feat(backend): test node cache selective invalidation

invoking needs access to `node_cache_size` to occur

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -47,9 +47,34 @@ 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.
 
-### Tests
+### 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)
 
-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 —
@@ -142,23 +167,6 @@ 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

@@ -1,89 +0,0 @@
-# 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

@@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
 Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](./contributingToFrontend.md)
+* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
@@ -38,9 +38,9 @@ There are two paths to making a development contribution:
 
 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, **@psychedelicious** is the best person to reach out to. 
+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 **@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 @imic or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.
+Please ping @imic1 or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

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

@@ -1,11 +1,13 @@
 ---
-title: Control Adapters
+title: ControlNet
 ---
 
-# :material-loupe: Control Adapters
+# :material-loupe: ControlNet
 
 ## 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
@@ -18,7 +20,7 @@ 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
@@ -28,7 +30,7 @@ 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
@@ -94,8 +96,6 @@ 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. 
@@ -120,7 +120,7 @@ With Pix2Pix, you can input an image into the controlnet, and then "instruct" th
 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.
 
@@ -132,31 +132,3 @@ 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/UTILITIES.md

@@ -1,336 +0,0 @@
----
-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

@@ -51,9 +51,6 @@ 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

@@ -147,7 +147,6 @@ 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

@@ -256,10 +256,6 @@ 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
@@ -300,18 +296,8 @@ 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)
 
-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:
+1. From the command line, run this command:
    ```bash
    git clone https://github.com/<your_github_username>/InvokeAI.git
    ```
@@ -319,10 +305,10 @@ If you have a "normal" installation, you should create a totally separate virtua
     This will create a directory named `InvokeAI` and populate it with the
     full source code from your fork of the InvokeAI repository.
 
-3. Activate the InvokeAI virtual environment as per step (4) of the manual
+2. Activate the InvokeAI virtual environment as per step (4) of the manual
 installation protocol (important!)
 
-4. Enter the InvokeAI repository directory and run one of these
+3. Enter the InvokeAI repository directory and run one of these
    commands, based on your GPU:
 
     === "CUDA (NVidia)"
@@ -348,15 +334,11 @@ installation protocol (important!)
     Be sure to pass `-e` (for an editable install) and don't forget the
     dot ("."). It is part of the command.
 
-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
+    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.
 
-    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
+4.  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

@@ -171,16 +171,3 @@ 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/nodes/NODES.md

@@ -4,12 +4,12 @@ The workflow editor is a blank canvas allowing for the use of individual functio
 
 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
+## UI 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".
+To add an input to the Linear UI, right click on the input 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. 
 
@@ -25,10 +25,6 @@ Any node or input field can be renamed in the workflow editor. If the input fiel
 * 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. 
-
 
 ## Important Concepts 
 

docs/nodes/communityNodes.md

@@ -8,21 +8,19 @@ To download a node, simply download the `.py` node file from the link and add it
 
 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
 
---------------------------------
-### Make 3D
+### FaceTools
 
-**Description:** Create compelling 3D stereo images from 2D originals.
+**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://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
+**Node Link:** https://github.com/ymgenesis/FaceTools/
 
-**Example Node Graph:**  https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
+**FaceMask Output Examples** 
 
-**Output Examples** 
-
-![Painting of a cozy delapidated house](https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png){: style="height:512px;width:512px"}
-![Photo of cute puppies](https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png){: style="height:512px;width:512px"}
+![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
@@ -45,52 +43,6 @@ To use a community workflow, download the the `.json` node graph file and load i
 
 **Node Link:** https://github.com/JPPhoto/image-picker-node
 
---------------------------------
-### 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:
-
-![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632){: style="height:512px;width:512px"}
-
-Highlights/Midtones/Shadows:
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" style="width: 30%" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" style="width: 30%" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" style="width: 30%" />
-
-Highlights/Midtones/Shadows (with LUT blur enabled):
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" style="width: 30%" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" style="width: 30%" />
-<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" style="width: 30%" />
-
---------------------------------
-### Halftone
-
-**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing.
-
-**Node Link:** https://github.com/JPPhoto/halftone-node
-
-**Example**
-
-Input:
-
-![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4){: style="height:512px;width:512px"}
-
-Halftone Output:
-
-![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f){: style="height:512px;width:512px"}
-
-CMYK Halftone Output:
-
-![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea){: style="height:512px;width:512px"}
-
 --------------------------------
 ### Retroize
 
@@ -125,7 +77,7 @@ Generated Prompt: An enchanted weapon will be usable by any character regardless
 **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)
 [Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
 
@@ -169,6 +121,18 @@ To be imported, an .obj must use triangulated meshes, so make sure to enable tha
 **Example Usage:**
 ![depth from obj usage graph](https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg)
 
+--------------------------------
+### Enhance Image (simple adjustments)
+
+**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.
+
+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/dwringer/image-enhance-node
+
+**Example Usage:**
+![enhance image usage graph](https://raw.githubusercontent.com/dwringer/image-enhance-node/main/image_enhance_usage.jpg)
+
 --------------------------------
 ### Generative Grammar-Based Prompt Nodes
 
@@ -189,28 +153,16 @@ This includes 3 Nodes:
 
 **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.
+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.
-- *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
 
-**Nodes and Output Examples:**
-![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg)
+**Example Usage:**
+![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_nodes_usage.jpg)
 
 --------------------------------
 ### Size Stepper Nodes
@@ -244,70 +196,6 @@ Results after using the depth controlnet
 
 --------------------------------
 
-### Prompt Tools 
-
-**Description:** A set of InvokeAI nodes that add general prompt manipulation tools.  These where written to accompany the PromptsFromFile node and other prompt generation nodes.
-
-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.
-
-See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
-
---------------------------------
-
-### XY Image to Grid and Images to Grids nodes
-
-**Description:** Image to grid nodes and supporting tools.
-
-1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then mutilple grids will be created until it runs out of images.
-2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images.  The XYImages collection has to be built using the supporoting nodes. See example node setups for more details.
-   
-
-See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
-
-**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
-
---------------------------------
-
-### Image to Character Art Image Node's
-
-**Description:** Group of nodes to convert an input image into ascii/unicode art Image
-
-**Node Link:** https://github.com/mickr777/imagetoasciiimage
-
-**Output Examples**
-
-<img src="https://github.com/invoke-ai/InvokeAI/assets/115216705/8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056" 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" />
-
---------------------------------
-
-### Grid to Gif
-
-**Description:** One node that turns a grid image into an image colletion, one node that turns an image collection into a gif
-
-**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py
-
-**Example Node Graph:**  https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json
-
-**Output Examples** 
-
-<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" />
-
---------------------------------
-
 ### Example Node Template
 
 **Description:** This node allows you to do super cool things with InvokeAI.

docs/nodes/defaultNodes.md

@@ -1,6 +1,6 @@
 # List of Default Nodes
 
-The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
+The table below contains a list of the default nodes shipped with InvokeAI and their descriptions. 
 
 | Node <img width=160 align="right"> | Function                                                                              |
 |: ---------------------------------- | :--------------------------------------------------------------------------------------|
@@ -17,12 +17,11 @@ 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|
-|[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|
+|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
 |Float Math             | Perform basic math operations on two floats|
 |Float Primitive Collection 			| A collection of float primitive values|
 |Float Primitive 			| A float primitive value|
@@ -77,7 +76,6 @@ 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|
@@ -99,6 +97,5 @@ 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

@@ -1,154 +0,0 @@
-# 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

@@ -9,6 +9,5 @@ If you're interested in finding more workflows, checkout the [#share-your-workfl
 * [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)
+* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)ß
+

installer/lib/installer.py

@@ -332,7 +332,6 @@ 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)):
@@ -341,17 +340,13 @@ class InvokeAiInstance:
                 new_argv.append(el)
                 new_argv.append(sys.argv[i + 1])
             elif el in ["-y", "--yes", "--yes-to-all"]:
-                auto_install = True
+                new_argv.append(el)
         sys.argv = new_argv
 
-        import messages
         import requests  # to catch download exceptions
+        from messages import introduction
 
-        auto_install = auto_install or messages.user_wants_auto_configuration()
-        if auto_install:
-            sys.argv.append("--yes")
-        else:
-            messages.introduction()
+        introduction()
 
         from invokeai.frontend.install.invokeai_configure import invokeai_configure
 

installer/lib/messages.py

@@ -7,7 +7,7 @@ import os
 import platform
 from pathlib import Path
 
-from prompt_toolkit import HTML, prompt
+from prompt_toolkit import prompt
 from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.validation import Validator
 from rich import box, print
@@ -65,50 +65,17 @@ def confirm_install(dest: Path) -> bool:
     if dest.exists():
         print(f":exclamation: Directory {dest} already exists :exclamation:")
         dest_confirmed = Confirm.ask(
-            ":stop_sign: (re)install in this location?",
+            ":stop_sign: Are you sure you want to (re)install in this location?",
             default=False,
         )
     else:
         print(f"InvokeAI will be installed in {dest}")
-        dest_confirmed = Confirm.ask("Use this location?", default=True)
+        dest_confirmed = not Confirm.ask("Would you like to pick a different location?", default=False)
     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

@@ -17,10 +17,9 @@ echo 6. Change InvokeAI startup options
 echo 7. Re-run the configure script to fix a broken install or to complete a major upgrade
 echo 8. Open the developer console
 echo 9. Update InvokeAI
-echo 10. Run the InvokeAI image database maintenance script
-echo 11. Command-line help
+echo 10. Command-line help
 echo Q - Quit
-set /P choice="Please enter 1-11, Q: [1] "
+set /P choice="Please enter 1-10, Q: [1] "
 if not defined choice set choice=1
 IF /I "%choice%" == "1" (
     echo Starting the InvokeAI browser-based UI..
@@ -59,11 +58,8 @@ 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-web.exe --help %*
+    python .venv\Scripts\invokeai.exe --help %*
     pause
     exit /b
 ) ELSE IF /I "%choice%" == "q" (

installer/templates/invoke.sh.in

@@ -46,9 +46,6 @@ 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
@@ -100,13 +97,13 @@ do_choice() {
         ;;
     10)
         clear
-        printf "Running the db maintenance script\n"
-        invokeai-db-maintenance --root ${INVOKEAI_ROOT}
+        printf "Command-line help\n"
+        invokeai --help
         ;;
-    11)
+    "HELP 1")
         clear
         printf "Command-line help\n"
-        invokeai-web --help
+        invokeai --help
         ;;
     *)
         clear
@@ -128,10 +125,7 @@ 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"
-	10 "Run the InvokeAI image database maintenance script"
-	11 "Command-line help"
-    )
+        9 "Update InvokeAI")
 
     choice=$(dialog --clear \
         --backtitle "\Zb\Zu\Z3InvokeAI" \
@@ -163,10 +157,9 @@ do_line_input() {
     printf "7: Re-run the configure script to fix a broken install\n"
     printf "8: Open the developer console\n"
     printf "9: Update InvokeAI\n"
-    printf "10: Run the InvokeAI image database maintenance script\n"
-    printf "11: Command-line help\n"
+    printf "10: Command-line help\n"
     printf "Q: Quit\n\n"
-    read -p "Please enter 1-11, Q: [1] " yn
+    read -p "Please enter 1-10, Q: [1] " yn
     choice=${yn:='1'}
     do_choice $choice
     clear

invokeai/app/api/dependencies.py

@@ -1,35 +1,35 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import sqlite3
 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.invocation_cache.invocation_cache_memory import MemoryInvocationCache
+from invokeai.app.services.resource_name import SimpleNameService
+from invokeai.app.services.session_processor.session_processor_default import DefaultSessionProcessor
+from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
+from invokeai.app.services.urls import LocalUrlService
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
-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.default_graphs import create_system_graphs
+from ..services.graph import GraphExecutionState, LibraryGraph
+from ..services.image_file_storage import DiskImageFileStorage
+from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
-from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
+from ..services.invocation_stats import InvocationStatsService
 from ..services.invoker import Invoker
-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 ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
+from ..services.model_manager_service import ModelManagerService
+from ..services.processor import DefaultInvocationProcessor
+from ..services.sqlite import SqliteItemStorage
+from ..services.thread import lock
 from .events import FastAPIEventService
 
 
@@ -49,7 +49,7 @@ def check_internet() -> bool:
         return False
 
 
-logger = InvokeAILogger.get_logger()
+logger = InvokeAILogger.getLogger()
 
 
 class ApiDependencies:
@@ -63,64 +63,100 @@ 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
 
-        db = SqliteDatabase(config, logger)
+        # TODO: build a file/path manager?
+        if config.use_memory_db:
+            db_location = ":memory:"
+        else:
+            db_path = config.db_path
+            db_path.parent.mkdir(parents=True, exist_ok=True)
+            db_location = str(db_path)
 
-        configuration = config
-        logger = logger
+        logger.info(f"Using database at {db_location}")
+        db_conn = sqlite3.connect(db_location, check_same_thread=False)  # TODO: figure out a better threading solution
+
+        if config.log_sql:
+            db_conn.set_trace_callback(print)
+        db_conn.execute("PRAGMA foreign_keys = ON;")
+
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
+            conn=db_conn, table_name="graph_executions", lock=lock
+        )
 
-        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"))
-        model_manager = ModelManagerService(config, logger)
-        names = SimpleNameService()
-        performance_statistics = InvocationStatsService()
-        processor = DefaultInvocationProcessor()
-        queue = MemoryInvocationQueue()
-        session_processor = DefaultSessionProcessor()
-        session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
+        image_record_storage = SqliteImageRecordStorage(conn=db_conn, lock=lock)
+        image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
+        names = SimpleNameService()
+        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
+
+        board_record_storage = SqliteBoardRecordStorage(conn=db_conn, lock=lock)
+        board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn, lock=lock)
+
+        boards = BoardService(
+            services=BoardServiceDependencies(
+                board_image_record_storage=board_image_record_storage,
+                board_record_storage=board_record_storage,
+                image_record_storage=image_record_storage,
+                url=urls,
+                logger=logger,
+            )
+        )
+
+        board_images = BoardImagesService(
+            services=BoardImagesServiceDependencies(
+                board_image_record_storage=board_image_record_storage,
+                board_record_storage=board_record_storage,
+                image_record_storage=image_record_storage,
+                url=urls,
+                logger=logger,
+            )
+        )
+
+        images = ImageService(
+            services=ImageServiceDependencies(
+                board_image_record_storage=board_image_record_storage,
+                image_record_storage=image_record_storage,
+                image_file_storage=image_file_storage,
+                url=urls,
+                logger=logger,
+                names=names,
+                graph_execution_manager=graph_execution_manager,
+            )
+        )
 
         services = InvocationServices(
-            board_image_records=board_image_records,
-            board_images=board_images,
-            board_records=board_records,
-            boards=boards,
-            configuration=configuration,
+            model_manager=ModelManagerService(config, logger),
             events=events,
-            graph_execution_manager=graph_execution_manager,
-            graph_library=graph_library,
-            image_files=image_files,
-            image_records=image_records,
-            images=images,
-            invocation_cache=invocation_cache,
             latents=latents,
+            images=images,
+            boards=boards,
+            board_images=board_images,
+            queue=MemoryInvocationQueue(),
+            graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, lock=lock, table_name="graphs"),
+            graph_execution_manager=graph_execution_manager,
+            processor=DefaultInvocationProcessor(),
+            configuration=config,
+            performance_statistics=InvocationStatsService(graph_execution_manager),
             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,
+            session_queue=SqliteSessionQueue(conn=db_conn, lock=lock),
+            session_processor=DefaultSessionProcessor(),
+            invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
 
-        db.clean()
+        try:
+            lock.acquire()
+            db_conn.execute("VACUUM;")
+            db_conn.commit()
+            logger.info("Cleaned database")
+        finally:
+            lock.release()
 
     @staticmethod
     def shutdown():

invokeai/app/api/events.py

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

invokeai/app/api/routers/app_info.py

@@ -7,7 +7,6 @@ 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
@@ -104,43 +103,3 @@ 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_records.board_records_common import BoardChanges
-from invokeai.app.services.boards.boards_common import BoardDTO
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+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 ..dependencies import ApiDependencies
 

invokeai/app/api/routers/images.py

@@ -8,9 +8,9 @@ from PIL import Image
 from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.metadata import ImageMetadata
-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 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 ..dependencies import ApiDependencies
 
@@ -322,20 +322,3 @@ 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

@@ -146,8 +146,7 @@ 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 checkpoints and rare SDv1 checkpoints",
-        default=None,
+        description="Prediction type for SDv2 checkpoint files", default="v_prediction"
     ),
 ) -> ImportModelResponse:
     """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""

invokeai/app/api/routers/session_queue.py

@@ -18,9 +18,9 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemDTO,
     SessionQueueStatus,
 )
-from invokeai.app.services.shared.graph import Graph
-from invokeai.app.services.shared.pagination import CursorPaginatedResults
+from invokeai.app.services.shared.models import CursorPaginatedResults
 
+from ...services.graph import Graph
 from ..dependencies import ApiDependencies
 
 session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])
@@ -84,7 +84,7 @@ async def list_queue_items(
     """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
+        queue_id=queue_id, limit=limit, status=status, order_id=cursor, priority=priority
     )
 
 
@@ -225,7 +225,7 @@ async def get_batch_status(
 )
 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"),
+    item_id: str = Path(description="The queue item to get"),
 ) -> SessionQueueItem:
     """Gets a queue item"""
     return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
@@ -240,7 +240,7 @@ async def get_queue_item(
 )
 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"),
+    item_id: str = Path(description="The queue item to cancel"),
 ) -> SessionQueueItem:
     """Deletes a queue item"""
 

invokeai/app/api/routers/sessions.py

@@ -6,12 +6,11 @@ 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.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
+from ...services.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
+from ...services.item_storage import PaginatedResults
 from ..dependencies import ApiDependencies
 
 session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])

invokeai/app/api/sockets.py

@@ -3,19 +3,16 @@
 from fastapi import FastAPI
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.typing import Event
-from socketio import ASGIApp, AsyncServer
+from fastapi_socketio import SocketManager
 
-from ..services.events.events_base import EventServiceBase
+from ..services.events import EventServiceBase
 
 
 class SocketIO:
-    __sio: AsyncServer
-    __app: ASGIApp
+    __sio: SocketManager
 
     def __init__(self, app: FastAPI):
-        self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
-        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
-        app.mount("/ws", self.__app)
+        self.__sio = SocketManager(app=app)
 
         self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)
         self.__sio.on("unsubscribe_queue", handler=self._handle_unsub_queue)

invokeai/app/api_app.py

@@ -8,6 +8,7 @@ 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
@@ -40,9 +41,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
 
 
-app_config = InvokeAIAppConfig.get_config()
-app_config.parse_args()
-logger = InvokeAILogger.get_logger(config=app_config)
+logger = InvokeAILogger.getLogger(config=app_config)
 
 # fix for windows mimetypes registry entries being borked
 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
@@ -224,7 +223,7 @@ def invoke_api():
                 exc_info=e,
             )
         else:
-            jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+            jurigged.watch(logger=InvokeAILogger.getLogger(name="jurigged").info)
 
     port = find_port(app_config.port)
     if port != app_config.port:
@@ -243,7 +242,7 @@ def invoke_api():
 
     # replace uvicorn's loggers with InvokeAI's for consistent appearance
     for logname in ["uvicorn.access", "uvicorn"]:
-        log = InvokeAILogger.get_logger(logname)
+        log = logging.getLogger(logname)
         log.handlers.clear()
         for ch in logger.handlers:
             log.addHandler(ch)

invokeai/app/cli_app.py

@@ -7,6 +7,8 @@ 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.
+config = InvokeAIAppConfig.get_config()
+config.parse_args()
 
 if True:  # hack to make flake8 happy with imports coming after setting up the config
     import argparse
@@ -59,9 +61,8 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     if torch.backends.mps.is_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
 
-config = InvokeAIAppConfig.get_config()
-config.parse_args()
-logger = InvokeAILogger().get_logger(config=config)
+
+logger = InvokeAILogger().getLogger(config=config)
 
 
 class CliCommand(BaseModel):

invokeai/app/invocations/baseinvocation.py

@@ -28,7 +28,7 @@ from pydantic import BaseModel, Field, validator
 from pydantic.fields import ModelField, Undefined
 from pydantic.typing import NoArgAnyCallable
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig
 
 if TYPE_CHECKING:
     from ..services.invocation_services import InvocationServices
@@ -67,8 +67,6 @@ class FieldDescriptions:
     width = "Width of output (px)"
     height = "Height of output (px)"
     control = "ControlNet(s) to apply"
-    ip_adapter = "IP-Adapter to apply"
-    t2i_adapter = "T2I-Adapter(s) to apply"
     denoised_latents = "Denoised latents tensor"
     latents = "Latents tensor"
     strength = "Strength of denoising (proportional to steps)"
@@ -89,12 +87,6 @@ class FieldDescriptions:
     num_1 = "The first number"
     num_2 = "The second number"
     mask = "The mask to use for the operation"
-    board = "The board to save the image to"
-    image = "The image to process"
-    tile_size = "Tile size"
-    inclusive_low = "The inclusive low value"
-    exclusive_high = "The exclusive high value"
-    decimal_places = "The number of decimal places to round to"
 
 
 class Input(str, Enum):
@@ -163,7 +155,6 @@ class UIType(str, Enum):
     VaeModel = "VaeModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
-    IPAdapterModel = "IPAdapterModelField"
     UNet = "UNetField"
     Vae = "VaeField"
     CLIP = "ClipField"
@@ -180,7 +171,6 @@ class UIType(str, Enum):
     WorkflowField = "WorkflowField"
     IsIntermediate = "IsIntermediate"
     MetadataField = "MetadataField"
-    BoardField = "BoardField"
     # endregion
 
 
@@ -432,22 +422,13 @@ class InvocationContext:
     services: InvocationServices
     graph_execution_state_id: str
     queue_id: str
-    queue_item_id: int
-    queue_batch_id: str
+    queue_item_id: str
 
-    def __init__(
-        self,
-        services: InvocationServices,
-        queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
-        graph_execution_state_id: str,
-    ):
+    def __init__(self, services: InvocationServices, queue_id: str, queue_item_id: str, graph_execution_state_id: str):
         self.services = services
         self.graph_execution_state_id = graph_execution_state_id
         self.queue_id = queue_id
         self.queue_item_id = queue_item_id
-        self.queue_batch_id = queue_batch_id
 
 
 class BaseInvocationOutput(BaseModel):
@@ -608,7 +589,7 @@ class BaseInvocation(ABC, BaseModel):
             if cached_value is None:
                 context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
                 output = self.invoke(context)
-                context.services.invocation_cache.save(key, output)
+                context.services.invocation_cache.save(output)
                 return output
             else:
                 context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
@@ -664,8 +645,6 @@ def invocation(
     :param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
     :param Optional[list[str]] tags: Adds tags to the invocation. Invocations may be searched for by their tags. Defaults to None.
     :param Optional[str] category: Adds a category to the invocation. Used to group the invocations in the UI. Defaults to None.
-    :param Optional[str] version: Adds a version to the invocation. Must be a valid semver string. Defaults to None.
-    :param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
     """
 
     def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]:

invokeai/app/invocations/compel.py

@@ -7,14 +7,14 @@ 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.conditioning_data import (
+from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import (
     BasicConditioningInfo,
-    ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.model_management.models import ModelNotFoundException, ModelType
+from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
 from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
@@ -99,15 +99,14 @@ 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 (
-                tokenizer,
-                ti_manager,
-            ),
-            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
-            text_encoder_info as text_encoder,
-        ):
+        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:
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -123,7 +122,7 @@ class CompelInvocation(BaseInvocation):
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
-            ec = ExtraConditioningInfo(
+            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -214,15 +213,14 @@ 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 (
-                tokenizer,
-                ti_manager,
-            ),
-            ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
-            text_encoder_info as text_encoder,
-        ):
+        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:
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -246,7 +244,7 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
-            ec = ExtraConditioningInfo(
+            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
                 tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
                 cross_attention_control_args=options.get("cross_attention_control", None),
             )
@@ -438,11 +436,9 @@ 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))
-        )
+        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

@@ -27,9 +27,9 @@ from PIL import Image
 from pydantic import BaseModel, 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,
@@ -38,6 +38,7 @@ from .baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -99,7 +100,7 @@ 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"
+        default=1.0, description="The weight given to the ControlNet", ui_type=UIType.Float
     )
     begin_step_percent: float = InputField(
         default=0, ge=-1, le=2, description="When the ControlNet is first applied (% of total steps)"
@@ -559,33 +560,3 @@ 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")
-        image = np.array(image, dtype=np.uint8)
-        height, width = image.shape[:2]
-
-        width_tile_size = min(self.color_map_tile_size, width)
-        height_tile_size = min(self.color_map_tile_size, height)
-
-        color_map = cv2.resize(
-            image,
-            (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

@@ -6,7 +6,7 @@ import numpy
 from PIL import Image, ImageOps
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 

invokeai/app/invocations/facetools.py

@@ -1,692 +0,0 @@
-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 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
-
-
-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 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 face in 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
-                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,
-    check_bounds: 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))
-
-            left_side = x_center - mesh_width
-            right_side = x_center + mesh_width
-            top_side = y_center - mesh_height
-            bottom_side = y_center + mesh_height
-            im_width, im_height = pil_image.size
-            over_w = im_width * 0.1
-            over_h = im_height * 0.1
-            if not check_bounds or (
-                (left_side >= -over_w)
-                and (right_side < im_width + over_w)
-                and (top_side >= -over_h)
-                and (bottom_side < im_height + over_h)
-            ):
-                x_center = float(x_center)
-                y_center = float(y_center)
-                face = FaceResultData(
-                    image=pil_image,
-                    mask=mask_pil or create_white_image(*pil_image.size),
-                    x_center=x_center + chunk_x_offset,
-                    y_center=y_center + chunk_y_offset,
-                    mesh_width=mesh_width,
-                    mesh_height=mesh_height,
-                )
-
-                result.append(face)
-            else:
-                context.services.logger.info("FaceTools --> Face out of bounds, ignoring.")
-
-    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,
-            check_bounds=False,
-        )
-    if should_chunk or len(result) == 0:
-        context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
-        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)
-            while fx <= (width - height):
-                x = int(fx)
-                image_chunks.append(image.crop((x, 0, x + height - 1, height - 1)))
-                x_offsets.append(x)
-                y_offsets.append(0)
-                fx += (width - height) / steps
-                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)
-            while fy <= (height - width):
-                y = int(fy)
-                image_chunks.append(image.crop((0, y, width - 1, y + width - 1)))
-                x_offsets.append(0)
-                y_offsets.append(y)
-                fy += (height - width) / steps
-                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="1.0.1")
-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="1.0.1")
-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")
-
-    @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="1.0.1"
-)
-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 BoardField, ColorField, ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
+from ..models.image import ImageCategory, ResourceOrigin
+from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -972,14 +972,13 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.0.1",
+    version="1.0.0",
     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)
+    image: ImageField = InputField(description="The image to load")
     metadata: CoreMetadata = InputField(
         default=None,
         description=FieldDescriptions.core_metadata,
@@ -993,7 +992,6 @@ class SaveImageInvocation(BaseInvocation):
             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,

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", version="1.0.0")
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
 class CV2InfillInvocation(BaseInvocation):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 

invokeai/app/invocations/ip_adapter.py

@@ -1,103 +0,0 @@
-import os
-from builtins import float
-from typing import List, Union
-
-from pydantic import BaseModel, 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")
-
-
-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')")
-
-
-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

@@ -1,16 +1,13 @@
 # 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,
@@ -22,7 +19,6 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import 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,
@@ -33,28 +29,24 @@ 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.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,
@@ -76,6 +68,7 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
+
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
@@ -198,7 +191,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.3.0",
+    version="1.0.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -212,7 +205,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3)
     steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
     cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
+        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, ui_type=UIType.Float, title="CFG Scale"
     )
     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)
@@ -222,18 +215,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
     unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
     control: Union[ControlField, list[ControlField]] = InputField(
         default=None,
+        description=FieldDescriptions.control,
         input=Input.Connection,
         ui_order=5,
     )
-    ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
-        description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
-    )
-    t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField(
-        description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7
-    )
     latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8
+        default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=6
     )
 
     @validator("cfg_scale")
@@ -335,6 +323,8 @@ 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,
@@ -354,205 +344,57 @@ class DenoiseLatentsInvocation(BaseInvocation):
         else:
             control_list = None
         if control_list is None:
-            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?
-        controlnet_data = []
-        for control_info in control_list:
-            control_model = exit_stack.enter_context(
-                context.services.model_manager.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_image_field = control_info.image
-            input_image = context.services.images.get_pil_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?
-            #        and do real check for classifier_free_guidance?
-            # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
-            control_image = prepare_control_image(
-                image=input_image,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                width=control_width_resize,
-                height=control_height_resize,
-                # batch_size=batch_size * num_images_per_prompt,
-                # num_images_per_prompt=num_images_per_prompt,
-                device=control_model.device,
-                dtype=control_model.dtype,
-                control_mode=control_info.control_mode,
-                resize_mode=control_info.resize_mode,
-            )
-            control_item = ControlNetData(
-                model=control_model,  # model object
-                image_tensor=control_image,
-                weight=control_info.control_weight,
-                begin_step_percent=control_info.begin_step_percent,
-                end_step_percent=control_info.end_step_percent,
-                control_mode=control_info.control_mode,
-                # any resizing needed should currently be happening in prepare_control_image(),
-                #    but adding resize_mode to ControlNetData in case needed in the future
-                resize_mode=control_info.resize_mode,
-            )
-            controlnet_data.append(control_item)
-            # MultiControlNetModel has been refactored out, just need list[ControlNetData]
-
-        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.services.model_manager.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,
-                    context=context,
-                )
-            )
-
-            image_encoder_model_info = context.services.model_manager.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,
-                context=context,
-            )
-
-            input_image = context.services.images.get_pil_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)
+            control_data = None
+            # from above handling, any control that is not None should now be of type list[ControlField]
+        else:
+            # 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 = []
+            for control_info in control_list:
+                control_model = exit_stack.enter_context(
+                    context.services.model_manager.get_model(
+                        model_name=control_info.control_model.model_name,
+                        model_type=ModelType.ControlNet,
+                        base_model=control_info.control_model.base_model,
+                        context=context,
+                    )
                 )
 
-            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,
+                control_models.append(control_model)
+                control_image_field = control_info.image
+                input_image = context.services.images.get_pil_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?
+                #        and do real check for classifier_free_guidance?
+                # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
+                control_image = prepare_control_image(
+                    image=input_image,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    width=control_width_resize,
+                    height=control_height_resize,
+                    # batch_size=batch_size * num_images_per_prompt,
+                    # num_images_per_prompt=num_images_per_prompt,
+                    device=control_model.device,
+                    dtype=control_model.dtype,
+                    control_mode=control_info.control_mode,
+                    resize_mode=control_info.resize_mode,
                 )
-            )
-
-        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.services.model_manager.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,
-                context=context,
-            )
-            image = context.services.images.get_pil_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}'."
+                control_item = ControlNetData(
+                    model=control_model,
+                    image_tensor=control_image,
+                    weight=control_info.control_weight,
+                    begin_step_percent=control_info.begin_step_percent,
+                    end_step_percent=control_info.end_step_percent,
+                    control_mode=control_info.control_mode,
+                    # any resizing needed should currently be happening in prepare_control_image(),
+                    #    but adding resize_mode to ControlNetData in case needed in the future
+                    resize_mode=control_info.resize_mode,
                 )
-
-            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
+                control_data.append(control_item)
+                # MultiControlNetModel has been refactored out, just need list[ControlNetData]
+        return control_data
 
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
@@ -625,12 +467,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             mask, masked_latents = self.prep_inpaint_mask(context, latents)
 
-            # 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
-            )
-
             # 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]
@@ -652,12 +488,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 **self.unet.unet.dict(),
                 context=context,
             )
-            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)
@@ -676,7 +509,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 pipeline = self.create_pipeline(unet, scheduler)
                 conditioning_data = self.get_conditioning_data(context, scheduler, unet, seed)
 
-                controlnet_data = self.prep_control_data(
+                control_data = self.prep_control_data(
+                    model=pipeline,
                     context=context,
                     control_input=self.control,
                     latents_shape=latents.shape,
@@ -685,13 +519,6 @@ 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,
@@ -710,9 +537,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     masked_latents=masked_latents,
                     num_inference_steps=num_inference_steps,
                     conditioning_data=conditioning_data,
-                    control_data=controlnet_data,
-                    ip_adapter_data=ip_adapter_data,
-                    t2i_adapter_data=t2i_adapter_data,
+                    control_data=control_data,  # list[ControlNetData]
                     callback=step_callback,
                 )
 
@@ -967,7 +792,8 @@ class ImageToLatentsInvocation(BaseInvocation):
             # non_noised_latents_from_image
             image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
             with torch.inference_mode():
-                latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
+                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 = vae.config.scaling_factor * latents
             latents = latents.to(dtype=orig_dtype)
@@ -994,18 +820,6 @@ 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
-
-    @_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):

invokeai/app/invocations/math.py

@@ -65,27 +65,13 @@ class DivideInvocation(BaseInvocation):
 class RandomIntInvocation(BaseInvocation):
     """Outputs a single random integer."""
 
-    low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
-    high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
+    low: int = InputField(default=0, description="The inclusive low value")
+    high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
 
     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="1.0.0")
-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",

invokeai/app/invocations/metadata.py

@@ -12,10 +12,7 @@ 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__
@@ -28,18 +25,6 @@ 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."""
 
@@ -57,14 +42,11 @@ class CoreMetadata(BaseModelExcludeNull):
     cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
     steps: int = Field(description="The number of steps used for inference")
     scheduler: str = Field(description="The scheduler used for inference")
-    clip_skip: Optional[int] = Field(
-        default=None,
+    clip_skip: int = Field(
         description="The number of skipped CLIP layers",
     )
     model: MainModelField = Field(description="The main model used for inference")
     controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
-    ipAdapters: list[IPAdapterMetadataField] = Field(description="The IP Adapters used for inference")
-    t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference")
     loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
     vae: Optional[VAEModelField] = Field(
         default=None,
@@ -134,14 +116,11 @@ class MetadataAccumulatorInvocation(BaseInvocation):
     cfg_scale: float = InputField(description="The classifier-free guidance scale parameter")
     steps: int = InputField(description="The number of steps used for inference")
     scheduler: str = InputField(description="The scheduler used for inference")
-    clip_skip: Optional[int] = Field(
-        default=None,
+    clip_skip: int = InputField(
         description="The number of skipped CLIP layers",
     )
     model: MainModelField = InputField(description="The main model used for inference")
     controlnets: list[ControlField] = InputField(description="The ControlNets used for inference")
-    ipAdapters: list[IPAdapterMetadataField] = InputField(description="The IP Adapters used for inference")
-    t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference")
     loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference")
     strength: Optional[float] = InputField(
         default=None,

invokeai/app/invocations/onnx.py

@@ -14,13 +14,13 @@ 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,
@@ -95,10 +95,9 @@ 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
@@ -166,6 +165,7 @@ 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
@@ -178,6 +178,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     control: Optional[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'")

invokeai/app/invocations/primitives.py

@@ -226,12 +226,6 @@ 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/t2i_adapter.py

@@ -1,83 +0,0 @@
-from typing import Union
-
-from pydantic import BaseModel, 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")
-
-
-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

@@ -4,14 +4,12 @@ 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 realesrgan import RealESRGANer
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
@@ -24,19 +22,13 @@ 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="1.1.0")
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
 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)"
-    )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get_pil_image(self.image.image_name)
@@ -94,11 +86,9 @@ 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
@@ -109,10 +99,6 @@ 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

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

invokeai/app/models/image.py

@@ -0,0 +1,71 @@
+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_record_storage.py

@@ -1,12 +1,55 @@
 import sqlite3
 import threading
+from abc import ABC, abstractmethod
 from typing import Optional, cast
 
-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
+from invokeai.app.services.image_record_storage import OffsetPaginatedResults
+from invokeai.app.services.models.image_record import ImageRecord, deserialize_image_record
 
-from .board_image_records_base import BoardImageRecordStorageBase
+
+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
 
 
 class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
@@ -14,11 +57,13 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
     _cursor: sqlite3.Cursor
     _lock: threading.Lock
 
-    def __init__(self, db: SqliteDatabase) -> None:
+    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
         super().__init__()
-        self._lock = db.lock
-        self._conn = db.conn
+        self._conn = conn
+        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
+        self._conn.row_factory = sqlite3.Row
         self._cursor = self._conn.cursor()
+        self._lock = lock
 
         try:
             self._lock.acquire()

invokeai/app/services/board_image_records/board_image_records_base.py

@@ -1,47 +0,0 @@
-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_images.py

@@ -0,0 +1,112 @@
+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

@@ -1,39 +0,0 @@
-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

@@ -1,38 +0,0 @@
-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_record_storage.py

@@ -1,20 +1,89 @@
 import sqlite3
 import threading
-from typing import Union, cast
+from abc import ABC, abstractmethod
+from typing import Optional, Union, cast
 
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from pydantic import BaseModel, Extra, Field
+
+from invokeai.app.services.image_record_storage import OffsetPaginatedResults
+from invokeai.app.services.models.board_record import BoardRecord, deserialize_board_record
 from invokeai.app.util.misc import uuid_string
 
-from .board_records_base import BoardRecordStorageBase
-from .board_records_common import (
-    BoardChanges,
-    BoardRecord,
-    BoardRecordDeleteException,
-    BoardRecordNotFoundException,
-    BoardRecordSaveException,
-    deserialize_board_record,
-)
+
+class BoardChanges(BaseModel, extra=Extra.forbid):
+    board_name: Optional[str] = Field(description="The board's new name.")
+    cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
+
+
+class BoardRecordNotFoundException(Exception):
+    """Raised when an board record is not found."""
+
+    def __init__(self, message="Board record not found"):
+        super().__init__(message)
+
+
+class BoardRecordSaveException(Exception):
+    """Raised when an board record cannot be saved."""
+
+    def __init__(self, message="Board record not saved"):
+        super().__init__(message)
+
+
+class BoardRecordDeleteException(Exception):
+    """Raised when an board record cannot be deleted."""
+
+    def __init__(self, message="Board record not deleted"):
+        super().__init__(message)
+
+
+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
 
 
 class SqliteBoardRecordStorage(BoardRecordStorageBase):
@@ -22,11 +91,13 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
     _cursor: sqlite3.Cursor
     _lock: threading.Lock
 
-    def __init__(self, db: SqliteDatabase) -> None:
+    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
         super().__init__()
-        self._lock = db.lock
-        self._conn = db.conn
+        self._conn = conn
+        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
+        self._conn.row_factory = sqlite3.Row
         self._cursor = self._conn.cursor()
+        self._lock = lock
 
         try:
             self._lock.acquire()

invokeai/app/services/board_records/board_records_base.py

@@ -1,55 +0,0 @@
-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

invokeai/app/services/boards.py

@@ -0,0 +1,158 @@
+from abc import ABC, abstractmethod
+from logging import Logger
+
+from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
+from invokeai.app.services.board_images import board_record_to_dto
+from invokeai.app.services.board_record_storage import BoardChanges, BoardRecordStorageBase
+from invokeai.app.services.image_record_storage import ImageRecordStorageBase, OffsetPaginatedResults
+from invokeai.app.services.models.board_record import BoardDTO
+from invokeai.app.services.urls import UrlServiceBase
+
+
+class BoardServiceABC(ABC):
+    """High-level service for board management."""
+
+    @abstractmethod
+    def create(
+        self,
+        board_name: str,
+    ) -> BoardDTO:
+        """Creates a board."""
+        pass
+
+    @abstractmethod
+    def get_dto(
+        self,
+        board_id: str,
+    ) -> BoardDTO:
+        """Gets a board."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        board_id: str,
+        changes: BoardChanges,
+    ) -> BoardDTO:
+        """Updates a board."""
+        pass
+
+    @abstractmethod
+    def delete(
+        self,
+        board_id: str,
+    ) -> None:
+        """Deletes a board."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+    ) -> OffsetPaginatedResults[BoardDTO]:
+        """Gets many boards."""
+        pass
+
+    @abstractmethod
+    def get_all(
+        self,
+    ) -> list[BoardDTO]:
+        """Gets all boards."""
+        pass
+
+
+class BoardServiceDependencies:
+    """Service dependencies for the BoardService."""
+
+    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 BoardService(BoardServiceABC):
+    _services: BoardServiceDependencies
+
+    def __init__(self, services: BoardServiceDependencies):
+        self._services = services
+
+    def create(
+        self,
+        board_name: str,
+    ) -> BoardDTO:
+        board_record = self._services.board_records.save(board_name)
+        return board_record_to_dto(board_record, None, 0)
+
+    def get_dto(self, board_id: str) -> BoardDTO:
+        board_record = self._services.board_records.get(board_id)
+        cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
+        if cover_image:
+            cover_image_name = cover_image.image_name
+        else:
+            cover_image_name = None
+        image_count = self._services.board_image_records.get_image_count_for_board(board_id)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
+
+    def update(
+        self,
+        board_id: str,
+        changes: BoardChanges,
+    ) -> BoardDTO:
+        board_record = self._services.board_records.update(board_id, changes)
+        cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
+        if cover_image:
+            cover_image_name = cover_image.image_name
+        else:
+            cover_image_name = None
+
+        image_count = self._services.board_image_records.get_image_count_for_board(board_id)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
+
+    def delete(self, board_id: str) -> None:
+        self._services.board_records.delete(board_id)
+
+    def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
+        board_records = self._services.board_records.get_many(offset, limit)
+        board_dtos = []
+        for r in board_records.items:
+            cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
+            if cover_image:
+                cover_image_name = cover_image.image_name
+            else:
+                cover_image_name = None
+
+            image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
+
+        return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
+
+    def get_all(self) -> list[BoardDTO]:
+        board_records = self._services.board_records.get_all()
+        board_dtos = []
+        for r in board_records:
+            cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
+            if cover_image:
+                cover_image_name = cover_image.image_name
+            else:
+                cover_image_name = None
+
+            image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
+
+        return board_dtos

invokeai/app/services/boards/boards_base.py

@@ -1,59 +0,0 @@
-from abc import ABC, abstractmethod
-
-from invokeai.app.services.board_records.board_records_common import BoardChanges
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-
-from .boards_common import BoardDTO
-
-
-class BoardServiceABC(ABC):
-    """High-level service for board management."""
-
-    @abstractmethod
-    def create(
-        self,
-        board_name: str,
-    ) -> BoardDTO:
-        """Creates a board."""
-        pass
-
-    @abstractmethod
-    def get_dto(
-        self,
-        board_id: str,
-    ) -> BoardDTO:
-        """Gets a board."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        board_id: str,
-        changes: BoardChanges,
-    ) -> BoardDTO:
-        """Updates a board."""
-        pass
-
-    @abstractmethod
-    def delete(
-        self,
-        board_id: str,
-    ) -> None:
-        """Deletes a board."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-    ) -> OffsetPaginatedResults[BoardDTO]:
-        """Gets many boards."""
-        pass
-
-    @abstractmethod
-    def get_all(
-        self,
-    ) -> list[BoardDTO]:
-        """Gets all boards."""
-        pass

invokeai/app/services/boards/boards_common.py

@@ -1,23 +0,0 @@
-from typing import Optional
-
-from pydantic import Field
-
-from ..board_records.board_records_common import BoardRecord
-
-
-class BoardDTO(BoardRecord):
-    """Deserialized board record with cover image URL and image count."""
-
-    cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
-    """The URL of the thumbnail of the most recent image in the board."""
-    image_count: int = Field(description="The number of images in the board.")
-    """The number of images in the board."""
-
-
-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/boards/boards_default.py

@@ -1,79 +0,0 @@
-from invokeai.app.services.board_records.board_records_common import BoardChanges
-from invokeai.app.services.boards.boards_common import BoardDTO
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-
-from .boards_base import BoardServiceABC
-from .boards_common import board_record_to_dto
-
-
-class BoardService(BoardServiceABC):
-    __invoker: Invoker
-
-    def start(self, invoker: Invoker) -> None:
-        self.__invoker = invoker
-
-    def create(
-        self,
-        board_name: str,
-    ) -> BoardDTO:
-        board_record = self.__invoker.services.board_records.save(board_name)
-        return board_record_to_dto(board_record, None, 0)
-
-    def get_dto(self, board_id: str) -> BoardDTO:
-        board_record = self.__invoker.services.board_records.get(board_id)
-        cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
-        if cover_image:
-            cover_image_name = cover_image.image_name
-        else:
-            cover_image_name = None
-        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
-        return board_record_to_dto(board_record, cover_image_name, image_count)
-
-    def update(
-        self,
-        board_id: str,
-        changes: BoardChanges,
-    ) -> BoardDTO:
-        board_record = self.__invoker.services.board_records.update(board_id, changes)
-        cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
-        if cover_image:
-            cover_image_name = cover_image.image_name
-        else:
-            cover_image_name = None
-
-        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
-        return board_record_to_dto(board_record, cover_image_name, image_count)
-
-    def delete(self, board_id: str) -> None:
-        self.__invoker.services.board_records.delete(board_id)
-
-    def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_many(offset, limit)
-        board_dtos = []
-        for r in board_records.items:
-            cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
-            if cover_image:
-                cover_image_name = cover_image.image_name
-            else:
-                cover_image_name = None
-
-            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
-
-        return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
-
-    def get_all(self) -> list[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_all()
-        board_dtos = []
-        for r in board_records:
-            cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
-            if cover_image:
-                cover_image_name = cover_image.image_name
-            else:
-                cover_image_name = None
-
-            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
-
-        return board_dtos

invokeai/app/services/config/__init__.py

@@ -2,5 +2,5 @@
 Init file for InvokeAI configure package
 """
 
-from .config_base import PagingArgumentParser  # noqa F401
-from .config_default import InvokeAIAppConfig, get_invokeai_config  # noqa F401
+from .base import PagingArgumentParser  # noqa F401
+from .invokeai_config import InvokeAIAppConfig, get_invokeai_config  # noqa F401

invokeai/app/services/config/base.py

@@ -12,6 +12,7 @@ from __future__ import annotations
 
 import argparse
 import os
+import pydoc
 import sys
 from argparse import ArgumentParser
 from pathlib import Path
@@ -20,7 +21,16 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from pydantic import BaseSettings
 
-from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
+
+class PagingArgumentParser(argparse.ArgumentParser):
+    """
+    A custom ArgumentParser that uses pydoc to page its output.
+    It also supports reading defaults from an init file.
+    """
+
+    def print_help(self, file=None):
+        text = self.format_help()
+        pydoc.pager(text)
 
 
 class InvokeAISettings(BaseSettings):
@@ -213,3 +223,18 @@ class InvokeAISettings(BaseSettings):
                 action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
                 help=field.field_info.description,
             )
+
+
+def int_or_float_or_str(value: str) -> Union[int, float, str]:
+    """
+    Workaround for argparse type checking.
+    """
+    try:
+        return int(value)
+    except Exception as e:  # noqa F841
+        pass
+    try:
+        return float(value)
+    except Exception as e:  # noqa F841
+        pass
+    return str(value)

invokeai/app/services/config/config_common.py

@@ -1,41 +0,0 @@
-# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
-
-"""
-Base class for the InvokeAI configuration system.
-It defines a type of pydantic BaseSettings object that
-is able to read and write from an omegaconf-based config file,
-with overriding of settings from environment variables and/or
-the command line.
-"""
-
-from __future__ import annotations
-
-import argparse
-import pydoc
-from typing import Union
-
-
-class PagingArgumentParser(argparse.ArgumentParser):
-    """
-    A custom ArgumentParser that uses pydoc to page its output.
-    It also supports reading defaults from an init file.
-    """
-
-    def print_help(self, file=None):
-        text = self.format_help()
-        pydoc.pager(text)
-
-
-def int_or_float_or_str(value: str) -> Union[int, float, str]:
-    """
-    Workaround for argparse type checking.
-    """
-    try:
-        return int(value)
-    except Exception as e:  # noqa F841
-        pass
-    try:
-        return float(value)
-    except Exception as e:  # noqa F841
-        pass
-    return str(value)

invokeai/app/services/config/invokeai_config.py

@@ -177,7 +177,7 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hint
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, parse_obj_as
 
-from .config_base import InvokeAISettings
+from .base import InvokeAISettings
 
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
@@ -241,8 +241,8 @@ class InvokeAIAppConfig(InvokeAISettings):
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
 
     # CACHE
-    ram                 : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", category="Model Cache", )
-    vram                : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", category="Model Cache", )
+    ram                 : Union[float, Literal["auto"]] = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Model Cache", )
+    vram                : Union[float, Literal["auto"]] = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category="Model Cache", )
     lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
 
     # DEVICE
@@ -255,7 +255,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
     force_tiled_decode  : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
     force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
-    png_compress_level  : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", category="Generation", )
 
     # QUEUE
     max_queue_size      : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )
@@ -278,7 +277,6 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     class Config:
         validate_assignment = True
-        env_prefix = "INVOKEAI"
 
     def parse_args(self, argv: Optional[list[str]] = None, conf: Optional[DictConfig] = None, clobber=False):
         """

invokeai/app/services/default_graphs.py

@@ -1,11 +1,10 @@
-from invokeai.app.services.item_storage.item_storage_base import ItemStorageABC
-
-from ...invocations.compel import CompelInvocation
-from ...invocations.image import ImageNSFWBlurInvocation
-from ...invocations.latent import DenoiseLatentsInvocation, LatentsToImageInvocation
-from ...invocations.noise import NoiseInvocation
-from ...invocations.primitives import IntegerInvocation
+from ..invocations.compel import CompelInvocation
+from ..invocations.image import ImageNSFWBlurInvocation
+from ..invocations.latent import DenoiseLatentsInvocation, LatentsToImageInvocation
+from ..invocations.noise import NoiseInvocation
+from ..invocations.primitives import IntegerInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
+from .item_storage import ItemStorageABC
 
 default_text_to_image_graph_id = "539b2af5-2b4d-4d8c-8071-e54a3255fc74"
 

invokeai/app/services/events.py

@@ -2,16 +2,10 @@
 
 from typing import Any, Optional
 
-from invokeai.app.invocations.model import ModelInfo
-from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
-from invokeai.app.services.session_queue.session_queue_common import (
-    BatchStatus,
-    EnqueueBatchResult,
-    SessionQueueItem,
-    SessionQueueStatus,
-)
+from invokeai.app.models.image import ProgressImage
+from invokeai.app.services.model_manager_service import BaseModelType, ModelInfo, ModelType, SubModelType
+from invokeai.app.services.session_queue.session_queue_common import EnqueueBatchResult, SessionQueueItem
 from invokeai.app.util.misc import get_timestamp
-from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
 
 
 class EventServiceBase:
@@ -35,8 +29,7 @@ class EventServiceBase:
     def emit_generator_progress(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         node: dict,
         source_node_id: str,
@@ -51,7 +44,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 node_id=node.get("id"),
                 source_node_id=source_node_id,
@@ -65,8 +57,7 @@ class EventServiceBase:
     def emit_invocation_complete(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         result: dict,
         node: dict,
@@ -78,7 +69,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 node=node,
                 source_node_id=source_node_id,
@@ -89,8 +79,7 @@ class EventServiceBase:
     def emit_invocation_error(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         node: dict,
         source_node_id: str,
@@ -103,7 +92,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 node=node,
                 source_node_id=source_node_id,
@@ -113,13 +101,7 @@ class EventServiceBase:
         )
 
     def emit_invocation_started(
-        self,
-        queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
-        graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
+        self, queue_id: str, queue_item_id: str, graph_execution_state_id: str, node: dict, source_node_id: str
     ) -> None:
         """Emitted when an invocation has started"""
         self.__emit_queue_event(
@@ -127,23 +109,19 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 node=node,
                 source_node_id=source_node_id,
             ),
         )
 
-    def emit_graph_execution_complete(
-        self, queue_id: str, queue_item_id: int, queue_batch_id: str, graph_execution_state_id: str
-    ) -> None:
+    def emit_graph_execution_complete(self, queue_id: str, queue_item_id: str, graph_execution_state_id: str) -> None:
         """Emitted when a session has completed all invocations"""
         self.__emit_queue_event(
             event_name="graph_execution_state_complete",
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
             ),
         )
@@ -151,8 +129,7 @@ class EventServiceBase:
     def emit_model_load_started(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         model_name: str,
         base_model: BaseModelType,
@@ -165,7 +142,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 model_name=model_name,
                 base_model=base_model,
@@ -177,8 +153,7 @@ class EventServiceBase:
     def emit_model_load_completed(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         model_name: str,
         base_model: BaseModelType,
@@ -192,7 +167,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 model_name=model_name,
                 base_model=base_model,
@@ -207,8 +181,7 @@ class EventServiceBase:
     def emit_session_retrieval_error(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         error_type: str,
         error: str,
@@ -219,7 +192,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 error_type=error_type,
                 error=error,
@@ -229,8 +201,7 @@ class EventServiceBase:
     def emit_invocation_retrieval_error(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
         node_id: str,
         error_type: str,
@@ -242,7 +213,6 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
                 node_id=node_id,
                 error_type=error_type,
@@ -253,8 +223,7 @@ class EventServiceBase:
     def emit_session_canceled(
         self,
         queue_id: str,
-        queue_item_id: int,
-        queue_batch_id: str,
+        queue_item_id: str,
         graph_execution_state_id: str,
     ) -> None:
         """Emitted when a session is canceled"""
@@ -263,36 +232,25 @@ class EventServiceBase:
             payload=dict(
                 queue_id=queue_id,
                 queue_item_id=queue_item_id,
-                queue_batch_id=queue_batch_id,
                 graph_execution_state_id=graph_execution_state_id,
             ),
         )
 
-    def emit_queue_item_status_changed(
-        self,
-        session_queue_item: SessionQueueItem,
-        batch_status: BatchStatus,
-        queue_status: SessionQueueStatus,
-    ) -> None:
+    def emit_queue_item_status_changed(self, session_queue_item: SessionQueueItem) -> None:
         """Emitted when a queue item's status changes"""
         self.__emit_queue_event(
             event_name="queue_item_status_changed",
             payload=dict(
-                queue_id=queue_status.queue_id,
-                queue_item=dict(
-                    queue_id=session_queue_item.queue_id,
-                    item_id=session_queue_item.item_id,
-                    status=session_queue_item.status,
-                    batch_id=session_queue_item.batch_id,
-                    session_id=session_queue_item.session_id,
-                    error=session_queue_item.error,
-                    created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
-                    updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
-                    started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
-                    completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
-                ),
-                batch_status=batch_status.dict(),
-                queue_status=queue_status.dict(),
+                queue_id=session_queue_item.queue_id,
+                queue_item_id=session_queue_item.item_id,
+                status=session_queue_item.status,
+                batch_id=session_queue_item.batch_id,
+                session_id=session_queue_item.session_id,
+                error=session_queue_item.error,
+                created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
+                updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
+                started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
+                completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
             ),
         )
 

invokeai/app/services/graph.py

@@ -2,15 +2,17 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, Union, cast, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, root_validator, validator
 from pydantic.fields import Field
 
+from invokeai.app.util.misc import uuid_string
+
 # Importing * is bad karma but needed here for node detection
-from invokeai.app.invocations import *  # noqa: F401 F403
-from invokeai.app.invocations.baseinvocation import (
+from ..invocations import *  # noqa: F401 F403
+from ..invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     Input,
@@ -21,7 +23,6 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.util.misc import uuid_string
 
 # in 3.10 this would be "from types import NoneType"
 NoneType = type(None)
@@ -116,10 +117,6 @@ def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
         if from_type is int and to_type is float:
             return True
 
-        # allow int|float -> str, pydantic will cast for us
-        if (from_type is int or from_type is float) and to_type is str:
-            return True
-
         # if not issubclass(from_type, to_type):
         if not is_union_subtype(from_type, to_type):
             return False
@@ -169,18 +166,6 @@ class NodeIdMismatchError(ValueError):
     pass
 
 
-class InvalidSubGraphError(ValueError):
-    pass
-
-
-class CyclicalGraphError(ValueError):
-    pass
-
-
-class UnknownGraphValidationError(ValueError):
-    pass
-
-
 # TODO: Create and use an Empty output?
 @invocation_output("graph_output")
 class GraphInvocationOutput(BaseInvocationOutput):
@@ -265,6 +250,59 @@ class Graph(BaseModel):
         default_factory=list,
     )
 
+    @root_validator
+    def validate_nodes_and_edges(cls, values):
+        """Validates that all edges match nodes in the graph"""
+        nodes = cast(Optional[dict[str, BaseInvocation]], values.get("nodes"))
+        edges = cast(Optional[list[Edge]], values.get("edges"))
+
+        if nodes is not None:
+            # Validate that all node ids are unique
+            node_ids = [n.id for n in nodes.values()]
+            duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
+            if duplicate_node_ids:
+                raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
+
+            # Validate that all node ids match the keys in the nodes dict
+            for k, v in nodes.items():
+                if k != v.id:
+                    raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
+
+        if edges is not None and nodes is not None:
+            # Validate that all edges match nodes in the graph
+            node_ids = set([e.source.node_id for e in edges] + [e.destination.node_id for e in edges])
+            missing_node_ids = [node_id for node_id in node_ids if node_id not in nodes]
+            if missing_node_ids:
+                raise NodeNotFoundError(
+                    f"All edges must reference nodes in the graph, missing nodes: {missing_node_ids}"
+                )
+
+            # Validate that all edge fields match node fields in the graph
+            for edge in edges:
+                source_node = nodes.get(edge.source.node_id, None)
+                if source_node is None:
+                    raise NodeFieldNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
+
+                destination_node = nodes.get(edge.destination.node_id, None)
+                if destination_node is None:
+                    raise NodeFieldNotFoundError(
+                        f"Edge destination node {edge.destination.node_id} does not exist in the graph"
+                    )
+
+                # output fields are not on the node object directly, they are on the output type
+                if edge.source.field not in source_node.get_output_type().__fields__:
+                    raise NodeFieldNotFoundError(
+                        f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
+                    )
+
+                # input fields are on the node
+                if edge.destination.field not in destination_node.__fields__:
+                    raise NodeFieldNotFoundError(
+                        f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
+                    )
+
+        return values
+
     def add_node(self, node: BaseInvocation) -> None:
         """Adds a node to a graph
 
@@ -335,108 +373,53 @@ class Graph(BaseModel):
         except KeyError:
             pass
 
-    def validate_self(self) -> None:
-        """
-        Validates the graph.
-
-        Raises an exception if the graph is invalid:
-        - `DuplicateNodeIdError`
-        - `NodeIdMismatchError`
-        - `InvalidSubGraphError`
-        - `NodeNotFoundError`
-        - `NodeFieldNotFoundError`
-        - `CyclicalGraphError`
-        - `InvalidEdgeError`
-        """
-
-        # Validate that all node ids are unique
-        node_ids = [n.id for n in self.nodes.values()]
-        duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
-        if duplicate_node_ids:
-            raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
-
-        # Validate that all node ids match the keys in the nodes dict
-        for k, v in self.nodes.items():
-            if k != v.id:
-                raise NodeIdMismatchError(f"Node ids must match, got {k} and {v.id}")
+    def is_valid(self) -> bool:
+        """Validates the graph."""
 
         # Validate all subgraphs
         for gn in (n for n in self.nodes.values() if isinstance(n, GraphInvocation)):
-            try:
-                gn.graph.validate_self()
-            except Exception as e:
-                raise InvalidSubGraphError(f"Subgraph {gn.id} is invalid") from e
+            if not gn.graph.is_valid():
+                return False
 
-        # Validate that all edges match nodes and fields in the graph
-        for edge in self.edges:
-            source_node = self.nodes.get(edge.source.node_id, None)
-            if source_node is None:
-                raise NodeNotFoundError(f"Edge source node {edge.source.node_id} does not exist in the graph")
-
-            destination_node = self.nodes.get(edge.destination.node_id, None)
-            if destination_node is None:
-                raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
-
-            # output fields are not on the node object directly, they are on the output type
-            if edge.source.field not in source_node.get_output_type().__fields__:
-                raise NodeFieldNotFoundError(
-                    f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
-                )
-
-            # input fields are on the node
-            if edge.destination.field not in destination_node.__fields__:
-                raise NodeFieldNotFoundError(
-                    f"Edge destination field {edge.destination.field} does not exist in node {edge.destination.node_id}"
-                )
+        # Validate all edges reference nodes in the graph
+        node_ids = set([e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges])
+        if not all((self.has_node(node_id) for node_id in node_ids)):
+            return False
 
         # Validate there are no cycles
         g = self.nx_graph_flat()
         if not nx.is_directed_acyclic_graph(g):
-            raise CyclicalGraphError("Graph contains cycles")
+            return False
 
         # Validate all edge connections are valid
-        for e in self.edges:
-            if not are_connections_compatible(
-                self.get_node(e.source.node_id),
-                e.source.field,
-                self.get_node(e.destination.node_id),
-                e.destination.field,
-            ):
-                raise InvalidEdgeError(
-                    f"Invalid edge from {e.source.node_id}.{e.source.field} to {e.destination.node_id}.{e.destination.field}"
+        if not all(
+            (
+                are_connections_compatible(
+                    self.get_node(e.source.node_id),
+                    e.source.field,
+                    self.get_node(e.destination.node_id),
+                    e.destination.field,
                 )
-
-        # Validate all iterators & collectors
-        # TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available
-        for n in self.nodes.values():
-            if isinstance(n, IterateInvocation) and not self._is_iterator_connection_valid(n.id):
-                raise InvalidEdgeError(f"Invalid iterator node {n.id}")
-            if isinstance(n, CollectInvocation) and not self._is_collector_connection_valid(n.id):
-                raise InvalidEdgeError(f"Invalid collector node {n.id}")
-
-        return None
-
-    def is_valid(self) -> bool:
-        """
-        Checks if the graph is valid.
-
-        Raises `UnknownGraphValidationError` if there is a problem validating the graph (not a validation error).
-        """
-        try:
-            self.validate_self()
-            return True
-        except (
-            DuplicateNodeIdError,
-            NodeIdMismatchError,
-            InvalidSubGraphError,
-            NodeNotFoundError,
-            NodeFieldNotFoundError,
-            CyclicalGraphError,
-            InvalidEdgeError,
+                for e in self.edges
+            )
         ):
             return False
-        except Exception as e:
-            raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
+
+        # Validate all iterators
+        # TODO: may need to validate all iterators in subgraphs so edge connections in parent graphs will be available
+        if not all(
+            (self._is_iterator_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, IterateInvocation))
+        ):
+            return False
+
+        # Validate all collectors
+        # TODO: may need to validate all collectors in subgraphs so edge connections in parent graphs will be available
+        if not all(
+            (self._is_collector_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, CollectInvocation))
+        ):
+            return False
+
+        return True
 
     def _validate_edge(self, edge: Edge):
         """Validates that a new edge doesn't create a cycle in the graph"""
@@ -817,12 +800,6 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
-    @validator("graph")
-    def graph_is_valid(cls, v: Graph):
-        """Validates that the graph is valid"""
-        v.validate_self()
-        return v
-
     class Config:
         schema_extra = {
             "required": [

invokeai/app/services/image_file_storage.py

@@ -1,5 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
 import json
+from abc import ABC, abstractmethod
 from pathlib import Path
 from queue import Queue
 from typing import Dict, Optional, Union
@@ -8,11 +9,67 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
-from invokeai.app.services.invoker import Invoker
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
-from .image_files_base import ImageFileStorageBase
-from .image_files_common import ImageFileDeleteException, ImageFileNotFoundException, ImageFileSaveException
+
+# TODO: Should these excpetions subclass existing python exceptions?
+class ImageFileNotFoundException(Exception):
+    """Raised when an image file is not found in storage."""
+
+    def __init__(self, message="Image file not found"):
+        super().__init__(message)
+
+
+class ImageFileSaveException(Exception):
+    """Raised when an image cannot be saved."""
+
+    def __init__(self, message="Image file not saved"):
+        super().__init__(message)
+
+
+class ImageFileDeleteException(Exception):
+    """Raised when an image cannot be deleted."""
+
+    def __init__(self, message="Image file not deleted"):
+        super().__init__(message)
+
+
+class ImageFileStorageBase(ABC):
+    """Low-level service responsible for storing and retrieving image files."""
+
+    @abstractmethod
+    def get(self, image_name: str) -> PILImageType:
+        """Retrieves an image as PIL Image."""
+        pass
+
+    @abstractmethod
+    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
+        """Gets the internal path to an image or thumbnail."""
+        pass
+
+    # TODO: We need to validate paths before starlette makes the FileResponse, else we get a
+    # 500 internal server error. I don't like having this method on the service.
+    @abstractmethod
+    def validate_path(self, path: str) -> bool:
+        """Validates the path given for an image or thumbnail."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image: PILImageType,
+        image_name: str,
+        metadata: Optional[dict] = None,
+        workflow: Optional[str] = None,
+        thumbnail_size: int = 256,
+    ) -> None:
+        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_name: str) -> None:
+        """Deletes an image and its thumbnail (if one exists)."""
+        pass
 
 
 class DiskImageFileStorage(ImageFileStorageBase):
@@ -22,7 +79,6 @@ class DiskImageFileStorage(ImageFileStorageBase):
     __cache_ids: Queue  # TODO: this is an incredibly naive cache
     __cache: Dict[Path, PILImageType]
     __max_cache_size: int
-    __invoker: Invoker
 
     def __init__(self, output_folder: Union[str, Path]):
         self.__cache = dict()
@@ -31,12 +87,10 @@ class DiskImageFileStorage(ImageFileStorageBase):
 
         self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__thumbnails_folder = self.__output_folder / "thumbnails"
+
         # Validate required output folders at launch
         self.__validate_storage_folders()
 
-    def start(self, invoker: Invoker) -> None:
-        self.__invoker = invoker
-
     def get(self, image_name: str) -> PILImageType:
         try:
             image_path = self.get_path(image_name)
@@ -80,12 +134,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
                 if original_workflow is not None:
                     pnginfo.add_text("invokeai_workflow", original_workflow)
 
-            image.save(
-                image_path,
-                "PNG",
-                pnginfo=pnginfo,
-                compress_level=self.__invoker.services.configuration.png_compress_level,
-            )
+            image.save(image_path, "PNG", pnginfo=pnginfo)
 
             thumbnail_name = get_thumbnail_name(image_name)
             thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)

invokeai/app/services/image_files/image_files_base.py

@@ -1,42 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from PIL.Image import Image as PILImageType
-
-
-class ImageFileStorageBase(ABC):
-    """Low-level service responsible for storing and retrieving image files."""
-
-    @abstractmethod
-    def get(self, image_name: str) -> PILImageType:
-        """Retrieves an image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
-        """Gets the internal path to an image or thumbnail."""
-        pass
-
-    # TODO: We need to validate paths before starlette makes the FileResponse, else we get a
-    # 500 internal server error. I don't like having this method on the service.
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates the path given for an image or thumbnail."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image: PILImageType,
-        image_name: str,
-        metadata: Optional[dict] = None,
-        workflow: Optional[str] = None,
-        thumbnail_size: int = 256,
-    ) -> None:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
-        pass

invokeai/app/services/image_files/image_files_common.py

@@ -1,20 +0,0 @@
-# TODO: Should these excpetions subclass existing python exceptions?
-class ImageFileNotFoundException(Exception):
-    """Raised when an image file is not found in storage."""
-
-    def __init__(self, message="Image file not found"):
-        super().__init__(message)
-
-
-class ImageFileSaveException(Exception):
-    """Raised when an image cannot be saved."""
-
-    def __init__(self, message="Image file not saved"):
-        super().__init__(message)
-
-
-class ImageFileDeleteException(Exception):
-    """Raised when an image cannot be deleted."""
-
-    def __init__(self, message="Image file not deleted"):
-        super().__init__(message)

invokeai/app/services/image_record_storage.py

@@ -1,36 +1,164 @@
 import json
 import sqlite3
 import threading
+from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Optional, cast
+from typing import Generic, Optional, TypeVar, cast
 
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from pydantic import BaseModel, Field
+from pydantic.generics import GenericModel
 
-from .image_records_base import ImageRecordStorageBase
-from .image_records_common import (
-    IMAGE_DTO_COLS,
-    ImageCategory,
-    ImageRecord,
-    ImageRecordChanges,
-    ImageRecordDeleteException,
-    ImageRecordNotFoundException,
-    ImageRecordSaveException,
-    ResourceOrigin,
-    deserialize_image_record,
+from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
+
+T = TypeVar("T", bound=BaseModel)
+
+
+class OffsetPaginatedResults(GenericModel, Generic[T]):
+    """Offset-paginated results"""
+
+    # fmt: off
+    items: list[T] = Field(description="Items")
+    offset: int = Field(description="Offset from which to retrieve items")
+    limit: int = Field(description="Limit of items to get")
+    total: int = Field(description="Total number of items in result")
+    # fmt: on
+
+
+# TODO: Should these excpetions subclass existing python exceptions?
+class ImageRecordNotFoundException(Exception):
+    """Raised when an image record is not found."""
+
+    def __init__(self, message="Image record not found"):
+        super().__init__(message)
+
+
+class ImageRecordSaveException(Exception):
+    """Raised when an image record cannot be saved."""
+
+    def __init__(self, message="Image record not saved"):
+        super().__init__(message)
+
+
+class ImageRecordDeleteException(Exception):
+    """Raised when an image record cannot be deleted."""
+
+    def __init__(self, message="Image record not deleted"):
+        super().__init__(message)
+
+
+IMAGE_DTO_COLS = ", ".join(
+    list(
+        map(
+            lambda c: "images." + c,
+            [
+                "image_name",
+                "image_origin",
+                "image_category",
+                "width",
+                "height",
+                "session_id",
+                "node_id",
+                "is_intermediate",
+                "created_at",
+                "updated_at",
+                "deleted_at",
+                "starred",
+            ],
+        )
+    )
 )
 
 
+class ImageRecordStorageBase(ABC):
+    """Low-level service responsible for interfacing with the image record store."""
+
+    # TODO: Implement an `update()` method
+
+    @abstractmethod
+    def get(self, image_name: str) -> ImageRecord:
+        """Gets an image record."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, image_name: str) -> Optional[dict]:
+        """Gets an image's metadata'."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        image_name: str,
+        changes: ImageRecordChanges,
+    ) -> None:
+        """Updates an image record."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: Optional[int] = None,
+        limit: Optional[int] = None,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+    ) -> OffsetPaginatedResults[ImageRecord]:
+        """Gets a page of image records."""
+        pass
+
+    # TODO: The database has a nullable `deleted_at` column, currently unused.
+    # Should we implement soft deletes? Would need coordination with ImageFileStorage.
+    @abstractmethod
+    def delete(self, image_name: str) -> None:
+        """Deletes an image record."""
+        pass
+
+    @abstractmethod
+    def delete_many(self, image_names: list[str]) -> None:
+        """Deletes many image records."""
+        pass
+
+    @abstractmethod
+    def delete_intermediates(self) -> list[str]:
+        """Deletes all intermediate image records, returning a list of deleted image names."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image_name: str,
+        image_origin: ResourceOrigin,
+        image_category: ImageCategory,
+        width: int,
+        height: int,
+        session_id: Optional[str],
+        node_id: Optional[str],
+        metadata: Optional[dict],
+        is_intermediate: bool = False,
+        starred: bool = False,
+    ) -> datetime:
+        """Saves an image record."""
+        pass
+
+    @abstractmethod
+    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
+        """Gets the most recent image for a board."""
+        pass
+
+
 class SqliteImageRecordStorage(ImageRecordStorageBase):
     _conn: sqlite3.Connection
     _cursor: sqlite3.Cursor
     _lock: threading.Lock
 
-    def __init__(self, db: SqliteDatabase) -> None:
+    def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
         super().__init__()
-        self._lock = db.lock
-        self._conn = db.conn
+        self._conn = conn
+        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
+        self._conn.row_factory = sqlite3.Row
         self._cursor = self._conn.cursor()
+        self._lock = lock
 
         try:
             self._lock.acquire()
@@ -456,7 +584,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 FROM images
                 JOIN board_images ON images.image_name = board_images.image_name
                 WHERE board_images.board_id = ?
-                ORDER BY images.starred DESC, images.created_at DESC
+                ORDER BY images.created_at DESC
                 LIMIT 1;
                 """,
                 (board_id,),

invokeai/app/services/image_records/image_records_base.py

@@ -1,84 +0,0 @@
-from abc import ABC, abstractmethod
-from datetime import datetime
-from typing import Optional
-
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-
-from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
-
-
-class ImageRecordStorageBase(ABC):
-    """Low-level service responsible for interfacing with the image record store."""
-
-    # TODO: Implement an `update()` method
-
-    @abstractmethod
-    def get(self, image_name: str) -> ImageRecord:
-        """Gets an image record."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, image_name: str) -> Optional[dict]:
-        """Gets an image's metadata'."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> None:
-        """Updates an image record."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: Optional[int] = None,
-        limit: Optional[int] = None,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageRecord]:
-        """Gets a page of image records."""
-        pass
-
-    # TODO: The database has a nullable `deleted_at` column, currently unused.
-    # Should we implement soft deletes? Would need coordination with ImageFileStorage.
-    @abstractmethod
-    def delete(self, image_name: str) -> None:
-        """Deletes an image record."""
-        pass
-
-    @abstractmethod
-    def delete_many(self, image_names: list[str]) -> None:
-        """Deletes many image records."""
-        pass
-
-    @abstractmethod
-    def delete_intermediates(self) -> list[str]:
-        """Deletes all intermediate image records, returning a list of deleted image names."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image_name: str,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        width: int,
-        height: int,
-        session_id: Optional[str],
-        node_id: Optional[str],
-        metadata: Optional[dict],
-        is_intermediate: bool = False,
-        starred: bool = False,
-    ) -> datetime:
-        """Saves an image record."""
-        pass
-
-    @abstractmethod
-    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
-        """Gets the most recent image for a board."""
-        pass

invokeai/app/services/images.py

@@ -0,0 +1,464 @@
+from abc import ABC, abstractmethod
+from logging import Logger
+from typing import TYPE_CHECKING, Callable, Optional
+
+from PIL.Image import Image as PILImageType
+
+from invokeai.app.invocations.metadata import ImageMetadata
+from invokeai.app.models.image import (
+    ImageCategory,
+    InvalidImageCategoryException,
+    InvalidOriginException,
+    ResourceOrigin,
+)
+from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
+from invokeai.app.services.image_file_storage import (
+    ImageFileDeleteException,
+    ImageFileNotFoundException,
+    ImageFileSaveException,
+    ImageFileStorageBase,
+)
+from invokeai.app.services.image_record_storage import (
+    ImageRecordDeleteException,
+    ImageRecordNotFoundException,
+    ImageRecordSaveException,
+    ImageRecordStorageBase,
+    OffsetPaginatedResults,
+)
+from invokeai.app.services.item_storage import ItemStorageABC
+from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
+from invokeai.app.services.resource_name import NameServiceBase
+from invokeai.app.services.urls import UrlServiceBase
+from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
+
+if TYPE_CHECKING:
+    from invokeai.app.services.graph import GraphExecutionState
+
+
+class ImageServiceABC(ABC):
+    """High-level service for image management."""
+
+    _on_changed_callbacks: list[Callable[[ImageDTO], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    @abstractmethod
+    def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
+        """Register a callback for when an item is changed"""
+        pass
+
+    @abstractmethod
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an item is deleted"""
+        pass
+
+    @abstractmethod
+    def _on_changed(self, item: ImageDTO) -> None:
+        pass
+
+    @abstractmethod
+    def _on_deleted(self, item_id: str) -> None:
+        pass
+
+    @abstractmethod
+    def create(
+        self,
+        image: PILImageType,
+        image_origin: ResourceOrigin,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        board_id: Optional[str] = None,
+        is_intermediate: bool = False,
+        metadata: Optional[dict] = None,
+        workflow: Optional[str] = None,
+    ) -> ImageDTO:
+        """Creates an image, storing the file and its metadata."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        image_name: str,
+        changes: ImageRecordChanges,
+    ) -> ImageDTO:
+        """Updates an image."""
+        pass
+
+    @abstractmethod
+    def get_pil_image(self, image_name: str) -> PILImageType:
+        """Gets an image as a PIL image."""
+        pass
+
+    @abstractmethod
+    def get_record(self, image_name: str) -> ImageRecord:
+        """Gets an image record."""
+        pass
+
+    @abstractmethod
+    def get_dto(self, image_name: str) -> ImageDTO:
+        """Gets an image DTO."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, image_name: str) -> ImageMetadata:
+        """Gets an image's metadata."""
+        pass
+
+    @abstractmethod
+    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
+        """Gets an image's path."""
+        pass
+
+    @abstractmethod
+    def validate_path(self, path: str) -> bool:
+        """Validates an image's path."""
+        pass
+
+    @abstractmethod
+    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
+        """Gets an image's or thumbnail's URL."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+    ) -> OffsetPaginatedResults[ImageDTO]:
+        """Gets a paginated list of image DTOs."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_name: str):
+        """Deletes an image."""
+        pass
+
+    @abstractmethod
+    def delete_intermediates(self) -> int:
+        """Deletes all intermediate images."""
+        pass
+
+    @abstractmethod
+    def delete_images_on_board(self, board_id: str):
+        """Deletes all images on a board."""
+        pass
+
+
+class ImageServiceDependencies:
+    """Service dependencies for the ImageService."""
+
+    image_records: ImageRecordStorageBase
+    image_files: ImageFileStorageBase
+    board_image_records: BoardImageRecordStorageBase
+    urls: UrlServiceBase
+    logger: Logger
+    names: NameServiceBase
+    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
+
+    def __init__(
+        self,
+        image_record_storage: ImageRecordStorageBase,
+        image_file_storage: ImageFileStorageBase,
+        board_image_record_storage: BoardImageRecordStorageBase,
+        url: UrlServiceBase,
+        logger: Logger,
+        names: NameServiceBase,
+        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
+    ):
+        self.image_records = image_record_storage
+        self.image_files = image_file_storage
+        self.board_image_records = board_image_record_storage
+        self.urls = url
+        self.logger = logger
+        self.names = names
+        self.graph_execution_manager = graph_execution_manager
+
+
+class ImageService(ImageServiceABC):
+    _services: ImageServiceDependencies
+    _on_changed_callbacks: list[Callable[[ImageDTO], None]] = list()
+    _on_deleted_callbacks: list[Callable[[str], None]] = list()
+
+    def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
+        """Register a callback for when an item is changed"""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an item is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, item: ImageDTO) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(item)
+
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)
+
+    def __init__(self, services: ImageServiceDependencies):
+        self._services = services
+
+    def create(
+        self,
+        image: PILImageType,
+        image_origin: ResourceOrigin,
+        image_category: ImageCategory,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        board_id: Optional[str] = None,
+        is_intermediate: bool = False,
+        metadata: Optional[dict] = None,
+        workflow: Optional[str] = None,
+    ) -> ImageDTO:
+        if image_origin not in ResourceOrigin:
+            raise InvalidOriginException
+
+        if image_category not in ImageCategory:
+            raise InvalidImageCategoryException
+
+        image_name = self._services.names.create_image_name()
+
+        # TODO: Do we want to store the graph in the image at all? I don't think so...
+        # graph = None
+        # if session_id is not None:
+        #     session_raw = self._services.graph_execution_manager.get_raw(session_id)
+        #     if session_raw is not None:
+        #         try:
+        #             graph = get_metadata_graph_from_raw_session(session_raw)
+        #         except Exception as e:
+        #             self._services.logger.warn(f"Failed to parse session graph: {e}")
+        #             graph = None
+
+        (width, height) = image.size
+
+        try:
+            # TODO: Consider using a transaction here to ensure consistency between storage and database
+            self._services.image_records.save(
+                # Non-nullable fields
+                image_name=image_name,
+                image_origin=image_origin,
+                image_category=image_category,
+                width=width,
+                height=height,
+                # Meta fields
+                is_intermediate=is_intermediate,
+                # Nullable fields
+                node_id=node_id,
+                metadata=metadata,
+                session_id=session_id,
+            )
+            if board_id is not None:
+                self._services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
+            self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, workflow=workflow)
+            image_dto = self.get_dto(image_name)
+
+            self._on_changed(image_dto)
+            return image_dto
+        except ImageRecordSaveException:
+            self._services.logger.error("Failed to save image record")
+            raise
+        except ImageFileSaveException:
+            self._services.logger.error("Failed to save image file")
+            raise
+        except Exception as e:
+            self._services.logger.error(f"Problem saving image record and file: {str(e)}")
+            raise e
+
+    def update(
+        self,
+        image_name: str,
+        changes: ImageRecordChanges,
+    ) -> ImageDTO:
+        try:
+            self._services.image_records.update(image_name, changes)
+            image_dto = self.get_dto(image_name)
+            self._on_changed(image_dto)
+            return image_dto
+        except ImageRecordSaveException:
+            self._services.logger.error("Failed to update image record")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem updating image record")
+            raise e
+
+    def get_pil_image(self, image_name: str) -> PILImageType:
+        try:
+            return self._services.image_files.get(image_name)
+        except ImageFileNotFoundException:
+            self._services.logger.error("Failed to get image file")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image file")
+            raise e
+
+    def get_record(self, image_name: str) -> ImageRecord:
+        try:
+            return self._services.image_records.get(image_name)
+        except ImageRecordNotFoundException:
+            self._services.logger.error("Image record not found")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image record")
+            raise e
+
+    def get_dto(self, image_name: str) -> ImageDTO:
+        try:
+            image_record = self._services.image_records.get(image_name)
+
+            image_dto = image_record_to_dto(
+                image_record,
+                self._services.urls.get_image_url(image_name),
+                self._services.urls.get_image_url(image_name, True),
+                self._services.board_image_records.get_board_for_image(image_name),
+            )
+
+            return image_dto
+        except ImageRecordNotFoundException:
+            self._services.logger.error("Image record not found")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image DTO")
+            raise e
+
+    def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
+        try:
+            image_record = self._services.image_records.get(image_name)
+            metadata = self._services.image_records.get_metadata(image_name)
+
+            if not image_record.session_id:
+                return ImageMetadata(metadata=metadata)
+
+            session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
+            graph = None
+
+            if session_raw:
+                try:
+                    graph = get_metadata_graph_from_raw_session(session_raw)
+                except Exception as e:
+                    self._services.logger.warn(f"Failed to parse session graph: {e}")
+                    graph = None
+
+            return ImageMetadata(graph=graph, metadata=metadata)
+        except ImageRecordNotFoundException:
+            self._services.logger.error("Image record not found")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem getting image DTO")
+            raise e
+
+    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
+        try:
+            return self._services.image_files.get_path(image_name, thumbnail)
+        except Exception as e:
+            self._services.logger.error("Problem getting image path")
+            raise e
+
+    def validate_path(self, path: str) -> bool:
+        try:
+            return self._services.image_files.validate_path(path)
+        except Exception as e:
+            self._services.logger.error("Problem validating image path")
+            raise e
+
+    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
+        try:
+            return self._services.urls.get_image_url(image_name, thumbnail)
+        except Exception as e:
+            self._services.logger.error("Problem getting image path")
+            raise e
+
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+    ) -> OffsetPaginatedResults[ImageDTO]:
+        try:
+            results = self._services.image_records.get_many(
+                offset,
+                limit,
+                image_origin,
+                categories,
+                is_intermediate,
+                board_id,
+            )
+
+            image_dtos = list(
+                map(
+                    lambda r: image_record_to_dto(
+                        r,
+                        self._services.urls.get_image_url(r.image_name),
+                        self._services.urls.get_image_url(r.image_name, True),
+                        self._services.board_image_records.get_board_for_image(r.image_name),
+                    ),
+                    results.items,
+                )
+            )
+
+            return OffsetPaginatedResults[ImageDTO](
+                items=image_dtos,
+                offset=results.offset,
+                limit=results.limit,
+                total=results.total,
+            )
+        except Exception as e:
+            self._services.logger.error("Problem getting paginated image DTOs")
+            raise e
+
+    def delete(self, image_name: str):
+        try:
+            self._services.image_files.delete(image_name)
+            self._services.image_records.delete(image_name)
+            self._on_deleted(image_name)
+        except ImageRecordDeleteException:
+            self._services.logger.error("Failed to delete image record")
+            raise
+        except ImageFileDeleteException:
+            self._services.logger.error("Failed to delete image file")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem deleting image record and file")
+            raise e
+
+    def delete_images_on_board(self, board_id: str):
+        try:
+            image_names = self._services.board_image_records.get_all_board_image_names_for_board(board_id)
+            for image_name in image_names:
+                self._services.image_files.delete(image_name)
+            self._services.image_records.delete_many(image_names)
+            for image_name in image_names:
+                self._on_deleted(image_name)
+        except ImageRecordDeleteException:
+            self._services.logger.error("Failed to delete image records")
+            raise
+        except ImageFileDeleteException:
+            self._services.logger.error("Failed to delete image files")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem deleting image records and files")
+            raise e
+
+    def delete_intermediates(self) -> int:
+        try:
+            image_names = self._services.image_records.delete_intermediates()
+            count = len(image_names)
+            for image_name in image_names:
+                self._services.image_files.delete(image_name)
+                self._on_deleted(image_name)
+            return count
+        except ImageRecordDeleteException:
+            self._services.logger.error("Failed to delete image records")
+            raise
+        except ImageFileDeleteException:
+            self._services.logger.error("Failed to delete image files")
+            raise
+        except Exception as e:
+            self._services.logger.error("Problem deleting image records and files")
+            raise e

invokeai/app/services/images/images_base.py

@@ -1,129 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable, Optional
-
-from PIL.Image import Image as PILImageType
-
-from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.services.image_records.image_records_common import (
-    ImageCategory,
-    ImageRecord,
-    ImageRecordChanges,
-    ResourceOrigin,
-)
-from invokeai.app.services.images.images_common import ImageDTO
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-
-
-class ImageServiceABC(ABC):
-    """High-level service for image management."""
-
-    _on_changed_callbacks: list[Callable[[ImageDTO], None]]
-    _on_deleted_callbacks: list[Callable[[str], None]]
-
-    def __init__(self) -> None:
-        self._on_changed_callbacks = list()
-        self._on_deleted_callbacks = list()
-
-    def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
-        """Register a callback for when an image is changed"""
-        self._on_changed_callbacks.append(on_changed)
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when an image is deleted"""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_changed(self, item: ImageDTO) -> None:
-        for callback in self._on_changed_callbacks:
-            callback(item)
-
-    def _on_deleted(self, item_id: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(item_id)
-
-    @abstractmethod
-    def create(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
-        is_intermediate: bool = False,
-        metadata: Optional[dict] = None,
-        workflow: Optional[str] = None,
-    ) -> ImageDTO:
-        """Creates an image, storing the file and its metadata."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> ImageDTO:
-        """Updates an image."""
-        pass
-
-    @abstractmethod
-    def get_pil_image(self, image_name: str) -> PILImageType:
-        """Gets an image as a PIL image."""
-        pass
-
-    @abstractmethod
-    def get_record(self, image_name: str) -> ImageRecord:
-        """Gets an image record."""
-        pass
-
-    @abstractmethod
-    def get_dto(self, image_name: str) -> ImageDTO:
-        """Gets an image DTO."""
-        pass
-
-    @abstractmethod
-    def get_metadata(self, image_name: str) -> ImageMetadata:
-        """Gets an image's metadata."""
-        pass
-
-    @abstractmethod
-    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
-        """Gets an image's path."""
-        pass
-
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image's path."""
-        pass
-
-    @abstractmethod
-    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
-        """Gets an image's or thumbnail's URL."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        """Gets a paginated list of image DTOs."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_name: str):
-        """Deletes an image."""
-        pass
-
-    @abstractmethod
-    def delete_intermediates(self) -> int:
-        """Deletes all intermediate images."""
-        pass
-
-    @abstractmethod
-    def delete_images_on_board(self, board_id: str):
-        """Deletes all images on a board."""
-        pass

invokeai/app/services/images/images_common.py

@@ -1,41 +0,0 @@
-from typing import Optional
-
-from pydantic import Field
-
-from invokeai.app.services.image_records.image_records_common import ImageRecord
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-
-
-class ImageUrlsDTO(BaseModelExcludeNull):
-    """The URLs for an image and its thumbnail."""
-
-    image_name: str = Field(description="The unique name of the image.")
-    """The unique name of the image."""
-    image_url: str = Field(description="The URL of the image.")
-    """The URL of the image."""
-    thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
-    """The URL of the image's thumbnail."""
-
-
-class ImageDTO(ImageRecord, ImageUrlsDTO):
-    """Deserialized image record, enriched for the frontend."""
-
-    board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
-    """The id of the board the image belongs to, if one exists."""
-
-    pass
-
-
-def image_record_to_dto(
-    image_record: ImageRecord,
-    image_url: str,
-    thumbnail_url: str,
-    board_id: Optional[str],
-) -> ImageDTO:
-    """Converts an image record to an image DTO."""
-    return ImageDTO(
-        **image_record.dict(),
-        image_url=image_url,
-        thumbnail_url=thumbnail_url,
-        board_id=board_id,
-    )

invokeai/app/services/images/images_default.py

@@ -1,286 +0,0 @@
-from typing import Optional
-
-from PIL.Image import Image as PILImageType
-
-from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
-
-from ..image_files.image_files_common import (
-    ImageFileDeleteException,
-    ImageFileNotFoundException,
-    ImageFileSaveException,
-)
-from ..image_records.image_records_common import (
-    ImageCategory,
-    ImageRecord,
-    ImageRecordChanges,
-    ImageRecordDeleteException,
-    ImageRecordNotFoundException,
-    ImageRecordSaveException,
-    InvalidImageCategoryException,
-    InvalidOriginException,
-    ResourceOrigin,
-)
-from .images_base import ImageServiceABC
-from .images_common import ImageDTO, image_record_to_dto
-
-
-class ImageService(ImageServiceABC):
-    __invoker: Invoker
-
-    def start(self, invoker: Invoker) -> None:
-        self.__invoker = invoker
-
-    def create(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
-        is_intermediate: bool = False,
-        metadata: Optional[dict] = None,
-        workflow: Optional[str] = None,
-    ) -> ImageDTO:
-        if image_origin not in ResourceOrigin:
-            raise InvalidOriginException
-
-        if image_category not in ImageCategory:
-            raise InvalidImageCategoryException
-
-        image_name = self.__invoker.services.names.create_image_name()
-
-        (width, height) = image.size
-
-        try:
-            # TODO: Consider using a transaction here to ensure consistency between storage and database
-            self.__invoker.services.image_records.save(
-                # Non-nullable fields
-                image_name=image_name,
-                image_origin=image_origin,
-                image_category=image_category,
-                width=width,
-                height=height,
-                # Meta fields
-                is_intermediate=is_intermediate,
-                # Nullable fields
-                node_id=node_id,
-                metadata=metadata,
-                session_id=session_id,
-            )
-            if board_id is not None:
-                self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
-            self.__invoker.services.image_files.save(
-                image_name=image_name, image=image, metadata=metadata, workflow=workflow
-            )
-            image_dto = self.get_dto(image_name)
-
-            self._on_changed(image_dto)
-            return image_dto
-        except ImageRecordSaveException:
-            self.__invoker.services.logger.error("Failed to save image record")
-            raise
-        except ImageFileSaveException:
-            self.__invoker.services.logger.error("Failed to save image file")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error(f"Problem saving image record and file: {str(e)}")
-            raise e
-
-    def update(
-        self,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> ImageDTO:
-        try:
-            self.__invoker.services.image_records.update(image_name, changes)
-            image_dto = self.get_dto(image_name)
-            self._on_changed(image_dto)
-            return image_dto
-        except ImageRecordSaveException:
-            self.__invoker.services.logger.error("Failed to update image record")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem updating image record")
-            raise e
-
-    def get_pil_image(self, image_name: str) -> PILImageType:
-        try:
-            return self.__invoker.services.image_files.get(image_name)
-        except ImageFileNotFoundException:
-            self.__invoker.services.logger.error("Failed to get image file")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image file")
-            raise e
-
-    def get_record(self, image_name: str) -> ImageRecord:
-        try:
-            return self.__invoker.services.image_records.get(image_name)
-        except ImageRecordNotFoundException:
-            self.__invoker.services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image record")
-            raise e
-
-    def get_dto(self, image_name: str) -> ImageDTO:
-        try:
-            image_record = self.__invoker.services.image_records.get(image_name)
-
-            image_dto = image_record_to_dto(
-                image_record,
-                self.__invoker.services.urls.get_image_url(image_name),
-                self.__invoker.services.urls.get_image_url(image_name, True),
-                self.__invoker.services.board_image_records.get_board_for_image(image_name),
-            )
-
-            return image_dto
-        except ImageRecordNotFoundException:
-            self.__invoker.services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image DTO")
-            raise e
-
-    def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
-        try:
-            image_record = self.__invoker.services.image_records.get(image_name)
-            metadata = self.__invoker.services.image_records.get_metadata(image_name)
-
-            if not image_record.session_id:
-                return ImageMetadata(metadata=metadata)
-
-            session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
-            graph = None
-
-            if session_raw:
-                try:
-                    graph = get_metadata_graph_from_raw_session(session_raw)
-                except Exception as e:
-                    self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
-                    graph = None
-
-            return ImageMetadata(graph=graph, metadata=metadata)
-        except ImageRecordNotFoundException:
-            self.__invoker.services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image DTO")
-            raise e
-
-    def get_path(self, image_name: str, thumbnail: bool = False) -> str:
-        try:
-            return self.__invoker.services.image_files.get_path(image_name, thumbnail)
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image path")
-            raise e
-
-    def validate_path(self, path: str) -> bool:
-        try:
-            return self.__invoker.services.image_files.validate_path(path)
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem validating image path")
-            raise e
-
-    def get_url(self, image_name: str, thumbnail: bool = False) -> str:
-        try:
-            return self.__invoker.services.urls.get_image_url(image_name, thumbnail)
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image path")
-            raise e
-
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        try:
-            results = self.__invoker.services.image_records.get_many(
-                offset,
-                limit,
-                image_origin,
-                categories,
-                is_intermediate,
-                board_id,
-            )
-
-            image_dtos = list(
-                map(
-                    lambda r: image_record_to_dto(
-                        r,
-                        self.__invoker.services.urls.get_image_url(r.image_name),
-                        self.__invoker.services.urls.get_image_url(r.image_name, True),
-                        self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
-                    ),
-                    results.items,
-                )
-            )
-
-            return OffsetPaginatedResults[ImageDTO](
-                items=image_dtos,
-                offset=results.offset,
-                limit=results.limit,
-                total=results.total,
-            )
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting paginated image DTOs")
-            raise e
-
-    def delete(self, image_name: str):
-        try:
-            self.__invoker.services.image_files.delete(image_name)
-            self.__invoker.services.image_records.delete(image_name)
-            self._on_deleted(image_name)
-        except ImageRecordDeleteException:
-            self.__invoker.services.logger.error("Failed to delete image record")
-            raise
-        except ImageFileDeleteException:
-            self.__invoker.services.logger.error("Failed to delete image file")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem deleting image record and file")
-            raise e
-
-    def delete_images_on_board(self, board_id: str):
-        try:
-            image_names = self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
-            for image_name in image_names:
-                self.__invoker.services.image_files.delete(image_name)
-            self.__invoker.services.image_records.delete_many(image_names)
-            for image_name in image_names:
-                self._on_deleted(image_name)
-        except ImageRecordDeleteException:
-            self.__invoker.services.logger.error("Failed to delete image records")
-            raise
-        except ImageFileDeleteException:
-            self.__invoker.services.logger.error("Failed to delete image files")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem deleting image records and files")
-            raise e
-
-    def delete_intermediates(self) -> int:
-        try:
-            image_names = self.__invoker.services.image_records.delete_intermediates()
-            count = len(image_names)
-            for image_name in image_names:
-                self.__invoker.services.image_files.delete(image_name)
-                self._on_deleted(image_name)
-            return count
-        except ImageRecordDeleteException:
-            self.__invoker.services.logger.error("Failed to delete image records")
-            raise
-        except ImageFileDeleteException:
-            self.__invoker.services.logger.error("Failed to delete image files")
-            raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem deleting image records and files")
-            raise e

invokeai/app/services/invocation_cache/invocation_cache_base.py

@@ -2,38 +2,24 @@ from abc import ABC, abstractmethod
 from typing import Optional, Union
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
-from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 
 
 class InvocationCacheBase(ABC):
-    """
-    Base class for invocation caches.
-    When an invocation is executed, it is hashed and its output stored in the cache.
-    When new invocations are executed, if they are flagged with `use_cache`, they
-    will attempt to pull their value from the cache before executing.
-
-    Implementations should register for the `on_deleted` event of the `images` and `latents`
-    services, and delete any cached outputs that reference the deleted image or latent.
-
-    See the memory implementation for an example.
-
-    Implementations should respect the `node_cache_size` configuration value, and skip all
-    cache logic if the value is set to 0.
-    """
+    """Base class for invocation caches."""
 
     @abstractmethod
     def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
-        """Retrieves an invocation output from the cache"""
+        """Retrieves and invocation output from the cache"""
         pass
 
     @abstractmethod
-    def save(self, key: Union[int, str], invocation_output: BaseInvocationOutput) -> None:
+    def save(self, value: BaseInvocationOutput) -> None:
         """Stores an invocation output in the cache"""
         pass
 
     @abstractmethod
     def delete(self, key: Union[int, str]) -> None:
-        """Deletes an invocation output from the cache"""
+        """Deleted an invocation output from the cache"""
         pass
 
     @abstractmethod
@@ -41,22 +27,8 @@ class InvocationCacheBase(ABC):
         """Clears the cache"""
         pass
 
+    @classmethod
     @abstractmethod
-    def create_key(self, invocation: BaseInvocation) -> int:
-        """Gets the key for the invocation's cache item"""
-        pass
-
-    @abstractmethod
-    def disable(self) -> None:
-        """Disables the cache, overriding the max cache size"""
-        pass
-
-    @abstractmethod
-    def enable(self) -> None:
-        """Enables the cache, letting the the max cache size take effect"""
-        pass
-
-    @abstractmethod
-    def get_status(self) -> InvocationCacheStatus:
-        """Returns the status of the cache"""
+    def create_key(cls, value: BaseInvocation) -> Union[int, str]:
+        """Creates the cache key for an invocation"""
         pass

invokeai/app/services/invocation_cache/invocation_cache_common.py

@@ -1,9 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class InvocationCacheStatus(BaseModel):
-    size: int = Field(description="The current size of the invocation cache")
-    hits: int = Field(description="The number of cache hits")
-    misses: int = Field(description="The number of cache misses")
-    enabled: bool = Field(description="Whether the invocation cache is enabled")
-    max_size: int = Field(description="The maximum size of the invocation cache")

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -1,126 +1,70 @@
-from collections import OrderedDict
-from dataclasses import dataclass, field
-from threading import Lock
+from queue import Queue
 from typing import Optional, Union
 
+
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
 from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
-from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.app.services.invoker import Invoker
 
 
-@dataclass(order=True)
-class CachedItem:
-    invocation_output: BaseInvocationOutput = field(compare=False)
-    invocation_output_json: str = field(compare=False)
-
-
 class MemoryInvocationCache(InvocationCacheBase):
-    _cache: OrderedDict[Union[int, str], CachedItem]
-    _max_cache_size: int
-    _disabled: bool
-    _hits: int
-    _misses: int
-    _invoker: Invoker
-    _lock: Lock
+    __cache: dict[Union[int, str], tuple[BaseInvocationOutput, str]]
+    __max_cache_size: int
+    __cache_ids: Queue
+    __invoker: Invoker
 
-    def __init__(self, max_cache_size: int = 0) -> None:
-        self._cache = OrderedDict()
-        self._max_cache_size = max_cache_size
-        self._disabled = False
-        self._hits = 0
-        self._misses = 0
-        self._lock = Lock()
+    def __init__(self, max_cache_size: int = 512) -> None:
+        self.__cache = dict()
+        self.__max_cache_size = max_cache_size
+        self.__cache_ids = Queue()
 
     def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        if self._max_cache_size == 0:
-            return
-        self._invoker.services.images.on_deleted(self._delete_by_match)
-        self._invoker.services.latents.on_deleted(self._delete_by_match)
+        self.__invoker = invoker
+        self.__invoker.services.images.on_deleted(self.delete_by_match)
 
     def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
-        with self._lock:
-            if self._max_cache_size == 0 or self._disabled:
-                return None
-            item = self._cache.get(key, None)
-            if item is not None:
-                self._hits += 1
-                self._cache.move_to_end(key)
-                return item.invocation_output
-            self._misses += 1
+        if self.__max_cache_size == 0:
             return None
 
-    def save(self, key: Union[int, str], invocation_output: BaseInvocationOutput) -> None:
-        with self._lock:
-            if self._max_cache_size == 0 or self._disabled or key in self._cache:
-                return
-            # If the cache is full, we need to remove the least used
-            number_to_delete = len(self._cache) + 1 - self._max_cache_size
-            self._delete_oldest_access(number_to_delete)
-            self._cache[key] = CachedItem(invocation_output, invocation_output.json())
+        item = self.__cache.get(key, None)
+        if item is not None:
+            return item[0]
 
-    def _delete_oldest_access(self, number_to_delete: int) -> None:
-        number_to_delete = min(number_to_delete, len(self._cache))
-        for _ in range(number_to_delete):
-            self._cache.popitem(last=False)
+    def save(self, value: BaseInvocationOutput) -> None:
+        if self.__max_cache_size == 0:
+            return None
 
-    def _delete(self, key: Union[int, str]) -> None:
-        if self._max_cache_size == 0:
-            return
-        if key in self._cache:
-            del self._cache[key]
+        value_json = value.json(exclude={"id"})
+        key = hash(value_json)
+
+        if key not in self.__cache:
+            self.__cache[key] = (value, value_json)
+            self.__cache_ids.put(key)
+            if self.__cache_ids.qsize() > self.__max_cache_size:
+                try:
+                    self.__cache.pop(self.__cache_ids.get())
+                except KeyError:
+                    pass
 
     def delete(self, key: Union[int, str]) -> None:
-        with self._lock:
-            return self._delete(key)
+        if self.__max_cache_size == 0:
+            return None
+
+        if key in self.__cache:
+            del self.__cache[key]
+
+    def delete_by_match(self, to_match: str) -> None:
+        to_delete = []
+        for name, item in self.__cache.items():
+            if to_match in item[1]:
+                to_delete.append(name)
+        for key in to_delete:
+            self.delete(key)
 
     def clear(self, *args, **kwargs) -> None:
-        with self._lock:
-            if self._max_cache_size == 0:
-                return
-            self._cache.clear()
-            self._misses = 0
-            self._hits = 0
+        self.__cache.clear()
+        self.__cache_ids = Queue()
 
-    @staticmethod
-    def create_key(invocation: BaseInvocation) -> int:
-        return hash(invocation.json(exclude={"id"}))
-
-    def disable(self) -> None:
-        with self._lock:
-            if self._max_cache_size == 0:
-                return
-            self._disabled = True
-
-    def enable(self) -> None:
-        with self._lock:
-            if self._max_cache_size == 0:
-                return
-            self._disabled = False
-
-    def get_status(self) -> InvocationCacheStatus:
-        with self._lock:
-            return InvocationCacheStatus(
-                hits=self._hits,
-                misses=self._misses,
-                enabled=not self._disabled and self._max_cache_size > 0,
-                size=len(self._cache),
-                max_size=self._max_cache_size,
-            )
-
-    def _delete_by_match(self, to_match: str) -> None:
-        with self._lock:
-            if self._max_cache_size == 0:
-                return
-            keys_to_delete = set()
-            for key, cached_item in self._cache.items():
-                if to_match in cached_item.invocation_output_json:
-                    keys_to_delete.add(key)
-            if not keys_to_delete:
-                return
-            for key in keys_to_delete:
-                self._delete(key)
-            self._invoker.services.logger.debug(
-                f"Deleted {len(keys_to_delete)} cached invocation outputs for {to_match}"
-            )
+    @classmethod
+    def create_key(cls, value: BaseInvocation) -> Union[int, str]:
+        return hash(value.json(exclude={"id"}))

invokeai/app/services/invocation_processor/invocation_processor_base.py

@@ -1,5 +0,0 @@
-from abc import ABC
-
-
-class InvocationProcessorABC(ABC):
-    pass

invokeai/app/services/invocation_processor/invocation_processor_common.py

@@ -1,15 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-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 CanceledException(Exception):
-    """Execution canceled by user."""
-
-    pass

invokeai/app/services/invocation_queue.py

@@ -1,11 +1,42 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import time
+from abc import ABC, abstractmethod
 from queue import Queue
 from typing import Optional
 
-from .invocation_queue_base import InvocationQueueABC
-from .invocation_queue_common import InvocationQueueItem
+from pydantic import BaseModel, Field
+
+
+class InvocationQueueItem(BaseModel):
+    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
+    invocation_id: str = Field(description="The ID of the node being invoked")
+    session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
+    session_queue_item_id: str = Field(
+        description="The ID of session queue item from which this invocation queue item came"
+    )
+    invoke_all: bool = Field(default=False)
+    timestamp: float = Field(default_factory=time.time)
+
+
+class InvocationQueueABC(ABC):
+    """Abstract base class for all invocation queues"""
+
+    @abstractmethod
+    def get(self) -> InvocationQueueItem:
+        pass
+
+    @abstractmethod
+    def put(self, item: Optional[InvocationQueueItem]) -> None:
+        pass
+
+    @abstractmethod
+    def cancel(self, graph_execution_state_id: str) -> None:
+        pass
+
+    @abstractmethod
+    def is_canceled(self, graph_execution_state_id: str) -> bool:
+        pass
 
 
 class MemoryInvocationQueue(InvocationQueueABC):

invokeai/app/services/invocation_queue/invocation_queue_base.py

@@ -1,26 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from .invocation_queue_common import InvocationQueueItem
-
-
-class InvocationQueueABC(ABC):
-    """Abstract base class for all invocation queues"""
-
-    @abstractmethod
-    def get(self) -> InvocationQueueItem:
-        pass
-
-    @abstractmethod
-    def put(self, item: Optional[InvocationQueueItem]) -> None:
-        pass
-
-    @abstractmethod
-    def cancel(self, graph_execution_state_id: str) -> None:
-        pass
-
-    @abstractmethod
-    def is_canceled(self, graph_execution_state_id: str) -> bool:
-        pass

invokeai/app/services/invocation_queue/invocation_queue_common.py

@@ -1,19 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-
-import time
-
-from pydantic import BaseModel, Field
-
-
-class InvocationQueueItem(BaseModel):
-    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
-    invocation_id: str = Field(description="The ID of the node being invoked")
-    session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
-    session_queue_item_id: int = Field(
-        description="The ID of session queue item from which this invocation queue item came"
-    )
-    session_queue_batch_id: str = Field(
-        description="The ID of the session batch from which this invocation queue item came"
-    )
-    invoke_all: bool = Field(default=False)
-    timestamp: float = Field(default_factory=time.time)

invokeai/app/services/invocation_services.py

@@ -6,27 +6,21 @@ from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from logging import Logger
 
-    from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
-    from .board_images.board_images_base import BoardImagesServiceABC
-    from .board_records.board_records_base import BoardRecordStorageBase
-    from .boards.boards_base import BoardServiceABC
-    from .config import InvokeAIAppConfig
-    from .events.events_base import EventServiceBase
-    from .image_files.image_files_base import ImageFileStorageBase
-    from .image_records.image_records_base import ImageRecordStorageBase
-    from .images.images_base import ImageServiceABC
-    from .invocation_cache.invocation_cache_base import InvocationCacheBase
-    from .invocation_processor.invocation_processor_base import InvocationProcessorABC
-    from .invocation_queue.invocation_queue_base import InvocationQueueABC
-    from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
-    from .item_storage.item_storage_base import ItemStorageABC
-    from .latents_storage.latents_storage_base import LatentsStorageBase
-    from .model_manager.model_manager_base import ModelManagerServiceBase
-    from .names.names_base import NameServiceBase
-    from .session_processor.session_processor_base import SessionProcessorBase
-    from .session_queue.session_queue_base import SessionQueueBase
-    from .shared.graph import GraphExecutionState, LibraryGraph
-    from .urls.urls_base import UrlServiceBase
+    from invokeai.app.services.board_images import BoardImagesServiceABC
+    from invokeai.app.services.boards import BoardServiceABC
+    from invokeai.app.services.config import InvokeAIAppConfig
+    from invokeai.app.services.events import EventServiceBase
+    from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
+    from invokeai.app.services.images import ImageServiceABC
+    from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
+    from invokeai.app.services.invocation_queue import InvocationQueueABC
+    from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
+    from invokeai.app.services.invoker import InvocationProcessorABC
+    from invokeai.app.services.item_storage import ItemStorageABC
+    from invokeai.app.services.latent_storage import LatentsStorageBase
+    from invokeai.app.services.model_manager_service import ModelManagerServiceBase
+    from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
+    from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
 
 
 class InvocationServices:
@@ -34,16 +28,12 @@ class InvocationServices:
 
     # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
     board_images: "BoardImagesServiceABC"
-    board_image_record_storage: "BoardImageRecordStorageBase"
     boards: "BoardServiceABC"
-    board_records: "BoardRecordStorageBase"
     configuration: "InvokeAIAppConfig"
     events: "EventServiceBase"
     graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
     graph_library: "ItemStorageABC[LibraryGraph]"
     images: "ImageServiceABC"
-    image_records: "ImageRecordStorageBase"
-    image_files: "ImageFileStorageBase"
     latents: "LatentsStorageBase"
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
@@ -53,22 +43,16 @@ class InvocationServices:
     session_queue: "SessionQueueBase"
     session_processor: "SessionProcessorBase"
     invocation_cache: "InvocationCacheBase"
-    names: "NameServiceBase"
-    urls: "UrlServiceBase"
 
     def __init__(
         self,
         board_images: "BoardImagesServiceABC",
-        board_image_records: "BoardImageRecordStorageBase",
         boards: "BoardServiceABC",
-        board_records: "BoardRecordStorageBase",
         configuration: "InvokeAIAppConfig",
         events: "EventServiceBase",
         graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
         graph_library: "ItemStorageABC[LibraryGraph]",
         images: "ImageServiceABC",
-        image_files: "ImageFileStorageBase",
-        image_records: "ImageRecordStorageBase",
         latents: "LatentsStorageBase",
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
@@ -78,20 +62,14 @@ class InvocationServices:
         session_queue: "SessionQueueBase",
         session_processor: "SessionProcessorBase",
         invocation_cache: "InvocationCacheBase",
-        names: "NameServiceBase",
-        urls: "UrlServiceBase",
     ):
         self.board_images = board_images
-        self.board_image_records = board_image_records
         self.boards = boards
-        self.board_records = board_records
         self.configuration = configuration
         self.events = events
         self.graph_execution_manager = graph_execution_manager
         self.graph_library = graph_library
         self.images = images
-        self.image_files = image_files
-        self.image_records = image_records
         self.latents = latents
         self.logger = logger
         self.model_manager = model_manager
@@ -101,5 +79,3 @@ class InvocationServices:
         self.session_queue = session_queue
         self.session_processor = session_processor
         self.invocation_cache = invocation_cache
-        self.names = names
-        self.urls = urls

invokeai/app/services/invocation_stats.py

@@ -1,35 +1,171 @@
+# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
+"""Utility to collect execution time and GPU usage stats on invocations in flight
+
+Usage:
+
+statistics = InvocationStatsService(graph_execution_manager)
+with statistics.collect_stats(invocation, graph_execution_state.id):
+      ... execute graphs...
+statistics.log_stats()
+
+Typical output:
+[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
+[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node                 Calls  Seconds  VRAM Used
+[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader        1   0.005s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip                1   0.004s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel                   2   0.512s     0.26G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int                 1   0.001s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size            1   0.001s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate                  1   0.001s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator     1   0.002s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise                    1   0.002s     0.01G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l                      1   3.541s     1.93G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i                      1   0.679s     0.58G
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME:  4.749s
+[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
+
+The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
+writes to the system log is stored in InvocationServices.performance_statistics.
+"""
+
 import time
+from abc import ABC, abstractmethod
+from contextlib import AbstractContextManager
+from dataclasses import dataclass, field
 from typing import Dict
 
 import psutil
 import torch
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.backend.model_management.model_cache import CacheStats
 
-from .invocation_stats_base import InvocationStatsServiceBase
-from .invocation_stats_common import GIG, NodeLog, NodeStats
+from ..invocations.baseinvocation import BaseInvocation
+from .graph import GraphExecutionState
+from .item_storage import ItemStorageABC
+from .model_manager_service import ModelManagerService
+
+# size of GIG in bytes
+GIG = 1073741824
+
+
+@dataclass
+class NodeStats:
+    """Class for tracking execution stats of an invocation node"""
+
+    calls: int = 0
+    time_used: float = 0.0  # seconds
+    max_vram: float = 0.0  # GB
+    cache_hits: int = 0
+    cache_misses: int = 0
+    cache_high_watermark: int = 0
+
+
+@dataclass
+class NodeLog:
+    """Class for tracking node usage"""
+
+    # {node_type => NodeStats}
+    nodes: Dict[str, NodeStats] = field(default_factory=dict)
+
+
+class InvocationStatsServiceBase(ABC):
+    "Abstract base class for recording node memory/time performance statistics"
+
+    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
+    # {graph_id => NodeLog}
+    _stats: Dict[str, NodeLog]
+    _cache_stats: Dict[str, CacheStats]
+    ram_used: float
+    ram_changed: float
+
+    @abstractmethod
+    def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
+        """
+        Initialize the InvocationStatsService and reset counters to zero
+        :param graph_execution_manager: Graph execution manager for this session
+        """
+        pass
+
+    @abstractmethod
+    def collect_stats(
+        self,
+        invocation: BaseInvocation,
+        graph_execution_state_id: str,
+    ) -> AbstractContextManager:
+        """
+        Return a context object that will capture the statistics on the execution
+        of invocaation. Use with: to place around the part of the code that executes the invocation.
+        :param invocation: BaseInvocation object from the current graph.
+        :param graph_execution_state: GraphExecutionState object from the current session.
+        """
+        pass
+
+    @abstractmethod
+    def reset_stats(self, graph_execution_state_id: str):
+        """
+        Reset all statistics for the indicated graph
+        :param graph_execution_state_id
+        """
+        pass
+
+    @abstractmethod
+    def reset_all_stats(self):
+        """Zero all statistics"""
+        pass
+
+    @abstractmethod
+    def update_invocation_stats(
+        self,
+        graph_id: str,
+        invocation_type: str,
+        time_used: float,
+        vram_used: float,
+    ):
+        """
+        Add timing information on execution of a node. Usually
+        used internally.
+        :param graph_id: ID of the graph that is currently executing
+        :param invocation_type: String literal type of the node
+        :param time_used: Time used by node's exection (sec)
+        :param vram_used: Maximum VRAM used during exection (GB)
+        """
+        pass
+
+    @abstractmethod
+    def log_stats(self):
+        """
+        Write out the accumulated statistics to the log or somewhere else.
+        """
+        pass
+
+    @abstractmethod
+    def update_mem_stats(
+        self,
+        ram_used: float,
+        ram_changed: float,
+    ):
+        """
+        Update the collector with RAM memory usage info.
+
+        :param ram_used: How much RAM is currently in use.
+        :param ram_changed: How much RAM changed since last generation.
+        """
+        pass
 
 
 class InvocationStatsService(InvocationStatsServiceBase):
     """Accumulate performance information about a running graph. Collects time spent in each node,
     as well as the maximum and current VRAM utilisation for CUDA systems"""
 
-    _invoker: Invoker
-
-    def __init__(self):
+    def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
+        self.graph_execution_manager = graph_execution_manager
         # {graph_id => NodeLog}
         self._stats: Dict[str, NodeLog] = {}
         self._cache_stats: Dict[str, CacheStats] = {}
         self.ram_used: float = 0.0
         self.ram_changed: float = 0.0
 
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-
     class StatsContext:
         """Context manager for collecting statistics."""
 
@@ -38,13 +174,13 @@ class InvocationStatsService(InvocationStatsServiceBase):
         graph_id: str
         start_time: float
         ram_used: int
-        model_manager: ModelManagerServiceBase
+        model_manager: ModelManagerService
 
         def __init__(
             self,
             invocation: BaseInvocation,
             graph_id: str,
-            model_manager: ModelManagerServiceBase,
+            model_manager: ModelManagerService,
             collector: "InvocationStatsServiceBase",
         ):
             """Initialize statistics for this run."""
@@ -81,11 +217,12 @@ class InvocationStatsService(InvocationStatsServiceBase):
         self,
         invocation: BaseInvocation,
         graph_execution_state_id: str,
+        model_manager: ModelManagerService,
     ) -> StatsContext:
         if not self._stats.get(graph_execution_state_id):  # first time we're seeing this
             self._stats[graph_execution_state_id] = NodeLog()
             self._cache_stats[graph_execution_state_id] = CacheStats()
-        return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
+        return self.StatsContext(invocation, graph_execution_state_id, model_manager, self)
 
     def reset_all_stats(self):
         """Zero all statistics"""
@@ -124,7 +261,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
         errored = set()
         for graph_id, node_log in self._stats.items():
             try:
-                current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
+                current_graph_state = self.graph_execution_manager.get(graph_id)
             except Exception:
                 errored.add(graph_id)
                 continue

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -1,121 +0,0 @@
-# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
-"""Utility to collect execution time and GPU usage stats on invocations in flight
-
-Usage:
-
-statistics = InvocationStatsService(graph_execution_manager)
-with statistics.collect_stats(invocation, graph_execution_state.id):
-      ... execute graphs...
-statistics.log_stats()
-
-Typical output:
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node                 Calls  Seconds  VRAM Used
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader        1   0.005s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip                1   0.004s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel                   2   0.512s     0.26G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int                 1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size            1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate                  1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator     1   0.002s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise                    1   0.002s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l                      1   3.541s     1.93G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i                      1   0.679s     0.58G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME:  4.749s
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
-
-The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
-writes to the system log is stored in InvocationServices.performance_statistics.
-"""
-
-from abc import ABC, abstractmethod
-from contextlib import AbstractContextManager
-from typing import Dict
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.backend.model_management.model_cache import CacheStats
-
-from .invocation_stats_common import NodeLog
-
-
-class InvocationStatsServiceBase(ABC):
-    "Abstract base class for recording node memory/time performance statistics"
-
-    # {graph_id => NodeLog}
-    _stats: Dict[str, NodeLog]
-    _cache_stats: Dict[str, CacheStats]
-    ram_used: float
-    ram_changed: float
-
-    @abstractmethod
-    def __init__(self):
-        """
-        Initialize the InvocationStatsService and reset counters to zero
-        """
-        pass
-
-    @abstractmethod
-    def collect_stats(
-        self,
-        invocation: BaseInvocation,
-        graph_execution_state_id: str,
-    ) -> AbstractContextManager:
-        """
-        Return a context object that will capture the statistics on the execution
-        of invocaation. Use with: to place around the part of the code that executes the invocation.
-        :param invocation: BaseInvocation object from the current graph.
-        :param graph_execution_state_id: The id of the current session.
-        """
-        pass
-
-    @abstractmethod
-    def reset_stats(self, graph_execution_state_id: str):
-        """
-        Reset all statistics for the indicated graph
-        :param graph_execution_state_id
-        """
-        pass
-
-    @abstractmethod
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        pass
-
-    @abstractmethod
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        """
-        Add timing information on execution of a node. Usually
-        used internally.
-        :param graph_id: ID of the graph that is currently executing
-        :param invocation_type: String literal type of the node
-        :param time_used: Time used by node's exection (sec)
-        :param vram_used: Maximum VRAM used during exection (GB)
-        """
-        pass
-
-    @abstractmethod
-    def log_stats(self):
-        """
-        Write out the accumulated statistics to the log or somewhere else.
-        """
-        pass
-
-    @abstractmethod
-    def update_mem_stats(
-        self,
-        ram_used: float,
-        ram_changed: float,
-    ):
-        """
-        Update the collector with RAM memory usage info.
-
-        :param ram_used: How much RAM is currently in use.
-        :param ram_changed: How much RAM changed since last generation.
-        """
-        pass

invokeai/app/services/invocation_stats/invocation_stats_common.py

@@ -1,25 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Dict
-
-# size of GIG in bytes
-GIG = 1073741824
-
-
-@dataclass
-class NodeStats:
-    """Class for tracking execution stats of an invocation node"""
-
-    calls: int = 0
-    time_used: float = 0.0  # seconds
-    max_vram: float = 0.0  # GB
-    cache_hits: int = 0
-    cache_misses: int = 0
-    cache_high_watermark: int = 0
-
-
-@dataclass
-class NodeLog:
-    """Class for tracking node usage"""
-
-    # {node_type => NodeStats}
-    nodes: Dict[str, NodeStats] = field(default_factory=dict)

invokeai/app/services/invoker.py

@@ -1,10 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from abc import ABC
 from typing import Optional
 
-from .invocation_queue.invocation_queue_common import InvocationQueueItem
+from .graph import Graph, GraphExecutionState
+from .invocation_queue import InvocationQueueItem
 from .invocation_services import InvocationServices
-from .shared.graph import Graph, GraphExecutionState
 
 
 class Invoker:
@@ -17,12 +18,7 @@ class Invoker:
         self._start()
 
     def invoke(
-        self,
-        session_queue_id: str,
-        session_queue_item_id: int,
-        session_queue_batch_id: str,
-        graph_execution_state: GraphExecutionState,
-        invoke_all: bool = False,
+        self, queue_id: str, queue_item_id: str, graph_execution_state: GraphExecutionState, invoke_all: bool = False
     ) -> Optional[str]:
         """Determines the next node to invoke and enqueues it, preparing if needed.
         Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
@@ -38,9 +34,8 @@ class Invoker:
         # Queue the invocation
         self.services.queue.put(
             InvocationQueueItem(
-                session_queue_id=session_queue_id,
-                session_queue_item_id=session_queue_item_id,
-                session_queue_batch_id=session_queue_batch_id,
+                session_queue_item_id=queue_item_id,
+                session_queue_id=queue_id,
                 graph_execution_state_id=graph_execution_state.id,
                 invocation_id=invocation.id,
                 invoke_all=invoke_all,
@@ -83,3 +78,7 @@ class Invoker:
             self.__stop_service(getattr(self.services, service))
 
         self.services.queue.put(None)
+
+
+class InvocationProcessorABC(ABC):
+    pass