feat(nodes): WIP restricted invocation context

Upgrade pydantic and fastapi to latest.

- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1

**Big Changes**

There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.

**Invocations**

The biggest change relates to invocation creation, instantiation and validation.

Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.

Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.

With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.

This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.

In the end, this implementation is cleaner.

**Invocation Fields**

In pydantic v2, you can no longer directly add or remove fields from a model.

Previously, we did this to add the `type` field to invocations.

**Invocation Decorators**

With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.

A similar technique is used for `invocation_output()`.

**Minor Changes**

There are a number of minor changes around the pydantic v2 models API.

**Protected `model_` Namespace**

All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".

Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.

```py
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=())
```

**Model Serialization**

Pydantic models no longer have `Model.dict()` or `Model.json()`.

Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.

**Model Deserialization**

Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.

Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.

```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```

**Field Customisation**

Pydantic `Field`s no longer accept arbitrary args.

Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.

**Schema Customisation**

FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.

This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised

The specific aren't important, but this does present additional surface area for bugs.

**Performance Improvements**

Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.

I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.

.github/ISSUE_TEMPLATE/FEATURE_REQUEST.yml

@@ -34,12 +34,9 @@ body:
     id: whatisexpected
     attributes:
       label: What should this feature add?
-      description: Please try to explain the functionality this feature should add
+      description: Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
       placeholder: |
-        Instead of one huge text field, it would be nice to have forms for bug-reports, feature-requests, ...
-        Great benefits with automatic labeling, assigning and other functionalitys not available in that form
-        via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
-        https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
+        I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
     validations:
       required: true
 

.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'
+        if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
         run: |
           pip install --upgrade requests
           python -c "\

.github/workflows/style-checks.yml

@@ -1,6 +1,4 @@
 name: style checks
-# just formatting and flake8 for now
-# TODO: add isort later
 
 on:
   pull_request:
@@ -20,8 +18,8 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install black flake8 Flake8-pyproject
+          pip install black flake8 Flake8-pyproject isort
 
-      # - run: isort --check-only .
+      - run: isort --check-only .
       - run: black --check .
       - run: flake8

.pre-commit-config.yaml

@@ -15,3 +15,10 @@ repos:
     language: system
     entry: flake8
     types: [python]
+
+  - id: isort
+    name: isort
+    stages: [commit]
+    language: system
+    entry: isort
+    types: [python]

README.md

@@ -46,13 +46,13 @@ the foundation for multiple commercial products.
   Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
   href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
   href="https://invoke-ai.github.io/InvokeAI/">Documentation and
-  Tutorials</a>] [<a
-  href="https://github.com/invoke-ai/InvokeAI/">Code and
-  Downloads</a>] [<a
-  href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
+  Tutorials</a>]
+  [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
   [<a
   href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
   Ideas & Q&A</a>] 
+   [<a
+  href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>] 
 
 <div align="center">
 
@@ -368,9 +368,9 @@ InvokeAI offers a locally hosted Web Server & React Frontend, with an industry l
 
 The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
 
-### *Node Architecture & Editor (Beta)*
+### *Workflows & Nodes*
 
-Invoke AI's backend is built on a graph-based execution architecture. This allows for customizable generation pipelines to be developed by professional users looking to create specific workflows to support their production use-cases, and will be extended in the future with additional capabilities.
+InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
 
 ### *Board & Gallery Management*
 
@@ -383,8 +383,9 @@ Invoke AI provides an organized gallery system for easily storing, accessing, an
 - *Upscaling Tools*
 - *Embedding Manager & Support*
 - *Model Manager & Support*
+- *Workflow creation & management*
 - *Node-Based Architecture*
-- *Node-Based Plug-&-Play UI (Beta)*
+
 
 ### Latest Changes
 
@@ -395,20 +396,18 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 ### Troubleshooting
 
 Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
-problems and other issues.
+problems and other issues. For more help, please join our [Discord][discord link]
 
 ## Contributing
 
 Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
 cleanup, testing, or code reviews, is very much encouraged to do so.
 
-To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
-
-If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
+Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
 
 If you are unfamiliar with how
-to contribute to GitHub projects, here is a
-[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.
+to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing: 
+[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
 
 We hope you enjoy using our software as much as we enjoy creating it,
 and we hope that some of those of you who are reading this will elect
@@ -424,7 +423,7 @@ their time, hard work and effort.
 
 ### Support
 
-For support, please use this repository's GitHub Issues tracking service, or join the Discord.
+For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
 
 Original portions of the software are Copyright (c) 2023 by respective contributors.
 

docs/contributing/CONTRIBUTING.md

@@ -1,39 +1,41 @@
-# How to Contribute
+# Contributing
 
-## Welcome to Invoke AI
 Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
 
 
-## Contributing to Invoke AI
+# Methods of Contributing to Invoke AI
 Anyone who wishes to contribute to InvokeAI, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation is very much encouraged to do so.
 
-To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
+## Development
+If you’d like to help with development, please see our [development guide](contribution_guides/development.md). 
 
-### Areas of contribution: 
+**New Contributors:** If you’re unfamiliar with contributing to open source projects, take a look at our [new contributor guide](contribution_guides/newContributorChecklist.md).
 
-#### Development
-If you’d like to help with development, please see our [development guide](contribution_guides/development.md). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
+## Nodes
+If you’d like to add a Node, please see our [nodes contribution guide](../nodes/contributingNodes.md).
 
-#### Nodes
-If you’d like to help with development, please see our [nodes contribution guide](/nodes/contributingNodes). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
+## Support and Triaging
+Helping support other users in [Discord](https://discord.gg/ZmtBAhwWhy) and on Github are valuable forms of contribution that we greatly appreciate. 
 
-#### Documentation
+We receive many issues and requests for help from users. We're limited in bandwidth relative to our the user base, so providing answers to questions or helping identify causes of issues is very helpful. By doing this, you enable us to spend time on the highest priority work. 
+
+## Documentation
 If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
 
-#### Translation
+## Translation
 If you'd like to help with translation, please see our [translation guide](contribution_guides/translation.md).
 
-#### Tutorials 
+## Tutorials 
 Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
 
 We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect to become part of our contributor community.
 
 
-### Contributors
+# Contributors
 
 This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
 
-### Code of Conduct
+# Code of Conduct
 
 The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
@@ -47,8 +49,7 @@ By making a contribution to this project, you certify that:
 This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
 
 This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
-
-### Support
+# Support
 
 For support, please use this repository's [GitHub Issues](https://github.com/invoke-ai/InvokeAI/issues), or join the [Discord](https://discord.gg/ZmtBAhwWhy).
 

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -47,34 +47,9 @@ pip install ".[dev,test]"
 These are optional groups of packages which are defined within the `pyproject.toml`
 and will be required for testing the changes you make to the code.
 
-### Running Tests
-
-We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
-be found under the `./tests` folder and can be run with a single `pytest`
-command. Optionally, to review test coverage you can append `--cov`.
-
-```zsh
-pytest --cov
-```
-
-Test outcomes and coverage will be reported in the terminal. In addition a more
-detailed report is created in both XML and HTML format in the `./coverage`
-folder. The HTML one in particular can help identify missing statements
-requiring tests to ensure coverage. This can be run by opening
-`./coverage/html/index.html`.
-
-For example.
-
-```zsh
-pytest --cov; open ./coverage/html/index.html
-```
-
-??? info "HTML coverage report output"
-
-    ![html-overview](../assets/contributing/html-overview.png)
-
-    ![html-detail](../assets/contributing/html-detail.png)
+### Tests
 
+See the [tests documentation](./TESTS.md) for information about running and writing tests.
 ### Reloading Changes
 
 Experimenting with changes to the Python source code is a drag if you have to re-start the server —
@@ -167,6 +142,23 @@ and so you'll have access to the same python environment as the InvokeAI app.
 
 This is _super_ handy.
 
+#### Enabling Type-Checking with Pylance
+
+We use python's typing system in InvokeAI. PR reviews will include checking that types are present and correct. We don't enforce types with `mypy` at this time, but that is on the horizon.
+
+Using a code analysis tool to automatically type check your code (and types) is very important when writing with types. These tools provide immediate feedback in your editor when types are incorrect, and following their suggestions lead to fewer runtime bugs.
+
+Pylance, installed at the beginning of this guide, is the de-facto python LSP (language server protocol). It provides type checking in the editor (among many other features). Once installed, you do need to enable type checking manually:
+
+- Open a python file
+- Look along the status bar in VSCode for `{ } Python`
+- Click the `{ }`
+- Turn type checking on - basic is fine
+
+You'll now see red squiggly lines where type issues are detected. Hover your cursor over the indicated symbols to see what's wrong.
+
+In 99% of cases when the type checker says there is a problem, there really is a problem, and you should take some time to understand and resolve what it is pointing out.
+
 #### Debugging configs with `launch.json`
 
 Debugging configs are managed in a `launch.json` file. Like most VSCode configs,

docs/contributing/TESTS.md

@@ -0,0 +1,89 @@
+# InvokeAI Backend Tests
+
+We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
+
+## Fast vs. Slow
+All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
+
+'Fast' tests are run to validate every PR, and are fast enough that they can be run routinely during development.
+
+'Slow' tests are currently only run manually on an ad-hoc basis. In the future, they may be automated to run nightly. Most developers are only expected to run the 'slow' tests that directly relate to the feature(s) that they are working on.
+
+As a rule of thumb, tests should be marked as 'slow' if there is a chance that they take >1s (e.g. on a CPU-only machine with slow internet connection). Common examples of slow tests are tests that depend on downloading a model, or running model inference.
+
+## Running Tests
+
+Below are some common test commands:
+```bash
+# Run the fast tests. (This implicitly uses the configured default option: `-m "not slow"`.)
+pytest tests/
+
+# Equivalent command to run the fast tests.
+pytest tests/ -m "not slow"
+
+# Run the slow tests.
+pytest tests/ -m "slow"
+
+# Run the slow tests from a specific file.
+pytest tests/path/to/slow_test.py -m "slow"
+
+# Run all tests (fast and slow).
+pytest tests -m ""
+```
+
+## Test Organization
+
+All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
+
+TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
+
+## Tests that depend on models
+
+There are a few things to keep in mind when adding tests that depend on models.
+
+1. If a required model is not already present, it should automatically be downloaded as part of the test setup.
+2. If a model is already downloaded, it should not be re-downloaded unnecessarily.
+3. Take reasonable care to keep the total number of models required for the tests low. Whenever possible, re-use models that are already required for other tests. If you are adding a new model, consider including a comment to explain why it is required/unique.
+
+There are several utilities to help with model setup for tests. Here is a sample test that depends on a model:
+```python
+import pytest
+import torch
+
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType
+from invokeai.backend.util.test_utils import install_and_load_model
+
+@pytest.mark.slow
+def test_model(model_installer, torch_device):
+    model_info = install_and_load_model(
+        model_installer=model_installer,
+        model_path_id_or_url="HF/dummy_model_id",
+        model_name="dummy_model",
+        base_model=BaseModelType.StableDiffusion1,
+        model_type=ModelType.Dummy,
+    )
+
+    dummy_input = build_dummy_input(torch_device)
+
+    with torch.no_grad(), model_info as model:
+        model.to(torch_device, dtype=torch.float32)
+        output = model(dummy_input)
+
+    # Validate output...
+
+```
+
+## Test Coverage
+
+To review test coverage, append `--cov` to your pytest command:
+```bash
+pytest tests/ --cov
+```
+
+Test outcomes and coverage will be reported in the terminal. In addition, a more detailed report is created in both XML and HTML format in the `./coverage` folder. The HTML output is particularly helpful in identifying untested statements where coverage should be improved. The HTML report can be viewed by opening `./coverage/html/index.html`.
+
+??? info "HTML coverage report output"
+
+    ![html-overview](../assets/contributing/html-overview.png)
+
+    ![html-detail](../assets/contributing/html-detail.png)

docs/contributing/contribution_guides/development.md

@@ -4,14 +4,21 @@
 
 If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
 
-For more information, please review our area specific documentation:
+
+## **Get Started**
+
+To get started, take a look at our [new contributors checklist](newContributorChecklist.md)
+
+Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
+* #### [Frontend Documentation](./contributingToFrontend.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
-If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md) or [translation](translation.md).
+
+
+If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md), [translation](translation.md) or helping support other users and triage issues as they're reported in GitHub.
 
 There are two paths to making a development contribution: 
 
@@ -23,67 +30,18 @@ There are two paths to making a development contribution:
 
 ## Best Practices: 
 * Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
-* Comments! Commenting your code helps reviwers easily understand your contribution
+* Comments! Commenting your code helps reviewers easily understand your contribution
 * Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
 * Make all communications public. This ensure knowledge is shared with the whole community
 
-## **How do I make a contribution?**
-
-Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
-
-Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
-
-1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
-2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
-3. Clone the repository to your local machine using:
-
-```bash
-git clone https://github.com/your-GitHub-username/InvokeAI.git
-```
-
-If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
-
-4. Create a new branch for your fix using:
-
-```bash
-git checkout -b branch-name-here
-```
-
-5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
-6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
-
-```bash
-git add insert-paths-of-changed-files-here
-```
-
-7. Store the contents of the index with a descriptive message.
-
-```bash
-git commit -m "Insert a short message of the changes made here"
-```
-
-8. Push the changes to the remote repository using
-
-```markdown
-git push origin branch-name-here
-```
-
-9. Submit a pull request to the **main** branch of the InvokeAI repository.
-10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
-11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
-12. Wait for the pull request to be reviewed by other collaborators.
-13. Make changes to the pull request if the reviewer(s) recommend them.
-14. Celebrate your success after your pull request is merged!
-
-If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
-
 ## **Where can I go for help?**
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@pyschedelicious** is the best person to reach out to. 
+For frontend related work, **@psychedelicious** is the best person to reach out to. 
+
+For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
 
-For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.
 
 ## **What does the Code of Conduct mean for me?**
 

docs/contributing/contribution_guides/documentation.md

@@ -10,4 +10,4 @@ When updating or creating documentation, please keep in mind InvokeAI is a tool
 
 ## Help & Questions
 
-Please ping @imic1 or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.
+Please ping @imic or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

docs/contributing/contribution_guides/newContributorChecklist.md

@@ -0,0 +1,68 @@
+# New Contributor Guide
+
+If you're a new contributor to InvokeAI or Open Source Projects, this is the guide for you. 
+
+## New Contributor Checklist
+- [x] Set up your local development environment & fork of InvokAI by following [the steps outlined here](../../installation/020_INSTALL_MANUAL.md#developer-install)
+- [x] Set up your local tooling with [this guide](InvokeAI/contributing/LOCAL_DEVELOPMENT/#developing-invokeai-in-vscode). Feel free to skip this step if you already have tooling you're comfortable with. 
+- [x] Familiarize yourself with [Git](https://www.atlassian.com/git) & our project structure by reading through the [development documentation](development.md)
+- [x] Join the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord
+- [x] Choose an issue to work on! This can be achieved by asking in the #dev-chat channel, tackling a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) or finding an item on the [roadmap](https://github.com/orgs/invoke-ai/projects/7). If nothing in any of those places catches your eye, feel free to work on something of interest to you! 
+- [x] Make your first Pull Request with the guide below
+- [x] Happy development! Don't be afraid to ask for help - we're happy to help you contribute!
+
+
+## How do I make a contribution?
+
+Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
+
+Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
+
+1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
+2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
+3. Clone the repository to your local machine using:
+```bash
+git clone https://github.com/your-GitHub-username/InvokeAI.git
+```
+If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
+4. Create a new branch for your fix using:
+```bash
+git checkout -b branch-name-here
+```
+5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
+6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
+```bash
+git add -A
+```
+7. Store the contents of the index with a descriptive message.
+```bash
+git commit -m "Insert a short message of the changes made here"
+```
+8. Push the changes to the remote repository using
+```bash
+git push origin branch-name-here
+```
+9. Submit a pull request to the **main** branch of the InvokeAI repository. If you're not sure how to, [follow this guide](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)
+10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
+11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
+12. Wait for the pull request to be reviewed by other collaborators.
+13. Make changes to the pull request if the reviewer(s) recommend them.
+14. Celebrate your success after your pull request is merged!
+
+If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
+
+
+## Best Practices: 
+* Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
+* Comments! Commenting your code helps reviewers easily understand your contribution
+* Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
+* Make all communications public. This ensure knowledge is shared with the whole community
+
+
+## **Where can I go for help?**
+
+If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
+
+For frontend related work, **@pyschedelicious** is the best person to reach out to. 
+
+For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.

docs/features/CONCEPTS.md

@@ -21,8 +21,8 @@ TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
 different TI training packages. InvokeAI supports both formats, but its
 [built-in TI training system](TRAINING.md) produces `.pt`.
 
-The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
-amassed a large ligrary of >800 community-contributed TI files covering a
+[Hugging Face](https://huggingface.co/sd-concepts-library) has
+amassed a large library of >800 community-contributed TI files covering a
 broad range of subjects and styles. You can also install your own or others' TI files 
 by placing them in the designated directory for the compatible model type
 

docs/features/CONFIGURATION.md

@@ -159,7 +159,7 @@ groups in `invokeia.yaml`:
 | `host`     | `localhost`      | Name or IP address of the network interface that the web server will listen on  |
 | `port`     | `9090`           | Network port number that the web server will listen on  |
 | `allow_origins`  | `[]`       | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
-| `allow_credentials | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
+| `allow_credentials` | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
 | `allow_methods` | `*`         | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API  |
 | `allow_headers` | `*`         | List of HTTP headers that the web server will accept when accessing the API  |
 

docs/features/CONTROLNET.md

@@ -1,13 +1,11 @@
 ---
-title: ControlNet
+title: Control Adapters
 ---
 
-# :material-loupe: ControlNet
+# :material-loupe: Control Adapters
 
 ## ControlNet
 
-ControlNet
-
 ControlNet is a powerful set of features developed by the open-source
 community (notably, Stanford researcher
 [**@ilyasviel**](https://github.com/lllyasviel)) that allows you to
@@ -20,7 +18,7 @@ towards generating images that better fit your desired style or
 outcome.
 
 
-### How it works
+#### How it works
 
 ControlNet works by analyzing an input image, pre-processing that
 image to identify relevant information that can be interpreted by each
@@ -30,7 +28,7 @@ composition, or other aspects of the image to better achieve a
 specific result.
 
 
-### Models
+#### Models
 
 InvokeAI provides access to a series of ControlNet models that provide
 different effects or styles in your generated images.  Currently
@@ -96,6 +94,8 @@ A model that generates normal maps from input images, allowing for more realisti
 **Image Segmentation**: 
 A model that divides input images into segments or regions, each of which corresponds to a different object or part of the image. (More details coming soon)
 
+**QR Code Monster**:
+A model that helps generate creative QR codes that still scan. Can also be used to create images with text, logos or shapes within them. 
 
 **Openpose**: 
 The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
@@ -104,7 +104,7 @@ The OpenPose control model allows for the identification of the general pose of
 
 The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.
 
-**Tile (experimental)**:
+**Tile**:
 
 The Tile model fills out details in the image to match the image, rather than the prompt. The Tile Model is a versatile tool that offers a range of functionalities. Its primary capabilities can be boiled down to two main behaviors:
 
@@ -117,12 +117,10 @@ The Tile Model can be a powerful tool in your arsenal for enhancing image qualit
 
 With Pix2Pix, you can input an image into the controlnet, and then "instruct" the model to change it using your prompt. For example, you can say "Make it winter" to add more wintry elements to a scene.
 
-**Inpaint**: Coming Soon - Currently this model is available but not functional on the Canvas. An upcoming release will provide additional capabilities for using this model when inpainting.
-
 Each of these models can be adjusted and combined with other ControlNet models to achieve different results, giving you even more control over your image generation process.
 
 
-## Using ControlNet
+### Using ControlNet
 
 To use ControlNet, you can simply select the desired model and adjust both the ControlNet and Pre-processor settings to achieve the desired result. You can also use multiple ControlNet models at the same time, allowing you to achieve even more complex effects or styles in your generated images.
 
@@ -134,3 +132,31 @@ Weight - Strength of the Controlnet model applied to the generation for the sect
 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 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.
+
+
+## IP-Adapter
+
+[IP-Adapter](https://ip-adapter.github.io) is a tooling that allows for image prompt capabilities with text-to-image diffusion models. IP-Adapter works by analyzing the given image prompt to extract features, then passing those features to the UNet along with any other conditioning provided. 
+
+![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)
+
+#### 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.  
+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
+
+IP-Adapter can be used by navigating to the *Control Adapters* options and enabling IP-Adapter. 
+
+IP-Adapter requires an image to be used as the Image Prompt. It can also be used in conjunction with text prompts, Image-to-Image, Inpainting, Outpainting, ControlNets and LoRAs.
+
+
+Each IP-Adapter has two settings that are applied to the IP-Adapter:
+
+* Weight - Strength of the IP-Adapter 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 IP-Adapter applied.

docs/features/MODEL_MERGING.md

@@ -2,17 +2,50 @@
 title: Model Merging
 ---
 
-# :material-image-off: Model Merging
-
-## How to Merge Models
-
-As of version 2.3, InvokeAI comes with a script that allows you to
-merge two or three diffusers-type models into a new merged model. The
+InvokeAI provides the ability to merge two or three diffusers-type models into a new merged model. The
 resulting model will combine characteristics of the original, and can
 be used to teach an old model new tricks.
 
+## How to Merge Models
+
+Model Merging can be be done by navigating to the Model Manager and clicking the "Merge Models" tab. From there, you can select the models and settings you want to use to merge th models. 
+
+## Settings
+
+* Model Selection: there are three multiple choice fields that
+  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.
+
+* 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
+  2d and (optionally) 3d models. So if you have two models named "A"
+  and "B", an alpha value of 0.25 will give you a merged model that is
+  25% A and 75% B.
+
+* Interpolation Method: This is the method used to combine
+  weights. The options are "weighted_sum" (the default), "sigmoid",
+  "inv_sigmoid" and "add_difference". Each produces slightly different
+  results. When three models are in use, only "add_difference" is
+  available.
+
+* Save Location: The location you want the merged model to be saved in. Default is in the InvokeAI root folder
+
+* Name for merged model: This is the name for the new model. Please
+  use InvokeAI conventions - only alphanumeric letters and the
+  characters ".+-".
+
+* Ignore Mismatches / Force: Not all models are compatible with each other. The merge
+  script will check for compatibility and refuse to merge ones that
+  are incompatible. Set this checkbox to try merging anyway.
+
+
+
 You may run the merge script by starting the invoke launcher
-(`invoke.sh` or `invoke.bat`) and choosing the option for _merge
+(`invoke.sh` or `invoke.bat`) and choosing the option (4) for _merge
 models_. This will launch a text-based interactive user interface that
 prompts you to select the models to merge, how to merge them, and the
 merged model name.
@@ -40,34 +73,4 @@ this to get back.
 If the merge runs successfully, it will create a new diffusers model
 under the selected name and register it with InvokeAI.
 
-## The Settings
-
-* Model Selection -- there are three multiple choice fields that
-  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.
-
-* 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
-  2d and (optionally) 3d models. So if you have two models named "A"
-  and "B", an alpha value of 0.25 will give you a merged model that is
-  25% A and 75% B.
-
-* Interpolation Method -- This is the method used to combine
-  weights. The options are "weighted_sum" (the default), "sigmoid",
-  "inv_sigmoid" and "add_difference". Each produces slightly different
-  results. When three models are in use, only "add_difference" is
-  available. (TODO: cite a reference that describes what these
-  interpolation methods actually do and how to decide among them).
-
-* Force -- Not all models are compatible with each other. The merge
-  script will check for compatibility and refuse to merge ones that
-  are incompatible. Set this checkbox to try merging anyway.
-
-* Name for merged model - This is the name for the new model. Please
-  use InvokeAI conventions - only alphanumeric letters and the
-  characters ".+-".
 

docs/features/PROMPTS.md

@@ -142,7 +142,7 @@ Prompt2prompt `.swap()` is not compatible with xformers, which will be temporari
 The `prompt2prompt` code is based off
 [bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
 
-### Escaping parentheses () and speech marks ""
+### Escaping parentheses and speech marks 
 
 If the model you are using has parentheses () or speech marks "" as part of its
 syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
@@ -246,7 +246,7 @@ To create a Dynamic Prompt, follow these steps:
     Within the braces, separate each option using a vertical bar |.
     If you want to include multiple options from a single group, prefix with the desired number and $$.
 
-For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {2$$style1|style2|style3}.
+For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {style1|style2|style3}.
 ### How Dynamic Prompts Work
 
 Once a Dynamic Prompt is configured, the system generates an array of combinations using the options provided. Each group of options in curly braces is treated independently, with the system selecting one option from each group. For a prefixed set (e.g., 2$$), the system will select two distinct options.
@@ -273,3 +273,36 @@ Below are some useful strategies for creating Dynamic Prompts:
     Experiment with different quantities for the prefix. For example, 3$$ will select three distinct options.
     Be aware of coherence in your prompts. Although the system can generate all possible combinations, not all may semantically make sense. Therefore, carefully choose the options for each group.
     Always review and fine-tune the generated prompts as needed. While Dynamic Prompts can help you generate a multitude of combinations, the final polishing and refining remain in your hands.
+
+
+## SDXL Prompting
+
+Prompting with SDXL is slightly different than prompting with SD1.5 or SD2.1 models - SDXL expects a prompt _and_ a style. 
+
+
+### Prompting 
+<figure markdown>
+
+![SDXL prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt.png)
+
+</figure>
+
+In the prompt box, enter a positive or negative prompt as you normally would. 
+
+For the style box you can enter a style that you want the image to be generated in. You can use styles from this example list, or any other style you wish: anime, photographic, digital art, comic book, fantasy art, analog film, neon punk, isometric, low poly, origami, line art, cinematic, 3d model, pixel art, etc.
+
+
+### Concatenated Prompts
+
+
+InvokeAI also has the option to concatenate the prompt and style inputs, by pressing the "link" button in the Positive Prompt box. 
+
+This concatenates the prompt & style inputs, and passes the joined prompt and style to the SDXL model. 
+![SDXL concatenated prompt boxes in InvokeAI](../assets/prompt_syntax/sdxl-prompt-concatenated.png)
+
+
+
+
+
+
+

docs/features/TRAINING.md

@@ -43,27 +43,22 @@ into the directory
 
 InvokeAI 2.3 and higher comes with a text console-based training front
 end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
-start the front end by selecting choice (3):
+start training tool selecting choice (3):
 
 ```sh
-Do you want to generate images using the
-1: Browser-based UI
-2: Command-line interface
-3: Run 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
-8: Open the developer console
-9: Update InvokeAI
-10: Command-line help
-Q: Quit
-
-Please enter 1-10, Q: [1] 
+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"
 ```
 
-From the command line, with the InvokeAI virtual environment active,
-you can launch the front end with the command `invokeai-ti --gui`.
+Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
+you can then launch the front end with the command `invokeai-ti --gui`.
 
 This will launch a text-based front end that will look like this:
 

docs/features/UTILITIES.md

@@ -0,0 +1,336 @@
+---
+title: Command-line Utilities
+---
+
+# :material-file-document: Utilities
+
+# 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
+familiar with Python can alternatively activate InvokeAI's virtual
+environment (typically, but not necessarily `invokeai/.venv`).
+
+In the developer's console, type the script's name to run it. To get a
+synopsis of what a utility does and the command-line arguments it
+accepts, pass it the `-h` argument, e.g.
+
+```bash
+invokeai-merge -h
+```
+## **invokeai-web**
+
+This script launches the web server and is effectively identical to
+selecting option [1] in the launcher. An advantage of launching the
+server from the command line is that you can override any setting
+configuration option in `invokeai.yaml` using like-named command-line
+arguments. For example, to temporarily change the size of the RAM
+cache to 7 GB, you can launch as follows:
+
+```bash
+invokeai-web --ram 7
+```
+
+## **invokeai-merge**
+
+This is the model merge script, the same as launcher option [4]. 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
+merges models named `stable-diffusion-1.5` and `inkdiffusion` into a new model named
+`my_new_model`:
+
+```bash
+invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffusion --merged_model_name my_new_model
+```
+
+## **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
+front end. It can also be run non-interactively. It has about a
+zillion arguments, but a typical training session can be launched
+with:
+
+```bash
+invokeai-ti --model stable-diffusion-1.5 \
+            --placeholder_token 'jello' \
+            --learnable_property object \
+			--num_train_epochs 50 \
+			--train_data_dir /path/to/training/images \
+			--output_dir /path/to/trained/model
+```
+
+(Note that \\ is the Linux/Mac long-line continuation character. Use ^
+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
+interactive console-based interface. It can also be used
+non-interactively to list, add and remove models as shown by these
+examples:
+
+* This will download and install three models from CivitAI, HuggingFace,
+and local disk:
+
+```bash
+invokeai-install --add https://civitai.com/api/download/models/161302 ^
+                  gsdf/Counterfeit-V3.0  ^
+				  D:\Models\merge_model_two.safetensors
+```
+(Note that ^ is the Windows long-line continuation character. Use \\ on
+Linux/Mac).
+
+* This will list installed models of type `main`:
+
+```bash
+invokeai-model-install --list-models main
+```
+
+* This will delete the models named `voxel-ish` and `realisticVision`:
+
+```bash
+invokeai-model-install --delete voxel-ish realisticVision
+```
+
+## **invokeai-configure**
+
+This is the console-based configure script that ran when InvokeAI was
+first installed. You can run it again at any time to change the
+configuration, repair a broken install.
+
+Called without any arguments, `invokeai-configure` enters interactive
+mode with two screens. The first screen is a form that provides access
+to most of InvokeAI's configuration options. The second screen lets
+you download, add, and delete models interactively. When you exit the
+second screen, the script will add any missing "support models"
+needed for core functionality, and any selected "sd weights" which are
+the model checkpoint/diffusers files.
+
+This behavior can be changed via a series of command-line
+arguments. Here are some of the useful ones:
+
+* `invokeai-configure --skip-sd-weights --skip-support-models`
+This will run just the configuration part of the utility, skipping
+downloading of support models and stable diffusion weights.
+
+* `invokeai-configure --yes`
+This will run the configure script non-interactively. It will set the
+configuration options to their default values, install/repair support
+models, and download the "recommended" set of SD models.
+
+* `invokeai-configure --yes --default_only` 
+This will run the configure script non-interactively. In contrast to
+the previous command, it will only download the default SD model,
+Stable Diffusion v1.5
+
+* `invokeai-configure --yes --default_only --skip-sd-weights` 
+This is similar to the previous command, but will not download any
+SD models at all. It is usually used to repair a broken install.
+
+By default, `invokeai-configure` runs on the currently active InvokeAI
+root folder. To run it against a different root, pass it the `--root
+</path/to/root>` argument.
+
+Lastly, you can use `invokeai-configure` to create a working root
+directory entirely from scratch. Assuming you wish to make a root directory
+named `InvokeAI-New`, run this command:
+
+```bash
+invokeai-configure --root InvokeAI-New --yes --default_only
+```
+This will create a minimally functional root directory. You can now
+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
+command-line arguments.
+
+## **invokeai-metadata**
+
+This is a script which takes a list of InvokeAI-generated images and
+outputs their metadata in the same JSON format that you get from the
+`</>` button in the Web GUI. For example:
+
+```bash
+$ invokeai-metadata ffe2a115-b492-493c-afff-7679aa034b50.png
+ffe2a115-b492-493c-afff-7679aa034b50.png:
+{
+    "app_version": "3.1.0",
+    "cfg_scale": 8.0,
+    "clip_skip": 0,
+    "controlnets": [],
+    "generation_mode": "sdxl_txt2img",
+    "height": 1024,
+    "loras": [],
+    "model": {
+        "base_model": "sdxl",
+        "model_name": "stable-diffusion-xl-base-1.0",
+        "model_type": "main"
+    },
+    "negative_prompt": "",
+    "negative_style_prompt": "",
+    "positive_prompt": "military grade sushi dinner for shock troopers",
+    "positive_style_prompt": "",
+    "rand_device": "cpu",
+    "refiner_cfg_scale": 7.5,
+    "refiner_model": {
+        "base_model": "sdxl-refiner",
+        "model_name": "sd_xl_refiner_1.0",
+        "model_type": "main"
+    },
+    "refiner_negative_aesthetic_score": 2.5,
+    "refiner_positive_aesthetic_score": 6.0,
+    "refiner_scheduler": "euler",
+    "refiner_start": 0.8,
+    "refiner_steps": 20,
+    "scheduler": "euler",
+    "seed": 387129902,
+    "steps": 25,
+    "width": 1024
+}
+```
+
+You may list multiple files on the command line.
+
+## **invokeai-import-images**
+
+InvokeAI uses a database to store information about images it
+generated, and just copying the image files from one InvokeAI root
+directory to another does not automatically import those images into
+the destination's gallery. This script allows you to bulk import
+images generated by one instance of InvokeAI into a gallery maintained
+by another. It also works on images generated by older versions of
+InvokeAI, going way back to version 1.
+
+This script has an interactive mode only. The following example shows
+it in action:
+
+```bash
+$ invokeai-import-images
+===============================================================================
+This script will import images generated by earlier versions of
+InvokeAI into the currently installed root directory:
+   /home/XXXX/invokeai-main
+If this is not what you want to do, type ctrl-C now to cancel.
+===============================================================================
+= Configuration & Settings
+Found invokeai.yaml file at /home/XXXX/invokeai-main/invokeai.yaml:
+  Database : /home/XXXX/invokeai-main/databases/invokeai.db
+  Outputs  : /home/XXXX/invokeai-main/outputs/images
+ 
+Use these paths for import (yes) or choose different ones (no) [Yn]:
+Inputs: Specify absolute path containing InvokeAI .png images to import: /home/XXXX/invokeai-2.3/outputs/images/
+Include files from subfolders recursively [yN]?
+
+Options for board selection for imported images:
+1) Select an existing board name. (found 4)
+2) Specify a board name to create/add to.
+3) Create/add to board named 'IMPORT'.
+4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_20230919T203519Z).
+5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5).
+Specify desired board option: 3
+
+===============================================================================
+= Import Settings Confirmation
+
+Database File Path               : /home/XXXX/invokeai-main/databases/invokeai.db
+Outputs/Images Directory         : /home/XXXX/invokeai-main/outputs/images
+Import Image Source Directory    : /home/XXXX/invokeai-2.3/outputs/images/
+  Recurse Source SubDirectories  : No
+Count of .png file(s) found      : 5785
+Board name option specified      : IMPORT
+Database backup will be taken at : /home/XXXX/invokeai-main/databases/backup
+
+Notes about the import process:
+- Source image files will not be modified, only copied to the outputs directory.
+- If the same file name already exists in the destination, the file will be skipped.
+- If the same file name already has a record in the database, the file will be skipped.
+- Invoke AI metadata tags will be updated/written into the imported copy only.
+- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)
+- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer.
+- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder.
+ 
+Do you wish to continue with the import [Yn] ?
+
+Making DB Backup at /home/lstein/invokeai-main/databases/backup/backup-20230919T203519Z-invokeai.db...Done!
+
+===============================================================================
+Importing /home/XXXX/invokeai-2.3/outputs/images/17d09907-297d-4db3-a18a-60b337feac66.png
+... (5785 more lines) ...
+===============================================================================
+= Import Complete - Elpased Time: 0.28 second(s)
+
+Source File(s)                          : 5785
+Total Imported                          : 5783
+Skipped b/c file already exists on disk : 1
+Skipped b/c file already exists in db   : 0
+Errors during import                    : 1
+```
+## **invokeai-db-maintenance**
+
+This script helps maintain the integrity of your InvokeAI database by
+finding and fixing three problems that can arise over time:
+
+1. An image was manually deleted from the outputs directory, leaving a
+   dangling image record in the InvokeAI database. This will cause a
+   black image to appear in the gallery. This is an "orphaned database
+   image record." The script can fix this by running a "clean"
+   operation on the database, removing the orphaned entries.
+   
+2. An image is present in the outputs directory but there is no
+   corresponding entry in the database. This can happen when the image
+   is added manually to the outputs directory, or if a crash occurred
+   after the image was generated but before the database was
+   completely updated. The symptom is that the image is present in the
+   outputs folder but doesn't appear in the InvokeAI gallery. This is
+   called an "orphaned image file." The script can fix this problem by
+   running an "archive" operation in which orphaned files are moved
+   into a directory named `outputs/images-archive`. If you wish, you
+   can then run `invokeai-image-import` to reimport these images back
+   into the database.
+   
+3. The thumbnail for an image is missing, again causing a black
+   gallery thumbnail. This is fixed by running the "thumbnaiils"
+   operation, which simply regenerates and re-registers the missing
+   thumbnail.
+   
+You can find and fix all three of these problems in a single go by
+executing this command:
+
+```bash
+invokeai-db-maintenance --operation all
+```
+
+Or you can run just the clean and thumbnail operations like this:
+
+```bash
+invokeai-db-maintenance -operation clean, thumbnail
+```
+
+If called without any arguments, the script will ask you which
+operations you wish to perform.
+
+## **invokeai-migrate3**
+
+This script will migrate settings and models (but not images!) from an
+InvokeAI v2.3 root folder to an InvokeAI 3.X folder. Call it with the
+source and destination root folders like this:
+
+```bash
+invokeai-migrate3 --from ~/invokeai-2.3 --to invokeai-3.1.1
+```
+
+Both directories must previously have been properly created and
+initialized by `invokeai-configure`. If you wish to migrate the images
+contained in the older root as well, you can use the
+`invokeai-image-migrate` script described earlier.
+
+---
+
+Copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/features/index.md

@@ -51,6 +51,9 @@ Prevent InvokeAI from displaying unwanted racy images.
 ### * [Controlling Logging](LOGGING.md)
 Control how InvokeAI logs status messages.
 
+### * [Command-line Utilities](UTILITIES.md)
+A list of the command-line utilities available with InvokeAI.
+
 <!-- OUT OF DATE
 ### * [Miscellaneous](OTHER.md)
 Run InvokeAI on Google Colab, generate images with repeating patterns,

docs/index.md

@@ -15,7 +15,8 @@ title: Home
 <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.1/css/fontawesome.min.css">
 <style>
     .button {
-      width: 300px;
+      width: 100%;
+      max-width: 100%;
       height: 50px;
       background-color: #448AFF;
       color: #fff;
@@ -27,8 +28,9 @@ title: Home
 
     .button-container {
       display: grid;
-      grid-template-columns: repeat(3, 300px);
+      grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
       gap: 20px;
+      justify-content: center;
     }
 
     .button:hover {
@@ -145,6 +147,7 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
 ### InvokeAI Configuration
 - [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
+- [Database Maintenance and other Command Line Utilities](features/UTILITIES.md)
 
 ## :octicons-log-16: Important Changes Since Version 2.3
 

docs/installation/020_INSTALL_MANUAL.md

@@ -256,6 +256,10 @@ manager, please follow these steps:
         *highly recommended** if your virtual environment is located outside of
         your runtime directory.
 
+    !!! tip
+
+        On linux, it is recommended to run invokeai with the following env var: `MALLOC_MMAP_THRESHOLD_=1048576`. For example: `MALLOC_MMAP_THRESHOLD_=1048576 invokeai --web`. This helps to prevent memory fragmentation that can lead to memory accumulation over time. This env var is set automatically when running via `invoke.sh`.
+
 10.  Render away!
 
     Browse the [features](../features/index.md) section to learn about all the
@@ -287,7 +291,7 @@ manager, please follow these steps:
     Leave off the `--gui` option to run the script using command-line arguments. Pass the `--help` argument
     to get usage instructions.
 
-### Developer Install
+## Developer Install
 
 If you have an interest in how InvokeAI works, or you would like to
 add features or bugfixes, you are encouraged to install the source
@@ -296,18 +300,29 @@ code for InvokeAI. For this to work, you will need to install the
 on your system, please see the [Git Installation
 Guide](https://github.com/git-guides/install-git)
 
-1. From the command line, run this command:
+You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
+
+If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
+
+> **Why do I need the frontend toolchain**?
+>
+> The InvokeAI project uses trunk-based development. That means our `main` branch is the development branch, and releases are tags on that branch. Because development is very active, we don't keep an updated build of the UI in `main` - we only build it for production releases.
+>
+> That means that between releases, to have a functioning application when running directly from the repo, you will need to run the UI in dev mode or build it regularly (any time the UI code changes).
+
+1. Create a fork of the InvokeAI repository through the GitHub UI or [this link](https://github.com/invoke-ai/InvokeAI/fork) 
+2. From the command line, run this command:
    ```bash
-   git clone https://github.com/invoke-ai/InvokeAI.git
+   git clone https://github.com/<your_github_username>/InvokeAI.git
    ```
 
     This will create a directory named `InvokeAI` and populate it with the
-    full source code from the InvokeAI repository.
+    full source code from your fork of the InvokeAI repository.
 
-2. Activate the InvokeAI virtual environment as per step (4) of the manual
+3. Activate the InvokeAI virtual environment as per step (4) of the manual
 installation protocol (important!)
 
-3. Enter the InvokeAI repository directory and run one of these
+4. Enter the InvokeAI repository directory and run one of these
    commands, based on your GPU:
 
     === "CUDA (NVidia)"
@@ -333,11 +348,15 @@ installation protocol (important!)
     Be sure to pass `-e` (for an editable install) and don't forget the
     dot ("."). It is part of the command.
 
-    You can now run `invokeai` and its related commands. The code will be
+5.  Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
+
+6.  You can now run `invokeai` and its related commands. The code will be
     read from the repository, so that you can edit the .py source files
     and watch the code's behavior change.
 
-4.  If you wish to contribute to the InvokeAI project, you are
+    When you pull in new changes to the repo, be sure to re-build the UI.
+
+7.  If you wish to contribute to the InvokeAI project, you are
     encouraged to establish a GitHub account and "fork"
     https://github.com/invoke-ai/InvokeAI into your own copy of the
     repository. You can then use GitHub functions to create and submit

docs/installation/050_INSTALLING_MODELS.md

@@ -171,3 +171,16 @@ subfolders and organize them as you wish.
 
 The location of the autoimport directories are controlled by settings
 in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
+
+### Installing models that live in HuggingFace subfolders
+
+On rare occasions you may need to install a diffusers-style model that
+lives in a subfolder of a HuggingFace repo id. In this event, simply
+add ":_subfolder-name_" to the end of the repo id. For example, if the
+repo id is "monster-labs/control_v1p_sd15_qrcode_monster" and the model
+you wish to fetch lives in a subfolder named "v2", then the repo id to
+pass to the various model installers should be 
+
+```
+monster-labs/control_v1p_sd15_qrcode_monster:v2
+```

docs/installation/INSTALLATION.md

@@ -17,14 +17,32 @@ This fork is supported across Linux, Windows and Macintosh. Linux users can use
 either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
-### [Installation Getting Started Guide](installation)
-#### **[Automated Installer](010_INSTALL_AUTOMATED.md)**
+
+## **[Automated Installer](010_INSTALL_AUTOMATED.md)**
 ✅ This is the recommended installation method for first-time users. 
-#### [Manual Installation](020_INSTALL_MANUAL.md)
-This method is recommended for experienced users and developers
-#### [Docker Installation](040_INSTALL_DOCKER.md)
-This method is recommended for those familiar with running Docker containers
-### Other Installation Guides
+
+  This is a script that will install all of InvokeAI's essential
+  third party libraries and InvokeAI itself. It includes access to a
+  "developer console" which will help us debug problems with you and
+  give you to access experimental features.
+
+## **[Manual Installation](020_INSTALL_MANUAL.md)**
+This method is recommended for experienced users and developers.
+
+  In this method you will manually run the commands needed to install
+  InvokeAI and its dependencies. We offer two recipes: one suited to
+  those who prefer the `conda` tool, and one suited to those who prefer
+  `pip` and Python virtual environments. In our hands the pip install
+  is faster and more reliable, but your mileage may vary.
+  Note that the conda installation method is currently deprecated and
+  will not be supported at some point in the future.
+
+## **[Docker Installation](040_INSTALL_DOCKER.md)**
+This method is recommended for those familiar with running Docker containers.
+
+We offer a method for creating Docker containers containing InvokeAI and its dependencies. This method is recommended for individuals with experience with Docker containers and understand the pluses and minuses of a container-based install.
+
+## Other Installation Guides
   - [PyPatchMatch](060_INSTALL_PATCHMATCH.md)
   - [XFormers](070_INSTALL_XFORMERS.md)
   - [CUDA and ROCm Drivers](030_INSTALL_CUDA_AND_ROCM.md)
@@ -63,43 +81,3 @@ images in full-precision mode:
 - GTX 1650 series cards
 - GTX 1660 series cards
 
-## Installation options
-
-1. [Automated Installer](010_INSTALL_AUTOMATED.md)
-
-    This is a script that will install all of InvokeAI's essential
-    third party libraries and InvokeAI itself. It includes access to a
-    "developer console" which will help us debug problems with you and
-    give you to access experimental features.
-
-
-    ✅ This is the recommended option for first time users. 
-
-2. [Manual Installation](020_INSTALL_MANUAL.md)
-
-    In this method you will manually run the commands needed to install
-    InvokeAI and its dependencies. We offer two recipes: one suited to
-    those who prefer the `conda` tool, and one suited to those who prefer
-    `pip` and Python virtual environments. In our hands the pip install
-    is faster and more reliable, but your mileage may vary.
-    Note that the conda installation method is currently deprecated and
-    will not be supported at some point in the future.
-
-    This method is recommended for users who have previously used `conda`
-    or `pip` in the past, developers, and anyone who wishes to remain on
-    the cutting edge of future InvokeAI development and is willing to put
-    up with occasional glitches and breakage.
-
-3. [Docker Installation](040_INSTALL_DOCKER.md)
-
-    We also offer a method for creating Docker containers containing
-    InvokeAI and its dependencies. This method is recommended for
-    individuals with experience with Docker containers and understand
-    the pluses and minuses of a container-based install.
-
-## Quick Guides
-
-* [Installing CUDA and ROCm Drivers](./030_INSTALL_CUDA_AND_ROCM.md)
-* [Installing XFormers](./070_INSTALL_XFORMERS.md)
-* [Installing PyPatchMatch](./060_INSTALL_PATCHMATCH.md)
-* [Installing New Models](./050_INSTALLING_MODELS.md)

docs/nodes/NODES.md

@@ -1,11 +1,34 @@
-# Using the Node Editor
+# Using the Workflow Editor
 
-The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. Nodes take in inputs on the left side of the node, and return an output on the right side of the node. A node graph is composed of multiple nodes that are connected together to create a workflow. Nodes' inputs and outputs are connected by dragging connectors from node to node. Inputs and outputs are color coded for ease of use.
+The workflow editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. Nodes take in inputs on the left side of the node, and return an output on the right side of the node. A node graph is composed of multiple nodes that are connected together to create a workflow. Nodes' inputs and outputs are connected by dragging connectors from node to node. Inputs and outputs are color coded for ease of use.
 
-To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
+If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Workflow Editor and build workflows to suit your needs.
 
+## Features
+
+### Linear View
+The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations. 
+
+To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
+
+The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity. 
+
+![linearview](../assets/nodes/linearview.png)
+
+### Renaming Fields and Nodes
+Any node or input field can be renamed in the workflow editor. If the input field you have renamed has been added to the Linear View, the changed name will be reflected in the Linear View and the node. 
+
+### Managing Nodes
+
+* Ctrl+C to copy a node
+* Ctrl+V to paste a node
+* Backspace/Delete to delete a node
+* Shift+Click to drag and select multiple nodes 
+
+### Node Caching 
+
+Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing. 
 
-If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Nodes Editor and build workflows to suit your needs.
 
 ## Important Concepts 
 
@@ -37,7 +60,7 @@ It is common to want to use both the same seed (for continuity) and random seeds
 
 ### ControlNet
 
-The ControlNet node outputs a Control, which can be provided as input to non-image *ToLatents nodes. Depending on the type of ControlNet desired, ControlNet nodes usually require an image processor node, such as a Canny Processor or Depth Processor, which prepares an input image for use with ControlNet.
+The ControlNet node outputs a Control, which can be provided as input to a Denoise Latents node. Depending on the type of ControlNet desired, ControlNet nodes usually require an image processor node, such as a Canny Processor or Depth Processor, which prepares an input image for use with ControlNet.
 
 ![groupscontrol](../assets/nodes/groupscontrol.png)
 
@@ -59,10 +82,9 @@ Iteration is a common concept in any processing, and means to repeat a process w
 
 ![groupsiterate](../assets/nodes/groupsiterate.png)
 
-### Multiple Image Generation + Random Seeds
+### Batch / Multiple Image Generation + Random Seeds
 
-Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. 
-
-To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
+Batch or multiple image generation in the workflow editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate, meaning this example will generate 4 images. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. This noise can then be fed to the Denoise Latents node for it to iterate through the denoising process with the different seeds provided.
 
 ![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)
+

docs/nodes/communityNodes.md

@@ -4,30 +4,46 @@ 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. Along with the node, an example node graph should be provided to help you get started with the node. 
+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 community node graph, download the the `.json` node graph file and load it into Invoke AI via the **Load Nodes** button on the Node Editor. 
+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
+- Community Nodes
+    + [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)
 
-### FaceTools
-
-**Description:** FaceTools is a collection of nodes created to manipulate faces as you would in Unified Canvas. It includes FaceMask, FaceOff, and FacePlace. FaceMask autodetects a face in the image using MediaPipe and creates a mask from it. FaceOff similarly detects a face, then takes the face off of the image by adding a square bounding box around it and cropping/scaling it. FacePlace puts the bounded face image from FaceOff back onto the original image. Using these nodes with other inpainting node(s), you can put new faces on existing things, put new things around existing faces, and work closer with a face as a bounded image. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control on FaceMask and FaceOff. See GitHub repository below for usage examples.
-
-**Node Link:** https://github.com/ymgenesis/FaceTools/
-
-**FaceMask Output Examples** 
-
-![5cc8abce-53b0-487a-b891-3bf94dcc8960](https://github.com/invoke-ai/InvokeAI/assets/25252829/43f36d24-1429-4ab1-bd06-a4bedfe0955e)
-![b920b710-1882-49a0-8d02-82dff2cca907](https://github.com/invoke-ai/InvokeAI/assets/25252829/7660c1ed-bf7d-4d0a-947f-1fc1679557ba)
-![71a91805-fda5-481c-b380-264665703133](https://github.com/invoke-ai/InvokeAI/assets/25252829/f8f6a2ee-2b68-4482-87da-b90221d5c3e2)
 
 --------------------------------
-### 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:**
+</br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
 
 --------------------------------
 ### Film Grain
@@ -37,22 +53,19 @@ To use a community node graph, download the the `.json` node graph file and load
 **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.
 
---------------------------------
-### Retroize
+**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
 
-**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:**
+</br><img src="https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg" width="500" />
 
 --------------------------------
 ### GPT2RandomPromptMaker
@@ -65,31 +78,133 @@ To use a community node graph, download the the `.json` node graph file and load
 
 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)
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c" width="200" />
+
+--------------------------------
+### Grid to Gif
+
+**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/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** 
+
+<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/input.png" width="300" />
+<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/output.gif" width="300" />
+
+--------------------------------
+### 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:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4" width="300" />
+
+Halftone Output:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f" width="300" />
+
+CMYK Halftone Output:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
+
+--------------------------------
+### Ideal Size
+
+**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.
+
+**Node Link:** https://github.com/JPPhoto/ideal-size-node
+
+--------------------------------
+### Image and Mask Composition Pack
+
+**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
+
+This includes 15 Nodes:
+
+- *Adjust Image Hue Plus* - Rotate the hue of an image in one of several different color spaces.
+- *Blend Latents/Noise (Masked)* - Use a mask to blend part of one latents tensor [including Noise outputs] into another. Can be used to "renoise" sections during a multi-stage [masked] denoising process.
+- *Enhance Image* - Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
+- *Equivalent Achromatic Lightness* - Calculates image lightness accounting for Helmholtz-Kohlrausch effect based on a method described by High, Green, and Nussbaum (2023).
+- *Text to Mask (Clipseg)* - Input a prompt and an image to generate a mask representing areas of the image matched by the prompt.
+- *Text to Mask Advanced (Clipseg)* - Output up to four prompt masks combined with logical "and", logical "or", or as separate channels of an RGBA image.
+- *Image Layer Blend* - Perform a layered blend of two images using alpha compositing. Opacity of top layer is selectable, with optional mask and several different blend modes/color spaces.
+- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
+- *Image Dilate or Erode* - Dilate or expand a mask (or any image!). This is equivalent to an expand/contract operation.
+- *Image Value Thresholds* - Clip an image to pure black/white beyond specified thresholds.
+- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
+- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
+- *Rotate/Flip Image* - Rotate an image in degrees clockwise/counterclockwise about its center, optionally resizing the image boundaries to fit, or flipping it about the vertical and/or horizontal axes.
+- *Shadows/Highlights/Midtones* - Extract three masks (with adjustable hard or soft thresholds) representing shadows, midtones, and highlights regions of an image.
+- *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
+  
+</br><img src="https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg" width="500" />
+
+--------------------------------
+### Image to Character Art Image Nodes
+
+**Description:** Group of nodes to convert an input image into ascii/unicode art Image
+
+**Node Link:** https://github.com/mickr777/imagetoasciiimage
+
+**Output Examples**
+
+<img src="https://user-images.githubusercontent.com/115216705/271817646-8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056.png" width="300" /><img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/3c4990eb-2f42-46b9-90f9-0088b939dc6a" width="300" /></br>
+<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/fee7f800-a4a8-41e2-a66b-c66e4343307e" width="300" />
+<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/1d9c1003-a45f-45c2-aac7-46470bb89330" width="300" />
+
+--------------------------------
+
+### 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.
+**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
 
 **Example Node Graph:**  https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
 
 **Output Example:** 
-=======
-![Example animation](https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif)
+
+<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
 [Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
 
 --------------------------------
+### 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** 
+
+<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
+<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
+
+--------------------------------
 ### 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"
@@ -99,7 +214,7 @@ Generated Prompt: An enchanted weapon will be usable by any character regardless
 "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."
 
-![glimmerwing_small](https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed)
+<img src="https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed" width="300" />
 
 **Requirement**
 
@@ -107,62 +222,37 @@ a Text-Generation-Webui instance (might work remotely too, but I never tried it)
 
 **Note**
 
-This node works best with SDXL models, especially as the style can be described independantly of the LLM's output.
+This node works best with SDXL models, especially as the style can be described independently of the LLM's output.
 
 --------------------------------
-### Depth Map from Wavefront OBJ
+### Prompt Tools 
 
-**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:** A set of InvokeAI nodes that add general prompt manipulation tools.  These were written to accompany the PromptsFromFile node and other prompt generation nodes.
 
-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.
+1. PromptJoin - Joins to prompts into one.
+2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
+3. PromptSplitNeg - splits a prompt into positive and negative using the old V2 method of [] for negative.
+4. PromptToFile - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
+5. PTFieldsCollect - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
+6. PTFieldsExpand - Takes Json string and converts it to individual generation parameters This can be fed from the Prompts to file node.
+7. PromptJoinThree -  Joins 3 prompt together.
+8. PromptStrength - This take a string and float and outputs another string in the format of (string)strength like the weighted format of compel. 
+9. PromptStrengthCombine - This takes a collection of prompt strength strings and outputs a string in the .and() or .blend() format that can be fed into a proper prompt node.
 
-**Node Link:** https://github.com/dwringer/depth-from-obj-node
+See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
 
-**Example Usage:**
-![depth from obj usage graph](https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg)
+**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
 
 --------------------------------
-### Enhance Image (simple adjustments)
+### Retroize
 
-**Description:** Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
+**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.
 
-Color inversion is toggled with a simple switch, while each of the four enhancer modes are activated by entering a value other than 1 in each corresponding input field. Values less than 1 will reduce the corresponding property, while values greater than 1 will enhance it.
+**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
 
-**Node Link:** https://github.com/dwringer/image-enhance-node
+**Retroize Output Examples**
 
-**Example Usage:**
-![enhance image usage graph](https://raw.githubusercontent.com/dwringer/image-enhance-node/main/image_enhance_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)
-
---------------------------------
-### Image and Mask Composition Pack
-
-**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
-
-This includes 4 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.
-- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
-- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
-- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
-
-**Node Link:** https://github.com/dwringer/composition-nodes
-
-**Example Usage:**
-![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_nodes_usage.jpg)
+<img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
 
 --------------------------------
 ### Size Stepper Nodes
@@ -174,10 +264,9 @@ A third node is included, *Random Switch (Integers)*, which is just a generic ve
 **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)
+</br><img src="https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg" width="500" />
 
 --------------------------------
-
 ### 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
@@ -186,16 +275,52 @@ A third node is included, *Random Switch (Integers)*, which is just a generic ve
 
 **Output Examples**
 
-![a3609d48-d9b7-41f0-b280-063d857986fb](https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36)
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36" width="300" />
 
 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)
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a" width="300" />
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc" width="300" />
+<img src="https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89" width="300" />
 
 --------------------------------
+### 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:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632" width="300" />
+
+Highlights/Midtones/Shadows:
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" width="300" />
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" width="300" />
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" width="300" />
+
+Highlights/Midtones/Shadows (with LUT blur enabled):
+
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" width="300" />
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
+<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
+
+--------------------------------
+### 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 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
+
+--------------------------------
 ### Example Node Template
 
 **Description:** This node allows you to do super cool things with InvokeAI.
@@ -206,7 +331,7 @@ Results after using the depth controlnet
 
 **Output Examples** 
 
-![Example Image](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png){: style="height:115px;width:240px"}
+</br><img src="https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png" width="500" />
 
 
 ## Disclaimer

docs/nodes/contributingNodes.md

@@ -4,10 +4,10 @@ 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 
-- Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
-    - Make sure you are following the template below and have provided all relevant details about the node and what it does.
-- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
+- 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.
+- 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.
 
 ### Community Node Template
 

docs/nodes/defaultNodes.md

@@ -17,11 +17,13 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Conditioning Primitive 			| A conditioning tensor primitive value|
 |Content Shuffle Processor 			| Applies content shuffle processing to image|
 |ControlNet 			| Collects ControlNet info to pass to other nodes|
-|OpenCV Inpaint 			| Simple inpaint using opencv.|
 |Denoise Latents 			| Denoises noisy latents to decodable images|
 |Divide Integers 			| Divides two numbers|
 |Dynamic Prompt 			| Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
-|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
+|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
+|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier)             | Identifies and labels faces in an image|
+|[FaceOff](./detailedNodes/faceTools.md#faceoff)             | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
+|Float Math             | Perform basic math operations on two floats|
 |Float Primitive Collection 			| A collection of float primitive values|
 |Float Primitive 			| A float primitive value|
 |Float Range 			| Creates a range|
@@ -29,6 +31,7 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Blur Image 			| Blurs an image|
 |Extract Image Channel 			| Gets a channel from an image.|
 |Image Primitive Collection 			| A collection of image primitive values|
+|Integer Math           | Perform basic math operations on two integers|
 |Convert Image Mode 			| Converts an image to a different mode.|
 |Crop Image 			| Crops an image to a specified box. The box can be outside of the image.|
 |Image Hue Adjustment 			| Adjusts the Hue of an image.|
@@ -42,6 +45,8 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |Paste Image 			| Pastes an image into another image.|
 |ImageProcessor 			| Base class for invocations that preprocess images for ControlNet|
 |Resize Image 			| Resizes an image to specific dimensions|
+|Round Float            | Rounds a float to a specified number of decimal places|
+|Float to Integer       | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
 |Scale Image 			| Scales an image by a factor|
 |Image to Latents 			| Encodes an image into latents.|
 |Add Invisible Watermark 			| Add an invisible watermark to an image|
@@ -72,6 +77,7 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |ONNX Prompt (Raw) 			| A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
 |ONNX Text to Latents 			| Generates latents from conditionings.|
 |ONNX Model Loader 			| Loads a main model, outputting its submodels.|
+|OpenCV Inpaint 			| Simple inpaint using opencv.|
 |Openpose Processor 			| Applies Openpose processing to image|
 |PIDI Processor 			| Applies PIDI processing to image|
 |Prompts from File 			| Loads prompts from a text file|
@@ -93,5 +99,6 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
 |String Primitive 			| A string primitive value|
 |Subtract Integers 			| Subtracts two numbers|
 |Tile Resample Processor 			| Tile resampler processor|
+|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
 |VAE Loader 			| Loads a VAE model, outputting a VaeLoaderOutput|
 |Zoe (Depth) Processor 			| Applies Zoe depth processing to image|

docs/nodes/detailedNodes/faceTools.md

@@ -0,0 +1,154 @@
+# Face Nodes
+
+## FaceOff
+
+FaceOff mimics a user finding a face in an image and resizing the bounding box
+around the head in Canvas.
+
+Enter a face ID (found with FaceIdentifier) to choose which face to mask.
+
+Just as you would add more context inside the bounding box by making it larger
+in Canvas, the node gives you a padding input (in pixels) which will
+simultaneously add more context, and increase the resolution of the bounding box
+so the face remains the same size inside it.
+
+The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
+threshold a detected face must reach for it to be processed. Lowering this value
+may help if detection is failing. If the detected masks are imperfect and stray
+too far outside/inside of faces, the node gives you X & Y offsets to shrink/grow
+the masks by a multiplier.
+
+FaceOff will output the face in a bounded image, taking the face off of the
+original image for input into any node that accepts image inputs. The node also
+outputs a face mask with the dimensions of the bounded image. The X & Y outputs
+are for connecting to the X & Y inputs of the Paste Image node, which will place
+the bounded image back on the original image using these coordinates.
+
+###### Inputs/Outputs
+
+| Input              | Description                                                                                                                                                                              |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Image              | Image for face detection                                                                                                                                                                 |
+| Face ID            | The face ID to process, numbered from 0. Multiple faces not supported. Find a face's ID with FaceIdentifier node.                                                                        |
+| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
+| X Offset           | X-axis offset of the mask                                                                                                                                                                |
+| Y Offset           | Y-axis offset of the mask                                                                                                                                                                |
+| Padding            | All-axis padding around the mask in pixels                                                                                                                                               |
+| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
+
+| Output        | Description                                      |
+| ------------- | ------------------------------------------------ |
+| Bounded Image | Original image bound, cropped, and resized       |
+| Width         | The width of the bounded image in pixels         |
+| Height        | The height of the bounded image in pixels        |
+| Mask          | The output mask                                  |
+| X             | The x coordinate of the bounding box's left side |
+| Y             | The y coordinate of the bounding box's top side  |
+
+## FaceMask
+
+FaceMask mimics a user drawing masks on faces in an image in Canvas.
+
+The "Face IDs" input allows the user to select specific faces to be masked.
+Leave empty to detect and mask all faces, or a comma-separated list for a
+specific combination of faces (ex: `1,2,4`). A single integer will detect and
+mask that specific face. Find face IDs with the FaceIdentifier node.
+
+The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
+threshold a detected face must reach for it to be processed. Lowering this value
+may help if detection is failing.
+
+If the detected masks are imperfect and stray too far outside/inside of faces,
+the node gives you X & Y offsets to shrink/grow the masks by a multiplier. All
+masks shrink/grow together by the X & Y offset values.
+
+By default, masks are created to change faces. When masks are inverted, they
+change surrounding areas, protecting faces.
+
+###### Inputs/Outputs
+
+| Input              | Description                                                                                                                                                                              |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Image              | Image for face detection                                                                                                                                                                 |
+| Face IDs           | Comma-separated list of face ids to mask eg '0,2,7'. Numbered from 0. Leave empty to mask all. Find face IDs with FaceIdentifier node.                                                   |
+| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
+| X Offset           | X-axis offset of the mask                                                                                                                                                                |
+| Y Offset           | Y-axis offset of the mask                                                                                                                                                                |
+| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
+| Invert Mask        | Toggle to invert the face mask                                                                                                                                                           |
+
+| Output | Description                       |
+| ------ | --------------------------------- |
+| Image  | The original image                |
+| Width  | The width of the image in pixels  |
+| Height | The height of the image in pixels |
+| Mask   | The output face mask              |
+
+## FaceIdentifier
+
+FaceIdentifier outputs an image with detected face IDs printed in white numbers
+onto each face.
+
+Face IDs can then be used in FaceMask and FaceOff to selectively mask all, a
+specific combination, or single faces.
+
+The FaceIdentifier output image is generated for user reference, and isn't meant
+to be passed on to other image-processing nodes.
+
+The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
+threshold a detected face must reach for it to be processed. Lowering this value
+may help if detection is failing. If an image is changed in the slightest, run
+it through FaceIdentifier again to get updated FaceIDs.
+
+###### Inputs/Outputs
+
+| Input              | Description                                                                                                                                                                              |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Image              | Image for face detection                                                                                                                                                                 |
+| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing)                                                                                                                    |
+| Chunk              | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
+
+| Output | Description                                                                                      |
+| ------ | ------------------------------------------------------------------------------------------------ |
+| Image  | The original image with small face ID numbers printed in white onto each face for user reference |
+| Width  | The width of the original image in pixels                                                        |
+| Height | The height of the original image in pixels                                                       |
+
+## Tips
+
+- If not all target faces are being detected, activate Chunk to bypass full
+  image face detection and greatly improve detection success.
+- Final results will vary between full-image detection and chunking for faces
+  that are detectable by both due to the nature of the process. Try either to
+  your taste.
+- Be sure Minimum Confidence is set the same when using FaceIdentifier with
+  FaceOff/FaceMask.
+- For FaceOff, use the color correction node before faceplace to correct edges
+  being noticeable in the final image (see example screenshot).
+- Non-inpainting models may struggle to paint/generate correctly around faces.
+- If your face won't change the way you want it to no matter what you change,
+  consider that the change you're trying to make is too much at that resolution.
+  For example, if an image is only 512x768 total, the face might only be 128x128
+  or 256x256, much smaller than the 512x512 your SD1.5 model was probably
+  trained on. Try increasing the resolution of the image by upscaling or
+  resizing, add padding to increase the bounding box's resolution, or use an
+  image where the face takes up more pixels.
+- If the resulting face seems out of place pasted back on the original image
+  (ie. too large, not proportional), add more padding on the FaceOff node to
+  give inpainting more context. Context and good prompting are important to
+  keeping things proportional.
+- If you find the mask is too big/small and going too far outside/inside the
+  area you want to affect, adjust the x & y offsets to shrink/grow the mask area
+- Use a higher denoise start value to resemble aspects of the original face or
+  surroundings. Denoise start = 0 & denoise end = 1 will make something new,
+  while denoise start = 0.50 & denoise end = 1 will be 50% old and 50% new.
+- mediapipe isn't good at detecting faces with lots of face paint, hair covering
+  the face, etc. Anything that obstructs the face will likely result in no faces
+  being detected.
+- If you find your face isn't being detected, try lowering the minimum
+  confidence value from 0.5. This could result in false positives, however
+  (random areas being detected as faces and masked).
+- After altering an image and wanting to process a different face in the newly
+  altered image, run the altered image through FaceIdentifier again to see the
+  new Face IDs. MediaPipe will most likely detect faces in a different order
+  after an image has been changed in the slightest.

docs/nodes/exampleWorkflows.md

@@ -1,15 +1,14 @@
 # Example Workflows
 
-TODO: Will update once uploading workflows is available.
+We've curated some example workflows for you to get started with Workflows in InvokeAI
 
-## Text2Image
+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!
 
-## Image2Image
+If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
 
-## ControlNet
-
-## Upscaling
-
-## Inpainting / Outpainting
-
-## LoRAs
+* [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)
+* [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)

docs/workflows/SDXL_Text_to_Image.json

@@ -0,0 +1,735 @@
+{
+  "name": "SDXL Text to Image",
+  "author": "InvokeAI",
+  "description": "Sample text to image workflow for SDXL",
+  "version": "1.0.1",
+  "contact": "invoke@invoke.ai",
+  "tags": "text2image, SDXL, default",
+  "notes": "",
+  "exposedFields": [
+    {
+      "nodeId": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
+      "fieldName": "model"
+    },
+    {
+      "nodeId": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "fieldName": "style"
+    },
+    {
+      "nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "fieldName": "style"
+    },
+    {
+      "nodeId": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "fieldName": "steps"
+    }
+  ],
+  "meta": {
+    "version": "1.0.0"
+  },
+  "nodes": [
+    {
+      "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+        "type": "sdxl_compel_prompt",
+        "inputs": {
+          "prompt": {
+            "id": "5a6889e6-95cb-462f-8f4a-6b93ae7afaec",
+            "name": "prompt",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Negative Prompt",
+            "value": ""
+          },
+          "style": {
+            "id": "f240d0e6-3a1c-4320-af23-20ebb707c276",
+            "name": "style",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Negative Style",
+            "value": ""
+          },
+          "original_width": {
+            "id": "05af07b0-99a0-4a68-8ad2-697bbdb7fc7e",
+            "name": "original_width",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "original_height": {
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+            "name": "original_height",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "crop_top": {
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+            "name": "crop_top",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "crop_left": {
+            "id": "349cf2e9-f3d0-4e16-9ae2-7097d25b6a51",
+            "name": "crop_left",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "target_width": {
+            "id": "44499347-7bd6-4a73-99d6-5a982786db05",
+            "name": "target_width",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "target_height": {
+            "id": "fda359b0-ab80-4f3c-805b-c9f61319d7d2",
+            "name": "target_height",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "clip": {
+            "id": "b447adaf-a649-4a76-a827-046a9fc8d89b",
+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "clip2": {
+            "id": "86ee4e32-08f9-4baa-9163-31d93f5c0187",
+            "name": "clip2",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "conditioning": {
+            "id": "7c10118e-7b4e-4911-b98e-d3ba6347dfd0",
+            "name": "conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "SDXL Negative Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 764,
+      "position": {
+        "x": 1275,
+        "y": -350
+      }
+    },
+    {
+      "id": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "55705012-79b9-4aac-9f26-c0b10309785b",
+        "type": "noise",
+        "inputs": {
+          "seed": {
+            "id": "6431737c-918a-425d-a3b4-5d57e2f35d4d",
+            "name": "seed",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "width": {
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+            "name": "width",
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+            "fieldKind": "input",
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+            "value": 1024
+          },
+          "height": {
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+            "name": "height",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
+          },
+          "use_cpu": {
+            "id": "c7c436d3-7a7a-4e76-91e4-c6deb271623c",
+            "name": "use_cpu",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": true
+          }
+        },
+        "outputs": {
+          "noise": {
+            "id": "50f650dc-0184-4e23-a927-0497a96fe954",
+            "name": "noise",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
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+          },
+          "height": {
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+            "fieldKind": "output"
+          }
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+      }
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+        "type": "l2i",
+        "inputs": {
+          "tiled": {
+            "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
+            "name": "tiled",
+            "type": "boolean",
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+          },
+          "fp32": {
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+            "fieldKind": "input",
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+            "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"
+          },
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+        "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+        "type": "rand_int",
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+            "name": "value",
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+      "data": {
+        "version": "1.0.0",
+        "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
+        "type": "sdxl_model_loader",
+        "inputs": {
+          "model": {
+            "id": "39f9e799-bc95-4318-a200-30eed9e60c42",
+            "name": "model",
+            "type": "SDXLMainModelField",
+            "fieldKind": "input",
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+            "value": {
+              "model_name": "stable-diffusion-xl-base-1.0",
+              "base_model": "sdxl",
+              "model_type": "main"
+            }
+          }
+        },
+        "outputs": {
+          "unet": {
+            "id": "2626a45e-59aa-4609-b131-2d45c5eaed69",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "output"
+          },
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+            "id": "7c9c42fa-93d5-4639-ab8b-c4d9b0559baf",
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+            "fieldKind": "output"
+          },
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+            "name": "clip2",
+            "type": "ClipField",
+            "fieldKind": "output"
+          },
+          "vae": {
+            "id": "ee6a6997-1b3c-4ff3-99ce-1e7bfba2750c",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "output"
+          }
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+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
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+      "width": 320,
+      "height": 234,
+      "position": {
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+        "y": 25
+      }
+    },
+    {
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+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+        "type": "sdxl_compel_prompt",
+        "inputs": {
+          "prompt": {
+            "id": "5a6889e6-95cb-462f-8f4a-6b93ae7afaec",
+            "name": "prompt",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Positive Prompt",
+            "value": ""
+          },
+          "style": {
+            "id": "f240d0e6-3a1c-4320-af23-20ebb707c276",
+            "name": "style",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Positive Style",
+            "value": ""
+          },
+          "original_width": {
+            "id": "05af07b0-99a0-4a68-8ad2-697bbdb7fc7e",
+            "name": "original_width",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1024
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+}

docs/workflows/Text_to_Image.json

@@ -0,0 +1,573 @@
+{
+  "name": "Text to Image",
+  "author": "InvokeAI",
+  "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
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+            "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0",
+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "conditioning": {
+            "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447",
+            "name": "conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "Positive Compel Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 235,
+      "position": {
+        "x": 1000,
+        "y": -75
+      }
+    },
+    {
+      "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+        "type": "rand_int",
+        "inputs": {
+          "low": {
+            "id": "3ec65a37-60ba-4b6c-a0b2-553dd7a84b84",
+            "name": "low",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "high": {
+            "id": "085f853a-1a5f-494d-8bec-e4ba29a3f2d1",
+            "name": "high",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 2147483647
+          }
+        },
+        "outputs": {
+          "value": {
+            "id": "812ade4d-7699-4261-b9fc-a6c9d2ab55ee",
+            "name": "value",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "Random Seed",
+        "isOpen": false,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 32,
+      "position": {
+        "x": 1000,
+        "y": 275
+      }
+    },
+    {
+      "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+        "type": "denoise_latents",
+        "inputs": {
+          "noise": {
+            "id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
+            "name": "noise",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "steps": {
+            "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
+            "name": "steps",
+            "type": "integer",
+            "fieldKind": "input",
+            "label": "",
+            "value": 36
+          },
+          "cfg_scale": {
+            "id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
+            "name": "cfg_scale",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 7.5
+          },
+          "denoising_start": {
+            "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
+            "name": "denoising_start",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 0
+          },
+          "denoising_end": {
+            "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
+            "name": "denoising_end",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1
+          },
+          "scheduler": {
+            "id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
+            "name": "scheduler",
+            "type": "Scheduler",
+            "fieldKind": "input",
+            "label": "",
+            "value": "euler"
+          },
+          "control": {
+            "id": "7abe57cc-469d-437e-ad72-a18efa28215f",
+            "name": "control",
+            "type": "ControlField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "latents": {
+            "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "denoise_mask": {
+            "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
+            "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",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true
+      },
+      "width": 320,
+      "height": 558,
+      "position": {
+        "x": 1400,
+        "y": 200
+      }
+    }
+  ],
+  "edges": [
+    {
+      "source": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+      "sourceHandle": "value",
+      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "targetHandle": "seed",
+      "id": "reactflow__edge-ea94bc37-d995-4a83-aa99-4af42479f2f2value-55705012-79b9-4aac-9f26-c0b10309785bseed",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "clip",
+      "target": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-7d8bf987-284f-413a-b2fd-d825445a5d6cclip",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "clip",
+      "target": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "targetHandle": "clip",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
+      "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",
+      "type": "default"
+    },
+    {
+      "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "sourceHandle": "conditioning",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "sourceHandle": "conditioning",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "unet",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
+      "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",
+      "type": "default"
+    }
+  ]
+}

installer/lib/installer.py

@@ -332,6 +332,7 @@ class InvokeAiInstance:
         Configure the InvokeAI runtime directory
         """
 
+        auto_install = False
         # set sys.argv to a consistent state
         new_argv = [sys.argv[0]]
         for i in range(1, len(sys.argv)):
@@ -340,13 +341,17 @@ class InvokeAiInstance:
                 new_argv.append(el)
                 new_argv.append(sys.argv[i + 1])
             elif el in ["-y", "--yes", "--yes-to-all"]:
-                new_argv.append(el)
+                auto_install = True
         sys.argv = new_argv
 
+        import messages
         import requests  # to catch download exceptions
-        from messages import introduction
 
-        introduction()
+        auto_install = auto_install or messages.user_wants_auto_configuration()
+        if auto_install:
+            sys.argv.append("--yes")
+        else:
+            messages.introduction()
 
         from invokeai.frontend.install.invokeai_configure import invokeai_configure
 

installer/lib/main.py

@@ -5,6 +5,7 @@ InvokeAI Installer
 import argparse
 import os
 from pathlib import Path
+
 from installer import Installer
 
 if __name__ == "__main__":

installer/lib/messages.py

@@ -7,7 +7,7 @@ import os
 import platform
 from pathlib import Path
 
-from prompt_toolkit import prompt
+from prompt_toolkit import HTML, prompt
 from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
@@ -65,17 +65,50 @@ def confirm_install(dest: Path) -> bool:
     if dest.exists():
         print(f":exclamation: Directory {dest} already exists :exclamation:")
         dest_confirmed = Confirm.ask(
-            ":stop_sign: Are you sure you want to (re)install in this location?",
+            ":stop_sign: (re)install in this location?",
             default=False,
         )
     else:
         print(f"InvokeAI will be installed in {dest}")
-        dest_confirmed = not Confirm.ask("Would you like to pick a different location?", default=False)
+        dest_confirmed = Confirm.ask("Use this location?", default=True)
     console.line()
 
     return dest_confirmed
 
 
+def user_wants_auto_configuration() -> bool:
+    """Prompt the user to choose between manual and auto configuration."""
+    console.rule("InvokeAI Configuration Section")
+    console.print(
+        Panel(
+            Group(
+                "\n".join(
+                    [
+                        "Libraries are installed and InvokeAI will now set up its root directory and configuration. Choose between:",
+                        "",
+                        "  * AUTOMATIC configuration:  install reasonable defaults and a minimal set of starter models.",
+                        "  * MANUAL configuration: manually inspect and adjust configuration options and pick from a larger set of starter models.",
+                        "",
+                        "Later you can fine tune your configuration by selecting option [6] 'Change InvokeAI startup options' from the invoke.bat/invoke.sh launcher script.",
+                    ]
+                ),
+            ),
+            box=box.MINIMAL,
+            padding=(1, 1),
+        )
+    )
+    choice = (
+        prompt(
+            HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
+            validator=Validator.from_callable(
+                lambda n: n == "" or n.startswith(("a", "A", "m", "M")), error_message="Please select 'a' or 'm'"
+            ),
+        )
+        or "a"
+    )
+    return choice.lower().startswith("a")
+
+
 def dest_path(dest=None) -> Path:
     """
     Prompt the user for the destination path and create the path

installer/templates/invoke.bat.in

@@ -17,9 +17,10 @@ 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. Command-line help
+echo 10. Run the InvokeAI image database maintenance script
+echo 11. Command-line help
 echo Q - Quit
-set /P choice="Please enter 1-10, Q: [1] "
+set /P choice="Please enter 1-11, Q: [1] "
 if not defined choice set choice=1
 IF /I "%choice%" == "1" (
     echo Starting the InvokeAI browser-based UI..
@@ -58,8 +59,11 @@ IF /I "%choice%" == "1" (
    echo Running invokeai-update...
    python -m invokeai.frontend.install.invokeai_update
 ) ELSE IF /I "%choice%" == "10" (
+   echo Running the db maintenance script...
+   python .venv\Scripts\invokeai-db-maintenance.exe
+) ELSE IF /I "%choice%" == "11" (
     echo Displaying command line help...
-    python .venv\Scripts\invokeai.exe --help %*
+    python .venv\Scripts\invokeai-web.exe --help %*
     pause
     exit /b
 ) ELSE IF /I "%choice%" == "q" (

installer/templates/invoke.sh.in

@@ -46,6 +46,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
+# Avoid glibc memory fragmentation. See invokeai/backend/model_management/README.md for details.
+export MALLOC_MMAP_THRESHOLD_=1048576
+
 # Primary function for the case statement to determine user input
 do_choice() {
     case $1 in
@@ -97,13 +100,13 @@ do_choice() {
         ;;
     10)
         clear
-        printf "Command-line help\n"
-        invokeai --help
+        printf "Running the db maintenance script\n"
+        invokeai-db-maintenance --root ${INVOKEAI_ROOT}
         ;;
-    "HELP 1")
+    11)
         clear
         printf "Command-line help\n"
-        invokeai --help
+        invokeai-web --help
         ;;
     *)
         clear
@@ -125,7 +128,10 @@ do_dialog() {
         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")
+        9 "Update InvokeAI"
+	10 "Run the InvokeAI image database maintenance script"
+	11 "Command-line help"
+    )
 
     choice=$(dialog --clear \
         --backtitle "\Zb\Zu\Z3InvokeAI" \
@@ -157,9 +163,10 @@ do_line_input() {
     printf "7: Re-run the configure script to fix a broken install\n"
     printf "8: Open the developer console\n"
     printf "9: Update InvokeAI\n"
-    printf "10: Command-line help\n"
+    printf "10: Run the InvokeAI image database maintenance script\n"
+    printf "11: Command-line help\n"
     printf "Q: Quit\n\n"
-    read -p "Please enter 1-10, Q: [1] " yn
+    read -p "Please enter 1-11, Q: [1] " yn
     choice=${yn:='1'}
     do_choice $choice
     clear

invokeai/app/api/dependencies.py

@@ -1,34 +1,35 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from logging import Logger
-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.config import InvokeAIAppConfig
-from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-from invokeai.app.services.images import ImageService, ImageServiceDependencies
-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 ..services.default_graphs import create_system_graphs
-from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-from ..services.graph import GraphExecutionState, LibraryGraph
-from ..services.image_file_storage import DiskImageFileStorage
-from ..services.invocation_queue import MemoryInvocationQueue
+from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
+from ..services.board_images.board_images_default import BoardImagesService
+from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
+from ..services.boards.boards_default import BoardService
+from ..services.config import InvokeAIAppConfig
+from ..services.image_files.image_files_disk import DiskImageFileStorage
+from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
+from ..services.images.images_default import ImageService
+from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
+from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
+from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
+from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
-from ..services.processor import DefaultInvocationProcessor
-from ..services.sqlite import SqliteItemStorage
-from ..services.model_manager_service import ModelManagerService
-from ..services.invocation_stats import InvocationStatsService
+from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
+from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
+from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
+from ..services.model_manager.model_manager_default import ModelManagerService
+from ..services.names.names_default import SimpleNameService
+from ..services.session_processor.session_processor_default import DefaultSessionProcessor
+from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
+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 .events import FastAPIEventService
 
 
@@ -48,7 +49,7 @@ def check_internet() -> bool:
         return False
 
 
-logger = InvokeAILogger.getLogger()
+logger = InvokeAILogger.get_logger()
 
 
 class ApiDependencies:
@@ -62,80 +63,65 @@ class ApiDependencies:
         logger.info(f"Root directory = {str(config.root_path)}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
-        events = FastAPIEventService(event_handler_id)
-
         output_folder = config.output_path
 
-        # TODO: build a file/path manager?
-        db_path = config.db_path
-        db_path.parent.mkdir(parents=True, exist_ok=True)
-        db_location = str(db_path)
+        db = SqliteDatabase(config, logger)
 
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-            filename=db_location, table_name="graph_executions"
-        )
+        configuration = config
+        logger = logger
 
-        urls = LocalUrlService()
-        image_record_storage = SqliteImageRecordStorage(db_location)
-        image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-        names = SimpleNameService()
+        board_image_records = SqliteBoardImageRecordStorage(db=db)
+        board_images = BoardImagesService()
+        board_records = SqliteBoardRecordStorage(db=db)
+        boards = BoardService()
+        events = FastAPIEventService(event_handler_id)
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
+        graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
+        image_files = DiskImageFileStorage(f"{output_folder}/images")
+        image_records = SqliteImageRecordStorage(db=db)
+        images = ImageService()
+        invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
-
-        board_record_storage = SqliteBoardRecordStorage(db_location)
-        board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
-
-        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,
-            )
-        )
+        model_manager = ModelManagerService(config, logger)
+        names = SimpleNameService()
+        performance_statistics = InvocationStatsService()
+        processor = DefaultInvocationProcessor()
+        queue = MemoryInvocationQueue()
+        session_processor = DefaultSessionProcessor()
+        session_queue = SqliteSessionQueue(db=db)
+        urls = LocalUrlService()
 
         services = InvocationServices(
-            model_manager=ModelManagerService(config, logger),
-            events=events,
-            latents=latents,
-            images=images,
-            boards=boards,
+            board_image_records=board_image_records,
             board_images=board_images,
-            queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
+            board_records=board_records,
+            boards=boards,
+            configuration=configuration,
+            events=events,
             graph_execution_manager=graph_execution_manager,
-            processor=DefaultInvocationProcessor(),
-            configuration=config,
-            performance_statistics=InvocationStatsService(graph_execution_manager),
+            graph_library=graph_library,
+            image_files=image_files,
+            image_records=image_records,
+            images=images,
+            invocation_cache=invocation_cache,
+            latents=latents,
             logger=logger,
+            model_manager=model_manager,
+            names=names,
+            performance_statistics=performance_statistics,
+            processor=processor,
+            queue=queue,
+            session_processor=session_processor,
+            session_queue=session_queue,
+            urls=urls,
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
 
+        db.clean()
+
     @staticmethod
     def shutdown():
         if ApiDependencies.invoker:

invokeai/app/api/events.py

@@ -7,7 +7,7 @@ from typing import Any
 
 from fastapi_events.dispatcher import dispatch
 
-from ..services.events import EventServiceBase
+from ..services.events.events_base import EventServiceBase
 
 
 class FastAPIEventService(EventServiceBase):

invokeai/app/api/routers/app_info.py

@@ -7,6 +7,7 @@ from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
+from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.patchmatch import PatchMatch
 from invokeai.backend.image_util.safety_checker import SafetyChecker
@@ -103,3 +104,43 @@ async def set_log_level(
     """Sets the log verbosity level"""
     ApiDependencies.invoker.services.logger.setLevel(level)
     return LogLevel(ApiDependencies.invoker.services.logger.level)
+
+
+@app_router.delete(
+    "/invocation_cache",
+    operation_id="clear_invocation_cache",
+    responses={200: {"description": "The operation was successful"}},
+)
+async def clear_invocation_cache() -> None:
+    """Clears the invocation cache"""
+    ApiDependencies.invoker.services.invocation_cache.clear()
+
+
+@app_router.put(
+    "/invocation_cache/enable",
+    operation_id="enable_invocation_cache",
+    responses={200: {"description": "The operation was successful"}},
+)
+async def enable_invocation_cache() -> None:
+    """Clears the invocation cache"""
+    ApiDependencies.invoker.services.invocation_cache.enable()
+
+
+@app_router.put(
+    "/invocation_cache/disable",
+    operation_id="disable_invocation_cache",
+    responses={200: {"description": "The operation was successful"}},
+)
+async def disable_invocation_cache() -> None:
+    """Clears the invocation cache"""
+    ApiDependencies.invoker.services.invocation_cache.disable()
+
+
+@app_router.get(
+    "/invocation_cache/status",
+    operation_id="get_invocation_cache_status",
+    responses={200: {"model": InvocationCacheStatus}},
+)
+async def get_invocation_cache_status() -> InvocationCacheStatus:
+    """Clears the invocation cache"""
+    return ApiDependencies.invoker.services.invocation_cache.get_status()

invokeai/app/api/routers/boards.py

@@ -4,9 +4,9 @@ from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
-from invokeai.app.services.board_record_storage import BoardChanges
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.board_record import BoardDTO
+from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.boards.boards_common import BoardDTO
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 
 from ..dependencies import ApiDependencies
 

invokeai/app/api/routers/images.py

@@ -1,20 +1,17 @@
 import io
 from typing import Optional
 
-from PIL import Image
 from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
+from PIL import Image
 from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import (
-    ImageDTO,
-    ImageRecordChanges,
-    ImageUrlsDTO,
-)
+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
+
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
@@ -45,7 +42,7 @@ 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()
@@ -325,3 +322,20 @@ async def unstar_images_in_list(
         return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to unstar images")
+
+
+class ImagesDownloaded(BaseModel):
+    response: Optional[str] = Field(
+        description="If defined, the message to display to the user when images begin downloading"
+    )
+
+
+@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
+async def download_images_from_list(
+    image_names: list[str] = Body(description="The list of names of images to download", embed=True),
+    board_id: Optional[str] = Body(
+        default=None, description="The board from which image should be downloaded from", embed=True
+    ),
+) -> ImagesDownloaded:
+    # return ImagesDownloaded(response="Your images are downloading")
+    raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")

invokeai/app/api/routers/models.py

@@ -2,29 +2,35 @@
 
 
 import pathlib
-from typing import Literal, List, 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
+from invokeai.backend.model_management import MergeInterpolationMethod
 from invokeai.backend.model_management.models import (
     OPENAPI_MODEL_CONFIGS,
-    SchedulerPredictionType,
-    ModelNotFoundException,
     InvalidModelException,
+    ModelNotFoundException,
+    SchedulerPredictionType,
 )
-from invokeai.backend.model_management import MergeInterpolationMethod
 
 from ..dependencies import ApiDependencies
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
 
 UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+update_models_response_adapter = TypeAdapter(UpdateModelResponse)
+
 ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+import_models_response_adapter = TypeAdapter(ImportModelResponse)
+
 ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+convert_models_response_adapter = 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)
+
+
+models_list_adapter = 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 = models_list_adapter.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 = update_models_response_adapter.validate_python(model_raw)
     except ModelNotFoundException as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
@@ -146,18 +160,21 @@ async def update_model(
 async def import_model(
     location: str = Body(description="A model path, repo_id or URL to import"),
     prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
-        description="Prediction type for SDv2 checkpoint files", default="v_prediction"
+        description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
+        default=None,
     ),
 ) -> 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)
 
@@ -169,7 +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 import_models_response_adapter.validate_python(model_raw)
 
     except ModelNotFoundException as e:
         logger.error(str(e))
@@ -203,13 +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 import_models_response_adapter.validate_python(model_raw)
     except ModelNotFoundException as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
@@ -221,7 +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,
 )
@@ -277,7 +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 = convert_models_response_adapter.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:
@@ -300,7 +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)
 
@@ -335,6 +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",
@@ -347,31 +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 '<MODELS>'}/{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 '<MODELS>'}/{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(
@@ -379,9 +417,12 @@ async def merge_models(
             base_model=base_model,
             model_type=ModelType.Main,
         )
-        response = parse_obj_as(ConvertModelResponse, model_raw)
+        response = convert_models_response_adapter.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

invokeai/app/api/routers/session_queue.py

@@ -0,0 +1,247 @@
+from typing import Optional
+
+from fastapi import Body, Path, Query
+from fastapi.routing import APIRouter
+from pydantic import BaseModel
+
+from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
+from invokeai.app.services.session_queue.session_queue_common import (
+    QUEUE_ITEM_STATUS,
+    Batch,
+    BatchStatus,
+    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
+
+session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])
+
+
+class SessionQueueAndProcessorStatus(BaseModel):
+    """The overall status of session queue and processor"""
+
+    queue: SessionQueueStatus
+    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",
+    responses={
+        201: {"model": EnqueueBatchResult},
+    },
+)
+async def enqueue_batch(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    batch: Batch = Body(description="Batch to process"),
+    prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
+) -> EnqueueBatchResult:
+    """Processes a batch and enqueues the output graphs for execution."""
+
+    return ApiDependencies.invoker.services.session_queue.enqueue_batch(queue_id=queue_id, batch=batch, prepend=prepend)
+
+
+@session_queue_router.get(
+    "/{queue_id}/list",
+    operation_id="list_queue_items",
+    responses={
+        200: {"model": CursorPaginatedResults[SessionQueueItemDTO]},
+    },
+)
+async def list_queue_items(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    limit: int = Query(default=50, description="The number of items to fetch"),
+    status: Optional[QUEUE_ITEM_STATUS] = Query(default=None, description="The status of items to fetch"),
+    cursor: Optional[int] = Query(default=None, description="The pagination cursor"),
+    priority: int = Query(default=0, description="The pagination cursor priority"),
+) -> CursorPaginatedResults[SessionQueueItemDTO]:
+    """Gets all queue items (without graphs)"""
+
+    return ApiDependencies.invoker.services.session_queue.list_queue_items(
+        queue_id=queue_id, limit=limit, status=status, cursor=cursor, priority=priority
+    )
+
+
+@session_queue_router.put(
+    "/{queue_id}/processor/resume",
+    operation_id="resume",
+    responses={200: {"model": SessionProcessorStatus}},
+)
+async def resume(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionProcessorStatus:
+    """Resumes session processor"""
+    return ApiDependencies.invoker.services.session_processor.resume()
+
+
+@session_queue_router.put(
+    "/{queue_id}/processor/pause",
+    operation_id="pause",
+    responses={200: {"model": SessionProcessorStatus}},
+)
+async def Pause(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionProcessorStatus:
+    """Pauses session processor"""
+    return ApiDependencies.invoker.services.session_processor.pause()
+
+
+@session_queue_router.put(
+    "/{queue_id}/cancel_by_batch_ids",
+    operation_id="cancel_by_batch_ids",
+    responses={200: {"model": CancelByBatchIDsResult}},
+)
+async def cancel_by_batch_ids(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    batch_ids: list[str] = Body(description="The list of batch_ids to cancel all queue items for", embed=True),
+) -> CancelByBatchIDsResult:
+    """Immediately cancels all queue items from the given batch ids"""
+    return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
+
+
+@session_queue_router.put(
+    "/{queue_id}/clear",
+    operation_id="clear",
+    responses={
+        200: {"model": ClearResult},
+    },
+)
+async def clear(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> ClearResult:
+    """Clears the queue entirely, immediately canceling the currently-executing session"""
+    queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+    if queue_item is not None:
+        ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
+    clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
+    return clear_result
+
+
+@session_queue_router.put(
+    "/{queue_id}/prune",
+    operation_id="prune",
+    responses={
+        200: {"model": PruneResult},
+    },
+)
+async def prune(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> PruneResult:
+    """Prunes all completed or errored queue items"""
+    return ApiDependencies.invoker.services.session_queue.prune(queue_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/current",
+    operation_id="get_current_queue_item",
+    responses={
+        200: {"model": Optional[SessionQueueItem]},
+    },
+)
+async def get_current_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> Optional[SessionQueueItem]:
+    """Gets the currently execution queue item"""
+    return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/next",
+    operation_id="get_next_queue_item",
+    responses={
+        200: {"model": Optional[SessionQueueItem]},
+    },
+)
+async def get_next_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> Optional[SessionQueueItem]:
+    """Gets the next queue item, without executing it"""
+    return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/status",
+    operation_id="get_queue_status",
+    responses={
+        200: {"model": SessionQueueAndProcessorStatus},
+    },
+)
+async def get_queue_status(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> SessionQueueAndProcessorStatus:
+    """Gets the status of the session queue"""
+    queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
+    processor = ApiDependencies.invoker.services.session_processor.get_status()
+    return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
+
+
+@session_queue_router.get(
+    "/{queue_id}/b/{batch_id}/status",
+    operation_id="get_batch_status",
+    responses={
+        200: {"model": BatchStatus},
+    },
+)
+async def get_batch_status(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    batch_id: str = Path(description="The batch to get the status of"),
+) -> BatchStatus:
+    """Gets the status of the session queue"""
+    return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
+
+
+@session_queue_router.get(
+    "/{queue_id}/i/{item_id}",
+    operation_id="get_queue_item",
+    responses={
+        200: {"model": SessionQueueItem},
+    },
+)
+async def get_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    item_id: int = Path(description="The queue item to get"),
+) -> SessionQueueItem:
+    """Gets a queue item"""
+    return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
+
+
+@session_queue_router.put(
+    "/{queue_id}/i/{item_id}/cancel",
+    operation_id="cancel_queue_item",
+    responses={
+        200: {"model": SessionQueueItem},
+    },
+)
+async def cancel_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    item_id: int = Path(description="The queue item to cancel"),
+) -> SessionQueueItem:
+    """Deletes a queue item"""
+
+    return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)

invokeai/app/api/routers/sessions.py

@@ -6,17 +6,12 @@ from fastapi import Body, HTTPException, Path, Query, Response
 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.graph import (
-    Edge,
-    EdgeConnection,
-    Graph,
-    GraphExecutionState,
-    NodeAlreadyExecutedError,
-)
-from ...services.item_storage import PaginatedResults
+from ...services.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
 from ..dependencies import ApiDependencies
 
 session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
@@ -29,12 +24,14 @@ session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
         200: {"model": GraphExecutionState},
         400: {"description": "Invalid json"},
     },
+    deprecated=True,
 )
 async def create_session(
-    graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with")
+    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(graph)
+    session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
     return session
 
 
@@ -42,6 +39,7 @@ async def create_session(
     "/",
     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"),
@@ -63,6 +61,7 @@ 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"),
@@ -83,6 +82,7 @@ async def get_session(
         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"),
@@ -115,6 +115,7 @@ async def add_node(
         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"),
@@ -148,6 +149,7 @@ async def update_node(
         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"),
@@ -178,6 +180,7 @@ async def delete_node(
         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"),
@@ -209,6 +212,7 @@ async def add_edge(
         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"),
@@ -247,8 +251,10 @@ async def delete_edge(
         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:
@@ -260,7 +266,7 @@ async def invoke_session(
     if session.is_complete():
         raise HTTPException(status_code=400)
 
-    ApiDependencies.invoker.invoke(session, invoke_all=all)
+    ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
     return Response(status_code=202)
 
 
@@ -268,6 +274,7 @@ async def invoke_session(
     "/{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"),

invokeai/app/api/routers/utilities.py

@@ -0,0 +1,42 @@
+from typing import Optional, Union
+
+from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
+from fastapi import Body
+from fastapi.routing import APIRouter
+from pydantic import BaseModel
+from pyparsing import ParseException
+
+utilities_router = APIRouter(prefix="/v1/utilities", tags=["utilities"])
+
+
+class DynamicPromptsResponse(BaseModel):
+    prompts: list[str]
+    error: Optional[str] = None
+
+
+@utilities_router.post(
+    "/dynamicprompts",
+    operation_id="parse_dynamicprompts",
+    responses={
+        200: {"model": DynamicPromptsResponse},
+    },
+)
+async def parse_dynamicprompts(
+    prompt: str = Body(description="The prompt to parse with dynamicprompts"),
+    max_prompts: int = Body(default=1000, description="The max number of prompts to generate"),
+    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:
+            generator = CombinatorialPromptGenerator()
+            prompts = generator.generate(prompt, max_prompts=max_prompts)
+        else:
+            generator = RandomPromptGenerator()
+            prompts = generator.generate(prompt, num_images=max_prompts)
+    except ParseException as e:
+        prompts = [prompt]
+        error = str(e)
+    return DynamicPromptsResponse(prompts=prompts if prompts else [""], error=error)

invokeai/app/api/sockets.py

@@ -3,34 +3,35 @@
 from fastapi import FastAPI
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event
-from fastapi_socketio import SocketManager
+from socketio import ASGIApp, AsyncServer
 
-from ..services.events import EventServiceBase
+from ..services.events.events_base import EventServiceBase
 
 
 class SocketIO:
-    __sio: SocketManager
+    __sio: AsyncServer
+    __app: ASGIApp
 
     def __init__(self, app: FastAPI):
-        self.__sio = SocketManager(app=app)
-        self.__sio.on("subscribe", handler=self._handle_sub)
-        self.__sio.on("unsubscribe", handler=self._handle_unsub)
+        self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
+        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
+        app.mount("/ws", self.__app)
 
-        local_handler.register(event_name=EventServiceBase.session_event, _func=self._handle_session_event)
+        self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)
+        self.__sio.on("unsubscribe_queue", handler=self._handle_unsub_queue)
+        local_handler.register(event_name=EventServiceBase.queue_event, _func=self._handle_queue_event)
 
-    async def _handle_session_event(self, event: Event):
+    async def _handle_queue_event(self, event: Event):
         await self.__sio.emit(
             event=event[1]["event"],
             data=event[1]["data"],
-            room=event[1]["data"]["graph_execution_state_id"],
+            room=event[1]["data"]["queue_id"],
         )
 
-    async def _handle_sub(self, sid, data, *args, **kwargs):
-        if "session" in data:
-            self.__sio.enter_room(sid, data["session"])
+    async def _handle_sub_queue(self, sid, data, *args, **kwargs):
+        if "queue_id" in data:
+            await self.__sio.enter_room(sid, data["queue_id"])
 
-        # @app.sio.on('unsubscribe')
-
-    async def _handle_unsub(self, sid, data, *args, **kwargs):
-        if "session" in data:
-            self.__sio.leave_room(sid, data["session"])
+    async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
+        if "queue_id" in data:
+            await self.__sio.enter_room(sid, data["queue_id"])

invokeai/app/api_app.py

@@ -1,10 +1,19 @@
-# Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+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.
+app_config = InvokeAIAppConfig.get_config()
+app_config.parse_args()
+
+if True:  # hack to make flake8 happy with imports coming after setting up the config
     import asyncio
-import logging
+    import mimetypes
     import socket
     from inspect import signature
     from pathlib import Path
 
+    import torch
     import uvicorn
     from fastapi import FastAPI
     from fastapi.middleware.cors import CORSMiddleware
@@ -13,25 +22,18 @@ from fastapi.openapi.utils import get_openapi
     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 .services.config import InvokeAIAppConfig
-from ..backend.util.logging import InvokeAILogger
-
-from invokeai.version.invokeai_version import __version__
-
-import invokeai.frontend.web as web_dir
-import mimetypes
-
-from .api.dependencies import ApiDependencies
-from .api.routers import sessions, models, images, boards, board_images, app_info
-from .api.sockets import SocketIO
-from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
-
-import torch
+    from pydantic.json_schema import models_json_schema
 
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
+    import invokeai.frontend.web as web_dir
+    from invokeai.version.invokeai_version import __version__
+
+    from ..backend.util.logging import InvokeAILogger
+    from .api.dependencies import ApiDependencies
+    from .api.routers import app_info, board_images, boards, images, models, session_queue, utilities
+    from .api.sockets import SocketIO
+    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
 
     if torch.backends.mps.is_available():
         # noinspection PyUnresolvedReferences
@@ -40,7 +42,7 @@ if torch.backends.mps.is_available():
 
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
-logger = InvokeAILogger.getLogger(config=app_config)
+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
@@ -49,7 +51,7 @@ 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)
@@ -61,10 +63,6 @@ app.add_middleware(
 
 socket_io = SocketIO(app)
 
-
-# Add startup event to load dependencies
-@app.on_event("startup")
-async def startup_event():
 app.add_middleware(
     CORSMiddleware,
     allow_origins=app_config.allow_origins,
@@ -73,6 +71,10 @@ async def startup_event():
     allow_headers=app_config.allow_headers,
 )
 
+
+# Add startup event to load dependencies
+@app.on_event("startup")
+async def startup_event():
     ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
 
 
@@ -83,12 +85,9 @@ async def shutdown_event():
 
 
 # Include all routers
-# TODO: REMOVE
-# app.include_router(
-#     invocation.invocation_router,
-#     prefix = '/api')
+# app.include_router(sessions.session_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")
 
@@ -100,6 +99,8 @@ 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")
+
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
@@ -111,6 +112,7 @@ 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
@@ -121,29 +123,32 @@ 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
 
@@ -166,7 +171,7 @@ def custom_openapi():
     return app.openapi_schema
 
 
-app.openapi = custom_openapi
+app.openapi = custom_openapi  # type: ignore [method-assign] # this is a valid assignment
 
 # Override API doc favicons
 app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
@@ -218,7 +223,7 @@ def invoke_api():
                 exc_info=e,
             )
         else:
-            jurigged.watch(logger=InvokeAILogger.getLogger(name="jurigged").info)
+            jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
 
     port = find_port(app_config.port)
     if port != app_config.port:
@@ -237,7 +242,7 @@ def invoke_api():
 
     # replace uvicorn's loggers with InvokeAI's for consistent appearance
     for logname in ["uvicorn.access", "uvicorn"]:
-        log = logging.getLogger(logname)
+        log = InvokeAILogger.get_logger(logname)
         log.handlers.clear()
         for ch in logger.handlers:
             log.addHandler(ch)

invokeai/app/cli/commands.py

@@ -1,16 +1,18 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from abc import ABC, abstractmethod
 import argparse
+from abc import ABC, abstractmethod
 from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
-from pydantic import BaseModel, Field
-import networkx as nx
+
 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 GraphExecutionState, LibraryGraph, Edge
+from ..services.graph import Edge, GraphExecutionState, LibraryGraph
 from ..services.invoker import Invoker
 
 
@@ -22,8 +24,8 @@ def add_field_argument(command_parser, name: str, field, default_override=None):
         if field.default_factory is None
         else field.default_factory()
     )
-    if get_origin(field.type_) == Literal:
-        allowed_values = get_args(field.type_)
+    if get_origin(field.annotation) == Literal:
+        allowed_values = get_args(field.annotation)
         allowed_types = set()
         for val in allowed_values:
             allowed_types.add(type(val))
@@ -36,15 +38,15 @@ def add_field_argument(command_parser, name: str, field, default_override=None):
             type=field_type,
             default=default,
             choices=allowed_values,
-            help=field.field_info.description,
+            help=field.description,
         )
     else:
         command_parser.add_argument(
             f"--{name}",
             dest=name,
-            type=field.type_,
+            type=field.annotation,
             default=default,
-            help=field.field_info.description,
+            help=field.description,
         )
 
 
@@ -140,7 +142,6 @@ 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):

invokeai/app/cli/completer.py

@@ -6,15 +6,15 @@ completer object.
 import atexit
 import readline
 import shlex
-
 from pathlib import Path
-from typing import List, Dict, Literal, get_args, get_type_hints, get_origin
+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 .commands import BaseCommand
 from ..services.invocation_services import InvocationServices
+from .commands import BaseCommand
 
 # singleton object, class variable
 completer = None

invokeai/app/cli_app.py

@@ -1,41 +1,43 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+# 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 Union, get_type_hints, Optional
+    from typing import Optional, Union, get_type_hints
 
+    import torch
     from pydantic import BaseModel, ValidationError
     from pydantic.fields import Field
 
-# This should come early so that the logger can pick up its configuration options
-from .services.config import InvokeAIAppConfig
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.version.invokeai_version import __version__
-
-
-from invokeai.app.services.board_image_record_storage import (
-    SqliteBoardImageRecordStorage,
-)
-from invokeai.app.services.board_images import (
-    BoardImagesService,
-    BoardImagesServiceDependencies,
-)
+    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.app.services.invocation_stats import InvocationStatsService
-from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
-from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
+    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,
@@ -49,20 +51,17 @@ 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
 
-import torch
-import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-
     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().getLogger(config=config)
+logger = InvokeAILogger().get_logger(config=config)
 
 
 class CliCommand(BaseModel):
@@ -252,19 +251,18 @@ def invoke_cli():
         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](
-        filename=db_location, table_name="graph_executions"
-    )
+    graph_execution_manager = SqliteItemStorage[GraphExecutionState](conn=db_conn, table_name="graph_executions")
 
     urls = LocalUrlService()
-    image_record_storage = SqliteImageRecordStorage(db_location)
+    image_record_storage = SqliteImageRecordStorage(conn=db_conn)
     image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
     names = SimpleNameService()
 
-    board_record_storage = SqliteBoardRecordStorage(db_location)
-    board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
+    board_record_storage = SqliteBoardRecordStorage(conn=db_conn)
+    board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn)
 
     boards = BoardService(
         services=BoardServiceDependencies(
@@ -306,12 +304,13 @@ def invoke_cli():
         boards=boards,
         board_images=board_images,
         queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
+        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)

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
 
@@ -38,14 +38,16 @@ class RangeInvocation(BaseInvocation):
     version="1.0.0",
 )
 class RangeOfSizeInvocation(BaseInvocation):
-    """Creates a range from start to start + size with step"""
+    """Creates a range from start to start + (size * step) incremented by step"""
 
     start: int = InputField(default=0, description="The start of the range")
-    size: int = InputField(default=1, description="The number of values")
+    size: int = InputField(default=1, gt=0, description="The number of values")
     step: int = InputField(default=1, description="The step of the range")
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
-        return IntegerCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
+        return IntegerCollectionOutput(
+            collection=list(range(self.start, self.start + (self.step * self.size), self.step))
+        )
 
 
 @invocation(
@@ -54,6 +56,7 @@ class RangeOfSizeInvocation(BaseInvocation):
     tags=["range", "integer", "random", "collection"],
     category="collections",
     version="1.0.0",
+    use_cache=False,
 )
 class RandomRangeInvocation(BaseInvocation):
     """Creates a collection of random numbers"""

invokeai/app/invocations/compel.py

@@ -1,21 +1,20 @@
 import re
 from dataclasses import dataclass
-from typing import List, Union
+from typing import List, Optional, Union
 
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
-from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
 
-from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
+from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
+    ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
 
-from ...backend.model_management.models import ModelType
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.models import ModelNotFoundException
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
+from ...backend.model_management.models import ModelNotFoundException, ModelType
 from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
@@ -44,7 +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."""
 
@@ -61,23 +66,21 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.services.model_manager.get_model(
-            **self.clip.tokenizer.dict(),
-            context=context,
+        tokenizer_info = context.get_model(
+            **self.clip.tokenizer.model_dump(),
         )
-        text_encoder_info = context.services.model_manager.get_model(
-            **self.clip.text_encoder.dict(),
-            context=context,
+        text_encoder_info = context.get_model(
+            **self.clip.text_encoder.model_dump(),
         )
 
         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.get_model(**lora.model_dump(exclude={"weight"}))
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
         ti_list = []
         for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
@@ -86,11 +89,10 @@ class CompelInvocation(BaseInvocation):
                 ti_list.append(
                     (
                         name,
-                        context.services.model_manager.get_model(
+                        context.get_model(
                             model_name=name,
                             base_model=self.clip.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
-                            context=context,
                         ).context.model,
                     )
                 )
@@ -100,14 +102,15 @@ class CompelInvocation(BaseInvocation):
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with ModelPatcher.apply_lora_text_encoder(
-            text_encoder_info.context.model, _lora_loader()
-        ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
+        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:
+            ),
+            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
+            text_encoder_info as text_encoder,
+        ):
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -118,12 +121,12 @@ class CompelInvocation(BaseInvocation):
 
             conjunction = Compel.parse_prompt_string(self.prompt)
 
-            if context.services.configuration.log_tokenization:
+            if context.config.log_tokenization:
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
-            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            ec = ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -139,8 +142,7 @@ class CompelInvocation(BaseInvocation):
             ]
         )
 
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-        context.services.latents.save(conditioning_name, conditioning_data)
+        conditioning_name = context.save_conditioning(conditioning_data)
 
         return ConditioningOutput(
             conditioning=ConditioningField(
@@ -160,11 +162,11 @@ 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 +174,11 @@ 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 +192,9 @@ 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
@@ -214,14 +222,15 @@ class SDXLPromptInvocationBase:
                 # print(traceback.format_exc())
                 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 (
+        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:
+            ),
+            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
+            text_encoder_info as text_encoder,
+        ):
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -245,7 +254,7 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
-            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            ec = ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -272,8 +281,16 @@ 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="")
@@ -309,7 +326,9 @@ 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,
@@ -320,7 +339,9 @@ 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,
@@ -358,7 +379,9 @@ 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="")
@@ -402,10 +425,16 @@ 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."""
 
@@ -420,7 +449,9 @@ 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
@@ -437,9 +468,11 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
         raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
 
     text_fragments = [
+        (
             x.text
             if type(x) is Fragment
             else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
+        )
         for x in parsed_prompt.children
     ]
     text = " ".join(text_fragments)

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
@@ -24,27 +24,24 @@ 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
 
 from ...backend.model_management import BaseModelType
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
-    UIType,
     invocation,
     invocation_output,
 )
 
-
 CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
 CONTROLNET_RESIZE_VALUES = Literal[
     "just_resize",
@@ -60,6 +57,8 @@ 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")
@@ -74,7 +73,7 @@ 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):
@@ -102,7 +101,7 @@ class ControlNetInvocation(BaseInvocation):
     image: ImageField = InputField(description="The control image")
     control_model: ControlNetModelField = InputField(description=FieldDescriptions.controlnet_model, input=Input.Direct)
     control_weight: Union[float, List[float]] = InputField(
-        default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
+        default=1.0, description="The weight given to the ControlNet"
     )
     begin_step_percent: float = InputField(
         default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
@@ -127,9 +126,7 @@ class ControlNetInvocation(BaseInvocation):
         )
 
 
-@invocation(
-    "image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
-)
+# This invocation exists for other invocations to subclass it - do not register with @invocation!
 class ImageProcessorInvocation(BaseInvocation):
     """Base class for invocations that preprocess images for ControlNet"""
 
@@ -396,9 +393,9 @@ 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()
@@ -562,3 +559,33 @@ class SamDetectorReproducibleColors(SamDetector):
             img[:, :] = ann_color
             final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
         return np.array(final_img, dtype=np.uint8)
+
+
+@invocation(
+    "color_map_image_processor",
+    title="Color Map Processor",
+    tags=["controlnet"],
+    category="controlnet",
+    version="1.0.0",
+)
+class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates a color map from the provided image"""
+
+    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
+
+    def run_processor(self, image: Image.Image):
+        image = image.convert("RGB")
+        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(
+            np_image,
+            (width // width_tile_size, height // height_tile_size),
+            interpolation=cv2.INTER_CUBIC,
+        )
+        color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
+        color_map = Image.fromarray(color_map)
+        return color_map

invokeai/app/invocations/cv.py

@@ -4,9 +4,10 @@
 import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
-from invokeai.app.invocations.primitives import ImageField, ImageOutput
 
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
+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
 
 

invokeai/app/invocations/facetools.py

@@ -0,0 +1,724 @@
+import math
+import re
+from pathlib import Path
+from typing import Optional, TypedDict
+
+import cv2
+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 field_validator
+
+import invokeai.assets.fonts as font_assets
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    InputField,
+    InvocationContext,
+    OutputField,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+
+
+@invocation_output("face_mask_output")
+class FaceMaskOutput(ImageOutput):
+    """Base class for FaceMask output"""
+
+    mask: ImageField = OutputField(description="The output mask")
+
+
+@invocation_output("face_off_output")
+class FaceOffOutput(ImageOutput):
+    """Base class for FaceOff Output"""
+
+    mask: ImageField = OutputField(description="The output mask")
+    x: int = OutputField(description="The x coordinate of the bounding box's left side")
+    y: int = OutputField(description="The y coordinate of the bounding box's top side")
+
+
+class FaceResultData(TypedDict):
+    image: ImageType
+    mask: ImageType
+    x_center: float
+    y_center: float
+    mesh_width: int
+    mesh_height: int
+    chunk_x_offset: int
+    chunk_y_offset: int
+
+
+class FaceResultDataWithId(FaceResultData):
+    face_id: int
+
+
+class ExtractFaceData(TypedDict):
+    bounded_image: ImageType
+    bounded_mask: ImageType
+    x_min: int
+    y_min: int
+    x_max: int
+    y_max: int
+
+
+class FaceMaskResult(TypedDict):
+    image: ImageType
+    mask: ImageType
+
+
+def create_white_image(w: int, h: int) -> ImageType:
+    return Image.new("L", (w, h), color=255)
+
+
+def create_black_image(w: int, h: int) -> ImageType:
+    return Image.new("L", (w, h), color=0)
+
+
+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]:
+    """Deduplicates a list of faces, adding IDs to them."""
+    deduped_faces: list[FaceResultData] = []
+
+    if len(face_result_list) == 0:
+        return list()
+
+    for candidate in face_result_list:
+        should_add = True
+        candidate_x_center = candidate["x_center"]
+        candidate_y_center = candidate["y_center"]
+        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
+            face_radius_h = face["mesh_height"] / 2
+            # Determine if the center of the candidate_face is inside the ellipse of the added face
+            # p < 1 -> Inside
+            # p = 1 -> Exactly on the ellipse
+            # p > 1 -> Outside
+            p = (math.pow((candidate_x_center - face_center_x), 2) / math.pow(face_radius_w, 2)) + (
+                math.pow((candidate_y_center - face_center_y), 2) / math.pow(face_radius_h, 2)
+            )
+
+            if p < 1:  # Inside of the already-added face's radius
+                deduped_faces[idx] = coalesce_faces(face, candidate)
+                should_add = False
+                break
+
+        if should_add is True:
+            deduped_faces.append(candidate)
+
+    sorted_faces = sorted(deduped_faces, key=lambda x: x["y_center"])
+    sorted_faces = sorted(sorted_faces, key=lambda x: x["x_center"])
+
+    # add face_id for reference
+    sorted_faces_with_ids: list[FaceResultDataWithId] = []
+    face_id_counter = 0
+    for face in sorted_faces:
+        sorted_faces_with_ids.append(
+            FaceResultDataWithId(
+                **face,
+                face_id=face_id_counter,
+            )
+        )
+        face_id_counter += 1
+
+    return sorted_faces_with_ids
+
+
+def generate_face_box_mask(
+    context: InvocationContext,
+    minimum_confidence: float,
+    x_offset: float,
+    y_offset: float,
+    pil_image: ImageType,
+    chunk_x_offset: int = 0,
+    chunk_y_offset: int = 0,
+    draw_mesh: bool = True,
+) -> list[FaceResultData]:
+    result = []
+    mask_pil = None
+
+    # Convert the PIL image to a NumPy array.
+    np_image = np.array(pil_image, dtype=np.uint8)
+
+    # Check if the input image has four channels (RGBA).
+    if np_image.shape[2] == 4:
+        # Convert RGBA to RGB by removing the alpha channel.
+        np_image = np_image[:, :, :3]
+
+    # Create a FaceMesh object for face landmark detection and mesh generation.
+    face_mesh = FaceMesh(
+        max_num_faces=999,
+        min_detection_confidence=minimum_confidence,
+        min_tracking_confidence=minimum_confidence,
+    )
+
+    # Detect the face landmarks and mesh in the input image.
+    results = face_mesh.process(np_image)
+
+    # Check if any face is detected.
+    if results.multi_face_landmarks:  # type: ignore # this are via protobuf and not typed
+        # Search for the face_id in the detected faces.
+        for face_id, face_landmarks in enumerate(results.multi_face_landmarks):  # type: ignore #this are via protobuf and not typed
+            # Get the bounding box of the face mesh.
+            x_coordinates = [landmark.x for landmark in face_landmarks.landmark]
+            y_coordinates = [landmark.y for landmark in face_landmarks.landmark]
+            x_min, x_max = min(x_coordinates), max(x_coordinates)
+            y_min, y_max = min(y_coordinates), max(y_coordinates)
+
+            # Calculate the width and height of the face mesh.
+            mesh_width = int((x_max - x_min) * np_image.shape[1])
+            mesh_height = int((y_max - y_min) * np_image.shape[0])
+
+            # Get the center of the face.
+            x_center = np.mean([landmark.x * np_image.shape[1] for landmark in face_landmarks.landmark])
+            y_center = np.mean([landmark.y * np_image.shape[0] for landmark in face_landmarks.landmark])
+
+            face_landmark_points = np.array(
+                [
+                    [landmark.x * np_image.shape[1], landmark.y * np_image.shape[0]]
+                    for landmark in face_landmarks.landmark
+                ]
+            )
+
+            # Apply the scaling offsets to the face landmark points with a multiplier.
+            scale_multiplier = 0.2
+            x_center = np.mean(face_landmark_points[:, 0])
+            y_center = np.mean(face_landmark_points[:, 1])
+
+            if draw_mesh:
+                x_scaled = face_landmark_points[:, 0] + scale_multiplier * x_offset * (
+                    face_landmark_points[:, 0] - x_center
+                )
+                y_scaled = face_landmark_points[:, 1] + scale_multiplier * y_offset * (
+                    face_landmark_points[:, 1] - y_center
+                )
+
+                convex_hull = cv2.convexHull(np.column_stack((x_scaled, y_scaled)).astype(np.int32))
+
+                # Generate a binary face mask using the face mesh.
+                mask_image = np.ones(np_image.shape[:2], dtype=np.uint8) * 255
+                cv2.fillConvexPoly(mask_image, convex_hull, 0)
+
+                # Convert the binary mask image to a PIL Image.
+                init_mask_pil = Image.fromarray(mask_image, mode="L")
+                w, h = init_mask_pil.size
+                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))
+
+            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)
+
+    return result
+
+
+def extract_face(
+    context: InvocationContext,
+    image: ImageType,
+    face: FaceResultData,
+    padding: int,
+) -> ExtractFaceData:
+    mask = face["mask"]
+    center_x = face["x_center"]
+    center_y = face["y_center"]
+    mesh_width = face["mesh_width"]
+    mesh_height = face["mesh_height"]
+
+    # Determine the minimum size of the square crop
+    min_size = min(mask.width, mask.height)
+
+    # Calculate the crop boundaries for the output image and mask.
+    mesh_width += 128 + padding  # add pixels to account for mask variance
+    mesh_height += 128 + padding  # add pixels to account for mask variance
+    crop_size = min(
+        max(mesh_width, mesh_height, 128), min_size
+    )  # Choose the smaller of the two (given value or face mask size)
+    if crop_size > 128:
+        crop_size = (crop_size + 7) // 8 * 8  # Ensure crop side is multiple of 8
+
+    # Calculate the actual crop boundaries within the bounds of the original image.
+    x_min = int(center_x - crop_size / 2)
+    y_min = int(center_y - crop_size / 2)
+    x_max = int(center_x + crop_size / 2)
+    y_max = int(center_y + crop_size / 2)
+
+    # Adjust the crop boundaries to stay within the original image's dimensions
+    if x_min < 0:
+        context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
+        x_max -= x_min
+        x_min = 0
+    elif x_max > mask.width:
+        context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
+        x_min -= x_max - mask.width
+        x_max = mask.width
+
+    if y_min < 0:
+        context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
+        y_max -= y_min
+        y_min = 0
+    elif y_max > mask.height:
+        context.services.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
+        y_min -= y_max - mask.height
+        y_max = mask.height
+
+    # Ensure the crop is square and adjust the boundaries if needed
+    if x_max - x_min != crop_size:
+        context.services.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
+        diff = crop_size - (x_max - x_min)
+        x_min -= diff // 2
+        x_max += diff - diff // 2
+
+    if y_max - y_min != crop_size:
+        context.services.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
+        diff = crop_size - (y_max - y_min)
+        y_min -= diff // 2
+        y_max += diff - diff // 2
+
+    context.services.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
+
+    # Crop the output image to the specified size with the center of the face mesh as the center.
+    mask = mask.crop((x_min, y_min, x_max, y_max))
+    bounded_image = image.crop((x_min, y_min, x_max, y_max))
+
+    # blur mask edge by small radius
+    mask = mask.filter(ImageFilter.GaussianBlur(radius=2))
+
+    return ExtractFaceData(
+        bounded_image=bounded_image,
+        bounded_mask=mask,
+        x_min=x_min,
+        y_min=y_min,
+        x_max=x_max,
+        y_max=y_max,
+    )
+
+
+def get_faces_list(
+    context: InvocationContext,
+    image: ImageType,
+    should_chunk: bool,
+    minimum_confidence: float,
+    x_offset: float,
+    y_offset: float,
+    draw_mesh: bool = True,
+) -> list[FaceResultDataWithId]:
+    result = []
+
+    # Generate the face box mask and get the center of the face.
+    if not should_chunk:
+        context.services.logger.info("FaceTools --> Attempting full image face detection.")
+        result = generate_face_box_mask(
+            context=context,
+            minimum_confidence=minimum_confidence,
+            x_offset=x_offset,
+            y_offset=y_offset,
+            pil_image=image,
+            chunk_x_offset=0,
+            chunk_y_offset=0,
+            draw_mesh=draw_mesh,
+        )
+    if should_chunk or len(result) == 0:
+        context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
+        width, height = image.size
+        image_chunks = []
+        x_offsets = []
+        y_offsets = []
+        result = []
+
+        # If width == height, there's nothing more we can do... otherwise...
+        if width > height:
+            # Landscape - slice the image horizontally
+            fx = 0.0
+            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, height)))
+                x_offsets.append(x)
+                y_offsets.append(0)
+                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) + 1
+            increment = (height - width) / (steps - 1)
+            while fy <= (height - width):
+                y = int(fy)
+                image_chunks.append(image.crop((0, y, width, y + width)))
+                x_offsets.append(0)
+                y_offsets.append(y)
+                fy += increment
+                context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
+
+        for idx in range(len(image_chunks)):
+            context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
+            result = result + generate_face_box_mask(
+                context=context,
+                minimum_confidence=minimum_confidence,
+                x_offset=x_offset,
+                y_offset=y_offset,
+                pil_image=image_chunks[idx],
+                chunk_x_offset=x_offsets[idx],
+                chunk_y_offset=y_offsets[idx],
+                draw_mesh=draw_mesh,
+            )
+
+        if len(result) == 0:
+            # Give up
+            context.services.logger.warning(
+                "FaceTools --> No face detected in chunked input image. Passing through original image."
+            )
+
+    all_faces = prepare_faces_list(result)
+
+    return all_faces
+
+
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
+class FaceOffInvocation(BaseInvocation):
+    """Bound, extract, and mask a face from an image using MediaPipe detection"""
+
+    image: ImageField = InputField(description="Image for face detection")
+    face_id: int = InputField(
+        default=0,
+        ge=0,
+        description="The face ID to process, numbered from 0. Multiple faces not supported. Find a face's ID with FaceIdentifier node.",
+    )
+    minimum_confidence: float = InputField(
+        default=0.5, description="Minimum confidence for face detection (lower if detection is failing)"
+    )
+    x_offset: float = InputField(default=0.0, description="X-axis offset of the mask")
+    y_offset: float = InputField(default=0.0, description="Y-axis offset of the mask")
+    padding: int = InputField(default=0, description="All-axis padding around the mask in pixels")
+    chunk: bool = InputField(
+        default=False,
+        description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
+    )
+
+    def faceoff(self, context: InvocationContext, image: ImageType) -> Optional[ExtractFaceData]:
+        all_faces = get_faces_list(
+            context=context,
+            image=image,
+            should_chunk=self.chunk,
+            minimum_confidence=self.minimum_confidence,
+            x_offset=self.x_offset,
+            y_offset=self.y_offset,
+            draw_mesh=True,
+        )
+
+        if len(all_faces) == 0:
+            context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
+            return None
+
+        if self.face_id > len(all_faces) - 1:
+            context.services.logger.warning(
+                f"FaceOff --> Face ID {self.face_id} is outside of the number of faces detected ({len(all_faces)}). Passing through original image."
+            )
+            return None
+
+        face_data = extract_face(context=context, image=image, face=all_faces[self.face_id], padding=self.padding)
+        # Convert the input image to RGBA mode to ensure it has an alpha channel.
+        face_data["bounded_image"] = face_data["bounded_image"].convert("RGBA")
+
+        return face_data
+
+    def invoke(self, context: InvocationContext) -> FaceOffOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        result = self.faceoff(context=context, image=image)
+
+        if result is None:
+            result_image = image
+            result_mask = create_white_image(*image.size)
+            x = 0
+            y = 0
+        else:
+            result_image = result["bounded_image"]
+            result_mask = result["bounded_mask"]
+            x = result["x_min"]
+            y = result["y_min"]
+
+        image_dto = context.services.images.create(
+            image=result_image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            workflow=self.workflow,
+        )
+
+        mask_dto = context.services.images.create(
+            image=result_mask,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.MASK,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        output = FaceOffOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+            mask=ImageField(image_name=mask_dto.image_name),
+            x=x,
+            y=y,
+        )
+
+        return output
+
+
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
+class FaceMaskInvocation(BaseInvocation):
+    """Face mask creation using mediapipe face detection"""
+
+    image: ImageField = InputField(description="Image to face detect")
+    face_ids: str = InputField(
+        default="",
+        description="Comma-separated list of face ids to mask eg '0,2,7'. Numbered from 0. Leave empty to mask all. Find face IDs with FaceIdentifier node.",
+    )
+    minimum_confidence: float = InputField(
+        default=0.5, description="Minimum confidence for face detection (lower if detection is failing)"
+    )
+    x_offset: float = InputField(default=0.0, description="Offset for the X-axis of the face mask")
+    y_offset: float = InputField(default=0.0, description="Offset for the Y-axis of the face mask")
+    chunk: bool = InputField(
+        default=False,
+        description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
+    )
+    invert_mask: bool = InputField(default=False, description="Toggle to invert the mask")
+
+    @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:
+            raise ValueError('Face IDs must be a comma-separated list of integers (e.g. "1,2,3")')
+        return v
+
+    def facemask(self, context: InvocationContext, image: ImageType) -> FaceMaskResult:
+        all_faces = get_faces_list(
+            context=context,
+            image=image,
+            should_chunk=self.chunk,
+            minimum_confidence=self.minimum_confidence,
+            x_offset=self.x_offset,
+            y_offset=self.y_offset,
+            draw_mesh=True,
+        )
+
+        mask_pil = create_white_image(*image.size)
+
+        id_range = list(range(0, len(all_faces)))
+        ids_to_extract = id_range
+        if self.face_ids != "":
+            parsed_face_ids = [int(id) for id in self.face_ids.split(",")]
+            # get requested face_ids that are in range
+            intersected_face_ids = set(parsed_face_ids) & set(id_range)
+
+            if len(intersected_face_ids) == 0:
+                id_range_str = ",".join([str(id) for id in id_range])
+                context.services.logger.warning(
+                    f"Face IDs must be in range of detected faces - requested {self.face_ids}, detected {id_range_str}. Passing through original image."
+                )
+                return FaceMaskResult(
+                    image=image,  # original image
+                    mask=mask_pil,  # white mask
+                )
+
+            ids_to_extract = list(intersected_face_ids)
+
+        for face_id in ids_to_extract:
+            face_data = extract_face(context=context, image=image, face=all_faces[face_id], padding=0)
+            face_mask_pil = face_data["bounded_mask"]
+            x_min = face_data["x_min"]
+            y_min = face_data["y_min"]
+            x_max = face_data["x_max"]
+            y_max = face_data["y_max"]
+
+            mask_pil.paste(
+                create_black_image(x_max - x_min, y_max - y_min),
+                box=(x_min, y_min),
+                mask=ImageOps.invert(face_mask_pil),
+            )
+
+        if self.invert_mask:
+            mask_pil = ImageOps.invert(mask_pil)
+
+        # Create an RGBA image with transparency
+        image = image.convert("RGBA")
+
+        return FaceMaskResult(
+            image=image,
+            mask=mask_pil,
+        )
+
+    def invoke(self, context: InvocationContext) -> FaceMaskOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        result = self.facemask(context=context, image=image)
+
+        image_dto = context.services.images.create(
+            image=result["image"],
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            workflow=self.workflow,
+        )
+
+        mask_dto = context.services.images.create(
+            image=result["mask"],
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.MASK,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        output = FaceMaskOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+            mask=ImageField(image_name=mask_dto.image_name),
+        )
+
+        return output
+
+
+@invocation(
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
+)
+class FaceIdentifierInvocation(BaseInvocation):
+    """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
+
+    image: ImageField = InputField(description="Image to face detect")
+    minimum_confidence: float = InputField(
+        default=0.5, description="Minimum confidence for face detection (lower if detection is failing)"
+    )
+    chunk: bool = InputField(
+        default=False,
+        description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
+    )
+
+    def faceidentifier(self, context: InvocationContext, image: ImageType) -> ImageType:
+        image = image.copy()
+
+        all_faces = get_faces_list(
+            context=context,
+            image=image,
+            should_chunk=self.chunk,
+            minimum_confidence=self.minimum_confidence,
+            x_offset=0,
+            y_offset=0,
+            draw_mesh=False,
+        )
+
+        # Note - font may be found either in the repo if running an editable install, or in the venv if running a package install
+        font_path = [x for x in [Path(y, "inter/Inter-Regular.ttf") for y in font_assets.__path__] if x.exists()]
+        font = ImageFont.truetype(font_path[0].as_posix(), FONT_SIZE)
+
+        # Paste face IDs on the output image
+        draw = ImageDraw.Draw(image)
+        for face in all_faces:
+            x_coord = face["x_center"]
+            y_coord = face["y_center"]
+            text = str(face["face_id"])
+            # get bbox of the text so we can center the id on the face
+            _, _, bbox_w, bbox_h = draw.textbbox(xy=(0, 0), text=text, font=font, stroke_width=FONT_STROKE_WIDTH)
+            x = x_coord - bbox_w / 2
+            y = y_coord - bbox_h / 2
+            draw.text(
+                xy=(x, y),
+                text=str(text),
+                fill=(255, 255, 255, 255),
+                font=font,
+                stroke_width=FONT_STROKE_WIDTH,
+                stroke_fill=(0, 0, 0, 255),
+            )
+
+        # Create an RGBA image with transparency
+        image = image.convert("RGBA")
+
+        return image
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        result_image = self.faceidentifier(context=context, image=image)
+
+        image_dto = context.services.images.create(
+            image=result_image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            workflow=self.workflow,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/image.py

@@ -8,12 +8,12 @@ import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from invokeai.app.invocations.metadata import CoreMetadata
-from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
+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 ..models.image import ImageCategory, ResourceOrigin
-from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -36,7 +36,13 @@ class ShowImageInvocation(BaseInvocation):
         )
 
 
-@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
+@invocation(
+    "blank_image",
+    title="Blank Image",
+    tags=["image"],
+    category="image",
+    version="1.0.0",
+)
 class BlankImageInvocation(BaseInvocation):
     """Creates a blank image and forwards it to the pipeline"""
 
@@ -65,7 +71,13 @@ class BlankImageInvocation(BaseInvocation):
         )
 
 
-@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
+@invocation(
+    "img_crop",
+    title="Crop Image",
+    tags=["image", "crop"],
+    category="image",
+    version="1.0.0",
+)
 class ImageCropInvocation(BaseInvocation):
     """Crops an image to a specified box. The box can be outside of the image."""
 
@@ -98,7 +110,13 @@ class ImageCropInvocation(BaseInvocation):
         )
 
 
-@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.0")
+@invocation(
+    "img_paste",
+    title="Paste Image",
+    tags=["image", "paste"],
+    category="image",
+    version="1.0.1",
+)
 class ImagePasteInvocation(BaseInvocation):
     """Pastes an image into another image."""
 
@@ -110,6 +128,7 @@ class ImagePasteInvocation(BaseInvocation):
     )
     x: int = InputField(default=0, description="The left x coordinate at which to paste the image")
     y: int = InputField(default=0, description="The top y coordinate at which to paste the image")
+    crop: bool = InputField(default=False, description="Crop to base image dimensions")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         base_image = context.services.images.get_pil_image(self.base_image.image_name)
@@ -129,6 +148,10 @@ class ImagePasteInvocation(BaseInvocation):
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
         new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
 
+        if self.crop:
+            base_w, base_h = base_image.size
+            new_image = new_image.crop((abs(min_x), abs(min_y), abs(min_x) + base_w, abs(min_y) + base_h))
+
         image_dto = context.services.images.create(
             image=new_image,
             image_origin=ResourceOrigin.INTERNAL,
@@ -146,7 +169,13 @@ class ImagePasteInvocation(BaseInvocation):
         )
 
 
-@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
+@invocation(
+    "tomask",
+    title="Mask from Alpha",
+    tags=["image", "mask"],
+    category="image",
+    version="1.0.0",
+)
 class MaskFromAlphaInvocation(BaseInvocation):
     """Extracts the alpha channel of an image as a mask."""
 
@@ -177,7 +206,13 @@ class MaskFromAlphaInvocation(BaseInvocation):
         )
 
 
-@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
+@invocation(
+    "img_mul",
+    title="Multiply Images",
+    tags=["image", "multiply"],
+    category="image",
+    version="1.0.0",
+)
 class ImageMultiplyInvocation(BaseInvocation):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
@@ -210,7 +245,13 @@ class ImageMultiplyInvocation(BaseInvocation):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
-@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
+@invocation(
+    "img_chan",
+    title="Extract Image Channel",
+    tags=["image", "channel"],
+    category="image",
+    version="1.0.0",
+)
 class ImageChannelInvocation(BaseInvocation):
     """Gets a channel from an image."""
 
@@ -242,7 +283,13 @@ class ImageChannelInvocation(BaseInvocation):
 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")
+@invocation(
+    "img_conv",
+    title="Convert Image Mode",
+    tags=["image", "convert"],
+    category="image",
+    version="1.0.0",
+)
 class ImageConvertInvocation(BaseInvocation):
     """Converts an image to a different mode."""
 
@@ -271,7 +318,13 @@ class ImageConvertInvocation(BaseInvocation):
         )
 
 
-@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
+@invocation(
+    "img_blur",
+    title="Blur Image",
+    tags=["image", "blur"],
+    category="image",
+    version="1.0.0",
+)
 class ImageBlurInvocation(BaseInvocation):
     """Blurs an image"""
 
@@ -325,20 +378,26 @@ PIL_RESAMPLING_MAP = {
 }
 
 
-@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
+@invocation(
+    "img_resize",
+    title="Resize Image",
+    tags=["image", "resize"],
+    category="image",
+    version="1.0.0",
+)
 class ImageResizeInvocation(BaseInvocation):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
-    width: int = InputField(default=512, ge=64, multiple_of=8, description="The width to resize to (px)")
-    height: int = InputField(default=512, ge=64, multiple_of=8, description="The height to resize to (px)")
+    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)
+        image = context.get_image(self.image.image_name)
 
         resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
 
@@ -347,25 +406,22 @@ class ImageResizeInvocation(BaseInvocation):
             resample=resample_mode,
         )
 
-        image_dto = context.services.images.create(
-            image=resize_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            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,
-            workflow=self.workflow,
-        )
+        image_name = context.save_image(image=resize_image)
 
         return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
+            image=ImageField(image_name=image_name),
+            width=resize_image.width,
+            height=resize_image.height,
         )
 
 
-@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
+@invocation(
+    "img_scale",
+    title="Scale Image",
+    tags=["image", "scale"],
+    category="image",
+    version="1.0.0",
+)
 class ImageScaleInvocation(BaseInvocation):
     """Scales an image by a factor"""
 
@@ -406,7 +462,13 @@ class ImageScaleInvocation(BaseInvocation):
         )
 
 
-@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
+@invocation(
+    "img_lerp",
+    title="Lerp Image",
+    tags=["image", "lerp"],
+    category="image",
+    version="1.0.0",
+)
 class ImageLerpInvocation(BaseInvocation):
     """Linear interpolation of all pixels of an image"""
 
@@ -439,7 +501,13 @@ class ImageLerpInvocation(BaseInvocation):
         )
 
 
-@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
+@invocation(
+    "img_ilerp",
+    title="Inverse Lerp Image",
+    tags=["image", "ilerp"],
+    category="image",
+    version="1.0.0",
+)
 class ImageInverseLerpInvocation(BaseInvocation):
     """Inverse linear interpolation of all pixels of an image"""
 
@@ -451,7 +519,7 @@ 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))
 
@@ -472,7 +540,13 @@ class ImageInverseLerpInvocation(BaseInvocation):
         )
 
 
-@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
+@invocation(
+    "img_nsfw",
+    title="Blur NSFW Image",
+    tags=["image", "nsfw"],
+    category="image",
+    version="1.0.0",
+)
 class ImageNSFWBlurInvocation(BaseInvocation):
     """Add blur to NSFW-flagged images"""
 
@@ -500,7 +574,7 @@ 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.model_dump() if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -510,7 +584,7 @@ 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")
@@ -518,7 +592,11 @@ 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):
     """Add an invisible watermark to an image"""
@@ -539,7 +617,7 @@ 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.model_dump() if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -550,7 +628,13 @@ class ImageWatermarkInvocation(BaseInvocation):
         )
 
 
-@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
+@invocation(
+    "mask_edge",
+    title="Mask Edge",
+    tags=["image", "mask", "inpaint"],
+    category="image",
+    version="1.0.0",
+)
 class MaskEdgeInvocation(BaseInvocation):
     """Applies an edge mask to an image"""
 
@@ -596,7 +680,11 @@ 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):
     """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@@ -627,7 +715,13 @@ class MaskCombineInvocation(BaseInvocation):
         )
 
 
-@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
+@invocation(
+    "color_correct",
+    title="Color Correct",
+    tags=["image", "color"],
+    category="image",
+    version="1.0.0",
+)
 class ColorCorrectInvocation(BaseInvocation):
     """
     Shifts the colors of a target image to match the reference image, optionally
@@ -737,7 +831,13 @@ class ColorCorrectInvocation(BaseInvocation):
         )
 
 
-@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
+@invocation(
+    "img_hue_adjust",
+    title="Adjust Image Hue",
+    tags=["image", "hue"],
+    category="image",
+    version="1.0.0",
+)
 class ImageHueAdjustmentInvocation(BaseInvocation):
     """Adjusts the Hue of an image."""
 
@@ -960,3 +1060,44 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
             width=image_dto.width,
             height=image_dto.height,
         )
+
+
+@invocation(
+    "save_image",
+    title="Save Image",
+    tags=["primitives", "image"],
+    category="primitives",
+    version="1.0.1",
+    use_cache=False,
+)
+class SaveImageInvocation(BaseInvocation):
+    """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: 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)
+
+        image_dto = context.services.images.create(
+            image=image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            board_id=self.board.board_id if self.board else None,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata.model_dump() if self.metadata else None,
+            workflow=self.workflow,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/infill.py

@@ -7,12 +7,12 @@ import numpy as np
 from PIL import Image, ImageOps
 
 from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 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 ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
@@ -269,7 +269,7 @@ class LaMaInfillInvocation(BaseInvocation):
         )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
 class CV2InfillInvocation(BaseInvocation):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 

invokeai/app/invocations/ip_adapter.py

@@ -0,0 +1,107 @@
+import os
+from builtins import float
+from typing import List, Union
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType
+from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
+
+
+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.")
+    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")
+    # weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
+    )
+
+
+@invocation_output("ip_adapter_output")
+class IPAdapterOutput(BaseInvocationOutput):
+    # Outputs
+    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")
+class IPAdapterInvocation(BaseInvocation):
+    """Collects IP-Adapter info to pass to other nodes."""
+
+    # Inputs
+    image: ImageField = InputField(description="The IP-Adapter image prompt.")
+    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"
+    )
+
+    begin_step_percent: float = InputField(
+        default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
+    )
+
+    def invoke(self, context: InvocationContext) -> IPAdapterOutput:
+        # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
+        ip_adapter_info = context.services.model_manager.model_info(
+            self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
+        )
+        # HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
+        # directly, and 2) we are reading from disk every time this invocation is called without caching the result.
+        # A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
+        # is currently messy due to differences between how the model info is generated when installing a model from
+        # disk vs. downloading the model.
+        image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
+            os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
+        )
+        image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
+        image_encoder_model = CLIPVisionModelField(
+            model_name=image_encoder_model_name,
+            base_model=BaseModelType.Any,
+        )
+        return IPAdapterOutput(
+            ip_adapter=IPAdapterField(
+                image=self.image,
+                ip_adapter_model=self.ip_adapter_model,
+                image_encoder_model=image_encoder_model,
+                weight=self.weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+            ),
+        )

invokeai/app/invocations/latent.py

@@ -1,13 +1,16 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
 from contextlib import ExitStack
+from functools import singledispatchmethod
 from typing import List, Literal, Optional, Union
 
 import einops
 import numpy as np
 import torch
 import torchvision.transforms as T
+from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.adapter import FullAdapterXL, T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -16,9 +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,
@@ -29,24 +33,28 @@ from invokeai.app.invocations.primitives import (
     LatentsOutput,
     build_latents_output,
 )
+from invokeai.app.invocations.t2i_adapter import T2IAdapterField
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
 
 from ...backend.model_management.lora import ModelPatcher
-from ...backend.model_management.seamless import set_seamless
 from ...backend.model_management.models import BaseModelType
+from ...backend.model_management.seamless import set_seamless
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
-    ConditioningData,
     ControlNetData,
+    IPAdapterData,
     StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
     image_resized_to_grid_as_tensor,
 )
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -63,8 +71,10 @@ from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
 
-DEFAULT_PRECISION = choose_precision(choose_torch_device())
+if choose_torch_device() == torch.device("mps"):
+    from torch import mps
 
+DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
@@ -74,12 +84,20 @@ 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:
@@ -87,7 +105,11 @@ 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."""
@@ -96,7 +118,11 @@ 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":
@@ -124,7 +150,7 @@ 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,
             )
 
@@ -156,9 +182,8 @@ def get_scheduler(
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.services.model_manager.get_model(
-        **scheduler_info.dict(),
-        context=context,
+    orig_scheduler_info = context.get_model(
+        **scheduler_info.model_dump(),
     )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
@@ -188,7 +213,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.0.0",
+    version="1.3.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -199,29 +224,64 @@ 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, ui_type=UIType.Float, title="CFG Scale"
+        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,
-        description=FieldDescriptions.control,
         input=Input.Connection,
         ui_order=5,
     )
-    latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
+    ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
+        description=FieldDescriptions.ip_adapter,
+        title="IP-Adapter",
+        default=None,
+        input=Input.Connection,
+        ui_order=6,
+    )
+    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
+    )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
+        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):
@@ -237,15 +297,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def dispatch_progress(
         self,
         context: InvocationContext,
-        source_node_id: str,
         intermediate_state: PipelineIntermediateState,
         base_model: BaseModelType,
     ) -> None:
         stable_diffusion_step_callback(
             context=context,
             intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
             base_model=base_model,
         )
 
@@ -256,11 +313,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
         unet,
         seed,
     ) -> ConditioningData:
-        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        positive_cond_data = context.get_conditioning(self.positive_conditioning.conditioning_name)
         c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
         extra_conditioning_info = c.extra_conditioning
 
-        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        negative_cond_data = context.get_conditioning(self.negative_conditioning.conditioning_name)
         uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
         conditioning_data = ConditioningData(
@@ -320,8 +377,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_control_data(
         self,
         context: InvocationContext,
-        # really only need model for dtype and device
-        model: StableDiffusionGeneratorPipeline,
         control_input: Union[ControlField, List[ControlField]],
         latents_shape: List[int],
         exit_stack: ExitStack,
@@ -341,26 +396,24 @@ class DenoiseLatentsInvocation(BaseInvocation):
         else:
             control_list = None
         if control_list is None:
-            control_data = None
-            # from above handling, any control that is not None should now be of type list[ControlField]
-        else:
+            return None
+        # After above handling, any control that is not None should now be of type list[ControlField].
+
         # FIXME: add checks to skip entry if model or image is None
         #        and if weight is None, populate with default 1.0?
-            control_data = []
-            control_models = []
+        controlnet_data = []
         for control_info in control_list:
             control_model = exit_stack.enter_context(
-                    context.services.model_manager.get_model(
+                context.get_model(
                     model_name=control_info.control_model.model_name,
                     model_type=ModelType.ControlNet,
                     base_model=control_info.control_model.base_model,
-                        context=context,
                 )
             )
 
-                control_models.append(control_model)
+            # control_models.append(control_model)
             control_image_field = control_info.image
-                input_image = context.services.images.get_pil_image(control_image_field.image_name)
+            input_image = context.get_image(control_image_field.image_name)
             # self.image.image_type, self.image.image_name
             # FIXME: still need to test with different widths, heights, devices, dtypes
             #        and add in batch_size, num_images_per_prompt?
@@ -379,7 +432,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 resize_mode=control_info.resize_mode,
             )
             control_item = ControlNetData(
-                    model=control_model,
+                model=control_model,  # model object
                 image_tensor=control_image,
                 weight=control_info.control_weight,
                 begin_step_percent=control_info.begin_step_percent,
@@ -389,9 +442,156 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 #    but adding resize_mode to ControlNetData in case needed in the future
                 resize_mode=control_info.resize_mode,
             )
-                control_data.append(control_item)
+            controlnet_data.append(control_item)
             # MultiControlNetModel has been refactored out, just need list[ControlNetData]
-        return control_data
+
+        return controlnet_data
+
+    def prep_ip_adapter_data(
+        self,
+        context: InvocationContext,
+        ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
+        conditioning_data: ConditioningData,
+        exit_stack: ExitStack,
+    ) -> Optional[list[IPAdapterData]]:
+        """If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
+        to the `conditioning_data` (in-place).
+        """
+        if ip_adapter is None:
+            return None
+
+        # ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
+        if not isinstance(ip_adapter, list):
+            ip_adapter = [ip_adapter]
+
+        if len(ip_adapter) == 0:
+            return None
+
+        ip_adapter_data_list = []
+        conditioning_data.ip_adapter_conditioning = []
+        for single_ip_adapter in ip_adapter:
+            ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
+                context.get_model(
+                    model_name=single_ip_adapter.ip_adapter_model.model_name,
+                    model_type=ModelType.IPAdapter,
+                    base_model=single_ip_adapter.ip_adapter_model.base_model,
+                )
+            )
+
+            image_encoder_model_info = context.get_model(
+                model_name=single_ip_adapter.image_encoder_model.model_name,
+                model_type=ModelType.CLIPVision,
+                base_model=single_ip_adapter.image_encoder_model.base_model,
+            )
+
+            input_image = context.get_image(single_ip_adapter.image.image_name)
+
+            # 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)
+                conditioning_data.ip_adapter_conditioning.append(
+                    IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
+                )
+
+            ip_adapter_data_list.append(
+                IPAdapterData(
+                    ip_adapter_model=ip_adapter_model,
+                    weight=single_ip_adapter.weight,
+                    begin_step_percent=single_ip_adapter.begin_step_percent,
+                    end_step_percent=single_ip_adapter.end_step_percent,
+                )
+            )
+
+        return ip_adapter_data_list
+
+    def run_t2i_adapters(
+        self,
+        context: InvocationContext,
+        t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
+        latents_shape: list[int],
+        do_classifier_free_guidance: bool,
+    ) -> Optional[list[T2IAdapterData]]:
+        if t2i_adapter is None:
+            return None
+
+        # Handle the possibility that t2i_adapter could be a list or a single T2IAdapterField.
+        if isinstance(t2i_adapter, T2IAdapterField):
+            t2i_adapter = [t2i_adapter]
+
+        if len(t2i_adapter) == 0:
+            return None
+
+        t2i_adapter_data = []
+        for t2i_adapter_field in t2i_adapter:
+            t2i_adapter_model_info = context.get_model(
+                model_name=t2i_adapter_field.t2i_adapter_model.model_name,
+                model_type=ModelType.T2IAdapter,
+                base_model=t2i_adapter_field.t2i_adapter_model.base_model,
+            )
+            image = context.get_image(t2i_adapter_field.image.image_name)
+
+            # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
+            if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
+                max_unet_downscale = 8
+            elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
+                max_unet_downscale = 4
+            else:
+                raise ValueError(
+                    f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
+                )
+
+            t2i_adapter_model: T2IAdapter
+            with t2i_adapter_model_info as t2i_adapter_model:
+                total_downscale_factor = t2i_adapter_model.total_downscale_factor
+                if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
+                    # HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
+                    # this PR: https://github.com/huggingface/diffusers/pull/5134.
+                    total_downscale_factor = total_downscale_factor // 2
+
+                # Resize the T2I-Adapter input image.
+                # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
+                # result will match the latent image's dimensions after max_unet_downscale is applied.
+                t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
+                t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
+
+                # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
+                # a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
+                # T2I-Adapter model.
+                #
+                # Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
+                # of the same requirements (e.g. preserving binary masks during resize).
+                t2i_image = prepare_control_image(
+                    image=image,
+                    do_classifier_free_guidance=False,
+                    width=t2i_input_width,
+                    height=t2i_input_height,
+                    num_channels=t2i_adapter_model.config.in_channels,
+                    device=t2i_adapter_model.device,
+                    dtype=t2i_adapter_model.dtype,
+                    resize_mode=t2i_adapter_field.resize_mode,
+                )
+
+                adapter_state = t2i_adapter_model(t2i_image)
+
+            if do_classifier_free_guidance:
+                for idx, value in enumerate(adapter_state):
+                    adapter_state[idx] = torch.cat([value] * 2, dim=0)
+
+            t2i_adapter_data.append(
+                T2IAdapterData(
+                    adapter_state=adapter_state,
+                    weight=t2i_adapter_field.weight,
+                    begin_step_percent=t2i_adapter_field.begin_step_percent,
+                    end_step_percent=t2i_adapter_field.end_step_percent,
+                )
+            )
+
+        return t2i_adapter_data
 
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
@@ -443,11 +643,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
             seed = None
             noise = None
             if self.noise is not None:
-                noise = context.services.latents.get(self.noise.latents_name)
+                noise = context.get_latents(self.noise.latents_name)
                 seed = self.noise.seed
 
             if self.latents is not None:
-                latents = context.services.latents.get(self.latents.latents_name)
+                latents = context.get_latents(self.latents.latents_name)
                 if seed is None:
                     seed = self.latents.seed
 
@@ -464,30 +664,36 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             mask, masked_latents = self.prep_inpaint_mask(context, latents)
 
-            # Get the source node id (we are invoking the prepared node)
-            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            # 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,
+            )
 
             def step_callback(state: PipelineIntermediateState):
-                self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
+                self.dispatch_progress(context, state, self.unet.unet.base_model)
 
             def _lora_loader():
                 for lora in self.unet.loras:
-                    lora_info = context.services.model_manager.get_model(
-                        **lora.dict(exclude={"weight"}),
-                        context=context,
+                    lora_info = context.get_model(
+                        **lora.model_dump(exclude={"weight"}),
                     )
                     yield (lora_info.context.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.services.model_manager.get_model(
-                **self.unet.unet.dict(),
-                context=context,
+            unet_info = context.get_model(
+                **self.unet.unet.model_dump(),
             )
-            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
-                unet_info.context.model, _lora_loader()
-            ), set_seamless(unet_info.context.model, self.unet.seamless_axes), unet_info as unet:
+            with (
+                ExitStack() as exit_stack,
+                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
+                set_seamless(unet_info.context.model, self.unet.seamless_axes),
+                unet_info as unet,
+            ):
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
@@ -506,8 +712,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 pipeline = self.create_pipeline(unet, scheduler)
                 conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
 
-                control_data = self.prep_control_data(
-                    model=pipeline,
+                controlnet_data = self.prep_control_data(
                     context=context,
                     control_input=self.control,
                     latents_shape=latents.shape,
@@ -516,6 +721,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     exit_stack=exit_stack,
                 )
 
+                ip_adapter_data = self.prep_ip_adapter_data(
+                    context=context,
+                    ip_adapter=self.ip_adapter,
+                    conditioning_data=conditioning_data,
+                    exit_stack=exit_stack,
+                )
+
                 num_inference_steps, timesteps, init_timestep = self.init_scheduler(
                     scheduler,
                     device=unet.device,
@@ -524,7 +736,10 @@ 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,
@@ -534,21 +749,28 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
-                    control_data=control_data,  # list[ControlNetData]
+                    control_data=controlnet_data,
+                    ip_adapter_data=ip_adapter_data,
+                    t2i_adapter_data=t2i_adapter_data,
                     callback=step_callback,
                 )
 
             # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
             result_latents = result_latents.to("cpu")
             torch.cuda.empty_cache()
+            if choose_torch_device() == torch.device("mps"):
+                mps.empty_cache()
 
-            name = f"{context.graph_execution_state_id}__{self.id}"
-            context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
+            latents_name = context.save_latents(result_latents)
+        return build_latents_output(latents_name=latents_name, latents=result_latents, seed=seed)
 
 
 @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):
     """Generates an image from latents."""
@@ -563,7 +785,7 @@ class LatentsToImageInvocation(BaseInvocation):
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
     fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
-    metadata: CoreMetadata = InputField(
+    metadata: Optional[CoreMetadata] = InputField(
         default=None,
         description=FieldDescriptions.core_metadata,
         ui_hidden=True,
@@ -571,11 +793,10 @@ class LatentsToImageInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
+        latents = context.get_latents(self.latents.latents_name)
 
-        vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
-            context=context,
+        vae_info = context.get_model(
+            **self.vae.vae.model_dump(),
         )
 
         with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
@@ -605,13 +826,15 @@ class LatentsToImageInvocation(BaseInvocation):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.services.configuration.tiled_decode:
+            if self.tiled or context.config.tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
 
             # clear memory as vae decode can request a lot
             torch.cuda.empty_cache()
+            if choose_torch_device() == torch.device("mps"):
+                mps.empty_cache()
 
             with torch.inference_mode():
                 # copied from diffusers pipeline
@@ -624,29 +847,28 @@ class LatentsToImageInvocation(BaseInvocation):
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
         torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
 
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            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,
-            workflow=self.workflow,
-        )
+        image_name = context.save_image(image, category=context.categories.GENERAL)
 
         return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
+            image=ImageField(image_name=image_name),
+            width=image.width,
+            height=image.height,
         )
 
 
 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."""
 
@@ -683,6 +905,8 @@ class ResizeLatentsInvocation(BaseInvocation):
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
         torch.cuda.empty_cache()
+        if device == torch.device("mps"):
+            mps.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, resized_latents)
@@ -690,7 +914,13 @@ 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."""
 
@@ -719,6 +949,8 @@ class ScaleLatentsInvocation(BaseInvocation):
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
         torch.cuda.empty_cache()
+        if device == torch.device("mps"):
+            mps.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, resized_latents)
@@ -727,7 +959,11 @@ 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."""
@@ -779,8 +1015,7 @@ class ImageToLatentsInvocation(BaseInvocation):
             # non_noised_latents_from_image
             image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
             with torch.inference_mode():
-                image_tensor_dist = vae.encode(image_tensor).latent_dist
-                latents = image_tensor_dist.sample().to(dtype=vae.dtype)  # FIXME: uses torch.randn. make reproducible!
+                latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
 
             latents = vae.config.scaling_factor * latents
             latents = latents.to(dtype=orig_dtype)
@@ -792,7 +1027,7 @@ 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,
         )
 
@@ -807,8 +1042,26 @@ class ImageToLatentsInvocation(BaseInvocation):
         context.services.latents.save(name, latents)
         return build_latents_output(latents_name=name, latents=latents, seed=None)
 
+    @singledispatchmethod
+    @staticmethod
+    def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        image_tensor_dist = vae.encode(image_tensor).latent_dist
+        latents = image_tensor_dist.sample().to(dtype=vae.dtype)  # FIXME: uses torch.randn. make reproducible!
+        return latents
 
-@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
+    @_encode_to_tensor.register
+    @staticmethod
+    def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
+        return vae.encode(image_tensor).latents
+
+
+@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."""
 
@@ -875,6 +1128,8 @@ class BlendLatentsInvocation(BaseInvocation):
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
         torch.cuda.empty_cache()
+        if device == torch.device("mps"):
+            mps.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, resized_latents)

invokeai/app/invocations/math.py

@@ -1,8 +1,11 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-import numpy as np
+from typing import Literal
 
-from invokeai.app.invocations.primitives import IntegerOutput
+import numpy as np
+from pydantic import field_validator
+
+from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
 
 from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
 
@@ -51,12 +54,238 @@ class DivideInvocation(BaseInvocation):
         return IntegerOutput(value=int(self.a / self.b))
 
 
-@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math", version="1.0.0")
+@invocation(
+    "rand_int",
+    title="Random Integer",
+    tags=["math", "random"],
+    category="math",
+    version="1.0.0",
+    use_cache=False,
+)
 class RandomIntInvocation(BaseInvocation):
     """Outputs a single random integer."""
 
-    low: int = InputField(default=0, description="The inclusive low value")
-    high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
+    low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
+    high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
 
     def invoke(self, context: InvocationContext) -> IntegerOutput:
         return IntegerOutput(value=np.random.randint(self.low, self.high))
+
+
+@invocation(
+    "rand_float",
+    title="Random Float",
+    tags=["math", "float", "random"],
+    category="math",
+    version="1.0.1",
+    use_cache=False,
+)
+class RandomFloatInvocation(BaseInvocation):
+    """Outputs a single random float"""
+
+    low: float = InputField(default=0.0, description=FieldDescriptions.inclusive_low)
+    high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high)
+    decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places)
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        random_float = np.random.uniform(self.low, self.high)
+        rounded_float = round(random_float, self.decimals)
+        return FloatOutput(value=rounded_float)
+
+
+@invocation(
+    "float_to_int",
+    title="Float To Integer",
+    tags=["math", "round", "integer", "float", "convert"],
+    category="math",
+    version="1.0.0",
+)
+class FloatToIntegerInvocation(BaseInvocation):
+    """Rounds a float number to (a multiple of) an integer."""
+
+    value: float = InputField(default=0, description="The value to round")
+    multiple: int = InputField(default=1, ge=1, title="Multiple of", description="The multiple to round to")
+    method: Literal["Nearest", "Floor", "Ceiling", "Truncate"] = InputField(
+        default="Nearest", description="The method to use for rounding"
+    )
+
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
+        if self.method == "Nearest":
+            return IntegerOutput(value=round(self.value / self.multiple) * self.multiple)
+        elif self.method == "Floor":
+            return IntegerOutput(value=np.floor(self.value / self.multiple) * self.multiple)
+        elif self.method == "Ceiling":
+            return IntegerOutput(value=np.ceil(self.value / self.multiple) * self.multiple)
+        else:  # self.method == "Truncate"
+            return IntegerOutput(value=int(self.value / self.multiple) * self.multiple)
+
+
+@invocation("round_float", title="Round Float", tags=["math", "round"], category="math", version="1.0.0")
+class RoundInvocation(BaseInvocation):
+    """Rounds a float to a specified number of decimal places."""
+
+    value: float = InputField(default=0, description="The float value")
+    decimals: int = InputField(default=0, description="The number of decimal places")
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        return FloatOutput(value=round(self.value, self.decimals))
+
+
+INTEGER_OPERATIONS = Literal[
+    "ADD",
+    "SUB",
+    "MUL",
+    "DIV",
+    "EXP",
+    "MOD",
+    "ABS",
+    "MIN",
+    "MAX",
+]
+
+
+INTEGER_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    MOD="Modulus A%B",
+    ABS="Absolute Value of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
+
+
+@invocation(
+    "integer_math",
+    title="Integer Math",
+    tags=[
+        "math",
+        "integer",
+        "add",
+        "subtract",
+        "multiply",
+        "divide",
+        "modulus",
+        "power",
+        "absolute value",
+        "min",
+        "max",
+    ],
+    category="math",
+    version="1.0.0",
+)
+class IntegerMathInvocation(BaseInvocation):
+    """Performs integer math."""
+
+    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)
+
+    @field_validator("b")
+    def no_unrepresentable_results(cls, v, values):
+        if values["operation"] == "DIV" and v == 0:
+            raise ValueError("Cannot divide by zero")
+        elif values["operation"] == "MOD" and v == 0:
+            raise ValueError("Cannot divide by zero")
+        elif values["operation"] == "EXP" and v < 0:
+            raise ValueError("Result of exponentiation is not an integer")
+        return v
+
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
+        # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
+        if self.operation == "ADD":
+            return IntegerOutput(value=self.a + self.b)
+        elif self.operation == "SUB":
+            return IntegerOutput(value=self.a - self.b)
+        elif self.operation == "MUL":
+            return IntegerOutput(value=self.a * self.b)
+        elif self.operation == "DIV":
+            return IntegerOutput(value=int(self.a / self.b))
+        elif self.operation == "EXP":
+            return IntegerOutput(value=self.a**self.b)
+        elif self.operation == "MOD":
+            return IntegerOutput(value=self.a % self.b)
+        elif self.operation == "ABS":
+            return IntegerOutput(value=abs(self.a))
+        elif self.operation == "MIN":
+            return IntegerOutput(value=min(self.a, self.b))
+        else:  # self.operation == "MAX":
+            return IntegerOutput(value=max(self.a, self.b))
+
+
+FLOAT_OPERATIONS = Literal[
+    "ADD",
+    "SUB",
+    "MUL",
+    "DIV",
+    "EXP",
+    "ABS",
+    "SQRT",
+    "MIN",
+    "MAX",
+]
+
+
+FLOAT_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    ABS="Absolute Value of A",
+    SQRT="Square Root of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
+
+
+@invocation(
+    "float_math",
+    title="Float Math",
+    tags=["math", "float", "add", "subtract", "multiply", "divide", "power", "root", "absolute value", "min", "max"],
+    category="math",
+    version="1.0.0",
+)
+class FloatMathInvocation(BaseInvocation):
+    """Performs floating point math."""
+
+    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)
+
+    @field_validator("b")
+    def no_unrepresentable_results(cls, v, values):
+        if values["operation"] == "DIV" and v == 0:
+            raise ValueError("Cannot divide by zero")
+        elif values["operation"] == "EXP" and values["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:
+            raise ValueError("Root operation resulted in a complex number")
+        return v
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        # Python doesn't support switch statements until 3.10, but InvokeAI supports back to 3.9
+        if self.operation == "ADD":
+            return FloatOutput(value=self.a + self.b)
+        elif self.operation == "SUB":
+            return FloatOutput(value=self.a - self.b)
+        elif self.operation == "MUL":
+            return FloatOutput(value=self.a * self.b)
+        elif self.operation == "DIV":
+            return FloatOutput(value=self.a / self.b)
+        elif self.operation == "EXP":
+            return FloatOutput(value=self.a**self.b)
+        elif self.operation == "SQRT":
+            return FloatOutput(value=np.sqrt(self.a))
+        elif self.operation == "ABS":
+            return FloatOutput(value=abs(self.a))
+        elif self.operation == "MIN":
+            return FloatOutput(value=min(self.a, self.b))
+        else:  # self.operation == "MAX":
+            return FloatOutput(value=max(self.a, self.b))

invokeai/app/invocations/metadata.py

@@ -12,7 +12,10 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.controlnet_image_processors import ControlField
+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__
@@ -25,29 +28,47 @@ class LoRAMetadataField(BaseModelExcludeNull):
     weight: float = Field(description="The weight of the LoRA model")
 
 
+class IPAdapterMetadataField(BaseModelExcludeNull):
+    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)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
+    )
+
+
 class CoreMetadata(BaseModelExcludeNull):
     """Core generation metadata for an image generated in InvokeAI."""
 
     app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
-    generation_mode: str = Field(
+    generation_mode: Optional[str] = Field(
+        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: int = Field(
+    positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
+    negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
+    width: Optional[int] = Field(default=None, description="The width parameter")
+    height: Optional[int] = Field(default=None, description="The height parameter")
+    seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
+    rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
+    cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
+    steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
+    scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
+    clip_skip: Optional[int] = Field(
+        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")
-    loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
+    model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
+    controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
+    ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
+        default=None, description="The IP Adapters used for inference"
+    )
+    t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
+    loras: Optional[list[LoRAMetadataField]] = Field(default=None, 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",
@@ -104,24 +125,34 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
 class MetadataAccumulatorInvocation(BaseInvocation):
     """Outputs a Core Metadata Object"""
 
-    generation_mode: str = InputField(
+    generation_mode: Optional[str] = InputField(
+        default=None,
         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: int = InputField(
+    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")
+    clip_skip: Optional[int] = InputField(
+        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")
-    loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference")
+    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"
+    )
+    ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
+        default=None, description="The IP Adapters used for inference"
+    )
+    t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
+        default=None, description="The IP Adapters 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",
@@ -135,6 +166,20 @@ class MetadataAccumulatorInvocation(BaseInvocation):
         description="The VAE used for decoding, if the main model's default was not used",
     )
 
+    # 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(
+        default=None,
+        description="The high resolution fix img2img strength used in the upscale pass.",
+    )
+
     # SDXL
     positive_style_prompt: Optional[str] = InputField(
         default=None,
@@ -178,4 +223,4 @@ class MetadataAccumulatorInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
         """Collects and outputs a CoreMetadata object"""
 
-        return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
+        return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))

invokeai/app/invocations/model.py

@@ -1,7 +1,7 @@
 import copy
 from typing import List, Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from .baseinvocation import (
@@ -24,6 +24,8 @@ 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")
@@ -65,6 +67,8 @@ 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 +76,16 @@ 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."""
 
@@ -86,7 +98,7 @@ class MainModelLoaderInvocation(BaseInvocation):
         model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.services.model_manager.model_exists(
+        if not context.model_exists(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
@@ -180,10 +192,16 @@ 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 +262,35 @@ 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:
@@ -330,6 +363,8 @@ class VAEModelField(BaseModel):
     model_name: str = Field(description="Name of the model")
     base_model: BaseModelType = Field(description="Base model")
 
+    model_config = ConfigDict(protected_namespaces=())
+
 
 @invocation_output("vae_loader_output")
 class VaeLoaderOutput(BaseInvocationOutput):
@@ -343,7 +378,10 @@ 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:
@@ -372,19 +410,31 @@ 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")

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)
@@ -118,6 +124,5 @@ class NoiseInvocation(BaseInvocation):
             seed=self.seed,
             use_cpu=self.use_cpu,
         )
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, noise)
-        return build_noise_output(latents_name=name, latents=noise, seed=self.seed)
+        latents_name = context.save_latents(noise)
+        return build_noise_output(latents_name=latents_name, latents=noise, seed=self.seed)

invokeai/app/invocations/onnx.py

@@ -9,24 +9,24 @@ from typing import List, Literal, Optional, 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
 from invokeai.backend import BaseModelType, ModelType, SubModelType
 
 from ...backend.model_management import ONNXModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util import choose_torch_device
-from ..models.image import ImageCategory, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     FieldDescriptions,
-    InputField,
     Input,
+    InputField,
     InvocationContext,
     OutputField,
     UIComponent,
@@ -63,14 +63,17 @@ 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
             ]
 
@@ -95,9 +98,10 @@ class ONNXPromptInvocation(BaseInvocation):
                     print(f'Warn: trigger: "{trigger}" not found')
             if loras or ti_list:
                 text_encoder.release_session()
-            with ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras), ONNXModelPatcher.apply_ti(
-                orig_tokenizer, text_encoder, ti_list
-            ) as (tokenizer, ti_manager):
+            with (
+                ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
+                ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
+            ):
                 text_encoder.create_session()
 
                 # copy from
@@ -165,7 +169,6 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
         default=7.5,
         ge=1,
         description=FieldDescriptions.cfg_scale,
-        ui_type=UIType.Float,
     )
     scheduler: SAMPLER_NAME_VALUES = InputField(
         default="euler", description=FieldDescriptions.scheduler, input=Input.Direct, ui_type=UIType.Scheduler
@@ -175,15 +178,14 @@ 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,
-        ui_type=UIType.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):
@@ -242,7 +244,7 @@ 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,
             )
 
@@ -255,12 +257,15 @@ 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
             ]
 
@@ -347,7 +352,7 @@ 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
@@ -376,7 +381,7 @@ 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.model_dump() if self.metadata else None,
             workflow=self.workflow,
         )
 
@@ -404,6 +409,8 @@ 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):

invokeai/app/invocations/param_easing.py

@@ -3,7 +3,6 @@ from typing import Literal, Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
-
 import PIL.Image
 from easing_functions import (
     BackEaseIn,
@@ -45,13 +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))
@@ -96,7 +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"""
 
@@ -160,7 +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):
@@ -200,7 +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)

invokeai/app/invocations/primitives.py

@@ -226,6 +226,12 @@ class ImageField(BaseModel):
     image_name: str = Field(description="The name of the image")
 
 
+class BoardField(BaseModel):
+    """A board primitive field"""
+
+    board_id: str = Field(description="The id of the board")
+
+
 @invocation_output("image_output")
 class ImageOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""

invokeai/app/invocations/prompt.py

@@ -3,18 +3,28 @@ 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
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
 
 
-@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt", version="1.0.0")
+@invocation(
+    "dynamic_prompt",
+    title="Dynamic Prompt",
+    tags=["prompt", "collection"],
+    category="prompt",
+    version="1.0.0",
+    use_cache=False,
+)
 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")
 
@@ -29,21 +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)
@@ -72,6 +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)

invokeai/app/invocations/strings.py

@@ -0,0 +1,139 @@
+# 2023 skunkworxdark (https://github.com/skunkworxdark)
+
+import re
+
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
+    invocation,
+    invocation_output,
+)
+from .primitives import StringOutput
+
+
+@invocation_output("string_pos_neg_output")
+class StringPosNegOutput(BaseInvocationOutput):
+    """Base class for invocations that output a positive and negative string"""
+
+    positive_string: str = OutputField(description="Positive string")
+    negative_string: str = OutputField(description="Negative string")
+
+
+@invocation(
+    "string_split_neg",
+    title="String Split Negative",
+    tags=["string", "split", "negative"],
+    category="string",
+    version="1.0.0",
+)
+class StringSplitNegInvocation(BaseInvocation):
+    """Splits string into two strings, inside [] goes into negative string everthing else goes into positive string. Each [ and ] character is replaced with a space"""
+
+    string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringPosNegOutput:
+        p_string = ""
+        n_string = ""
+        brackets_depth = 0
+        escaped = False
+
+        for char in self.string or "":
+            if char == "[" and not escaped:
+                n_string += " "
+                brackets_depth += 1
+            elif char == "]" and not escaped:
+                brackets_depth -= 1
+                char = " "
+            elif brackets_depth > 0:
+                n_string += char
+            else:
+                p_string += char
+
+            # keep track of the escape char but only if it isn't escaped already
+            if char == "\\" and not escaped:
+                escaped = True
+            else:
+                escaped = False
+
+        return StringPosNegOutput(positive_string=p_string, negative_string=n_string)
+
+
+@invocation_output("string_2_output")
+class String2Output(BaseInvocationOutput):
+    """Base class for invocations that output two strings"""
+
+    string_1: str = OutputField(description="string 1")
+    string_2: str = OutputField(description="string 2")
+
+
+@invocation("string_split", title="String Split", tags=["string", "split"], category="string", version="1.0.0")
+class StringSplitInvocation(BaseInvocation):
+    """Splits string into two strings, based on the first occurance of the delimiter. The delimiter will be removed from the string"""
+
+    string: str = InputField(default="", description="String to split", ui_component=UIComponent.Textarea)
+    delimiter: str = InputField(
+        default="", description="Delimiter to spilt with. blank will split on the first whitespace"
+    )
+
+    def invoke(self, context: InvocationContext) -> String2Output:
+        result = self.string.split(self.delimiter, 1)
+        if len(result) == 2:
+            part1, part2 = result
+        else:
+            part1 = result[0]
+            part2 = ""
+
+        return String2Output(string_1=part1, string_2=part2)
+
+
+@invocation("string_join", title="String Join", tags=["string", "join"], category="string", version="1.0.0")
+class StringJoinInvocation(BaseInvocation):
+    """Joins string left to string right"""
+
+    string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea)
+    string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        return StringOutput(value=((self.string_left or "") + (self.string_right or "")))
+
+
+@invocation("string_join_three", title="String Join Three", tags=["string", "join"], category="string", version="1.0.0")
+class StringJoinThreeInvocation(BaseInvocation):
+    """Joins string left to string middle to string right"""
+
+    string_left: str = InputField(default="", description="String Left", ui_component=UIComponent.Textarea)
+    string_middle: str = InputField(default="", description="String Middle", ui_component=UIComponent.Textarea)
+    string_right: str = InputField(default="", description="String Right", ui_component=UIComponent.Textarea)
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        return StringOutput(value=((self.string_left or "") + (self.string_middle or "") + (self.string_right or "")))
+
+
+@invocation(
+    "string_replace", title="String Replace", tags=["string", "replace", "regex"], category="string", version="1.0.0"
+)
+class StringReplaceInvocation(BaseInvocation):
+    """Replaces the search string with the replace string"""
+
+    string: str = InputField(default="", description="String to work on", ui_component=UIComponent.Textarea)
+    search_string: str = InputField(default="", description="String to search for", ui_component=UIComponent.Textarea)
+    replace_string: str = InputField(
+        default="", description="String to replace the search", ui_component=UIComponent.Textarea
+    )
+    use_regex: bool = InputField(
+        default=False, description="Use search string as a regex expression (non regex is case insensitive)"
+    )
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        pattern = self.search_string or ""
+        new_string = self.string or ""
+        if len(pattern) > 0:
+            if not self.use_regex:
+                # None regex so make case insensitve
+                pattern = "(?i)" + re.escape(pattern)
+            new_string = re.sub(pattern, (self.replace_string or ""), new_string)
+        return StringOutput(value=new_string)

invokeai/app/invocations/t2i_adapter.py

@@ -0,0 +1,85 @@
+from typing import Union
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.backend.model_management.models.base import BaseModelType
+
+
+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.")
+    t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
+    weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+
+@invocation_output("t2i_adapter_output")
+class T2IAdapterOutput(BaseInvocationOutput):
+    t2i_adapter: T2IAdapterField = OutputField(description=FieldDescriptions.t2i_adapter, title="T2I Adapter")
+
+
+@invocation(
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
+)
+class T2IAdapterInvocation(BaseInvocation):
+    """Collects T2I-Adapter info to pass to other nodes."""
+
+    # Inputs
+    image: ImageField = InputField(description="The IP-Adapter image prompt.")
+    t2i_adapter_model: T2IAdapterModelField = InputField(
+        description="The T2I-Adapter model.",
+        title="T2I-Adapter Model",
+        input=Input.Direct,
+        ui_order=-1,
+    )
+    weight: Union[float, list[float]] = InputField(
+        default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
+    )
+    begin_step_percent: float = InputField(
+        default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(
+        default="just_resize",
+        description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
+    )
+
+    def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
+        return T2IAdapterOutput(
+            t2i_adapter=T2IAdapterField(
+                image=self.image,
+                t2i_adapter_model=self.t2i_adapter_model,
+                weight=self.weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+                resize_mode=self.resize_mode,
+            )
+        )

invokeai/app/invocations/upscale.py

@@ -4,12 +4,15 @@ from typing import Literal
 
 import cv2 as cv
 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.models.image import ImageCategory, ResourceOrigin
+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
 
@@ -22,13 +25,21 @@ ESRGAN_MODELS = Literal[
     "RealESRGAN_x2plus.pth",
 ]
 
+if choose_torch_device() == torch.device("mps"):
+    from torch import mps
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
+
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
 class ESRGANInvocation(BaseInvocation):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
     model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
+    tile_size: int = InputField(
+        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)
@@ -86,9 +97,11 @@ class ESRGANInvocation(BaseInvocation):
             model_path=str(models_path / esrgan_model_path),
             model=rrdbnet_model,
             half=False,
+            tile=self.tile_size,
         )
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
+        # TODO: This strips the alpha... is that okay?
         cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
 
         # We can pass an `outscale` value here, but it just resizes the image by that factor after
@@ -99,6 +112,10 @@ class ESRGANInvocation(BaseInvocation):
         # back to PIL
         pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
 
+        torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
+
         image_dto = context.services.images.create(
             image=pil_image,
             image_origin=ResourceOrigin.INTERNAL,

invokeai/app/models/exceptions.py

@@ -1,4 +0,0 @@
-class CanceledException(Exception):
-    """Execution canceled by user."""
-
-    pass

invokeai/app/models/image.py

@@ -1,71 +0,0 @@
-from enum import Enum
-
-from pydantic import BaseModel, Field
-
-from invokeai.app.util.metaenum import MetaEnum
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
-
-
-class ResourceOrigin(str, Enum, metaclass=MetaEnum):
-    """The origin of a resource (eg image).
-
-    - INTERNAL: The resource was created by the application.
-    - EXTERNAL: The resource was not created by the application.
-    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
-    """
-
-    INTERNAL = "internal"
-    """The resource was created by the application."""
-    EXTERNAL = "external"
-    """The resource was not created by the application.
-    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
-    """
-
-
-class InvalidOriginException(ValueError):
-    """Raised when a provided value is not a valid ResourceOrigin.
-
-    Subclasses `ValueError`.
-    """
-
-    def __init__(self, message="Invalid resource origin."):
-        super().__init__(message)
-
-
-class ImageCategory(str, Enum, metaclass=MetaEnum):
-    """The category of an image.
-
-    - GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
-    - MASK: The image is a mask image.
-    - CONTROL: The image is a ControlNet control image.
-    - USER: The image is a user-provide image.
-    - OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
-    """
-
-    GENERAL = "general"
-    """GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
-    MASK = "mask"
-    """MASK: The image is a mask image."""
-    CONTROL = "control"
-    """CONTROL: The image is a ControlNet control image."""
-    USER = "user"
-    """USER: The image is a user-provide image."""
-    OTHER = "other"
-    """OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
-
-
-class InvalidImageCategoryException(ValueError):
-    """Raised when a provided value is not a valid ImageCategory.
-
-    Subclasses `ValueError`.
-    """
-
-    def __init__(self, message="Invalid image category."):
-        super().__init__(message)

invokeai/app/services/board_image_records/board_image_records_base.py

@@ -0,0 +1,47 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class BoardImageRecordStorageBase(ABC):
+    """Abstract base class for the one-to-many board-image relationship record storage."""
+
+    @abstractmethod
+    def add_image_to_board(
+        self,
+        board_id: str,
+        image_name: str,
+    ) -> None:
+        """Adds an image to a board."""
+        pass
+
+    @abstractmethod
+    def remove_image_from_board(
+        self,
+        image_name: str,
+    ) -> None:
+        """Removes an image from a board."""
+        pass
+
+    @abstractmethod
+    def get_all_board_image_names_for_board(
+        self,
+        board_id: str,
+    ) -> list[str]:
+        """Gets all board images for a board, as a list of the image names."""
+        pass
+
+    @abstractmethod
+    def get_board_for_image(
+        self,
+        image_name: str,
+    ) -> Optional[str]:
+        """Gets an image's board id, if it has one."""
+        pass
+
+    @abstractmethod
+    def get_image_count_for_board(
+        self,
+        board_id: str,
+    ) -> int:
+        """Gets the number of images for a board."""
+        pass

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -1,79 +1,27 @@
-from abc import ABC, abstractmethod
 import sqlite3
 import threading
 from typing import Optional, cast
 
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import (
-    ImageRecord,
-    deserialize_image_record,
-)
+from invokeai.app.services.image_records.image_records_common import ImageRecord, deserialize_image_record
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite import SqliteDatabase
 
-
-class BoardImageRecordStorageBase(ABC):
-    """Abstract base class for the one-to-many board-image relationship record storage."""
-
-    @abstractmethod
-    def add_image_to_board(
-        self,
-        board_id: str,
-        image_name: str,
-    ) -> None:
-        """Adds an image to a board."""
-        pass
-
-    @abstractmethod
-    def remove_image_from_board(
-        self,
-        image_name: str,
-    ) -> None:
-        """Removes an image from a board."""
-        pass
-
-    @abstractmethod
-    def get_all_board_image_names_for_board(
-        self,
-        board_id: str,
-    ) -> list[str]:
-        """Gets all board images for a board, as a list of the image names."""
-        pass
-
-    @abstractmethod
-    def get_board_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        """Gets an image's board id, if it has one."""
-        pass
-
-    @abstractmethod
-    def get_image_count_for_board(
-        self,
-        board_id: str,
-    ) -> int:
-        """Gets the number of images for a board."""
-        pass
+from .board_image_records_base import BoardImageRecordStorageBase
 
 
 class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
-    _filename: str
     _conn: sqlite3.Connection
     _cursor: sqlite3.Cursor
-    _lock: threading.Lock
+    _lock: threading.RLock
 
-    def __init__(self, filename: str) -> None:
+    def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._filename = filename
-        self._conn = sqlite3.connect(filename, check_same_thread=False)
-        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
-        self._conn.row_factory = sqlite3.Row
+        self._lock = db.lock
+        self._conn = db.conn
         self._cursor = self._conn.cursor()
-        self._lock = threading.Lock()
 
         try:
             self._lock.acquire()
-            # Enable foreign keys
-            self._conn.execute("PRAGMA foreign_keys = ON;")
             self._create_tables()
             self._conn.commit()
         finally:

invokeai/app/services/board_images.py

@@ -1,115 +0,0 @@
-from abc import ABC, abstractmethod
-from logging import Logger
-from typing import Optional
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
-from invokeai.app.services.board_record_storage import (
-    BoardRecord,
-    BoardRecordStorageBase,
-)
-
-from invokeai.app.services.image_record_storage import ImageRecordStorageBase
-from invokeai.app.services.models.board_record import BoardDTO
-from invokeai.app.services.urls import UrlServiceBase
-
-
-class BoardImagesServiceABC(ABC):
-    """High-level service for board-image relationship management."""
-
-    @abstractmethod
-    def add_image_to_board(
-        self,
-        board_id: str,
-        image_name: str,
-    ) -> None:
-        """Adds an image to a board."""
-        pass
-
-    @abstractmethod
-    def remove_image_from_board(
-        self,
-        image_name: str,
-    ) -> None:
-        """Removes an image from a board."""
-        pass
-
-    @abstractmethod
-    def get_all_board_image_names_for_board(
-        self,
-        board_id: str,
-    ) -> list[str]:
-        """Gets all board images for a board, as a list of the image names."""
-        pass
-
-    @abstractmethod
-    def get_board_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        """Gets an image's board id, if it has one."""
-        pass
-
-
-class BoardImagesServiceDependencies:
-    """Service dependencies for the BoardImagesService."""
-
-    board_image_records: BoardImageRecordStorageBase
-    board_records: BoardRecordStorageBase
-    image_records: ImageRecordStorageBase
-    urls: UrlServiceBase
-    logger: Logger
-
-    def __init__(
-        self,
-        board_image_record_storage: BoardImageRecordStorageBase,
-        image_record_storage: ImageRecordStorageBase,
-        board_record_storage: BoardRecordStorageBase,
-        url: UrlServiceBase,
-        logger: Logger,
-    ):
-        self.board_image_records = board_image_record_storage
-        self.image_records = image_record_storage
-        self.board_records = board_record_storage
-        self.urls = url
-        self.logger = logger
-
-
-class BoardImagesService(BoardImagesServiceABC):
-    _services: BoardImagesServiceDependencies
-
-    def __init__(self, services: BoardImagesServiceDependencies):
-        self._services = services
-
-    def add_image_to_board(
-        self,
-        board_id: str,
-        image_name: str,
-    ) -> None:
-        self._services.board_image_records.add_image_to_board(board_id, image_name)
-
-    def remove_image_from_board(
-        self,
-        image_name: str,
-    ) -> None:
-        self._services.board_image_records.remove_image_from_board(image_name)
-
-    def get_all_board_image_names_for_board(
-        self,
-        board_id: str,
-    ) -> list[str]:
-        return self._services.board_image_records.get_all_board_image_names_for_board(board_id)
-
-    def get_board_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        board_id = self._services.board_image_records.get_board_for_image(image_name)
-        return board_id
-
-
-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"}),
-        cover_image_name=cover_image_name,
-        image_count=image_count,
-    )

invokeai/app/services/board_images/board_images_base.py

@@ -0,0 +1,39 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class BoardImagesServiceABC(ABC):
+    """High-level service for board-image relationship management."""
+
+    @abstractmethod
+    def add_image_to_board(
+        self,
+        board_id: str,
+        image_name: str,
+    ) -> None:
+        """Adds an image to a board."""
+        pass
+
+    @abstractmethod
+    def remove_image_from_board(
+        self,
+        image_name: str,
+    ) -> None:
+        """Removes an image from a board."""
+        pass
+
+    @abstractmethod
+    def get_all_board_image_names_for_board(
+        self,
+        board_id: str,
+    ) -> list[str]:
+        """Gets all board images for a board, as a list of the image names."""
+        pass
+
+    @abstractmethod
+    def get_board_for_image(
+        self,
+        image_name: str,
+    ) -> Optional[str]:
+        """Gets an image's board id, if it has one."""
+        pass

invokeai/app/services/board_images/board_images_default.py

@@ -0,0 +1,38 @@
+from typing import Optional
+
+from invokeai.app.services.invoker import Invoker
+
+from .board_images_base import BoardImagesServiceABC
+
+
+class BoardImagesService(BoardImagesServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def add_image_to_board(
+        self,
+        board_id: str,
+        image_name: str,
+    ) -> None:
+        self.__invoker.services.board_image_records.add_image_to_board(board_id, image_name)
+
+    def remove_image_from_board(
+        self,
+        image_name: str,
+    ) -> None:
+        self.__invoker.services.board_image_records.remove_image_from_board(image_name)
+
+    def get_all_board_image_names_for_board(
+        self,
+        board_id: str,
+    ) -> list[str]:
+        return self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
+
+    def get_board_for_image(
+        self,
+        image_name: str,
+    ) -> Optional[str]:
+        board_id = self.__invoker.services.board_image_records.get_board_for_image(image_name)
+        return board_id

invokeai/app/services/board_records/board_records_base.py

@@ -0,0 +1,55 @@
+from abc import ABC, abstractmethod
+
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+
+from .board_records_common import BoardChanges, BoardRecord
+
+
+class BoardRecordStorageBase(ABC):
+    """Low-level service responsible for interfacing with the board record store."""
+
+    @abstractmethod
+    def delete(self, board_id: str) -> None:
+        """Deletes a board record."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        board_name: str,
+    ) -> BoardRecord:
+        """Saves a board record."""
+        pass
+
+    @abstractmethod
+    def get(
+        self,
+        board_id: str,
+    ) -> BoardRecord:
+        """Gets a board record."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        board_id: str,
+        changes: BoardChanges,
+    ) -> BoardRecord:
+        """Updates a board record."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+    ) -> OffsetPaginatedResults[BoardRecord]:
+        """Gets many board records."""
+        pass
+
+    @abstractmethod
+    def get_all(
+        self,
+    ) -> list[BoardRecord]:
+        """Gets all board records."""
+        pass