diff --git a/.gitattributes b/.gitattributes index 23dfb4efbd..4c9fc0120e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -2,3 +2,4 @@ # Only affects text files and ignores other file types. # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/ * text=auto +docker/** text eol=lf \ No newline at end of file diff --git a/.github/workflows/pypi-release.yml b/.github/workflows/pypi-release.yml index 9e58fb3ae0..5b7d2cd2fa 100644 --- a/.github/workflows/pypi-release.yml +++ b/.github/workflows/pypi-release.yml @@ -28,7 +28,7 @@ jobs: run: twine check dist/* - name: check PyPI versions - if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3' || github.ref == 'refs/heads/v3.3.0post1' + if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/') run: | pip install --upgrade requests python -c "\ diff --git a/.gitignore b/.gitignore index 44a0864b5b..2b99d137b1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,8 +1,5 @@ .idea/ -# ignore the Anaconda/Miniconda installer used while building Docker image -anaconda.sh - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -136,12 +133,10 @@ celerybeat.pid # Environments .env -.venv +.venv* env/ venv/ ENV/ -env.bak/ -venv.bak/ # Spyder project settings .spyderproject @@ -186,11 +181,6 @@ cython_debug/ .scratch/ .vscode/ -# ignore environment.yml and requirements.txt -# these are links to the real files in environments-and-requirements -environment.yml -requirements.txt - # source installer files installer/*zip installer/install.bat diff --git a/README.md b/README.md index 15c5747ea8..932bd79ae9 100644 --- a/README.md +++ b/README.md @@ -123,7 +123,7 @@ and go to http://localhost:9090. ### Command-Line Installation (for developers and users familiar with Terminals) -You must have Python 3.9 through 3.11 installed on your machine. Earlier or +You must have Python 3.10 through 3.11 installed on your machine. Earlier or later versions are not supported. Node.js also needs to be installed along with yarn (can be installed with the command `npm install -g yarn` if needed) diff --git a/docker/.env.sample b/docker/.env.sample index 7e414ecd65..98ad307c04 100644 --- a/docker/.env.sample +++ b/docker/.env.sample @@ -1,13 +1,15 @@ ## Make a copy of this file named `.env` and fill in the values below. -## Any environment variables supported by InvokeAI can be specified here. +## Any environment variables supported by InvokeAI can be specified here, +## in addition to the examples below. # INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data. # Outputs will also be stored here by default. # This **must** be an absolute path. INVOKEAI_ROOT= -HUGGINGFACE_TOKEN= +# Get this value from your HuggingFace account settings page. +# HUGGING_FACE_HUB_TOKEN= -## optional variables specific to the docker setup -# GPU_DRIVER=cuda -# CONTAINER_UID=1000 \ No newline at end of file +## optional variables specific to the docker setup. +# GPU_DRIVER=cuda # or rocm +# CONTAINER_UID=1000 diff --git a/docker/Dockerfile b/docker/Dockerfile index e158c681a4..6aa6a43a1a 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -2,7 +2,7 @@ ## Builder stage -FROM library/ubuntu:22.04 AS builder +FROM library/ubuntu:23.04 AS builder ARG DEBIAN_FRONTEND=noninteractive RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache @@ -10,7 +10,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ --mount=type=cache,target=/var/lib/apt,sharing=locked \ apt update && apt-get install -y \ git \ - python3.10-venv \ + python3-venv \ python3-pip \ build-essential @@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai ENV VIRTUAL_ENV=/opt/venv/invokeai ENV PATH="$VIRTUAL_ENV/bin:$PATH" -ARG TORCH_VERSION=2.0.1 -ARG TORCHVISION_VERSION=0.15.2 +ARG TORCH_VERSION=2.1.0 +ARG TORCHVISION_VERSION=0.16 ARG GPU_DRIVER=cuda ARG TARGETPLATFORM="linux/amd64" # unused but available @@ -35,9 +35,9 @@ RUN --mount=type=cache,target=/root/.cache/pip \ if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \ extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \ elif [ "$GPU_DRIVER" = "rocm" ]; then \ - extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \ + extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \ else \ - extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \ + extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \ fi &&\ pip install $extra_index_url_arg \ torch==$TORCH_VERSION \ @@ -70,7 +70,7 @@ RUN --mount=type=cache,target=/usr/lib/node_modules \ #### Runtime stage --------------------------------------- -FROM library/ubuntu:22.04 AS runtime +FROM library/ubuntu:23.04 AS runtime ARG DEBIAN_FRONTEND=noninteractive ENV PYTHONUNBUFFERED=1 @@ -85,6 +85,7 @@ RUN apt update && apt install -y --no-install-recommends \ iotop \ bzip2 \ gosu \ + magic-wormhole \ libglib2.0-0 \ libgl1-mesa-glx \ python3-venv \ @@ -94,10 +95,6 @@ RUN apt update && apt install -y --no-install-recommends \ libstdc++-10-dev &&\ apt-get clean && apt-get autoclean -# globally add magic-wormhole -# for ease of transferring data to and from the container -# when running in sandboxed cloud environments; e.g. Runpod etc. -RUN pip install magic-wormhole ENV INVOKEAI_SRC=/opt/invokeai ENV VIRTUAL_ENV=/opt/venv/invokeai @@ -120,9 +117,7 @@ WORKDIR ${INVOKEAI_SRC} RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc RUN python3 -c "from patchmatch import patch_match" -# Create unprivileged user and make the local dir -RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke -RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT} +RUN mkdir -p ${INVOKEAI_ROOT} && chown -R 1000:1000 ${INVOKEAI_ROOT} COPY docker/docker-entrypoint.sh ./ ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"] diff --git a/docker/README.md b/docker/README.md index 91f7fb8c51..4291ece25f 100644 --- a/docker/README.md +++ b/docker/README.md @@ -5,7 +5,7 @@ All commands are to be run from the `docker` directory: `cd docker` #### Linux 1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`) -2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04). +2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository). - The deprecated `docker-compose` (hyphenated) CLI continues to work for now. 3. Ensure docker daemon is able to access the GPU. - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) @@ -20,7 +20,6 @@ This is done via Docker Desktop preferences ## Quickstart - 1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to: a. the desired location of the InvokeAI runtime directory, or b. an existing, v3.0.0 compatible runtime directory. @@ -42,20 +41,22 @@ The Docker daemon on the system must be already set up to use the GPU. In case o Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used. -You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated. +You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated. -Example (most values are optional): +Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended): -``` +```bash INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai HUGGINGFACE_TOKEN=the_actual_token CONTAINER_UID=1000 GPU_DRIVER=cuda ``` +Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail. + ## Even Moar Customizing! -See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below. +See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below. ### Reconfigure the runtime directory @@ -63,7 +64,7 @@ Can be used to download additional models from the supported model list In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory -``` +```yaml command: - invokeai-configure - --yes @@ -71,7 +72,7 @@ command: Or install models: -``` +```yaml command: - invokeai-model-install -``` \ No newline at end of file +``` diff --git a/docker/build.sh b/docker/build.sh index db25439840..3b3875c15c 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -5,7 +5,7 @@ build_args="" [[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env) -echo "docker-compose build args:" +echo "docker compose build args:" echo $build_args -docker-compose build $build_args +docker compose build $build_args diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml index 85deac428e..f7e92d6bf5 100644 --- a/docker/docker-compose.yml +++ b/docker/docker-compose.yml @@ -15,6 +15,10 @@ services: - driver: nvidia count: 1 capabilities: [gpu] + # For AMD support, comment out the deploy section above and uncomment the devices section below: + #devices: + # - /dev/kfd:/dev/kfd + # - /dev/dri:/dev/dri build: context: .. dockerfile: docker/Dockerfile diff --git a/docker/docker-entrypoint.sh b/docker/docker-entrypoint.sh index 6d776feb0e..7a9e6921ce 100755 --- a/docker/docker-entrypoint.sh +++ b/docker/docker-entrypoint.sh @@ -19,7 +19,7 @@ set -e -o pipefail # Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS. USER_ID=${CONTAINER_UID:-1000} -USER=invoke +USER=ubuntu usermod -u ${USER_ID} ${USER} 1>/dev/null configure() { diff --git a/docker/run.sh b/docker/run.sh index bb764ec022..4b595b06df 100755 --- a/docker/run.sh +++ b/docker/run.sh @@ -1,8 +1,11 @@ #!/usr/bin/env bash set -e +# This script is provided for backwards compatibility with the old docker setup. +# it doesn't do much aside from wrapping the usual docker compose CLI. + SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}") cd "$SCRIPTDIR" || exit 1 -docker-compose up --build -d -docker-compose logs -f +docker compose up -d +docker compose logs -f diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index ca765b3ca6..24bd5ad7dd 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -488,7 +488,7 @@ sections describe what's new for InvokeAI. - A choice of installer scripts that automate installation and configuration. See - [Installation](installation/index.md). + [Installation](installation/INSTALLATION.md). - A streamlined manual installation process that works for both Conda and PIP-only installs. See [Manual Installation](installation/020_INSTALL_MANUAL.md). @@ -657,7 +657,7 @@ sections describe what's new for InvokeAI. ## v1.13 (3 September 2022) -- Support image variations (see [VARIATIONS](features/VARIATIONS.md) +- Support image variations (see [VARIATIONS](deprecated/VARIATIONS.md) ([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers) - Supports a Google Colab notebook for a standalone server running on Google diff --git a/CODE_OF_CONDUCT.md b/docs/CODE_OF_CONDUCT.md similarity index 100% rename from CODE_OF_CONDUCT.md rename to docs/CODE_OF_CONDUCT.md diff --git a/docs/contributing/contribution_guides/development.md b/docs/contributing/contribution_guides/development.md index 086fd6e90d..2f50d7f579 100644 --- a/docs/contributing/contribution_guides/development.md +++ b/docs/contributing/contribution_guides/development.md @@ -45,5 +45,5 @@ For backend related work, please reach out to **@blessedcoolant**, **@lstein**, ## **What does the Code of Conduct mean for me?** -Our [Code of Conduct](CODE_OF_CONDUCT.md) means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code. +Our [Code of Conduct](../../CODE_OF_CONDUCT.md) means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code. diff --git a/docs/deprecated/CLI.md b/docs/deprecated/CLI.md index eaa215c8dd..b40aeffc37 100644 --- a/docs/deprecated/CLI.md +++ b/docs/deprecated/CLI.md @@ -211,8 +211,8 @@ Here are the invoke> command that apply to txt2img: | `--facetool ` | `-ft ` | `-ft gfpgan` | Select face restoration algorithm to use: gfpgan, codeformer | | `--codeformer_fidelity` | `-cf ` | `0.75` | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality | | `--save_original` | `-save_orig` | `False` | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. | -| `--variation ` | `-v` | `0.0` | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S` and `-n` to generate a series a riffs on a starting image. See [Variations](../features/VARIATIONS.md). | -| `--with_variations ` | | `None` | Combine two or more variations. See [Variations](../features/VARIATIONS.md) for now to use this. | +| `--variation ` | `-v` | `0.0` | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S` and `-n` to generate a series a riffs on a starting image. See [Variations](VARIATIONS.md). | +| `--with_variations ` | | `None` | Combine two or more variations. See [Variations](VARIATIONS.md) for now to use this. | | `--save_intermediates ` | | `None` | Save the image from every nth step into an "intermediates" folder inside the output directory | | `--h_symmetry_time_pct ` | | `None` | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) | | `--v_symmetry_time_pct ` | | `None` | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) | diff --git a/docs/other/TRANSLATION.md b/docs/deprecated/TRANSLATION.md similarity index 100% rename from docs/other/TRANSLATION.md rename to docs/deprecated/TRANSLATION.md diff --git a/docs/features/VARIATIONS.md b/docs/deprecated/VARIATIONS.md similarity index 97% rename from docs/features/VARIATIONS.md rename to docs/deprecated/VARIATIONS.md index e6e21490c8..0c09b71836 100644 --- a/docs/features/VARIATIONS.md +++ b/docs/deprecated/VARIATIONS.md @@ -126,6 +126,6 @@ amounts of image-to-image variation even when the seed is fixed and the `-v` argument is very low. Others are more deterministic. Feel free to experiment until you find the combination that you like. -Also be aware of the [Perlin Noise](OTHER.md#thresholding-and-perlin-noise-initialization-options) +Also be aware of the [Perlin Noise](../features/OTHER.md#thresholding-and-perlin-noise-initialization-options) feature, which provides another way of introducing variability into your image generation requests. diff --git a/docs/features/CONCEPTS.md b/docs/features/CONCEPTS.md index df9ee5bd26..5f3d2d961f 100644 --- a/docs/features/CONCEPTS.md +++ b/docs/features/CONCEPTS.md @@ -28,8 +28,9 @@ by placing them in the designated directory for the compatible model type ### An Example -Here are a few examples to illustrate how it works. All these images were -generated using the command-line client and the Stable Diffusion 1.5 model: +Here are a few examples to illustrate how it works. All these images +were generated using the legacy command-line client and the Stable +Diffusion 1.5 model: | Japanese gardener | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> | | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: | diff --git a/docs/features/CONFIGURATION.md b/docs/features/CONFIGURATION.md index cfd65f8a61..f83caf522d 100644 --- a/docs/features/CONFIGURATION.md +++ b/docs/features/CONFIGURATION.md @@ -82,7 +82,7 @@ format of YAML files can be found [here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/). You can fix a broken `invokeai.yaml` by deleting it and running the -configuration script again -- option [7] in the launcher, "Re-run the +configuration script again -- option [6] in the launcher, "Re-run the configure script". #### Reading Environment Variables diff --git a/docs/features/CONTROLNET.md b/docs/features/CONTROLNET.md index 8284ddf75d..dc773f1ec9 100644 --- a/docs/features/CONTROLNET.md +++ b/docs/features/CONTROLNET.md @@ -17,9 +17,6 @@ image generation, providing you with a way to direct the network towards generating images that better fit your desired style or outcome. - -#### How it works - ControlNet works by analyzing an input image, pre-processing that image to identify relevant information that can be interpreted by each specific ControlNet model, and then inserting that control information @@ -27,35 +24,21 @@ into the generation process. This can be used to adjust the style, composition, or other aspects of the image to better achieve a specific result. - -#### Models +#### Installation InvokeAI provides access to a series of ControlNet models that provide -different effects or styles in your generated images. Currently -InvokeAI only supports "diffuser" style ControlNet models. These are -folders that contain the files `config.json` and/or -`diffusion_pytorch_model.safetensors` and -`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is -the name of the model. +different effects or styles in your generated images. -***InvokeAI does not currently support checkpoint-format -ControlNets. These come in the form of a single file with the -extension `.safetensors`.*** +To install ControlNet Models: -Diffuser-style ControlNet models are available at HuggingFace -(http://huggingface.co) and accessed via their repo IDs (identifiers -in the format "author/modelname"). The easiest way to install them is +1. The easiest way to install them is to use the InvokeAI model installer application. Use the -`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate +`invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate to the CONTROLNETS section. Select the models you wish to install and press "APPLY CHANGES". You may also enter additional HuggingFace -repo_ids in the "Additional models" textbox: +repo_ids in the "Additional models" textbox. +2. Using the "Add Model" function of the model manager, enter the HuggingFace Repo ID of the ControlNet. The ID is in the format "author/repoName" -![Model Installer - -Controlnetl](../assets/installing-models/model-installer-controlnet.png){:width="640px"} - -Command-line users can launch the model installer using the command -`invokeai-model-install`. _Be aware that some ControlNet models require additional code functionality in order to work properly, so just installing a @@ -63,6 +46,17 @@ third-party ControlNet model may not have the desired effect._ Please read and follow the documentation for installing a third party model not currently included among InvokeAI's default list. +Currently InvokeAI **only** supports 🤗 Diffusers-format ControlNet models. These are +folders that contain the files `config.json` and/or +`diffusion_pytorch_model.safetensors` and +`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is +the name of the model. + +🤗 Diffusers-format ControlNet models are available at HuggingFace +(http://huggingface.co) and accessed via their repo IDs (identifiers +in the format "author/modelname"). + +#### ControlNet Models The models currently supported include: **Canny**: @@ -133,6 +127,29 @@ Start/End - 0 represents the start of the generation, 1 represents the end. The Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke. +## T2I-Adapter +[T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) is a tool similar to ControlNet that allows for control over the generation process by providing control information during the generation process. T2I-Adapter models tend to be smaller and more efficient than ControlNets. + +##### Installation +To install T2I-Adapter Models: + +1. The easiest way to install models is +to use the InvokeAI model installer application. Use the +`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate +to the T2I-Adapters section. Select the models you wish to install and +press "APPLY CHANGES". You may also enter additional HuggingFace +repo_ids in the "Additional models" textbox. +2. Using the "Add Model" function of the model manager, enter the HuggingFace Repo ID of the T2I-Adapter. The ID is in the format "author/repoName" + +#### Usage +Each T2I Adapter has two settings that are applied. + +Weight - Strength of the model applied to the generation for the section, defined by start/end. + +Start/End - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied. + +Additionally, each section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process. + ## IP-Adapter @@ -140,13 +157,13 @@ Additionally, each ControlNet section can be expanded in order to manipulate set ![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg) -![IP-Adapter + IMG2IMG](https://github.com/tencent-ailab/IP-Adapter/blob/main/assets/demo/image-to-image.jpg) +![IP-Adapter + IMG2IMG](https://raw.githubusercontent.com/tencent-ailab/IP-Adapter/main/assets/demo/image-to-image.jpg) #### Installation There are several ways to install IP-Adapter models with an existing InvokeAI installation: -1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [5] to download models. -2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models. +1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models. +2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models. 3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder. #### Using IP-Adapter diff --git a/docs/features/MODEL_MERGING.md b/docs/features/MODEL_MERGING.md index 6adf4db16a..e384662ef5 100644 --- a/docs/features/MODEL_MERGING.md +++ b/docs/features/MODEL_MERGING.md @@ -16,9 +16,10 @@ Model Merging can be be done by navigating to the Model Manager and clicking the display all the diffusers-style models that InvokeAI knows about. If you do not see the model you are looking for, then it is probably a legacy checkpoint model and needs to be converted using the - `invoke` command-line client and its `!optimize` command. You - must select at least two models to merge. The third can be left at - "None" if you desire. + "Convert" option in the Web-based Model Manager tab. + + You must select at least two models to merge. The third can be left + at "None" if you desire. * Alpha: This is the ratio to use when combining models. It ranges from 0 to 1. The higher the value, the more weight is given to the diff --git a/docs/features/UTILITIES.md b/docs/features/UTILITIES.md index a73118d55a..2d62fe3a79 100644 --- a/docs/features/UTILITIES.md +++ b/docs/features/UTILITIES.md @@ -8,7 +8,7 @@ title: Command-line Utilities InvokeAI comes with several scripts that are accessible via the command line. To access these commands, start the "developer's -console" from the launcher (`invoke.bat` menu item [8]). Users who are +console" from the launcher (`invoke.bat` menu item [7]). Users who are familiar with Python can alternatively activate InvokeAI's virtual environment (typically, but not necessarily `invokeai/.venv`). @@ -34,7 +34,7 @@ invokeai-web --ram 7 ## **invokeai-merge** -This is the model merge script, the same as launcher option [4]. Call +This is the model merge script, the same as launcher option [3]. Call it with the `--gui` command-line argument to start the interactive console-based GUI. Alternatively, you can run it non-interactively using command-line arguments as illustrated in the example below which @@ -48,7 +48,7 @@ invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffus ## **invokeai-ti** This is the textual inversion training script that is run by launcher -option [3]. Call it with `--gui` to run the interactive console-based +option [2]. Call it with `--gui` to run the interactive console-based front end. It can also be run non-interactively. It has about a zillion arguments, but a typical training session can be launched with: @@ -68,7 +68,7 @@ in Windows). ## **invokeai-install** This is the console-based model install script that is run by launcher -option [5]. If called without arguments, it will launch the +option [4]. If called without arguments, it will launch the interactive console-based interface. It can also be used non-interactively to list, add and remove models as shown by these examples: @@ -148,7 +148,7 @@ launch the web server against it with `invokeai-web --root InvokeAI-New`. ## **invokeai-update** This is the interactive console-based script that is run by launcher -menu item [9] to update to a new version of InvokeAI. It takes no +menu item [8] to update to a new version of InvokeAI. It takes no command-line arguments. ## **invokeai-metadata** diff --git a/docs/features/index.md b/docs/features/index.md index bd37366314..6315b20ca5 100644 --- a/docs/features/index.md +++ b/docs/features/index.md @@ -28,7 +28,7 @@ Learn how to install and use ControlNet models for fine control over image output. ### * [Image-to-Image Guide](IMG2IMG.md) -Use a seed image to build new creations in the CLI. +Use a seed image to build new creations. ## Model Management diff --git a/docs/help/gettingStartedWithAI.md b/docs/help/gettingStartedWithAI.md index 1f22e1edba..617bd60401 100644 --- a/docs/help/gettingStartedWithAI.md +++ b/docs/help/gettingStartedWithAI.md @@ -57,7 +57,9 @@ Prompts provide the models directions on what to generate. As a general rule of Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!) -Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at ****. Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision! +Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at https://models.invoke.ai + +Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision! - *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas* diff --git a/docs/index.md b/docs/index.md index 9a426e5684..8c9ed5b7f8 100644 --- a/docs/index.md +++ b/docs/index.md @@ -143,7 +143,6 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM. ### Prompt Engineering - [Prompt Syntax](features/PROMPTS.md) -- [Generating Variations](features/VARIATIONS.md) ### InvokeAI Configuration - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md) @@ -166,10 +165,8 @@ still a work in progress, but coming soon. ### Command-Line Interface Retired -The original "invokeai" command-line interface has been retired. The -`invokeai` command will now launch a new command-line client that can -be used by developers to create and test nodes. It is not intended to -be used for routine image generation or manipulation. +All "invokeai" command-line interfaces have been retired as of version +3.4. To launch the Web GUI from the command-line, use the command `invokeai-web` rather than the traditional `invokeai --web`. diff --git a/docs/installation/010_INSTALL_AUTOMATED.md b/docs/installation/010_INSTALL_AUTOMATED.md index 0937c07bca..52192f33c0 100644 --- a/docs/installation/010_INSTALL_AUTOMATED.md +++ b/docs/installation/010_INSTALL_AUTOMATED.md @@ -40,7 +40,7 @@ experimental versions later. this, open up a command-line window ("Terminal" on Linux and Macintosh, "Command" or "Powershell" on Windows) and type `python --version`. If Python is installed, it will print out the version - number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet + number. If it is version `3.10.*` or `3.11.*` you meet requirements. !!! warning "What to do if you have an unsupported version" @@ -48,7 +48,7 @@ experimental versions later. Go to [Python Downloads](https://www.python.org/downloads/) and download the appropriate installer package for your platform. We recommend [Version - 3.10.9](https://www.python.org/downloads/release/python-3109/), + 3.10.12](https://www.python.org/downloads/release/python-3109/), which has been extensively tested with InvokeAI. _Please select your platform in the section below for platform-specific diff --git a/docs/installation/020_INSTALL_MANUAL.md b/docs/installation/020_INSTALL_MANUAL.md index a19992d266..27484c0ffd 100644 --- a/docs/installation/020_INSTALL_MANUAL.md +++ b/docs/installation/020_INSTALL_MANUAL.md @@ -32,7 +32,7 @@ gaming): * **Python** - version 3.9 through 3.11 + version 3.10 through 3.11 * **CUDA Tools** @@ -65,7 +65,7 @@ gaming): To install InvokeAI with virtual environments and the PIP package manager, please follow these steps: -1. Please make sure you are using Python 3.9 through 3.11. The rest of the install +1. Please make sure you are using Python 3.10 through 3.11. The rest of the install procedure depends on this and will not work with other versions: ```bash diff --git a/docs/installation/040_INSTALL_DOCKER.md b/docs/installation/040_INSTALL_DOCKER.md index fd75067cf1..a550056ce1 100644 --- a/docs/installation/040_INSTALL_DOCKER.md +++ b/docs/installation/040_INSTALL_DOCKER.md @@ -4,30 +4,31 @@ title: Installing with Docker # :fontawesome-brands-docker: Docker -!!! warning "For most users" +!!! warning "macOS and AMD GPU Users" - We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md) + We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md), + because Docker containers can not access the GPU on macOS. -!!! tip "For developers" +!!! warning "AMD GPU Users" - For container-related development tasks or for enabling easy - deployment to other environments (on-premises or cloud), follow these - instructions. + Container support for AMD GPUs has been reported to work by the community, but has not received + extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and + use the `build.sh` script to build the image for this to take effect at build time. - For general use, install locally to leverage your machine's GPU. +!!! tip "Linux and Windows Users" + + For optimal performance, configure your Docker daemon to access your machine's GPU. + Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/). + Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) ## Why containers? -They provide a flexible, reliable way to build and deploy InvokeAI. You'll also -use a Docker volume to store the largest model files and image outputs as a -first step in decoupling storage and compute. Future enhancements can do this -for other assets. See [Processes](https://12factor.net/processes) under the -Twelve-Factor App methodology for details on why running applications in such a -stateless fashion is important. +They provide a flexible, reliable way to build and deploy InvokeAI. +See [Processes](https://12factor.net/processes) under the Twelve-Factor App +methodology for details on why running applications in such a stateless fashion is important. -You can specify the target platform when building the image and running the -container. You'll also need to specify the InvokeAI requirements file that -matches the container's OS and the architecture it will run on. +The container is configured for CUDA by default, but can be built to support AMD GPUs +by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time. Developers on Apple silicon (M1/M2): You [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224) @@ -36,6 +37,16 @@ development purposes it's fine. Once you're done with development tasks on your laptop you can build for the target platform and architecture and deploy to another environment with NVIDIA GPUs on-premises or in the cloud. +## TL;DR + +This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options. + + ```bash + # docker compose commands should be run from the `docker` directory + cd docker + docker compose up + ``` + ## Installation in a Linux container (desktop) ### Prerequisites @@ -58,222 +69,44 @@ a token and copy it, since you will need in for the next step. ### Setup -Set the fork you want to use and other variables. +Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary. -!!! tip +Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail. - I preffer to save my env vars - in the repository root in a `.env` (or `.envrc`) file to automatically re-apply - them when I come back. - -The build- and run- scripts contain default values for almost everything, -besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you -created in the last step. - -Some Suggestions of variables you may want to change besides the Token: +At a minimum, you might want to set the `INVOKEAI_ROOT` environment variable +to point to the location where you wish to store your InvokeAI models, configuration, and outputs.
| Environment-Variable | Default value | Description | | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `HUGGING_FACE_HUB_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models | -| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name | -| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored | -| `ARCH` | arch of the build machine | Can be changed if you want to build the image for another arch | -| `CONTAINER_REGISTRY` | ghcr.io | Name of the Container Registry to use for the full tag | -| `CONTAINER_REPOSITORY` | `$(whoami)/${REPOSITORY_NAME}` | Name of the Container Repository | -| `CONTAINER_FLAVOR` | `cuda` | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. | -| `CONTAINER_TAG` | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}` | The Container Repository / Tag which will be used | -| `INVOKE_DOCKERFILE` | `Dockerfile` | The Dockerfile which should be built, handy for development | -| `PIP_EXTRA_INDEX_URL` | | If you want to use a custom pip-extra-index-url | +| `INVOKEAI_ROOT` | `~/invokeai` | **Required** - the location of your InvokeAI root directory. It will be created if it does not exist. +| `HUGGING_FACE_HUB_TOKEN` | | InvokeAI will work without it, but some of the integrations with HuggingFace (like downloading from models from private repositories) may not work| +| `GPU_DRIVER` | `cuda` | Optionally change this to `rocm` to build the image for AMD GPUs. NOTE: Use the `build.sh` script to build the image for this to take effect.
#### Build the Image -I provided a build script, which is located next to the Dockerfile in -`docker/build.sh`. It can be executed from repository root like this: +Use the standard `docker compose build` command from within the `docker` directory. -```bash -./docker/build.sh -``` - -The build Script not only builds the container, but also creates the docker -volume if not existing yet. +If using an AMD GPU: + a: set the `GPU_DRIVER=rocm` environment variable in `docker-compose.yml` and continue using `docker compose build` as usual, or + b: set `GPU_DRIVER=rocm` in the `.env` file and use the `build.sh` script, provided for convenience #### Run the Container -After the build process is done, you can run the container via the provided -`docker/run.sh` script +Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual. -```bash -./docker/run.sh -``` +Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090) -When used without arguments, the container will start the webserver and provide -you the link to open it. But if you want to use some other parameters you can -also do so. +## Troubleshooting / FAQ -!!! example "run script example" - - ```bash - ./docker/run.sh "banana sushi" -Ak_lms -S42 -s10 - ``` - - This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps. - - Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments) - ---- - -## Running the container on your GPU - -If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running -the container with an extra environment variable to enable GPU usage and have -the process run much faster: - -```bash -GPU_FLAGS=all ./docker/run.sh -``` - -This passes the `--gpus all` to docker and uses the GPU. - -If you don't have a GPU (or your host is not yet setup to use it) you will see a -message like this: - -`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].` - -You can use the full set of GPU combinations documented here: - -https://docs.docker.com/config/containers/resource_constraints/#gpu - -For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to -choose a specific device identified by a UUID. - ---- - -!!! warning "Deprecated" - - From here on you will find the the previous Docker-Docs, which will still - provide some usefull informations. - -## Usage (time to have fun) - -### Startup - -If you're on a **Linux container** the `invoke` script is **automatically -started** and the output dir set to the Docker volume you created earlier. - -If you're **directly on macOS follow these startup instructions**. With the -Conda environment activated (`conda activate ldm`), run the interactive -interface that combines the functionality of the original scripts `txt2img` and -`img2img`: Use the more accurate but VRAM-intensive full precision math because -half-precision requires autocast and won't work. By default the images are saved -in `outputs/img-samples/`. - -```Shell -python3 scripts/invoke.py --full_precision -``` - -You'll get the script's prompt. You can see available options or quit. - -```Shell -invoke> -h -invoke> q -``` - -### Text to Image - -For quick (but bad) image results test with 5 steps (default 50) and 1 sample -image. This will let you know that everything is set up correctly. Then increase -steps to 100 or more for good (but slower) results. The prompt can be in quotes -or not. - -```Shell -invoke> The hulk fighting with sheldon cooper -s5 -n1 -invoke> "woman closeup highly detailed" -s 150 -# Reuse previous seed and apply face restoration -invoke> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75 -``` - -You'll need to experiment to see if face restoration is making it better or -worse for your specific prompt. - -If you're on a container the output is set to the Docker volume. You can copy it -wherever you want. You can download it from the Docker Desktop app, Volumes, -my-vol, data. Or you can copy it from your Mac terminal. Keep in mind -`docker cp` can't expand `*.png` so you'll need to specify the image file name. - -On your host Mac (you can use the name of any container that mounted the -volume): - -```Shell -docker cp dummy:/data/000001.928403745.png /Users//Pictures -``` - -### Image to Image - -You can also do text-guided image-to-image translation. For example, turning a -sketch into a detailed drawing. - -`strength` is a value between 0.0 and 1.0 that controls the amount of noise that -is added to the input image. Values that approach 1.0 allow for lots of -variations but will also produce images that are not semantically consistent -with the input. 0.0 preserves image exactly, 1.0 replaces it completely. - -Make sure your input image size dimensions are multiples of 64 e.g. 512x512. -Otherwise you'll get `Error: product of dimension sizes > 2**31'`. If you still -get the error -[try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.) -like 512x256. - -If you're on a Docker container, copy your input image into the Docker volume - -```Shell -docker cp /Users//Pictures/sketch-mountains-input.jpg dummy:/data/ -``` - -Try it out generating an image (or more). The `invoke` script needs absolute -paths to find the image so don't use `~`. - -If you're on your Mac - -```Shell -invoke> "A fantasy landscape, trending on artstation" -I /Users//Pictures/sketch-mountains-input.jpg --strength 0.75 --steps 100 -n4 -``` - -If you're on a Linux container on your Mac - -```Shell -invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1 -``` - -### Web Interface - -You can use the `invoke` script with a graphical web interface. Start the web -server with: - -```Shell -python3 scripts/invoke.py --full_precision --web -``` - -If it's running on your Mac point your Mac web browser to - - -Press Control-C at the command line to stop the web server. - -### Notes - -Some text you can add at the end of the prompt to make it very pretty: - -```Shell -cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed, painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida -``` - -The original scripts should work as well. - -```Shell -python3 scripts/orig_scripts/txt2img.py --help -python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1 --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray" -python3 scripts/orig_scripts/txt2img.py --ddim_steps 5 --n_iter 1 --n_samples 1 --plms --prompt "ocean" # or --klms -``` +- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error. +- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings, + and you may have cloned this repository before the issue was fixed. To solve this, please change + the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode + (`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL. + Finally, you may delete `docker-entrypoint.sh` followed by `git pull; git checkout docker/docker-entrypoint.sh` + to reset the file to its most recent version. + For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers) diff --git a/docs/installation/050_INSTALLING_MODELS.md b/docs/installation/050_INSTALLING_MODELS.md index d455d2146f..5333e2aa88 100644 --- a/docs/installation/050_INSTALLING_MODELS.md +++ b/docs/installation/050_INSTALLING_MODELS.md @@ -84,7 +84,7 @@ InvokeAI root directory's `autoimport` folder. ### Installation via `invokeai-model-install` -From the `invoke` launcher, choose option [5] "Download and install +From the `invoke` launcher, choose option [4] "Download and install models." This will launch the same script that prompted you to select models at install time. You can use this to add models that you skipped the first time around. It is all right to specify a model that diff --git a/docs/installation/060_INSTALL_PATCHMATCH.md b/docs/installation/060_INSTALL_PATCHMATCH.md index ccfd19d207..a9646f8b60 100644 --- a/docs/installation/060_INSTALL_PATCHMATCH.md +++ b/docs/installation/060_INSTALL_PATCHMATCH.md @@ -59,8 +59,7 @@ Prior to installing PyPatchMatch, you need to take the following steps: `from patchmatch import patch_match`: It should look like the following: ```py - Python 3.9.5 (default, Nov 23 2021, 15:27:38) - [GCC 9.3.0] on linux + Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from patchmatch import patch_match Compiling and loading c extensions from "/home/lstein/Projects/InvokeAI/.invokeai-env/src/pypatchmatch/patchmatch". diff --git a/docs/installation/deprecated_documentation/INSTALL_LINUX.md b/docs/installation/deprecated_documentation/INSTALL_LINUX.md index 1e66698ec2..97060f85ad 100644 --- a/docs/installation/deprecated_documentation/INSTALL_LINUX.md +++ b/docs/installation/deprecated_documentation/INSTALL_LINUX.md @@ -79,7 +79,7 @@ title: Manual Installation, Linux and obtaining an access token for downloading. It will then download and install the weights files for you. - Please look [here](../INSTALL_MANUAL.md) for a manual process for doing + Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing the same thing. 7. Start generating images! @@ -112,7 +112,7 @@ title: Manual Installation, Linux To use an alternative model you may invoke the `!switch` command in the CLI, or pass `--model ` during `invoke.py` launch for either the CLI or the Web UI. See [Command Line - Client](../../features/CLI.md#model-selection-and-importation). The + Client](../../deprecated/CLI.md#model-selection-and-importation). The model names are defined in `configs/models.yaml`. 8. Subsequently, to relaunch the script, be sure to run "conda activate diff --git a/docs/installation/deprecated_documentation/INSTALL_MAC.md b/docs/installation/deprecated_documentation/INSTALL_MAC.md index 7a3c5b564f..dea3c329a7 100644 --- a/docs/installation/deprecated_documentation/INSTALL_MAC.md +++ b/docs/installation/deprecated_documentation/INSTALL_MAC.md @@ -150,7 +150,7 @@ will do our best to help. To use an alternative model you may invoke the `!switch` command in the CLI, or pass `--model ` during `invoke.py` launch for either the CLI or the Web UI. See [Command Line - Client](../../features/CLI.md#model-selection-and-importation). The + Client](../../deprecated/CLI.md#model-selection-and-importation). The model names are defined in `configs/models.yaml`. --- diff --git a/docs/installation/deprecated_documentation/INSTALL_SOURCE.md b/docs/installation/deprecated_documentation/INSTALL_SOURCE.md index 2b1b750fbf..b71cd68ab7 100644 --- a/docs/installation/deprecated_documentation/INSTALL_SOURCE.md +++ b/docs/installation/deprecated_documentation/INSTALL_SOURCE.md @@ -128,7 +128,7 @@ python scripts/invoke.py --web --max_load_models=3 \ ``` These options are described in detail in the -[Command-Line Interface](../../features/CLI.md) documentation. +[Command-Line Interface](../../deprecated/CLI.md) documentation. ## Troubleshooting diff --git a/docs/installation/deprecated_documentation/INSTALL_WINDOWS.md b/docs/installation/deprecated_documentation/INSTALL_WINDOWS.md index 19acb832e4..9536f09db2 100644 --- a/docs/installation/deprecated_documentation/INSTALL_WINDOWS.md +++ b/docs/installation/deprecated_documentation/INSTALL_WINDOWS.md @@ -75,7 +75,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan obtaining an access token for downloading. It will then download and install the weights files for you. - Please look [here](../INSTALL_MANUAL.md) for a manual process for doing the + Please look [here](../020_INSTALL_MANUAL.md) for a manual process for doing the same thing. 8. Start generating images! @@ -108,7 +108,7 @@ Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehan To use an alternative model you may invoke the `!switch` command in the CLI, or pass `--model ` during `invoke.py` launch for either the CLI or the Web UI. See [Command Line - Client](../../features/CLI.md#model-selection-and-importation). The + Client](../../deprecated/CLI.md#model-selection-and-importation). The model names are defined in `configs/models.yaml`. 9. Subsequently, to relaunch the script, first activate the Anaconda diff --git a/docs/nodes/communityNodes.md b/docs/nodes/communityNodes.md index 47ed407695..d24873b24c 100644 --- a/docs/nodes/communityNodes.md +++ b/docs/nodes/communityNodes.md @@ -4,32 +4,58 @@ These are nodes that have been developed by the community, for the community. If If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md). -To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node. +To use a node, add the node to the `nodes` folder found in your InvokeAI install location. + +The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future. + +If you'd prefer, you can also just download the `.py` file from the linked repository and add it to the `nodes` folder. To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. --------------------------------- +- Community Nodes + + [Average Images](#average-images) + + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj) + + [Film Grain](#film-grain) + + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes) + + [GPT2RandomPromptMaker](#gpt2randompromptmaker) + + [Grid to Gif](#grid-to-gif) + + [Halftone](#halftone) + + [Ideal Size](#ideal-size) + + [Image and Mask Composition Pack](#image-and-mask-composition-pack) + + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes) + + [Image Picker](#image-picker) + + [Load Video Frame](#load-video-frame) + + [Make 3D](#make-3d) + + [Oobabooga](#oobabooga) + + [Prompt Tools](#prompt-tools) + + [Retroize](#retroize) + + [Size Stepper Nodes](#size-stepper-nodes) + + [Text font to Image](#text-font-to-image) + + [Thresholding](#thresholding) + + [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes) +- [Example Node Template](#example-node-template) +- [Disclaimer](#disclaimer) +- [Help](#help) + -------------------------------- -### Make 3D +### Average Images -**Description:** Create compelling 3D stereo images from 2D originals. +**Description:** This node takes in a collection of images of the same size and averages them as output. It converts everything to RGB mode first. -**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d) - -**Example Node Graph:** https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false - -**Output Examples** - -![Painting of a cozy delapidated house](https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png){: style="height:512px;width:512px"} -![Photo of cute puppies](https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png){: style="height:512px;width:512px"} +**Node Link:** https://github.com/JPPhoto/average-images-node -------------------------------- -### Ideal Size +### Depth Map from Wavefront OBJ -**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of. +**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation. -**Node Link:** https://github.com/JPPhoto/ideal-size-node +To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations. + +**Node Link:** https://github.com/dwringer/depth-from-obj-node + +**Example Usage:** +
-------------------------------- ### Film Grain @@ -39,68 +65,19 @@ To use a community workflow, download the the `.json` node graph file and load i **Node Link:** https://github.com/JPPhoto/film-grain-node -------------------------------- -### Image Picker +### Generative Grammar-Based Prompt Nodes -**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose. +**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no nonterminal terms remain in the string. -**Node Link:** https://github.com/JPPhoto/image-picker-node +This includes 3 Nodes: +- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output) +- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading +- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts. --------------------------------- -### Thresholding +**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes -**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image. - -**Node Link:** https://github.com/JPPhoto/thresholding-node - -**Examples** - -Input: - -![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632){: style="height:512px;width:512px"} - -Highlights/Midtones/Shadows: - - - - - -Highlights/Midtones/Shadows (with LUT blur enabled): - - - - - --------------------------------- -### Halftone - -**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing. - -**Node Link:** https://github.com/JPPhoto/halftone-node - -**Example** - -Input: - -![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4){: style="height:512px;width:512px"} - -Halftone Output: - -![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f){: style="height:512px;width:512px"} - -CMYK Halftone Output: - -![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea){: style="height:512px;width:512px"} - --------------------------------- -### Retroize - -**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images. - -**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/ - -**Retroize Output Examples** - -![image](https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974) +**Example Usage:** +
-------------------------------- ### GPT2RandomPromptMaker @@ -113,76 +90,49 @@ CMYK Halftone Output: Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment. -![9acf5aef-7254-40dd-95b3-8eac431dfab0 (1)](https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c) + -------------------------------- -### Load Video Frame +### Grid to Gif -**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs. +**Description:** One node that turns a grid image into an image collection, one node that turns an image collection into a gif. -**Node Link:** https://github.com/helix4u/load_video_frame +**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py -**Example Node Graph:** https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json +**Example Node Graph:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json -**Output Example:** +**Output Examples** -![Example animation](https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif) -[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4) + + -------------------------------- +### Halftone -### Oobabooga +**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing. -**Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input. +**Node Link:** https://github.com/JPPhoto/halftone-node -**Link:** https://github.com/sammyf/oobabooga-node +**Example** +Input: -**Example:** + -"describe a new mystical creature in its natural environment" +Halftone Output: -*can return* + -"The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface. -As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace." +CMYK Halftone Output: -![glimmerwing_small](https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed) - -**Requirement** - -a Text-Generation-Webui instance (might work remotely too, but I never tried it) and obviously InvokeAI 3.x - -**Note** - -This node works best with SDXL models, especially as the style can be described independantly of the LLM's output. + -------------------------------- -### Depth Map from Wavefront OBJ +### Ideal Size -**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation. +**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of. -To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations. - -**Node Link:** https://github.com/dwringer/depth-from-obj-node - -**Example Usage:** -![depth from obj usage graph](https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg) - --------------------------------- -### Generative Grammar-Based Prompt Nodes - -**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no more nonterminal terms remain in the string. - -This includes 3 Nodes: -- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output) -- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading -- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts. - -**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes - -**Example Usage:** -![lookups usage example graph](https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg) +**Node Link:** https://github.com/JPPhoto/ideal-size-node -------------------------------- ### Image and Mask Composition Pack @@ -208,45 +158,84 @@ This includes 15 Nodes: - *Text Mask (simple 2D)* - create and position a white on black (or black on white) line of text using any font locally available to Invoke. **Node Link:** https://github.com/dwringer/composition-nodes - -**Nodes and Output Examples:** -![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg) + +
-------------------------------- -### Size Stepper Nodes +### Image to Character Art Image Nodes -**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes. +**Description:** Group of nodes to convert an input image into ascii/unicode art Image -A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection. - -**Node Link:** https://github.com/dwringer/size-stepper-nodes - -**Example Usage:** -![size stepper usage graph](https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg) - --------------------------------- - -### Text font to Image - -**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line - -**Node Link:** https://github.com/mickr777/textfontimage +**Node Link:** https://github.com/mickr777/imagetoasciiimage **Output Examples** -![a3609d48-d9b7-41f0-b280-063d857986fb](https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36) - -Results after using the depth controlnet - -![9133eabb-bcda-4326-831e-1b641228b178](https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a) -![4f9a3fa8-9be9-4236-8a3e-fcec66decd2a](https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc) -![babd69c4-9d60-4a55-a834-5e8397f62610](https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89) +
+ + -------------------------------- +### Image Picker + +**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose. + +**Node Link:** https://github.com/JPPhoto/image-picker-node + +-------------------------------- +### Load Video Frame + +**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs. + +**Node Link:** https://github.com/helix4u/load_video_frame + +**Output Example:** + + +-------------------------------- +### Make 3D + +**Description:** Create compelling 3D stereo images from 2D originals. + +**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d) + +**Example Node Graph:** https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false + +**Output Examples** + + + + +-------------------------------- +### Oobabooga + +**Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input. + +**Link:** https://github.com/sammyf/oobabooga-node + +**Example:** + +"describe a new mystical creature in its natural environment" + +*can return* + +"The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface. +As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace." + + + +**Requirement** + +a Text-Generation-Webui instance (might work remotely too, but I never tried it) and obviously InvokeAI 3.x + +**Note** + +This node works best with SDXL models, especially as the style can be described independently of the LLM's output. + +-------------------------------- ### Prompt Tools -**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These where written to accompany the PromptsFromFile node and other prompt generation nodes. +**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These were written to accompany the PromptsFromFile node and other prompt generation nodes. 1. PromptJoin - Joins to prompts into one. 2. PromptReplace - performs a search and replace on a prompt. With the option of using regex. @@ -263,62 +252,94 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai **Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes -------------------------------- +### Retroize +**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images. + +**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/ + +**Retroize Output Examples** + + + +-------------------------------- +### Size Stepper Nodes + +**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes. + +A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection. + +**Node Link:** https://github.com/dwringer/size-stepper-nodes + +**Example Usage:** +
+ +-------------------------------- +### Text font to Image + +**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line + +**Node Link:** https://github.com/mickr777/textfontimage + +**Output Examples** + + + +Results after using the depth controlnet + + + + + +-------------------------------- +### Thresholding + +**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image. + +**Node Link:** https://github.com/JPPhoto/thresholding-node + +**Examples** + +Input: + + + +Highlights/Midtones/Shadows: + + + + + +Highlights/Midtones/Shadows (with LUT blur enabled): + + + + + +-------------------------------- ### XY Image to Grid and Images to Grids nodes **Description:** Image to grid nodes and supporting tools. -1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then mutilple grids will be created until it runs out of images. -2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporoting nodes. See example node setups for more details. - +1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images. +2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details. See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md **Node Link:** https://github.com/skunkworxdark/XYGrid_nodes -------------------------------- - -### Image to Character Art Image Node's - -**Description:** Group of nodes to convert an input image into ascii/unicode art Image - -**Node Link:** https://github.com/mickr777/imagetoasciiimage - -**Output Examples** - - -
- - - --------------------------------- - -### Grid to Gif - -**Description:** One node that turns a grid image into an image colletion, one node that turns an image collection into a gif - -**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py - -**Example Node Graph:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json - -**Output Examples** - - - - --------------------------------- - ### Example Node Template **Description:** This node allows you to do super cool things with InvokeAI. -**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py +**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py -**Example Node Graph:** https://github.com/invoke-ai/InvokeAI/fake_node_graph.json +**Example Workflow:** https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json **Output Examples** -![Example Image](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png){: style="height:115px;width:240px"} +
## Disclaimer diff --git a/docs/nodes/contributingNodes.md b/docs/nodes/contributingNodes.md index c58a56e4e6..7a30c8aeb0 100644 --- a/docs/nodes/contributingNodes.md +++ b/docs/nodes/contributingNodes.md @@ -4,7 +4,7 @@ To learn about the specifics of creating a new node, please visit our [Node crea Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: -- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node. +- Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily. - Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list - Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node. - A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project. diff --git a/docs/nodes/exampleWorkflows.md b/docs/nodes/exampleWorkflows.md index f0159bd77c..568283585c 100644 --- a/docs/nodes/exampleWorkflows.md +++ b/docs/nodes/exampleWorkflows.md @@ -2,13 +2,17 @@ We've curated some example workflows for you to get started with Workflows in InvokeAI -To use them, right click on your desired workflow, press "Download Linked File". You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images! +To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images! If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord. * [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json) -* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json) -* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json) -* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json) +* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_Text_to_Image.json) +* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json) +* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json) +* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json) +* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json) +* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json.json) * [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json) * [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json) +* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/QR_Code_Monster.json) \ No newline at end of file diff --git a/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json b/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json similarity index 55% rename from docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json rename to docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json index c963ace025..17222aa002 100644 --- a/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json +++ b/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json @@ -1,5 +1,5 @@ { - "name": "ESRGAN img2img upscale w_ Lineart ControlNet", + "name": "ESRGAN img2img upscale w_ Canny ControlNet", "author": "InvokeAI", "description": "Sample workflow for using Upscaling with ControlNet with SD1.5", "version": "1.0.1", @@ -8,20 +8,16 @@ "notes": "", "exposedFields": [ { - "nodeId": "aba70c57-2495-4ec1-8d23-02b1d11445c7", + "nodeId": "d8ace142-c05f-4f1d-8982-88dc7473958d", "fieldName": "model" }, { - "nodeId": "c394834e-cab7-4c0c-919e-2e35eba7f34e", - "fieldName": "prompt" - }, - { - "nodeId": "465c7e6e-278f-49b0-87ab-642e88cd076f", - "fieldName": "prompt" - }, - { - "nodeId": "4e2833b2-5d35-45ec-ae65-89ea1846a3f4", + "nodeId": "771bdf6a-0813-4099-a5d8-921a138754d4", "fieldName": "image" + }, + { + "nodeId": "63b6ab7e-5b05-4d1b-a3b1-42d8e53ce16b", + "fieldName": "prompt" } ], "meta": { @@ -29,14 +25,60 @@ }, "nodes": [ { - "id": "aba70c57-2495-4ec1-8d23-02b1d11445c7", + "id": "e8bf67fe-67de-4227-87eb-79e86afdfc74", "type": "invocation", "data": { - "id": "aba70c57-2495-4ec1-8d23-02b1d11445c7", + "id": "e8bf67fe-67de-4227-87eb-79e86afdfc74", + "type": "compel", + "inputs": { + "prompt": { + "id": "5f762fae-d791-42d9-8ab5-2b830c33ff20", + "name": "prompt", + "type": "string", + "fieldKind": "input", + "label": "", + "value": "" + }, + "clip": { + "id": "8ac95f40-317d-4513-bbba-b99effd3b438", + "name": "clip", + "type": "ClipField", + "fieldKind": "input", + "label": "" + } + }, + "outputs": { + "conditioning": { + "id": "46c65b2b-c0b5-40c2-b183-74e9451c6d56", + "name": "conditioning", + "type": "ConditioningField", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 261, + "position": { + "x": 1261.0015571435993, + "y": 1513.9276360694537 + } + }, + { + "id": "d8ace142-c05f-4f1d-8982-88dc7473958d", + "type": "invocation", + "data": { + "id": "d8ace142-c05f-4f1d-8982-88dc7473958d", "type": "main_model_loader", "inputs": { "model": { - "id": "4fcc98ee-1c70-4ad3-aaee-df72e0d4ecb9", + "id": "b35ae88a-f2d2-43f6-958c-8c624391250f", "name": "model", "type": "MainModelField", "fieldKind": "input", @@ -50,19 +92,19 @@ }, "outputs": { "unet": { - "id": "3ecbc0ff-a7b0-43de-a81f-039210cbda50", + "id": "02f243cb-c6e2-42c5-8be9-ef0519d54383", "name": "unet", "type": "UNetField", "fieldKind": "output" }, "clip": { - "id": "4e55bd72-5409-4fba-9929-4177e4ae9c34", + "id": "7762ed13-5b28-40f4-85f1-710942ceb92a", "name": "clip", "type": "ClipField", "fieldKind": "output" }, "vae": { - "id": "0095495c-4424-451f-a8f5-26dc840a3c56", + "id": "69566153-1918-417d-a3bb-32e9e857ef6b", "name": "vae", "type": "VaeField", "fieldKind": "output" @@ -72,256 +114,26 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 202, + "height": 226, "position": { - "x": 175, - "y": 300 + "x": 433.44132965778, + "y": 1419.9552496403696 } }, { - "id": "167ffc36-4cb7-425c-ae55-e88f80a1d6fe", + "id": "771bdf6a-0813-4099-a5d8-921a138754d4", "type": "invocation", "data": { - "id": "167ffc36-4cb7-425c-ae55-e88f80a1d6fe", - "type": "noise", - "inputs": { - "seed": { - "id": "a64e30f7-f9f4-4d67-a5aa-af5924205f92", - "name": "seed", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "width": { - "id": "054eaa35-63af-41c9-b13f-e9c2aaeeca43", - "name": "width", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 512 - }, - "height": { - "id": "6e9a642c-fe1d-477f-ae66-1706471f7d73", - "name": "height", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 512 - }, - "use_cpu": { - "id": "caa3c8ba-aed5-44d8-88d9-4f48a75d59a4", - "name": "use_cpu", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": true - } - }, - "outputs": { - "noise": { - "id": "133dbb18-f862-430b-b9a0-613aa8e61c7d", - "name": "noise", - "type": "LatentsField", - "fieldKind": "output" - }, - "width": { - "id": "2e31961a-af0c-497b-9ae2-429cb6c2f5a1", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "eca33bb8-37bb-4bfd-b7c7-3dba01207374", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": false, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 32, - "position": { - "x": 1400, - "y": 650 - } - }, - { - "id": "c2172a8b-1b5f-4330-acbe-dd2565c3b988", - "type": "invocation", - "data": { - "id": "c2172a8b-1b5f-4330-acbe-dd2565c3b988", - "type": "l2i", - "inputs": { - "tiled": { - "id": "406bccc1-d757-4578-b46e-be6141c03385", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "960ec115-547c-45c8-af2a-569214d9409c", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "latents": { - "id": "c8658cc5-3762-499d-9cad-eceb8f9dde4e", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "c35cf05c-0985-4bc4-9b05-0c89799bb888", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "image": { - "id": "25afb2bc-c964-4cd3-8332-4d0e9ea65d3a", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "aff7065e-8ce0-44aa-bb6e-d16925279fdd", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "6f5e8706-7a9b-4455-beb8-3d2e2ceabbc2", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": true, - "isIntermediate": false - }, - "width": 320, - "height": 266, - "position": { - "x": 2225, - "y": 450 - } - }, - { - "id": "c394834e-cab7-4c0c-919e-2e35eba7f34e", - "type": "invocation", - "data": { - "id": "c394834e-cab7-4c0c-919e-2e35eba7f34e", - "type": "compel", - "inputs": { - "prompt": { - "id": "fef594dd-07d3-47e6-97d0-1803b55a0f26", - "name": "prompt", - "type": "string", - "fieldKind": "input", - "label": "Positive Prompt", - "value": "tiger" - }, - "clip": { - "id": "e5639045-aa13-48c2-a172-869774aecab6", - "name": "clip", - "type": "ClipField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "conditioning": { - "id": "3249093e-0bc4-42a7-8a9b-2172fb89e915", - "name": "conditioning", - "type": "ConditioningField", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 235, - "position": { - "x": 975, - "y": -25 - } - }, - { - "id": "465c7e6e-278f-49b0-87ab-642e88cd076f", - "type": "invocation", - "data": { - "id": "465c7e6e-278f-49b0-87ab-642e88cd076f", - "type": "compel", - "inputs": { - "prompt": { - "id": "9a3ac8ea-7655-4806-ab8c-b7a18a253181", - "name": "prompt", - "type": "string", - "fieldKind": "input", - "label": "Negative Prompt", - "value": "" - }, - "clip": { - "id": "1d348def-bb7d-4bab-b983-9f55c933ea67", - "name": "clip", - "type": "ClipField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "conditioning": { - "id": "f92febc1-67c4-45d4-b2e4-9ba470e4ccef", - "name": "conditioning", - "type": "ConditioningField", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 235, - "position": { - "x": 975, - "y": 250 - } - }, - { - "id": "4e2833b2-5d35-45ec-ae65-89ea1846a3f4", - "type": "invocation", - "data": { - "id": "4e2833b2-5d35-45ec-ae65-89ea1846a3f4", + "id": "771bdf6a-0813-4099-a5d8-921a138754d4", "type": "image", "inputs": { "image": { - "id": "0b2f59b4-9994-4c99-9309-5434c746abb9", + "id": "0f6d68a2-38bd-4f65-a112-0a256c7a2678", "name": "image", "type": "ImageField", "fieldKind": "input", @@ -330,19 +142,19 @@ }, "outputs": { "image": { - "id": "9a41092e-50ec-4530-95b2-33d9207a8f50", + "id": "76f6f9b6-755b-4373-93fa-6a779998d2c8", "name": "image", "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "6462e3b2-6450-45fd-9fee-0fbe25537ed0", + "id": "6858e46b-707c-444f-beda-9b5f4aecfdf8", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "94c0d477-8753-4976-ba91-b9eb7ad71285", + "id": "421bdc6e-ecd1-4935-9665-d38ab8314f79", "name": "height", "type": "integer", "fieldKind": "output" @@ -352,299 +164,70 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 225, "position": { - "x": 50, - "y": 750 + "x": 11.612243766002848, + "y": 1989.909405085168 } }, { - "id": "a7f1336d-516d-4735-826f-3c633dfaa5e8", + "id": "f7564dd2-9539-47f2-ac13-190804461f4e", "type": "invocation", "data": { - "id": "a7f1336d-516d-4735-826f-3c633dfaa5e8", - "type": "i2l", - "inputs": { - "image": { - "id": "b69df743-8045-4ffe-bb14-71b7f9c17c5f", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "a98a3497-34c5-46f7-9eaf-c24eab5d481a", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - }, - "tiled": { - "id": "706dc2c8-1820-42a7-98e1-bcd631737e7b", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "5fc3bc04-c66e-46db-be2a-470c9d64b0d8", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - } - }, - "outputs": { - "latents": { - "id": "50b89da5-e7ed-45cd-b74e-83c07e510ccd", - "name": "latents", - "type": "LatentsField", - "fieldKind": "output" - }, - "width": { - "id": "7be69dad-837f-4c98-8ae2-c7aacaa44b52", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "66e3c7ec-3848-4afb-84bb-ff3a09e47089", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 299, - "position": { - "x": 975, - "y": 525 - } - }, - { - "id": "61613ab2-784d-4a5c-8576-18fd5da065ef", - "type": "invocation", - "data": { - "id": "61613ab2-784d-4a5c-8576-18fd5da065ef", - "type": "denoise_latents", - "inputs": { - "noise": { - "id": "6b894db2-ee45-45b6-b531-573311ddea73", - "name": "noise", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "steps": { - "id": "7e7693fe-6c0c-464a-8535-2ed517766c19", - "name": "steps", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 64 - }, - "cfg_scale": { - "id": "890ba738-690c-44a3-97cd-c589257b531a", - "name": "cfg_scale", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 7.5 - }, - "denoising_start": { - "id": "0e157ca8-5c06-499a-be6d-283fb834df32", - "name": "denoising_start", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.7 - }, - "denoising_end": { - "id": "915bc497-cb58-40a5-8089-2ece7213be21", - "name": "denoising_end", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 1 - }, - "scheduler": { - "id": "694471cd-8708-411b-9d88-2ae725254ff0", - "name": "scheduler", - "type": "Scheduler", - "fieldKind": "input", - "label": "", - "value": "euler_a" - }, - "control": { - "id": "b14cff5e-30ea-4d61-b27c-8247063699ad", - "name": "control", - "type": "ControlField", - "fieldKind": "input", - "label": "" - }, - "latents": { - "id": "0528dbfd-d661-4c73-b493-722b27e37201", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "denoise_mask": { - "id": "ea7d6070-4f00-4b77-b49e-ffe1ca0e3fea", - "name": "denoise_mask", - "type": "DenoiseMaskField", - "fieldKind": "input", - "label": "" - }, - "positive_conditioning": { - "id": "56a3fa76-9eb4-4680-8a4d-169696034525", - "name": "positive_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "negative_conditioning": { - "id": "0eed85bc-d5bd-4de4-8155-0c08f1ac5e32", - "name": "negative_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "87ab9e45-a92f-4d47-a9f6-a65fe23373de", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "latents": { - "id": "d12e0bd6-7d35-4d12-84c0-540c26ba01c8", - "name": "latents", - "type": "LatentsField", - "fieldKind": "output" - }, - "width": { - "id": "201ca444-2d10-4bab-b6be-e1b5be97e1b0", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "74ce84a5-b68c-4503-8a7d-bc017024678a", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 558, - "position": { - "x": 1800, - "y": 350 - } - }, - { - "id": "39abdb3e-f8d1-4b0a-bab1-547d9e25b78e", - "type": "invocation", - "data": { - "id": "39abdb3e-f8d1-4b0a-bab1-547d9e25b78e", - "type": "rand_int", - "inputs": { - "low": { - "id": "10eff2d0-929f-45ca-a1ba-68c3e742db71", - "name": "low", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "high": { - "id": "b6e39169-e6ee-496a-8046-5444497036c2", - "name": "high", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 2147483647 - } - }, - "outputs": { - "value": { - "id": "cc477a7a-36bc-458f-b7ac-6717bac6f12b", - "name": "value", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": false, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 32, - "position": { - "x": 975, - "y": 850 - } - }, - { - "id": "08c41d50-fb10-43a9-a58b-fc15ee678a83", - "type": "invocation", - "data": { - "id": "08c41d50-fb10-43a9-a58b-fc15ee678a83", + "id": "f7564dd2-9539-47f2-ac13-190804461f4e", "type": "esrgan", "inputs": { + "metadata": { + "id": "8fa0c7eb-5bd3-4575-98e7-72285c532504", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, "image": { - "id": "01b92996-26aa-412b-9eba-d13cb7b370a8", + "id": "3c949799-a504-41c9-b342-cff4b8146c48", "name": "image", "type": "ImageField", "fieldKind": "input", "label": "" }, "model_name": { - "id": "3fc7bf6a-e181-4236-b746-48b011351af1", + "id": "77cb4750-53d6-4c2c-bb5c-145981acbf17", "name": "model_name", "type": "enum", "fieldKind": "input", "label": "", - "value": "RealESRGAN_x2plus.pth" + "value": "RealESRGAN_x4plus.pth" + }, + "tile_size": { + "id": "7787b3ad-46ee-4248-995f-bc740e1f988b", + "name": "tile_size", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 400 } }, "outputs": { "image": { - "id": "6feb0664-c61d-4fcd-8226-ed81591dcb0c", + "id": "37e6308e-e926-4e07-b0db-4e8601f495d0", "name": "image", "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "61650a80-7d2f-4509-8600-574c5cc6e569", + "id": "c194d84a-fac7-4856-b646-d08477a5ad2b", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "fcb00841-f068-475e-ac90-0874313fd7fa", + "id": "b2a6206c-a9c8-4271-a055-0b93a7f7d505", "name": "height", "type": "integer", "fieldKind": "output" @@ -654,31 +237,106 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.1.0" }, "width": 320, - "height": 282, + "height": 339, "position": { - "x": 475, - "y": 725 + "x": 436.07457889056195, + "y": 1967.3109314112623 } }, { - "id": "30598d37-cf80-4e12-896a-7683cf727e77", + "id": "1d887701-df21-4966-ae6e-a7d82307d7bd", "type": "invocation", "data": { - "id": "30598d37-cf80-4e12-896a-7683cf727e77", + "id": "1d887701-df21-4966-ae6e-a7d82307d7bd", + "type": "canny_image_processor", + "inputs": { + "metadata": { + "id": "52c877c8-25d9-4949-8518-f536fcdd152d", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, + "image": { + "id": "e0af11fe-4f95-4193-a599-cf40b6a963f5", + "name": "image", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "low_threshold": { + "id": "ab775f7b-f556-4298-a9d6-2274f3a6c77c", + "name": "low_threshold", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 100 + }, + "high_threshold": { + "id": "9e58b615-06e4-417f-b0d8-63f1574cd174", + "name": "high_threshold", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 200 + } + }, + "outputs": { + "image": { + "id": "61feb8bf-95c9-4634-87e2-887fc43edbdf", + "name": "image", + "type": "ImageField", + "fieldKind": "output" + }, + "width": { + "id": "9e203e41-73f7-4cfa-bdca-5040e5e60c55", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "ec7d99dc-0d82-4495-a759-6423808bff1c", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 339, + "position": { + "x": 1221.7155516160597, + "y": 1971.0099052871012 + } + }, + { + "id": "ca1d020c-89a8-4958-880a-016d28775cfa", + "type": "invocation", + "data": { + "id": "ca1d020c-89a8-4958-880a-016d28775cfa", "type": "controlnet", "inputs": { "image": { - "id": "d44259eb-bc93-4d4b-9665-a7895e5a77ab", + "id": "2973c126-e301-4595-a7dc-d6e1729ccdbf", "name": "image", "type": "ImageField", "fieldKind": "input", "label": "" }, "control_model": { - "id": "2021065f-d5c9-47ad-bfea-03eea03a19ce", + "id": "4bb4d987-8491-4839-b41b-6e2f546fe2d0", "name": "control_model", "type": "ControlNetModelField", "fieldKind": "input", @@ -689,15 +347,15 @@ } }, "control_weight": { - "id": "f856e29f-303f-4507-8c39-71ccc636f67c", + "id": "a3cf387a-b58f-4058-858f-6a918efac609", "name": "control_weight", - "type": "float", + "type": "FloatPolymorphic", "fieldKind": "input", "label": "", "value": 1 }, "begin_step_percent": { - "id": "f9187fa7-1510-439d-9c9b-e5fa990639b0", + "id": "e0614f69-8a58-408b-9238-d3a44a4db4e0", "name": "begin_step_percent", "type": "float", "fieldKind": "input", @@ -705,7 +363,7 @@ "value": 0 }, "end_step_percent": { - "id": "3f7c15c3-2b80-49a8-8eec-57d277c37364", + "id": "ac683539-b6ed-4166-9294-2040e3ede206", "name": "end_step_percent", "type": "float", "fieldKind": "input", @@ -713,7 +371,7 @@ "value": 1 }, "control_mode": { - "id": "f34cb991-ecb5-423c-865f-9890b2fa9d23", + "id": "f00b21de-cbd7-4901-8efc-e7134a2dc4c8", "name": "control_mode", "type": "enum", "fieldKind": "input", @@ -721,7 +379,7 @@ "value": "balanced" }, "resize_mode": { - "id": "552142f6-f6a6-4291-803c-68caefec7c6d", + "id": "cafb60ee-3959-4d57-a06c-13b83be6ea4f", "name": "resize_mode", "type": "enum", "fieldKind": "input", @@ -731,7 +389,7 @@ }, "outputs": { "control": { - "id": "6160265b-4a8d-4fc5-8e1f-d793a353d2db", + "id": "dfb88dd1-12bf-4034-9268-e726f894c131", "name": "control", "type": "ControlField", "fieldKind": "output" @@ -741,101 +399,218 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 480, + "height": 508, "position": { - "x": 1375, - "y": 875 + "x": 1681.7783532660528, + "y": 1845.0516454465633 } }, { - "id": "b79732f8-6126-4d06-9c8a-4e84bd5b1ac9", + "id": "f50624ce-82bf-41d0-bdf7-8aab11a80d48", "type": "invocation", "data": { - "id": "b79732f8-6126-4d06-9c8a-4e84bd5b1ac9", - "type": "vae_loader", + "id": "f50624ce-82bf-41d0-bdf7-8aab11a80d48", + "type": "noise", "inputs": { - "vae_model": { - "id": "b68038ae-b8cb-4e29-9581-da50d55af462", - "name": "vae_model", - "type": "VaeModelField", - "fieldKind": "input", - "label": "", - "value": { - "model_name": "sd-vae-ft-mse", - "base_model": "sd-1" - } - } - }, - "outputs": { - "vae": { - "id": "82f4ba9d-5e3a-4b48-b7a4-37de956663d7", - "name": "vae", - "type": "VaeField", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 138, - "position": { - "x": 175, - "y": 525 - } - }, - { - "id": "b10d39ab-7bc9-48bc-b883-2fd50920876d", - "type": "invocation", - "data": { - "id": "b10d39ab-7bc9-48bc-b883-2fd50920876d", - "type": "canny_image_processor", - "inputs": { - "image": { - "id": "dee9cdf2-9b3f-4d20-8433-ef7d6f6526bd", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "low_threshold": { - "id": "4eaefd37-9fbe-4b73-8a17-c60d4e1d7e39", - "name": "low_threshold", + "seed": { + "id": "f76b0e01-b601-423f-9b5f-ab7a1f10fe82", + "name": "seed", "type": "integer", "fieldKind": "input", "label": "", - "value": 100 + "value": 0 }, - "high_threshold": { - "id": "c394720b-546b-464e-8f53-d20bfda4ee04", - "name": "high_threshold", + "width": { + "id": "eec326d6-710c-45de-a25c-95704c80d7e2", + "name": "width", "type": "integer", "fieldKind": "input", "label": "", - "value": 200 + "value": 512 + }, + "height": { + "id": "2794a27d-5337-43ca-95d9-41b673642c94", + "name": "height", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 512 + }, + "use_cpu": { + "id": "ae7654e3-979e-44a1-8968-7e3199e91e66", + "name": "use_cpu", + "type": "boolean", + "fieldKind": "input", + "label": "", + "value": true } }, "outputs": { - "image": { - "id": "80fcdf76-dfc3-41a1-99ea-5ab2aa5fde07", - "name": "image", - "type": "ImageField", + "noise": { + "id": "8b6dc166-4ead-4124-8ac9-529814b0cbb9", + "name": "noise", + "type": "LatentsField", "fieldKind": "output" }, "width": { - "id": "3fa5e5a9-ff60-44d2-8d91-635d0c798f15", + "id": "e3fe3940-a277-4838-a448-5f81f2a7d99d", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "01a10c42-d485-4436-af8b-03d71c59bc8c", + "id": "48ecd6ef-c216-40d5-9d1b-d37bd00c82e7", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": false, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 32, + "position": { + "x": 1660.5387878479382, + "y": 1664.7391082353483 + } + }, + { + "id": "c3737554-8d87-48ff-a6f8-e71d2867f434", + "type": "invocation", + "data": { + "id": "c3737554-8d87-48ff-a6f8-e71d2867f434", + "type": "denoise_latents", + "inputs": { + "positive_conditioning": { + "id": "e127084b-72f5-4fe4-892b-84f34f88bce9", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "72cde4ee-55de-4d3e-9057-74e741c04e20", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "noise": { + "id": "747f7023-1c19-465b-bec8-1d9695dd3505", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "80860292-633c-46f2-83d0-60d0029b65d2", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "ebc71e6f-9148-4f12-b455-5e1f179d1c3a", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": "ced44b8f-3bad-4c34-8113-13bc0faed28a", + "name": "denoising_start", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "denoising_end": { + "id": "79bf4b77-3502-4f72-ba8b-269c4c3c5c72", + "name": "denoising_end", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 1 + }, + "scheduler": { + "id": "ed56e2b8-f477-41a2-b9f5-f15f4933ae65", + "name": "scheduler", + "type": "Scheduler", + "fieldKind": "input", + "label": "", + "value": "euler" + }, + "unet": { + "id": "146b790c-b08e-437c-a2e1-e393c2c1c41a", + "name": "unet", + "type": "UNetField", + "fieldKind": "input", + "label": "" + }, + "control": { + "id": "75ed3df1-d261-4b8e-a89b-341c4d7161fb", + "name": "control", + "type": "ControlPolymorphic", + "fieldKind": "input", + "label": "" + }, + "ip_adapter": { + "id": "eab9a61d-9b64-44d3-8d90-4686f5887cb0", + "name": "ip_adapter", + "type": "IPAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "t2i_adapter": { + "id": "2dc8d637-58fd-4069-ad33-85c32d958b7b", + "name": "t2i_adapter", + "type": "T2IAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "latents": { + "id": "6767e40a-97c6-4487-b3c9-cad1c150bf9f", + "name": "latents", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "denoise_mask": { + "id": "6251efda-d97d-4ff1-94b5-8cc6b458c184", + "name": "denoise_mask", + "type": "DenoiseMaskField", + "fieldKind": "input", + "label": "" + } + }, + "outputs": { + "latents": { + "id": "4e7986a4-dff2-4448-b16b-1af477b81f8b", + "name": "latents", + "type": "LatentsField", + "fieldKind": "output" + }, + "width": { + "id": "dad525dd-d2f8-4f07-8c8d-51f2a3c5456e", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "af03a089-4739-40c6-8b48-25d458d63c2f", "name": "height", "type": "integer", "fieldKind": "output" @@ -845,165 +620,344 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - 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"id": "reactflow__edge-08c41d50-fb10-43a9-a58b-fc15ee678a83image-b10d39ab-7bc9-48bc-b883-2fd50920876dimage", + "id": "reactflow__edge-f7564dd2-9539-47f2-ac13-190804461f4eimage-5ca498a4-c8c8-4580-a396-0c984317205dimage", "type": "default" }, { - "source": "b10d39ab-7bc9-48bc-b883-2fd50920876d", - "sourceHandle": "image", - "target": "30598d37-cf80-4e12-896a-7683cf727e77", - "targetHandle": "image", - "id": "reactflow__edge-b10d39ab-7bc9-48bc-b883-2fd50920876dimage-30598d37-cf80-4e12-896a-7683cf727e77image", + "source": "d8ace142-c05f-4f1d-8982-88dc7473958d", + "sourceHandle": "unet", + "target": "c3737554-8d87-48ff-a6f8-e71d2867f434", + "targetHandle": "unet", + "id": "reactflow__edge-d8ace142-c05f-4f1d-8982-88dc7473958dunet-c3737554-8d87-48ff-a6f8-e71d2867f434unet", "type": "default" } ] diff --git a/docs/workflows/FaceMask.json b/docs/workflows/FaceMask.json index 589ac097e4..54c1f800b6 100644 --- a/docs/workflows/FaceMask.json +++ b/docs/workflows/FaceMask.json @@ -1,7 +1,7 @@ { "name": "FaceMask", "author": "YMGenesis", - "description": "21 September 2023\n\nPlace an image with recognizable face(s) in Image Primitive, and write what sort of new face you want in the top prompt text box. See Notes for more info.", + "description": "Place an image with recognizable face(s) in Image Primitive, and write what sort of new face you want in the top prompt text box. See Notes for more info.", "version": "1.0", "contact": "YMGenesis on InvokeAI Discord", "tags": "facemask, facetools", @@ -56,14 +56,14 @@ "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 226, "position": { - "x": 4625, - "y": 1275 + "x": 4103.832039728059, + "y": 1987.4435345183065 } }, { @@ -102,14 +102,14 @@ "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 259, + "height": 261, "position": { - "x": 5025, - "y": 1275 + "x": 4988.591898842789, + "y": 1700.8901379603535 } }, { @@ -148,14 +148,14 @@ "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 259, + "height": 261, "position": { - "x": 5025, - "y": 1550 + "x": 4986.228031951785, + "y": 1987.19695578231 } }, { @@ -219,91 +219,18 @@ } }, "label": "", - "isOpen": true, + "isOpen": false, "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 388, + "height": 32, "position": { - "x": 5425, - "y": 1525 - } - }, - { - "id": "3b2ebc7f-251d-4726-b1b8-5331355f8626", - "type": "invocation", - "data": { - "id": "3b2ebc7f-251d-4726-b1b8-5331355f8626", - "type": "l2i", - "inputs": { - "tiled": { - "id": "8265e595-8f76-4615-8b70-3a0fca854cbf", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "cc2dd2ee-63f6-40e7-937e-d82086685abe", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "latents": { - "id": "fbbab00e-b47f-4f45-801a-a719873429ca", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "476e6075-c62d-4d42-afcf-a18e4c4a47fb", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "image": { - "id": "65086674-a0f7-4d6c-a02e-101010b34d74", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "e32514a9-50d5-423e-95d3-5d9fd460adaa", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "4d6557ec-2967-40ec-bcae-52725ffe766c", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true, - "version": "1.0.0", - "useCache": true - }, - "width": 320, - "height": 266, - "position": { - "x": 5825, - "y": 1275 + "x": 5398.389401611981, + "y": 2019.4053462371755 } }, { @@ -339,18 +266,18 @@ } }, "label": "", - "isOpen": true, + "isOpen": false, "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": false + "useCache": false, + "version": "1.0.0" }, "width": 320, - "height": 218, + "height": 32, "position": { - "x": 5425, - "y": 1275 + "x": 5386.304039775159, + "y": 1979.791644235275 } }, { @@ -407,18 +334,18 @@ } }, "label": "", - "isOpen": true, + "isOpen": false, "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 316, + "height": 32, "position": { - "x": 5025, - "y": 2175 + "x": 5009.179957658444, + "y": 2346.7322639404283 } }, { @@ -461,86 +388,14 @@ "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 225, "position": { - "x": 4625, - "y": 1525 - } - }, - { - "id": "31e99fe1-c39d-4cca-bd89-9ec27cb3a8a7", - "type": "invocation", - "data": { - "id": "31e99fe1-c39d-4cca-bd89-9ec27cb3a8a7", - "type": "color_correct", - "inputs": { - "image": { - "id": "8523ebb4-b6ef-4ba2-a0fc-32a5beb69c65", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "reference": { - "id": "b4d476d4-cdca-4757-a7e3-509614ce956e", - "name": "reference", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "mask": { - "id": "66b192f5-50a6-4241-aac8-4ed68a34ac90", - "name": "mask", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "mask_blur_radius": { - "id": "deac73dc-2ad0-4e31-9519-920c56746c4e", - "name": "mask_blur_radius", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 8 - } - }, - "outputs": { - "image": { - "id": "d5eb4f4e-e277-435c-a960-0cf1731cac4b", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "9fef7aab-0294-43ac-adcf-f53fe3188823", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "148b6497-7a4e-43b4-bbba-a18e9426db37", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": true, - "isIntermediate": false, - "version": "1.0.0", - "useCache": true - }, - "width": 320, - "height": 396, - "position": { - "x": 5825, - "y": 1575 + "x": 4107.933245141945, + "y": 2255.443448115275 } }, { @@ -602,44 +457,197 @@ } }, "label": "", + "isOpen": false, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 32, + "position": { + "x": 5006.155303630073, + "y": 2277.2727128782517 + } + }, + { + "id": "d14b4d95-bf74-4ec5-827b-4c9e797c7ae9", + "type": "invocation", + "data": { + "id": "d14b4d95-bf74-4ec5-827b-4c9e797c7ae9", + "type": "denoise_latents", + "inputs": { + "noise": { + "id": "175b4d0a-3017-46e2-933f-c02f1cfb29b2", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "dd174b3e-3f6c-46cb-a703-3c6f3b3c72f1", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "0ea30aa7-8747-4c93-87e8-3c84e0dfd187", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": "a6392edb-8895-41ed-918b-0ba8d2ac72ac", + "name": "denoising_start", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "denoising_end": { + "id": "1d1807cc-a24d-426e-9de5-a7e61d45c006", + "name": "denoising_end", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 1 + }, + "scheduler": { + "id": "947a5212-8923-4d5d-934c-dbc5879b9d07", + "name": "scheduler", + "type": "Scheduler", + "fieldKind": "input", + "label": "", + "value": "euler" + }, + "control": { + "id": "585378b9-2686-4573-b762-3dc2d6179193", + "name": "control", + "type": "ControlPolymorphic", + "fieldKind": "input", + "label": "" + }, + "ip_adapter": { + "id": "191f4687-fdcc-45da-859f-71fd5091a8bd", + "name": "ip_adapter", + "type": "IPAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "t2i_adapter": { + "id": "39fc54ae-2141-4cab-9c01-c6c415f964cd", + "name": "t2i_adapter", + "type": "T2IAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "latents": { + "id": "7f1a388e-8355-496c-a45d-fce5b8685a63", + "name": "latents", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "denoise_mask": { + "id": "de922941-b2d8-4c57-92a7-201f9ddaf262", + "name": "denoise_mask", + "type": "DenoiseMaskField", + "fieldKind": "input", + "label": "" + }, + "positive_conditioning": { + "id": "2b42b4e8-4795-4fcc-bef1-c08cb8e25e0a", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "ff29b7d7-1bff-4aa9-b5c0-f8786a55023a", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "unet": { + "id": "0155f1cb-152b-4097-9395-afcc745c697b", + "name": "unet", + "type": "UNetField", + "fieldKind": "input", + "label": "" + } + }, + "outputs": { + "latents": { + "id": "03cfb327-02a1-4fbe-b7ce-b07fd501d2b8", + "name": "latents", + "type": "LatentsField", + "fieldKind": "output" + }, + "width": { + "id": "3ffb87d7-b5a6-4b35-bdf1-2bb9b718d815", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "2c1f0588-943a-4fd1-b75b-48d04c944296", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", "isOpen": true, "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.3.0" }, "width": 320, - "height": 323, + "height": 646, "position": { - "x": 5025, - "y": 1825 + "x": 5512.059705982663, + "y": 2103.8364934988267 } }, { - "id": "c2032405-817c-494d-b1e5-441e74d248d2", + "id": "eb725b0b-1fa6-4f79-aedb-52c19afcfad9", "type": "invocation", "data": { - "id": "c2032405-817c-494d-b1e5-441e74d248d2", + "id": "eb725b0b-1fa6-4f79-aedb-52c19afcfad9", "type": "face_mask_detection", "inputs": { + "metadata": { + "id": "a56bf310-b5c3-4440-8ba6-79f5e434a9e6", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, "image": { - "id": "e04ef12b-5eb8-40ba-9e46-93f360be12e6", + "id": "11ece2fd-57ee-4504-b1f5-8d0c9332d785", "name": "image", "type": "ImageField", "fieldKind": "input", "label": "" }, "face_ids": { - "id": "63ce222d-2dd3-41a3-976d-a15e5c6bc6d2", + "id": "c5eaafc3-70f4-4bcd-9df7-0d4cd26e1734", "name": "face_ids", "type": "string", "fieldKind": "input", "label": "", - "value": "0" + "value": "" }, "minimum_confidence": { - "id": "0b5969ae-afa6-4007-be70-4dc2a86dda4a", + "id": "ab85fdbd-d61e-4584-8595-c1cea1ffb288", "name": "minimum_confidence", "type": "float", "fieldKind": "input", @@ -647,7 +655,7 @@ "value": 0.5 }, "x_offset": { - "id": "5dc14f5e-02ad-42c9-809c-e708f107c2cd", + "id": "e381e04d-b54d-4457-9b6e-b3b554a8e343", "name": "x_offset", "type": "float", "fieldKind": "input", @@ -655,7 +663,7 @@ "value": 0 }, "y_offset": { - "id": "0d84f0c3-6983-4902-b3ed-d8cd8797e35d", + "id": "98e771c8-df97-4a57-a5d4-9601dac68338", "name": "y_offset", "type": "float", "fieldKind": "input", @@ -663,7 +671,7 @@ "value": 0 }, "chunk": { - "id": "00edd0c0-ee3f-4739-8a22-895b88d83d78", + "id": "ae4af045-99f9-4d84-81d2-438dc3d13b8d", "name": "chunk", "type": "boolean", "fieldKind": "input", @@ -671,7 +679,7 @@ "value": false }, "invert_mask": { - "id": "cbf70aeb-ceec-45c2-8042-3921c3134baf", + "id": "a6a0fe16-da45-46aa-9f85-9469fde40d71", "name": "invert_mask", "type": "boolean", "fieldKind": "input", @@ -681,25 +689,25 @@ }, "outputs": { "image": { - "id": "fe460f4c-7aa2-409a-bc02-a07e69cf9f2a", + "id": "7de8643a-c6b1-4260-b843-728c8d0fc6d4", "name": "image", "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "26cf35c6-2ccd-4298-a7db-b1c2fddb3fc9", + "id": "fb1450b6-de42-465a-98d7-1dc93ceb20d7", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "3c4e7666-d039-49a5-b4c6-c5d9ba11f1bb", + "id": "cea5abb6-1584-440a-9ee2-1e4c926235e7", "name": "height", "type": "integer", "fieldKind": "output" }, "mask": { - "id": "35f88fb5-94c1-4643-a5c6-cc288602f37d", + "id": "272e4224-1736-42de-895d-096309259ac7", "name": "mask", "type": "ImageField", "fieldKind": "output" @@ -710,135 +718,76 @@ "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.0.0", - "useCache": true + "useCache": true, + "version": "1.0.2" }, "width": 320, - "height": 583, + "height": 585, "position": { - "x": 4625, - "y": 1900 + "x": 4559.385043470649, + "y": 2082.7157021692556 } }, { - "id": "441fdd15-1a49-413c-bd2a-637a105ad370", + "id": "e4681270-ea7e-4063-9116-880408854eee", "type": "invocation", "data": { - "id": "441fdd15-1a49-413c-bd2a-637a105ad370", - "type": "denoise_latents", + "id": "e4681270-ea7e-4063-9116-880408854eee", + "type": "l2i", "inputs": { - "noise": { - "id": "86b6226c-1780-49d7-bbed-c4a5440eebb2", - "name": "noise", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "steps": { - "id": "f3cae478-9f7c-4836-b8f8-3a83ef2fdcc3", - "name": "steps", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 20 - }, - "cfg_scale": { - "id": "f7d64dc5-ea3d-44a8-8951-56622f0548ee", - "name": "cfg_scale", - "type": "FloatPolymorphic", - "fieldKind": "input", - "label": "", - "value": 5 - }, - "denoising_start": { - "id": "01409326-116d-41b5-9c3d-abaa2a5182aa", - "name": "denoising_start", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.3 - }, - "denoising_end": { - "id": "445b857b-a136-4736-b677-b2a701d651d4", - "name": "denoising_end", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 1 - }, - "scheduler": { - "id": "8e7d75ef-6cf0-467e-8067-595bb96c9636", - "name": "scheduler", - "type": "Scheduler", - "fieldKind": "input", - "label": "", - "value": "euler" - }, - "control": { - "id": "ad0c589a-b5e9-4892-9173-a709ee8672db", - "name": "control", - "type": "ControlPolymorphic", - "fieldKind": "input", - "label": "" - }, - "ip_adapter": { - "id": "843f92c7-2714-46ea-ab9d-fc568aa684f3", - "name": "ip_adapter", - "type": "IPAdapterField", + "metadata": { + "id": "1632b6ac-605d-42a7-853c-65539a6f664e", + "name": "metadata", + "type": "MetadataField", "fieldKind": "input", "label": "" }, "latents": { - "id": "d21e2b79-344d-438f-8841-c5c2dcb82e77", + "id": "9986c874-6d4b-47fc-895a-88933ef2b473", "name": "latents", "type": "LatentsField", "fieldKind": "input", "label": "" }, - "denoise_mask": { - "id": "e8cf20d0-76e0-4543-b11f-2a4d209a6489", - "name": "denoise_mask", - "type": "DenoiseMaskField", + "vae": { + "id": "d5842416-e575-4e35-a5d0-fd1ce4401b52", + "name": "vae", + "type": "VaeField", "fieldKind": "input", "label": "" }, - "positive_conditioning": { - "id": "3ab3bd3b-4ecd-4b87-9ab3-5de24828f3cd", - "name": "positive_conditioning", - "type": "ConditioningField", + "tiled": { + "id": "6a84e45e-5f54-4952-9285-9dedc6d056d5", + "name": "tiled", + "type": "boolean", "fieldKind": "input", - "label": "" + "label": "", + "value": false }, - "negative_conditioning": { - "id": "6e2907e9-779d-4984-9c79-6032bf75ab0c", - "name": "negative_conditioning", - "type": "ConditioningField", + "fp32": { + "id": "2fa37674-8685-4ec4-87d9-d4683131d79c", + "name": "fp32", + "type": "boolean", "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "bb038dd9-cb2e-41df-a830-a5e1d5696c31", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" + "label": "", + "value": false } }, "outputs": { - "latents": { - "id": "82c61fed-7cf5-44d4-8d00-f42350619487", - "name": "latents", - "type": "LatentsField", + "image": { + "id": "b1856446-1b03-4825-aae0-0859e27c3c8c", + "name": "image", + "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "73ab2f4f-898f-44a1-a46c-c0d4aaa9c2e3", + "id": "c95d92f6-2416-4fc8-a542-998c7c3fac73", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "92b783b2-8741-4089-8125-1ff2f2d15372", + "id": "7033a118-3b20-4c25-8450-e15d7fc8657c", "name": "height", "type": "integer", "fieldKind": "output" @@ -849,18 +798,109 @@ "notes": "", "embedWorkflow": false, "isIntermediate": true, - "version": "1.1.0", - "useCache": true + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 614, + "height": 267, "position": { - "x": 5425, - "y": 1925 + "x": 5941.909912847396, + "y": 2111.4771842290065 + } + }, + { + "id": "7bc3c331-4658-46fd-8736-fe3043fcd9d1", + "type": "invocation", + "data": { + "id": "7bc3c331-4658-46fd-8736-fe3043fcd9d1", + "type": "color_correct", + "inputs": { + "metadata": { + "id": "203d08d1-586f-47c9-95a1-2afa1db23751", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, + "image": { + "id": "50bc42c6-c3b8-44c4-89fe-ef2edd9b67f4", + "name": "image", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "reference": { + "id": "2eef99df-b1f8-441c-a316-466a46812df0", + "name": "reference", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "mask": { + "id": "6cfde62b-e6fc-4f13-91dc-e679b26ec04b", + "name": "mask", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "mask_blur_radius": { + "id": "51125aec-bbcb-4ae0-b618-a9bc632a5a86", + "name": "mask_blur_radius", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 8 + } + }, + "outputs": { + "image": { + "id": "4c82dcb2-1c55-4b6d-a42c-061478881393", + "name": "image", + "type": "ImageField", + "fieldKind": "output" + }, + "width": { + "id": "b7d23fea-e361-468d-96e7-9b243592e904", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "d056d182-d261-4037-bb5e-caec9bda9ca6", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 396, + "position": { + "x": 6399.947577155154, + "y": 2127.7477011465667 } } ], "edges": [ + { + "source": "27dd9fc3-8c6e-4602-8754-e9ca2f478d68", + "target": "098898c8-7a20-4d78-9363-296d42e3d8da", + "id": "27dd9fc3-8c6e-4602-8754-e9ca2f478d68-098898c8-7a20-4d78-9363-296d42e3d8da-collapsed", + "type": "collapsed" + }, + { + "source": "a6d08bcb-0b52-4dd8-9247-8b6480238c6d", + "target": "098898c8-7a20-4d78-9363-296d42e3d8da", + "id": "a6d08bcb-0b52-4dd8-9247-8b6480238c6d-098898c8-7a20-4d78-9363-296d42e3d8da-collapsed", + "type": "collapsed" + }, { "source": "c9897be0-7f59-4388-816d-86cb72cc4036", "sourceHandle": "clip", @@ -877,14 +917,6 @@ "id": 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"reactflow__edge-441fdd15-1a49-413c-bd2a-637a105ad370latents-3b2ebc7f-251d-4726-b1b8-5331355f8626latents", + "id": "reactflow__edge-a6d08bcb-0b52-4dd8-9247-8b6480238c6dlatents-d14b4d95-bf74-4ec5-827b-4c9e797c7ae9latents", + "type": "default" + }, + { + "source": "3fac3aa6-910a-4a90-a8b6-5b7e1611efba", + "sourceHandle": "image", + "target": "eb725b0b-1fa6-4f79-aedb-52c19afcfad9", + "targetHandle": "image", + "id": "reactflow__edge-3fac3aa6-910a-4a90-a8b6-5b7e1611efbaimage-eb725b0b-1fa6-4f79-aedb-52c19afcfad9image", + "type": "default" + }, + { + "source": "eb725b0b-1fa6-4f79-aedb-52c19afcfad9", + "sourceHandle": "image", + "target": "bcbdc4ea-1fad-40d4-8632-70f84116f4b6", + "targetHandle": "image", + "id": "reactflow__edge-eb725b0b-1fa6-4f79-aedb-52c19afcfad9image-bcbdc4ea-1fad-40d4-8632-70f84116f4b6image", + "type": "default" + }, + { + "source": "eb725b0b-1fa6-4f79-aedb-52c19afcfad9", + "sourceHandle": "mask", + "target": "bcbdc4ea-1fad-40d4-8632-70f84116f4b6", + "targetHandle": 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"reactflow__edge-d14b4d95-bf74-4ec5-827b-4c9e797c7ae9latents-e4681270-ea7e-4063-9116-880408854eeelatents", + "type": "default" + }, + { + "source": "3fac3aa6-910a-4a90-a8b6-5b7e1611efba", + "sourceHandle": "image", + "target": "7bc3c331-4658-46fd-8736-fe3043fcd9d1", + "targetHandle": "reference", + "id": "reactflow__edge-3fac3aa6-910a-4a90-a8b6-5b7e1611efbaimage-7bc3c331-4658-46fd-8736-fe3043fcd9d1reference", + "type": "default" + }, + { + "source": "e4681270-ea7e-4063-9116-880408854eee", + "sourceHandle": "image", + "target": "7bc3c331-4658-46fd-8736-fe3043fcd9d1", + "targetHandle": "image", + "id": "reactflow__edge-e4681270-ea7e-4063-9116-880408854eeeimage-7bc3c331-4658-46fd-8736-fe3043fcd9d1image", + "type": "default" + }, + { + "source": "eb725b0b-1fa6-4f79-aedb-52c19afcfad9", + "sourceHandle": "mask", + "target": "7bc3c331-4658-46fd-8736-fe3043fcd9d1", + "targetHandle": "mask", + "id": "reactflow__edge-eb725b0b-1fa6-4f79-aedb-52c19afcfad9mask-7bc3c331-4658-46fd-8736-fe3043fcd9d1mask", "type": "default" } ] diff --git a/docs/workflows/FaceOff_FaceScale2x.json b/docs/workflows/FaceOff_FaceScale2x.json index fa3cb80e9b..d1707e5e73 100644 --- a/docs/workflows/FaceOff_FaceScale2x.json +++ b/docs/workflows/FaceOff_FaceScale2x.json @@ -227,85 +227,12 @@ "version": "1.0.0" }, "width": 320, - "height": 388, + "height": 389, "position": { "x": 5425, "y": 1525 } }, - { - "id": "3b2ebc7f-251d-4726-b1b8-5331355f8626", - "type": "invocation", - "data": { - "id": "3b2ebc7f-251d-4726-b1b8-5331355f8626", - "type": "l2i", - "inputs": { - "tiled": { - "id": "8265e595-8f76-4615-8b70-3a0fca854cbf", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "cc2dd2ee-63f6-40e7-937e-d82086685abe", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "latents": { - "id": "fbbab00e-b47f-4f45-801a-a719873429ca", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "476e6075-c62d-4d42-afcf-a18e4c4a47fb", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "image": { - "id": "65086674-a0f7-4d6c-a02e-101010b34d74", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "e32514a9-50d5-423e-95d3-5d9fd460adaa", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "4d6557ec-2967-40ec-bcae-52725ffe766c", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true, - "useCache": true, - "version": "1.0.0" - }, - "width": 320, - "height": 266, - "position": { - "x": 5825, - "y": 1275 - } - }, { "id": "27dd9fc3-8c6e-4602-8754-e9ca2f478d68", "type": "invocation", @@ -415,7 +342,7 @@ "version": "1.0.0" }, "width": 320, - "height": 316, + "height": 318, "position": { "x": 5025, "y": 2175 @@ -471,94 +398,6 @@ "y": 1525 } }, - { - "id": "5b15322a-1ea2-4f23-b422-c2dea5f594e2", - "type": "invocation", - "data": { - "id": "5b15322a-1ea2-4f23-b422-c2dea5f594e2", - "type": "img_paste", - "inputs": { - "base_image": { - "id": "67487235-b302-4636-b8d5-b893bbd56db1", - "name": "base_image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "image": { - "id": "049e467a-2a5f-4017-b330-3ec97519eeef", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "mask": { - "id": "39396a0b-c12d-4384-bd24-f9358ece80b0", - "name": "mask", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "x": { - "id": "39bf90a6-8db3-4b12-8c3c-97a665e0d319", - "name": "x", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "y": { - "id": "7352707c-a833-45b3-83a5-c7b733741a5e", - "name": "y", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "crop": { - "id": "c9225c97-04a8-42f5-9cda-333a9c475abb", - "name": "crop", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - } - }, - "outputs": { - "image": { - "id": "a8a59a34-59d6-4edf-972c-d5b189e488a2", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "151b99f7-4736-4d87-b49b-a82d18b70a6f", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "a3b4856e-6136-4d29-a2d5-b8db7d1f7f55", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": true, - "isIntermediate": false, - "useCache": true, - "version": "1.0.1" - }, - "width": 320, - "height": 502, - "position": { - "x": 5825, - "y": 2300 - } - }, { "id": "a6d08bcb-0b52-4dd8-9247-8b6480238c6d", "type": "invocation", @@ -626,340 +465,51 @@ "version": "1.0.0" }, "width": 320, - "height": 323, + "height": 325, "position": { "x": 5025, "y": 1825 } }, { - "id": "233aeb43-980a-4578-bdb7-5488d54d1bbf", + "id": "01a35dfd-b4bd-4901-8088-49972eac7582", "type": "invocation", "data": { - "id": "233aeb43-980a-4578-bdb7-5488d54d1bbf", - "type": "img_scale", + "id": "01a35dfd-b4bd-4901-8088-49972eac7582", + "type": "l2i", "inputs": { - "image": { - "id": "81fe81ca-7532-472f-a1a7-84123ce007cd", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "scale_factor": { - "id": "0c0092eb-90c9-44d3-9388-421071352571", - "name": "scale_factor", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 2 - }, - "resample_mode": { - "id": "521fa4d3-7117-48e6-82c5-27c83822cf2f", - "name": "resample_mode", - "type": "enum", - "fieldKind": "input", - "label": "", - "value": "bicubic" - } - }, - "outputs": { - "image": { - "id": "0110d34f-0cd0-456a-9f76-e6e2feb8e495", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "e9daa3a1-11ff-41f8-bc72-aa96c497ff13", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "0d52aba2-9181-4822-ba97-c947e455c9e2", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "Scale Image (Mask Up)", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true, - "useCache": true, - "version": "1.0.0" - }, - "width": 320, - "height": 339, - "position": { - "x": 5025, - "y": 2500 - } - }, - { - "id": "8bdaaacf-077d-4b73-b03c-ad7a2fdcc0b6", - "type": "invocation", - "data": { - "id": "8bdaaacf-077d-4b73-b03c-ad7a2fdcc0b6", - "type": "img_scale", - "inputs": { - "image": { - "id": "432f3c88-285d-42b7-a42e-24ae97a4751a", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "scale_factor": { - "id": "b3dd9aff-0c84-4ad7-bfd9-a8fa1ccae58b", - "name": "scale_factor", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.5 - }, - "resample_mode": { - "id": "2eb9d0a7-d0f8-474e-b238-548d864fbd8e", - "name": "resample_mode", - "type": "enum", - "fieldKind": "input", - "label": "", - "value": "bicubic" - } - }, - "outputs": { - "image": { - "id": "9c089c7e-930c-4d1f-b2ab-a957ecf288aa", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "f880ce2a-5f1c-4f0c-9398-095cf082d353", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "fde5f8e6-d5fc-4d06-a1b1-9880baabf3a6", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "Scale Image (Face Down)", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true, - "useCache": true, - "version": "1.0.0" - }, - "width": 320, - "height": 339, - "position": { - "x": 5825, - "y": 1550 - } - }, - { - "id": "9e356e8e-d03d-4de9-b522-026c8751d6d4", - "type": "invocation", - "data": { - "id": "9e356e8e-d03d-4de9-b522-026c8751d6d4", - "type": "denoise_latents", - "inputs": { - "noise": { - "id": "d7a78bb2-e3d2-4e5d-b494-1cc1e5f0cc84", - "name": "noise", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "steps": { - "id": "2d9aba03-011e-4858-b5e2-a1301565dde9", - "name": "steps", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 20 - }, - "cfg_scale": { - "id": "3528c5bb-12a2-4094-a35f-c5d07ddae5ee", - "name": "cfg_scale", - "type": "FloatPolymorphic", - "fieldKind": "input", - "label": "", - "value": 5 - }, - "denoising_start": { - "id": "c997229c-8f35-4427-8fcc-99a3dcaaf5a3", - "name": "denoising_start", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.3 - }, - "denoising_end": { - "id": "abbb8f6b-f643-4cb2-9147-227feafd8aa6", - "name": "denoising_end", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 1 - }, - "scheduler": { - "id": "2fcd2bdc-de01-422f-963d-551ff58f7b70", - "name": "scheduler", - "type": "Scheduler", - "fieldKind": "input", - "label": "", - "value": "euler" - }, - "control": { - "id": "1ad815aa-0957-420b-a782-7e07a5e16a05", - "name": "control", - "type": "ControlPolymorphic", - "fieldKind": "input", - "label": "" - }, - "ip_adapter": { - "id": "8350154f-b179-44d6-88c8-08c8fa940648", - "name": "ip_adapter", - "type": "IPAdapterField", + "metadata": { + "id": "ce479dbf-d12f-43e7-9047-ec0e6bd838a7", + "name": "metadata", + "type": "MetadataField", "fieldKind": "input", "label": "" }, "latents": { - "id": "bf8c9465-743e-4b27-b370-e63053dc2175", + "id": "0d3f0abc-5c60-495e-af10-8136b25532d0", "name": "latents", "type": "LatentsField", "fieldKind": "input", "label": "" }, - "denoise_mask": { - "id": "4487f154-4f2f-459b-8a69-561ea892eea7", - "name": "denoise_mask", - "type": "DenoiseMaskField", + "vae": { + "id": "c0563226-c977-456a-8cf5-2873ab56a9f4", + "name": "vae", + "type": "VaeField", "fieldKind": "input", "label": "" }, - "positive_conditioning": { - "id": "a757a5d4-3aaa-4cd9-9a9d-bfb01b0676e4", - "name": "positive_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "negative_conditioning": { - "id": "68d0ce84-615a-4062-96bc-3599029a4b63", - "name": "negative_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "0de640f1-2170-4983-88d3-5af648da1b5d", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "latents": { - "id": "3198ada0-5926-41b3-89f0-8b572af78824", - "name": "latents", - "type": "LatentsField", - "fieldKind": "output" - }, - "width": { - "id": "3cea129c-3145-4f89-a5f1-849863e05001", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "ed204db5-ce67-4795-873a-67b5d6a099a6", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true, - "useCache": true, - "version": "1.1.0" - }, - "width": 320, - "height": 614, - "position": { - "x": 5425, - "y": 1925 - } - }, - { - "id": "cd886d23-b786-4d0e-83e6-b6f1cf0ddc95", - "type": "invocation", - "data": { - "id": "cd886d23-b786-4d0e-83e6-b6f1cf0ddc95", - "type": "face_off", - "inputs": { - "image": { - "id": "4c4611d3-baab-47e8-a814-18ea86f1aff2", - "name": "image", - "type": "ImageField", - "fieldKind": "input", - "label": "" - }, - "face_id": { - "id": "cfc00e4b-c60e-44b4-b0da-f26da1b4060e", - "name": "face_id", - "type": "integer", + "tiled": { + "id": "f909d67c-f789-4daa-8528-50113bba78ea", + "name": "tiled", + "type": "boolean", "fieldKind": "input", "label": "", - "value": 0 + "value": false }, - "minimum_confidence": { - "id": "4ba35ea2-d1b3-4d24-88c5-737fdd396210", - "name": "minimum_confidence", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.5 - }, - "x_offset": { - "id": "c79b5f0a-df14-41e1-9f17-11f6b3ace52f", - "name": "x_offset", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "y_offset": { - "id": "ec8eb73f-2853-41b8-aa9d-c93b6f9f08bd", - "name": "y_offset", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "padding": { - "id": "a7a649f9-0b79-4f12-a482-b3af800b6584", - "name": "padding", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "chunk": { - "id": "df654e9c-dda3-414d-b11b-51b36071ae62", - "name": "chunk", + "fp32": { + "id": "d7c26ac3-df0f-47ab-aa91-0c5bfbe1ea38", + "name": "fp32", "type": "boolean", "fieldKind": "input", "label": "", @@ -968,40 +518,22 @@ }, "outputs": { "image": { - "id": "4292e151-cd37-4cdf-9f1b-2b3e0cc6d831", + "id": "380a7af7-bba5-434b-9996-47b8194d69d4", "name": "image", "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "8e83877f-36ed-470c-889a-174a35259e6f", + "id": "a0f2a9cf-77f6-42a5-801d-30f635d1de26", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "40487efd-937a-491e-a1c3-e791afe32cd6", + "id": "890ec07c-9bc0-46f6-b962-8a6fd149d562", "name": "height", "type": "integer", "fieldKind": "output" - }, - "mask": { - "id": "2cc63f32-0f8e-46a9-bed3-9f47a8b834f2", - "name": "mask", - "type": "ImageField", - "fieldKind": "output" - }, - "x": { - "id": "12451165-986b-4eab-8159-55b8d7ce87ab", - "name": "x", - "type": "integer", - "fieldKind": "output" - }, - "y": { - "id": "928c0d08-613c-4c03-9a7e-a3087f45032b", - "name": "y", - "type": "integer", - "fieldKind": "output" } }, "label": "", @@ -1013,28 +545,35 @@ "version": "1.0.0" }, "width": 320, - "height": 655, + "height": 267, "position": { - "x": 4625, - "y": 1800 + "x": 5810.2137275110845, + "y": 1270.641572039504 } }, { - "id": "7ad41b86-c089-430c-b70c-3c1cbf886ed9", + "id": "43c00342-9ca3-498a-8635-c4c716e32d5f", "type": "invocation", "data": { - "id": "7ad41b86-c089-430c-b70c-3c1cbf886ed9", + "id": "43c00342-9ca3-498a-8635-c4c716e32d5f", "type": "img_scale", "inputs": { + "metadata": { + "id": "d35fa75e-439b-4fda-9064-4ed73f378de6", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, "image": { - "id": "6db4f3c8-ade5-46c7-8c39-f8b8bd949912", + "id": "e8c46885-2ec1-4fb9-8d34-07534e62ec97", "name": "image", "type": "ImageField", "fieldKind": "input", "label": "" }, "scale_factor": { - "id": "ac4f04dc-2fc8-424f-bcab-292d6139a763", + "id": "94b04846-9f70-44ac-ae82-99af13632255", "name": "scale_factor", "type": "float", "fieldKind": "input", @@ -1042,7 +581,7 @@ "value": 2 }, "resample_mode": { - "id": "1f2fdad6-1d75-42a9-8f20-dfe5361804ad", + "id": "2fbcd8bb-6668-4661-868a-1452f0e73e6d", "name": "resample_mode", "type": "enum", "fieldKind": "input", @@ -1052,25 +591,25 @@ }, "outputs": { "image": { - "id": "ab6ca82e-f936-47be-8640-1b1649032dfd", + "id": "96600931-0da3-4c68-842f-d06f2d93f383", "name": "image", "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "bf4f0ecd-7628-46b8-9654-e728dc69985b", + "id": "ff158681-580c-4a51-a2c8-c4bd2058a011", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "07e63e0c-2da3-445f-bb34-82b9eef6edfb", + "id": "2a332d0e-4442-4a6e-8cf9-5e2e42f713c6", "name": "height", "type": "integer", "fieldKind": "output" } }, - "label": "Scale Image (Face Up)", + "label": "", "isOpen": true, "notes": "", "embedWorkflow": false, @@ -1081,40 +620,47 @@ "width": 320, "height": 339, "position": { - "x": 4625, - "y": 2500 + "x": 5808.235568293352, + "y": 1551.540180957498 } }, { - "id": "98a614c8-24df-413f-b001-a728b354e931", + "id": "0c71919b-a030-44fb-8c09-1baf37088d20", "type": "invocation", "data": { - "id": "98a614c8-24df-413f-b001-a728b354e931", + "id": "0c71919b-a030-44fb-8c09-1baf37088d20", "type": "color_correct", "inputs": { + "metadata": { + "id": "89536a0d-1c62-4975-ada9-33f359837481", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, "image": { - "id": "0f45f05a-192c-4951-9b94-32b32cc68cc2", + "id": "10d7c1da-e8e1-4226-ad9f-1bde9816c118", "name": "image", "type": "ImageField", "fieldKind": "input", "label": "" }, "reference": { - "id": "4a430d07-f13e-4889-a58e-a241e05bf142", + "id": "9c7354ed-33dc-462a-bc57-717082eb3f45", "name": "reference", "type": "ImageField", "fieldKind": "input", "label": "" }, "mask": { - "id": "c3bf8c13-0766-49e5-96cc-26a6e9dbafc9", + "id": "03aee643-6873-421c-8caf-1e05e85f6a9d", "name": "mask", "type": "ImageField", "fieldKind": "input", "label": "" }, "mask_blur_radius": { - "id": "c0a35154-41dc-494c-8807-f4cca2b996e9", + "id": "2d2dffa6-f719-4ec9-a201-c07a3a914c77", "name": "mask_blur_radius", "type": "float", "fieldKind": "input", @@ -1124,19 +670,19 @@ }, "outputs": { "image": { - "id": "27559b60-0a58-4615-8f5a-a46d85be1d89", + "id": "b652c45e-e797-47d2-9175-aa77ede6a8f5", "name": "image", "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "f03d5175-d0d8-4f4f-970f-7792d72a69c7", + "id": "385015ed-2c6b-4b72-adda-ba5639841352", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "7a7b98e5-8b34-411c-b51c-e3174a9de7f6", + "id": "fe6632e0-560c-4c56-9b52-6e5e5d7eb642", "name": "height", "type": "integer", "fieldKind": "output" @@ -1153,8 +699,518 @@ "width": 320, "height": 396, "position": { - "x": 5825, - "y": 1900 + "x": 5806.25740907562, + "y": 1903.6525217138576 + } + }, + { + "id": "4e11665c-b932-493d-ab4f-019bed730b47", + "type": "invocation", + "data": { + "id": "4e11665c-b932-493d-ab4f-019bed730b47", + "type": "img_paste", + "inputs": { + "metadata": { + "id": "6f365d9d-bf84-4053-b936-8035a4c7c991", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, + "base_image": { + "id": "fb323f35-d1fd-4d20-b145-9a3db7a57a28", + "name": "base_image", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "image": { + "id": "ea76a063-ec51-4b2b-bfa8-8df9c64ecc2e", + "name": "image", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "mask": { + "id": "9b3f194a-d7f1-45f3-989a-6b2096dd3fd6", + "name": "mask", + "type": "ImageField", + "fieldKind": "input", + "label": "" + }, + "x": { + "id": "d74b8e26-bc59-4a27-bb00-f769b81e8ef0", + "name": "x", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "y": { + "id": "6ada744a-8f67-49c6-a8b6-df03b3397d73", + "name": "y", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "crop": { + "id": "dec1e8e1-2fb0-43e8-9ddf-0eb478e5e567", + "name": "crop", + "type": "boolean", + "fieldKind": "input", + "label": "", + "value": false + } + }, + "outputs": { + "image": { + "id": "4a01e54d-9a7b-458d-aa87-640ce8c5cd62", + "name": "image", + "type": "ImageField", + "fieldKind": "output" + }, + "width": { + "id": "be903c7c-b867-4938-9a0e-de8a6676ae89", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "7dbb23a1-bedc-418c-8c27-09cf24e0e777", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.1" + }, + "width": 320, + "height": 504, + "position": { + "x": 5810.103371182836, + "y": 2315.9071582023707 + } + }, + { + "id": "9a6a35cd-5c05-4df1-81bf-e40a1954c618", + "type": "invocation", + "data": { + "id": "9a6a35cd-5c05-4df1-81bf-e40a1954c618", + "type": "denoise_latents", + "inputs": { + "positive_conditioning": { + "id": "1039d676-b54f-4848-903d-a5e3eef84781", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "52e4b032-e32f-48b1-ac02-b17f0dff78f6", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "noise": { + "id": "7c6c35bb-576c-42fa-8191-169afff13d73", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "e0df62c7-edb8-4777-8ca1-c7a6fb30f6a2", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "e1a87d18-671d-4a6e-a528-7d4376500211", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": "60ab8906-162c-4817-bd46-f5d7a93aa213", + "name": "denoising_start", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "denoising_end": { + "id": "c8f73ade-d5a4-4e69-8462-c0f9ee3dfb96", + "name": "denoising_end", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 1 + }, + "scheduler": { + "id": "e2872b56-4250-4a0d-86b3-37c60485fdc7", + "name": "scheduler", + "type": "Scheduler", + "fieldKind": "input", + "label": "", + "value": "euler" + }, + "unet": { + "id": "b1f93d41-b399-4774-a913-4a0c0c6d5f66", + "name": 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Best result with value between 1-2.\n\n1 = 512\n2 = 1024\n\n", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 161, + "position": { + "x": 2505.1027422955735, + "y": -447.7244534284996 + } + }, + { + "id": "e41c9dcc-a460-439a-85f3-eb18c74d9411", + "type": "invocation", + "data": { + "id": "e41c9dcc-a460-439a-85f3-eb18c74d9411", + "type": "denoise_latents", + "inputs": { + "noise": { + "id": "2223c30c-e3ef-4a2b-9ae8-31f9f2b38f68", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "09c2a8c9-0771-4e35-b674-26633fe5d740", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "dcef4b34-926f-46c1-9e57-c6401f1ac901", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": 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"9755ae4c-ef30-4db3-80f6-a31f98979a11", + "targetHandle": "control", + "id": "reactflow__edge-2ac03cf6-0326-454a-bed0-d8baef2bf30dcontrol-9755ae4c-ef30-4db3-80f6-a31f98979a11control", + "type": "default" + }, + { + "source": "9755ae4c-ef30-4db3-80f6-a31f98979a11", + "sourceHandle": "latents", + "target": "28542b66-5a00-4780-a318-0a036d2df914", + "targetHandle": "latents", + "id": "reactflow__edge-9755ae4c-ef30-4db3-80f6-a31f98979a11latents-28542b66-5a00-4780-a318-0a036d2df914latents", + "type": "default" + }, + { + "source": "59349822-af20-4e0e-a53f-3ba135d00c3f", + "sourceHandle": "value", + "target": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a", + "targetHandle": "seed", + "id": "reactflow__edge-59349822-af20-4e0e-a53f-3ba135d00c3fvalue-280fd8a7-3b0c-49fe-8be4-6246e08b6c9aseed", + "type": "default" + } + ] +} \ No newline at end of file diff --git a/docs/workflows/SDXL_Text_to_Image.json b/docs/workflows/SDXL_Text_to_Image.json index 57f95293eb..af11731703 100644 --- a/docs/workflows/SDXL_Text_to_Image.json +++ b/docs/workflows/SDXL_Text_to_Image.json @@ -26,10 +26,6 @@ { "nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", "fieldName": "style" - }, - { - "nodeId": "87ee6243-fb0d-4f77-ad5f-56591659339e", - "fieldName": "steps" } ], "meta": { @@ -40,7 +36,6 @@ "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", "type": "invocation", "data": { - "version": "1.0.0", "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", "type": "sdxl_compel_prompt", "inputs": { @@ -135,10 +130,12 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 764, + "height": 793, "position": { "x": 1275, "y": -350 @@ -148,7 +145,6 @@ "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "invocation", "data": { - "version": "1.0.0", "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "noise", "inputs": { @@ -209,7 +205,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -218,83 +216,10 @@ "y": -300 } }, - { - "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "type": "invocation", - "data": { - "version": "1.0.0", - "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "type": "l2i", - "inputs": { - "tiled": { - "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "b146d873-ffb9-4767-986a-5360504841a2", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": true - }, - "latents": { - "id": "65441abd-7713-4b00-9d8d-3771404002e8", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "a478b833-6e13-4611-9a10-842c89603c74", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "image": { - "id": "c87ae925-f858-417a-8940-8708ba9b4b53", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "23e41c00-a354-48e8-8f59-5875679c27ab", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": true, - "isIntermediate": false - }, - "width": 320, - "height": 224, - "position": { - "x": 2025, - "y": -250 - } - }, { "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "type": "invocation", "data": { - "version": "1.0.0", "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "type": "rand_int", "inputs": { @@ -327,7 +252,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": false, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -340,7 +267,6 @@ "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "type": "invocation", "data": { - "version": "1.0.0", "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "type": "sdxl_model_loader", "inputs": { @@ -351,7 +277,7 @@ "fieldKind": "input", "label": "", "value": { - "model_name": "stable-diffusion-xl-base-1.0", + "model_name": "stable-diffusion-xl-base-1-0", "base_model": "sdxl", "model_type": "main" } @@ -387,10 +313,12 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 234, + "height": 258, "position": { "x": 475, "y": 25 @@ -400,7 +328,6 @@ "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08", "type": "invocation", "data": { - "version": "1.0.0", "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08", "type": "sdxl_compel_prompt", "inputs": { @@ -495,128 +422,77 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 764, + "height": 793, "position": { "x": 900, "y": -350 } }, { - "id": "87ee6243-fb0d-4f77-ad5f-56591659339e", + "id": "63e91020-83b2-4f35-b174-ad9692aabb48", "type": "invocation", "data": { - "version": "1.0.0", - "id": "87ee6243-fb0d-4f77-ad5f-56591659339e", - "type": "denoise_latents", + "id": "63e91020-83b2-4f35-b174-ad9692aabb48", + "type": "l2i", "inputs": { - "noise": { - "id": "4884a4b7-cc19-4fea-83c7-1f940e6edd24", - "name": "noise", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "steps": { - "id": "4c61675c-b6b9-41ac-b187-b5c13b587039", - "name": "steps", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 36 - }, - "cfg_scale": { - "id": "f8213f35-4637-4a1a-83f4-1f8cfb9ccd2c", - "name": "cfg_scale", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 7.5 - }, - "denoising_start": { - "id": "01e2f30d-0acd-4e21-98b9-a9b8e24c6db2", - "name": "denoising_start", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "denoising_end": { - "id": "3db95479-a73b-4c75-9b44-08daec16b224", - "name": "denoising_end", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 1 - }, - "scheduler": { - "id": "db8430a9-64c3-4c54-ae38-9f597cf7b6d5", - "name": "scheduler", - "type": "Scheduler", - "fieldKind": "input", - "label": "", - "value": "euler" - }, - "control": { - "id": "599b49e8-6435-4576-be41-a5155f3a17e3", - "name": "control", - "type": "ControlField", + "metadata": { + "id": "88971324-3fdb-442d-b8b7-7612478a8622", + "name": "metadata", + "type": "MetadataField", "fieldKind": "input", "label": "" }, "latents": { - "id": "226f9e91-454e-4159-9fa6-019c0cf29277", + "id": "da0e40cb-c49f-4fa5-9856-338b91a65f6b", "name": "latents", "type": "LatentsField", "fieldKind": "input", "label": "" }, - "denoise_mask": { - "id": "de019cb6-7fb5-45bf-a266-22e20889893f", - "name": "denoise_mask", - "type": "DenoiseMaskField", + "vae": { + "id": "ae5164ce-1710-4ec5-a83a-6113a0d1b5c0", + "name": "vae", + "type": "VaeField", "fieldKind": "input", "label": "" }, - "positive_conditioning": { - "id": "02fc400a-110d-470e-8411-f404f966a949", - "name": "positive_conditioning", - "type": "ConditioningField", + "tiled": { + "id": "2ccfd535-1a7b-4ecf-84db-9430a64fb3d7", + "name": "tiled", + "type": "boolean", "fieldKind": "input", - "label": "" + "label": "", + "value": false }, - "negative_conditioning": { - "id": "4bd3bdfa-fcf4-42be-8e47-1e314255798f", - "name": "negative_conditioning", - "type": "ConditioningField", + "fp32": { + "id": "64f07d5a-54a2-429c-8c5b-0c2a3a8e5cd5", + "name": "fp32", + "type": "boolean", "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "7c2d58a8-b5f1-4e63-8ffd-8ada52c35832", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" + "label": "", + "value": false } }, "outputs": { - "latents": { - "id": "6a6fa492-de26-4e95-b1d9-a322fe37eb13", - "name": "latents", - "type": "LatentsField", + "image": { + "id": "9b281eaa-6504-407d-a5ca-1e5e8020a4bf", + "name": "image", + "type": "ImageField", "fieldKind": "output" }, "width": { - "id": "a9790729-7d6c-4418-903d-4da961fccf56", + "id": "98e545f3-b53b-490d-b94d-bed9418ccc75", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "fa74efe5-7330-4a3c-b256-c82a544585b4", + "id": "4a74bd43-d7f7-4c7f-bb3b-d09bb2992c46", "name": "height", "type": "integer", "fieldKind": "output" @@ -626,13 +502,161 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": false, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 558, + "height": 267, "position": { - "x": 1650, - "y": -250 + "x": 2112.5626808057173, + "y": -174.24042139280238 + } + }, + { + "id": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb", + "type": "invocation", + "data": { + "id": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb", + "type": "denoise_latents", + "inputs": { + "positive_conditioning": { + "id": "29b73dfa-a06e-4b4a-a844-515b9eb93a81", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "a81e6f5b-f4de-4919-b483-b6e2f067465a", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "noise": { + "id": "4ba06bb7-eb45-4fb9-9984-31001b545587", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "36ee8a45-ca69-44bc-9bc3-aa881e6045c0", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "2a2024e0-a736-46ec-933c-c1c1ebe96943", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": "be219d5e-41b7-430a-8fb5-bc21a31ad219", + "name": "denoising_start", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "denoising_end": { + "id": "3adfb7ae-c9f7-4a40-b6e0-4c2050bd1a99", + "name": "denoising_end", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 1 + }, + "scheduler": { + "id": "14423e0d-7215-4ee0-b065-f9e95eaa8d7d", + "name": "scheduler", + "type": "Scheduler", + "fieldKind": "input", + "label": "", + "value": "euler" + }, + "unet": { + "id": "e73bbf98-6489-492b-b83c-faed215febac", + "name": "unet", + "type": "UNetField", + "fieldKind": "input", + "label": "" + }, + "control": { + "id": "dab351b3-0c86-4ea5-9782-4e8edbfb0607", + "name": "control", + "type": "ControlPolymorphic", + "fieldKind": "input", + "label": "" + }, + "ip_adapter": { + "id": "192daea0-a90a-43cc-a2ee-0114a8e90318", + "name": "ip_adapter", + "type": "IPAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "t2i_adapter": { + "id": "ee386a55-d4c7-48c1-ac57-7bc4e3aada7a", + "name": "t2i_adapter", + "type": "T2IAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "latents": { + "id": "3a922c6a-3d8c-4c9e-b3ec-2f4d81cda077", + "name": "latents", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "denoise_mask": { + "id": "cd7ce032-835f-495f-8b45-d57272f33132", + "name": "denoise_mask", + "type": "DenoiseMaskField", + "fieldKind": "input", + "label": "" + } + }, + "outputs": { + "latents": { + "id": "6260b84f-8361-470a-98d8-5b22a45c2d8c", + "name": "latents", + "type": "LatentsField", + "fieldKind": "output" + }, + "width": { + "id": "aede0ecf-25b6-46be-aa30-b77f79715deb", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "519abf62-d475-48ef-ab8f-66136bc0e499", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.4.0" + }, + "width": 320, + "height": 646, + "position": { + "x": 1642.955772577545, + "y": -230.2485847594651 } } ], @@ -686,50 +710,42 @@ { "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "sourceHandle": "vae", - "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", + "target": "63e91020-83b2-4f35-b174-ad9692aabb48", "targetHandle": "vae", - "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae", - "type": "default" - }, - { - "source": "87ee6243-fb0d-4f77-ad5f-56591659339e", - "sourceHandle": "latents", - "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "targetHandle": "latents", - "id": "reactflow__edge-87ee6243-fb0d-4f77-ad5f-56591659339elatents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents", - "type": "default" - }, - { - "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08", - "sourceHandle": "conditioning", - "target": "87ee6243-fb0d-4f77-ad5f-56591659339e", - "targetHandle": "positive_conditioning", - "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-87ee6243-fb0d-4f77-ad5f-56591659339epositive_conditioning", - "type": "default" - }, - { - "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", - "sourceHandle": "conditioning", - "target": "87ee6243-fb0d-4f77-ad5f-56591659339e", - "targetHandle": "negative_conditioning", - "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-87ee6243-fb0d-4f77-ad5f-56591659339enegative_conditioning", + "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-63e91020-83b2-4f35-b174-ad9692aabb48vae", "type": "default" }, { "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "sourceHandle": "unet", - "target": "87ee6243-fb0d-4f77-ad5f-56591659339e", + "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb", "targetHandle": "unet", - "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-87ee6243-fb0d-4f77-ad5f-56591659339eunet", + "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbunet", + "type": "default" + }, + { + "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08", + "sourceHandle": "conditioning", + "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb", + "targetHandle": "positive_conditioning", + "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbpositive_conditioning", + "type": "default" + }, + { + "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", + "sourceHandle": "conditioning", + "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb", + "targetHandle": "negative_conditioning", + "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnegative_conditioning", "type": "default" }, { "source": "55705012-79b9-4aac-9f26-c0b10309785b", "sourceHandle": "noise", - "target": "87ee6243-fb0d-4f77-ad5f-56591659339e", + "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb", "targetHandle": "noise", - "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-87ee6243-fb0d-4f77-ad5f-56591659339enoise", + "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnoise", "type": "default" } ] -} +} \ No newline at end of file diff --git a/docs/workflows/SDXL_w_Refiner_Text_to_Image.json b/docs/workflows/SDXL_w_Refiner_Text_to_Image.json index 22ffb8262d..f70d974702 100644 --- a/docs/workflows/SDXL_w_Refiner_Text_to_Image.json +++ b/docs/workflows/SDXL_w_Refiner_Text_to_Image.json @@ -11,10 +11,6 @@ "nodeId": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "fieldName": "model" }, - { - "nodeId": "65b56526-ef0a-4c1f-adda-1017c925b063", - "fieldName": "steps" - }, { "nodeId": "06a30867-1e9d-461f-bd58-14a63cc997dd", "fieldName": "scheduler" @@ -23,10 +19,6 @@ "nodeId": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", "fieldName": "model" }, - { - "nodeId": "a9352523-613a-43e3-b97f-dade7ec317e5", - "fieldName": "steps" - }, { "nodeId": "b2b35add-929d-4538-aecb-02c661768b29", "fieldName": "value" @@ -48,142 +40,10 @@ "version": "1.0.0" }, "nodes": [ - { - "id": "a9352523-613a-43e3-b97f-dade7ec317e5", - "type": "invocation", - "data": { - "version": "1.0.0", - "id": "a9352523-613a-43e3-b97f-dade7ec317e5", - "type": "denoise_latents", - "inputs": { - "noise": { - "id": "962fb1ba-341c-441c-940b-1543caafab29", - "name": "noise", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "steps": { - "id": "2b76247b-cc60-4ef0-8a51-290700590805", - "name": "steps", - "type": "integer", - "fieldKind": "input", - "label": "Refiner Steps", - "value": 36 - }, - "cfg_scale": { - "id": "f13c5cf5-6198-4183-9b47-0a44c5666a2a", - "name": "cfg_scale", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 7.5 - }, - "denoising_start": { - "id": "397bb49d-7d00-465b-a918-456910d7fedb", - "name": "denoising_start", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.8 - }, - "denoising_end": { - "id": "dac6aa2a-d074-4e86-af0c-def573dd69ac", - "name": "denoising_end", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 1 - }, - "scheduler": { - "id": "34f9f11c-f2fc-48b2-b015-ededbf2d000f", - "name": "scheduler", - "type": "Scheduler", - "fieldKind": "input", - "label": "", - "value": "euler" - }, - "control": { - "id": "80c69321-e712-453b-b8a8-b4e03d37844c", - "name": "control", - "type": "ControlField", - "fieldKind": "input", - "label": "" - }, - "latents": { - "id": "8122d26c-ad2f-4f65-93d5-9ebb426bdba4", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "denoise_mask": { - "id": "5dc048d6-28c3-4db4-9e8b-652006616c17", - "name": "denoise_mask", - "type": "DenoiseMaskField", - "fieldKind": "input", - "label": "" - }, - "positive_conditioning": { - "id": "dd5cab6f-6dbd-4791-a21a-ef0544f26f8f", - "name": "positive_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "negative_conditioning": { - "id": "5efedcb9-3286-426a-ad57-f77b2d7d1898", - "name": "negative_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "3ebc07f0-4cd7-4f4d-a5b3-a8ce13383305", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "latents": { - "id": "ca9c565a-1dda-428c-9fdf-7c51eb7fa9c5", - "name": "latents", - "type": "LatentsField", - "fieldKind": "output" - }, - "width": { - "id": "d91b7cbd-fe5c-4c92-923e-241d1a63648c", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "af9ee999-a666-42a8-8e5c-d04518c4aa8e", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 558, - "position": { - "x": 1650, - "y": -150 - } - }, { "id": "b2b35add-929d-4538-aecb-02c661768b29", "type": "invocation", "data": { - "version": "1.0.0", "id": "b2b35add-929d-4538-aecb-02c661768b29", "type": "string", "inputs": { @@ -208,7 +68,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -221,7 +83,6 @@ "id": "8d54b9db-3662-43af-8369-9a277e063f3b", "type": "invocation", "data": { - "version": "1.0.0", "id": "8d54b9db-3662-43af-8369-9a277e063f3b", "type": "string", "inputs": { @@ -246,7 +107,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -259,7 +122,6 @@ "id": "f1a6a160-4c36-4902-8eeb-8b1c23e81bc8", "type": "invocation", "data": { - "version": "1.0.0", "id": "f1a6a160-4c36-4902-8eeb-8b1c23e81bc8", "type": "string", "inputs": { @@ -284,7 +146,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -297,7 +161,6 @@ "id": "fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1", "type": "invocation", "data": { - "version": "1.0.0", "id": "fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1", "type": "sdxl_refiner_compel_prompt", "inputs": { @@ -369,10 +232,12 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 520, + "height": 547, "position": { "x": 1625, "y": -925 @@ -382,7 +247,6 @@ "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", "type": "invocation", "data": { - "version": "1.0.0", "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", "type": "sdxl_compel_prompt", "inputs": { @@ -477,10 +341,12 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 764, + "height": 793, "position": { "x": 900, "y": -925 @@ -490,7 +356,6 @@ "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "invocation", "data": { - "version": "1.0.0", "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "noise", "inputs": { @@ -551,7 +416,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -560,83 +427,10 @@ "y": -200 } }, - { - "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "type": "invocation", - "data": { - "version": "1.0.0", - "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "type": "l2i", - "inputs": { - "tiled": { - "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "b146d873-ffb9-4767-986a-5360504841a2", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": true - }, - "latents": { - "id": "65441abd-7713-4b00-9d8d-3771404002e8", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "a478b833-6e13-4611-9a10-842c89603c74", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "image": { - "id": "c87ae925-f858-417a-8940-8708ba9b4b53", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "23e41c00-a354-48e8-8f59-5875679c27ab", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": true, - "isIntermediate": false - }, - "width": 320, - "height": 266, - "position": { - "x": 2075, - "y": -400 - } - }, { "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "type": "invocation", "data": { - "version": "1.0.0", "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "type": "rand_int", "inputs": { @@ -669,7 +463,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": false, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -682,7 +478,6 @@ "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "type": "invocation", "data": { - "version": "1.0.0", "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", "type": "sdxl_model_loader", "inputs": { @@ -729,7 +524,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -742,7 +539,6 @@ "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08", "type": "invocation", "data": { - "version": "1.0.0", "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08", "type": "sdxl_compel_prompt", "inputs": { @@ -837,10 +633,12 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 764, + "height": 793, "position": { "x": 550, "y": -925 @@ -850,7 +648,6 @@ "id": "f0e06b70-9f53-44e3-8f5f-63d813b6b579", "type": "invocation", "data": { - "version": "1.0.0", "id": "f0e06b70-9f53-44e3-8f5f-63d813b6b579", "type": "sdxl_refiner_compel_prompt", "inputs": { @@ -922,10 +719,12 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 520, + "height": 547, "position": { "x": 1275, "y": -925 @@ -935,7 +734,6 @@ "id": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", "type": "invocation", "data": { - "version": "1.0.0", "id": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", "type": "sdxl_refiner_model_loader", "inputs": { @@ -976,7 +774,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -989,7 +789,6 @@ "id": "5639e3bc-b769-4ae5-9262-72db703c5a7b", "type": "invocation", "data": { - "version": "1.0.0", "id": "5639e3bc-b769-4ae5-9262-72db703c5a7b", "type": "string", "inputs": { @@ -1014,7 +813,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -1023,142 +824,10 @@ "y": 25 } }, - { - "id": "65b56526-ef0a-4c1f-adda-1017c925b063", - "type": "invocation", - "data": { - "version": "1.0.0", - "id": "65b56526-ef0a-4c1f-adda-1017c925b063", - "type": "denoise_latents", - "inputs": { - "noise": { - "id": "962fb1ba-341c-441c-940b-1543caafab29", - "name": "noise", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "steps": { - "id": "2b76247b-cc60-4ef0-8a51-290700590805", - "name": "steps", - "type": "integer", - "fieldKind": "input", - "label": "", - "value": 36 - }, - "cfg_scale": { - "id": "f13c5cf5-6198-4183-9b47-0a44c5666a2a", - "name": "cfg_scale", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 7.5 - }, - "denoising_start": { - "id": "397bb49d-7d00-465b-a918-456910d7fedb", - "name": "denoising_start", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0 - }, - "denoising_end": { - "id": "dac6aa2a-d074-4e86-af0c-def573dd69ac", - "name": "denoising_end", - "type": "float", - "fieldKind": "input", - "label": "", - "value": 0.8 - }, - "scheduler": { - "id": "34f9f11c-f2fc-48b2-b015-ededbf2d000f", - "name": "scheduler", - "type": "Scheduler", - "fieldKind": "input", - "label": "", - "value": "euler" - }, - "control": { - "id": "80c69321-e712-453b-b8a8-b4e03d37844c", - "name": "control", - "type": "ControlField", - "fieldKind": "input", - "label": "" - }, - "latents": { - "id": "8122d26c-ad2f-4f65-93d5-9ebb426bdba4", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "denoise_mask": { - "id": "5dc048d6-28c3-4db4-9e8b-652006616c17", - "name": "denoise_mask", - "type": "DenoiseMaskField", - "fieldKind": "input", - "label": "" - }, - "positive_conditioning": { - "id": "dd5cab6f-6dbd-4791-a21a-ef0544f26f8f", - "name": "positive_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "negative_conditioning": { - "id": "5efedcb9-3286-426a-ad57-f77b2d7d1898", - "name": "negative_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "3ebc07f0-4cd7-4f4d-a5b3-a8ce13383305", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "latents": { - "id": "ca9c565a-1dda-428c-9fdf-7c51eb7fa9c5", - "name": "latents", - "type": "LatentsField", - "fieldKind": "output" - }, - "width": { - "id": "d91b7cbd-fe5c-4c92-923e-241d1a63648c", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "af9ee999-a666-42a8-8e5c-d04518c4aa8e", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": false, - "isIntermediate": true - }, - "width": 320, - "height": 558, - "position": { - "x": 1275, - "y": -150 - } - }, { "id": "06a30867-1e9d-461f-bd58-14a63cc997dd", "type": "invocation", "data": { - "version": "1.0.0", "id": "06a30867-1e9d-461f-bd58-14a63cc997dd", "type": "scheduler", "inputs": { @@ -1183,7 +852,9 @@ "isOpen": false, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, "height": 32, @@ -1191,6 +862,378 @@ "x": 700, "y": 125 } + }, + { + "id": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "type": "invocation", + "data": { + "id": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "type": "denoise_latents", + "inputs": { + "positive_conditioning": { + "id": "73b2ebc2-4a56-4809-b8ab-b78fde786961", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "04d1bfbb-6cdc-4c16-8e08-290ba86ca8ba", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "noise": { + "id": "39ea4659-ea69-415f-85c0-a06f94d53e14", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "dfd3c295-adae-499a-8c94-3c6c6d9ece0e", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "2ae0c196-8c94-4ea8-a9fc-1be06938a0c3", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": "3d085ec1-14de-4eef-9853-2edf5d81daac", + "name": "denoising_start", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "denoising_end": { + "id": "1a820924-15ca-4ba5-b981-6b588e486a5b", + "name": "denoising_end", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 1 + }, + "scheduler": { + "id": "d0d19fab-5001-4c5d-b664-031df1a65311", + "name": "scheduler", + "type": "Scheduler", + "fieldKind": "input", + "label": "", + "value": "euler" + }, + "unet": { + "id": "efbdecd1-5c07-420c-bd58-52de43fcde4c", + "name": "unet", + "type": "UNetField", + "fieldKind": "input", + "label": "" + }, + "control": { + "id": "e1a457c4-5546-4c02-83e1-092776b27cd1", + "name": "control", + "type": "ControlPolymorphic", + "fieldKind": "input", + "label": "" + }, + "ip_adapter": { + "id": "d4082d78-7f17-4f87-af05-5a76129737ba", + "name": "ip_adapter", + "type": "IPAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "t2i_adapter": { + "id": "4841595c-f81b-440a-9377-fe89b26b42ac", + "name": "t2i_adapter", + "type": "T2IAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "latents": { + "id": "60bbfc7e-6641-4354-b678-12029c580aa9", + "name": "latents", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "denoise_mask": { + "id": "b2876171-e4c5-45cf-a352-852047c902fc", + "name": "denoise_mask", + "type": "DenoiseMaskField", + "fieldKind": "input", + "label": "" + } + }, + "outputs": { + "latents": { + "id": "62705a29-cc3a-4154-8f62-a8f821daf861", + "name": "latents", + "type": "LatentsField", + "fieldKind": "output" + }, + "width": { + "id": "eb2e2312-1e64-4008-a64f-6783d49dde29", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "7bd8d012-edcf-4def-98eb-7ebdd724c7c5", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": true, + "useCache": true, + "version": "1.4.0" + }, + "width": 320, + "height": 646, + "position": { + "x": 1269.2683722842958, + "y": -119.4839111990423 + } + }, + { + "id": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "type": "invocation", + "data": { + "id": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "type": "denoise_latents", + "inputs": { + "positive_conditioning": { + "id": "a9c932a9-6164-4333-bade-3909c9a3ce59", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "a57fced5-aca6-40c9-8197-ce4f01433111", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "noise": { + "id": "b4cbec14-c24e-4ec2-bda3-8fc19c089717", + "name": "noise", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "steps": { + "id": "24dd36f7-fdf1-40c9-945c-216471b44a2f", + "name": "steps", + "type": "integer", + "fieldKind": "input", + "label": "", + "value": 10 + }, + "cfg_scale": { + "id": "5f3a7f0c-5088-49e9-b490-75822d0c20cc", + "name": "cfg_scale", + "type": "FloatPolymorphic", + "fieldKind": "input", + "label": "", + "value": 7.5 + }, + "denoising_start": { + "id": "b326ffde-625c-49c5-b5e1-90b79df80979", + "name": "denoising_start", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 0 + }, + "denoising_end": { + "id": "d4a458ef-5576-4c5d-8e6d-ee04c9c7c4dc", + "name": "denoising_end", + "type": "float", + "fieldKind": "input", + "label": "", + "value": 1 + }, + "scheduler": { + "id": "95aba2d0-c470-44f2-a25c-a192600be6da", + "name": "scheduler", + "type": "Scheduler", + "fieldKind": "input", + "label": "", + "value": "euler" + }, + "unet": { + "id": "68a8636e-3b2f-4a95-bd05-d86a01edb74a", + "name": "unet", + "type": "UNetField", + "fieldKind": "input", + "label": "" + }, + "control": { + "id": "508e68a6-1cfc-4121-baed-b829b2886474", + "name": "control", + "type": "ControlPolymorphic", + "fieldKind": "input", + "label": "" + }, + "ip_adapter": { + "id": "e976e72f-8bd1-44d4-ad75-8410db221e3f", + "name": "ip_adapter", + "type": "IPAdapterPolymorphic", + "fieldKind": "input", + 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false, + "isIntermediate": true, + "useCache": true, + "version": "1.4.0" + }, + "width": 320, + "height": 646, + "position": { + "x": 1672.552348276784, + "y": -118.3156091718022 + } + }, + { + "id": "17eb4b88-bdd8-4984-affa-26586b146866", + "type": "invocation", + "data": { + "id": "17eb4b88-bdd8-4984-affa-26586b146866", + "type": "l2i", + "inputs": { + "metadata": { + "id": "04f49f70-8ee6-43e3-ac63-af02e5b34204", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, + "latents": { + "id": "d27c4313-01db-45cb-b9f4-6a827d0d766a", + "name": "latents", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "vae": { + "id": "3751b0f6-69f3-4f95-a7f9-476b2d31e9f9", + "name": "vae", + "type": "VaeField", + "fieldKind": "input", + "label": "" + }, + "tiled": { + "id": "07e5e79a-b452-4beb-b26f-715da2387ac7", + "name": "tiled", + "type": "boolean", + "fieldKind": "input", + "label": "", + "value": false + }, + "fp32": { + "id": "afe954f6-ecaf-4eac-98ee-23f4d0eb7a6b", + "name": "fp32", + "type": "boolean", + "fieldKind": "input", + "label": "", + "value": false + } + }, + "outputs": { + "image": { + "id": "98515f37-9fe7-420e-839b-6e349d9407df", + "name": "image", + "type": "ImageField", + "fieldKind": "output" + }, + "width": { + "id": "5203f61f-02db-423c-9c85-80aa20816dea", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "499e6152-c604-4dad-84c3-8c5b26a39919", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": false, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 267, + "position": { + "x": 2045.2934900771834, + "y": -362.2916292593367 + } } ], "edges": [ @@ -1296,102 +1339,6 @@ "id": "reactflow__edge-8d54b9db-3662-43af-8369-9a277e063f3bvalue-fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1style", "type": "default" }, - { - "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08", - "sourceHandle": "conditioning", - "target": "65b56526-ef0a-4c1f-adda-1017c925b063", - "targetHandle": "positive_conditioning", - "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-65b56526-ef0a-4c1f-adda-1017c925b063positive_conditioning", - "type": "default" - }, - { - "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", - "sourceHandle": "conditioning", - "target": "65b56526-ef0a-4c1f-adda-1017c925b063", - "targetHandle": "negative_conditioning", - "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-65b56526-ef0a-4c1f-adda-1017c925b063negative_conditioning", - "type": "default" - }, - { - "source": "55705012-79b9-4aac-9f26-c0b10309785b", - "sourceHandle": "noise", - "target": "65b56526-ef0a-4c1f-adda-1017c925b063", - "targetHandle": "noise", - "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-65b56526-ef0a-4c1f-adda-1017c925b063noise", - "type": "default" - }, - { - "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", - "sourceHandle": "unet", - "target": "65b56526-ef0a-4c1f-adda-1017c925b063", - "targetHandle": "unet", - "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-65b56526-ef0a-4c1f-adda-1017c925b063unet", - "type": "default" - }, - { - "source": "fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1", - "sourceHandle": "conditioning", - "target": "a9352523-613a-43e3-b97f-dade7ec317e5", - "targetHandle": "negative_conditioning", - "id": "reactflow__edge-fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1conditioning-a9352523-613a-43e3-b97f-dade7ec317e5negative_conditioning", - "type": "default" - }, - { - "source": "f0e06b70-9f53-44e3-8f5f-63d813b6b579", - "sourceHandle": "conditioning", - "target": "a9352523-613a-43e3-b97f-dade7ec317e5", - "targetHandle": "positive_conditioning", - "id": "reactflow__edge-f0e06b70-9f53-44e3-8f5f-63d813b6b579conditioning-a9352523-613a-43e3-b97f-dade7ec317e5positive_conditioning", - "type": "default" - }, - { - "source": "a9352523-613a-43e3-b97f-dade7ec317e5", - "sourceHandle": "latents", - "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "targetHandle": "latents", - "id": "reactflow__edge-a9352523-613a-43e3-b97f-dade7ec317e5latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents", - "type": "default" - }, - { - "source": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", - "sourceHandle": "vae", - "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "targetHandle": "vae", - "id": "reactflow__edge-62bdf243-d98f-4508-b6b5-c3af00ef49f0vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae", - "type": "default" - }, - { - "source": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", - "sourceHandle": "unet", - "target": "a9352523-613a-43e3-b97f-dade7ec317e5", - "targetHandle": "unet", - "id": "reactflow__edge-62bdf243-d98f-4508-b6b5-c3af00ef49f0unet-a9352523-613a-43e3-b97f-dade7ec317e5unet", - "type": "default" - }, - { - "source": "65b56526-ef0a-4c1f-adda-1017c925b063", - "sourceHandle": "latents", - "target": "a9352523-613a-43e3-b97f-dade7ec317e5", - "targetHandle": "latents", - "id": "reactflow__edge-65b56526-ef0a-4c1f-adda-1017c925b063latents-a9352523-613a-43e3-b97f-dade7ec317e5latents", - "type": "default" - }, - { - "source": "06a30867-1e9d-461f-bd58-14a63cc997dd", - "sourceHandle": "scheduler", - "target": "65b56526-ef0a-4c1f-adda-1017c925b063", - "targetHandle": "scheduler", - "id": "reactflow__edge-06a30867-1e9d-461f-bd58-14a63cc997ddscheduler-65b56526-ef0a-4c1f-adda-1017c925b063scheduler", - "type": "default" - }, - { - "source": "06a30867-1e9d-461f-bd58-14a63cc997dd", - "sourceHandle": "scheduler", - "target": "a9352523-613a-43e3-b97f-dade7ec317e5", - "targetHandle": "scheduler", - "id": "reactflow__edge-06a30867-1e9d-461f-bd58-14a63cc997ddscheduler-a9352523-613a-43e3-b97f-dade7ec317e5scheduler", - "type": "default" - }, { "source": "8d54b9db-3662-43af-8369-9a277e063f3b", "sourceHandle": "value", @@ -1399,6 +1346,94 @@ "targetHandle": "style", "id": "reactflow__edge-8d54b9db-3662-43af-8369-9a277e063f3bvalue-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204style", "type": "default" + }, + { + "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", + "sourceHandle": "conditioning", + "target": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "targetHandle": "negative_conditioning", + "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-84df8f00-ea7e-499f-ab86-d019ddea5393negative_conditioning", + "type": "default" + }, + { + "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08", + "sourceHandle": "conditioning", + "target": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "targetHandle": "positive_conditioning", + "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-84df8f00-ea7e-499f-ab86-d019ddea5393positive_conditioning", + "type": "default" + }, + { + "source": "55705012-79b9-4aac-9f26-c0b10309785b", + "sourceHandle": "noise", + "target": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "targetHandle": "noise", + "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-84df8f00-ea7e-499f-ab86-d019ddea5393noise", + "type": "default" + }, + { + "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22", + "sourceHandle": "unet", + "target": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "targetHandle": "unet", + "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-84df8f00-ea7e-499f-ab86-d019ddea5393unet", + "type": "default" + }, + { + "source": "06a30867-1e9d-461f-bd58-14a63cc997dd", + "sourceHandle": "scheduler", + "target": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "targetHandle": "scheduler", + "id": "reactflow__edge-06a30867-1e9d-461f-bd58-14a63cc997ddscheduler-84df8f00-ea7e-499f-ab86-d019ddea5393scheduler", + "type": "default" + }, + { + "source": "84df8f00-ea7e-499f-ab86-d019ddea5393", + "sourceHandle": "latents", + "target": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "targetHandle": "latents", + "id": "reactflow__edge-84df8f00-ea7e-499f-ab86-d019ddea5393latents-3d40eda5-ff7b-4dff-8d2e-4f44742faa1blatents", + "type": "default" + }, + { + "source": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", + "sourceHandle": "unet", + "target": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "targetHandle": "unet", + "id": "reactflow__edge-62bdf243-d98f-4508-b6b5-c3af00ef49f0unet-3d40eda5-ff7b-4dff-8d2e-4f44742faa1bunet", + "type": "default" + }, + { + "source": "f0e06b70-9f53-44e3-8f5f-63d813b6b579", + "sourceHandle": "conditioning", + "target": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "targetHandle": "positive_conditioning", + "id": "reactflow__edge-f0e06b70-9f53-44e3-8f5f-63d813b6b579conditioning-3d40eda5-ff7b-4dff-8d2e-4f44742faa1bpositive_conditioning", + "type": "default" + }, + { + "source": "fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1", + "sourceHandle": "conditioning", + "target": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "targetHandle": "negative_conditioning", + "id": "reactflow__edge-fbb2f1a0-2e68-411d-a955-60c3b8a6f2d1conditioning-3d40eda5-ff7b-4dff-8d2e-4f44742faa1bnegative_conditioning", + "type": "default" + }, + { + "source": "3d40eda5-ff7b-4dff-8d2e-4f44742faa1b", + "sourceHandle": "latents", + "target": "17eb4b88-bdd8-4984-affa-26586b146866", + "targetHandle": "latents", + "id": "reactflow__edge-3d40eda5-ff7b-4dff-8d2e-4f44742faa1blatents-17eb4b88-bdd8-4984-affa-26586b146866latents", + "type": "default" + }, + { + "source": "62bdf243-d98f-4508-b6b5-c3af00ef49f0", + "sourceHandle": "vae", + "target": "17eb4b88-bdd8-4984-affa-26586b146866", + "targetHandle": "vae", + "id": "reactflow__edge-62bdf243-d98f-4508-b6b5-c3af00ef49f0vae-17eb4b88-bdd8-4984-affa-26586b146866vae", + "type": "default" } ] -} +} \ No newline at end of file diff --git a/docs/workflows/Text_to_Image.json b/docs/workflows/Text_to_Image.json index 7239a2247f..a49ce7bf93 100644 --- a/docs/workflows/Text_to_Image.json +++ b/docs/workflows/Text_to_Image.json @@ -18,10 +18,6 @@ { "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402", "fieldName": "prompt" - }, - { - "nodeId": "75899702-fa44-46d2-b2d5-3e17f234c3e7", - "fieldName": "steps" } ], "meta": { @@ -32,7 +28,6 @@ "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402", "type": "invocation", "data": { - "version": "1.0.0", "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402", "type": "compel", "inputs": { @@ -64,20 +59,21 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 235, + "height": 261, "position": { - "x": 1400, - "y": -75 + "x": 995.7263915923627, + "y": 239.67783573351227 } }, { "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "invocation", "data": { - "version": "1.0.0", "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "noise", "inputs": { @@ -138,92 +134,21 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 364, + "height": 389, "position": { - "x": 1000, - "y": 350 - } - }, - { - "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "type": "invocation", - "data": { - "version": "1.0.0", - "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "type": "l2i", - "inputs": { - "tiled": { - "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df", - "name": "tiled", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "fp32": { - "id": "b146d873-ffb9-4767-986a-5360504841a2", - "name": "fp32", - "type": "boolean", - "fieldKind": "input", - "label": "", - "value": false - }, - "latents": { - "id": "65441abd-7713-4b00-9d8d-3771404002e8", - "name": "latents", - "type": "LatentsField", - "fieldKind": "input", - "label": "" - }, - "vae": { - "id": "a478b833-6e13-4611-9a10-842c89603c74", - "name": "vae", - "type": "VaeField", - "fieldKind": "input", - "label": "" - } - }, - "outputs": { - "image": { - "id": "c87ae925-f858-417a-8940-8708ba9b4b53", - "name": "image", - "type": "ImageField", - "fieldKind": "output" - }, - "width": { - "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c", - "name": "width", - "type": "integer", - "fieldKind": "output" - }, - "height": { - "id": "23e41c00-a354-48e8-8f59-5875679c27ab", - "name": "height", - "type": "integer", - "fieldKind": "output" - } - }, - "label": "", - "isOpen": true, - "notes": "", - "embedWorkflow": true, - "isIntermediate": false - }, - "width": 320, - "height": 266, - "position": { - "x": 1800, - "y": 200 + "x": 993.4442117555518, + "y": 605.6757415334787 } }, { "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "type": "invocation", "data": { - "version": "1.0.0", "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "type": "main_model_loader", "inputs": { @@ -261,23 +186,24 @@ } }, "label": "", - "isOpen": false, + "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 32, + "height": 226, "position": { - "x": 1000, - "y": 200 + "x": 163.04436745878343, + "y": 254.63156870373479 } }, { "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c", "type": "invocation", "data": { - "version": "1.0.0", "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c", "type": "compel", "inputs": { @@ -309,20 +235,21 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.0.0" }, "width": 320, - "height": 235, + "height": 261, "position": { - "x": 1000, - "y": -75 + "x": 595.7263915923627, + "y": 239.67783573351227 } }, { "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "type": "invocation", "data": { - "version": "1.0.0", "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2", "type": "rand_int", "inputs": { @@ -352,51 +279,66 @@ } }, "label": "Random Seed", - "isOpen": false, + "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": false, + "version": "1.0.0" }, "width": 320, - "height": 32, + "height": 218, "position": { - "x": 1000, - "y": 275 + "x": 541.094822888628, + "y": 694.5704476446829 } }, { - "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7", + "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c", "type": "invocation", "data": { - "version": "1.0.0", - "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7", + "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c", "type": "denoise_latents", "inputs": { + "positive_conditioning": { + "id": "90b7f4f8-ada7-4028-8100-d2e54f192052", + "name": "positive_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, + "negative_conditioning": { + "id": "9393779e-796c-4f64-b740-902a1177bf53", + "name": "negative_conditioning", + "type": "ConditioningField", + "fieldKind": "input", + "label": "" + }, "noise": { - "id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b", + "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1", "name": "noise", "type": "LatentsField", "fieldKind": "input", "label": "" }, "steps": { - "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c", + "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88", "name": "steps", "type": "integer", "fieldKind": "input", "label": "", - "value": 36 + "value": 10 }, "cfg_scale": { - "id": "107267ce-4666-4cd7-94b3-7476b7973ae9", + "id": "87dd04d3-870e-49e1-98bf-af003a810109", "name": "cfg_scale", - "type": "float", + "type": "FloatPolymorphic", "fieldKind": "input", "label": "", "value": 7.5 }, "denoising_start": { - "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1", + "id": "f369d80f-4931-4740-9bcd-9f0620719fab", "name": "denoising_start", "type": "float", "fieldKind": "input", @@ -404,7 +346,7 @@ "value": 0 }, "denoising_end": { - "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f", + "id": "747d10e5-6f02-445c-994c-0604d814de8c", "name": "denoising_end", "type": "float", "fieldKind": "input", @@ -412,71 +354,71 @@ "value": 1 }, "scheduler": { - "id": "53092874-a43b-4623-91a2-76e62fdb1f2e", + "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b", "name": "scheduler", "type": "Scheduler", "fieldKind": "input", "label": "", "value": "euler" }, + "unet": { + "id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b", + "name": "unet", + "type": "UNetField", + "fieldKind": "input", + "label": "" + }, "control": { - "id": "7abe57cc-469d-437e-ad72-a18efa28215f", + "id": "077b64cb-34be-4fcc-83f2-e399807a02bd", "name": "control", - "type": "ControlField", + "type": "ControlPolymorphic", + "fieldKind": "input", + "label": "" + }, + "ip_adapter": { + "id": "1d6948f7-3a65-4a65-a20c-768b287251aa", + "name": "ip_adapter", + "type": "IPAdapterPolymorphic", + "fieldKind": "input", + "label": "" + }, + "t2i_adapter": { + "id": "75e67b09-952f-4083-aaf4-6b804d690412", + "name": "t2i_adapter", + "type": "T2IAdapterPolymorphic", "fieldKind": "input", "label": "" }, "latents": { - "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129", + "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43", "name": "latents", "type": "LatentsField", "fieldKind": "input", "label": "" }, "denoise_mask": { - "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1", + "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa", "name": "denoise_mask", "type": "DenoiseMaskField", "fieldKind": "input", "label": "" - }, - "positive_conditioning": { - "id": "c7160303-8a23-4f15-9197-855d48802a7f", - "name": "positive_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "negative_conditioning": { - "id": "fd750efa-1dfc-4d0b-accb-828e905ba320", - "name": "negative_conditioning", - "type": "ConditioningField", - "fieldKind": "input", - "label": "" - }, - "unet": { - "id": "af1f41ba-ce2a-4314-8d7f-494bb5800381", - "name": "unet", - "type": "UNetField", - "fieldKind": "input", - "label": "" } }, "outputs": { "latents": { - "id": "8508d04d-f999-4a44-94d0-388ab1401d27", + "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38", "name": "latents", "type": "LatentsField", "fieldKind": "output" }, "width": { - "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e", + "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0", "name": "width", "type": "integer", "fieldKind": "output" }, "height": { - "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96", + "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d", "name": "height", "type": "integer", "fieldKind": "output" @@ -486,13 +428,95 @@ "isOpen": true, "notes": "", "embedWorkflow": false, - "isIntermediate": true + "isIntermediate": true, + "useCache": true, + "version": "1.4.0" }, "width": 320, - "height": 558, + "height": 646, "position": { - "x": 1400, - "y": 200 + "x": 1476.5794704734735, + "y": 256.80174342731783 + } + }, + { + "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f", + "type": "invocation", + "data": { + "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f", + "type": "l2i", + "inputs": { + "metadata": { + "id": "ab375f12-0042-4410-9182-29e30db82c85", + "name": "metadata", + "type": "MetadataField", + "fieldKind": "input", + "label": "" + }, + "latents": { + "id": "3a7e7efd-bff5-47d7-9d48-615127afee78", + "name": "latents", + "type": "LatentsField", + "fieldKind": "input", + "label": "" + }, + "vae": { + "id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b", + "name": "vae", + "type": "VaeField", + "fieldKind": "input", + "label": "" + }, + "tiled": { + "id": "da52059a-0cee-4668-942f-519aa794d739", + "name": "tiled", + "type": "boolean", + "fieldKind": "input", + "label": "", + "value": false + }, + "fp32": { + "id": "c4841df3-b24e-4140-be3b-ccd454c2522c", + "name": "fp32", + "type": "boolean", + "fieldKind": "input", + "label": "", + "value": false + } + }, + "outputs": { + "image": { + "id": "72d667d0-cf85-459d-abf2-28bd8b823fe7", + "name": "image", + "type": "ImageField", + "fieldKind": "output" + }, + "width": { + "id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc", + "name": "width", + "type": "integer", + "fieldKind": "output" + }, + "height": { + "id": "230f359c-b4ea-436c-b372-332d7dcdca85", + "name": "height", + "type": "integer", + "fieldKind": "output" + } + }, + "label": "", + "isOpen": true, + "notes": "", + "embedWorkflow": false, + "isIntermediate": false, + "useCache": true, + "version": "1.0.0" + }, + "width": 320, + "height": 267, + "position": { + "x": 2037.9648469717395, + "y": 426.10844427600136 } } ], @@ -522,52 +546,52 @@ "type": "default" }, { - "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", - "sourceHandle": "vae", - "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "targetHandle": "vae", - "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae", - "type": "default" - }, - { - "source": "75899702-fa44-46d2-b2d5-3e17f234c3e7", - "sourceHandle": "latents", - "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9", - "targetHandle": "latents", - "id": "reactflow__edge-75899702-fa44-46d2-b2d5-3e17f234c3e7latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents", + "source": "55705012-79b9-4aac-9f26-c0b10309785b", + "sourceHandle": "noise", + "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c", + "targetHandle": "noise", + "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise", "type": "default" }, { "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c", "sourceHandle": "conditioning", - "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", + "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c", "targetHandle": "positive_conditioning", - "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning", + "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning", "type": "default" }, { "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402", "sourceHandle": "conditioning", - "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", + "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c", "targetHandle": "negative_conditioning", - "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning", + "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning", "type": "default" }, { "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", "sourceHandle": "unet", - "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", + "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c", "targetHandle": "unet", - "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet", + "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet", "type": "default" }, { - "source": "55705012-79b9-4aac-9f26-c0b10309785b", - "sourceHandle": "noise", - "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7", - "targetHandle": "noise", - "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-75899702-fa44-46d2-b2d5-3e17f234c3e7noise", + "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c", + "sourceHandle": "latents", + "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f", + "targetHandle": "latents", + "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents", + "type": "default" + }, + { + "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8", + "sourceHandle": "vae", + "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f", + "targetHandle": "vae", + "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae", "type": "default" } ] -} +} \ No newline at end of file diff --git a/installer/install.bat.in b/installer/install.bat.in index ffe96d4355..5fa76471de 100644 --- a/installer/install.bat.in +++ b/installer/install.bat.in @@ -1,7 +1,7 @@ @echo off setlocal EnableExtensions EnableDelayedExpansion -@rem This script requires the user to install Python 3.9 or higher. All other +@rem This script requires the user to install Python 3.10 or higher. All other @rem requirements are downloaded as needed. @rem change to the script's directory @@ -19,7 +19,7 @@ set INVOKEAI_VERSION=latest set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/ set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/#troubleshooting set PYTHON_URL=https://www.python.org/downloads/windows/ -set MINIMUM_PYTHON_VERSION=3.9.0 +set MINIMUM_PYTHON_VERSION=3.10.0 set PYTHON_URL=https://www.python.org/downloads/release/python-3109/ set err_msg=An error has occurred and the script could not continue. @@ -28,8 +28,7 @@ set err_msg=An error has occurred and the script could not continue. echo This script will install InvokeAI and its dependencies. echo. echo BEFORE YOU START PLEASE MAKE SURE TO DO THE FOLLOWING -echo 1. Install python 3.9 or 3.10. Python version 3.11 and above are -echo not supported at the moment. +echo 1. Install python 3.10 or 3.11. Python version 3.9 is no longer supported. echo 2. Double-click on the file WinLongPathsEnabled.reg in order to echo enable long path support on your system. echo 3. Install the Visual C++ core libraries. @@ -46,19 +45,19 @@ echo ***** Checking and Updating Python ***** call python --version >.tmp1 2>.tmp2 if %errorlevel% == 1 ( - set err_msg=Please install Python 3.10. See %INSTRUCTIONS% for details. + set err_msg=Please install Python 3.10-11. See %INSTRUCTIONS% for details. goto err_exit ) for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i if "%python_version%" == "" ( - set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.9 from %PYTHON_URL% + set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.12 from %PYTHON_URL% goto err_exit ) call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version% if %errorlevel% == 1 ( - set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.9 from %PYTHON_URL% + set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.12 from %PYTHON_URL% goto err_exit ) diff --git a/installer/install.sh.in b/installer/install.sh.in index 1b8ba92ea6..9cf41192bf 100755 --- a/installer/install.sh.in +++ b/installer/install.sh.in @@ -8,10 +8,10 @@ cd $scriptdir function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; } -MINIMUM_PYTHON_VERSION=3.9.0 +MINIMUM_PYTHON_VERSION=3.10.0 MAXIMUM_PYTHON_VERSION=3.11.100 PYTHON="" -for candidate in python3.11 python3.10 python3.9 python3 python ; do +for candidate in python3.11 python3.10 python3 python ; do if ppath=`which $candidate`; then # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational # we check that this found executable can actually run diff --git a/installer/lib/installer.py b/installer/lib/installer.py index 70ed4d4331..bf48e3b06d 100644 --- a/installer/lib/installer.py +++ b/installer/lib/installer.py @@ -13,7 +13,7 @@ from pathlib import Path from tempfile import TemporaryDirectory from typing import Union -SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100" +SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100" INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"] BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp" @@ -67,7 +67,6 @@ class Installer: # Cleaning up temporary directories on Windows results in a race condition # and a stack trace. # `ignore_cleanup_errors` was only added in Python 3.10 - # users of Python 3.9 will see a gnarly stack trace on installer exit if OS == "Windows" and int(platform.python_version_tuple()[1]) >= 10: venv_dir = TemporaryDirectory(prefix=BOOTSTRAP_VENV_PREFIX, ignore_cleanup_errors=True) else: @@ -139,13 +138,6 @@ class Installer: except shutil.SameFileError: venv.create(venv_dir, with_pip=True, symlinks=True) - # upgrade pip in Python 3.9 environments - if int(platform.python_version_tuple()[1]) == 9: - from plumbum import FG, local - - pip = local[get_pip_from_venv(venv_dir)] - pip["install", "--upgrade", "pip"] & FG - return venv_dir def install( diff --git a/installer/readme.txt b/installer/readme.txt index b9a97e2093..ef040c3913 100644 --- a/installer/readme.txt +++ b/installer/readme.txt @@ -4,7 +4,7 @@ Project homepage: https://github.com/invoke-ai/InvokeAI Preparations: - You will need to install Python 3.9 or higher for this installer + You will need to install Python 3.10 or higher for this installer to work. Instructions are given here: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/ @@ -14,15 +14,15 @@ Preparations: python --version If all is well, it will print "Python 3.X.X", where the version number - is at least 3.9.*, and not higher than 3.11.*. + is at least 3.10.*, and not higher than 3.11.*. If this works, check the version of the Python package manager, pip: pip --version You should get a message that indicates that the pip package - installer was derived from Python 3.9 or 3.10. For example: - "pip 22.3.1 from /usr/bin/pip (python 3.9)" + installer was derived from Python 3.10 or 3.11. For example: + "pip 22.0.1 from /usr/bin/pip (python 3.10)" Long Paths on Windows: diff --git a/installer/templates/invoke.bat.in b/installer/templates/invoke.bat.in index 227091b33a..ee6d56fc56 100644 --- a/installer/templates/invoke.bat.in +++ b/installer/templates/invoke.bat.in @@ -9,41 +9,37 @@ set INVOKEAI_ROOT=. :start echo Desired action: echo 1. Generate images with the browser-based interface -echo 2. Explore InvokeAI nodes using a command-line interface -echo 3. Run textual inversion training -echo 4. Merge models (diffusers type only) -echo 5. Download and install models -echo 6. Change InvokeAI startup options -echo 7. Re-run the configure script to fix a broken install or to complete a major upgrade -echo 8. Open the developer console -echo 9. Update InvokeAI -echo 10. Run the InvokeAI image database maintenance script -echo 11. Command-line help +echo 2. Run textual inversion training +echo 3. Merge models (diffusers type only) +echo 4. Download and install models +echo 5. Change InvokeAI startup options +echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade +echo 7. Open the developer console +echo 8. Update InvokeAI +echo 9. Run the InvokeAI image database maintenance script +echo 10. Command-line help echo Q - Quit -set /P choice="Please enter 1-11, Q: [1] " +set /P choice="Please enter 1-10, Q: [1] " if not defined choice set choice=1 IF /I "%choice%" == "1" ( echo Starting the InvokeAI browser-based UI.. python .venv\Scripts\invokeai-web.exe %* ) ELSE IF /I "%choice%" == "2" ( - echo Starting the InvokeAI command-line.. - python .venv\Scripts\invokeai.exe %* -) ELSE IF /I "%choice%" == "3" ( echo Starting textual inversion training.. python .venv\Scripts\invokeai-ti.exe --gui -) ELSE IF /I "%choice%" == "4" ( +) ELSE IF /I "%choice%" == "3" ( echo Starting model merging script.. python .venv\Scripts\invokeai-merge.exe --gui -) ELSE IF /I "%choice%" == "5" ( +) ELSE IF /I "%choice%" == "4" ( echo Running invokeai-model-install... python .venv\Scripts\invokeai-model-install.exe -) ELSE IF /I "%choice%" == "6" ( +) ELSE IF /I "%choice%" == "5" ( echo Running invokeai-configure... python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models -) ELSE IF /I "%choice%" == "7" ( +) ELSE IF /I "%choice%" == "6" ( echo Running invokeai-configure... python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight -) ELSE IF /I "%choice%" == "8" ( +) ELSE IF /I "%choice%" == "7" ( echo Developer Console echo Python command is: where python @@ -55,13 +51,13 @@ IF /I "%choice%" == "1" ( echo ************************* echo *** Type `exit` to quit this shell and deactivate the Python virtual environment *** call cmd /k -) ELSE IF /I "%choice%" == "9" ( +) ELSE IF /I "%choice%" == "8" ( echo Running invokeai-update... python -m invokeai.frontend.install.invokeai_update -) ELSE IF /I "%choice%" == "10" ( +) ELSE IF /I "%choice%" == "9" ( echo Running the db maintenance script... python .venv\Scripts\invokeai-db-maintenance.exe -) ELSE IF /I "%choice%" == "11" ( +) ELSE IF /I "%choice%" == "10" ( echo Displaying command line help... python .venv\Scripts\invokeai-web.exe --help %* pause diff --git a/installer/templates/invoke.sh.in b/installer/templates/invoke.sh.in index 6cf6967608..3230c9f442 100644 --- a/installer/templates/invoke.sh.in +++ b/installer/templates/invoke.sh.in @@ -58,52 +58,47 @@ do_choice() { invokeai-web $PARAMS ;; 2) - clear - printf "Explore InvokeAI nodes using a command-line interface\n" - invokeai $PARAMS - ;; - 3) clear printf "Textual inversion training\n" invokeai-ti --gui $PARAMS ;; - 4) + 3) clear printf "Merge models (diffusers type only)\n" invokeai-merge --gui $PARAMS ;; - 5) + 4) clear printf "Download and install models\n" invokeai-model-install --root ${INVOKEAI_ROOT} ;; - 6) + 5) clear printf "Change InvokeAI startup options\n" invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models ;; - 7) + 6) clear printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n" invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights ;; - 8) + 7) clear printf "Open the developer console\n" file_name=$(basename "${BASH_SOURCE[0]}") bash --init-file "$file_name" ;; - 9) + 8) clear printf "Update InvokeAI\n" python -m invokeai.frontend.install.invokeai_update ;; - 10) + 9) clear printf "Running the db maintenance script\n" invokeai-db-maintenance --root ${INVOKEAI_ROOT} ;; - 11) + 10) clear printf "Command-line help\n" invokeai-web --help @@ -121,16 +116,15 @@ do_choice() { do_dialog() { options=( 1 "Generate images with a browser-based interface" - 2 "Explore InvokeAI nodes using a command-line interface" - 3 "Textual inversion training" - 4 "Merge models (diffusers type only)" - 5 "Download and install models" - 6 "Change InvokeAI startup options" - 7 "Re-run the configure script to fix a broken install or to complete a major upgrade" - 8 "Open the developer console" - 9 "Update InvokeAI" - 10 "Run the InvokeAI image database maintenance script" - 11 "Command-line help" + 2 "Textual inversion training" + 3 "Merge models (diffusers type only)" + 4 "Download and install models" + 5 "Change InvokeAI startup options" + 6 "Re-run the configure script to fix a broken install or to complete a major upgrade" + 7 "Open the developer console" + 8 "Update InvokeAI" + 9 "Run the InvokeAI image database maintenance script" + 10 "Command-line help" ) choice=$(dialog --clear \ @@ -155,18 +149,17 @@ do_line_input() { printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n" printf "What would you like to do?\n" printf "1: Generate images using the browser-based interface\n" - printf "2: Explore InvokeAI nodes using the command-line interface\n" - printf "3: Run textual inversion training\n" - printf "4: Merge models (diffusers type only)\n" - printf "5: Download and install models\n" - printf "6: Change InvokeAI startup options\n" - printf "7: Re-run the configure script to fix a broken install\n" - printf "8: Open the developer console\n" - printf "9: Update InvokeAI\n" - printf "10: Run the InvokeAI image database maintenance script\n" - printf "11: Command-line help\n" + printf "2: Run textual inversion training\n" + printf "3: Merge models (diffusers type only)\n" + printf "4: Download and install models\n" + printf "5: Change InvokeAI startup options\n" + printf "6: Re-run the configure script to fix a broken install\n" + printf "7: Open the developer console\n" + printf "8: Update InvokeAI\n" + printf "9: Run the InvokeAI image database maintenance script\n" + printf "10: Command-line help\n" printf "Q: Quit\n\n" - read -p "Please enter 1-11, Q: [1] " yn + read -p "Please enter 1-10, Q: [1] " yn choice=${yn:='1'} do_choice $choice clear diff --git a/invokeai/app/api/dependencies.py b/invokeai/app/api/dependencies.py index c9a2f0a843..e7c8fa7fae 100644 --- a/invokeai/app/api/dependencies.py +++ b/invokeai/app/api/dependencies.py @@ -2,6 +2,7 @@ from logging import Logger +from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage from invokeai.backend.util.logging import InvokeAILogger from invokeai.version.invokeai_version import __version__ @@ -30,6 +31,7 @@ from ..services.shared.default_graphs import create_system_graphs from ..services.shared.graph import GraphExecutionState, LibraryGraph from ..services.shared.sqlite import SqliteDatabase from ..services.urls.urls_default import LocalUrlService +from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage from .events import FastAPIEventService @@ -90,6 +92,8 @@ class ApiDependencies: session_processor = DefaultSessionProcessor() session_queue = SqliteSessionQueue(db=db) urls = LocalUrlService() + workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db) + workflow_records = SqliteWorkflowRecordsStorage(db=db) services = InvocationServices( board_image_records=board_image_records, @@ -114,6 +118,8 @@ class ApiDependencies: session_processor=session_processor, session_queue=session_queue, urls=urls, + workflow_image_records=workflow_image_records, + workflow_records=workflow_records, ) create_system_graphs(services.graph_library) diff --git a/invokeai/app/api/routers/images.py b/invokeai/app/api/routers/images.py index 7b61887eb8..e8c8c693b3 100644 --- a/invokeai/app/api/routers/images.py +++ b/invokeai/app/api/routers/images.py @@ -1,13 +1,14 @@ import io +import traceback from typing import Optional 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, Field +from pydantic import BaseModel, Field, ValidationError -from invokeai.app.invocations.metadata import ImageMetadata +from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO from invokeai.app.services.shared.pagination import OffsetPaginatedResults @@ -42,20 +43,41 @@ async def upload_image( crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"), ) -> ImageDTO: """Uploads an image""" - if not file.content_type.startswith("image"): + if not file.content_type or not file.content_type.startswith("image"): raise HTTPException(status_code=415, detail="Not an image") - contents = await file.read() + metadata = None + workflow = None + contents = await file.read() try: pil_image = Image.open(io.BytesIO(contents)) if crop_visible: bbox = pil_image.getbbox() pil_image = pil_image.crop(bbox) except Exception: - # Error opening the image + ApiDependencies.invoker.services.logger.error(traceback.format_exc()) raise HTTPException(status_code=415, detail="Failed to read image") + # TODO: retain non-invokeai metadata on upload? + # attempt to parse metadata from image + metadata_raw = pil_image.info.get("invokeai_metadata", None) + if metadata_raw: + try: + metadata = MetadataFieldValidator.validate_json(metadata_raw) + except ValidationError: + ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image") + pass + + # attempt to parse workflow from image + workflow_raw = pil_image.info.get("invokeai_workflow", None) + if workflow_raw is not None: + try: + workflow = WorkflowFieldValidator.validate_json(workflow_raw) + except ValidationError: + ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image") + pass + try: image_dto = ApiDependencies.invoker.services.images.create( image=pil_image, @@ -63,6 +85,8 @@ async def upload_image( image_category=image_category, session_id=session_id, board_id=board_id, + metadata=metadata, + workflow=workflow, is_intermediate=is_intermediate, ) @@ -71,6 +95,7 @@ async def upload_image( return image_dto except Exception: + ApiDependencies.invoker.services.logger.error(traceback.format_exc()) raise HTTPException(status_code=500, detail="Failed to create image") @@ -87,7 +112,7 @@ async def delete_image( pass -@images_router.post("/clear-intermediates", operation_id="clear_intermediates") +@images_router.delete("/intermediates", operation_id="clear_intermediates") async def clear_intermediates() -> int: """Clears all intermediates""" @@ -99,6 +124,17 @@ async def clear_intermediates() -> int: pass +@images_router.get("/intermediates", operation_id="get_intermediates_count") +async def get_intermediates_count() -> int: + """Gets the count of intermediate images""" + + try: + return ApiDependencies.invoker.services.images.get_intermediates_count() + except Exception: + raise HTTPException(status_code=500, detail="Failed to get intermediates") + pass + + @images_router.patch( "/i/{image_name}", operation_id="update_image", @@ -135,11 +171,11 @@ async def get_image_dto( @images_router.get( "/i/{image_name}/metadata", operation_id="get_image_metadata", - response_model=ImageMetadata, + response_model=Optional[MetadataField], ) async def get_image_metadata( image_name: str = Path(description="The name of image to get"), -) -> ImageMetadata: +) -> Optional[MetadataField]: """Gets an image's metadata""" try: diff --git a/invokeai/app/api/routers/models.py b/invokeai/app/api/routers/models.py index ebc40f5ce5..afa7d8df82 100644 --- a/invokeai/app/api/routers/models.py +++ b/invokeai/app/api/routers/models.py @@ -2,11 +2,11 @@ import pathlib -from typing import List, Literal, Optional, Union +from typing import Annotated, List, Literal, Optional, Union from fastapi import Body, Path, Query, Response from fastapi.routing import APIRouter -from pydantic import BaseModel, parse_obj_as +from pydantic import BaseModel, ConfigDict, Field, TypeAdapter from starlette.exceptions import HTTPException from invokeai.backend import BaseModelType, ModelType @@ -23,8 +23,14 @@ from ..dependencies import ApiDependencies models_router = APIRouter(prefix="/v1/models", tags=["models"]) UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] +UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse) + ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] +ImportModelResponseValidator = TypeAdapter(ImportModelResponse) + ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] +ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse) + MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)] @@ -32,6 +38,11 @@ ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)] class ModelsList(BaseModel): models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]] + model_config = ConfigDict(use_enum_values=True) + + +ModelsListValidator = TypeAdapter(ModelsList) + @models_router.get( "/", @@ -49,7 +60,7 @@ async def list_models( models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)) else: models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type) - models = parse_obj_as(ModelsList, {"models": models_raw}) + models = ModelsListValidator.validate_python({"models": models_raw}) return models @@ -105,11 +116,14 @@ async def update_model( info.path = new_info.get("path") # replace empty string values with None/null to avoid phenomenon of vae: '' - info_dict = info.dict() + info_dict = info.model_dump() 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( @@ -117,7 +131,7 @@ async def update_model( base_model=base_model, model_type=model_type, ) - model_response = parse_obj_as(UpdateModelResponse, model_raw) + model_response = UpdateModelResponseValidator.validate_python(model_raw) except ModelNotFoundException as e: raise HTTPException(status_code=404, detail=str(e)) except ValueError as e: @@ -152,13 +166,15 @@ async def import_model( ) -> ImportModelResponse: """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically""" + location = location.strip("\"' ") items_to_import = {location} prediction_types = {x.value: x for x in SchedulerPredictionType} logger = ApiDependencies.invoker.services.logger try: installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import( - items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type) + items_to_import=items_to_import, + prediction_type_helper=lambda x: prediction_types.get(prediction_type), ) info = installed_models.get(location) @@ -170,7 +186,7 @@ async def import_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_name=info.name, base_model=info.base_model, model_type=info.model_type ) - return parse_obj_as(ImportModelResponse, model_raw) + return ImportModelResponseValidator.validate_python(model_raw) except ModelNotFoundException as e: logger.error(str(e)) @@ -204,13 +220,18 @@ async def add_model( try: ApiDependencies.invoker.services.model_manager.add_model( - info.model_name, info.base_model, info.model_type, model_attributes=info.dict() + info.model_name, + info.base_model, + info.model_type, + model_attributes=info.model_dump(), ) logger.info(f"Successfully added {info.model_name}") model_raw = ApiDependencies.invoker.services.model_manager.list_model( - model_name=info.model_name, base_model=info.base_model, model_type=info.model_type + model_name=info.model_name, + base_model=info.base_model, + model_type=info.model_type, ) - return parse_obj_as(ImportModelResponse, model_raw) + return ImportModelResponseValidator.validate_python(model_raw) except ModelNotFoundException as e: logger.error(str(e)) raise HTTPException(status_code=404, detail=str(e)) @@ -222,7 +243,10 @@ async def add_model( @models_router.delete( "/{base_model}/{model_type}/{model_name}", operation_id="del_model", - responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}}, + responses={ + 204: {"description": "Model deleted successfully"}, + 404: {"description": "Model not found"}, + }, status_code=204, response_model=None, ) @@ -278,7 +302,7 @@ async def convert_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_name, base_model=base_model, model_type=model_type ) - response = parse_obj_as(ConvertModelResponse, model_raw) + response = ConvertModelResponseValidator.validate_python(model_raw) except ModelNotFoundException as e: raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}") except ValueError as e: @@ -301,7 +325,8 @@ async def search_for_models( ) -> List[pathlib.Path]: if not search_path.is_dir(): raise HTTPException( - status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory" + status_code=404, + detail=f"The search path '{search_path}' does not exist or is not directory", ) return ApiDependencies.invoker.services.model_manager.search_for_models(search_path) @@ -336,6 +361,26 @@ async def sync_to_config() -> bool: return True +# There's some weird pydantic-fastapi behaviour that requires this to be a separate class +# TODO: After a few updates, see if it works inside the route operation handler? +class MergeModelsBody(BaseModel): + model_names: List[str] = Field(description="model name", min_length=2, max_length=3) + merged_model_name: Optional[str] = Field(description="Name of destination model") + alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5) + interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method") + force: Optional[bool] = Field( + description="Force merging of models created with different versions of diffusers", + default=False, + ) + + merge_dest_directory: Optional[str] = Field( + description="Save the merged model to the designated directory (with 'merged_model_name' appended)", + default=None, + ) + + model_config = ConfigDict(protected_namespaces=()) + + @models_router.put( "/merge/{base_model}", operation_id="merge_models", @@ -348,31 +393,23 @@ async def sync_to_config() -> bool: response_model=MergeModelResponse, ) async def merge_models( + body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)], base_model: BaseModelType = Path(description="Base model"), - model_names: List[str] = Body(description="model name", min_items=2, max_items=3), - merged_model_name: Optional[str] = Body(description="Name of destination model"), - alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5), - interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"), - force: Optional[bool] = Body( - description="Force merging of models created with different versions of diffusers", default=False - ), - merge_dest_directory: Optional[str] = Body( - description="Save the merged model to the designated directory (with 'merged_model_name' appended)", - default=None, - ), ) -> MergeModelResponse: """Convert a checkpoint model into a diffusers model""" logger = ApiDependencies.invoker.services.logger try: - logger.info(f"Merging models: {model_names} into {merge_dest_directory or ''}/{merged_model_name}") - dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None + logger.info( + f"Merging models: {body.model_names} into {body.merge_dest_directory or ''}/{body.merged_model_name}" + ) + dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None result = ApiDependencies.invoker.services.model_manager.merge_models( - model_names, - base_model, - merged_model_name=merged_model_name or "+".join(model_names), - alpha=alpha, - interp=interp, - force=force, + model_names=body.model_names, + base_model=base_model, + merged_model_name=body.merged_model_name or "+".join(body.model_names), + alpha=body.alpha, + interp=body.interp, + force=body.force, merge_dest_directory=dest, ) model_raw = ApiDependencies.invoker.services.model_manager.list_model( @@ -380,9 +417,12 @@ async def merge_models( base_model=base_model, model_type=ModelType.Main, ) - response = parse_obj_as(ConvertModelResponse, model_raw) + response = ConvertModelResponseValidator.validate_python(model_raw) except ModelNotFoundException: - raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found") + raise HTTPException( + status_code=404, + detail=f"One or more of the models '{body.model_names}' not found", + ) except ValueError as e: raise HTTPException(status_code=400, detail=str(e)) return response diff --git a/invokeai/app/api/routers/session_queue.py b/invokeai/app/api/routers/session_queue.py index 7ecb0504a3..40f1f2213b 100644 --- a/invokeai/app/api/routers/session_queue.py +++ b/invokeai/app/api/routers/session_queue.py @@ -12,13 +12,11 @@ from invokeai.app.services.session_queue.session_queue_common import ( CancelByBatchIDsResult, ClearResult, EnqueueBatchResult, - EnqueueGraphResult, PruneResult, SessionQueueItem, SessionQueueItemDTO, SessionQueueStatus, ) -from invokeai.app.services.shared.graph import Graph from invokeai.app.services.shared.pagination import CursorPaginatedResults from ..dependencies import ApiDependencies @@ -33,23 +31,6 @@ class SessionQueueAndProcessorStatus(BaseModel): processor: SessionProcessorStatus -@session_queue_router.post( - "/{queue_id}/enqueue_graph", - operation_id="enqueue_graph", - responses={ - 201: {"model": EnqueueGraphResult}, - }, -) -async def enqueue_graph( - queue_id: str = Path(description="The queue id to perform this operation on"), - graph: Graph = Body(description="The graph to enqueue"), - prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"), -) -> EnqueueGraphResult: - """Enqueues a graph for single execution.""" - - return ApiDependencies.invoker.services.session_queue.enqueue_graph(queue_id=queue_id, graph=graph, prepend=prepend) - - @session_queue_router.post( "/{queue_id}/enqueue_batch", operation_id="enqueue_batch", diff --git a/invokeai/app/api/routers/sessions.py b/invokeai/app/api/routers/sessions.py index cd93a267ad..fb850d0b2b 100644 --- a/invokeai/app/api/routers/sessions.py +++ b/invokeai/app/api/routers/sessions.py @@ -1,57 +1,50 @@ # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) -from typing import Annotated, Optional, Union -from fastapi import Body, HTTPException, Path, Query, Response +from fastapi import HTTPException, Path from fastapi.routing import APIRouter -from pydantic.fields import Field -from invokeai.app.services.shared.pagination import PaginatedResults - -# Importing * is bad karma but needed here for node detection -from ...invocations import * # noqa: F401 F403 -from ...invocations.baseinvocation import BaseInvocation -from ...services.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError +from ...services.shared.graph import GraphExecutionState from ..dependencies import ApiDependencies session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"]) -@session_router.post( - "/", - operation_id="create_session", - responses={ - 200: {"model": GraphExecutionState}, - 400: {"description": "Invalid json"}, - }, - deprecated=True, -) -async def create_session( - queue_id: str = Query(default="", description="The id of the queue to associate the session with"), - graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"), -) -> GraphExecutionState: - """Creates a new session, optionally initializing it with an invocation graph""" - session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph) - return session +# @session_router.post( +# "/", +# operation_id="create_session", +# responses={ +# 200: {"model": GraphExecutionState}, +# 400: {"description": "Invalid json"}, +# }, +# deprecated=True, +# ) +# async def create_session( +# queue_id: str = Query(default="", description="The id of the queue to associate the session with"), +# graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"), +# ) -> GraphExecutionState: +# """Creates a new session, optionally initializing it with an invocation graph""" +# session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph) +# return session -@session_router.get( - "/", - operation_id="list_sessions", - responses={200: {"model": PaginatedResults[GraphExecutionState]}}, - deprecated=True, -) -async def list_sessions( - page: int = Query(default=0, description="The page of results to get"), - per_page: int = Query(default=10, description="The number of results per page"), - query: str = Query(default="", description="The query string to search for"), -) -> PaginatedResults[GraphExecutionState]: - """Gets a list of sessions, optionally searching""" - if query == "": - result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page) - else: - result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page) - return result +# @session_router.get( +# "/", +# operation_id="list_sessions", +# responses={200: {"model": PaginatedResults[GraphExecutionState]}}, +# deprecated=True, +# ) +# async def list_sessions( +# page: int = Query(default=0, description="The page of results to get"), +# per_page: int = Query(default=10, description="The number of results per page"), +# query: str = Query(default="", description="The query string to search for"), +# ) -> PaginatedResults[GraphExecutionState]: +# """Gets a list of sessions, optionally searching""" +# if query == "": +# result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page) +# else: +# result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page) +# return result @session_router.get( @@ -61,7 +54,6 @@ async def list_sessions( 200: {"model": GraphExecutionState}, 404: {"description": "Session not found"}, }, - deprecated=True, ) async def get_session( session_id: str = Path(description="The id of the session to get"), @@ -74,211 +66,211 @@ async def get_session( return session -@session_router.post( - "/{session_id}/nodes", - operation_id="add_node", - responses={ - 200: {"model": str}, - 400: {"description": "Invalid node or link"}, - 404: {"description": "Session not found"}, - }, - deprecated=True, -) -async def add_node( - session_id: str = Path(description="The id of the session"), - node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore - description="The node to add" - ), -) -> str: - """Adds a node to the graph""" - session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) - if session is None: - raise HTTPException(status_code=404) +# @session_router.post( +# "/{session_id}/nodes", +# operation_id="add_node", +# responses={ +# 200: {"model": str}, +# 400: {"description": "Invalid node or link"}, +# 404: {"description": "Session not found"}, +# }, +# deprecated=True, +# ) +# async def add_node( +# session_id: str = Path(description="The id of the session"), +# node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore +# description="The node to add" +# ), +# ) -> str: +# """Adds a node to the graph""" +# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) +# if session is None: +# raise HTTPException(status_code=404) - try: - session.add_node(node) - ApiDependencies.invoker.services.graph_execution_manager.set( - session - ) # TODO: can this be done automatically, or add node through an API? - return session.id - except NodeAlreadyExecutedError: - raise HTTPException(status_code=400) - except IndexError: - raise HTTPException(status_code=400) +# try: +# session.add_node(node) +# ApiDependencies.invoker.services.graph_execution_manager.set( +# session +# ) # TODO: can this be done automatically, or add node through an API? +# return session.id +# except NodeAlreadyExecutedError: +# raise HTTPException(status_code=400) +# except IndexError: +# raise HTTPException(status_code=400) -@session_router.put( - "/{session_id}/nodes/{node_path}", - operation_id="update_node", - responses={ - 200: {"model": GraphExecutionState}, - 400: {"description": "Invalid node or link"}, - 404: {"description": "Session not found"}, - }, - deprecated=True, -) -async def update_node( - session_id: str = Path(description="The id of the session"), - node_path: str = Path(description="The path to the node in the graph"), - node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore - description="The new node" - ), -) -> GraphExecutionState: - """Updates a node in the graph and removes all linked edges""" - session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) - if session is None: - raise HTTPException(status_code=404) +# @session_router.put( +# "/{session_id}/nodes/{node_path}", +# operation_id="update_node", +# responses={ +# 200: {"model": GraphExecutionState}, +# 400: {"description": "Invalid node or link"}, +# 404: {"description": "Session not found"}, +# }, +# deprecated=True, +# ) +# async def update_node( +# session_id: str = Path(description="The id of the session"), +# node_path: str = Path(description="The path to the node in the graph"), +# node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore +# description="The new node" +# ), +# ) -> GraphExecutionState: +# """Updates a node in the graph and removes all linked edges""" +# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) +# if session is None: +# raise HTTPException(status_code=404) - try: - session.update_node(node_path, node) - ApiDependencies.invoker.services.graph_execution_manager.set( - session - ) # TODO: can this be done automatically, or add node through an API? - return session - except NodeAlreadyExecutedError: - raise HTTPException(status_code=400) - except IndexError: - raise HTTPException(status_code=400) +# try: +# session.update_node(node_path, node) +# ApiDependencies.invoker.services.graph_execution_manager.set( +# session +# ) # TODO: can this be done automatically, or add node through an API? +# return session +# except NodeAlreadyExecutedError: +# raise HTTPException(status_code=400) +# except IndexError: +# raise HTTPException(status_code=400) -@session_router.delete( - "/{session_id}/nodes/{node_path}", - operation_id="delete_node", - responses={ - 200: {"model": GraphExecutionState}, - 400: {"description": "Invalid node or link"}, - 404: {"description": "Session not found"}, - }, - deprecated=True, -) -async def delete_node( - session_id: str = Path(description="The id of the session"), - node_path: str = Path(description="The path to the node to delete"), -) -> GraphExecutionState: - """Deletes a node in the graph and removes all linked edges""" - session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) - if session is None: - raise HTTPException(status_code=404) +# @session_router.delete( +# "/{session_id}/nodes/{node_path}", +# operation_id="delete_node", +# responses={ +# 200: {"model": GraphExecutionState}, +# 400: {"description": "Invalid node or link"}, +# 404: {"description": "Session not found"}, +# }, +# deprecated=True, +# ) +# async def delete_node( +# session_id: str = Path(description="The id of the session"), +# node_path: str = Path(description="The path to the node to delete"), +# ) -> GraphExecutionState: +# """Deletes a node in the graph and removes all linked edges""" +# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) +# if session is None: +# raise HTTPException(status_code=404) - try: - session.delete_node(node_path) - ApiDependencies.invoker.services.graph_execution_manager.set( - session - ) # TODO: can this be done automatically, or add node through an API? - return session - except NodeAlreadyExecutedError: - raise HTTPException(status_code=400) - except IndexError: - raise HTTPException(status_code=400) +# try: +# session.delete_node(node_path) +# ApiDependencies.invoker.services.graph_execution_manager.set( +# session +# ) # TODO: can this be done automatically, or add node through an API? +# return session +# except NodeAlreadyExecutedError: +# raise HTTPException(status_code=400) +# except IndexError: +# raise HTTPException(status_code=400) -@session_router.post( - "/{session_id}/edges", - operation_id="add_edge", - responses={ - 200: {"model": GraphExecutionState}, - 400: {"description": "Invalid node or link"}, - 404: {"description": "Session not found"}, - }, - deprecated=True, -) -async def add_edge( - session_id: str = Path(description="The id of the session"), - edge: Edge = Body(description="The edge to add"), -) -> GraphExecutionState: - """Adds an edge to the graph""" - session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) - if session is None: - raise HTTPException(status_code=404) +# @session_router.post( +# "/{session_id}/edges", +# operation_id="add_edge", +# responses={ +# 200: {"model": GraphExecutionState}, +# 400: {"description": "Invalid node or link"}, +# 404: {"description": "Session not found"}, +# }, +# deprecated=True, +# ) +# async def add_edge( +# session_id: str = Path(description="The id of the session"), +# edge: Edge = Body(description="The edge to add"), +# ) -> GraphExecutionState: +# """Adds an edge to the graph""" +# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) +# if session is None: +# raise HTTPException(status_code=404) - try: - session.add_edge(edge) - ApiDependencies.invoker.services.graph_execution_manager.set( - session - ) # TODO: can this be done automatically, or add node through an API? - return session - except NodeAlreadyExecutedError: - raise HTTPException(status_code=400) - except IndexError: - raise HTTPException(status_code=400) +# try: +# session.add_edge(edge) +# ApiDependencies.invoker.services.graph_execution_manager.set( +# session +# ) # TODO: can this be done automatically, or add node through an API? +# return session +# except NodeAlreadyExecutedError: +# raise HTTPException(status_code=400) +# except IndexError: +# raise HTTPException(status_code=400) -# TODO: the edge being in the path here is really ugly, find a better solution -@session_router.delete( - "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}", - operation_id="delete_edge", - responses={ - 200: {"model": GraphExecutionState}, - 400: {"description": "Invalid node or link"}, - 404: {"description": "Session not found"}, - }, - deprecated=True, -) -async def delete_edge( - session_id: str = Path(description="The id of the session"), - from_node_id: str = Path(description="The id of the node the edge is coming from"), - from_field: str = Path(description="The field of the node the edge is coming from"), - to_node_id: str = Path(description="The id of the node the edge is going to"), - to_field: str = Path(description="The field of the node the edge is going to"), -) -> GraphExecutionState: - """Deletes an edge from the graph""" - session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) - if session is None: - raise HTTPException(status_code=404) +# # TODO: the edge being in the path here is really ugly, find a better solution +# @session_router.delete( +# "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}", +# operation_id="delete_edge", +# responses={ +# 200: {"model": GraphExecutionState}, +# 400: {"description": "Invalid node or link"}, +# 404: {"description": "Session not found"}, +# }, +# deprecated=True, +# ) +# async def delete_edge( +# session_id: str = Path(description="The id of the session"), +# from_node_id: str = Path(description="The id of the node the edge is coming from"), +# from_field: str = Path(description="The field of the node the edge is coming from"), +# to_node_id: str = Path(description="The id of the node the edge is going to"), +# to_field: str = Path(description="The field of the node the edge is going to"), +# ) -> GraphExecutionState: +# """Deletes an edge from the graph""" +# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) +# if session is None: +# raise HTTPException(status_code=404) - try: - edge = Edge( - source=EdgeConnection(node_id=from_node_id, field=from_field), - destination=EdgeConnection(node_id=to_node_id, field=to_field), - ) - session.delete_edge(edge) - ApiDependencies.invoker.services.graph_execution_manager.set( - session - ) # TODO: can this be done automatically, or add node through an API? - return session - except NodeAlreadyExecutedError: - raise HTTPException(status_code=400) - except IndexError: - raise HTTPException(status_code=400) +# try: +# edge = Edge( +# source=EdgeConnection(node_id=from_node_id, field=from_field), +# destination=EdgeConnection(node_id=to_node_id, field=to_field), +# ) +# session.delete_edge(edge) +# ApiDependencies.invoker.services.graph_execution_manager.set( +# session +# ) # TODO: can this be done automatically, or add node through an API? +# return session +# except NodeAlreadyExecutedError: +# raise HTTPException(status_code=400) +# except IndexError: +# raise HTTPException(status_code=400) -@session_router.put( - "/{session_id}/invoke", - operation_id="invoke_session", - responses={ - 200: {"model": None}, - 202: {"description": "The invocation is queued"}, - 400: {"description": "The session has no invocations ready to invoke"}, - 404: {"description": "Session not found"}, - }, - deprecated=True, -) -async def invoke_session( - queue_id: str = Query(description="The id of the queue to associate the session with"), - session_id: str = Path(description="The id of the session to invoke"), - all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"), -) -> Response: - """Invokes a session""" - session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) - if session is None: - raise HTTPException(status_code=404) +# @session_router.put( +# "/{session_id}/invoke", +# operation_id="invoke_session", +# responses={ +# 200: {"model": None}, +# 202: {"description": "The invocation is queued"}, +# 400: {"description": "The session has no invocations ready to invoke"}, +# 404: {"description": "Session not found"}, +# }, +# deprecated=True, +# ) +# async def invoke_session( +# queue_id: str = Query(description="The id of the queue to associate the session with"), +# session_id: str = Path(description="The id of the session to invoke"), +# all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"), +# ) -> Response: +# """Invokes a session""" +# session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) +# if session is None: +# raise HTTPException(status_code=404) - if session.is_complete(): - raise HTTPException(status_code=400) +# if session.is_complete(): +# raise HTTPException(status_code=400) - ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all) - return Response(status_code=202) +# ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all) +# return Response(status_code=202) -@session_router.delete( - "/{session_id}/invoke", - operation_id="cancel_session_invoke", - responses={202: {"description": "The invocation is canceled"}}, - deprecated=True, -) -async def cancel_session_invoke( - session_id: str = Path(description="The id of the session to cancel"), -) -> Response: - """Invokes a session""" - ApiDependencies.invoker.cancel(session_id) - return Response(status_code=202) +# @session_router.delete( +# "/{session_id}/invoke", +# operation_id="cancel_session_invoke", +# responses={202: {"description": "The invocation is canceled"}}, +# deprecated=True, +# ) +# async def cancel_session_invoke( +# session_id: str = Path(description="The id of the session to cancel"), +# ) -> Response: +# """Invokes a session""" +# ApiDependencies.invoker.cancel(session_id) +# return Response(status_code=202) diff --git a/invokeai/app/api/routers/utilities.py b/invokeai/app/api/routers/utilities.py index e664cb9070..476d10e2c0 100644 --- a/invokeai/app/api/routers/utilities.py +++ b/invokeai/app/api/routers/utilities.py @@ -1,4 +1,4 @@ -from typing import Optional +from typing import Optional, Union from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator from fastapi import Body @@ -27,6 +27,7 @@ async def parse_dynamicprompts( combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"), ) -> DynamicPromptsResponse: """Creates a batch process""" + generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator] try: error: Optional[str] = None if combinatorial: diff --git a/invokeai/app/api/routers/workflows.py b/invokeai/app/api/routers/workflows.py new file mode 100644 index 0000000000..36de31fb51 --- /dev/null +++ b/invokeai/app/api/routers/workflows.py @@ -0,0 +1,20 @@ +from fastapi import APIRouter, Path + +from invokeai.app.api.dependencies import ApiDependencies +from invokeai.app.invocations.baseinvocation import WorkflowField + +workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"]) + + +@workflows_router.get( + "/i/{workflow_id}", + operation_id="get_workflow", + responses={ + 200: {"model": WorkflowField}, + }, +) +async def get_workflow( + workflow_id: str = Path(description="The workflow to get"), +) -> WorkflowField: + """Gets a workflow""" + return ApiDependencies.invoker.services.workflow_records.get(workflow_id) diff --git a/invokeai/app/api/sockets.py b/invokeai/app/api/sockets.py index f41c38786c..5725901de0 100644 --- a/invokeai/app/api/sockets.py +++ b/invokeai/app/api/sockets.py @@ -30,8 +30,8 @@ class SocketIO: async def _handle_sub_queue(self, sid, data, *args, **kwargs): if "queue_id" in data: - self.__sio.enter_room(sid, data["queue_id"]) + await self.__sio.enter_room(sid, data["queue_id"]) async def _handle_unsub_queue(self, sid, data, *args, **kwargs): if "queue_id" in data: - self.__sio.enter_room(sid, data["queue_id"]) + await self.__sio.enter_room(sid, data["queue_id"]) diff --git a/invokeai/app/api_app.py b/invokeai/app/api_app.py index fdbd64b30d..51aa14c75b 100644 --- a/invokeai/app/api_app.py +++ b/invokeai/app/api_app.py @@ -1,3 +1,7 @@ +from typing import Any + +from fastapi.responses import HTMLResponse + from .services.config import InvokeAIAppConfig # parse_args() must be called before any other imports. if it is not called first, consumers of the config @@ -13,17 +17,20 @@ if True: # hack to make flake8 happy with imports coming after setting up the c from inspect import signature from pathlib import Path - import torch import uvicorn from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware + from fastapi.middleware.gzip import GZipMiddleware from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html from fastapi.openapi.utils import get_openapi + from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles from fastapi_events.handlers.local import local_handler from fastapi_events.middleware import EventHandlerASGIMiddleware - from pydantic.schema import schema + from pydantic.json_schema import models_json_schema + from torch.backends.mps import is_available as is_mps_available + # for PyCharm: # noinspection PyUnresolvedReferences import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import) import invokeai.frontend.web as web_dir @@ -31,19 +38,27 @@ if True: # hack to make flake8 happy with imports coming after setting up the c from ..backend.util.logging import InvokeAILogger from .api.dependencies import ApiDependencies - from .api.routers import app_info, board_images, boards, images, models, session_queue, sessions, utilities + from .api.routers import ( + app_info, + board_images, + boards, + images, + models, + session_queue, + sessions, + utilities, + workflows, + ) from .api.sockets import SocketIO from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField - if torch.backends.mps.is_available(): - # noinspection PyUnresolvedReferences + if is_mps_available(): import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import) app_config = InvokeAIAppConfig.get_config() app_config.parse_args() logger = InvokeAILogger.get_logger(config=app_config) - # fix for windows mimetypes registry entries being borked # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352 mimetypes.add_type("application/javascript", ".js") @@ -51,7 +66,7 @@ mimetypes.add_type("text/css", ".css") # Create the app # TODO: create this all in a method so configuration/etc. can be passed in? -app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None) +app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False) # Add event handler event_handler_id: int = id(app) @@ -63,53 +78,45 @@ app.add_middleware( socket_io = SocketIO(app) +app.add_middleware( + CORSMiddleware, + allow_origins=app_config.allow_origins, + allow_credentials=app_config.allow_credentials, + allow_methods=app_config.allow_methods, + allow_headers=app_config.allow_headers, +) + +app.add_middleware(GZipMiddleware, minimum_size=1000) + # Add startup event to load dependencies @app.on_event("startup") -async def startup_event(): - app.add_middleware( - CORSMiddleware, - allow_origins=app_config.allow_origins, - allow_credentials=app_config.allow_credentials, - allow_methods=app_config.allow_methods, - allow_headers=app_config.allow_headers, - ) - +async def startup_event() -> None: ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger) # Shut down threads @app.on_event("shutdown") -async def shutdown_event(): +async def shutdown_event() -> None: ApiDependencies.shutdown() # Include all routers -# TODO: REMOVE -# app.include_router( -# invocation.invocation_router, -# prefix = '/api') - app.include_router(sessions.session_router, prefix="/api") app.include_router(utilities.utilities_router, prefix="/api") - app.include_router(models.models_router, prefix="/api") - app.include_router(images.images_router, prefix="/api") - app.include_router(boards.boards_router, prefix="/api") - app.include_router(board_images.board_images_router, prefix="/api") - app.include_router(app_info.app_router, prefix="/api") - app.include_router(session_queue.session_queue_router, prefix="/api") +app.include_router(workflows.workflows_router, prefix="/api") # Build a custom OpenAPI to include all outputs # TODO: can outputs be included on metadata of invocation schemas somehow? -def custom_openapi(): +def custom_openapi() -> dict[str, Any]: if app.openapi_schema: return app.openapi_schema openapi_schema = get_openapi( @@ -117,6 +124,7 @@ def custom_openapi(): description="An API for invoking AI image operations", version="1.0.0", routes=app.routes, + separate_input_output_schemas=False, # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/ ) # Add all outputs @@ -127,29 +135,32 @@ def custom_openapi(): output_type = signature(invoker.invoke).return_annotation output_types.add(output_type) - output_schemas = schema(output_types, ref_prefix="#/components/schemas/") - for schema_key, output_schema in output_schemas["definitions"].items(): - output_schema["class"] = "output" - openapi_schema["components"]["schemas"][schema_key] = output_schema - + output_schemas = models_json_schema( + models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}" + ) + for schema_key, output_schema in output_schemas[1]["$defs"].items(): # TODO: note that we assume the schema_key here is the TYPE.__name__ # This could break in some cases, figure out a better way to do it output_type_titles[schema_key] = output_schema["title"] # Add Node Editor UI helper schemas - ui_config_schemas = schema([UIConfigBase, _InputField, _OutputField], ref_prefix="#/components/schemas/") - for schema_key, ui_config_schema in ui_config_schemas["definitions"].items(): + ui_config_schemas = models_json_schema( + [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")], + ref_template="#/components/schemas/{model}", + ) + for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items(): openapi_schema["components"]["schemas"][schema_key] = ui_config_schema # Add a reference to the output type to additionalProperties of the invoker schema for invoker in all_invocations: invoker_name = invoker.__name__ - output_type = signature(invoker.invoke).return_annotation + output_type = signature(obj=invoker.invoke).return_annotation output_type_title = output_type_titles[output_type.__name__] - invoker_schema = openapi_schema["components"]["schemas"][invoker_name] + invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"] outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"} invoker_schema["output"] = outputs_ref invoker_schema["class"] = "invocation" + openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output" from invokeai.backend.model_management.models import get_model_config_enums @@ -160,7 +171,6 @@ def custom_openapi(): # print(f"Config with name {name} already defined") continue - # "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"} openapi_schema["components"]["schemas"][name] = dict( title=name, description="An enumeration.", @@ -172,36 +182,45 @@ def custom_openapi(): return app.openapi_schema -app.openapi = custom_openapi - -# Override API doc favicons -app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static") +app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment @app.get("/docs", include_in_schema=False) -def overridden_swagger(): +def overridden_swagger() -> HTMLResponse: return get_swagger_ui_html( - openapi_url=app.openapi_url, + openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string title=app.title, - swagger_favicon_url="/static/favicon.ico", + swagger_favicon_url="/static/docs/favicon.ico", ) @app.get("/redoc", include_in_schema=False) -def overridden_redoc(): +def overridden_redoc() -> HTMLResponse: return get_redoc_html( - openapi_url=app.openapi_url, + openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string title=app.title, - redoc_favicon_url="/static/favicon.ico", + redoc_favicon_url="/static/docs/favicon.ico", ) -# Must mount *after* the other routes else it borks em -app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui") +web_root_path = Path(list(web_dir.__path__)[0]) -def invoke_api(): - def find_port(port: int): +# Cannot add headers to StaticFiles, so we must serve index.html with a custom route +# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release +@app.get("/", include_in_schema=False, name="ui_root") +def get_index() -> FileResponse: + return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"}) + + +# # Must mount *after* the other routes else it borks em +app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static") # docs favicon is in here +app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets") +app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales") + + +def invoke_api() -> None: + def find_port(port: int) -> int: """Find a port not in use starting at given port""" # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon! # https://github.com/WaylonWalker @@ -236,7 +255,7 @@ def invoke_api(): app=app, host=app_config.host, port=port, - loop=loop, + loop="asyncio", log_level=app_config.log_level, ) server = uvicorn.Server(config) diff --git a/invokeai/app/cli/commands.py b/invokeai/app/cli/commands.py deleted file mode 100644 index b000abcf6a..0000000000 --- a/invokeai/app/cli/commands.py +++ /dev/null @@ -1,313 +0,0 @@ -# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) - -import argparse -from abc import ABC, abstractmethod -from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints - -import matplotlib.pyplot as plt -import networkx as nx -from pydantic import BaseModel, Field - -import invokeai.backend.util.logging as logger - -from ..invocations.baseinvocation import BaseInvocation -from ..invocations.image import ImageField -from ..services.graph import Edge, GraphExecutionState, LibraryGraph -from ..services.invoker import Invoker - - -def add_field_argument(command_parser, name: str, field, default_override=None): - default = ( - default_override - if default_override is not None - else field.default - if field.default_factory is None - else field.default_factory() - ) - if get_origin(field.type_) == Literal: - allowed_values = get_args(field.type_) - allowed_types = set() - for val in allowed_values: - allowed_types.add(type(val)) - allowed_types_list = list(allowed_types) - field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list] # type: ignore - - command_parser.add_argument( - f"--{name}", - dest=name, - type=field_type, - default=default, - choices=allowed_values, - help=field.field_info.description, - ) - else: - command_parser.add_argument( - f"--{name}", - dest=name, - type=field.type_, - default=default, - help=field.field_info.description, - ) - - -def add_parsers( - subparsers, - commands: list[type], - command_field: str = "type", - exclude_fields: list[str] = ["id", "type"], - add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None, -): - """Adds parsers for each command to the subparsers""" - - # Create subparsers for each command - for command in commands: - hints = get_type_hints(command) - cmd_name = get_args(hints[command_field])[0] - command_parser = subparsers.add_parser(cmd_name, help=command.__doc__) - - if add_arguments is not None: - add_arguments(command_parser) - - # Convert all fields to arguments - fields = command.__fields__ # type: ignore - for name, field in fields.items(): - if name in exclude_fields: - continue - - add_field_argument(command_parser, name, field) - - -def add_graph_parsers( - subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None -): - for graph in graphs: - command_parser = subparsers.add_parser(graph.name, help=graph.description) - - if add_arguments is not None: - add_arguments(command_parser) - - # Add arguments for inputs - for exposed_input in graph.exposed_inputs: - node = graph.graph.get_node(exposed_input.node_path) - field = node.__fields__[exposed_input.field] - default_override = getattr(node, exposed_input.field) - add_field_argument(command_parser, exposed_input.alias, field, default_override) - - -class CliContext: - invoker: Invoker - session: GraphExecutionState - parser: argparse.ArgumentParser - defaults: dict[str, Any] - graph_nodes: dict[str, str] - nodes_added: list[str] - - def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser): - self.invoker = invoker - self.session = session - self.parser = parser - self.defaults = dict() - self.graph_nodes = dict() - self.nodes_added = list() - - def get_session(self): - self.session = self.invoker.services.graph_execution_manager.get(self.session.id) - return self.session - - def reset(self): - self.session = self.invoker.create_execution_state() - self.graph_nodes = dict() - self.nodes_added = list() - # Leave defaults unchanged - - def add_node(self, node: BaseInvocation): - self.get_session() - self.session.graph.add_node(node) - self.nodes_added.append(node.id) - self.invoker.services.graph_execution_manager.set(self.session) - - def add_edge(self, edge: Edge): - self.get_session() - self.session.add_edge(edge) - self.invoker.services.graph_execution_manager.set(self.session) - - -class ExitCli(Exception): - """Exception to exit the CLI""" - - pass - - -class BaseCommand(ABC, BaseModel): - """A CLI command""" - - # All commands must include a type name like this: - # type: Literal['your_command_name'] = 'your_command_name' - - @classmethod - def get_all_subclasses(cls): - subclasses = [] - toprocess = [cls] - while len(toprocess) > 0: - next = toprocess.pop(0) - next_subclasses = next.__subclasses__() - subclasses.extend(next_subclasses) - toprocess.extend(next_subclasses) - return subclasses - - @classmethod - def get_commands(cls): - return tuple(BaseCommand.get_all_subclasses()) - - @classmethod - def get_commands_map(cls): - # Get the type strings out of the literals and into a dictionary - return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses())) - - @abstractmethod - def run(self, context: CliContext) -> None: - """Run the command. Raise ExitCli to exit.""" - pass - - -class ExitCommand(BaseCommand): - """Exits the CLI""" - - type: Literal["exit"] = "exit" - - def run(self, context: CliContext) -> None: - raise ExitCli() - - -class HelpCommand(BaseCommand): - """Shows help""" - - type: Literal["help"] = "help" - - def run(self, context: CliContext) -> None: - context.parser.print_help() - - -def get_graph_execution_history( - graph_execution_state: GraphExecutionState, -) -> Iterable[str]: - """Gets the history of fully-executed invocations for a graph execution""" - return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes) - - -def get_invocation_command(invocation) -> str: - fields = invocation.__fields__.items() - type_hints = get_type_hints(type(invocation)) - command = [invocation.type] - for name, field in fields: - if name in ["id", "type"]: - continue - - # TODO: add links - - # Skip image fields when serializing command - type_hint = type_hints.get(name) or None - if type_hint is ImageField or ImageField in get_args(type_hint): - continue - - field_value = getattr(invocation, name) - field_default = field.default - if field_value != field_default: - if type_hint is str or str in get_args(type_hint): - command.append(f'--{name} "{field_value}"') - else: - command.append(f"--{name} {field_value}") - - return " ".join(command) - - -class HistoryCommand(BaseCommand): - """Shows the invocation history""" - - type: Literal["history"] = "history" - - # Inputs - # fmt: off - count: int = Field(default=5, gt=0, description="The number of history entries to show") - # fmt: on - - def run(self, context: CliContext) -> None: - history = list(get_graph_execution_history(context.get_session())) - for i in range(min(self.count, len(history))): - entry_id = history[-1 - i] - entry = context.get_session().graph.get_node(entry_id) - logger.info(f"{entry_id}: {get_invocation_command(entry)}") - - -class SetDefaultCommand(BaseCommand): - """Sets a default value for a field""" - - type: Literal["default"] = "default" - - # Inputs - # fmt: off - field: str = Field(description="The field to set the default for") - value: str = Field(description="The value to set the default to, or None to clear the default") - # fmt: on - - def run(self, context: CliContext) -> None: - if self.value is None: - if self.field in context.defaults: - del context.defaults[self.field] - else: - context.defaults[self.field] = self.value - - -class DrawGraphCommand(BaseCommand): - """Debugs a graph""" - - type: Literal["draw_graph"] = "draw_graph" - - def run(self, context: CliContext) -> None: - session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id) - nxgraph = session.graph.nx_graph_flat() - - # Draw the networkx graph - plt.figure(figsize=(20, 20)) - pos = nx.spectral_layout(nxgraph) - nx.draw_networkx_nodes(nxgraph, pos, node_size=1000) - nx.draw_networkx_edges(nxgraph, pos, width=2) - nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif") - plt.axis("off") - plt.show() - - -class DrawExecutionGraphCommand(BaseCommand): - """Debugs an execution graph""" - - type: Literal["draw_xgraph"] = "draw_xgraph" - - def run(self, context: CliContext) -> None: - session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id) - nxgraph = session.execution_graph.nx_graph_flat() - - # Draw the networkx graph - plt.figure(figsize=(20, 20)) - pos = nx.spectral_layout(nxgraph) - nx.draw_networkx_nodes(nxgraph, pos, node_size=1000) - nx.draw_networkx_edges(nxgraph, pos, width=2) - nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif") - plt.axis("off") - plt.show() - - -class SortedHelpFormatter(argparse.HelpFormatter): - def _iter_indented_subactions(self, action): - try: - get_subactions = action._get_subactions - except AttributeError: - pass - else: - self._indent() - if isinstance(action, argparse._SubParsersAction): - for subaction in sorted(get_subactions(), key=lambda x: x.dest): - yield subaction - else: - for subaction in get_subactions(): - yield subaction - self._dedent() diff --git a/invokeai/app/cli/completer.py b/invokeai/app/cli/completer.py deleted file mode 100644 index 5aece8a058..0000000000 --- a/invokeai/app/cli/completer.py +++ /dev/null @@ -1,171 +0,0 @@ -""" -Readline helper functions for cli_app.py -You may import the global singleton `completer` to get access to the -completer object. -""" -import atexit -import readline -import shlex -from pathlib import Path -from typing import Dict, List, Literal, get_args, get_origin, get_type_hints - -import invokeai.backend.util.logging as logger - -from ...backend import ModelManager -from ..invocations.baseinvocation import BaseInvocation -from ..services.invocation_services import InvocationServices -from .commands import BaseCommand - -# singleton object, class variable -completer = None - - -class Completer(object): - def __init__(self, model_manager: ModelManager): - self.commands = self.get_commands() - self.matches = None - self.linebuffer = None - self.manager = model_manager - return - - def complete(self, text, state): - """ - Complete commands and switches fromm the node CLI command line. - Switches are determined in a context-specific manner. - """ - - buffer = readline.get_line_buffer() - if state == 0: - options = None - try: - current_command, current_switch = self.get_current_command(buffer) - options = self.get_command_options(current_command, current_switch) - except IndexError: - pass - options = options or list(self.parse_commands().keys()) - - if not text: # first time - self.matches = options - else: - self.matches = [s for s in options if s and s.startswith(text)] - - try: - match = self.matches[state] - except IndexError: - match = None - return match - - @classmethod - def get_commands(self) -> List[object]: - """ - Return a list of all the client commands and invocations. - """ - return BaseCommand.get_commands() + BaseInvocation.get_invocations() - - def get_current_command(self, buffer: str) -> tuple[str, str]: - """ - Parse the readline buffer to find the most recent command and its switch. - """ - if len(buffer) == 0: - return None, None - tokens = shlex.split(buffer) - command = None - switch = None - for t in tokens: - if t[0].isalpha(): - if switch is None: - command = t - else: - switch = t - # don't try to autocomplete switches that are already complete - if switch and buffer.endswith(" "): - switch = None - return command or "", switch or "" - - def parse_commands(self) -> Dict[str, List[str]]: - """ - Return a dict in which the keys are the command name - and the values are the parameters the command takes. - """ - result = dict() - for command in self.commands: - hints = get_type_hints(command) - name = get_args(hints["type"])[0] - result.update({name: hints}) - return result - - def get_command_options(self, command: str, switch: str) -> List[str]: - """ - Return all the parameters that can be passed to the command as - command-line switches. Returns None if the command is unrecognized. - """ - parsed_commands = self.parse_commands() - if command not in parsed_commands: - return None - - # handle switches in the format "-foo=bar" - argument = None - if switch and "=" in switch: - switch, argument = switch.split("=") - - parameter = switch.strip("-") - if parameter in parsed_commands[command]: - if argument is None: - return self.get_parameter_options(parameter, parsed_commands[command][parameter]) - else: - return [ - f"--{parameter}={x}" - for x in self.get_parameter_options(parameter, parsed_commands[command][parameter]) - ] - else: - return [f"--{x}" for x in parsed_commands[command].keys()] - - def get_parameter_options(self, parameter: str, typehint) -> List[str]: - """ - Given a parameter type (such as Literal), offers autocompletions. - """ - if get_origin(typehint) == Literal: - return get_args(typehint) - if parameter == "model": - return self.manager.model_names() - - def _pre_input_hook(self): - if self.linebuffer: - readline.insert_text(self.linebuffer) - readline.redisplay() - self.linebuffer = None - - -def set_autocompleter(services: InvocationServices) -> Completer: - global completer - - if completer: - return completer - - completer = Completer(services.model_manager) - - readline.set_completer(completer.complete) - try: - readline.set_auto_history(True) - except AttributeError: - # pyreadline3 does not have a set_auto_history() method - pass - readline.set_pre_input_hook(completer._pre_input_hook) - readline.set_completer_delims(" ") - readline.parse_and_bind("tab: complete") - readline.parse_and_bind("set print-completions-horizontally off") - readline.parse_and_bind("set page-completions on") - readline.parse_and_bind("set skip-completed-text on") - readline.parse_and_bind("set show-all-if-ambiguous on") - - histfile = Path(services.configuration.root_dir / ".invoke_history") - try: - readline.read_history_file(histfile) - readline.set_history_length(1000) - except FileNotFoundError: - pass - except OSError: # file likely corrupted - newname = f"{histfile}.old" - logger.error(f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}") - histfile.replace(Path(newname)) - atexit.register(readline.write_history_file, histfile) diff --git a/invokeai/app/cli_app.py b/invokeai/app/cli_app.py deleted file mode 100644 index 2f8a4d2cbd..0000000000 --- a/invokeai/app/cli_app.py +++ /dev/null @@ -1,484 +0,0 @@ -# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team - -from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache - -from .services.config import InvokeAIAppConfig - -# parse_args() must be called before any other imports. if it is not called first, consumers of the config -# which are imported/used before parse_args() is called will get the default config values instead of the -# values from the command line or config file. - -if True: # hack to make flake8 happy with imports coming after setting up the config - import argparse - import re - import shlex - import sqlite3 - import sys - import time - from typing import Optional, Union, get_type_hints - - import torch - from pydantic import BaseModel, ValidationError - from pydantic.fields import Field - - import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import) - from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage - from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies - from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage - from invokeai.app.services.boards import BoardService, BoardServiceDependencies - from invokeai.app.services.image_record_storage import SqliteImageRecordStorage - from invokeai.app.services.images import ImageService, ImageServiceDependencies - from invokeai.app.services.invocation_stats import InvocationStatsService - from invokeai.app.services.resource_name import SimpleNameService - from invokeai.app.services.urls import LocalUrlService - from invokeai.backend.util.logging import InvokeAILogger - from invokeai.version.invokeai_version import __version__ - - from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers - from .cli.completer import set_autocompleter - from .invocations.baseinvocation import BaseInvocation - from .services.default_graphs import create_system_graphs, default_text_to_image_graph_id - from .services.events import EventServiceBase - from .services.graph import ( - Edge, - EdgeConnection, - GraphExecutionState, - GraphInvocation, - LibraryGraph, - are_connection_types_compatible, - ) - from .services.image_file_storage import DiskImageFileStorage - from .services.invocation_queue import MemoryInvocationQueue - from .services.invocation_services import InvocationServices - from .services.invoker import Invoker - from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage - from .services.model_manager_service import ModelManagerService - from .services.processor import DefaultInvocationProcessor - from .services.sqlite import SqliteItemStorage - - if torch.backends.mps.is_available(): - import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import) - -config = InvokeAIAppConfig.get_config() -config.parse_args() -logger = InvokeAILogger().get_logger(config=config) - - -class CliCommand(BaseModel): - command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore - - -class InvalidArgs(Exception): - pass - - -def add_invocation_args(command_parser): - # Add linking capability - command_parser.add_argument( - "--link", - "-l", - action="append", - nargs=3, - help="A link in the format 'source_node source_field dest_field'. source_node can be relative to history (e.g. -1)", - ) - - command_parser.add_argument( - "--link_node", - "-ln", - action="append", - help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)", - ) - - -def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser: - # Create invocation parser - parser = argparse.ArgumentParser(formatter_class=SortedHelpFormatter) - - def exit(*args, **kwargs): - raise InvalidArgs - - parser.exit = exit - subparsers = parser.add_subparsers(dest="type") - - # Create subparsers for each invocation - invocations = BaseInvocation.get_all_subclasses() - add_parsers(subparsers, invocations, add_arguments=add_invocation_args) - - # Create subparsers for each command - commands = BaseCommand.get_all_subclasses() - add_parsers(subparsers, commands, exclude_fields=["type"]) - - # Create subparsers for exposed CLI graphs - # TODO: add a way to identify these graphs - text_to_image = services.graph_library.get(default_text_to_image_graph_id) - add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args) - - return parser - - -class NodeField: - alias: str - node_path: str - field: str - field_type: type - - def __init__(self, alias: str, node_path: str, field: str, field_type: type): - self.alias = alias - self.node_path = node_path - self.field = field - self.field_type = field_type - - -def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str, NodeField]: - return {k: NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()} - - -def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField: - """Gets the node field for the specified field alias""" - exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias) - node_type = type(graph.graph.get_node(exposed_input.node_path)) - return NodeField( - alias=exposed_input.alias, - node_path=f"{node_id}.{exposed_input.node_path}", - field=exposed_input.field, - field_type=get_type_hints(node_type)[exposed_input.field], - ) - - -def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField: - """Gets the node field for the specified field alias""" - exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias) - node_type = type(graph.graph.get_node(exposed_output.node_path)) - node_output_type = node_type.get_output_type() - return NodeField( - alias=exposed_output.alias, - node_path=f"{node_id}.{exposed_output.node_path}", - field=exposed_output.field, - field_type=get_type_hints(node_output_type)[exposed_output.field], - ) - - -def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]: - """Gets the inputs for the specified invocation from the context""" - node_type = type(invocation) - if node_type is not GraphInvocation: - return fields_from_type_hints(get_type_hints(node_type), invocation.id) - else: - graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id]) - return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs} - - -def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]: - """Gets the outputs for the specified invocation from the context""" - node_type = type(invocation) - if node_type is not GraphInvocation: - return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id) - else: - graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id]) - return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs} - - -def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliContext) -> list[Edge]: - """Generates all possible edges between two invocations""" - afields = get_node_outputs(a, context) - bfields = get_node_inputs(b, context) - - matching_fields = set(afields.keys()).intersection(bfields.keys()) - - # Remove invalid fields - invalid_fields = set(["type", "id"]) - matching_fields = matching_fields.difference(invalid_fields) - - # Validate types - matching_fields = [ - f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type) - ] - - edges = [ - Edge( - source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field), - destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field), - ) - for alias in matching_fields - ] - return edges - - -class SessionError(Exception): - """Raised when a session error has occurred""" - - pass - - -def invoke_all(context: CliContext): - """Runs all invocations in the specified session""" - context.invoker.invoke(context.session, invoke_all=True) - while not context.get_session().is_complete(): - # Wait some time - time.sleep(0.1) - - # Print any errors - if context.session.has_error(): - for n in context.session.errors: - context.invoker.services.logger.error( - f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}" - ) - - raise SessionError() - - -def invoke_cli(): - logger.info(f"InvokeAI version {__version__}") - # get the optional list of invocations to execute on the command line - parser = config.get_parser() - parser.add_argument("commands", nargs="*") - invocation_commands = parser.parse_args().commands - - # get the optional file to read commands from. - # Simplest is to use it for STDIN - if infile := config.from_file: - sys.stdin = open(infile, "r") - - model_manager = ModelManagerService(config, logger) - - events = EventServiceBase() - output_folder = config.output_path - - # TODO: build a file/path manager? - if config.use_memory_db: - db_location = ":memory:" - else: - db_location = config.db_path - db_location.parent.mkdir(parents=True, exist_ok=True) - - db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution - logger.info(f'InvokeAI database location is "{db_location}"') - - graph_execution_manager = SqliteItemStorage[GraphExecutionState](conn=db_conn, table_name="graph_executions") - - urls = LocalUrlService() - image_record_storage = SqliteImageRecordStorage(conn=db_conn) - image_file_storage = DiskImageFileStorage(f"{output_folder}/images") - names = SimpleNameService() - - board_record_storage = SqliteBoardRecordStorage(conn=db_conn) - board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn) - - boards = BoardService( - services=BoardServiceDependencies( - board_image_record_storage=board_image_record_storage, - board_record_storage=board_record_storage, - image_record_storage=image_record_storage, - url=urls, - logger=logger, - ) - ) - - board_images = BoardImagesService( - services=BoardImagesServiceDependencies( - board_image_record_storage=board_image_record_storage, - board_record_storage=board_record_storage, - image_record_storage=image_record_storage, - url=urls, - logger=logger, - ) - ) - - images = ImageService( - services=ImageServiceDependencies( - board_image_record_storage=board_image_record_storage, - image_record_storage=image_record_storage, - image_file_storage=image_file_storage, - url=urls, - logger=logger, - names=names, - graph_execution_manager=graph_execution_manager, - ) - ) - - services = InvocationServices( - model_manager=model_manager, - events=events, - latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")), - images=images, - boards=boards, - board_images=board_images, - queue=MemoryInvocationQueue(), - graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, table_name="graphs"), - graph_execution_manager=graph_execution_manager, - processor=DefaultInvocationProcessor(), - performance_statistics=InvocationStatsService(graph_execution_manager), - logger=logger, - configuration=config, - invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size), - ) - - system_graphs = create_system_graphs(services.graph_library) - system_graph_names = set([g.name for g in system_graphs]) - set_autocompleter(services) - - invoker = Invoker(services) - session: GraphExecutionState = invoker.create_execution_state() - parser = get_command_parser(services) - - re_negid = re.compile("^-[0-9]+$") - - # Uncomment to print out previous sessions at startup - # print(services.session_manager.list()) - - context = CliContext(invoker, session, parser) - set_autocompleter(services) - - command_line_args_exist = len(invocation_commands) > 0 - done = False - - while not done: - try: - if command_line_args_exist: - cmd_input = invocation_commands.pop(0) - done = len(invocation_commands) == 0 - else: - cmd_input = input("invoke> ") - except (KeyboardInterrupt, EOFError): - # Ctrl-c exits - break - - try: - # Refresh the state of the session - # history = list(get_graph_execution_history(context.session)) - history = list(reversed(context.nodes_added)) - - # Split the command for piping - cmds = cmd_input.split("|") - start_id = len(context.nodes_added) - current_id = start_id - new_invocations = list() - for cmd in cmds: - if cmd is None or cmd.strip() == "": - raise InvalidArgs("Empty command") - - # Parse args to create invocation - args = vars(context.parser.parse_args(shlex.split(cmd.strip()))) - - # Override defaults - for field_name, field_default in context.defaults.items(): - if field_name in args: - args[field_name] = field_default - - # Parse invocation - command: CliCommand = None # type:ignore - system_graph: Optional[LibraryGraph] = None - if args["type"] in system_graph_names: - system_graph = next(filter(lambda g: g.name == args["type"], system_graphs)) - invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id)) - for exposed_input in system_graph.exposed_inputs: - if exposed_input.alias in args: - node = invocation.graph.get_node(exposed_input.node_path) - field = exposed_input.field - setattr(node, field, args[exposed_input.alias]) - command = CliCommand(command=invocation) - context.graph_nodes[invocation.id] = system_graph.id - else: - args["id"] = current_id - command = CliCommand(command=args) - - if command is None: - continue - - # Run any CLI commands immediately - if isinstance(command.command, BaseCommand): - # Invoke all current nodes to preserve operation order - invoke_all(context) - - # Run the command - command.command.run(context) - continue - - # TODO: handle linking with library graphs - # Pipe previous command output (if there was a previous command) - edges: list[Edge] = list() - if len(history) > 0 or current_id != start_id: - from_id = history[0] if current_id == start_id else str(current_id - 1) - from_node = ( - next(filter(lambda n: n[0].id == from_id, new_invocations))[0] - if current_id != start_id - else context.session.graph.get_node(from_id) - ) - matching_edges = generate_matching_edges(from_node, command.command, context) - edges.extend(matching_edges) - - # Parse provided links - if "link_node" in args and args["link_node"]: - for link in args["link_node"]: - node_id = link - if re_negid.match(node_id): - node_id = str(current_id + int(node_id)) - - link_node = context.session.graph.get_node(node_id) - matching_edges = generate_matching_edges(link_node, command.command, context) - matching_destinations = [e.destination for e in matching_edges] - edges = [e for e in edges if e.destination not in matching_destinations] - edges.extend(matching_edges) - - if "link" in args and args["link"]: - for link in args["link"]: - edges = [ - e - for e in edges - if e.destination.node_id != command.command.id or e.destination.field != link[2] - ] - - node_id = link[0] - if re_negid.match(node_id): - node_id = str(current_id + int(node_id)) - - # TODO: handle missing input/output - node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]] - node_input = get_node_inputs(command.command, context)[link[2]] - - edges.append( - Edge( - source=EdgeConnection(node_id=node_output.node_path, field=node_output.field), - destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field), - ) - ) - - new_invocations.append((command.command, edges)) - - current_id = current_id + 1 - - # Add the node to the session - context.add_node(command.command) - for edge in edges: - print(edge) - context.add_edge(edge) - - # Execute all remaining nodes - invoke_all(context) - - except InvalidArgs: - invoker.services.logger.warning('Invalid command, use "help" to list commands') - continue - - except ValidationError: - invoker.services.logger.warning('Invalid command arguments, run " --help" for summary') - - except SessionError: - # Start a new session - invoker.services.logger.warning("Session error: creating a new session") - context.reset() - - except ExitCli: - break - - except SystemExit: - continue - - invoker.stop() - - -if __name__ == "__main__": - if config.version: - print(f"InvokeAI version {__version__}") - else: - invoke_cli() diff --git a/invokeai/app/invocations/__init__.py b/invokeai/app/invocations/__init__.py index 6407a1cdee..32cf73d215 100644 --- a/invokeai/app/invocations/__init__.py +++ b/invokeai/app/invocations/__init__.py @@ -1,8 +1,28 @@ -import os +import shutil +import sys +from importlib.util import module_from_spec, spec_from_file_location +from pathlib import Path -__all__ = [] +from invokeai.app.services.config.config_default import InvokeAIAppConfig -dirname = os.path.dirname(os.path.abspath(__file__)) -for f in os.listdir(dirname): - if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py": - __all__.append(f[:-3]) +custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute()) +custom_nodes_path.mkdir(parents=True, exist_ok=True) + +custom_nodes_init_path = str(custom_nodes_path / "__init__.py") +custom_nodes_readme_path = str(custom_nodes_path / "README.md") + +# copy our custom nodes __init__.py to the custom nodes directory +shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path) +shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path) + +# Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically +spec = spec_from_file_location("custom_nodes", custom_nodes_init_path) +if spec is None or spec.loader is None: + raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}") +module = module_from_spec(spec) +sys.modules[spec.name] = module +spec.loader.exec_module(module) + +# add core nodes to __all__ +python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py")) +__all__ = list(f.stem for f in python_files) # type: ignore diff --git a/invokeai/app/invocations/baseinvocation.py b/invokeai/app/invocations/baseinvocation.py index d82b94d0e9..c5347bee38 100644 --- a/invokeai/app/invocations/baseinvocation.py +++ b/invokeai/app/invocations/baseinvocation.py @@ -2,33 +2,21 @@ from __future__ import annotations -import json +import inspect import re from abc import ABC, abstractmethod from enum import Enum from inspect import signature -from typing import ( - TYPE_CHECKING, - AbstractSet, - Any, - Callable, - ClassVar, - Literal, - Mapping, - Optional, - Type, - TypeVar, - Union, - get_args, - get_type_hints, -) +from types import UnionType +from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union import semver -from pydantic import BaseModel, Field, validator -from pydantic.fields import ModelField, Undefined -from pydantic.typing import NoArgAnyCallable +from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model +from pydantic.fields import FieldInfo, _Unset +from pydantic_core import PydanticUndefined from invokeai.app.services.config.config_default import InvokeAIAppConfig +from invokeai.app.util.misc import uuid_string if TYPE_CHECKING: from ..services.invocation_services import InvocationServices @@ -38,6 +26,10 @@ class InvalidVersionError(ValueError): pass +class InvalidFieldError(TypeError): + pass + + class FieldDescriptions: denoising_start = "When to start denoising, expressed a percentage of total steps" denoising_end = "When to stop denoising, expressed a percentage of total steps" @@ -72,7 +64,12 @@ class FieldDescriptions: denoised_latents = "Denoised latents tensor" latents = "Latents tensor" strength = "Strength of denoising (proportional to steps)" - core_metadata = "Optional core metadata to be written to image" + metadata = "Optional metadata to be saved with the image" + metadata_collection = "Collection of Metadata" + metadata_item_polymorphic = "A single metadata item or collection of metadata items" + metadata_item_label = "Label for this metadata item" + metadata_item_value = "The value for this metadata item (may be any type)" + workflow = "Optional workflow to be saved with the image" interp_mode = "Interpolation mode" torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)" fp32 = "Whether or not to use full float32 precision" @@ -95,6 +92,10 @@ class FieldDescriptions: inclusive_low = "The inclusive low value" exclusive_high = "The exclusive high value" decimal_places = "The number of decimal places to round to" + freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.' + freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.' + freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features." + freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features." class Input(str, Enum): @@ -179,8 +180,12 @@ class UIType(str, Enum): Scheduler = "Scheduler" WorkflowField = "WorkflowField" IsIntermediate = "IsIntermediate" - MetadataField = "MetadataField" BoardField = "BoardField" + Any = "Any" + MetadataItem = "MetadataItem" + MetadataItemCollection = "MetadataItemCollection" + MetadataItemPolymorphic = "MetadataItemPolymorphic" + MetadataDict = "MetadataDict" # endregion @@ -211,6 +216,11 @@ class _InputField(BaseModel): ui_choice_labels: Optional[dict[str, str]] item_default: Optional[Any] + model_config = ConfigDict( + validate_assignment=True, + json_schema_serialization_defaults_required=True, + ) + class _OutputField(BaseModel): """ @@ -224,34 +234,36 @@ class _OutputField(BaseModel): ui_type: Optional[UIType] ui_order: Optional[int] + model_config = ConfigDict( + validate_assignment=True, + json_schema_serialization_defaults_required=True, + ) + + +def get_type(klass: BaseModel) -> str: + """Helper function to get an invocation or invocation output's type. This is the default value of the `type` field.""" + return klass.model_fields["type"].default + def InputField( - *args: Any, - default: Any = Undefined, - default_factory: Optional[NoArgAnyCallable] = None, - alias: Optional[str] = None, - title: Optional[str] = None, - description: Optional[str] = None, - exclude: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, - include: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, - const: Optional[bool] = None, - gt: Optional[float] = None, - ge: Optional[float] = None, - lt: Optional[float] = None, - le: Optional[float] = None, - multiple_of: Optional[float] = None, - allow_inf_nan: Optional[bool] = None, - max_digits: Optional[int] = None, - decimal_places: Optional[int] = None, - min_items: Optional[int] = None, - max_items: Optional[int] = None, - unique_items: Optional[bool] = None, - min_length: Optional[int] = None, - max_length: Optional[int] = None, - allow_mutation: bool = True, - regex: Optional[str] = None, - discriminator: Optional[str] = None, - repr: bool = True, + # copied from pydantic's Field + default: Any = _Unset, + default_factory: Callable[[], Any] | None = _Unset, + title: str | None = _Unset, + description: str | None = _Unset, + pattern: str | None = _Unset, + strict: bool | None = _Unset, + gt: float | None = _Unset, + ge: float | None = _Unset, + lt: float | None = _Unset, + le: float | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + # custom input: Input = Input.Any, ui_type: Optional[UIType] = None, ui_component: Optional[UIComponent] = None, @@ -259,7 +271,6 @@ def InputField( ui_order: Optional[int] = None, ui_choice_labels: Optional[dict[str, str]] = None, item_default: Optional[Any] = None, - **kwargs: Any, ) -> Any: """ Creates an input field for an invocation. @@ -289,18 +300,27 @@ def InputField( : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \ : param bool item_default: [None] Specifies the default item value, if this is a collection input. \ - Ignored for non-collection fields.. + Ignored for non-collection fields. """ - return Field( - *args, + + json_schema_extra_: dict[str, Any] = dict( + input=input, + ui_type=ui_type, + ui_component=ui_component, + ui_hidden=ui_hidden, + ui_order=ui_order, + item_default=item_default, + ui_choice_labels=ui_choice_labels, + _field_kind="input", + ) + + field_args = dict( default=default, default_factory=default_factory, - alias=alias, title=title, description=description, - exclude=exclude, - include=include, - const=const, + pattern=pattern, + strict=strict, gt=gt, ge=ge, lt=lt, @@ -309,57 +329,92 @@ def InputField( allow_inf_nan=allow_inf_nan, max_digits=max_digits, decimal_places=decimal_places, - min_items=min_items, - max_items=max_items, - unique_items=unique_items, min_length=min_length, max_length=max_length, - allow_mutation=allow_mutation, - regex=regex, - discriminator=discriminator, - repr=repr, - input=input, - ui_type=ui_type, - ui_component=ui_component, - ui_hidden=ui_hidden, - ui_order=ui_order, - item_default=item_default, - ui_choice_labels=ui_choice_labels, - **kwargs, + ) + + """ + Invocation definitions have their fields typed correctly for their `invoke()` functions. + This typing is often more specific than the actual invocation definition requires, because + fields may have values provided only by connections. + + For example, consider an ResizeImageInvocation with an `image: ImageField` field. + + `image` is required during the call to `invoke()`, but when the python class is instantiated, + the field may not be present. This is fine, because that image field will be provided by a + an ancestor node that outputs the image. + + So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then + we need to handle a lot of extra logic in the `invoke()` function to check if the field has a + value or not. This is very tedious. + + Ideally, the invocation definition would be able to specify that the field is required during + invocation, but optional during instantiation. So the field would be typed as `image: ImageField`, + but when calling the `invoke()` function, we raise an error if the field is not present. + + To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do + extra validation when calling `invoke()`. + + There is some additional logic here to cleaning create the pydantic field via the wrapper. + """ + + # Filter out field args not provided + provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined} + + if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined): + raise ValueError("Cannot specify both default and default_factory") + + # because we are manually making fields optional, we need to store the original required bool for reference later + if default is PydanticUndefined and default_factory is PydanticUndefined: + json_schema_extra_.update(dict(orig_required=True)) + else: + json_schema_extra_.update(dict(orig_required=False)) + + # make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one + if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined: + default_ = None if default is PydanticUndefined else default + provided_args.update(dict(default=default_)) + if default is not PydanticUndefined: + # before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value + json_schema_extra_.update(dict(default=default)) + json_schema_extra_.update(dict(orig_default=default)) + elif default is not PydanticUndefined and default_factory is PydanticUndefined: + default_ = default + provided_args.update(dict(default=default_)) + json_schema_extra_.update(dict(orig_default=default_)) + elif default_factory is not PydanticUndefined: + provided_args.update(dict(default_factory=default_factory)) + # TODO: cannot serialize default_factory... + # json_schema_extra_.update(dict(orig_default_factory=default_factory)) + + return Field( + **provided_args, + json_schema_extra=json_schema_extra_, ) def OutputField( - *args: Any, - default: Any = Undefined, - default_factory: Optional[NoArgAnyCallable] = None, - alias: Optional[str] = None, - title: Optional[str] = None, - description: Optional[str] = None, - exclude: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, - include: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, - const: Optional[bool] = None, - gt: Optional[float] = None, - ge: Optional[float] = None, - lt: Optional[float] = None, - le: Optional[float] = None, - multiple_of: Optional[float] = None, - allow_inf_nan: Optional[bool] = None, - max_digits: Optional[int] = None, - decimal_places: Optional[int] = None, - min_items: Optional[int] = None, - max_items: Optional[int] = None, - unique_items: Optional[bool] = None, - min_length: Optional[int] = None, - max_length: Optional[int] = None, - allow_mutation: bool = True, - regex: Optional[str] = None, - discriminator: Optional[str] = None, - repr: bool = True, + # copied from pydantic's Field + default: Any = _Unset, + default_factory: Callable[[], Any] | None = _Unset, + title: str | None = _Unset, + description: str | None = _Unset, + pattern: str | None = _Unset, + strict: bool | None = _Unset, + gt: float | None = _Unset, + ge: float | None = _Unset, + lt: float | None = _Unset, + le: float | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + # custom ui_type: Optional[UIType] = None, ui_hidden: bool = False, ui_order: Optional[int] = None, - **kwargs: Any, ) -> Any: """ Creates an output field for an invocation output. @@ -379,15 +434,12 @@ def OutputField( : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \ """ return Field( - *args, default=default, default_factory=default_factory, - alias=alias, title=title, description=description, - exclude=exclude, - include=include, - const=const, + pattern=pattern, + strict=strict, gt=gt, ge=ge, lt=lt, @@ -396,19 +448,14 @@ def OutputField( allow_inf_nan=allow_inf_nan, max_digits=max_digits, decimal_places=decimal_places, - min_items=min_items, - max_items=max_items, - unique_items=unique_items, min_length=min_length, max_length=max_length, - allow_mutation=allow_mutation, - regex=regex, - discriminator=discriminator, - repr=repr, - ui_type=ui_type, - ui_hidden=ui_hidden, - ui_order=ui_order, - **kwargs, + json_schema_extra=dict( + ui_type=ui_type, + ui_hidden=ui_hidden, + ui_order=ui_order, + _field_kind="output", + ), ) @@ -422,7 +469,13 @@ class UIConfigBase(BaseModel): title: Optional[str] = Field(default=None, description="The node's display name") category: Optional[str] = Field(default=None, description="The node's category") version: Optional[str] = Field( - default=None, description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".' + default=None, + description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".', + ) + + model_config = ConfigDict( + validate_assignment=True, + json_schema_serialization_defaults_required=True, ) @@ -457,23 +510,39 @@ class BaseInvocationOutput(BaseModel): All invocation outputs must use the `@invocation_output` decorator to provide their unique type. """ - @classmethod - def get_all_subclasses_tuple(cls): - subclasses = [] - toprocess = [cls] - while len(toprocess) > 0: - next = toprocess.pop(0) - next_subclasses = next.__subclasses__() - subclasses.extend(next_subclasses) - toprocess.extend(next_subclasses) - return tuple(subclasses) + _output_classes: ClassVar[set[BaseInvocationOutput]] = set() - class Config: - @staticmethod - def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None: - if "required" not in schema or not isinstance(schema["required"], list): - schema["required"] = list() - schema["required"].extend(["type"]) + @classmethod + def register_output(cls, output: BaseInvocationOutput) -> None: + cls._output_classes.add(output) + + @classmethod + def get_outputs(cls) -> Iterable[BaseInvocationOutput]: + return cls._output_classes + + @classmethod + def get_outputs_union(cls) -> UnionType: + outputs_union = Union[tuple(cls._output_classes)] # type: ignore [valid-type] + return outputs_union # type: ignore [return-value] + + @classmethod + def get_output_types(cls) -> Iterable[str]: + return map(lambda i: get_type(i), BaseInvocationOutput.get_outputs()) + + @staticmethod + def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None: + # Because we use a pydantic Literal field with default value for the invocation type, + # it will be typed as optional in the OpenAPI schema. Make it required manually. + if "required" not in schema or not isinstance(schema["required"], list): + schema["required"] = list() + schema["required"].extend(["type"]) + + model_config = ConfigDict( + protected_namespaces=(), + validate_assignment=True, + json_schema_serialization_defaults_required=True, + json_schema_extra=json_schema_extra, + ) class RequiredConnectionException(Exception): @@ -492,110 +561,94 @@ class MissingInputException(Exception): class BaseInvocation(ABC, BaseModel): """ - A node to process inputs and produce outputs. - May use dependency injection in __init__ to receive providers. - All invocations must use the `@invocation` decorator to provide their unique type. """ + _invocation_classes: ClassVar[set[BaseInvocation]] = set() + @classmethod - def get_all_subclasses(cls): + def register_invocation(cls, invocation: BaseInvocation) -> None: + cls._invocation_classes.add(invocation) + + @classmethod + def get_invocations_union(cls) -> UnionType: + invocations_union = Union[tuple(cls._invocation_classes)] # type: ignore [valid-type] + return invocations_union # type: ignore [return-value] + + @classmethod + def get_invocations(cls) -> Iterable[BaseInvocation]: app_config = InvokeAIAppConfig.get_config() - subclasses = [] - toprocess = [cls] - while len(toprocess) > 0: - next = toprocess.pop(0) - next_subclasses = next.__subclasses__() - subclasses.extend(next_subclasses) - toprocess.extend(next_subclasses) - allowed_invocations = [] - for sc in subclasses: + allowed_invocations: set[BaseInvocation] = set() + for sc in cls._invocation_classes: + invocation_type = get_type(sc) is_in_allowlist = ( - sc.__fields__.get("type").default in app_config.allow_nodes - if isinstance(app_config.allow_nodes, list) - else True + invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True ) - is_in_denylist = ( - sc.__fields__.get("type").default in app_config.deny_nodes - if isinstance(app_config.deny_nodes, list) - else False + invocation_type in app_config.deny_nodes if isinstance(app_config.deny_nodes, list) else False ) - if is_in_allowlist and not is_in_denylist: - allowed_invocations.append(sc) + allowed_invocations.add(sc) return allowed_invocations @classmethod - def get_invocations(cls): - return tuple(BaseInvocation.get_all_subclasses()) - - @classmethod - def get_invocations_map(cls): + def get_invocations_map(cls) -> dict[str, BaseInvocation]: # Get the type strings out of the literals and into a dictionary return dict( map( - lambda t: (get_args(get_type_hints(t)["type"])[0], t), - BaseInvocation.get_all_subclasses(), + lambda i: (get_type(i), i), + BaseInvocation.get_invocations(), ) ) @classmethod - def get_output_type(cls): + def get_invocation_types(cls) -> Iterable[str]: + return map(lambda i: get_type(i), BaseInvocation.get_invocations()) + + @classmethod + def get_output_type(cls) -> BaseInvocationOutput: return signature(cls.invoke).return_annotation - class Config: - validate_assignment = True - validate_all = True - - @staticmethod - def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None: - uiconfig = getattr(model_class, "UIConfig", None) - if uiconfig and hasattr(uiconfig, "title"): - schema["title"] = uiconfig.title - if uiconfig and hasattr(uiconfig, "tags"): - schema["tags"] = uiconfig.tags - if uiconfig and hasattr(uiconfig, "category"): - schema["category"] = uiconfig.category - if uiconfig and hasattr(uiconfig, "version"): - schema["version"] = uiconfig.version - if "required" not in schema or not isinstance(schema["required"], list): - schema["required"] = list() - schema["required"].extend(["type", "id"]) + @staticmethod + def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None: + # Add the various UI-facing attributes to the schema. These are used to build the invocation templates. + uiconfig = getattr(model_class, "UIConfig", None) + if uiconfig and hasattr(uiconfig, "title"): + schema["title"] = uiconfig.title + if uiconfig and hasattr(uiconfig, "tags"): + schema["tags"] = uiconfig.tags + if uiconfig and hasattr(uiconfig, "category"): + schema["category"] = uiconfig.category + if uiconfig and hasattr(uiconfig, "version"): + schema["version"] = uiconfig.version + if "required" not in schema or not isinstance(schema["required"], list): + schema["required"] = list() + schema["required"].extend(["type", "id"]) @abstractmethod def invoke(self, context: InvocationContext) -> BaseInvocationOutput: """Invoke with provided context and return outputs.""" pass - def __init__(self, **data): - # nodes may have required fields, that can accept input from connections - # on instantiation of the model, we need to exclude these from validation - restore = dict() - try: - field_names = list(self.__fields__.keys()) - for field_name in field_names: - # if the field is required and may get its value from a connection, exclude it from validation - field = self.__fields__[field_name] - _input = field.field_info.extra.get("input", None) - if _input in [Input.Connection, Input.Any] and field.required: - if field_name not in data: - restore[field_name] = self.__fields__.pop(field_name) - # instantiate the node, which will validate the data - super().__init__(**data) - finally: - # restore the removed fields - for field_name, field in restore.items(): - self.__fields__[field_name] = field - def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput: - for field_name, field in self.__fields__.items(): - _input = field.field_info.extra.get("input", None) - if field.required and not hasattr(self, field_name): - if _input == Input.Connection: - raise RequiredConnectionException(self.__fields__["type"].default, field_name) - elif _input == Input.Any: - raise MissingInputException(self.__fields__["type"].default, field_name) + for field_name, field in self.model_fields.items(): + if not field.json_schema_extra or callable(field.json_schema_extra): + # something has gone terribly awry, we should always have this and it should be a dict + continue + + # Here we handle the case where the field is optional in the pydantic class, but required + # in the `invoke()` method. + + orig_default = field.json_schema_extra.get("orig_default", PydanticUndefined) + orig_required = field.json_schema_extra.get("orig_required", True) + input_ = field.json_schema_extra.get("input", None) + if orig_default is not PydanticUndefined and not hasattr(self, field_name): + setattr(self, field_name, orig_default) + if orig_required and orig_default is PydanticUndefined and getattr(self, field_name) is None: + if input_ == Input.Connection: + raise RequiredConnectionException(self.model_fields["type"].default, field_name) + elif input_ == Input.Any: + raise MissingInputException(self.model_fields["type"].default, field_name) # skip node cache codepath if it's disabled if context.services.configuration.node_cache_size == 0: @@ -618,35 +671,96 @@ class BaseInvocation(ABC, BaseModel): return self.invoke(context) def get_type(self) -> str: - return self.__fields__["type"].default + return self.model_fields["type"].default id: str = Field( - description="The id of this instance of an invocation. Must be unique among all instances of invocations." + default_factory=uuid_string, + description="The id of this instance of an invocation. Must be unique among all instances of invocations.", + json_schema_extra=dict(_field_kind="internal"), ) - is_intermediate: bool = InputField( - default=False, description="Whether or not this is an intermediate invocation.", ui_type=UIType.IsIntermediate + is_intermediate: bool = Field( + default=False, + description="Whether or not this is an intermediate invocation.", + json_schema_extra=dict(ui_type=UIType.IsIntermediate, _field_kind="internal"), ) - workflow: Optional[str] = InputField( - default=None, - description="The workflow to save with the image", - ui_type=UIType.WorkflowField, + use_cache: bool = Field( + default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal") ) - use_cache: bool = InputField(default=True, description="Whether or not to use the cache") - - @validator("workflow", pre=True) - def validate_workflow_is_json(cls, v): - if v is None: - return None - try: - json.loads(v) - except json.decoder.JSONDecodeError: - raise ValueError("Workflow must be valid JSON") - return v UIConfig: ClassVar[Type[UIConfigBase]] + model_config = ConfigDict( + protected_namespaces=(), + validate_assignment=True, + json_schema_extra=json_schema_extra, + json_schema_serialization_defaults_required=True, + coerce_numbers_to_str=True, + ) -GenericBaseInvocation = TypeVar("GenericBaseInvocation", bound=BaseInvocation) + +TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation) + + +RESERVED_INPUT_FIELD_NAMES = { + "id", + "is_intermediate", + "use_cache", + "type", + "workflow", + "metadata", +} + +RESERVED_OUTPUT_FIELD_NAMES = {"type"} + + +class _Model(BaseModel): + pass + + +# Get all pydantic model attrs, methods, etc +RESERVED_PYDANTIC_FIELD_NAMES = set(map(lambda m: m[0], inspect.getmembers(_Model()))) + + +def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None: + """ + Validates the fields of an invocation or invocation output: + - must not override any pydantic reserved fields + - must be created via `InputField`, `OutputField`, or be an internal field defined in this file + """ + for name, field in model_fields.items(): + if name in RESERVED_PYDANTIC_FIELD_NAMES: + raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)') + + field_kind = ( + # _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file + field.json_schema_extra.get("_field_kind", None) + if field.json_schema_extra + else None + ) + + # must have a field_kind + if field_kind is None or field_kind not in {"input", "output", "internal"}: + raise InvalidFieldError( + f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)' + ) + + if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES: + raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)') + + if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES: + raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)') + + # internal fields *must* be in the reserved list + if ( + field_kind == "internal" + and name not in RESERVED_INPUT_FIELD_NAMES + and name not in RESERVED_OUTPUT_FIELD_NAMES + ): + raise InvalidFieldError( + f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)' + ) + + return None def invocation( @@ -656,9 +770,9 @@ def invocation( category: Optional[str] = None, version: Optional[str] = None, use_cache: Optional[bool] = True, -) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]: +) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]: """ - Adds metadata to an invocation. + Registers an invocation. :param str invocation_type: The type of the invocation. Must be unique among all invocations. :param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None. @@ -668,12 +782,17 @@ def invocation( :param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor. """ - def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]: + def wrapper(cls: Type[TBaseInvocation]) -> Type[TBaseInvocation]: # Validate invocation types on creation of invocation classes # TODO: ensure unique? if re.compile(r"^\S+$").match(invocation_type) is None: raise ValueError(f'"invocation_type" must consist of non-whitespace characters, got "{invocation_type}"') + if invocation_type in BaseInvocation.get_invocation_types(): + raise ValueError(f'Invocation type "{invocation_type}" already exists') + + validate_fields(cls.model_fields, invocation_type) + # Add OpenAPI schema extras uiconf_name = cls.__qualname__ + ".UIConfig" if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name: @@ -691,59 +810,114 @@ def invocation( raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e cls.UIConfig.version = version if use_cache is not None: - cls.__fields__["use_cache"].default = use_cache + cls.model_fields["use_cache"].default = use_cache + + # Add the invocation type to the model. + + # You'd be tempted to just add the type field and rebuild the model, like this: + # cls.model_fields.update(type=FieldInfo.from_annotated_attribute(Literal[invocation_type], invocation_type)) + # cls.model_rebuild() or cls.model_rebuild(force=True) + + # Unfortunately, because the `GraphInvocation` uses a forward ref in its `graph` field's annotation, this does + # not work. Instead, we have to create a new class with the type field and patch the original class with it. - # Add the invocation type to the pydantic model of the invocation invocation_type_annotation = Literal[invocation_type] # type: ignore - invocation_type_field = ModelField.infer( - name="type", - value=invocation_type, - annotation=invocation_type_annotation, - class_validators=None, - config=cls.__config__, + invocation_type_field = Field( + title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal") ) - cls.__fields__.update({"type": invocation_type_field}) - # to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html - if annotations := cls.__dict__.get("__annotations__", None): - annotations.update({"type": invocation_type_annotation}) + + docstring = cls.__doc__ + cls = create_model( + cls.__qualname__, + __base__=cls, + __module__=cls.__module__, + type=(invocation_type_annotation, invocation_type_field), + ) + cls.__doc__ = docstring + + # TODO: how to type this correctly? it's typed as ModelMetaclass, a private class in pydantic + BaseInvocation.register_invocation(cls) # type: ignore + return cls return wrapper -GenericBaseInvocationOutput = TypeVar("GenericBaseInvocationOutput", bound=BaseInvocationOutput) +TBaseInvocationOutput = TypeVar("TBaseInvocationOutput", bound=BaseInvocationOutput) def invocation_output( output_type: str, -) -> Callable[[Type[GenericBaseInvocationOutput]], Type[GenericBaseInvocationOutput]]: +) -> Callable[[Type[TBaseInvocationOutput]], Type[TBaseInvocationOutput]]: """ Adds metadata to an invocation output. :param str output_type: The type of the invocation output. Must be unique among all invocation outputs. """ - def wrapper(cls: Type[GenericBaseInvocationOutput]) -> Type[GenericBaseInvocationOutput]: + def wrapper(cls: Type[TBaseInvocationOutput]) -> Type[TBaseInvocationOutput]: # Validate output types on creation of invocation output classes # TODO: ensure unique? if re.compile(r"^\S+$").match(output_type) is None: raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"') - # Add the output type to the pydantic model of the invocation output - output_type_annotation = Literal[output_type] # type: ignore - output_type_field = ModelField.infer( - name="type", - value=output_type, - annotation=output_type_annotation, - class_validators=None, - config=cls.__config__, - ) - cls.__fields__.update({"type": output_type_field}) + if output_type in BaseInvocationOutput.get_output_types(): + raise ValueError(f'Invocation type "{output_type}" already exists') - # to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html - if annotations := cls.__dict__.get("__annotations__", None): - annotations.update({"type": output_type_annotation}) + validate_fields(cls.model_fields, output_type) + + # Add the output type to the model. + + output_type_annotation = Literal[output_type] # type: ignore + output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal")) + + docstring = cls.__doc__ + cls = create_model( + cls.__qualname__, + __base__=cls, + __module__=cls.__module__, + type=(output_type_annotation, output_type_field), + ) + cls.__doc__ = docstring + + BaseInvocationOutput.register_output(cls) # type: ignore # TODO: how to type this correctly? return cls return wrapper + + +class WorkflowField(RootModel): + """ + Pydantic model for workflows with custom root of type dict[str, Any]. + Workflows are stored without a strict schema. + """ + + root: dict[str, Any] = Field(description="The workflow") + + +WorkflowFieldValidator = TypeAdapter(WorkflowField) + + +class WithWorkflow(BaseModel): + workflow: Optional[WorkflowField] = Field( + default=None, description=FieldDescriptions.workflow, json_schema_extra=dict(_field_kind="internal") + ) + + +class MetadataField(RootModel): + """ + Pydantic model for metadata with custom root of type dict[str, Any]. + Metadata is stored without a strict schema. + """ + + root: dict[str, Any] = Field(description="The metadata") + + +MetadataFieldValidator = TypeAdapter(MetadataField) + + +class WithMetadata(BaseModel): + metadata: Optional[MetadataField] = Field( + default=None, description=FieldDescriptions.metadata, json_schema_extra=dict(_field_kind="internal") + ) diff --git a/invokeai/app/invocations/collections.py b/invokeai/app/invocations/collections.py index 83863422f8..f26eebe1ff 100644 --- a/invokeai/app/invocations/collections.py +++ b/invokeai/app/invocations/collections.py @@ -2,7 +2,7 @@ import numpy as np -from pydantic import validator +from pydantic import ValidationInfo, field_validator from invokeai.app.invocations.primitives import IntegerCollectionOutput from invokeai.app.util.misc import SEED_MAX, get_random_seed @@ -20,9 +20,9 @@ class RangeInvocation(BaseInvocation): stop: int = InputField(default=10, description="The stop of the range") step: int = InputField(default=1, description="The step of the range") - @validator("stop") - def stop_gt_start(cls, v, values): - if "start" in values and v <= values["start"]: + @field_validator("stop") + def stop_gt_start(cls, v: int, info: ValidationInfo): + if "start" in info.data and v <= info.data["start"]: raise ValueError("stop must be greater than start") return v diff --git a/invokeai/app/invocations/compel.py b/invokeai/app/invocations/compel.py index b2634c2c56..3a7d5e9e4d 100644 --- a/invokeai/app/invocations/compel.py +++ b/invokeai/app/invocations/compel.py @@ -1,6 +1,6 @@ import re from dataclasses import dataclass -from typing import List, Union +from typing import List, Optional, Union import torch from compel import Compel, ReturnedEmbeddingsType @@ -43,7 +43,13 @@ class ConditioningFieldData: # PerpNeg = "perp_neg" -@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning", version="1.0.0") +@invocation( + "compel", + title="Prompt", + tags=["prompt", "compel"], + category="conditioning", + version="1.0.0", +) class CompelInvocation(BaseInvocation): """Parse prompt using compel package to conditioning.""" @@ -61,17 +67,19 @@ class CompelInvocation(BaseInvocation): @torch.no_grad() def invoke(self, context: InvocationContext) -> ConditioningOutput: tokenizer_info = context.services.model_manager.get_model( - **self.clip.tokenizer.dict(), + **self.clip.tokenizer.model_dump(), context=context, ) text_encoder_info = context.services.model_manager.get_model( - **self.clip.text_encoder.dict(), + **self.clip.text_encoder.model_dump(), context=context, ) def _lora_loader(): for lora in self.clip.loras: - lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context) + lora_info = context.services.model_manager.get_model( + **lora.model_dump(exclude={"weight"}), context=context + ) yield (lora_info.context.model, lora.weight) del lora_info return @@ -100,13 +108,14 @@ class CompelInvocation(BaseInvocation): 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, self.clip.skipped_layers), text_encoder_info as text_encoder, + # Apply the LoRA after text_encoder has been moved to its target device for faster patching. + ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()), ): compel = Compel( tokenizer=tokenizer, @@ -160,11 +169,11 @@ class SDXLPromptInvocationBase: zero_on_empty: bool, ): tokenizer_info = context.services.model_manager.get_model( - **clip_field.tokenizer.dict(), + **clip_field.tokenizer.model_dump(), context=context, ) text_encoder_info = context.services.model_manager.get_model( - **clip_field.text_encoder.dict(), + **clip_field.text_encoder.model_dump(), context=context, ) @@ -172,7 +181,11 @@ class SDXLPromptInvocationBase: 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), + ( + 1, + cpu_text_encoder.config.max_position_embeddings, + cpu_text_encoder.config.hidden_size, + ), dtype=text_encoder_info.context.cache.precision, ) if get_pooled: @@ -186,7 +199,9 @@ class SDXLPromptInvocationBase: def _lora_loader(): for lora in clip_field.loras: - lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context) + lora_info = context.services.model_manager.get_model( + **lora.model_dump(exclude={"weight"}), context=context + ) yield (lora_info.context.model, lora.weight) del lora_info return @@ -215,13 +230,14 @@ class SDXLPromptInvocationBase: print(f'Warn: trigger: "{trigger}" not found') 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, ), ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers), text_encoder_info as text_encoder, + # Apply the LoRA after text_encoder has been moved to its target device for faster patching. + ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix), ): compel = Compel( tokenizer=tokenizer, @@ -273,8 +289,16 @@ class SDXLPromptInvocationBase: class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): """Parse prompt using compel package to conditioning.""" - prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea) - style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea) + prompt: str = InputField( + default="", + description=FieldDescriptions.compel_prompt, + ui_component=UIComponent.Textarea, + ) + style: str = InputField( + default="", + description=FieldDescriptions.compel_prompt, + ui_component=UIComponent.Textarea, + ) original_width: int = InputField(default=1024, description="") original_height: int = InputField(default=1024, description="") crop_top: int = InputField(default=0, description="") @@ -310,7 +334,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): [ c1, torch.zeros( - (c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]), device=c1.device, dtype=c1.dtype + (c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]), + device=c1.device, + dtype=c1.dtype, ), ], dim=1, @@ -321,7 +347,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): [ c2, torch.zeros( - (c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]), device=c2.device, dtype=c2.dtype + (c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]), + device=c2.device, + dtype=c2.dtype, ), ], dim=1, @@ -359,7 +387,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase """Parse prompt using compel package to conditioning.""" style: str = InputField( - default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea + default="", + description=FieldDescriptions.compel_prompt, + ui_component=UIComponent.Textarea, ) # TODO: ? original_width: int = InputField(default=1024, description="") original_height: int = InputField(default=1024, description="") @@ -403,10 +433,16 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase class ClipSkipInvocationOutput(BaseInvocationOutput): """Clip skip node output""" - clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP") + clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP") -@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning", version="1.0.0") +@invocation( + "clip_skip", + title="CLIP Skip", + tags=["clipskip", "clip", "skip"], + category="conditioning", + version="1.0.0", +) class ClipSkipInvocation(BaseInvocation): """Skip layers in clip text_encoder model.""" @@ -421,7 +457,9 @@ class ClipSkipInvocation(BaseInvocation): def get_max_token_count( - tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False + tokenizer, + prompt: Union[FlattenedPrompt, Blend, Conjunction], + truncate_if_too_long=False, ) -> int: if type(prompt) is Blend: blend: Blend = prompt diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py index 59a36935df..7c76b70e7f 100644 --- a/invokeai/app/invocations/controlnet_image_processors.py +++ b/invokeai/app/invocations/controlnet_image_processors.py @@ -2,7 +2,7 @@ # initial implementation by Gregg Helt, 2023 # heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux from builtins import bool, float -from typing import Dict, List, Literal, Optional, Union +from typing import Dict, List, Literal, Union import cv2 import numpy as np @@ -24,7 +24,7 @@ from controlnet_aux import ( ) from controlnet_aux.util import HWC3, ade_palette from PIL import Image -from pydantic import BaseModel, Field, validator +from pydantic import BaseModel, ConfigDict, Field, field_validator from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin @@ -38,6 +38,8 @@ from .baseinvocation import ( InputField, InvocationContext, OutputField, + WithMetadata, + WithWorkflow, invocation, invocation_output, ) @@ -57,6 +59,8 @@ class ControlNetModelField(BaseModel): model_name: str = Field(description="Name of the ControlNet model") base_model: BaseModelType = Field(description="Base model") + model_config = ConfigDict(protected_namespaces=()) + class ControlField(BaseModel): image: ImageField = Field(description="The control image") @@ -71,7 +75,7 @@ class ControlField(BaseModel): control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use") resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use") - @validator("control_weight") + @field_validator("control_weight") def validate_control_weight(cls, v): """Validate that all control weights in the valid range""" if isinstance(v, list): @@ -124,15 +128,13 @@ class ControlNetInvocation(BaseInvocation): ) -@invocation( - "image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0" -) -class ImageProcessorInvocation(BaseInvocation): +# This invocation exists for other invocations to subclass it - do not register with @invocation! +class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Base class for invocations that preprocess images for ControlNet""" image: ImageField = InputField(description="The image to process") - def run_processor(self, image): + def run_processor(self, image: Image.Image) -> Image.Image: # superclass just passes through image without processing return image @@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation): session_id=context.graph_execution_state_id, node_id=self.id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -393,9 +396,9 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation): detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res) image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res) - h: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `h` parameter") - w: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `w` parameter") - f: Optional[int] = InputField(default=256, ge=0, description="Content shuffle `f` parameter") + h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter") + w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter") + f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter") def run_processor(self, image): content_shuffle_processor = ContentShuffleDetector() @@ -575,14 +578,14 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation): def run_processor(self, image: Image.Image): image = image.convert("RGB") - image = np.array(image, dtype=np.uint8) - height, width = image.shape[:2] + np_image = np.array(image, dtype=np.uint8) + height, width = np_image.shape[:2] width_tile_size = min(self.color_map_tile_size, width) height_tile_size = min(self.color_map_tile_size, height) color_map = cv2.resize( - image, + np_image, (width // width_tile_size, height // height_tile_size), interpolation=cv2.INTER_CUBIC, ) diff --git a/invokeai/app/invocations/custom_nodes/README.md b/invokeai/app/invocations/custom_nodes/README.md new file mode 100644 index 0000000000..d93bb65539 --- /dev/null +++ b/invokeai/app/invocations/custom_nodes/README.md @@ -0,0 +1,51 @@ +# Custom Nodes / Node Packs + +Copy your node packs to this directory. + +When nodes are added or changed, you must restart the app to see the changes. + +## Directory Structure + +For a node pack to be loaded, it must be placed in a directory alongside this +file. Here's an example structure: + +```py +. +├── __init__.py # Invoke-managed custom node loader +│ +├── cool_node +│ ├── __init__.py # see example below +│ └── cool_node.py +│ +└── my_node_pack + ├── __init__.py # see example below + ├── tasty_node.py + ├── bodacious_node.py + ├── utils.py + └── extra_nodes + └── fancy_node.py +``` + +## Node Pack `__init__.py` + +Each node pack must have an `__init__.py` file that imports its nodes. + +The structure of each node or node pack is otherwise not important. + +Here are examples, based on the example directory structure. + +### `cool_node/__init__.py` + +```py +from .cool_node import CoolInvocation +``` + +### `my_node_pack/__init__.py` + +```py +from .tasty_node import TastyInvocation +from .bodacious_node import BodaciousInvocation +from .extra_nodes.fancy_node import FancyInvocation +``` + +Only nodes imported in the `__init__.py` file are loaded. diff --git a/invokeai/app/invocations/custom_nodes/init.py b/invokeai/app/invocations/custom_nodes/init.py new file mode 100644 index 0000000000..c6708e95a7 --- /dev/null +++ b/invokeai/app/invocations/custom_nodes/init.py @@ -0,0 +1,51 @@ +""" +Invoke-managed custom node loader. See README.md for more information. +""" + +import sys +from importlib.util import module_from_spec, spec_from_file_location +from pathlib import Path + +from invokeai.backend.util.logging import InvokeAILogger + +logger = InvokeAILogger.get_logger() +loaded_count = 0 + + +for d in Path(__file__).parent.iterdir(): + # skip files + if not d.is_dir(): + continue + + # skip hidden directories + if d.name.startswith("_") or d.name.startswith("."): + continue + + # skip directories without an `__init__.py` + init = d / "__init__.py" + if not init.exists(): + continue + + module_name = init.parent.stem + + # skip if already imported + if module_name in globals(): + continue + + # we have a legit module to import + spec = spec_from_file_location(module_name, init.absolute()) + + if spec is None or spec.loader is None: + logger.warn(f"Could not load {init}") + continue + + module = module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + + loaded_count += 1 + + del init, module_name + + +logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}") diff --git a/invokeai/app/invocations/cv.py b/invokeai/app/invocations/cv.py index 3b85955d74..e5cfd327c1 100644 --- a/invokeai/app/invocations/cv.py +++ b/invokeai/app/invocations/cv.py @@ -8,11 +8,11 @@ from PIL import Image, ImageOps from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin -from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation +from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation @invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0") -class CvInpaintInvocation(BaseInvocation): +class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Simple inpaint using opencv.""" image: ImageField = InputField(description="The image to inpaint") diff --git a/invokeai/app/invocations/facetools.py b/invokeai/app/invocations/facetools.py index a433fac792..0bb24ef69d 100644 --- a/invokeai/app/invocations/facetools.py +++ b/invokeai/app/invocations/facetools.py @@ -8,7 +8,7 @@ import numpy as np from mediapipe.python.solutions.face_mesh import FaceMesh # type: ignore[import] from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps from PIL.Image import Image as ImageType -from pydantic import validator +from pydantic import field_validator import invokeai.assets.fonts as font_assets from invokeai.app.invocations.baseinvocation import ( @@ -16,6 +16,8 @@ from invokeai.app.invocations.baseinvocation import ( InputField, InvocationContext, OutputField, + WithMetadata, + WithWorkflow, invocation, invocation_output, ) @@ -46,6 +48,8 @@ class FaceResultData(TypedDict): y_center: float mesh_width: int mesh_height: int + chunk_x_offset: int + chunk_y_offset: int class FaceResultDataWithId(FaceResultData): @@ -78,6 +82,48 @@ FONT_SIZE = 32 FONT_STROKE_WIDTH = 4 +def coalesce_faces(face1: FaceResultData, face2: FaceResultData) -> FaceResultData: + face1_x_offset = face1["chunk_x_offset"] - min(face1["chunk_x_offset"], face2["chunk_x_offset"]) + face2_x_offset = face2["chunk_x_offset"] - min(face1["chunk_x_offset"], face2["chunk_x_offset"]) + face1_y_offset = face1["chunk_y_offset"] - min(face1["chunk_y_offset"], face2["chunk_y_offset"]) + face2_y_offset = face2["chunk_y_offset"] - min(face1["chunk_y_offset"], face2["chunk_y_offset"]) + + new_im_width = ( + max(face1["image"].width, face2["image"].width) + + max(face1["chunk_x_offset"], face2["chunk_x_offset"]) + - min(face1["chunk_x_offset"], face2["chunk_x_offset"]) + ) + new_im_height = ( + max(face1["image"].height, face2["image"].height) + + max(face1["chunk_y_offset"], face2["chunk_y_offset"]) + - min(face1["chunk_y_offset"], face2["chunk_y_offset"]) + ) + pil_image = Image.new(mode=face1["image"].mode, size=(new_im_width, new_im_height)) + pil_image.paste(face1["image"], (face1_x_offset, face1_y_offset)) + pil_image.paste(face2["image"], (face2_x_offset, face2_y_offset)) + + # Mask images are always from the origin + new_mask_im_width = max(face1["mask"].width, face2["mask"].width) + new_mask_im_height = max(face1["mask"].height, face2["mask"].height) + mask_pil = create_white_image(new_mask_im_width, new_mask_im_height) + black_image = create_black_image(face1["mask"].width, face1["mask"].height) + mask_pil.paste(black_image, (0, 0), ImageOps.invert(face1["mask"])) + black_image = create_black_image(face2["mask"].width, face2["mask"].height) + mask_pil.paste(black_image, (0, 0), ImageOps.invert(face2["mask"])) + + new_face = FaceResultData( + image=pil_image, + mask=mask_pil, + x_center=max(face1["x_center"], face2["x_center"]), + y_center=max(face1["y_center"], face2["y_center"]), + mesh_width=max(face1["mesh_width"], face2["mesh_width"]), + mesh_height=max(face1["mesh_height"], face2["mesh_height"]), + chunk_x_offset=max(face1["chunk_x_offset"], face2["chunk_x_offset"]), + chunk_y_offset=max(face2["chunk_y_offset"], face2["chunk_y_offset"]), + ) + return new_face + + def prepare_faces_list( face_result_list: list[FaceResultData], ) -> list[FaceResultDataWithId]: @@ -91,7 +137,7 @@ def prepare_faces_list( should_add = True candidate_x_center = candidate["x_center"] candidate_y_center = candidate["y_center"] - for face in deduped_faces: + for idx, face in enumerate(deduped_faces): face_center_x = face["x_center"] face_center_y = face["y_center"] face_radius_w = face["mesh_width"] / 2 @@ -105,6 +151,7 @@ def prepare_faces_list( ) if p < 1: # Inside of the already-added face's radius + deduped_faces[idx] = coalesce_faces(face, candidate) should_add = False break @@ -138,7 +185,6 @@ def generate_face_box_mask( chunk_x_offset: int = 0, chunk_y_offset: int = 0, draw_mesh: bool = True, - check_bounds: bool = True, ) -> list[FaceResultData]: result = [] mask_pil = None @@ -211,33 +257,20 @@ def generate_face_box_mask( mask_pil = create_white_image(w + chunk_x_offset, h + chunk_y_offset) mask_pil.paste(init_mask_pil, (chunk_x_offset, chunk_y_offset)) - left_side = x_center - mesh_width - right_side = x_center + mesh_width - top_side = y_center - mesh_height - bottom_side = y_center + mesh_height - im_width, im_height = pil_image.size - over_w = im_width * 0.1 - over_h = im_height * 0.1 - if not check_bounds or ( - (left_side >= -over_w) - and (right_side < im_width + over_w) - and (top_side >= -over_h) - and (bottom_side < im_height + over_h) - ): - x_center = float(x_center) - y_center = float(y_center) - face = FaceResultData( - image=pil_image, - mask=mask_pil or create_white_image(*pil_image.size), - x_center=x_center + chunk_x_offset, - y_center=y_center + chunk_y_offset, - mesh_width=mesh_width, - mesh_height=mesh_height, - ) + x_center = float(x_center) + y_center = float(y_center) + face = FaceResultData( + image=pil_image, + mask=mask_pil or create_white_image(*pil_image.size), + x_center=x_center + chunk_x_offset, + y_center=y_center + chunk_y_offset, + mesh_width=mesh_width, + mesh_height=mesh_height, + chunk_x_offset=chunk_x_offset, + chunk_y_offset=chunk_y_offset, + ) - result.append(face) - else: - context.services.logger.info("FaceTools --> Face out of bounds, ignoring.") + result.append(face) return result @@ -346,7 +379,6 @@ def get_faces_list( chunk_x_offset=0, chunk_y_offset=0, draw_mesh=draw_mesh, - check_bounds=False, ) if should_chunk or len(result) == 0: context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).") @@ -360,24 +392,26 @@ def get_faces_list( if width > height: # Landscape - slice the image horizontally fx = 0.0 - steps = int(width * 2 / height) + steps = int(width * 2 / height) + 1 + increment = (width - height) / (steps - 1) while fx <= (width - height): x = int(fx) - image_chunks.append(image.crop((x, 0, x + height - 1, height - 1))) + image_chunks.append(image.crop((x, 0, x + height, height))) x_offsets.append(x) y_offsets.append(0) - fx += (width - height) / steps + fx += increment context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}") elif height > width: # Portrait - slice the image vertically fy = 0.0 - steps = int(height * 2 / width) + steps = int(height * 2 / width) + 1 + increment = (height - width) / (steps - 1) while fy <= (height - width): y = int(fy) - image_chunks.append(image.crop((0, y, width - 1, y + width - 1))) + image_chunks.append(image.crop((0, y, width, y + width))) x_offsets.append(0) y_offsets.append(y) - fy += (height - width) / steps + fy += increment context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}") for idx in range(len(image_chunks)): @@ -404,8 +438,8 @@ def get_faces_list( return all_faces -@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.1") -class FaceOffInvocation(BaseInvocation): +@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2") +class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Bound, extract, and mask a face from an image using MediaPipe detection""" image: ImageField = InputField(description="Image for face detection") @@ -498,8 +532,8 @@ class FaceOffInvocation(BaseInvocation): return output -@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.1") -class FaceMaskInvocation(BaseInvocation): +@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2") +class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Face mask creation using mediapipe face detection""" image: ImageField = InputField(description="Image to face detect") @@ -518,7 +552,7 @@ class FaceMaskInvocation(BaseInvocation): ) invert_mask: bool = InputField(default=False, description="Toggle to invert the mask") - @validator("face_ids") + @field_validator("face_ids") def validate_comma_separated_ints(cls, v) -> str: comma_separated_ints_regex = re.compile(r"^\d*(,\d+)*$") if comma_separated_ints_regex.match(v) is None: @@ -616,9 +650,9 @@ class FaceMaskInvocation(BaseInvocation): @invocation( - "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.1" + "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2" ) -class FaceIdentifierInvocation(BaseInvocation): +class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Outputs an image with detected face IDs printed on each face. For use with other FaceTools.""" image: ImageField = InputField(description="Image to face detect") diff --git a/invokeai/app/invocations/image.py b/invokeai/app/invocations/image.py index 2d59a567c0..9a4e9da954 100644 --- a/invokeai/app/invocations/image.py +++ b/invokeai/app/invocations/image.py @@ -7,13 +7,21 @@ import cv2 import numpy from PIL import Image, ImageChops, ImageFilter, ImageOps -from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark from invokeai.backend.image_util.safety_checker import SafetyChecker -from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation +from .baseinvocation import ( + BaseInvocation, + FieldDescriptions, + Input, + InputField, + InvocationContext, + WithMetadata, + WithWorkflow, + invocation, +) @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0") @@ -37,7 +45,7 @@ class ShowImageInvocation(BaseInvocation): @invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0") -class BlankImageInvocation(BaseInvocation): +class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Creates a blank image and forwards it to the pipeline""" width: int = InputField(default=512, description="The width of the image") @@ -55,6 +63,7 @@ class BlankImageInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -66,7 +75,7 @@ class BlankImageInvocation(BaseInvocation): @invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0") -class ImageCropInvocation(BaseInvocation): +class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Crops an image to a specified box. The box can be outside of the image.""" image: ImageField = InputField(description="The image to crop") @@ -88,6 +97,7 @@ class ImageCropInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -99,7 +109,7 @@ class ImageCropInvocation(BaseInvocation): @invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1") -class ImagePasteInvocation(BaseInvocation): +class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Pastes an image into another image.""" base_image: ImageField = InputField(description="The base image") @@ -141,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -152,7 +163,7 @@ class ImagePasteInvocation(BaseInvocation): @invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0") -class MaskFromAlphaInvocation(BaseInvocation): +class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Extracts the alpha channel of an image as a mask.""" image: ImageField = InputField(description="The image to create the mask from") @@ -172,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -183,7 +195,7 @@ class MaskFromAlphaInvocation(BaseInvocation): @invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0") -class ImageMultiplyInvocation(BaseInvocation): +class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Multiplies two images together using `PIL.ImageChops.multiply()`.""" image1: ImageField = InputField(description="The first image to multiply") @@ -202,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -216,7 +229,7 @@ IMAGE_CHANNELS = Literal["A", "R", "G", "B"] @invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0") -class ImageChannelInvocation(BaseInvocation): +class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Gets a channel from an image.""" image: ImageField = InputField(description="The image to get the channel from") @@ -234,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -248,7 +262,7 @@ IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F @invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0") -class ImageConvertInvocation(BaseInvocation): +class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Converts an image to a different mode.""" image: ImageField = InputField(description="The image to convert") @@ -266,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -277,7 +292,7 @@ class ImageConvertInvocation(BaseInvocation): @invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0") -class ImageBlurInvocation(BaseInvocation): +class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Blurs an image""" image: ImageField = InputField(description="The image to blur") @@ -300,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -331,16 +347,13 @@ PIL_RESAMPLING_MAP = { @invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0") -class ImageResizeInvocation(BaseInvocation): +class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Resizes an image to specific dimensions""" image: ImageField = InputField(description="The image to resize") width: int = InputField(default=512, gt=0, description="The width to resize to (px)") height: int = InputField(default=512, gt=0, description="The height to resize to (px)") resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode") - metadata: Optional[CoreMetadata] = InputField( - default=None, description=FieldDescriptions.core_metadata, ui_hidden=True - ) def invoke(self, context: InvocationContext) -> ImageOutput: image = context.services.images.get_pil_image(self.image.image_name) @@ -359,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, - metadata=self.metadata.dict() if self.metadata else None, + metadata=self.metadata, workflow=self.workflow, ) @@ -371,7 +384,7 @@ class ImageResizeInvocation(BaseInvocation): @invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0") -class ImageScaleInvocation(BaseInvocation): +class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Scales an image by a factor""" image: ImageField = InputField(description="The image to scale") @@ -401,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -412,7 +426,7 @@ class ImageScaleInvocation(BaseInvocation): @invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0") -class ImageLerpInvocation(BaseInvocation): +class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Linear interpolation of all pixels of an image""" image: ImageField = InputField(description="The image to lerp") @@ -434,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -445,7 +460,7 @@ class ImageLerpInvocation(BaseInvocation): @invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0") -class ImageInverseLerpInvocation(BaseInvocation): +class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Inverse linear interpolation of all pixels of an image""" image: ImageField = InputField(description="The image to lerp") @@ -456,7 +471,7 @@ class ImageInverseLerpInvocation(BaseInvocation): image = context.services.images.get_pil_image(self.image.image_name) image_arr = numpy.asarray(image, dtype=numpy.float32) - image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255 + image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255 # type: ignore [assignment] ilerp_image = Image.fromarray(numpy.uint8(image_arr)) @@ -467,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -478,13 +494,10 @@ class ImageInverseLerpInvocation(BaseInvocation): @invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0") -class ImageNSFWBlurInvocation(BaseInvocation): +class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Add blur to NSFW-flagged images""" image: ImageField = InputField(description="The image to check") - metadata: Optional[CoreMetadata] = InputField( - default=None, description=FieldDescriptions.core_metadata, ui_hidden=True - ) def invoke(self, context: InvocationContext) -> ImageOutput: image = context.services.images.get_pil_image(self.image.image_name) @@ -505,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, - metadata=self.metadata.dict() if self.metadata else None, + metadata=self.metadata, workflow=self.workflow, ) @@ -515,7 +528,7 @@ class ImageNSFWBlurInvocation(BaseInvocation): height=image_dto.height, ) - def _get_caution_img(self) -> Image: + def _get_caution_img(self) -> Image.Image: import invokeai.app.assets.images as image_assets caution = Image.open(Path(image_assets.__path__[0]) / "caution.png") @@ -523,16 +536,17 @@ class ImageNSFWBlurInvocation(BaseInvocation): @invocation( - "img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0" + "img_watermark", + title="Add Invisible Watermark", + tags=["image", "watermark"], + category="image", + version="1.0.0", ) -class ImageWatermarkInvocation(BaseInvocation): +class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Add an invisible watermark to an image""" image: ImageField = InputField(description="The image to check") text: str = InputField(default="InvokeAI", description="Watermark text") - metadata: Optional[CoreMetadata] = InputField( - default=None, description=FieldDescriptions.core_metadata, ui_hidden=True - ) def invoke(self, context: InvocationContext) -> ImageOutput: image = context.services.images.get_pil_image(self.image.image_name) @@ -544,7 +558,7 @@ class ImageWatermarkInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, - metadata=self.metadata.dict() if self.metadata else None, + metadata=self.metadata, workflow=self.workflow, ) @@ -556,7 +570,7 @@ class ImageWatermarkInvocation(BaseInvocation): @invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0") -class MaskEdgeInvocation(BaseInvocation): +class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Applies an edge mask to an image""" image: ImageField = InputField(description="The image to apply the mask to") @@ -590,6 +604,7 @@ class MaskEdgeInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -601,9 +616,13 @@ class MaskEdgeInvocation(BaseInvocation): @invocation( - "mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0" + "mask_combine", + title="Combine Masks", + tags=["image", "mask", "multiply"], + category="image", + version="1.0.0", ) -class MaskCombineInvocation(BaseInvocation): +class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.""" mask1: ImageField = InputField(description="The first mask to combine") @@ -622,6 +641,7 @@ class MaskCombineInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -633,7 +653,7 @@ class MaskCombineInvocation(BaseInvocation): @invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0") -class ColorCorrectInvocation(BaseInvocation): +class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata): """ 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. @@ -732,6 +752,7 @@ class ColorCorrectInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -743,7 +764,7 @@ class ColorCorrectInvocation(BaseInvocation): @invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0") -class ImageHueAdjustmentInvocation(BaseInvocation): +class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Adjusts the Hue of an image.""" image: ImageField = InputField(description="The image to adjust") @@ -771,6 +792,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation): node_id=self.id, is_intermediate=self.is_intermediate, session_id=context.graph_execution_state_id, + metadata=self.metadata, workflow=self.workflow, ) @@ -846,7 +868,7 @@ CHANNEL_FORMATS = { category="image", version="1.0.0", ) -class ImageChannelOffsetInvocation(BaseInvocation): +class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Add or subtract a value from a specific color channel of an image.""" image: ImageField = InputField(description="The image to adjust") @@ -880,6 +902,7 @@ class ImageChannelOffsetInvocation(BaseInvocation): node_id=self.id, is_intermediate=self.is_intermediate, session_id=context.graph_execution_state_id, + metadata=self.metadata, workflow=self.workflow, ) @@ -916,7 +939,7 @@ class ImageChannelOffsetInvocation(BaseInvocation): category="image", version="1.0.0", ) -class ImageChannelMultiplyInvocation(BaseInvocation): +class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Scale a specific color channel of an image.""" image: ImageField = InputField(description="The image to adjust") @@ -956,6 +979,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation): is_intermediate=self.is_intermediate, session_id=context.graph_execution_state_id, workflow=self.workflow, + metadata=self.metadata, ) return ImageOutput( @@ -975,16 +999,11 @@ class ImageChannelMultiplyInvocation(BaseInvocation): version="1.0.1", use_cache=False, ) -class SaveImageInvocation(BaseInvocation): +class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Saves an image. Unlike an image primitive, this invocation stores a copy of the image.""" image: ImageField = InputField(description=FieldDescriptions.image) - board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct) - metadata: CoreMetadata = InputField( - default=None, - description=FieldDescriptions.core_metadata, - ui_hidden=True, - ) + board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct) def invoke(self, context: InvocationContext) -> ImageOutput: image = context.services.images.get_pil_image(self.image.image_name) @@ -997,7 +1016,7 @@ class SaveImageInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, - metadata=self.metadata.dict() if self.metadata else None, + metadata=self.metadata, workflow=self.workflow, ) diff --git a/invokeai/app/invocations/infill.py b/invokeai/app/invocations/infill.py index d8384290f3..b100fe7c4e 100644 --- a/invokeai/app/invocations/infill.py +++ b/invokeai/app/invocations/infill.py @@ -13,7 +13,7 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint from invokeai.backend.image_util.lama import LaMA from invokeai.backend.image_util.patchmatch import PatchMatch -from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation +from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES @@ -119,7 +119,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int] @invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") -class InfillColorInvocation(BaseInvocation): +class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Infills transparent areas of an image with a solid color""" image: ImageField = InputField(description="The image to infill") @@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -154,7 +155,7 @@ class InfillColorInvocation(BaseInvocation): @invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") -class InfillTileInvocation(BaseInvocation): +class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Infills transparent areas of an image with tiles of the image""" image: ImageField = InputField(description="The image to infill") @@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -192,7 +194,7 @@ class InfillTileInvocation(BaseInvocation): @invocation( "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0" ) -class InfillPatchMatchInvocation(BaseInvocation): +class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Infills transparent areas of an image using the PatchMatch algorithm""" image: ImageField = InputField(description="The image to infill") @@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) @@ -243,7 +246,7 @@ class InfillPatchMatchInvocation(BaseInvocation): @invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") -class LaMaInfillInvocation(BaseInvocation): +class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Infills transparent areas of an image using the LaMa model""" image: ImageField = InputField(description="The image to infill") @@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, + workflow=self.workflow, ) return ImageOutput( @@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation): ) -@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0") -class CV2InfillInvocation(BaseInvocation): +@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint") +class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Infills transparent areas of an image using OpenCV Inpainting""" image: ImageField = InputField(description="The image to infill") @@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, + workflow=self.workflow, ) return ImageOutput( diff --git a/invokeai/app/invocations/ip_adapter.py b/invokeai/app/invocations/ip_adapter.py index 3e3a3d9b1f..4fa256552d 100644 --- a/invokeai/app/invocations/ip_adapter.py +++ b/invokeai/app/invocations/ip_adapter.py @@ -2,7 +2,7 @@ import os from builtins import float from typing import List, Union -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from invokeai.app.invocations.baseinvocation import ( BaseInvocation, @@ -25,14 +25,18 @@ class IPAdapterModelField(BaseModel): model_name: str = Field(description="Name of the IP-Adapter model") base_model: BaseModelType = Field(description="Base model") + model_config = ConfigDict(protected_namespaces=()) + class CLIPVisionModelField(BaseModel): model_name: str = Field(description="Name of the CLIP Vision image encoder model") base_model: BaseModelType = Field(description="Base model (usually 'Any')") + model_config = ConfigDict(protected_namespaces=()) + class IPAdapterField(BaseModel): - image: ImageField = Field(description="The IP-Adapter image prompt.") + image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).") ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.") image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.") weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet") @@ -51,19 +55,19 @@ class IPAdapterOutput(BaseInvocationOutput): ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter") -@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0") +@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0") class IPAdapterInvocation(BaseInvocation): """Collects IP-Adapter info to pass to other nodes.""" # Inputs - image: ImageField = InputField(description="The IP-Adapter image prompt.") + image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).") ip_adapter_model: IPAdapterModelField = InputField( description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1 ) # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float) weight: Union[float, List[float]] = InputField( - default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight" + default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight" ) begin_step_percent: float = InputField( diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py index 7ca8cbbe6c..fac680c78b 100644 --- a/invokeai/app/invocations/latent.py +++ b/invokeai/app/invocations/latent.py @@ -19,11 +19,10 @@ from diffusers.models.attention_processor import ( ) from diffusers.schedulers import DPMSolverSDEScheduler from diffusers.schedulers import SchedulerMixin as Scheduler -from pydantic import validator +from pydantic import field_validator from torchvision.transforms.functional import resize as tv_resize from invokeai.app.invocations.ip_adapter import IPAdapterField -from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.primitives import ( DenoiseMaskField, DenoiseMaskOutput, @@ -64,6 +63,8 @@ from .baseinvocation import ( InvocationContext, OutputField, UIType, + WithMetadata, + WithWorkflow, invocation, invocation_output, ) @@ -84,12 +85,20 @@ class SchedulerOutput(BaseInvocationOutput): scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler) -@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0") +@invocation( + "scheduler", + title="Scheduler", + tags=["scheduler"], + category="latents", + version="1.0.0", +) class SchedulerInvocation(BaseInvocation): """Selects a scheduler.""" scheduler: SAMPLER_NAME_VALUES = InputField( - default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler + default="euler", + description=FieldDescriptions.scheduler, + ui_type=UIType.Scheduler, ) def invoke(self, context: InvocationContext) -> SchedulerOutput: @@ -97,7 +106,11 @@ class SchedulerInvocation(BaseInvocation): @invocation( - "create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0" + "create_denoise_mask", + title="Create Denoise Mask", + tags=["mask", "denoise"], + category="latents", + version="1.0.0", ) class CreateDenoiseMaskInvocation(BaseInvocation): """Creates mask for denoising model run.""" @@ -106,7 +119,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation): image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1) mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2) tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3) - fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4) + fp32: bool = InputField( + default=DEFAULT_PRECISION == "float32", + description=FieldDescriptions.fp32, + ui_order=4, + ) def prep_mask_tensor(self, mask_image): if mask_image.mode != "L": @@ -134,7 +151,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation): if image is not None: vae_info = context.services.model_manager.get_model( - **self.vae.vae.dict(), + **self.vae.vae.model_dump(), context=context, ) @@ -167,7 +184,7 @@ def get_scheduler( ) -> Scheduler: scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"]) orig_scheduler_info = context.services.model_manager.get_model( - **scheduler_info.dict(), + **scheduler_info.model_dump(), context=context, ) with orig_scheduler_info as orig_scheduler: @@ -198,7 +215,7 @@ def get_scheduler( title="Denoise Latents", tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"], category="latents", - version="1.3.0", + version="1.4.0", ) class DenoiseLatentsInvocation(BaseInvocation): """Denoises noisy latents to decodable images""" @@ -209,34 +226,64 @@ class DenoiseLatentsInvocation(BaseInvocation): negative_conditioning: ConditioningField = InputField( description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1 ) - noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3) + noise: Optional[LatentsField] = InputField( + default=None, + description=FieldDescriptions.noise, + input=Input.Connection, + ui_order=3, + ) steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps) cfg_scale: Union[float, List[float]] = InputField( default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale" ) - denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start) + denoising_start: float = InputField( + default=0.0, + ge=0, + le=1, + description=FieldDescriptions.denoising_start, + ) denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end) scheduler: SAMPLER_NAME_VALUES = InputField( - default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler + default="euler", + description=FieldDescriptions.scheduler, + ui_type=UIType.Scheduler, ) - unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2) - control: Union[ControlField, list[ControlField]] = InputField( + unet: UNetField = InputField( + description=FieldDescriptions.unet, + input=Input.Connection, + title="UNet", + ui_order=2, + ) + control: Optional[Union[ControlField, list[ControlField]]] = InputField( default=None, input=Input.Connection, ui_order=5, ) ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField( - description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6 + description=FieldDescriptions.ip_adapter, + title="IP-Adapter", + default=None, + input=Input.Connection, + ui_order=6, ) - t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField( - description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7 + t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField( + description=FieldDescriptions.t2i_adapter, + title="T2I-Adapter", + default=None, + input=Input.Connection, + ui_order=7, + ) + latents: Optional[LatentsField] = InputField( + default=None, description=FieldDescriptions.latents, input=Input.Connection ) - latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection) denoise_mask: Optional[DenoiseMaskField] = InputField( - default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8 + default=None, + description=FieldDescriptions.mask, + input=Input.Connection, + ui_order=8, ) - @validator("cfg_scale") + @field_validator("cfg_scale") def ge_one(cls, v): """validate that all cfg_scale values are >= 1""" if isinstance(v, list): @@ -259,7 +306,7 @@ class DenoiseLatentsInvocation(BaseInvocation): stable_diffusion_step_callback( context=context, intermediate_state=intermediate_state, - node=self.dict(), + node=self.model_dump(), source_node_id=source_node_id, base_model=base_model, ) @@ -445,15 +492,21 @@ class DenoiseLatentsInvocation(BaseInvocation): context=context, ) - input_image = context.services.images.get_pil_image(single_ip_adapter.image.image_name) + # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here. + single_ipa_images = single_ip_adapter.image + if not isinstance(single_ipa_images, list): + single_ipa_images = [single_ipa_images] + + single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images] # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments. with image_encoder_model_info as image_encoder_model: # Get image embeddings from CLIP and ImageProjModel. image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds( - input_image, image_encoder_model + single_ipa_images, image_encoder_model ) + conditioning_data.ip_adapter_conditioning.append( IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds) ) @@ -628,7 +681,10 @@ class DenoiseLatentsInvocation(BaseInvocation): # TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets, # below. Investigate whether this is appropriate. t2i_adapter_data = self.run_t2i_adapters( - context, self.t2i_adapter, latents.shape, do_classifier_free_guidance=True + context, + self.t2i_adapter, + latents.shape, + do_classifier_free_guidance=True, ) # Get the source node id (we are invoking the prepared node) @@ -641,7 +697,7 @@ class DenoiseLatentsInvocation(BaseInvocation): def _lora_loader(): for lora in self.unet.loras: lora_info = context.services.model_manager.get_model( - **lora.dict(exclude={"weight"}), + **lora.model_dump(exclude={"weight"}), context=context, ) yield (lora_info.context.model, lora.weight) @@ -649,14 +705,17 @@ class DenoiseLatentsInvocation(BaseInvocation): return unet_info = context.services.model_manager.get_model( - **self.unet.unet.dict(), + **self.unet.unet.model_dump(), context=context, ) with ( ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), + ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet, + # Apply the LoRA after unet has been moved to its target device for faster patching. + ModelPatcher.apply_lora_unet(unet, _lora_loader()), ): latents = latents.to(device=unet.device, dtype=unet.dtype) if noise is not None: @@ -700,7 +759,10 @@ class DenoiseLatentsInvocation(BaseInvocation): denoising_end=self.denoising_end, ) - result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( + ( + result_latents, + result_attention_map_saver, + ) = pipeline.latents_from_embeddings( latents=latents, timesteps=timesteps, init_timestep=init_timestep, @@ -728,9 +790,13 @@ class DenoiseLatentsInvocation(BaseInvocation): @invocation( - "l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0" + "l2i", + title="Latents to Image", + tags=["latents", "image", "vae", "l2i"], + category="latents", + version="1.0.0", ) -class LatentsToImageInvocation(BaseInvocation): +class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Generates an image from latents.""" latents: LatentsField = InputField( @@ -743,18 +809,13 @@ class LatentsToImageInvocation(BaseInvocation): ) tiled: bool = InputField(default=False, description=FieldDescriptions.tiled) fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32) - metadata: CoreMetadata = InputField( - default=None, - description=FieldDescriptions.core_metadata, - ui_hidden=True, - ) @torch.no_grad() def invoke(self, context: InvocationContext) -> ImageOutput: latents = context.services.latents.get(self.latents.latents_name) vae_info = context.services.model_manager.get_model( - **self.vae.vae.dict(), + **self.vae.vae.model_dump(), context=context, ) @@ -816,7 +877,7 @@ class LatentsToImageInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, - metadata=self.metadata.dict() if self.metadata else None, + metadata=self.metadata, workflow=self.workflow, ) @@ -830,7 +891,13 @@ class LatentsToImageInvocation(BaseInvocation): LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"] -@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0") +@invocation( + "lresize", + title="Resize Latents", + tags=["latents", "resize"], + category="latents", + version="1.0.0", +) class ResizeLatentsInvocation(BaseInvocation): """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.""" @@ -876,7 +943,13 @@ class ResizeLatentsInvocation(BaseInvocation): return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed) -@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0") +@invocation( + "lscale", + title="Scale Latents", + tags=["latents", "resize"], + category="latents", + version="1.0.0", +) class ScaleLatentsInvocation(BaseInvocation): """Scales latents by a given factor.""" @@ -915,7 +988,11 @@ class ScaleLatentsInvocation(BaseInvocation): @invocation( - "i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0" + "i2l", + title="Image to Latents", + tags=["latents", "image", "vae", "i2l"], + category="latents", + version="1.0.0", ) class ImageToLatentsInvocation(BaseInvocation): """Encodes an image into latents.""" @@ -979,7 +1056,7 @@ class ImageToLatentsInvocation(BaseInvocation): image = context.services.images.get_pil_image(self.image.image_name) vae_info = context.services.model_manager.get_model( - **self.vae.vae.dict(), + **self.vae.vae.model_dump(), context=context, ) @@ -1007,7 +1084,13 @@ class ImageToLatentsInvocation(BaseInvocation): return vae.encode(image_tensor).latents -@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0") +@invocation( + "lblend", + title="Blend Latents", + tags=["latents", "blend"], + category="latents", + version="1.0.0", +) class BlendLatentsInvocation(BaseInvocation): """Blend two latents using a given alpha. Latents must have same size.""" diff --git a/invokeai/app/invocations/math.py b/invokeai/app/invocations/math.py index b52cbb28bf..ad676e824c 100644 --- a/invokeai/app/invocations/math.py +++ b/invokeai/app/invocations/math.py @@ -3,7 +3,7 @@ from typing import Literal import numpy as np -from pydantic import validator +from pydantic import ValidationInfo, field_validator from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput @@ -72,7 +72,14 @@ class RandomIntInvocation(BaseInvocation): return IntegerOutput(value=np.random.randint(self.low, self.high)) -@invocation("rand_float", title="Random Float", tags=["math", "float", "random"], category="math", version="1.0.0") +@invocation( + "rand_float", + title="Random Float", + tags=["math", "float", "random"], + category="math", + version="1.0.1", + use_cache=False, +) class RandomFloatInvocation(BaseInvocation): """Outputs a single random float""" @@ -175,16 +182,16 @@ class IntegerMathInvocation(BaseInvocation): operation: INTEGER_OPERATIONS = InputField( default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS ) - a: int = InputField(default=0, description=FieldDescriptions.num_1) - b: int = InputField(default=0, description=FieldDescriptions.num_2) + a: int = InputField(default=1, description=FieldDescriptions.num_1) + b: int = InputField(default=1, description=FieldDescriptions.num_2) - @validator("b") - def no_unrepresentable_results(cls, v, values): - if values["operation"] == "DIV" and v == 0: + @field_validator("b") + def no_unrepresentable_results(cls, v: int, info: ValidationInfo): + if info.data["operation"] == "DIV" and v == 0: raise ValueError("Cannot divide by zero") - elif values["operation"] == "MOD" and v == 0: + elif info.data["operation"] == "MOD" and v == 0: raise ValueError("Cannot divide by zero") - elif values["operation"] == "EXP" and v < 0: + elif info.data["operation"] == "EXP" and v < 0: raise ValueError("Result of exponentiation is not an integer") return v @@ -249,16 +256,16 @@ class FloatMathInvocation(BaseInvocation): operation: FLOAT_OPERATIONS = InputField( default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS ) - a: float = InputField(default=0, description=FieldDescriptions.num_1) - b: float = InputField(default=0, description=FieldDescriptions.num_2) + a: float = InputField(default=1, description=FieldDescriptions.num_1) + b: float = InputField(default=1, description=FieldDescriptions.num_2) - @validator("b") - def no_unrepresentable_results(cls, v, values): - if values["operation"] == "DIV" and v == 0: + @field_validator("b") + def no_unrepresentable_results(cls, v: float, info: ValidationInfo): + if info.data["operation"] == "DIV" and v == 0: raise ValueError("Cannot divide by zero") - elif values["operation"] == "EXP" and values["a"] == 0 and v < 0: + elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0: raise ValueError("Cannot raise zero to a negative power") - elif values["operation"] == "EXP" and type(values["a"] ** v) is complex: + elif info.data["operation"] == "EXP" and type(info.data["a"] ** v) is complex: raise ValueError("Root operation resulted in a complex number") return v diff --git a/invokeai/app/invocations/metadata.py b/invokeai/app/invocations/metadata.py index cecc01096a..0f45fc5a36 100644 --- a/invokeai/app/invocations/metadata.py +++ b/invokeai/app/invocations/metadata.py @@ -1,13 +1,16 @@ -from typing import Optional +from typing import Any, Literal, Optional, Union -from pydantic import Field +from pydantic import BaseModel, ConfigDict, Field from invokeai.app.invocations.baseinvocation import ( BaseInvocation, BaseInvocationOutput, + FieldDescriptions, InputField, InvocationContext, + MetadataField, OutputField, + UIType, invocation, invocation_output, ) @@ -16,133 +19,133 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField from invokeai.app.invocations.primitives import ImageField from invokeai.app.invocations.t2i_adapter import T2IAdapterField -from invokeai.app.util.model_exclude_null import BaseModelExcludeNull from ...version import __version__ -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 MetadataItemField(BaseModel): + label: str = Field(description=FieldDescriptions.metadata_item_label) + value: Any = Field(description=FieldDescriptions.metadata_item_value) -class IPAdapterMetadataField(BaseModelExcludeNull): +class LoRAMetadataField(BaseModel): + """LoRA Metadata Field""" + + lora: LoRAModelField = Field(description=FieldDescriptions.lora_model) + weight: float = Field(description=FieldDescriptions.lora_weight) + + +class IPAdapterMetadataField(BaseModel): + """IP Adapter Field, minus the CLIP Vision Encoder model""" + image: ImageField = Field(description="The IP-Adapter image prompt.") - ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.") - weight: float = Field(description="The weight of the IP-Adapter model") - begin_step_percent: float = Field( - default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)" + ip_adapter_model: IPAdapterModelField = Field( + description="The IP-Adapter model.", ) - end_step_percent: float = Field( - default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)" + weight: Union[float, list[float]] = Field( + description="The weight given to the IP-Adapter", + ) + begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)") + end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)") + + +@invocation_output("metadata_item_output") +class MetadataItemOutput(BaseInvocationOutput): + """Metadata Item Output""" + + item: MetadataItemField = OutputField(description="Metadata Item") + + +@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0") +class MetadataItemInvocation(BaseInvocation): + """Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value.""" + + label: str = InputField(description=FieldDescriptions.metadata_item_label) + value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any) + + def invoke(self, context: InvocationContext) -> MetadataItemOutput: + return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value)) + + +@invocation_output("metadata_output") +class MetadataOutput(BaseInvocationOutput): + metadata: MetadataField = OutputField(description="Metadata Dict") + + +@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0") +class MetadataInvocation(BaseInvocation): + """Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict.""" + + items: Union[list[MetadataItemField], MetadataItemField] = InputField( + description=FieldDescriptions.metadata_item_polymorphic ) + def invoke(self, context: InvocationContext) -> MetadataOutput: + if isinstance(self.items, MetadataItemField): + # single metadata item + data = {self.items.label: self.items.value} + else: + # collection of metadata items + data = {item.label: item.value for item in self.items} -class CoreMetadata(BaseModelExcludeNull): - """Core generation metadata for an image generated in InvokeAI.""" + # add app version + data.update({"app_version": __version__}) + return MetadataOutput(metadata=MetadataField.model_validate(data)) - app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image") - generation_mode: str = Field( + +@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0") +class MergeMetadataInvocation(BaseInvocation): + """Merged a collection of MetadataDict into a single MetadataDict.""" + + collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection) + + def invoke(self, context: InvocationContext) -> MetadataOutput: + data = {} + for item in self.collection: + data.update(item.model_dump()) + + return MetadataOutput(metadata=MetadataField.model_validate(data)) + + +GENERATION_MODES = Literal[ + "txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint" +] + + +@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0") +class CoreMetadataInvocation(BaseInvocation): + """Collects core generation metadata into a MetadataField""" + + generation_mode: Optional[GENERATION_MODES] = InputField( + default=None, description="The generation mode that output this image", ) - created_by: Optional[str] = Field(description="The name of the creator of the image") - positive_prompt: str = Field(description="The positive prompt parameter") - negative_prompt: str = Field(description="The negative prompt parameter") - width: int = Field(description="The width parameter") - height: int = Field(description="The height parameter") - seed: int = Field(description="The seed used for noise generation") - rand_device: str = Field(description="The device used for random number generation") - cfg_scale: float = Field(description="The classifier-free guidance scale parameter") - steps: int = Field(description="The number of steps used for inference") - scheduler: str = Field(description="The scheduler used for inference") - clip_skip: Optional[int] = Field( + positive_prompt: Optional[str] = InputField(default=None, description="The positive prompt parameter") + negative_prompt: Optional[str] = InputField(default=None, description="The negative prompt parameter") + width: Optional[int] = InputField(default=None, description="The width parameter") + height: Optional[int] = InputField(default=None, description="The height parameter") + seed: Optional[int] = InputField(default=None, description="The seed used for noise generation") + rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation") + cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter") + steps: Optional[int] = InputField(default=None, description="The number of steps used for inference") + scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference") + seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis") + seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis") + clip_skip: Optional[int] = InputField( default=None, description="The number of skipped CLIP layers", ) - model: MainModelField = Field(description="The main model used for inference") - controlnets: list[ControlField] = Field(description="The ControlNets used for inference") - ipAdapters: list[IPAdapterMetadataField] = Field(description="The IP Adapters used for inference") - t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference") - loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference") - vae: Optional[VAEModelField] = Field( - default=None, - description="The VAE used for decoding, if the main model's default was not used", + model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference") + controlnets: Optional[list[ControlField]] = InputField( + default=None, description="The ControlNets used for inference" ) - - # Latents-to-Latents - strength: Optional[float] = Field( - default=None, - description="The strength used for latents-to-latents", + ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField( + default=None, description="The IP Adapters used for inference" ) - init_image: Optional[str] = Field(default=None, description="The name of the initial image") - - # SDXL - positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter") - negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter") - - # SDXL Refiner - refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used") - refiner_cfg_scale: Optional[float] = Field( - default=None, - description="The classifier-free guidance scale parameter used for the refiner", + t2iAdapters: Optional[list[T2IAdapterField]] = InputField( + default=None, description="The IP Adapters used for inference" ) - refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner") - refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner") - refiner_positive_aesthetic_score: Optional[float] = Field( - default=None, description="The aesthetic score used for the refiner" - ) - refiner_negative_aesthetic_score: Optional[float] = Field( - default=None, description="The aesthetic score used for the refiner" - ) - refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising") - - -class ImageMetadata(BaseModelExcludeNull): - """An image's generation metadata""" - - metadata: Optional[dict] = Field( - default=None, - description="The image's core metadata, if it was created in the Linear or Canvas UI", - ) - graph: Optional[dict] = Field(default=None, description="The graph that created the image") - - -@invocation_output("metadata_accumulator_output") -class MetadataAccumulatorOutput(BaseInvocationOutput): - """The output of the MetadataAccumulator node""" - - metadata: CoreMetadata = OutputField(description="The core metadata for the image") - - -@invocation( - "metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0" -) -class MetadataAccumulatorInvocation(BaseInvocation): - """Outputs a Core Metadata Object""" - - generation_mode: str = InputField( - description="The generation mode that output this image", - ) - positive_prompt: str = InputField(description="The positive prompt parameter") - negative_prompt: str = InputField(description="The negative prompt parameter") - width: int = InputField(description="The width parameter") - height: int = InputField(description="The height parameter") - seed: int = InputField(description="The seed used for noise generation") - rand_device: str = InputField(description="The device used for random number generation") - cfg_scale: float = InputField(description="The classifier-free guidance scale parameter") - steps: int = InputField(description="The number of steps used for inference") - scheduler: str = InputField(description="The scheduler used for inference") - clip_skip: Optional[int] = Field( - default=None, - description="The number of skipped CLIP layers", - ) - model: MainModelField = InputField(description="The main model used for inference") - controlnets: list[ControlField] = InputField(description="The ControlNets used for inference") - ipAdapters: list[IPAdapterMetadataField] = InputField(description="The IP Adapters used for inference") - t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference") - loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference") + loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference") strength: Optional[float] = InputField( default=None, description="The strength used for latents-to-latents", @@ -158,9 +161,11 @@ class MetadataAccumulatorInvocation(BaseInvocation): # High resolution fix metadata. hrf_width: Optional[int] = InputField( + default=None, description="The high resolution fix height and width multipler.", ) hrf_height: Optional[int] = InputField( + default=None, description="The high resolution fix height and width multipler.", ) hrf_strength: Optional[float] = InputField( @@ -208,7 +213,13 @@ class MetadataAccumulatorInvocation(BaseInvocation): description="The start value used for refiner denoising", ) - def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput: + def invoke(self, context: InvocationContext) -> MetadataOutput: """Collects and outputs a CoreMetadata object""" - return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict())) + return MetadataOutput( + metadata=MetadataField.model_validate( + self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"}) + ) + ) + + model_config = ConfigDict(extra="allow") diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py index 571cb2e730..32815191c2 100644 --- a/invokeai/app/invocations/model.py +++ b/invokeai/app/invocations/model.py @@ -1,7 +1,9 @@ import copy from typing import List, Optional -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field + +from invokeai.app.invocations.shared import FreeUConfig from ...backend.model_management import BaseModelType, ModelType, SubModelType from .baseinvocation import ( @@ -24,6 +26,8 @@ class ModelInfo(BaseModel): model_type: ModelType = Field(description="Info to load submodel") submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel") + model_config = ConfigDict(protected_namespaces=()) + class LoraInfo(ModelInfo): weight: float = Field(description="Lora's weight which to use when apply to model") @@ -34,6 +38,7 @@ class UNetField(BaseModel): scheduler: ModelInfo = Field(description="Info to load scheduler submodel") loras: List[LoraInfo] = Field(description="Loras to apply on model loading") seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless') + freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration") class ClipField(BaseModel): @@ -49,15 +54,34 @@ class VaeField(BaseModel): seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless') -@invocation_output("model_loader_output") -class ModelLoaderOutput(BaseInvocationOutput): - """Model loader output""" +@invocation_output("unet_output") +class UNetOutput(BaseInvocationOutput): + """Base class for invocations that output a UNet field""" unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet") - clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP") + + +@invocation_output("vae_output") +class VAEOutput(BaseInvocationOutput): + """Base class for invocations that output a VAE field""" + vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") +@invocation_output("clip_output") +class CLIPOutput(BaseInvocationOutput): + """Base class for invocations that output a CLIP field""" + + clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP") + + +@invocation_output("model_loader_output") +class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput): + """Model loader output""" + + pass + + class MainModelField(BaseModel): """Main model field""" @@ -65,6 +89,8 @@ class MainModelField(BaseModel): base_model: BaseModelType = Field(description="Base model") model_type: ModelType = Field(description="Model Type") + model_config = ConfigDict(protected_namespaces=()) + class LoRAModelField(BaseModel): """LoRA model field""" @@ -72,8 +98,16 @@ class LoRAModelField(BaseModel): model_name: str = Field(description="Name of the LoRA model") base_model: BaseModelType = Field(description="Base model") + model_config = ConfigDict(protected_namespaces=()) -@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0") + +@invocation( + "main_model_loader", + title="Main Model", + tags=["model"], + category="model", + version="1.0.0", +) class MainModelLoaderInvocation(BaseInvocation): """Loads a main model, outputting its submodels.""" @@ -180,10 +214,16 @@ class LoraLoaderInvocation(BaseInvocation): lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA") weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight) unet: Optional[UNetField] = InputField( - default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet" + default=None, + description=FieldDescriptions.unet, + input=Input.Connection, + title="UNet", ) clip: Optional[ClipField] = InputField( - default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP" + default=None, + description=FieldDescriptions.clip, + input=Input.Connection, + title="CLIP", ) def invoke(self, context: InvocationContext) -> LoraLoaderOutput: @@ -244,20 +284,35 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput): clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2") -@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0") +@invocation( + "sdxl_lora_loader", + title="SDXL LoRA", + tags=["lora", "model"], + category="model", + version="1.0.0", +) class SDXLLoraLoaderInvocation(BaseInvocation): """Apply selected lora to unet and text_encoder.""" lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA") weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight) unet: Optional[UNetField] = InputField( - default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet" + default=None, + description=FieldDescriptions.unet, + input=Input.Connection, + title="UNet", ) clip: Optional[ClipField] = InputField( - default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1" + default=None, + description=FieldDescriptions.clip, + input=Input.Connection, + title="CLIP 1", ) clip2: Optional[ClipField] = InputField( - default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2" + default=None, + description=FieldDescriptions.clip, + input=Input.Connection, + title="CLIP 2", ) def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput: @@ -330,12 +385,7 @@ class VAEModelField(BaseModel): model_name: str = Field(description="Name of the model") base_model: BaseModelType = Field(description="Base model") - -@invocation_output("vae_loader_output") -class VaeLoaderOutput(BaseInvocationOutput): - """VAE output""" - - vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") + model_config = ConfigDict(protected_namespaces=()) @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0") @@ -343,10 +393,13 @@ class VaeLoaderInvocation(BaseInvocation): """Loads a VAE model, outputting a VaeLoaderOutput""" vae_model: VAEModelField = InputField( - description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, title="VAE" + description=FieldDescriptions.vae_model, + input=Input.Direct, + ui_type=UIType.VaeModel, + title="VAE", ) - def invoke(self, context: InvocationContext) -> VaeLoaderOutput: + def invoke(self, context: InvocationContext) -> VAEOutput: base_model = self.vae_model.base_model model_name = self.vae_model.model_name model_type = ModelType.Vae @@ -357,7 +410,7 @@ class VaeLoaderInvocation(BaseInvocation): model_type=model_type, ): raise Exception(f"Unkown vae name: {model_name}!") - return VaeLoaderOutput( + return VAEOutput( vae=VaeField( vae=ModelInfo( model_name=model_name, @@ -372,19 +425,31 @@ class VaeLoaderInvocation(BaseInvocation): class SeamlessModeOutput(BaseInvocationOutput): """Modified Seamless Model output""" - unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet") - vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE") + unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet") + vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE") -@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0") +@invocation( + "seamless", + title="Seamless", + tags=["seamless", "model"], + category="model", + version="1.0.0", +) class SeamlessModeInvocation(BaseInvocation): """Applies the seamless transformation to the Model UNet and VAE.""" unet: Optional[UNetField] = InputField( - default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet" + default=None, + description=FieldDescriptions.unet, + input=Input.Connection, + title="UNet", ) vae: Optional[VaeField] = InputField( - default=None, description=FieldDescriptions.vae_model, input=Input.Connection, title="VAE" + default=None, + description=FieldDescriptions.vae_model, + input=Input.Connection, + title="VAE", ) seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless") seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless") @@ -407,3 +472,24 @@ class SeamlessModeInvocation(BaseInvocation): vae.seamless_axes = seamless_axes_list return SeamlessModeOutput(unet=unet, vae=vae) + + +@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.0") +class FreeUInvocation(BaseInvocation): + """ + Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2): + + SD1.5: 1.2/1.4/0.9/0.2, + SD2: 1.1/1.2/0.9/0.2, + SDXL: 1.1/1.2/0.6/0.4, + """ + + unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet") + b1: float = InputField(default=1.2, ge=-1, le=3, description=FieldDescriptions.freeu_b1) + b2: float = InputField(default=1.4, ge=-1, le=3, description=FieldDescriptions.freeu_b2) + s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1) + s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2) + + def invoke(self, context: InvocationContext) -> UNetOutput: + self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2) + return UNetOutput(unet=self.unet) diff --git a/invokeai/app/invocations/noise.py b/invokeai/app/invocations/noise.py index c46747aa89..3c1651a2f0 100644 --- a/invokeai/app/invocations/noise.py +++ b/invokeai/app/invocations/noise.py @@ -2,7 +2,7 @@ import torch -from pydantic import validator +from pydantic import field_validator from invokeai.app.invocations.latent import LatentsField from invokeai.app.util.misc import SEED_MAX, get_random_seed @@ -65,7 +65,7 @@ Nodes class NoiseOutput(BaseInvocationOutput): """Invocation noise output""" - noise: LatentsField = OutputField(default=None, description=FieldDescriptions.noise) + noise: LatentsField = OutputField(description=FieldDescriptions.noise) width: int = OutputField(description=FieldDescriptions.width) height: int = OutputField(description=FieldDescriptions.height) @@ -78,7 +78,13 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int): ) -@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents", version="1.0.0") +@invocation( + "noise", + title="Noise", + tags=["latents", "noise"], + category="latents", + version="1.0.0", +) class NoiseInvocation(BaseInvocation): """Generates latent noise.""" @@ -105,7 +111,7 @@ class NoiseInvocation(BaseInvocation): description="Use CPU for noise generation (for reproducible results across platforms)", ) - @validator("seed", pre=True) + @field_validator("seed", mode="before") def modulo_seed(cls, v): """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range.""" return v % (SEED_MAX + 1) diff --git a/invokeai/app/invocations/onnx.py b/invokeai/app/invocations/onnx.py index 35f8ed965e..140505f736 100644 --- a/invokeai/app/invocations/onnx.py +++ b/invokeai/app/invocations/onnx.py @@ -4,15 +4,14 @@ import inspect import re # from contextlib import ExitStack -from typing import List, Literal, Optional, Union +from typing import List, Literal, Union import numpy as np import torch from diffusers.image_processor import VaeImageProcessor -from pydantic import BaseModel, Field, validator +from pydantic import BaseModel, ConfigDict, Field, field_validator from tqdm import tqdm -from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.app.util.step_callback import stable_diffusion_step_callback @@ -31,6 +30,8 @@ from .baseinvocation import ( OutputField, UIComponent, UIType, + WithMetadata, + WithWorkflow, invocation, invocation_output, ) @@ -63,14 +64,17 @@ class ONNXPromptInvocation(BaseInvocation): def invoke(self, context: InvocationContext) -> ConditioningOutput: tokenizer_info = context.services.model_manager.get_model( - **self.clip.tokenizer.dict(), + **self.clip.tokenizer.model_dump(), ) text_encoder_info = context.services.model_manager.get_model( - **self.clip.text_encoder.dict(), + **self.clip.text_encoder.model_dump(), ) with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder: # , ExitStack() as stack: loras = [ - (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) + ( + context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model, + lora.weight, + ) for lora in self.clip.loras ] @@ -175,14 +179,14 @@ class ONNXTextToLatentsInvocation(BaseInvocation): description=FieldDescriptions.unet, input=Input.Connection, ) - control: Optional[Union[ControlField, list[ControlField]]] = InputField( + control: Union[ControlField, list[ControlField]] = InputField( default=None, description=FieldDescriptions.control, ) # seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", ) # seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'") - @validator("cfg_scale") + @field_validator("cfg_scale") def ge_one(cls, v): """validate that all cfg_scale values are >= 1""" if isinstance(v, list): @@ -241,7 +245,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation): stable_diffusion_step_callback( context=context, intermediate_state=intermediate_state, - node=self.dict(), + node=self.model_dump(), source_node_id=source_node_id, ) @@ -254,12 +258,15 @@ class ONNXTextToLatentsInvocation(BaseInvocation): eta=0.0, ) - unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) + unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump()) with unet_info as unet: # , ExitStack() as stack: # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras] loras = [ - (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) + ( + context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model, + lora.weight, + ) for lora in self.unet.loras ] @@ -321,7 +328,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation): category="image", version="1.0.0", ) -class ONNXLatentsToImageInvocation(BaseInvocation): +class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow): """Generates an image from latents.""" latents: LatentsField = InputField( @@ -332,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation): description=FieldDescriptions.vae, input=Input.Connection, ) - metadata: Optional[CoreMetadata] = InputField( - default=None, - description=FieldDescriptions.core_metadata, - ui_hidden=True, - ) # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)") def invoke(self, context: InvocationContext) -> ImageOutput: @@ -346,7 +348,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation): raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}") vae_info = context.services.model_manager.get_model( - **self.vae.vae.dict(), + **self.vae.vae.model_dump(), ) # clear memory as vae decode can request a lot @@ -375,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, - metadata=self.metadata.dict() if self.metadata else None, + metadata=self.metadata, workflow=self.workflow, ) @@ -403,6 +405,8 @@ class OnnxModelField(BaseModel): base_model: BaseModelType = Field(description="Base model") model_type: ModelType = Field(description="Model Type") + model_config = ConfigDict(protected_namespaces=()) + @invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0") class OnnxModelLoaderInvocation(BaseInvocation): diff --git a/invokeai/app/invocations/param_easing.py b/invokeai/app/invocations/param_easing.py index 7c327a6657..0e86fb978b 100644 --- a/invokeai/app/invocations/param_easing.py +++ b/invokeai/app/invocations/param_easing.py @@ -44,13 +44,22 @@ from invokeai.app.invocations.primitives import FloatCollectionOutput from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation -@invocation("float_range", title="Float Range", tags=["math", "range"], category="math", version="1.0.0") +@invocation( + "float_range", + title="Float Range", + tags=["math", "range"], + category="math", + version="1.0.0", +) class FloatLinearRangeInvocation(BaseInvocation): """Creates a range""" start: float = InputField(default=5, description="The first value of the range") stop: float = InputField(default=10, description="The last value of the range") - steps: int = InputField(default=30, description="number of values to interpolate over (including start and stop)") + steps: int = InputField( + default=30, + description="number of values to interpolate over (including start and stop)", + ) def invoke(self, context: InvocationContext) -> FloatCollectionOutput: param_list = list(np.linspace(self.start, self.stop, self.steps)) @@ -95,7 +104,13 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))] # actually I think for now could just use CollectionOutput (which is list[Any] -@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step", version="1.0.0") +@invocation( + "step_param_easing", + title="Step Param Easing", + tags=["step", "easing"], + category="step", + version="1.0.0", +) class StepParamEasingInvocation(BaseInvocation): """Experimental per-step parameter easing for denoising steps""" @@ -159,7 +174,9 @@ class StepParamEasingInvocation(BaseInvocation): context.services.logger.debug("base easing duration: " + str(base_easing_duration)) even_num_steps = num_easing_steps % 2 == 0 # even number of steps easing_function = easing_class( - start=self.start_value, end=self.end_value, duration=base_easing_duration - 1 + start=self.start_value, + end=self.end_value, + duration=base_easing_duration - 1, ) base_easing_vals = list() for step_index in range(base_easing_duration): @@ -199,7 +216,11 @@ class StepParamEasingInvocation(BaseInvocation): # else: # no mirroring (default) - easing_function = easing_class(start=self.start_value, end=self.end_value, duration=num_easing_steps - 1) + easing_function = easing_class( + start=self.start_value, + end=self.end_value, + duration=num_easing_steps - 1, + ) for step_index in range(num_easing_steps): step_val = easing_function.ease(step_index) easing_list.append(step_val) diff --git a/invokeai/app/invocations/primitives.py b/invokeai/app/invocations/primitives.py index c314edfd15..537d616f1f 100644 --- a/invokeai/app/invocations/primitives.py +++ b/invokeai/app/invocations/primitives.py @@ -251,7 +251,9 @@ class ImageCollectionOutput(BaseInvocationOutput): @invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0") -class ImageInvocation(BaseInvocation): +class ImageInvocation( + BaseInvocation, +): """An image primitive value""" image: ImageField = InputField(description="The image to load") @@ -291,7 +293,7 @@ class DenoiseMaskField(BaseModel): """An inpaint mask field""" mask_name: str = Field(description="The name of the mask image") - masked_latents_name: Optional[str] = Field(description="The name of the masked image latents") + masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents") @invocation_output("denoise_mask_output") diff --git a/invokeai/app/invocations/prompt.py b/invokeai/app/invocations/prompt.py index b3d482b779..cb43a52447 100644 --- a/invokeai/app/invocations/prompt.py +++ b/invokeai/app/invocations/prompt.py @@ -3,7 +3,7 @@ from typing import Optional, Union import numpy as np from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator -from pydantic import validator +from pydantic import field_validator from invokeai.app.invocations.primitives import StringCollectionOutput @@ -21,7 +21,10 @@ from .baseinvocation import BaseInvocation, InputField, InvocationContext, UICom class DynamicPromptInvocation(BaseInvocation): """Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator""" - prompt: str = InputField(description="The prompt to parse with dynamicprompts", ui_component=UIComponent.Textarea) + prompt: str = InputField( + description="The prompt to parse with dynamicprompts", + ui_component=UIComponent.Textarea, + ) max_prompts: int = InputField(default=1, description="The number of prompts to generate") combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator") @@ -36,21 +39,31 @@ class DynamicPromptInvocation(BaseInvocation): return StringCollectionOutput(collection=prompts) -@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt", version="1.0.0") +@invocation( + "prompt_from_file", + title="Prompts from File", + tags=["prompt", "file"], + category="prompt", + version="1.0.0", +) class PromptsFromFileInvocation(BaseInvocation): """Loads prompts from a text file""" file_path: str = InputField(description="Path to prompt text file") pre_prompt: Optional[str] = InputField( - default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea + default=None, + description="String to prepend to each prompt", + ui_component=UIComponent.Textarea, ) post_prompt: Optional[str] = InputField( - default=None, description="String to append to each prompt", ui_component=UIComponent.Textarea + default=None, + description="String to append to each prompt", + ui_component=UIComponent.Textarea, ) start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from") max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)") - @validator("file_path") + @field_validator("file_path") def file_path_exists(cls, v): if not exists(v): raise ValueError(FileNotFoundError) @@ -79,6 +92,10 @@ class PromptsFromFileInvocation(BaseInvocation): def invoke(self, context: InvocationContext) -> StringCollectionOutput: prompts = self.promptsFromFile( - self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts + self.file_path, + self.pre_prompt, + self.post_prompt, + self.start_line, + self.max_prompts, ) return StringCollectionOutput(collection=prompts) diff --git a/invokeai/app/invocations/shared.py b/invokeai/app/invocations/shared.py new file mode 100644 index 0000000000..db742a3433 --- /dev/null +++ b/invokeai/app/invocations/shared.py @@ -0,0 +1,16 @@ +from pydantic import BaseModel, Field + +from invokeai.app.invocations.baseinvocation import FieldDescriptions + + +class FreeUConfig(BaseModel): + """ + Configuration for the FreeU hyperparameters. + - https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu + - https://github.com/ChenyangSi/FreeU + """ + + s1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s1) + s2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s2) + b1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b1) + b2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b2) diff --git a/invokeai/app/invocations/t2i_adapter.py b/invokeai/app/invocations/t2i_adapter.py index e1bd8d0d04..76c250a552 100644 --- a/invokeai/app/invocations/t2i_adapter.py +++ b/invokeai/app/invocations/t2i_adapter.py @@ -1,6 +1,6 @@ from typing import Union -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from invokeai.app.invocations.baseinvocation import ( BaseInvocation, @@ -23,6 +23,8 @@ class T2IAdapterModelField(BaseModel): model_name: str = Field(description="Name of the T2I-Adapter model") base_model: BaseModelType = Field(description="Base model") + model_config = ConfigDict(protected_namespaces=()) + class T2IAdapterField(BaseModel): image: ImageField = Field(description="The T2I-Adapter image prompt.") diff --git a/invokeai/app/invocations/upscale.py b/invokeai/app/invocations/upscale.py index e26c1b9084..1167914aca 100644 --- a/invokeai/app/invocations/upscale.py +++ b/invokeai/app/invocations/upscale.py @@ -7,13 +7,14 @@ import numpy as np import torch from basicsr.archs.rrdbnet_arch import RRDBNet from PIL import Image +from pydantic import ConfigDict from realesrgan import RealESRGANer from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin from invokeai.backend.util.devices import choose_torch_device -from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation +from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation # TODO: Populate this from disk? # TODO: Use model manager to load? @@ -29,7 +30,7 @@ if choose_torch_device() == torch.device("mps"): @invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0") -class ESRGANInvocation(BaseInvocation): +class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata): """Upscales an image using RealESRGAN.""" image: ImageField = InputField(description="The input image") @@ -38,6 +39,8 @@ class ESRGANInvocation(BaseInvocation): default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)" ) + model_config = ConfigDict(protected_namespaces=()) + def invoke(self, context: InvocationContext) -> ImageOutput: image = context.services.images.get_pil_image(self.image.image_name) models_path = context.services.configuration.models_path @@ -120,6 +123,7 @@ class ESRGANInvocation(BaseInvocation): node_id=self.id, session_id=context.graph_execution_state_id, is_intermediate=self.is_intermediate, + metadata=self.metadata, workflow=self.workflow, ) diff --git a/invokeai/app/services/board_image_records/board_image_records_sqlite.py b/invokeai/app/services/board_image_records/board_image_records_sqlite.py index df7505b797..9f4e4379bc 100644 --- a/invokeai/app/services/board_image_records/board_image_records_sqlite.py +++ b/invokeai/app/services/board_image_records/board_image_records_sqlite.py @@ -12,7 +12,7 @@ from .board_image_records_base import BoardImageRecordStorageBase class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase): _conn: sqlite3.Connection _cursor: sqlite3.Cursor - _lock: threading.Lock + _lock: threading.RLock def __init__(self, db: SqliteDatabase) -> None: super().__init__() diff --git a/invokeai/app/services/board_records/board_records_common.py b/invokeai/app/services/board_records/board_records_common.py index e0264dde0d..d08951b499 100644 --- a/invokeai/app/services/board_records/board_records_common.py +++ b/invokeai/app/services/board_records/board_records_common.py @@ -1,7 +1,7 @@ from datetime import datetime from typing import Optional, Union -from pydantic import BaseModel, Extra, Field +from pydantic import BaseModel, Field from invokeai.app.util.misc import get_iso_timestamp from invokeai.app.util.model_exclude_null import BaseModelExcludeNull @@ -18,9 +18,9 @@ class BoardRecord(BaseModelExcludeNull): """The created timestamp of the image.""" updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.") """The updated timestamp of the image.""" - deleted_at: Union[datetime, str, None] = Field(description="The deleted timestamp of the board.") + deleted_at: Optional[Union[datetime, str]] = Field(default=None, description="The deleted timestamp of the board.") """The updated timestamp of the image.""" - cover_image_name: Optional[str] = Field(description="The name of the cover image of the board.") + cover_image_name: Optional[str] = Field(default=None, description="The name of the cover image of the board.") """The name of the cover image of the board.""" @@ -46,9 +46,9 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord: ) -class BoardChanges(BaseModel, extra=Extra.forbid): - board_name: Optional[str] = Field(description="The board's new name.") - cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.") +class BoardChanges(BaseModel, extra="forbid"): + board_name: Optional[str] = Field(default=None, description="The board's new name.") + cover_image_name: Optional[str] = Field(default=None, description="The name of the board's new cover image.") class BoardRecordNotFoundException(Exception): diff --git a/invokeai/app/services/board_records/board_records_sqlite.py b/invokeai/app/services/board_records/board_records_sqlite.py index b2ddc931f5..9e3423ab19 100644 --- a/invokeai/app/services/board_records/board_records_sqlite.py +++ b/invokeai/app/services/board_records/board_records_sqlite.py @@ -20,7 +20,7 @@ from .board_records_common import ( class SqliteBoardRecordStorage(BoardRecordStorageBase): _conn: sqlite3.Connection _cursor: sqlite3.Cursor - _lock: threading.Lock + _lock: threading.RLock def __init__(self, db: SqliteDatabase) -> None: super().__init__() diff --git a/invokeai/app/services/boards/boards_common.py b/invokeai/app/services/boards/boards_common.py index e22e1915fe..0cb54102bb 100644 --- a/invokeai/app/services/boards/boards_common.py +++ b/invokeai/app/services/boards/boards_common.py @@ -17,7 +17,7 @@ class BoardDTO(BoardRecord): def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO: """Converts a board record to a board DTO.""" return BoardDTO( - **board_record.dict(exclude={"cover_image_name"}), + **board_record.model_dump(exclude={"cover_image_name"}), cover_image_name=cover_image_name, image_count=image_count, ) diff --git a/invokeai/app/services/config/config_base.py b/invokeai/app/services/config/config_base.py index a07e14252a..9405c1dfae 100644 --- a/invokeai/app/services/config/config_base.py +++ b/invokeai/app/services/config/config_base.py @@ -18,7 +18,7 @@ from pathlib import Path from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints from omegaconf import DictConfig, ListConfig, OmegaConf -from pydantic import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str @@ -32,12 +32,14 @@ class InvokeAISettings(BaseSettings): initconf: ClassVar[Optional[DictConfig]] = None argparse_groups: ClassVar[Dict] = {} + model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True) + def parse_args(self, argv: Optional[list] = sys.argv[1:]): parser = self.get_parser() opt, unknown_opts = parser.parse_known_args(argv) if len(unknown_opts) > 0: print("Unknown args:", unknown_opts) - for name in self.__fields__: + for name in self.model_fields: if name not in self._excluded(): value = getattr(opt, name) if isinstance(value, ListConfig): @@ -54,10 +56,12 @@ class InvokeAISettings(BaseSettings): cls = self.__class__ type = get_args(get_type_hints(cls)["type"])[0] field_dict = dict({type: dict()}) - for name, field in self.__fields__.items(): + for name, field in self.model_fields.items(): if name in cls._excluded_from_yaml(): continue - category = field.field_info.extra.get("category") or "Uncategorized" + category = ( + field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized" + ) value = getattr(self, name) if category not in field_dict[type]: field_dict[type][category] = dict() @@ -73,7 +77,7 @@ class InvokeAISettings(BaseSettings): else: settings_stanza = "Uncategorized" - env_prefix = getattr(cls.Config, "env_prefix", None) + env_prefix = getattr(cls.model_config, "env_prefix", None) env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper() initconf = ( @@ -89,14 +93,18 @@ class InvokeAISettings(BaseSettings): for key, value in os.environ.items(): upcase_environ[key.upper()] = value - fields = cls.__fields__ + fields = cls.model_fields cls.argparse_groups = {} for name, field in fields.items(): if name not in cls._excluded(): current_default = field.default - category = field.field_info.extra.get("category", "Uncategorized") + category = ( + field.json_schema_extra.get("category", "Uncategorized") + if field.json_schema_extra + else "Uncategorized" + ) env_name = env_prefix + "_" + name if category in initconf and name in initconf.get(category): field.default = initconf.get(category).get(name) @@ -146,11 +154,6 @@ class InvokeAISettings(BaseSettings): "tiled_decode", ] - class Config: - env_file_encoding = "utf-8" - arbitrary_types_allowed = True - case_sensitive = True - @classmethod def add_field_argument(cls, command_parser, name: str, field, default_override=None): field_type = get_type_hints(cls).get(name) @@ -161,7 +164,7 @@ class InvokeAISettings(BaseSettings): if field.default_factory is None else field.default_factory() ) - if category := field.field_info.extra.get("category"): + if category := (field.json_schema_extra.get("category", None) if field.json_schema_extra else None): if category not in cls.argparse_groups: cls.argparse_groups[category] = command_parser.add_argument_group(category) argparse_group = cls.argparse_groups[category] @@ -169,7 +172,7 @@ class InvokeAISettings(BaseSettings): argparse_group = command_parser if get_origin(field_type) == Literal: - allowed_values = get_args(field.type_) + allowed_values = get_args(field.annotation) allowed_types = set() for val in allowed_values: allowed_types.add(type(val)) @@ -182,7 +185,7 @@ class InvokeAISettings(BaseSettings): type=field_type, default=default, choices=allowed_values, - help=field.field_info.description, + help=field.description, ) elif get_origin(field_type) == Union: @@ -191,7 +194,7 @@ class InvokeAISettings(BaseSettings): dest=name, type=int_or_float_or_str, default=default, - help=field.field_info.description, + help=field.description, ) elif get_origin(field_type) == list: @@ -199,17 +202,17 @@ class InvokeAISettings(BaseSettings): f"--{name}", dest=name, nargs="*", - type=field.type_, + type=field.annotation, default=default, - action=argparse.BooleanOptionalAction if field.type_ == bool else "store", - help=field.field_info.description, + action=argparse.BooleanOptionalAction if field.annotation == bool else "store", + help=field.description, ) else: argparse_group.add_argument( f"--{name}", dest=name, - type=field.type_, + type=field.annotation, default=default, - action=argparse.BooleanOptionalAction if field.type_ == bool else "store", - help=field.field_info.description, + action=argparse.BooleanOptionalAction if field.annotation == bool else "store", + help=field.description, ) diff --git a/invokeai/app/services/config/config_default.py b/invokeai/app/services/config/config_default.py index 2a42c99bd8..f0e9dbcda4 100644 --- a/invokeai/app/services/config/config_default.py +++ b/invokeai/app/services/config/config_default.py @@ -45,6 +45,7 @@ InvokeAI: ram: 13.5 vram: 0.25 lazy_offload: true + log_memory_usage: false Device: device: auto precision: auto @@ -144,8 +145,8 @@ which is set to the desired top-level name. For example, to create a class InvokeBatch(InvokeAISettings): type: Literal["InvokeBatch"] = "InvokeBatch" - node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources') - cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources') + node_count : int = Field(default=1, description="Number of nodes to run on", json_schema_extra=dict(category='Resources')) + cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", json_schema_extra=dict(category='Resources')) This will now read and write from the "InvokeBatch" section of the config file, look for environment variables named INVOKEBATCH_*, and @@ -175,7 +176,8 @@ from pathlib import Path from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints from omegaconf import DictConfig, OmegaConf -from pydantic import Field, parse_obj_as +from pydantic import Field, TypeAdapter +from pydantic_settings import SettingsConfigDict from .config_base import InvokeAISettings @@ -185,6 +187,21 @@ LEGACY_INIT_FILE = Path("invokeai.init") DEFAULT_MAX_VRAM = 0.5 +class Categories(object): + WebServer = dict(category="Web Server") + Features = dict(category="Features") + Paths = dict(category="Paths") + Logging = dict(category="Logging") + Development = dict(category="Development") + Other = dict(category="Other") + ModelCache = dict(category="Model Cache") + Device = dict(category="Device") + Generation = dict(category="Generation") + Queue = dict(category="Queue") + Nodes = dict(category="Nodes") + MemoryPerformance = dict(category="Memory/Performance") + + class InvokeAIAppConfig(InvokeAISettings): """ Generate images using Stable Diffusion. Use "invokeai" to launch @@ -201,86 +218,90 @@ class InvokeAIAppConfig(InvokeAISettings): type: Literal["InvokeAI"] = "InvokeAI" # WEB - host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server') - port : int = Field(default=9090, description="Port to bind to", category='Web Server') - allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server') - allow_credentials : bool = Field(default=True, description="Allow CORS credentials", category='Web Server') - allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server') - allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server') + host : str = Field(default="127.0.0.1", description="IP address to bind to", json_schema_extra=Categories.WebServer) + port : int = Field(default=9090, description="Port to bind to", json_schema_extra=Categories.WebServer) + allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", json_schema_extra=Categories.WebServer) + allow_credentials : bool = Field(default=True, description="Allow CORS credentials", json_schema_extra=Categories.WebServer) + allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", json_schema_extra=Categories.WebServer) + allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", json_schema_extra=Categories.WebServer) # FEATURES - esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features') - 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') - ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features') + esrgan : bool = Field(default=True, description="Enable/disable upscaling code", json_schema_extra=Categories.Features) + internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", json_schema_extra=Categories.Features) + log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features) + patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", json_schema_extra=Categories.Features) + ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', json_schema_extra=Categories.Features) # 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') - controlnet_dir : Path = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths') - conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths') - models_dir : Path = Field(default='models', description='Path to the models directory', category='Paths') - legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths') - db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths') - outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths') - use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths') - from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths') + root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths) + autoimport_dir : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths) + lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths) + embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths) + controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths) + conf_path : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths) + models_dir : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths) + legacy_conf_dir : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths) + db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths) + outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths) + use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths) + custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths) + from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths) # LOGGING - log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=", "syslog=path|address:host:port", "http="', category="Logging") + log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=", "syslog=path|address:host:port", "http="', json_schema_extra=Categories.Logging) # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues - log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging") - log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging") - log_sql : bool = Field(default=False, description="Log SQL queries", category="Logging") + log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', json_schema_extra=Categories.Logging) + log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", json_schema_extra=Categories.Logging) + log_sql : bool = Field(default=False, description="Log SQL queries", json_schema_extra=Categories.Logging) - dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development") + dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", json_schema_extra=Categories.Development) - version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other") + version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other) # CACHE - ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", category="Model Cache", ) - vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", category="Model Cache", ) - lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", ) + ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, ) + vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, ) + lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, ) + log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache) # DEVICE - device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", category="Device", ) - precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", category="Device", ) + device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device) + precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device) # GENERATION - sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", ) - attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", category="Generation", ) - attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", ) - force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",) - force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",) - png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", category="Generation", ) + sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation) + attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation) + attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation) + force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation) + png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation) # QUEUE - max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", ) + max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue) # NODES - allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes") - deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes") - node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", category="Nodes", ) + allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes) + deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes) + node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes) # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES - 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 : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance') - max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance') - max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", 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') + always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance) + free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance) + max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance) + max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance) + xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance) + tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance) # See InvokeAIAppConfig subclass below for CACHE and DEVICE categories # fmt: on - class Config: - validate_assignment = True - env_prefix = "INVOKEAI" + model_config = SettingsConfigDict(validate_assignment=True, env_prefix="INVOKEAI") - def parse_args(self, argv: Optional[list[str]] = None, conf: Optional[DictConfig] = None, clobber=False): + def parse_args( + self, + argv: Optional[list[str]] = None, + conf: Optional[DictConfig] = None, + clobber=False, + ): """ Update settings with contents of init file, environment, and command-line settings. @@ -308,7 +329,11 @@ class InvokeAIAppConfig(InvokeAISettings): if self.singleton_init and not clobber: hints = get_type_hints(self.__class__) for k in self.singleton_init: - setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k])) + setattr( + self, + k, + TypeAdapter(hints[k]).validate_python(self.singleton_init[k]), + ) @classmethod def get_config(cls, **kwargs) -> InvokeAIAppConfig: @@ -388,6 +413,13 @@ class InvokeAIAppConfig(InvokeAISettings): """ return self._resolve(self.models_dir) + @property + def custom_nodes_path(self) -> Path: + """ + Path to the custom nodes directory + """ + return self._resolve(self.custom_nodes_dir) + # the following methods support legacy calls leftover from the Globals era @property def full_precision(self) -> bool: diff --git a/invokeai/app/services/events/events_base.py b/invokeai/app/services/events/events_base.py index 8685db3717..ad00815151 100644 --- a/invokeai/app/services/events/events_base.py +++ b/invokeai/app/services/events/events_base.py @@ -2,7 +2,6 @@ from typing import Any, Optional -from invokeai.app.invocations.model import ModelInfo from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage from invokeai.app.services.session_queue.session_queue_common import ( BatchStatus, @@ -11,6 +10,7 @@ from invokeai.app.services.session_queue.session_queue_common import ( SessionQueueStatus, ) from invokeai.app.util.misc import get_timestamp +from invokeai.backend.model_management.model_manager import ModelInfo from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType @@ -55,7 +55,7 @@ class EventServiceBase: graph_execution_state_id=graph_execution_state_id, node_id=node.get("id"), source_node_id=source_node_id, - progress_image=progress_image.dict() if progress_image is not None else None, + progress_image=progress_image.model_dump() if progress_image is not None else None, step=step, order=order, total_steps=total_steps, @@ -291,8 +291,8 @@ class EventServiceBase: started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None, completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None, ), - batch_status=batch_status.dict(), - queue_status=queue_status.dict(), + batch_status=batch_status.model_dump(), + queue_status=queue_status.model_dump(), ), ) diff --git a/invokeai/app/services/image_files/image_files_base.py b/invokeai/app/services/image_files/image_files_base.py index d998f9024b..91e18f30fc 100644 --- a/invokeai/app/services/image_files/image_files_base.py +++ b/invokeai/app/services/image_files/image_files_base.py @@ -1,8 +1,11 @@ from abc import ABC, abstractmethod +from pathlib import Path from typing import Optional from PIL.Image import Image as PILImageType +from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField + class ImageFileStorageBase(ABC): """Low-level service responsible for storing and retrieving image files.""" @@ -13,7 +16,7 @@ class ImageFileStorageBase(ABC): pass @abstractmethod - def get_path(self, image_name: str, thumbnail: bool = False) -> str: + def get_path(self, image_name: str, thumbnail: bool = False) -> Path: """Gets the internal path to an image or thumbnail.""" pass @@ -29,8 +32,8 @@ class ImageFileStorageBase(ABC): self, image: PILImageType, image_name: str, - metadata: Optional[dict] = None, - workflow: Optional[str] = None, + metadata: Optional[MetadataField] = None, + workflow: Optional[WorkflowField] = None, thumbnail_size: int = 256, ) -> None: """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp.""" diff --git a/invokeai/app/services/image_files/image_files_disk.py b/invokeai/app/services/image_files/image_files_disk.py index 9111a71605..91c1e14789 100644 --- a/invokeai/app/services/image_files/image_files_disk.py +++ b/invokeai/app/services/image_files/image_files_disk.py @@ -1,5 +1,4 @@ # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team -import json from pathlib import Path from queue import Queue from typing import Dict, Optional, Union @@ -8,6 +7,7 @@ from PIL import Image, PngImagePlugin from PIL.Image import Image as PILImageType from send2trash import send2trash +from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField from invokeai.app.services.invoker import Invoker from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail @@ -55,8 +55,8 @@ class DiskImageFileStorage(ImageFileStorageBase): self, image: PILImageType, image_name: str, - metadata: Optional[dict] = None, - workflow: Optional[str] = None, + metadata: Optional[MetadataField] = None, + workflow: Optional[WorkflowField] = None, thumbnail_size: int = 256, ) -> None: try: @@ -65,20 +65,10 @@ class DiskImageFileStorage(ImageFileStorageBase): pnginfo = PngImagePlugin.PngInfo() - if metadata is not None or workflow is not None: - if metadata is not None: - pnginfo.add_text("invokeai_metadata", json.dumps(metadata)) - if workflow is not None: - pnginfo.add_text("invokeai_workflow", workflow) - else: - # For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back - # TODO: retain non-invokeai metadata on save... - original_metadata = image.info.get("invokeai_metadata", None) - if original_metadata is not None: - pnginfo.add_text("invokeai_metadata", original_metadata) - original_workflow = image.info.get("invokeai_workflow", None) - if original_workflow is not None: - pnginfo.add_text("invokeai_workflow", original_workflow) + if metadata is not None: + pnginfo.add_text("invokeai_metadata", metadata.model_dump_json()) + if workflow is not None: + pnginfo.add_text("invokeai_workflow", workflow.model_dump_json()) image.save( image_path, diff --git a/invokeai/app/services/image_records/image_records_base.py b/invokeai/app/services/image_records/image_records_base.py index 58db6feb23..655e4b4fb8 100644 --- a/invokeai/app/services/image_records/image_records_base.py +++ b/invokeai/app/services/image_records/image_records_base.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from datetime import datetime from typing import Optional +from invokeai.app.invocations.metadata import MetadataField from invokeai.app.services.shared.pagination import OffsetPaginatedResults from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin @@ -18,7 +19,7 @@ class ImageRecordStorageBase(ABC): pass @abstractmethod - def get_metadata(self, image_name: str) -> Optional[dict]: + def get_metadata(self, image_name: str) -> Optional[MetadataField]: """Gets an image's metadata'.""" pass @@ -34,8 +35,8 @@ class ImageRecordStorageBase(ABC): @abstractmethod def get_many( self, - offset: Optional[int] = None, - limit: Optional[int] = None, + offset: int = 0, + limit: int = 10, image_origin: Optional[ResourceOrigin] = None, categories: Optional[list[ImageCategory]] = None, is_intermediate: Optional[bool] = None, @@ -61,6 +62,11 @@ class ImageRecordStorageBase(ABC): """Deletes all intermediate image records, returning a list of deleted image names.""" pass + @abstractmethod + def get_intermediates_count(self) -> int: + """Gets a count of all intermediate images.""" + pass + @abstractmethod def save( self, @@ -69,11 +75,11 @@ class ImageRecordStorageBase(ABC): image_category: ImageCategory, width: int, height: int, - session_id: Optional[str], - node_id: Optional[str], - metadata: Optional[dict], - is_intermediate: bool = False, - starred: bool = False, + is_intermediate: Optional[bool] = False, + starred: Optional[bool] = False, + session_id: Optional[str] = None, + node_id: Optional[str] = None, + metadata: Optional[MetadataField] = None, ) -> datetime: """Saves an image record.""" pass diff --git a/invokeai/app/services/image_records/image_records_common.py b/invokeai/app/services/image_records/image_records_common.py index 39fac92048..5a6e5652c9 100644 --- a/invokeai/app/services/image_records/image_records_common.py +++ b/invokeai/app/services/image_records/image_records_common.py @@ -3,7 +3,7 @@ import datetime from enum import Enum from typing import Optional, Union -from pydantic import Extra, Field, StrictBool, StrictStr +from pydantic import Field, StrictBool, StrictStr from invokeai.app.util.metaenum import MetaEnum from invokeai.app.util.misc import get_iso_timestamp @@ -129,7 +129,9 @@ class ImageRecord(BaseModelExcludeNull): """The created timestamp of the image.""" updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.") """The updated timestamp of the image.""" - deleted_at: Union[datetime.datetime, str, None] = Field(description="The deleted timestamp of the image.") + deleted_at: Optional[Union[datetime.datetime, str]] = Field( + default=None, description="The deleted timestamp of the image." + ) """The deleted timestamp of the image.""" is_intermediate: bool = Field(description="Whether this is an intermediate image.") """Whether this is an intermediate image.""" @@ -147,7 +149,7 @@ class ImageRecord(BaseModelExcludeNull): """Whether this image is starred.""" -class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid): +class ImageRecordChanges(BaseModelExcludeNull, extra="allow"): """A set of changes to apply to an image record. Only limited changes are valid: diff --git a/invokeai/app/services/image_records/image_records_sqlite.py b/invokeai/app/services/image_records/image_records_sqlite.py index c0783fdf2f..239917b728 100644 --- a/invokeai/app/services/image_records/image_records_sqlite.py +++ b/invokeai/app/services/image_records/image_records_sqlite.py @@ -1,9 +1,9 @@ -import json import sqlite3 import threading from datetime import datetime -from typing import Optional, cast +from typing import Optional, Union, cast +from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator from invokeai.app.services.shared.pagination import OffsetPaginatedResults from invokeai.app.services.shared.sqlite import SqliteDatabase @@ -24,7 +24,7 @@ from .image_records_common import ( class SqliteImageRecordStorage(ImageRecordStorageBase): _conn: sqlite3.Connection _cursor: sqlite3.Cursor - _lock: threading.Lock + _lock: threading.RLock def __init__(self, db: SqliteDatabase) -> None: super().__init__() @@ -117,7 +117,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): """ ) - def get(self, image_name: str) -> Optional[ImageRecord]: + def get(self, image_name: str) -> ImageRecord: try: self._lock.acquire() @@ -141,22 +141,26 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): return deserialize_image_record(dict(result)) - def get_metadata(self, image_name: str) -> Optional[dict]: + def get_metadata(self, image_name: str) -> Optional[MetadataField]: try: self._lock.acquire() self._cursor.execute( """--sql - SELECT images.metadata FROM images + SELECT metadata FROM images WHERE image_name = ?; """, (image_name,), ) result = cast(Optional[sqlite3.Row], self._cursor.fetchone()) - if not result or not result[0]: - return None - return json.loads(result[0]) + + if not result: + raise ImageRecordNotFoundException + + as_dict = dict(result) + metadata_raw = cast(Optional[str], as_dict.get("metadata", None)) + return MetadataFieldValidator.validate_json(metadata_raw) if metadata_raw is not None else None except sqlite3.Error as e: self._conn.rollback() raise ImageRecordNotFoundException from e @@ -223,8 +227,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): def get_many( self, - offset: Optional[int] = None, - limit: Optional[int] = None, + offset: int = 0, + limit: int = 10, image_origin: Optional[ResourceOrigin] = None, categories: Optional[list[ImageCategory]] = None, is_intermediate: Optional[bool] = None, @@ -249,7 +253,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): """ query_conditions = "" - query_params = [] + query_params: list[Union[int, str, bool]] = [] if image_origin is not None: query_conditions += """--sql @@ -297,11 +301,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): images_query += query_conditions + query_pagination + ";" # Add all the parameters images_params = query_params.copy() - - if limit is not None: - images_params.append(limit) - if offset is not None: - images_params.append(offset) + # Add the pagination parameters + images_params.extend([limit, offset]) # Build the list of images, deserializing each row self._cursor.execute(images_query, images_params) @@ -357,6 +358,24 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): finally: self._lock.release() + def get_intermediates_count(self) -> int: + try: + self._lock.acquire() + self._cursor.execute( + """--sql + SELECT COUNT(*) FROM images + WHERE is_intermediate = TRUE; + """ + ) + count = cast(int, self._cursor.fetchone()[0]) + self._conn.commit() + return count + except sqlite3.Error as e: + self._conn.rollback() + raise ImageRecordDeleteException from e + finally: + self._lock.release() + def delete_intermediates(self) -> list[str]: try: self._lock.acquire() @@ -387,16 +406,16 @@ class SqliteImageRecordStorage(ImageRecordStorageBase): image_name: str, image_origin: ResourceOrigin, image_category: ImageCategory, - session_id: Optional[str], width: int, height: int, - node_id: Optional[str], - metadata: Optional[dict], - is_intermediate: bool = False, - starred: bool = False, + is_intermediate: Optional[bool] = False, + starred: Optional[bool] = False, + session_id: Optional[str] = None, + node_id: Optional[str] = None, + metadata: Optional[MetadataField] = None, ) -> datetime: try: - metadata_json = None if metadata is None else json.dumps(metadata) + metadata_json = metadata.model_dump_json() if metadata is not None else None self._lock.acquire() self._cursor.execute( """--sql diff --git a/invokeai/app/services/images/images_base.py b/invokeai/app/services/images/images_base.py index 71581099a3..50a3a5fb82 100644 --- a/invokeai/app/services/images/images_base.py +++ b/invokeai/app/services/images/images_base.py @@ -3,7 +3,7 @@ from typing import Callable, Optional from PIL.Image import Image as PILImageType -from invokeai.app.invocations.metadata import ImageMetadata +from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField from invokeai.app.services.image_records.image_records_common import ( ImageCategory, ImageRecord, @@ -49,9 +49,9 @@ class ImageServiceABC(ABC): node_id: Optional[str] = None, session_id: Optional[str] = None, board_id: Optional[str] = None, - is_intermediate: bool = False, - metadata: Optional[dict] = None, - workflow: Optional[str] = None, + is_intermediate: Optional[bool] = False, + metadata: Optional[MetadataField] = None, + workflow: Optional[WorkflowField] = None, ) -> ImageDTO: """Creates an image, storing the file and its metadata.""" pass @@ -81,7 +81,7 @@ class ImageServiceABC(ABC): pass @abstractmethod - def get_metadata(self, image_name: str) -> ImageMetadata: + def get_metadata(self, image_name: str) -> Optional[MetadataField]: """Gets an image's metadata.""" pass @@ -123,6 +123,11 @@ class ImageServiceABC(ABC): """Deletes all intermediate images.""" pass + @abstractmethod + def get_intermediates_count(self) -> int: + """Gets the number of intermediate images.""" + pass + @abstractmethod def delete_images_on_board(self, board_id: str): """Deletes all images on a board.""" diff --git a/invokeai/app/services/images/images_common.py b/invokeai/app/services/images/images_common.py index f8b63a16c1..198c26c3a2 100644 --- a/invokeai/app/services/images/images_common.py +++ b/invokeai/app/services/images/images_common.py @@ -20,10 +20,15 @@ class ImageUrlsDTO(BaseModelExcludeNull): class ImageDTO(ImageRecord, ImageUrlsDTO): """Deserialized image record, enriched for the frontend.""" - board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.") + board_id: Optional[str] = Field( + default=None, 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 + workflow_id: Optional[str] = Field( + default=None, + description="The workflow that generated this image.", + ) + """The workflow that generated this image.""" def image_record_to_dto( @@ -31,11 +36,13 @@ def image_record_to_dto( image_url: str, thumbnail_url: str, board_id: Optional[str], + workflow_id: Optional[str], ) -> ImageDTO: """Converts an image record to an image DTO.""" return ImageDTO( - **image_record.dict(), + **image_record.model_dump(), image_url=image_url, thumbnail_url=thumbnail_url, board_id=board_id, + workflow_id=workflow_id, ) diff --git a/invokeai/app/services/images/images_default.py b/invokeai/app/services/images/images_default.py index 9134b9a4f6..8eb768a1b9 100644 --- a/invokeai/app/services/images/images_default.py +++ b/invokeai/app/services/images/images_default.py @@ -2,10 +2,9 @@ from typing import Optional from PIL.Image import Image as PILImageType -from invokeai.app.invocations.metadata import ImageMetadata +from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField from invokeai.app.services.invoker import Invoker from invokeai.app.services.shared.pagination import OffsetPaginatedResults -from invokeai.app.util.metadata import get_metadata_graph_from_raw_session from ..image_files.image_files_common import ( ImageFileDeleteException, @@ -41,9 +40,9 @@ class ImageService(ImageServiceABC): node_id: Optional[str] = None, session_id: Optional[str] = None, board_id: Optional[str] = None, - is_intermediate: bool = False, - metadata: Optional[dict] = None, - workflow: Optional[str] = None, + is_intermediate: Optional[bool] = False, + metadata: Optional[MetadataField] = None, + workflow: Optional[WorkflowField] = None, ) -> ImageDTO: if image_origin not in ResourceOrigin: raise InvalidOriginException @@ -56,6 +55,12 @@ class ImageService(ImageServiceABC): (width, height) = image.size try: + if workflow is not None: + created_workflow = self.__invoker.services.workflow_records.create(workflow) + workflow_id = created_workflow.model_dump()["id"] + else: + workflow_id = None + # TODO: Consider using a transaction here to ensure consistency between storage and database self.__invoker.services.image_records.save( # Non-nullable fields @@ -73,6 +78,8 @@ class ImageService(ImageServiceABC): ) if board_id is not None: self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name) + if workflow_id is not None: + self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name) self.__invoker.services.image_files.save( image_name=image_name, image=image, metadata=metadata, workflow=workflow ) @@ -132,10 +139,11 @@ class ImageService(ImageServiceABC): image_record = self.__invoker.services.image_records.get(image_name) image_dto = image_record_to_dto( - image_record, - self.__invoker.services.urls.get_image_url(image_name), - self.__invoker.services.urls.get_image_url(image_name, True), - self.__invoker.services.board_image_records.get_board_for_image(image_name), + image_record=image_record, + image_url=self.__invoker.services.urls.get_image_url(image_name), + thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True), + board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name), + workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name), ) return image_dto @@ -146,25 +154,22 @@ class ImageService(ImageServiceABC): self.__invoker.services.logger.error("Problem getting image DTO") raise e - def get_metadata(self, image_name: str) -> Optional[ImageMetadata]: + def get_metadata(self, image_name: str) -> Optional[MetadataField]: try: - image_record = self.__invoker.services.image_records.get(image_name) - metadata = self.__invoker.services.image_records.get_metadata(image_name) + return self.__invoker.services.image_records.get_metadata(image_name) + except ImageRecordNotFoundException: + self.__invoker.services.logger.error("Image record not found") + raise + except Exception as e: + self.__invoker.services.logger.error("Problem getting image DTO") + raise e - if not image_record.session_id: - return ImageMetadata(metadata=metadata) - - session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id) - graph = None - - if session_raw: - try: - graph = get_metadata_graph_from_raw_session(session_raw) - except Exception as e: - self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}") - graph = None - - return ImageMetadata(graph=graph, metadata=metadata) + def get_workflow(self, image_name: str) -> Optional[WorkflowField]: + try: + workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name) + if workflow_id is None: + return None + return self.__invoker.services.workflow_records.get(workflow_id) except ImageRecordNotFoundException: self.__invoker.services.logger.error("Image record not found") raise @@ -174,7 +179,7 @@ class ImageService(ImageServiceABC): def get_path(self, image_name: str, thumbnail: bool = False) -> str: try: - return self.__invoker.services.image_files.get_path(image_name, thumbnail) + return str(self.__invoker.services.image_files.get_path(image_name, thumbnail)) except Exception as e: self.__invoker.services.logger.error("Problem getting image path") raise e @@ -215,10 +220,11 @@ class ImageService(ImageServiceABC): image_dtos = list( map( lambda r: image_record_to_dto( - r, - self.__invoker.services.urls.get_image_url(r.image_name), - self.__invoker.services.urls.get_image_url(r.image_name, True), - self.__invoker.services.board_image_records.get_board_for_image(r.image_name), + image_record=r, + image_url=self.__invoker.services.urls.get_image_url(r.image_name), + thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True), + board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name), + workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name), ), results.items, ) @@ -284,3 +290,10 @@ class ImageService(ImageServiceABC): except Exception as e: self.__invoker.services.logger.error("Problem deleting image records and files") raise e + + def get_intermediates_count(self) -> int: + try: + return self.__invoker.services.image_records.get_intermediates_count() + except Exception as e: + self.__invoker.services.logger.error("Problem getting intermediates count") + raise e diff --git a/invokeai/app/services/invocation_cache/invocation_cache_memory.py b/invokeai/app/services/invocation_cache/invocation_cache_memory.py index 817dbb958e..4a503b3c6b 100644 --- a/invokeai/app/services/invocation_cache/invocation_cache_memory.py +++ b/invokeai/app/services/invocation_cache/invocation_cache_memory.py @@ -58,7 +58,12 @@ class MemoryInvocationCache(InvocationCacheBase): # If the cache is full, we need to remove the least used number_to_delete = len(self._cache) + 1 - self._max_cache_size self._delete_oldest_access(number_to_delete) - self._cache[key] = CachedItem(invocation_output, invocation_output.json()) + self._cache[key] = CachedItem( + invocation_output, + invocation_output.model_dump_json( + warnings=False, exclude_defaults=True, exclude_unset=True, include={"type"} + ), + ) def _delete_oldest_access(self, number_to_delete: int) -> None: number_to_delete = min(number_to_delete, len(self._cache)) @@ -85,7 +90,7 @@ class MemoryInvocationCache(InvocationCacheBase): @staticmethod def create_key(invocation: BaseInvocation) -> int: - return hash(invocation.json(exclude={"id"})) + return hash(invocation.model_dump_json(exclude={"id"}, warnings=False)) def disable(self) -> None: with self._lock: diff --git a/invokeai/app/services/invocation_processor/invocation_processor_default.py b/invokeai/app/services/invocation_processor/invocation_processor_default.py index 349c4a03e4..c59fb678ef 100644 --- a/invokeai/app/services/invocation_processor/invocation_processor_default.py +++ b/invokeai/app/services/invocation_processor/invocation_processor_default.py @@ -89,7 +89,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC): queue_item_id=queue_item.session_queue_item_id, queue_id=queue_item.session_queue_id, graph_execution_state_id=graph_execution_state.id, - node=invocation.dict(), + node=invocation.model_dump(), source_node_id=source_node_id, ) @@ -127,9 +127,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC): queue_item_id=queue_item.session_queue_item_id, queue_id=queue_item.session_queue_id, graph_execution_state_id=graph_execution_state.id, - node=invocation.dict(), + node=invocation.model_dump(), source_node_id=source_node_id, - result=outputs.dict(), + result=outputs.model_dump(), ) self.__invoker.services.performance_statistics.log_stats() @@ -157,7 +157,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC): queue_item_id=queue_item.session_queue_item_id, queue_id=queue_item.session_queue_id, graph_execution_state_id=graph_execution_state.id, - node=invocation.dict(), + node=invocation.model_dump(), source_node_id=source_node_id, error_type=e.__class__.__name__, error=error, @@ -187,7 +187,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC): queue_item_id=queue_item.session_queue_item_id, queue_id=queue_item.session_queue_id, graph_execution_state_id=graph_execution_state.id, - node=invocation.dict(), + node=invocation.model_dump(), source_node_id=source_node_id, error_type=e.__class__.__name__, error=traceback.format_exc(), diff --git a/invokeai/app/services/invocation_services.py b/invokeai/app/services/invocation_services.py index ba53ea50cf..d405201f4e 100644 --- a/invokeai/app/services/invocation_services.py +++ b/invokeai/app/services/invocation_services.py @@ -27,6 +27,8 @@ if TYPE_CHECKING: from .session_queue.session_queue_base import SessionQueueBase from .shared.graph import GraphExecutionState, LibraryGraph from .urls.urls_base import UrlServiceBase + from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase + from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase class InvocationServices: @@ -55,6 +57,8 @@ class InvocationServices: invocation_cache: "InvocationCacheBase" names: "NameServiceBase" urls: "UrlServiceBase" + workflow_image_records: "WorkflowImageRecordsStorageBase" + workflow_records: "WorkflowRecordsStorageBase" def __init__( self, @@ -80,6 +84,8 @@ class InvocationServices: invocation_cache: "InvocationCacheBase", names: "NameServiceBase", urls: "UrlServiceBase", + workflow_image_records: "WorkflowImageRecordsStorageBase", + workflow_records: "WorkflowRecordsStorageBase", ): self.board_images = board_images self.board_image_records = board_image_records @@ -103,3 +109,5 @@ class InvocationServices: self.invocation_cache = invocation_cache self.names = names self.urls = urls + self.workflow_image_records = workflow_image_records + self.workflow_records = workflow_records diff --git a/invokeai/app/services/invocation_stats/invocation_stats_default.py b/invokeai/app/services/invocation_stats/invocation_stats_default.py index 2041ab6190..be019b6820 100644 --- a/invokeai/app/services/invocation_stats/invocation_stats_default.py +++ b/invokeai/app/services/invocation_stats/invocation_stats_default.py @@ -72,7 +72,7 @@ class InvocationStatsService(InvocationStatsServiceBase): ) self.collector.update_invocation_stats( graph_id=self.graph_id, - invocation_type=self.invocation.type, # type: ignore - `type` is not on the `BaseInvocation` model, but *is* on all invocations + invocation_type=self.invocation.type, # type: ignore # `type` is not on the `BaseInvocation` model, but *is* on all invocations time_used=time.time() - self.start_time, vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0, ) diff --git a/invokeai/app/services/item_storage/item_storage_sqlite.py b/invokeai/app/services/item_storage/item_storage_sqlite.py index b810baf9fd..d0249ebfa6 100644 --- a/invokeai/app/services/item_storage/item_storage_sqlite.py +++ b/invokeai/app/services/item_storage/item_storage_sqlite.py @@ -2,7 +2,7 @@ import sqlite3 import threading from typing import Generic, Optional, TypeVar, get_args -from pydantic import BaseModel, parse_raw_as +from pydantic import BaseModel, TypeAdapter from invokeai.app.services.shared.pagination import PaginatedResults from invokeai.app.services.shared.sqlite import SqliteDatabase @@ -17,7 +17,8 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]): _conn: sqlite3.Connection _cursor: sqlite3.Cursor _id_field: str - _lock: threading.Lock + _lock: threading.RLock + _validator: Optional[TypeAdapter[T]] def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"): super().__init__() @@ -27,6 +28,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]): self._table_name = table_name self._id_field = id_field # TODO: validate that T has this field self._cursor = self._conn.cursor() + self._validator: Optional[TypeAdapter[T]] = None self._create_table() @@ -45,16 +47,21 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]): self._lock.release() def _parse_item(self, item: str) -> T: - # __orig_class__ is technically an implementation detail of the typing module, not a supported API - item_type = get_args(self.__orig_class__)[0] # type: ignore - return parse_raw_as(item_type, item) + if self._validator is None: + """ + We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so + we can create it when it is first needed instead. + __orig_class__ is technically an implementation detail of the typing module, not a supported API + """ + self._validator = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined] + return self._validator.validate_json(item) def set(self, item: T): try: self._lock.acquire() self._cursor.execute( f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""", - (item.json(),), + (item.model_dump_json(warnings=False, exclude_none=True),), ) self._conn.commit() finally: diff --git a/invokeai/app/services/model_manager/__init__.py b/invokeai/app/services/model_manager/__init__.py index e69de29bb2..3d6a9c248c 100644 --- a/invokeai/app/services/model_manager/__init__.py +++ b/invokeai/app/services/model_manager/__init__.py @@ -0,0 +1 @@ +from .model_manager_default import ModelManagerService # noqa F401 diff --git a/invokeai/app/services/model_manager/model_manager_base.py b/invokeai/app/services/model_manager/model_manager_base.py index bb9110ba0a..4c2fc4c085 100644 --- a/invokeai/app/services/model_manager/model_manager_base.py +++ b/invokeai/app/services/model_manager/model_manager_base.py @@ -231,7 +231,7 @@ class ModelManagerServiceBase(ABC): def merge_models( self, model_names: List[str] = Field( - default=None, min_items=2, max_items=3, description="List of model names to merge" + default=None, min_length=2, max_length=3, description="List of model names to merge" ), base_model: Union[BaseModelType, str] = Field( default=None, description="Base model shared by all models to be merged" diff --git a/invokeai/app/services/model_manager/model_manager_default.py b/invokeai/app/services/model_manager/model_manager_default.py index 263f804b4d..cdb3e59a91 100644 --- a/invokeai/app/services/model_manager/model_manager_default.py +++ b/invokeai/app/services/model_manager/model_manager_default.py @@ -327,7 +327,7 @@ class ModelManagerService(ModelManagerServiceBase): def merge_models( self, model_names: List[str] = Field( - default=None, min_items=2, max_items=3, description="List of model names to merge" + default=None, min_length=2, max_length=3, description="List of model names to merge" ), base_model: Union[BaseModelType, str] = Field( default=None, description="Base model shared by all models to be merged" diff --git a/invokeai/app/services/session_queue/session_queue_base.py b/invokeai/app/services/session_queue/session_queue_base.py index b5272f1868..e0b6e4f528 100644 --- a/invokeai/app/services/session_queue/session_queue_base.py +++ b/invokeai/app/services/session_queue/session_queue_base.py @@ -9,7 +9,6 @@ from invokeai.app.services.session_queue.session_queue_common import ( CancelByQueueIDResult, ClearResult, EnqueueBatchResult, - EnqueueGraphResult, IsEmptyResult, IsFullResult, PruneResult, @@ -17,7 +16,6 @@ from invokeai.app.services.session_queue.session_queue_common import ( SessionQueueItemDTO, SessionQueueStatus, ) -from invokeai.app.services.shared.graph import Graph from invokeai.app.services.shared.pagination import CursorPaginatedResults @@ -29,11 +27,6 @@ class SessionQueueBase(ABC): """Dequeues the next session queue item.""" pass - @abstractmethod - def enqueue_graph(self, queue_id: str, graph: Graph, prepend: bool) -> EnqueueGraphResult: - """Enqueues a single graph for execution.""" - pass - @abstractmethod def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult: """Enqueues all permutations of a batch for execution.""" diff --git a/invokeai/app/services/session_queue/session_queue_common.py b/invokeai/app/services/session_queue/session_queue_common.py index 2d40a5b0c4..69e6a3ab87 100644 --- a/invokeai/app/services/session_queue/session_queue_common.py +++ b/invokeai/app/services/session_queue/session_queue_common.py @@ -3,8 +3,8 @@ import json from itertools import chain, product from typing import Generator, Iterable, Literal, NamedTuple, Optional, TypeAlias, Union, cast -from pydantic import BaseModel, Field, StrictStr, parse_raw_as, root_validator, validator -from pydantic.json import pydantic_encoder +from pydantic import BaseModel, ConfigDict, Field, StrictStr, TypeAdapter, field_validator, model_validator +from pydantic_core import to_jsonable_python from invokeai.app.invocations.baseinvocation import BaseInvocation from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError @@ -17,7 +17,7 @@ class BatchZippedLengthError(ValueError): """Raise when a batch has items of different lengths.""" -class BatchItemsTypeError(TypeError): +class BatchItemsTypeError(ValueError): # this cannot be a TypeError in pydantic v2 """Raise when a batch has items of different types.""" @@ -70,7 +70,7 @@ class Batch(BaseModel): default=1, ge=1, description="Int stating how many times to iterate through all possible batch indices" ) - @validator("data") + @field_validator("data") def validate_lengths(cls, v: Optional[BatchDataCollection]): if v is None: return v @@ -81,7 +81,7 @@ class Batch(BaseModel): raise BatchZippedLengthError("Zipped batch items must all have the same length") return v - @validator("data") + @field_validator("data") def validate_types(cls, v: Optional[BatchDataCollection]): if v is None: return v @@ -94,7 +94,7 @@ class Batch(BaseModel): raise BatchItemsTypeError("All items in a batch must have the same type") return v - @validator("data") + @field_validator("data") def validate_unique_field_mappings(cls, v: Optional[BatchDataCollection]): if v is None: return v @@ -107,34 +107,35 @@ class Batch(BaseModel): paths.add(pair) return v - @root_validator(skip_on_failure=True) + @model_validator(mode="after") def validate_batch_nodes_and_edges(cls, values): - batch_data_collection = cast(Optional[BatchDataCollection], values["data"]) + batch_data_collection = cast(Optional[BatchDataCollection], values.data) if batch_data_collection is None: return values - graph = cast(Graph, values["graph"]) + graph = cast(Graph, values.graph) for batch_data_list in batch_data_collection: for batch_data in batch_data_list: try: node = cast(BaseInvocation, graph.get_node(batch_data.node_path)) except NodeNotFoundError: raise NodeNotFoundError(f"Node {batch_data.node_path} not found in graph") - if batch_data.field_name not in node.__fields__: + if batch_data.field_name not in node.model_fields: raise NodeNotFoundError(f"Field {batch_data.field_name} not found in node {batch_data.node_path}") return values - @validator("graph") + @field_validator("graph") def validate_graph(cls, v: Graph): v.validate_self() return v - class Config: - schema_extra = { - "required": [ + model_config = ConfigDict( + json_schema_extra=dict( + required=[ "graph", "runs", ] - } + ) + ) # endregion Batch @@ -146,15 +147,21 @@ DEFAULT_QUEUE_ID = "default" QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"] +NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue]) + def get_field_values(queue_item_dict: dict) -> Optional[list[NodeFieldValue]]: field_values_raw = queue_item_dict.get("field_values", None) - return parse_raw_as(list[NodeFieldValue], field_values_raw) if field_values_raw is not None else None + return NodeFieldValueValidator.validate_json(field_values_raw) if field_values_raw is not None else None + + +GraphExecutionStateValidator = TypeAdapter(GraphExecutionState) def get_session(queue_item_dict: dict) -> GraphExecutionState: session_raw = queue_item_dict.get("session", "{}") - return parse_raw_as(GraphExecutionState, session_raw) + session = GraphExecutionStateValidator.validate_json(session_raw, strict=False) + return session class SessionQueueItemWithoutGraph(BaseModel): @@ -178,14 +185,14 @@ class SessionQueueItemWithoutGraph(BaseModel): ) @classmethod - def from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO": + def queue_item_dto_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO": # must parse these manually queue_item_dict["field_values"] = get_field_values(queue_item_dict) return SessionQueueItemDTO(**queue_item_dict) - class Config: - schema_extra = { - "required": [ + model_config = ConfigDict( + json_schema_extra=dict( + required=[ "item_id", "status", "batch_id", @@ -196,7 +203,8 @@ class SessionQueueItemWithoutGraph(BaseModel): "created_at", "updated_at", ] - } + ) + ) class SessionQueueItemDTO(SessionQueueItemWithoutGraph): @@ -207,15 +215,15 @@ class SessionQueueItem(SessionQueueItemWithoutGraph): session: GraphExecutionState = Field(description="The fully-populated session to be executed") @classmethod - def from_dict(cls, queue_item_dict: dict) -> "SessionQueueItem": + def queue_item_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItem": # must parse these manually queue_item_dict["field_values"] = get_field_values(queue_item_dict) queue_item_dict["session"] = get_session(queue_item_dict) return SessionQueueItem(**queue_item_dict) - class Config: - schema_extra = { - "required": [ + model_config = ConfigDict( + json_schema_extra=dict( + required=[ "item_id", "status", "batch_id", @@ -227,7 +235,8 @@ class SessionQueueItem(SessionQueueItemWithoutGraph): "created_at", "updated_at", ] - } + ) + ) # endregion Queue Items @@ -267,14 +276,6 @@ class EnqueueBatchResult(BaseModel): priority: int = Field(description="The priority of the enqueued batch") -class EnqueueGraphResult(BaseModel): - enqueued: int = Field(description="The total number of queue items enqueued") - requested: int = Field(description="The total number of queue items requested to be enqueued") - batch: Batch = Field(description="The batch that was enqueued") - priority: int = Field(description="The priority of the enqueued batch") - queue_item: SessionQueueItemDTO = Field(description="The queue item that was enqueued") - - class ClearResult(BaseModel): """Result of clearing the session queue""" @@ -321,7 +322,7 @@ def populate_graph(graph: Graph, node_field_values: Iterable[NodeFieldValue]) -> """ Populates the given graph with the given batch data items. """ - graph_clone = graph.copy(deep=True) + graph_clone = graph.model_copy(deep=True) for item in node_field_values: node = graph_clone.get_node(item.node_path) if node is None: @@ -354,7 +355,7 @@ def create_session_nfv_tuples( for item in batch_datum.items ] node_field_values_to_zip.append(node_field_values) - data.append(list(zip(*node_field_values_to_zip))) + data.append(list(zip(*node_field_values_to_zip))) # type: ignore [arg-type] # create generator to yield session,nfv tuples count = 0 @@ -409,11 +410,11 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new values_to_insert.append( SessionQueueValueToInsert( queue_id, # queue_id - session.json(), # session (json) + session.model_dump_json(warnings=False, exclude_none=True), # session (json) session.id, # session_id batch.batch_id, # batch_id # must use pydantic_encoder bc field_values is a list of models - json.dumps(field_values, default=pydantic_encoder) if field_values else None, # field_values (json) + json.dumps(field_values, default=to_jsonable_python) if field_values else None, # field_values (json) priority, # priority ) ) @@ -421,3 +422,6 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new # endregion Util + +Batch.model_rebuild(force=True) +SessionQueueItem.model_rebuild(force=True) diff --git a/invokeai/app/services/session_queue/session_queue_sqlite.py b/invokeai/app/services/session_queue/session_queue_sqlite.py index 0e12382392..7259a7bd0c 100644 --- a/invokeai/app/services/session_queue/session_queue_sqlite.py +++ b/invokeai/app/services/session_queue/session_queue_sqlite.py @@ -17,7 +17,6 @@ from invokeai.app.services.session_queue.session_queue_common import ( CancelByQueueIDResult, ClearResult, EnqueueBatchResult, - EnqueueGraphResult, IsEmptyResult, IsFullResult, PruneResult, @@ -28,7 +27,6 @@ from invokeai.app.services.session_queue.session_queue_common import ( calc_session_count, prepare_values_to_insert, ) -from invokeai.app.services.shared.graph import Graph from invokeai.app.services.shared.pagination import CursorPaginatedResults from invokeai.app.services.shared.sqlite import SqliteDatabase @@ -37,7 +35,7 @@ class SqliteSessionQueue(SessionQueueBase): __invoker: Invoker __conn: sqlite3.Connection __cursor: sqlite3.Cursor - __lock: threading.Lock + __lock: threading.RLock def start(self, invoker: Invoker) -> None: self.__invoker = invoker @@ -255,32 +253,6 @@ class SqliteSessionQueue(SessionQueueBase): ) return cast(Union[int, None], self.__cursor.fetchone()[0]) or 0 - def enqueue_graph(self, queue_id: str, graph: Graph, prepend: bool) -> EnqueueGraphResult: - enqueue_result = self.enqueue_batch(queue_id=queue_id, batch=Batch(graph=graph), prepend=prepend) - try: - self.__lock.acquire() - self.__cursor.execute( - """--sql - SELECT * - FROM session_queue - WHERE queue_id = ? - AND batch_id = ? - """, - (queue_id, enqueue_result.batch.batch_id), - ) - result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone()) - except Exception: - self.__conn.rollback() - raise - finally: - self.__lock.release() - if result is None: - raise SessionQueueItemNotFoundError(f"No queue item with batch id {enqueue_result.batch.batch_id}") - return EnqueueGraphResult( - **enqueue_result.dict(), - queue_item=SessionQueueItemDTO.from_dict(dict(result)), - ) - def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult: try: self.__lock.acquire() @@ -351,7 +323,7 @@ class SqliteSessionQueue(SessionQueueBase): self.__lock.release() if result is None: return None - queue_item = SessionQueueItem.from_dict(dict(result)) + queue_item = SessionQueueItem.queue_item_from_dict(dict(result)) queue_item = self._set_queue_item_status(item_id=queue_item.item_id, status="in_progress") return queue_item @@ -380,7 +352,7 @@ class SqliteSessionQueue(SessionQueueBase): self.__lock.release() if result is None: return None - return SessionQueueItem.from_dict(dict(result)) + return SessionQueueItem.queue_item_from_dict(dict(result)) def get_current(self, queue_id: str) -> Optional[SessionQueueItem]: try: @@ -404,7 +376,7 @@ class SqliteSessionQueue(SessionQueueBase): self.__lock.release() if result is None: return None - return SessionQueueItem.from_dict(dict(result)) + return SessionQueueItem.queue_item_from_dict(dict(result)) def _set_queue_item_status( self, item_id: int, status: QUEUE_ITEM_STATUS, error: Optional[str] = None @@ -564,7 +536,7 @@ class SqliteSessionQueue(SessionQueueBase): queue_item = self.get_queue_item(item_id) if queue_item.status not in ["canceled", "failed", "completed"]: status = "failed" if error is not None else "canceled" - queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error) + queue_item = self._set_queue_item_status(item_id=item_id, status=status, error=error) # type: ignore [arg-type] # mypy seems to not narrow the Literals here self.__invoker.services.queue.cancel(queue_item.session_id) self.__invoker.services.events.emit_session_canceled( queue_item_id=queue_item.item_id, @@ -699,7 +671,7 @@ class SqliteSessionQueue(SessionQueueBase): self.__lock.release() if result is None: raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}") - return SessionQueueItem.from_dict(dict(result)) + return SessionQueueItem.queue_item_from_dict(dict(result)) def list_queue_items( self, @@ -751,7 +723,7 @@ class SqliteSessionQueue(SessionQueueBase): params.append(limit + 1) self.__cursor.execute(query, params) results = cast(list[sqlite3.Row], self.__cursor.fetchall()) - items = [SessionQueueItemDTO.from_dict(dict(result)) for result in results] + items = [SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)) for result in results] has_more = False if len(items) > limit: # remove the extra item diff --git a/invokeai/app/services/shared/default_graphs.py b/invokeai/app/services/shared/default_graphs.py index b2d0a1f0b6..9a6e2456cb 100644 --- a/invokeai/app/services/shared/default_graphs.py +++ b/invokeai/app/services/shared/default_graphs.py @@ -80,10 +80,10 @@ def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[Li # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes graphs: list[LibraryGraph] = list() - # text_to_image = graph_library.get(default_text_to_image_graph_id) + text_to_image = graph_library.get(default_text_to_image_graph_id) - # # TODO: Check if the graph is the same as the default one, and if not, update it - # #if text_to_image is None: + # TODO: Check if the graph is the same as the default one, and if not, update it + # if text_to_image is None: text_to_image = create_text_to_image() graph_library.set(text_to_image) diff --git a/invokeai/app/services/shared/graph.py b/invokeai/app/services/shared/graph.py index dab045af9d..b84d456071 100644 --- a/invokeai/app/services/shared/graph.py +++ b/invokeai/app/services/shared/graph.py @@ -5,7 +5,7 @@ import itertools from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints import networkx as nx -from pydantic import BaseModel, root_validator, validator +from pydantic import BaseModel, ConfigDict, field_validator, model_validator from pydantic.fields import Field # Importing * is bad karma but needed here for node detection @@ -193,7 +193,7 @@ class GraphInvocation(BaseInvocation): """Execute a graph""" # TODO: figure out how to create a default here - graph: "Graph" = Field(description="The graph to run", default=None) + graph: "Graph" = InputField(description="The graph to run", default=None) def invoke(self, context: InvocationContext) -> GraphInvocationOutput: """Invoke with provided services and return outputs.""" @@ -235,7 +235,8 @@ class CollectInvocationOutput(BaseInvocationOutput): class CollectInvocation(BaseInvocation): """Collects values into a collection""" - item: Any = InputField( + item: Optional[Any] = InputField( + default=None, description="The item to collect (all inputs must be of the same type)", ui_type=UIType.CollectionItem, title="Collection Item", @@ -250,8 +251,8 @@ class CollectInvocation(BaseInvocation): return CollectInvocationOutput(collection=copy.copy(self.collection)) -InvocationsUnion = Union[BaseInvocation.get_invocations()] # type: ignore -InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()] # type: ignore +InvocationsUnion: Any = BaseInvocation.get_invocations_union() +InvocationOutputsUnion: Any = BaseInvocationOutput.get_outputs_union() class Graph(BaseModel): @@ -378,13 +379,13 @@ class Graph(BaseModel): raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph") # output fields are not on the node object directly, they are on the output type - if edge.source.field not in source_node.get_output_type().__fields__: + if edge.source.field not in source_node.get_output_type().model_fields: raise NodeFieldNotFoundError( f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}" ) # input fields are on the node - if edge.destination.field not in destination_node.__fields__: + if edge.destination.field not in destination_node.model_fields: raise NodeFieldNotFoundError( f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}" ) @@ -395,24 +396,24 @@ class Graph(BaseModel): raise CyclicalGraphError("Graph contains cycles") # Validate all edge connections are valid - for e in self.edges: + for edge in self.edges: if not are_connections_compatible( - self.get_node(e.source.node_id), - e.source.field, - self.get_node(e.destination.node_id), - e.destination.field, + self.get_node(edge.source.node_id), + edge.source.field, + self.get_node(edge.destination.node_id), + edge.destination.field, ): raise InvalidEdgeError( - f"Invalid edge from {e.source.node_id}.{e.source.field} to {e.destination.node_id}.{e.destination.field}" + f"Invalid edge from {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}" ) # Validate all iterators & collectors # TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available - for n in self.nodes.values(): - if isinstance(n, IterateInvocation) and not self._is_iterator_connection_valid(n.id): - raise InvalidEdgeError(f"Invalid iterator node {n.id}") - if isinstance(n, CollectInvocation) and not self._is_collector_connection_valid(n.id): - raise InvalidEdgeError(f"Invalid collector node {n.id}") + for node in self.nodes.values(): + if isinstance(node, IterateInvocation) and not self._is_iterator_connection_valid(node.id): + raise InvalidEdgeError(f"Invalid iterator node {node.id}") + if isinstance(node, CollectInvocation) and not self._is_collector_connection_valid(node.id): + raise InvalidEdgeError(f"Invalid collector node {node.id}") return None @@ -438,6 +439,14 @@ class Graph(BaseModel): except Exception as e: raise UnknownGraphValidationError(f"Problem validating graph {e}") from e + def _is_destination_field_Any(self, edge: Edge) -> bool: + """Checks if the destination field for an edge is of type typing.Any""" + return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any + + def _is_destination_field_list_of_Any(self, edge: Edge) -> bool: + """Checks if the destination field for an edge is of type typing.Any""" + return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any] + def _validate_edge(self, edge: Edge): """Validates that a new edge doesn't create a cycle in the graph""" @@ -490,8 +499,19 @@ class Graph(BaseModel): f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}" ) - # Validate if collector output type matches input type (if this edge results in both being set) - if isinstance(from_node, CollectInvocation) and edge.source.field == "collection": + # Validate that we are not connecting collector to iterator (currently unsupported) + if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation): + raise InvalidEdgeError( + f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}" + ) + + # Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any] + if ( + isinstance(from_node, CollectInvocation) + and edge.source.field == "collection" + and not self._is_destination_field_list_of_Any(edge) + and not self._is_destination_field_Any(edge) + ): if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination): raise InvalidEdgeError( f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}" @@ -594,7 +614,7 @@ class Graph(BaseModel): def _get_input_edges_and_graphs( self, node_path: str, prefix: Optional[str] = None - ) -> list[tuple["Graph", str, Edge]]: + ) -> list[tuple["Graph", Union[str, None], Edge]]: """Gets all input edges for a node along with the graph they are in and the graph's path""" edges = list() @@ -636,7 +656,7 @@ class Graph(BaseModel): def _get_output_edges_and_graphs( self, node_path: str, prefix: Optional[str] = None - ) -> list[tuple["Graph", str, Edge]]: + ) -> list[tuple["Graph", Union[str, None], Edge]]: """Gets all output edges for a node along with the graph they are in and the graph's path""" edges = list() @@ -724,16 +744,15 @@ class Graph(BaseModel): # Get the input root type input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore - # Verify that all outputs are lists - # if not all((get_origin(f) == list for f in output_fields)): - # return False - # Verify that all outputs are lists if not all(is_list_or_contains_list(f) for f in output_fields): return False # Verify that all outputs match the input type (are a base class or the same class) - if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)): + if not all( + is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0]) + for f in output_fields + ): return False return True @@ -817,15 +836,15 @@ class GraphExecutionState(BaseModel): default_factory=dict, ) - @validator("graph") + @field_validator("graph") def graph_is_valid(cls, v: Graph): """Validates that the graph is valid""" v.validate_self() return v - class Config: - schema_extra = { - "required": [ + model_config = ConfigDict( + json_schema_extra=dict( + required=[ "id", "graph", "execution_graph", @@ -836,7 +855,8 @@ class GraphExecutionState(BaseModel): "prepared_source_mapping", "source_prepared_mapping", ] - } + ) + ) def next(self) -> Optional[BaseInvocation]: """Gets the next node ready to execute.""" @@ -910,7 +930,7 @@ class GraphExecutionState(BaseModel): input_collection = getattr(input_collection_prepared_node_output, input_collection_edge.source.field) self_iteration_count = len(input_collection) - new_nodes = list() + new_nodes: list[str] = list() if self_iteration_count == 0: # TODO: should this raise a warning? It might just happen if an empty collection is input, and should be valid. return new_nodes @@ -920,7 +940,7 @@ class GraphExecutionState(BaseModel): # Create new edges for this iteration # For collect nodes, this may contain multiple inputs to the same field - new_edges = list() + new_edges: list[Edge] = list() for edge in input_edges: for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge.source.node_id): new_edge = Edge( @@ -1179,18 +1199,18 @@ class LibraryGraph(BaseModel): description="The outputs exposed by this graph", default_factory=list ) - @validator("exposed_inputs", "exposed_outputs") - def validate_exposed_aliases(cls, v): + @field_validator("exposed_inputs", "exposed_outputs") + def validate_exposed_aliases(cls, v: list[Union[ExposedNodeInput, ExposedNodeOutput]]): if len(v) != len(set(i.alias for i in v)): raise ValueError("Duplicate exposed alias") return v - @root_validator + @model_validator(mode="after") def validate_exposed_nodes(cls, values): - graph = values["graph"] + graph = values.graph # Validate exposed inputs - for exposed_input in values["exposed_inputs"]: + for exposed_input in values.exposed_inputs: if not graph.has_node(exposed_input.node_path): raise ValueError(f"Exposed input node {exposed_input.node_path} does not exist") node = graph.get_node(exposed_input.node_path) @@ -1200,7 +1220,7 @@ class LibraryGraph(BaseModel): ) # Validate exposed outputs - for exposed_output in values["exposed_outputs"]: + for exposed_output in values.exposed_outputs: if not graph.has_node(exposed_output.node_path): raise ValueError(f"Exposed output node {exposed_output.node_path} does not exist") node = graph.get_node(exposed_output.node_path) @@ -1212,4 +1232,6 @@ class LibraryGraph(BaseModel): return values -GraphInvocation.update_forward_refs() +GraphInvocation.model_rebuild(force=True) +Graph.model_rebuild(force=True) +GraphExecutionState.model_rebuild(force=True) diff --git a/invokeai/app/services/shared/pagination.py b/invokeai/app/services/shared/pagination.py index 85c8fb984e..ea342b1101 100644 --- a/invokeai/app/services/shared/pagination.py +++ b/invokeai/app/services/shared/pagination.py @@ -1,12 +1,11 @@ from typing import Generic, TypeVar from pydantic import BaseModel, Field -from pydantic.generics import GenericModel GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel) -class CursorPaginatedResults(GenericModel, Generic[GenericBaseModel]): +class CursorPaginatedResults(BaseModel, Generic[GenericBaseModel]): """ Cursor-paginated results Generic must be a Pydantic model @@ -17,7 +16,7 @@ class CursorPaginatedResults(GenericModel, Generic[GenericBaseModel]): items: list[GenericBaseModel] = Field(..., description="Items") -class OffsetPaginatedResults(GenericModel, Generic[GenericBaseModel]): +class OffsetPaginatedResults(BaseModel, Generic[GenericBaseModel]): """ Offset-paginated results Generic must be a Pydantic model @@ -29,7 +28,7 @@ class OffsetPaginatedResults(GenericModel, Generic[GenericBaseModel]): items: list[GenericBaseModel] = Field(description="Items") -class PaginatedResults(GenericModel, Generic[GenericBaseModel]): +class PaginatedResults(BaseModel, Generic[GenericBaseModel]): """ Paginated results Generic must be a Pydantic model diff --git a/invokeai/app/services/shared/sqlite.py b/invokeai/app/services/shared/sqlite.py index c41dbbe606..3c75c3d6a7 100644 --- a/invokeai/app/services/shared/sqlite.py +++ b/invokeai/app/services/shared/sqlite.py @@ -9,7 +9,7 @@ sqlite_memory = ":memory:" class SqliteDatabase: conn: sqlite3.Connection - lock: threading.Lock + lock: threading.RLock _logger: Logger _config: InvokeAIAppConfig @@ -27,7 +27,7 @@ class SqliteDatabase: self._logger.info(f"Using database at {location}") self.conn = sqlite3.connect(location, check_same_thread=False) - self.lock = threading.Lock() + self.lock = threading.RLock() self.conn.row_factory = sqlite3.Row if self._config.log_sql: diff --git a/invokeai/app/cli/__init__.py b/invokeai/app/services/workflow_image_records/__init__.py similarity index 100% rename from invokeai/app/cli/__init__.py rename to invokeai/app/services/workflow_image_records/__init__.py diff --git a/invokeai/app/services/workflow_image_records/workflow_image_records_base.py b/invokeai/app/services/workflow_image_records/workflow_image_records_base.py new file mode 100644 index 0000000000..d99a2ba106 --- /dev/null +++ b/invokeai/app/services/workflow_image_records/workflow_image_records_base.py @@ -0,0 +1,23 @@ +from abc import ABC, abstractmethod +from typing import Optional + + +class WorkflowImageRecordsStorageBase(ABC): + """Abstract base class for the one-to-many workflow-image relationship record storage.""" + + @abstractmethod + def create( + self, + workflow_id: str, + image_name: str, + ) -> None: + """Creates a workflow-image record.""" + pass + + @abstractmethod + def get_workflow_for_image( + self, + image_name: str, + ) -> Optional[str]: + """Gets an image's workflow id, if it has one.""" + pass diff --git a/invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py b/invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py new file mode 100644 index 0000000000..ec7a73f1d5 --- /dev/null +++ b/invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py @@ -0,0 +1,122 @@ +import sqlite3 +import threading +from typing import Optional, cast + +from invokeai.app.services.shared.sqlite import SqliteDatabase +from invokeai.app.services.workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase + + +class SqliteWorkflowImageRecordsStorage(WorkflowImageRecordsStorageBase): + """SQLite implementation of WorkflowImageRecordsStorageBase.""" + + _conn: sqlite3.Connection + _cursor: sqlite3.Cursor + _lock: threading.RLock + + def __init__(self, db: SqliteDatabase) -> None: + super().__init__() + self._lock = db.lock + self._conn = db.conn + self._cursor = self._conn.cursor() + + try: + self._lock.acquire() + self._create_tables() + self._conn.commit() + finally: + self._lock.release() + + def _create_tables(self) -> None: + # Create the `workflow_images` junction table. + self._cursor.execute( + """--sql + CREATE TABLE IF NOT EXISTS workflow_images ( + workflow_id TEXT NOT NULL, + image_name TEXT NOT NULL, + created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')), + -- updated via trigger + updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')), + -- Soft delete, currently unused + deleted_at DATETIME, + -- enforce one-to-many relationship between workflows and images using PK + -- (we can extend this to many-to-many later) + PRIMARY KEY (image_name), + FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE CASCADE, + FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE + ); + """ + ) + + # Add index for workflow id + self._cursor.execute( + """--sql + CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id ON workflow_images (workflow_id); + """ + ) + + # Add index for workflow id, sorted by created_at + self._cursor.execute( + """--sql + CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id_created_at ON workflow_images (workflow_id, created_at); + """ + ) + + # Add trigger for `updated_at`. + self._cursor.execute( + """--sql + CREATE TRIGGER IF NOT EXISTS tg_workflow_images_updated_at + AFTER UPDATE + ON workflow_images FOR EACH ROW + BEGIN + UPDATE workflow_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW') + WHERE workflow_id = old.workflow_id AND image_name = old.image_name; + END; + """ + ) + + def create( + self, + workflow_id: str, + image_name: str, + ) -> None: + """Creates a workflow-image record.""" + try: + self._lock.acquire() + self._cursor.execute( + """--sql + INSERT INTO workflow_images (workflow_id, image_name) + VALUES (?, ?); + """, + (workflow_id, image_name), + ) + self._conn.commit() + except sqlite3.Error as e: + self._conn.rollback() + raise e + finally: + self._lock.release() + + def get_workflow_for_image( + self, + image_name: str, + ) -> Optional[str]: + """Gets an image's workflow id, if it has one.""" + try: + self._lock.acquire() + self._cursor.execute( + """--sql + SELECT workflow_id + FROM workflow_images + WHERE image_name = ?; + """, + (image_name,), + ) + result = self._cursor.fetchone() + if result is None: + return None + return cast(str, result[0]) + except sqlite3.Error as e: + self._conn.rollback() + raise e + finally: + self._lock.release() diff --git a/invokeai/app/services/workflow_records/__init__.py b/invokeai/app/services/workflow_records/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/invokeai/app/services/workflow_records/workflow_records_base.py b/invokeai/app/services/workflow_records/workflow_records_base.py new file mode 100644 index 0000000000..d5a4b25ce4 --- /dev/null +++ b/invokeai/app/services/workflow_records/workflow_records_base.py @@ -0,0 +1,17 @@ +from abc import ABC, abstractmethod + +from invokeai.app.invocations.baseinvocation import WorkflowField + + +class WorkflowRecordsStorageBase(ABC): + """Base class for workflow storage services.""" + + @abstractmethod + def get(self, workflow_id: str) -> WorkflowField: + """Get workflow by id.""" + pass + + @abstractmethod + def create(self, workflow: WorkflowField) -> WorkflowField: + """Creates a workflow.""" + pass diff --git a/invokeai/app/services/workflow_records/workflow_records_common.py b/invokeai/app/services/workflow_records/workflow_records_common.py new file mode 100644 index 0000000000..3a2b13f565 --- /dev/null +++ b/invokeai/app/services/workflow_records/workflow_records_common.py @@ -0,0 +1,2 @@ +class WorkflowNotFoundError(Exception): + """Raised when a workflow is not found""" diff --git a/invokeai/app/services/workflow_records/workflow_records_sqlite.py b/invokeai/app/services/workflow_records/workflow_records_sqlite.py new file mode 100644 index 0000000000..b0952e8234 --- /dev/null +++ b/invokeai/app/services/workflow_records/workflow_records_sqlite.py @@ -0,0 +1,102 @@ +import sqlite3 +import threading + +from invokeai.app.invocations.baseinvocation import WorkflowField, WorkflowFieldValidator +from invokeai.app.services.invoker import Invoker +from invokeai.app.services.shared.sqlite import SqliteDatabase +from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase +from invokeai.app.services.workflow_records.workflow_records_common import WorkflowNotFoundError +from invokeai.app.util.misc import uuid_string + + +class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase): + _invoker: Invoker + _conn: sqlite3.Connection + _cursor: sqlite3.Cursor + _lock: threading.RLock + + def __init__(self, db: SqliteDatabase) -> None: + super().__init__() + self._lock = db.lock + self._conn = db.conn + self._cursor = self._conn.cursor() + self._create_tables() + + def start(self, invoker: Invoker) -> None: + self._invoker = invoker + + def get(self, workflow_id: str) -> WorkflowField: + try: + self._lock.acquire() + self._cursor.execute( + """--sql + SELECT workflow + FROM workflows + WHERE workflow_id = ?; + """, + (workflow_id,), + ) + row = self._cursor.fetchone() + if row is None: + raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found") + return WorkflowFieldValidator.validate_json(row[0]) + except Exception: + self._conn.rollback() + raise + finally: + self._lock.release() + + def create(self, workflow: WorkflowField) -> WorkflowField: + try: + # workflows do not have ids until they are saved + workflow_id = uuid_string() + workflow.root["id"] = workflow_id + self._lock.acquire() + self._cursor.execute( + """--sql + INSERT INTO workflows(workflow) + VALUES (?); + """, + (workflow.model_dump_json(),), + ) + self._conn.commit() + except Exception: + self._conn.rollback() + raise + finally: + self._lock.release() + return self.get(workflow_id) + + def _create_tables(self) -> None: + try: + self._lock.acquire() + self._cursor.execute( + """--sql + CREATE TABLE IF NOT EXISTS workflows ( + workflow TEXT NOT NULL, + workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index + created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')), + updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger + ); + """ + ) + + self._cursor.execute( + """--sql + CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at + AFTER UPDATE + ON workflows FOR EACH ROW + BEGIN + UPDATE workflows + SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW') + WHERE workflow_id = old.workflow_id; + END; + """ + ) + + self._conn.commit() + except Exception: + self._conn.rollback() + raise + finally: + self._lock.release() diff --git a/invokeai/app/util/controlnet_utils.py b/invokeai/app/util/controlnet_utils.py index e6f34a4c44..51ceec2edd 100644 --- a/invokeai/app/util/controlnet_utils.py +++ b/invokeai/app/util/controlnet_utils.py @@ -265,7 +265,7 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device: def prepare_control_image( - image: Image, + image: Image.Image, width: int, height: int, num_channels: int = 3, diff --git a/invokeai/app/util/misc.py b/invokeai/app/util/misc.py index 6d56652ed4..910b05d8dd 100644 --- a/invokeai/app/util/misc.py +++ b/invokeai/app/util/misc.py @@ -1,4 +1,5 @@ import datetime +import typing import uuid import numpy as np @@ -27,3 +28,8 @@ def get_random_seed(): def uuid_string(): res = uuid.uuid4() return str(res) + + +def is_optional(value: typing.Any): + """Checks if a value is typed as Optional. Note that Optional is sugar for Union[x, None].""" + return typing.get_origin(value) is typing.Union and type(None) in typing.get_args(value) diff --git a/invokeai/app/util/model_exclude_null.py b/invokeai/app/util/model_exclude_null.py index b75f127ec7..6da41039b4 100644 --- a/invokeai/app/util/model_exclude_null.py +++ b/invokeai/app/util/model_exclude_null.py @@ -13,11 +13,11 @@ From https://github.com/tiangolo/fastapi/discussions/8882#discussioncomment-5154 class BaseModelExcludeNull(BaseModel): - def dict(self, *args, **kwargs) -> dict[str, Any]: + def model_dump(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) + return super().model_dump(*args, exclude_none=True, **kwargs) pass diff --git a/invokeai/assets/__init__.py b/invokeai/assets/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/invokeai/backend/image_util/invisible_watermark.py b/invokeai/backend/image_util/invisible_watermark.py index 3e8604f9c3..37b3ca918c 100644 --- a/invokeai/backend/image_util/invisible_watermark.py +++ b/invokeai/backend/image_util/invisible_watermark.py @@ -20,12 +20,12 @@ class InvisibleWatermark: """ @classmethod - def invisible_watermark_available(self) -> bool: + def invisible_watermark_available(cls) -> bool: return config.invisible_watermark @classmethod - def add_watermark(self, image: Image, watermark_text: str) -> Image: - if not self.invisible_watermark_available(): + def add_watermark(cls, image: Image.Image, watermark_text: str) -> Image.Image: + if not cls.invisible_watermark_available(): return image logger.debug(f'Applying invisible watermark "{watermark_text}"') bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR) diff --git a/invokeai/backend/image_util/safety_checker.py b/invokeai/backend/image_util/safety_checker.py index fd1f05f10e..b9649925e1 100644 --- a/invokeai/backend/image_util/safety_checker.py +++ b/invokeai/backend/image_util/safety_checker.py @@ -26,8 +26,8 @@ class SafetyChecker: tried_load: bool = False @classmethod - def _load_safety_checker(self): - if self.tried_load: + def _load_safety_checker(cls): + if cls.tried_load: return if config.nsfw_checker: @@ -35,31 +35,31 @@ class SafetyChecker: from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from transformers import AutoFeatureExtractor - self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH) - self.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH) + cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH) + cls.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH) logger.info("NSFW checker initialized") except Exception as e: logger.warning(f"Could not load NSFW checker: {str(e)}") else: logger.info("NSFW checker loading disabled") - self.tried_load = True + cls.tried_load = True @classmethod - def safety_checker_available(self) -> bool: - self._load_safety_checker() - return self.safety_checker is not None + def safety_checker_available(cls) -> bool: + cls._load_safety_checker() + return cls.safety_checker is not None @classmethod - def has_nsfw_concept(self, image: Image) -> bool: - if not self.safety_checker_available(): + def has_nsfw_concept(cls, image: Image.Image) -> bool: + if not cls.safety_checker_available(): return False device = choose_torch_device() - features = self.feature_extractor([image], return_tensors="pt") + features = cls.feature_extractor([image], return_tensors="pt") features.to(device) - self.safety_checker.to(device) + cls.safety_checker.to(device) x_image = np.array(image).astype(np.float32) / 255.0 x_image = x_image[None].transpose(0, 3, 1, 2) with SilenceWarnings(): - checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values) + checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values) return has_nsfw_concept[0] diff --git a/invokeai/backend/image_util/txt2mask.py b/invokeai/backend/image_util/txt2mask.py index 12db54b0db..de0c6a1652 100644 --- a/invokeai/backend/image_util/txt2mask.py +++ b/invokeai/backend/image_util/txt2mask.py @@ -41,18 +41,18 @@ config = InvokeAIAppConfig.get_config() class SegmentedGrayscale(object): - def __init__(self, image: Image, heatmap: torch.Tensor): + def __init__(self, image: Image.Image, heatmap: torch.Tensor): self.heatmap = heatmap self.image = image - def to_grayscale(self, invert: bool = False) -> Image: + def to_grayscale(self, invert: bool = False) -> Image.Image: return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255))) - def to_mask(self, threshold: float = 0.5) -> Image: + def to_mask(self, threshold: float = 0.5) -> Image.Image: discrete_heatmap = self.heatmap.lt(threshold).int() return self._rescale(Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L")) - def to_transparent(self, invert: bool = False) -> Image: + def to_transparent(self, invert: bool = False) -> Image.Image: transparent_image = self.image.copy() # For img2img, we want the selected regions to be transparent, # but to_grayscale() returns the opposite. Thus invert. @@ -61,7 +61,7 @@ class SegmentedGrayscale(object): return transparent_image # unscales and uncrops the 352x352 heatmap so that it matches the image again - def _rescale(self, heatmap: Image) -> Image: + def _rescale(self, heatmap: Image.Image) -> Image.Image: size = self.image.width if (self.image.width > self.image.height) else self.image.height resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS) return resized_image.crop((0, 0, self.image.width, self.image.height)) @@ -82,7 +82,7 @@ class Txt2Mask(object): self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir) @torch.no_grad() - def segment(self, image, prompt: str) -> SegmentedGrayscale: + def segment(self, image: Image.Image, prompt: str) -> SegmentedGrayscale: """ Given a prompt string such as "a bagel", tries to identify the object in the provided image and returns a SegmentedGrayscale object in which the brighter @@ -99,7 +99,7 @@ class Txt2Mask(object): heatmap = torch.sigmoid(outputs.logits) return SegmentedGrayscale(image, heatmap) - def _scale_and_crop(self, image: Image) -> Image: + def _scale_and_crop(self, image: Image.Image) -> Image.Image: scaled_image = Image.new("RGB", (CLIPSEG_SIZE, CLIPSEG_SIZE)) if image.width > image.height: # width is constraint scale = CLIPSEG_SIZE / image.width diff --git a/invokeai/backend/image_util/util.py b/invokeai/backend/image_util/util.py index bc7fa01e3b..7eceb9be82 100644 --- a/invokeai/backend/image_util/util.py +++ b/invokeai/backend/image_util/util.py @@ -9,7 +9,7 @@ class InitImageResizer: def __init__(self, Image): self.image = Image - def resize(self, width=None, height=None) -> Image: + def resize(self, width=None, height=None) -> Image.Image: """ Return a copy of the image resized to fit within a box width x height. The aspect ratio is diff --git a/invokeai/backend/install/invokeai_configure.py b/invokeai/backend/install/invokeai_configure.py index d4bcea64d0..59cf1260ba 100755 --- a/invokeai/backend/install/invokeai_configure.py +++ b/invokeai/backend/install/invokeai_configure.py @@ -793,7 +793,11 @@ def migrate_init_file(legacy_format: Path): old = legacy_parser.parse_args([f"@{str(legacy_format)}"]) new = InvokeAIAppConfig.get_config() - fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"] + fields = [ + x + for x, y in InvokeAIAppConfig.model_fields.items() + if (y.json_schema_extra.get("category", None) if y.json_schema_extra else None) != "DEPRECATED" + ] for attr in fields: if hasattr(old, attr): try: diff --git a/invokeai/backend/install/model_install_backend.py b/invokeai/backend/install/model_install_backend.py index 1481300c77..9784aa0ac2 100644 --- a/invokeai/backend/install/model_install_backend.py +++ b/invokeai/backend/install/model_install_backend.py @@ -236,9 +236,16 @@ class ModelInstall(object): if not models_installed: models_installed = dict() + model_path_id_or_url = str(model_path_id_or_url).strip("\"' ") + # A little hack to allow nested routines to retrieve info on the requested ID self.current_id = model_path_id_or_url path = Path(model_path_id_or_url) + + # fix relative paths + if path.exists() and not path.is_absolute(): + path = path.absolute() # make relative to current WD + # checkpoint file, or similar if path.is_file(): models_installed.update({str(path): self._install_path(path)}) @@ -453,6 +460,12 @@ class ModelInstall(object): possible_conf = path.with_suffix(".yaml") if possible_conf.exists(): legacy_conf = str(self.relative_to_root(possible_conf)) + else: + legacy_conf = Path( + self.config.root_path, + "configs/controlnet", + ("cldm_v15.yaml" if info.base_type == BaseModelType("sd-1") else "cldm_v21.yaml"), + ) if legacy_conf: attributes.update(dict(config=str(legacy_conf))) diff --git a/invokeai/backend/ip_adapter/attention_processor.py b/invokeai/backend/ip_adapter/attention_processor.py index 2873c52322..c06d7d113c 100644 --- a/invokeai/backend/ip_adapter/attention_processor.py +++ b/invokeai/backend/ip_adapter/attention_processor.py @@ -67,6 +67,12 @@ class IPAttnProcessor2_0(torch.nn.Module): temb=None, ip_adapter_image_prompt_embeds=None, ): + """Apply IP-Adapter attention. + + Args: + ip_adapter_image_prompt_embeds (torch.Tensor): The image prompt embeddings. + Shape: (batch_size, num_ip_images, seq_len, ip_embedding_len). + """ residual = hidden_states if attn.spatial_norm is not None: @@ -127,26 +133,35 @@ class IPAttnProcessor2_0(torch.nn.Module): for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales): # The batch dimensions should match. assert ipa_embed.shape[0] == encoder_hidden_states.shape[0] - # The channel dimensions should match. - assert ipa_embed.shape[2] == encoder_hidden_states.shape[2] + # The token_len dimensions should match. + assert ipa_embed.shape[-1] == encoder_hidden_states.shape[-1] ip_hidden_states = ipa_embed + # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding) + ip_key = ipa_weights.to_k_ip(ip_hidden_states) ip_value = ipa_weights.to_v_ip(ip_hidden_states) + # Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads) + ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - # The output of sdpa has shape: (batch, num_heads, seq_len, head_dim) + # Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 ip_hidden_states = F.scaled_dot_product_attention( query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False ) + # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim) + ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) ip_hidden_states = ip_hidden_states.to(query.dtype) + # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim) + hidden_states = hidden_states + scale * ip_hidden_states # linear proj diff --git a/invokeai/backend/model_management/lora.py b/invokeai/backend/model_management/lora.py index bb44455c88..18eff002b4 100644 --- a/invokeai/backend/model_management/lora.py +++ b/invokeai/backend/model_management/lora.py @@ -1,6 +1,6 @@ from __future__ import annotations -import copy +import pickle from contextlib import contextmanager from pathlib import Path from typing import Any, Dict, List, Optional, Tuple, Union @@ -12,6 +12,8 @@ from diffusers.models import UNet2DConditionModel from safetensors.torch import load_file from transformers import CLIPTextModel, CLIPTokenizer +from invokeai.app.invocations.shared import FreeUConfig + from .models.lora import LoRAModel """ @@ -54,24 +56,6 @@ class ModelPatcher: return (module_key, module) - @staticmethod - def _lora_forward_hook( - applied_loras: List[Tuple[LoRAModel, float]], - layer_name: str, - ): - def lora_forward(module, input_h, output): - if len(applied_loras) == 0: - return output - - for lora, weight in applied_loras: - layer = lora.layers.get(layer_name, None) - if layer is None: - continue - output += layer.forward(module, input_h, weight) - return output - - return lora_forward - @classmethod @contextmanager def apply_lora_unet( @@ -129,21 +113,40 @@ class ModelPatcher: if not layer_key.startswith(prefix): continue + # TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This + # should be improved in the following ways: + # 1. The key mapping could be more-efficiently pre-computed. This would save time every time a + # LoRA model is applied. + # 2. From an API perspective, there's no reason that the `ModelPatcher` should be aware of the + # intricacies of Stable Diffusion key resolution. It should just expect the input LoRA + # weights to have valid keys. module_key, module = cls._resolve_lora_key(model, layer_key, prefix) + + # All of the LoRA weight calculations will be done on the same device as the module weight. + # (Performance will be best if this is a CUDA device.) + device = module.weight.device + dtype = module.weight.dtype + if module_key not in original_weights: original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True) - # enable autocast to calc fp16 loras on cpu - # 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(original_weights[module_key]) * lora_weight * layer_scale + + # We intentionally move to the target device first, then cast. Experimentally, this was found to + # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the + # same thing in a single call to '.to(...)'. + layer.to(device=device) + layer.to(dtype=torch.float32) + # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA + # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed. + layer_weight = layer.get_weight(module.weight) * (lora_weight * layer_scale) + layer.to(device="cpu") if module.weight.shape != layer_weight.shape: # TODO: debug on lycoris layer_weight = layer_weight.reshape(module.weight.shape) - module.weight += layer_weight.to(device=module.weight.device, dtype=module.weight.dtype) + module.weight += layer_weight.to(dtype=dtype) yield # wait for context manager exit @@ -164,7 +167,13 @@ class ModelPatcher: new_tokens_added = None try: - ti_tokenizer = copy.deepcopy(tokenizer) + # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a + # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after + # exiting this `apply_ti(...)` context manager. + # + # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`, + # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs). + ti_tokenizer = pickle.loads(pickle.dumps(tokenizer)) ti_manager = TextualInversionManager(ti_tokenizer) init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings @@ -196,7 +205,9 @@ class ModelPatcher: if model_embeddings.weight.data[token_id].shape != embedding.shape: raise ValueError( - f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {model_embeddings.weight.data[token_id].shape[0]}." + f"Cannot load embedding for {trigger}. It was trained on a model with token dimension" + f" {embedding.shape[0]}, but the current model has token dimension" + f" {model_embeddings.weight.data[token_id].shape[0]}." ) model_embeddings.weight.data[token_id] = embedding.to( @@ -231,6 +242,25 @@ class ModelPatcher: while len(skipped_layers) > 0: text_encoder.text_model.encoder.layers.append(skipped_layers.pop()) + @classmethod + @contextmanager + def apply_freeu( + cls, + unet: UNet2DConditionModel, + freeu_config: Optional[FreeUConfig] = None, + ): + did_apply_freeu = False + try: + if freeu_config is not None: + unet.enable_freeu(b1=freeu_config.b1, b2=freeu_config.b2, s1=freeu_config.s1, s2=freeu_config.s2) + did_apply_freeu = True + + yield + + finally: + if did_apply_freeu: + unet.disable_freeu() + class TextualInversionModel: embedding: torch.Tensor # [n, 768]|[n, 1280] @@ -257,7 +287,8 @@ class TextualInversionModel: if "string_to_param" in state_dict: if len(state_dict["string_to_param"]) > 1: print( - f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first token will be used.' + f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first' + " token will be used." ) result.embedding = next(iter(state_dict["string_to_param"].values())) @@ -435,7 +466,13 @@ class ONNXModelPatcher: orig_embeddings = None try: - ti_tokenizer = copy.deepcopy(tokenizer) + # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a + # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after + # exiting this `apply_ti(...)` context manager. + # + # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`, + # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs). + ti_tokenizer = pickle.loads(pickle.dumps(tokenizer)) ti_manager = TextualInversionManager(ti_tokenizer) def _get_trigger(ti_name, index): @@ -470,7 +507,9 @@ class ONNXModelPatcher: if embeddings[token_id].shape != embedding.shape: raise ValueError( - f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {embeddings[token_id].shape[0]}." + f"Cannot load embedding for {trigger}. It was trained on a model with token dimension" + f" {embedding.shape[0]}, but the current model has token dimension" + f" {embeddings[token_id].shape[0]}." ) embeddings[token_id] = embedding diff --git a/invokeai/backend/model_management/memory_snapshot.py b/invokeai/backend/model_management/memory_snapshot.py index 4f43affcf7..fe54af191c 100644 --- a/invokeai/backend/model_management/memory_snapshot.py +++ b/invokeai/backend/model_management/memory_snapshot.py @@ -55,14 +55,16 @@ class MemorySnapshot: try: malloc_info = LibcUtil().mallinfo2() - except OSError: - # This is expected in environments that do not have the 'libc.so.6' shared library. + except (OSError, AttributeError): + # OSError: This is expected in environments that do not have the 'libc.so.6' shared library. + # AttributeError: This is expected in environments that have `libc.so.6` but do not have the `mallinfo2` (e.g. glibc < 2.33) + # TODO: Does `mallinfo` work? malloc_info = None return cls(process_ram, vram, malloc_info) -def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnapshot) -> str: +def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: Optional[MemorySnapshot]) -> str: """Get a pretty string describing the difference between two `MemorySnapshot`s.""" def get_msg_line(prefix: str, val1: int, val2: int): @@ -71,6 +73,9 @@ def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnaps msg = "" + if snapshot_1 is None or snapshot_2 is None: + return msg + msg += get_msg_line("Process RAM", snapshot_1.process_ram, snapshot_2.process_ram) if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None: diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py index 2a6079b2c4..83af789219 100644 --- a/invokeai/backend/model_management/model_cache.py +++ b/invokeai/backend/model_management/model_cache.py @@ -117,6 +117,7 @@ class ModelCache(object): lazy_offloading: bool = True, sha_chunksize: int = 16777216, logger: types.ModuleType = logger, + log_memory_usage: bool = False, ): """ :param max_cache_size: Maximum size of the RAM cache [6.0 GB] @@ -126,6 +127,10 @@ class ModelCache(object): :param lazy_offloading: Keep model in VRAM until another model needs to be loaded :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially :param sha_chunksize: Chunksize to use when calculating sha256 model hash + :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache + operation, and the result will be logged (at debug level). There is a time cost to capturing the memory + snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's + behaviour. """ self.model_infos: Dict[str, ModelBase] = dict() # allow lazy offloading only when vram cache enabled @@ -137,6 +142,7 @@ class ModelCache(object): self.storage_device: torch.device = storage_device self.sha_chunksize = sha_chunksize self.logger = logger + self._log_memory_usage = log_memory_usage # used for stats collection self.stats = None @@ -144,6 +150,11 @@ class ModelCache(object): self._cached_models = dict() self._cache_stack = list() + def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]: + if self._log_memory_usage: + return MemorySnapshot.capture() + return None + def get_key( self, model_path: str, @@ -223,10 +234,10 @@ class ModelCache(object): # Load the model from disk and capture a memory snapshot before/after. start_load_time = time.time() - snapshot_before = MemorySnapshot.capture() + snapshot_before = self._capture_memory_snapshot() with skip_torch_weight_init(): model = model_info.get_model(child_type=submodel, torch_dtype=self.precision) - snapshot_after = MemorySnapshot.capture() + snapshot_after = self._capture_memory_snapshot() end_load_time = time.time() self_reported_model_size_after_load = model_info.get_size(submodel) @@ -275,9 +286,9 @@ class ModelCache(object): return start_model_to_time = time.time() - snapshot_before = MemorySnapshot.capture() + snapshot_before = self._capture_memory_snapshot() cache_entry.model.to(target_device) - snapshot_after = MemorySnapshot.capture() + snapshot_after = self._capture_memory_snapshot() end_model_to_time = time.time() self.logger.debug( f"Moved model '{key}' from {source_device} to" @@ -286,7 +297,12 @@ class ModelCache(object): f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}" ) - if snapshot_before.vram is not None and snapshot_after.vram is not None: + if ( + snapshot_before is not None + and snapshot_after is not None + and snapshot_before.vram is not None + and snapshot_after.vram is not None + ): vram_change = abs(snapshot_before.vram - snapshot_after.vram) # If the estimated model size does not match the change in VRAM, log a warning. @@ -422,12 +438,17 @@ class ModelCache(object): self.logger.debug(f"Before unloading: cached_models={len(self._cached_models)}") pos = 0 + models_cleared = 0 while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack): model_key = self._cache_stack[pos] cache_entry = self._cached_models[model_key] refs = sys.getrefcount(cache_entry.model) + # HACK: This is a workaround for a memory-management issue that we haven't tracked down yet. We are directly + # going against the advice in the Python docs by using `gc.get_referrers(...)` in this way: + # https://docs.python.org/3/library/gc.html#gc.get_referrers + # manualy clear local variable references of just finished function calls # for some reason python don't want to collect it even by gc.collect() immidiately if refs > 2: @@ -453,15 +474,16 @@ class ModelCache(object): f" refs: {refs}" ) - # 2 refs: + # Expected refs: # 1 from cache_entry # 1 from getrefcount function # 1 from onnx runtime object - if not cache_entry.locked and refs <= 3 if "onnx" in model_key else 2: + if not cache_entry.locked and refs <= (3 if "onnx" in model_key else 2): self.logger.debug( f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)" ) current_size -= cache_entry.size + models_cleared += 1 if self.stats: self.stats.cleared += 1 del self._cache_stack[pos] @@ -471,7 +493,20 @@ class ModelCache(object): else: pos += 1 - gc.collect() + if models_cleared > 0: + # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but + # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost + # is high even if no garbage gets collected.) + # + # Calling gc.collect(...) when a model is cleared seems like a good middle-ground: + # - If models had to be cleared, it's a signal that we are close to our memory limit. + # - If models were cleared, there's a good chance that there's a significant amount of garbage to be + # collected. + # + # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up + # immediately when their reference count hits 0. + gc.collect() + torch.cuda.empty_cache() if choose_torch_device() == torch.device("mps"): mps.empty_cache() @@ -491,7 +526,6 @@ class ModelCache(object): vram_in_use = torch.cuda.memory_allocated() self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB") - gc.collect() torch.cuda.empty_cache() if choose_torch_device() == torch.device("mps"): mps.empty_cache() diff --git a/invokeai/backend/model_management/model_load_optimizations.py b/invokeai/backend/model_management/model_load_optimizations.py index f835079213..8dc8a8793e 100644 --- a/invokeai/backend/model_management/model_load_optimizations.py +++ b/invokeai/backend/model_management/model_load_optimizations.py @@ -17,7 +17,7 @@ def skip_torch_weight_init(): completely unnecessary if the intent is to load checkpoint weights from disk for the layer. This context manager monkey-patches common torch layers to skip the weight initialization step. """ - torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd] + torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd, torch.nn.Embedding] saved_functions = [m.reset_parameters for m in torch_modules] try: diff --git a/invokeai/backend/model_management/model_manager.py b/invokeai/backend/model_management/model_manager.py index 7bb188cb4e..da4239fa07 100644 --- a/invokeai/backend/model_management/model_manager.py +++ b/invokeai/backend/model_management/model_manager.py @@ -236,13 +236,13 @@ import types from dataclasses import dataclass from pathlib import Path from shutil import move, rmtree -from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union +from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union, cast import torch import yaml from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field import invokeai.backend.util.logging as logger from invokeai.app.services.config import InvokeAIAppConfig @@ -294,6 +294,8 @@ class AddModelResult(BaseModel): base_model: BaseModelType = Field(description="The base model") config: ModelConfigBase = Field(description="The configuration of the model") + model_config = ConfigDict(protected_namespaces=()) + MAX_CACHE_SIZE = 6.0 # GB @@ -349,6 +351,7 @@ class ModelManager(object): precision=precision, sequential_offload=sequential_offload, logger=logger, + log_memory_usage=self.app_config.log_memory_usage, ) self._read_models(config) @@ -576,7 +579,7 @@ class ModelManager(object): """ model_key = self.create_key(model_name, base_model, model_type) if model_key in self.models: - return self.models[model_key].dict(exclude_defaults=True) + return self.models[model_key].model_dump(exclude_defaults=True) else: return None # TODO: None or empty dict on not found @@ -632,7 +635,7 @@ class ModelManager(object): continue model_dict = dict( - **model_config.dict(exclude_defaults=True), + **model_config.model_dump(exclude_defaults=True), # OpenAPIModelInfoBase model_name=cur_model_name, base_model=cur_base_model, @@ -900,14 +903,16 @@ class ModelManager(object): Write current configuration out to the indicated file. """ data_to_save = dict() - data_to_save["__metadata__"] = self.config_meta.dict() + data_to_save["__metadata__"] = self.config_meta.model_dump() for model_key, model_config in self.models.items(): model_name, base_model, model_type = self.parse_key(model_key) 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"}) + data_to_save[model_key] = cast(BaseModel, model_config).model_dump( + exclude_defaults=True, exclude={"error"}, mode="json" + ) # alias for config file data_to_save[model_key]["format"] = data_to_save[model_key].pop("model_format") @@ -986,6 +991,8 @@ class ModelManager(object): for model_path in models_dir.iterdir(): if model_path not in loaded_files: # TODO: check + if model_path.name.startswith("."): + continue model_name = model_path.name if model_path.is_dir() else model_path.stem model_key = self.create_key(model_name, cur_base_model, cur_model_type) @@ -1005,6 +1012,8 @@ class ModelManager(object): self.logger.warning(f"Not a valid model: {model_path}. {e}") except NotImplementedError as e: self.logger.warning(e) + except Exception as e: + self.logger.warning(f"Error loading model {model_path}. {e}") imported_models = self.scan_autoimport_directory() if (new_models_found or imported_models) and self.config_path: diff --git a/invokeai/backend/model_management/models/__init__.py b/invokeai/backend/model_management/models/__init__.py index bf4b208395..0afd731032 100644 --- a/invokeai/backend/model_management/models/__init__.py +++ b/invokeai/backend/model_management/models/__init__.py @@ -2,7 +2,7 @@ import inspect from enum import Enum from typing import Literal, get_origin -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict, create_model from .base import ( # noqa: F401 BaseModelType, @@ -106,6 +106,8 @@ class OpenAPIModelInfoBase(BaseModel): base_model: BaseModelType model_type: ModelType + model_config = ConfigDict(protected_namespaces=()) + for base_model, models in MODEL_CLASSES.items(): for model_type, model_class in models.items(): @@ -121,17 +123,11 @@ for base_model, models in MODEL_CLASSES.items(): if openapi_cfg_name in vars(): continue - api_wrapper = type( + api_wrapper = create_model( openapi_cfg_name, - (cfg, OpenAPIModelInfoBase), - dict( - __annotations__=dict( - model_type=Literal[model_type.value], - ), - ), + __base__=(cfg, OpenAPIModelInfoBase), + model_type=(Literal[model_type], model_type), # type: ignore ) - - # globals()[openapi_cfg_name] = api_wrapper vars()[openapi_cfg_name] = api_wrapper OPENAPI_MODEL_CONFIGS.append(api_wrapper) diff --git a/invokeai/backend/model_management/models/base.py b/invokeai/backend/model_management/models/base.py index 6e507735d4..f735e37189 100644 --- a/invokeai/backend/model_management/models/base.py +++ b/invokeai/backend/model_management/models/base.py @@ -19,7 +19,7 @@ from diffusers import logging as diffusers_logging from onnx import numpy_helper from onnxruntime import InferenceSession, SessionOptions, get_available_providers from picklescan.scanner import scan_file_path -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from transformers import logging as transformers_logging @@ -86,14 +86,21 @@ class ModelError(str, Enum): NotFound = "not_found" +def model_config_json_schema_extra(schema: dict[str, Any]) -> None: + if "required" not in schema: + schema["required"] = [] + schema["required"].append("model_type") + + class ModelConfigBase(BaseModel): path: str # or Path description: Optional[str] = Field(None) model_format: Optional[str] = Field(None) error: Optional[ModelError] = Field(None) - class Config: - use_enum_values = True + model_config = ConfigDict( + use_enum_values=True, protected_namespaces=(), json_schema_extra=model_config_json_schema_extra + ) class EmptyConfigLoader(ConfigMixin): diff --git a/invokeai/backend/model_management/models/controlnet.py b/invokeai/backend/model_management/models/controlnet.py index 359df91a82..6a42b59fe1 100644 --- a/invokeai/backend/model_management/models/controlnet.py +++ b/invokeai/backend/model_management/models/controlnet.py @@ -132,13 +132,14 @@ def _convert_controlnet_ckpt_and_cache( model_path: str, output_path: str, base_model: BaseModelType, - model_config: ControlNetModel.CheckpointConfig, + model_config: str, ) -> str: """ Convert the controlnet from checkpoint format to diffusers format, cache it to disk, and return Path to converted file. If already on disk then just returns Path. """ + print(f"DEBUG: controlnet config = {model_config}") app_config = InvokeAIAppConfig.get_config() weights = app_config.root_path / model_path output_path = Path(output_path) diff --git a/invokeai/backend/model_management/models/ip_adapter.py b/invokeai/backend/model_management/models/ip_adapter.py index 63694af0c8..c60edd0abe 100644 --- a/invokeai/backend/model_management/models/ip_adapter.py +++ b/invokeai/backend/model_management/models/ip_adapter.py @@ -58,14 +58,16 @@ class IPAdapterModel(ModelBase): def get_model( self, - torch_dtype: Optional[torch.dtype], + torch_dtype: torch.dtype, child_type: Optional[SubModelType] = None, ) -> typing.Union[IPAdapter, IPAdapterPlus]: if child_type is not None: raise ValueError("There are no child models in an IP-Adapter model.") model = build_ip_adapter( - ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype + ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), + device=torch.device("cpu"), + dtype=torch_dtype, ) self.model_size = model.calc_size() diff --git a/invokeai/backend/model_management/models/lora.py b/invokeai/backend/model_management/models/lora.py index b6f321d60b..6ececa7df1 100644 --- a/invokeai/backend/model_management/models/lora.py +++ b/invokeai/backend/model_management/models/lora.py @@ -440,33 +440,19 @@ class IA3Layer(LoRALayerBase): 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, @@ -475,8 +461,6 @@ class LoRAModelRaw: # (torch.nn.Module): # 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 @@ -557,8 +541,6 @@ class LoRAModelRaw: # (torch.nn.Module): file_path = Path(file_path) model = cls( - device=device, - dtype=dtype, name=file_path.stem, # TODO: layers=dict(), ) diff --git a/invokeai/backend/model_management/seamless.py b/invokeai/backend/model_management/seamless.py index 7138f2e123..bfdf9e0c53 100644 --- a/invokeai/backend/model_management/seamless.py +++ b/invokeai/backend/model_management/seamless.py @@ -96,7 +96,7 @@ def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axe finally: for module, orig_conv_forward in to_restore: module._conv_forward = orig_conv_forward - if hasattr(m, "asymmetric_padding_mode"): - del m.asymmetric_padding_mode - if hasattr(m, "asymmetric_padding"): - del m.asymmetric_padding + if hasattr(module, "asymmetric_padding_mode"): + del module.asymmetric_padding_mode + if hasattr(module, "asymmetric_padding"): + del module.asymmetric_padding diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py index 0943b78bf8..1b65326f6e 100644 --- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py +++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py @@ -546,11 +546,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): # Handle ControlNet(s) and T2I-Adapter(s) down_block_additional_residuals = None mid_block_additional_residual = None - if control_data is not None and t2i_adapter_data is not None: - # TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility - # between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers. - raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).") - elif control_data is not None: + down_intrablock_additional_residuals = None + # if control_data is not None and t2i_adapter_data is not None: + # TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility + # between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers. + # raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).") + # elif control_data is not None: + if control_data is not None: down_block_additional_residuals, mid_block_additional_residual = self.invokeai_diffuser.do_controlnet_step( control_data=control_data, sample=latent_model_input, @@ -559,7 +561,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): total_step_count=total_step_count, conditioning_data=conditioning_data, ) - elif t2i_adapter_data is not None: + # elif t2i_adapter_data is not None: + if t2i_adapter_data is not None: accum_adapter_state = None for single_t2i_adapter_data in t2i_adapter_data: # Determine the T2I-Adapter weights for the current denoising step. @@ -584,7 +587,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): for idx, value in enumerate(single_t2i_adapter_data.adapter_state): accum_adapter_state[idx] += value * t2i_adapter_weight - down_block_additional_residuals = accum_adapter_state + # down_block_additional_residuals = accum_adapter_state + down_intrablock_additional_residuals = accum_adapter_state uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step( sample=latent_model_input, @@ -593,8 +597,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline): total_step_count=total_step_count, conditioning_data=conditioning_data, # extra: - down_block_additional_residuals=down_block_additional_residuals, - mid_block_additional_residual=mid_block_additional_residual, + down_block_additional_residuals=down_block_additional_residuals, # for ControlNet + mid_block_additional_residual=mid_block_additional_residual, # for ControlNet + down_intrablock_additional_residuals=down_intrablock_additional_residuals, # for T2I-Adapter ) guidance_scale = conditioning_data.guidance_scale diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py index 7c3f835a44..6a63c225fc 100644 --- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py +++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py @@ -55,11 +55,11 @@ class PostprocessingSettings: class IPAdapterConditioningInfo: cond_image_prompt_embeds: torch.Tensor """IP-Adapter image encoder conditioning embeddings. - Shape: (batch_size, num_tokens, encoding_dim). + Shape: (num_images, num_tokens, encoding_dim). """ uncond_image_prompt_embeds: torch.Tensor """IP-Adapter image encoding embeddings to use for unconditional generation. - Shape: (batch_size, num_tokens, encoding_dim). + Shape: (num_images, num_tokens, encoding_dim). """ diff --git a/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py b/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py index abef979b1c..b5ea40185a 100644 --- a/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py +++ b/invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py @@ -1,7 +1,8 @@ import math +from typing import Optional -import PIL import torch +from PIL import Image from torchvision.transforms.functional import InterpolationMode from torchvision.transforms.functional import resize as tv_resize @@ -11,7 +12,7 @@ class AttentionMapSaver: self.token_ids = token_ids self.latents_shape = latents_shape # self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]]) - self.collated_maps = {} + self.collated_maps: dict[str, torch.Tensor] = {} def clear_maps(self): self.collated_maps = {} @@ -38,9 +39,10 @@ class AttentionMapSaver: def write_maps_to_disk(self, path: str): pil_image = self.get_stacked_maps_image() - pil_image.save(path, "PNG") + if pil_image is not None: + pil_image.save(path, "PNG") - def get_stacked_maps_image(self) -> PIL.Image: + def get_stacked_maps_image(self) -> Optional[Image.Image]: """ Scale all collected attention maps to the same size, blend them together and return as an image. :return: An image containing a vertical stack of blended attention maps, one for each requested token. @@ -95,4 +97,4 @@ class AttentionMapSaver: return None merged_bytes = merged.mul(0xFF).byte() - return PIL.Image.fromarray(merged_bytes.numpy(), mode="L") + return Image.fromarray(merged_bytes.numpy(), mode="L") diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py index c12c86ed92..b7c0058fe9 100644 --- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py +++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py @@ -260,7 +260,6 @@ class InvokeAIDiffuserComponent: conditioning_data, **kwargs, ) - else: ( unconditioned_next_x, @@ -345,9 +344,12 @@ class InvokeAIDiffuserComponent: cross_attention_kwargs = None if conditioning_data.ip_adapter_conditioning is not None: + # Note that we 'stack' to produce tensors of shape (batch_size, num_ip_images, seq_len, token_len). cross_attention_kwargs = { "ip_adapter_image_prompt_embeds": [ - torch.cat([ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds]) + torch.stack( + [ipa_conditioning.uncond_image_prompt_embeds, ipa_conditioning.cond_image_prompt_embeds] + ) for ipa_conditioning in conditioning_data.ip_adapter_conditioning ] } @@ -407,6 +409,15 @@ class InvokeAIDiffuserComponent: uncond_down_block.append(_uncond_down) cond_down_block.append(_cond_down) + uncond_down_intrablock, cond_down_intrablock = None, None + down_intrablock_additional_residuals = kwargs.pop("down_intrablock_additional_residuals", None) + if down_intrablock_additional_residuals is not None: + uncond_down_intrablock, cond_down_intrablock = [], [] + for down_intrablock in down_intrablock_additional_residuals: + _uncond_down, _cond_down = down_intrablock.chunk(2) + uncond_down_intrablock.append(_uncond_down) + cond_down_intrablock.append(_cond_down) + uncond_mid_block, cond_mid_block = None, None mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None) if mid_block_additional_residual is not None: @@ -415,9 +426,10 @@ class InvokeAIDiffuserComponent: # Run unconditional UNet denoising. cross_attention_kwargs = None if conditioning_data.ip_adapter_conditioning is not None: + # Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len). cross_attention_kwargs = { "ip_adapter_image_prompt_embeds": [ - ipa_conditioning.uncond_image_prompt_embeds + torch.unsqueeze(ipa_conditioning.uncond_image_prompt_embeds, dim=0) for ipa_conditioning in conditioning_data.ip_adapter_conditioning ] } @@ -437,6 +449,7 @@ class InvokeAIDiffuserComponent: cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=uncond_down_block, mid_block_additional_residual=uncond_mid_block, + down_intrablock_additional_residuals=uncond_down_intrablock, added_cond_kwargs=added_cond_kwargs, **kwargs, ) @@ -444,9 +457,10 @@ class InvokeAIDiffuserComponent: # Run conditional UNet denoising. cross_attention_kwargs = None if conditioning_data.ip_adapter_conditioning is not None: + # Note that we 'unsqueeze' to produce tensors of shape (batch_size=1, num_ip_images, seq_len, token_len). cross_attention_kwargs = { "ip_adapter_image_prompt_embeds": [ - ipa_conditioning.cond_image_prompt_embeds + torch.unsqueeze(ipa_conditioning.cond_image_prompt_embeds, dim=0) for ipa_conditioning in conditioning_data.ip_adapter_conditioning ] } @@ -465,6 +479,7 @@ class InvokeAIDiffuserComponent: cross_attention_kwargs=cross_attention_kwargs, down_block_additional_residuals=cond_down_block, mid_block_additional_residual=cond_mid_block, + down_intrablock_additional_residuals=cond_down_intrablock, added_cond_kwargs=added_cond_kwargs, **kwargs, ) @@ -489,6 +504,15 @@ class InvokeAIDiffuserComponent: uncond_down_block.append(_uncond_down) cond_down_block.append(_cond_down) + uncond_down_intrablock, cond_down_intrablock = None, None + down_intrablock_additional_residuals = kwargs.pop("down_intrablock_additional_residuals", None) + if down_intrablock_additional_residuals is not None: + uncond_down_intrablock, cond_down_intrablock = [], [] + for down_intrablock in down_intrablock_additional_residuals: + _uncond_down, _cond_down = down_intrablock.chunk(2) + uncond_down_intrablock.append(_uncond_down) + cond_down_intrablock.append(_cond_down) + uncond_mid_block, cond_mid_block = None, None mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None) if mid_block_additional_residual is not None: @@ -517,6 +541,7 @@ class InvokeAIDiffuserComponent: {"swap_cross_attn_context": cross_attn_processor_context}, down_block_additional_residuals=uncond_down_block, mid_block_additional_residual=uncond_mid_block, + down_intrablock_additional_residuals=uncond_down_intrablock, added_cond_kwargs=added_cond_kwargs, **kwargs, ) @@ -536,6 +561,7 @@ class InvokeAIDiffuserComponent: {"swap_cross_attn_context": cross_attn_processor_context}, down_block_additional_residuals=cond_down_block, mid_block_additional_residual=cond_mid_block, + down_intrablock_additional_residuals=cond_down_intrablock, added_cond_kwargs=added_cond_kwargs, **kwargs, ) diff --git a/invokeai/backend/training/textual_inversion_training.py b/invokeai/backend/training/textual_inversion_training.py index 153bd0fcc4..9bc1d188bc 100644 --- a/invokeai/backend/training/textual_inversion_training.py +++ b/invokeai/backend/training/textual_inversion_training.py @@ -41,7 +41,7 @@ from transformers import CLIPTextModel, CLIPTokenizer # invokeai stuff from invokeai.app.services.config import InvokeAIAppConfig, PagingArgumentParser -from invokeai.app.services.model_manager_service import ModelManagerService +from invokeai.app.services.model_manager import ModelManagerService from invokeai.backend.model_management.models import SubModelType if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): diff --git a/invokeai/configs/INITIAL_MODELS.yaml b/invokeai/configs/INITIAL_MODELS.yaml index b6883ea915..67fcad4055 100644 --- a/invokeai/configs/INITIAL_MODELS.yaml +++ b/invokeai/configs/INITIAL_MODELS.yaml @@ -117,9 +117,6 @@ sd-1/embedding/EasyNegative: recommended: True sd-1/embedding/ahx-beta-453407d: repo_id: sd-concepts-library/ahx-beta-453407d -sd-1/lora/LowRA: - path: https://civitai.com/api/download/models/63006 - recommended: True sd-1/lora/Ink scenery: path: https://civitai.com/api/download/models/83390 sd-1/ip_adapter/ip_adapter_sd15: diff --git a/invokeai/configs/controlnet/cldm_v15.yaml b/invokeai/configs/controlnet/cldm_v15.yaml new file mode 100644 index 0000000000..fde1825577 --- /dev/null +++ b/invokeai/configs/controlnet/cldm_v15.yaml @@ -0,0 +1,79 @@ +model: + target: cldm.cldm.ControlLDM + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + control_key: "hint" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + only_mid_control: False + + control_stage_config: + target: cldm.cldm.ControlNet + params: + image_size: 32 # unused + in_channels: 4 + hint_channels: 3 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + unet_config: + target: cldm.cldm.ControlledUnetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/invokeai/configs/controlnet/cldm_v21.yaml b/invokeai/configs/controlnet/cldm_v21.yaml new file mode 100644 index 0000000000..fc65193647 --- /dev/null +++ b/invokeai/configs/controlnet/cldm_v21.yaml @@ -0,0 +1,85 @@ +model: + target: cldm.cldm.ControlLDM + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + control_key: "hint" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + only_mid_control: False + + control_stage_config: + target: cldm.cldm.ControlNet + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 4 + hint_channels: 3 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + unet_config: + target: cldm.cldm.ControlledUnetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/invokeai/frontend/install/invokeai_update.py b/invokeai/frontend/install/invokeai_update.py index 87661da79f..b065fcfe5b 100644 --- a/invokeai/frontend/install/invokeai_update.py +++ b/invokeai/frontend/install/invokeai_update.py @@ -50,7 +50,7 @@ def invokeai_is_running() -> bool: return False -def welcome(versions: dict): +def welcome(latest_release: str, latest_prerelease: str): @group() def text(): yield f"InvokeAI Version: [bold yellow]{__version__}" @@ -61,7 +61,8 @@ def welcome(versions: dict): yield "making the web frontend unusable. Please downgrade to the latest release if this happens." yield "" yield "[bold yellow]Options:" - yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic]) + yield f"""[1] Update to the latest [bold]official release[/bold] ([italic]{latest_release}[/italic]) +[2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/italic]) [2] Manually enter the [bold]tag name[/bold] for the version you wish to update to [3] Manually enter the [bold]branch name[/bold] for the version you wish to update to""" @@ -92,12 +93,17 @@ def get_extras(): def main(): versions = get_versions() + released_versions = [x for x in versions if not (x["draft"] or x["prerelease"])] + prerelease_versions = [x for x in versions if not x["draft"] and x["prerelease"]] + latest_release = released_versions[0]["tag_name"] if len(released_versions) else None + latest_prerelease = prerelease_versions[0]["tag_name"] if len(prerelease_versions) else None + if invokeai_is_running(): print(":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]") input("Press any key to continue...") return - welcome(versions) + welcome(latest_release, latest_prerelease) tag = None branch = None @@ -105,11 +111,13 @@ def main(): choice = Prompt.ask("Choice:", choices=["1", "2", "3", "4"], default="1") if choice == "1": - release = versions[0]["tag_name"] + release = latest_release elif choice == "2": + release = latest_prerelease + elif choice == "3": while not tag: tag = Prompt.ask("Enter an InvokeAI tag name") - elif choice == "3": + elif choice == "4": while not branch: branch = Prompt.ask("Enter an InvokeAI branch name") diff --git a/invokeai/frontend/merge/merge_diffusers.py b/invokeai/frontend/merge/merge_diffusers.py index 8fa02cb49c..d515c5b4ee 100644 --- a/invokeai/frontend/merge/merge_diffusers.py +++ b/invokeai/frontend/merge/merge_diffusers.py @@ -9,7 +9,7 @@ import curses import sys from argparse import Namespace from pathlib import Path -from typing import List, Optional +from typing import List import npyscreen from npyscreen import widget @@ -131,6 +131,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction): values=[ "Models Built on SD-1.x", "Models Built on SD-2.x", + "Models Built on SDXL", ], value=[self.current_base], columns=4, @@ -273,9 +274,10 @@ class mergeModelsForm(npyscreen.FormMultiPageAction): else: interp = self.interpolations[self.merge_method.value[0]] + bases = ["sd-1", "sd-2", "sdxl"] args = dict( model_names=models, - base_model=tuple(BaseModelType)[self.base_select.value[0]], + base_model=BaseModelType(bases[self.base_select.value[0]]), alpha=self.alpha.value, interp=interp, force=self.force.value, @@ -309,7 +311,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction): else: return True - def get_model_names(self, base_model: Optional[BaseModelType] = None) -> List[str]: + def get_model_names(self, base_model: BaseModelType = BaseModelType.StableDiffusion1) -> List[str]: model_names = [ info["model_name"] for info in self.model_manager.list_models(model_type=ModelType.Main, base_model=base_model) @@ -318,7 +320,8 @@ class mergeModelsForm(npyscreen.FormMultiPageAction): return sorted(model_names) def _populate_models(self, value=None): - base_model = tuple(BaseModelType)[value[0]] + bases = ["sd-1", "sd-2", "sdxl"] + base_model = BaseModelType(bases[value[0]]) self.model_names = self.get_model_names(base_model) models_plus_none = self.model_names.copy() diff --git a/invokeai/frontend/web/package.json b/invokeai/frontend/web/package.json index e5f1513c89..59e672544a 100644 --- a/invokeai/frontend/web/package.json +++ b/invokeai/frontend/web/package.json @@ -54,42 +54,42 @@ ] }, "dependencies": { - "@chakra-ui/anatomy": "^2.2.0", - "@chakra-ui/icons": "^2.1.0", - "@chakra-ui/react": "^2.8.0", + "@chakra-ui/anatomy": "^2.2.1", + "@chakra-ui/icons": "^2.1.1", + "@chakra-ui/react": "^2.8.1", "@chakra-ui/styled-system": "^2.9.1", - "@chakra-ui/theme-tools": "^2.1.0", + "@chakra-ui/theme-tools": "^2.1.1", "@dagrejs/graphlib": "^2.1.13", "@dnd-kit/core": "^6.0.8", "@dnd-kit/modifiers": "^6.0.1", "@dnd-kit/utilities": "^3.2.1", "@emotion/react": "^11.11.1", "@emotion/styled": "^11.11.0", - "@floating-ui/react-dom": "^2.0.1", - "@fontsource-variable/inter": "^5.0.8", - "@fontsource/inter": "^5.0.8", + "@floating-ui/react-dom": "^2.0.2", + "@fontsource-variable/inter": "^5.0.13", + "@fontsource/inter": "^5.0.13", "@mantine/core": "^6.0.19", "@mantine/form": "^6.0.19", "@mantine/hooks": "^6.0.19", "@nanostores/react": "^0.7.1", - "@reduxjs/toolkit": "^1.9.5", - "@roarr/browser-log-writer": "^1.1.5", + "@reduxjs/toolkit": "^1.9.7", + "@roarr/browser-log-writer": "^1.3.0", "@stevebel/png": "^1.5.1", "compare-versions": "^6.1.0", "dateformat": "^5.0.3", - "formik": "^2.4.3", - "framer-motion": "^10.16.1", + "formik": "^2.4.5", + "framer-motion": "^10.16.4", "fuse.js": "^6.6.2", - "i18next": "^23.4.4", + "i18next": "^23.5.1", "i18next-browser-languagedetector": "^7.0.2", - "i18next-http-backend": "^2.2.1", - "konva": "^9.2.0", + "i18next-http-backend": "^2.2.2", + "konva": "^9.2.2", "lodash-es": "^4.17.21", "nanostores": "^0.9.2", "new-github-issue-url": "^1.0.0", - "openapi-fetch": "^0.7.4", - "overlayscrollbars": "^2.2.0", - "overlayscrollbars-react": "^0.5.0", + "openapi-fetch": "^0.7.10", + "overlayscrollbars": "^2.3.2", + "overlayscrollbars-react": "^0.5.2", "patch-package": "^8.0.0", "query-string": "^8.1.0", "react": "^18.2.0", @@ -98,25 +98,25 @@ "react-dropzone": "^14.2.3", "react-error-boundary": "^4.0.11", "react-hotkeys-hook": "4.4.1", - "react-i18next": "^13.1.2", - "react-icons": "^4.10.1", + "react-i18next": "^13.3.0", + "react-icons": "^4.11.0", "react-konva": "^18.2.10", - "react-redux": "^8.1.2", + "react-redux": "^8.1.3", "react-resizable-panels": "^0.0.55", "react-use": "^17.4.0", - "react-virtuoso": "^4.5.0", - "react-zoom-pan-pinch": "^3.0.8", - "reactflow": "^11.8.3", + "react-virtuoso": "^4.6.1", + "react-zoom-pan-pinch": "^3.2.0", + "reactflow": "^11.9.3", "redux-dynamic-middlewares": "^2.2.0", - "redux-remember": "^4.0.1", + "redux-remember": "^4.0.4", "roarr": "^7.15.1", - "serialize-error": "^11.0.1", + "serialize-error": "^11.0.2", "socket.io-client": "^4.7.2", - "type-fest": "^4.2.0", + "type-fest": "^4.4.0", "use-debounce": "^9.0.4", "use-image": "^1.1.1", - "uuid": "^9.0.0", - "zod": "^3.22.2", + "uuid": "^9.0.1", + "zod": "^3.22.4", "zod-validation-error": "^1.5.0" }, "peerDependencies": { @@ -129,40 +129,40 @@ "devDependencies": { "@chakra-ui/cli": "^2.4.1", "@types/dateformat": "^5.0.0", - "@types/lodash-es": "^4.14.194", - "@types/node": "^20.5.1", - "@types/react": "^18.2.20", - "@types/react-dom": "^18.2.6", - "@types/react-redux": "^7.1.25", - "@types/react-transition-group": "^4.4.6", - "@types/uuid": "^9.0.2", - "@typescript-eslint/eslint-plugin": "^6.4.1", - "@typescript-eslint/parser": "^6.4.1", - "@vitejs/plugin-react-swc": "^3.3.2", - "axios": "^1.4.0", + "@types/lodash-es": "^4.17.9", + "@types/node": "^20.8.6", + "@types/react": "^18.2.28", + "@types/react-dom": "^18.2.13", + "@types/react-redux": "^7.1.27", + "@types/react-transition-group": "^4.4.7", + "@types/uuid": "^9.0.5", + "@typescript-eslint/eslint-plugin": "^6.7.5", + "@typescript-eslint/parser": "^6.7.5", + "@vitejs/plugin-react-swc": "^3.4.0", + "axios": "^1.5.1", "babel-plugin-transform-imports": "^2.0.0", - "concurrently": "^8.2.0", - "eslint": "^8.47.0", + "concurrently": "^8.2.1", + "eslint": "^8.51.0", "eslint-config-prettier": "^9.0.0", - "eslint-plugin-prettier": "^5.0.0", + "eslint-plugin-prettier": "^5.0.1", "eslint-plugin-react": "^7.33.2", "eslint-plugin-react-hooks": "^4.6.0", "form-data": "^4.0.0", "husky": "^8.0.3", - "lint-staged": "^14.0.1", + "lint-staged": "^15.0.1", "madge": "^6.1.0", "openapi-types": "^12.1.3", - "openapi-typescript": "^6.5.2", + "openapi-typescript": "^6.7.0", "postinstall-postinstall": "^2.1.0", - "prettier": "^3.0.2", + "prettier": "^3.0.3", "rollup-plugin-visualizer": "^5.9.2", "ts-toolbelt": "^9.6.0", "typescript": "^5.2.2", - "vite": "^4.4.9", + "vite": "^4.4.11", "vite-plugin-css-injected-by-js": "^3.3.0", - "vite-plugin-dts": "^3.5.2", + "vite-plugin-dts": "^3.6.0", "vite-plugin-eslint": "^1.8.1", - "vite-tsconfig-paths": "^4.2.0", + "vite-tsconfig-paths": "^4.2.1", "yarn": "^1.22.19" } } diff --git a/invokeai/frontend/web/public/locales/de.json b/invokeai/frontend/web/public/locales/de.json index 9baa6eb6a2..4f91f84b35 100644 --- a/invokeai/frontend/web/public/locales/de.json +++ b/invokeai/frontend/web/public/locales/de.json @@ -4,14 +4,14 @@ "reportBugLabel": "Fehler melden", "settingsLabel": "Einstellungen", "img2img": "Bild zu Bild", - "nodes": "Knoten", + "nodes": "Knoten Editor", "langGerman": "Deutsch", "nodesDesc": "Ein knotenbasiertes System, für die Erzeugung von Bildern, ist derzeit in der Entwicklung. Bleiben Sie gespannt auf Updates zu dieser fantastischen Funktion.", "postProcessing": "Nachbearbeitung", "postProcessDesc1": "InvokeAI bietet eine breite Palette von Nachbearbeitungsfunktionen. Bildhochskalierung und Gesichtsrekonstruktion sind bereits in der WebUI verfügbar. Sie können sie über das Menü Erweiterte Optionen der Reiter Text in Bild und Bild in Bild aufrufen. Sie können Bilder auch direkt bearbeiten, indem Sie die Schaltflächen für Bildaktionen oberhalb der aktuellen Bildanzeige oder im Viewer verwenden.", "postProcessDesc2": "Eine spezielle Benutzeroberfläche wird in Kürze veröffentlicht, um erweiterte Nachbearbeitungs-Workflows zu erleichtern.", "postProcessDesc3": "Die InvokeAI Kommandozeilen-Schnittstelle bietet verschiedene andere Funktionen, darunter Embiggen.", - "training": "Training", + "training": "trainieren", "trainingDesc1": "Ein spezieller Arbeitsablauf zum Trainieren Ihrer eigenen Embeddings und Checkpoints mit Textual Inversion und Dreambooth über die Weboberfläche.", "trainingDesc2": "InvokeAI unterstützt bereits das Training von benutzerdefinierten Embeddings mit Textual Inversion unter Verwendung des Hauptskripts.", "upload": "Hochladen", @@ -38,14 +38,14 @@ "statusUpscalingESRGAN": "Hochskalierung (ESRGAN)", "statusLoadingModel": "Laden des Modells", "statusModelChanged": "Modell Geändert", - "cancel": "Abbruch", + "cancel": "Abbrechen", "accept": "Annehmen", "back": "Zurück", "langEnglish": "Englisch", "langDutch": "Niederländisch", "langFrench": "Französisch", "langItalian": "Italienisch", - "langPortuguese": "Portogisisch", + "langPortuguese": "Portugiesisch", "langRussian": "Russisch", "langUkranian": "Ukrainisch", "hotkeysLabel": "Tastenkombinationen", @@ -58,12 +58,44 @@ "langArabic": "Arabisch", "langKorean": "Koreanisch", "langHebrew": "Hebräisch", - "langSpanish": "Spanisch" + "langSpanish": "Spanisch", + "t2iAdapter": "T2I Adapter", + "communityLabel": "Gemeinschaft", + "dontAskMeAgain": "Frag mich nicht nochmal", + "loadingInvokeAI": "Lade Invoke AI", + "statusMergedModels": "Modelle zusammengeführt", + "areYouSure": "Bist du dir sicher?", + "statusConvertingModel": "Model konvertieren", + "on": "An", + "nodeEditor": "Knoten Editor", + "statusMergingModels": "Modelle zusammenführen", + "langSimplifiedChinese": "Vereinfachtes Chinesisch", + "ipAdapter": "IP Adapter", + "controlAdapter": "Control Adapter", + "auto": "Automatisch", + "controlNet": "ControlNet", + "imageFailedToLoad": "Kann Bild nicht laden", + "statusModelConverted": "Model konvertiert", + "modelManager": "Model Manager", + "lightMode": "Heller Modus", + "generate": "Erstellen", + "learnMore": "Mehr lernen", + "darkMode": "Dunkler Modus", + "loading": "Lade", + "random": "Zufall", + "batch": "Stapel-Manager", + "advanced": "Erweitert", + "langBrPortuguese": "Portugiesisch (Brasilien)", + "unifiedCanvas": "Einheitliche Leinwand", + "openInNewTab": "In einem neuem Tab öffnen", + "statusProcessing": "wird bearbeitet", + "linear": "Linear", + "imagePrompt": "Bild Prompt" }, "gallery": { "generations": "Erzeugungen", "showGenerations": "Zeige Erzeugnisse", - "uploads": "Hochgelades", + "uploads": "Uploads", "showUploads": "Zeige Uploads", "galleryImageSize": "Bildgröße", "galleryImageResetSize": "Größe zurücksetzen", @@ -73,7 +105,15 @@ "singleColumnLayout": "Einspaltiges Layout", "allImagesLoaded": "Alle Bilder geladen", "loadMore": "Mehr laden", - "noImagesInGallery": "Keine Bilder in der Galerie" + "noImagesInGallery": "Keine Bilder in der Galerie", + "loading": "Lade", + "preparingDownload": "bereite Download vor", + "preparingDownloadFailed": "Problem beim Download vorbereiten", + "deleteImage": "Lösche Bild", + "images": "Bilder", + "copy": "Kopieren", + "download": "Runterladen", + "setCurrentImage": "Setze aktuelle Bild" }, "hotkeys": { "keyboardShortcuts": "Tastenkürzel", @@ -82,7 +122,8 @@ "galleryHotkeys": "Galerie Tastenkürzel", "unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel", "invoke": { - "desc": "Ein Bild erzeugen" + "desc": "Ein Bild erzeugen", + "title": "Invoke" }, "cancel": { "title": "Abbrechen", @@ -166,7 +207,7 @@ }, "toggleGalleryPin": { "title": "Galerie anheften umschalten", - "desc": "Heftet die Galerie an die Benutzeroberfläche bzw. löst die sie." + "desc": "Heftet die Galerie an die Benutzeroberfläche bzw. löst die sie" }, "increaseGalleryThumbSize": { "title": "Größe der Galeriebilder erhöhen", @@ -279,6 +320,10 @@ "acceptStagingImage": { "title": "Staging-Bild akzeptieren", "desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs" + }, + "nodesHotkeys": "Knoten Tastenkürzel", + "addNodes": { + "title": "Knotenpunkt hinzufügen" } }, "modelManager": { @@ -295,7 +340,7 @@ "config": "Konfiguration", "configValidationMsg": "Pfad zur Konfigurationsdatei Ihres Models.", "modelLocation": "Ort des Models", - "modelLocationValidationMsg": "Pfad zum Speicherort Ihres Models.", + "modelLocationValidationMsg": "Pfad zum Speicherort Ihres Models", "vaeLocation": "VAE Ort", "vaeLocationValidationMsg": "Pfad zum Speicherort Ihres VAE.", "width": "Breite", @@ -328,11 +373,63 @@ "deleteModel": "Model löschen", "deleteConfig": "Konfiguration löschen", "deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?", - "deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.", + "deleteMsg2": "Dadurch WIRD das Modell von der Festplatte gelöscht WENN es im InvokeAI Root Ordner liegt. Wenn es in einem anderem Ordner liegt wird das Modell NICHT von der Festplatte gelöscht.", "customConfig": "Benutzerdefinierte Konfiguration", "invokeRoot": "InvokeAI Ordner", "formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.", - "checkpointModels": "Kontrollpunkte" + "checkpointModels": "Kontrollpunkte", + "convert": "Umwandeln", + "addCheckpointModel": "Kontrollpunkt / SafeTensors Modell hinzufügen", + "allModels": "Alle Modelle", + "alpha": "Alpha", + "addDifference": "Unterschied hinzufügen", + "convertToDiffusersHelpText2": "Bei diesem Vorgang wird Ihr Eintrag im Modell-Manager durch die Diffusor-Version desselben Modells ersetzt.", + "convertToDiffusersHelpText5": "Bitte stellen Sie sicher, dass Sie über genügend Speicherplatz verfügen. Die Modelle sind in der Regel zwischen 2 GB und 7 GB groß.", + "convertToDiffusersHelpText3": "Ihre Kontrollpunktdatei auf der Festplatte wird NICHT gelöscht oder in irgendeiner Weise verändert. Sie können Ihren Kontrollpunkt dem Modell-Manager wieder hinzufügen, wenn Sie dies wünschen.", + "convertToDiffusersHelpText4": "Dies ist ein einmaliger Vorgang. Er kann je nach den Spezifikationen Ihres Computers etwa 30-60 Sekunden dauern.", + "convertToDiffusersHelpText6": "Möchten Sie dieses Modell konvertieren?", + "custom": "Benutzerdefiniert", + "modelConverted": "Modell umgewandelt", + "inverseSigmoid": "Inverses Sigmoid", + "invokeAIFolder": "Invoke AI Ordner", + "formMessageDiffusersModelLocationDesc": "Bitte geben Sie mindestens einen an.", + "customSaveLocation": "Benutzerdefinierter Speicherort", + "formMessageDiffusersVAELocation": "VAE Speicherort", + "mergedModelCustomSaveLocation": "Benutzerdefinierter Pfad", + "modelMergeHeaderHelp2": "Nur Diffusers sind für die Zusammenführung verfügbar. Wenn Sie ein Kontrollpunktmodell zusammenführen möchten, konvertieren Sie es bitte zuerst in Diffusers.", + "manual": "Manuell", + "modelManager": "Modell Manager", + "modelMergeAlphaHelp": "Alpha steuert die Überblendungsstärke für die Modelle. Niedrigere Alphawerte führen zu einem geringeren Einfluss des zweiten Modells.", + "modelMergeHeaderHelp1": "Sie können bis zu drei verschiedene Modelle miteinander kombinieren, um eine Mischung zu erstellen, die Ihren Bedürfnissen entspricht.", + "ignoreMismatch": "Unstimmigkeiten zwischen ausgewählten Modellen ignorieren", + "model": "Modell", + "convertToDiffusersSaveLocation": "Speicherort", + "pathToCustomConfig": "Pfad zur benutzerdefinierten Konfiguration", + "v1": "v1", + "modelMergeInterpAddDifferenceHelp": "In diesem Modus wird zunächst Modell 3 von Modell 2 subtrahiert. Die resultierende Version wird mit Modell 1 mit dem oben eingestellten Alphasatz gemischt.", + "modelTwo": "Modell 2", + "modelOne": "Modell 1", + "v2_base": "v2 (512px)", + "scanForModels": "Nach Modellen suchen", + "name": "Name", + "safetensorModels": "SafeTensors", + "pickModelType": "Modell Typ auswählen", + "sameFolder": "Gleicher Ordner", + "modelThree": "Modell 3", + "v2_768": "v2 (768px)", + "none": "Nix", + "repoIDValidationMsg": "Online Repo Ihres Modells", + "vaeRepoIDValidationMsg": "Online Repo Ihrer VAE", + "importModels": "Importiere Modelle", + "merge": "Zusammenführen", + "addDiffuserModel": "Diffusers hinzufügen", + "advanced": "Erweitert", + "closeAdvanced": "Schließe Erweitert", + "convertingModelBegin": "Konvertiere Modell. Bitte warten.", + "customConfigFileLocation": "Benutzerdefinierte Konfiguration Datei Speicherort", + "baseModel": "Basis Modell", + "convertToDiffusers": "Konvertiere zu Diffusers", + "diffusersModels": "Diffusers" }, "parameters": { "images": "Bilder", @@ -352,7 +449,7 @@ "type": "Art", "strength": "Stärke", "upscaling": "Hochskalierung", - "upscale": "Hochskalieren", + "upscale": "Hochskalieren (Shift + U)", "upscaleImage": "Bild hochskalieren", "scale": "Maßstab", "otherOptions": "Andere Optionen", @@ -369,7 +466,7 @@ "seamCorrectionHeader": "Nahtkorrektur", "infillScalingHeader": "Infill und Skalierung", "img2imgStrength": "Bild-zu-Bild-Stärke", - "toggleLoopback": "Toggle Loopback", + "toggleLoopback": "Loopback umschalten", "sendTo": "Senden an", "sendToImg2Img": "Senden an Bild zu Bild", "sendToUnifiedCanvas": "Senden an Unified Canvas", @@ -384,8 +481,20 @@ "initialImage": "Ursprüngliches Bild", "showOptionsPanel": "Optionsleiste zeigen", "cancel": { - "setType": "Abbruchart festlegen" - } + "setType": "Abbruchart festlegen", + "immediate": "Sofort abbrechen", + "schedule": "Abbrechen nach der aktuellen Iteration", + "isScheduled": "Abbrechen" + }, + "copyImage": "Bild kopieren", + "denoisingStrength": "Stärke der Entrauschung", + "symmetry": "Symmetrie", + "imageToImage": "Bild zu Bild", + "info": "Information", + "general": "Allgemein", + "hiresStrength": "High Res Stärke", + "hidePreview": "Verstecke Vorschau", + "showPreview": "Zeige Vorschau" }, "settings": { "displayInProgress": "Bilder in Bearbeitung anzeigen", @@ -396,7 +505,9 @@ "resetWebUI": "Web-Oberfläche zurücksetzen", "resetWebUIDesc1": "Das Zurücksetzen der Web-Oberfläche setzt nur den lokalen Cache des Browsers mit Ihren Bildern und gespeicherten Einstellungen zurück. Es werden keine Bilder von der Festplatte gelöscht.", "resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.", - "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt. Aktualisieren Sie die Seite, um sie neu zu laden." + "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.", + "models": "Modelle", + "useSlidersForAll": "Schieberegler für alle Optionen verwenden" }, "toast": { "tempFoldersEmptied": "Temp-Ordner geleert", @@ -406,7 +517,7 @@ "imageCopied": "Bild kopiert", "imageLinkCopied": "Bildlink kopiert", "imageNotLoaded": "Kein Bild geladen", - "imageNotLoadedDesc": "Kein Bild gefunden, das an das Bild zu Bild-Modul gesendet werden kann", + "imageNotLoadedDesc": "Konnte kein Bild finden", "imageSavedToGallery": "Bild in die Galerie gespeichert", "canvasMerged": "Leinwand zusammengeführt", "sentToImageToImage": "Gesendet an Bild zu Bild", @@ -476,7 +587,7 @@ "autoSaveToGallery": "Automatisch in Galerie speichern", "saveBoxRegionOnly": "Nur Auswahlbox speichern", "limitStrokesToBox": "Striche auf Box beschränken", - "showCanvasDebugInfo": "Leinwand-Debug-Infos anzeigen", + "showCanvasDebugInfo": "Zusätzliche Informationen zur Leinwand anzeigen", "clearCanvasHistory": "Leinwand-Verlauf löschen", "clearHistory": "Verlauf löschen", "clearCanvasHistoryMessage": "Wenn Sie den Verlauf der Leinwand löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.", @@ -501,14 +612,17 @@ "betaClear": "Löschen", "betaDarkenOutside": "Außen abdunkeln", "betaLimitToBox": "Begrenzung auf das Feld", - "betaPreserveMasked": "Maskiertes bewahren" + "betaPreserveMasked": "Maskiertes bewahren", + "antialiasing": "Kantenglättung", + "showResultsOn": "Zeige Ergebnisse (An)", + "showResultsOff": "Zeige Ergebnisse (Aus)" }, "accessibility": { "modelSelect": "Model Auswahl", "uploadImage": "Bild hochladen", "previousImage": "Voriges Bild", "useThisParameter": "Benutze diesen Parameter", - "copyMetadataJson": "Kopiere metadata JSON", + "copyMetadataJson": "Kopiere Metadaten JSON", "zoomIn": "Vergrößern", "rotateClockwise": "Im Uhrzeigersinn drehen", "flipHorizontally": "Horizontal drehen", @@ -517,9 +631,191 @@ "toggleAutoscroll": "Auroscroll ein/ausschalten", "toggleLogViewer": "Log Betrachter ein/ausschalten", "showOptionsPanel": "Zeige Optionen", - "reset": "Zurücksetzen", + "reset": "Zurücksetzten", "nextImage": "Nächstes Bild", "zoomOut": "Verkleinern", - "rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen" + "rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen", + "showGalleryPanel": "Galeriefenster anzeigen", + "exitViewer": "Betrachten beenden", + "menu": "Menü", + "loadMore": "Mehr laden", + "invokeProgressBar": "Invoke Fortschrittsanzeige" + }, + "boards": { + "autoAddBoard": "Automatisches Hinzufügen zum Ordner", + "topMessage": "Dieser Ordner enthält Bilder die in den folgenden Funktionen verwendet werden:", + "move": "Bewegen", + "menuItemAutoAdd": "Automatisches Hinzufügen zu diesem Ordner", + "myBoard": "Meine Ordner", + "searchBoard": "Ordner durchsuchen...", + "noMatching": "Keine passenden Ordner", + "selectBoard": "Ordner aussuchen", + "cancel": "Abbrechen", + "addBoard": "Ordner hinzufügen", + "uncategorized": "Nicht kategorisiert", + "downloadBoard": "Ordner runterladen", + "changeBoard": "Ordner wechseln", + "loading": "Laden...", + "clearSearch": "Suche leeren", + "bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden." + }, + "controlnet": { + "showAdvanced": "Zeige Erweitert", + "contentShuffleDescription": "Mischt den Inhalt von einem Bild", + "addT2IAdapter": "$t(common.t2iAdapter) hinzufügen", + "importImageFromCanvas": "Importieren Bild von Zeichenfläche", + "lineartDescription": "Konvertiere Bild zu Lineart", + "importMaskFromCanvas": "Importiere Maske von Zeichenfläche", + "hed": "HED", + "hideAdvanced": "Verstecke Erweitert", + "contentShuffle": "Inhalt mischen", + "controlNetEnabledT2IDisabled": "$t(common.controlNet) ist aktiv, $t(common.t2iAdapter) ist deaktiviert", + "ipAdapterModel": "Adapter Modell", + "beginEndStepPercent": "Start / Ende Step Prozent", + "duplicate": "Kopieren", + "f": "F", + "h": "H", + "depthMidasDescription": "Tiefenmap erstellen mit Midas", + "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))", + "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert", + "weight": "Breite", + "selectModel": "Wähle ein Modell", + "depthMidas": "Tiefe (Midas)", + "w": "W", + "addControlNet": "$t(common.controlNet) hinzufügen", + "none": "Kein", + "incompatibleBaseModel": "Inkompatibles Basismodell:", + "enableControlnet": "Aktiviere ControlNet", + "detectResolution": "Auflösung erkennen", + "controlNetT2IMutexDesc": "$t(common.controlNet) und $t(common.t2iAdapter) zur gleichen Zeit wird nicht unterstützt.", + "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))", + "fill": "Füllen", + "addIPAdapter": "$t(common.ipAdapter) hinzufügen", + "colorMapDescription": "Erstelle eine Farbkarte von diesem Bild", + "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))", + "imageResolution": "Bild Auflösung", + "depthZoe": "Tiefe (Zoe)", + "colorMap": "Farbe", + "lowThreshold": "Niedrige Schwelle", + "highThreshold": "Hohe Schwelle", + "toggleControlNet": "Schalten ControlNet um", + "delete": "Löschen", + "controlAdapter_one": "Control Adapter", + "controlAdapter_other": "Control Adapters", + "colorMapTileSize": "Tile Größe", + "depthZoeDescription": "Tiefenmap erstellen mit Zoe", + "setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe", + "handAndFace": "Hand und Gesicht", + "enableIPAdapter": "Aktiviere IP Adapter", + "resize": "Größe ändern", + "resetControlImage": "Zurücksetzen vom Referenz Bild", + "balanced": "Ausgewogen", + "prompt": "Prompt", + "resizeMode": "Größenänderungsmodus", + "processor": "Prozessor", + "saveControlImage": "Speichere Referenz Bild", + "safe": "Speichern", + "ipAdapterImageFallback": "Kein IP Adapter Bild ausgewählt", + "resetIPAdapterImage": "Zurücksetzen vom IP Adapter Bild" + }, + "queue": { + "status": "Status", + "cancelTooltip": "Aktuellen Aufgabe abbrechen", + "queueEmpty": "Warteschlange leer", + "in_progress": "In Arbeit", + "queueFront": "An den Anfang der Warteschlange tun", + "completed": "Fertig", + "queueBack": "In die Warteschlange", + "clearFailed": "Probleme beim leeren der Warteschlange", + "clearSucceeded": "Warteschlange geleert", + "pause": "Pause", + "cancelSucceeded": "Auftrag abgebrochen", + "queue": "Warteschlange", + "batch": "Stapel", + "pending": "Ausstehend", + "clear": "Leeren", + "prune": "Leeren", + "total": "Gesamt", + "canceled": "Abgebrochen", + "clearTooltip": "Abbrechen und alle Aufträge leeren", + "current": "Aktuell", + "failed": "Fehler", + "cancelItem": "Abbruch Auftrag", + "next": "Nächste", + "cancel": "Abbruch", + "session": "Sitzung", + "queueTotal": "{{total}} Gesamt", + "resume": "Wieder aufnehmen", + "item": "Auftrag", + "notReady": "Warteschlange noch nicht bereit", + "batchValues": "Stapel Werte", + "queueCountPrediction": "{{predicted}} zur Warteschlange hinzufügen", + "queuedCount": "{{pending}} wartenden Elemente", + "clearQueueAlertDialog": "Die Warteschlange leeren, stoppt den aktuellen Prozess und leert die Warteschlange komplett.", + "completedIn": "Fertig in", + "cancelBatchSucceeded": "Stapel abgebrochen", + "cancelBatch": "Stapel stoppen", + "enqueueing": "Stapel in der Warteschlange", + "queueMaxExceeded": "Maximum von {{max_queue_size}} Elementen erreicht, würde {{skip}} Elemente überspringen", + "cancelBatchFailed": "Problem beim Abbruch vom Stapel", + "clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?" + }, + "metadata": { + "negativePrompt": "Negativ Beschreibung", + "metadata": "Meta-Data", + "strength": "Bild zu Bild stärke", + "imageDetails": "Bild Details", + "model": "Modell", + "noImageDetails": "Keine Bild Details gefunden", + "cfgScale": "CFG-Skala", + "fit": "Bild zu Bild passen", + "height": "Höhe", + "noMetaData": "Keine Meta-Data gefunden", + "width": "Breite", + "createdBy": "Erstellt von", + "steps": "Schritte" + }, + "popovers": { + "noiseUseCPU": { + "heading": "Nutze Prozessor rauschen" + }, + "paramModel": { + "heading": "Modell" + }, + "paramIterations": { + "heading": "Iterationen" + }, + "paramCFGScale": { + "heading": "CFG-Skala" + }, + "paramSteps": { + "heading": "Schritte" + }, + "lora": { + "heading": "LoRA Gewichte" + }, + "infillMethod": { + "heading": "Füllmethode" + }, + "paramVAE": { + "heading": "VAE" + } + }, + "ui": { + "lockRatio": "Verhältnis sperren", + "hideProgressImages": "Verstecke Prozess Bild", + "showProgressImages": "Zeige Prozess Bild" + }, + "invocationCache": { + "disable": "Deaktivieren", + "misses": "Cache Nötig", + "hits": "Cache Treffer", + "enable": "Aktivieren", + "clear": "Leeren" + }, + "embedding": { + "noMatchingEmbedding": "Keine passenden Embeddings", + "addEmbedding": "Embedding hinzufügen", + "incompatibleModel": "Inkompatibles Basismodell:" } } diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json index dd96193d5d..564ed174a8 100644 --- a/invokeai/frontend/web/public/locales/en.json +++ b/invokeai/frontend/web/public/locales/en.json @@ -70,8 +70,8 @@ "langDutch": "Nederlands", "langEnglish": "English", "langFrench": "Français", - "langGerman": "Deutsch", - "langHebrew": "עברית", + "langGerman": "German", + "langHebrew": "Hebrew", "langItalian": "Italiano", "langJapanese": "日本語", "langKorean": "한국어", @@ -137,9 +137,9 @@ "controlnet": { "controlAdapter_one": "Control Adapter", "controlAdapter_other": "Control Adapters", - "controlnet": "$t(controlnet.controlAdapter) #{{number}} ($t(common.controlNet))", - "ip_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.ipAdapter))", - "t2i_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.t2iAdapter))", + "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))", + "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))", + "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))", "addControlNet": "Add $t(common.controlNet)", "addIPAdapter": "Add $t(common.ipAdapter)", "addT2IAdapter": "Add $t(common.t2iAdapter)", @@ -722,7 +722,9 @@ "noMatchingModels": "No matching Models", "noModelsAvailable": "No models available", "selectLoRA": "Select a LoRA", - "selectModel": "Select a Model" + "selectModel": "Select a Model", + "noLoRAsInstalled": "No LoRAs installed", + "noRefinerModelsInstalled": "No SDXL Refiner models installed" }, "nodes": { "addNode": "Add Node", @@ -1115,13 +1117,13 @@ "showProgressInViewer": "Show Progress Images in Viewer", "ui": "User Interface", "useSlidersForAll": "Use Sliders For All Options", + "clearIntermediatesDisabled": "Queue must be empty to clear intermediates", "clearIntermediatesDesc1": "Clearing intermediates will reset your Canvas and ControlNet state.", "clearIntermediatesDesc2": "Intermediate images are byproducts of generation, different from the result images in the gallery. Clearing intermediates will free disk space.", "clearIntermediatesDesc3": "Your gallery images will not be deleted.", "clearIntermediates": "Clear Intermediates", "clearIntermediatesWithCount_one": "Clear {{count}} Intermediate", "clearIntermediatesWithCount_other": "Clear {{count}} Intermediates", - "clearIntermediatesWithCount_zero": "No Intermediates to Clear", "intermediatesCleared_one": "Cleared {{count}} Intermediate", "intermediatesCleared_other": "Cleared {{count}} Intermediates", "intermediatesClearedFailed": "Problem Clearing Intermediates" @@ -1256,11 +1258,15 @@ }, "compositingBlur": { "heading": "Blur", - "paragraphs": ["The blur radius of the mask."] + "paragraphs": [ + "The blur radius of the mask." + ] }, "compositingBlurMethod": { "heading": "Blur Method", - "paragraphs": ["The method of blur applied to the masked area."] + "paragraphs": [ + "The method of blur applied to the masked area." + ] }, "compositingCoherencePass": { "heading": "Coherence Pass", @@ -1270,7 +1276,9 @@ }, "compositingCoherenceMode": { "heading": "Mode", - "paragraphs": ["The mode of the Coherence Pass."] + "paragraphs": [ + "The mode of the Coherence Pass." + ] }, "compositingCoherenceSteps": { "heading": "Steps", @@ -1288,7 +1296,9 @@ }, "compositingMaskAdjustments": { "heading": "Mask Adjustments", - "paragraphs": ["Adjust the mask."] + "paragraphs": [ + "Adjust the mask." + ] }, "controlNetBeginEnd": { "heading": "Begin / End Step Percentage", @@ -1346,7 +1356,9 @@ }, "infillMethod": { "heading": "Infill Method", - "paragraphs": ["Method to infill the selected area."] + "paragraphs": [ + "Method to infill the selected area." + ] }, "lora": { "heading": "LoRA Weight", diff --git a/invokeai/frontend/web/public/locales/it.json b/invokeai/frontend/web/public/locales/it.json index 42836e4d0f..c358f415b2 100644 --- a/invokeai/frontend/web/public/locales/it.json +++ b/invokeai/frontend/web/public/locales/it.json @@ -87,7 +87,9 @@ "learnMore": "Per saperne di più", "ipAdapter": "Adattatore IP", "t2iAdapter": "Adattatore T2I", - "controlAdapter": "Adattatore di Controllo" + "controlAdapter": "Adattatore di Controllo", + "controlNet": "ControlNet", + "auto": "Automatico" }, "gallery": { "generations": "Generazioni", @@ -115,7 +117,10 @@ "currentlyInUse": "Questa immagine è attualmente utilizzata nelle seguenti funzionalità:", "copy": "Copia", "download": "Scarica", - "setCurrentImage": "Imposta come immagine corrente" + "setCurrentImage": "Imposta come immagine corrente", + "preparingDownload": "Preparazione del download", + "preparingDownloadFailed": "Problema durante la preparazione del download", + "downloadSelection": "Scarica gli elementi selezionati" }, "hotkeys": { "keyboardShortcuts": "Tasti rapidi", @@ -468,7 +473,8 @@ "useCustomConfig": "Utilizza configurazione personalizzata", "closeAdvanced": "Chiudi Avanzate", "modelType": "Tipo di modello", - "customConfigFileLocation": "Posizione del file di configurazione personalizzato" + "customConfigFileLocation": "Posizione del file di configurazione personalizzato", + "vaePrecision": "Precisione VAE" }, "parameters": { "images": "Immagini", @@ -570,9 +576,12 @@ "systemBusy": "Sistema occupato", "unableToInvoke": "Impossibile invocare", "systemDisconnected": "Sistema disconnesso", - "noControlImageForControlAdapter": "L'adattatore di controllo {{number}} non ha un'immagine di controllo", - "noModelForControlAdapter": "Nessun modello selezionato per l'adattatore di controllo {{number}}.", - "incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo {{number}} non è compatibile con il modello principale." + "noControlImageForControlAdapter": "L'adattatore di controllo #{{number}} non ha un'immagine di controllo", + "noModelForControlAdapter": "Nessun modello selezionato per l'adattatore di controllo #{{number}}.", + "incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo #{{number}} non è compatibile con il modello principale.", + "missingNodeTemplate": "Modello di nodo mancante", + "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante", + "missingFieldTemplate": "Modello di campo mancante" }, "enableNoiseSettings": "Abilita le impostazioni del rumore", "cpuNoise": "Rumore CPU", @@ -583,7 +592,7 @@ "iterations": "Iterazioni", "iterationsWithCount_one": "{{count}} Iterazione", "iterationsWithCount_many": "{{count}} Iterazioni", - "iterationsWithCount_other": "", + "iterationsWithCount_other": "{{count}} Iterazioni", "seamlessX&Y": "Senza cuciture X & Y", "isAllowedToUpscale": { "useX2Model": "L'immagine è troppo grande per l'ampliamento con il modello x4, utilizza il modello x2", @@ -591,7 +600,8 @@ }, "seamlessX": "Senza cuciture X", "seamlessY": "Senza cuciture Y", - "imageActions": "Azioni Immagine" + "imageActions": "Azioni Immagine", + "aspectRatioFree": "Libere" }, "settings": { "models": "Modelli", @@ -620,7 +630,19 @@ "beta": "Beta", "enableNodesEditor": "Abilita l'editor dei nodi", "experimental": "Sperimentale", - "autoChangeDimensions": "Aggiorna L/A alle impostazioni predefinite del modello in caso di modifica" + "autoChangeDimensions": "Aggiorna L/A alle impostazioni predefinite del modello in caso di modifica", + "clearIntermediates": "Cancella le immagini intermedie", + "clearIntermediatesDesc3": "Le immagini della galleria non verranno eliminate.", + "clearIntermediatesDesc2": "Le immagini intermedie sono sottoprodotti della generazione, diversi dalle immagini risultanti nella galleria. La cancellazione degli intermedi libererà spazio su disco.", + "intermediatesCleared_one": "Cancellata {{count}} immagine intermedia", + "intermediatesCleared_many": "Cancellate {{count}} immagini intermedie", + "intermediatesCleared_other": "Cancellate {{count}} immagini intermedie", + "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato di Tela Unificata e ControlNet.", + "intermediatesClearedFailed": "Problema con la cancellazione delle immagini intermedie", + "clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia", + "clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie", + "clearIntermediatesWithCount_other": "Cancella {{count}} immagini intermedie", + "clearIntermediatesDisabled": "La coda deve essere vuota per cancellare le immagini intermedie" }, "toast": { "tempFoldersEmptied": "Cartella temporanea svuotata", @@ -670,9 +692,9 @@ "nodesUnrecognizedTypes": "Impossibile caricare. Il grafico ha tipi di dati non riconosciuti", "nodesNotValidJSON": "JSON non valido", "nodesBrokenConnections": "Impossibile caricare. Alcune connessioni sono interrotte.", - "baseModelChangedCleared_one": "Il modello base è stato modificato, cancellato o disabilitato {{number}} sotto-modello incompatibile", - "baseModelChangedCleared_many": "", - "baseModelChangedCleared_other": "", + "baseModelChangedCleared_one": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modello incompatibile", + "baseModelChangedCleared_many": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili", + "baseModelChangedCleared_other": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili", "imageSavingFailed": "Salvataggio dell'immagine non riuscito", "canvasSentControlnetAssets": "Tela inviata a ControlNet & Risorse", "problemCopyingCanvasDesc": "Impossibile copiare la tela", @@ -866,7 +888,145 @@ "workflowValidation": "Errore di convalida del flusso di lavoro", "workflowAuthor": "Autore", "workflowName": "Nome", - "workflowNotes": "Note" + "workflowNotes": "Note", + "unhandledInputProperty": "Proprietà di input non gestita", + "versionUnknown": " Versione sconosciuta", + "unableToValidateWorkflow": "Impossibile convalidare il flusso di lavoro", + "updateApp": "Aggiorna App", + "problemReadingWorkflow": "Problema durante la lettura del flusso di lavoro dall'immagine", + "unableToLoadWorkflow": "Impossibile caricare il flusso di lavoro", + "updateNode": "Aggiorna nodo", + "version": "Versione", + "notes": "Note", + "problemSettingTitle": "Problema nell'impostazione del titolo", + "unkownInvocation": "Tipo di invocazione sconosciuta", + "unknownTemplate": "Modello sconosciuto", + "nodeType": "Tipo di nodo", + "vaeField": "VAE", + "unhandledOutputProperty": "Proprietà di output non gestita", + "notesDescription": "Aggiunge note sul tuo flusso di lavoro", + "unknownField": "Campo sconosciuto", + "unknownNode": "Nodo sconosciuto", + "vaeFieldDescription": "Sotto modello VAE.", + "booleanPolymorphicDescription": "Una raccolta di booleani.", + "missingTemplate": "Modello mancante", + "outputSchemaNotFound": "Schema di output non trovato", + "colorFieldDescription": "Un colore RGBA.", + "maybeIncompatible": "Potrebbe essere incompatibile con quello installato", + "noNodeSelected": "Nessun nodo selezionato", + "colorPolymorphic": "Colore polimorfico", + "booleanCollectionDescription": "Una raccolta di booleani.", + "colorField": "Colore", + "nodeTemplate": "Modello di nodo", + "nodeOpacity": "Opacità del nodo", + "pickOne": "Sceglierne uno", + "outputField": "Campo di output", + "nodeSearch": "Cerca nodi", + "nodeOutputs": "Uscite del nodo", + "collectionItem": "Oggetto della raccolta", + "noConnectionInProgress": "Nessuna connessione in corso", + "noConnectionData": "Nessun dato di connessione", + "outputFields": "Campi di output", + "cannotDuplicateConnection": "Impossibile creare connessioni duplicate", + "booleanPolymorphic": "Polimorfico booleano", + "colorPolymorphicDescription": "Una collezione di colori polimorfici.", + "missingCanvaInitImage": "Immagine iniziale della tela mancante", + "clipFieldDescription": "Sottomodelli di tokenizzatore e codificatore di testo.", + "noImageFoundState": "Nessuna immagine iniziale trovata nello stato", + "clipField": "CLIP", + "noMatchingNodes": "Nessun nodo corrispondente", + "noFieldType": "Nessun tipo di campo", + "colorCollection": "Una collezione di colori.", + "noOutputSchemaName": "Nessun nome dello schema di output trovato nell'oggetto di riferimento", + "boolean": "Booleani", + "missingCanvaInitMaskImages": "Immagini di inizializzazione e maschera della tela mancanti", + "oNNXModelField": "Modello ONNX", + "node": "Nodo", + "booleanDescription": "I booleani sono veri o falsi.", + "collection": "Raccolta", + "cannotConnectInputToInput": "Impossibile collegare Input a Input", + "cannotConnectOutputToOutput": "Impossibile collegare Output ad Output", + "booleanCollection": "Raccolta booleana", + "cannotConnectToSelf": "Impossibile connettersi a se stesso", + "mismatchedVersion": "Ha una versione non corrispondente", + "outputNode": "Nodo di Output", + "loadingNodes": "Caricamento nodi...", + "oNNXModelFieldDescription": "Campo del modello ONNX.", + "denoiseMaskFieldDescription": "La maschera di riduzione del rumore può essere passata tra i nodi", + "floatCollectionDescription": "Una raccolta di numeri virgola mobile.", + "enum": "Enumeratore", + "float": "In virgola mobile", + "doesNotExist": "non esiste", + "currentImageDescription": "Visualizza l'immagine corrente nell'editor dei nodi", + "fieldTypesMustMatch": "I tipi di campo devono corrispondere", + "edge": "Bordo", + "enumDescription": "Gli enumeratori sono valori che possono essere una delle diverse opzioni.", + "denoiseMaskField": "Maschera riduzione rumore", + "currentImage": "Immagine corrente", + "floatCollection": "Raccolta in virgola mobile", + "inputField": "Campo di Input", + "controlFieldDescription": "Informazioni di controllo passate tra i nodi.", + "skippingUnknownOutputType": "Tipo di campo di output sconosciuto saltato", + "latentsFieldDescription": "Le immagini latenti possono essere passate tra i nodi.", + "ipAdapterPolymorphicDescription": "Una raccolta di adattatori IP.", + "latentsPolymorphicDescription": "Le immagini latenti possono essere passate tra i nodi.", + "ipAdapterCollection": "Raccolta Adattatori IP", + "conditioningCollection": "Raccolta condizionamenti", + "ipAdapterPolymorphic": "Adattatore IP Polimorfico", + "integerPolymorphicDescription": "Una raccolta di numeri interi.", + "conditioningCollectionDescription": "Il condizionamento può essere passato tra i nodi.", + "skippingReservedFieldType": "Tipo di campo riservato saltato", + "conditioningPolymorphic": "Condizionamento Polimorfico", + "integer": "Numero Intero", + "latentsCollection": "Raccolta Latenti", + "sourceNode": "Nodo di origine", + "integerDescription": "Gli interi sono numeri senza punto decimale.", + "stringPolymorphic": "Stringa polimorfica", + "conditioningPolymorphicDescription": "Il condizionamento può essere passato tra i nodi.", + "skipped": "Saltato", + "imagePolymorphic": "Immagine Polimorfica", + "imagePolymorphicDescription": "Una raccolta di immagini.", + "floatPolymorphic": "Numeri in virgola mobile Polimorfici", + "ipAdapterCollectionDescription": "Una raccolta di adattatori IP.", + "stringCollectionDescription": "Una raccolta di stringhe.", + "unableToParseNode": "Impossibile analizzare il nodo", + "controlCollection": "Raccolta di Controllo", + "stringCollection": "Raccolta di stringhe", + "inputMayOnlyHaveOneConnection": "L'ingresso può avere solo una connessione", + "ipAdapter": "Adattatore IP", + "integerCollection": "Raccolta di numeri interi", + "controlCollectionDescription": "Informazioni di controllo passate tra i nodi.", + "skippedReservedInput": "Campo di input riservato saltato", + "inputNode": "Nodo di Input", + "imageField": "Immagine", + "skippedReservedOutput": "Campo di output riservato saltato", + "integerCollectionDescription": "Una raccolta di numeri interi.", + "conditioningFieldDescription": "Il condizionamento può essere passato tra i nodi.", + "stringDescription": "Le stringhe sono testo.", + "integerPolymorphic": "Numero intero Polimorfico", + "ipAdapterModel": "Modello Adattatore IP", + "latentsPolymorphic": "Latenti polimorfici", + "skippingInputNoTemplate": "Campo di input senza modello saltato", + "ipAdapterDescription": "Un adattatore di prompt di immagini (Adattatore IP).", + "stringPolymorphicDescription": "Una raccolta di stringhe.", + "skippingUnknownInputType": "Tipo di campo di input sconosciuto saltato", + "controlField": "Controllo", + "ipAdapterModelDescription": "Campo Modello adattatore IP", + "invalidOutputSchema": "Schema di output non valido", + "floatDescription": "I numeri in virgola mobile sono numeri con un punto decimale.", + "floatPolymorphicDescription": "Una raccolta di numeri in virgola mobile.", + "conditioningField": "Condizionamento", + "string": "Stringa", + "latentsField": "Latenti", + "connectionWouldCreateCycle": "La connessione creerebbe un ciclo", + "inputFields": "Campi di Input", + "uNetFieldDescription": "Sub-modello UNet.", + "imageCollectionDescription": "Una raccolta di immagini.", + "imageFieldDescription": "Le immagini possono essere passate tra i nodi.", + "unableToParseEdge": "Impossibile analizzare il bordo", + "latentsCollectionDescription": "Le immagini latenti possono essere passate tra i nodi.", + "imageCollection": "Raccolta Immagini", + "loRAModelField": "LoRA" }, "boards": { "autoAddBoard": "Aggiungi automaticamente bacheca", @@ -883,7 +1043,8 @@ "searchBoard": "Cerca bacheche ...", "noMatching": "Nessuna bacheca corrispondente", "selectBoard": "Seleziona una Bacheca", - "uncategorized": "Non categorizzato" + "uncategorized": "Non categorizzato", + "downloadBoard": "Scarica la bacheca" }, "controlnet": { "contentShuffleDescription": "Rimescola il contenuto di un'immagine", @@ -951,8 +1112,13 @@ "addControlNet": "Aggiungi $t(common.controlNet)", "controlNetT2IMutexDesc": "$t(common.controlNet) e $t(common.t2iAdapter) contemporaneamente non sono attualmente supportati.", "addIPAdapter": "Aggiungi $t(common.ipAdapter)", - "controlAdapter": "Adattatore di Controllo", - "megaControl": "Mega ControlNet" + "controlAdapter_one": "Adattatore di Controllo", + "controlAdapter_many": "Adattatori di Controllo", + "controlAdapter_other": "Adattatori di Controllo", + "megaControl": "Mega ControlNet", + "minConfidence": "Confidenza minima", + "scribble": "Scribble", + "amult": "Angolo di illuminazione" }, "queue": { "queueFront": "Aggiungi all'inizio della coda", @@ -979,7 +1145,9 @@ "pause": "Sospendi", "pruneTooltip": "Rimuovi {{item_count}} elementi completati", "cancelSucceeded": "Elemento annullato", - "batchQueuedDesc": "Aggiunte {{item_count}} sessioni a {{direction}} della coda", + "batchQueuedDesc_one": "Aggiunta {{count}} sessione a {{direction}} della coda", + "batchQueuedDesc_many": "Aggiunte {{count}} sessioni a {{direction}} della coda", + "batchQueuedDesc_other": "Aggiunte {{count}} sessioni a {{direction}} della coda", "graphQueued": "Grafico in coda", "batch": "Lotto", "clearQueueAlertDialog": "Lo svuotamento della coda annulla immediatamente tutti gli elementi in elaborazione e cancella completamente la coda.", @@ -1025,7 +1193,9 @@ "noLoRAsAvailable": "Nessun LoRA disponibile", "noModelsAvailable": "Nessun modello disponibile", "selectModel": "Seleziona un modello", - "selectLoRA": "Seleziona un LoRA" + "selectLoRA": "Seleziona un LoRA", + "noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato", + "noLoRAsInstalled": "Nessun LoRA installato" }, "invocationCache": { "disable": "Disabilita", @@ -1056,7 +1226,7 @@ "maxPrompts": "Numero massimo di prompt", "promptsWithCount_one": "{{count}} Prompt", "promptsWithCount_many": "{{count}} Prompt", - "promptsWithCount_other": "", + "promptsWithCount_other": "{{count}} Prompt", "dynamicPrompts": "Prompt dinamici" }, "popovers": { @@ -1268,7 +1438,8 @@ "controlNet": { "paragraphs": [ "ControlNet fornisce una guida al processo di generazione, aiutando a creare immagini con composizione, struttura o stile controllati, a seconda del modello selezionato." - ] + ], + "heading": "ControlNet" } }, "sdxl": { @@ -1313,6 +1484,8 @@ "createdBy": "Creato da", "workflow": "Flusso di lavoro", "steps": "Passi", - "scheduler": "Campionatore" + "scheduler": "Campionatore", + "recallParameters": "Richiama i parametri", + "noRecallParameters": "Nessun parametro da richiamare trovato" } } diff --git a/invokeai/frontend/web/public/locales/ja.json b/invokeai/frontend/web/public/locales/ja.json index a53ea50b46..c7718e7b7c 100644 --- a/invokeai/frontend/web/public/locales/ja.json +++ b/invokeai/frontend/web/public/locales/ja.json @@ -1,6 +1,6 @@ { "common": { - "languagePickerLabel": "言語選択", + "languagePickerLabel": "言語", "reportBugLabel": "バグ報告", "settingsLabel": "設定", "langJapanese": "日本語", @@ -63,11 +63,34 @@ "langFrench": "Français", "langGerman": "Deutsch", "langPortuguese": "Português", - "nodes": "ノード", + "nodes": "ワークフローエディター", "langKorean": "한국어", "langPolish": "Polski", "txt2img": "txt2img", - "postprocessing": "Post Processing" + "postprocessing": "Post Processing", + "t2iAdapter": "T2I アダプター", + "communityLabel": "コミュニティ", + "dontAskMeAgain": "次回から確認しない", + "areYouSure": "本当によろしいですか？", + "on": "オン", + "nodeEditor": "ノードエディター", + "ipAdapter": "IPアダプター", + "controlAdapter": "コントロールアダプター", + "auto": "自動", + "openInNewTab": "新しいタブで開く", + "controlNet": "コントロールネット", + "statusProcessing": "処理中", + "linear": "リニア", + "imageFailedToLoad": "画像が読み込めません", + "imagePrompt": "画像プロンプト", + "modelManager": "モデルマネージャー", + "lightMode": "ライトモード", + "generate": "生成", + "learnMore": "もっと学ぶ", + "darkMode": "ダークモード", + "random": "ランダム", + "batch": "バッチマネージャー", + "advanced": "高度な設定" }, "gallery": { "uploads": "アップロード", @@ -274,7 +297,7 @@ "config": "Config", "configValidationMsg": "モデルの設定ファイルへのパス", "modelLocation": "モデルの場所", - "modelLocationValidationMsg": "モデルが配置されている場所へのパス。", + "modelLocationValidationMsg": "ディフューザーモデルのあるローカルフォルダーのパスを入力してください", "repo_id": "Repo ID", "repoIDValidationMsg": "モデルのリモートリポジトリ", "vaeLocation": "VAEの場所", @@ -309,12 +332,79 @@ "delete": "削除", "deleteModel": "モデルを削除", "deleteConfig": "設定を削除", - "deleteMsg1": "InvokeAIからこのモデルエントリーを削除してよろしいですか?", - "deleteMsg2": "これは、ドライブからモデルのCheckpointファイルを削除するものではありません。必要であればそれらを読み込むことができます。", + "deleteMsg1": "InvokeAIからこのモデルを削除してよろしいですか?", + "deleteMsg2": "これは、モデルがInvokeAIルートフォルダ内にある場合、ディスクからモデルを削除します。カスタム保存場所を使用している場合、モデルはディスクから削除されません。", "formMessageDiffusersModelLocation": "Diffusersモデルの場所", "formMessageDiffusersModelLocationDesc": "最低でも1つは入力してください。", "formMessageDiffusersVAELocation": "VAEの場所s", - "formMessageDiffusersVAELocationDesc": "指定しない場合、InvokeAIは上記のモデルの場所にあるVAEファイルを探します。" + "formMessageDiffusersVAELocationDesc": "指定しない場合、InvokeAIは上記のモデルの場所にあるVAEファイルを探します。", + "importModels": "モデルをインポート", + "custom": "カスタム", + "none": "なし", + "convert": "変換", + "statusConverting": "変換中", + "cannotUseSpaces": "スペースは使えません", + "convertToDiffusersHelpText6": "このモデルを変換しますか？", + "checkpointModels": "チェックポイント", + "settings": "設定", + "convertingModelBegin": "モデルを変換しています...", + "baseModel": "ベースモデル", + "modelDeleteFailed": "モデルの削除ができませんでした", + "convertToDiffusers": "ディフューザーに変換", + "alpha": "アルファ", + "diffusersModels": "ディフューザー", + "pathToCustomConfig": "カスタム設定のパス", + "noCustomLocationProvided": "カスタムロケーションが指定されていません", + "modelConverted": "モデル変換が完了しました", + "weightedSum": "重み付け総和", + "inverseSigmoid": "逆シグモイド", + "invokeAIFolder": "Invoke AI フォルダ", + "syncModelsDesc": "モデルがバックエンドと同期していない場合、このオプションを使用してモデルを更新できます。通常、モデル.yamlファイルを手動で更新したり、アプリケーションの起動後にモデルをInvokeAIルートフォルダに追加した場合に便利です。", + "noModels": "モデルが見つかりません", + "sigmoid": "シグモイド", + "merge": "マージ", + "modelMergeInterpAddDifferenceHelp": "このモードでは、モデル3がまずモデル2から減算されます。その結果得られたバージョンが、上記で設定されたアルファ率でモデル1とブレンドされます。", + "customConfig": "カスタム設定", + "predictionType": "予測タイプ（安定したディフュージョン 2.x モデルおよび一部の安定したディフュージョン 1.x モデル用）", + "selectModel": "モデルを選択", + "modelSyncFailed": "モデルの同期に失敗しました", + "quickAdd": "クイック追加", + "simpleModelDesc": "ローカルのDiffusersモデル、ローカルのチェックポイント/safetensorsモデル、HuggingFaceリポジトリのID、またはチェックポイント/ DiffusersモデルのURLへのパスを指定してください。", + "customSaveLocation": "カスタム保存場所", + "advanced": "高度な設定", + "modelDeleted": "モデルが削除されました", + "convertToDiffusersHelpText2": "このプロセスでは、モデルマネージャーのエントリーを同じモデルのディフューザーバージョンに置き換えます。", + "modelUpdateFailed": "モデル更新が失敗しました", + "useCustomConfig": "カスタム設定を使用する", + "convertToDiffusersHelpText5": "十分なディスク空き容量があることを確認してください。モデルは一般的に2GBから7GBのサイズがあります。", + "modelConversionFailed": "モデル変換が失敗しました", + "modelEntryDeleted": "モデルエントリーが削除されました", + "syncModels": "モデルを同期", + "mergedModelSaveLocation": "保存場所", + "closeAdvanced": "高度な設定を閉じる", + "modelType": "モデルタイプ", + "modelsMerged": "モデルマージ完了", + "modelsMergeFailed": "モデルマージ失敗", + "scanForModels": "モデルをスキャン", + "customConfigFileLocation": "カスタム設定ファイルの場所", + "convertToDiffusersHelpText1": "このモデルは 🧨 Diffusers フォーマットに変換されます。", + "modelsSynced": "モデルが同期されました", + "invokeRoot": "InvokeAIフォルダ", + "mergedModelCustomSaveLocation": "カスタムパス", + "mergeModels": "マージモデル", + "interpolationType": "補間タイプ", + "modelMergeHeaderHelp2": "マージできるのはDiffusersのみです。チェックポイントモデルをマージしたい場合は、まずDiffusersに変換してください。", + "convertToDiffusersSaveLocation": "保存場所", + "pickModelType": "モデルタイプを選択", + "sameFolder": "同じフォルダ", + "convertToDiffusersHelpText3": "チェックポイントファイルは、InvokeAIルートフォルダ内にある場合、ディスクから削除されます。カスタムロケーションにある場合は、削除されません。", + "loraModels": "LoRA", + "modelMergeAlphaHelp": "アルファはモデルのブレンド強度を制御します。アルファ値が低いと、2番目のモデルの影響が低くなります。", + "addDifference": "差分を追加", + "modelMergeHeaderHelp1": "あなたのニーズに適したブレンドを作成するために、異なるモデルを最大3つまでマージすることができます。", + "ignoreMismatch": "選択されたモデル間の不一致を無視する", + "convertToDiffusersHelpText4": "これは一回限りのプロセスです。コンピュータの仕様によっては、約30秒から60秒かかる可能性があります。", + "mergedModelName": "マージされたモデル名" }, "parameters": { "images": "画像", @@ -440,7 +530,8 @@ "next": "次", "accept": "同意", "showHide": "表示/非表示", - "discardAll": "すべて破棄" + "discardAll": "すべて破棄", + "snapToGrid": "グリッドにスナップ" }, "accessibility": { "modelSelect": "モデルを選択", @@ -452,7 +543,7 @@ "useThisParameter": "このパラメータを使用する", "copyMetadataJson": "メタデータをコピー(JSON)", "zoomIn": "ズームイン", - "exitViewer": "ExitViewer", + "exitViewer": "ビューアーを終了", "zoomOut": "ズームアウト", "rotateCounterClockwise": "反時計回りに回転", "rotateClockwise": "時計回りに回転", @@ -461,6 +552,265 @@ "toggleAutoscroll": "自動スクロールの切替", "modifyConfig": "Modify Config", "toggleLogViewer": "Log Viewerの切替", - "showOptionsPanel": "オプションパネルを表示" + "showOptionsPanel": "サイドパネルを表示", + "showGalleryPanel": "ギャラリーパネルを表示", + "menu": "メニュー", + "loadMore": "さらに読み込む" + }, + "controlnet": { + "resize": "リサイズ", + "showAdvanced": "高度な設定を表示", + "addT2IAdapter": "$t(common.t2iAdapter)を追加", + "importImageFromCanvas": "キャンバスから画像をインポート", + "lineartDescription": "画像を線画に変換", + "importMaskFromCanvas": "キャンバスからマスクをインポート", + "hideAdvanced": "高度な設定を非表示", + "ipAdapterModel": "アダプターモデル", + "resetControlImage": "コントロール画像をリセット", + "beginEndStepPercent": "開始 / 終了ステップパーセンテージ", + "duplicate": "複製", + "balanced": "バランス", + "prompt": "プロンプト", + "depthMidasDescription": "Midasを使用して深度マップを生成", + "openPoseDescription": "Openposeを使用してポーズを推定", + "control": "コントロール", + "resizeMode": "リサイズモード", + "weight": "重み", + "selectModel": "モデルを選択", + "crop": "切り抜き", + "w": "幅", + "processor": "プロセッサー", + "addControlNet": "$t(common.controlNet)を追加", + "none": "なし", + "incompatibleBaseModel": "互換性のないベースモデル:", + "enableControlnet": "コントロールネットを有効化", + "detectResolution": "検出解像度", + "controlNetT2IMutexDesc": "$t(common.controlNet)と$t(common.t2iAdapter)の同時使用は現在サポートされていません。", + "pidiDescription": "PIDI画像処理", + "controlMode": "コントロールモード", + "fill": "塗りつぶし", + "cannyDescription": "Canny 境界検出", + "addIPAdapter": "$t(common.ipAdapter)を追加", + "colorMapDescription": "画像からカラーマップを生成", + "lineartAnimeDescription": "アニメスタイルの線画処理", + "imageResolution": "画像解像度", + "megaControl": "メガコントロール", + "lowThreshold": "最低閾値", + "autoConfigure": "プロセッサーを自動設定", + "highThreshold": "最大閾値", + "saveControlImage": "コントロール画像を保存", + "toggleControlNet": "このコントロールネットを切り替え", + "delete": "削除", + "controlAdapter_other": "コントロールアダプター", + "colorMapTileSize": "タイルサイズ", + "ipAdapterImageFallback": "IP Adapterの画像が選択されていません", + "mediapipeFaceDescription": "Mediapipeを使用して顔を検出", + "depthZoeDescription": "Zoeを使用して深度マップを生成", + "setControlImageDimensions": "コントロール画像のサイズを幅と高さにセット", + "resetIPAdapterImage": "IP Adapterの画像をリセット", + "handAndFace": "手と顔", + "enableIPAdapter": "IP Adapterを有効化", + "amult": "a_mult", + "contentShuffleDescription": "画像の内容をシャッフルします", + "bgth": "bg_th", + "controlNetEnabledT2IDisabled": "$t(common.controlNet) が有効化され、$t(common.t2iAdapter)s が無効化されました", + "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))", + "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) が有効化され、$t(common.controlNet)s が無効化されました", + "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))", + "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))", + "minConfidence": "最小確信度", + "colorMap": "Color", + "noneDescription": "処理は行われていません", + "canny": "Canny", + "hedDescription": "階層的エッジ検出", + "maxFaces": "顔の最大数" + }, + "metadata": { + "seamless": "シームレス", + "Threshold": "ノイズ閾値", + "seed": "シード", + "width": "幅", + "workflow": "ワークフロー", + "steps": "ステップ", + "scheduler": "スケジューラー", + "positivePrompt": "ポジティブプロンプト", + "strength": "Image to Image 強度", + "perlin": "パーリンノイズ", + "recallParameters": "パラメータを呼び出す" + }, + "queue": { + "queueEmpty": "キューが空です", + "pauseSucceeded": "処理が一時停止されました", + "queueFront": "キューの先頭へ追加", + "queueBack": "キューに追加", + "queueCountPrediction": "{{predicted}}をキューに追加", + "queuedCount": "保留中 {{pending}}", + "pause": "一時停止", + "queue": "キュー", + "pauseTooltip": "処理を一時停止", + "cancel": "キャンセル", + "queueTotal": "合計 {{total}}", + "resumeSucceeded": "処理が再開されました", + "resumeTooltip": "処理を再開", + "resume": "再会", + "status": "ステータス", + "pruneSucceeded": "キューから完了アイテム{{item_count}}件を削除しました", + "cancelTooltip": "現在のアイテムをキャンセル", + "in_progress": "進行中", + "notReady": "キューに追加できません", + "batchFailedToQueue": "バッチをキューに追加できませんでした", + "completed": "完了", + "batchValues": "バッチの値", + "cancelFailed": "アイテムのキャンセルに問題があります", + "batchQueued": "バッチをキューに追加しました", + "pauseFailed": "処理の一時停止に問題があります", + "clearFailed": "キューのクリアに問題があります", + "front": "先頭", + "clearSucceeded": "キューがクリアされました", + "pruneTooltip": "{{item_count}} の完了アイテムを削除", + "cancelSucceeded": "アイテムがキャンセルされました", + "batchQueuedDesc_other": "{{count}} セッションをキューの{{direction}}に追加しました", + "graphQueued": "グラフをキューに追加しました", + "batch": "バッチ", + "clearQueueAlertDialog": "キューをクリアすると、処理中のアイテムは直ちにキャンセルされ、キューは完全にクリアされます。", + "pending": "保留中", + "resumeFailed": "処理の再開に問題があります", + "clear": "クリア", + "total": "合計", + "canceled": "キャンセル", + "pruneFailed": "キューの削除に問題があります", + "cancelBatchSucceeded": "バッチがキャンセルされました", + "clearTooltip": "全てのアイテムをキャンセルしてクリア", + "current": "現在", + "failed": "失敗", + "cancelItem": "項目をキャンセル", + "next": "次", + "cancelBatch": "バッチをキャンセル", + "session": "セッション", + "enqueueing": "バッチをキューに追加", + "queueMaxExceeded": "{{max_queue_size}} の最大値を超えたため、{{skip}} をスキップします", + "cancelBatchFailed": "バッチのキャンセルに問題があります", + "clearQueueAlertDialog2": "キューをクリアしてもよろしいですか？", + "item": "アイテム", + "graphFailedToQueue": "グラフをキューに追加できませんでした" + }, + "models": { + "noMatchingModels": "一致するモデルがありません", + "loading": "読み込み中", + "noMatchingLoRAs": "一致するLoRAがありません", + "noLoRAsAvailable": "使用可能なLoRAがありません", + "noModelsAvailable": "使用可能なモデルがありません", + "selectModel": "モデルを選択してください", + "selectLoRA": "LoRAを選択してください" + }, + "nodes": { + "addNode": "ノードを追加", + "boardField": "ボード", + "boolean": "ブーリアン", + "boardFieldDescription": "ギャラリーボード", + "addNodeToolTip": "ノードを追加 (Shift+A, Space)", + "booleanPolymorphicDescription": "ブーリアンのコレクション。", + "inputField": "入力フィールド", + "latentsFieldDescription": "潜在空間はノード間で伝達できます。", + "floatCollectionDescription": "浮動小数点のコレクション。", + "missingTemplate": "テンプレートが見つかりません", + "ipAdapterPolymorphicDescription": "IP-Adaptersのコレクション。", + "latentsPolymorphicDescription": "潜在空間はノード間で伝達できます。", + "colorFieldDescription": "RGBAカラー。", + "ipAdapterCollection": "IP-Adapterコレクション", + "conditioningCollection": "条件付きコレクション", + "hideGraphNodes": "グラフオーバーレイを非表示", + "loadWorkflow": "ワークフローを読み込み", + "integerPolymorphicDescription": "整数のコレクション。", + "hideLegendNodes": "フィールドタイプの凡例を非表示", + "float": "浮動小数点", + "booleanCollectionDescription": "ブーリアンのコレクション。", + "integer": "整数", + "colorField": "カラー", + "nodeTemplate": "ノードテンプレート", + "integerDescription": "整数は小数点を持たない数値です。", + "imagePolymorphicDescription": "画像のコレクション。", + "doesNotExist": "存在しません", + "ipAdapterCollectionDescription": "IP-Adaptersのコレクション。", + "inputMayOnlyHaveOneConnection": "入力は1つの接続しか持つことができません", + "nodeOutputs": "ノード出力", + "currentImageDescription": "ノードエディタ内の現在の画像を表示", + "downloadWorkflow": "ワークフローのJSONをダウンロード", + "integerCollection": "整数コレクション", + "collectionItem": "コレクションアイテム", + "fieldTypesMustMatch": "フィールドタイプが一致している必要があります", + "edge": "輪郭", + "inputNode": "入力ノード", + "imageField": "画像", + "animatedEdgesHelp": "選択したエッジおよび選択したノードに接続されたエッジをアニメーション化します", + "cannotDuplicateConnection": "重複した接続は作れません", + "noWorkflow": "ワークフローがありません", + "integerCollectionDescription": "整数のコレクション。", + "colorPolymorphicDescription": "カラーのコレクション。", + "missingCanvaInitImage": "キャンバスの初期画像が見つかりません", + "clipFieldDescription": "トークナイザーとテキストエンコーダーサブモデル。", + "fullyContainNodesHelp": "ノードは選択ボックス内に完全に存在する必要があります", + "clipField": "クリップ", + "nodeType": "ノードタイプ", + "executionStateInProgress": "処理中", + "executionStateError": "エラー", + "ipAdapterModel": "IP-Adapterモデル", + "ipAdapterDescription": "イメージプロンプトアダプター(IP-Adapter)。", + "missingCanvaInitMaskImages": "キャンバスの初期画像およびマスクが見つかりません", + "hideMinimapnodes": "ミニマップを非表示", + "fitViewportNodes": "全体を表示", + "executionStateCompleted": "完了", + "node": "ノード", + "currentImage": "現在の画像", + "controlField": "コントロール", + "booleanDescription": "ブーリアンはtrueかfalseです。", + "collection": "コレクション", + "ipAdapterModelDescription": "IP-Adapterモデルフィールド", + "cannotConnectInputToInput": "入力から入力には接続できません", + "invalidOutputSchema": "無効な出力スキーマ", + "floatDescription": "浮動小数点は、小数点を持つ数値です。", + "floatPolymorphicDescription": "浮動小数点のコレクション。", + "floatCollection": "浮動小数点コレクション", + "latentsField": "潜在空間", + "cannotConnectOutputToOutput": "出力から出力には接続できません", + "booleanCollection": "ブーリアンコレクション", + "cannotConnectToSelf": "自身のノードには接続できません", + "inputFields": "入力フィールド(複数)", + "colorCodeEdges": "カラー-Code Edges", + "imageCollectionDescription": "画像のコレクション。", + "loadingNodes": "ノードを読み込み中...", + "imageCollection": "画像コレクション" + }, + "boards": { + "autoAddBoard": "自動追加するボード", + "move": "移動", + "menuItemAutoAdd": "このボードに自動追加", + "myBoard": "マイボード", + "searchBoard": "ボードを検索...", + "noMatching": "一致するボードがありません", + "selectBoard": "ボードを選択", + "cancel": "キャンセル", + "addBoard": "ボードを追加", + "uncategorized": "未分類", + "downloadBoard": "ボードをダウンロード", + "changeBoard": "ボードを変更", + "loading": "ロード中...", + "topMessage": "このボードには、以下の機能で使用されている画像が含まれています：", + "bottomMessage": "このボードおよび画像を削除すると、現在これらを利用している機能はリセットされます。", + "clearSearch": "検索をクリア" + }, + "embedding": { + "noMatchingEmbedding": "一致する埋め込みがありません", + "addEmbedding": "埋め込みを追加", + "incompatibleModel": "互換性のないベースモデル:" + }, + "invocationCache": { + "invocationCache": "呼び出しキャッシュ", + "clearSucceeded": "呼び出しキャッシュをクリアしました", + "clearFailed": "呼び出しキャッシュのクリアに問題があります", + "enable": "有効", + "clear": "クリア", + "maxCacheSize": "最大キャッシュサイズ", + "cacheSize": "キャッシュサイズ" } } diff --git a/invokeai/frontend/web/public/locales/nl.json b/invokeai/frontend/web/public/locales/nl.json index f682886dae..2d50a602d1 100644 --- a/invokeai/frontend/web/public/locales/nl.json +++ b/invokeai/frontend/web/public/locales/nl.json @@ -79,7 +79,18 @@ "modelManager": "Modelbeheer", "darkMode": "Donkere modus", "lightMode": "Lichte modus", - "communityLabel": "Gemeenschap" + "communityLabel": "Gemeenschap", + "t2iAdapter": "T2I-adapter", + "on": "Aan", + "nodeEditor": "Knooppunteditor", + "ipAdapter": "IP-adapter", + "controlAdapter": "Control-adapter", + "auto": "Autom.", + "controlNet": "ControlNet", + "statusProcessing": "Bezig met verwerken", + "imageFailedToLoad": "Kan afbeelding niet laden", + "learnMore": "Meer informatie", + "advanced": "Uitgebreid" }, "gallery": { "generations": "Gegenereerde afbeeldingen", @@ -95,12 +106,22 @@ "allImagesLoaded": "Alle afbeeldingen geladen", "loadMore": "Laad meer", "noImagesInGallery": "Geen afbeeldingen om te tonen", - "deleteImage": "Wis afbeelding", - "deleteImageBin": "Gewiste afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.", - "deleteImagePermanent": "Gewiste afbeeldingen kunnen niet worden hersteld.", + "deleteImage": "Verwijder afbeelding", + "deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.", + "deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.", "assets": "Eigen onderdelen", "images": "Afbeeldingen", - "autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken" + "autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken", + "featuresWillReset": "Als je deze afbeelding verwijdert, dan worden deze functies onmiddellijk teruggezet.", + "loading": "Bezig met laden", + "unableToLoad": "Kan galerij niet laden", + "preparingDownload": "Bezig met voorbereiden van download", + "preparingDownloadFailed": "Fout bij voorbereiden van download", + "downloadSelection": "Download selectie", + "currentlyInUse": "Deze afbeelding is momenteel in gebruik door de volgende functies:", + "copy": "Kopieer", + "download": "Download", + "setCurrentImage": "Stel in als huidige afbeelding" }, "hotkeys": { "keyboardShortcuts": "Sneltoetsen", @@ -332,7 +353,7 @@ "config": "Configuratie", "configValidationMsg": "Pad naar het configuratiebestand van je model.", "modelLocation": "Locatie model", - "modelLocationValidationMsg": "Pad naar waar je model zich bevindt.", + "modelLocationValidationMsg": "Geef het pad naar een lokale map waar je Diffusers-model wordt bewaard", "vaeLocation": "Locatie VAE", "vaeLocationValidationMsg": "Pad naar waar je VAE zich bevindt.", "width": "Breedte", @@ -365,11 +386,11 @@ "deleteModel": "Verwijder model", "deleteConfig": "Verwijder configuratie", "deleteMsg1": "Weet je zeker dat je dit model wilt verwijderen uit InvokeAI?", - "deleteMsg2": "Hiermee ZAL het model van schijf worden verwijderd als het zich bevindt in de InvokeAI-beginmap. Als je het model vanaf een eigen locatie gebruikt, dan ZAL het model NIET van schijf worden verwijderd.", + "deleteMsg2": "Hiermee ZAL het model van schijf worden verwijderd als het zich bevindt in de beginmap van InvokeAI. Als je het model vanaf een eigen locatie gebruikt, dan ZAL het model NIET van schijf worden verwijderd.", "formMessageDiffusersVAELocationDesc": "Indien niet opgegeven, dan zal InvokeAI kijken naar het VAE-bestand in de hierboven gegeven modellocatie.", "repoIDValidationMsg": "Online repository van je model", "formMessageDiffusersModelLocation": "Locatie Diffusers-model", - "convertToDiffusersHelpText3": "Je checkpoint-bestand op schijf ZAL worden verwijderd als het zich in de InvokeAI root map bevindt. Het zal NIET worden verwijderd als het zich in een andere locatie bevindt.", + "convertToDiffusersHelpText3": "Je checkpoint-bestand op de schijf ZAL worden verwijderd als het zich in de beginmap van InvokeAI bevindt. Het ZAL NIET worden verwijderd als het zich in een andere locatie bevindt.", "convertToDiffusersHelpText6": "Wil je dit model omzetten?", "allModels": "Alle modellen", "checkpointModels": "Checkpoints", @@ -437,14 +458,24 @@ "noCustomLocationProvided": "Geen Aangepaste Locatie Opgegeven", "syncModels": "Synchroniseer Modellen", "modelsSynced": "Modellen Gesynchroniseerd", - "modelSyncFailed": "Synchronisatie Modellen Gefaald", + "modelSyncFailed": "Synchronisatie modellen mislukt", "modelDeleteFailed": "Model kon niet verwijderd worden", "convertingModelBegin": "Model aan het converteren. Even geduld.", "importModels": "Importeer Modellen", - "syncModelsDesc": "Als je modellen niet meer synchroon zijn met de backend, kan je ze met deze optie verversen. Dit wordt typisch gebruikt in het geval je het models.yaml bestand met de hand bewerkt of als je modellen aan de InvokeAI root map toevoegt nadat de applicatie gestart werd.", + "syncModelsDesc": "Als je modellen niet meer synchroon zijn met de backend, kan je ze met deze optie vernieuwen. Dit wordt meestal gebruikt in het geval je het bestand models.yaml met de hand bewerkt of als je modellen aan de beginmap van InvokeAI toevoegt nadat de applicatie gestart is.", "loraModels": "LoRA's", "onnxModels": "Onnx", - "oliveModels": "Olives" + "oliveModels": "Olives", + "noModels": "Geen modellen gevonden", + "predictionType": "Soort voorspelling (voor Stable Diffusion 2.x-modellen en incidentele Stable Diffusion 1.x-modellen)", + "quickAdd": "Voeg snel toe", + "simpleModelDesc": "Geef een pad naar een lokaal Diffusers-model, lokale-checkpoint- / safetensors-model, een HuggingFace-repo-ID of een url naar een checkpoint- / Diffusers-model.", + "advanced": "Uitgebreid", + "useCustomConfig": "Gebruik eigen configuratie", + "closeAdvanced": "Sluit uitgebreid", + "modelType": "Soort model", + "customConfigFileLocation": "Locatie eigen configuratiebestand", + "vaePrecision": "Nauwkeurigheid VAE" }, "parameters": { "images": "Afbeeldingen", @@ -465,7 +496,7 @@ "type": "Soort", "strength": "Sterkte", "upscaling": "Opschalen", - "upscale": "Schaal op", + "upscale": "Vergroot (Shift + U)", "upscaleImage": "Schaal afbeelding op", "scale": "Schaal", "otherOptions": "Andere opties", @@ -496,7 +527,7 @@ "useInitImg": "Gebruik initiële afbeelding", "info": "Info", "initialImage": "Initiële afbeelding", - "showOptionsPanel": "Toon deelscherm Opties", + "showOptionsPanel": "Toon deelscherm Opties (O of T)", "symmetry": "Symmetrie", "hSymmetryStep": "Stap horiz. symmetrie", "vSymmetryStep": "Stap vert. symmetrie", @@ -504,7 +535,8 @@ "immediate": "Annuleer direct", "isScheduled": "Annuleren", "setType": "Stel annuleervorm in", - "schedule": "Annuleer na huidige iteratie" + "schedule": "Annuleer na huidige iteratie", + "cancel": "Annuleer" }, "general": "Algemeen", "copyImage": "Kopieer afbeelding", @@ -520,7 +552,7 @@ "boundingBoxWidth": "Tekenvak breedte", "boundingBoxHeight": "Tekenvak hoogte", "clipSkip": "Overslaan CLIP", - "aspectRatio": "Verhouding", + "aspectRatio": "Beeldverhouding", "negativePromptPlaceholder": "Negatieve prompt", "controlNetControlMode": "Aansturingsmodus", "positivePromptPlaceholder": "Positieve prompt", @@ -532,7 +564,46 @@ "coherenceSteps": "Stappen", "coherenceStrength": "Sterkte", "seamHighThreshold": "Hoog", - "seamLowThreshold": "Laag" + "seamLowThreshold": "Laag", + "invoke": { + "noNodesInGraph": "Geen knooppunten in graaf", + "noModelSelected": "Geen model ingesteld", + "invoke": "Start", + "noPrompts": "Geen prompts gegenereerd", + "systemBusy": "Systeem is bezig", + "noInitialImageSelected": "Geen initiële afbeelding gekozen", + "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} invoer ontbreekt", + "noControlImageForControlAdapter": "Controle-adapter #{{number}} heeft geen controle-afbeelding", + "noModelForControlAdapter": "Control-adapter #{{number}} heeft geen model ingesteld staan.", + "unableToInvoke": "Kan niet starten", + "incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is ongeldig in combinatie met het hoofdmodel.", + "systemDisconnected": "Systeem is niet verbonden", + "missingNodeTemplate": "Knooppuntsjabloon ontbreekt", + "readyToInvoke": "Klaar om te starten", + "missingFieldTemplate": "Veldsjabloon ontbreekt", + "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan" + }, + "seamlessX&Y": "Naadloos X en Y", + "isAllowedToUpscale": { + "useX2Model": "Afbeelding is te groot om te vergroten met het x4-model. Gebruik hiervoor het x2-model", + "tooLarge": "Afbeelding is te groot om te vergoten. Kies een kleinere afbeelding" + }, + "aspectRatioFree": "Vrij", + "cpuNoise": "CPU-ruis", + "patchmatchDownScaleSize": "Verklein", + "gpuNoise": "GPU-ruis", + "seamlessX": "Naadloos X", + "useCpuNoise": "Gebruik CPU-ruis", + "clipSkipWithLayerCount": "Overslaan CLIP {{layerCount}}", + "seamlessY": "Naadloos Y", + "manualSeed": "Handmatige seedwaarde", + "imageActions": "Afbeeldingshandeling", + "randomSeed": "Willekeurige seedwaarde", + "iterations": "Iteraties", + "iterationsWithCount_one": "{{count}} iteratie", + "iterationsWithCount_other": "{{count}} iteraties", + "enableNoiseSettings": "Schakel ruisinstellingen in", + "coherenceMode": "Modus" }, "settings": { "models": "Modellen", @@ -544,14 +615,14 @@ "resetWebUI": "Herstel web-UI", "resetWebUIDesc1": "Herstel web-UI herstelt alleen de lokale afbeeldingscache en de onthouden instellingen van je browser. Het verwijdert geen afbeeldingen van schijf.", "resetWebUIDesc2": "Als afbeeldingen niet getoond worden in de galerij of iets anders werkt niet, probeer dan eerst deze herstelfunctie voordat je een fout aanmeldt op GitHub.", - "resetComplete": "Webgebruikersinterface is hersteld.", + "resetComplete": "Webinterface is hersteld.", "useSlidersForAll": "Gebruik schuifbalken voor alle opties", - "consoleLogLevel": "Logboekniveau", + "consoleLogLevel": "Niveau logboek", "shouldLogToConsole": "Schrijf logboek naar console", "developer": "Ontwikkelaar", "general": "Algemeen", "showProgressInViewer": "Toon voortgangsafbeeldingen in viewer", - "generation": "Generatie", + "generation": "Genereren", "ui": "Gebruikersinterface", "antialiasProgressImages": "Voer anti-aliasing uit op voortgangsafbeeldingen", "showAdvancedOptions": "Toon uitgebreide opties", @@ -560,8 +631,17 @@ "beta": "Bèta", "experimental": "Experimenteel", "alternateCanvasLayout": "Omwisselen Canvas Layout", - "enableNodesEditor": "Knopen Editor Inschakelen", - "autoChangeDimensions": "Werk bij wijziging afmetingen bij naar modelstandaard" + "enableNodesEditor": "Schakel Knooppunteditor in", + "autoChangeDimensions": "Werk B/H bij naar modelstandaard bij wijziging", + "clearIntermediates": "Wis tussentijdse afbeeldingen", + "clearIntermediatesDesc3": "Je galerijafbeeldingen zullen niet worden verwijderd.", + "clearIntermediatesWithCount_one": "Wis {{count}} tussentijdse afbeelding", + "clearIntermediatesWithCount_other": "Wis {{count}} tussentijdse afbeeldingen", + "clearIntermediatesDesc2": "Tussentijdse afbeeldingen zijn nevenproducten bij het genereren. Deze wijken af van de uitvoerafbeeldingen in de galerij. Als je tussentijdse afbeeldingen wist, wordt schijfruimte vrijgemaakt.", + "intermediatesCleared_one": "{{count}} tussentijdse afbeelding gewist", + "intermediatesCleared_other": "{{count}} tussentijdse afbeeldingen gewist", + "clearIntermediatesDesc1": "Als je tussentijdse afbeeldingen wist, dan wordt de staat hersteld van je canvas en van ControlNet.", + "intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen" }, "toast": { "tempFoldersEmptied": "Tijdelijke map geleegd", @@ -610,7 +690,42 @@ "nodesCorruptedGraph": "Kan niet laden. Graph lijkt corrupt.", "nodesUnrecognizedTypes": "Laden mislukt. Graph heeft onherkenbare types", "nodesBrokenConnections": "Laden mislukt. Sommige verbindingen zijn verbroken.", - "nodesNotValidGraph": "Geen geldige knooppunten graph" + "nodesNotValidGraph": "Geen geldige knooppunten graph", + "baseModelChangedCleared_one": "Basismodel is gewijzigd: {{count}} niet-compatibel submodel weggehaald of uitgeschakeld", + "baseModelChangedCleared_other": "Basismodel is gewijzigd: {{count}} niet-compatibele submodellen weggehaald of uitgeschakeld", + "imageSavingFailed": "Fout bij bewaren afbeelding", + "canvasSentControlnetAssets": "Canvas gestuurd naar ControlNet en Assets", + "problemCopyingCanvasDesc": "Kan basislaag niet exporteren", + "loadedWithWarnings": "Werkstroom geladen met waarschuwingen", + "setInitialImage": "Ingesteld als initiële afbeelding", + "canvasCopiedClipboard": "Canvas gekopieerd naar klembord", + "setControlImage": "Ingesteld als controle-afbeelding", + "setNodeField": "Ingesteld als knooppuntveld", + "problemSavingMask": "Fout bij bewaren masker", + "problemSavingCanvasDesc": "Kan basislaag niet exporteren", + "maskSavedAssets": "Masker bewaard in Assets", + "modelAddFailed": "Fout bij toevoegen model", + "problemDownloadingCanvas": "Fout bij downloaden van canvas", + "problemMergingCanvas": "Fout bij samenvoegen canvas", + "setCanvasInitialImage": "Ingesteld als initiële canvasafbeelding", + "imageUploaded": "Afbeelding geüpload", + "addedToBoard": "Toegevoegd aan bord", + "workflowLoaded": "Werkstroom geladen", + "modelAddedSimple": "Model toegevoegd", + "problemImportingMaskDesc": "Kan masker niet exporteren", + "problemCopyingCanvas": "Fout bij kopiëren canvas", + "problemSavingCanvas": "Fout bij bewaren canvas", + "canvasDownloaded": "Canvas gedownload", + "setIPAdapterImage": "Ingesteld als IP-adapterafbeelding", + "problemMergingCanvasDesc": "Kan basislaag niet exporteren", + "problemDownloadingCanvasDesc": "Kan basislaag niet exporteren", + "problemSavingMaskDesc": "Kan masker niet exporteren", + "imageSaved": "Afbeelding bewaard", + "maskSentControlnetAssets": "Masker gestuurd naar ControlNet en Assets", + "canvasSavedGallery": "Canvas bewaard in galerij", + "imageUploadFailed": "Fout bij uploaden afbeelding", + "modelAdded": "Model toegevoegd: {{modelName}}", + "problemImportingMask": "Fout bij importeren masker" }, "tooltip": { "feature": { @@ -685,7 +800,9 @@ "betaDarkenOutside": "Verduister buiten tekenvak", "betaLimitToBox": "Beperk tot tekenvak", "betaPreserveMasked": "Behoud masker", - "antialiasing": "Anti-aliasing" + "antialiasing": "Anti-aliasing", + "showResultsOn": "Toon resultaten (aan)", + "showResultsOff": "Toon resultaten (uit)" }, "accessibility": { "exitViewer": "Stop viewer", @@ -707,7 +824,9 @@ "toggleAutoscroll": "Autom. scrollen aan/uit", "toggleLogViewer": "Logboekviewer aan/uit", "showOptionsPanel": "Toon zijscherm", - "menu": "Menu" + "menu": "Menu", + "showGalleryPanel": "Toon deelscherm Galerij", + "loadMore": "Laad meer" }, "ui": { "showProgressImages": "Toon voortgangsafbeeldingen", @@ -730,6 +849,661 @@ "resetWorkflow": "Herstel werkstroom", "resetWorkflowDesc": "Weet je zeker dat je deze werkstroom wilt herstellen?", "resetWorkflowDesc2": "Herstel van een werkstroom haalt alle knooppunten, randen en werkstroomdetails weg.", - "downloadWorkflow": "Download JSON van werkstroom" + "downloadWorkflow": "Download JSON van werkstroom", + "booleanPolymorphicDescription": "Een verzameling Booleanse waarden.", + "scheduler": "Planner", + "inputField": "Invoerveld", + "controlFieldDescription": "Controlegegevens doorgegeven tussen knooppunten.", + "skippingUnknownOutputType": "Overslaan van onbekend soort uitvoerveld", + "latentsFieldDescription": "Latents kunnen worden doorgegeven tussen knooppunten.", + "denoiseMaskFieldDescription": "Ontruisingsmasker kan worden doorgegeven tussen knooppunten", + "floatCollectionDescription": "Een verzameling zwevende-kommagetallen.", + "missingTemplate": "Ontbrekende sjabloon", + "outputSchemaNotFound": "Uitvoerschema niet gevonden", + "ipAdapterPolymorphicDescription": "Een verzameling IP-adapters.", + "workflowDescription": "Korte beschrijving", + "latentsPolymorphicDescription": "Latents kunnen worden doorgegeven tussen knooppunten.", + "colorFieldDescription": "Een RGBA-kleur.", + "mainModelField": "Model", + "unhandledInputProperty": "Onverwerkt invoerkenmerk", + "versionUnknown": " Versie onbekend", + "ipAdapterCollection": "Verzameling IP-adapters", + "conditioningCollection": "Verzameling conditionering", + "maybeIncompatible": "Is mogelijk niet compatibel met geïnstalleerde knooppunten", + "ipAdapterPolymorphic": "Polymorfisme IP-adapter", + "noNodeSelected": "Geen knooppunt gekozen", + "addNode": "Voeg knooppunt toe", + "unableToValidateWorkflow": "Kan werkstroom niet valideren", + "enum": "Enumeratie", + "integerPolymorphicDescription": "Een verzameling gehele getallen.", + "noOutputRecorded": "Geen uitvoer opgenomen", + "updateApp": "Werk app bij", + "conditioningCollectionDescription": "Conditionering kan worden doorgegeven tussen knooppunten.", + "colorPolymorphic": "Polymorfisme kleur", + "colorCodeEdgesHelp": "Kleurgecodeerde randen op basis van hun verbonden velden", + "collectionDescription": "TODO", + "float": "Zwevende-kommagetal", + "workflowContact": "Contactpersoon", + "skippingReservedFieldType": "Overslaan van gereserveerd veldsoort", + "animatedEdges": "Geanimeerde randen", + "booleanCollectionDescription": "Een verzameling van Booleanse waarden.", + "sDXLMainModelFieldDescription": "SDXL-modelveld.", + "conditioningPolymorphic": "Polymorfisme conditionering", + "integer": "Geheel getal", + "colorField": "Kleur", + "boardField": "Bord", + "nodeTemplate": "Sjabloon knooppunt", + "latentsCollection": "Verzameling latents", + "problemReadingWorkflow": "Fout bij lezen van werkstroom uit afbeelding", + "sourceNode": "Bronknooppunt", + "nodeOpacity": "Dekking knooppunt", + "pickOne": "Kies er een", + "collectionItemDescription": "TODO", + "integerDescription": "Gehele getallen zijn getallen zonder een decimaalteken.", + "outputField": "Uitvoerveld", + "unableToLoadWorkflow": "Kan werkstroom niet valideren", + "snapToGrid": "Lijn uit op raster", + "stringPolymorphic": "Polymorfisme tekenreeks", + "conditioningPolymorphicDescription": "Conditionering kan worden doorgegeven tussen knooppunten.", + "noFieldsLinearview": "Geen velden toegevoegd aan lineaire weergave", + "skipped": "Overgeslagen", + "imagePolymorphic": "Polymorfisme afbeelding", + "nodeSearch": "Zoek naar knooppunten", + "updateNode": "Werk knooppunt bij", + "sDXLRefinerModelFieldDescription": "Beschrijving", + "imagePolymorphicDescription": "Een verzameling afbeeldingen.", + "floatPolymorphic": "Polymorfisme zwevende-kommagetal", + "version": "Versie", + "doesNotExist": "bestaat niet", + "ipAdapterCollectionDescription": "Een verzameling van IP-adapters.", + "stringCollectionDescription": "Een verzameling tekenreeksen.", + "unableToParseNode": "Kan knooppunt niet inlezen", + "controlCollection": "Controle-verzameling", + "validateConnections": "Valideer verbindingen en graaf", + "stringCollection": "Verzameling tekenreeksen", + "inputMayOnlyHaveOneConnection": "Invoer mag slechts een enkele verbinding hebben", + "notes": "Opmerkingen", + "uNetField": "UNet", + "nodeOutputs": "Uitvoer knooppunt", + "currentImageDescription": "Toont de huidige afbeelding in de knooppunteditor", + "validateConnectionsHelp": "Voorkom dat er ongeldige verbindingen worden gelegd en dat er ongeldige grafen worden aangeroepen", + "problemSettingTitle": "Fout bij instellen titel", + "ipAdapter": "IP-adapter", + "integerCollection": "Verzameling gehele getallen", + "collectionItem": "Verzamelingsonderdeel", + "noConnectionInProgress": "Geen verbinding bezig te maken", + "vaeModelField": "VAE", + "controlCollectionDescription": "Controlegegevens doorgegeven tussen knooppunten.", + "skippedReservedInput": "Overgeslagen gereserveerd invoerveld", + "workflowVersion": "Versie", + "noConnectionData": "Geen verbindingsgegevens", + "outputFields": "Uitvoervelden", + "fieldTypesMustMatch": "Veldsoorten moeten overeenkomen", + "workflow": "Werkstroom", + "edge": "Rand", + "inputNode": "Invoerknooppunt", + "enumDescription": "Enumeraties zijn waarden die uit een aantal opties moeten worden gekozen.", + "unkownInvocation": "Onbekende aanroepsoort", + "loRAModelFieldDescription": "TODO", + "imageField": "Afbeelding", + "skippedReservedOutput": "Overgeslagen gereserveerd uitvoerveld", + "animatedEdgesHelp": "Animeer gekozen randen en randen verbonden met de gekozen knooppunten", + "cannotDuplicateConnection": "Kan geen dubbele verbindingen maken", + "booleanPolymorphic": "Polymorfisme Booleaanse waarden", + "unknownTemplate": "Onbekend sjabloon", + "noWorkflow": "Geen werkstroom", + "removeLinearView": "Verwijder uit lineaire weergave", + "colorCollectionDescription": "TODO", + "integerCollectionDescription": "Een verzameling gehele getallen.", + "colorPolymorphicDescription": "Een verzameling kleuren.", + "sDXLMainModelField": "SDXL-model", + "workflowTags": "Labels", + "denoiseMaskField": "Ontruisingsmasker", + "schedulerDescription": "Beschrijving", + "missingCanvaInitImage": "Ontbrekende initialisatie-afbeelding voor canvas", + "conditioningFieldDescription": "Conditionering kan worden doorgegeven tussen knooppunten.", + "clipFieldDescription": "Submodellen voor tokenizer en text_encoder.", + "fullyContainNodesHelp": "Knooppunten moeten zich volledig binnen het keuzevak bevinden om te worden gekozen", + "noImageFoundState": "Geen initiële afbeelding gevonden in de staat", + "workflowValidation": "Validatiefout werkstroom", + "clipField": "Clip", + "stringDescription": "Tekenreeksen zijn tekst.", + "nodeType": "Soort knooppunt", + "noMatchingNodes": "Geen overeenkomende knooppunten", + "fullyContainNodes": "Omvat knooppunten volledig om ze te kiezen", + "integerPolymorphic": "Polymorfisme geheel getal", + "executionStateInProgress": "Bezig", + "noFieldType": "Geen soort veld", + "colorCollection": "Een verzameling kleuren.", + "executionStateError": "Fout", + "noOutputSchemaName": "Geen naam voor uitvoerschema gevonden in referentieobject", + "ipAdapterModel": "Model IP-adapter", + "latentsPolymorphic": "Polymorfisme latents", + "vaeModelFieldDescription": "Beschrijving", + "skippingInputNoTemplate": "Overslaan van invoerveld zonder sjabloon", + "ipAdapterDescription": "Een Afbeeldingsprompt-adapter (IP-adapter).", + "boolean": "Booleaanse waarden", + "missingCanvaInitMaskImages": "Ontbrekende initialisatie- en maskerafbeeldingen voor canvas", + "problemReadingMetadata": "Fout bij lezen van metagegevens uit afbeelding", + "stringPolymorphicDescription": "Een verzameling tekenreeksen.", + "oNNXModelField": "ONNX-model", + "executionStateCompleted": "Voltooid", + "node": "Knooppunt", + "skippingUnknownInputType": "Overslaan van onbekend soort invoerveld", + "workflowAuthor": "Auteur", + "currentImage": "Huidige afbeelding", + "controlField": "Controle", + "workflowName": "Naam", + "booleanDescription": "Booleanse waarden zijn waar en onwaar.", + "collection": "Verzameling", + "ipAdapterModelDescription": "Modelveld IP-adapter", + "cannotConnectInputToInput": "Kan invoer niet aan invoer verbinden", + "invalidOutputSchema": "Ongeldig uitvoerschema", + "boardFieldDescription": "Een galerijbord", + "floatDescription": "Zwevende-kommagetallen zijn getallen met een decimaalteken.", + "floatPolymorphicDescription": "Een verzameling zwevende-kommagetallen.", + "vaeField": "Vae", + "conditioningField": "Conditionering", + "unhandledOutputProperty": "Onverwerkt uitvoerkenmerk", + "workflowNotes": "Opmerkingen", + "string": "Tekenreeks", + "floatCollection": "Verzameling zwevende-kommagetallen", + "latentsField": "Latents", + "cannotConnectOutputToOutput": "Kan uitvoer niet aan uitvoer verbinden", + "booleanCollection": "Verzameling Booleaanse waarden", + "connectionWouldCreateCycle": "Verbinding zou cyclisch worden", + "cannotConnectToSelf": "Kan niet aan zichzelf verbinden", + "notesDescription": "Voeg opmerkingen toe aan je werkstroom", + "unknownField": "Onbekend veld", + "inputFields": "Invoervelden", + "colorCodeEdges": "Kleurgecodeerde randen", + "uNetFieldDescription": "UNet-submodel.", + "unknownNode": "Onbekend knooppunt", + "imageCollectionDescription": "Een verzameling afbeeldingen.", + "mismatchedVersion": "Heeft niet-overeenkomende versie", + "vaeFieldDescription": "Vae-submodel.", + "imageFieldDescription": "Afbeeldingen kunnen worden doorgegeven tussen knooppunten.", + "outputNode": "Uitvoerknooppunt", + "addNodeToolTip": "Voeg knooppunt toe (Shift+A, spatie)", + "loadingNodes": "Bezig met laden van knooppunten...", + "snapToGridHelp": "Lijn knooppunten uit op raster bij verplaatsing", + "workflowSettings": "Instellingen werkstroomeditor", + "mainModelFieldDescription": "TODO", + "sDXLRefinerModelField": "Verfijningsmodel", + "loRAModelField": "LoRA", + "unableToParseEdge": "Kan rand niet inlezen", + "latentsCollectionDescription": "Latents kunnen worden doorgegeven tussen knooppunten.", + "oNNXModelFieldDescription": "ONNX-modelveld.", + "imageCollection": "Afbeeldingsverzameling" + }, + "controlnet": { + "amult": "a_mult", + "resize": "Schaal", + "showAdvanced": "Toon uitgebreide opties", + "contentShuffleDescription": "Verschuift het materiaal in de afbeelding", + "bgth": "bg_th", + "addT2IAdapter": "Voeg $t(common.t2iAdapter) toe", + "pidi": "PIDI", + "importImageFromCanvas": "Importeer afbeelding uit canvas", + "lineartDescription": "Zet afbeelding om naar line-art", + "normalBae": "Normale BAE", + "importMaskFromCanvas": "Importeer masker uit canvas", + "hed": "HED", + "hideAdvanced": "Verberg uitgebreid", + "contentShuffle": "Verschuif materiaal", + "controlNetEnabledT2IDisabled": "$t(common.controlNet) ingeschakeld, $t(common.t2iAdapter)s uitgeschakeld", + "ipAdapterModel": "Adaptermodel", + "resetControlImage": "Herstel controle-afbeelding", + "beginEndStepPercent": "Percentage begin-/eindstap", + "mlsdDescription": "Minimalistische herkenning lijnsegmenten", + "duplicate": "Maak kopie", + "balanced": "Gebalanceerd", + "f": "F", + "h": "H", + "prompt": "Prompt", + "depthMidasDescription": "Genereer diepteblad via Midas", + "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))", + "openPoseDescription": "Menselijke pose-benadering via Openpose", + "control": "Controle", + "resizeMode": "Modus schaling", + "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ingeschakeld, $t(common.controlNet)s uitgeschakeld", + "coarse": "Grof", + "weight": "Gewicht", + "selectModel": "Kies een model", + "crop": "Snij bij", + "depthMidas": "Diepte (Midas)", + "w": "B", + "processor": "Verwerker", + "addControlNet": "Voeg $t(common.controlNet) toe", + "none": "Geen", + "incompatibleBaseModel": "Niet-compatibel basismodel:", + "enableControlnet": "Schakel ControlNet in", + "detectResolution": "Herken resolutie", + "controlNetT2IMutexDesc": "Gelijktijdig gebruik van $t(common.controlNet) en $t(common.t2iAdapter) wordt op dit moment niet ondersteund.", + "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))", + "pidiDescription": "PIDI-afbeeldingsverwerking", + "mediapipeFace": "Mediapipe - Gezicht", + "mlsd": "M-LSD", + "controlMode": "Controlemodus", + "fill": "Vul", + "cannyDescription": "Herkenning Canny-rand", + "addIPAdapter": "Voeg $t(common.ipAdapter) toe", + "lineart": "Line-art", + "colorMapDescription": "Genereert een kleurenblad van de afbeelding", + "lineartAnimeDescription": "Lineartverwerking in anime-stijl", + "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))", + "minConfidence": "Min. vertrouwensniveau", + "imageResolution": "Resolutie afbeelding", + "megaControl": "Zeer veel controle", + "depthZoe": "Diepte (Zoe)", + "colorMap": "Kleur", + "lowThreshold": "Lage drempelwaarde", + "autoConfigure": "Configureer verwerker automatisch", + "highThreshold": "Hoge drempelwaarde", + "normalBaeDescription": "Normale BAE-verwerking", + "noneDescription": "Geen verwerking toegepast", + "saveControlImage": "Bewaar controle-afbeelding", + "openPose": "Openpose", + "toggleControlNet": "Zet deze ControlNet aan/uit", + "delete": "Verwijder", + "controlAdapter_one": "Control-adapter", + "controlAdapter_other": "Control-adapters", + "safe": "Veilig", + "colorMapTileSize": "Grootte tegel", + "lineartAnime": "Line-art voor anime", + "ipAdapterImageFallback": "Geen IP-adapterafbeelding gekozen", + "mediapipeFaceDescription": "Gezichtsherkenning met Mediapipe", + "canny": "Canny", + "depthZoeDescription": "Genereer diepteblad via Zoe", + "hedDescription": "Herkenning van holistisch-geneste randen", + "setControlImageDimensions": "Stel afmetingen controle-afbeelding in op B/H", + "scribble": "Krabbel", + "resetIPAdapterImage": "Herstel IP-adapterafbeelding", + "handAndFace": "Hand en gezicht", + "enableIPAdapter": "Schakel IP-adapter in", + "maxFaces": "Max. gezichten" + }, + "dynamicPrompts": { + "seedBehaviour": { + "perPromptDesc": "Gebruik een verschillende seedwaarde per afbeelding", + "perIterationLabel": "Seedwaarde per iteratie", + "perIterationDesc": "Gebruik een verschillende seedwaarde per iteratie", + "perPromptLabel": "Seedwaarde per afbeelding", + "label": "Gedrag seedwaarde" + }, + "enableDynamicPrompts": "Schakel dynamische prompts in", + "combinatorial": "Combinatorisch genereren", + "maxPrompts": "Max. prompts", + "promptsWithCount_one": "{{count}} prompt", + "promptsWithCount_other": "{{count}} prompts", + "dynamicPrompts": "Dynamische prompts" + }, + "popovers": { + "noiseUseCPU": { + "paragraphs": [ + "Bepaalt of ruis wordt gegenereerd op de CPU of de GPU.", + "Met CPU-ruis ingeschakeld zal een bepaalde seedwaarde dezelfde afbeelding opleveren op welke machine dan ook.", + "Er is geen prestatieverschil bij het inschakelen van CPU-ruis." + ], + "heading": "Gebruik CPU-ruis" + }, + "paramScheduler": { + "paragraphs": [ + "De planner bepaalt hoe ruis per iteratie wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model." + ], + "heading": "Planner" + }, + "scaleBeforeProcessing": { + "paragraphs": [ + "Schaalt het gekozen gebied naar de grootte die het meest geschikt is voor het model, vooraf aan het proces van het afbeeldingen genereren." + ], + "heading": "Schaal vooraf aan verwerking" + }, + "compositingMaskAdjustments": { + "heading": "Aanpassingen masker", + "paragraphs": [ + "Pas het masker aan." + ] + }, + "paramRatio": { + "heading": "Beeldverhouding", + "paragraphs": [ + "De beeldverhouding van de afmetingen van de afbeelding die wordt gegenereerd.", + "Een afbeeldingsgrootte (in aantal pixels) equivalent aan 512x512 wordt aanbevolen voor SD1.5-modellen. Een grootte-equivalent van 1024x1024 wordt aanbevolen voor SDXL-modellen." + ] + }, + "compositingCoherenceSteps": { + "heading": "Stappen", + "paragraphs": [ + "Het aantal te gebruiken ontruisingsstappen in de coherentiefase.", + "Gelijk aan de hoofdparameter Stappen." + ] + }, + "dynamicPrompts": { + "paragraphs": [ + "Dynamische prompts vormt een enkele prompt om in vele.", + "De basissyntax is \"a {red|green|blue} ball\". Dit zal de volgende drie prompts geven: \"a red ball\", \"a green ball\" en \"a blue ball\".", + "Gebruik de syntax zo vaak als je wilt in een enkele prompt, maar zorg ervoor dat het aantal gegenereerde prompts in lijn ligt met de instelling Max. prompts." + ], + "heading": "Dynamische prompts" + }, + "paramVAE": { + "paragraphs": [ + "Het model gebruikt voor het vertalen van AI-uitvoer naar de uiteindelijke afbeelding." + ], + "heading": "VAE" + }, + "compositingBlur": { + "heading": "Vervaging", + "paragraphs": [ + "De vervagingsstraal van het masker." + ] + }, + "paramIterations": { + "paragraphs": [ + "Het aantal te genereren afbeeldingen.", + "Als dynamische prompts is ingeschakeld, dan zal elke prompt dit aantal keer gegenereerd worden." + ], + "heading": "Iteraties" + }, + "paramVAEPrecision": { + "heading": "Nauwkeurigheid VAE", + "paragraphs": [ + "De nauwkeurigheid gebruikt tijdens de VAE-codering en -decodering. FP16/halve nauwkeurig is efficiënter, ten koste van kleine afbeeldingsvariaties." + ] + }, + "compositingCoherenceMode": { + "heading": "Modus", + "paragraphs": [ + "De modus van de coherentiefase." + ] + }, + "paramSeed": { + "paragraphs": [ + "Bepaalt de startruis die gebruikt wordt bij het genereren.", + "Schakel \"Willekeurige seedwaarde\" uit om identieke resultaten te krijgen met dezelfde genereer-instellingen." + ], + "heading": "Seedwaarde" + }, + "controlNetResizeMode": { + "heading": "Schaalmodus", + "paragraphs": [ + "Hoe de ControlNet-afbeelding zal worden geschaald aan de uitvoergrootte van de afbeelding." + ] + }, + "controlNetBeginEnd": { + "paragraphs": [ + "Op welke stappen van het ontruisingsproces ControlNet worden toegepast.", + "ControlNets die worden toegepast aan het begin begeleiden het compositieproces. ControlNets die worden toegepast aan het eind zorgen voor details." + ], + "heading": "Percentage begin- / eindstap" + }, + "dynamicPromptsSeedBehaviour": { + "paragraphs": [ + "Bepaalt hoe de seedwaarde wordt gebruikt bij het genereren van prompts.", + "Per iteratie zal een unieke seedwaarde worden gebruikt voor elke iteratie. Gebruik dit om de promptvariaties binnen een enkele seedwaarde te verkennen.", + "Bijvoorbeeld: als je vijf prompts heb, dan zal voor elke afbeelding dezelfde seedwaarde gebruikt worden.", + "De optie Per afbeelding zal een unieke seedwaarde voor elke afbeelding gebruiken. Dit biedt meer variatie." + ], + "heading": "Gedrag seedwaarde" + }, + "clipSkip": { + "paragraphs": [ + "Kies hoeveel CLIP-modellagen je wilt overslaan.", + "Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen.", + "Een hogere waarde geeft meestal een minder gedetailleerde afbeelding." + ], + "heading": "Overslaan CLIP" + }, + "paramModel": { + "heading": "Model", + "paragraphs": [ + "Model gebruikt voor de ontruisingsstappen.", + "Verschillende modellen zijn meestal getraind om zich te specialiseren in het maken van bepaalde esthetische resultaten en materiaal." + ] + }, + "compositingCoherencePass": { + "heading": "Coherentiefase", + "paragraphs": [ + "Een tweede ronde ontruising helpt bij het samenstellen van de erin- of eruitgetekende afbeelding." + ] + }, + "paramDenoisingStrength": { + "paragraphs": [ + "Hoeveel ruis wordt toegevoegd aan de invoerafbeelding.", + "0 levert een identieke afbeelding op, waarbij 1 een volledig nieuwe afbeelding oplevert." + ], + "heading": "Ontruisingssterkte" + }, + "compositingStrength": { + "heading": "Sterkte", + "paragraphs": [ + "Ontruisingssterkte voor de coherentiefase.", + "Gelijk aan de parameter Ontruisingssterkte Afbeelding naar afbeelding." + ] + }, + "paramNegativeConditioning": { + "paragraphs": [ + "Het genereerproces voorkomt de gegeven begrippen in de negatieve prompt. Gebruik dit om bepaalde zaken of voorwerpen uit te sluiten van de uitvoerafbeelding.", + "Ondersteunt Compel-syntax en -embeddingen." + ], + "heading": "Negatieve prompt" + }, + "compositingBlurMethod": { + "heading": "Vervagingsmethode", + "paragraphs": [ + "De methode van de vervaging die wordt toegepast op het gemaskeerd gebied." + ] + }, + "dynamicPromptsMaxPrompts": { + "heading": "Max. prompts", + "paragraphs": [ + "Beperkt het aantal prompts die kunnen worden gegenereerd door dynamische prompts." + ] + }, + "infillMethod": { + "paragraphs": [ + "Methode om een gekozen gebied in te vullen." + ], + "heading": "Invulmethode" + }, + "controlNetWeight": { + "heading": "Gewicht", + "paragraphs": [ + "Hoe sterk ControlNet effect heeft op de gegeneerde afbeelding." + ] + }, + "controlNet": { + "heading": "ControlNet", + "paragraphs": [ + "ControlNets begeleidt het genereerproces, waarbij geholpen wordt bij het maken van afbeeldingen met aangestuurde compositie, structuur of stijl, afhankelijk van het gekozen model." + ] + }, + "paramCFGScale": { + "heading": "CFG-schaal", + "paragraphs": [ + "Bepaalt hoeveel je prompt invloed heeft op het genereerproces." + ] + }, + "controlNetControlMode": { + "paragraphs": [ + "Geeft meer gewicht aan ofwel de prompt danwel ControlNet." + ], + "heading": "Controlemodus" + }, + "paramSteps": { + "heading": "Stappen", + "paragraphs": [ + "Het aantal uit te voeren stappen tijdens elke generatie.", + "Een hoger aantal stappen geven meestal betere afbeeldingen, ten koste van een hogere benodigde tijd om te genereren." + ] + }, + "paramPositiveConditioning": { + "heading": "Positieve prompt", + "paragraphs": [ + "Begeleidt het generartieproces. Gebruik een woord of frase naar keuze.", + "Syntaxes en embeddings voor Compel en dynamische prompts." + ] + }, + "lora": { + "heading": "Gewicht LoRA", + "paragraphs": [ + "Een hogere LoRA-gewicht zal leiden tot een groter effect op de uiteindelijke afbeelding." + ] + } + }, + "metadata": { + "seamless": "Naadloos", + "positivePrompt": "Positieve prompt", + "negativePrompt": "Negatieve prompt", + "generationMode": "Genereermodus", + "Threshold": "Drempelwaarde ruis", + "metadata": "Metagegevens", + "strength": "Sterkte Afbeelding naar afbeelding", + "seed": "Seedwaarde", + "imageDetails": "Afbeeldingsdetails", + "perlin": "Perlin-ruis", + "model": "Model", + "noImageDetails": "Geen afbeeldingsdetails gevonden", + "hiresFix": "Optimalisatie voor hoge resolutie", + "cfgScale": "CFG-schaal", + "fit": "Schaal aanpassen in Afbeelding naar afbeelding", + "initImage": "Initiële afbeelding", + "recallParameters": "Opnieuw aan te roepen parameters", + "height": "Hoogte", + "variations": "Paren seedwaarde-gewicht", + "noMetaData": "Geen metagegevens gevonden", + "width": "Breedte", + "createdBy": "Gemaakt door", + "workflow": "Werkstroom", + "steps": "Stappen", + "scheduler": "Planner", + "noRecallParameters": "Geen opnieuw uit te voeren parameters gevonden" + }, + "queue": { + "status": "Status", + "pruneSucceeded": "{{item_count}} voltooide onderdelen uit wachtrij opgeruimd", + "cancelTooltip": "Annuleer huidig onderdeel", + "queueEmpty": "Wachtrij leeg", + "pauseSucceeded": "Verwerker onderbroken", + "in_progress": "Bezig", + "queueFront": "Voeg vooraan toe in wachtrij", + "notReady": "Fout bij plaatsen in wachtrij", + "batchFailedToQueue": "Fout bij reeks in wachtrij plaatsen", + "completed": "Voltooid", + "queueBack": "Voeg toe aan wachtrij", + "batchValues": "Reekswaarden", + "cancelFailed": "Fout bij annuleren onderdeel", + "queueCountPrediction": "Voeg {{predicted}} toe aan wachtrij", + "batchQueued": "Reeks in wachtrij geplaatst", + "pauseFailed": "Fout bij onderbreken verwerker", + "clearFailed": "Fout bij wissen van wachtrij", + "queuedCount": "{{pending}} wachtend", + "front": "begin", + "clearSucceeded": "Wachtrij gewist", + "pause": "Onderbreek", + "pruneTooltip": "Ruim {{item_count}} voltooide onderdelen op", + "cancelSucceeded": "Onderdeel geannuleerd", + "batchQueuedDesc_one": "Voeg {{count}} sessie toe aan het {{direction}} van de wachtrij", + "batchQueuedDesc_other": "Voeg {{count}} sessies toe aan het {{direction}} van de wachtrij", + "graphQueued": "Graaf in wachtrij geplaatst", + "queue": "Wachtrij", + "batch": "Reeks", + "clearQueueAlertDialog": "Als je de wachtrij onmiddellijk wist, dan worden alle onderdelen die bezig zijn geannuleerd en wordt de wachtrij volledig gewist.", + "pending": "Wachtend", + "completedIn": "Voltooid na", + "resumeFailed": "Fout bij hervatten verwerker", + "clear": "Wis", + "prune": "Ruim op", + "total": "Totaal", + "canceled": "Geannuleerd", + "pruneFailed": "Fout bij opruimen van wachtrij", + "cancelBatchSucceeded": "Reeks geannuleerd", + "clearTooltip": "Annuleer en wis alle onderdelen", + "current": "Huidig", + "pauseTooltip": "Onderbreek verwerker", + "failed": "Mislukt", + "cancelItem": "Annuleer onderdeel", + "next": "Volgende", + "cancelBatch": "Annuleer reeks", + "back": "eind", + "cancel": "Annuleer", + "session": "Sessie", + "queueTotal": "Totaal {{total}}", + "resumeSucceeded": "Verwerker hervat", + "enqueueing": "Bezig met toevoegen van reeks aan wachtrij", + "resumeTooltip": "Hervat verwerker", + "queueMaxExceeded": "Max. aantal van {{max_queue_size}} overschreden, {{skip}} worden overgeslagen", + "resume": "Hervat", + "cancelBatchFailed": "Fout bij annuleren van reeks", + "clearQueueAlertDialog2": "Weet je zeker dat je de wachtrij wilt wissen?", + "item": "Onderdeel", + "graphFailedToQueue": "Fout bij toevoegen graaf aan wachtrij" + }, + "sdxl": { + "refinerStart": "Startwaarde verfijning", + "selectAModel": "Kies een model", + "scheduler": "Planner", + "cfgScale": "CFG-schaal", + "negStylePrompt": "Negatieve-stijlprompt", + "noModelsAvailable": "Geen modellen beschikbaar", + "refiner": "Verfijning", + "negAestheticScore": "Negatieve esthetische score", + "useRefiner": "Gebruik verfijning", + "denoisingStrength": "Sterkte ontruising", + "refinermodel": "Verfijningsmodel", + "posAestheticScore": "Positieve esthetische score", + "concatPromptStyle": "Plak prompt- en stijltekst aan elkaar", + "loading": "Bezig met laden...", + "steps": "Stappen", + "posStylePrompt": "Positieve-stijlprompt" + }, + "models": { + "noMatchingModels": "Geen overeenkomend modellen", + "loading": "bezig met laden", + "noMatchingLoRAs": "Geen overeenkomende LoRA's", + "noLoRAsAvailable": "Geen LoRA's beschikbaar", + "noModelsAvailable": "Geen modellen beschikbaar", + "selectModel": "Kies een model", + "selectLoRA": "Kies een LoRA" + }, + "boards": { + "autoAddBoard": "Voeg automatisch bord toe", + "topMessage": "Dit bord bevat afbeeldingen die in gebruik zijn door de volgende functies:", + "move": "Verplaats", + "menuItemAutoAdd": "Voeg dit automatisch toe aan bord", + "myBoard": "Mijn bord", + "searchBoard": "Zoek borden...", + "noMatching": "Geen overeenkomende borden", + "selectBoard": "Kies een bord", + "cancel": "Annuleer", + "addBoard": "Voeg bord toe", + "bottomMessage": "Als je dit bord en alle afbeeldingen erop verwijdert, dan worden alle functies teruggezet die ervan gebruik maken.", + "uncategorized": "Zonder categorie", + "downloadBoard": "Download bord", + "changeBoard": "Wijzig bord", + "loading": "Bezig met laden...", + "clearSearch": "Maak zoekopdracht leeg" + }, + "invocationCache": { + "disable": "Schakel uit", + "misses": "Mislukt cacheverzoek", + "enableFailed": "Fout bij inschakelen aanroepcache", + "invocationCache": "Aanroepcache", + "clearSucceeded": "Aanroepcache gewist", + "enableSucceeded": "Aanroepcache ingeschakeld", + "clearFailed": "Fout bij wissen aanroepcache", + "hits": "Gelukt cacheverzoek", + "disableSucceeded": "Aanroepcache uitgeschakeld", + "disableFailed": "Fout bij uitschakelen aanroepcache", + "enable": "Schakel in", + "clear": "Wis", + "maxCacheSize": "Max. grootte cache", + "cacheSize": "Grootte cache" + }, + "embedding": { + "noMatchingEmbedding": "Geen overeenkomende embeddings", + "addEmbedding": "Voeg embedding toe", + "incompatibleModel": "Niet-compatibel basismodel:" } } diff --git a/invokeai/frontend/web/public/locales/zh_CN.json b/invokeai/frontend/web/public/locales/zh_CN.json index d91bf0b4bb..729d7652e3 100644 --- a/invokeai/frontend/web/public/locales/zh_CN.json +++ b/invokeai/frontend/web/public/locales/zh_CN.json @@ -88,7 +88,9 @@ "t2iAdapter": "T2I Adapter", "ipAdapter": "IP Adapter", "controlAdapter": "Control Adapter", - "controlNet": "ControlNet" + "controlNet": "ControlNet", + "on": "开", + "auto": "自动" }, "gallery": { "generations": "生成的图像", @@ -472,7 +474,8 @@ "vae": "VAE", "oliveModels": "Olive", "loraModels": "LoRA", - "alpha": "Alpha" + "alpha": "Alpha", + "vaePrecision": "VAE 精度" }, "parameters": { "images": "图像", @@ -595,7 +598,11 @@ "useX2Model": "图像太大，无法使用 x4 模型，使用 x2 模型作为替代", "tooLarge": "图像太大无法进行放大，请选择更小的图像" }, - "iterationsWithCount_other": "{{count}} 次迭代生成" + "iterationsWithCount_other": "{{count}} 次迭代生成", + "seamlessX&Y": "无缝 X & Y", + "aspectRatioFree": "自由", + "seamlessX": "无缝 X", + "seamlessY": "无缝 Y" }, "settings": { "models": "模型", @@ -628,10 +635,11 @@ "clearIntermediates": "清除中间产物", "clearIntermediatesDesc3": "您图库中的图像不会被删除。", "clearIntermediatesDesc2": "中间产物图像是生成过程中产生的副产品，与图库中的结果图像不同。清除中间产物可释放磁盘空间。", - "intermediatesCleared_other": "已清除 {{number}} 个中间产物", + "intermediatesCleared_other": "已清除 {{count}} 个中间产物", "clearIntermediatesDesc1": "清除中间产物会重置您的画布和 ControlNet 状态。", "intermediatesClearedFailed": "清除中间产物时出现问题", - "noIntermediates": "没有可清除的中间产物" + "clearIntermediatesWithCount_other": "清除 {{count}} 个中间产物", + "clearIntermediatesDisabled": "队列为空才能清理中间产物" }, "toast": { "tempFoldersEmptied": "临时文件夹已清空", @@ -714,7 +722,7 @@ "canvasSavedGallery": "画布已保存到图库", "imageUploadFailed": "图像上传失败", "problemImportingMask": "导入遮罩时出现问题", - "baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{number}} 个不兼容的子模型" + "baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型" }, "unifiedCanvas": { "layer": "图层", @@ -858,7 +866,7 @@ "version": "版本", "validateConnections": "验证连接和节点图", "inputMayOnlyHaveOneConnection": "输入仅能有一个连接", - "notes": "节点", + "notes": "注释", "nodeOutputs": "节点输出", "currentImageDescription": "在节点编辑器中显示当前图像", "validateConnectionsHelp": "防止建立无效连接和调用无效节点图", @@ -884,11 +892,11 @@ "currentImage": "当前图像", "workflowName": "名称", "cannotConnectInputToInput": "无法将输入连接到输入", - "workflowNotes": "节点", + "workflowNotes": "注释", "cannotConnectOutputToOutput": "无法将输出连接到输出", "connectionWouldCreateCycle": "连接将创建一个循环", "cannotConnectToSelf": "无法连接自己", - "notesDescription": "添加有关您的工作流的节点", + "notesDescription": "添加有关您的工作流的注释", "unknownField": "未知", "colorCodeEdges": "边缘颜色编码", "unknownNode": "未知节点", @@ -1003,7 +1011,27 @@ "booleanCollection": "布尔值合集", "imageCollectionDescription": "一个图像合集。", "loRAModelField": "LoRA", - "imageCollection": "图像合集" + "imageCollection": "图像合集", + "ipAdapterPolymorphicDescription": "一个 IP-Adapters Collection 合集。", + "ipAdapterCollection": "IP-Adapters 合集", + "conditioningCollection": "条件合集", + "ipAdapterPolymorphic": "IP-Adapters 多态", + "conditioningCollectionDescription": "条件可以在节点间传递。", + "colorPolymorphic": "颜色多态", + "conditioningPolymorphic": "条件多态", + "latentsCollection": "Latents 合集", + "stringPolymorphic": "字符多态", + "conditioningPolymorphicDescription": "条件可以在节点间传递。", + "imagePolymorphic": "图像多态", + "floatPolymorphic": "浮点多态", + "ipAdapterCollectionDescription": "一个 IP-Adapters Collection 合集。", + "ipAdapter": "IP-Adapter", + "booleanPolymorphic": "布尔多态", + "conditioningFieldDescription": "条件可以在节点间传递。", + "integerPolymorphic": "整数多态", + "latentsPolymorphic": "Latents 多态", + "conditioningField": "条件", + "latentsField": "Latents" }, "controlnet": { "resize": "直接缩放", @@ -1073,21 +1101,21 @@ "contentShuffle": "Content Shuffle", "f": "F", "h": "H", - "controlnet": "$t(controlnet.controlAdapter) #{{number}} ($t(common.controlNet))", + "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))", "control": "Control (普通控制)", "coarse": "Coarse", "depthMidas": "Depth (Midas)", "w": "W", - "ip_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.ipAdapter))", + "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))", "mediapipeFace": "Mediapipe Face", "mlsd": "M-LSD", "lineart": "Lineart", - "t2i_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.t2iAdapter))", + "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))", "megaControl": "Mega Control (超级控制)", "depthZoe": "Depth (Zoe)", "colorMap": "Color", "openPose": "Openpose", - "controlAdapter": "Control Adapter", + "controlAdapter_other": "Control Adapters", "lineartAnime": "Lineart Anime", "canny": "Canny" }, @@ -1141,7 +1169,7 @@ "queuedCount": "{{pending}} 待处理", "front": "前", "pruneTooltip": "修剪 {{item_count}} 个已完成的项目", - "batchQueuedDesc": "在队列的 {{direction}} 中添加了 {{item_count}} 个会话", + "batchQueuedDesc_other": "在队列的 {{direction}} 中添加了 {{count}} 个会话", "graphQueued": "节点图已加入队列", "back": "后", "session": "会话", @@ -1192,7 +1220,9 @@ "steps": "步数", "scheduler": "调度器", "seamless": "无缝", - "fit": "图生图适应" + "fit": "图生图匹配", + "recallParameters": "召回参数", + "noRecallParameters": "未找到要召回的参数" }, "models": { "noMatchingModels": "无相匹配的模型", 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 cbc88966a7..772ea216c0 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts @@ -12,6 +12,7 @@ import { addFirstListImagesListener } from './listeners/addFirstListImagesListen import { addAnyEnqueuedListener } from './listeners/anyEnqueued'; import { addAppConfigReceivedListener } from './listeners/appConfigReceived'; import { addAppStartedListener } from './listeners/appStarted'; +import { addBatchEnqueuedListener } from './listeners/batchEnqueued'; import { addDeleteBoardAndImagesFulfilledListener } from './listeners/boardAndImagesDeleted'; import { addBoardIdSelectedListener } from './listeners/boardIdSelected'; import { addCanvasCopiedToClipboardListener } from './listeners/canvasCopiedToClipboard'; @@ -71,8 +72,6 @@ import { addStagingAreaImageSavedListener } from './listeners/stagingAreaImageSa import { addTabChangedListener } from './listeners/tabChanged'; import { addUpscaleRequestedListener } from './listeners/upscaleRequested'; import { addWorkflowLoadedListener } from './listeners/workflowLoaded'; -import { addBatchEnqueuedListener } from './listeners/batchEnqueued'; -import { addControlAdapterAddedOrEnabledListener } from './listeners/controlAdapterAddedOrEnabled'; export const listenerMiddleware = createListenerMiddleware(); @@ -200,7 +199,3 @@ addTabChangedListener(); // Dynamic prompts addDynamicPromptsListener(); - -// Display toast when controlnet or t2i adapter enabled -// TODO: Remove when they can both be enabled at same time -addControlAdapterAddedOrEnabledListener(); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/anyEnqueued.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/anyEnqueued.ts index ff11491b53..3f0e3342f9 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/anyEnqueued.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/anyEnqueued.ts @@ -1,15 +1,9 @@ -import { isAnyOf } from '@reduxjs/toolkit'; import { queueApi } from 'services/api/endpoints/queue'; import { startAppListening } from '..'; -const matcher = isAnyOf( - queueApi.endpoints.enqueueBatch.matchFulfilled, - queueApi.endpoints.enqueueGraph.matchFulfilled -); - export const addAnyEnqueuedListener = () => { startAppListening({ - matcher, + matcher: queueApi.endpoints.enqueueBatch.matchFulfilled, effect: async (_, { dispatch, getState }) => { const { data } = queueApi.endpoints.getQueueStatus.select()(getState()); diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts index 62f7b60036..35c1affb97 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/canvasMerged.ts @@ -44,7 +44,7 @@ export const addCanvasMergedListener = () => { } const baseLayerRect = canvasBaseLayer.getClientRect({ - relativeTo: canvasBaseLayer.getParent(), + relativeTo: canvasBaseLayer.getParent() ?? undefined, }); const imageDTO = await dispatch( diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlAdapterAddedOrEnabled.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlAdapterAddedOrEnabled.ts deleted file mode 100644 index bc5387c1fb..0000000000 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/controlAdapterAddedOrEnabled.ts +++ /dev/null @@ -1,87 +0,0 @@ -import { isAnyOf } from '@reduxjs/toolkit'; -import { - controlAdapterAdded, - controlAdapterAddedFromImage, - controlAdapterIsEnabledChanged, - controlAdapterRecalled, - selectControlAdapterAll, - selectControlAdapterById, -} from 'features/controlAdapters/store/controlAdaptersSlice'; -import { ControlAdapterType } from 'features/controlAdapters/store/types'; -import { addToast } from 'features/system/store/systemSlice'; -import i18n from 'i18n'; -import { startAppListening } from '..'; - -const isAnyControlAdapterAddedOrEnabled = isAnyOf( - controlAdapterAdded, - controlAdapterAddedFromImage, - controlAdapterRecalled, - controlAdapterIsEnabledChanged -); - -/** - * Until we can have both controlnet and t2i adapter enabled at once, they are mutually exclusive - * This displays a toast when one is enabled and the other is already enabled, or one is added - * with the other enabled - */ -export const addControlAdapterAddedOrEnabledListener = () => { - startAppListening({ - matcher: isAnyControlAdapterAddedOrEnabled, - effect: async (action, { dispatch, getOriginalState }) => { - const controlAdapters = getOriginalState().controlAdapters; - - const hasEnabledControlNets = selectControlAdapterAll( - controlAdapters - ).some((ca) => ca.isEnabled && ca.type === 'controlnet'); - - const hasEnabledT2IAdapters = selectControlAdapterAll( - controlAdapters - ).some((ca) => ca.isEnabled && ca.type === 't2i_adapter'); - - let caType: ControlAdapterType | null = null; - - if (controlAdapterAdded.match(action)) { - caType = action.payload.type; - } - - if (controlAdapterAddedFromImage.match(action)) { - caType = action.payload.type; - } - - if (controlAdapterRecalled.match(action)) { - caType = action.payload.type; - } - - if (controlAdapterIsEnabledChanged.match(action)) { - const _caType = selectControlAdapterById( - controlAdapters, - action.payload.id - )?.type; - if (!_caType) { - return; - } - caType = _caType; - } - - if ( - (caType === 'controlnet' && hasEnabledT2IAdapters) || - (caType === 't2i_adapter' && hasEnabledControlNets) - ) { - const title = - caType === 'controlnet' - ? i18n.t('controlnet.controlNetEnabledT2IDisabled') - : i18n.t('controlnet.t2iEnabledControlNetDisabled'); - - const description = i18n.t('controlnet.controlNetT2IMutexDesc'); - - dispatch( - addToast({ - title, - description, - status: 'warning', - }) - ); - } - }, - }); -}; 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 f3db0ea65f..a454e5ca48 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 @@ -1,22 +1,22 @@ import { logger } from 'app/logging/logger'; import { parseify } from 'common/util/serialize'; +import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions'; import { - pendingControlImagesCleared, controlAdapterImageChanged, - selectControlAdapterById, controlAdapterProcessedImageChanged, + pendingControlImagesCleared, + selectControlAdapterById, } from 'features/controlAdapters/store/controlAdaptersSlice'; +import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types'; import { SAVE_IMAGE } from 'features/nodes/util/graphBuilders/constants'; import { addToast } from 'features/system/store/systemSlice'; import { t } from 'i18next'; import { imagesApi } from 'services/api/endpoints/images'; import { queueApi } from 'services/api/endpoints/queue'; import { isImageOutput } from 'services/api/guards'; -import { Graph, ImageDTO } from 'services/api/types'; +import { BatchConfig, ImageDTO } from 'services/api/types'; import { socketInvocationComplete } from 'services/events/actions'; import { startAppListening } from '..'; -import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions'; -import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types'; export const addControlNetImageProcessedListener = () => { startAppListening({ @@ -37,41 +37,46 @@ export const addControlNetImageProcessedListener = () => { // ControlNet one-off procressing graph is just the processor node, no edges. // Also we need to grab the image. - const graph: Graph = { - nodes: { - [ca.processorNode.id]: { - ...ca.processorNode, - is_intermediate: true, - image: { image_name: ca.controlImage }, - }, - [SAVE_IMAGE]: { - id: SAVE_IMAGE, - type: 'save_image', - is_intermediate: true, - use_cache: false, + + const enqueueBatchArg: BatchConfig = { + prepend: true, + batch: { + graph: { + nodes: { + [ca.processorNode.id]: { + ...ca.processorNode, + is_intermediate: true, + image: { image_name: ca.controlImage }, + }, + [SAVE_IMAGE]: { + id: SAVE_IMAGE, + type: 'save_image', + is_intermediate: true, + use_cache: false, + }, + }, + edges: [ + { + source: { + node_id: ca.processorNode.id, + field: 'image', + }, + destination: { + node_id: SAVE_IMAGE, + field: 'image', + }, + }, + ], }, + runs: 1, }, - edges: [ - { - source: { - node_id: ca.processorNode.id, - field: 'image', - }, - destination: { - node_id: SAVE_IMAGE, - field: 'image', - }, - }, - ], }; + try { const req = dispatch( - queueApi.endpoints.enqueueGraph.initiate( - { graph, prepend: true }, - { - fixedCacheKey: 'enqueueGraph', - } - ) + queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, { + fixedCacheKey: 'enqueueBatch', + }) ); const enqueueResult = await req.unwrap(); req.reset(); @@ -83,8 +88,8 @@ export const addControlNetImageProcessedListener = () => { const [invocationCompleteAction] = await take( (action): action is ReturnType => socketInvocationComplete.match(action) && - action.payload.data.graph_execution_state_id === - enqueueResult.queue_item.session_id && + action.payload.data.queue_batch_id === + enqueueResult.batch.batch_id && action.payload.data.source_node_id === SAVE_IMAGE ); @@ -116,7 +121,10 @@ export const addControlNetImageProcessedListener = () => { ); } } catch (error) { - log.error({ graph: parseify(graph) }, t('queue.graphFailedToQueue')); + log.error( + { enqueueBatchArg: parseify(enqueueBatchArg) }, + t('queue.graphFailedToQueue') + ); // handle usage-related errors if (error instanceof Object) { 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 79a09c628f..bd5422841f 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 @@ -151,7 +151,9 @@ export const addRequestedSingleImageDeletionListener = () => { if (wasImageDeleted) { dispatch( - api.util.invalidateTags([{ type: 'Board', id: imageDTO.board_id }]) + api.util.invalidateTags([ + { type: 'Board', id: imageDTO.board_id ?? 'none' }, + ]) ); } }, diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/upscaleRequested.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/upscaleRequested.ts index c252f412a6..9ddcdc9701 100644 --- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/upscaleRequested.ts +++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/upscaleRequested.ts @@ -6,7 +6,7 @@ import { addToast } from 'features/system/store/systemSlice'; import { t } from 'i18next'; import { queueApi } from 'services/api/endpoints/queue'; import { startAppListening } from '..'; -import { ImageDTO } from 'services/api/types'; +import { BatchConfig, ImageDTO } from 'services/api/types'; import { createIsAllowedToUpscaleSelector } from 'features/parameters/hooks/useIsAllowedToUpscale'; export const upscaleRequested = createAction<{ imageDTO: ImageDTO }>( @@ -44,20 +44,23 @@ export const addUpscaleRequestedListener = () => { const { esrganModelName } = state.postprocessing; const { autoAddBoardId } = state.gallery; - const graph = buildAdHocUpscaleGraph({ - image_name, - esrganModelName, - autoAddBoardId, - }); + const enqueueBatchArg: BatchConfig = { + prepend: true, + batch: { + graph: buildAdHocUpscaleGraph({ + image_name, + esrganModelName, + autoAddBoardId, + }), + runs: 1, + }, + }; try { const req = dispatch( - queueApi.endpoints.enqueueGraph.initiate( - { graph, prepend: true }, - { - fixedCacheKey: 'enqueueGraph', - } - ) + queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, { + fixedCacheKey: 'enqueueBatch', + }) ); const enqueueResult = await req.unwrap(); @@ -67,7 +70,10 @@ export const addUpscaleRequestedListener = () => { t('queue.graphQueued') ); } catch (error) { - log.error({ graph: parseify(graph) }, t('queue.graphFailedToQueue')); + log.error( + { enqueueBatchArg: parseify(enqueueBatchArg) }, + t('queue.graphFailedToQueue') + ); // handle usage-related errors if (error instanceof Object) { diff --git a/invokeai/frontend/web/src/app/types/invokeai.ts b/invokeai/frontend/web/src/app/types/invokeai.ts index 39e4ffd27a..0fe7a36052 100644 --- a/invokeai/frontend/web/src/app/types/invokeai.ts +++ b/invokeai/frontend/web/src/app/types/invokeai.ts @@ -59,6 +59,8 @@ export type AppConfig = { nodesAllowlist: string[] | undefined; nodesDenylist: string[] | undefined; maxUpscalePixels?: number; + metadataFetchDebounce?: number; + workflowFetchDebounce?: number; sd: { defaultModel?: string; disabledControlNetModels: string[]; diff --git a/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx b/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx index 7c85b3557e..5ea17f788c 100644 --- a/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx +++ b/invokeai/frontend/web/src/common/components/IAIMantineMultiSelect.tsx @@ -6,7 +6,7 @@ import { useMantineMultiSelectStyles } from 'mantine-theme/hooks/useMantineMulti import { KeyboardEvent, RefObject, memo, useCallback } from 'react'; type IAIMultiSelectProps = Omit & { - tooltip?: string; + tooltip?: string | null; inputRef?: RefObject; label?: string; }; diff --git a/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx b/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx index 39fe7ead3c..675314b421 100644 --- a/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx +++ b/invokeai/frontend/web/src/common/components/IAIMantineSearchableSelect.tsx @@ -12,7 +12,7 @@ export type IAISelectDataType = { }; type IAISelectProps = Omit & { - tooltip?: string; + tooltip?: string | null; label?: string; inputRef?: RefObject; }; diff --git a/invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx b/invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx index 8cc08d2304..9541015b65 100644 --- a/invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx +++ b/invokeai/frontend/web/src/common/components/IAIMantineSelect.tsx @@ -10,7 +10,7 @@ export type IAISelectDataType = { }; export type IAISelectProps = Omit & { - tooltip?: string; + tooltip?: string | null; inputRef?: RefObject; label?: string; }; diff --git a/invokeai/frontend/web/src/common/hooks/useImage.ts b/invokeai/frontend/web/src/common/hooks/useImage.ts new file mode 100644 index 0000000000..60c973ce59 --- /dev/null +++ b/invokeai/frontend/web/src/common/hooks/useImage.ts @@ -0,0 +1,102 @@ +import { useLayoutEffect, useRef, useState } from 'react'; + +// Adapted from https://github.com/konvajs/use-image + +type CrossOrigin = 'anonymous' | 'use-credentials'; +type ReferrerPolicy = + | 'no-referrer' + | 'no-referrer-when-downgrade' + | 'origin' + | 'origin-when-cross-origin' + | 'same-origin' + | 'strict-origin' + | 'strict-origin-when-cross-origin' + | 'unsafe-url'; +type ImageStatus = 'loaded' | 'loading' | 'failed'; + +export const useImage = ( + url: string, + crossOrigin?: CrossOrigin, + referrerpolicy?: ReferrerPolicy +): [undefined | HTMLImageElement, ImageStatus, Blob | null] => { + // lets use refs for image and status + // so we can update them during render + // to have instant update in status/image when new data comes in + const statusRef = useRef('loading'); + const imageRef = useRef(); + const blobRef = useRef(null); + + // we are not going to use token + // but we need to just to trigger state update + const [_, setStateToken] = useState(0); + + // keep track of old props to trigger changes + const oldUrl = useRef(); + const oldCrossOrigin = useRef(); + const oldReferrerPolicy = useRef(); + + if ( + oldUrl.current !== url || + oldCrossOrigin.current !== crossOrigin || + oldReferrerPolicy.current !== referrerpolicy + ) { + statusRef.current = 'loading'; + imageRef.current = undefined; + oldUrl.current = url; + oldCrossOrigin.current = crossOrigin; + oldReferrerPolicy.current = referrerpolicy; + } + + useLayoutEffect( + function () { + if (!url) { + return; + } + const img = document.createElement('img'); + + function onload() { + statusRef.current = 'loaded'; + imageRef.current = img; + const canvas = document.createElement('canvas'); + canvas.width = img.clientWidth; + canvas.height = img.clientHeight; + + const context = canvas.getContext('2d'); + if (context) { + context.drawImage(img, 0, 0); + canvas.toBlob(function (blob) { + blobRef.current = blob; + }, 'image/png'); + } + setStateToken(Math.random()); + } + + function onerror() { + statusRef.current = 'failed'; + imageRef.current = undefined; + setStateToken(Math.random()); + } + + img.addEventListener('load', onload); + img.addEventListener('error', onerror); + if (crossOrigin) { + img.crossOrigin = crossOrigin; + } + if (referrerpolicy) { + img.referrerPolicy = referrerpolicy; + } + img.src = url; + + return function cleanup() { + img.removeEventListener('load', onload); + img.removeEventListener('error', onerror); + }; + }, + [url, crossOrigin, referrerpolicy] + ); + + // return array because it is better to use in case of several useImage hooks + // const [background, backgroundStatus] = useImage(url1); + // const [patter] = useImage(url2); + return [imageRef.current, statusRef.current, blobRef.current]; +}; diff --git a/invokeai/frontend/web/src/common/hooks/useImageUrlToBlob.ts b/invokeai/frontend/web/src/common/hooks/useImageUrlToBlob.ts new file mode 100644 index 0000000000..77538a929d --- /dev/null +++ b/invokeai/frontend/web/src/common/hooks/useImageUrlToBlob.ts @@ -0,0 +1,40 @@ +import { useCallback } from 'react'; +import { $authToken } from 'services/api/client'; + +/** + * Converts an image URL to a Blob by creating an element, drawing it to canvas + * and then converting the canvas to a Blob. + * + * @returns A function that takes a URL and returns a Promise that resolves with a Blob + */ +export const useImageUrlToBlob = () => { + const imageUrlToBlob = useCallback( + async (url: string) => + new Promise((resolve) => { + const img = new Image(); + img.onload = () => { + const canvas = document.createElement('canvas'); + canvas.width = img.width; + canvas.height = img.height; + + const context = canvas.getContext('2d'); + if (!context) { + return; + } + context.drawImage(img, 0, 0); + resolve( + new Promise((resolve) => { + canvas.toBlob(function (blob) { + resolve(blob); + }, 'image/png'); + }) + ); + }; + img.crossOrigin = $authToken.get() ? 'use-credentials' : 'anonymous'; + img.src = url; + }), + [] + ); + + return imageUrlToBlob; +}; diff --git a/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts b/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts index 0ade036987..5bdc59d345 100644 --- a/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts +++ b/invokeai/frontend/web/src/features/canvas/hooks/useColorUnderCursor.ts @@ -37,7 +37,12 @@ const useColorPicker = () => { 1 ).data; - if (!(a && r && g && b)) { + if ( + r === undefined || + g === undefined || + b === undefined || + a === undefined + ) { return; } diff --git a/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts b/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts index 77fae4e0a1..a5520322be 100644 --- a/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts +++ b/invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts @@ -30,6 +30,7 @@ import { isCanvasMaskLine, } from './canvasTypes'; import { appSocketQueueItemStatusChanged } from 'services/events/actions'; +import { queueApi } from 'services/api/endpoints/queue'; export const initialLayerState: CanvasLayerState = { objects: [], @@ -812,6 +813,20 @@ export const canvasSlice = createSlice({ ); } }); + builder.addMatcher( + queueApi.endpoints.clearQueue.matchFulfilled, + (state) => { + state.batchIds = []; + } + ); + builder.addMatcher( + queueApi.endpoints.cancelByBatchIds.matchFulfilled, + (state, action) => { + state.batchIds = state.batchIds.filter( + (id) => !action.meta.arg.originalArgs.batch_ids.includes(id) + ); + } + ); }, }); diff --git a/invokeai/frontend/web/src/features/controlAdapters/components/ControlAdaptersCollapse.tsx b/invokeai/frontend/web/src/features/controlAdapters/components/ControlAdaptersCollapse.tsx index d1c3f6f0b9..2cad007a03 100644 --- a/invokeai/frontend/web/src/features/controlAdapters/components/ControlAdaptersCollapse.tsx +++ b/invokeai/frontend/web/src/features/controlAdapters/components/ControlAdaptersCollapse.tsx @@ -90,9 +90,7 @@ const ControlAdaptersCollapse = () => { return ( diff --git a/invokeai/frontend/web/src/features/controlAdapters/store/constants.ts b/invokeai/frontend/web/src/features/controlAdapters/store/constants.ts index c35847d323..db2311f3f5 100644 --- a/invokeai/frontend/web/src/features/controlAdapters/store/constants.ts +++ b/invokeai/frontend/web/src/features/controlAdapters/store/constants.ts @@ -246,7 +246,7 @@ export const CONTROLNET_MODEL_DEFAULT_PROCESSORS: { mlsd: 'mlsd_image_processor', depth: 'midas_depth_image_processor', bae: 'normalbae_image_processor', - sketch: 'lineart_image_processor', + sketch: 'pidi_image_processor', scribble: 'lineart_image_processor', lineart: 'lineart_image_processor', lineart_anime: 'lineart_anime_image_processor', diff --git a/invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts b/invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts index a3645fad9d..9e293f1104 100644 --- a/invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts +++ b/invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts @@ -88,61 +88,6 @@ export const selectValidT2IAdapters = (controlAdapters: ControlAdaptersState) => (ca.processorType === 'none' && Boolean(ca.controlImage))) ); -// TODO: I think we can safely remove this? -// const disableAllIPAdapters = ( -// state: ControlAdaptersState, -// exclude?: string -// ) => { -// const updates: Update[] = selectAllIPAdapters(state) -// .filter((ca) => ca.id !== exclude) -// .map((ca) => ({ -// id: ca.id, -// changes: { isEnabled: false }, -// })); -// caAdapter.updateMany(state, updates); -// }; - -const disableAllControlNets = ( - state: ControlAdaptersState, - exclude?: string -) => { - const updates: Update[] = selectAllControlNets(state) - .filter((ca) => ca.id !== exclude) - .map((ca) => ({ - id: ca.id, - changes: { isEnabled: false }, - })); - caAdapter.updateMany(state, updates); -}; - -const disableAllT2IAdapters = ( - state: ControlAdaptersState, - exclude?: string -) => { - const updates: Update[] = selectAllT2IAdapters(state) - .filter((ca) => ca.id !== exclude) - .map((ca) => ({ - id: ca.id, - changes: { isEnabled: false }, - })); - caAdapter.updateMany(state, updates); -}; - -const disableIncompatibleControlAdapters = ( - state: ControlAdaptersState, - type: ControlAdapterType, - exclude?: string -) => { - if (type === 'controlnet') { - // we cannot do controlnet + t2i adapter, if we are enabled a controlnet, disable all t2is - disableAllT2IAdapters(state, exclude); - } - if (type === 't2i_adapter') { - // we cannot do controlnet + t2i adapter, if we are enabled a t2i, disable controlnets - disableAllControlNets(state, exclude); - } -}; - export const controlAdaptersSlice = createSlice({ name: 'controlAdapters', initialState: initialControlAdapterState, @@ -158,7 +103,6 @@ export const controlAdaptersSlice = createSlice({ ) => { const { id, type, overrides } = action.payload; caAdapter.addOne(state, buildControlAdapter(id, type, overrides)); - disableIncompatibleControlAdapters(state, type, id); }, prepare: ({ type, @@ -175,8 +119,6 @@ export const controlAdaptersSlice = createSlice({ action: PayloadAction ) => { caAdapter.addOne(state, action.payload); - const { type, id } = action.payload; - disableIncompatibleControlAdapters(state, type, id); }, controlAdapterDuplicated: { reducer: ( @@ -196,8 +138,6 @@ export const controlAdaptersSlice = createSlice({ isEnabled: true, }); caAdapter.addOne(state, newControlAdapter); - const { type } = newControlAdapter; - disableIncompatibleControlAdapters(state, type, newId); }, prepare: (id: string) => { return { payload: { id, newId: uuidv4() } }; @@ -217,7 +157,6 @@ export const controlAdaptersSlice = createSlice({ state, buildControlAdapter(id, type, { controlImage }) ); - disableIncompatibleControlAdapters(state, type, id); }, prepare: (payload: { type: ControlAdapterType; @@ -235,12 +174,6 @@ export const controlAdaptersSlice = createSlice({ ) => { const { id, isEnabled } = action.payload; caAdapter.updateOne(state, { id, changes: { isEnabled } }); - if (isEnabled) { - // we are enabling a control adapter. due to limitations in the current system, we may need to disable other adapters - // TODO: disable when multiple IP adapters are supported - const ca = selectControlAdapterById(state, id); - ca && disableIncompatibleControlAdapters(state, ca.type, id); - } }, controlAdapterImageChanged: ( state, diff --git a/invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx b/invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx index f34235bab2..3ce6cdc99e 100644 --- a/invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx +++ b/invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx @@ -16,15 +16,13 @@ const ParamDynamicPromptsCollapse = () => { () => createSelector(stateSelector, ({ dynamicPrompts }) => { const count = dynamicPrompts.prompts.length; - if (count === 1) { - return t('dynamicPrompts.promptsWithCount_one', { - count, - }); - } else { + if (count > 1) { return t('dynamicPrompts.promptsWithCount_other', { count, }); } + + return; }), [t] ); diff --git a/invokeai/frontend/web/src/features/dynamicPrompts/store/dynamicPromptsSlice.ts b/invokeai/frontend/web/src/features/dynamicPrompts/store/dynamicPromptsSlice.ts index 32e24845ea..4c2cd31eca 100644 --- a/invokeai/frontend/web/src/features/dynamicPrompts/store/dynamicPromptsSlice.ts +++ b/invokeai/frontend/web/src/features/dynamicPrompts/store/dynamicPromptsSlice.ts @@ -39,7 +39,10 @@ export const dynamicPromptsSlice = createSlice({ promptsChanged: (state, action: PayloadAction) => { state.prompts = action.payload; }, - parsingErrorChanged: (state, action: PayloadAction) => { + parsingErrorChanged: ( + state, + action: PayloadAction + ) => { state.parsingError = action.payload; }, isErrorChanged: (state, action: PayloadAction) => { 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 57f06a0cea..36a251952c 100644 --- a/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx @@ -27,7 +27,7 @@ import { setShouldShowImageDetails, setShouldShowProgressInViewer, } from 'features/ui/store/uiSlice'; -import { memo, useCallback, useMemo } from 'react'; +import { memo, useCallback } from 'react'; import { useHotkeys } from 'react-hotkeys-hook'; import { useTranslation } from 'react-i18next'; import { @@ -38,10 +38,9 @@ import { FaSeedling, } from 'react-icons/fa'; import { FaCircleNodes, FaEllipsis } from 'react-icons/fa6'; -import { - useGetImageDTOQuery, - useGetImageMetadataFromFileQuery, -} from 'services/api/endpoints/images'; +import { useGetImageDTOQuery } from 'services/api/endpoints/images'; +import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata'; +import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow'; import { menuListMotionProps } from 'theme/components/menu'; import { sentImageToImg2Img } from '../../store/actions'; import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems'; @@ -89,7 +88,6 @@ const CurrentImageButtons = () => { shouldShowImageDetails, lastSelectedImage, shouldShowProgressInViewer, - shouldFetchMetadataFromApi, } = useAppSelector(currentImageButtonsSelector); const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled; @@ -104,23 +102,12 @@ const CurrentImageButtons = () => { lastSelectedImage?.image_name ?? skipToken ); - const getMetadataArg = useMemo(() => { - if (lastSelectedImage) { - return { image: lastSelectedImage, shouldFetchMetadataFromApi }; - } else { - return skipToken; - } - }, [lastSelectedImage, shouldFetchMetadataFromApi]); + const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata( + lastSelectedImage?.image_name + ); - const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery( - getMetadataArg, - { - selectFromResult: (res) => ({ - isLoading: res.isFetching, - metadata: res?.currentData?.metadata, - workflow: res?.currentData?.workflow, - }), - } + const { workflow, isLoading: isLoadingWorkflow } = useDebouncedWorkflow( + lastSelectedImage?.workflow_id ); const handleLoadWorkflow = useCallback(() => { @@ -257,7 +244,7 @@ const CurrentImageButtons = () => { } tooltip={`${t('nodes.loadWorkflow')} (W)`} aria-label={`${t('nodes.loadWorkflow')} (W)`} @@ -265,7 +252,7 @@ const CurrentImageButtons = () => { onClick={handleLoadWorkflow} /> } tooltip={`${t('parameters.usePrompt')} (P)`} aria-label={`${t('parameters.usePrompt')} (P)`} @@ -273,7 +260,7 @@ const CurrentImageButtons = () => { onClick={handleUsePrompt} /> } tooltip={`${t('parameters.useSeed')} (S)`} aria-label={`${t('parameters.useSeed')} (S)`} @@ -281,7 +268,7 @@ const CurrentImageButtons = () => { onClick={handleUseSeed} /> } tooltip={`${t('parameters.useAll')} (A)`} aria-label={`${t('parameters.useAll')} (A)`} diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx index 35a4e9f18c..ed12eb5ff4 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx @@ -2,7 +2,7 @@ import { Flex, MenuItem, Spinner } from '@chakra-ui/react'; import { useStore } from '@nanostores/react'; import { useAppToaster } from 'app/components/Toaster'; import { $customStarUI } from 'app/store/nanostores/customStarUI'; -import { useAppDispatch, useAppSelector } from 'app/store/storeHooks'; +import { useAppDispatch } from 'app/store/storeHooks'; import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice'; import { imagesToChangeSelected, @@ -32,12 +32,12 @@ import { import { FaCircleNodes } from 'react-icons/fa6'; import { MdStar, MdStarBorder } from 'react-icons/md'; import { - useGetImageMetadataFromFileQuery, useStarImagesMutation, useUnstarImagesMutation, } from 'services/api/endpoints/images'; +import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata'; +import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow'; import { ImageDTO } from 'services/api/types'; -import { configSelector } from '../../../system/store/configSelectors'; import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions'; type SingleSelectionMenuItemsProps = { @@ -53,18 +53,13 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { const toaster = useAppToaster(); const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled; - const { shouldFetchMetadataFromApi } = useAppSelector(configSelector); const customStarUi = useStore($customStarUI); - const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery( - { image: imageDTO, shouldFetchMetadataFromApi }, - { - selectFromResult: (res) => ({ - isLoading: res.isFetching, - metadata: res?.currentData?.metadata, - workflow: res?.currentData?.workflow, - }), - } + const { metadata, isLoading: isLoadingMetadata } = useDebouncedMetadata( + imageDTO?.image_name + ); + const { workflow, isLoading: isLoadingWorkflow } = useDebouncedWorkflow( + imageDTO?.workflow_id ); const [starImages] = useStarImagesMutation(); @@ -181,17 +176,17 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { {t('parameters.downloadImage')} : } + icon={isLoadingWorkflow ? : } onClickCapture={handleLoadWorkflow} - isDisabled={isLoading || !workflow} + isDisabled={isLoadingWorkflow || !workflow} > {t('nodes.loadWorkflow')} : } + icon={isLoadingMetadata ? : } onClickCapture={handleRecallPrompt} isDisabled={ - isLoading || + isLoadingMetadata || (metadata?.positive_prompt === undefined && metadata?.negative_prompt === undefined) } @@ -199,16 +194,16 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => { {t('parameters.usePrompt')} : } + icon={isLoadingMetadata ? : } onClickCapture={handleRecallSeed} - isDisabled={isLoading || metadata?.seed === undefined} + isDisabled={isLoadingMetadata || metadata?.seed === undefined} > {t('parameters.useSeed')} : } + icon={isLoadingMetadata ? : } onClickCapture={handleUseAllParameters} - isDisabled={isLoading || !metadata} + isDisabled={isLoadingMetadata || !metadata} > {t('parameters.useAll')} diff --git a/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx b/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx index e9cb3ffcaf..29637e4a3c 100644 --- a/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx +++ b/invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx @@ -10,15 +10,14 @@ import { Text, } from '@chakra-ui/react'; import { IAINoContentFallback } from 'common/components/IAIImageFallback'; +import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent'; import { memo } from 'react'; -import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images'; +import { useTranslation } from 'react-i18next'; +import { useDebouncedMetadata } from 'services/api/hooks/useDebouncedMetadata'; +import { useDebouncedWorkflow } from 'services/api/hooks/useDebouncedWorkflow'; import { ImageDTO } from 'services/api/types'; import DataViewer from './DataViewer'; import ImageMetadataActions from './ImageMetadataActions'; -import { useAppSelector } from '../../../../app/store/storeHooks'; -import { configSelector } from '../../../system/store/configSelectors'; -import { useTranslation } from 'react-i18next'; -import ScrollableContent from 'features/nodes/components/sidePanel/ScrollableContent'; type ImageMetadataViewerProps = { image: ImageDTO; @@ -32,17 +31,8 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => { // }); const { t } = useTranslation(); - const { shouldFetchMetadataFromApi } = useAppSelector(configSelector); - - const { metadata, workflow } = useGetImageMetadataFromFileQuery( - { image, shouldFetchMetadataFromApi }, - { - selectFromResult: (res) => ({ - metadata: res?.currentData?.metadata, - workflow: res?.currentData?.workflow, - }), - } - ); + const { metadata } = useDebouncedMetadata(image.image_name); + const { workflow } = useDebouncedWorkflow(image.workflow_id); return ( { const dispatch = useAppDispatch(); const { loras } = useAppSelector(selector); const { data: loraModels } = useGetLoRAModelsQuery(); - + const { t } = useTranslation(); const currentMainModel = useAppSelector( (state: RootState) => state.generation.model ); @@ -79,7 +80,7 @@ const ParamLoRASelect = () => { return ( - No LoRAs Loaded + {t('models.noLoRAsInstalled')} ); diff --git a/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx b/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx index 447dfcbd97..3c06b9f9da 100644 --- a/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx +++ b/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/EmbedWorkflowCheckbox.tsx @@ -1,13 +1,13 @@ import { Checkbox, Flex, FormControl, FormLabel } from '@chakra-ui/react'; import { useAppDispatch } from 'app/store/storeHooks'; import { useEmbedWorkflow } from 'features/nodes/hooks/useEmbedWorkflow'; -import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput'; +import { useWithWorkflow } from 'features/nodes/hooks/useWithWorkflow'; import { nodeEmbedWorkflowChanged } from 'features/nodes/store/nodesSlice'; import { ChangeEvent, memo, useCallback } from 'react'; const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => { const dispatch = useAppDispatch(); - const hasImageOutput = useHasImageOutput(nodeId); + const withWorkflow = useWithWorkflow(nodeId); const embedWorkflow = useEmbedWorkflow(nodeId); const handleChange = useCallback( (e: ChangeEvent) => { @@ -21,7 +21,7 @@ const EmbedWorkflowCheckbox = ({ nodeId }: { nodeId: string }) => { [dispatch, nodeId] ); - if (!hasImageOutput) { + if (!withWorkflow) { return null; } diff --git a/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx b/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx index ec5085221e..1424c6b837 100644 --- a/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx +++ b/invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx @@ -1,11 +1,11 @@ import { Flex } from '@chakra-ui/react'; +import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput'; import { DRAG_HANDLE_CLASSNAME } from 'features/nodes/types/constants'; import { memo } from 'react'; +import { useFeatureStatus } from '../../../../../system/hooks/useFeatureStatus'; import EmbedWorkflowCheckbox from './EmbedWorkflowCheckbox'; import SaveToGalleryCheckbox from './SaveToGalleryCheckbox'; import UseCacheCheckbox from './UseCacheCheckbox'; -import { useHasImageOutput } from 'features/nodes/hooks/useHasImageOutput'; -import { useFeatureStatus } from '../../../../../system/hooks/useFeatureStatus'; type Props = { nodeId: string; diff --git a/invokeai/frontend/web/src/features/nodes/hooks/useAnyOrDirectInputFieldNames.ts b/invokeai/frontend/web/src/features/nodes/hooks/useAnyOrDirectInputFieldNames.ts index 36f2e8a62c..dda2efc156 100644 --- a/invokeai/frontend/web/src/features/nodes/hooks/useAnyOrDirectInputFieldNames.ts +++ b/invokeai/frontend/web/src/features/nodes/hooks/useAnyOrDirectInputFieldNames.ts @@ -9,6 +9,7 @@ import { POLYMORPHIC_TYPES, TYPES_WITH_INPUT_COMPONENTS, } from '../types/constants'; +import { getSortedFilteredFieldNames } from '../util/getSortedFilteredFieldNames'; export const useAnyOrDirectInputFieldNames = (nodeId: string) => { const selector = useMemo( @@ -24,17 +25,13 @@ export const useAnyOrDirectInputFieldNames = (nodeId: string) => { if (!nodeTemplate) { return []; } - return map(nodeTemplate.inputs) - .filter( - (field) => - (['any', 'direct'].includes(field.input) || - POLYMORPHIC_TYPES.includes(field.type)) && - TYPES_WITH_INPUT_COMPONENTS.includes(field.type) - ) - .filter((field) => !field.ui_hidden) - .sort((a, b) => (a.ui_order ?? 0) - (b.ui_order ?? 0)) - .map((field) => field.name) - .filter((fieldName) => fieldName !== 'is_intermediate'); + const fields = map(nodeTemplate.inputs).filter( + (field) => + (['any', 'direct'].includes(field.input) || + POLYMORPHIC_TYPES.includes(field.type)) && + TYPES_WITH_INPUT_COMPONENTS.includes(field.type) + ); + return getSortedFilteredFieldNames(fields); }, defaultSelectorOptions ), diff --git a/invokeai/frontend/web/src/features/nodes/hooks/useConnectionInputFieldNames.ts b/invokeai/frontend/web/src/features/nodes/hooks/useConnectionInputFieldNames.ts index eea874cc87..9fb31df801 100644 --- a/invokeai/frontend/web/src/features/nodes/hooks/useConnectionInputFieldNames.ts +++ b/invokeai/frontend/web/src/features/nodes/hooks/useConnectionInputFieldNames.ts @@ -9,6 +9,7 @@ import { TYPES_WITH_INPUT_COMPONENTS, } from '../types/constants'; import { isInvocationNode } from '../types/types'; +import { getSortedFilteredFieldNames } from '../util/getSortedFilteredFieldNames'; export const useConnectionInputFieldNames = (nodeId: string) => { const selector = useMemo( @@ -24,17 +25,16 @@ export const useConnectionInputFieldNames = (nodeId: string) => { if (!nodeTemplate) { return []; } - return map(nodeTemplate.inputs) - .filter( - (field) => - (field.input === 'connection' && - !POLYMORPHIC_TYPES.includes(field.type)) || - !TYPES_WITH_INPUT_COMPONENTS.includes(field.type) - ) - .filter((field) => !field.ui_hidden) - .sort((a, b) => (a.ui_order ?? 0) - (b.ui_order ?? 0)) - .map((field) => field.name) - .filter((fieldName) => fieldName !== 'is_intermediate'); + + // get the visible fields + const fields = map(nodeTemplate.inputs).filter( + (field) => + (field.input === 'connection' && + !POLYMORPHIC_TYPES.includes(field.type)) || + !TYPES_WITH_INPUT_COMPONENTS.includes(field.type) + ); + + return getSortedFilteredFieldNames(fields); }, defaultSelectorOptions ), diff --git a/invokeai/frontend/web/src/features/nodes/hooks/useOutputFieldNames.ts b/invokeai/frontend/web/src/features/nodes/hooks/useOutputFieldNames.ts index 97956a4889..e0a1e5433e 100644 --- a/invokeai/frontend/web/src/features/nodes/hooks/useOutputFieldNames.ts +++ b/invokeai/frontend/web/src/features/nodes/hooks/useOutputFieldNames.ts @@ -5,6 +5,7 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions'; import { map } from 'lodash-es'; import { useMemo } from 'react'; import { isInvocationNode } from '../types/types'; +import { getSortedFilteredFieldNames } from '../util/getSortedFilteredFieldNames'; export const useOutputFieldNames = (nodeId: string) => { const selector = useMemo( @@ -20,11 +21,8 @@ export const useOutputFieldNames = (nodeId: string) => { if (!nodeTemplate) { return []; } - return map(nodeTemplate.outputs) - .filter((field) => !field.ui_hidden) - .sort((a, b) => (a.ui_order ?? 0) - (b.ui_order ?? 0)) - .map((field) => field.name) - .filter((fieldName) => fieldName !== 'is_intermediate'); + + return getSortedFilteredFieldNames(map(nodeTemplate.outputs)); }, defaultSelectorOptions ), diff --git a/invokeai/frontend/web/src/features/nodes/hooks/useWithWorkflow.ts b/invokeai/frontend/web/src/features/nodes/hooks/useWithWorkflow.ts new file mode 100644 index 0000000000..3c83e01731 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/hooks/useWithWorkflow.ts @@ -0,0 +1,31 @@ +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 { useMemo } from 'react'; +import { isInvocationNode } from '../types/types'; + +export const useWithWorkflow = (nodeId: string) => { + const selector = useMemo( + () => + createSelector( + stateSelector, + ({ nodes }) => { + const node = nodes.nodes.find((node) => node.id === nodeId); + if (!isInvocationNode(node)) { + return false; + } + const nodeTemplate = nodes.nodeTemplates[node?.data.type ?? '']; + if (!nodeTemplate) { + return false; + } + return nodeTemplate.withWorkflow; + }, + defaultSelectorOptions + ), + [nodeId] + ); + + const withWorkflow = useAppSelector(selector); + return withWorkflow; +}; diff --git a/invokeai/frontend/web/src/features/nodes/store/util/validateSourceAndTargetTypes.ts b/invokeai/frontend/web/src/features/nodes/store/util/validateSourceAndTargetTypes.ts index 8c2bef34fe..2f47e47a78 100644 --- a/invokeai/frontend/web/src/features/nodes/store/util/validateSourceAndTargetTypes.ts +++ b/invokeai/frontend/web/src/features/nodes/store/util/validateSourceAndTargetTypes.ts @@ -69,6 +69,8 @@ export const validateSourceAndTargetTypes = ( (sourceType === 'integer' || sourceType === 'float') && targetType === 'string'; + const isTargetAnyType = targetType === 'Any'; + return ( isCollectionItemToNonCollection || isNonCollectionToCollectionItem || @@ -76,6 +78,7 @@ export const validateSourceAndTargetTypes = ( isGenericCollectionToAnyCollectionOrPolymorphic || isCollectionToGenericCollection || isIntToFloat || - isIntOrFloatToString + isIntOrFloatToString || + isTargetAnyType ); }; diff --git a/invokeai/frontend/web/src/features/nodes/types/constants.ts b/invokeai/frontend/web/src/features/nodes/types/constants.ts index 076f71cc02..c6eec736da 100644 --- a/invokeai/frontend/web/src/features/nodes/types/constants.ts +++ b/invokeai/frontend/web/src/features/nodes/types/constants.ts @@ -33,6 +33,8 @@ export const COLLECTION_TYPES: FieldType[] = [ 'ColorCollection', 'T2IAdapterCollection', 'IPAdapterCollection', + 'MetadataItemCollection', + 'MetadataCollection', ]; export const POLYMORPHIC_TYPES: FieldType[] = [ @@ -47,6 +49,7 @@ export const POLYMORPHIC_TYPES: FieldType[] = [ 'ColorPolymorphic', 'T2IAdapterPolymorphic', 'IPAdapterPolymorphic', + 'MetadataItemPolymorphic', ]; export const MODEL_TYPES: FieldType[] = [ @@ -78,6 +81,8 @@ export const COLLECTION_MAP: FieldTypeMapWithNumber = { ColorField: 'ColorCollection', T2IAdapterField: 'T2IAdapterCollection', IPAdapterField: 'IPAdapterCollection', + MetadataItemField: 'MetadataItemCollection', + MetadataField: 'MetadataCollection', }; export const isCollectionItemType = ( itemType: string | undefined @@ -97,6 +102,7 @@ export const SINGLE_TO_POLYMORPHIC_MAP: FieldTypeMapWithNumber = { ColorField: 'ColorPolymorphic', T2IAdapterField: 'T2IAdapterPolymorphic', IPAdapterField: 'IPAdapterPolymorphic', + MetadataItemField: 'MetadataItemPolymorphic', }; export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = { @@ -111,6 +117,7 @@ export const POLYMORPHIC_TO_SINGLE_MAP: FieldTypeMap = { ColorPolymorphic: 'ColorField', T2IAdapterPolymorphic: 'T2IAdapterField', IPAdapterPolymorphic: 'IPAdapterField', + MetadataItemPolymorphic: 'MetadataItemField', }; export const TYPES_WITH_INPUT_COMPONENTS: FieldType[] = [ @@ -144,6 +151,37 @@ export const isPolymorphicItemType = ( Boolean(itemType && itemType in SINGLE_TO_POLYMORPHIC_MAP); export const FIELDS: Record = { + Any: { + color: 'gray.500', + description: 'Any field type is accepted.', + title: 'Any', + }, + MetadataField: { + color: 'gray.500', + description: 'A metadata dict.', + title: 'Metadata Dict', + }, + MetadataCollection: { + color: 'gray.500', + description: 'A collection of metadata dicts.', + title: 'Metadata Dict Collection', + }, + MetadataItemField: { + color: 'gray.500', + description: 'A metadata item.', + title: 'Metadata Item', + }, + MetadataItemCollection: { + color: 'gray.500', + description: 'Any field type is accepted.', + title: 'Metadata Item Collection', + }, + MetadataItemPolymorphic: { + color: 'gray.500', + description: + 'MetadataItem or MetadataItemCollection field types are accepted.', + title: 'Metadata Item Polymorphic', + }, boolean: { color: 'green.500', description: t('nodes.booleanDescription'), diff --git a/invokeai/frontend/web/src/features/nodes/types/types.ts b/invokeai/frontend/web/src/features/nodes/types/types.ts index f7ef848211..ba1ca05c4d 100644 --- a/invokeai/frontend/web/src/features/nodes/types/types.ts +++ b/invokeai/frontend/web/src/features/nodes/types/types.ts @@ -10,7 +10,7 @@ import { } from 'features/parameters/types/parameterSchemas'; import i18n from 'i18next'; import { has, keyBy } from 'lodash-es'; -import { OpenAPIV3 } from 'openapi-types'; +import { OpenAPIV3_1 } from 'openapi-types'; import { RgbaColor } from 'react-colorful'; import { Node } from 'reactflow'; import { Graph, _InputField, _OutputField } from 'services/api/types'; @@ -54,6 +54,10 @@ export type InvocationTemplate = { * The type of this node's output */ outputType: string; // TODO: generate a union of output types + /** + * Whether or not this invocation supports workflows + */ + withWorkflow: boolean; /** * The invocation's version. */ @@ -72,6 +76,7 @@ export type FieldUIConfig = { // TODO: Get this from the OpenAPI schema? may be tricky... export const zFieldType = z.enum([ + 'Any', 'BoardField', 'boolean', 'BooleanCollection', @@ -109,6 +114,11 @@ export const zFieldType = z.enum([ 'LatentsPolymorphic', 'LoRAModelField', 'MainModelField', + 'MetadataField', + 'MetadataCollection', + 'MetadataItemField', + 'MetadataItemCollection', + 'MetadataItemPolymorphic', 'ONNXModelField', 'Scheduler', 'SDXLMainModelField', @@ -685,6 +695,57 @@ export type CollectionItemInputFieldValue = z.infer< typeof zCollectionItemInputFieldValue >; +export const zMetadataItemField = z.object({ + label: z.string(), + value: z.any(), +}); +export type MetadataItemField = z.infer; + +export const zMetadataItemInputFieldValue = zInputFieldValueBase.extend({ + type: z.literal('MetadataItemField'), + value: zMetadataItemField.optional(), +}); +export type MetadataItemInputFieldValue = z.infer< + typeof zMetadataItemInputFieldValue +>; + +export const zMetadataItemCollectionInputFieldValue = + zInputFieldValueBase.extend({ + type: z.literal('MetadataItemCollection'), + value: z.array(zMetadataItemField).optional(), + }); +export type MetadataItemCollectionInputFieldValue = z.infer< + typeof zMetadataItemCollectionInputFieldValue +>; + +export const zMetadataItemPolymorphicInputFieldValue = + zInputFieldValueBase.extend({ + type: z.literal('MetadataItemPolymorphic'), + value: z + .union([zMetadataItemField, z.array(zMetadataItemField)]) + .optional(), + }); +export type MetadataItemPolymorphicInputFieldValue = z.infer< + typeof zMetadataItemPolymorphicInputFieldValue +>; + +export const zMetadataField = z.record(z.any()); +export type MetadataField = z.infer; + +export const zMetadataInputFieldValue = zInputFieldValueBase.extend({ + type: z.literal('MetadataField'), + value: zMetadataField.optional(), +}); +export type MetadataInputFieldValue = z.infer; + +export const zMetadataCollectionInputFieldValue = zInputFieldValueBase.extend({ + type: z.literal('MetadataCollection'), + value: z.array(zMetadataField).optional(), +}); +export type MetadataCollectionInputFieldValue = z.infer< + typeof zMetadataCollectionInputFieldValue +>; + export const zColorField = z.object({ r: z.number().int().min(0).max(255), g: z.number().int().min(0).max(255), @@ -723,7 +784,13 @@ export type SchedulerInputFieldValue = z.infer< typeof zSchedulerInputFieldValue >; +export const zAnyInputFieldValue = zInputFieldValueBase.extend({ + type: z.literal('Any'), + value: z.any().optional(), +}); + export const zInputFieldValue = z.discriminatedUnion('type', [ + zAnyInputFieldValue, zBoardInputFieldValue, zBooleanCollectionInputFieldValue, zBooleanInputFieldValue, @@ -774,6 +841,11 @@ export const zInputFieldValue = z.discriminatedUnion('type', [ zUNetInputFieldValue, zVaeInputFieldValue, zVaeModelInputFieldValue, + zMetadataItemInputFieldValue, + zMetadataItemCollectionInputFieldValue, + zMetadataItemPolymorphicInputFieldValue, + zMetadataInputFieldValue, + zMetadataCollectionInputFieldValue, ]); export type InputFieldValue = z.infer; @@ -786,14 +858,19 @@ export type InputFieldTemplateBase = { fieldKind: 'input'; } & _InputField; +export type AnyInputFieldTemplate = InputFieldTemplateBase & { + type: 'Any'; + default: undefined; +}; + export type IntegerInputFieldTemplate = InputFieldTemplateBase & { type: 'integer'; default: number; multipleOf?: number; maximum?: number; - exclusiveMaximum?: boolean; + exclusiveMaximum?: number; minimum?: number; - exclusiveMinimum?: boolean; + exclusiveMinimum?: number; }; export type IntegerCollectionInputFieldTemplate = InputFieldTemplateBase & { @@ -814,9 +891,9 @@ export type FloatInputFieldTemplate = InputFieldTemplateBase & { default: number; multipleOf?: number; maximum?: number; - exclusiveMaximum?: boolean; + exclusiveMaximum?: number; minimum?: number; - exclusiveMinimum?: boolean; + exclusiveMinimum?: number; }; export type FloatCollectionInputFieldTemplate = InputFieldTemplateBase & { @@ -939,6 +1016,11 @@ export type UNetInputFieldTemplate = InputFieldTemplateBase & { type: 'UNetField'; }; +export type MetadataItemFieldTemplate = InputFieldTemplateBase & { + default: undefined; + type: 'MetadataItemField'; +}; + export type ClipInputFieldTemplate = InputFieldTemplateBase & { default: undefined; type: 'ClipField'; @@ -1087,6 +1169,34 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & { type: 'WorkflowField'; }; +export type MetadataItemInputFieldTemplate = InputFieldTemplateBase & { + default: undefined; + type: 'MetadataItemField'; +}; + +export type MetadataItemCollectionInputFieldTemplate = + InputFieldTemplateBase & { + default: undefined; + type: 'MetadataItemCollection'; + }; + +export type MetadataItemPolymorphicInputFieldTemplate = Omit< + MetadataItemInputFieldTemplate, + 'type' +> & { + type: 'MetadataItemPolymorphic'; +}; + +export type MetadataInputFieldTemplate = InputFieldTemplateBase & { + default: undefined; + type: 'MetadataField'; +}; + +export type MetadataCollectionInputFieldTemplate = InputFieldTemplateBase & { + default: undefined; + type: 'MetadataCollection'; +}; + /** * An input field template is generated on each page load from the OpenAPI schema. * @@ -1094,6 +1204,7 @@ export type WorkflowInputFieldTemplate = InputFieldTemplateBase & { * maximum length, pattern to match, etc). */ export type InputFieldTemplate = + | AnyInputFieldTemplate | BoardInputFieldTemplate | BooleanCollectionInputFieldTemplate | BooleanPolymorphicInputFieldTemplate @@ -1143,7 +1254,12 @@ export type InputFieldTemplate = | T2IAdapterPolymorphicInputFieldTemplate | UNetInputFieldTemplate | VaeInputFieldTemplate - | VaeModelInputFieldTemplate; + | VaeModelInputFieldTemplate + | MetadataItemInputFieldTemplate + | MetadataItemCollectionInputFieldTemplate + | MetadataInputFieldTemplate + | MetadataItemPolymorphicInputFieldTemplate + | MetadataCollectionInputFieldTemplate; export const isInputFieldValue = ( field?: InputFieldValue | OutputFieldValue @@ -1163,20 +1279,20 @@ export type TypeHints = { }; export type InvocationSchemaExtra = { - output: OpenAPIV3.ReferenceObject; // the output of the invocation + output: OpenAPIV3_1.ReferenceObject; // the output of the invocation title: string; category?: string; tags?: string[]; version?: string; properties: Omit< - NonNullable & + NonNullable & (_InputField | _OutputField), 'type' > & { - type: Omit & { + type: Omit & { default: AnyInvocationType; }; - use_cache: Omit & { + use_cache: Omit & { default: boolean; }; }; @@ -1187,17 +1303,17 @@ export type InvocationSchemaType = { }; export type InvocationBaseSchemaObject = Omit< - OpenAPIV3.BaseSchemaObject, + OpenAPIV3_1.BaseSchemaObject, 'title' | 'type' | 'properties' > & InvocationSchemaExtra; export type InvocationOutputSchemaObject = Omit< - OpenAPIV3.SchemaObject, + OpenAPIV3_1.SchemaObject, 'properties' > & { - properties: OpenAPIV3.SchemaObject['properties'] & { - type: Omit & { + properties: OpenAPIV3_1.SchemaObject['properties'] & { + type: Omit & { default: string; }; } & { @@ -1205,14 +1321,18 @@ export type InvocationOutputSchemaObject = Omit< }; }; -export type InvocationFieldSchema = OpenAPIV3.SchemaObject & _InputField; +export type InvocationFieldSchema = OpenAPIV3_1.SchemaObject & _InputField; + +export type OpenAPIV3_1SchemaOrRef = + | OpenAPIV3_1.ReferenceObject + | OpenAPIV3_1.SchemaObject; export interface ArraySchemaObject extends InvocationBaseSchemaObject { - type: OpenAPIV3.ArraySchemaObjectType; - items: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject; + type: OpenAPIV3_1.ArraySchemaObjectType; + items: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject; } export interface NonArraySchemaObject extends InvocationBaseSchemaObject { - type?: OpenAPIV3.NonArraySchemaObjectType; + type?: OpenAPIV3_1.NonArraySchemaObjectType; } export type InvocationSchemaObject = ( @@ -1221,46 +1341,46 @@ export type InvocationSchemaObject = ( ) & { class: 'invocation' }; export const isSchemaObject = ( - obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined -): obj is OpenAPIV3.SchemaObject => Boolean(obj && !('$ref' in obj)); + obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject | undefined +): obj is OpenAPIV3_1.SchemaObject => Boolean(obj && !('$ref' in obj)); export const isArraySchemaObject = ( - obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined -): obj is OpenAPIV3.ArraySchemaObject => + obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject | undefined +): obj is OpenAPIV3_1.ArraySchemaObject => Boolean(obj && !('$ref' in obj) && obj.type === 'array'); export const isNonArraySchemaObject = ( - obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined -): obj is OpenAPIV3.NonArraySchemaObject => + obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject | undefined +): obj is OpenAPIV3_1.NonArraySchemaObject => Boolean(obj && !('$ref' in obj) && obj.type !== 'array'); export const isRefObject = ( - obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject | undefined -): obj is OpenAPIV3.ReferenceObject => Boolean(obj && '$ref' in obj); + obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject | undefined +): obj is OpenAPIV3_1.ReferenceObject => Boolean(obj && '$ref' in obj); export const isInvocationSchemaObject = ( obj: - | OpenAPIV3.ReferenceObject - | OpenAPIV3.SchemaObject + | OpenAPIV3_1.ReferenceObject + | OpenAPIV3_1.SchemaObject | InvocationSchemaObject ): obj is InvocationSchemaObject => 'class' in obj && obj.class === 'invocation'; export const isInvocationOutputSchemaObject = ( obj: - | OpenAPIV3.ReferenceObject - | OpenAPIV3.SchemaObject + | OpenAPIV3_1.ReferenceObject + | OpenAPIV3_1.SchemaObject | InvocationOutputSchemaObject ): obj is InvocationOutputSchemaObject => 'class' in obj && obj.class === 'output'; export const isInvocationFieldSchema = ( - obj: OpenAPIV3.ReferenceObject | OpenAPIV3.SchemaObject + obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject ): obj is InvocationFieldSchema => !('$ref' in obj); export type InvocationEdgeExtra = { type: 'default' | 'collapsed' }; -const zLoRAMetadataItem = z.object({ +export const zLoRAMetadataItem = z.object({ lora: zLoRAModelField.deepPartial(), weight: z.number(), }); diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts index 1f7fe81620..92e44e9ab2 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts @@ -1,5 +1,13 @@ -import { isBoolean, isInteger, isNumber, isString } from 'lodash-es'; -import { OpenAPIV3 } from 'openapi-types'; +import { + isArray, + isBoolean, + isInteger, + isNumber, + isString, + startCase, +} from 'lodash-es'; +import { OpenAPIV3_1 } from 'openapi-types'; +import { ControlField } from 'services/api/types'; import { COLLECTION_MAP, POLYMORPHIC_TYPES, @@ -8,36 +16,70 @@ import { isPolymorphicItemType, } from '../types/constants'; import { + AnyInputFieldTemplate, + BoardInputFieldTemplate, BooleanCollectionInputFieldTemplate, BooleanInputFieldTemplate, + BooleanPolymorphicInputFieldTemplate, ClipInputFieldTemplate, CollectionInputFieldTemplate, CollectionItemInputFieldTemplate, + ColorCollectionInputFieldTemplate, ColorInputFieldTemplate, + ColorPolymorphicInputFieldTemplate, + ConditioningCollectionInputFieldTemplate, + ConditioningField, ConditioningInputFieldTemplate, + ConditioningPolymorphicInputFieldTemplate, + ControlCollectionInputFieldTemplate, ControlInputFieldTemplate, ControlNetModelInputFieldTemplate, + ControlPolymorphicInputFieldTemplate, DenoiseMaskInputFieldTemplate, EnumInputFieldTemplate, FieldType, FloatCollectionInputFieldTemplate, - FloatPolymorphicInputFieldTemplate, FloatInputFieldTemplate, + FloatPolymorphicInputFieldTemplate, + IPAdapterCollectionInputFieldTemplate, + IPAdapterField, + IPAdapterInputFieldTemplate, + IPAdapterModelInputFieldTemplate, + IPAdapterPolymorphicInputFieldTemplate, ImageCollectionInputFieldTemplate, + ImageField, ImageInputFieldTemplate, + ImagePolymorphicInputFieldTemplate, + InputFieldTemplate, InputFieldTemplateBase, IntegerCollectionInputFieldTemplate, IntegerInputFieldTemplate, + IntegerPolymorphicInputFieldTemplate, InvocationFieldSchema, InvocationSchemaObject, + LatentsCollectionInputFieldTemplate, + LatentsField, LatentsInputFieldTemplate, + LatentsPolymorphicInputFieldTemplate, LoRAModelInputFieldTemplate, MainModelInputFieldTemplate, + MetadataCollectionInputFieldTemplate, + MetadataInputFieldTemplate, + MetadataItemCollectionInputFieldTemplate, + MetadataItemInputFieldTemplate, + MetadataItemPolymorphicInputFieldTemplate, + OpenAPIV3_1SchemaOrRef, SDXLMainModelInputFieldTemplate, SDXLRefinerModelInputFieldTemplate, SchedulerInputFieldTemplate, StringCollectionInputFieldTemplate, StringInputFieldTemplate, + StringPolymorphicInputFieldTemplate, + T2IAdapterCollectionInputFieldTemplate, + T2IAdapterField, + T2IAdapterInputFieldTemplate, + T2IAdapterModelInputFieldTemplate, + T2IAdapterPolymorphicInputFieldTemplate, UNetInputFieldTemplate, VaeInputFieldTemplate, VaeModelInputFieldTemplate, @@ -45,35 +87,7 @@ import { isNonArraySchemaObject, isRefObject, isSchemaObject, - ControlPolymorphicInputFieldTemplate, - ColorPolymorphicInputFieldTemplate, - ColorCollectionInputFieldTemplate, - IntegerPolymorphicInputFieldTemplate, - StringPolymorphicInputFieldTemplate, - BooleanPolymorphicInputFieldTemplate, - ImagePolymorphicInputFieldTemplate, - LatentsPolymorphicInputFieldTemplate, - LatentsCollectionInputFieldTemplate, - ConditioningPolymorphicInputFieldTemplate, - ConditioningCollectionInputFieldTemplate, - ControlCollectionInputFieldTemplate, - ImageField, - LatentsField, - ConditioningField, - IPAdapterField, - IPAdapterInputFieldTemplate, - IPAdapterModelInputFieldTemplate, - IPAdapterPolymorphicInputFieldTemplate, - IPAdapterCollectionInputFieldTemplate, - T2IAdapterField, - T2IAdapterInputFieldTemplate, - T2IAdapterModelInputFieldTemplate, - T2IAdapterPolymorphicInputFieldTemplate, - T2IAdapterCollectionInputFieldTemplate, - BoardInputFieldTemplate, - InputFieldTemplate, } from '../types/types'; -import { ControlField } from 'services/api/types'; export type BaseFieldProperties = 'name' | 'title' | 'description'; @@ -90,7 +104,7 @@ export type BuildInputFieldArg = { * @example * refObjectToFieldType({ "$ref": "#/components/schemas/ImageField" }) --> 'ImageField' */ -export const refObjectToSchemaName = (refObject: OpenAPIV3.ReferenceObject) => +export const refObjectToSchemaName = (refObject: OpenAPIV3_1.ReferenceObject) => refObject.$ref.split('/').slice(-1)[0]; const buildIntegerInputFieldTemplate = ({ @@ -111,7 +125,10 @@ const buildIntegerInputFieldTemplate = ({ template.maximum = schemaObject.maximum; } - if (schemaObject.exclusiveMaximum !== undefined) { + if ( + schemaObject.exclusiveMaximum !== undefined && + isNumber(schemaObject.exclusiveMaximum) + ) { template.exclusiveMaximum = schemaObject.exclusiveMaximum; } @@ -119,7 +136,10 @@ const buildIntegerInputFieldTemplate = ({ template.minimum = schemaObject.minimum; } - if (schemaObject.exclusiveMinimum !== undefined) { + if ( + schemaObject.exclusiveMinimum !== undefined && + isNumber(schemaObject.exclusiveMinimum) + ) { template.exclusiveMinimum = schemaObject.exclusiveMinimum; } @@ -144,7 +164,10 @@ const buildIntegerPolymorphicInputFieldTemplate = ({ template.maximum = schemaObject.maximum; } - if (schemaObject.exclusiveMaximum !== undefined) { + if ( + schemaObject.exclusiveMaximum !== undefined && + isNumber(schemaObject.exclusiveMaximum) + ) { template.exclusiveMaximum = schemaObject.exclusiveMaximum; } @@ -152,7 +175,10 @@ const buildIntegerPolymorphicInputFieldTemplate = ({ template.minimum = schemaObject.minimum; } - if (schemaObject.exclusiveMinimum !== undefined) { + if ( + schemaObject.exclusiveMinimum !== undefined && + isNumber(schemaObject.exclusiveMinimum) + ) { template.exclusiveMinimum = schemaObject.exclusiveMinimum; } @@ -195,7 +221,10 @@ const buildFloatInputFieldTemplate = ({ template.maximum = schemaObject.maximum; } - if (schemaObject.exclusiveMaximum !== undefined) { + if ( + schemaObject.exclusiveMaximum !== undefined && + isNumber(schemaObject.exclusiveMaximum) + ) { template.exclusiveMaximum = schemaObject.exclusiveMaximum; } @@ -203,7 +232,10 @@ const buildFloatInputFieldTemplate = ({ template.minimum = schemaObject.minimum; } - if (schemaObject.exclusiveMinimum !== undefined) { + if ( + schemaObject.exclusiveMinimum !== undefined && + isNumber(schemaObject.exclusiveMinimum) + ) { template.exclusiveMinimum = schemaObject.exclusiveMinimum; } @@ -227,7 +259,10 @@ const buildFloatPolymorphicInputFieldTemplate = ({ template.maximum = schemaObject.maximum; } - if (schemaObject.exclusiveMaximum !== undefined) { + if ( + schemaObject.exclusiveMaximum !== undefined && + isNumber(schemaObject.exclusiveMaximum) + ) { template.exclusiveMaximum = schemaObject.exclusiveMaximum; } @@ -235,7 +270,10 @@ const buildFloatPolymorphicInputFieldTemplate = ({ template.minimum = schemaObject.minimum; } - if (schemaObject.exclusiveMinimum !== undefined) { + if ( + schemaObject.exclusiveMinimum !== undefined && + isNumber(schemaObject.exclusiveMinimum) + ) { template.exclusiveMinimum = schemaObject.exclusiveMinimum; } return template; @@ -819,6 +857,78 @@ const buildCollectionItemInputFieldTemplate = ({ return template; }; +const buildAnyInputFieldTemplate = ({ + baseField, +}: BuildInputFieldArg): AnyInputFieldTemplate => { + const template: AnyInputFieldTemplate = { + ...baseField, + type: 'Any', + default: undefined, + }; + + return template; +}; + +const buildMetadataItemInputFieldTemplate = ({ + baseField, +}: BuildInputFieldArg): MetadataItemInputFieldTemplate => { + const template: MetadataItemInputFieldTemplate = { + ...baseField, + type: 'MetadataItemField', + default: undefined, + }; + + return template; +}; + +const buildMetadataItemCollectionInputFieldTemplate = ({ + baseField, +}: BuildInputFieldArg): MetadataItemCollectionInputFieldTemplate => { + const template: MetadataItemCollectionInputFieldTemplate = { + ...baseField, + type: 'MetadataItemCollection', + default: undefined, + }; + + return template; +}; + +const buildMetadataItemPolymorphicInputFieldTemplate = ({ + baseField, +}: BuildInputFieldArg): MetadataItemPolymorphicInputFieldTemplate => { + const template: MetadataItemPolymorphicInputFieldTemplate = { + ...baseField, + type: 'MetadataItemPolymorphic', + default: undefined, + }; + + return template; +}; + +const buildMetadataDictInputFieldTemplate = ({ + baseField, +}: BuildInputFieldArg): MetadataInputFieldTemplate => { + const template: MetadataInputFieldTemplate = { + ...baseField, + type: 'MetadataField', + default: undefined, + }; + + return template; +}; + +const buildMetadataCollectionInputFieldTemplate = ({ + baseField, +}: BuildInputFieldArg): MetadataCollectionInputFieldTemplate => { + const template: MetadataCollectionInputFieldTemplate = { + ...baseField, + type: 'MetadataCollection', + default: undefined, + }; + + return template; +}; + const buildColorInputFieldTemplate = ({ schemaObject, baseField, @@ -872,84 +982,106 @@ const buildSchedulerInputFieldTemplate = ({ }; export const getFieldType = ( - schemaObject: InvocationFieldSchema + schemaObject: OpenAPIV3_1SchemaOrRef ): string | undefined => { - if (schemaObject?.ui_type) { - return schemaObject.ui_type; - } else if (!schemaObject.type) { - // if schemaObject has no type, then it should have one of allOf, anyOf, oneOf + if (isSchemaObject(schemaObject)) { + if (!schemaObject.type) { + // if schemaObject has no type, then it should have one of allOf, anyOf, oneOf - if (schemaObject.allOf) { - const allOf = schemaObject.allOf; - if (allOf && allOf[0] && isRefObject(allOf[0])) { - return refObjectToSchemaName(allOf[0]); - } - } else if (schemaObject.anyOf) { - const anyOf = schemaObject.anyOf; - /** - * Handle Polymorphic inputs, eg string | string[]. In OpenAPI, this is: - * - an `anyOf` with two items - * - one is an `ArraySchemaObject` with a single `SchemaObject or ReferenceObject` of type T in its `items` - * - the other is a `SchemaObject` or `ReferenceObject` of type T - * - * Any other cases we ignore. - */ - - let firstType: string | undefined; - let secondType: string | undefined; - - if (isArraySchemaObject(anyOf[0])) { - // first is array, second is not - const first = anyOf[0].items; - const second = anyOf[1]; - if (isRefObject(first) && isRefObject(second)) { - firstType = refObjectToSchemaName(first); - secondType = refObjectToSchemaName(second); - } else if ( - isNonArraySchemaObject(first) && - isNonArraySchemaObject(second) - ) { - firstType = first.type; - secondType = second.type; + if (schemaObject.allOf) { + const allOf = schemaObject.allOf; + if (allOf && allOf[0] && isRefObject(allOf[0])) { + return refObjectToSchemaName(allOf[0]); } - } else if (isArraySchemaObject(anyOf[1])) { - // first is not array, second is - const first = anyOf[0]; - const second = anyOf[1].items; - if (isRefObject(first) && isRefObject(second)) { - firstType = refObjectToSchemaName(first); - secondType = refObjectToSchemaName(second); - } else if ( - isNonArraySchemaObject(first) && - isNonArraySchemaObject(second) - ) { - firstType = first.type; - secondType = second.type; + } else if (schemaObject.anyOf) { + // ignore null types + const anyOf = schemaObject.anyOf.filter((i) => { + if (isSchemaObject(i)) { + if (i.type === 'null') { + return false; + } + } + return true; + }); + if (anyOf.length === 1) { + if (isRefObject(anyOf[0])) { + return refObjectToSchemaName(anyOf[0]); + } else if (isSchemaObject(anyOf[0])) { + return getFieldType(anyOf[0]); + } + } + /** + * Handle Polymorphic inputs, eg string | string[]. In OpenAPI, this is: + * - an `anyOf` with two items + * - one is an `ArraySchemaObject` with a single `SchemaObject or ReferenceObject` of type T in its `items` + * - the other is a `SchemaObject` or `ReferenceObject` of type T + * + * Any other cases we ignore. + */ + + let firstType: string | undefined; + let secondType: string | undefined; + + if (isArraySchemaObject(anyOf[0])) { + // first is array, second is not + const first = anyOf[0].items; + const second = anyOf[1]; + if (isRefObject(first) && isRefObject(second)) { + firstType = refObjectToSchemaName(first); + secondType = refObjectToSchemaName(second); + } else if ( + isNonArraySchemaObject(first) && + isNonArraySchemaObject(second) + ) { + firstType = first.type; + secondType = second.type; + } + } else if (isArraySchemaObject(anyOf[1])) { + // first is not array, second is + const first = anyOf[0]; + const second = anyOf[1].items; + if (isRefObject(first) && isRefObject(second)) { + firstType = refObjectToSchemaName(first); + secondType = refObjectToSchemaName(second); + } else if ( + isNonArraySchemaObject(first) && + isNonArraySchemaObject(second) + ) { + firstType = first.type; + secondType = second.type; + } + } + if (firstType === secondType && isPolymorphicItemType(firstType)) { + return SINGLE_TO_POLYMORPHIC_MAP[firstType]; } } - if (firstType === secondType && isPolymorphicItemType(firstType)) { - return SINGLE_TO_POLYMORPHIC_MAP[firstType]; + } else if (schemaObject.enum) { + return 'enum'; + } else if (schemaObject.type) { + if (schemaObject.type === 'number') { + // floats are "number" in OpenAPI, while ints are "integer" - we need to distinguish them + return 'float'; + } else if (schemaObject.type === 'array') { + const itemType = isSchemaObject(schemaObject.items) + ? schemaObject.items.type + : refObjectToSchemaName(schemaObject.items); + + if (isArray(itemType)) { + // This is a nested array, which we don't support + return; + } + + if (isCollectionItemType(itemType)) { + return COLLECTION_MAP[itemType]; + } + + return; + } else if (!isArray(schemaObject.type)) { + return schemaObject.type; } } - } else if (schemaObject.enum) { - return 'enum'; - } else if (schemaObject.type) { - if (schemaObject.type === 'number') { - // floats are "number" in OpenAPI, while ints are "integer" - we need to distinguish them - return 'float'; - } else if (schemaObject.type === 'array') { - const itemType = isSchemaObject(schemaObject.items) - ? schemaObject.items.type - : refObjectToSchemaName(schemaObject.items); - - if (isCollectionItemType(itemType)) { - return COLLECTION_MAP[itemType]; - } - - return; - } else { - return schemaObject.type; - } + } else if (isRefObject(schemaObject)) { + return refObjectToSchemaName(schemaObject); } return; }; @@ -958,6 +1090,7 @@ const TEMPLATE_BUILDER_MAP: { [key in FieldType]?: (arg: BuildInputFieldArg) => InputFieldTemplate; } = { BoardField: buildBoardInputFieldTemplate, + Any: buildAnyInputFieldTemplate, boolean: buildBooleanInputFieldTemplate, BooleanCollection: buildBooleanCollectionInputFieldTemplate, BooleanPolymorphic: buildBooleanPolymorphicInputFieldTemplate, @@ -993,6 +1126,11 @@ const TEMPLATE_BUILDER_MAP: { LatentsField: buildLatentsInputFieldTemplate, LatentsPolymorphic: buildLatentsPolymorphicInputFieldTemplate, LoRAModelField: buildLoRAModelInputFieldTemplate, + MetadataItemField: buildMetadataItemInputFieldTemplate, + MetadataItemCollection: buildMetadataItemCollectionInputFieldTemplate, + MetadataItemPolymorphic: buildMetadataItemPolymorphicInputFieldTemplate, + MetadataField: buildMetadataDictInputFieldTemplate, + MetadataCollection: buildMetadataCollectionInputFieldTemplate, MainModelField: buildMainModelInputFieldTemplate, Scheduler: buildSchedulerInputFieldTemplate, SDXLMainModelField: buildSDXLMainModelInputFieldTemplate, @@ -1025,7 +1163,15 @@ export const buildInputFieldTemplate = ( name: string, fieldType: FieldType ) => { - const { input, ui_hidden, ui_component, ui_type, ui_order } = fieldSchema; + const { + input, + ui_hidden, + ui_component, + ui_type, + ui_order, + ui_choice_labels, + item_default, + } = fieldSchema; const extra = { // TODO: Can we support polymorphic inputs in the UI? @@ -1035,11 +1181,13 @@ export const buildInputFieldTemplate = ( ui_type, required: nodeSchema.required?.includes(name) ?? false, ui_order, + ui_choice_labels, + item_default, }; const baseField = { name, - title: fieldSchema.title ?? '', + title: fieldSchema.title ?? (name ? startCase(name) : ''), description: fieldSchema.description ?? '', fieldKind: 'input' as const, ...extra, diff --git a/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts b/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts index 97f520379a..ca2513649d 100644 --- a/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts +++ b/invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts @@ -3,6 +3,7 @@ import { FieldType, InputFieldTemplate, InputFieldValue } from '../types/types'; const FIELD_VALUE_FALLBACK_MAP: { [key in FieldType]: InputFieldValue['value']; } = { + Any: undefined, enum: '', BoardField: undefined, boolean: false, @@ -38,6 +39,11 @@ const FIELD_VALUE_FALLBACK_MAP: { LatentsCollection: [], LatentsField: undefined, LatentsPolymorphic: undefined, + MetadataItemField: undefined, + MetadataItemCollection: [], + MetadataItemPolymorphic: undefined, + MetadataField: undefined, + MetadataCollection: [], LoRAModelField: undefined, MainModelField: undefined, ONNXModelField: undefined, diff --git a/invokeai/frontend/web/src/features/nodes/util/getMetadataAndWorkflowFromImageBlob.ts b/invokeai/frontend/web/src/features/nodes/util/getMetadataAndWorkflowFromImageBlob.ts deleted file mode 100644 index b46a701757..0000000000 --- a/invokeai/frontend/web/src/features/nodes/util/getMetadataAndWorkflowFromImageBlob.ts +++ /dev/null @@ -1,45 +0,0 @@ -import * as png from '@stevebel/png'; -import { logger } from 'app/logging/logger'; -import { parseify } from 'common/util/serialize'; -import { - ImageMetadataAndWorkflow, - zCoreMetadata, - zWorkflow, -} from 'features/nodes/types/types'; -import { get } from 'lodash-es'; - -export const getMetadataAndWorkflowFromImageBlob = async ( - image: Blob -): Promise => { - const data: ImageMetadataAndWorkflow = {}; - const buffer = await image.arrayBuffer(); - const text = png.decode(buffer).text; - - const rawMetadata = get(text, 'invokeai_metadata'); - if (rawMetadata) { - const metadataResult = zCoreMetadata.safeParse(JSON.parse(rawMetadata)); - if (metadataResult.success) { - data.metadata = metadataResult.data; - } else { - logger('system').error( - { error: parseify(metadataResult.error) }, - 'Problem reading metadata from image' - ); - } - } - - const rawWorkflow = get(text, 'invokeai_workflow'); - if (rawWorkflow) { - const workflowResult = zWorkflow.safeParse(JSON.parse(rawWorkflow)); - if (workflowResult.success) { - data.workflow = workflowResult.data; - } else { - logger('system').error( - { error: parseify(workflowResult.error) }, - 'Problem reading workflow from image' - ); - } - } - - return data; -}; diff --git a/invokeai/frontend/web/src/features/nodes/util/getSortedFilteredFieldNames.ts b/invokeai/frontend/web/src/features/nodes/util/getSortedFilteredFieldNames.ts new file mode 100644 index 0000000000..b235fe8a07 --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/getSortedFilteredFieldNames.ts @@ -0,0 +1,20 @@ +import { isNil } from 'lodash-es'; +import { InputFieldTemplate, OutputFieldTemplate } from '../types/types'; + +export const getSortedFilteredFieldNames = ( + fields: InputFieldTemplate[] | OutputFieldTemplate[] +): string[] => { + const visibleFields = fields.filter((field) => !field.ui_hidden); + + // we want explicitly ordered fields to be before unordered fields; split the list + const orderedFields = visibleFields + .filter((f) => !isNil(f.ui_order)) + .sort((a, b) => (a.ui_order ?? 0) - (b.ui_order ?? 0)); + const unorderedFields = visibleFields.filter((f) => isNil(f.ui_order)); + + // concat the lists, and return the field names, skipping `is_intermediate` + return orderedFields + .concat(unorderedFields) + .map((f) => f.name) + .filter((fieldName) => fieldName !== 'is_intermediate'); +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts index 552a9c6c9b..60d4e36dca 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts @@ -5,14 +5,14 @@ import { CollectInvocation, ControlField, ControlNetInvocation, - MetadataAccumulatorInvocation, + CoreMetadataInvocation, } from 'services/api/types'; import { NonNullableGraph } from '../../types/types'; import { CANVAS_COHERENCE_DENOISE_LATENTS, CONTROL_NET_COLLECT, - METADATA_ACCUMULATOR, } from './constants'; +import { upsertMetadata } from './metadata'; export const addControlNetToLinearGraph = ( state: RootState, @@ -23,9 +23,11 @@ export const addControlNetToLinearGraph = ( (ca) => ca.model?.base_model === state.generation.model?.base_model ); - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; + // const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as + // | MetadataAccumulatorInvocation + // | undefined; + + const controlNetMetadata: CoreMetadataInvocation['controlnets'] = []; if (validControlNets.length) { // Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect @@ -43,6 +45,16 @@ export const addControlNetToLinearGraph = ( }, }); + if (CANVAS_COHERENCE_DENOISE_LATENTS in graph.nodes) { + graph.edges.push({ + source: { node_id: CONTROL_NET_COLLECT, field: 'collection' }, + destination: { + node_id: CANVAS_COHERENCE_DENOISE_LATENTS, + field: 'control', + }, + }); + } + validControlNets.forEach((controlNet) => { if (!controlNet.model) { return; @@ -89,15 +101,9 @@ export const addControlNetToLinearGraph = ( graph.nodes[controlNetNode.id] = controlNetNode as ControlNetInvocation; - if (metadataAccumulator?.controlnets) { - // metadata accumulator only needs a control field - not the whole node - // extract what we need and add to the accumulator - const controlField = omit(controlNetNode, [ - 'id', - 'type', - ]) as ControlField; - metadataAccumulator.controlnets.push(controlField); - } + controlNetMetadata.push( + omit(controlNetNode, ['id', 'type', 'is_intermediate']) as ControlField + ); graph.edges.push({ source: { node_id: controlNetNode.id, field: 'control' }, @@ -106,16 +112,7 @@ export const addControlNetToLinearGraph = ( field: 'item', }, }); - - if (CANVAS_COHERENCE_DENOISE_LATENTS in graph.nodes) { - graph.edges.push({ - source: { node_id: controlNetNode.id, field: 'control' }, - destination: { - node_id: CANVAS_COHERENCE_DENOISE_LATENTS, - field: 'control', - }, - }); - } }); + upsertMetadata(graph, { controlnets: controlNetMetadata }); } }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts index 4b4a8a8a03..8c23ae667e 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts @@ -1,25 +1,25 @@ +import { logger } from 'app/logging/logger'; import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { DenoiseLatentsInvocation, - ResizeLatentsInvocation, - NoiseInvocation, - LatentsToImageInvocation, Edge, + LatentsToImageInvocation, + NoiseInvocation, + ResizeLatentsInvocation, } from 'services/api/types'; import { - LATENTS_TO_IMAGE, DENOISE_LATENTS, - NOISE, - MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, - LATENTS_TO_IMAGE_HRF, DENOISE_LATENTS_HRF, - RESCALE_LATENTS, + LATENTS_TO_IMAGE, + LATENTS_TO_IMAGE_HRF, + MAIN_MODEL_LOADER, + NOISE, NOISE_HRF, + RESCALE_LATENTS, VAE_LOADER, } from './constants'; -import { logger } from 'app/logging/logger'; +import { upsertMetadata } from './metadata'; // Copy certain connections from previous DENOISE_LATENTS to new DENOISE_LATENTS_HRF. function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void { @@ -71,10 +71,8 @@ export const addHrfToGraph = ( } const log = logger('txt2img'); - const { vae } = state.generation; + const { vae, hrfWidth, hrfHeight, hrfStrength } = state.generation; const isAutoVae = !vae; - const hrfWidth = state.generation.hrfWidth; - const hrfHeight = state.generation.hrfHeight; // Pre-existing (original) graph nodes. const originalDenoiseLatentsNode = graph.nodes[DENOISE_LATENTS] as @@ -116,7 +114,7 @@ export const addHrfToGraph = ( cfg_scale: originalDenoiseLatentsNode?.cfg_scale, scheduler: originalDenoiseLatentsNode?.scheduler, steps: originalDenoiseLatentsNode?.steps, - denoising_start: 1 - state.generation.hrfStrength, + denoising_start: 1 - hrfStrength, denoising_end: 1, }; @@ -221,16 +219,6 @@ export const addHrfToGraph = ( field: 'latents', }, }, - { - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE_HRF, - field: 'metadata', - }, - }, { source: { node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER, @@ -243,5 +231,11 @@ export const addHrfToGraph = ( } ); + upsertMetadata(graph, { + hrf_height: hrfHeight, + hrf_width: hrfWidth, + hrf_strength: hrfStrength, + }); + copyConnectionsToDenoiseLatentsHrf(graph); }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts index f96b2e52ed..93c6cdb284 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addIPAdapterToLinearGraph.ts @@ -1,16 +1,18 @@ import { RootState } from 'app/store/store'; import { selectValidIPAdapters } from 'features/controlAdapters/store/controlAdaptersSlice'; +import { omit } from 'lodash-es'; import { CollectInvocation, + CoreMetadataInvocation, IPAdapterInvocation, - MetadataAccumulatorInvocation, + IPAdapterMetadataField, } from 'services/api/types'; import { NonNullableGraph } from '../../types/types'; import { CANVAS_COHERENCE_DENOISE_LATENTS, IP_ADAPTER_COLLECT, - METADATA_ACCUMULATOR, } from './constants'; +import { upsertMetadata } from './metadata'; export const addIPAdapterToLinearGraph = ( state: RootState, @@ -21,10 +23,6 @@ export const addIPAdapterToLinearGraph = ( (ca) => ca.model?.base_model === state.generation.model?.base_model ); - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; - if (validIPAdapters.length) { // Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect const ipAdapterCollectNode: CollectInvocation = { @@ -32,15 +30,26 @@ export const addIPAdapterToLinearGraph = ( type: 'collect', is_intermediate: true, }; - graph.nodes[ipAdapterCollectNode.id] = ipAdapterCollectNode; + graph.nodes[IP_ADAPTER_COLLECT] = ipAdapterCollectNode; graph.edges.push({ - source: { node_id: ipAdapterCollectNode.id, field: 'collection' }, + source: { node_id: IP_ADAPTER_COLLECT, field: 'collection' }, destination: { node_id: baseNodeId, field: 'ip_adapter', }, }); + if (CANVAS_COHERENCE_DENOISE_LATENTS in graph.nodes) { + graph.edges.push({ + source: { node_id: IP_ADAPTER_COLLECT, field: 'collection' }, + destination: { + node_id: CANVAS_COHERENCE_DENOISE_LATENTS, + field: 'ip_adapter', + }, + }); + } + const ipAdapterMetdata: CoreMetadataInvocation['ipAdapters'] = []; + validIPAdapters.forEach((ipAdapter) => { if (!ipAdapter.model) { return; @@ -66,19 +75,13 @@ export const addIPAdapterToLinearGraph = ( graph.nodes[ipAdapterNode.id] = ipAdapterNode as IPAdapterInvocation; - if (metadataAccumulator?.ipAdapters) { - const ipAdapterField = { - image: { - image_name: ipAdapter.controlImage, - }, - weight, - ip_adapter_model: model, - begin_step_percent: beginStepPct, - end_step_percent: endStepPct, - }; - - metadataAccumulator.ipAdapters.push(ipAdapterField); - } + ipAdapterMetdata.push( + omit(ipAdapterNode, [ + 'id', + 'type', + 'is_intermediate', + ]) as IPAdapterMetadataField + ); graph.edges.push({ source: { node_id: ipAdapterNode.id, field: 'ip_adapter' }, @@ -87,16 +90,8 @@ export const addIPAdapterToLinearGraph = ( field: 'item', }, }); - - if (CANVAS_COHERENCE_DENOISE_LATENTS in graph.nodes) { - graph.edges.push({ - source: { node_id: ipAdapterNode.id, field: 'ip_adapter' }, - destination: { - node_id: CANVAS_COHERENCE_DENOISE_LATENTS, - field: 'ip_adapter', - }, - }); - } }); + + upsertMetadata(graph, { ipAdapters: ipAdapterMetdata }); } }; 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 e199a78a20..66c2bd0444 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts @@ -2,20 +2,20 @@ import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; import { forEach, size } from 'lodash-es'; import { + CoreMetadataInvocation, LoraLoaderInvocation, - MetadataAccumulatorInvocation, } from 'services/api/types'; import { + CANVAS_COHERENCE_DENOISE_LATENTS, CANVAS_INPAINT_GRAPH, CANVAS_OUTPAINT_GRAPH, - CANVAS_COHERENCE_DENOISE_LATENTS, CLIP_SKIP, LORA_LOADER, MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, POSITIVE_CONDITIONING, } from './constants'; +import { upsertMetadata } from './metadata'; export const addLoRAsToGraph = ( state: RootState, @@ -33,29 +33,29 @@ export const addLoRAsToGraph = ( const { loras } = state.lora; const loraCount = size(loras); - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; - if (loraCount > 0) { - // Remove modelLoaderNodeId unet connection to feed it to LoRAs - graph.edges = graph.edges.filter( - (e) => - !( - e.source.node_id === modelLoaderNodeId && - ['unet'].includes(e.source.field) - ) - ); - // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs - graph.edges = graph.edges.filter( - (e) => - !(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field)) - ); + if (loraCount === 0) { + return; } + // Remove modelLoaderNodeId unet connection to feed it to LoRAs + graph.edges = graph.edges.filter( + (e) => + !( + e.source.node_id === modelLoaderNodeId && + ['unet'].includes(e.source.field) + ) + ); + // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs + graph.edges = graph.edges.filter( + (e) => + !(e.source.node_id === CLIP_SKIP && ['clip'].includes(e.source.field)) + ); + // we need to remember the last lora so we can chain from it let lastLoraNodeId = ''; let currentLoraIndex = 0; + const loraMetadata: CoreMetadataInvocation['loras'] = []; forEach(loras, (lora) => { const { model_name, base_model, weight } = lora; @@ -69,13 +69,10 @@ export const addLoRAsToGraph = ( weight, }; - // add the lora to the metadata accumulator - if (metadataAccumulator?.loras) { - metadataAccumulator.loras.push({ - lora: { model_name, base_model }, - weight, - }); - } + loraMetadata.push({ + lora: { model_name, base_model }, + weight, + }); // add to graph graph.nodes[currentLoraNodeId] = loraLoaderNode; @@ -182,4 +179,6 @@ export const addLoRAsToGraph = ( lastLoraNodeId = currentLoraNodeId; currentLoraIndex += 1; }); + + upsertMetadata(graph, { loras: loraMetadata }); }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts index cb052984d4..04841f0def 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLLoRAstoGraph.ts @@ -1,14 +1,14 @@ 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'; + LoRAMetadataItem, + NonNullableGraph, + zLoRAMetadataItem, +} from 'features/nodes/types/types'; +import { forEach, size } from 'lodash-es'; +import { SDXLLoraLoaderInvocation } from 'services/api/types'; import { CANVAS_COHERENCE_DENOISE_LATENTS, LORA_LOADER, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, POSITIVE_CONDITIONING, SDXL_CANVAS_INPAINT_GRAPH, @@ -17,6 +17,7 @@ import { SDXL_REFINER_INPAINT_CREATE_MASK, SEAMLESS, } from './constants'; +import { upsertMetadata } from './metadata'; export const addSDXLLoRAsToGraph = ( state: RootState, @@ -34,9 +35,12 @@ export const addSDXLLoRAsToGraph = ( const { loras } = state.lora; const loraCount = size(loras); - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; + + if (loraCount === 0) { + return; + } + + const loraMetadata: LoRAMetadataItem[] = []; // Handle Seamless Plugs const unetLoaderId = modelLoaderNodeId; @@ -47,22 +51,17 @@ export const addSDXLLoRAsToGraph = ( clipLoaderId = SDXL_MODEL_LOADER; } - if (loraCount > 0) { - // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs - graph.edges = graph.edges.filter( - (e) => - !( - e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field) - ) && - !( - e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field) - ) && - !( - e.source.node_id === clipLoaderId && - ['clip2'].includes(e.source.field) - ) - ); - } + // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs + graph.edges = graph.edges.filter( + (e) => + !( + e.source.node_id === unetLoaderId && ['unet'].includes(e.source.field) + ) && + !( + e.source.node_id === clipLoaderId && ['clip'].includes(e.source.field) + ) && + !(e.source.node_id === clipLoaderId && ['clip2'].includes(e.source.field)) + ); // we need to remember the last lora so we can chain from it let lastLoraNodeId = ''; @@ -80,16 +79,12 @@ export const addSDXLLoRAsToGraph = ( weight, }; - // add the lora to the metadata accumulator - if (metadataAccumulator) { - if (!metadataAccumulator.loras) { - metadataAccumulator.loras = []; - } - metadataAccumulator.loras.push({ + loraMetadata.push( + zLoRAMetadataItem.parse({ lora: { model_name, base_model }, weight, - }); - } + }) + ); // add to graph graph.nodes[currentLoraNodeId] = loraLoaderNode; @@ -242,4 +237,6 @@ export const addSDXLLoRAsToGraph = ( lastLoraNodeId = currentLoraNodeId; currentLoraIndex += 1; }); + + upsertMetadata(graph, { loras: loraMetadata }); }; 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 a6ee6a091d..136263f63e 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSDXLRefinerToGraph.ts @@ -2,7 +2,6 @@ import { RootState } from 'app/store/store'; import { CreateDenoiseMaskInvocation, ImageDTO, - MetadataAccumulatorInvocation, SeamlessModeInvocation, } from 'services/api/types'; import { NonNullableGraph } from '../../types/types'; @@ -12,7 +11,6 @@ import { LATENTS_TO_IMAGE, MASK_COMBINE, MASK_RESIZE_UP, - METADATA_ACCUMULATOR, SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH, SDXL_CANVAS_INPAINT_GRAPH, SDXL_CANVAS_OUTPAINT_GRAPH, @@ -26,6 +24,7 @@ import { SDXL_REFINER_SEAMLESS, } from './constants'; import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt'; +import { upsertMetadata } from './metadata'; export const addSDXLRefinerToGraph = ( state: RootState, @@ -58,21 +57,15 @@ export const addSDXLRefinerToGraph = ( return; } - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; - - if (metadataAccumulator) { - metadataAccumulator.refiner_model = refinerModel; - 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; - } + upsertMetadata(graph, { + refiner_model: refinerModel, + refiner_positive_aesthetic_score: refinerPositiveAestheticScore, + refiner_negative_aesthetic_score: refinerNegativeAestheticScore, + refiner_cfg_scale: refinerCFGScale, + refiner_scheduler: refinerScheduler, + refiner_start: refinerStart, + refiner_steps: refinerSteps, + }); const modelLoaderId = modelLoaderNodeId ? modelLoaderNodeId diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts index d5a6addf8a..79aace8f62 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts @@ -1,19 +1,15 @@ +import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; +import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; +import { SaveImageInvocation } from 'services/api/types'; import { CANVAS_OUTPUT, LATENTS_TO_IMAGE, LATENTS_TO_IMAGE_HRF, - METADATA_ACCUMULATOR, NSFW_CHECKER, SAVE_IMAGE, WATERMARKER, } from './constants'; -import { - MetadataAccumulatorInvocation, - SaveImageInvocation, -} from 'services/api/types'; -import { RootState } from 'app/store/store'; -import { activeTabNameSelector } from 'features/ui/store/uiSelectors'; /** * Set the `use_cache` field on the linear/canvas graph's final image output node to False. @@ -37,23 +33,6 @@ export const addSaveImageNode = ( graph.nodes[SAVE_IMAGE] = saveImageNode; - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; - - if (metadataAccumulator) { - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: SAVE_IMAGE, - field: 'metadata', - }, - }); - } - const destination = { node_id: SAVE_IMAGE, field: 'image', diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts index bdbaacd384..ba341a8a3d 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSeamlessToLinearGraph.ts @@ -1,6 +1,7 @@ import { RootState } from 'app/store/store'; import { SeamlessModeInvocation } from 'services/api/types'; import { NonNullableGraph } from '../../types/types'; +import { upsertMetadata } from './metadata'; import { CANVAS_COHERENCE_DENOISE_LATENTS, CANVAS_INPAINT_GRAPH, @@ -31,6 +32,17 @@ export const addSeamlessToLinearGraph = ( seamless_y: seamlessYAxis, } as SeamlessModeInvocation; + if (seamlessXAxis) { + upsertMetadata(graph, { + seamless_x: seamlessXAxis, + }); + } + if (seamlessYAxis) { + upsertMetadata(graph, { + seamless_y: seamlessYAxis, + }); + } + let denoisingNodeId = DENOISE_LATENTS; if ( diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts index f07edcb220..71c2aaeede 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addT2IAdapterToLinearGraph.ts @@ -3,15 +3,15 @@ import { selectValidT2IAdapters } from 'features/controlAdapters/store/controlAd import { omit } from 'lodash-es'; import { CollectInvocation, - MetadataAccumulatorInvocation, + CoreMetadataInvocation, T2IAdapterInvocation, } from 'services/api/types'; import { NonNullableGraph, T2IAdapterField } from '../../types/types'; import { CANVAS_COHERENCE_DENOISE_LATENTS, - METADATA_ACCUMULATOR, T2I_ADAPTER_COLLECT, } from './constants'; +import { upsertMetadata } from './metadata'; export const addT2IAdaptersToLinearGraph = ( state: RootState, @@ -22,10 +22,6 @@ export const addT2IAdaptersToLinearGraph = ( (ca) => ca.model?.base_model === state.generation.model?.base_model ); - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; - if (validT2IAdapters.length) { // Even though denoise_latents' control input is polymorphic, keep it simple and always use a collect const t2iAdapterCollectNode: CollectInvocation = { @@ -42,6 +38,17 @@ export const addT2IAdaptersToLinearGraph = ( }, }); + if (CANVAS_COHERENCE_DENOISE_LATENTS in graph.nodes) { + graph.edges.push({ + source: { node_id: T2I_ADAPTER_COLLECT, field: 'collection' }, + destination: { + node_id: CANVAS_COHERENCE_DENOISE_LATENTS, + field: 't2i_adapter', + }, + }); + } + const t2iAdapterMetdata: CoreMetadataInvocation['t2iAdapters'] = []; + validT2IAdapters.forEach((t2iAdapter) => { if (!t2iAdapter.model) { return; @@ -86,15 +93,13 @@ export const addT2IAdaptersToLinearGraph = ( graph.nodes[t2iAdapterNode.id] = t2iAdapterNode as T2IAdapterInvocation; - if (metadataAccumulator?.ipAdapters) { - // metadata accumulator only needs a control field - not the whole node - // extract what we need and add to the accumulator - const t2iAdapterField = omit(t2iAdapterNode, [ + t2iAdapterMetdata.push( + omit(t2iAdapterNode, [ 'id', 'type', - ]) as T2IAdapterField; - metadataAccumulator.t2iAdapters.push(t2iAdapterField); - } + 'is_intermediate', + ]) as T2IAdapterField + ); graph.edges.push({ source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' }, @@ -103,16 +108,8 @@ export const addT2IAdaptersToLinearGraph = ( field: 'item', }, }); - - if (CANVAS_COHERENCE_DENOISE_LATENTS in graph.nodes) { - graph.edges.push({ - source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' }, - destination: { - node_id: CANVAS_COHERENCE_DENOISE_LATENTS, - field: 't2i_adapter', - }, - }); - } }); + + upsertMetadata(graph, { t2iAdapters: t2iAdapterMetdata }); } }; 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 696c8afff2..f049a89e36 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts @@ -1,6 +1,5 @@ import { RootState } from 'app/store/store'; import { NonNullableGraph } from 'features/nodes/types/types'; -import { MetadataAccumulatorInvocation } from 'services/api/types'; import { CANVAS_COHERENCE_INPAINT_CREATE_MASK, CANVAS_IMAGE_TO_IMAGE_GRAPH, @@ -14,7 +13,6 @@ import { INPAINT_IMAGE, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, ONNX_MODEL_LOADER, SDXL_CANVAS_IMAGE_TO_IMAGE_GRAPH, SDXL_CANVAS_INPAINT_GRAPH, @@ -26,6 +24,7 @@ import { TEXT_TO_IMAGE_GRAPH, VAE_LOADER, } from './constants'; +import { upsertMetadata } from './metadata'; export const addVAEToGraph = ( state: RootState, @@ -41,9 +40,6 @@ export const addVAEToGraph = ( ); const isAutoVae = !vae; - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; if (!isAutoVae) { graph.nodes[VAE_LOADER] = { @@ -181,7 +177,7 @@ export const addVAEToGraph = ( } } - if (vae && metadataAccumulator) { - metadataAccumulator.vae = vae; + if (vae) { + upsertMetadata(graph, { vae }); } }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts index 4e515906b6..c43437e4fc 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/addWatermarkerToGraph.ts @@ -5,14 +5,8 @@ import { ImageNSFWBlurInvocation, ImageWatermarkInvocation, LatentsToImageInvocation, - MetadataAccumulatorInvocation, } from 'services/api/types'; -import { - LATENTS_TO_IMAGE, - METADATA_ACCUMULATOR, - NSFW_CHECKER, - WATERMARKER, -} from './constants'; +import { LATENTS_TO_IMAGE, NSFW_CHECKER, WATERMARKER } from './constants'; export const addWatermarkerToGraph = ( state: RootState, @@ -32,10 +26,6 @@ export const addWatermarkerToGraph = ( | ImageNSFWBlurInvocation | undefined; - const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as - | MetadataAccumulatorInvocation - | undefined; - if (!nodeToAddTo) { // something has gone terribly awry return; @@ -80,17 +70,4 @@ export const addWatermarkerToGraph = ( }, }); } - - if (metadataAccumulator) { - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: WATERMARKER, - field: 'metadata', - }, - }); - } }; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts index c612e88598..5af8edbdfc 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildAdHocUpscaleGraph.ts @@ -1,12 +1,13 @@ +import { BoardId } from 'features/gallery/store/types'; import { NonNullableGraph } from 'features/nodes/types/types'; import { ESRGANModelName } from 'features/parameters/store/postprocessingSlice'; import { - Graph, ESRGANInvocation, + Graph, SaveImageInvocation, } from 'services/api/types'; import { REALESRGAN as ESRGAN, SAVE_IMAGE } from './constants'; -import { BoardId } from 'features/gallery/store/types'; +import { addCoreMetadataNode, upsertMetadata } from './metadata'; type Arg = { image_name: string; @@ -55,5 +56,10 @@ export const buildAdHocUpscaleGraph = ({ ], }; + addCoreMetadataNode(graph, {}); + upsertMetadata(graph, { + esrgan_model: esrganModelName, + }); + 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 59bdf669e6..9d957c3a4a 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts @@ -20,12 +20,12 @@ import { IMG2IMG_RESIZE, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, POSITIVE_CONDITIONING, SEAMLESS, } from './constants'; +import { addCoreMetadataNode } from './metadata'; /** * Builds the Canvas tab's Image to Image graph. @@ -308,10 +308,7 @@ export const buildCanvasImageToImageGraph = ( }); } - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'img2img', cfg_scale, width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width, @@ -325,15 +322,10 @@ export const buildCanvasImageToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, // option; set in addVAEToGraph - controlnets: [], // populated in addControlNetToLinearGraph - loras: [], // populated in addLoRAsToGraph - ipAdapters: [], // populated in addIPAdapterToLinearGraph - t2iAdapters: [], clip_skip: clipSkip, strength, init_image: initialImage.image_name, - }; + }); // Add Seamless To Graph if (seamlessXAxis || seamlessYAxis) { diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts index b9c0c9eff3..c1ecde5395 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLImageToImageGraph.ts @@ -16,7 +16,6 @@ import { IMAGE_TO_LATENTS, IMG2IMG_RESIZE, LATENTS_TO_IMAGE, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, POSITIVE_CONDITIONING, @@ -28,6 +27,7 @@ import { } from './constants'; import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt'; import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph'; +import { addCoreMetadataNode } from './metadata'; /** * Builds the Canvas tab's Image to Image graph. @@ -319,10 +319,7 @@ export const buildCanvasSDXLImageToImageGraph = ( }); } - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'img2img', cfg_scale, width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width, @@ -336,24 +333,8 @@ export const buildCanvasSDXLImageToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, // option; set in addVAEToGraph - controlnets: [], // populated in addControlNetToLinearGraph - loras: [], // populated in addLoRAsToGraph - ipAdapters: [], // populated in addIPAdapterToLinearGraph - t2iAdapters: [], strength, init_image: initialImage.image_name, - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: CANVAS_OUTPUT, - field: 'metadata', - }, }); // Add Seamless To 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 index df636669dc..e43891eba4 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasSDXLTextToImageGraph.ts @@ -18,7 +18,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { CANVAS_OUTPUT, LATENTS_TO_IMAGE, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, ONNX_MODEL_LOADER, @@ -30,6 +29,7 @@ import { SEAMLESS, } from './constants'; import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt'; +import { addCoreMetadataNode } from './metadata'; /** * Builds the Canvas tab's Text to Image graph. @@ -301,10 +301,7 @@ export const buildCanvasSDXLTextToImageGraph = ( }); } - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'txt2img', cfg_scale, width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width, @@ -318,22 +315,6 @@ export const buildCanvasSDXLTextToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, // option; set in addVAEToGraph - controlnets: [], // populated in addControlNetToLinearGraph - loras: [], // populated in addLoRAsToGraph - ipAdapters: [], // populated in addIPAdapterToLinearGraph - t2iAdapters: [], - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: CANVAS_OUTPUT, - field: 'metadata', - }, }); // Add Seamless To 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 38f11f14ac..6e48c14086 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts @@ -21,13 +21,13 @@ import { DENOISE_LATENTS, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, ONNX_MODEL_LOADER, POSITIVE_CONDITIONING, SEAMLESS, } from './constants'; +import { addCoreMetadataNode } from './metadata'; /** * Builds the Canvas tab's Text to Image graph. @@ -289,10 +289,7 @@ export const buildCanvasTextToImageGraph = ( }); } - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'txt2img', cfg_scale, width: !isUsingScaledDimensions ? width : scaledBoundingBoxDimensions.width, @@ -306,23 +303,7 @@ export const buildCanvasTextToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, // option; set in addVAEToGraph - controlnets: [], // populated in addControlNetToLinearGraph - loras: [], // populated in addLoRAsToGraph - ipAdapters: [], // populated in addIPAdapterToLinearGraph - t2iAdapters: [], clip_skip: clipSkip, - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: CANVAS_OUTPUT, - field: 'metadata', - }, }); // Add Seamless To Graph diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearBatchConfig.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearBatchConfig.ts index 9c25ee3b8f..59f8d4123f 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearBatchConfig.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearBatchConfig.ts @@ -2,15 +2,16 @@ import { NUMPY_RAND_MAX } from 'app/constants'; import { RootState } from 'app/store/store'; import { generateSeeds } from 'common/util/generateSeeds'; import { NonNullableGraph } from 'features/nodes/types/types'; -import { range, unset } from 'lodash-es'; +import { range } from 'lodash-es'; import { components } from 'services/api/schema'; import { Batch, BatchConfig } from 'services/api/types'; import { CANVAS_COHERENCE_NOISE, - METADATA_ACCUMULATOR, + METADATA, NOISE, POSITIVE_CONDITIONING, } from './constants'; +import { getHasMetadata, removeMetadata } from './metadata'; export const prepareLinearUIBatch = ( state: RootState, @@ -24,7 +25,6 @@ export const prepareLinearUIBatch = ( const data: Batch['data'] = []; if (prompts.length === 1) { - unset(graph.nodes[METADATA_ACCUMULATOR], 'seed'); const seeds = generateSeeds({ count: iterations, start: shouldRandomizeSeed ? undefined : seed, @@ -40,9 +40,11 @@ export const prepareLinearUIBatch = ( }); } - if (graph.nodes[METADATA_ACCUMULATOR]) { + if (getHasMetadata(graph)) { + // add to metadata + removeMetadata(graph, 'seed'); zipped.push({ - node_path: METADATA_ACCUMULATOR, + node_path: METADATA, field_name: 'seed', items: seeds, }); @@ -77,9 +79,11 @@ export const prepareLinearUIBatch = ( }); } - if (graph.nodes[METADATA_ACCUMULATOR]) { + // add to metadata + if (getHasMetadata(graph)) { + removeMetadata(graph, 'seed'); firstBatchDatumList.push({ - node_path: METADATA_ACCUMULATOR, + node_path: METADATA, field_name: 'seed', items: seeds, }); @@ -106,13 +110,17 @@ export const prepareLinearUIBatch = ( items: seeds, }); } - if (graph.nodes[METADATA_ACCUMULATOR]) { + + // add to metadata + if (getHasMetadata(graph)) { + removeMetadata(graph, 'seed'); secondBatchDatumList.push({ - node_path: METADATA_ACCUMULATOR, + node_path: METADATA, field_name: 'seed', items: seeds, }); } + if (graph.nodes[CANVAS_COHERENCE_NOISE]) { secondBatchDatumList.push({ node_path: CANVAS_COHERENCE_NOISE, @@ -137,17 +145,17 @@ export const prepareLinearUIBatch = ( }); } - if (graph.nodes[METADATA_ACCUMULATOR]) { + // add to metadata + if (getHasMetadata(graph)) { + removeMetadata(graph, 'positive_prompt'); firstBatchDatumList.push({ - node_path: METADATA_ACCUMULATOR, + node_path: METADATA, field_name: 'positive_prompt', items: extendedPrompts, }); } if (shouldConcatSDXLStylePrompt && model?.base_model === 'sdxl') { - unset(graph.nodes[METADATA_ACCUMULATOR], 'positive_style_prompt'); - const stylePrompts = extendedPrompts.map((p) => [p, positiveStylePrompt].join(' ') ); @@ -160,11 +168,13 @@ export const prepareLinearUIBatch = ( }); } - if (graph.nodes[METADATA_ACCUMULATOR]) { + // add to metadata + if (getHasMetadata(graph)) { + removeMetadata(graph, 'positive_style_prompt'); firstBatchDatumList.push({ - node_path: METADATA_ACCUMULATOR, + node_path: METADATA, field_name: 'positive_style_prompt', - items: stylePrompts, + items: extendedPrompts, }); } } 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 0eeba988f2..3b13c746c9 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts @@ -21,13 +21,13 @@ import { IMAGE_TO_LATENTS, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, POSITIVE_CONDITIONING, RESIZE, SEAMLESS, } from './constants'; +import { addCoreMetadataNode } from './metadata'; /** * Builds the Image to Image tab graph. @@ -311,10 +311,7 @@ export const buildLinearImageToImageGraph = ( }); } - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'img2img', cfg_scale, height, @@ -326,25 +323,9 @@ export const buildLinearImageToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, // option; set in addVAEToGraph - controlnets: [], // populated in addControlNetToLinearGraph - loras: [], // populated in addLoRAsToGraph - ipAdapters: [], // populated in addIPAdapterToLinearGraph - t2iAdapters: [], // populated in addT2IAdapterToLinearGraph clip_skip: clipSkip, strength, init_image: initialImage.imageName, - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, }); // Add Seamless To Graph 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 f818768fb5..54f8e05d21 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLImageToImageGraph.ts @@ -18,7 +18,6 @@ import { addWatermarkerToGraph } from './addWatermarkerToGraph'; import { IMAGE_TO_LATENTS, LATENTS_TO_IMAGE, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, POSITIVE_CONDITIONING, @@ -30,6 +29,7 @@ import { SEAMLESS, } from './constants'; import { buildSDXLStylePrompts } from './helpers/craftSDXLStylePrompt'; +import { addCoreMetadataNode } from './metadata'; /** * Builds the Image to Image tab graph. @@ -331,10 +331,7 @@ export const buildLinearSDXLImageToImageGraph = ( }); } - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'sdxl_img2img', cfg_scale, height, @@ -346,26 +343,10 @@ export const buildLinearSDXLImageToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, - controlnets: [], - loras: [], - ipAdapters: [], - t2iAdapters: [], - strength: strength, + strength, init_image: initialImage.imageName, positive_style_prompt: positiveStylePrompt, negative_style_prompt: negativeStylePrompt, - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, }); // Add Seamless To 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 4cb90678c3..37fbbf7f43 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearSDXLTextToImageGraph.ts @@ -11,9 +11,9 @@ import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph'; import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph'; import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { addCoreMetadataNode } from './metadata'; import { LATENTS_TO_IMAGE, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, POSITIVE_CONDITIONING, @@ -225,10 +225,7 @@ export const buildLinearSDXLTextToImageGraph = ( ], }; - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'sdxl_txt2img', cfg_scale, height, @@ -240,24 +237,8 @@ export const buildLinearSDXLTextToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, - controlnets: [], - loras: [], - ipAdapters: [], - t2iAdapters: [], positive_style_prompt: positiveStylePrompt, negative_style_prompt: negativeStylePrompt, - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, }); // Add Seamless To 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 e692e12fa4..8e0143f180 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts @@ -15,12 +15,12 @@ import { addSeamlessToLinearGraph } from './addSeamlessToLinearGraph'; import { addT2IAdaptersToLinearGraph } from './addT2IAdapterToLinearGraph'; import { addVAEToGraph } from './addVAEToGraph'; import { addWatermarkerToGraph } from './addWatermarkerToGraph'; +import { addCoreMetadataNode } from './metadata'; import { CLIP_SKIP, DENOISE_LATENTS, LATENTS_TO_IMAGE, MAIN_MODEL_LOADER, - METADATA_ACCUMULATOR, NEGATIVE_CONDITIONING, NOISE, ONNX_MODEL_LOADER, @@ -48,10 +48,6 @@ export const buildLinearTextToImageGraph = ( seamlessXAxis, seamlessYAxis, seed, - hrfWidth, - hrfHeight, - hrfStrength, - hrfEnabled: hrfEnabled, } = state.generation; const use_cpu = shouldUseCpuNoise; @@ -238,10 +234,7 @@ export const buildLinearTextToImageGraph = ( ], }; - // add metadata accumulator, which is only mostly populated - some fields are added later - graph.nodes[METADATA_ACCUMULATOR] = { - id: METADATA_ACCUMULATOR, - type: 'metadata_accumulator', + addCoreMetadataNode(graph, { generation_mode: 'txt2img', cfg_scale, height, @@ -253,26 +246,7 @@ export const buildLinearTextToImageGraph = ( steps, rand_device: use_cpu ? 'cpu' : 'cuda', scheduler, - vae: undefined, // option; set in addVAEToGraph - controlnets: [], // populated in addControlNetToLinearGraph - loras: [], // populated in addLoRAsToGraph - ipAdapters: [], // populated in addIPAdapterToLinearGraph - t2iAdapters: [], // populated in addT2IAdapterToLinearGraph clip_skip: clipSkip, - hrf_width: hrfEnabled ? hrfWidth : undefined, - hrf_height: hrfEnabled ? hrfHeight : undefined, - hrf_strength: hrfEnabled ? hrfStrength : undefined, - }; - - graph.edges.push({ - source: { - node_id: METADATA_ACCUMULATOR, - field: 'metadata', - }, - destination: { - node_id: LATENTS_TO_IMAGE, - field: 'metadata', - }, }); // Add Seamless To Graph diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts index 7be06ac110..eb782f456a 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildNodesGraph.ts @@ -35,7 +35,6 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => { const { nodes, edges } = nodesState; const filteredNodes = nodes.filter(isInvocationNode); - const workflowJSON = JSON.stringify(buildWorkflow(nodesState)); // Reduce the node editor nodes into invocation graph nodes const parsedNodes = filteredNodes.reduce>( @@ -68,7 +67,7 @@ export const buildNodesGraph = (nodesState: NodesState): Graph => { if (embedWorkflow) { // add the workflow to the node - Object.assign(graphNode, { workflow: workflowJSON }); + Object.assign(graphNode, { workflow: buildWorkflow(nodesState) }); } // Add it to the nodes object 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 7d547d09e6..51dc94769f 100644 --- a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts @@ -56,7 +56,14 @@ export const IP_ADAPTER = 'ip_adapter'; export const DYNAMIC_PROMPT = 'dynamic_prompt'; export const IMAGE_COLLECTION = 'image_collection'; export const IMAGE_COLLECTION_ITERATE = 'image_collection_iterate'; -export const METADATA_ACCUMULATOR = 'metadata_accumulator'; +export const METADATA = 'core_metadata'; +export const BATCH_METADATA = 'batch_metadata'; +export const BATCH_METADATA_COLLECT = 'batch_metadata_collect'; +export const BATCH_SEED = 'batch_seed'; +export const BATCH_PROMPT = 'batch_prompt'; +export const BATCH_STYLE_PROMPT = 'batch_style_prompt'; +export const METADATA_COLLECT = 'metadata_collect'; +export const MERGE_METADATA = 'merge_metadata'; export const REALESRGAN = 'esrgan'; export const DIVIDE = 'divide'; export const SCALE = 'scale_image'; diff --git a/invokeai/frontend/web/src/features/nodes/util/graphBuilders/metadata.ts b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/metadata.ts new file mode 100644 index 0000000000..b673be9e4a --- /dev/null +++ b/invokeai/frontend/web/src/features/nodes/util/graphBuilders/metadata.ts @@ -0,0 +1,66 @@ +import { NonNullableGraph } from 'features/nodes/types/types'; +import { CoreMetadataInvocation } from 'services/api/types'; +import { JsonObject } from 'type-fest'; +import { METADATA, SAVE_IMAGE } from './constants'; + +export const addCoreMetadataNode = ( + graph: NonNullableGraph, + metadata: Partial +): void => { + graph.nodes[METADATA] = { + id: METADATA, + type: 'core_metadata', + ...metadata, + }; + + graph.edges.push({ + source: { + node_id: METADATA, + field: 'metadata', + }, + destination: { + node_id: SAVE_IMAGE, + field: 'metadata', + }, + }); + + return; +}; + +export const upsertMetadata = ( + graph: NonNullableGraph, + metadata: Partial | JsonObject +): void => { + const metadataNode = graph.nodes[METADATA] as + | CoreMetadataInvocation + | undefined; + + if (!metadataNode) { + return; + } + + Object.assign(metadataNode, metadata); +}; + +export const removeMetadata = ( + graph: NonNullableGraph, + key: keyof CoreMetadataInvocation +): void => { + const metadataNode = graph.nodes[METADATA] as + | CoreMetadataInvocation + | undefined; + + if (!metadataNode) { + return; + } + + delete metadataNode[key]; +}; + +export const getHasMetadata = (graph: NonNullableGraph): boolean => { + const metadataNode = graph.nodes[METADATA] as + | CoreMetadataInvocation + | undefined; + + return Boolean(metadataNode); +}; diff --git a/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts b/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts index 69d8d9dd4c..7c6f4e638f 100644 --- a/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts +++ b/invokeai/frontend/web/src/features/nodes/util/parseSchema.ts @@ -1,10 +1,9 @@ import { logger } from 'app/logging/logger'; import { parseify } from 'common/util/serialize'; -import { reduce } from 'lodash-es'; -import { OpenAPIV3 } from 'openapi-types'; +import { reduce, startCase } from 'lodash-es'; +import { OpenAPIV3_1 } from 'openapi-types'; import { AnyInvocationType } from 'services/events/types'; import { - FieldType, InputFieldTemplate, InvocationSchemaObject, InvocationTemplate, @@ -16,18 +15,11 @@ import { } from '../types/types'; import { buildInputFieldTemplate, getFieldType } from './fieldTemplateBuilders'; -const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'metadata', 'use_cache']; +const RESERVED_INPUT_FIELD_NAMES = ['id', 'type', 'use_cache']; const RESERVED_OUTPUT_FIELD_NAMES = ['type']; -const RESERVED_FIELD_TYPES = [ - 'WorkflowField', - 'MetadataField', - 'IsIntermediate', -]; +const RESERVED_FIELD_TYPES = ['IsIntermediate']; -const invocationDenylist: AnyInvocationType[] = [ - 'graph', - 'metadata_accumulator', -]; +const invocationDenylist: AnyInvocationType[] = ['graph']; const isReservedInputField = (nodeType: string, fieldName: string) => { if (RESERVED_INPUT_FIELD_NAMES.includes(fieldName)) { @@ -42,7 +34,7 @@ const isReservedInputField = (nodeType: string, fieldName: string) => { return false; }; -const isReservedFieldType = (fieldType: FieldType) => { +const isReservedFieldType = (fieldType: string) => { if (RESERVED_FIELD_TYPES.includes(fieldType)) { return true; } @@ -60,7 +52,7 @@ const isNotInDenylist = (schema: InvocationSchemaObject) => !invocationDenylist.includes(schema.properties.type.default); export const parseSchema = ( - openAPI: OpenAPIV3.Document, + openAPI: OpenAPIV3_1.Document, nodesAllowlistExtra: string[] | undefined = undefined, nodesDenylistExtra: string[] | undefined = undefined ): Record => { @@ -86,6 +78,7 @@ export const parseSchema = ( const tags = schema.tags ?? []; const description = schema.description ?? ''; const version = schema.version; + let withWorkflow = false; const inputs = reduce( schema.properties, @@ -110,9 +103,9 @@ export const parseSchema = ( return inputsAccumulator; } - const fieldType = getFieldType(property); + const fieldType = property.ui_type ?? getFieldType(property); - if (!isFieldType(fieldType)) { + if (!fieldType) { logger('nodes').warn( { node: type, @@ -120,11 +113,16 @@ export const parseSchema = ( fieldType, field: parseify(property), }, - 'Skipping unknown input field type' + 'Missing input field type' ); return inputsAccumulator; } + if (fieldType === 'WorkflowField') { + withWorkflow = true; + return inputsAccumulator; + } + if (isReservedFieldType(fieldType)) { logger('nodes').trace( { @@ -133,7 +131,20 @@ export const parseSchema = ( fieldType, field: parseify(property), }, - 'Skipping reserved field type' + `Skipping reserved input field type: ${fieldType}` + ); + return inputsAccumulator; + } + + if (!isFieldType(fieldType)) { + logger('nodes').warn( + { + node: type, + fieldName: propertyName, + fieldType, + field: parseify(property), + }, + `Skipping unknown input field type: ${fieldType}` ); return inputsAccumulator; } @@ -146,7 +157,7 @@ export const parseSchema = ( ); if (!field) { - logger('nodes').debug( + logger('nodes').warn( { node: type, fieldName: propertyName, @@ -209,7 +220,7 @@ export const parseSchema = ( return outputsAccumulator; } - const fieldType = getFieldType(property); + const fieldType = property.ui_type ?? getFieldType(property); if (!isFieldType(fieldType)) { logger('nodes').warn( @@ -222,7 +233,8 @@ export const parseSchema = ( outputsAccumulator[propertyName] = { fieldKind: 'output', name: propertyName, - title: property.title ?? '', + title: + property.title ?? (propertyName ? startCase(propertyName) : ''), description: property.description ?? '', type: fieldType, ui_hidden: property.ui_hidden ?? false, @@ -247,6 +259,7 @@ export const parseSchema = ( inputs, outputs, useCache, + withWorkflow, }; Object.assign(invocationsAccumulator, { [type]: invocation }); diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx index 85b6eaa903..718b38cfba 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx @@ -28,9 +28,7 @@ export default function ParamAdvancedCollapse() { const activeLabel = useMemo(() => { const activeLabel: string[] = []; - if (shouldUseCpuNoise) { - activeLabel.push(t('parameters.cpuNoise')); - } else { + if (!shouldUseCpuNoise) { activeLabel.push(t('parameters.gpuNoise')); } diff --git a/invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx b/invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx index ef0db1af6d..63709f23aa 100644 --- a/invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx +++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx @@ -4,12 +4,13 @@ import { RootState, 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 { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; import { useMemo } from 'react'; +import { useTranslation } from 'react-i18next'; +import ParamHrfHeight from './ParamHrfHeight'; import ParamHrfStrength from './ParamHrfStrength'; import ParamHrfToggle from './ParamHrfToggle'; import ParamHrfWidth from './ParamHrfWidth'; -import ParamHrfHeight from './ParamHrfHeight'; -import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus'; const selector = createSelector( stateSelector, @@ -22,15 +23,14 @@ const selector = createSelector( ); export default function ParamHrfCollapse() { + const { t } = useTranslation(); const isHRFFeatureEnabled = useFeatureStatus('hrf').isFeatureEnabled; const { hrfEnabled } = useAppSelector(selector); const activeLabel = useMemo(() => { if (hrfEnabled) { - return 'On'; - } else { - return 'Off'; + return t('common.on'); } - }, [hrfEnabled]); + }, [t, hrfEnabled]); if (!isHRFFeatureEnabled) { return null; diff --git a/invokeai/frontend/web/src/features/queue/components/common/QueueItemCard.tsx b/invokeai/frontend/web/src/features/queue/components/common/QueueItemCard.tsx index 9cc991335e..d441be4ecb 100644 --- a/invokeai/frontend/web/src/features/queue/components/common/QueueItemCard.tsx +++ b/invokeai/frontend/web/src/features/queue/components/common/QueueItemCard.tsx @@ -7,7 +7,7 @@ const QueueItemCard = ({ session_queue_item, label, }: { - session_queue_item?: components['schemas']['SessionQueueItem']; + session_queue_item?: components['schemas']['SessionQueueItem'] | null; label: string; }) => { return ( diff --git a/invokeai/frontend/web/src/features/queue/hooks/useIsQueueMutationInProgress.ts b/invokeai/frontend/web/src/features/queue/hooks/useIsQueueMutationInProgress.ts index abb3967b92..9947c17086 100644 --- a/invokeai/frontend/web/src/features/queue/hooks/useIsQueueMutationInProgress.ts +++ b/invokeai/frontend/web/src/features/queue/hooks/useIsQueueMutationInProgress.ts @@ -3,7 +3,6 @@ import { // useCancelByBatchIdsMutation, useClearQueueMutation, useEnqueueBatchMutation, - useEnqueueGraphMutation, usePruneQueueMutation, useResumeProcessorMutation, usePauseProcessorMutation, @@ -14,10 +13,6 @@ export const useIsQueueMutationInProgress = () => { useEnqueueBatchMutation({ fixedCacheKey: 'enqueueBatch', }); - const [_triggerEnqueueGraph, { isLoading: isLoadingEnqueueGraph }] = - useEnqueueGraphMutation({ - fixedCacheKey: 'enqueueGraph', - }); const [_triggerResumeProcessor, { isLoading: isLoadingResumeProcessor }] = useResumeProcessorMutation({ fixedCacheKey: 'resumeProcessor', @@ -44,7 +39,6 @@ export const useIsQueueMutationInProgress = () => { // }); return ( isLoadingEnqueueBatch || - isLoadingEnqueueGraph || isLoadingResumeProcessor || isLoadingPauseProcessor || isLoadingCancelQueue || diff --git a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx index 6ac3034834..ac33891a6c 100644 --- a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx +++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx @@ -1,4 +1,4 @@ -import { Flex } from '@chakra-ui/react'; +import { Flex, Text } from '@chakra-ui/react'; import { createSelector } from '@reduxjs/toolkit'; import { stateSelector } from 'app/store/store'; import { useAppSelector } from 'app/store/storeHooks'; @@ -14,6 +14,7 @@ import ParamSDXLRefinerStart from './SDXLRefiner/ParamSDXLRefinerStart'; import ParamSDXLRefinerSteps from './SDXLRefiner/ParamSDXLRefinerSteps'; import ParamUseSDXLRefiner from './SDXLRefiner/ParamUseSDXLRefiner'; import { useTranslation } from 'react-i18next'; +import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable'; const selector = createSelector( stateSelector, @@ -31,6 +32,19 @@ const selector = createSelector( const ParamSDXLRefinerCollapse = () => { const { activeLabel, shouldUseSliders } = useAppSelector(selector); const { t } = useTranslation(); + const isRefinerAvailable = useIsRefinerAvailable(); + + if (!isRefinerAvailable) { + return ( + + + + {t('models.noRefinerModelsInstalled')} + + + + ); + } return ( diff --git a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsClearIntermediates.tsx b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsClearIntermediates.tsx index 423ce41bcd..c419fa716f 100644 --- a/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsClearIntermediates.tsx +++ b/invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsClearIntermediates.tsx @@ -1,8 +1,9 @@ import { Heading, Text } from '@chakra-ui/react'; import { useAppDispatch } from 'app/store/storeHooks'; import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice'; -import { useCallback, useEffect } from 'react'; +import { useCallback } from 'react'; import { useTranslation } from 'react-i18next'; +import { useGetQueueStatusQuery } from 'services/api/endpoints/queue'; import IAIButton from '../../../../common/components/IAIButton'; import { useClearIntermediatesMutation, @@ -16,13 +17,26 @@ export default function SettingsClearIntermediates() { const { t } = useTranslation(); const dispatch = useAppDispatch(); - const { data: intermediatesCount, refetch: updateIntermediatesCount } = - useGetIntermediatesCountQuery(); + const { data: intermediatesCount } = useGetIntermediatesCountQuery( + undefined, + { + refetchOnMountOrArgChange: true, + } + ); const [clearIntermediates, { isLoading: isLoadingClearIntermediates }] = useClearIntermediatesMutation(); + const { data: queueStatus } = useGetQueueStatusQuery(); + const hasPendingItems = + queueStatus && + (queueStatus.queue.in_progress > 0 || queueStatus.queue.pending > 0); + const handleClickClearIntermediates = useCallback(() => { + if (hasPendingItems) { + return; + } + clearIntermediates() .unwrap() .then((clearedCount) => { @@ -43,21 +57,19 @@ export default function SettingsClearIntermediates() { }) ); }); - }, [t, clearIntermediates, dispatch]); - - useEffect(() => { - // update the count on mount - updateIntermediatesCount(); - }, [updateIntermediatesCount]); + }, [t, clearIntermediates, dispatch, hasPendingItems]); return ( {t('settings.clearIntermediates')} {t('settings.clearIntermediatesWithCount', { count: intermediatesCount ?? 0, diff --git a/invokeai/frontend/web/src/features/system/util/copyBlobToClipboard.ts b/invokeai/frontend/web/src/features/system/util/copyBlobToClipboard.ts index cf59f2a687..b5e896f3bf 100644 --- a/invokeai/frontend/web/src/features/system/util/copyBlobToClipboard.ts +++ b/invokeai/frontend/web/src/features/system/util/copyBlobToClipboard.ts @@ -2,7 +2,7 @@ * Copies a blob to the clipboard by calling navigator.clipboard.write(). */ export const copyBlobToClipboard = ( - blob: Promise, + blob: Promise | Blob, type = 'image/png' ) => { navigator.clipboard.write([ 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 6837a2e853..e5c68ba6cf 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 @@ -112,7 +112,7 @@ export default function MergeModelsPanel() { } }); - const mergeModelsInfo: MergeModelConfig = { + const mergeModelsInfo: MergeModelConfig['body'] = { model_names: models_names, merged_model_name: mergedModelName !== '' ? mergedModelName : models_names.join('-'), @@ -125,7 +125,7 @@ export default function MergeModelsPanel() { mergeModels({ base_model: baseModel, - body: mergeModelsInfo, + body: { body: mergeModelsInfo }, }) .unwrap() .then((_) => { diff --git a/invokeai/frontend/web/src/features/ui/hooks/useCopyImageToClipboard.ts b/invokeai/frontend/web/src/features/ui/hooks/useCopyImageToClipboard.ts index 4b42a45e93..ef9db44a9d 100644 --- a/invokeai/frontend/web/src/features/ui/hooks/useCopyImageToClipboard.ts +++ b/invokeai/frontend/web/src/features/ui/hooks/useCopyImageToClipboard.ts @@ -1,11 +1,13 @@ import { useAppToaster } from 'app/components/Toaster'; +import { useImageUrlToBlob } from 'common/hooks/useImageUrlToBlob'; +import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard'; import { useCallback, useMemo } from 'react'; import { useTranslation } from 'react-i18next'; -import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard'; export const useCopyImageToClipboard = () => { const toaster = useAppToaster(); const { t } = useTranslation(); + const imageUrlToBlob = useImageUrlToBlob(); const isClipboardAPIAvailable = useMemo(() => { return Boolean(navigator.clipboard) && Boolean(window.ClipboardItem); @@ -23,15 +25,13 @@ export const useCopyImageToClipboard = () => { }); } try { - const getImageBlob = async () => { - const response = await fetch(image_url); - if (!response.ok) { - throw new Error(`Problem retrieving image data`); - } - return await response.blob(); - }; + const blob = await imageUrlToBlob(image_url); - copyBlobToClipboard(getImageBlob()); + if (!blob) { + throw new Error('Unable to create Blob'); + } + + copyBlobToClipboard(blob); toaster({ title: t('toast.imageCopied'), @@ -49,7 +49,7 @@ export const useCopyImageToClipboard = () => { }); } }, - [isClipboardAPIAvailable, t, toaster] + [imageUrlToBlob, isClipboardAPIAvailable, t, toaster] ); return { isClipboardAPIAvailable, copyImageToClipboard }; diff --git a/invokeai/frontend/web/src/services/api/endpoints/images.ts b/invokeai/frontend/web/src/services/api/endpoints/images.ts index 3fa606d4b6..166d00a3db 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/images.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/images.ts @@ -1,20 +1,15 @@ import { EntityState, Update } from '@reduxjs/toolkit'; -import { fetchBaseQuery } from '@reduxjs/toolkit/dist/query'; import { PatchCollection } from '@reduxjs/toolkit/dist/query/core/buildThunks'; +import { logger } from 'app/logging/logger'; import { ASSETS_CATEGORIES, BoardId, IMAGE_CATEGORIES, IMAGE_LIMIT, } from 'features/gallery/store/types'; -import { - ImageMetadataAndWorkflow, - zCoreMetadata, -} from 'features/nodes/types/types'; -import { getMetadataAndWorkflowFromImageBlob } from 'features/nodes/util/getMetadataAndWorkflowFromImageBlob'; +import { CoreMetadata, zCoreMetadata } from 'features/nodes/types/types'; import { keyBy } from 'lodash-es'; import { ApiTagDescription, LIST_TAG, api } from '..'; -import { $authToken, $projectId } from '../client'; import { components, paths } from '../schema'; import { DeleteBoardResult, @@ -23,7 +18,6 @@ import { ListImagesArgs, OffsetPaginatedResults_ImageDTO_, PostUploadAction, - UnsafeImageMetadata, } from '../types'; import { getCategories, @@ -100,14 +94,12 @@ export const imagesApi = api.injectEndpoints({ keepUnusedDataFor: 86400, }), getIntermediatesCount: build.query({ - query: () => ({ url: getListImagesUrl({ is_intermediate: true }) }), + query: () => ({ url: 'images/intermediates' }), providesTags: ['IntermediatesCount'], - transformResponse: (response: OffsetPaginatedResults_ImageDTO_) => { - // TODO: This is storing a primitive value in the cache. `immer` cannot track state changes, so - // attempts to use manual cache updates on this value will fail. This should be changed into an - // object. - return response.total; - }, + }), + clearIntermediates: build.mutation({ + query: () => ({ url: `images/intermediates`, method: 'DELETE' }), + invalidatesTags: ['IntermediatesCount'], }), getImageDTO: build.query({ query: (image_name) => ({ url: `images/i/${image_name}` }), @@ -116,79 +108,26 @@ export const imagesApi = api.injectEndpoints({ ], keepUnusedDataFor: 86400, // 24 hours }), - getImageMetadata: build.query({ + getImageMetadata: build.query({ query: (image_name) => ({ url: `images/i/${image_name}/metadata` }), providesTags: (result, error, image_name) => [ { type: 'ImageMetadata', id: image_name }, ], - keepUnusedDataFor: 86400, // 24 hours - }), - getImageMetadataFromFile: build.query< - ImageMetadataAndWorkflow, - { image: ImageDTO; shouldFetchMetadataFromApi: boolean } - >({ - queryFn: async ( - args: { image: ImageDTO; shouldFetchMetadataFromApi: boolean }, - api, - extraOptions, - fetchWithBaseQuery + transformResponse: ( + response: paths['/api/v1/images/i/{image_name}/metadata']['get']['responses']['200']['content']['application/json'] ) => { - if (args.shouldFetchMetadataFromApi) { - let metadata; - const metadataResponse = await fetchWithBaseQuery( - `images/i/${args.image.image_name}/metadata` - ); - if (metadataResponse.data) { - const metadataResult = zCoreMetadata.safeParse( - // eslint-disable-next-line @typescript-eslint/no-explicit-any - (metadataResponse.data as any)?.metadata - ); - if (metadataResult.success) { - metadata = metadataResult.data; - } + if (response) { + const result = zCoreMetadata.safeParse(response); + if (result.success) { + return result.data; + } else { + logger('images').warn('Problem parsing metadata'); } - return { data: { metadata } }; - } else { - const authToken = $authToken.get(); - const projectId = $projectId.get(); - const customBaseQuery = fetchBaseQuery({ - baseUrl: '', - prepareHeaders: (headers) => { - if (authToken) { - headers.set('Authorization', `Bearer ${authToken}`); - } - if (projectId) { - headers.set('project-id', projectId); - } - - return headers; - }, - responseHandler: async (res) => { - return await res.blob(); - }, - }); - - const response = await customBaseQuery( - args.image.image_url, - api, - extraOptions - ); - const data = await getMetadataAndWorkflowFromImageBlob( - response.data as Blob - ); - - return { data }; } + return; }, - providesTags: (result, error, { image }) => [ - { type: 'ImageMetadataFromFile', id: image.image_name }, - ], keepUnusedDataFor: 86400, // 24 hours }), - clearIntermediates: build.mutation({ - query: () => ({ url: `images/clear-intermediates`, method: 'POST' }), - invalidatesTags: ['IntermediatesCount'], - }), deleteImage: build.mutation({ query: ({ image_name }) => ({ url: `images/i/${image_name}`, @@ -520,7 +459,7 @@ export const imagesApi = api.injectEndpoints({ // assume all images are on the same board/category if (images[0]) { const categories = getCategories(images[0]); - const boardId = images[0].board_id; + const boardId = images[0].board_id ?? undefined; return [ { @@ -637,7 +576,7 @@ export const imagesApi = api.injectEndpoints({ // assume all images are on the same board/category if (images[0]) { const categories = getCategories(images[0]); - const boardId = images[0].board_id; + const boardId = images[0].board_id ?? undefined; return [ { type: 'ImageList', @@ -1635,6 +1574,5 @@ export const { useDeleteBoardMutation, useStarImagesMutation, useUnstarImagesMutation, - useGetImageMetadataFromFileQuery, useBulkDownloadImagesMutation, } = imagesApi; diff --git a/invokeai/frontend/web/src/services/api/endpoints/queue.ts b/invokeai/frontend/web/src/services/api/endpoints/queue.ts index ab75964e89..d44e333850 100644 --- a/invokeai/frontend/web/src/services/api/endpoints/queue.ts +++ b/invokeai/frontend/web/src/services/api/endpoints/queue.ts @@ -83,30 +83,6 @@ export const queueApi = api.injectEndpoints({ } }, }), - enqueueGraph: build.mutation< - paths['/api/v1/queue/{queue_id}/enqueue_graph']['post']['responses']['201']['content']['application/json'], - paths['/api/v1/queue/{queue_id}/enqueue_graph']['post']['requestBody']['content']['application/json'] - >({ - query: (arg) => ({ - url: `queue/${$queueId.get()}/enqueue_graph`, - body: arg, - method: 'POST', - }), - invalidatesTags: [ - 'SessionQueueStatus', - 'CurrentSessionQueueItem', - 'NextSessionQueueItem', - ], - onQueryStarted: async (arg, api) => { - const { dispatch, queryFulfilled } = api; - try { - await queryFulfilled; - resetListQueryData(dispatch); - } catch { - // no-op - } - }, - }), resumeProcessor: build.mutation< paths['/api/v1/queue/{queue_id}/processor/resume']['put']['responses']['200']['content']['application/json'], void @@ -341,7 +317,6 @@ export const queueApi = api.injectEndpoints({ export const { useCancelByBatchIdsMutation, - useEnqueueGraphMutation, useEnqueueBatchMutation, usePauseProcessorMutation, useResumeProcessorMutation, diff --git a/invokeai/frontend/web/src/services/api/endpoints/workflows.ts b/invokeai/frontend/web/src/services/api/endpoints/workflows.ts new file mode 100644 index 0000000000..1792788d57 --- /dev/null +++ b/invokeai/frontend/web/src/services/api/endpoints/workflows.ts @@ -0,0 +1,30 @@ +import { logger } from 'app/logging/logger'; +import { Workflow, zWorkflow } from 'features/nodes/types/types'; +import { api } from '..'; +import { paths } from '../schema'; + +export const workflowsApi = api.injectEndpoints({ + endpoints: (build) => ({ + getWorkflow: build.query({ + query: (workflow_id) => `workflows/i/${workflow_id}`, + providesTags: (result, error, workflow_id) => [ + { type: 'Workflow', id: workflow_id }, + ], + transformResponse: ( + response: paths['/api/v1/workflows/i/{workflow_id}']['get']['responses']['200']['content']['application/json'] + ) => { + if (response) { + const result = zWorkflow.safeParse(response); + if (result.success) { + return result.data; + } else { + logger('images').warn('Problem parsing workflow'); + } + } + return; + }, + }), + }), +}); + +export const { useGetWorkflowQuery } = workflowsApi; diff --git a/invokeai/frontend/web/src/services/api/hooks/useDebouncedMetadata.ts b/invokeai/frontend/web/src/services/api/hooks/useDebouncedMetadata.ts new file mode 100644 index 0000000000..023b3c140c --- /dev/null +++ b/invokeai/frontend/web/src/services/api/hooks/useDebouncedMetadata.ts @@ -0,0 +1,21 @@ +import { skipToken } from '@reduxjs/toolkit/query'; +import { useDebounce } from 'use-debounce'; +import { useGetImageMetadataQuery } from '../endpoints/images'; +import { useAppSelector } from 'app/store/storeHooks'; + +export const useDebouncedMetadata = (imageName?: string | null) => { + const metadataFetchDebounce = useAppSelector( + (state) => state.config.metadataFetchDebounce + ); + + const [debouncedImageName] = useDebounce( + imageName, + metadataFetchDebounce ?? 0 + ); + + const { data: metadata, isLoading } = useGetImageMetadataQuery( + debouncedImageName ?? skipToken + ); + + return { metadata, isLoading }; +}; diff --git a/invokeai/frontend/web/src/services/api/hooks/useDebouncedWorkflow.ts b/invokeai/frontend/web/src/services/api/hooks/useDebouncedWorkflow.ts new file mode 100644 index 0000000000..2731597b2c --- /dev/null +++ b/invokeai/frontend/web/src/services/api/hooks/useDebouncedWorkflow.ts @@ -0,0 +1,21 @@ +import { skipToken } from '@reduxjs/toolkit/query'; +import { useAppSelector } from 'app/store/storeHooks'; +import { useDebounce } from 'use-debounce'; +import { useGetWorkflowQuery } from '../endpoints/workflows'; + +export const useDebouncedWorkflow = (workflowId?: string | null) => { + const workflowFetchDebounce = useAppSelector( + (state) => state.config.workflowFetchDebounce + ); + + const [debouncedWorkflowID] = useDebounce( + workflowId, + workflowFetchDebounce ?? 0 + ); + + const { data: workflow, isLoading } = useGetWorkflowQuery( + debouncedWorkflowID ?? skipToken + ); + + return { workflow, isLoading }; +}; diff --git a/invokeai/frontend/web/src/services/api/index.ts b/invokeai/frontend/web/src/services/api/index.ts index f423b2b0ed..b7595b3d52 100644 --- a/invokeai/frontend/web/src/services/api/index.ts +++ b/invokeai/frontend/web/src/services/api/index.ts @@ -37,6 +37,7 @@ export const tagTypes = [ 'ControlNetModel', 'LoRAModel', 'SDXLRefinerModel', + 'Workflow', ] as const; export type ApiTagDescription = TagDescription<(typeof tagTypes)[number]>; export const LIST_TAG = 'LIST'; diff --git a/invokeai/frontend/web/src/services/api/schema.d.ts b/invokeai/frontend/web/src/services/api/schema.d.ts index d71d01e6af..8ac2e2dcb2 100644 --- a/invokeai/frontend/web/src/services/api/schema.d.ts +++ b/invokeai/frontend/web/src/services/api/schema.d.ts @@ -5,80 +5,13 @@ export type paths = { - "/api/v1/sessions/": { - /** - * List Sessions - * @deprecated - * @description Gets a list of sessions, optionally searching - */ - get: operations["list_sessions"]; - /** - * Create Session - * @deprecated - * @description Creates a new session, optionally initializing it with an invocation graph - */ - post: operations["create_session"]; - }; "/api/v1/sessions/{session_id}": { /** * Get Session - * @deprecated * @description Gets a session */ get: operations["get_session"]; }; - "/api/v1/sessions/{session_id}/nodes": { - /** - * Add Node - * @deprecated - * @description Adds a node to the graph - */ - post: operations["add_node"]; - }; - "/api/v1/sessions/{session_id}/nodes/{node_path}": { - /** - * Update Node - * @deprecated - * @description Updates a node in the graph and removes all linked edges - */ - put: operations["update_node"]; - /** - * Delete Node - * @deprecated - * @description Deletes a node in the graph and removes all linked edges - */ - delete: operations["delete_node"]; - }; - "/api/v1/sessions/{session_id}/edges": { - /** - * Add Edge - * @deprecated - * @description Adds an edge to the graph - */ - post: operations["add_edge"]; - }; - "/api/v1/sessions/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}": { - /** - * Delete Edge - * @deprecated - * @description Deletes an edge from the graph - */ - delete: operations["delete_edge"]; - }; - "/api/v1/sessions/{session_id}/invoke": { - /** - * Invoke Session - * @deprecated - * @description Invokes a session - */ - put: operations["invoke_session"]; - /** - * Cancel Session Invoke - * @deprecated - * @description Invokes a session - */ - delete: operations["cancel_session_invoke"]; - }; "/api/v1/utilities/dynamicprompts": { /** * Parse Dynamicprompts @@ -176,12 +109,17 @@ export type paths = { */ patch: operations["update_image"]; }; - "/api/v1/images/clear-intermediates": { + "/api/v1/images/intermediates": { + /** + * Get Intermediates Count + * @description Gets the count of intermediate images + */ + get: operations["get_intermediates_count"]; /** * Clear Intermediates * @description Clears all intermediates */ - post: operations["clear_intermediates"]; + delete: operations["clear_intermediates"]; }; "/api/v1/images/i/{image_name}/metadata": { /** @@ -349,13 +287,6 @@ export type paths = { */ get: operations["get_invocation_cache_status"]; }; - "/api/v1/queue/{queue_id}/enqueue_graph": { - /** - * Enqueue Graph - * @description Enqueues a graph for single execution. - */ - post: operations["enqueue_graph"]; - }; "/api/v1/queue/{queue_id}/enqueue_batch": { /** * Enqueue Batch @@ -447,6 +378,13 @@ export type paths = { */ put: operations["cancel_queue_item"]; }; + "/api/v1/workflows/i/{workflow_id}": { + /** + * Get Workflow + * @description Gets a workflow + */ + get: operations["get_workflow"]; + }; }; export type webhooks = Record; @@ -482,11 +420,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -506,9 +439,9 @@ export type components = { */ b?: number; /** - * Type + * type * @default add - * @enum {string} + * @constant */ type: "add"; }; @@ -551,7 +484,6 @@ export type components = { }; /** * BaseModelType - * @description An enumeration. * @enum {string} */ BaseModelType: "any" | "sd-1" | "sd-2" | "sdxl" | "sdxl-refiner"; @@ -566,11 +498,8 @@ export type components = { * Data * @description The batch data collection. */ - data?: components["schemas"]["BatchDatum"][][]; - /** - * Graph - * @description The graph to initialize the session with - */ + data?: components["schemas"]["BatchDatum"][][] | null; + /** @description The graph to initialize the session with */ graph: components["schemas"]["Graph"]; /** * Runs @@ -645,6 +574,10 @@ export type components = { * @description Creates a blank image and forwards it to the pipeline */ BlankImageInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -656,11 +589,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -687,20 +615,19 @@ export type components = { */ mode?: "RGB" | "RGBA"; /** - * Color * @description The color of the image * @default { - * "r": 0, - * "g": 0, + * "a": 255, * "b": 0, - * "a": 255 + * "g": 0, + * "r": 0 * } */ color?: components["schemas"]["ColorField"]; /** - * Type + * type * @default blank_image - * @enum {string} + * @constant */ type: "blank_image"; }; @@ -720,26 +647,15 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Latents A - * @description Latents tensor - */ + /** @description Latents tensor */ latents_a?: components["schemas"]["LatentsField"]; - /** - * Latents B - * @description Latents tensor - */ + /** @description Latents tensor */ latents_b?: components["schemas"]["LatentsField"]; /** * Alpha @@ -748,9 +664,9 @@ export type components = { */ alpha?: number; /** - * Type + * type * @default lblend - * @enum {string} + * @constant */ type: "lblend"; }; @@ -760,12 +676,12 @@ export type components = { * Board Name * @description The board's new name. */ - board_name?: string; + board_name?: string | null; /** * Cover Image Name * @description The name of the board's new cover image. */ - cover_image_name?: string; + cover_image_name?: string | null; }; /** * BoardDTO @@ -796,12 +712,12 @@ export type components = { * Deleted At * @description The deleted timestamp of the board. */ - deleted_at?: string; + deleted_at?: string | null; /** * Cover Image Name * @description The name of the board's cover image. */ - cover_image_name?: string; + cover_image_name: string | null; /** * Image Count * @description The number of images in the board. @@ -872,14 +788,11 @@ export type components = { * Board Id * @description The board from which image should be downloaded from */ - board_id?: string; + board_id?: string | null; }; /** Body_enqueue_batch */ Body_enqueue_batch: { - /** - * Batch - * @description Batch to process - */ + /** @description Batch to process */ batch: components["schemas"]["Batch"]; /** * Prepend @@ -888,20 +801,6 @@ export type components = { */ prepend?: boolean; }; - /** Body_enqueue_graph */ - Body_enqueue_graph: { - /** - * Graph - * @description The graph to enqueue - */ - graph: components["schemas"]["Graph"]; - /** - * Prepend - * @description Whether or not to prepend this batch in the queue - * @default false - */ - prepend?: boolean; - }; /** Body_import_model */ Body_import_model: { /** @@ -912,41 +811,13 @@ export type components = { /** * Prediction Type * @description Prediction type for SDv2 checkpoints and rare SDv1 checkpoints - * @enum {string} */ - prediction_type?: "v_prediction" | "epsilon" | "sample"; + prediction_type?: ("v_prediction" | "epsilon" | "sample") | null; }; /** Body_merge_models */ Body_merge_models: { - /** - * Model Names - * @description model name - */ - model_names: string[]; - /** - * Merged Model Name - * @description Name of destination model - */ - merged_model_name: string; - /** - * Alpha - * @description Alpha weighting strength to apply to 2d and 3d models - * @default 0.5 - */ - alpha?: number; - /** @description Interpolation method */ - interp: components["schemas"]["MergeInterpolationMethod"]; - /** - * Force - * @description Force merging of models created with different versions of diffusers - * @default false - */ - force?: boolean; - /** - * Merge Dest Directory - * @description Save the merged model to the designated directory (with 'merged_model_name' appended) - */ - merge_dest_directory?: string; + /** @description Model configuration */ + body: components["schemas"]["MergeModelsBody"]; }; /** Body_parse_dynamicprompts */ Body_parse_dynamicprompts: { @@ -1024,11 +895,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1041,9 +907,9 @@ export type components = { */ collection?: boolean[]; /** - * Type + * type * @default boolean_collection - * @enum {string} + * @constant */ type: "boolean_collection"; }; @@ -1058,9 +924,9 @@ export type components = { */ collection: boolean[]; /** - * Type + * type * @default boolean_collection_output - * @enum {string} + * @constant */ type: "boolean_collection_output"; }; @@ -1080,11 +946,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1098,9 +959,9 @@ export type components = { */ value?: boolean; /** - * Type + * type * @default boolean - * @enum {string} + * @constant */ type: "boolean"; }; @@ -1115,9 +976,9 @@ export type components = { */ value: boolean; /** - * Type + * type * @default boolean_output - * @enum {string} + * @constant */ type: "boolean_output"; }; @@ -1128,19 +989,20 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default clip_vision + * @constant */ model_type: "clip_vision"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "diffusers"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; }; /** CLIPVisionModelField */ CLIPVisionModelField: { @@ -1157,6 +1019,10 @@ export type components = { * @description Infills transparent areas of an image using OpenCV Inpainting */ CV2InfillInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -1168,26 +1034,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to infill - */ + /** @description The image to infill */ image?: components["schemas"]["ImageField"]; /** - * Type + * type * @default infill_cv2 - * @enum {string} + * @constant */ type: "infill_cv2"; }; @@ -1207,6 +1065,10 @@ export type components = { * @description Canny edge detection for ControlNet */ CannyImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -1218,28 +1080,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default canny_image_processor - * @enum {string} - */ - type: "canny_image_processor"; /** * Low Threshold * @description The low threshold of the Canny pixel gradient (0-255) @@ -1252,6 +1100,12 @@ export type components = { * @default 200 */ high_threshold?: number; + /** + * type + * @default canny_image_processor + * @constant + */ + type: "canny_image_processor"; }; /** * ClearResult @@ -1266,15 +1120,9 @@ export type components = { }; /** ClipField */ ClipField: { - /** - * Tokenizer - * @description Info to load tokenizer submodel - */ + /** @description Info to load tokenizer submodel */ tokenizer: components["schemas"]["ModelInfo"]; - /** - * Text Encoder - * @description Info to load text_encoder submodel - */ + /** @description Info to load text_encoder submodel */ text_encoder: components["schemas"]["ModelInfo"]; /** * Skipped Layers @@ -1303,11 +1151,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1326,9 +1169,9 @@ export type components = { */ skipped_layers?: number; /** - * Type + * type * @default clip_skip - * @enum {string} + * @constant */ type: "clip_skip"; }; @@ -1341,11 +1184,11 @@ export type components = { * CLIP * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip?: components["schemas"]["ClipField"]; + clip?: components["schemas"]["ClipField"] | null; /** - * Type + * type * @default clip_skip_output - * @enum {string} + * @constant */ type: "clip_skip_output"; }; @@ -1365,11 +1208,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1387,18 +1225,13 @@ export type components = { */ collection?: unknown[]; /** - * Type + * type * @default collect - * @enum {string} + * @constant */ type: "collect"; }; - /** - * CollectInvocationOutput - * @description Base class for all invocation outputs. - * - * All invocation outputs must use the `@invocation_output` decorator to provide their unique type. - */ + /** CollectInvocationOutput */ CollectInvocationOutput: { /** * Collection @@ -1406,9 +1239,9 @@ export type components = { */ collection: unknown[]; /** - * Type + * type * @default collect_output - * @enum {string} + * @constant */ type: "collect_output"; }; @@ -1423,9 +1256,9 @@ export type components = { */ collection: components["schemas"]["ColorField"][]; /** - * Type + * type * @default color_collection_output - * @enum {string} + * @constant */ type: "color_collection_output"; }; @@ -1435,6 +1268,10 @@ export type components = { * using a mask to only color-correct certain regions of the target image. */ ColorCorrectInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -1446,32 +1283,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to color-correct - */ + /** @description The image to color-correct */ image?: components["schemas"]["ImageField"]; - /** - * Reference - * @description Reference image for color-correction - */ + /** @description Reference image for color-correction */ reference?: components["schemas"]["ImageField"]; - /** - * Mask - * @description Mask to use when applying color-correction - */ - mask?: components["schemas"]["ImageField"]; + /** @description Mask to use when applying color-correction */ + mask?: components["schemas"]["ImageField"] | null; /** * Mask Blur Radius * @description Mask blur radius @@ -1479,9 +1302,9 @@ export type components = { */ mask_blur_radius?: number; /** - * Type + * type * @default color_correct - * @enum {string} + * @constant */ type: "color_correct"; }; @@ -1527,11 +1350,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1539,20 +1357,19 @@ export type components = { */ use_cache?: boolean; /** - * Color * @description The color value * @default { - * "r": 0, - * "g": 0, + * "a": 255, * "b": 0, - * "a": 255 + * "g": 0, + * "r": 0 * } */ color?: components["schemas"]["ColorField"]; /** - * Type + * type * @default color - * @enum {string} + * @constant */ type: "color"; }; @@ -1561,6 +1378,10 @@ export type components = { * @description Generates a color map from the provided image */ ColorMapImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -1572,49 +1393,38 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default color_map_image_processor - * @enum {string} - */ - type: "color_map_image_processor"; /** * Color Map Tile Size * @description Tile size * @default 64 */ color_map_tile_size?: number; + /** + * type + * @default color_map_image_processor + * @constant + */ + type: "color_map_image_processor"; }; /** * ColorOutput * @description Base class for nodes that output a single color */ ColorOutput: { - /** - * Color - * @description The output color - */ + /** @description The output color */ color: components["schemas"]["ColorField"]; /** - * Type + * type * @default color_output - * @enum {string} + * @constant */ type: "color_output"; }; @@ -1634,11 +1444,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1651,17 +1456,17 @@ export type components = { * @default */ prompt?: string; - /** - * Type - * @default compel - * @enum {string} - */ - type: "compel"; /** * CLIP * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ clip?: components["schemas"]["ClipField"]; + /** + * type + * @default compel + * @constant + */ + type: "compel"; }; /** * Conditioning Collection Primitive @@ -1679,11 +1484,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -1696,9 +1496,9 @@ export type components = { */ collection?: components["schemas"]["ConditioningField"][]; /** - * Type + * type * @default conditioning_collection - * @enum {string} + * @constant */ type: "conditioning_collection"; }; @@ -1713,9 +1513,9 @@ export type components = { */ collection: components["schemas"]["ConditioningField"][]; /** - * Type + * type * @default conditioning_collection_output - * @enum {string} + * @constant */ type: "conditioning_collection_output"; }; @@ -1746,26 +1546,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Conditioning - * @description Conditioning tensor - */ + /** @description Conditioning tensor */ conditioning?: components["schemas"]["ConditioningField"]; /** - * Type + * type * @default conditioning - * @enum {string} + * @constant */ type: "conditioning"; }; @@ -1774,15 +1566,12 @@ export type components = { * @description Base class for nodes that output a single conditioning tensor */ ConditioningOutput: { - /** - * Conditioning - * @description Conditioning tensor - */ + /** @description Conditioning tensor */ conditioning: components["schemas"]["ConditioningField"]; /** - * Type + * type * @default conditioning_output - * @enum {string} + * @constant */ type: "conditioning_output"; }; @@ -1791,6 +1580,10 @@ export type components = { * @description Applies content shuffle processing to image */ ContentShuffleImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -1802,28 +1595,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default content_shuffle_image_processor - * @enum {string} - */ - type: "content_shuffle_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -1854,18 +1633,18 @@ export type components = { * @default 256 */ f?: number; + /** + * type + * @default content_shuffle_image_processor + * @constant + */ + type: "content_shuffle_image_processor"; }; /** ControlField */ ControlField: { - /** - * Image - * @description The control image - */ + /** @description The control image */ image: components["schemas"]["ImageField"]; - /** - * Control Model - * @description The ControlNet model to use - */ + /** @description The ControlNet model to use */ control_model: components["schemas"]["ControlNetModelField"]; /** * Control Weight @@ -1916,26 +1695,15 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The control image - */ + /** @description The control image */ image?: components["schemas"]["ImageField"]; - /** - * Control Model - * @description ControlNet model to load - */ + /** @description ControlNet model to load */ control_model: components["schemas"]["ControlNetModelField"]; /** * Control Weight @@ -1970,9 +1738,9 @@ export type components = { */ resize_mode?: "just_resize" | "crop_resize" | "fill_resize" | "just_resize_simple"; /** - * Type + * type * @default controlnet - * @enum {string} + * @constant */ type: "controlnet"; }; @@ -1983,19 +1751,20 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default controlnet + * @constant */ model_type: "controlnet"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "checkpoint"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Config */ config: string; }; @@ -2006,19 +1775,20 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default controlnet + * @constant */ model_type: "controlnet"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "diffusers"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; }; /** * ControlNetModelField @@ -2038,174 +1808,200 @@ export type components = { * @description node output for ControlNet info */ ControlOutput: { - /** - * Control - * @description ControlNet(s) to apply - */ + /** @description ControlNet(s) to apply */ control: components["schemas"]["ControlField"]; /** - * Type + * type * @default control_output - * @enum {string} + * @constant */ type: "control_output"; }; /** - * CoreMetadata - * @description Core generation metadata for an image generated in InvokeAI. + * Core Metadata + * @description Collects core generation metadata into a MetadataField */ - CoreMetadata: { + CoreMetadataInvocation: { /** - * App Version - * @description The version of InvokeAI used to generate this image - * @default 3.2.0 + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. */ - app_version?: string; + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; /** * Generation Mode * @description The generation mode that output this image */ - generation_mode: string; - /** - * Created By - * @description The name of the creator of the image - */ - created_by?: string; + generation_mode?: ("txt2img" | "img2img" | "inpaint" | "outpaint" | "sdxl_txt2img" | "sdxl_img2img" | "sdxl_inpaint" | "sdxl_outpaint") | null; /** * Positive Prompt * @description The positive prompt parameter */ - positive_prompt: string; + positive_prompt?: string | null; /** * Negative Prompt * @description The negative prompt parameter */ - negative_prompt: string; + negative_prompt?: string | null; /** * Width * @description The width parameter */ - width: number; + width?: number | null; /** * Height * @description The height parameter */ - height: number; + height?: number | null; /** * Seed * @description The seed used for noise generation */ - seed: number; + seed?: number | null; /** * Rand Device * @description The device used for random number generation */ - rand_device: string; + rand_device?: string | null; /** * Cfg Scale * @description The classifier-free guidance scale parameter */ - cfg_scale: number; + cfg_scale?: number | null; /** * Steps * @description The number of steps used for inference */ - steps: number; + steps?: number | null; /** * Scheduler * @description The scheduler used for inference */ - scheduler: string; + scheduler?: string | null; + /** + * Seamless X + * @description Whether seamless tiling was used on the X axis + */ + seamless_x?: boolean | null; + /** + * Seamless Y + * @description Whether seamless tiling was used on the Y axis + */ + seamless_y?: boolean | null; /** * Clip Skip * @description The number of skipped CLIP layers */ - clip_skip?: number; - /** - * Model - * @description The main model used for inference - */ - model: components["schemas"]["MainModelField"]; + clip_skip?: number | null; + /** @description The main model used for inference */ + model?: components["schemas"]["MainModelField"] | null; /** * Controlnets * @description The ControlNets used for inference */ - controlnets: components["schemas"]["ControlField"][]; + controlnets?: components["schemas"]["ControlField"][] | null; /** * Ipadapters * @description The IP Adapters used for inference */ - ipAdapters: components["schemas"]["IPAdapterMetadataField"][]; + ipAdapters?: components["schemas"]["IPAdapterMetadataField"][] | null; /** * T2Iadapters * @description The IP Adapters used for inference */ - t2iAdapters: components["schemas"]["T2IAdapterField"][]; + t2iAdapters?: components["schemas"]["T2IAdapterField"][] | null; /** * Loras * @description The LoRAs used for inference */ - loras: components["schemas"]["LoRAMetadataField"][]; - /** - * Vae - * @description The VAE used for decoding, if the main model's default was not used - */ - vae?: components["schemas"]["VAEModelField"]; + loras?: components["schemas"]["LoRAMetadataField"][] | null; /** * Strength * @description The strength used for latents-to-latents */ - strength?: number; + strength?: number | null; /** * Init Image * @description The name of the initial image */ - init_image?: string; + init_image?: string | null; + /** @description The VAE used for decoding, if the main model's default was not used */ + vae?: components["schemas"]["VAEModelField"] | null; + /** + * Hrf Width + * @description The high resolution fix height and width multipler. + */ + hrf_width?: number | null; + /** + * Hrf Height + * @description The high resolution fix height and width multipler. + */ + hrf_height?: number | null; + /** + * Hrf Strength + * @description The high resolution fix img2img strength used in the upscale pass. + */ + hrf_strength?: number | null; /** * Positive Style Prompt * @description The positive style prompt parameter */ - positive_style_prompt?: string; + positive_style_prompt?: string | null; /** * Negative Style Prompt * @description The negative style prompt parameter */ - negative_style_prompt?: string; - /** - * Refiner Model - * @description The SDXL Refiner model used - */ - refiner_model?: components["schemas"]["MainModelField"]; + negative_style_prompt?: string | null; + /** @description The SDXL Refiner model used */ + refiner_model?: components["schemas"]["MainModelField"] | null; /** * Refiner Cfg Scale * @description The classifier-free guidance scale parameter used for the refiner */ - refiner_cfg_scale?: number; + refiner_cfg_scale?: number | null; /** * Refiner Steps * @description The number of steps used for the refiner */ - refiner_steps?: number; + refiner_steps?: number | null; /** * Refiner Scheduler * @description The scheduler used for the refiner */ - refiner_scheduler?: string; + refiner_scheduler?: string | null; /** * Refiner Positive Aesthetic Score * @description The aesthetic score used for the refiner */ - refiner_positive_aesthetic_score?: number; + refiner_positive_aesthetic_score?: number | null; /** * Refiner Negative Aesthetic Score * @description The aesthetic score used for the refiner */ - refiner_negative_aesthetic_score?: number; + refiner_negative_aesthetic_score?: number | null; /** * Refiner Start * @description The start value used for refiner denoising */ - refiner_start?: number; + refiner_start?: number | null; + /** + * type + * @default core_metadata + * @constant + */ + type: "core_metadata"; + [key: string]: unknown; }; /** * Create Denoise Mask @@ -2223,31 +2019,17 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Vae - * @description VAE - */ + /** @description VAE */ vae?: components["schemas"]["VaeField"]; - /** - * Image - * @description Image which will be masked - */ - image?: components["schemas"]["ImageField"]; - /** - * Mask - * @description The mask to use when pasting - */ + /** @description Image which will be masked */ + image?: components["schemas"]["ImageField"] | null; + /** @description The mask to use when pasting */ mask?: components["schemas"]["ImageField"]; /** * Tiled @@ -2262,17 +2044,13 @@ export type components = { */ fp32?: boolean; /** - * Type + * type * @default create_denoise_mask - * @enum {string} + * @constant */ type: "create_denoise_mask"; }; - /** - * CursorPaginatedResults[SessionQueueItemDTO] - * @description Cursor-paginated results - * Generic must be a Pydantic model - */ + /** CursorPaginatedResults[SessionQueueItemDTO] */ CursorPaginatedResults_SessionQueueItemDTO_: { /** * Limit @@ -2295,6 +2073,10 @@ export type components = { * @description Simple inpaint using opencv. */ CvInpaintInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -2306,31 +2088,20 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to inpaint - */ + /** @description The image to inpaint */ image?: components["schemas"]["ImageField"]; - /** - * Mask - * @description The mask to use when inpainting - */ + /** @description The mask to use when inpainting */ mask?: components["schemas"]["ImageField"]; /** - * Type + * type * @default cv_inpaint - * @enum {string} + * @constant */ type: "cv_inpaint"; }; @@ -2373,22 +2144,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Noise - * @description Noise tensor - */ - noise?: components["schemas"]["LatentsField"]; + /** @description Positive conditioning tensor */ + positive_conditioning?: components["schemas"]["ConditioningField"]; + /** @description Negative conditioning tensor */ + negative_conditioning?: components["schemas"]["ConditioningField"]; + /** @description Noise tensor */ + noise?: components["schemas"]["LatentsField"] | null; /** * Steps * @description Number of steps to run @@ -2420,49 +2187,33 @@ export type components = { * @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"; - /** Control */ - control?: components["schemas"]["ControlField"] | components["schemas"]["ControlField"][]; - /** - * IP-Adapter - * @description IP-Adapter to apply - */ - ip_adapter?: components["schemas"]["IPAdapterField"] | components["schemas"]["IPAdapterField"][]; - /** - * T2I-Adapter - * @description T2I-Adapter(s) to apply - */ - t2i_adapter?: components["schemas"]["T2IAdapterField"] | components["schemas"]["T2IAdapterField"][]; - /** - * Latents - * @description Latents tensor - */ - latents?: components["schemas"]["LatentsField"]; - /** - * Denoise Mask - * @description The mask to use for the operation - */ - denoise_mask?: components["schemas"]["DenoiseMaskField"]; - /** - * Type - * @default denoise_latents - * @enum {string} - */ - type: "denoise_latents"; - /** - * Positive Conditioning - * @description Positive conditioning tensor - */ - positive_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Negative Conditioning - * @description Negative conditioning tensor - */ - negative_conditioning?: components["schemas"]["ConditioningField"]; /** * UNet * @description UNet (scheduler, LoRAs) */ unet?: components["schemas"]["UNetField"]; + /** Control */ + control?: components["schemas"]["ControlField"] | components["schemas"]["ControlField"][] | null; + /** + * IP-Adapter + * @description IP-Adapter to apply + */ + ip_adapter?: components["schemas"]["IPAdapterField"] | components["schemas"]["IPAdapterField"][] | null; + /** + * T2I-Adapter + * @description T2I-Adapter(s) to apply + */ + t2i_adapter?: components["schemas"]["T2IAdapterField"] | components["schemas"]["T2IAdapterField"][] | null; + /** @description Latents tensor */ + latents?: components["schemas"]["LatentsField"] | null; + /** @description The mask to use for the operation */ + denoise_mask?: components["schemas"]["DenoiseMaskField"] | null; + /** + * type + * @default denoise_latents + * @constant + */ + type: "denoise_latents"; }; /** * DenoiseMaskField @@ -2478,22 +2229,19 @@ export type components = { * Masked Latents Name * @description The name of the masked image latents */ - masked_latents_name?: string; + masked_latents_name: string | null; }; /** * DenoiseMaskOutput * @description Base class for nodes that output a single image */ DenoiseMaskOutput: { - /** - * Denoise Mask - * @description Mask for denoise model run - */ + /** @description Mask for denoise model run */ denoise_mask: components["schemas"]["DenoiseMaskField"]; /** - * Type + * type * @default denoise_mask_output - * @enum {string} + * @constant */ type: "denoise_mask_output"; }; @@ -2513,11 +2261,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -2537,9 +2280,9 @@ export type components = { */ b?: number; /** - * Type + * type * @default div - * @enum {string} + * @constant */ type: "div"; }; @@ -2559,11 +2302,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -2588,9 +2326,9 @@ export type components = { */ combinatorial?: boolean; /** - * Type + * type * @default dynamic_prompt - * @enum {string} + * @constant */ type: "dynamic_prompt"; }; @@ -2599,13 +2337,17 @@ export type components = { /** Prompts */ prompts: string[]; /** Error */ - error?: string; + error?: string | null; }; /** * Upscale (RealESRGAN) * @description Upscales an image using RealESRGAN. */ ESRGANInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -2617,21 +2359,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The input image - */ + /** @description The input image */ image?: components["schemas"]["ImageField"]; /** * Model Name @@ -2647,23 +2381,17 @@ export type components = { */ tile_size?: number; /** - * Type + * type * @default esrgan - * @enum {string} + * @constant */ type: "esrgan"; }; /** Edge */ Edge: { - /** - * Source - * @description The connection for the edge's from node and field - */ + /** @description The connection for the edge's from node and field */ source: components["schemas"]["EdgeConnection"]; - /** - * Destination - * @description The connection for the edge's to node and field - */ + /** @description The connection for the edge's to node and field */ destination: components["schemas"]["EdgeConnection"]; }; /** EdgeConnection */ @@ -2696,10 +2424,7 @@ export type components = { * @description The total number of queue items requested to be enqueued */ requested: number; - /** - * Batch - * @description The batch that was enqueued - */ + /** @description The batch that was enqueued */ batch: components["schemas"]["Batch"]; /** * Priority @@ -2707,39 +2432,15 @@ export type components = { */ priority: number; }; - /** EnqueueGraphResult */ - EnqueueGraphResult: { - /** - * Enqueued - * @description The total number of queue items enqueued - */ - enqueued: number; - /** - * Requested - * @description The total number of queue items requested to be enqueued - */ - requested: number; - /** - * Batch - * @description The batch that was enqueued - */ - batch: components["schemas"]["Batch"]; - /** - * Priority - * @description The priority of the enqueued batch - */ - priority: number; - /** - * Queue Item - * @description The queue item that was enqueued - */ - queue_item: components["schemas"]["SessionQueueItemDTO"]; - }; /** * FaceIdentifier * @description Outputs an image with detected face IDs printed on each face. For use with other FaceTools. */ FaceIdentifierInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -2751,21 +2452,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description Image to face detect - */ + /** @description Image to face detect */ image?: components["schemas"]["ImageField"]; /** * Minimum Confidence @@ -2780,9 +2473,9 @@ export type components = { */ chunk?: boolean; /** - * Type + * type * @default face_identifier - * @enum {string} + * @constant */ type: "face_identifier"; }; @@ -2791,6 +2484,10 @@ export type components = { * @description Face mask creation using mediapipe face detection */ FaceMaskInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -2802,21 +2499,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description Image to face detect - */ + /** @description Image to face detect */ image?: components["schemas"]["ImageField"]; /** * Face Ids @@ -2855,9 +2544,9 @@ export type components = { */ invert_mask?: boolean; /** - * Type + * type * @default face_mask_detection - * @enum {string} + * @constant */ type: "face_mask_detection"; }; @@ -2866,10 +2555,7 @@ export type components = { * @description Base class for FaceMask output */ FaceMaskOutput: { - /** - * Image - * @description The output image - */ + /** @description The output image */ image: components["schemas"]["ImageField"]; /** * Width @@ -2882,15 +2568,12 @@ export type components = { */ height: number; /** - * Type + * type * @default face_mask_output - * @enum {string} + * @constant */ type: "face_mask_output"; - /** - * Mask - * @description The output mask - */ + /** @description The output mask */ mask: components["schemas"]["ImageField"]; }; /** @@ -2898,6 +2581,10 @@ export type components = { * @description Bound, extract, and mask a face from an image using MediaPipe detection */ FaceOffInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -2909,21 +2596,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description Image for face detection - */ + /** @description Image for face detection */ image?: components["schemas"]["ImageField"]; /** * Face Id @@ -2962,9 +2641,9 @@ export type components = { */ chunk?: boolean; /** - * Type + * type * @default face_off - * @enum {string} + * @constant */ type: "face_off"; }; @@ -2973,10 +2652,7 @@ export type components = { * @description Base class for FaceOff Output */ FaceOffOutput: { - /** - * Image - * @description The output image - */ + /** @description The output image */ image: components["schemas"]["ImageField"]; /** * Width @@ -2989,15 +2665,12 @@ export type components = { */ height: number; /** - * Type + * type * @default face_off_output - * @enum {string} + * @constant */ type: "face_off_output"; - /** - * Mask - * @description The output mask - */ + /** @description The output mask */ mask: components["schemas"]["ImageField"]; /** * X @@ -3026,11 +2699,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3043,9 +2711,9 @@ export type components = { */ collection?: number[]; /** - * Type + * type * @default float_collection - * @enum {string} + * @constant */ type: "float_collection"; }; @@ -3060,9 +2728,9 @@ export type components = { */ collection: number[]; /** - * Type + * type * @default float_collection_output - * @enum {string} + * @constant */ type: "float_collection_output"; }; @@ -3082,11 +2750,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3100,9 +2763,9 @@ export type components = { */ value?: number; /** - * Type + * type * @default float - * @enum {string} + * @constant */ type: "float"; }; @@ -3122,11 +2785,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3152,9 +2810,9 @@ export type components = { */ steps?: number; /** - * Type + * type * @default float_range - * @enum {string} + * @constant */ type: "float_range"; }; @@ -3174,11 +2832,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3205,9 +2858,9 @@ export type components = { */ b?: number; /** - * Type + * type * @default float_math - * @enum {string} + * @constant */ type: "float_math"; }; @@ -3222,9 +2875,9 @@ export type components = { */ value: number; /** - * Type + * type * @default float_output - * @enum {string} + * @constant */ type: "float_output"; }; @@ -3244,11 +2897,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3275,9 +2923,9 @@ export type components = { */ method?: "Nearest" | "Floor" | "Ceiling" | "Truncate"; /** - * Type + * type * @default float_to_int - * @enum {string} + * @constant */ type: "float_to_int"; }; @@ -3293,7 +2941,7 @@ export type components = { * @description The nodes in this graph */ nodes?: { - [key: string]: components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["BlankImageInvocation"] | 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"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["CV2InfillInvocation"] | 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"]["ColorMapImageProcessorInvocation"]; + [key: string]: components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["ColorCorrectInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["MaskEdgeInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["MergeMetadataInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["CoreMetadataInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["ColorMapImageProcessorInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["MetadataInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["TestInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["ImageChannelOffsetInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["TestInvocation3"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["MetadataItemInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["BlankImageInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["BooleanInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["TestInvocation2"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["MaskCombineInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["CV2InfillInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["StringReplaceInvocation"]; }; /** * Edges @@ -3311,15 +2959,9 @@ export type components = { * @description The id of the execution state */ id: string; - /** - * Graph - * @description The graph being executed - */ + /** @description The graph being executed */ graph: components["schemas"]["Graph"]; - /** - * Execution Graph - * @description The expanded graph of activated and executed nodes - */ + /** @description The expanded graph of activated and executed nodes */ execution_graph: components["schemas"]["Graph"]; /** * Executed @@ -3336,7 +2978,7 @@ export type components = { * @description The results of node executions */ results: { - [key: string]: components["schemas"]["BooleanOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["ImageOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["String2Output"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["FaceOffOutput"]; + [key: string]: components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["StringPosNegOutput"] | components["schemas"]["T2IAdapterOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["MetadataItemOutput"] | components["schemas"]["MetadataOutput"] | components["schemas"]["IPAdapterOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["IntegerCollectionOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["ConditioningOutput"] | components["schemas"]["DenoiseMaskOutput"] | components["schemas"]["StringCollectionOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["SchedulerOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["ColorCollectionOutput"] | components["schemas"]["BooleanCollectionOutput"] | components["schemas"]["LatentsCollectionOutput"] | components["schemas"]["IntegerOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["ColorOutput"] | components["schemas"]["BooleanOutput"] | components["schemas"]["SeamlessModeOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["CollectInvocationOutput"] | components["schemas"]["ConditioningCollectionOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["FaceMaskOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["FaceOffOutput"] | components["schemas"]["String2Output"] | components["schemas"]["ImageOutput"]; }; /** * Errors @@ -3376,40 +3018,27 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Graph - * @description The graph to run - */ + /** @description The graph to run */ graph?: components["schemas"]["Graph"]; /** - * Type + * type * @default graph - * @enum {string} + * @constant */ type: "graph"; }; - /** - * GraphInvocationOutput - * @description Base class for all invocation outputs. - * - * All invocation outputs must use the `@invocation_output` decorator to provide their unique type. - */ + /** GraphInvocationOutput */ GraphInvocationOutput: { /** - * Type + * type * @default graph_output - * @enum {string} + * @constant */ type: "graph_output"; }; @@ -3423,6 +3052,10 @@ export type components = { * @description Applies HED edge detection to image */ HedImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3434,28 +3067,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default hed_image_processor - * @enum {string} - */ - type: "hed_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -3474,23 +3093,23 @@ export type components = { * @default false */ scribble?: boolean; + /** + * type + * @default hed_image_processor + * @constant + */ + type: "hed_image_processor"; }; /** IPAdapterField */ IPAdapterField: { /** * Image - * @description The IP-Adapter image prompt. - */ - image: components["schemas"]["ImageField"]; - /** - * Ip Adapter Model - * @description The IP-Adapter model to use. + * @description The IP-Adapter image prompt(s). */ + image: components["schemas"]["ImageField"] | components["schemas"]["ImageField"][]; + /** @description The IP-Adapter model to use. */ ip_adapter_model: components["schemas"]["IPAdapterModelField"]; - /** - * Image Encoder Model - * @description The name of the CLIP image encoder model. - */ + /** @description The name of the CLIP image encoder model. */ image_encoder_model: components["schemas"]["CLIPVisionModelField"]; /** * Weight @@ -3527,11 +3146,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3540,9 +3154,9 @@ export type components = { use_cache?: boolean; /** * Image - * @description The IP-Adapter image prompt. + * @description The IP-Adapter image prompt(s). */ - image?: components["schemas"]["ImageField"]; + image?: components["schemas"]["ImageField"] | components["schemas"]["ImageField"][]; /** * IP-Adapter Model * @description The IP-Adapter model. @@ -3567,41 +3181,36 @@ export type components = { */ end_step_percent?: number; /** - * Type + * type * @default ip_adapter - * @enum {string} + * @constant */ type: "ip_adapter"; }; - /** IPAdapterMetadataField */ + /** + * IPAdapterMetadataField + * @description IP Adapter Field, minus the CLIP Vision Encoder model + */ IPAdapterMetadataField: { - /** - * Image - * @description The IP-Adapter image prompt. - */ + /** @description The IP-Adapter image prompt. */ image: components["schemas"]["ImageField"]; - /** - * Ip Adapter Model - * @description The IP-Adapter model to use. - */ + /** @description The IP-Adapter model. */ ip_adapter_model: components["schemas"]["IPAdapterModelField"]; /** * Weight - * @description The weight of the IP-Adapter model + * @description The weight given to the IP-Adapter */ - weight: number; + weight: number | number[]; /** * Begin Step Percent * @description When the IP-Adapter is first applied (% of total steps) - * @default 0 */ - begin_step_percent?: number; + begin_step_percent: number; /** * End Step Percent * @description When the IP-Adapter is last applied (% of total steps) - * @default 1 */ - end_step_percent?: number; + end_step_percent: number; }; /** IPAdapterModelField */ IPAdapterModelField: { @@ -3620,26 +3229,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default ip_adapter + * @constant */ model_type: "ip_adapter"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "invokeai"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; }; - /** - * IPAdapterOutput - * @description Base class for all invocation outputs. - * - * All invocation outputs must use the `@invocation_output` decorator to provide their unique type. - */ + /** IPAdapterOutput */ IPAdapterOutput: { /** * IP-Adapter @@ -3647,9 +3252,9 @@ export type components = { */ ip_adapter: components["schemas"]["IPAdapterField"]; /** - * Type + * type * @default ip_adapter_output - * @enum {string} + * @constant */ type: "ip_adapter_output"; }; @@ -3658,6 +3263,10 @@ export type components = { * @description Blurs an image */ ImageBlurInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3669,21 +3278,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to blur - */ + /** @description The image to blur */ image?: components["schemas"]["ImageField"]; /** * Radius @@ -3699,9 +3300,9 @@ export type components = { */ blur_type?: "gaussian" | "box"; /** - * Type + * type * @default img_blur - * @enum {string} + * @constant */ type: "img_blur"; }; @@ -3722,6 +3323,10 @@ export type components = { * @description Gets a channel from an image. */ ImageChannelInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3733,21 +3338,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to get the channel from - */ + /** @description The image to get the channel from */ image?: components["schemas"]["ImageField"]; /** * Channel @@ -3757,9 +3354,9 @@ export type components = { */ channel?: "A" | "R" | "G" | "B"; /** - * Type + * type * @default img_chan - * @enum {string} + * @constant */ type: "img_chan"; }; @@ -3768,6 +3365,10 @@ export type components = { * @description Scale a specific color channel of an image. */ ImageChannelMultiplyInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3779,21 +3380,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to adjust - */ + /** @description The image to adjust */ image?: components["schemas"]["ImageField"]; /** * Channel @@ -3814,9 +3407,9 @@ export type components = { */ invert_channel?: boolean; /** - * Type + * type * @default img_channel_multiply - * @enum {string} + * @constant */ type: "img_channel_multiply"; }; @@ -3825,6 +3418,10 @@ export type components = { * @description Add or subtract a value from a specific color channel of an image. */ ImageChannelOffsetInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3836,21 +3433,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to adjust - */ + /** @description The image to adjust */ image?: components["schemas"]["ImageField"]; /** * Channel @@ -3865,9 +3454,9 @@ export type components = { */ offset?: number; /** - * Type + * type * @default img_channel_offset - * @enum {string} + * @constant */ type: "img_channel_offset"; }; @@ -3887,11 +3476,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -3904,9 +3488,9 @@ export type components = { */ collection?: components["schemas"]["ImageField"][]; /** - * Type + * type * @default image_collection - * @enum {string} + * @constant */ type: "image_collection"; }; @@ -3921,9 +3505,9 @@ export type components = { */ collection: components["schemas"]["ImageField"][]; /** - * Type + * type * @default image_collection_output - * @enum {string} + * @constant */ type: "image_collection_output"; }; @@ -3932,6 +3516,10 @@ export type components = { * @description Converts an image to a different mode. */ ImageConvertInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3943,21 +3531,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to convert - */ + /** @description The image to convert */ image?: components["schemas"]["ImageField"]; /** * Mode @@ -3967,9 +3547,9 @@ export type components = { */ mode?: "L" | "RGB" | "RGBA" | "CMYK" | "YCbCr" | "LAB" | "HSV" | "I" | "F"; /** - * Type + * type * @default img_conv - * @enum {string} + * @constant */ type: "img_conv"; }; @@ -3978,6 +3558,10 @@ export type components = { * @description Crops an image to a specified box. The box can be outside of the image. */ ImageCropInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -3989,21 +3573,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to crop - */ + /** @description The image to crop */ image?: components["schemas"]["ImageField"]; /** * X @@ -4030,9 +3606,9 @@ export type components = { */ height?: number; /** - * Type + * type * @default img_crop - * @enum {string} + * @constant */ type: "img_crop"; }; @@ -4084,7 +3660,7 @@ export type components = { * Deleted At * @description The deleted timestamp of the image. */ - deleted_at?: string; + deleted_at?: string | null; /** * Is Intermediate * @description Whether this is an intermediate image. @@ -4094,12 +3670,12 @@ export type components = { * Session Id * @description The session ID that generated this image, if it is a generated image. */ - session_id?: string; + session_id?: string | null; /** * Node Id * @description The node ID that generated this image, if it is a generated image. */ - node_id?: string; + node_id?: string | null; /** * Starred * @description Whether this image is starred. @@ -4109,7 +3685,12 @@ export type components = { * Board Id * @description The id of the board the image belongs to, if one exists. */ - board_id?: string; + board_id?: string | null; + /** + * Workflow Id + * @description The workflow that generated this image. + */ + workflow_id?: string | null; }; /** * ImageField @@ -4127,6 +3708,10 @@ export type components = { * @description Adjusts the Hue of an image. */ ImageHueAdjustmentInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4138,21 +3723,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to adjust - */ + /** @description The image to adjust */ image?: components["schemas"]["ImageField"]; /** * Hue @@ -4161,9 +3738,9 @@ export type components = { */ hue?: number; /** - * Type + * type * @default img_hue_adjust - * @enum {string} + * @constant */ type: "img_hue_adjust"; }; @@ -4172,6 +3749,10 @@ export type components = { * @description Inverse linear interpolation of all pixels of an image */ ImageInverseLerpInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4183,21 +3764,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to lerp - */ + /** @description The image to lerp */ image?: components["schemas"]["ImageField"]; /** * Min @@ -4212,9 +3785,9 @@ export type components = { */ max?: number; /** - * Type + * type * @default img_ilerp - * @enum {string} + * @constant */ type: "img_ilerp"; }; @@ -4234,26 +3807,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to load - */ + /** @description The image to load */ image?: components["schemas"]["ImageField"]; /** - * Type + * type * @default image - * @enum {string} + * @constant */ type: "image"; }; @@ -4262,6 +3827,10 @@ export type components = { * @description Linear interpolation of all pixels of an image */ ImageLerpInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4273,21 +3842,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to lerp - */ + /** @description The image to lerp */ image?: components["schemas"]["ImageField"]; /** * Min @@ -4302,33 +3863,21 @@ export type components = { */ max?: number; /** - * Type + * type * @default img_lerp - * @enum {string} + * @constant */ type: "img_lerp"; }; - /** - * ImageMetadata - * @description An image's generation metadata - */ - ImageMetadata: { - /** - * Metadata - * @description The image's core metadata, if it was created in the Linear or Canvas UI - */ - metadata?: Record; - /** - * Graph - * @description The graph that created the image - */ - graph?: Record; - }; /** * Multiply Images * @description Multiplies two images together using `PIL.ImageChops.multiply()`. */ ImageMultiplyInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4340,31 +3889,20 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image1 - * @description The first image to multiply - */ + /** @description The first image to multiply */ image1?: components["schemas"]["ImageField"]; - /** - * Image2 - * @description The second image to multiply - */ + /** @description The second image to multiply */ image2?: components["schemas"]["ImageField"]; /** - * Type + * type * @default img_mul - * @enum {string} + * @constant */ type: "img_mul"; }; @@ -4373,6 +3911,10 @@ export type components = { * @description Add blur to NSFW-flagged images */ ImageNSFWBlurInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4384,43 +3926,27 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; + /** @description The image to check */ + image?: components["schemas"]["ImageField"]; /** - * Metadata - * @description Optional core metadata to be written to image - */ - metadata?: components["schemas"]["CoreMetadata"]; - /** - * Type + * type * @default img_nsfw - * @enum {string} + * @constant */ type: "img_nsfw"; - /** - * Image - * @description The image to check - */ - image?: components["schemas"]["ImageField"]; }; /** * ImageOutput * @description Base class for nodes that output a single image */ ImageOutput: { - /** - * Image - * @description The output image - */ + /** @description The output image */ image: components["schemas"]["ImageField"]; /** * Width @@ -4433,9 +3959,9 @@ export type components = { */ height: number; /** - * Type + * type * @default image_output - * @enum {string} + * @constant */ type: "image_output"; }; @@ -4444,6 +3970,10 @@ export type components = { * @description Pastes an image into another image. */ ImagePasteInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4455,32 +3985,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Base Image - * @description The base image - */ + /** @description The base image */ base_image?: components["schemas"]["ImageField"]; - /** - * Image - * @description The image to paste - */ + /** @description The image to paste */ image?: components["schemas"]["ImageField"]; - /** - * Mask - * @description The mask to use when pasting - */ - mask?: components["schemas"]["ImageField"]; + /** @description The mask to use when pasting */ + mask?: components["schemas"]["ImageField"] | null; /** * X * @description The left x coordinate at which to paste the image @@ -4500,51 +4016,12 @@ export type components = { */ crop?: boolean; /** - * Type + * type * @default img_paste - * @enum {string} + * @constant */ type: "img_paste"; }; - /** - * Base Image Processor - * @description Base class for invocations that preprocess images for ControlNet - */ - ImageProcessorInvocation: { - /** - * Id - * @description The id of this instance of an invocation. Must be unique among all instances of invocations. - */ - id: string; - /** - * Is Intermediate - * @description Whether or not this is an intermediate invocation. - * @default false - */ - is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; - /** - * Use Cache - * @description Whether or not to use the cache - * @default true - */ - use_cache?: boolean; - /** - * Image - * @description The image to process - */ - image?: components["schemas"]["ImageField"]; - /** - * Type - * @default image_processor - * @enum {string} - */ - type: "image_processor"; - }; /** * ImageRecordChanges * @description A set of changes to apply to an image record. @@ -4557,28 +4034,33 @@ export type components = { */ ImageRecordChanges: { /** @description The image's new category. */ - image_category?: components["schemas"]["ImageCategory"]; + image_category?: components["schemas"]["ImageCategory"] | null; /** * Session Id * @description The image's new session ID. */ - session_id?: string; + session_id?: string | null; /** * Is Intermediate * @description The image's new `is_intermediate` flag. */ - is_intermediate?: boolean; + is_intermediate?: boolean | null; /** * Starred * @description The image's new `starred` state */ - starred?: boolean; + starred?: boolean | null; + [key: string]: unknown; }; /** * Resize Image * @description Resizes an image to specific dimensions */ ImageResizeInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4590,21 +4072,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to resize - */ + /** @description The image to resize */ image?: components["schemas"]["ImageField"]; /** * Width @@ -4626,14 +4100,9 @@ export type components = { */ resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos"; /** - * Metadata - * @description Optional core metadata to be written to image - */ - metadata?: components["schemas"]["CoreMetadata"]; - /** - * Type + * type * @default img_resize - * @enum {string} + * @constant */ type: "img_resize"; }; @@ -4642,6 +4111,10 @@ export type components = { * @description Scales an image by a factor */ ImageScaleInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4653,21 +4126,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to scale - */ + /** @description The image to scale */ image?: components["schemas"]["ImageField"]; /** * Scale Factor @@ -4683,9 +4148,9 @@ export type components = { */ resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos"; /** - * Type + * type * @default img_scale - * @enum {string} + * @constant */ type: "img_scale"; }; @@ -4705,26 +4170,15 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to encode - */ + /** @description The image to encode */ image?: components["schemas"]["ImageField"]; - /** - * Vae - * @description VAE - */ + /** @description VAE */ vae?: components["schemas"]["VaeField"]; /** * Tiled @@ -4739,9 +4193,9 @@ export type components = { */ fp32?: boolean; /** - * Type + * type * @default i2l - * @enum {string} + * @constant */ type: "i2l"; }; @@ -4771,6 +4225,10 @@ export type components = { * @description Add an invisible watermark to an image */ ImageWatermarkInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4782,21 +4240,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to check - */ + /** @description The image to check */ image?: components["schemas"]["ImageField"]; /** * Text @@ -4805,14 +4255,9 @@ export type components = { */ text?: string; /** - * Metadata - * @description Optional core metadata to be written to image - */ - metadata?: components["schemas"]["CoreMetadata"]; - /** - * Type + * type * @default img_watermark - * @enum {string} + * @constant */ type: "img_watermark"; }; @@ -4822,7 +4267,7 @@ export type components = { * Response * @description If defined, the message to display to the user when images begin downloading */ - response?: string; + response: string | null; }; /** ImagesUpdatedFromListResult */ ImagesUpdatedFromListResult: { @@ -4837,6 +4282,10 @@ export type components = { * @description Infills transparent areas of an image with a solid color */ InfillColorInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4848,37 +4297,28 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to infill - */ + /** @description The image to infill */ image?: components["schemas"]["ImageField"]; /** - * Color * @description The color to use to infill * @default { - * "r": 127, - * "g": 127, + * "a": 255, * "b": 127, - * "a": 255 + * "g": 127, + * "r": 127 * } */ color?: components["schemas"]["ColorField"]; /** - * Type + * type * @default infill_rgba - * @enum {string} + * @constant */ type: "infill_rgba"; }; @@ -4887,6 +4327,10 @@ export type components = { * @description Infills transparent areas of an image using the PatchMatch algorithm */ InfillPatchMatchInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4898,21 +4342,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to infill - */ + /** @description The image to infill */ image?: components["schemas"]["ImageField"]; /** * Downscale @@ -4928,9 +4364,9 @@ export type components = { */ resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos"; /** - * Type + * type * @default infill_patchmatch - * @enum {string} + * @constant */ type: "infill_patchmatch"; }; @@ -4939,6 +4375,10 @@ export type components = { * @description Infills transparent areas of an image with tiles of the image */ InfillTileInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -4950,21 +4390,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to infill - */ + /** @description The image to infill */ image?: components["schemas"]["ImageField"]; /** * Tile Size @@ -4978,9 +4410,9 @@ export type components = { */ seed?: number; /** - * Type + * type * @default infill_tile - * @enum {string} + * @constant */ type: "infill_tile"; }; @@ -5000,11 +4432,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -5017,9 +4444,9 @@ export type components = { */ collection?: number[]; /** - * Type + * type * @default integer_collection - * @enum {string} + * @constant */ type: "integer_collection"; }; @@ -5034,9 +4461,9 @@ export type components = { */ collection: number[]; /** - * Type + * type * @default integer_collection_output - * @enum {string} + * @constant */ type: "integer_collection_output"; }; @@ -5056,11 +4483,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -5074,9 +4496,9 @@ export type components = { */ value?: number; /** - * Type + * type * @default integer - * @enum {string} + * @constant */ type: "integer"; }; @@ -5096,11 +4518,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -5127,9 +4544,9 @@ export type components = { */ b?: number; /** - * Type + * type * @default integer_math - * @enum {string} + * @constant */ type: "integer_math"; }; @@ -5144,9 +4561,9 @@ export type components = { */ value: number; /** - * Type + * type * @default integer_output - * @enum {string} + * @constant */ type: "integer_output"; }; @@ -5194,11 +4611,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -5217,9 +4629,9 @@ export type components = { */ index?: number; /** - * Type + * type * @default iterate - * @enum {string} + * @constant */ type: "iterate"; }; @@ -5232,11 +4644,11 @@ export type components = { * Collection Item * @description The item being iterated over */ - item?: unknown; + item: unknown; /** - * Type + * type * @default iterate_output - * @enum {string} + * @constant */ type: "iterate_output"; }; @@ -5245,6 +4657,10 @@ export type components = { * @description Infills transparent areas of an image using the LaMa model */ LaMaInfillInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5256,26 +4672,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to infill - */ + /** @description The image to infill */ image?: components["schemas"]["ImageField"]; /** - * Type + * type * @default infill_lama - * @enum {string} + * @constant */ type: "infill_lama"; }; @@ -5295,11 +4703,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -5312,9 +4715,9 @@ export type components = { */ collection?: components["schemas"]["LatentsField"][]; /** - * Type + * type * @default latents_collection - * @enum {string} + * @constant */ type: "latents_collection"; }; @@ -5329,9 +4732,9 @@ export type components = { */ collection: components["schemas"]["LatentsField"][]; /** - * Type + * type * @default latents_collection_output - * @enum {string} + * @constant */ type: "latents_collection_output"; }; @@ -5349,7 +4752,7 @@ export type components = { * Seed * @description Seed used to generate this latents */ - seed?: number; + seed?: number | null; }; /** * Latents Primitive @@ -5367,26 +4770,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Latents - * @description The latents tensor - */ + /** @description The latents tensor */ latents?: components["schemas"]["LatentsField"]; /** - * Type + * type * @default latents - * @enum {string} + * @constant */ type: "latents"; }; @@ -5395,10 +4790,7 @@ export type components = { * @description Base class for nodes that output a single latents tensor */ LatentsOutput: { - /** - * Latents - * @description Latents tensor - */ + /** @description Latents tensor */ latents: components["schemas"]["LatentsField"]; /** * Width @@ -5411,9 +4803,9 @@ export type components = { */ height: number; /** - * Type + * type * @default latents_output - * @enum {string} + * @constant */ type: "latents_output"; }; @@ -5422,6 +4814,10 @@ export type components = { * @description Generates an image from latents. */ LatentsToImageInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5433,17 +4829,16 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; + /** @description Latents tensor */ + latents?: components["schemas"]["LatentsField"]; + /** @description VAE */ + vae?: components["schemas"]["VaeField"]; /** * Tiled * @description Processing using overlapping tiles (reduce memory consumption) @@ -5457,32 +4852,21 @@ export type components = { */ fp32?: boolean; /** - * Metadata - * @description Optional core metadata to be written to image - */ - metadata?: components["schemas"]["CoreMetadata"]; - /** - * Type + * type * @default l2i - * @enum {string} + * @constant */ type: "l2i"; - /** - * Latents - * @description Latents tensor - */ - latents?: components["schemas"]["LatentsField"]; - /** - * Vae - * @description VAE - */ - vae?: components["schemas"]["VaeField"]; }; /** * Leres (Depth) Processor * @description Applies leres processing to image */ LeresImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5494,28 +4878,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default leres_image_processor - * @enum {string} - */ - type: "leres_image_processor"; /** * Thr A * @description Leres parameter `thr_a` @@ -5546,12 +4916,22 @@ export type components = { * @default 512 */ image_resolution?: number; + /** + * type + * @default leres_image_processor + * @constant + */ + type: "leres_image_processor"; }; /** * Lineart Anime Processor * @description Applies line art anime processing to image */ LineartAnimeImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5563,28 +4943,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default lineart_anime_image_processor - * @enum {string} - */ - type: "lineart_anime_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -5597,12 +4963,22 @@ export type components = { * @default 512 */ image_resolution?: number; + /** + * type + * @default lineart_anime_image_processor + * @constant + */ + type: "lineart_anime_image_processor"; }; /** * Lineart Processor * @description Applies line art processing to image */ LineartImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5614,28 +4990,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default lineart_image_processor - * @enum {string} - */ - type: "lineart_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -5654,20 +5016,23 @@ export type components = { * @default false */ coarse?: boolean; + /** + * type + * @default lineart_image_processor + * @constant + */ + type: "lineart_image_processor"; }; /** * LoRAMetadataField - * @description LoRA metadata for an image generated in InvokeAI. + * @description LoRA Metadata Field */ LoRAMetadataField: { - /** - * Lora - * @description The LoRA model - */ + /** @description LoRA model to load */ lora: components["schemas"]["LoRAModelField"]; /** * Weight - * @description The weight of the LoRA model + * @description The weight at which the LoRA is applied to each model */ weight: number; }; @@ -5678,15 +5043,16 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default lora + * @constant */ model_type: "lora"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; model_format: components["schemas"]["LoRAModelFormat"]; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; }; /** * LoRAModelField @@ -5703,13 +5069,11 @@ export type components = { }; /** * LoRAModelFormat - * @description An enumeration. * @enum {string} */ LoRAModelFormat: "lycoris" | "diffusers"; /** * LogLevel - * @description An enumeration. * @enum {integer} */ LogLevel: 0 | 10 | 20 | 30 | 40 | 50; @@ -5725,7 +5089,7 @@ export type components = { /** @description Info to load submodel */ model_type: components["schemas"]["ModelType"]; /** @description Info to load submodel */ - submodel?: components["schemas"]["SubModelType"]; + submodel?: components["schemas"]["SubModelType"] | null; /** * Weight * @description Lora's weight which to use when apply to model @@ -5748,11 +5112,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -5774,16 +5133,16 @@ export type components = { * UNet * @description UNet (scheduler, LoRAs) */ - unet?: components["schemas"]["UNetField"]; + unet?: components["schemas"]["UNetField"] | null; /** * CLIP * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip?: components["schemas"]["ClipField"]; + clip?: components["schemas"]["ClipField"] | null; /** - * Type + * type * @default lora_loader - * @enum {string} + * @constant */ type: "lora_loader"; }; @@ -5796,16 +5155,16 @@ export type components = { * UNet * @description UNet (scheduler, LoRAs) */ - unet?: components["schemas"]["UNetField"]; + unet?: components["schemas"]["UNetField"] | null; /** * CLIP * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip?: components["schemas"]["ClipField"]; + clip?: components["schemas"]["ClipField"] | null; /** - * Type + * type * @default lora_loader_output - * @enum {string} + * @constant */ type: "lora_loader_output"; }; @@ -5840,26 +5199,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Model - * @description Main model (UNet, VAE, CLIP) to load - */ + /** @description Main model (UNet, VAE, CLIP) to load */ model: components["schemas"]["MainModelField"]; /** - * Type + * type * @default main_model_loader - * @enum {string} + * @constant */ type: "main_model_loader"; }; @@ -5868,6 +5219,10 @@ export type components = { * @description Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`. */ MaskCombineInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5879,31 +5234,20 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Mask1 - * @description The first mask to combine - */ + /** @description The first mask to combine */ mask1?: components["schemas"]["ImageField"]; - /** - * Mask2 - * @description The second image to combine - */ + /** @description The second image to combine */ mask2?: components["schemas"]["ImageField"]; /** - * Type + * type * @default mask_combine - * @enum {string} + * @constant */ type: "mask_combine"; }; @@ -5912,6 +5256,10 @@ export type components = { * @description Applies an edge mask to an image */ MaskEdgeInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5923,21 +5271,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to apply the mask to - */ + /** @description The image to apply the mask to */ image?: components["schemas"]["ImageField"]; /** * Edge Size @@ -5960,9 +5300,9 @@ export type components = { */ high_threshold?: number; /** - * Type + * type * @default mask_edge - * @enum {string} + * @constant */ type: "mask_edge"; }; @@ -5971,6 +5311,10 @@ export type components = { * @description Extracts the alpha channel of an image as a mask. */ MaskFromAlphaInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -5982,21 +5326,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to create the mask from - */ + /** @description The image to create the mask from */ image?: components["schemas"]["ImageField"]; /** * Invert @@ -6005,9 +5341,9 @@ export type components = { */ invert?: boolean; /** - * Type + * type * @default tomask - * @enum {string} + * @constant */ type: "tomask"; }; @@ -6016,6 +5352,10 @@ export type components = { * @description Applies mediapipe face processing to image */ MediapipeFaceProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -6027,28 +5367,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default mediapipe_face_processor - * @enum {string} - */ - type: "mediapipe_face_processor"; /** * Max Faces * @description Maximum number of faces to detect @@ -6061,18 +5387,23 @@ export type components = { * @default 0.5 */ min_confidence?: number; + /** + * type + * @default mediapipe_face_processor + * @constant + */ + type: "mediapipe_face_processor"; }; /** * MergeInterpolationMethod - * @description An enumeration. * @enum {string} */ MergeInterpolationMethod: "weighted_sum" | "sigmoid" | "inv_sigmoid" | "add_difference"; /** - * Metadata Accumulator - * @description Outputs a Core Metadata Object + * Metadata Merge + * @description Merged a collection of MetadataDict into a single MetadataDict. */ - MetadataAccumulatorInvocation: { + MergeMetadataInvocation: { /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -6085,10 +5416,77 @@ export type components = { */ is_intermediate?: boolean; /** - * Workflow - * @description The workflow to save with the image + * Use Cache + * @description Whether or not to use the cache + * @default true */ - workflow?: string; + use_cache?: boolean; + /** + * Collection + * @description Collection of Metadata + */ + collection?: components["schemas"]["MetadataField"][]; + /** + * type + * @default merge_metadata + * @constant + */ + type: "merge_metadata"; + }; + /** MergeModelsBody */ + MergeModelsBody: { + /** + * Model Names + * @description model name + */ + model_names: string[]; + /** + * Merged Model Name + * @description Name of destination model + */ + merged_model_name: string | null; + /** + * Alpha + * @description Alpha weighting strength to apply to 2d and 3d models + * @default 0.5 + */ + alpha?: number | null; + /** @description Interpolation method */ + interp: components["schemas"]["MergeInterpolationMethod"] | null; + /** + * Force + * @description Force merging of models created with different versions of diffusers + * @default false + */ + force?: boolean | null; + /** + * Merge Dest Directory + * @description Save the merged model to the designated directory (with 'merged_model_name' appended) + */ + merge_dest_directory?: string | null; + }; + /** + * MetadataField + * @description Pydantic model for metadata with custom root of type dict[str, Any]. + * Metadata is stored without a strict schema. + */ + MetadataField: Record; + /** + * Metadata + * @description Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict. + */ + MetadataInvocation: { + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; /** * Use Cache * @description Whether or not to use the cache @@ -6096,189 +5494,103 @@ export type components = { */ use_cache?: boolean; /** - * Generation Mode - * @description The generation mode that output this image + * Items + * @description A single metadata item or collection of metadata items */ - generation_mode?: string; + items?: components["schemas"]["MetadataItemField"][] | components["schemas"]["MetadataItemField"]; /** - * Positive Prompt - * @description The positive prompt parameter + * type + * @default metadata + * @constant */ - positive_prompt?: string; + type: "metadata"; + }; + /** MetadataItemField */ + MetadataItemField: { /** - * Negative Prompt - * @description The negative prompt parameter + * Label + * @description Label for this metadata item */ - negative_prompt?: string; + label: string; /** - * Width - * @description The width parameter + * Value + * @description The value for this metadata item (may be any type) */ - width?: number; - /** - * Height - * @description The height parameter - */ - height?: number; - /** - * Seed - * @description The seed used for noise generation - */ - seed?: number; - /** - * Rand Device - * @description The device used for random number generation - */ - rand_device?: string; - /** - * Cfg Scale - * @description The classifier-free guidance scale parameter - */ - cfg_scale?: number; - /** - * Steps - * @description The number of steps used for inference - */ - steps?: number; - /** - * Scheduler - * @description The scheduler used for inference - */ - scheduler?: string; - /** - * Clip Skip - * @description The number of skipped CLIP layers - */ - clip_skip?: number; - /** - * Model - * @description The main model used for inference - */ - model?: components["schemas"]["MainModelField"]; - /** - * Controlnets - * @description The ControlNets used for inference - */ - controlnets?: components["schemas"]["ControlField"][]; - /** - * Ipadapters - * @description The IP Adapters used for inference - */ - ipAdapters?: components["schemas"]["IPAdapterMetadataField"][]; - /** - * T2Iadapters - * @description The IP Adapters used for inference - */ - t2iAdapters: components["schemas"]["T2IAdapterField"][]; - /** - * Loras - * @description The LoRAs used for inference - */ - loras?: components["schemas"]["LoRAMetadataField"][]; - /** - * Strength - * @description The strength used for latents-to-latents - */ - strength?: number; - /** - * Init Image - * @description The name of the initial image - */ - init_image?: string; - /** - * Vae - * @description The VAE used for decoding, if the main model's default was not used - */ - vae?: components["schemas"]["VAEModelField"]; - /** - * High Resolution Fix Width - * @description The high resolution fix height and width multiplier. - */ - hrf_width?: number; - /** - * High Resolution Fix Height - * @description The high resolution fix height and width multiplier. - */ - hrf_height?: number; - /** - * High Resolution Strength - * @description The high resolution fix img2img strength used in the upscale pass. - */ - hrf_strength?: number; - /** - * Positive Style Prompt - * @description The positive style prompt parameter - */ - positive_style_prompt?: string; - /** - * Negative Style Prompt - * @description The negative style prompt parameter - */ - negative_style_prompt?: string; - /** - * Refiner Model - * @description The SDXL Refiner model used - */ - refiner_model?: components["schemas"]["MainModelField"]; - /** - * Refiner Cfg Scale - * @description The classifier-free guidance scale parameter used for the refiner - */ - refiner_cfg_scale?: number; - /** - * Refiner Steps - * @description The number of steps used for the refiner - */ - refiner_steps?: number; - /** - * Refiner Scheduler - * @description The scheduler used for the refiner - */ - refiner_scheduler?: string; - /** - * Refiner Positive Aesthetic Score - * @description The aesthetic score used for the refiner - */ - 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 - */ - refiner_start?: number; - /** - * Type - * @default metadata_accumulator - * @enum {string} - */ - type: "metadata_accumulator"; + value: unknown; }; /** - * MetadataAccumulatorOutput - * @description The output of the MetadataAccumulator node + * Metadata Item + * @description Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value. */ - MetadataAccumulatorOutput: { + MetadataItemInvocation: { /** - * Metadata - * @description The core metadata for the image + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. */ - metadata: components["schemas"]["CoreMetadata"]; + id: string; /** - * Type - * @default metadata_accumulator_output - * @enum {string} + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false */ - type: "metadata_accumulator_output"; + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** + * Label + * @description Label for this metadata item + */ + label?: string; + /** + * Value + * @description The value for this metadata item (may be any type) + */ + value?: unknown; + /** + * type + * @default metadata_item + * @constant + */ + type: "metadata_item"; + }; + /** + * MetadataItemOutput + * @description Metadata Item Output + */ + MetadataItemOutput: { + /** @description Metadata Item */ + item: components["schemas"]["MetadataItemField"]; + /** + * type + * @default metadata_item_output + * @constant + */ + type: "metadata_item_output"; + }; + /** MetadataOutput */ + MetadataOutput: { + /** @description Metadata Dict */ + metadata: components["schemas"]["MetadataField"]; + /** + * type + * @default metadata_output + * @constant + */ + type: "metadata_output"; }; /** * Midas Depth Processor * @description Applies Midas depth processing to image */ MidasDepthImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -6290,28 +5602,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default midas_depth_image_processor - * @enum {string} - */ - type: "midas_depth_image_processor"; /** * A Mult * @description Midas parameter `a_mult` (a = a_mult * PI) @@ -6324,12 +5622,22 @@ export type components = { * @default 0.1 */ bg_th?: number; + /** + * type + * @default midas_depth_image_processor + * @constant + */ + type: "midas_depth_image_processor"; }; /** * MLSD Processor * @description Applies MLSD processing to image */ MlsdImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -6341,28 +5649,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default mlsd_image_processor - * @enum {string} - */ - type: "mlsd_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -6387,11 +5681,16 @@ export type components = { * @default 0.1 */ thr_d?: number; + /** + * type + * @default mlsd_image_processor + * @constant + */ + type: "mlsd_image_processor"; }; /** * ModelError - * @description An enumeration. - * @enum {string} + * @constant */ ModelError: "not_found"; /** ModelInfo */ @@ -6406,7 +5705,7 @@ export type components = { /** @description Info to load submodel */ model_type: components["schemas"]["ModelType"]; /** @description Info to load submodel */ - submodel?: components["schemas"]["SubModelType"]; + submodel?: components["schemas"]["SubModelType"] | null; }; /** * ModelLoaderOutput @@ -6429,21 +5728,19 @@ export type components = { */ vae: components["schemas"]["VaeField"]; /** - * Type + * type * @default model_loader_output - * @enum {string} + * @constant */ type: "model_loader_output"; }; /** * ModelType - * @description An enumeration. * @enum {string} */ ModelType: "onnx" | "main" | "vae" | "lora" | "controlnet" | "embedding" | "ip_adapter" | "clip_vision" | "t2i_adapter"; /** * ModelVariantType - * @description An enumeration. * @enum {string} */ ModelVariantType: "normal" | "inpaint" | "depth"; @@ -6468,11 +5765,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -6492,9 +5784,9 @@ export type components = { */ b?: number; /** - * Type + * type * @default mul - * @enum {string} + * @constant */ type: "mul"; }; @@ -6532,11 +5824,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -6567,9 +5854,9 @@ export type components = { */ use_cpu?: boolean; /** - * Type + * type * @default noise - * @enum {string} + * @constant */ type: "noise"; }; @@ -6578,11 +5865,8 @@ export type components = { * @description Invocation noise output */ NoiseOutput: { - /** - * Noise - * @description Noise tensor - */ - noise?: components["schemas"]["LatentsField"]; + /** @description Noise tensor */ + noise: components["schemas"]["LatentsField"]; /** * Width * @description Width of output (px) @@ -6594,9 +5878,9 @@ export type components = { */ height: number; /** - * Type + * type * @default noise_output - * @enum {string} + * @constant */ type: "noise_output"; }; @@ -6605,6 +5889,10 @@ export type components = { * @description Applies NormalBae processing to image */ NormalbaeImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -6616,28 +5904,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default normalbae_image_processor - * @enum {string} - */ - type: "normalbae_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -6650,12 +5924,22 @@ export type components = { * @default 512 */ image_resolution?: number; + /** + * type + * @default normalbae_image_processor + * @constant + */ + type: "normalbae_image_processor"; }; /** * ONNX Latents to Image * @description Generates an image from latents. */ ONNXLatentsToImageInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -6667,36 +5951,20 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Latents - * @description Denoised latents tensor - */ + /** @description Denoised latents tensor */ latents?: components["schemas"]["LatentsField"]; - /** - * Vae - * @description VAE - */ + /** @description VAE */ vae?: components["schemas"]["VaeField"]; /** - * Metadata - * @description Optional core metadata to be written to image - */ - metadata?: components["schemas"]["CoreMetadata"]; - /** - * Type + * type * @default l2i_onnx - * @enum {string} + * @constant */ type: "l2i_onnx"; }; @@ -6726,19 +5994,13 @@ export type components = { */ vae_encoder?: components["schemas"]["VaeField"]; /** - * Type + * type * @default model_loader_output_onnx - * @enum {string} + * @constant */ type: "model_loader_output_onnx"; }; - /** - * ONNX Prompt (Raw) - * @description A node to process inputs and produce outputs. - * May use dependency injection in __init__ to receive providers. - * - * All invocations must use the `@invocation` decorator to provide their unique type. - */ + /** ONNX Prompt (Raw) */ ONNXPromptInvocation: { /** * Id @@ -6751,11 +6013,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -6768,15 +6025,12 @@ export type components = { * @default */ prompt?: string; - /** - * Clip - * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count - */ + /** @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ clip?: components["schemas"]["ClipField"]; /** - * Type + * type * @default prompt_onnx - * @enum {string} + * @constant */ type: "prompt_onnx"; }; @@ -6787,19 +6041,20 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default onnx + * @constant */ model_type: "onnx"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "onnx"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; variant: components["schemas"]["ModelVariantType"]; }; /** ONNXStableDiffusion2ModelConfig */ @@ -6809,19 +6064,20 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default onnx + * @constant */ model_type: "onnx"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "onnx"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; variant: components["schemas"]["ModelVariantType"]; prediction_type: components["schemas"]["SchedulerPredictionType"]; /** Upcast Attention */ @@ -6843,31 +6099,17 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Positive Conditioning - * @description Positive conditioning tensor - */ + /** @description Positive conditioning tensor */ positive_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Negative Conditioning - * @description Negative conditioning tensor - */ + /** @description Negative conditioning tensor */ negative_conditioning?: components["schemas"]["ConditioningField"]; - /** - * Noise - * @description Noise tensor - */ + /** @description Noise tensor */ noise?: components["schemas"]["LatentsField"]; /** * Steps @@ -6895,10 +6137,7 @@ export type components = { * @enum {string} */ precision?: "tensor(bool)" | "tensor(int8)" | "tensor(uint8)" | "tensor(int16)" | "tensor(uint16)" | "tensor(int32)" | "tensor(uint32)" | "tensor(int64)" | "tensor(uint64)" | "tensor(float16)" | "tensor(float)" | "tensor(double)"; - /** - * Unet - * @description UNet (scheduler, LoRAs) - */ + /** @description UNet (scheduler, LoRAs) */ unet?: components["schemas"]["UNetField"]; /** * Control @@ -6906,17 +6145,13 @@ export type components = { */ control?: components["schemas"]["ControlField"] | components["schemas"]["ControlField"][]; /** - * Type + * type * @default t2l_onnx - * @enum {string} + * @constant */ type: "t2l_onnx"; }; - /** - * OffsetPaginatedResults[BoardDTO] - * @description Offset-paginated results - * Generic must be a Pydantic model - */ + /** OffsetPaginatedResults[BoardDTO] */ OffsetPaginatedResults_BoardDTO_: { /** * Limit @@ -6939,11 +6174,7 @@ export type components = { */ items: components["schemas"]["BoardDTO"][]; }; - /** - * OffsetPaginatedResults[ImageDTO] - * @description Offset-paginated results - * Generic must be a Pydantic model - */ + /** OffsetPaginatedResults[ImageDTO] */ OffsetPaginatedResults_ImageDTO_: { /** * Limit @@ -6997,26 +6228,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Model - * @description ONNX Main model (UNet, VAE, CLIP) to load - */ + /** @description ONNX Main model (UNet, VAE, CLIP) to load */ model: components["schemas"]["OnnxModelField"]; /** - * Type + * type * @default onnx_model_loader - * @enum {string} + * @constant */ type: "onnx_model_loader"; }; @@ -7025,6 +6248,10 @@ export type components = { * @description Applies Openpose processing to image */ OpenposeImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -7036,28 +6263,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default openpose_image_processor - * @enum {string} - */ - type: "openpose_image_processor"; /** * Hand And Face * @description Whether to use hands and face mode @@ -7076,44 +6289,22 @@ export type components = { * @default 512 */ image_resolution?: number; - }; - /** - * PaginatedResults[GraphExecutionState] - * @description Paginated results - * Generic must be a Pydantic model - */ - PaginatedResults_GraphExecutionState_: { /** - * Page - * @description Current Page + * type + * @default openpose_image_processor + * @constant */ - page: number; - /** - * Pages - * @description Total number of pages - */ - pages: number; - /** - * Per Page - * @description Number of items per page - */ - per_page: number; - /** - * Total - * @description Total number of items in result - */ - total: number; - /** - * Items - * @description Items - */ - items: components["schemas"]["GraphExecutionState"][]; + type: "openpose_image_processor"; }; /** * PIDI Processor * @description Applies PIDI processing to image */ PidiImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -7125,28 +6316,14 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default pidi_image_processor - * @enum {string} - */ - type: "pidi_image_processor"; /** * Detect Resolution * @description Pixel resolution for detection @@ -7171,6 +6348,12 @@ export type components = { * @default false */ scribble?: boolean; + /** + * type + * @default pidi_image_processor + * @constant + */ + type: "pidi_image_processor"; }; /** * Prompts from File @@ -7188,11 +6371,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7208,12 +6386,12 @@ export type components = { * Pre Prompt * @description String to prepend to each prompt */ - pre_prompt?: string; + pre_prompt?: string | null; /** * Post Prompt * @description String to append to each prompt */ - post_prompt?: string; + post_prompt?: string | null; /** * Start Line * @description Line in the file to start start from @@ -7227,9 +6405,9 @@ export type components = { */ max_prompts?: number; /** - * Type + * type * @default prompt_from_file - * @enum {string} + * @constant */ type: "prompt_from_file"; }; @@ -7260,15 +6438,10 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache - * @default true + * @default false */ use_cache?: boolean; /** @@ -7290,9 +6463,9 @@ export type components = { */ decimals?: number; /** - * Type + * type * @default rand_float - * @enum {string} + * @constant */ type: "rand_float"; }; @@ -7312,11 +6485,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7336,9 +6504,9 @@ export type components = { */ high?: number; /** - * Type + * type * @default rand_int - * @enum {string} + * @constant */ type: "rand_int"; }; @@ -7358,11 +6526,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7393,9 +6556,9 @@ export type components = { */ seed?: number; /** - * Type + * type * @default random_range - * @enum {string} + * @constant */ type: "random_range"; }; @@ -7415,11 +6578,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7445,9 +6603,9 @@ export type components = { */ step?: number; /** - * Type + * type * @default range - * @enum {string} + * @constant */ type: "range"; }; @@ -7467,11 +6625,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7497,9 +6650,9 @@ export type components = { */ step?: number; /** - * Type + * type * @default range_of_size - * @enum {string} + * @constant */ type: "range_of_size"; }; @@ -7527,21 +6680,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Latents - * @description Latents tensor - */ + /** @description Latents tensor */ latents?: components["schemas"]["LatentsField"]; /** * Width @@ -7567,9 +6712,9 @@ export type components = { */ antialias?: boolean; /** - * Type + * type * @default lresize - * @enum {string} + * @constant */ type: "lresize"; }; @@ -7599,11 +6744,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7623,9 +6763,9 @@ export type components = { */ decimals?: number; /** - * Type + * type * @default round_float - * @enum {string} + * @constant */ type: "round_float"; }; @@ -7645,11 +6785,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7709,9 +6844,9 @@ export type components = { */ clip2?: components["schemas"]["ClipField"]; /** - * Type + * type * @default sdxl_compel_prompt - * @enum {string} + * @constant */ type: "sdxl_compel_prompt"; }; @@ -7731,11 +6866,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7757,21 +6887,21 @@ export type components = { * UNet * @description UNet (scheduler, LoRAs) */ - unet?: components["schemas"]["UNetField"]; + unet?: components["schemas"]["UNetField"] | null; /** * CLIP 1 * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip?: components["schemas"]["ClipField"]; + clip?: components["schemas"]["ClipField"] | null; /** * CLIP 2 * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip2?: components["schemas"]["ClipField"]; + clip2?: components["schemas"]["ClipField"] | null; /** - * Type + * type * @default sdxl_lora_loader - * @enum {string} + * @constant */ type: "sdxl_lora_loader"; }; @@ -7784,21 +6914,21 @@ export type components = { * UNet * @description UNet (scheduler, LoRAs) */ - unet?: components["schemas"]["UNetField"]; + unet?: components["schemas"]["UNetField"] | null; /** * CLIP 1 * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip?: components["schemas"]["ClipField"]; + clip?: components["schemas"]["ClipField"] | null; /** * CLIP 2 * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ - clip2?: components["schemas"]["ClipField"]; + clip2?: components["schemas"]["ClipField"] | null; /** - * Type + * type * @default sdxl_lora_loader_output - * @enum {string} + * @constant */ type: "sdxl_lora_loader_output"; }; @@ -7818,26 +6948,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Model - * @description SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load - */ + /** @description SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load */ model: components["schemas"]["MainModelField"]; /** - * Type + * type * @default sdxl_model_loader - * @enum {string} + * @constant */ type: "sdxl_model_loader"; }; @@ -7867,9 +6989,9 @@ export type components = { */ vae: components["schemas"]["VaeField"]; /** - * Type + * type * @default sdxl_model_loader_output - * @enum {string} + * @constant */ type: "sdxl_model_loader_output"; }; @@ -7889,11 +7011,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -7932,15 +7049,12 @@ export type components = { * @default 6 */ aesthetic_score?: number; - /** - * Clip2 - * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count - */ + /** @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count */ clip2?: components["schemas"]["ClipField"]; /** - * Type + * type * @default sdxl_refiner_compel_prompt - * @enum {string} + * @constant */ type: "sdxl_refiner_compel_prompt"; }; @@ -7960,26 +7074,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Model - * @description SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load - */ + /** @description SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load */ model: components["schemas"]["MainModelField"]; /** - * Type + * type * @default sdxl_refiner_model_loader - * @enum {string} + * @constant */ type: "sdxl_refiner_model_loader"; }; @@ -8004,9 +7110,9 @@ export type components = { */ vae: components["schemas"]["VaeField"]; /** - * Type + * type * @default sdxl_refiner_model_loader_output - * @enum {string} + * @constant */ type: "sdxl_refiner_model_loader_output"; }; @@ -8015,6 +7121,10 @@ export type components = { * @description Saves an image. Unlike an image primitive, this invocation stores a copy of the image. */ SaveImageInvocation: { + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -8026,36 +7136,20 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default false */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Board - * @description The board to save the image to - */ + /** @description The board to save the image to */ board?: components["schemas"]["BoardField"]; /** - * Metadata - * @description Optional core metadata to be written to image - */ - metadata?: components["schemas"]["CoreMetadata"]; - /** - * Type + * type * @default save_image - * @enum {string} + * @constant */ type: "save_image"; }; @@ -8075,21 +7169,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Latents - * @description Latents tensor - */ + /** @description Latents tensor */ latents?: components["schemas"]["LatentsField"]; /** * Scale Factor @@ -8110,9 +7196,9 @@ export type components = { */ antialias?: boolean; /** - * Type + * type * @default lscale - * @enum {string} + * @constant */ type: "lscale"; }; @@ -8132,11 +7218,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -8151,18 +7232,13 @@ export type components = { */ 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 + * type * @default scheduler - * @enum {string} + * @constant */ type: "scheduler"; }; - /** - * SchedulerOutput - * @description Base class for all invocation outputs. - * - * All invocation outputs must use the `@invocation_output` decorator to provide their unique type. - */ + /** SchedulerOutput */ SchedulerOutput: { /** * Scheduler @@ -8171,15 +7247,14 @@ export type components = { */ 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 + * type * @default scheduler_output - * @enum {string} + * @constant */ type: "scheduler_output"; }; /** * SchedulerPredictionType - * @description An enumeration. * @enum {string} */ SchedulerPredictionType: "epsilon" | "v_prediction" | "sample"; @@ -8199,11 +7274,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -8214,12 +7284,12 @@ export type components = { * UNet * @description UNet (scheduler, LoRAs) */ - unet?: components["schemas"]["UNetField"]; + unet?: components["schemas"]["UNetField"] | null; /** * VAE * @description VAE model to load */ - vae?: components["schemas"]["VaeField"]; + vae?: components["schemas"]["VaeField"] | null; /** * Seamless Y * @description Specify whether Y axis is seamless @@ -8233,9 +7303,9 @@ export type components = { */ seamless_x?: boolean; /** - * Type + * type * @default seamless - * @enum {string} + * @constant */ type: "seamless"; }; @@ -8248,16 +7318,16 @@ export type components = { * UNet * @description UNet (scheduler, LoRAs) */ - unet?: components["schemas"]["UNetField"]; + unet?: components["schemas"]["UNetField"] | null; /** * VAE * @description VAE */ - vae?: components["schemas"]["VaeField"]; + vae?: components["schemas"]["VaeField"] | null; /** - * Type + * type * @default seamless_output - * @enum {string} + * @constant */ type: "seamless_output"; }; @@ -8266,6 +7336,10 @@ export type components = { * @description Applies segment anything processing to image */ SegmentAnythingProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -8277,26 +7351,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; /** - * Type + * type * @default segment_anything_processor - * @enum {string} + * @constant */ type: "segment_anything_processor"; }; @@ -8321,10 +7387,7 @@ export type components = { queue: components["schemas"]["SessionQueueStatus"]; processor: components["schemas"]["SessionProcessorStatus"]; }; - /** - * SessionQueueItem - * @description Session queue item without the full graph. Used for serialization. - */ + /** SessionQueueItem */ SessionQueueItem: { /** * Item Id @@ -8358,7 +7421,7 @@ export type components = { * Error * @description The error message if this queue item errored */ - error?: string; + error?: string | null; /** * Created At * @description When this queue item was created @@ -8373,12 +7436,12 @@ export type components = { * Started At * @description When this queue item was started */ - started_at?: string; + started_at?: string | null; /** * Completed At * @description When this queue item was completed */ - completed_at?: string; + completed_at?: string | null; /** * Queue Id * @description The id of the queue with which this item is associated @@ -8388,17 +7451,11 @@ export type components = { * Field Values * @description The field values that were used for this queue item */ - field_values?: components["schemas"]["NodeFieldValue"][]; - /** - * Session - * @description The fully-populated session to be executed - */ + field_values?: components["schemas"]["NodeFieldValue"][] | null; + /** @description The fully-populated session to be executed */ session: components["schemas"]["GraphExecutionState"]; }; - /** - * SessionQueueItemDTO - * @description Session queue item without the full graph. Used for serialization. - */ + /** SessionQueueItemDTO */ SessionQueueItemDTO: { /** * Item Id @@ -8432,7 +7489,7 @@ export type components = { * Error * @description The error message if this queue item errored */ - error?: string; + error?: string | null; /** * Created At * @description When this queue item was created @@ -8447,12 +7504,12 @@ export type components = { * Started At * @description When this queue item was started */ - started_at?: string; + started_at?: string | null; /** * Completed At * @description When this queue item was completed */ - completed_at?: string; + completed_at?: string | null; /** * Queue Id * @description The id of the queue with which this item is associated @@ -8462,7 +7519,7 @@ export type components = { * Field Values * @description The field values that were used for this queue item */ - field_values?: components["schemas"]["NodeFieldValue"][]; + field_values?: components["schemas"]["NodeFieldValue"][] | null; }; /** SessionQueueStatus */ SessionQueueStatus: { @@ -8475,17 +7532,17 @@ export type components = { * Item Id * @description The current queue item id */ - item_id?: number; + item_id: number | null; /** * Batch Id * @description The current queue item's batch id */ - batch_id?: string; + batch_id: string | null; /** * Session Id * @description The current queue item's session id */ - session_id?: string; + session_id: string | null; /** * Pending * @description Number of queue items with status 'pending' @@ -8533,26 +7590,18 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to show - */ + /** @description The image to show */ image?: components["schemas"]["ImageField"]; /** - * Type + * type * @default show_image - * @enum {string} + * @constant */ type: "show_image"; }; @@ -8563,21 +7612,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default main + * @constant */ model_type: "main"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "checkpoint"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Vae */ - vae?: string; + vae?: string | null; /** Config */ config: string; variant: components["schemas"]["ModelVariantType"]; @@ -8589,21 +7639,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default main + * @constant */ model_type: "main"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "diffusers"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Vae */ - vae?: string; + vae?: string | null; variant: components["schemas"]["ModelVariantType"]; }; /** StableDiffusion2ModelCheckpointConfig */ @@ -8613,21 +7664,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default main + * @constant */ model_type: "main"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "checkpoint"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Vae */ - vae?: string; + vae?: string | null; /** Config */ config: string; variant: components["schemas"]["ModelVariantType"]; @@ -8639,21 +7691,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default main + * @constant */ model_type: "main"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "diffusers"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Vae */ - vae?: string; + vae?: string | null; variant: components["schemas"]["ModelVariantType"]; }; /** StableDiffusionXLModelCheckpointConfig */ @@ -8663,21 +7716,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default main + * @constant */ model_type: "main"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "checkpoint"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Vae */ - vae?: string; + vae?: string | null; /** Config */ config: string; variant: components["schemas"]["ModelVariantType"]; @@ -8689,21 +7743,22 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default main + * @constant */ model_type: "main"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "diffusers"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; /** Vae */ - vae?: string; + vae?: string | null; variant: components["schemas"]["ModelVariantType"]; }; /** @@ -8722,11 +7777,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -8774,12 +7824,12 @@ export type components = { * Pre Start Value * @description value before easing start */ - pre_start_value?: number; + pre_start_value?: number | null; /** * Post End Value * @description value after easing end */ - post_end_value?: number; + post_end_value?: number | null; /** * Mirror * @description include mirror of easing function @@ -8793,9 +7843,9 @@ export type components = { */ show_easing_plot?: boolean; /** - * Type + * type * @default step_param_easing - * @enum {string} + * @constant */ type: "step_param_easing"; }; @@ -8815,9 +7865,9 @@ export type components = { */ string_2: string; /** - * Type + * type * @default string_2_output - * @enum {string} + * @constant */ type: "string_2_output"; }; @@ -8837,11 +7887,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -8854,9 +7899,9 @@ export type components = { */ collection?: string[]; /** - * Type + * type * @default string_collection - * @enum {string} + * @constant */ type: "string_collection"; }; @@ -8871,9 +7916,9 @@ export type components = { */ collection: string[]; /** - * Type + * type * @default string_collection_output - * @enum {string} + * @constant */ type: "string_collection_output"; }; @@ -8893,11 +7938,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -8911,9 +7951,9 @@ export type components = { */ value?: string; /** - * Type + * type * @default string - * @enum {string} + * @constant */ type: "string"; }; @@ -8933,11 +7973,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -8957,9 +7992,9 @@ export type components = { */ string_right?: string; /** - * Type + * type * @default string_join - * @enum {string} + * @constant */ type: "string_join"; }; @@ -8979,11 +8014,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -9009,9 +8039,9 @@ export type components = { */ string_right?: string; /** - * Type + * type * @default string_join_three - * @enum {string} + * @constant */ type: "string_join_three"; }; @@ -9026,9 +8056,9 @@ export type components = { */ value: string; /** - * Type + * type * @default string_output - * @enum {string} + * @constant */ type: "string_output"; }; @@ -9048,9 +8078,9 @@ export type components = { */ negative_string: string; /** - * Type + * type * @default string_pos_neg_output - * @enum {string} + * @constant */ type: "string_pos_neg_output"; }; @@ -9070,11 +8100,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -9106,9 +8131,9 @@ export type components = { */ use_regex?: boolean; /** - * Type + * type * @default string_replace - * @enum {string} + * @constant */ type: "string_replace"; }; @@ -9128,11 +8153,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -9152,9 +8172,9 @@ export type components = { */ delimiter?: string; /** - * Type + * type * @default string_split - * @enum {string} + * @constant */ type: "string_split"; }; @@ -9174,11 +8194,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -9192,15 +8207,14 @@ export type components = { */ string?: string; /** - * Type + * type * @default string_split_neg - * @enum {string} + * @constant */ type: "string_split_neg"; }; /** * SubModelType - * @description An enumeration. * @enum {string} */ SubModelType: "unet" | "text_encoder" | "text_encoder_2" | "tokenizer" | "tokenizer_2" | "vae" | "vae_decoder" | "vae_encoder" | "scheduler" | "safety_checker"; @@ -9220,11 +8234,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -9244,23 +8253,17 @@ export type components = { */ b?: number; /** - * Type + * type * @default sub - * @enum {string} + * @constant */ type: "sub"; }; /** T2IAdapterField */ T2IAdapterField: { - /** - * Image - * @description The T2I-Adapter image prompt. - */ + /** @description The T2I-Adapter image prompt. */ image: components["schemas"]["ImageField"]; - /** - * T2I Adapter Model - * @description The T2I-Adapter model to use. - */ + /** @description The T2I-Adapter model to use. */ t2i_adapter_model: components["schemas"]["T2IAdapterModelField"]; /** * Weight @@ -9304,21 +8307,13 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The IP-Adapter image prompt. - */ + /** @description The IP-Adapter image prompt. */ image?: components["schemas"]["ImageField"]; /** * T2I-Adapter Model @@ -9351,9 +8346,9 @@ export type components = { */ resize_mode?: "just_resize" | "crop_resize" | "fill_resize" | "just_resize_simple"; /** - * Type + * type * @default t2i_adapter - * @enum {string} + * @constant */ type: "t2i_adapter"; }; @@ -9364,19 +8359,20 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default t2i_adapter + * @constant */ model_type: "t2i_adapter"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; /** * Model Format - * @enum {string} + * @constant */ model_format: "diffusers"; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; }; /** T2IAdapterModelField */ T2IAdapterModelField: { @@ -9388,12 +8384,7 @@ export type components = { /** @description Base model */ base_model: components["schemas"]["BaseModelType"]; }; - /** - * T2IAdapterOutput - * @description Base class for all invocation outputs. - * - * All invocation outputs must use the `@invocation_output` decorator to provide their unique type. - */ + /** T2IAdapterOutput */ T2IAdapterOutput: { /** * T2I Adapter @@ -9401,35 +8392,14 @@ export type components = { */ t2i_adapter: components["schemas"]["T2IAdapterField"]; /** - * Type + * type * @default t2i_adapter_output - * @enum {string} + * @constant */ type: "t2i_adapter_output"; }; - /** TextualInversionModelConfig */ - TextualInversionModelConfig: { - /** Model Name */ - model_name: string; - base_model: components["schemas"]["BaseModelType"]; - /** - * Model Type - * @enum {string} - */ - model_type: "embedding"; - /** Path */ - path: string; - /** Description */ - description?: string; - /** Model Format */ - model_format: null; - error?: components["schemas"]["ModelError"]; - }; - /** - * Tile Resample Processor - * @description Tile resampler processor - */ - TileResamplerProcessorInvocation: { + /** TestInvocation */ + TestInvocation: { /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -9442,45 +8412,141 @@ export type components = { */ is_intermediate?: boolean; /** - * Workflow - * @description The workflow to save with the image + * Use Cache + * @description Whether or not to use the cache + * @default true */ - workflow?: string; + use_cache?: boolean; + /** A */ + a?: string; + /** + * type + * @default test_invocation + * @constant + */ + type: "test_invocation"; + }; + /** TestInvocation2 */ + TestInvocation2: { + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; + /** A */ + a?: string; /** - * Image - * @description The image to process + * type + * @default test_invocation_2 + * @constant */ + type: "test_invocation_2"; + }; + /** TestInvocation3 */ + TestInvocation3: { + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** A */ + a?: string; + /** + * type + * @default test_invocation_3 + * @constant + */ + type: "test_invocation_3"; + }; + /** TextualInversionModelConfig */ + TextualInversionModelConfig: { + /** Model Name */ + model_name: string; + base_model: components["schemas"]["BaseModelType"]; + /** + * Model Type + * @default embedding + * @constant + */ + model_type: "embedding"; + /** Path */ + path: string; + /** Description */ + description?: string | null; + /** Model Format */ + model_format: null; + error?: components["schemas"]["ModelError"] | null; + }; + /** + * Tile Resample Processor + * @description Tile resampler processor + */ + TileResamplerProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; + /** + * Id + * @description The id of this instance of an invocation. Must be unique among all instances of invocations. + */ + id: string; + /** + * Is Intermediate + * @description Whether or not this is an intermediate invocation. + * @default false + */ + is_intermediate?: boolean; + /** + * Use Cache + * @description Whether or not to use the cache + * @default true + */ + use_cache?: boolean; + /** @description The image to process */ image?: components["schemas"]["ImageField"]; - /** - * Type - * @default tile_image_processor - * @enum {string} - */ - type: "tile_image_processor"; /** * Down Sampling Rate * @description Down sampling rate * @default 1 */ down_sampling_rate?: number; + /** + * type + * @default tile_image_processor + * @constant + */ + type: "tile_image_processor"; }; /** UNetField */ UNetField: { - /** - * Unet - * @description Info to load unet submodel - */ + /** @description Info to load unet submodel */ unet: components["schemas"]["ModelInfo"]; - /** - * Scheduler - * @description Info to load scheduler submodel - */ + /** @description Info to load scheduler submodel */ scheduler: components["schemas"]["ModelInfo"]; /** * Loras @@ -9521,10 +8587,7 @@ export type components = { }; /** VaeField */ VaeField: { - /** - * Vae - * @description Info to load vae submodel - */ + /** @description Info to load vae submodel */ vae: components["schemas"]["ModelInfo"]; /** * Seamless Axes @@ -9548,11 +8611,6 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache @@ -9565,9 +8623,9 @@ export type components = { */ vae_model: components["schemas"]["VAEModelField"]; /** - * Type + * type * @default vae_loader - * @enum {string} + * @constant */ type: "vae_loader"; }; @@ -9582,9 +8640,9 @@ export type components = { */ vae: components["schemas"]["VaeField"]; /** - * Type + * type * @default vae_loader_output - * @enum {string} + * @constant */ type: "vae_loader_output"; }; @@ -9595,19 +8653,19 @@ export type components = { base_model: components["schemas"]["BaseModelType"]; /** * Model Type - * @enum {string} + * @default vae + * @constant */ model_type: "vae"; /** Path */ path: string; /** Description */ - description?: string; + description?: string | null; model_format: components["schemas"]["VaeModelFormat"]; - error?: components["schemas"]["ModelError"]; + error?: components["schemas"]["ModelError"] | null; }; /** * VaeModelFormat - * @description An enumeration. * @enum {string} */ VaeModelFormat: "checkpoint" | "diffusers"; @@ -9620,11 +8678,21 @@ export type components = { /** Error Type */ type: string; }; + /** + * WorkflowField + * @description Pydantic model for workflows with custom root of type dict[str, Any]. + * Workflows are stored without a strict schema. + */ + WorkflowField: Record; /** * Zoe (Depth) Processor * @description Applies Zoe depth processing to image */ ZoeDepthImageProcessorInvocation: { + /** @description Optional workflow to be saved with the image */ + workflow?: components["schemas"]["WorkflowField"] | null; + /** @description Optional metadata to be saved with the image */ + metadata?: components["schemas"]["MetadataField"] | null; /** * Id * @description The id of this instance of an invocation. Must be unique among all instances of invocations. @@ -9636,56 +8704,21 @@ export type components = { * @default false */ is_intermediate?: boolean; - /** - * Workflow - * @description The workflow to save with the image - */ - workflow?: string; /** * Use Cache * @description Whether or not to use the cache * @default true */ use_cache?: boolean; - /** - * Image - * @description The image to process - */ + /** @description The image to process */ image?: components["schemas"]["ImageField"]; /** - * Type + * type * @default zoe_depth_image_processor - * @enum {string} + * @constant */ type: "zoe_depth_image_processor"; }; - /** - * UIConfigBase - * @description Provides additional node configuration to the UI. - * This is used internally by the @invocation decorator logic. Do not use this directly. - */ - UIConfigBase: { - /** - * Tags - * @description The node's tags - */ - tags?: string[]; - /** - * Title - * @description The node's display name - */ - title?: string; - /** - * Category - * @description The node's category - */ - category?: string; - /** - * Version - * @description The node's version. Should be a valid semver string e.g. "1.0.0" or "3.8.13". - */ - version?: string; - }; /** * Input * @description The type of input a field accepts. @@ -9695,19 +8728,49 @@ export type components = { * @enum {string} */ Input: "connection" | "direct" | "any"; - /** - * UIType - * @description Type hints for the UI. - * If a field should be provided a data type that does not exactly match the python type of the field, use this to provide the type that should be used instead. See the node development docs for detail on adding a new field type, which involves client-side changes. - * @enum {string} - */ - UIType: "boolean" | "ColorField" | "ConditioningField" | "ControlField" | "float" | "ImageField" | "integer" | "LatentsField" | "string" | "BooleanCollection" | "ColorCollection" | "ConditioningCollection" | "ControlCollection" | "FloatCollection" | "ImageCollection" | "IntegerCollection" | "LatentsCollection" | "StringCollection" | "BooleanPolymorphic" | "ColorPolymorphic" | "ConditioningPolymorphic" | "ControlPolymorphic" | "FloatPolymorphic" | "ImagePolymorphic" | "IntegerPolymorphic" | "LatentsPolymorphic" | "StringPolymorphic" | "MainModelField" | "SDXLMainModelField" | "SDXLRefinerModelField" | "ONNXModelField" | "VaeModelField" | "LoRAModelField" | "ControlNetModelField" | "IPAdapterModelField" | "UNetField" | "VaeField" | "ClipField" | "Collection" | "CollectionItem" | "enum" | "Scheduler" | "WorkflowField" | "IsIntermediate" | "MetadataField" | "BoardField"; /** * UIComponent * @description The type of UI component to use for a field, used to override the default components, which are inferred from the field type. * @enum {string} */ UIComponent: "none" | "textarea" | "slider"; + /** + * UIConfigBase + * @description Provides additional node configuration to the UI. + * This is used internally by the @invocation decorator logic. Do not use this directly. + */ + UIConfigBase: { + /** + * Tags + * @description The node's tags + */ + tags: string[] | null; + /** + * Title + * @description The node's display name + * @default null + */ + title: string | null; + /** + * Category + * @description The node's category + * @default null + */ + category: string | null; + /** + * Version + * @description The node's version. Should be a valid semver string e.g. "1.0.0" or "3.8.13". + * @default null + */ + version: string | null; + }; + /** + * UIType + * @description Type hints for the UI. + * If a field should be provided a data type that does not exactly match the python type of the field, use this to provide the type that should be used instead. See the node development docs for detail on adding a new field type, which involves client-side changes. + * @enum {string} + */ + UIType: "boolean" | "ColorField" | "ConditioningField" | "ControlField" | "float" | "ImageField" | "integer" | "LatentsField" | "string" | "BooleanCollection" | "ColorCollection" | "ConditioningCollection" | "ControlCollection" | "FloatCollection" | "ImageCollection" | "IntegerCollection" | "LatentsCollection" | "StringCollection" | "BooleanPolymorphic" | "ColorPolymorphic" | "ConditioningPolymorphic" | "ControlPolymorphic" | "FloatPolymorphic" | "ImagePolymorphic" | "IntegerPolymorphic" | "LatentsPolymorphic" | "StringPolymorphic" | "MainModelField" | "SDXLMainModelField" | "SDXLRefinerModelField" | "ONNXModelField" | "VaeModelField" | "LoRAModelField" | "ControlNetModelField" | "IPAdapterModelField" | "UNetField" | "VaeField" | "ClipField" | "Collection" | "CollectionItem" | "enum" | "Scheduler" | "WorkflowField" | "IsIntermediate" | "BoardField" | "Any" | "MetadataItem" | "MetadataItemCollection" | "MetadataItemPolymorphic" | "MetadataDict"; /** * _InputField * @description *DO NOT USE* @@ -9719,16 +8782,16 @@ export type components = { input: components["schemas"]["Input"]; /** Ui Hidden */ ui_hidden: boolean; - ui_type?: components["schemas"]["UIType"]; - ui_component?: components["schemas"]["UIComponent"]; + ui_type: components["schemas"]["UIType"] | null; + ui_component: components["schemas"]["UIComponent"] | null; /** Ui Order */ - ui_order?: number; + ui_order: number | null; /** Ui Choice Labels */ - ui_choice_labels?: { + ui_choice_labels: { [key: string]: string; - }; + } | null; /** Item Default */ - item_default?: unknown; + item_default: unknown; }; /** * _OutputField @@ -9740,22 +8803,16 @@ export type components = { _OutputField: { /** Ui Hidden */ ui_hidden: boolean; - ui_type?: components["schemas"]["UIType"]; + ui_type: components["schemas"]["UIType"] | null; /** Ui Order */ - ui_order?: number; + ui_order: number | null; }; /** - * StableDiffusion1ModelFormat + * IPAdapterModelFormat * @description An enumeration. * @enum {string} */ - StableDiffusion1ModelFormat: "checkpoint" | "diffusers"; - /** - * StableDiffusionXLModelFormat - * @description An enumeration. - * @enum {string} - */ - StableDiffusionXLModelFormat: "checkpoint" | "diffusers"; + IPAdapterModelFormat: "invokeai"; /** * IPAdapterModelFormat * @description An enumeration. @@ -9768,30 +8825,36 @@ export type components = { * @enum {string} */ T2IAdapterModelFormat: "diffusers"; - /** - * ControlNetModelFormat - * @description An enumeration. - * @enum {string} - */ - ControlNetModelFormat: "checkpoint" | "diffusers"; /** * StableDiffusion2ModelFormat * @description An enumeration. * @enum {string} */ StableDiffusion2ModelFormat: "checkpoint" | "diffusers"; - /** - * CLIPVisionModelFormat - * @description An enumeration. - * @enum {string} - */ - CLIPVisionModelFormat: "diffusers"; /** * StableDiffusionOnnxModelFormat * @description An enumeration. * @enum {string} */ StableDiffusionOnnxModelFormat: "olive" | "onnx"; + /** + * StableDiffusion1ModelFormat + * @description An enumeration. + * @enum {string} + */ + StableDiffusion1ModelFormat: "checkpoint" | "diffusers"; + /** + * ControlNetModelFormat + * @description An enumeration. + * @enum {string} + */ + ControlNetModelFormat: "checkpoint" | "diffusers"; + /** + * StableDiffusionXLModelFormat + * @description An enumeration. + * @enum {string} + */ + StableDiffusionXLModelFormat: "checkpoint" | "diffusers"; }; responses: never; parameters: never; @@ -9806,76 +8869,8 @@ export type external = Record; export type operations = { - /** - * List Sessions - * @deprecated - * @description Gets a list of sessions, optionally searching - */ - list_sessions: { - parameters: { - query?: { - /** @description The page of results to get */ - page?: number; - /** @description The number of results per page */ - per_page?: number; - /** @description The query string to search for */ - query?: string; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["PaginatedResults_GraphExecutionState_"]; - }; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Create Session - * @deprecated - * @description Creates a new session, optionally initializing it with an invocation graph - */ - create_session: { - parameters: { - query?: { - /** @description The id of the queue to associate the session with */ - queue_id?: string; - }; - }; - requestBody?: { - content: { - "application/json": components["schemas"]["Graph"]; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["GraphExecutionState"]; - }; - }; - /** @description Invalid json */ - 400: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; /** * Get Session - * @deprecated * @description Gets a session */ get_session: { @@ -9904,284 +8899,6 @@ export type operations = { }; }; }; - /** - * Add Node - * @deprecated - * @description Adds a node to the graph - */ - add_node: { - parameters: { - path: { - /** @description The id of the session */ - session_id: string; - }; - }; - requestBody: { - content: { - "application/json": components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["BlankImageInvocation"] | 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"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["CV2InfillInvocation"] | 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"]["ColorMapImageProcessorInvocation"]; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": string; - }; - }; - /** @description Invalid node or link */ - 400: { - content: never; - }; - /** @description Session not found */ - 404: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Update Node - * @deprecated - * @description Updates a node in the graph and removes all linked edges - */ - update_node: { - parameters: { - path: { - /** @description The id of the session */ - session_id: string; - /** @description The path to the node in the graph */ - node_path: string; - }; - }; - requestBody: { - content: { - "application/json": components["schemas"]["BooleanInvocation"] | components["schemas"]["BooleanCollectionInvocation"] | components["schemas"]["IntegerInvocation"] | components["schemas"]["IntegerCollectionInvocation"] | components["schemas"]["FloatInvocation"] | components["schemas"]["FloatCollectionInvocation"] | components["schemas"]["StringInvocation"] | components["schemas"]["StringCollectionInvocation"] | components["schemas"]["ImageInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["LatentsInvocation"] | components["schemas"]["LatentsCollectionInvocation"] | components["schemas"]["ColorInvocation"] | components["schemas"]["ConditioningInvocation"] | components["schemas"]["ConditioningCollectionInvocation"] | components["schemas"]["FaceOffInvocation"] | components["schemas"]["FaceMaskInvocation"] | components["schemas"]["FaceIdentifierInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["SeamlessModeInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["IPAdapterInvocation"] | components["schemas"]["T2IAdapterInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["SchedulerInvocation"] | components["schemas"]["CreateDenoiseMaskInvocation"] | components["schemas"]["DenoiseLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["BlendLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["BlankImageInvocation"] | 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"]["ImageChannelOffsetInvocation"] | components["schemas"]["ImageChannelMultiplyInvocation"] | components["schemas"]["SaveImageInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["RandomFloatInvocation"] | components["schemas"]["FloatToIntegerInvocation"] | components["schemas"]["RoundInvocation"] | components["schemas"]["IntegerMathInvocation"] | components["schemas"]["FloatMathInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["StringSplitNegInvocation"] | components["schemas"]["StringSplitInvocation"] | components["schemas"]["StringJoinInvocation"] | components["schemas"]["StringJoinThreeInvocation"] | components["schemas"]["StringReplaceInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["LaMaInfillInvocation"] | components["schemas"]["CV2InfillInvocation"] | 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"]["ColorMapImageProcessorInvocation"]; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["GraphExecutionState"]; - }; - }; - /** @description Invalid node or link */ - 400: { - content: never; - }; - /** @description Session not found */ - 404: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Delete Node - * @deprecated - * @description Deletes a node in the graph and removes all linked edges - */ - delete_node: { - parameters: { - path: { - /** @description The id of the session */ - session_id: string; - /** @description The path to the node to delete */ - node_path: string; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["GraphExecutionState"]; - }; - }; - /** @description Invalid node or link */ - 400: { - content: never; - }; - /** @description Session not found */ - 404: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Add Edge - * @deprecated - * @description Adds an edge to the graph - */ - add_edge: { - parameters: { - path: { - /** @description The id of the session */ - session_id: string; - }; - }; - requestBody: { - content: { - "application/json": components["schemas"]["Edge"]; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["GraphExecutionState"]; - }; - }; - /** @description Invalid node or link */ - 400: { - content: never; - }; - /** @description Session not found */ - 404: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Delete Edge - * @deprecated - * @description Deletes an edge from the graph - */ - delete_edge: { - parameters: { - path: { - /** @description The id of the session */ - session_id: string; - /** @description The id of the node the edge is coming from */ - from_node_id: string; - /** @description The field of the node the edge is coming from */ - from_field: string; - /** @description The id of the node the edge is going to */ - to_node_id: string; - /** @description The field of the node the edge is going to */ - to_field: string; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": components["schemas"]["GraphExecutionState"]; - }; - }; - /** @description Invalid node or link */ - 400: { - content: never; - }; - /** @description Session not found */ - 404: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Invoke Session - * @deprecated - * @description Invokes a session - */ - invoke_session: { - parameters: { - query: { - /** @description The id of the queue to associate the session with */ - queue_id: string; - /** @description Whether or not to invoke all remaining invocations */ - all?: boolean; - }; - path: { - /** @description The id of the session to invoke */ - session_id: string; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": unknown; - }; - }; - /** @description The invocation is queued */ - 202: { - content: never; - }; - /** @description The session has no invocations ready to invoke */ - 400: { - content: never; - }; - /** @description Session not found */ - 404: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; - /** - * Cancel Session Invoke - * @deprecated - * @description Invokes a session - */ - cancel_session_invoke: { - parameters: { - path: { - /** @description The id of the session to cancel */ - session_id: string; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": unknown; - }; - }; - /** @description The invocation is canceled */ - 202: { - content: never; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; /** * Parse Dynamicprompts * @description Creates a batch process @@ -10215,9 +8932,9 @@ export type operations = { parameters: { query?: { /** @description Base models to include */ - base_models?: components["schemas"]["BaseModelType"][]; + base_models?: components["schemas"]["BaseModelType"][] | null; /** @description The type of model to get */ - model_type?: components["schemas"]["ModelType"]; + model_type?: components["schemas"]["ModelType"] | null; }; }; responses: { @@ -10400,7 +9117,7 @@ export type operations = { parameters: { query?: { /** @description Save the converted model to the designated directory */ - convert_dest_directory?: string; + convert_dest_directory?: string | null; }; path: { /** @description Base model */ @@ -10541,11 +9258,11 @@ export type operations = { /** @description Whether this is an intermediate image */ is_intermediate: boolean; /** @description The board to add this image to, if any */ - board_id?: string; + board_id?: string | null; /** @description The session ID associated with this upload, if any */ - session_id?: string; + session_id?: string | null; /** @description Whether to crop the image */ - crop_visible?: boolean; + crop_visible?: boolean | null; }; }; requestBody: { @@ -10655,6 +9372,20 @@ export type operations = { }; }; }; + /** + * Get Intermediates Count + * @description Gets the count of intermediate images + */ + get_intermediates_count: { + responses: { + /** @description Successful Response */ + 200: { + content: { + "application/json": number; + }; + }; + }; + }; /** * Clear Intermediates * @description Clears all intermediates @@ -10664,7 +9395,7 @@ export type operations = { /** @description Successful Response */ 200: { content: { - "application/json": unknown; + "application/json": number; }; }; }; @@ -10684,7 +9415,7 @@ export type operations = { /** @description Successful Response */ 200: { content: { - "application/json": components["schemas"]["ImageMetadata"]; + "application/json": components["schemas"]["MetadataField"] | null; }; }; /** @description Validation Error */ @@ -10789,13 +9520,13 @@ export type operations = { parameters: { query?: { /** @description The origin of images to list. */ - image_origin?: components["schemas"]["ResourceOrigin"]; + image_origin?: components["schemas"]["ResourceOrigin"] | null; /** @description The categories of image to include. */ - categories?: components["schemas"]["ImageCategory"][]; + categories?: components["schemas"]["ImageCategory"][] | null; /** @description Whether to list intermediate images. */ - is_intermediate?: boolean; + is_intermediate?: boolean | null; /** @description The board id to filter by. Use 'none' to find images without a board. */ - board_id?: string; + board_id?: string | null; /** @description The page offset */ offset?: number; /** @description The number of images per page */ @@ -10913,11 +9644,11 @@ export type operations = { parameters: { query?: { /** @description Whether to list all boards */ - all?: boolean; + all?: boolean | null; /** @description The page offset */ - offset?: number; + offset?: number | null; /** @description The number of boards per page */ - limit?: number; + limit?: number | null; }; }; responses: { @@ -10995,7 +9726,7 @@ export type operations = { parameters: { query?: { /** @description Permanently delete all images on the board */ - include_images?: boolean; + include_images?: boolean | null; }; path: { /** @description The id of board to delete */ @@ -11291,43 +10022,6 @@ export type operations = { }; }; }; - /** - * Enqueue Graph - * @description Enqueues a graph for single execution. - */ - enqueue_graph: { - parameters: { - path: { - /** @description The queue id to perform this operation on */ - queue_id: string; - }; - }; - requestBody: { - content: { - "application/json": components["schemas"]["Body_enqueue_graph"]; - }; - }; - responses: { - /** @description Successful Response */ - 200: { - content: { - "application/json": unknown; - }; - }; - /** @description Created */ - 201: { - content: { - "application/json": components["schemas"]["EnqueueGraphResult"]; - }; - }; - /** @description Validation Error */ - 422: { - content: { - "application/json": components["schemas"]["HTTPValidationError"]; - }; - }; - }; - }; /** * Enqueue Batch * @description Processes a batch and enqueues the output graphs for execution. @@ -11348,7 +10042,7 @@ export type operations = { /** @description Successful Response */ 200: { content: { - "application/json": unknown; + "application/json": components["schemas"]["EnqueueBatchResult"]; }; }; /** @description Created */ @@ -11375,9 +10069,9 @@ export type operations = { /** @description The number of items to fetch */ limit?: number; /** @description The status of items to fetch */ - status?: "pending" | "in_progress" | "completed" | "failed" | "canceled"; + status?: ("pending" | "in_progress" | "completed" | "failed" | "canceled") | null; /** @description The pagination cursor */ - cursor?: number; + cursor?: number | null; /** @description The pagination cursor priority */ priority?: number; }; @@ -11551,7 +10245,7 @@ export type operations = { /** @description Successful Response */ 200: { content: { - "application/json": components["schemas"]["SessionQueueItem"]; + "application/json": components["schemas"]["SessionQueueItem"] | null; }; }; /** @description Validation Error */ @@ -11577,7 +10271,7 @@ export type operations = { /** @description Successful Response */ 200: { content: { - "application/json": components["schemas"]["SessionQueueItem"]; + "application/json": components["schemas"]["SessionQueueItem"] | null; }; }; /** @description Validation Error */ @@ -11698,4 +10392,30 @@ export type operations = { }; }; }; + /** + * Get Workflow + * @description Gets a workflow + */ + get_workflow: { + parameters: { + path: { + /** @description The workflow to get */ + workflow_id: string; + }; + }; + responses: { + /** @description Successful Response */ + 200: { + content: { + "application/json": components["schemas"]["WorkflowField"]; + }; + }; + /** @description Validation Error */ + 422: { + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; }; diff --git a/invokeai/frontend/web/src/services/api/types.ts b/invokeai/frontend/web/src/services/api/types.ts index 6fda849b89..085ea65327 100644 --- a/invokeai/frontend/web/src/services/api/types.ts +++ b/invokeai/frontend/web/src/services/api/types.ts @@ -26,15 +26,6 @@ export type BatchConfig = paths['/api/v1/queue/{queue_id}/enqueue_batch']['post']['requestBody']['content']['application/json']; export type EnqueueBatchResult = components['schemas']['EnqueueBatchResult']; -export type EnqueueGraphResult = components['schemas']['EnqueueGraphResult']; - -/** - * This is an unsafe type; the object inside is not guaranteed to be valid. - */ -export type UnsafeImageMetadata = { - metadata: s['CoreMetadata']; - graph: NonNullable; -}; export type _InputField = s['_InputField']; export type _OutputField = s['_OutputField']; @@ -51,7 +42,6 @@ export type ImageChanges = s['ImageRecordChanges']; export type ImageCategory = s['ImageCategory']; export type ResourceOrigin = s['ResourceOrigin']; export type ImageField = s['ImageField']; -export type ImageMetadata = s['ImageMetadata']; export type OffsetPaginatedResults_BoardDTO_ = s['OffsetPaginatedResults_BoardDTO_']; export type OffsetPaginatedResults_ImageDTO_ = @@ -146,13 +136,19 @@ export type ImageCollectionInvocation = s['ImageCollectionInvocation']; export type MainModelLoaderInvocation = s['MainModelLoaderInvocation']; export type OnnxModelLoaderInvocation = s['OnnxModelLoaderInvocation']; export type LoraLoaderInvocation = s['LoraLoaderInvocation']; -export type MetadataAccumulatorInvocation = s['MetadataAccumulatorInvocation']; export type ESRGANInvocation = s['ESRGANInvocation']; export type DivideInvocation = s['DivideInvocation']; export type ImageNSFWBlurInvocation = s['ImageNSFWBlurInvocation']; export type ImageWatermarkInvocation = s['ImageWatermarkInvocation']; export type SeamlessModeInvocation = s['SeamlessModeInvocation']; export type SaveImageInvocation = s['SaveImageInvocation']; +export type MetadataInvocation = s['MetadataInvocation']; +export type CoreMetadataInvocation = s['CoreMetadataInvocation']; +export type MetadataItemInvocation = s['MetadataItemInvocation']; +export type MergeMetadataInvocation = s['MergeMetadataInvocation']; +export type IPAdapterMetadataField = s['IPAdapterMetadataField']; +export type T2IAdapterField = s['T2IAdapterField']; +export type LoRAMetadataField = s['LoRAMetadataField']; // ControlNet Nodes export type ControlNetInvocation = s['ControlNetInvocation']; diff --git a/invokeai/frontend/web/static/dream_web/favicon.ico b/invokeai/frontend/web/static/docs/favicon.ico similarity index 100% rename from invokeai/frontend/web/static/dream_web/favicon.ico rename to invokeai/frontend/web/static/docs/favicon.ico diff --git a/invokeai/frontend/web/static/dream_web/index.css b/invokeai/frontend/web/static/dream_web/index.css deleted file mode 100644 index 25a0994a3d..0000000000 --- a/invokeai/frontend/web/static/dream_web/index.css +++ /dev/null @@ -1,179 +0,0 @@ -:root { - --fields-dark:#DCDCDC; - --fields-light:#F5F5F5; -} - -* { - font-family: 'Arial'; - font-size: 100%; -} -body { - font-size: 1em; -} -textarea { - font-size: 0.95em; -} -header, form, #progress-section { - margin-left: auto; - margin-right: auto; - max-width: 1024px; - text-align: center; -} -fieldset { - border: none; - line-height: 2.2em; -} -fieldset > legend { - width: auto; - margin-left: 0; - margin-right: auto; - font-weight:bold; -} -select, input { - margin-right: 10px; - padding: 2px; -} -input:disabled { - cursor:auto; -} -input[type=submit] { - cursor: pointer; - background-color: #666; - color: white; -} -input[type=checkbox] { - cursor: pointer; - margin-right: 0px; - width: 20px; - height: 20px; - vertical-align: middle; -} -input#seed { - margin-right: 0px; -} -div { - padding: 10px 10px 10px 10px; -} -header { - margin-bottom: 16px; -} -header h1 { - margin-bottom: 0; - font-size: 2em; -} -#search-box { - display: flex; -} -#scaling-inprocess-message { - font-weight: bold; - font-style: italic; - display: none; -} -#prompt { - flex-grow: 1; - padding: 5px 10px 5px 10px; - border: 1px solid #999; - outline: none; -} -#submit { - padding: 5px 10px 5px 10px; - border: 1px solid #999; -} -#reset-all, #remove-image { - margin-top: 12px; - font-size: 0.8em; - background-color: pink; - border: 1px solid #999; - border-radius: 4px; -} -#results { - text-align: center; - margin: auto; - padding-top: 10px; -} -#results figure { - display: inline-block; - margin: 10px; -} -#results figcaption { - font-size: 0.8em; - padding: 3px; - color: #888; - cursor: pointer; -} -#results img { - border-radius: 5px; - object-fit: contain; - background-color: var(--fields-dark); -} -#fieldset-config { - line-height:2em; -} -input[type="number"] { - width: 60px; -} -#seed { - width: 150px; -} -button#reset-seed { - font-size: 1.7em; - background: #efefef; - border: 1px solid #999; - border-radius: 4px; - line-height: 0.8; - margin: 0 10px 0 0; - padding: 0 5px 3px; - vertical-align: middle; -} -label { - white-space: nowrap; -} -#progress-section { - display: none; -} -#progress-image { - width: 30vh; - height: 30vh; - object-fit: contain; - background-color: var(--fields-dark); -} -#cancel-button { - cursor: pointer; - color: red; -} -#txt2img { - background-color: var(--fields-dark); -} -#variations { - background-color: var(--fields-light); -} -#initimg { - background-color: var(--fields-dark); -} -#img2img { - background-color: var(--fields-light); -} -#initimg > :not(legend) { - background-color: var(--fields-light); - margin: .5em; -} - -#postprocess, #initimg { - display:flex; - flex-wrap:wrap; - padding: 0; - margin-top: 1em; - background-color: var(--fields-dark); -} -#postprocess > fieldset, #initimg > * { - flex-grow: 1; -} -#postprocess > fieldset { - background-color: var(--fields-dark); -} -#progress-section { - background-color: var(--fields-light); -} -#no-results-message:not(:only-child) { - display: none; -} diff --git a/invokeai/frontend/web/static/dream_web/index.html b/invokeai/frontend/web/static/dream_web/index.html deleted file mode 100644 index feb542adb2..0000000000 --- a/invokeai/frontend/web/static/dream_web/index.html +++ /dev/null @@ -1,187 +0,0 @@ - - - - Stable Diffusion Dream Server - - - - - - - - - - - -
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Stable Diffusion Dream Server

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- For news and support for this web service, visit our GitHub - site -
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- - - diff --git a/invokeai/frontend/web/static/dream_web/index.js b/invokeai/frontend/web/static/dream_web/index.js deleted file mode 100644 index 438232f0c7..0000000000 --- a/invokeai/frontend/web/static/dream_web/index.js +++ /dev/null @@ -1,409 +0,0 @@ -const socket = io(); - -var priorResultsLoadState = { - page: 0, - pages: 1, - per_page: 10, - total: 20, - offset: 0, // number of items generated since last load - loading: false, - initialized: false, -}; - -function loadPriorResults() { - // Fix next page by offset - let offsetPages = - priorResultsLoadState.offset / priorResultsLoadState.per_page; - priorResultsLoadState.page += offsetPages; - priorResultsLoadState.pages += offsetPages; - priorResultsLoadState.total += priorResultsLoadState.offset; - priorResultsLoadState.offset = 0; - - if (priorResultsLoadState.loading) { - return; - } - - if (priorResultsLoadState.page >= priorResultsLoadState.pages) { - return; // Nothing more to load - } - - // Load - priorResultsLoadState.loading = true; - let url = new URL('/api/images', document.baseURI); - url.searchParams.append( - 'page', - priorResultsLoadState.initialized - ? priorResultsLoadState.page + 1 - : priorResultsLoadState.page - ); - url.searchParams.append('per_page', priorResultsLoadState.per_page); - fetch(url.href, { - method: 'GET', - headers: new Headers({ 'content-type': 'application/json' }), - }) - .then((response) => response.json()) - .then((data) => { - priorResultsLoadState.page = data.page; - priorResultsLoadState.pages = data.pages; - priorResultsLoadState.per_page = data.per_page; - priorResultsLoadState.total = data.total; - - data.items.forEach(function (dreamId, index) { - let src = 'api/images/' + dreamId; - fetch('/api/images/' + dreamId + '/metadata', { - method: 'GET', - headers: new Headers({ 'content-type': 'application/json' }), - }) - .then((response) => response.json()) - .then((metadata) => { - let seed = metadata.seed || 0; // TODO: Parse old metadata - appendOutput(src, seed, metadata, true); - }); - }); - - // Load until page is full - if (!priorResultsLoadState.initialized) { - if (document.body.scrollHeight <= window.innerHeight) { - loadPriorResults(); - } - } - }) - .finally(() => { - priorResultsLoadState.loading = false; - priorResultsLoadState.initialized = true; - }); -} - -function resetForm() { - var form = document.getElementById('generate-form'); - form.querySelector('fieldset').removeAttribute('disabled'); -} - -function initProgress(totalSteps, showProgressImages) { - // TODO: Progress could theoretically come from multiple jobs at the same time (in the future) - let progressSectionEle = document.querySelector('#progress-section'); - progressSectionEle.style.display = 'initial'; - let progressEle = document.querySelector('#progress-bar'); - progressEle.setAttribute('max', totalSteps); - - let progressImageEle = document.querySelector('#progress-image'); - progressImageEle.src = BLANK_IMAGE_URL; - progressImageEle.style.display = showProgressImages ? 'initial' : 'none'; -} - -function setProgress(step, totalSteps, src) { - let progressEle = document.querySelector('#progress-bar'); - progressEle.setAttribute('value', step); - - if (src) { - let progressImageEle = document.querySelector('#progress-image'); - progressImageEle.src = src; - } -} - -function resetProgress(hide = true) { - if (hide) { - let progressSectionEle = document.querySelector('#progress-section'); - progressSectionEle.style.display = 'none'; - } - let progressEle = document.querySelector('#progress-bar'); - progressEle.setAttribute('value', 0); -} - -function toBase64(file) { - return new Promise((resolve, reject) => { - const r = new FileReader(); - r.readAsDataURL(file); - r.onload = () => resolve(r.result); - r.onerror = (error) => reject(error); - }); -} - -function ondragdream(event) { - let dream = event.target.dataset.dream; - event.dataTransfer.setData('dream', dream); -} - -function seedClick(event) { - // Get element - var image = event.target.closest('figure').querySelector('img'); - var dream = JSON.parse(decodeURIComponent(image.dataset.dream)); - - let form = document.querySelector('#generate-form'); - for (const [k, v] of new FormData(form)) { - if (k == 'initimg') { - continue; - } - let formElem = form.querySelector(`*[name=${k}]`); - formElem.value = dream[k] !== undefined ? dream[k] : formElem.defaultValue; - } - - document.querySelector('#seed').value = dream.seed; - document.querySelector('#iterations').value = 1; // Reset to 1 iteration since we clicked a single image (not a full job) - - // NOTE: leaving this manual for the user for now - it was very confusing with this behavior - // document.querySelector("#with_variations").value = variations || ''; - // if (document.querySelector("#variation_amount").value <= 0) { - // document.querySelector("#variation_amount").value = 0.2; - // } - - saveFields(document.querySelector('#generate-form')); -} - -function appendOutput(src, seed, config, toEnd = false) { - let outputNode = document.createElement('figure'); - let altText = seed.toString() + ' | ' + config.prompt; - - // img needs width and height for lazy loading to work - // TODO: store the full config in a data attribute on the image? - const figureContents = ` - - ${altText} - -
${seed}
- `; - - outputNode.innerHTML = figureContents; - - if (toEnd) { - document.querySelector('#results').append(outputNode); - } else { - document.querySelector('#results').prepend(outputNode); - } - document.querySelector('#no-results-message')?.remove(); -} - -function saveFields(form) { - for (const [k, v] of new FormData(form)) { - if (typeof v !== 'object') { - // Don't save 'file' type - localStorage.setItem(k, v); - } - } -} - -function loadFields(form) { - for (const [k, v] of new FormData(form)) { - const item = localStorage.getItem(k); - if (item != null) { - form.querySelector(`*[name=${k}]`).value = item; - } - } -} - -function clearFields(form) { - localStorage.clear(); - let prompt = form.prompt.value; - form.reset(); - form.prompt.value = prompt; -} - -const BLANK_IMAGE_URL = - 'data:image/svg+xml,'; -async function generateSubmit(form) { - // Convert file data to base64 - // TODO: Should probably uplaod files with formdata or something, and store them in the backend? - let formData = Object.fromEntries(new FormData(form)); - if (!formData.enable_generate && !formData.enable_init_image) { - gen_label = document.querySelector('label[for=enable_generate]').innerHTML; - initimg_label = document.querySelector( - 'label[for=enable_init_image]' - ).innerHTML; - alert(`Error: one of "${gen_label}" or "${initimg_label}" must be set`); - } - - formData.initimg_name = formData.initimg.name; - formData.initimg = - formData.initimg.name !== '' ? await toBase64(formData.initimg) : null; - - // Evaluate all checkboxes - let checkboxes = form.querySelectorAll('input[type=checkbox]'); - checkboxes.forEach(function (checkbox) { - if (checkbox.checked) { - formData[checkbox.name] = 'true'; - } - }); - - let strength = formData.strength; - let totalSteps = formData.initimg - ? Math.floor(strength * formData.steps) - : formData.steps; - let showProgressImages = formData.progress_images; - - // Set enabling flags - - // Initialize the progress bar - initProgress(totalSteps, showProgressImages); - - // POST, use response to listen for events - fetch(form.action, { - method: form.method, - headers: new Headers({ 'content-type': 'application/json' }), - body: JSON.stringify(formData), - }) - .then((response) => response.json()) - .then((data) => { - var jobId = data.jobId; - socket.emit('join_room', { room: jobId }); - }); - - form.querySelector('fieldset').setAttribute('disabled', ''); -} - -function fieldSetEnableChecked(event) { - cb = event.target; - fields = cb.closest('fieldset'); - fields.disabled = !cb.checked; -} - -// Socket listeners -socket.on('job_started', (data) => {}); - -socket.on('dream_result', (data) => { - var jobId = data.jobId; - var dreamId = data.dreamId; - var dreamRequest = data.dreamRequest; - var src = 'api/images/' + dreamId; - - priorResultsLoadState.offset += 1; - appendOutput(src, dreamRequest.seed, dreamRequest); - - resetProgress(false); -}); - -socket.on('dream_progress', (data) => { - // TODO: it'd be nice if we could get a seed reported here, but the generator would need to be updated - var step = data.step; - var totalSteps = data.totalSteps; - var jobId = data.jobId; - var dreamId = data.dreamId; - - var progressType = data.progressType; - if (progressType === 'GENERATION') { - var src = data.hasProgressImage - ? 'api/intermediates/' + dreamId + '/' + step - : null; - setProgress(step, totalSteps, src); - } else if (progressType === 'UPSCALING_STARTED') { - // step and totalSteps are used for upscale count on this message - document.getElementById('processing_cnt').textContent = step; - document.getElementById('processing_total').textContent = totalSteps; - document.getElementById('scaling-inprocess-message').style.display = - 'block'; - } else if (progressType == 'UPSCALING_DONE') { - document.getElementById('scaling-inprocess-message').style.display = 'none'; - } -}); - -socket.on('job_canceled', (data) => { - resetForm(); - resetProgress(); -}); - -socket.on('job_done', (data) => { - jobId = data.jobId; - socket.emit('leave_room', { room: jobId }); - - resetForm(); - resetProgress(); -}); - -window.onload = async () => { - document.querySelector('#prompt').addEventListener('keydown', (e) => { - if (e.key === 'Enter' && !e.shiftKey) { - const form = e.target.form; - generateSubmit(form); - } - }); - document.querySelector('#generate-form').addEventListener('submit', (e) => { - e.preventDefault(); - const form = e.target; - - generateSubmit(form); - }); - document.querySelector('#generate-form').addEventListener('change', (e) => { - saveFields(e.target.form); - }); - document.querySelector('#reset-seed').addEventListener('click', (e) => { - document.querySelector('#seed').value = 0; - saveFields(e.target.form); - }); - document.querySelector('#reset-all').addEventListener('click', (e) => { - clearFields(e.target.form); - }); - document.querySelector('#remove-image').addEventListener('click', (e) => { - initimg.value = null; - }); - loadFields(document.querySelector('#generate-form')); - - document.querySelector('#cancel-button').addEventListener('click', () => { - fetch('/api/cancel').catch((e) => { - console.error(e); - }); - }); - document.documentElement.addEventListener('keydown', (e) => { - if (e.key === 'Escape') - fetch('/api/cancel').catch((err) => { - console.error(err); - }); - }); - - if (!config.gfpgan_model_exists) { - document.querySelector('#gfpgan').style.display = 'none'; - } - - window.addEventListener('scroll', () => { - if (window.innerHeight + window.pageYOffset >= document.body.offsetHeight) { - loadPriorResults(); - } - }); - - // Enable/disable forms by checkboxes - document - .querySelectorAll('legend > input[type=checkbox]') - .forEach(function (cb) { - cb.addEventListener('change', fieldSetEnableChecked); - fieldSetEnableChecked({ target: cb }); - }); - - // Load some of the previous results - loadPriorResults(); - - // Image drop/upload WIP - /* - let drop = document.getElementById('dropper'); - function ondrop(event) { - let dreamData = event.dataTransfer.getData('dream'); - if (dreamData) { - var dream = JSON.parse(decodeURIComponent(dreamData)); - alert(dream.dreamId); - } - }; - - function ondragenter(event) { - event.preventDefault(); - }; - - function ondragover(event) { - event.preventDefault(); - }; - - function ondragleave(event) { - - } - - drop.addEventListener('drop', ondrop); - drop.addEventListener('dragenter', ondragenter); - drop.addEventListener('dragover', ondragover); - drop.addEventListener('dragleave', ondragleave); - */ -}; diff --git a/invokeai/frontend/web/static/dream_web/test.html b/invokeai/frontend/web/static/dream_web/test.html deleted file mode 100644 index cbb746a5a1..0000000000 --- a/invokeai/frontend/web/static/dream_web/test.html +++ /dev/null @@ -1,246 +0,0 @@ - - - InvokeAI Test - - - - - - - - - - - - - - - -
- -
- - - - diff --git a/invokeai/frontend/web/static/legacy_web/favicon.ico b/invokeai/frontend/web/static/legacy_web/favicon.ico deleted file mode 100644 index 51eb844a6a..0000000000 Binary files a/invokeai/frontend/web/static/legacy_web/favicon.ico and /dev/null differ diff --git a/invokeai/frontend/web/static/legacy_web/index.css b/invokeai/frontend/web/static/legacy_web/index.css deleted file mode 100644 index 51f0f267c3..0000000000 --- a/invokeai/frontend/web/static/legacy_web/index.css +++ /dev/null @@ -1,152 +0,0 @@ -* { - font-family: 'Arial'; - font-size: 100%; -} -body { - font-size: 1em; -} -textarea { - font-size: 0.95em; -} -header, form, #progress-section { - margin-left: auto; - margin-right: auto; - max-width: 1024px; - text-align: center; -} -fieldset { - border: none; - line-height: 2.2em; -} -select, input { - margin-right: 10px; - padding: 2px; -} -input[type=submit] { - background-color: #666; - color: white; -} -input[type=checkbox] { - margin-right: 0px; - width: 20px; - height: 20px; - vertical-align: middle; -} -input#seed { - margin-right: 0px; -} -div { - padding: 10px 10px 10px 10px; -} -header { - margin-bottom: 16px; -} -header h1 { - margin-bottom: 0; - font-size: 2em; -} -#search-box { - display: flex; -} -#scaling-inprocess-message { - font-weight: bold; - font-style: italic; - display: none; -} -#prompt { - flex-grow: 1; - padding: 5px 10px 5px 10px; - border: 1px solid #999; - outline: none; -} -#submit { - padding: 5px 10px 5px 10px; - border: 1px solid #999; -} -#reset-all, #remove-image { - margin-top: 12px; - font-size: 0.8em; - background-color: pink; - border: 1px solid #999; - border-radius: 4px; -} -#results { - text-align: center; - margin: auto; - padding-top: 10px; -} -#results figure { - display: inline-block; - margin: 10px; -} -#results figcaption { - font-size: 0.8em; - padding: 3px; - color: #888; - cursor: pointer; -} -#results img { - border-radius: 5px; - object-fit: cover; -} -#fieldset-config { - line-height:2em; - background-color: #F0F0F0; -} -input[type="number"] { - width: 60px; -} -#seed { - width: 150px; -} -button#reset-seed { - font-size: 1.7em; - background: #efefef; - border: 1px solid #999; - border-radius: 4px; - line-height: 0.8; - margin: 0 10px 0 0; - padding: 0 5px 3px; - vertical-align: middle; -} -label { - white-space: nowrap; -} -#progress-section { - display: none; -} -#progress-image { - width: 30vh; - height: 30vh; -} -#cancel-button { - cursor: pointer; - color: red; -} -#basic-parameters { - background-color: #EEEEEE; -} -#txt2img { - background-color: #DCDCDC; -} -#variations { - background-color: #EEEEEE; -} -#img2img { - background-color: #DCDCDC; -} -#gfpgan { - background-color: #EEEEEE; -} -#progress-section { - background-color: #F5F5F5; -} -.section-header { - text-align: left; - font-weight: bold; - padding: 0 0 0 0; -} -#no-results-message:not(:only-child) { - display: none; -} - diff --git a/invokeai/frontend/web/static/legacy_web/index.html b/invokeai/frontend/web/static/legacy_web/index.html deleted file mode 100644 index c96eed54c3..0000000000 --- a/invokeai/frontend/web/static/legacy_web/index.html +++ /dev/null @@ -1,137 +0,0 @@ - - - Stable Diffusion Dream Server - - - - - - - - -
-

Stable Diffusion Dream Server

-
- For news and support for this web service, visit our GitHub site -
-
- -
-
-
- -
-
-
Basic options
- - - - - - - - - - -
- - - - - - - - - -
- - - - - -
- - - - - - -
-
-
Image-to-image options
- - - -
- - - - -
-
-
Post-processing options
- - - - - - -
-
-
-
-
- - ✖ -
- -
- Postprocessing...1/3 -
- -
- -
-
-

No results...

 -
-
-
- - diff --git a/invokeai/frontend/web/static/legacy_web/index.js b/invokeai/frontend/web/static/legacy_web/index.js deleted file mode 100644 index a150f3f2e9..0000000000 --- a/invokeai/frontend/web/static/legacy_web/index.js +++ /dev/null @@ -1,234 +0,0 @@ -function toBase64(file) { - return new Promise((resolve, reject) => { - const r = new FileReader(); - r.readAsDataURL(file); - r.onload = () => resolve(r.result); - r.onerror = (error) => reject(error); - }); -} - -function appendOutput(src, seed, config) { - let outputNode = document.createElement('figure'); - - let variations = config.with_variations; - if (config.variation_amount > 0) { - variations = - (variations ? variations + ',' : '') + - seed + - ':' + - config.variation_amount; - } - let baseseed = - config.with_variations || config.variation_amount > 0 ? config.seed : seed; - let altText = - baseseed + ' | ' + (variations ? variations + ' | ' : '') + config.prompt; - - // img needs width and height for lazy loading to work - const figureContents = ` - - ${altText} - -
${seed}
- `; - - outputNode.innerHTML = figureContents; - let figcaption = outputNode.querySelector('figcaption'); - - // Reload image config - figcaption.addEventListener('click', () => { - let form = document.querySelector('#generate-form'); - for (const [k, v] of new FormData(form)) { - if (k == 'initimg') { - continue; - } - form.querySelector(`*[name=${k}]`).value = config[k]; - } - - document.querySelector('#seed').value = baseseed; - document.querySelector('#with_variations').value = variations || ''; - if (document.querySelector('#variation_amount').value <= 0) { - document.querySelector('#variation_amount').value = 0.2; - } - - saveFields(document.querySelector('#generate-form')); - }); - - document.querySelector('#results').prepend(outputNode); -} - -function saveFields(form) { - for (const [k, v] of new FormData(form)) { - if (typeof v !== 'object') { - // Don't save 'file' type - localStorage.setItem(k, v); - } - } -} - -function loadFields(form) { - for (const [k, v] of new FormData(form)) { - const item = localStorage.getItem(k); - if (item != null) { - form.querySelector(`*[name=${k}]`).value = item; - } - } -} - -function clearFields(form) { - localStorage.clear(); - let prompt = form.prompt.value; - form.reset(); - form.prompt.value = prompt; -} - -const BLANK_IMAGE_URL = - 'data:image/svg+xml,'; -async function generateSubmit(form) { - const prompt = document.querySelector('#prompt').value; - - // Convert file data to base64 - let formData = Object.fromEntries(new FormData(form)); - formData.initimg_name = formData.initimg.name; - formData.initimg = - formData.initimg.name !== '' ? await toBase64(formData.initimg) : null; - - let strength = formData.strength; - let totalSteps = formData.initimg - ? Math.floor(strength * formData.steps) - : formData.steps; - - let progressSectionEle = document.querySelector('#progress-section'); - progressSectionEle.style.display = 'initial'; - let progressEle = document.querySelector('#progress-bar'); - progressEle.setAttribute('max', totalSteps); - let progressImageEle = document.querySelector('#progress-image'); - progressImageEle.src = BLANK_IMAGE_URL; - - progressImageEle.style.display = {}.hasOwnProperty.call( - formData, - 'progress_images' - ) - ? 'initial' - : 'none'; - - // Post as JSON, using Fetch streaming to get results - fetch(form.action, { - method: form.method, - body: JSON.stringify(formData), - }).then(async (response) => { - const reader = response.body.getReader(); - - let noOutputs = true; - while (true) { - let { value, done } = await reader.read(); - value = new TextDecoder().decode(value); - if (done) { - progressSectionEle.style.display = 'none'; - break; - } - - for (let event of value.split('\n').filter((e) => e !== '')) { - const data = JSON.parse(event); - - if (data.event === 'result') { - noOutputs = false; - appendOutput(data.url, data.seed, data.config); - progressEle.setAttribute('value', 0); - progressEle.setAttribute('max', totalSteps); - } else if (data.event === 'upscaling-started') { - document.getElementById('processing_cnt').textContent = - data.processed_file_cnt; - document.getElementById('scaling-inprocess-message').style.display = - 'block'; - } else if (data.event === 'upscaling-done') { - document.getElementById('scaling-inprocess-message').style.display = - 'none'; - } else if (data.event === 'step') { - progressEle.setAttribute('value', data.step); - if (data.url) { - progressImageEle.src = data.url; - } - } else if (data.event === 'canceled') { - // avoid alerting as if this were an error case - noOutputs = false; - } - } - } - - // Re-enable form, remove no-results-message - form.querySelector('fieldset').removeAttribute('disabled'); - document.querySelector('#prompt').value = prompt; - document.querySelector('progress').setAttribute('value', '0'); - - if (noOutputs) { - alert('Error occurred while generating.'); - } - }); - - // Disable form while generating - form.querySelector('fieldset').setAttribute('disabled', ''); - document.querySelector('#prompt').value = `Generating: "${prompt}"`; -} - -async function fetchRunLog() { - try { - let response = await fetch('/run_log.json'); - const data = await response.json(); - for (let item of data.run_log) { - appendOutput(item.url, item.seed, item); - } - } catch (e) { - console.error(e); - } -} - -window.onload = async () => { - document.querySelector('#prompt').addEventListener('keydown', (e) => { - if (e.key === 'Enter' && !e.shiftKey) { - const form = e.target.form; - generateSubmit(form); - } - }); - document.querySelector('#generate-form').addEventListener('submit', (e) => { - e.preventDefault(); - const form = e.target; - - generateSubmit(form); - }); - document.querySelector('#generate-form').addEventListener('change', (e) => { - saveFields(e.target.form); - }); - document.querySelector('#reset-seed').addEventListener('click', (e) => { - document.querySelector('#seed').value = -1; - saveFields(e.target.form); - }); - document.querySelector('#reset-all').addEventListener('click', (e) => { - clearFields(e.target.form); - }); - document.querySelector('#remove-image').addEventListener('click', (e) => { - initimg.value = null; - }); - loadFields(document.querySelector('#generate-form')); - - document.querySelector('#cancel-button').addEventListener('click', () => { - fetch('/cancel').catch((e) => { - console.error(e); - }); - }); - document.documentElement.addEventListener('keydown', (e) => { - if (e.key === 'Escape') - fetch('/cancel').catch((err) => { - console.error(err); - }); - }); - - if (!config.gfpgan_model_exists) { - document.querySelector('#gfpgan').style.display = 'none'; - } - await fetchRunLog(); -}; diff --git a/invokeai/frontend/web/yarn.lock b/invokeai/frontend/web/yarn.lock index 2a531175d4..cc257115da 100644 --- a/invokeai/frontend/web/yarn.lock +++ b/invokeai/frontend/web/yarn.lock @@ -50,13 +50,20 @@ resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.22.10.tgz#e37634f9a12a1716136c44624ef54283cabd3f55" integrity sha512-lNbdGsQb9ekfsnjFGhEiF4hfFqGgfOP3H3d27re3n+CGhNuTSUEQdfWk556sTLNTloczcdM5TYF2LhzmDQKyvQ== -"@babel/runtime@^7.0.0", "@babel/runtime@^7.1.2", "@babel/runtime@^7.10.2", "@babel/runtime@^7.12.1", "@babel/runtime@^7.12.13", "@babel/runtime@^7.12.5", "@babel/runtime@^7.13.10", "@babel/runtime@^7.18.3", "@babel/runtime@^7.19.4", "@babel/runtime@^7.21.0", "@babel/runtime@^7.22.5", "@babel/runtime@^7.9.2": +"@babel/runtime@^7.0.0", "@babel/runtime@^7.1.2", "@babel/runtime@^7.12.1", "@babel/runtime@^7.12.13", "@babel/runtime@^7.12.5", "@babel/runtime@^7.18.3", "@babel/runtime@^7.19.4", "@babel/runtime@^7.21.0", "@babel/runtime@^7.22.5", "@babel/runtime@^7.9.2": version "7.22.6" resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.22.6.tgz#57d64b9ae3cff1d67eb067ae117dac087f5bd438" integrity sha512-wDb5pWm4WDdF6LFUde3Jl8WzPA+3ZbxYqkC6xAXuD3irdEHN1k0NfTRrJD8ZD378SJ61miMLCqIOXYhd8x+AJQ== dependencies: regenerator-runtime "^0.13.11" +"@babel/runtime@^7.10.2", "@babel/runtime@^7.13.10": + version "7.23.2" + resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.23.2.tgz#062b0ac103261d68a966c4c7baf2ae3e62ec3885" + integrity sha512-mM8eg4yl5D6i3lu2QKPuPH4FArvJ8KhTofbE7jwMUv9KX5mBvwPAqnV3MlyBNqdp9RyRKP6Yck8TrfYrPvX3bg== + dependencies: + regenerator-runtime "^0.14.0" + "@babel/types@^7.22.5", "@babel/types@^7.4": version "7.22.5" resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.22.5.tgz#cd93eeaab025880a3a47ec881f4b096a5b786fbe" @@ -66,33 +73,33 @@ "@babel/helper-validator-identifier" "^7.22.5" to-fast-properties "^2.0.0" -"@chakra-ui/accordion@2.3.0": - version "2.3.0" - resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.3.0.tgz#2c85fd2d2734b176f019f8db9f4e075007b4e1fb" - integrity sha512-A4TkRw3Jnt+Fam6dSSJ62rskdrvjF3JGctYcfXlojfFIpHPuIw4pDwfZgNAxlaxWkcj0e7JJKlQ88dnZW+QfFg== +"@chakra-ui/accordion@2.3.1": + version "2.3.1" + resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.3.1.tgz#a326509e286a5c4e8478de9bc2b4b05017039e6b" + integrity sha512-FSXRm8iClFyU+gVaXisOSEw0/4Q+qZbFRiuhIAkVU6Boj0FxAMrlo9a8AV5TuF77rgaHytCdHk0Ng+cyUijrag== dependencies: "@chakra-ui/descendant" "3.1.0" - "@chakra-ui/icon" "3.1.0" + "@chakra-ui/icon" "3.2.0" "@chakra-ui/react-context" "2.1.0" "@chakra-ui/react-use-controllable-state" "2.1.0" "@chakra-ui/react-use-merge-refs" "2.1.0" "@chakra-ui/shared-utils" "2.0.5" "@chakra-ui/transition" "2.1.0" -"@chakra-ui/alert@2.2.0": - version "2.2.0" - resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.2.0.tgz#b59eadca4f083674dfcd43651b6c47b953c2c984" - integrity sha512-De+BT88iYOu3Con7MxQeICb1SwgAdVdgpHIYjTh3qvGlNXAQjs81rhG0fONXvwW1FIYletvr9DY2Tlg8xJe7tQ== +"@chakra-ui/alert@2.2.1": + version "2.2.1" + resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.2.1.tgz#69f4fae19e4f8204ae1db906784139d416063d04" + integrity sha512-GduIqqWCkvID8hxRlKw29Jp3w93r/E9S30J2F8By3ODon9Bhk1o/KVolcPiSiQvRwKNBJCd/rBTpPpLkB+s7pw== dependencies: - "@chakra-ui/icon" "3.1.0" + "@chakra-ui/icon" "3.2.0" "@chakra-ui/react-context" "2.1.0" "@chakra-ui/shared-utils" "2.0.5" "@chakra-ui/spinner" "2.1.0" -"@chakra-ui/anatomy@2.2.0", "@chakra-ui/anatomy@^2.2.0": - version "2.2.0" - resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.2.0.tgz#788229829f853dcd03314cd7ddd4f19f056ec24e" - integrity sha512-cD8Ms5C8+dFda0LrORMdxiFhAZwOIY1BSlCadz6/mHUIgNdQy13AHPrXiq6qWdMslqVHq10k5zH7xMPLt6kjFg== +"@chakra-ui/anatomy@2.2.1", "@chakra-ui/anatomy@^2.2.1": + version "2.2.1" + resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.2.1.tgz#f7ef088dcb8be4f1d075f37101830199fb93f763" + integrity sha512-bbmyWTGwQo+aHYDMtLIj7k7hcWvwE7GFVDViLFArrrPhfUTDdQTNqhiDp1N7eh2HLyjNhc2MKXV8s2KTQqkmTg== "@chakra-ui/avatar@2.3.0": version "2.3.0" @@ -137,12 +144,12 @@ dependencies: "@chakra-ui/shared-utils" "2.0.5" -"@chakra-ui/checkbox@2.3.0": - version "2.3.0" - resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.3.0.tgz#6a103555100008fcde0b25a4f3f87267dd6ea073" - 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version "2.1.0" - resolved "https://registry.yarnpkg.com/@chakra-ui/close-button/-/close-button-2.1.0.tgz#5af435a62919793dc713be321eaff1f61749b6d5" - integrity sha512-KfJcz6UAaR2dDWSIv6UrCGkZQS54Fjl+DEEVOUTJ7gf4KOP4FQZCkv8hqsAB9FeCtnwU43adq2oaw3aZH/Uzew== +"@chakra-ui/close-button@2.1.1": + version "2.1.1" + resolved "https://registry.yarnpkg.com/@chakra-ui/close-button/-/close-button-2.1.1.tgz#995b245c56eb41465a71d8667840c238618a7b66" + integrity sha512-gnpENKOanKexswSVpVz7ojZEALl2x5qjLYNqSQGbxz+aP9sOXPfUS56ebyBrre7T7exuWGiFeRwnM0oVeGPaiw== dependencies: - "@chakra-ui/icon" "3.1.0" + "@chakra-ui/icon" "3.2.0" "@chakra-ui/color-mode@2.2.0": version "2.2.0" @@ -203,10 +210,10 @@ "@chakra-ui/react-use-callback-ref" "2.1.0" "@chakra-ui/shared-utils" "2.0.5" -"@chakra-ui/css-reset@2.2.0": - version "2.2.0" - resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.2.0.tgz#7bd8da563941709cd68e2d1cf1e71279bec038ea" - integrity sha512-nn7hjquIrPwCzwI4d/Y4wzM5A5xAeswREOfT8gT0Yd+U+Qnw3pPT8NPLbNJ3DvuOfJaCV6/N5ld/6RRTgYF/sQ== +"@chakra-ui/css-reset@2.3.0": + version "2.3.0" + resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.3.0.tgz#83e3160a9c2a12431cad0ee27ebfbf3aedc5c9c7" + integrity sha512-cQwwBy5O0jzvl0K7PLTLgp8ijqLPKyuEMiDXwYzl95seD3AoeuoCLyzZcJtVqaUZ573PiBdAbY/IlZcwDOItWg== "@chakra-ui/descendant@3.1.0": version "3.1.0" @@ -249,40 +256,40 @@ "@chakra-ui/dom-utils" "2.1.0" react-focus-lock "^2.9.4" -"@chakra-ui/form-control@2.1.0": - 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integrity sha512-UCB0ioiyj8CRjtrvaceBLqqhZCVP+1B8+NWQhmdsm0VXOJtobBCf1dBQmebCCo34qZmUwZfIH2MZLqNHazrfjg== +compute-scroll-into-view@3.0.3: + version "3.0.3" + resolved "https://registry.yarnpkg.com/compute-scroll-into-view/-/compute-scroll-into-view-3.0.3.tgz#c418900a5c56e2b04b885b54995df164535962b1" + integrity sha512-nadqwNxghAGTamwIqQSG433W6OADZx2vCo3UXHNrzTRHK/htu+7+L0zhjEoaeaQVNAi3YgqWDv8+tzf0hRfR+A== concat-map@0.0.1: version "0.0.1" resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b" integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg== -concurrently@^8.2.0: - version "8.2.0" - resolved "https://registry.yarnpkg.com/concurrently/-/concurrently-8.2.0.tgz#cdc9f621a4d913366600355d68254df2c5e782f3" - integrity sha512-nnLMxO2LU492mTUj9qX/az/lESonSZu81UznYDoXtz1IQf996ixVqPAgHXwvHiHCAef/7S8HIK+fTFK7Ifk8YA== +concurrently@^8.2.1: + version "8.2.1" + resolved "https://registry.yarnpkg.com/concurrently/-/concurrently-8.2.1.tgz#bcab9cacc38c23c503839583151e0fa96fd5b584" + integrity sha512-nVraf3aXOpIcNud5pB9M82p1tynmZkrSGQ1p6X/VY8cJ+2LMVqAgXsJxYYefACSHbTYlm92O1xuhdGTjwoEvbQ== dependencies: chalk "^4.1.2" date-fns "^2.30.0" @@ -3642,10 +3672,10 @@ eslint-config-prettier@^9.0.0: resolved "https://registry.yarnpkg.com/eslint-config-prettier/-/eslint-config-prettier-9.0.0.tgz#eb25485946dd0c66cd216a46232dc05451518d1f" integrity sha512-IcJsTkJae2S35pRsRAwoCE+925rJJStOdkKnLVgtE+tEpqU0EVVM7OqrwxqgptKdX29NUwC82I5pXsGFIgSevw== -eslint-plugin-prettier@^5.0.0: - 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integrity sha512-spUQWrdPt+pRVP1TTJLmfRNJJHHZryFmptzcafwSvHsceV81djHOdnEeDmkdotZyLNjDhrOasNK8nikkoG1O8Q== +eslint@^8.51.0: + version "8.51.0" + resolved "https://registry.yarnpkg.com/eslint/-/eslint-8.51.0.tgz#4a82dae60d209ac89a5cff1604fea978ba4950f3" + integrity sha512-2WuxRZBrlwnXi+/vFSJyjMqrNjtJqiasMzehF0shoLaW7DzS3/9Yvrmq5JiT66+pNjiX4UBnLDiKHcWAr/OInA== dependencies: "@eslint-community/eslint-utils" "^4.2.0" "@eslint-community/regexpp" "^4.6.1" "@eslint/eslintrc" "^2.1.2" - "@eslint/js" "^8.47.0" - "@humanwhocodes/config-array" "^0.11.10" + "@eslint/js" "8.51.0" + "@humanwhocodes/config-array" "^0.11.11" "@humanwhocodes/module-importer" "^1.0.1" "@nodelib/fs.walk" "^1.2.8" ajv "^6.12.4" @@ -3791,19 +3821,19 @@ eventemitter3@^5.0.1: resolved "https://registry.yarnpkg.com/eventemitter3/-/eventemitter3-5.0.1.tgz#53f5ffd0a492ac800721bb42c66b841de96423c4" integrity sha512-GWkBvjiSZK87ELrYOSESUYeVIc9mvLLf/nXalMOS5dYrgZq9o5OVkbZAVM06CVxYsCwH9BDZFPlQTlPA1j4ahA== -execa@7.2.0, execa@^7.1.1: - 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version "4.2.0" - resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-4.2.0.tgz#e259430307710e77721ecf6f545840acad72195f" - integrity sha512-5zknd7Dss75pMSED270A1RQS3KloqRJA9XbXLe0eCxyw7xXFb3rd+9B0UQ/0E+LQT6lnrLviEolYORlRWamn4w== +type-fest@^4.4.0: + version "4.4.0" + resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-4.4.0.tgz#061cd10ff55664bb7174218cdf78c28c48f71c69" + integrity sha512-HT3RRs7sTfY22KuPQJkD/XjbTbxgP2Je5HPt6H6JEGvcjHd5Lqru75EbrP3tb4FYjNJ+DjLp+MNQTFQU0mhXNw== typed-array-buffer@^1.0.0: version "1.0.0" @@ -6747,6 +6813,11 @@ unbox-primitive@^1.0.2: has-symbols "^1.0.3" which-boxed-primitive "^1.0.2" +undici-types@~5.25.1: + version "5.25.3" + resolved "https://registry.yarnpkg.com/undici-types/-/undici-types-5.25.3.tgz#e044115914c85f0bcbb229f346ab739f064998c3" + integrity sha512-Ga1jfYwRn7+cP9v8auvEXN1rX3sWqlayd4HP7OKk4mZWylEmu3KzXDUGrQUN6Ol7qo1gPvB2e5gX6udnyEPgdA== + undici@^5.23.0: version "5.23.0" resolved "https://registry.yarnpkg.com/undici/-/undici-5.23.0.tgz#e7bdb0ed42cebe7b7aca87ced53e6eaafb8f8ca0" @@ -6833,10 +6904,10 @@ util-deprecate@^1.0.1: resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf" integrity sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw== -uuid@^9.0.0: - version "9.0.0" - resolved "https://registry.yarnpkg.com/uuid/-/uuid-9.0.0.tgz#592f550650024a38ceb0c562f2f6aa435761efb5" - integrity sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg== +uuid@^9.0.1: + version "9.0.1" + resolved "https://registry.yarnpkg.com/uuid/-/uuid-9.0.1.tgz#e188d4c8853cc722220392c424cd637f32293f30" + integrity sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA== validator@^13.7.0: version "13.9.0" @@ -6848,12 +6919,12 @@ vite-plugin-css-injected-by-js@^3.3.0: resolved "https://registry.yarnpkg.com/vite-plugin-css-injected-by-js/-/vite-plugin-css-injected-by-js-3.3.0.tgz#c19480a9e42a95c5bced976a9dde1446f9bd91ff" integrity sha512-xG+jyHNCmUqi/TXp6q88wTJGeAOrNLSyUUTp4qEQ9QZLGcHWQQsCsSSKa59rPMQr8sOzfzmWDd8enGqfH/dBew== -vite-plugin-dts@^3.5.2: - version "3.5.2" - resolved "https://registry.yarnpkg.com/vite-plugin-dts/-/vite-plugin-dts-3.5.2.tgz#429612f727f1bf4eff1f22e29c04b52a75d398b8" - integrity sha512-iKc851+jdHEoN1ifbOEsoMs+/Zg26PE1EyO2Jc+4apOWRoaeK2zRJnaStgUuJaVaEcAjTqWzpNgCAMq7iO6DWA== +vite-plugin-dts@^3.6.0: + version "3.6.0" + resolved "https://registry.yarnpkg.com/vite-plugin-dts/-/vite-plugin-dts-3.6.0.tgz#8d1052e93ae4efef1b0860e44411570a58c77517" + integrity sha512-doxhDRFJCZD2sGjIp4V800nm8Y19GvmwckjG5vYPuiqJ7OBjc9NlW1Vp9Gkyh2aXlUs1jTDRH/lxWfcsPLOQHg== dependencies: - "@microsoft/api-extractor" "^7.36.3" + "@microsoft/api-extractor" "^7.36.4" "@rollup/pluginutils" "^5.0.2" "@vue/language-core" "^1.8.8" debug "^4.3.4" @@ -6869,19 +6940,19 @@ vite-plugin-eslint@^1.8.1: "@types/eslint" "^8.4.5" rollup "^2.77.2" -vite-tsconfig-paths@^4.2.0: - version "4.2.0" - resolved "https://registry.yarnpkg.com/vite-tsconfig-paths/-/vite-tsconfig-paths-4.2.0.tgz#bd2647d3eadafb65a10fc98a2ca565211f2eaf63" - integrity sha512-jGpus0eUy5qbbMVGiTxCL1iB9ZGN6Bd37VGLJU39kTDD6ZfULTTb1bcc5IeTWqWJKiWV5YihCaibeASPiGi8kw== +vite-tsconfig-paths@^4.2.1: + version "4.2.1" + resolved "https://registry.yarnpkg.com/vite-tsconfig-paths/-/vite-tsconfig-paths-4.2.1.tgz#e53b89096b91d31a6d1e26f75999ea8c336a89ed" + integrity sha512-GNUI6ZgPqT3oervkvzU+qtys83+75N/OuDaQl7HmOqFTb0pjZsuARrRipsyJhJ3enqV8beI1xhGbToR4o78nSQ== dependencies: debug "^4.1.1" globrex "^0.1.2" tsconfck "^2.1.0" -vite@^4.4.9: - version "4.4.9" - resolved "https://registry.yarnpkg.com/vite/-/vite-4.4.9.tgz#1402423f1a2f8d66fd8d15e351127c7236d29d3d" - integrity sha512-2mbUn2LlUmNASWwSCNSJ/EG2HuSRTnVNaydp6vMCm5VIqJsjMfbIWtbH2kDuwUVW5mMUKKZvGPX/rqeqVvv1XA== +vite@^4.4.11: + version "4.4.11" + resolved "https://registry.yarnpkg.com/vite/-/vite-4.4.11.tgz#babdb055b08c69cfc4c468072a2e6c9ca62102b0" + integrity sha512-ksNZJlkcU9b0lBwAGZGGaZHCMqHsc8OpgtoYhsQ4/I2v5cnpmmmqe5pM4nv/4Hn6G/2GhTdj0DhZh2e+Er1q5A== dependencies: esbuild "^0.18.10" postcss "^8.4.27" @@ -7036,16 +7107,21 @@ yallist@^4.0.0: resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72" integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A== -yaml@2.3.1, yaml@^2.2.2: - version "2.3.1" - resolved "https://registry.yarnpkg.com/yaml/-/yaml-2.3.1.tgz#02fe0975d23cd441242aa7204e09fc28ac2ac33b" - integrity sha512-2eHWfjaoXgTBC2jNM1LRef62VQa0umtvRiDSk6HSzW7RvS5YtkabJrwYLLEKWBc8a5U2PTSCs+dJjUTJdlHsWQ== +yaml@2.3.2: + version "2.3.2" + resolved "https://registry.yarnpkg.com/yaml/-/yaml-2.3.2.tgz#f522db4313c671a0ca963a75670f1c12ea909144" + integrity sha512-N/lyzTPaJasoDmfV7YTrYCI0G/3ivm/9wdG0aHuheKowWQwGTsK0Eoiw6utmzAnI6pkJa0DUVygvp3spqqEKXg== yaml@^1.10.0: version "1.10.2" resolved "https://registry.yarnpkg.com/yaml/-/yaml-1.10.2.tgz#2301c5ffbf12b467de8da2333a459e29e7920e4b" integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg== +yaml@^2.2.2: + version "2.3.1" + resolved "https://registry.yarnpkg.com/yaml/-/yaml-2.3.1.tgz#02fe0975d23cd441242aa7204e09fc28ac2ac33b" + integrity sha512-2eHWfjaoXgTBC2jNM1LRef62VQa0umtvRiDSk6HSzW7RvS5YtkabJrwYLLEKWBc8a5U2PTSCs+dJjUTJdlHsWQ== + yargs-parser@^21.1.1: version "21.1.1" resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35" @@ -7090,10 +7166,10 @@ zod-validation-error@^1.5.0: resolved "https://registry.yarnpkg.com/zod-validation-error/-/zod-validation-error-1.5.0.tgz#2b355007a1c3b7fb04fa476bfad4e7b3fd5491e3" integrity sha512-/7eFkAI4qV0tcxMBB/3+d2c1P6jzzZYdYSlBuAklzMuCrJu5bzJfHS0yVAS87dRHVlhftd6RFJDIvv03JgkSbw== -zod@^3.22.2: - version "3.22.2" - resolved "https://registry.yarnpkg.com/zod/-/zod-3.22.2.tgz#3add8c682b7077c05ac6f979fea6998b573e157b" - integrity sha512-wvWkphh5WQsJbVk1tbx1l1Ly4yg+XecD+Mq280uBGt9wa5BKSWf4Mhp6GmrkPixhMxmabYY7RbzlwVP32pbGCg== +zod@^3.22.4: + version "3.22.4" + resolved "https://registry.yarnpkg.com/zod/-/zod-3.22.4.tgz#f31c3a9386f61b1f228af56faa9255e845cf3fff" + integrity sha512-iC+8Io04lddc+mVqQ9AZ7OQ2MrUKGN+oIQyq1vemgt46jwCwLfhq7/pwnBnNXXXZb8VTVLKwp9EDkx+ryxIWmg== zustand@^4.4.1: version "4.4.1" diff --git a/mkdocs.yml b/mkdocs.yml index f95d83ac8f..97b2a16f19 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -134,6 +134,7 @@ nav: - List of Default Nodes: 'nodes/defaultNodes.md' - Workflow Editor Usage: 'nodes/NODES.md' - ComfyUI to InvokeAI: 'nodes/comfyToInvoke.md' + - Facetool Node: 'nodes/detailedNodes/faceTools.md' - Contributing Nodes: 'nodes/contributingNodes.md' - Features: - Overview: 'features/index.md' @@ -144,7 +145,7 @@ nav: - Image-to-Image: 'features/IMG2IMG.md' - Controlling Logging: 'features/LOGGING.md' - Model Merging: 'features/MODEL_MERGING.md' - - Using Nodes : './nodes/overview' + - Using Nodes : 'nodes/overview.md' - NSFW Checker: 'features/WATERMARK+NSFW.md' - Postprocessing: 'features/POSTPROCESS.md' - Prompting Features: 'features/PROMPTS.md' @@ -152,15 +153,18 @@ nav: - Unified Canvas: 'features/UNIFIED_CANVAS.md' - InvokeAI Web Server: 'features/WEB.md' - WebUI Hotkeys: "features/WEBUIHOTKEYS.md" + - Maintenance Utilities: "features/UTILITIES.md" - Other: 'features/OTHER.md' - Contributing: - How to Contribute: 'contributing/CONTRIBUTING.md' + - InvokeAI Code of Conduct: 'CODE_OF_CONDUCT.md' - Development: - Overview: 'contributing/contribution_guides/development.md' - New Contributors: 'contributing/contribution_guides/newContributorChecklist.md' - InvokeAI Architecture: 'contributing/ARCHITECTURE.md' - Frontend Documentation: 'contributing/contribution_guides/contributingToFrontend.md' - Local Development: 'contributing/LOCAL_DEVELOPMENT.md' + - Adding Tests: 'contributing/TESTS.md' - Documentation: 'contributing/contribution_guides/documentation.md' - Nodes: 'contributing/INVOCATIONS.md' - Translation: 'contributing/contribution_guides/translation.md' @@ -168,9 +172,12 @@ nav: - Changelog: 'CHANGELOG.md' - Deprecated: - Command Line Interface: 'deprecated/CLI.md' + - Variations: 'deprecated/VARIATIONS.md' + - Translations: 'deprecated/TRANSLATION.md' - Embiggen: 'deprecated/EMBIGGEN.md' - Inpainting: 'deprecated/INPAINTING.md' - Outpainting: 'deprecated/OUTPAINTING.md' + - Troubleshooting: 'help/deprecated/TROUBLESHOOT.md' - Help: - Getting Started: 'help/gettingStartedWithAI.md' - Diffusion Overview: 'help/diffusion.md' diff --git a/pyproject.toml b/pyproject.toml index 180901bfa5..a65505755d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta" [project] name = "InvokeAI" description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process" -requires-python = ">=3.9, <3.12" +requires-python = ">=3.10, <3.12" readme = { content-type = "text/markdown", file = "README.md" } keywords = ["stable-diffusion", "AI"] dynamic = ["version"] @@ -35,32 +35,33 @@ dependencies = [ "accelerate~=0.23.0", "albumentations", "click", - "clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip", + "clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip", "compel~=2.0.2", "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 + "timm==0.6.13", # needed to override timm latest in controlnet_aux, see https://github.com/isl-org/ZoeDepth/issues/26 "datasets", # When bumping diffusers beyond 0.21, make sure to address this: # https://github.com/invoke-ai/InvokeAI/blob/fc09ab7e13cb7ca5389100d149b6422ace7b8ed3/invokeai/app/invocations/latent.py#L513 - "diffusers[torch]~=0.21.0", + "diffusers[torch]~=0.22.0", "dnspython~=2.4.0", "dynamicprompts", "easing-functions", "einops", "facexlib", - "fastapi==0.88.0", - "fastapi-events==0.8.0", + "fastapi~=0.103.2", + "fastapi-events~=0.9.1", "huggingface-hub~=0.16.4", - "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids - "matplotlib", # needed for plotting of Penner easing functions - "mediapipe", # needed for "mediapipeface" controlnet model + "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids + "matplotlib", # needed for plotting of Penner easing functions + "mediapipe", # needed for "mediapipeface" controlnet model "numpy", "npyscreen", "omegaconf", "onnx", "onnxruntime", "opencv-python", - "pydantic==1.*", + "pydantic~=2.4.2", + "pydantic-settings~=2.0.3", "picklescan", "pillow", "prompt-toolkit", @@ -69,7 +70,7 @@ dependencies = [ 'pyperclip', "pyreadline3", "python-multipart", - "python-socketio", + "python-socketio~=5.10.0", "pytorch-lightning", "realesrgan", "requests~=2.28.2", @@ -79,8 +80,8 @@ dependencies = [ "semver~=3.0.1", "send2trash", "test-tube~=0.7.5", - "torch~=2.0.1", - "torchvision~=0.15.2", + "torch~=2.1.0", + "torchvision~=0.16", "torchmetrics~=0.11.0", "torchsde~=0.2.5", "transformers~=4.31.0", @@ -95,21 +96,20 @@ dependencies = [ "mkdocs-git-revision-date-localized-plugin", "mkdocs-redirects==1.2.0", ] -"dev" = [ - "jurigged", - "pudb", -] +"dev" = ["jurigged", "pudb"] "test" = [ "black", "flake8", "Flake8-pyproject", "isort", + "mypy", "pre-commit", "pytest>6.0.0", "pytest-cov", "pytest-datadir", ] "xformers" = [ + "xformers==0.0.21; sys_platform!='darwin'", "triton; sys_platform=='linux'", ] @@ -122,6 +122,9 @@ dependencies = [ "onnx-directml" = [ "onnxruntime-directml", ] +"onnx" = ["onnxruntime"] +"onnx-cuda" = ["onnxruntime-gpu"] +"onnx-directml" = ["onnxruntime-directml"] [project.scripts] @@ -129,10 +132,9 @@ dependencies = [ "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 +# shortcut commands to start web ui # "invokeai --web" will launch the web interface -# "invokeai" will launch the CLI -"invokeai" = "invokeai.frontend.legacy_launch_invokeai:main" +# "invokeai" = "invokeai.frontend.legacy_launch_invokeai:main" # new shortcut to launch web interface "invokeai-web" = "invokeai.app.api_app:invoke_api" @@ -145,7 +147,6 @@ dependencies = [ "invokeai-migrate3" = "invokeai.backend.install.migrate_to_3:main" "invokeai-update" = "invokeai.frontend.install.invokeai_update:main" "invokeai-metadata" = "invokeai.backend.image_util.invoke_metadata:main" -"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" "invokeai-db-maintenance" = "invokeai.backend.util.db_maintenance:main" @@ -163,26 +164,31 @@ version = { attr = "invokeai.version.__version__" } [tool.setuptools.packages.find] "where" = ["."] "include" = [ - "invokeai.assets.fonts*","invokeai.version*", - "invokeai.generator*","invokeai.backend*", - "invokeai.frontend*", "invokeai.frontend.web.dist*", - "invokeai.frontend.web.static*", - "invokeai.configs*", - "invokeai.app*", + "invokeai.assets.fonts*", + "invokeai.version*", + "invokeai.generator*", + "invokeai.backend*", + "invokeai.frontend*", + "invokeai.frontend.web.dist*", + "invokeai.frontend.web.static*", + "invokeai.configs*", + "invokeai.app*", ] [tool.setuptools.package-data] +"invokeai.app.assets" = ["**/*.png"] "invokeai.assets.fonts" = ["**/*.ttf"] "invokeai.backend" = ["**.png"] "invokeai.configs" = ["*.example", "**/*.yaml", "*.txt"] "invokeai.frontend.web.dist" = ["**"] "invokeai.frontend.web.static" = ["**"] +"invokeai.app.invocations" = ["**"] #=== Begin: PyTest and Coverage [tool.pytest.ini_options] addopts = "--cov-report term --cov-report html --cov-report xml --strict-markers -m \"not slow\"" markers = [ - "slow: Marks tests as slow. Disabled by default. To run all tests, use -m \"\". To run only slow tests, use -m \"slow\"." + "slow: Marks tests as slow. Disabled by default. To run all tests, use -m \"\". To run only slow tests, use -m \"slow\".", ] [tool.coverage.run] branch = true @@ -190,7 +196,7 @@ source = ["invokeai"] omit = ["*tests*", "*migrations*", ".venv/*", "*.env"] [tool.coverage.report] show_missing = true -fail_under = 85 # let's set something sensible on Day 1 ... +fail_under = 85 # let's set something sensible on Day 1 ... [tool.coverage.json] output = "coverage/coverage.json" pretty_print = true @@ -209,7 +215,8 @@ exclude = [ "__pycache__", "build", "dist", - "invokeai/frontend/web/node_modules/" + "invokeai/frontend/web/node_modules/", + ".venv*", ] [tool.black] @@ -218,3 +225,53 @@ line-length = 120 [tool.isort] profile = "black" line_length = 120 + +[tool.mypy] +ignore_missing_imports = true # ignores missing types in third-party libraries + +[[tool.mypy.overrides]] +follow_imports = "skip" +module = [ + "invokeai.app.api.routers.models", + "invokeai.app.invocations.compel", + "invokeai.app.invocations.latent", + "invokeai.app.services.config.config_base", + "invokeai.app.services.config.config_default", + "invokeai.app.services.invocation_stats.invocation_stats_default", + "invokeai.app.services.model_manager.model_manager_base", + "invokeai.app.services.model_manager.model_manager_default", + "invokeai.app.util.controlnet_utils", + "invokeai.backend.image_util.txt2mask", + "invokeai.backend.image_util.safety_checker", + "invokeai.backend.image_util.patchmatch", + "invokeai.backend.image_util.invisible_watermark", + "invokeai.backend.install.model_install_backend", + "invokeai.backend.ip_adapter.ip_adapter", + "invokeai.backend.ip_adapter.resampler", + "invokeai.backend.ip_adapter.unet_patcher", + "invokeai.backend.model_management.convert_ckpt_to_diffusers", + "invokeai.backend.model_management.lora", + "invokeai.backend.model_management.model_cache", + "invokeai.backend.model_management.model_manager", + "invokeai.backend.model_management.model_merge", + "invokeai.backend.model_management.model_probe", + "invokeai.backend.model_management.model_search", + "invokeai.backend.model_management.models.*", # this is needed to ignore the module's `__init__.py` + "invokeai.backend.model_management.models.base", + "invokeai.backend.model_management.models.controlnet", + "invokeai.backend.model_management.models.ip_adapter", + "invokeai.backend.model_management.models.lora", + "invokeai.backend.model_management.models.sdxl", + "invokeai.backend.model_management.models.stable_diffusion", + "invokeai.backend.model_management.models.vae", + "invokeai.backend.model_management.seamless", + "invokeai.backend.model_management.util", + "invokeai.backend.stable_diffusion.diffusers_pipeline", + "invokeai.backend.stable_diffusion.diffusion.cross_attention_control", + "invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion", + "invokeai.backend.util.hotfixes", + "invokeai.backend.util.logging", + "invokeai.backend.util.mps_fixes", + "invokeai.backend.util.util", + "invokeai.frontend.install.model_install", +] diff --git a/tests/backend/ip_adapter/test_ip_adapter.py b/tests/backend/ip_adapter/test_ip_adapter.py index 7f634ee1fe..6712196778 100644 --- a/tests/backend/ip_adapter/test_ip_adapter.py +++ b/tests/backend/ip_adapter/test_ip_adapter.py @@ -65,7 +65,10 @@ def test_ip_adapter_unet_patch(model_params, model_installer, torch_device): ip_adapter.to(torch_device, dtype=torch.float32) unet.to(torch_device, dtype=torch.float32) - cross_attention_kwargs = {"ip_adapter_image_prompt_embeds": [torch.randn((1, 4, 768)).to(torch_device)]} + # ip_embeds shape: (batch_size, num_ip_images, seq_len, ip_image_embedding_len) + ip_embeds = torch.randn((1, 3, 4, 768)).to(torch_device) + + cross_attention_kwargs = {"ip_adapter_image_prompt_embeds": [ip_embeds]} ip_adapter_unet_patcher = UNetPatcher([ip_adapter]) with ip_adapter_unet_patcher.apply_ip_adapter_attention(unet): output = unet(**dummy_unet_input, cross_attention_kwargs=cross_attention_kwargs).sample diff --git a/tests/backend/model_management/test_libc_util.py b/tests/backend/model_management/test_libc_util.py index a517db4c90..e13a2fd3a2 100644 --- a/tests/backend/model_management/test_libc_util.py +++ b/tests/backend/model_management/test_libc_util.py @@ -11,7 +11,10 @@ def test_libc_util_mallinfo2(): # TODO: Set the expected result preemptively based on the system properties. pytest.xfail("libc shared library is not available on this system.") - info = libc.mallinfo2() + try: + info = libc.mallinfo2() + except AttributeError: + pytest.xfail("`mallinfo2` is not available on this system, likely due to glibc < 2.33.") assert info.arena > 0 diff --git a/tests/backend/model_management/test_lora.py b/tests/backend/model_management/test_lora.py new file mode 100644 index 0000000000..14bcc87c89 --- /dev/null +++ b/tests/backend/model_management/test_lora.py @@ -0,0 +1,102 @@ +# test that if the model's device changes while the lora is applied, the weights can still be restored + +# test that LoRA patching works on both CPU and CUDA + +import pytest +import torch + +from invokeai.backend.model_management.lora import ModelPatcher +from invokeai.backend.model_management.models.lora import LoRALayer, LoRAModelRaw + + +@pytest.mark.parametrize( + "device", + [ + "cpu", + pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")), + ], +) +@torch.no_grad() +def test_apply_lora(device): + """Test the basic behavior of ModelPatcher.apply_lora(...). Check that patching and unpatching produce the correct + result, and that model/LoRA tensors are moved between devices as expected. + """ + + linear_in_features = 4 + linear_out_features = 8 + lora_dim = 2 + model = torch.nn.ModuleDict( + {"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device=device, dtype=torch.float16)} + ) + + lora_layers = { + "linear_layer_1": LoRALayer( + layer_key="linear_layer_1", + values={ + "lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16), + "lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16), + }, + ) + } + lora = LoRAModelRaw("lora_name", lora_layers) + + lora_weight = 0.5 + orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone() + expected_patched_linear_weight = orig_linear_weight + (lora_dim * lora_weight) + + with ModelPatcher.apply_lora(model, [(lora, lora_weight)], prefix=""): + # After patching, all LoRA layer weights should have been moved back to the cpu. + assert lora_layers["linear_layer_1"].up.device.type == "cpu" + assert lora_layers["linear_layer_1"].down.device.type == "cpu" + + # After patching, the patched model should still be on its original device. + assert model["linear_layer_1"].weight.data.device.type == device + + torch.testing.assert_close(model["linear_layer_1"].weight.data, expected_patched_linear_weight) + + # After unpatching, the original model weights should have been restored on the original device. + assert model["linear_layer_1"].weight.data.device.type == device + torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device") +@torch.no_grad() +def test_apply_lora_change_device(): + """Test that if LoRA patching is applied on the CPU, and then the patched model is moved to the GPU, unpatching + still behaves correctly. + """ + linear_in_features = 4 + linear_out_features = 8 + lora_dim = 2 + # Initialize the model on the CPU. + model = torch.nn.ModuleDict( + {"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device="cpu", dtype=torch.float16)} + ) + + lora_layers = { + "linear_layer_1": LoRALayer( + layer_key="linear_layer_1", + values={ + "lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16), + "lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16), + }, + ) + } + lora = LoRAModelRaw("lora_name", lora_layers) + + orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone() + + with ModelPatcher.apply_lora(model, [(lora, 0.5)], prefix=""): + # After patching, all LoRA layer weights should have been moved back to the cpu. + assert lora_layers["linear_layer_1"].up.device.type == "cpu" + assert lora_layers["linear_layer_1"].down.device.type == "cpu" + + # After patching, the patched model should still be on the CPU. + assert model["linear_layer_1"].weight.data.device.type == "cpu" + + # Move the model to the GPU. + assert model.to("cuda") + + # After unpatching, the original model weights should have been restored on the GPU. + assert model["linear_layer_1"].weight.data.device.type == "cuda" + torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight, check_device=False) diff --git a/tests/backend/model_management/test_memory_snapshot.py b/tests/backend/model_management/test_memory_snapshot.py index 80aed7b7ba..216cd62171 100644 --- a/tests/backend/model_management/test_memory_snapshot.py +++ b/tests/backend/model_management/test_memory_snapshot.py @@ -13,10 +13,11 @@ def test_memory_snapshot_capture(): snapshots = [ - MemorySnapshot(process_ram=1.0, vram=2.0, malloc_info=Struct_mallinfo2()), - MemorySnapshot(process_ram=1.0, vram=2.0, malloc_info=None), - MemorySnapshot(process_ram=1.0, vram=None, malloc_info=Struct_mallinfo2()), - MemorySnapshot(process_ram=1.0, vram=None, malloc_info=None), + MemorySnapshot(process_ram=1, vram=2, malloc_info=Struct_mallinfo2()), + MemorySnapshot(process_ram=1, vram=2, malloc_info=None), + MemorySnapshot(process_ram=1, vram=None, malloc_info=Struct_mallinfo2()), + MemorySnapshot(process_ram=1, vram=None, malloc_info=None), + None, ] @@ -26,10 +27,12 @@ def test_get_pretty_snapshot_diff(snapshot_1, snapshot_2): """Test that get_pretty_snapshot_diff() works with various combinations of missing MemorySnapshot fields.""" msg = get_pretty_snapshot_diff(snapshot_1, snapshot_2) - expected_lines = 1 - if snapshot_1.vram is not None and snapshot_2.vram is not None: + expected_lines = 0 + if snapshot_1 is not None and snapshot_2 is not None: expected_lines += 1 - if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None: - expected_lines += 5 + if snapshot_1.vram is not None and snapshot_2.vram is not None: + expected_lines += 1 + if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None: + expected_lines += 5 assert len(msg.splitlines()) == expected_lines diff --git a/tests/backend/model_management/test_model_load_optimization.py b/tests/backend/model_management/test_model_load_optimization.py index 43f007e972..a4fe1dd597 100644 --- a/tests/backend/model_management/test_model_load_optimization.py +++ b/tests/backend/model_management/test_model_load_optimization.py @@ -11,6 +11,7 @@ from invokeai.backend.model_management.model_load_optimizations import _no_op, s (torch.nn.Conv1d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}), (torch.nn.Conv2d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}), (torch.nn.Conv3d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}), + (torch.nn.Embedding, {"num_embeddings": 10, "embedding_dim": 10}), ], ) def test_skip_torch_weight_init_linear(torch_module, layer_args): @@ -36,12 +37,14 @@ def test_skip_torch_weight_init_linear(torch_module, layer_args): # Check that reset_parameters is skipped while `skip_torch_weight_init()` is active. assert reset_params_fn_during == _no_op assert not torch.allclose(layer_before.weight, layer_during.weight) - assert not torch.allclose(layer_before.bias, layer_during.bias) + if hasattr(layer_before, "bias"): + assert not torch.allclose(layer_before.bias, layer_during.bias) # Check that the original behavior is restored after `skip_torch_weight_init()` ends. assert reset_params_fn_before is reset_params_fn_after assert torch.allclose(layer_before.weight, layer_after.weight) - assert torch.allclose(layer_before.bias, layer_after.bias) + if hasattr(layer_before, "bias"): + assert torch.allclose(layer_before.bias, layer_after.bias) def test_skip_torch_weight_init_restores_base_class_behavior(): diff --git a/tests/nodes/test_graph_execution_state.py b/tests/nodes/test_graph_execution_state.py index 27b8a58bea..171cdfdb6f 100644 --- a/tests/nodes/test_graph_execution_state.py +++ b/tests/nodes/test_graph_execution_state.py @@ -75,6 +75,8 @@ def mock_services() -> InvocationServices: session_processor=None, # type: ignore session_queue=None, # type: ignore urls=None, # type: ignore + workflow_records=None, # type: ignore + workflow_image_records=None, # type: ignore ) diff --git a/tests/nodes/test_invoker.py b/tests/nodes/test_invoker.py index 105f7417cd..25b02955b0 100644 --- a/tests/nodes/test_invoker.py +++ b/tests/nodes/test_invoker.py @@ -80,6 +80,8 @@ def mock_services() -> InvocationServices: session_processor=None, # type: ignore session_queue=None, # type: ignore urls=None, # type: ignore + workflow_records=None, # type: ignore + workflow_image_records=None, # type: ignore ) diff --git a/tests/nodes/test_node_graph.py b/tests/nodes/test_node_graph.py index 822ffc1588..e2a50e61e5 100644 --- a/tests/nodes/test_node_graph.py +++ b/tests/nodes/test_node_graph.py @@ -1,4 +1,5 @@ import pytest +from pydantic import TypeAdapter from invokeai.app.invocations.baseinvocation import ( BaseInvocation, @@ -9,7 +10,12 @@ from invokeai.app.invocations.baseinvocation import ( ) from invokeai.app.invocations.image import ShowImageInvocation from invokeai.app.invocations.math import AddInvocation, SubtractInvocation -from invokeai.app.invocations.primitives import FloatInvocation, IntegerInvocation +from invokeai.app.invocations.primitives import ( + FloatCollectionInvocation, + FloatInvocation, + IntegerInvocation, + StringInvocation, +) from invokeai.app.invocations.upscale import ESRGANInvocation from invokeai.app.services.shared.default_graphs import create_text_to_image from invokeai.app.services.shared.graph import ( @@ -26,8 +32,11 @@ from invokeai.app.services.shared.graph import ( ) from .test_nodes import ( + AnyTypeTestInvocation, ImageToImageTestInvocation, ListPassThroughInvocation, + PolymorphicStringTestInvocation, + PromptCollectionTestInvocation, PromptTestInvocation, TextToImageTestInvocation, ) @@ -593,20 +602,21 @@ def test_graph_can_serialize(): g.add_edge(e) # Not throwing on this line is sufficient - _ = g.json() + _ = g.model_dump_json() def test_graph_can_deserialize(): g = Graph() n1 = TextToImageTestInvocation(id="1", prompt="Banana sushi") - n2 = ESRGANInvocation(id="2") + n2 = ImageToImageTestInvocation(id="2") g.add_node(n1) g.add_node(n2) e = create_edge(n1.id, "image", n2.id, "image") g.add_edge(e) - json = g.json() - g2 = Graph.parse_raw(json) + json = g.model_dump_json() + GraphValidator = TypeAdapter(Graph) + g2 = GraphValidator.validate_json(json) assert g2 is not None assert g2.nodes["1"] is not None @@ -619,7 +629,7 @@ def test_graph_can_deserialize(): def test_invocation_decorator(): - invocation_type = "test_invocation" + invocation_type = "test_invocation_decorator" title = "Test Invocation" tags = ["first", "second", "third"] category = "category" @@ -630,7 +640,7 @@ def test_invocation_decorator(): def invoke(self): pass - schema = TestInvocation.schema() + schema = TestInvocation.model_json_schema() assert schema.get("title") == title assert schema.get("tags") == tags @@ -640,18 +650,17 @@ def test_invocation_decorator(): def test_invocation_version_must_be_semver(): - invocation_type = "test_invocation" valid_version = "1.0.0" invalid_version = "not_semver" - @invocation(invocation_type, version=valid_version) + @invocation("test_invocation_version_valid", version=valid_version) class ValidVersionInvocation(BaseInvocation): def invoke(self): pass with pytest.raises(InvalidVersionError): - @invocation(invocation_type, version=invalid_version) + @invocation("test_invocation_version_invalid", version=invalid_version) class InvalidVersionInvocation(BaseInvocation): def invoke(self): pass @@ -691,7 +700,145 @@ def test_ints_do_not_accept_floats(): g.add_edge(e) +def test_polymorphic_accepts_single(): + g = Graph() + n1 = StringInvocation(id="1", value="banana") + n2 = PolymorphicStringTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e1 = create_edge(n1.id, "value", n2.id, "value") + # Not throwing on this line is sufficient + g.add_edge(e1) + + +def test_polymorphic_accepts_collection_of_same_base_type(): + g = Graph() + n1 = PromptCollectionTestInvocation(id="1", collection=["banana", "sundae"]) + n2 = PolymorphicStringTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e1 = create_edge(n1.id, "collection", n2.id, "value") + # Not throwing on this line is sufficient + g.add_edge(e1) + + +def test_polymorphic_does_not_accept_collection_of_different_base_type(): + g = Graph() + n1 = FloatCollectionInvocation(id="1", collection=[1.0, 2.0, 3.0]) + n2 = PolymorphicStringTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e1 = create_edge(n1.id, "collection", n2.id, "value") + with pytest.raises(InvalidEdgeError): + g.add_edge(e1) + + +def test_polymorphic_does_not_accept_generic_collection(): + g = Graph() + n1 = IntegerInvocation(id="1", value=1) + n2 = IntegerInvocation(id="2", value=2) + n3 = CollectInvocation(id="3") + n4 = PolymorphicStringTestInvocation(id="4") + g.add_node(n1) + g.add_node(n2) + g.add_node(n3) + g.add_node(n4) + e1 = create_edge(n1.id, "value", n3.id, "item") + e2 = create_edge(n2.id, "value", n3.id, "item") + e3 = create_edge(n3.id, "collection", n4.id, "value") + g.add_edge(e1) + g.add_edge(e2) + with pytest.raises(InvalidEdgeError): + g.add_edge(e3) + + +def test_any_accepts_integer(): + g = Graph() + n1 = IntegerInvocation(id="1", value=1) + n2 = AnyTypeTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e = create_edge(n1.id, "value", n2.id, "value") + # Not throwing on this line is sufficient + g.add_edge(e) + + +def test_any_accepts_string(): + g = Graph() + n1 = StringInvocation(id="1", value="banana sundae") + n2 = AnyTypeTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e = create_edge(n1.id, "value", n2.id, "value") + # Not throwing on this line is sufficient + g.add_edge(e) + + +def test_any_accepts_generic_collection(): + g = Graph() + n1 = IntegerInvocation(id="1", value=1) + n2 = IntegerInvocation(id="2", value=2) + n3 = CollectInvocation(id="3") + n4 = AnyTypeTestInvocation(id="4") + g.add_node(n1) + g.add_node(n2) + g.add_node(n3) + g.add_node(n4) + e1 = create_edge(n1.id, "value", n3.id, "item") + e2 = create_edge(n2.id, "value", n3.id, "item") + e3 = create_edge(n3.id, "collection", n4.id, "value") + g.add_edge(e1) + g.add_edge(e2) + # Not throwing on this line is sufficient + g.add_edge(e3) + + +def test_any_accepts_prompt_collection(): + g = Graph() + n1 = PromptCollectionTestInvocation(id="1", collection=["banana", "sundae"]) + n2 = AnyTypeTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e = create_edge(n1.id, "collection", n2.id, "value") + # Not throwing on this line is sufficient + g.add_edge(e) + + +def test_any_accepts_any(): + g = Graph() + n1 = AnyTypeTestInvocation(id="1") + n2 = AnyTypeTestInvocation(id="2") + g.add_node(n1) + g.add_node(n2) + e = create_edge(n1.id, "value", n2.id, "value") + # Not throwing on this line is sufficient + g.add_edge(e) + + +def test_iterate_accepts_collection(): + """We need to update the validation for Collect -> Iterate to traverse to the Iterate + node's output and compare that against the item type of the Collect node's collection. Until + then, Collect nodes may not output into Iterate nodes.""" + g = Graph() + n1 = IntegerInvocation(id="1", value=1) + n2 = IntegerInvocation(id="2", value=2) + n3 = CollectInvocation(id="3") + n4 = IterateInvocation(id="4") + g.add_node(n1) + g.add_node(n2) + g.add_node(n3) + g.add_node(n4) + e1 = create_edge(n1.id, "value", n3.id, "item") + e2 = create_edge(n2.id, "value", n3.id, "item") + e3 = create_edge(n3.id, "collection", n4.id, "collection") + g.add_edge(e1) + g.add_edge(e2) + # Once we fix the validation logic as described, this should should not raise an error + with pytest.raises(InvalidEdgeError, match="Cannot connect collector to iterator"): + g.add_edge(e3) + + def test_graph_can_generate_schema(): # Not throwing on this line is sufficient # NOTE: if this test fails, it's PROBABLY because a new invocation type is breaking schema generation - _ = Graph.schema_json(indent=2) + _ = Graph.model_json_schema() diff --git a/tests/nodes/test_nodes.py b/tests/nodes/test_nodes.py index 471c72a005..51b33dd4c7 100644 --- a/tests/nodes/test_nodes.py +++ b/tests/nodes/test_nodes.py @@ -1,11 +1,11 @@ from typing import Any, Callable, Union -from pydantic import Field - from invokeai.app.invocations.baseinvocation import ( BaseInvocation, BaseInvocationOutput, + InputField, InvocationContext, + OutputField, invocation, invocation_output, ) @@ -15,12 +15,12 @@ from invokeai.app.invocations.image import ImageField # Define test invocations before importing anything that uses invocations @invocation_output("test_list_output") class ListPassThroughInvocationOutput(BaseInvocationOutput): - collection: list[ImageField] = Field(default_factory=list) + collection: list[ImageField] = OutputField(default_factory=list) @invocation("test_list") class ListPassThroughInvocation(BaseInvocation): - collection: list[ImageField] = Field(default_factory=list) + collection: list[ImageField] = InputField(default_factory=list) def invoke(self, context: InvocationContext) -> ListPassThroughInvocationOutput: return ListPassThroughInvocationOutput(collection=self.collection) @@ -28,12 +28,12 @@ class ListPassThroughInvocation(BaseInvocation): @invocation_output("test_prompt_output") class PromptTestInvocationOutput(BaseInvocationOutput): - prompt: str = Field(default="") + prompt: str = OutputField(default="") @invocation("test_prompt") class PromptTestInvocation(BaseInvocation): - prompt: str = Field(default="") + prompt: str = InputField(default="") def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput: return PromptTestInvocationOutput(prompt=self.prompt) @@ -47,13 +47,13 @@ class ErrorInvocation(BaseInvocation): @invocation_output("test_image_output") class ImageTestInvocationOutput(BaseInvocationOutput): - image: ImageField = Field() + image: ImageField = OutputField() @invocation("test_text_to_image") class TextToImageTestInvocation(BaseInvocation): - prompt: str = Field(default="") - prompt2: str = Field(default="") + prompt: str = InputField(default="") + prompt2: str = InputField(default="") def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput: return ImageTestInvocationOutput(image=ImageField(image_name=self.id)) @@ -61,8 +61,8 @@ class TextToImageTestInvocation(BaseInvocation): @invocation("test_image_to_image") class ImageToImageTestInvocation(BaseInvocation): - prompt: str = Field(default="") - image: Union[ImageField, None] = Field(default=None) + prompt: str = InputField(default="") + image: Union[ImageField, None] = InputField(default=None) def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput: return ImageTestInvocationOutput(image=ImageField(image_name=self.id)) @@ -70,17 +70,40 @@ class ImageToImageTestInvocation(BaseInvocation): @invocation_output("test_prompt_collection_output") class PromptCollectionTestInvocationOutput(BaseInvocationOutput): - collection: list[str] = Field(default_factory=list) + collection: list[str] = OutputField(default_factory=list) @invocation("test_prompt_collection") class PromptCollectionTestInvocation(BaseInvocation): - collection: list[str] = Field() + collection: list[str] = InputField() def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput: return PromptCollectionTestInvocationOutput(collection=self.collection.copy()) +@invocation_output("test_any_output") +class AnyTypeTestInvocationOutput(BaseInvocationOutput): + value: Any = OutputField() + + +@invocation("test_any") +class AnyTypeTestInvocation(BaseInvocation): + value: Any = InputField(default=None) + + def invoke(self, context: InvocationContext) -> AnyTypeTestInvocationOutput: + return AnyTypeTestInvocationOutput(value=self.value) + + +@invocation("test_polymorphic") +class PolymorphicStringTestInvocation(BaseInvocation): + value: Union[str, list[str]] = InputField(default="") + + def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput: + if isinstance(self.value, str): + return PromptCollectionTestInvocationOutput(collection=[self.value]) + return PromptCollectionTestInvocationOutput(collection=self.value) + + # Importing these must happen after test invocations are defined or they won't register from invokeai.app.services.events.events_base import EventServiceBase # noqa: E402 from invokeai.app.services.shared.graph import Edge, EdgeConnection # noqa: E402 diff --git a/tests/nodes/test_session_queue.py b/tests/nodes/test_session_queue.py index 6dd7c4845a..cdab5729f8 100644 --- a/tests/nodes/test_session_queue.py +++ b/tests/nodes/test_session_queue.py @@ -1,5 +1,5 @@ import pytest -from pydantic import ValidationError, parse_raw_as +from pydantic import TypeAdapter, ValidationError from invokeai.app.services.session_queue.session_queue_common import ( Batch, @@ -150,8 +150,9 @@ def test_prepare_values_to_insert(batch_data_collection, batch_graph): values = prepare_values_to_insert(queue_id="default", batch=b, priority=0, max_new_queue_items=1000) assert len(values) == 8 + GraphExecutionStateValidator = TypeAdapter(GraphExecutionState) # graph should be serialized - ges = parse_raw_as(GraphExecutionState, values[0].session) + ges = GraphExecutionStateValidator.validate_json(values[0].session) # graph values should be populated assert ges.graph.get_node("1").prompt == "Banana sushi" @@ -160,15 +161,16 @@ def test_prepare_values_to_insert(batch_data_collection, batch_graph): assert ges.graph.get_node("4").prompt == "Nissan" # session ids should match deserialized graph - assert [v.session_id for v in values] == [parse_raw_as(GraphExecutionState, v.session).id for v in values] + assert [v.session_id for v in values] == [GraphExecutionStateValidator.validate_json(v.session).id for v in values] # should unique session ids sids = [v.session_id for v in values] assert len(sids) == len(set(sids)) + NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue]) # should have 3 node field values assert type(values[0].field_values) is str - assert len(parse_raw_as(list[NodeFieldValue], values[0].field_values)) == 3 + assert len(NodeFieldValueValidator.validate_json(values[0].field_values)) == 3 # should have batch id and priority assert all(v.batch_id == b.batch_id for v in values) diff --git a/tests/nodes/test_sqlite.py b/tests/nodes/test_sqlite.py index 6e4da8b36e..818f9d048f 100644 --- a/tests/nodes/test_sqlite.py +++ b/tests/nodes/test_sqlite.py @@ -15,7 +15,8 @@ class TestModel(BaseModel): @pytest.fixture def db() -> SqliteItemStorage[TestModel]: sqlite_db = SqliteDatabase(InvokeAIAppConfig(use_memory_db=True), InvokeAILogger.get_logger()) - return SqliteItemStorage[TestModel](db=sqlite_db, table_name="test", id_field="id") + sqlite_item_storage = SqliteItemStorage[TestModel](db=sqlite_db, table_name="test", id_field="id") + return sqlite_item_storage def test_sqlite_service_can_create_and_get(db: SqliteItemStorage[TestModel]):