: 'noise', + id: NOISE, + is_intermediate: true, + width, + height, + use_cpu, + }, + [t2lNode.id]: t2lNode, + [CANVAS_OUTPUT]: { + type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i', + id: CANVAS_OUTPUT, + is_intermediate: !shouldAutoSave, + fp32: vaePrecision === 'fp32' ? true : false, + }, + }, + edges: [ + // Connect Model Loader to UNet and CLIP + { + source: { + node_id: modelLoaderNodeId, + field: 'unet', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'unet', + }, + }, + { + source: { + node_id: modelLoaderNodeId, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: modelLoaderNodeId, + field: 'clip2', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip2', + }, + }, + { + source: { + node_id: modelLoaderNodeId, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: modelLoaderNodeId, + field: 'clip2', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip2', + }, + }, + // Connect everything to Denoise Latents + { + source: { + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'positive_conditioning', + }, + }, + { + source: { + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'negative_conditioning', + }, + }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'noise', + }, + }, + // Decode Denoised Latents To Image + { + source: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'latents', + }, + }, + ], + }; + + // add metadata accumulator, which is only mostly populated - some fields are added later + graph.nodes[METADATA_ACCUMULATOR] = { + id: METADATA_ACCUMULATOR, + type: 'metadata_accumulator', + generation_mode: 'txt2img', + cfg_scale, + height, + width, + positive_prompt: '', // set in addDynamicPromptsToGraph + negative_prompt: negativePrompt, + model, + seed: 0, // set in addDynamicPromptsToGraph + steps, + rand_device: use_cpu ? 'cpu' : 'cuda', + scheduler, + vae: undefined, // option; set in addVAEToGraph + controlnets: [], // populated in addControlNetToLinearGraph + loras: [], // populated in addLoRAsToGraph + clip_skip: clipSkip, + }; + + graph.edges.push({ + source: { + node_id: METADATA_ACCUMULATOR, + field: 'metadata', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'metadata', + }, + }); + + // Add Refiner if enabled + if (shouldUseSDXLRefiner) { + addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS); + } + + // add LoRA support + addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId); + + // optionally add custom VAE + addVAEToGraph(state, graph, modelLoaderNodeId); + + // add dynamic prompts - also sets up core iteration and seed + addDynamicPromptsToGraph(state, graph); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS); + + // NSFW & watermark - must be last thing added to graph + if (state.system.shouldUseNSFWChecker) { + // must add before watermarker! + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); + } + + if (state.system.shouldUseWatermarker) { + // must add after nsfw checker! + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); + } + + return graph; +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts index 5b636b482a..4548a7e099 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts @@ -2,6 +2,10 @@ import { logger } from 'app/logging/logger'; import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { + DenoiseLatentsInvocation, + ONNXTextToLatentsInvocation, +} from 'services/api/types'; import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; @@ -9,21 +13,17 @@ import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { + CANVAS_OUTPUT, + CANVAS_TEXT_TO_IMAGE_GRAPH, CLIP_SKIP, - LATENTS_TO_IMAGE, + DENOISE_LATENTS, MAIN_MODEL_LOADER, - ONNX_MODEL_LOADER, METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, + ONNX_MODEL_LOADER, POSITIVE_CONDITIONING, - TEXT_TO_IMAGE_GRAPH, - TEXT_TO_LATENTS, } from './constants'; -import { - ONNXTextToLatentsInvocation, - TextToLatentsInvocation, -} from 'services/api/types'; /** * Builds the Canvas tab's Text to Image graph. @@ -57,31 +57,38 @@ export const buildCanvasTextToImageGraph = ( const use_cpu = shouldUseNoiseSettings ? shouldUseCpuNoise : initialGenerationState.shouldUseCpuNoise; + const isUsingOnnxModel = model.model_type === 'onnx'; + const modelLoaderNodeId = isUsingOnnxModel ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER; + const modelLoaderNodeType = isUsingOnnxModel ? 'onnx_model_loader' : 'main_model_loader'; - const t2lNode: TextToLatentsInvocation | ONNXTextToLatentsInvocation = + + const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation = isUsingOnnxModel ? { type: 't2l_onnx', - id: TEXT_TO_LATENTS, + id: DENOISE_LATENTS, is_intermediate: true, cfg_scale, scheduler, steps, } : { - type: 't2l', - id: TEXT_TO_LATENTS, + type: 'denoise_latents', + id: DENOISE_LATENTS, is_intermediate: true, cfg_scale, scheduler, steps, + denoising_start: 0, + denoising_end: 1, }; + /** * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the * full graph here as a template. Then use the parameters from app state and set friendlier node @@ -94,8 +101,20 @@ export const buildCanvasTextToImageGraph = ( // copy-pasted graph from node editor, filled in with state values & friendly node ids // TODO: Actually create the graph correctly for ONNX const graph: NonNullableGraph = { - id: TEXT_TO_IMAGE_GRAPH, + id: CANVAS_TEXT_TO_IMAGE_GRAPH, nodes: { + [modelLoaderNodeId]: { + type: modelLoaderNodeType, + id: modelLoaderNodeId, + is_intermediate: true, + model, + }, + [CLIP_SKIP]: { + type: 'clip_skip', + id: CLIP_SKIP, + is_intermediate: true, + skipped_layers: clipSkip, + }, [POSITIVE_CONDITIONING]: { type: isUsingOnnxModel ? 'prompt_onnx' : 'compel', id: POSITIVE_CONDITIONING, @@ -117,93 +136,74 @@ export const buildCanvasTextToImageGraph = ( use_cpu, }, [t2lNode.id]: t2lNode, - [modelLoaderNodeId]: { - type: modelLoaderNodeType, - id: modelLoaderNodeId, - is_intermediate: true, - model, - }, - [CLIP_SKIP]: { - type: 'clip_skip', - id: CLIP_SKIP, - is_intermediate: true, - skipped_layers: clipSkip, - }, - [LATENTS_TO_IMAGE]: { + [CANVAS_OUTPUT]: { type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i', - id: LATENTS_TO_IMAGE, + id: CANVAS_OUTPUT, is_intermediate: !shouldAutoSave, }, }, edges: [ + // Connect Model Loader to UNet & CLIP Skip { source: { - node_id: NEGATIVE_CONDITIONING, - field: 'conditioning', + node_id: modelLoaderNodeId, + field: 'unet', }, destination: { - node_id: TEXT_TO_LATENTS, - field: 'negative_conditioning', + node_id: DENOISE_LATENTS, + field: 'unet', }, }, + { + source: { + node_id: modelLoaderNodeId, + field: 'clip', + }, + destination: { + node_id: CLIP_SKIP, + field: 'clip', + }, + }, + // Connect CLIP Skip to Conditioning + { + source: { + node_id: CLIP_SKIP, + field: 'clip', + }, + destination: { + node_id: POSITIVE_CONDITIONING, + field: 'clip', + }, + }, + { + source: { + node_id: CLIP_SKIP, + field: 'clip', + }, + destination: { + node_id: NEGATIVE_CONDITIONING, + field: 'clip', + }, + }, + // Connect everything to Denoise Latents { source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning', }, destination: { - node_id: TEXT_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'positive_conditioning', }, }, { source: { - node_id: modelLoaderNodeId, - field: 'clip', - }, - destination: { - node_id: CLIP_SKIP, - field: 'clip', - }, - }, - { - source: { - node_id: CLIP_SKIP, - field: 'clip', - }, - destination: { - node_id: POSITIVE_CONDITIONING, - field: 'clip', - }, - }, - { - source: { - node_id: CLIP_SKIP, - field: 'clip', - }, - destination: { node_id: NEGATIVE_CONDITIONING, - field: 'clip', - }, - }, - { - source: { - node_id: modelLoaderNodeId, - field: 'unet', + field: 'conditioning', }, destination: { - node_id: TEXT_TO_LATENTS, - field: 'unet', - }, - }, - { - source: { - node_id: TEXT_TO_LATENTS, - field: 'latents', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'latents', + node_id: DENOISE_LATENTS, + field: 'negative_conditioning', }, }, { @@ -212,10 +212,21 @@ export const buildCanvasTextToImageGraph = ( field: 'noise', }, destination: { - node_id: TEXT_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'noise', }, }, + // Decode denoised latents to image + { + source: { + node_id: DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: CANVAS_OUTPUT, + field: 'latents', + }, + }, ], }; @@ -246,32 +257,32 @@ export const buildCanvasTextToImageGraph = ( field: 'metadata', }, destination: { - node_id: LATENTS_TO_IMAGE, + node_id: CANVAS_OUTPUT, field: 'metadata', }, }); - // add LoRA support - addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, modelLoaderNodeId); - // optionally add custom VAE addVAEToGraph(state, graph, modelLoaderNodeId); + // add LoRA support + addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId); + // add dynamic prompts - also sets up core iteration and seed addDynamicPromptsToGraph(state, graph); // add controlnet, mutating `graph` - addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS); + addControlNetToLinearGraph(state, graph, DENOISE_LATENTS); // NSFW & watermark - must be last thing added to graph if (state.system.shouldUseNSFWChecker) { // must add before watermarker! - addNSFWCheckerToGraph(state, graph); + addNSFWCheckerToGraph(state, graph, CANVAS_OUTPUT); } if (state.system.shouldUseWatermarker) { // must add after nsfw checker! - addWatermarkerToGraph(state, graph); + addWatermarkerToGraph(state, graph, CANVAS_OUTPUT); } return graph; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts index f264edc6be..982a09357f 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts @@ -14,10 +14,10 @@ import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CLIP_SKIP, + DENOISE_LATENTS, IMAGE_TO_IMAGE_GRAPH, IMAGE_TO_LATENTS, LATENTS_TO_IMAGE, - LATENTS_TO_LATENTS, MAIN_MODEL_LOADER, METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, @@ -118,13 +118,14 @@ export const buildLinearImageToImageGraph = ( id: LATENTS_TO_IMAGE, fp32: vaePrecision === 'fp32' ? true : false, }, - [LATENTS_TO_LATENTS]: { - type: 'l2l', - id: LATENTS_TO_LATENTS, + [DENOISE_LATENTS]: { + type: 'denoise_latents', + id: DENOISE_LATENTS, cfg_scale, scheduler, steps, - strength, + denoising_start: 1 - strength, + denoising_end: 1, }, [IMAGE_TO_LATENTS]: { type: 'i2l', @@ -137,13 +138,14 @@ export const buildLinearImageToImageGraph = ( }, }, edges: [ + // Connect Model Loader to UNet and CLIP Skip { source: { node_id: MAIN_MODEL_LOADER, field: 'unet', }, destination: { - node_id: LATENTS_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'unet', }, }, @@ -157,6 +159,7 @@ export const buildLinearImageToImageGraph = ( field: 'clip', }, }, + // Connect CLIP Skip to Conditioning { source: { node_id: CLIP_SKIP, @@ -177,24 +180,25 @@ export const buildLinearImageToImageGraph = ( field: 'clip', }, }, + // Connect everything to Denoise Latents { source: { - node_id: LATENTS_TO_LATENTS, - field: 'latents', + node_id: POSITIVE_CONDITIONING, + field: 'conditioning', }, destination: { - node_id: LATENTS_TO_IMAGE, - field: 'latents', + node_id: DENOISE_LATENTS, + field: 'positive_conditioning', }, }, { source: { - node_id: IMAGE_TO_LATENTS, - field: 'latents', + node_id: NEGATIVE_CONDITIONING, + field: 'conditioning', }, destination: { - node_id: LATENTS_TO_LATENTS, - field: 'latents', + node_id: DENOISE_LATENTS, + field: 'negative_conditioning', }, }, { @@ -203,28 +207,29 @@ export const buildLinearImageToImageGraph = ( field: 'noise', }, destination: { - node_id: LATENTS_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'noise', }, }, { source: { - node_id: NEGATIVE_CONDITIONING, - field: 'conditioning', + node_id: IMAGE_TO_LATENTS, + field: 'latents', }, destination: { - node_id: LATENTS_TO_LATENTS, - field: 'negative_conditioning', + node_id: DENOISE_LATENTS, + field: 'latents', }, }, + // Decode denoised latents to image { source: { - node_id: POSITIVE_CONDITIONING, - field: 'conditioning', + node_id: DENOISE_LATENTS, + field: 'latents', }, destination: { - node_id: LATENTS_TO_LATENTS, - field: 'positive_conditioning', + node_id: LATENTS_TO_IMAGE, + field: 'latents', }, }, ], @@ -333,17 +338,17 @@ export const buildLinearImageToImageGraph = ( }, }); - // add LoRA support - addLoRAsToGraph(state, graph, LATENTS_TO_LATENTS); - // optionally add custom VAE - addVAEToGraph(state, graph); + addVAEToGraph(state, graph, MAIN_MODEL_LOADER); + + // add LoRA support + addLoRAsToGraph(state, graph, DENOISE_LATENTS); // add dynamic prompts - also sets up core iteration and seed addDynamicPromptsToGraph(state, graph); // add controlnet, mutating `graph` - addControlNetToLinearGraph(state, graph, LATENTS_TO_LATENTS); + addControlNetToLinearGraph(state, graph, DENOISE_LATENTS); // NSFW & watermark - must be last thing added to graph if (state.system.shouldUseNSFWChecker) { diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts index a260dbc467..42ea07c923 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts @@ -6,9 +6,12 @@ import { ImageResizeInvocation, ImageToLatentsInvocation, } from 'services/api/types'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph'; import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { IMAGE_TO_LATENTS, @@ -18,10 +21,11 @@ import { NOISE, POSITIVE_CONDITIONING, RESIZE, + SDXL_DENOISE_LATENTS, SDXL_IMAGE_TO_IMAGE_GRAPH, - SDXL_LATENTS_TO_LATENTS, SDXL_MODEL_LOADER, } from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; /** * Builds the Image to Image tab graph. @@ -79,6 +83,10 @@ export const buildLinearSDXLImageToImageGraph = ( ? shouldUseCpuNoise : initialGenerationState.shouldUseCpuNoise; + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt); + // copy-pasted graph from node editor, filled in with state values & friendly node ids const graph: NonNullableGraph = { id: SDXL_IMAGE_TO_IMAGE_GRAPH, @@ -92,17 +100,13 @@ export const buildLinearSDXLImageToImageGraph = ( type: 'sdxl_compel_prompt', id: POSITIVE_CONDITIONING, prompt: positivePrompt, - style: shouldConcatSDXLStylePrompt - ? `${positivePrompt} ${positiveStylePrompt}` - : positiveStylePrompt, + style: craftedPositiveStylePrompt, }, [NEGATIVE_CONDITIONING]: { type: 'sdxl_compel_prompt', id: NEGATIVE_CONDITIONING, prompt: negativePrompt, - style: shouldConcatSDXLStylePrompt - ? `${negativePrompt} ${negativeStylePrompt}` - : negativeStylePrompt, + style: craftedNegativeStylePrompt, }, [NOISE]: { type: 'noise', @@ -114,9 +118,9 @@ export const buildLinearSDXLImageToImageGraph = ( id: LATENTS_TO_IMAGE, fp32: vaePrecision === 'fp32' ? true : false, }, - [SDXL_LATENTS_TO_LATENTS]: { - type: 'l2l_sdxl', - id: SDXL_LATENTS_TO_LATENTS, + [SDXL_DENOISE_LATENTS]: { + type: 'denoise_latents', + id: SDXL_DENOISE_LATENTS, cfg_scale, scheduler, steps, @@ -136,36 +140,17 @@ export const buildLinearSDXLImageToImageGraph = ( }, }, edges: [ + // Connect Model Loader to UNet, CLIP & VAE { source: { node_id: SDXL_MODEL_LOADER, field: 'unet', }, destination: { - node_id: SDXL_LATENTS_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'unet', }, }, - { - source: { - node_id: SDXL_MODEL_LOADER, - field: 'vae', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'vae', - }, - }, - { - source: { - node_id: SDXL_MODEL_LOADER, - field: 'vae', - }, - destination: { - node_id: IMAGE_TO_LATENTS, - field: 'vae', - }, - }, { source: { node_id: SDXL_MODEL_LOADER, @@ -206,43 +191,14 @@ export const buildLinearSDXLImageToImageGraph = ( field: 'clip2', }, }, - { - source: { - node_id: SDXL_LATENTS_TO_LATENTS, - field: 'latents', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'latents', - }, - }, - { - source: { - node_id: IMAGE_TO_LATENTS, - field: 'latents', - }, - destination: { - node_id: SDXL_LATENTS_TO_LATENTS, - field: 'latents', - }, - }, - { - source: { - node_id: NOISE, - field: 'noise', - }, - destination: { - node_id: SDXL_LATENTS_TO_LATENTS, - field: 'noise', - }, - }, + // Connect everything to Denoise Latents { source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning', }, destination: { - node_id: SDXL_LATENTS_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'positive_conditioning', }, }, @@ -252,10 +208,41 @@ export const buildLinearSDXLImageToImageGraph = ( field: 'conditioning', }, destination: { - node_id: SDXL_LATENTS_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'negative_conditioning', }, }, + { + source: { + node_id: NOISE, + field: 'noise', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'noise', + }, + }, + { + source: { + node_id: IMAGE_TO_LATENTS, + field: 'latents', + }, + destination: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + }, + // Decode Denoised Latents To Image + { + source: { + node_id: SDXL_DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, ], }; @@ -364,11 +351,19 @@ export const buildLinearSDXLImageToImageGraph = ( }, }); + addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER); + // Add Refiner if enabled if (shouldUseSDXLRefiner) { - addSDXLRefinerToGraph(state, graph, SDXL_LATENTS_TO_LATENTS); + addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS); } + // optionally add custom VAE + addVAEToGraph(state, graph, SDXL_MODEL_LOADER); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS); + // add dynamic prompts - also sets up core iteration and seed addDynamicPromptsToGraph(state, graph); diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts index c10e7831d3..a74884f23b 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts @@ -2,9 +2,12 @@ import { logger } from 'app/logging/logger'; import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph'; +import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph'; import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph'; +import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { LATENTS_TO_IMAGE, @@ -12,10 +15,11 @@ import { NEGATIVE_CONDITIONING, NOISE, POSITIVE_CONDITIONING, + SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER, SDXL_TEXT_TO_IMAGE_GRAPH, - SDXL_TEXT_TO_LATENTS, } from './constants'; +import { craftSDXLStylePrompt } from './helpers/craftSDXLStylePrompt'; export const buildLinearSDXLTextToImageGraph = ( state: RootState @@ -39,8 +43,8 @@ export const buildLinearSDXLTextToImageGraph = ( const { positiveStylePrompt, negativeStylePrompt, - shouldConcatSDXLStylePrompt, shouldUseSDXLRefiner, + shouldConcatSDXLStylePrompt, refinerStart, } = state.sdxl; @@ -53,6 +57,10 @@ export const buildLinearSDXLTextToImageGraph = ( throw new Error('No model found in state'); } + // Construct Style Prompt + const { craftedPositiveStylePrompt, craftedNegativeStylePrompt } = + craftSDXLStylePrompt(state, shouldConcatSDXLStylePrompt); + /** * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the * full graph here as a template. Then use the parameters from app state and set friendlier node @@ -75,17 +83,13 @@ export const buildLinearSDXLTextToImageGraph = ( type: 'sdxl_compel_prompt', id: POSITIVE_CONDITIONING, prompt: positivePrompt, - style: shouldConcatSDXLStylePrompt - ? `${positivePrompt} ${positiveStylePrompt}` - : positiveStylePrompt, + style: craftedPositiveStylePrompt, }, [NEGATIVE_CONDITIONING]: { type: 'sdxl_compel_prompt', id: NEGATIVE_CONDITIONING, prompt: negativePrompt, - style: shouldConcatSDXLStylePrompt - ? `${negativePrompt} ${negativeStylePrompt}` - : negativeStylePrompt, + style: craftedNegativeStylePrompt, }, [NOISE]: { type: 'noise', @@ -94,12 +98,13 @@ export const buildLinearSDXLTextToImageGraph = ( height, use_cpu, }, - [SDXL_TEXT_TO_LATENTS]: { - type: 't2l_sdxl', - id: SDXL_TEXT_TO_LATENTS, + [SDXL_DENOISE_LATENTS]: { + type: 'denoise_latents', + id: SDXL_DENOISE_LATENTS, cfg_scale, scheduler, steps, + denoising_start: 0, denoising_end: shouldUseSDXLRefiner ? refinerStart : 1, }, [LATENTS_TO_IMAGE]: { @@ -109,26 +114,17 @@ export const buildLinearSDXLTextToImageGraph = ( }, }, edges: [ + // Connect Model Loader to UNet, VAE & CLIP { source: { node_id: SDXL_MODEL_LOADER, field: 'unet', }, destination: { - node_id: SDXL_TEXT_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'unet', }, }, - { - source: { - node_id: SDXL_MODEL_LOADER, - field: 'vae', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'vae', - }, - }, { source: { node_id: SDXL_MODEL_LOADER, @@ -169,13 +165,14 @@ export const buildLinearSDXLTextToImageGraph = ( field: 'clip2', }, }, + // Connect everything to Denoise Latents { source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning', }, destination: { - node_id: SDXL_TEXT_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'positive_conditioning', }, }, @@ -185,7 +182,7 @@ export const buildLinearSDXLTextToImageGraph = ( field: 'conditioning', }, destination: { - node_id: SDXL_TEXT_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'negative_conditioning', }, }, @@ -195,13 +192,14 @@ export const buildLinearSDXLTextToImageGraph = ( field: 'noise', }, destination: { - node_id: SDXL_TEXT_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'noise', }, }, + // Decode Denoised Latents To Image { source: { - node_id: SDXL_TEXT_TO_LATENTS, + node_id: SDXL_DENOISE_LATENTS, field: 'latents', }, destination: { @@ -248,9 +246,18 @@ export const buildLinearSDXLTextToImageGraph = ( // Add Refiner if enabled if (shouldUseSDXLRefiner) { - addSDXLRefinerToGraph(state, graph, SDXL_TEXT_TO_LATENTS); + addSDXLRefinerToGraph(state, graph, SDXL_DENOISE_LATENTS); } + // optionally add custom VAE + addVAEToGraph(state, graph, SDXL_MODEL_LOADER); + + // add LoRA support + addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, SDXL_MODEL_LOADER); + + // add controlnet, mutating `graph` + addControlNetToLinearGraph(state, graph, SDXL_DENOISE_LATENTS); + // add dynamic prompts - also sets up core iteration and seed addDynamicPromptsToGraph(state, graph); diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts index 9dcc502d14..99a1ec7420 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts @@ -2,6 +2,10 @@ import { logger } from 'app/logging/logger'; import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { initialGenerationState } from 'features/parameters/store/generationSlice'; +import { + DenoiseLatentsInvocation, + ONNXTextToLatentsInvocation, +} from 'services/api/types'; import { addControlNetToLinearGraph } from './addControlNetToLinearGraph'; import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph'; import { addLoRAsToGraph } from './addLoRAsToGraph'; @@ -10,20 +14,16 @@ import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CLIP_SKIP, + DENOISE_LATENTS, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, - ONNX_MODEL_LOADER, METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, + ONNX_MODEL_LOADER, POSITIVE_CONDITIONING, TEXT_TO_IMAGE_GRAPH, - TEXT_TO_LATENTS, } from './constants'; -import { - ONNXTextToLatentsInvocation, - TextToLatentsInvocation, -} from 'services/api/types'; export const buildLinearTextToImageGraph = ( state: RootState @@ -54,30 +54,36 @@ export const buildLinearTextToImageGraph = ( } const isUsingOnnxModel = model.model_type === 'onnx'; + const modelLoaderNodeId = isUsingOnnxModel ? ONNX_MODEL_LOADER : MAIN_MODEL_LOADER; + const modelLoaderNodeType = isUsingOnnxModel ? 'onnx_model_loader' : 'main_model_loader'; - const t2lNode: TextToLatentsInvocation | ONNXTextToLatentsInvocation = + + const t2lNode: DenoiseLatentsInvocation | ONNXTextToLatentsInvocation = isUsingOnnxModel ? { type: 't2l_onnx', - id: TEXT_TO_LATENTS, + id: DENOISE_LATENTS, is_intermediate: true, cfg_scale, scheduler, steps, } : { - type: 't2l', - id: TEXT_TO_LATENTS, + type: 'denoise_latents', + id: DENOISE_LATENTS, is_intermediate: true, cfg_scale, scheduler, steps, + denoising_start: 0, + denoising_end: 1, }; + /** * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the * full graph here as a template. Then use the parameters from app state and set friendlier node @@ -93,6 +99,18 @@ export const buildLinearTextToImageGraph = ( const graph: NonNullableGraph = { id: TEXT_TO_IMAGE_GRAPH, nodes: { + [modelLoaderNodeId]: { + type: modelLoaderNodeType, + id: modelLoaderNodeId, + is_intermediate: true, + model, + }, + [CLIP_SKIP]: { + type: 'clip_skip', + id: CLIP_SKIP, + skipped_layers: clipSkip, + is_intermediate: true, + }, [POSITIVE_CONDITIONING]: { type: isUsingOnnxModel ? 'prompt_onnx' : 'compel', id: POSITIVE_CONDITIONING, @@ -114,18 +132,6 @@ export const buildLinearTextToImageGraph = ( is_intermediate: true, }, [t2lNode.id]: t2lNode, - [modelLoaderNodeId]: { - type: modelLoaderNodeType, - id: modelLoaderNodeId, - is_intermediate: true, - model, - }, - [CLIP_SKIP]: { - type: 'clip_skip', - id: CLIP_SKIP, - skipped_layers: clipSkip, - is_intermediate: true, - }, [LATENTS_TO_IMAGE]: { type: isUsingOnnxModel ? 'l2i_onnx' : 'l2i', id: LATENTS_TO_IMAGE, @@ -133,6 +139,17 @@ export const buildLinearTextToImageGraph = ( }, }, edges: [ + // Connect Model Loader to UNet and CLIP Skip + { + source: { + node_id: modelLoaderNodeId, + field: 'unet', + }, + destination: { + node_id: DENOISE_LATENTS, + field: 'unet', + }, + }, { source: { node_id: modelLoaderNodeId, @@ -143,16 +160,7 @@ export const buildLinearTextToImageGraph = ( field: 'clip', }, }, - { - source: { - node_id: modelLoaderNodeId, - field: 'unet', - }, - destination: { - node_id: TEXT_TO_LATENTS, - field: 'unet', - }, - }, + // Connect CLIP Skip to Conditioning { source: { node_id: CLIP_SKIP, @@ -173,13 +181,14 @@ export const buildLinearTextToImageGraph = ( field: 'clip', }, }, + // Connect everything to Denoise Latents { source: { node_id: POSITIVE_CONDITIONING, field: 'conditioning', }, destination: { - node_id: TEXT_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'positive_conditioning', }, }, @@ -189,30 +198,31 @@ export const buildLinearTextToImageGraph = ( field: 'conditioning', }, destination: { - node_id: TEXT_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'negative_conditioning', }, }, - { - source: { - node_id: TEXT_TO_LATENTS, - field: 'latents', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'latents', - }, - }, { source: { node_id: NOISE, field: 'noise', }, destination: { - node_id: TEXT_TO_LATENTS, + node_id: DENOISE_LATENTS, field: 'noise', }, }, + // Decode Denoised Latents To Image + { + source: { + node_id: DENOISE_LATENTS, + field: 'latents', + }, + destination: { + node_id: LATENTS_TO_IMAGE, + field: 'latents', + }, + }, ], }; @@ -248,17 +258,17 @@ export const buildLinearTextToImageGraph = ( }, }); - // add LoRA support - addLoRAsToGraph(state, graph, TEXT_TO_LATENTS, modelLoaderNodeId); - // optionally add custom VAE addVAEToGraph(state, graph, modelLoaderNodeId); + // add LoRA support + addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId); + // add dynamic prompts - also sets up core iteration and seed addDynamicPromptsToGraph(state, graph); // add controlnet, mutating `graph` - addControlNetToLinearGraph(state, graph, TEXT_TO_LATENTS); + addControlNetToLinearGraph(state, graph, DENOISE_LATENTS); // NSFW & watermark - must be last thing added to graph if (state.system.shouldUseNSFWChecker) { diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts index 7fa87c7f20..3e213120b3 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts @@ -1,7 +1,7 @@ // friendly node ids export const POSITIVE_CONDITIONING = 'positive_conditioning'; export const NEGATIVE_CONDITIONING = 'negative_conditioning'; -export const TEXT_TO_LATENTS = 'text_to_latents'; +export const DENOISE_LATENTS = 'denoise_latents'; export const LATENTS_TO_IMAGE = 'latents_to_image'; export const NSFW_CHECKER = 'nsfw_checker'; export const WATERMARKER = 'invisible_watermark'; @@ -17,7 +17,24 @@ export const CLIP_SKIP = 'clip_skip'; export const IMAGE_TO_LATENTS = 'image_to_latents'; export const LATENTS_TO_LATENTS = 'latents_to_latents'; export const RESIZE = 'resize_image'; +export const CANVAS_OUTPUT = 'canvas_output'; export const INPAINT = 'inpaint'; +export const INPAINT_SEAM_FIX = 'inpaint_seam_fix'; +export const INPAINT_IMAGE = 'inpaint_image'; +export const SCALED_INPAINT_IMAGE = 'scaled_inpaint_image'; +export const INPAINT_IMAGE_RESIZE_UP = 'inpaint_image_resize_up'; +export const INPAINT_IMAGE_RESIZE_DOWN = 'inpaint_image_resize_down'; +export const INPAINT_INFILL = 'inpaint_infill'; +export const INPAINT_INFILL_RESIZE_DOWN = 'inpaint_infill_resize_down'; +export const INPAINT_FINAL_IMAGE = 'inpaint_final_image'; +export const MASK_FROM_ALPHA = 'tomask'; +export const MASK_EDGE = 'mask_edge'; +export const MASK_BLUR = 'mask_blur'; +export const MASK_COMBINE = 'mask_combine'; +export const MASK_RESIZE_UP = 'mask_resize_up'; +export const MASK_RESIZE_DOWN = 'mask_resize_down'; +export const COLOR_CORRECT = 'color_correct'; +export const PASTE_IMAGE = 'img_paste'; export const CONTROL_NET_COLLECT = 'control_net_collect'; export const DYNAMIC_PROMPT = 'dynamic_prompt'; export const IMAGE_COLLECTION = 'image_collection'; @@ -27,18 +44,26 @@ export const REALESRGAN = 'esrgan'; export const DIVIDE = 'divide'; export const SCALE = 'scale_image'; export const SDXL_MODEL_LOADER = 'sdxl_model_loader'; -export const SDXL_TEXT_TO_LATENTS = 't2l_sdxl'; -export const SDXL_LATENTS_TO_LATENTS = 'l2l_sdxl'; +export const SDXL_DENOISE_LATENTS = 'sdxl_denoise_latents'; export const SDXL_REFINER_MODEL_LOADER = 'sdxl_refiner_model_loader'; export const SDXL_REFINER_POSITIVE_CONDITIONING = 'sdxl_refiner_positive_conditioning'; export const SDXL_REFINER_NEGATIVE_CONDITIONING = 'sdxl_refiner_negative_conditioning'; -export const SDXL_REFINER_LATENTS_TO_LATENTS = 'l2l_sdxl_refiner'; +export const SDXL_REFINER_DENOISE_LATENTS = 'sdxl_refiner_denoise_latents'; // friendly graph ids export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph'; +export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph'; +export const CANVAS_TEXT_TO_IMAGE_GRAPH = 'canvas_text_to_image_graph'; +export const CANVAS_IMAGE_TO_IMAGE_GRAPH = 'canvas_image_to_image_graph'; +export const CANVAS_INPAINT_GRAPH = 'canvas_inpaint_graph'; +export const CANVAS_OUTPAINT_GRAPH = 'canvas_outpaint_graph'; export const SDXL_TEXT_TO_IMAGE_GRAPH = 'sdxl_text_to_image_graph'; export const SDXL_IMAGE_TO_IMAGE_GRAPH = 'sxdl_image_to_image_graph'; -export const IMAGE_TO_IMAGE_GRAPH = 'image_to_image_graph'; -export const INPAINT_GRAPH = 'inpaint_graph'; +export const SDXL_CANVAS_TEXT_TO_IMAGE_GRAPH = + 'sdxl_canvas_text_to_image_graph'; +export const SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH = + 'sdxl_canvas_image_to_image_graph'; +export const SDXL_CANVAS_INPAINT_GRAPH = 'sdxl_canvas_inpaint_graph'; +export const SDXL_CANVAS_OUTPAINT_GRAPH = 'sdxl_canvas_outpaint_graph'; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/helpers/craftSDXLStylePrompt.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/helpers/craftSDXLStylePrompt.ts new file mode 100644 index 0000000000..f46d5cc5dc --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/helpers/craftSDXLStylePrompt.ts @@ -0,0 +1,28 @@ +import { RootState } from 'app/store/store'; + +export const craftSDXLStylePrompt = ( + state: RootState, + shouldConcatSDXLStylePrompt: boolean +) => { + const { positivePrompt, negativePrompt } = state.generation; + const { positiveStylePrompt, negativeStylePrompt } = state.sdxl; + + let craftedPositiveStylePrompt = positiveStylePrompt; + let craftedNegativeStylePrompt = negativeStylePrompt; + + if (shouldConcatSDXLStylePrompt) { + if (positiveStylePrompt.length > 0) { + craftedPositiveStylePrompt = `${positivePrompt} ${positiveStylePrompt}`; + } else { + craftedPositiveStylePrompt = positivePrompt; + } + + if (negativeStylePrompt.length > 0) { + craftedNegativeStylePrompt = `${negativePrompt} ${negativeStylePrompt}`; + } else { + craftedNegativeStylePrompt = negativePrompt; + } + } + + return { craftedPositiveStylePrompt, craftedNegativeStylePrompt }; +}; diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse.tsx new file mode 100644 index 0000000000..9ca6503d3d --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse.tsx @@ -0,0 +1,21 @@ +import { Flex } from '@chakra-ui/react'; +import IAICollapse from 'common/components/IAICollapse'; +import { memo } from 'react'; +import { useTranslation } from 'react-i18next'; +import ParamMaskBlur from './ParamMaskBlur'; +import ParamMaskBlurMethod from './ParamMaskBlurMethod'; + +const ParamMaskAdjustmentCollapse = () => { + const { t } = useTranslation(); + + return ( + + + + + + + ); +}; + +export default memo(ParamMaskAdjustmentCollapse); diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamBlur.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlur.tsx similarity index 62% rename from invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamBlur.tsx rename to invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlur.tsx index 5c20ba7a13..82b82228e2 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamBlur.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlur.tsx @@ -1,31 +1,31 @@ import type { RootState } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import IAISlider from 'common/components/IAISlider'; -import { setSeamBlur } from 'features/parameters/store/generationSlice'; +import { setMaskBlur } from 'features/parameters/store/generationSlice'; import { useTranslation } from 'react-i18next'; -export default function ParamSeamBlur() { +export default function ParamMaskBlur() { const dispatch = useAppDispatch(); - const seamBlur = useAppSelector( - (state: RootState) => state.generation.seamBlur + const maskBlur = useAppSelector( + (state: RootState) => state.generation.maskBlur ); const { t } = useTranslation(); return ( { - dispatch(setSeamBlur(v)); + dispatch(setMaskBlur(v)); }} withInput withSliderMarks withReset handleReset={() => { - dispatch(setSeamBlur(16)); + dispatch(setMaskBlur(16)); }} /> ); diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlurMethod.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlurMethod.tsx new file mode 100644 index 0000000000..fa20dcdbcc --- /dev/null +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskBlurMethod.tsx @@ -0,0 +1,36 @@ +import { SelectItem } from '@mantine/core'; +import { RootState } from 'app/store/store'; + +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import IAIMantineSelect from 'common/components/IAIMantineSelect'; +import { setMaskBlurMethod } from 'features/parameters/store/generationSlice'; +import { useTranslation } from 'react-i18next'; + +type MaskBlurMethods = 'box' | 'gaussian'; + +const maskBlurMethods: SelectItem[] = [ + { label: 'Box Blur', value: 'box' }, + { label: 'Gaussian Blur', value: 'gaussian' }, +]; + +export default function ParamMaskBlurMethod() { + const maskBlurMethod = useAppSelector( + (state: RootState) => state.generation.maskBlurMethod + ); + const dispatch = useAppDispatch(); + const { t } = useTranslation(); + + const handleMaskBlurMethodChange = (v: string | null) => { + if (!v) return; + dispatch(setMaskBlurMethod(v as MaskBlurMethods)); + }; + + return ( + + ); +} diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse.tsx deleted file mode 100644 index 88d839fa15..0000000000 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse.tsx +++ /dev/null @@ -1,22 +0,0 @@ -import IAICollapse from 'common/components/IAICollapse'; -import { memo } from 'react'; -import { useTranslation } from 'react-i18next'; -import ParamSeamBlur from './ParamSeamBlur'; -import ParamSeamSize from './ParamSeamSize'; -import ParamSeamSteps from './ParamSeamSteps'; -import ParamSeamStrength from './ParamSeamStrength'; - -const ParamSeamCorrectionCollapse = () => { - const { t } = useTranslation(); - - return ( - - - - - - - ); -}; - -export default memo(ParamSeamCorrectionCollapse); diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSize.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSize.tsx deleted file mode 100644 index 8e56cded7b..0000000000 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSize.tsx +++ /dev/null @@ -1,31 +0,0 @@ -import type { RootState } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import IAISlider from 'common/components/IAISlider'; -import { setSeamSize } from 'features/parameters/store/generationSlice'; -import { useTranslation } from 'react-i18next'; - -export default function ParamSeamSize() { - const dispatch = useAppDispatch(); - const { t } = useTranslation(); - - const seamSize = useAppSelector( - (state: RootState) => state.generation.seamSize - ); - - return ( - { - dispatch(setSeamSize(v)); - }} - withInput - withSliderMarks - withReset - handleReset={() => dispatch(setSeamSize(96))} - /> - ); -} diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSteps.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSteps.tsx deleted file mode 100644 index 8ca5226621..0000000000 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamSteps.tsx +++ /dev/null @@ -1,32 +0,0 @@ -import type { RootState } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import IAISlider from 'common/components/IAISlider'; -import { setSeamSteps } from 'features/parameters/store/generationSlice'; -import { useTranslation } from 'react-i18next'; - -export default function ParamSeamSteps() { - const { t } = useTranslation(); - const seamSteps = useAppSelector( - (state: RootState) => state.generation.seamSteps - ); - const dispatch = useAppDispatch(); - - return ( - { - dispatch(setSeamSteps(v)); - }} - withInput - withSliderMarks - withReset - handleReset={() => { - dispatch(setSeamSteps(30)); - }} - /> - ); -} diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamStrength.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamStrength.tsx deleted file mode 100644 index de74156cd3..0000000000 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamStrength.tsx +++ /dev/null @@ -1,32 +0,0 @@ -import { RootState } from 'app/store/store'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; -import IAISlider from 'common/components/IAISlider'; -import { setSeamStrength } from 'features/parameters/store/generationSlice'; -import { useTranslation } from 'react-i18next'; - -export default function ParamSeamStrength() { - const dispatch = useAppDispatch(); - const { t } = useTranslation(); - const seamStrength = useAppSelector( - (state: RootState) => state.generation.seamStrength - ); - - return ( - { - dispatch(setSeamStrength(v)); - }} - withInput - withSliderMarks - withReset - handleReset={() => { - dispatch(setSeamStrength(0.7)); - }} - /> - ); -} diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse.tsx index 418ed9278f..c4d2d35f8f 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse.tsx @@ -16,7 +16,10 @@ import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; import { map } from 'lodash-es'; import { Fragment, memo, useCallback } from 'react'; import { FaPlus } from 'react-icons/fa'; -import { useGetControlNetModelsQuery } from 'services/api/endpoints/models'; +import { + controlNetModelsAdapter, + useGetControlNetModelsQuery, +} from 'services/api/endpoints/models'; import { v4 as uuidv4 } from 'uuid'; const selector = createSelector( @@ -42,7 +45,9 @@ const ParamControlNetCollapse = () => { const dispatch = useAppDispatch(); const { firstModel } = useGetControlNetModelsQuery(undefined, { selectFromResult: (result) => { - const firstModel = result.data?.entities[result.data?.ids[0]]; + const firstModel = result.data + ? controlNetModelsAdapter.getSelectors().selectAll(result.data)[0] + : undefined; return { firstModel, }; @@ -95,7 +100,7 @@ const ParamControlNetCollapse = () => { {controlNetsArray.map((c, i) => ( {i > 0 && } - + ))} diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx index 0a18d4f556..05b5b6468a 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/MainModel/ParamMainModelSelect.tsx @@ -15,11 +15,11 @@ import { modelIdToMainModelParam } from 'features/parameters/util/modelIdToMainM import SyncModelsButton from 'features/ui/components/tabs/ModelManager/subpanels/ModelManagerSettingsPanel/SyncModelsButton'; import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; import { forEach } from 'lodash-es'; +import { NON_REFINER_BASE_MODELS } from 'services/api/constants'; import { useGetMainModelsQuery, useGetOnnxModelsQuery, } from 'services/api/endpoints/models'; -import { NON_REFINER_BASE_MODELS } from 'services/api/constants'; import { useFeatureStatus } from '../../../../system/hooks/useFeatureStatus'; const selector = createSelector( @@ -52,10 +52,7 @@ const ParamMainModelSelect = () => { const data: SelectItem[] = []; forEach(mainModels.entities, (model, id) => { - if ( - !model || - (activeTabName === 'unifiedCanvas' && model.base_model === 'sdxl') - ) { + if (!model) { return; } diff --git a/invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts b/invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts index cb2361524d..95caf9a9e1 100644 --- a/invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts +++ b/invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts @@ -4,16 +4,16 @@ import { refinerModelChanged, setNegativeStylePromptSDXL, setPositiveStylePromptSDXL, - setRefinerAestheticScore, setRefinerCFGScale, + setRefinerNegativeAestheticScore, + setRefinerPositiveAestheticScore, setRefinerScheduler, setRefinerStart, setRefinerSteps, } from 'features/sdxl/store/sdxlSlice'; import { useCallback } from 'react'; import { useTranslation } from 'react-i18next'; -import { UnsafeImageMetadata } from 'services/api/endpoints/images'; -import { ImageDTO } from 'services/api/types'; +import { ImageDTO, UnsafeImageMetadata } from 'services/api/types'; import { initialImageSelected, modelSelected } from '../store/actions'; import { setCfgScale, @@ -34,8 +34,9 @@ import { isValidPositivePrompt, isValidSDXLNegativeStylePrompt, isValidSDXLPositiveStylePrompt, - isValidSDXLRefinerAestheticScore, isValidSDXLRefinerModel, + isValidSDXLRefinerNegativeAestheticScore, + isValidSDXLRefinerPositiveAestheticScore, isValidSDXLRefinerStart, isValidScheduler, isValidSeed, @@ -339,7 +340,8 @@ export const useRecallParameters = () => { refiner_cfg_scale, refiner_steps, refiner_scheduler, - refiner_aesthetic_store, + refiner_positive_aesthetic_store, + refiner_negative_aesthetic_store, refiner_start, } = metadata; @@ -398,8 +400,24 @@ export const useRecallParameters = () => { dispatch(setRefinerScheduler(refiner_scheduler)); } - if (isValidSDXLRefinerAestheticScore(refiner_aesthetic_store)) { - dispatch(setRefinerAestheticScore(refiner_aesthetic_store)); + if ( + isValidSDXLRefinerPositiveAestheticScore( + refiner_positive_aesthetic_store + ) + ) { + dispatch( + setRefinerPositiveAestheticScore(refiner_positive_aesthetic_store) + ); + } + + if ( + isValidSDXLRefinerNegativeAestheticScore( + refiner_negative_aesthetic_store + ) + ) { + dispatch( + setRefinerNegativeAestheticScore(refiner_negative_aesthetic_store) + ); } if (isValidSDXLRefinerStart(refiner_start)) { diff --git a/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts b/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts index 12f2c3eabf..0173391833 100644 --- a/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts +++ b/invokeai/frontend/web/src/features/parameters/store/generationSlice.ts @@ -4,11 +4,13 @@ import { roundToMultiple } from 'common/util/roundDownToMultiple'; import { configChanged } from 'features/system/store/configSlice'; import { clamp } from 'lodash-es'; import { ImageDTO } from 'services/api/types'; + import { clipSkipMap } from '../types/constants'; import { CfgScaleParam, HeightParam, MainModelParam, + MaskBlurMethodParam, NegativePromptParam, OnnxModelParam, PositivePromptParam, @@ -33,10 +35,8 @@ export interface GenerationState { positivePrompt: PositivePromptParam; negativePrompt: NegativePromptParam; scheduler: SchedulerParam; - seamBlur: number; - seamSize: number; - seamSteps: number; - seamStrength: number; + maskBlur: number; + maskBlurMethod: MaskBlurMethodParam; seed: SeedParam; seedWeights: string; shouldFitToWidthHeight: boolean; @@ -72,10 +72,8 @@ export const initialGenerationState: GenerationState = { positivePrompt: '', negativePrompt: '', scheduler: 'euler', - seamBlur: 16, - seamSize: 96, - seamSteps: 30, - seamStrength: 0.7, + maskBlur: 16, + maskBlurMethod: 'box', seed: 0, seedWeights: '', shouldFitToWidthHeight: true, @@ -196,17 +194,11 @@ export const generationSlice = createSlice({ clearInitialImage: (state) => { state.initialImage = undefined; }, - setSeamSize: (state, action: PayloadAction) => { - state.seamSize = action.payload; + setMaskBlur: (state, action: PayloadAction) => { + state.maskBlur = action.payload; }, - setSeamBlur: (state, action: PayloadAction) => { - state.seamBlur = action.payload; - }, - setSeamStrength: (state, action: PayloadAction) => { - state.seamStrength = action.payload; - }, - setSeamSteps: (state, action: PayloadAction) => { - state.seamSteps = action.payload; + setMaskBlurMethod: (state, action: PayloadAction) => { + state.maskBlurMethod = action.payload; }, setTileSize: (state, action: PayloadAction) => { state.tileSize = action.payload; @@ -312,10 +304,8 @@ export const { setPositivePrompt, setNegativePrompt, setScheduler, - setSeamBlur, - setSeamSize, - setSeamSteps, - setSeamStrength, + setMaskBlur, + setMaskBlurMethod, setSeed, setSeedWeights, setShouldFitToWidthHeight, diff --git a/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts b/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts index ac799ac600..25905e1e14 100644 --- a/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts +++ b/invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts @@ -353,22 +353,40 @@ export const isValidPrecision = (val: unknown): val is PrecisionParam => zPrecision.safeParse(val).success; /** - * Zod schema for SDXL refiner aesthetic score parameter + * Zod schema for SDXL refiner positive aesthetic score parameter */ -export const zSDXLRefinerAestheticScore = z.number().min(1).max(10); +export const zSDXLRefinerPositiveAestheticScore = z.number().min(1).max(10); /** - * Type alias for SDXL refiner aesthetic score parameter, inferred from its zod schema + * Type alias for SDXL refiner aesthetic positive score parameter, inferred from its zod schema */ -export type SDXLRefinerAestheticScoreParam = z.infer< - typeof zSDXLRefinerAestheticScore +export type SDXLRefinerPositiveAestheticScoreParam = z.infer< + typeof zSDXLRefinerPositiveAestheticScore >; /** - * Validates/type-guards a value as a SDXL refiner aesthetic score parameter + * Validates/type-guards a value as a SDXL refiner positive aesthetic score parameter */ -export const isValidSDXLRefinerAestheticScore = ( +export const isValidSDXLRefinerPositiveAestheticScore = ( val: unknown -): val is SDXLRefinerAestheticScoreParam => - zSDXLRefinerAestheticScore.safeParse(val).success; +): val is SDXLRefinerPositiveAestheticScoreParam => + zSDXLRefinerPositiveAestheticScore.safeParse(val).success; + +/** + * Zod schema for SDXL refiner negative aesthetic score parameter + */ +export const zSDXLRefinerNegativeAestheticScore = z.number().min(1).max(10); +/** + * Type alias for SDXL refiner aesthetic negative score parameter, inferred from its zod schema + */ +export type SDXLRefinerNegativeAestheticScoreParam = z.infer< + typeof zSDXLRefinerNegativeAestheticScore +>; +/** + * Validates/type-guards a value as a SDXL refiner negative aesthetic score parameter + */ +export const isValidSDXLRefinerNegativeAestheticScore = ( + val: unknown +): val is SDXLRefinerNegativeAestheticScoreParam => + zSDXLRefinerNegativeAestheticScore.safeParse(val).success; /** * Zod schema for SDXL start parameter @@ -385,6 +403,21 @@ export const isValidSDXLRefinerStart = ( val: unknown ): val is SDXLRefinerStartParam => zSDXLRefinerstart.safeParse(val).success; +/** + * Zod schema for a mask blur method parameter + */ +export const zMaskBlurMethod = z.enum(['box', 'gaussian']); +/** + * Type alias for mask blur method parameter, inferred from its zod schema + */ +export type MaskBlurMethodParam = z.infer; +/** + * Validates/type-guards a value as a mask blur method parameter + */ +export const isValidMaskBlurMethod = ( + val: unknown +): val is MaskBlurMethodParam => zMaskBlurMethod.safeParse(val).success; + // /** // * Zod schema for BaseModelType // */ diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx index 37e1718dc6..3b186006f1 100644 --- a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx @@ -4,9 +4,10 @@ import { stateSelector } from 'app/store/store'; import { useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAICollapse from 'common/components/IAICollapse'; -import ParamSDXLRefinerAestheticScore from './SDXLRefiner/ParamSDXLRefinerAestheticScore'; import ParamSDXLRefinerCFGScale from './SDXLRefiner/ParamSDXLRefinerCFGScale'; import ParamSDXLRefinerModelSelect from './SDXLRefiner/ParamSDXLRefinerModelSelect'; +import ParamSDXLRefinerNegativeAestheticScore from './SDXLRefiner/ParamSDXLRefinerNegativeAestheticScore'; +import ParamSDXLRefinerPositiveAestheticScore from './SDXLRefiner/ParamSDXLRefinerPositiveAestheticScore'; import ParamSDXLRefinerScheduler from './SDXLRefiner/ParamSDXLRefinerScheduler'; import ParamSDXLRefinerStart from './SDXLRefiner/ParamSDXLRefinerStart'; import ParamSDXLRefinerSteps from './SDXLRefiner/ParamSDXLRefinerSteps'; @@ -38,7 +39,8 @@ const ParamSDXLRefinerCollapse = () => { - + + diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx index c0b143a557..a6ee21ab68 100644 --- a/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx @@ -1,4 +1,6 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse'; +import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse'; +import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse'; import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse'; import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons'; import ParamSDXLPromptArea from './ParamSDXLPromptArea'; @@ -12,6 +14,8 @@ const SDXLImageToImageTabParameters = () => { + + diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerNegativeAestheticScore.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerNegativeAestheticScore.tsx new file mode 100644 index 0000000000..4dad3f519a --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerNegativeAestheticScore.tsx @@ -0,0 +1,60 @@ +import { createSelector } from '@reduxjs/toolkit'; +import { stateSelector } from 'app/store/store'; +import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import IAISlider from 'common/components/IAISlider'; +import { setRefinerNegativeAestheticScore } from 'features/sdxl/store/sdxlSlice'; +import { memo, useCallback } from 'react'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; + +const selector = createSelector( + [stateSelector], + ({ sdxl, hotkeys }) => { + const { refinerNegativeAestheticScore } = sdxl; + const { shift } = hotkeys; + + return { + refinerNegativeAestheticScore, + shift, + }; + }, + defaultSelectorOptions +); + +const ParamSDXLRefinerNegativeAestheticScore = () => { + const { refinerNegativeAestheticScore, shift } = useAppSelector(selector); + + const isRefinerAvailable = useIsRefinerAvailable(); + + const dispatch = useAppDispatch(); + + const handleChange = useCallback( + (v: number) => dispatch(setRefinerNegativeAestheticScore(v)), + [dispatch] + ); + + const handleReset = useCallback( + () => dispatch(setRefinerNegativeAestheticScore(2.5)), + [dispatch] + ); + + return ( + + ); +}; + +export default memo(ParamSDXLRefinerNegativeAestheticScore); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerPositiveAestheticScore.tsx similarity index 66% rename from invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx rename to invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerPositiveAestheticScore.tsx index 9c9c4b2f89..47842629f6 100644 --- a/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerAestheticScore.tsx +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerPositiveAestheticScore.tsx @@ -3,50 +3,50 @@ import { stateSelector } from 'app/store/store'; import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import IAISlider from 'common/components/IAISlider'; -import { setRefinerAestheticScore } from 'features/sdxl/store/sdxlSlice'; +import { setRefinerPositiveAestheticScore } from 'features/sdxl/store/sdxlSlice'; import { memo, useCallback } from 'react'; import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; const selector = createSelector( [stateSelector], ({ sdxl, hotkeys }) => { - const { refinerAestheticScore } = sdxl; + const { refinerPositiveAestheticScore } = sdxl; const { shift } = hotkeys; return { - refinerAestheticScore, + refinerPositiveAestheticScore, shift, }; }, defaultSelectorOptions ); -const ParamSDXLRefinerAestheticScore = () => { - const { refinerAestheticScore, shift } = useAppSelector(selector); +const ParamSDXLRefinerPositiveAestheticScore = () => { + const { refinerPositiveAestheticScore, shift } = useAppSelector(selector); const isRefinerAvailable = useIsRefinerAvailable(); const dispatch = useAppDispatch(); const handleChange = useCallback( - (v: number) => dispatch(setRefinerAestheticScore(v)), + (v: number) => dispatch(setRefinerPositiveAestheticScore(v)), [dispatch] ); const handleReset = useCallback( - () => dispatch(setRefinerAestheticScore(6)), + () => dispatch(setRefinerPositiveAestheticScore(6)), [dispatch] ); return ( { ); }; -export default memo(ParamSDXLRefinerAestheticScore); +export default memo(ParamSDXLRefinerPositiveAestheticScore); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx index 35bc0b4284..c562951c4d 100644 --- a/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx @@ -1,4 +1,6 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse'; +import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse'; +import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse'; import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse'; import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons'; import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters'; @@ -12,6 +14,8 @@ const SDXLTextToImageTabParameters = () => { + + diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabCoreParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabCoreParameters.tsx new file mode 100644 index 0000000000..7db6ccc219 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabCoreParameters.tsx @@ -0,0 +1,75 @@ +import { Box, Flex } from '@chakra-ui/react'; +import { createSelector } from '@reduxjs/toolkit'; +import { stateSelector } from 'app/store/store'; +import { useAppSelector } from 'app/store/storeHooks'; +import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; +import IAICollapse from 'common/components/IAICollapse'; +import ParamBoundingBoxSize from 'features/parameters/components/Parameters/Canvas/BoundingBox/ParamBoundingBoxSize'; +import ParamCFGScale from 'features/parameters/components/Parameters/Core/ParamCFGScale'; +import ParamIterations from 'features/parameters/components/Parameters/Core/ParamIterations'; +import ParamModelandVAEandScheduler from 'features/parameters/components/Parameters/Core/ParamModelandVAEandScheduler'; +import ParamSteps from 'features/parameters/components/Parameters/Core/ParamSteps'; +import ParamSeedFull from 'features/parameters/components/Parameters/Seed/ParamSeedFull'; +import { memo } from 'react'; +import ParamSDXLImg2ImgDenoisingStrength from './ParamSDXLImg2ImgDenoisingStrength'; + +const selector = createSelector( + stateSelector, + ({ ui, generation }) => { + const { shouldUseSliders } = ui; + const { shouldRandomizeSeed } = generation; + + const activeLabel = !shouldRandomizeSeed ? 'Manual Seed' : undefined; + + return { shouldUseSliders, activeLabel }; + }, + defaultSelectorOptions +); + +const SDXLUnifiedCanvasTabCoreParameters = () => { + const { shouldUseSliders, activeLabel } = useAppSelector(selector); + + return ( + + + {shouldUseSliders ? ( + <> + + + + + + + + + + ) : ( + <> + + + + + + + + + + + + )} + + + + ); +}; + +export default memo(SDXLUnifiedCanvasTabCoreParameters); diff --git a/invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabParameters.tsx b/invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabParameters.tsx new file mode 100644 index 0000000000..c6af754ad9 --- /dev/null +++ b/invokeai/frontend/web/src/features/sdxl/components/SDXLUnifiedCanvasTabParameters.tsx @@ -0,0 +1,27 @@ +import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse'; +import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse'; +import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse'; +import ParamMaskAdjustmentCollapse from 'features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse'; +import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse'; +import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse'; +import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons'; +import ParamSDXLPromptArea from './ParamSDXLPromptArea'; +import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse'; +import SDXLUnifiedCanvasTabCoreParameters from './SDXLUnifiedCanvasTabCoreParameters'; + +export default function SDXLUnifiedCanvasTabParameters() { + return ( + <> + + + + + + + + + + + + ); +} diff --git a/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts b/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts index 7ee3ea1d4f..7670790f05 100644 --- a/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts +++ b/invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts @@ -16,7 +16,8 @@ type SDXLInitialState = { refinerSteps: number; refinerCFGScale: number; refinerScheduler: SchedulerParam; - refinerAestheticScore: number; + refinerPositiveAestheticScore: number; + refinerNegativeAestheticScore: number; refinerStart: number; }; @@ -30,7 +31,8 @@ const sdxlInitialState: SDXLInitialState = { refinerSteps: 20, refinerCFGScale: 7.5, refinerScheduler: 'euler', - refinerAestheticScore: 6, + refinerPositiveAestheticScore: 6, + refinerNegativeAestheticScore: 2.5, refinerStart: 0.7, }; @@ -68,8 +70,17 @@ const sdxlSlice = createSlice({ setRefinerScheduler: (state, action: PayloadAction) => { state.refinerScheduler = action.payload; }, - setRefinerAestheticScore: (state, action: PayloadAction) => { - state.refinerAestheticScore = action.payload; + setRefinerPositiveAestheticScore: ( + state, + action: PayloadAction + ) => { + state.refinerPositiveAestheticScore = action.payload; + }, + setRefinerNegativeAestheticScore: ( + state, + action: PayloadAction + ) => { + state.refinerNegativeAestheticScore = action.payload; }, setRefinerStart: (state, action: PayloadAction) => { state.refinerStart = action.payload; @@ -87,7 +98,8 @@ export const { setRefinerSteps, setRefinerCFGScale, setRefinerScheduler, - setRefinerAestheticScore, + setRefinerPositiveAestheticScore, + setRefinerNegativeAestheticScore, setRefinerStart, } = sdxlSlice.actions; diff --git a/invokeai/frontend/web/src/features/system/store/systemSlice.ts b/invokeai/frontend/web/src/features/system/store/systemSlice.ts index fba980131e..079819fce7 100644 --- a/invokeai/frontend/web/src/features/system/store/systemSlice.ts +++ b/invokeai/frontend/web/src/features/system/store/systemSlice.ts @@ -365,12 +365,19 @@ export const systemSlice = createSlice({ state.statusTranslationKey = 'common.statusConnected'; state.progressImage = null; + let errorDescription = undefined; + + if (action.payload?.status === 422) { + errorDescription = 'Validation Error'; + } else if (action.payload?.error) { + errorDescription = action.payload?.error as string; + } + state.toastQueue.push( makeToast({ title: t('toast.serverError'), status: 'error', - description: - action.payload?.status === 422 ? 'Validation Error' : undefined, + description: errorDescription, }) ); }); diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddCheckpoint.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddCheckpoint.tsx index fd5106b289..5f82483cd3 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddCheckpoint.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddCheckpoint.tsx @@ -28,9 +28,7 @@ export default function AdvancedAddCheckpoint( const advancedAddCheckpointForm = useForm({ initialValues: { - model_name: model_path - ? model_path.split('\\').splice(-1)[0].split('.')[0] - : '', + model_name: model_path?.split('\\').splice(-1)[0]?.split('.')[0] ?? '', base_model: 'sd-1', model_type: 'main', path: model_path ? model_path : '', diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddDiffusers.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddDiffusers.tsx index 376631bd1f..ec2d3f037a 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddDiffusers.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/AddModelsPanel/AdvancedAddDiffusers.tsx @@ -25,7 +25,7 @@ export default function AdvancedAddDiffusers(props: AdvancedAddDiffusersProps) { const advancedAddDiffusersForm = useForm({ initialValues: { - model_name: model_path ? model_path.split('\\').splice(-1)[0] : '', + model_name: model_path?.split('\\').splice(-1)[0] ?? '', base_model: 'sd-1', model_type: 'main', path: model_path ? model_path : '', diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx index 4ad8fbaba6..6837a2e853 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/MergeModelsPanel.tsx @@ -59,10 +59,10 @@ export default function MergeModelsPanel() { }, [sd1DiffusersModels, sd2DiffusersModels]); const [modelOne, setModelOne] = useState( - Object.keys(modelsMap[baseModel as keyof typeof modelsMap])[0] + Object.keys(modelsMap[baseModel as keyof typeof modelsMap])?.[0] ?? null ); const [modelTwo, setModelTwo] = useState( - Object.keys(modelsMap[baseModel as keyof typeof modelsMap])[1] + Object.keys(modelsMap[baseModel as keyof typeof modelsMap])?.[1] ?? null ); const [modelThree, setModelThree] = useState(null); @@ -106,8 +106,9 @@ export default function MergeModelsPanel() { let modelsToMerge: (string | null)[] = [modelOne, modelTwo, modelThree]; modelsToMerge = modelsToMerge.filter((model) => model !== null); modelsToMerge.forEach((model) => { - if (model) { - models_names.push(model?.split('/')[2]); + const n = model?.split('/')?.[2]; + if (n) { + models_names.push(n); } }); diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelConvert.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelConvert.tsx index 1aec7d5c05..045745e206 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelConvert.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelConvert.tsx @@ -47,13 +47,11 @@ export default function ModelConvert(props: ModelConvertProps) { }; const modelConvertHandler = () => { - const responseBody = { + const queryArg = { base_model: model.base_model, model_name: model.model_name, - params: { - convert_dest_directory: - saveLocation === 'Custom' ? customSaveLocation : undefined, - }, + convert_dest_directory: + saveLocation === 'Custom' ? customSaveLocation : undefined, }; if (saveLocation === 'Custom' && customSaveLocation === '') { @@ -74,14 +72,14 @@ export default function ModelConvert(props: ModelConvertProps) { title: `${t('modelManager.convertingModelBegin')}: ${ model.model_name }`, - status: 'success', + status: 'info', }) ) ); - convertModel(responseBody) + convertModel(queryArg) .unwrap() - .then((_) => { + .then(() => { dispatch( addToast( makeToast({ @@ -91,7 +89,7 @@ export default function ModelConvert(props: ModelConvertProps) { ) ); }) - .catch((_) => { + .catch(() => { dispatch( addToast( makeToast({ diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx index de7ce3b084..fcfffee48b 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx @@ -2,10 +2,10 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse'; import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse'; import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse'; -import ParamSeamCorrectionCollapse from 'features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse'; import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse'; import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse'; // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse'; +import ParamMaskAdjustmentCollapse from 'features/parameters/components/Parameters/Canvas/MaskAdjustment/ParamMaskAdjustmentCollapse'; import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea'; import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons'; import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters'; @@ -21,7 +21,7 @@ const UnifiedCanvasParameters = () => { {/* */} - + diff --git a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasTab.tsx b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasTab.tsx index 4c36c45e13..0a5b872e4b 100644 --- a/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasTab.tsx +++ b/invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasTab.tsx @@ -1,14 +1,22 @@ import { Flex } from '@chakra-ui/react'; +import { RootState } from 'app/store/store'; +import { useAppSelector } from 'app/store/storeHooks'; +import SDXLUnifiedCanvasTabParameters from 'features/sdxl/components/SDXLUnifiedCanvasTabParameters'; import { memo } from 'react'; import ParametersPinnedWrapper from '../../ParametersPinnedWrapper'; import UnifiedCanvasContent from './UnifiedCanvasContent'; import UnifiedCanvasParameters from './UnifiedCanvasParameters'; const UnifiedCanvasTab = () => { + const model = useAppSelector((state: RootState) => state.generation.model); return ( - + {model && model.base_model === 'sdxl' ? ( + + ) : ( + + )} diff --git a/invokeai/frontend/web/src/features/ui/store/uiSelectors.ts b/invokeai/frontend/web/src/features/ui/store/uiSelectors.ts index fa152e9ce5..5427fa9d3b 100644 --- a/invokeai/frontend/web/src/features/ui/store/uiSelectors.ts +++ b/invokeai/frontend/web/src/features/ui/store/uiSelectors.ts @@ -2,12 +2,12 @@ import { createSelector } from '@reduxjs/toolkit'; import { RootState } from 'app/store/store'; import { isEqual } from 'lodash-es'; -import { tabMap } from './tabMap'; +import { InvokeTabName, tabMap } from './tabMap'; import { UIState } from './uiTypes'; export const activeTabNameSelector = createSelector( (state: RootState) => state.ui, - (ui: UIState) => tabMap[ui.activeTab], + (ui: UIState) => tabMap[ui.activeTab] as InvokeTabName, { memoizeOptions: { equalityCheck: isEqual, diff --git a/invokeai/frontend/web/src/services/api/client.ts b/invokeai/frontend/web/src/services/api/client.ts index dd4caa460e..87deda7d36 100644 --- a/invokeai/frontend/web/src/services/api/client.ts +++ b/invokeai/frontend/web/src/services/api/client.ts @@ -16,6 +16,11 @@ export const $authToken = atom(); */ export const $baseUrl = atom(); +/** + * The optional project-id header. + */ +export const $projectId = atom(); + /** * Autogenerated, type-safe fetch client for the API. Used when RTK Query is not an option. * Dynamically updates when the token or base url changes. @@ -24,9 +29,12 @@ export const $baseUrl = atom(); * @example * const { get, post, del } = $client.get(); */ -export const $client = computed([$authToken, $baseUrl], (authToken, baseUrl) => +export const $client = computed([$authToken, $baseUrl, $projectId], (authToken, baseUrl, projectId) => createClient({ - headers: authToken ? { Authorization: `Bearer ${authToken}` } : {}, + headers: { + ...(authToken ? { Authorization: `Bearer ${authToken}` } : {}), + ...(projectId ? { "project-id": projectId } : {}) + }, // do not include `api/v1` in the base url for this client baseUrl: `${baseUrl ?? ''}`, }) diff --git a/invokeai/frontend/web/src/services/api/endpoints/boardImages.ts b/invokeai/frontend/web/src/services/api/endpoints/boardImages.ts deleted file mode 100644 index 2dc292321e..0000000000 --- a/invokeai/frontend/web/src/services/api/endpoints/boardImages.ts +++ /dev/null @@ -1,36 +0,0 @@ -import { api } from '..'; - -export const boardImagesApi = api.injectEndpoints({ - endpoints: (build) => ({ - /** - * Board Images Queries - */ - // listBoardImages: build.query< - // OffsetPaginatedResults_ImageDTO_, - // ListBoardImagesArg - // >({ - // query: ({ board_id, offset, limit }) => ({ - // url: `board_images/${board_id}`, - // method: 'GET', - // }), - // providesTags: (result, error, arg) => { - // // any list of boardimages - // const tags: ApiFullTagDescription[] = [ - // { type: 'BoardImage', id: `${arg.board_id}_${LIST_TAG}` }, - // ]; - // if (result) { - // // and individual tags for each boardimage - // tags.push( - // ...result.items.map(({ board_id, image_name }) => ({ - // type: 'BoardImage' as const, - // id: `${board_id}_${image_name}`, - // })) - // ); - // } - // return tags; - // }, - // }), - }), -}); - -// export const { useListBoardImagesQuery } = boardImagesApi; diff --git a/invokeai/frontend/web/src/services/api/endpoints/boards.ts b/invokeai/frontend/web/src/services/api/endpoints/boards.ts index 73b894b492..9d9fa11da8 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/boards.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/boards.ts @@ -1,28 +1,16 @@ -import { Update } from '@reduxjs/toolkit'; import { ASSETS_CATEGORIES, IMAGE_CATEGORIES, } from 'features/gallery/store/types'; import { BoardDTO, - ImageDTO, + ListBoardsArg, OffsetPaginatedResults_BoardDTO_, + OffsetPaginatedResults_ImageDTO_, + UpdateBoardArg, } from 'services/api/types'; import { ApiFullTagDescription, LIST_TAG, api } from '..'; -import { paths } from '../schema'; -import { getListImagesUrl, imagesAdapter, imagesApi } from './images'; - -type ListBoardsArg = NonNullable< - paths['/api/v1/boards/']['get']['parameters']['query'] ->; - -type UpdateBoardArg = - paths['/api/v1/boards/{board_id}']['patch']['parameters']['path'] & { - changes: paths['/api/v1/boards/{board_id}']['patch']['requestBody']['content']['application/json']; - }; - -type DeleteBoardResult = - paths['/api/v1/boards/{board_id}']['delete']['responses']['200']['content']['application/json']; +import { getListImagesUrl } from '../util'; export const boardsApi = api.injectEndpoints({ endpoints: (build) => ({ @@ -82,6 +70,44 @@ export const boardsApi = api.injectEndpoints({ keepUnusedDataFor: 0, }), + getBoardImagesTotal: build.query({ + query: (board_id) => ({ + url: getListImagesUrl({ + board_id: board_id ?? 'none', + categories: IMAGE_CATEGORIES, + is_intermediate: false, + limit: 0, + offset: 0, + }), + method: 'GET', + }), + providesTags: (result, error, arg) => [ + { type: 'BoardImagesTotal', id: arg ?? 'none' }, + ], + transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => { + return response.total; + }, + }), + + getBoardAssetsTotal: build.query({ + query: (board_id) => ({ + url: getListImagesUrl({ + board_id: board_id ?? 'none', + categories: ASSETS_CATEGORIES, + is_intermediate: false, + limit: 0, + offset: 0, + }), + method: 'GET', + }), + providesTags: (result, error, arg) => [ + { type: 'BoardAssetsTotal', id: arg ?? 'none' }, + ], + transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => { + return response.total; + }, + }), + /** * Boards Mutations */ @@ -105,176 +131,15 @@ export const boardsApi = api.injectEndpoints({ { type: 'Board', id: arg.board_id }, ], }), - - deleteBoard: build.mutation({ - query: (board_id) => ({ url: `boards/${board_id}`, method: 'DELETE' }), - invalidatesTags: (result, error, board_id) => [ - { type: 'Board', id: LIST_TAG }, - // invalidate the 'No Board' cache - { - type: 'ImageList', - id: getListImagesUrl({ - board_id: 'none', - categories: IMAGE_CATEGORIES, - }), - }, - { - type: 'ImageList', - id: getListImagesUrl({ - board_id: 'none', - categories: ASSETS_CATEGORIES, - }), - }, - { type: 'BoardImagesTotal', id: 'none' }, - { type: 'BoardAssetsTotal', id: 'none' }, - ], - async onQueryStarted(board_id, { dispatch, queryFulfilled, getState }) { - /** - * Cache changes for deleteBoard: - * - Update every image in the 'getImageDTO' cache that has the board_id - * - Update every image in the 'All Images' cache that has the board_id - * - Update every image in the 'All Assets' cache that has the board_id - * - Invalidate the 'No Board' cache: - * Ideally we'd be able to insert all deleted images into the cache, but we don't - * have access to the deleted images DTOs - only the names, and a network request - * for all of a board's DTOs could be very large. Instead, we invalidate the 'No Board' - * cache. - */ - - try { - const { data } = await queryFulfilled; - const { deleted_board_images } = data; - - // update getImageDTO caches - deleted_board_images.forEach((image_id) => { - dispatch( - imagesApi.util.updateQueryData( - 'getImageDTO', - image_id, - (draft) => { - draft.board_id = undefined; - } - ) - ); - }); - - // update 'All Images' & 'All Assets' caches - const queryArgsToUpdate = [ - { - categories: IMAGE_CATEGORIES, - }, - { - categories: ASSETS_CATEGORIES, - }, - ]; - - const updates: Update[] = deleted_board_images.map( - (image_name) => ({ - id: image_name, - changes: { board_id: undefined }, - }) - ); - - queryArgsToUpdate.forEach((queryArgs) => { - dispatch( - imagesApi.util.updateQueryData( - 'listImages', - queryArgs, - (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.updateMany(draft, updates); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; - } - ) - ); - }); - } catch { - //no-op - } - }, - }), - - deleteBoardAndImages: build.mutation({ - query: (board_id) => ({ - url: `boards/${board_id}`, - method: 'DELETE', - params: { include_images: true }, - }), - invalidatesTags: (result, error, board_id) => [ - { type: 'Board', id: LIST_TAG }, - { - type: 'ImageList', - id: getListImagesUrl({ - board_id: 'none', - categories: IMAGE_CATEGORIES, - }), - }, - { - type: 'ImageList', - id: getListImagesUrl({ - board_id: 'none', - categories: ASSETS_CATEGORIES, - }), - }, - { type: 'BoardImagesTotal', id: 'none' }, - { type: 'BoardAssetsTotal', id: 'none' }, - ], - async onQueryStarted(board_id, { dispatch, queryFulfilled, getState }) { - /** - * Cache changes for deleteBoardAndImages: - * - ~~Remove every image in the 'getImageDTO' cache that has the board_id~~ - * This isn't actually possible, you cannot remove cache entries with RTK Query. - * Instead, we rely on the UI to remove all components that use the deleted images. - * - Remove every image in the 'All Images' cache that has the board_id - * - Remove every image in the 'All Assets' cache that has the board_id - */ - - try { - const { data } = await queryFulfilled; - const { deleted_images } = data; - - // update 'All Images' & 'All Assets' caches - const queryArgsToUpdate = [ - { - categories: IMAGE_CATEGORIES, - }, - { - categories: ASSETS_CATEGORIES, - }, - ]; - - queryArgsToUpdate.forEach((queryArgs) => { - dispatch( - imagesApi.util.updateQueryData( - 'listImages', - queryArgs, - (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.removeMany( - draft, - deleted_images - ); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; - } - ) - ); - }); - } catch { - //no-op - } - }, - }), }), }); export const { useListBoardsQuery, useListAllBoardsQuery, + useGetBoardImagesTotalQuery, + useGetBoardAssetsTotalQuery, useCreateBoardMutation, useUpdateBoardMutation, - useDeleteBoardMutation, - useDeleteBoardAndImagesMutation, useListAllImageNamesForBoardQuery, } = boardsApi; diff --git a/invokeai/frontend/web/src/services/api/endpoints/images.ts b/invokeai/frontend/web/src/services/api/endpoints/images.ts index e8740a418b..0c52258f9d 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/images.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/images.ts @@ -1,93 +1,38 @@ -import { EntityState, createEntityAdapter } from '@reduxjs/toolkit'; +import { EntityState, Update } from '@reduxjs/toolkit'; import { PatchCollection } from '@reduxjs/toolkit/dist/query/core/buildThunks'; -import { dateComparator } from 'common/util/dateComparator'; import { ASSETS_CATEGORIES, BoardId, IMAGE_CATEGORIES, + IMAGE_LIMIT, } from 'features/gallery/store/types'; -import queryString from 'query-string'; -import { ApiFullTagDescription, api } from '..'; -import { components, paths } from '../schema'; +import { keyBy } from 'lodash'; +import { ApiFullTagDescription, LIST_TAG, api } from '..'; +import { components } from '../schema'; import { + DeleteBoardResult, ImageCategory, ImageDTO, + ListImagesArgs, OffsetPaginatedResults_ImageDTO_, PostUploadAction, + UnsafeImageMetadata, } from '../types'; - -const getIsImageInDateRange = ( - data: ImageCache | undefined, - imageDTO: ImageDTO -) => { - if (!data) { - return false; - } - const cacheImageDTOS = imagesSelectors.selectAll(data); - - if (cacheImageDTOS.length > 1) { - // Images are sorted by `created_at` DESC - // check if the image is newer than the oldest image in the cache - const createdDate = new Date(imageDTO.created_at); - const oldestDate = new Date( - cacheImageDTOS[cacheImageDTOS.length - 1].created_at - ); - return createdDate >= oldestDate; - } else if ([0, 1].includes(cacheImageDTOS.length)) { - // if there are only 1 or 0 images in the cache, we consider the image to be in the date range - return true; - } - return false; -}; - -const getCategories = (imageDTO: ImageDTO) => { - if (IMAGE_CATEGORIES.includes(imageDTO.image_category)) { - return IMAGE_CATEGORIES; - } - return ASSETS_CATEGORIES; -}; - -export type ListImagesArgs = NonNullable< - paths['/api/v1/images/']['get']['parameters']['query'] ->; - -/** - * This is an unsafe type; the object inside is not guaranteed to be valid. - */ -export type UnsafeImageMetadata = { - metadata: components['schemas']['CoreMetadata']; - graph: NonNullable; -}; - -export type ImageCache = EntityState & { total: number }; - -// The adapter is not actually the data store - it just provides helper functions to interact -// with some other store of data. We will use the RTK Query cache as that store. -export const imagesAdapter = createEntityAdapter({ - selectId: (image) => image.image_name, - sortComparer: (a, b) => dateComparator(b.updated_at, a.updated_at), -}); - -// We want to also store the images total in the cache. When we initialize the cache state, -// we will provide this type arg so the adapter knows we want the total. -export type AdditionalImagesAdapterState = { total: number }; - -// Create selectors for the adapter. -export const imagesSelectors = imagesAdapter.getSelectors(); - -// Helper to create the url for the listImages endpoint. Also we use it to create the cache key. -export const getListImagesUrl = (queryArgs: ListImagesArgs) => - `images/?${queryString.stringify(queryArgs, { arrayFormat: 'none' })}`; +import { + getCategories, + getIsImageInDateRange, + getListImagesUrl, + imagesAdapter, + imagesSelectors, +} from '../util'; +import { boardsApi } from './boards'; export const imagesApi = api.injectEndpoints({ endpoints: (build) => ({ /** * Image Queries */ - listImages: build.query< - EntityState & { total: number }, - ListImagesArgs - >({ + listImages: build.query, ListImagesArgs>({ query: (queryArgs) => ({ // Use the helper to create the URL. url: getListImagesUrl(queryArgs), @@ -110,23 +55,17 @@ export const imagesApi = api.injectEndpoints({ return cacheKey; }, transformResponse(response: OffsetPaginatedResults_ImageDTO_) { - const { total, items: images } = response; - // Use the adapter to convert the response to the right shape, and adding the new total. + const { items: images } = response; + // Use the adapter to convert the response to the right shape. // The trick is to just provide an empty state and add the images array to it. This returns // a properly shaped EntityState. - return imagesAdapter.addMany( - imagesAdapter.getInitialState({ - total, - }), - images - ); + return imagesAdapter.addMany(imagesAdapter.getInitialState(), images); }, merge: (cache, response) => { // Here we actually update the cache. `response` here is the output of `transformResponse` // above. In a similar vein to `transformResponse`, we can use the imagesAdapter to get - // things in the right shape. Also update the total image count. + // things in the right shape. imagesAdapter.addMany(cache, imagesSelectors.selectAll(response)); - cache.total = response.total; }, forceRefetch({ currentArg, previousArg }) { // Refetch when the offset changes (which means we are on a new page). @@ -161,69 +100,26 @@ export const imagesApi = api.injectEndpoints({ }, }), getImageDTO: build.query({ - query: (image_name) => ({ url: `images/${image_name}` }), - providesTags: (result, error, arg) => { - const tags: ApiFullTagDescription[] = [{ type: 'Image', id: arg }]; - if (result?.board_id) { - tags.push({ type: 'Board', id: result.board_id }); - } - return tags; - }, + query: (image_name) => ({ url: `images/i/${image_name}` }), + providesTags: (result, error, image_name) => [ + { type: 'Image', id: image_name }, + ], keepUnusedDataFor: 86400, // 24 hours }), getImageMetadata: build.query({ - query: (image_name) => ({ url: `images/${image_name}/metadata` }), - providesTags: (result, error, arg) => { - const tags: ApiFullTagDescription[] = [ - { type: 'ImageMetadata', id: arg }, - ]; - return tags; - }, + query: (image_name) => ({ url: `images/i/${image_name}/metadata` }), + providesTags: (result, error, image_name) => [ + { type: 'ImageMetadata', id: image_name }, + ], keepUnusedDataFor: 86400, // 24 hours }), - getBoardImagesTotal: build.query({ - query: (board_id) => ({ - url: getListImagesUrl({ - board_id: board_id ?? 'none', - categories: IMAGE_CATEGORIES, - is_intermediate: false, - limit: 0, - offset: 0, - }), - method: 'GET', - }), - providesTags: (result, error, arg) => [ - { type: 'BoardImagesTotal', id: arg ?? 'none' }, - ], - transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => { - return response.total; - }, - }), - getBoardAssetsTotal: build.query({ - query: (board_id) => ({ - url: getListImagesUrl({ - board_id: board_id ?? 'none', - categories: ASSETS_CATEGORIES, - is_intermediate: false, - limit: 0, - offset: 0, - }), - method: 'GET', - }), - providesTags: (result, error, arg) => [ - { type: 'BoardAssetsTotal', id: arg ?? 'none' }, - ], - transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => { - return response.total; - }, - }), clearIntermediates: build.mutation({ query: () => ({ url: `images/clear-intermediates`, method: 'POST' }), invalidatesTags: ['IntermediatesCount'], }), deleteImage: build.mutation({ query: ({ image_name }) => ({ - url: `images/${image_name}`, + url: `images/i/${image_name}`, method: 'DELETE', }), invalidatesTags: (result, error, { board_id }) => [ @@ -240,33 +136,84 @@ export const imagesApi = api.injectEndpoints({ const { image_name, board_id } = imageDTO; - // Store patches so we can undo if the query fails - const patches: PatchCollection[] = []; + const queryArg = { + board_id: board_id ?? 'none', + categories: getCategories(imageDTO), + }; - // determine `categories`, i.e. do we update "All Images" or "All Assets" - // $cache = [board_id|no_board]/[images|assets] - const categories = getCategories(imageDTO); - - // *remove* from $cache - patches.push( - dispatch( - imagesApi.util.updateQueryData( - 'listImages', - { board_id: board_id ?? 'none', categories }, - (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.removeOne(draft, image_name); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; - } - ) - ) + const patch = dispatch( + imagesApi.util.updateQueryData('listImages', queryArg, (draft) => { + imagesAdapter.removeOne(draft, image_name); + }) ); try { await queryFulfilled; } catch { - patches.forEach((patchResult) => patchResult.undo()); + patch.undo(); + } + }, + }), + deleteImages: build.mutation< + components['schemas']['DeleteImagesFromListResult'], + { imageDTOs: ImageDTO[] } + >({ + query: ({ imageDTOs }) => { + const image_names = imageDTOs.map((imageDTO) => imageDTO.image_name); + return { + url: `images/delete`, + method: 'POST', + body: { + image_names, + }, + }; + }, + invalidatesTags: (result, error, { imageDTOs }) => { + // for now, assume bulk delete is all on one board + const boardId = imageDTOs[0]?.board_id; + return [ + { type: 'BoardImagesTotal', id: boardId ?? 'none' }, + { type: 'BoardAssetsTotal', id: boardId ?? 'none' }, + ]; + }, + async onQueryStarted({ imageDTOs }, { dispatch, queryFulfilled }) { + /** + * Cache changes for `deleteImages`: + * - *remove* the deleted images from their boards + * + * Unfortunately we cannot do an optimistic update here due to how immer handles patching + * arrays. You have to undo *all* patches, else the entity adapter's `ids` array is borked. + * So we have to wait for the query to complete before updating the cache. + */ + try { + const { data } = await queryFulfilled; + + // convert to an object so we can access the successfully delete image DTOs by name + const groupedImageDTOs = keyBy(imageDTOs, 'image_name'); + + data.deleted_images.forEach((image_name) => { + const imageDTO = groupedImageDTOs[image_name]; + + // should never be undefined + if (imageDTO) { + const queryArg = { + board_id: imageDTO.board_id ?? 'none', + categories: getCategories(imageDTO), + }; + // remove all deleted images from their boards + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + queryArg, + (draft) => { + imagesAdapter.removeOne(draft, image_name); + } + ) + ); + } + }); + } catch { + // } }, }), @@ -278,7 +225,7 @@ export const imagesApi = api.injectEndpoints({ { imageDTO: ImageDTO; is_intermediate: boolean } >({ query: ({ imageDTO, is_intermediate }) => ({ - url: `images/${imageDTO.image_name}`, + url: `images/i/${imageDTO.image_name}`, method: 'PATCH', body: { is_intermediate }, }), @@ -329,20 +276,13 @@ export const imagesApi = api.injectEndpoints({ 'listImages', { board_id: imageDTO.board_id ?? 'none', categories }, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.removeOne( - draft, - imageDTO.image_name - ); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.removeOne(draft, imageDTO.image_name); } ) ) ); } else { // ELSE (it is being changed to a non-intermediate): - console.log(imageDTO); const queryArgs = { board_id: imageDTO.board_id ?? 'none', categories, @@ -352,6 +292,16 @@ export const imagesApi = api.injectEndpoints({ getState() ); + const { data: total } = IMAGE_CATEGORIES.includes( + imageDTO.image_category + ) + ? boardsApi.endpoints.getBoardImagesTotal.select( + imageDTO.board_id ?? 'none' + )(getState()) + : boardsApi.endpoints.getBoardAssetsTotal.select( + imageDTO.board_id ?? 'none' + )(getState()); + // IF it eligible for insertion into existing $cache // "eligible" means either: // - The cache is fully populated, with all images in the db cached @@ -359,8 +309,7 @@ export const imagesApi = api.injectEndpoints({ // - The image's `created_at` is within the range of the cached images const isCacheFullyPopulated = - currentCache.data && - currentCache.data.ids.length >= currentCache.data.total; + currentCache.data && currentCache.data.ids.length >= (total ?? 0); const isInDateRange = getIsImageInDateRange( currentCache.data, @@ -375,10 +324,7 @@ export const imagesApi = api.injectEndpoints({ 'listImages', queryArgs, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.upsertOne(draft, imageDTO); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.upsertOne(draft, imageDTO); } ) ) @@ -401,7 +347,7 @@ export const imagesApi = api.injectEndpoints({ { imageDTO: ImageDTO; session_id: string } >({ query: ({ imageDTO, session_id }) => ({ - url: `images/${imageDTO.image_name}`, + url: `images/i/${imageDTO.image_name}`, method: 'PATCH', body: { session_id }, }), @@ -464,14 +410,14 @@ export const imagesApi = api.injectEndpoints({ const formData = new FormData(); formData.append('file', file); return { - url: `images/`, + url: `images/upload`, method: 'POST', body: formData, params: { image_category, is_intermediate, session_id, - board_id, + board_id: board_id === 'none' ? undefined : board_id, crop_visible, }, }; @@ -524,10 +470,7 @@ export const imagesApi = api.injectEndpoints({ categories, }, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.addOne(draft, imageDTO); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.addOne(draft, imageDTO); } ) ); @@ -543,6 +486,158 @@ export const imagesApi = api.injectEndpoints({ } }, }), + + deleteBoard: build.mutation({ + query: (board_id) => ({ url: `boards/${board_id}`, method: 'DELETE' }), + invalidatesTags: (result, error, board_id) => [ + { type: 'Board', id: LIST_TAG }, + // invalidate the 'No Board' cache + { + type: 'ImageList', + id: getListImagesUrl({ + board_id: 'none', + categories: IMAGE_CATEGORIES, + }), + }, + { + type: 'ImageList', + id: getListImagesUrl({ + board_id: 'none', + categories: ASSETS_CATEGORIES, + }), + }, + { type: 'BoardImagesTotal', id: 'none' }, + { type: 'BoardAssetsTotal', id: 'none' }, + ], + async onQueryStarted(board_id, { dispatch, queryFulfilled, getState }) { + /** + * Cache changes for deleteBoard: + * - Update every image in the 'getImageDTO' cache that has the board_id + * - Update every image in the 'All Images' cache that has the board_id + * - Update every image in the 'All Assets' cache that has the board_id + * - Invalidate the 'No Board' cache: + * Ideally we'd be able to insert all deleted images into the cache, but we don't + * have access to the deleted images DTOs - only the names, and a network request + * for all of a board's DTOs could be very large. Instead, we invalidate the 'No Board' + * cache. + */ + + try { + const { data } = await queryFulfilled; + const { deleted_board_images } = data; + + // update getImageDTO caches + deleted_board_images.forEach((image_id) => { + dispatch( + imagesApi.util.updateQueryData( + 'getImageDTO', + image_id, + (draft) => { + draft.board_id = undefined; + } + ) + ); + }); + + // update 'All Images' & 'All Assets' caches + const queryArgsToUpdate = [ + { + categories: IMAGE_CATEGORIES, + }, + { + categories: ASSETS_CATEGORIES, + }, + ]; + + const updates: Update[] = deleted_board_images.map( + (image_name) => ({ + id: image_name, + changes: { board_id: undefined }, + }) + ); + + queryArgsToUpdate.forEach((queryArgs) => { + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + queryArgs, + (draft) => { + imagesAdapter.updateMany(draft, updates); + } + ) + ); + }); + } catch { + //no-op + } + }, + }), + + deleteBoardAndImages: build.mutation({ + query: (board_id) => ({ + url: `boards/${board_id}`, + method: 'DELETE', + params: { include_images: true }, + }), + invalidatesTags: (result, error, board_id) => [ + { type: 'Board', id: LIST_TAG }, + { + type: 'ImageList', + id: getListImagesUrl({ + board_id: 'none', + categories: IMAGE_CATEGORIES, + }), + }, + { + type: 'ImageList', + id: getListImagesUrl({ + board_id: 'none', + categories: ASSETS_CATEGORIES, + }), + }, + { type: 'BoardImagesTotal', id: 'none' }, + { type: 'BoardAssetsTotal', id: 'none' }, + ], + async onQueryStarted(board_id, { dispatch, queryFulfilled, getState }) { + /** + * Cache changes for deleteBoardAndImages: + * - ~~Remove every image in the 'getImageDTO' cache that has the board_id~~ + * This isn't actually possible, you cannot remove cache entries with RTK Query. + * Instead, we rely on the UI to remove all components that use the deleted images. + * - Remove every image in the 'All Images' cache that has the board_id + * - Remove every image in the 'All Assets' cache that has the board_id + */ + + try { + const { data } = await queryFulfilled; + const { deleted_images } = data; + + // update 'All Images' & 'All Assets' caches + const queryArgsToUpdate = [ + { + categories: IMAGE_CATEGORIES, + }, + { + categories: ASSETS_CATEGORIES, + }, + ]; + + queryArgsToUpdate.forEach((queryArgs) => { + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + queryArgs, + (draft) => { + imagesAdapter.removeMany(draft, deleted_images); + } + ) + ); + }); + } catch { + //no-op + } + }, + }), addImageToBoard: build.mutation< void, { board_id: BoardId; imageDTO: ImageDTO } @@ -556,10 +651,13 @@ export const imagesApi = api.injectEndpoints({ }; }, invalidatesTags: (result, error, { board_id, imageDTO }) => [ + // refresh the board itself { type: 'Board', id: board_id }, + // update old board totals { type: 'BoardImagesTotal', id: board_id }, - { type: 'BoardImagesTotal', id: imageDTO.board_id ?? 'none' }, { type: 'BoardAssetsTotal', id: board_id }, + // update new board totals + { type: 'BoardImagesTotal', id: imageDTO.board_id ?? 'none' }, { type: 'BoardAssetsTotal', id: imageDTO.board_id ?? 'none' }, ], async onQueryStarted( @@ -589,7 +687,7 @@ export const imagesApi = api.injectEndpoints({ 'getImageDTO', imageDTO.image_name, (draft) => { - Object.assign(draft, { board_id }); + draft.board_id = board_id; } ) ) @@ -606,13 +704,7 @@ export const imagesApi = api.injectEndpoints({ categories, }, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.removeOne( - draft, - imageDTO.image_name - ); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.removeOne(draft, imageDTO.image_name); } ) ) @@ -630,9 +722,18 @@ export const imagesApi = api.injectEndpoints({ // OR // - The image's `created_at` is within the range of the cached images + const { data: total } = IMAGE_CATEGORIES.includes( + imageDTO.image_category + ) + ? boardsApi.endpoints.getBoardImagesTotal.select( + imageDTO.board_id ?? 'none' + )(getState()) + : boardsApi.endpoints.getBoardAssetsTotal.select( + imageDTO.board_id ?? 'none' + )(getState()); + const isCacheFullyPopulated = - currentCache.data && - currentCache.data.ids.length >= currentCache.data.total; + currentCache.data && currentCache.data.ids.length >= (total ?? 0); const isInDateRange = getIsImageInDateRange( currentCache.data, @@ -647,10 +748,7 @@ export const imagesApi = api.injectEndpoints({ 'listImages', queryArgs, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.addOne(draft, imageDTO); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.addOne(draft, imageDTO); } ) ) @@ -667,20 +765,26 @@ export const imagesApi = api.injectEndpoints({ }), removeImageFromBoard: build.mutation({ query: ({ imageDTO }) => { - const { board_id, image_name } = imageDTO; + const { image_name } = imageDTO; return { url: `board_images/`, method: 'DELETE', - body: { board_id, image_name }, + body: { image_name }, }; }, - invalidatesTags: (result, error, { imageDTO }) => [ - { type: 'Board', id: imageDTO.board_id }, - { type: 'BoardImagesTotal', id: imageDTO.board_id }, - { type: 'BoardImagesTotal', id: 'none' }, - { type: 'BoardAssetsTotal', id: imageDTO.board_id }, - { type: 'BoardAssetsTotal', id: 'none' }, - ], + invalidatesTags: (result, error, { imageDTO }) => { + const { board_id } = imageDTO; + return [ + // invalidate the image's old board + { type: 'Board', id: board_id ?? 'none' }, + // update old board totals + { type: 'BoardImagesTotal', id: board_id ?? 'none' }, + { type: 'BoardAssetsTotal', id: board_id ?? 'none' }, + // update the no_board totals + { type: 'BoardImagesTotal', id: 'none' }, + { type: 'BoardAssetsTotal', id: 'none' }, + ]; + }, async onQueryStarted( { imageDTO }, { dispatch, queryFulfilled, getState } @@ -704,7 +808,7 @@ export const imagesApi = api.injectEndpoints({ 'getImageDTO', imageDTO.image_name, (draft) => { - Object.assign(draft, { board_id: undefined }); + draft.board_id = undefined; } ) ) @@ -720,13 +824,7 @@ export const imagesApi = api.injectEndpoints({ categories, }, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.removeOne( - draft, - imageDTO.image_name - ); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.removeOne(draft, imageDTO.image_name); } ) ) @@ -744,9 +842,18 @@ export const imagesApi = api.injectEndpoints({ // OR // - The image's `created_at` is within the range of the cached images + const { data: total } = IMAGE_CATEGORIES.includes( + imageDTO.image_category + ) + ? boardsApi.endpoints.getBoardImagesTotal.select( + imageDTO.board_id ?? 'none' + )(getState()) + : boardsApi.endpoints.getBoardAssetsTotal.select( + imageDTO.board_id ?? 'none' + )(getState()); + const isCacheFullyPopulated = - currentCache.data && - currentCache.data.ids.length >= currentCache.data.total; + currentCache.data && currentCache.data.ids.length >= (total ?? 0); const isInDateRange = getIsImageInDateRange( currentCache.data, @@ -761,10 +868,7 @@ export const imagesApi = api.injectEndpoints({ 'listImages', queryArgs, (draft) => { - const oldTotal = draft.total; - const newState = imagesAdapter.upsertOne(draft, imageDTO); - const delta = newState.total - oldTotal; - draft.total = draft.total + delta; + imagesAdapter.upsertOne(draft, imageDTO); } ) ) @@ -778,6 +882,263 @@ export const imagesApi = api.injectEndpoints({ } }, }), + addImagesToBoard: build.mutation< + components['schemas']['AddImagesToBoardResult'], + { + board_id: string; + imageDTOs: ImageDTO[]; + } + >({ + query: ({ board_id, imageDTOs }) => ({ + url: `board_images/batch`, + method: 'POST', + body: { + image_names: imageDTOs.map((i) => i.image_name), + board_id, + }, + }), + invalidatesTags: (result, error, { imageDTOs, board_id }) => { + //assume all images are being moved from one board for now + const oldBoardId = imageDTOs[0]?.board_id; + return [ + // update the destination board + { type: 'Board', id: board_id ?? 'none' }, + // update new board totals + { type: 'BoardImagesTotal', id: board_id ?? 'none' }, + { type: 'BoardAssetsTotal', id: board_id ?? 'none' }, + // update old board totals + { type: 'BoardImagesTotal', id: oldBoardId ?? 'none' }, + { type: 'BoardAssetsTotal', id: oldBoardId ?? 'none' }, + // update the no_board totals + { type: 'BoardImagesTotal', id: 'none' }, + { type: 'BoardAssetsTotal', id: 'none' }, + ]; + }, + async onQueryStarted( + { board_id: new_board_id, imageDTOs }, + { dispatch, queryFulfilled, getState } + ) { + try { + const { data } = await queryFulfilled; + const { added_image_names } = data; + + /** + * Cache changes for addImagesToBoard: + * - *update* getImageDTO for each image + * - *add* to board_id/[images|assets] + * - *remove* from [old_board_id|no_board]/[images|assets] + */ + + added_image_names.forEach((image_name) => { + dispatch( + imagesApi.util.updateQueryData( + 'getImageDTO', + image_name, + (draft) => { + draft.board_id = new_board_id; + } + ) + ); + + const imageDTO = imageDTOs.find((i) => i.image_name === image_name); + + if (!imageDTO) { + return; + } + + const categories = getCategories(imageDTO); + const old_board_id = imageDTO.board_id; + + // remove from the old board + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + { board_id: old_board_id ?? 'none', categories }, + (draft) => { + imagesAdapter.removeOne(draft, imageDTO.image_name); + } + ) + ); + + const queryArgs = { + board_id: new_board_id, + categories, + }; + + const currentCache = imagesApi.endpoints.listImages.select( + queryArgs + )(getState()); + + const { data: previousTotal } = IMAGE_CATEGORIES.includes( + imageDTO.image_category + ) + ? boardsApi.endpoints.getBoardImagesTotal.select( + new_board_id ?? 'none' + )(getState()) + : boardsApi.endpoints.getBoardAssetsTotal.select( + new_board_id ?? 'none' + )(getState()); + + const isCacheFullyPopulated = + currentCache.data && + currentCache.data.ids.length >= (previousTotal ?? 0); + + const isInDateRange = + (previousTotal || 0) >= IMAGE_LIMIT + ? getIsImageInDateRange(currentCache.data, imageDTO) + : true; + + if (isCacheFullyPopulated || isInDateRange) { + // *upsert* to $cache + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + queryArgs, + (draft) => { + imagesAdapter.upsertOne(draft, { + ...imageDTO, + board_id: new_board_id, + }); + } + ) + ); + } + }); + } catch { + // no-op + } + }, + }), + removeImagesFromBoard: build.mutation< + components['schemas']['RemoveImagesFromBoardResult'], + { + imageDTOs: ImageDTO[]; + } + >({ + query: ({ imageDTOs }) => ({ + url: `board_images/batch/delete`, + method: 'POST', + body: { + image_names: imageDTOs.map((i) => i.image_name), + }, + }), + invalidatesTags: (result, error, { imageDTOs }) => { + const touchedBoardIds: string[] = []; + const tags: ApiFullTagDescription[] = [ + { type: 'BoardImagesTotal', id: 'none' }, + { type: 'BoardAssetsTotal', id: 'none' }, + ]; + + result?.removed_image_names.forEach((image_name) => { + const board_id = imageDTOs.find( + (i) => i.image_name === image_name + )?.board_id; + + if (!board_id || touchedBoardIds.includes(board_id)) { + return; + } + + tags.push({ type: 'Board', id: board_id }); + tags.push({ type: 'BoardImagesTotal', id: board_id }); + tags.push({ type: 'BoardAssetsTotal', id: board_id }); + }); + + return tags; + }, + async onQueryStarted( + { imageDTOs }, + { dispatch, queryFulfilled, getState } + ) { + try { + const { data } = await queryFulfilled; + const { removed_image_names } = data; + + /** + * Cache changes for removeImagesFromBoard: + * - *update* getImageDTO for each image + * - *remove* from old_board_id/[images|assets] + * - *add* to no_board/[images|assets] + */ + + removed_image_names.forEach((image_name) => { + dispatch( + imagesApi.util.updateQueryData( + 'getImageDTO', + image_name, + (draft) => { + draft.board_id = undefined; + } + ) + ); + + const imageDTO = imageDTOs.find((i) => i.image_name === image_name); + + if (!imageDTO) { + return; + } + + const categories = getCategories(imageDTO); + + // remove from the old board + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + { board_id: imageDTO.board_id ?? 'none', categories }, + (draft) => { + imagesAdapter.removeOne(draft, imageDTO.image_name); + } + ) + ); + + // add to `no_board` + const queryArgs = { + board_id: 'none', + categories, + }; + + const currentCache = imagesApi.endpoints.listImages.select( + queryArgs + )(getState()); + + const { data: total } = IMAGE_CATEGORIES.includes( + imageDTO.image_category + ) + ? boardsApi.endpoints.getBoardImagesTotal.select( + imageDTO.board_id ?? 'none' + )(getState()) + : boardsApi.endpoints.getBoardAssetsTotal.select( + imageDTO.board_id ?? 'none' + )(getState()); + + const isCacheFullyPopulated = + currentCache.data && currentCache.data.ids.length >= (total ?? 0); + + const isInDateRange = + (total || 0) >= IMAGE_LIMIT + ? getIsImageInDateRange(currentCache.data, imageDTO) + : true; + + if (isCacheFullyPopulated || isInDateRange) { + // *upsert* to $cache + dispatch( + imagesApi.util.updateQueryData( + 'listImages', + queryArgs, + (draft) => { + imagesAdapter.upsertOne(draft, { + ...imageDTO, + board_id: 'none', + }); + } + ) + ); + } + }); + } catch { + // no-op + } + }, + }), }), }); @@ -788,10 +1149,15 @@ export const { useGetImageDTOQuery, useGetImageMetadataQuery, useDeleteImageMutation, - useGetBoardImagesTotalQuery, - useGetBoardAssetsTotalQuery, + useDeleteImagesMutation, useUploadImageMutation, + useClearIntermediatesMutation, + useAddImagesToBoardMutation, + useRemoveImagesFromBoardMutation, useAddImageToBoardMutation, useRemoveImageFromBoardMutation, - useClearIntermediatesMutation, + useChangeImageIsIntermediateMutation, + useChangeImageSessionIdMutation, + useDeleteBoardAndImagesMutation, + useDeleteBoardMutation, } = imagesApi; diff --git a/invokeai/frontend/web/src/services/api/endpoints/models.ts b/invokeai/frontend/web/src/services/api/endpoints/models.ts index a7b1323f36..33eb1fbdc2 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/models.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/models.ts @@ -5,7 +5,6 @@ import { BaseModelType, CheckpointModelConfig, ControlNetModelConfig, - ConvertModelConfig, DiffusersModelConfig, ImportModelConfig, LoRAModelConfig, @@ -83,7 +82,7 @@ type DeleteLoRAModelResponse = void; type ConvertMainModelArg = { base_model: BaseModelType; model_name: string; - params: ConvertModelConfig; + convert_dest_directory?: string; }; type ConvertMainModelResponse = @@ -122,7 +121,7 @@ type CheckpointConfigsResponse = type SearchFolderArg = operations['search_for_models']['parameters']['query']; -const mainModelsAdapter = createEntityAdapter({ +export const mainModelsAdapter = createEntityAdapter({ sortComparer: (a, b) => a.model_name.localeCompare(b.model_name), }); @@ -132,15 +131,15 @@ const onnxModelsAdapter = createEntityAdapter({ const loraModelsAdapter = createEntityAdapter({ sortComparer: (a, b) => a.model_name.localeCompare(b.model_name), }); -const controlNetModelsAdapter = +export const controlNetModelsAdapter = createEntityAdapter({ sortComparer: (a, b) => a.model_name.localeCompare(b.model_name), }); -const textualInversionModelsAdapter = +export const textualInversionModelsAdapter = createEntityAdapter({ sortComparer: (a, b) => a.model_name.localeCompare(b.model_name), }); -const vaeModelsAdapter = createEntityAdapter({ +export const vaeModelsAdapter = createEntityAdapter({ sortComparer: (a, b) => a.model_name.localeCompare(b.model_name), }); @@ -320,11 +319,11 @@ export const modelsApi = api.injectEndpoints({ ConvertMainModelResponse, ConvertMainModelArg >({ - query: ({ base_model, model_name, params }) => { + query: ({ base_model, model_name, convert_dest_directory }) => { return { url: `models/convert/${base_model}/main/${model_name}`, method: 'PUT', - params: params, + params: { convert_dest_directory }, }; }, invalidatesTags: [ diff --git a/invokeai/frontend/web/src/services/api/hooks/useBoardName.ts b/invokeai/frontend/web/src/services/api/hooks/useBoardName.ts index 748f2c8f6e..ce0cff7b8a 100644 --- a/invokeai/frontend/web/src/services/api/hooks/useBoardName.ts +++ b/invokeai/frontend/web/src/services/api/hooks/useBoardName.ts @@ -1,7 +1,7 @@ import { BoardId } from 'features/gallery/store/types'; import { useListAllBoardsQuery } from '../endpoints/boards'; -export const useBoardName = (board_id: BoardId | null | undefined) => { +export const useBoardName = (board_id: BoardId) => { const { boardName } = useListAllBoardsQuery(undefined, { selectFromResult: ({ data }) => { const selectedBoard = data?.find((b) => b.board_id === board_id); diff --git a/invokeai/frontend/web/src/services/api/hooks/useBoardTotal.ts b/invokeai/frontend/web/src/services/api/hooks/useBoardTotal.ts index dd144ffe00..a350979b89 100644 --- a/invokeai/frontend/web/src/services/api/hooks/useBoardTotal.ts +++ b/invokeai/frontend/web/src/services/api/hooks/useBoardTotal.ts @@ -4,7 +4,7 @@ import { useMemo } from 'react'; import { useGetBoardAssetsTotalQuery, useGetBoardImagesTotalQuery, -} from '../endpoints/images'; +} from '../endpoints/boards'; export const useBoardTotal = (board_id: BoardId) => { const galleryView = useAppSelector((state) => state.gallery.galleryView); diff --git a/invokeai/frontend/web/src/services/api/index.ts b/invokeai/frontend/web/src/services/api/index.ts index 0a0391898c..a9de7130c9 100644 --- a/invokeai/frontend/web/src/services/api/index.ts +++ b/invokeai/frontend/web/src/services/api/index.ts @@ -6,7 +6,7 @@ import { createApi, fetchBaseQuery, } from '@reduxjs/toolkit/query/react'; -import { $authToken, $baseUrl } from 'services/api/client'; +import { $authToken, $baseUrl, $projectId } from 'services/api/client'; export const tagTypes = [ 'Board', @@ -30,6 +30,7 @@ const dynamicBaseQuery: BaseQueryFn< > = async (args, api, extraOptions) => { const baseUrl = $baseUrl.get(); const authToken = $authToken.get(); + const projectId = $projectId.get(); const rawBaseQuery = fetchBaseQuery({ baseUrl: `${baseUrl ?? ''}/api/v1`, @@ -37,6 +38,9 @@ const dynamicBaseQuery: BaseQueryFn< if (authToken) { headers.set('Authorization', `Bearer ${authToken}`); } + if (projectId) { + headers.set("project-id", projectId) + } return headers; }, diff --git a/invokeai/frontend/web/src/services/api/schema.d.ts b/invokeai/frontend/web/src/services/api/schema.d.ts index 80f0933f37..0bfa7c334f 100644 --- a/invokeai/frontend/web/src/services/api/schema.d.ts +++ b/invokeai/frontend/web/src/services/api/schema.d.ts @@ -135,19 +135,14 @@ export type paths = { */ put: operations["merge_models"]; }; - "/api/v1/images/": { - /** - * List Image Dtos - * @description Gets a list of image DTOs - */ - get: operations["list_image_dtos"]; + "/api/v1/images/upload": { /** * Upload Image * @description Uploads an image */ post: operations["upload_image"]; }; - "/api/v1/images/{image_name}": { + "/api/v1/images/i/{image_name}": { /** * Get Image Dto * @description Gets an image's DTO @@ -171,34 +166,50 @@ export type paths = { */ post: operations["clear_intermediates"]; }; - "/api/v1/images/{image_name}/metadata": { + "/api/v1/images/i/{image_name}/metadata": { /** * Get Image Metadata * @description Gets an image's metadata */ get: operations["get_image_metadata"]; }; - "/api/v1/images/{image_name}/full": { + "/api/v1/images/i/{image_name}/full": { /** * Get Image Full * @description Gets a full-resolution image file */ get: operations["get_image_full"]; + /** + * Get Image Full + * @description Gets a full-resolution image file + */ + head: operations["get_image_full"]; }; - "/api/v1/images/{image_name}/thumbnail": { + "/api/v1/images/i/{image_name}/thumbnail": { /** * Get Image Thumbnail * @description Gets a thumbnail image file */ get: operations["get_image_thumbnail"]; }; - "/api/v1/images/{image_name}/urls": { + "/api/v1/images/i/{image_name}/urls": { /** * Get Image Urls * @description Gets an image and thumbnail URL */ get: operations["get_image_urls"]; }; + "/api/v1/images/": { + /** + * List Image Dtos + * @description Gets a list of image DTOs + */ + get: operations["list_image_dtos"]; + }; + "/api/v1/images/delete": { + /** Delete Images From List */ + post: operations["delete_images_from_list"]; + }; "/api/v1/boards/": { /** * List Boards @@ -237,15 +248,29 @@ export type paths = { }; "/api/v1/board_images/": { /** - * Create Board Image + * Add Image To Board * @description Creates a board_image */ - post: operations["create_board_image"]; + post: operations["add_image_to_board"]; /** - * Remove Board Image - * @description Deletes a board_image + * Remove Image From Board + * @description Removes an image from its board, if it had one */ - delete: operations["remove_board_image"]; + delete: operations["remove_image_from_board"]; + }; + "/api/v1/board_images/batch": { + /** + * Add Images To Board + * @description Adds a list of images to a board + */ + post: operations["add_images_to_board"]; + }; + "/api/v1/board_images/batch/delete": { + /** + * Remove Images From Board + * @description Removes a list of images from their board, if they had one + */ + post: operations["remove_images_from_board"]; }; "/api/v1/app/version": { /** Get Version */ @@ -273,6 +298,19 @@ export type webhooks = Record; export type components = { schemas: { + /** AddImagesToBoardResult */ + AddImagesToBoardResult: { + /** + * Board Id + * @description The id of the board the images were added to + */ + board_id: string; + /** + * Added Image Names + * @description The image names that were added to the board + */ + added_image_names: (string)[]; + }; /** * AddInvocation * @description Adds two numbers @@ -405,8 +443,8 @@ export type components = { */ image_count: number; }; - /** Body_create_board_image */ - Body_create_board_image: { + /** Body_add_image_to_board */ + Body_add_image_to_board: { /** * Board Id * @description The id of the board to add to @@ -418,6 +456,27 @@ export type components = { */ image_name: string; }; + /** Body_add_images_to_board */ + Body_add_images_to_board: { + /** + * Board Id + * @description The id of the board to add to + */ + board_id: string; + /** + * Image Names + * @description The names of the images to add + */ + image_names: (string)[]; + }; + /** Body_delete_images_from_list */ + Body_delete_images_from_list: { + /** + * Image Names + * @description The list of names of images to delete + */ + image_names: (string)[]; + }; /** Body_import_model */ Body_import_model: { /** @@ -465,19 +524,22 @@ export type components = { */ merge_dest_directory?: string; }; - /** Body_remove_board_image */ - Body_remove_board_image: { - /** - * Board Id - * @description The id of the board - */ - board_id: string; + /** Body_remove_image_from_board */ + Body_remove_image_from_board: { /** * Image Name * @description The name of the image to remove */ image_name: string; }; + /** Body_remove_images_from_board */ + Body_remove_images_from_board: { + /** + * Image Names + * @description The names of the images to remove + */ + image_names: (string)[]; + }; /** Body_upload_image */ Body_upload_image: { /** @@ -650,6 +712,51 @@ export type components = { */ collection: (unknown)[]; }; + /** + * ColorCorrectInvocation + * @description Shifts the colors of a target image to match the reference image, optionally + * using a mask to only color-correct certain regions of the target image. + */ + ColorCorrectInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default color_correct + * @enum {string} + */ + type?: "color_correct"; + /** + * Image + * @description The image to color-correct + */ + image?: components["schemas"]["ImageField"]; + /** + * Reference + * @description Reference image for color-correction + */ + reference?: components["schemas"]["ImageField"]; + /** + * Mask + * @description Mask to use when applying color-correction + */ + mask?: components["schemas"]["ImageField"]; + /** + * Mask Blur Radius + * @description Mask blur radius + * @default 8 + */ + mask_blur_radius?: number; + }; /** ColorField */ ColorField: { /** @@ -980,6 +1087,12 @@ export type components = { * @description Core generation metadata for an image generated in InvokeAI. */ CoreMetadata: { + /** + * App Version + * @description The version of InvokeAI used to generate this image + * @default 3.0.2 + */ + app_version?: string; /** * Generation Mode * @description The generation mode that output this image @@ -1096,10 +1209,15 @@ export type components = { */ refiner_scheduler?: string; /** - * Refiner Aesthetic Store + * Refiner Positive Aesthetic Store * @description The aesthetic score used for the refiner */ - refiner_aesthetic_store?: number; + refiner_positive_aesthetic_store?: number; + /** + * Refiner Negative Aesthetic Store + * @description The aesthetic score used for the refiner + */ + refiner_negative_aesthetic_store?: number; /** * Refiner Start * @description The start value used for refiner denoising @@ -1157,6 +1275,98 @@ export type components = { */ deleted_images: (string)[]; }; + /** DeleteImagesFromListResult */ + DeleteImagesFromListResult: { + /** Deleted Images */ + deleted_images: (string)[]; + }; + /** + * DenoiseLatentsInvocation + * @description Denoises noisy latents to decodable images + */ + DenoiseLatentsInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default denoise_latents + * @enum {string} + */ + type?: "denoise_latents"; + /** + * Positive Conditioning + * @description Positive conditioning for generation + */ + positive_conditioning?: components["schemas"]["ConditioningField"]; + /** + * Negative Conditioning + * @description Negative conditioning for generation + */ + negative_conditioning?: components["schemas"]["ConditioningField"]; + /** + * Noise + * @description The noise to use + */ + noise?: components["schemas"]["LatentsField"]; + /** + * Steps + * @description The number of steps to use to generate the image + * @default 10 + */ + steps?: number; + /** + * Cfg Scale + * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt + * @default 7.5 + */ + cfg_scale?: number | (number)[]; + /** + * Denoising Start + * @default 0 + */ + denoising_start?: number; + /** + * Denoising End + * @default 1 + */ + denoising_end?: number; + /** + * Scheduler + * @description The scheduler to use + * @default euler + * @enum {string} + */ + scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc"; + /** + * Unet + * @description UNet submodel + */ + unet?: components["schemas"]["UNetField"]; + /** + * Control + * @description The control to use + */ + control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[]; + /** + * Latents + * @description The latents to use as a base image + */ + latents?: components["schemas"]["LatentsField"]; + /** + * Mask + * @description Mask + */ + mask?: components["schemas"]["ImageField"]; + }; /** * DivideInvocation * @description Divides two numbers @@ -1381,7 +1591,7 @@ export type components = { * @description The nodes in this graph */ nodes?: { - [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined; + [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"]) | undefined; }; /** * Edges @@ -1424,7 +1634,7 @@ export type components = { * @description The results of node executions */ results: { - [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined; + [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined; }; /** * Errors @@ -1842,6 +2052,40 @@ export type components = { */ image_name: string; }; + /** + * ImageHueAdjustmentInvocation + * @description Adjusts the Hue of an image. + */ + ImageHueAdjustmentInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default img_hue_adjust + * @enum {string} + */ + type?: "img_hue_adjust"; + /** + * Image + * @description The image to adjust + */ + image?: components["schemas"]["ImageField"]; + /** + * Hue + * @description The degrees by which to rotate the hue, 0-360 + * @default 0 + */ + hue?: number; + }; /** * ImageInverseLerpInvocation * @description Inverse linear interpolation of all pixels of an image @@ -1922,6 +2166,40 @@ export type components = { */ max?: number; }; + /** + * ImageLuminosityAdjustmentInvocation + * @description Adjusts the Luminosity (Value) of an image. + */ + ImageLuminosityAdjustmentInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default img_luminosity_adjust + * @enum {string} + */ + type?: "img_luminosity_adjust"; + /** + * Image + * @description The image to adjust + */ + image?: components["schemas"]["ImageField"]; + /** + * Luminosity + * @description The factor by which to adjust the luminosity (value) + * @default 1 + */ + luminosity?: number; + }; /** * ImageMetadata * @description An image's generation metadata @@ -2177,6 +2455,40 @@ export type components = { */ resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos"; }; + /** + * ImageSaturationAdjustmentInvocation + * @description Adjusts the Saturation of an image. + */ + ImageSaturationAdjustmentInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default img_saturation_adjust + * @enum {string} + */ + type?: "img_saturation_adjust"; + /** + * Image + * @description The image to adjust + */ + image?: components["schemas"]["ImageField"]; + /** + * Saturation + * @description The factor by which to adjust the saturation + * @default 1 + */ + saturation?: number; + }; /** * ImageScaleInvocation * @description Scales an image by a factor @@ -2429,171 +2741,6 @@ export type components = { */ seed?: number; }; - /** - * InpaintInvocation - * @description Generates an image using inpaint. - */ - InpaintInvocation: { - /** - * Id - * @description The id of this node. Must be unique among all nodes. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this node is an intermediate node. - * @default false - */ - is_intermediate?: boolean; - /** - * Type - * @default inpaint - * @enum {string} - */ - type?: "inpaint"; - /** - * Positive Conditioning - * @description Positive conditioning for generation - */ - positive_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Negative Conditioning - * @description Negative conditioning for generation - */ - negative_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Seed - * @description The seed to use (omit for random) - */ - seed?: number; - /** - * Steps - * @description The number of steps to use to generate the image - * @default 30 - */ - steps?: number; - /** - * Width - * @description The width of the resulting image - * @default 512 - */ - width?: number; - /** - * Height - * @description The height of the resulting image - * @default 512 - */ - height?: number; - /** - * Cfg Scale - * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt - * @default 7.5 - */ - cfg_scale?: number; - /** - * Scheduler - * @description The scheduler to use - * @default euler - * @enum {string} - */ - scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc"; - /** - * Unet - * @description UNet model - */ - unet?: components["schemas"]["UNetField"]; - /** - * Vae - * @description Vae model - */ - vae?: components["schemas"]["VaeField"]; - /** - * Image - * @description The input image - */ - image?: components["schemas"]["ImageField"]; - /** - * Strength - * @description The strength of the original image - * @default 0.75 - */ - strength?: number; - /** - * Fit - * @description Whether or not the result should be fit to the aspect ratio of the input image - * @default true - */ - fit?: boolean; - /** - * Mask - * @description The mask - */ - mask?: components["schemas"]["ImageField"]; - /** - * Seam Size - * @description The seam inpaint size (px) - * @default 96 - */ - seam_size?: number; - /** - * Seam Blur - * @description The seam inpaint blur radius (px) - * @default 16 - */ - seam_blur?: number; - /** - * Seam Strength - * @description The seam inpaint strength - * @default 0.75 - */ - seam_strength?: number; - /** - * Seam Steps - * @description The number of steps to use for seam inpaint - * @default 30 - */ - seam_steps?: number; - /** - * Tile Size - * @description The tile infill method size (px) - * @default 32 - */ - tile_size?: number; - /** - * Infill Method - * @description The method used to infill empty regions (px) - * @default patchmatch - * @enum {string} - */ - infill_method?: "patchmatch" | "tile" | "solid"; - /** - * Inpaint Width - * @description The width of the inpaint region (px) - */ - inpaint_width?: number; - /** - * Inpaint Height - * @description The height of the inpaint region (px) - */ - inpaint_height?: number; - /** - * Inpaint Fill - * @description The solid infill method color - * @default { - * "r": 127, - * "g": 127, - * "b": 127, - * "a": 255 - * } - */ - inpaint_fill?: components["schemas"]["ColorField"]; - /** - * Inpaint Replace - * @description The amount by which to replace masked areas with latent noise - * @default 0 - */ - inpaint_replace?: number; - }; /** * IntCollectionOutput * @description A collection of integers @@ -2690,6 +2837,11 @@ export type components = { * @description The name of the latents */ latents_name: string; + /** + * Seed + * @description Seed used to generate this latents + */ + seed?: number; }; /** * LatentsOutput @@ -2768,84 +2920,6 @@ export type components = { */ metadata?: components["schemas"]["CoreMetadata"]; }; - /** - * LatentsToLatentsInvocation - * @description Generates latents using latents as base image. - */ - LatentsToLatentsInvocation: { - /** - * Id - * @description The id of this node. Must be unique among all nodes. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this node is an intermediate node. - * @default false - */ - is_intermediate?: boolean; - /** - * Type - * @default l2l - * @enum {string} - */ - type?: "l2l"; - /** - * Positive Conditioning - * @description Positive conditioning for generation - */ - positive_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Negative Conditioning - * @description Negative conditioning for generation - */ - negative_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Noise - * @description The noise to use - */ - noise?: components["schemas"]["LatentsField"]; - /** - * Steps - * @description The number of steps to use to generate the image - * @default 10 - */ - steps?: number; - /** - * Cfg Scale - * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt - * @default 7.5 - */ - cfg_scale?: number | (number)[]; - /** - * Scheduler - * @description The scheduler to use - * @default euler - * @enum {string} - */ - scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc"; - /** - * Unet - * @description UNet submodel - */ - unet?: components["schemas"]["UNetField"]; - /** - * Control - * @description The control to use - */ - control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[]; - /** - * Latents - * @description The latents to use as a base image - */ - latents?: components["schemas"]["LatentsField"]; - /** - * Strength - * @description The strength of the latents to use - * @default 0.7 - */ - strength?: number; - }; /** * LeresImageProcessorInvocation * @description Applies leres processing to image @@ -3204,6 +3278,87 @@ export type components = { */ model: components["schemas"]["MainModelField"]; }; + /** + * MaskCombineInvocation + * @description Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`. + */ + MaskCombineInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default mask_combine + * @enum {string} + */ + type?: "mask_combine"; + /** + * Mask1 + * @description The first mask to combine + */ + mask1?: components["schemas"]["ImageField"]; + /** + * Mask2 + * @description The second image to combine + */ + mask2?: components["schemas"]["ImageField"]; + }; + /** + * MaskEdgeInvocation + * @description Applies an edge mask to an image + */ + MaskEdgeInvocation: { + /** + * Id + * @description The id of this node. Must be unique among all nodes. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this node is an intermediate node. + * @default false + */ + is_intermediate?: boolean; + /** + * Type + * @default mask_edge + * @enum {string} + */ + type?: "mask_edge"; + /** + * Image + * @description The image to apply the mask to + */ + image?: components["schemas"]["ImageField"]; + /** + * Edge Size + * @description The size of the edge + */ + edge_size: number; + /** + * Edge Blur + * @description The amount of blur on the edge + */ + edge_blur: number; + /** + * Low Threshold + * @description First threshold for the hysteresis procedure in Canny edge detection + */ + low_threshold: number; + /** + * High Threshold + * @description Second threshold for the hysteresis procedure in Canny edge detection + */ + high_threshold: number; + }; /** * MaskFromAlphaInvocation * @description Extracts the alpha channel of an image as a mask. @@ -3449,10 +3604,15 @@ export type components = { */ refiner_scheduler?: string; /** - * Refiner Aesthetic Store + * Refiner Positive Aesthetic Score * @description The aesthetic score used for the refiner */ - refiner_aesthetic_store?: number; + refiner_positive_aesthetic_score?: number; + /** + * Refiner Negative Aesthetic Score + * @description The aesthetic score used for the refiner + */ + refiner_negative_aesthetic_score?: number; /** * Refiner Start * @description The start value used for refiner denoising @@ -4627,6 +4787,14 @@ export type components = { */ step?: number; }; + /** RemoveImagesFromBoardResult */ + RemoveImagesFromBoardResult: { + /** + * Removed Image Names + * @description The image names that were removed from their board + */ + removed_image_names: (string)[]; + }; /** * ResizeLatentsInvocation * @description Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. @@ -4766,10 +4934,10 @@ export type components = { clip2?: components["schemas"]["ClipField"]; }; /** - * SDXLLatentsToLatentsInvocation - * @description Generates latents from conditionings. + * SDXLLoraLoaderInvocation + * @description Apply selected lora to unet and text_encoder. */ - SDXLLatentsToLatentsInvocation: { + SDXLLoraLoaderInvocation: { /** * Id * @description The id of this node. Must be unique among all nodes. @@ -4783,64 +4951,63 @@ export type components = { is_intermediate?: boolean; /** * Type - * @default l2l_sdxl + * @default sdxl_lora_loader * @enum {string} */ - type?: "l2l_sdxl"; + type?: "sdxl_lora_loader"; /** - * Positive Conditioning - * @description Positive conditioning for generation + * Lora + * @description Lora model name */ - positive_conditioning?: components["schemas"]["ConditioningField"]; + lora?: components["schemas"]["LoRAModelField"]; /** - * Negative Conditioning - * @description Negative conditioning for generation + * Weight + * @description With what weight to apply lora + * @default 0.75 */ - negative_conditioning?: components["schemas"]["ConditioningField"]; + weight?: number; /** - * Noise - * @description The noise to use + * Unet + * @description UNet model for applying lora */ - noise?: components["schemas"]["LatentsField"]; + unet?: components["schemas"]["UNetField"]; /** - * Steps - * @description The number of steps to use to generate the image - * @default 10 + * Clip + * @description Clip model for applying lora */ - steps?: number; + clip?: components["schemas"]["ClipField"]; /** - * Cfg Scale - * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt - * @default 7.5 + * Clip2 + * @description Clip2 model for applying lora */ - cfg_scale?: number | (number)[]; + clip2?: components["schemas"]["ClipField"]; + }; + /** + * SDXLLoraLoaderOutput + * @description Model loader output + */ + SDXLLoraLoaderOutput: { /** - * Scheduler - * @description The scheduler to use - * @default euler + * Type + * @default sdxl_lora_loader_output * @enum {string} */ - scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc"; + type?: "sdxl_lora_loader_output"; /** * Unet * @description UNet submodel */ unet?: components["schemas"]["UNetField"]; /** - * Latents - * @description Initial latents + * Clip + * @description Tokenizer and text_encoder submodels */ - latents?: components["schemas"]["LatentsField"]; + clip?: components["schemas"]["ClipField"]; /** - * Denoising Start - * @default 0 + * Clip2 + * @description Tokenizer2 and text_encoder2 submodels */ - denoising_start?: number; - /** - * Denoising End - * @default 1 - */ - denoising_end?: number; + clip2?: components["schemas"]["ClipField"]; }; /** * SDXLModelLoaderInvocation @@ -4902,81 +5069,6 @@ export type components = { */ vae?: components["schemas"]["VaeField"]; }; - /** - * SDXLRawPromptInvocation - * @description Pass unmodified prompt to conditioning without compel processing. - */ - SDXLRawPromptInvocation: { - /** - * Id - * @description The id of this node. Must be unique among all nodes. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this node is an intermediate node. - * @default false - */ - is_intermediate?: boolean; - /** - * Type - * @default sdxl_raw_prompt - * @enum {string} - */ - type?: "sdxl_raw_prompt"; - /** - * Prompt - * @description Prompt - * @default - */ - prompt?: string; - /** - * Style - * @description Style prompt - * @default - */ - style?: string; - /** - * Original Width - * @default 1024 - */ - original_width?: number; - /** - * Original Height - * @default 1024 - */ - original_height?: number; - /** - * Crop Top - * @default 0 - */ - crop_top?: number; - /** - * Crop Left - * @default 0 - */ - crop_left?: number; - /** - * Target Width - * @default 1024 - */ - target_width?: number; - /** - * Target Height - * @default 1024 - */ - target_height?: number; - /** - * Clip - * @description Clip to use - */ - clip?: components["schemas"]["ClipField"]; - /** - * Clip2 - * @description Clip2 to use - */ - clip2?: components["schemas"]["ClipField"]; - }; /** * SDXLRefinerCompelPromptInvocation * @description Parse prompt using compel package to conditioning. @@ -5091,132 +5183,6 @@ export type components = { */ vae?: components["schemas"]["VaeField"]; }; - /** - * SDXLRefinerRawPromptInvocation - * @description Parse prompt using compel package to conditioning. - */ - SDXLRefinerRawPromptInvocation: { - /** - * Id - * @description The id of this node. Must be unique among all nodes. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this node is an intermediate node. - * @default false - */ - is_intermediate?: boolean; - /** - * Type - * @default sdxl_refiner_raw_prompt - * @enum {string} - */ - type?: "sdxl_refiner_raw_prompt"; - /** - * Style - * @description Style prompt - * @default - */ - style?: string; - /** - * Original Width - * @default 1024 - */ - original_width?: number; - /** - * Original Height - * @default 1024 - */ - original_height?: number; - /** - * Crop Top - * @default 0 - */ - crop_top?: number; - /** - * Crop Left - * @default 0 - */ - crop_left?: number; - /** - * Aesthetic Score - * @default 6 - */ - aesthetic_score?: number; - /** - * Clip2 - * @description Clip to use - */ - clip2?: components["schemas"]["ClipField"]; - }; - /** - * SDXLTextToLatentsInvocation - * @description Generates latents from conditionings. - */ - SDXLTextToLatentsInvocation: { - /** - * Id - * @description The id of this node. Must be unique among all nodes. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this node is an intermediate node. - * @default false - */ - is_intermediate?: boolean; - /** - * Type - * @default t2l_sdxl - * @enum {string} - */ - type?: "t2l_sdxl"; - /** - * Positive Conditioning - * @description Positive conditioning for generation - */ - positive_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Negative Conditioning - * @description Negative conditioning for generation - */ - negative_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Noise - * @description The noise to use - */ - noise?: components["schemas"]["LatentsField"]; - /** - * Steps - * @description The number of steps to use to generate the image - * @default 10 - */ - steps?: number; - /** - * Cfg Scale - * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt - * @default 7.5 - */ - cfg_scale?: number | (number)[]; - /** - * Scheduler - * @description The scheduler to use - * @default euler - * @enum {string} - */ - scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc"; - /** - * Unet - * @description UNet submodel - */ - unet?: components["schemas"]["UNetField"]; - /** - * Denoising End - * @default 1 - */ - denoising_end?: number; - }; /** * ScaleLatentsInvocation * @description Scales latents by a given factor. @@ -5615,73 +5581,6 @@ export type components = { */ b?: number; }; - /** - * TextToLatentsInvocation - * @description Generates latents from conditionings. - */ - TextToLatentsInvocation: { - /** - * Id - * @description The id of this node. Must be unique among all nodes. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this node is an intermediate node. - * @default false - */ - is_intermediate?: boolean; - /** - * Type - * @default t2l - * @enum {string} - */ - type?: "t2l"; - /** - * Positive Conditioning - * @description Positive conditioning for generation - */ - positive_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Negative Conditioning - * @description Negative conditioning for generation - */ - negative_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Noise - * @description The noise to use - */ - noise?: components["schemas"]["LatentsField"]; - /** - * Steps - * @description The number of steps to use to generate the image - * @default 10 - */ - steps?: number; - /** - * Cfg Scale - * @description The Classifier-Free Guidance, higher values may result in a result closer to the prompt - * @default 7.5 - */ - cfg_scale?: number | (number)[]; - /** - * Scheduler - * @description The scheduler to use - * @default euler - * @enum {string} - */ - scheduler?: "ddim" | "ddpm" | "deis" | "lms" | "lms_k" | "pndm" | "heun" | "heun_k" | "euler" | "euler_k" | "euler_a" | "kdpm_2" | "kdpm_2_a" | "dpmpp_2s" | "dpmpp_2s_k" | "dpmpp_2m" | "dpmpp_2m_k" | "dpmpp_2m_sde" | "dpmpp_2m_sde_k" | "dpmpp_sde" | "dpmpp_sde_k" | "unipc"; - /** - * Unet - * @description UNet submodel - */ - unet?: components["schemas"]["UNetField"]; - /** - * Control - * @description The control to use - */ - control?: components["schemas"]["ControlField"] | (components["schemas"]["ControlField"])[]; - }; /** TextualInversionModelConfig */ TextualInversionModelConfig: { /** Model Name */ @@ -5897,6 +5796,12 @@ export type components = { * @enum {string} */ ControlNetModelFormat: "checkpoint" | "diffusers"; + /** + * StableDiffusion2ModelFormat + * @description An enumeration. + * @enum {string} + */ + StableDiffusion2ModelFormat: "checkpoint" | "diffusers"; /** * StableDiffusionXLModelFormat * @description An enumeration. @@ -5909,12 +5814,6 @@ export type components = { * @enum {string} */ StableDiffusionOnnxModelFormat: "olive" | "onnx"; - /** - * StableDiffusion2ModelFormat - * @description An enumeration. - * @enum {string} - */ - StableDiffusion2ModelFormat: "checkpoint" | "diffusers"; /** * StableDiffusion1ModelFormat * @description An enumeration. @@ -6031,7 +5930,7 @@ export type operations = { }; requestBody: { content: { - "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]; + "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"]; }; }; responses: { @@ -6068,7 +5967,7 @@ export type operations = { }; requestBody: { content: { - "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]; + "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"]; }; }; responses: { @@ -6547,42 +6446,6 @@ export type operations = { }; }; }; - /** - * List Image Dtos - * @description Gets a list of image DTOs - */ - list_image_dtos: { - parameters: { - query?: { - /** @description The origin of images to list. */ - image_origin?: components["schemas"]["ResourceOrigin"]; - /** @description The categories of image to include. */ - categories?: (components["schemas"]["ImageCategory"])[]; - /** @description Whether to list intermediate images. */ - is_intermediate?: boolean; - /** @description The board id to filter by. Use 'none' to find images without a board. */ - board_id?: string; - /** @description The page offset */ - offset?: number; - /** @description The number of images per page */ - limit?: number; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["OffsetPaginatedResults_ImageDTO_"]; - }; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; /** * Upload Image * @description Uploads an image @@ -6829,6 +6692,64 @@ export type operations = { }; }; }; + /** + * List Image Dtos + * @description Gets a list of image DTOs + */ + list_image_dtos: { + parameters: { + query?: { + /** @description The origin of images to list. */ + image_origin?: components["schemas"]["ResourceOrigin"]; + /** @description The categories of image to include. */ + categories?: (components["schemas"]["ImageCategory"])[]; + /** @description Whether to list intermediate images. */ + is_intermediate?: boolean; + /** @description The board id to filter by. Use 'none' to find images without a board. */ + board_id?: string; + /** @description The page offset */ + offset?: number; + /** @description The number of images per page */ + limit?: number; + }; + }; + responses: { + /** @description Successful Response */ + 200: { + content: { + "application/json": components["schemas"]["OffsetPaginatedResults_ImageDTO_"]; + }; + }; + /** @description Validation Error */ + 422: { + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + /** Delete Images From List */ + delete_images_from_list: { + requestBody: { + content: { + "application/json": components["schemas"]["Body_delete_images_from_list"]; + }; + }; + responses: { + /** @description Successful Response */ + 200: { + content: { + "application/json": components["schemas"]["DeleteImagesFromListResult"]; + }; + }; + /** @description Validation Error */ + 422: { + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; /** * List Boards * @description Gets a list of boards @@ -6999,13 +6920,13 @@ export type operations = { }; }; /** - * Create Board Image + * Add Image To Board * @description Creates a board_image */ - create_board_image: { + add_image_to_board: { requestBody: { content: { - "application/json": components["schemas"]["Body_create_board_image"]; + "application/json": components["schemas"]["Body_add_image_to_board"]; }; }; responses: { @@ -7024,13 +6945,13 @@ export type operations = { }; }; /** - * Remove Board Image - * @description Deletes a board_image + * Remove Image From Board + * @description Removes an image from its board, if it had one */ - remove_board_image: { + remove_image_from_board: { requestBody: { content: { - "application/json": components["schemas"]["Body_remove_board_image"]; + "application/json": components["schemas"]["Body_remove_image_from_board"]; }; }; responses: { @@ -7048,6 +6969,56 @@ export type operations = { }; }; }; + /** + * Add Images To Board + * @description Adds a list of images to a board + */ + add_images_to_board: { + requestBody: { + content: { + "application/json": components["schemas"]["Body_add_images_to_board"]; + }; + }; + responses: { + /** @description Images were added to board successfully */ + 201: { + content: { + "application/json": components["schemas"]["AddImagesToBoardResult"]; + }; + }; + /** @description Validation Error */ + 422: { + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + /** + * Remove Images From Board + * @description Removes a list of images from their board, if they had one + */ + remove_images_from_board: { + requestBody: { + content: { + "application/json": components["schemas"]["Body_remove_images_from_board"]; + }; + }; + responses: { + /** @description Images were removed from board successfully */ + 201: { + content: { + "application/json": components["schemas"]["RemoveImagesFromBoardResult"]; + }; + }; + /** @description Validation Error */ + 422: { + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; /** Get Version */ app_version: { responses: { diff --git a/invokeai/frontend/web/src/services/api/thunks/session.ts b/invokeai/frontend/web/src/services/api/thunks/session.ts index 5588f25b46..29b032d1c3 100644 --- a/invokeai/frontend/web/src/services/api/thunks/session.ts +++ b/invokeai/frontend/web/src/services/api/thunks/session.ts @@ -60,6 +60,9 @@ type InvokedSessionThunkConfig = { const isErrorWithStatus = (error: unknown): error is { status: number } => isObject(error) && 'status' in error; +const isErrorWithDetail = (error: unknown): error is { detail: string } => + isObject(error) && 'detail' in error; + /** * `SessionsService.invokeSession()` thunk */ @@ -85,7 +88,15 @@ export const sessionInvoked = createAsyncThunk< error: (error as any).body.detail, }); } - return rejectWithValue({ arg, status: response.status, error }); + if (isErrorWithDetail(error) && response.status === 403) { + return rejectWithValue({ + arg, + status: response.status, + error: error.detail + }); + } + if (error) + return rejectWithValue({ arg, status: response.status, error }); } }); diff --git a/invokeai/frontend/web/src/services/api/types.d.ts b/invokeai/frontend/web/src/services/api/types.ts similarity index 83% rename from invokeai/frontend/web/src/services/api/types.d.ts rename to invokeai/frontend/web/src/services/api/types.ts index 2ee508fe48..435b605489 100644 --- a/invokeai/frontend/web/src/services/api/types.d.ts +++ b/invokeai/frontend/web/src/services/api/types.ts @@ -1,13 +1,40 @@ import { UseToastOptions } from '@chakra-ui/react'; +import { EntityState } from '@reduxjs/toolkit'; import { O } from 'ts-toolbelt'; -import { components } from './schema'; +import { components, paths } from './schema'; -type schemas = components['schemas']; +export type ImageCache = EntityState; + +export type ListImagesArgs = NonNullable< + paths['/api/v1/images/']['get']['parameters']['query'] +>; + +export type DeleteBoardResult = + paths['/api/v1/boards/{board_id}']['delete']['responses']['200']['content']['application/json']; + +export type ListBoardsArg = NonNullable< + paths['/api/v1/boards/']['get']['parameters']['query'] +>; + +export type UpdateBoardArg = + paths['/api/v1/boards/{board_id}']['patch']['parameters']['path'] & { + changes: paths['/api/v1/boards/{board_id}']['patch']['requestBody']['content']['application/json']; + }; + +/** + * This is an unsafe type; the object inside is not guaranteed to be valid. + */ +export type UnsafeImageMetadata = { + metadata: components['schemas']['CoreMetadata']; + graph: NonNullable; +}; /** * Marks the `type` property as required. Use for nodes. */ -type TypeReq = O.Required; +type TypeReq = O.Required; + +// Extracted types from API schema // App Info export type AppVersion = components['schemas']['AppVersion']; @@ -72,7 +99,6 @@ export type AnyModelConfig = | OnnxModelConfig; export type MergeModelConfig = components['schemas']['Body_merge_models']; -export type ConvertModelConfig = components['schemas']['Body_convert_model']; export type ImportModelConfig = components['schemas']['Body_import_model']; // Graphs @@ -94,9 +120,6 @@ export type RandomRangeInvocation = TypeReq< export type RangeOfSizeInvocation = TypeReq< components['schemas']['RangeOfSizeInvocation'] >; -export type InpaintInvocation = TypeReq< - components['schemas']['InpaintInvocation'] ->; export type ImageResizeInvocation = TypeReq< components['schemas']['ImageResizeInvocation'] >; @@ -113,14 +136,11 @@ export type DynamicPromptInvocation = TypeReq< components['schemas']['DynamicPromptInvocation'] >; export type NoiseInvocation = TypeReq; -export type TextToLatentsInvocation = TypeReq< - components['schemas']['TextToLatentsInvocation'] ->; export type ONNXTextToLatentsInvocation = TypeReq< components['schemas']['ONNXTextToLatentsInvocation'] >; -export type LatentsToLatentsInvocation = TypeReq< - components['schemas']['LatentsToLatentsInvocation'] +export type DenoiseLatentsInvocation = TypeReq< + components['schemas']['DenoiseLatentsInvocation'] >; export type ImageToLatentsInvocation = TypeReq< components['schemas']['ImageToLatentsInvocation'] @@ -140,6 +160,9 @@ export type OnnxModelLoaderInvocation = TypeReq< export type LoraLoaderInvocation = TypeReq< components['schemas']['LoraLoaderInvocation'] >; +export type SDXLLoraLoaderInvocation = TypeReq< + components['schemas']['SDXLLoraLoaderInvocation'] +>; export type MetadataAccumulatorInvocation = TypeReq< components['schemas']['MetadataAccumulatorInvocation'] >; @@ -149,12 +172,27 @@ export type ESRGANInvocation = TypeReq< export type DivideInvocation = TypeReq< components['schemas']['DivideInvocation'] >; +export type InfillTileInvocation = TypeReq< + components['schemas']['InfillTileInvocation'] +>; +export type InfillPatchmatchInvocation = TypeReq< + components['schemas']['InfillPatchMatchInvocation'] +>; export type ImageNSFWBlurInvocation = TypeReq< components['schemas']['ImageNSFWBlurInvocation'] >; export type ImageWatermarkInvocation = TypeReq< components['schemas']['ImageWatermarkInvocation'] >; +export type ImageBlurInvocation = TypeReq< + components['schemas']['ImageBlurInvocation'] +>; +export type ColorCorrectInvocation = TypeReq< + components['schemas']['ColorCorrectInvocation'] +>; +export type ImagePasteInvocation = TypeReq< + components['schemas']['ImagePasteInvocation'] +>; // ControlNet Nodes export type ControlNetInvocation = TypeReq< diff --git a/invokeai/frontend/web/src/services/api/util.ts b/invokeai/frontend/web/src/services/api/util.ts new file mode 100644 index 0000000000..20c9baedbb --- /dev/null +++ b/invokeai/frontend/web/src/services/api/util.ts @@ -0,0 +1,56 @@ +import { + ASSETS_CATEGORIES, + IMAGE_CATEGORIES, +} from 'features/gallery/store/types'; +import { ImageCache, ImageDTO, ListImagesArgs } from './types'; +import { createEntityAdapter } from '@reduxjs/toolkit'; +import { dateComparator } from 'common/util/dateComparator'; +import queryString from 'query-string'; + +export const getIsImageInDateRange = ( + data: ImageCache | undefined, + imageDTO: ImageDTO +) => { + if (!data) { + return false; + } + const cacheImageDTOS = imagesSelectors.selectAll(data); + + if (cacheImageDTOS.length > 1) { + // Images are sorted by `created_at` DESC + // check if the image is newer than the oldest image in the cache + const createdDate = new Date(imageDTO.created_at); + const oldestImage = cacheImageDTOS[cacheImageDTOS.length - 1]; + if (!oldestImage) { + // satisfy TS gods, we already confirmed the array has more than one image + return false; + } + const oldestDate = new Date(oldestImage.created_at); + return createdDate >= oldestDate; + } else if ([0, 1].includes(cacheImageDTOS.length)) { + // if there are only 1 or 0 images in the cache, we consider the image to be in the date range + return true; + } + return false; +}; + +export const getCategories = (imageDTO: ImageDTO) => { + if (IMAGE_CATEGORIES.includes(imageDTO.image_category)) { + return IMAGE_CATEGORIES; + } + return ASSETS_CATEGORIES; +}; + +// The adapter is not actually the data store - it just provides helper functions to interact +// with some other store of data. We will use the RTK Query cache as that store. +export const imagesAdapter = createEntityAdapter({ + selectId: (image) => image.image_name, + sortComparer: (a, b) => dateComparator(b.updated_at, a.updated_at), +}); + +// Create selectors for the adapter. +export const imagesSelectors = imagesAdapter.getSelectors(); + +// Helper to create the url for the listImages endpoint. Also we use it to create the cache key. +export const getListImagesUrl = (queryArgs: ListImagesArgs) => + `images/?${queryString.stringify(queryArgs, { arrayFormat: 'none' })}`; diff --git a/invokeai/frontend/web/src/services/events/middleware.ts b/invokeai/frontend/web/src/services/events/middleware.ts index 0c3b275274..56992be672 100644 --- a/invokeai/frontend/web/src/services/events/middleware.ts +++ b/invokeai/frontend/web/src/services/events/middleware.ts @@ -13,7 +13,8 @@ import { socketSubscribed, socketUnsubscribed } from './actions'; export const socketMiddleware = () => { let areListenersSet = false; - let socketUrl = `ws://${window.location.host}`; + const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws'; + let socketUrl = `${wsProtocol}://${window.location.host}`; const socketOptions: Parameters[0] = { timeout: 60000, diff --git a/invokeai/frontend/web/src/theme/util/generateColorPalette.ts b/invokeai/frontend/web/src/theme/util/generateColorPalette.ts index 6d90a070c0..63a5c06219 100644 --- a/invokeai/frontend/web/src/theme/util/generateColorPalette.ts +++ b/invokeai/frontend/web/src/theme/util/generateColorPalette.ts @@ -22,7 +22,7 @@ export function generateColorPalette( ]; const p = colorSteps.reduce((palette, step, index) => { - const A = alpha ? lightnessSteps[index] / 100 : 1; + const A = alpha ? (lightnessSteps[index] as number) / 100 : 1; // Lightness should be 50% for alpha colors const L = alpha ? 50 : lightnessSteps[colorSteps.length - 1 - index]; diff --git a/invokeai/frontend/web/tsconfig.json b/invokeai/frontend/web/tsconfig.json index e722e2f9a8..c43d5dd86d 100644 --- a/invokeai/frontend/web/tsconfig.json +++ b/invokeai/frontend/web/tsconfig.json @@ -13,6 +13,8 @@ "moduleResolution": "Node", // TODO: Disabled for IDE performance issues with our translation JSON // "resolveJsonModule": true, + "noUncheckedIndexedAccess": true, + "strictNullChecks": true, "isolatedModules": true, "noEmit": true, "jsx": "react-jsx", diff --git a/invokeai/version/invokeai_version.py b/invokeai/version/invokeai_version.py index 2e0d56b8ff..ef3bc05f1a 100644 --- a/invokeai/version/invokeai_version.py +++ b/invokeai/version/invokeai_version.py @@ -1 +1 @@ -__version__ = "3.0.1post3" +__version__ = "3.0.2post1" diff --git a/pyproject.toml b/pyproject.toml index b3f12481a8..6e5b754914 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,7 +40,7 @@ dependencies = [ "controlnet-aux>=0.0.6", "timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26 "datasets", - "diffusers[torch]~=0.19.0", + "diffusers[torch]~=0.19.3", "dnspython~=2.4.0", "dynamicprompts", "easing-functions", @@ -77,7 +77,7 @@ dependencies = [ "realesrgan", "requests~=2.28.2", "rich~=13.3", - "safetensors~=0.3.0", + "safetensors==0.3.1", "scikit-image~=0.21.0", "send2trash", "test-tube~=0.7.5", @@ -100,7 +100,7 @@ dependencies = [ "dev" = [ "pudb", ] -"test" = ["pytest>6.0.0", "pytest-cov", "black"] +"test" = ["pytest>6.0.0", "pytest-cov", "pytest-datadir", "black"] "xformers" = [ "xformers~=0.0.19; sys_platform!='darwin'", "triton; sys_platform=='linux'", @@ -118,7 +118,7 @@ dependencies = [ [project.scripts] # legacy entrypoints; provided for backwards compatibility -"configure_invokeai.py" = "invokeai.frontend.install:invokeai_configure" +"configure_invokeai.py" = "invokeai.frontend.install.invokeai_configure:invokeai_configure" "textual_inversion.py" = "invokeai.frontend.training:invokeai_textual_inversion" # shortcut commands to start cli and web @@ -130,15 +130,16 @@ dependencies = [ "invokeai-web" = "invokeai.app.api_app:invoke_api" # full commands -"invokeai-configure" = "invokeai.frontend.install:invokeai_configure" +"invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure" "invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers" "invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion" -"invokeai-model-install" = "invokeai.frontend.install:invokeai_model_install" +"invokeai-model-install" = "invokeai.frontend.install.model_install:main" "invokeai-migrate3" = "invokeai.backend.install.migrate_to_3:main" -"invokeai-update" = "invokeai.frontend.install:invokeai_update" +"invokeai-update" = "invokeai.frontend.install.invokeai_update:main" "invokeai-metadata" = "invokeai.frontend.CLI.sd_metadata:print_metadata" "invokeai-node-cli" = "invokeai.app.cli_app:invoke_cli" "invokeai-node-web" = "invokeai.app.api_app:invoke_api" +"invokeai-import-images" = "invokeai.frontend.install.import_images:main" [project.urls] "Homepage" = "https://invoke-ai.github.io/InvokeAI/" diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py index 61d32b6df5..b40194c6ce 100755 --- a/scripts/configure_invokeai.py +++ b/scripts/configure_invokeai.py @@ -2,7 +2,7 @@ # Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein) import warnings -from invokeai.frontend.install import invokeai_configure as configure +from invokeai.frontend.install.invokeai_configure import invokeai_configure as configure if __name__ == "__main__": warnings.warn("configure_invokeai.py is deprecated, running 'invokeai-configure'...", DeprecationWarning) diff --git a/scripts/create_checkpoint_template.py b/scripts/create_checkpoint_template.py new file mode 100755 index 0000000000..7ff201c841 --- /dev/null +++ b/scripts/create_checkpoint_template.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python +""" +Read a checkpoint/safetensors file and write out a template .json file containing +its metadata for use in fast model probing. +""" + +import sys +import argparse +import json + +from pathlib import Path + +from invokeai.backend.model_management.models.base import read_checkpoint_meta + +parser = argparse.ArgumentParser(description="Create a .json template from checkpoint/safetensors model") +parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file") +parser.add_argument("--template", "--out", type=Path, help="Path to the output .json file") + +opt = parser.parse_args() +ckpt = read_checkpoint_meta(opt.checkpoint) +while "state_dict" in ckpt: + ckpt = ckpt["state_dict"] + +tmpl = {} + +for key, tensor in ckpt.items(): + tmpl[key] = list(tensor.shape) + +try: + with open(opt.template, "w") as f: + json.dump(tmpl, f) + print(f"Template written out as {opt.template}") +except Exception as e: + print(f"An exception occurred while writing template: {str(e)}") diff --git a/scripts/dream.py b/scripts/dream.py index 12176db41e..d2735046a4 100755 --- a/scripts/dream.py +++ b/scripts/dream.py @@ -2,10 +2,12 @@ # Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein) import warnings -from invokeai.frontend.CLI import invokeai_command_line_interface as main warnings.warn( "dream.py is being deprecated, please run invoke.py for the " "new UI/API or legacy_api.py for the old API", DeprecationWarning, ) -main() + +from invokeai.app.cli_app import invoke_cli + +invoke_cli() diff --git a/scripts/probe-model.py b/scripts/probe-model.py index 7281dafc3f..4cf2c50263 100755 --- a/scripts/probe-model.py +++ b/scripts/probe-model.py @@ -9,8 +9,10 @@ parser = argparse.ArgumentParser(description="Probe model type") parser.add_argument( "model_path", type=Path, + nargs="+", ) args = parser.parse_args() -info = ModelProbe().probe(args.model_path) -print(info) +for path in args.model_path: + info = ModelProbe().probe(path) + print(f"{path}: {info}") diff --git a/scripts/verify_checkpoint_template.py b/scripts/verify_checkpoint_template.py new file mode 100755 index 0000000000..15194290f5 --- /dev/null +++ b/scripts/verify_checkpoint_template.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python +""" +Read a checkpoint/safetensors file and compare it to a template .json. +Returns True if their metadata match. +""" + +import sys +import argparse +import json + +from pathlib import Path + +from invokeai.backend.model_management.models.base import read_checkpoint_meta + +parser = argparse.ArgumentParser(description="Compare a checkpoint/safetensors file to a JSON metadata template.") +parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file") +parser.add_argument("--template", "--out", type=Path, help="Path to the template .json file to match against") + +opt = parser.parse_args() +ckpt = read_checkpoint_meta(opt.checkpoint) +while "state_dict" in ckpt: + ckpt = ckpt["state_dict"] + +checkpoint_metadata = {} + +for key, tensor in ckpt.items(): + checkpoint_metadata[key] = list(tensor.shape) + +with open(opt.template, "r") as f: + template = json.load(f) + +if checkpoint_metadata == template: + print("True") + sys.exit(0) +else: + print("False") + sys.exit(-1) diff --git a/shell.nix b/shell.nix deleted file mode 100644 index 3de8c91b38..0000000000 --- a/shell.nix +++ /dev/null @@ -1,162 +0,0 @@ -{ pkgs ? import {} - , lib ? pkgs.lib - , stdenv ? pkgs.stdenv - , fetchurl ? pkgs.fetchurl - , runCommand ? pkgs.runCommand - , makeWrapper ? pkgs.makeWrapper - , mkShell ? pkgs.mkShell - , buildFHSUserEnv ? pkgs.buildFHSUserEnv - , frameworks ? pkgs.darwin.apple_sdk.frameworks -}: - -# Setup InvokeAI environment using nix -# Simple usage: -# nix-shell -# python3 scripts/preload_models.py -# python3 scripts/invoke.py -h - -let - conda-shell = { url, sha256, installPath, packages, shellHook }: - let - src = fetchurl { inherit url sha256; }; - libPath = lib.makeLibraryPath ([] ++ lib.optionals (stdenv.isLinux) [ pkgs.zlib ]); - condaArch = if stdenv.system == "aarch64-darwin" then "osx-arm64" else ""; - installer = - if stdenv.isDarwin then - runCommand "conda-install" { - nativeBuildInputs = [ makeWrapper ]; - } '' - mkdir -p $out/bin - cp ${src} $out/bin/miniconda-installer.sh - chmod +x $out/bin/miniconda-installer.sh - makeWrapper \ - $out/bin/miniconda-installer.sh \ - $out/bin/conda-install \ - --add-flags "-p ${installPath}" \ - --add-flags "-b" - '' - else if stdenv.isLinux then - runCommand "conda-install" { - nativeBuildInputs = [ makeWrapper ]; - buildInputs = [ pkgs.zlib ]; - } - # on line 10, we have 'unset LD_LIBRARY_PATH' - # we have to comment it out however in a way that the number of bytes in the - # file does not change. So we replace the 'u' in the line with a '#' - # The reason is that the binary payload is encoded as number - # of bytes from the top of the installer script - # and unsetting the library path prevents the zlib library from being discovered - '' - mkdir -p $out/bin - sed 's/unset LD_LIBRARY_PATH/#nset LD_LIBRARY_PATH/' ${src} > $out/bin/miniconda-installer.sh - chmod +x $out/bin/miniconda-installer.sh - makeWrapper \ - $out/bin/miniconda-installer.sh \ - $out/bin/conda-install \ - --add-flags "-p ${installPath}" \ - --add-flags "-b" \ - --prefix "LD_LIBRARY_PATH" : "${libPath}" - '' - else {}; - - hook = '' - export CONDA_SUBDIR=${condaArch} - '' + shellHook; - - fhs = buildFHSUserEnv { - name = "conda-shell"; - targetPkgs = pkgs: [ stdenv.cc pkgs.git installer ] ++ packages; - profile = hook; - runScript = "bash"; - }; - - shell = mkShell { - shellHook = if stdenv.isDarwin then hook else "conda-shell; exit"; - packages = if stdenv.isDarwin then [ pkgs.git installer ] ++ packages else [ fhs ]; - }; - in shell; - - packages = with pkgs; [ - cmake - protobuf - libiconv - rustc - cargo - rustPlatform.bindgenHook - ]; - - env = { - aarch64-darwin = { - envFile = "environment-mac.yml"; - condaPath = (builtins.toString ./.) + "/.conda"; - ptrSize = "8"; - }; - x86_64-linux = { - envFile = "environment.yml"; - condaPath = (builtins.toString ./.) + "/.conda"; - ptrSize = "8"; - }; - }; - - envFile = env.${stdenv.system}.envFile; - installPath = env.${stdenv.system}.condaPath; - ptrSize = env.${stdenv.system}.ptrSize; - shellHook = '' - conda-install - - # tmpdir is too small in nix - export TMPDIR="${installPath}/tmp" - - # Add conda to PATH - export PATH="${installPath}/bin:$PATH" - - # Allows `conda activate` to work properly - source ${installPath}/etc/profile.d/conda.sh - - # Paths for gcc if compiling some C sources with pip - export NIX_CFLAGS_COMPILE="-I${installPath}/include -I$TMPDIR/include" - export NIX_CFLAGS_LINK="-L${installPath}/lib $BINDGEN_EXTRA_CLANG_ARGS" - - export PIP_EXISTS_ACTION=w - - # rust-onig fails (think it writes config.h to wrong location) - mkdir -p "$TMPDIR/include" - cat <<'EOF' > "$TMPDIR/include/config.h" - #define HAVE_PROTOTYPES 1 - #define STDC_HEADERS 1 - #define HAVE_STRING_H 1 - #define HAVE_STDARG_H 1 - #define HAVE_STDLIB_H 1 - #define HAVE_LIMITS_H 1 - #define HAVE_INTTYPES_H 1 - #define SIZEOF_INT 4 - #define SIZEOF_SHORT 2 - #define SIZEOF_LONG ${ptrSize} - #define SIZEOF_VOIDP ${ptrSize} - #define SIZEOF_LONG_LONG 8 - EOF - - conda env create -f "${envFile}" || conda env update --prune -f "${envFile}" - conda activate invokeai - ''; - - version = "4.12.0"; - conda = { - aarch64-darwin = { - shell = conda-shell { - inherit shellHook installPath; - url = "https://repo.anaconda.com/miniconda/Miniconda3-py39_${version}-MacOSX-arm64.sh"; - sha256 = "4bd112168cc33f8a4a60d3ef7e72b52a85972d588cd065be803eb21d73b625ef"; - packages = [ frameworks.Security ] ++ packages; - }; - }; - x86_64-linux = { - shell = conda-shell { - inherit shellHook installPath; - url = "https://repo.continuum.io/miniconda/Miniconda3-py39_${version}-Linux-x86_64.sh"; - sha256 = "78f39f9bae971ec1ae7969f0516017f2413f17796670f7040725dd83fcff5689"; - packages = with pkgs; [ libGL glib ] ++ packages; - }; - }; - }; -in conda.${stdenv.system}.shell diff --git a/tests/nodes/test_graph_execution_state.py b/tests/nodes/test_graph_execution_state.py index 1a24b76c1c..276dee7c98 100644 --- a/tests/nodes/test_graph_execution_state.py +++ b/tests/nodes/test_graph_execution_state.py @@ -16,6 +16,7 @@ from invokeai.app.invocations.baseinvocation import ( from invokeai.app.invocations.collections import RangeInvocation from invokeai.app.invocations.math import AddInvocation, MultiplyInvocation from invokeai.app.services.invocation_services import InvocationServices +from invokeai.app.services.invocation_stats import InvocationStatsService from invokeai.app.services.graph import ( Graph, CollectInvocation, @@ -41,6 +42,9 @@ def simple_graph(): @pytest.fixture def mock_services() -> InvocationServices: # NOTE: none of these are actually called by the test invocations + graph_execution_manager = SqliteItemStorage[GraphExecutionState]( + filename=sqlite_memory, table_name="graph_executions" + ) return InvocationServices( model_manager=None, # type: ignore events=TestEventService(), @@ -52,9 +56,8 @@ def mock_services() -> InvocationServices: board_images=None, # type: ignore queue=MemoryInvocationQueue(), graph_library=SqliteItemStorage[LibraryGraph](filename=sqlite_memory, table_name="graphs"), - graph_execution_manager=SqliteItemStorage[GraphExecutionState]( - filename=sqlite_memory, table_name="graph_executions" - ), + graph_execution_manager=graph_execution_manager, + performance_statistics=InvocationStatsService(graph_execution_manager), processor=DefaultInvocationProcessor(), configuration=None, # type: ignore ) diff --git a/tests/nodes/test_invoker.py b/tests/nodes/test_invoker.py index 7c21d2a7f8..677b0a372d 100644 --- a/tests/nodes/test_invoker.py +++ b/tests/nodes/test_invoker.py @@ -11,6 +11,7 @@ from invokeai.app.services.processor import DefaultInvocationProcessor from invokeai.app.services.sqlite import SqliteItemStorage, sqlite_memory from invokeai.app.services.invoker import Invoker from invokeai.app.services.invocation_services import InvocationServices +from invokeai.app.services.invocation_stats import InvocationStatsService from invokeai.app.services.graph import ( Graph, GraphExecutionState, @@ -34,6 +35,9 @@ def simple_graph(): @pytest.fixture def mock_services() -> InvocationServices: # NOTE: none of these are actually called by the test invocations + graph_execution_manager = SqliteItemStorage[GraphExecutionState]( + filename=sqlite_memory, table_name="graph_executions" + ) return InvocationServices( model_manager=None, # type: ignore events=TestEventService(), @@ -45,10 +49,9 @@ def mock_services() -> InvocationServices: board_images=None, # type: ignore queue=MemoryInvocationQueue(), graph_library=SqliteItemStorage[LibraryGraph](filename=sqlite_memory, table_name="graphs"), - graph_execution_manager=SqliteItemStorage[GraphExecutionState]( - filename=sqlite_memory, table_name="graph_executions" - ), + graph_execution_manager=graph_execution_manager, processor=DefaultInvocationProcessor(), + performance_statistics=InvocationStatsService(graph_execution_manager), configuration=None, # type: ignore ) diff --git a/tests/test_config.py b/tests/test_config.py index 5d3dc46aa4..721edce487 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -84,6 +84,21 @@ def test_env_override(): assert conf.max_cache_size == 20 +def test_root_resists_cwd(): + previous = os.environ["INVOKEAI_ROOT"] + cwd = Path(os.getcwd()).resolve() + + os.environ["INVOKEAI_ROOT"] = "." + conf = InvokeAIAppConfig.get_config() + conf.parse_args([]) + assert conf.root_path == cwd + + os.chdir("..") + assert conf.root_path == cwd + os.environ["INVOKEAI_ROOT"] = previous + os.chdir(cwd) + + def test_type_coercion(): conf = InvokeAIAppConfig().get_config() conf.parse_args(argv=["--root=/tmp/foobar"]) diff --git a/tests/test_model_manager.py b/tests/test_model_manager.py new file mode 100644 index 0000000000..4aa2c4d3b2 --- /dev/null +++ b/tests/test_model_manager.py @@ -0,0 +1,47 @@ +from pathlib import Path + +import pytest + +from invokeai.app.services.config import InvokeAIAppConfig +from invokeai.backend import ModelManager, BaseModelType, ModelType, SubModelType + +BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main) +VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main) +VAE_NULL_OVERRIDE_MODEL_NAME = ("SDXL with empty VAE", BaseModelType.StableDiffusionXL, ModelType.Main) + + +@pytest.fixture +def model_manager(datadir) -> ModelManager: + InvokeAIAppConfig.get_config(root=datadir) + return ModelManager(datadir / "configs" / "relative_sub.models.yaml") + + +def test_get_model_names(model_manager: ModelManager): + names = model_manager.model_names() + assert names[:2] == [BASIC_MODEL_NAME, VAE_OVERRIDE_MODEL_NAME] + + +def test_get_model_path_for_diffusers(model_manager: ModelManager, datadir: Path): + model_config = model_manager._get_model_config(BASIC_MODEL_NAME[1], BASIC_MODEL_NAME[0], BASIC_MODEL_NAME[2]) + top_model_path, is_override = model_manager._get_model_path(model_config) + expected_model_path = datadir / "models" / "sdxl" / "main" / "SDXL base 1_0" + assert top_model_path == expected_model_path + assert not is_override + + +def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir: Path): + model_config = model_manager._get_model_config( + VAE_OVERRIDE_MODEL_NAME[1], VAE_OVERRIDE_MODEL_NAME[0], VAE_OVERRIDE_MODEL_NAME[2] + ) + vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae) + expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix" + assert vae_model_path == expected_vae_path + assert is_override + + +def test_get_model_path_for_null_overridden_vae(model_manager: ModelManager, datadir: Path): + model_config = model_manager._get_model_config( + VAE_NULL_OVERRIDE_MODEL_NAME[1], VAE_NULL_OVERRIDE_MODEL_NAME[0], VAE_NULL_OVERRIDE_MODEL_NAME[2] + ) + vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae) + assert not is_override diff --git a/tests/test_model_manager/configs/relative_sub.models.yaml b/tests/test_model_manager/configs/relative_sub.models.yaml new file mode 100644 index 0000000000..2e26710d13 --- /dev/null +++ b/tests/test_model_manager/configs/relative_sub.models.yaml @@ -0,0 +1,22 @@ +__metadata__: + version: 3.0.0 + +sdxl/main/SDXL base: + path: sdxl/main/SDXL base 1_0 + description: SDXL base v1.0 + variant: normal + format: diffusers + +sdxl/main/SDXL with VAE: + path: sdxl/main/SDXL base 1_0 + description: SDXL with customized VAE + vae: sdxl/vae/sdxl-vae-fp16-fix/ + variant: normal + format: diffusers + +sdxl/main/SDXL with empty VAE: + path: sdxl/main/SDXL base 1_0 + description: SDXL with customized VAE + vae: '' + variant: normal + format: diffusers diff --git a/tests/test_model_manager/models/sdxl/main/SDXL base 1_0/model_index.json b/tests/test_model_manager/models/sdxl/main/SDXL base 1_0/model_index.json new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_model_manager/models/sdxl/vae/sdxl-vae-fp16-fix/config.json b/tests/test_model_manager/models/sdxl/vae/sdxl-vae-fp16-fix/config.json new file mode 100644 index 0000000000..e69de29bb2