Update invokeai_version.py

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Translation: InvokeAI/Web UI

.github/workflows/typegen-checks.yml

@@ -0,0 +1,85 @@
+# Runs typegen schema quality checks.
+# Frontend types should match the server.
+#
+# Checks for changes to files before running the checks.
+# If always_run is true, always runs the checks.
+
+name: 'typegen checks'
+
+on:
+  push:
+    branches:
+      - 'main'
+  pull_request:
+    types:
+      - 'ready_for_review'
+      - 'opened'
+      - 'synchronize'
+  merge_group:
+  workflow_dispatch:
+    inputs:
+      always_run:
+        description: 'Always run the checks'
+        required: true
+        type: boolean
+        default: true
+  workflow_call:
+    inputs:
+      always_run:
+        description: 'Always run the checks'
+        required: true
+        type: boolean
+        default: true
+
+jobs:
+  typegen-checks:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 15 # expected run time: <5 min
+    steps:
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: check for changed files
+        if: ${{ inputs.always_run != true }}
+        id: changed-files
+        uses: tj-actions/changed-files@v42
+        with:
+          files_yaml: |
+            src:
+              - 'pyproject.toml'
+              - 'invokeai/**'
+
+      - name: setup python
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
+      - name: install python dependencies
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        run: pip3 install --use-pep517 --editable="."
+
+      - name: install frontend dependencies
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        uses: ./.github/actions/install-frontend-deps
+
+      - name: copy schema
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        run: cp invokeai/frontend/web/src/services/api/schema.ts invokeai/frontend/web/src/services/api/schema_orig.ts
+        shell: bash
+
+      - name: generate schema
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        run: make frontend-typegen
+        shell: bash
+
+      - name: compare files
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        run: |
+          if ! diff invokeai/frontend/web/src/services/api/schema.ts invokeai/frontend/web/src/services/api/schema_orig.ts; then
+            echo "Files are different!";
+            exit 1;
+          fi
+        shell: bash

.nvmrc

@@ -0,0 +1 @@
+v22.12.0

README.md

@@ -30,51 +30,12 @@ Invoke is available in two editions:
 |----------------------------------------------------------------------------------------------------------------------------|
 |  [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]  | 
 
-</div>
+# Installation
 
-## Quick Start
+To get started with Invoke, [Download the Installer](https://www.invoke.com/downloads).
 
-1. Download and unzip the installer from the bottom of the [latest release][latest release link].
-2. Run the installer script.
+For detailed step by step instructions, or for instructions on manual/docker installations, visit our documentation on [Installation and Updates][installation docs]
 
-   - **Windows**: Double-click on the `install.bat` script.
-   - **macOS**: Open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press enter.
-   - **Linux**: Run `install.sh`.
-
-3. When prompted, enter a location for the install and select your GPU type.
-4. Once the install finishes, find the directory you selected during install. The default location is `C:\Users\Username\invokeai` for Windows or `~/invokeai` for Linux/macOS.
-5. Run the launcher script (`invoke.bat` for Windows, `invoke.sh` for macOS and Linux) the same way you ran the installer script in step 2.
-6. Select option 1 to start the application. Once it starts up, open your browser and go to <http://localhost:9090>.
-7. Open the model manager tab to install a starter model and then you'll be ready to generate.
-
-More detail, including hardware requirements and manual install instructions, are available in the [installation documentation][installation docs].
-
-## Docker Container
-
-We publish official container images in Github Container Registry: https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai. Both CUDA and ROCm images are available. Check the above link for relevant tags.
-
-> [!IMPORTANT]
-> Ensure that Docker is set up to use the GPU. Refer to [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] documentation.
-
-### Generate!
-
-Run the container, modifying the command as necessary:
-
-```bash
-docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
-```
-
-Then open `http://localhost:9090` and install some models using the Model Manager tab to begin generating.
-
-For ROCm, add `--device /dev/kfd --device /dev/dri` to the `docker run` command.
-
-### Persist your data
-
-You will likely want to persist your workspace outside of the container. Use the `--volume /home/myuser/invokeai:/invokeai` flag to mount some local directory (using its **absolute** path) to the `/invokeai` path inside the container. Your generated images and models will reside there. You can use this directory with other InvokeAI installations, or switch between runtime directories as needed.
-
-### DIY
-
-Build your own image and customize the environment to match your needs using our `docker-compose` stack. See [README.md](./docker/README.md) in the [docker](./docker) directory.
 
 ## Troubleshooting, FAQ and Support
 

docs/configuration.md

@@ -39,7 +39,7 @@ It has two sections - one for internal use and one for user settings:
 
 ```yaml
 # Internal metadata - do not edit:
-schema_version: 4
+schema_version: 4.0.2
 
 # Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:
 host: 0.0.0.0 # serve the app on your local network
@@ -83,6 +83,10 @@ A subset of settings may be specified using CLI args:
 - `--root`: specify the root directory
 - `--config`: override the default `invokeai.yaml` file location
 
+### Low-VRAM Mode
+
+See the [Low-VRAM mode docs][low-vram] for details on enabling this feature.
+
 ### All Settings
 
 Following the table are additional explanations for certain settings.
@@ -114,6 +118,10 @@ remote_api_tokens:
 
 The provided token will be added as a `Bearer` token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.
 
+!!! tip "HuggingFace Models"
+
+    If you get an error when installing a HF model using a URL instead of repo id, you may need to [set up a HF API token](https://huggingface.co/settings/tokens) and add an entry for it under `remote_api_tokens`. Use `huggingface.co` for `url_regex`.
+
 #### Model Hashing
 
 Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.
@@ -181,3 +189,4 @@ The `log_format` option provides several alternative formats:
 
 [basic guide to yaml files]: https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/
 [Model Marketplace API Keys]: #model-marketplace-api-keys
+[low-vram]: ./features/low-vram.md

docs/contributing/MODEL_MANAGER.md

@@ -1364,7 +1364,6 @@ the in-memory loaded model:
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
 | `model`        | AnyModel               | The instantiated model (details below) |
-| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
 ### get_model_by_key(key, [submodel]) -> LoadedModel
 

docs/contributing/dev-environment.md

@@ -1,12 +1,10 @@
 # Dev Environment
 
-To make changes to Invoke's backend, frontend, or documentation, you'll need to set up a dev environment.
+To make changes to Invoke's backend, frontend or documentation, you'll need to set up a dev environment.
 
-If you just want to use Invoke, you should use the [installer][installer link].
+If you only want to make changes to the docs site, you can skip the frontend dev environment setup as described in the below guide.
 
-!!! info "Why do I need the frontend toolchain?"
-
-    The repo doesn't contain a build of the frontend. You'll be responsible for rebuilding it every time you pull in new changes, or run it in dev mode (which incurs a substantial performance penalty).
+If you just want to use Invoke, you should use the [launcher][launcher link].
 
 !!! warning
 
@@ -17,84 +15,66 @@ If you just want to use Invoke, you should use the [installer][installer link].
 ## Setup
 
 1. Run through the [requirements][requirements link].
+
 2. [Fork and clone][forking link] the [InvokeAI repo][repo link].
+
 3. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
-4. Create a python virtual environment inside the directory you just created:
+
+4. Follow the [manual install][manual install link] guide, with some modifications to the install command:
+
+      - Use `.` instead of `invokeai` to install from the current directory. You don't need to specify the version.
+
+      - Add `-e` after the `install` operation to make this an [editable install][editable install link]. That means your changes to the python code will be reflected when you restart the Invoke server.
+
+      - When installing the `invokeai` package, add the `dev`, `test` and `docs` package options to the package specifier. You may or may not need the `xformers` option - follow the manual install guide to figure that out. So, your package specifier will be either `".[dev,test,docs]"` or `".[dev,test,docs,xformers]"`. Note the quotes!
+
+     With the modifications made, the install command should look something like this:
 
       ```sh
-      python3 -m venv .venv --prompt InvokeAI-Dev
+      uv pip install -e ".[dev,test,docs,xformers]" --python 3.11 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
       ```
 
-5. Activate the venv (you'll need to do this every time you want to run the app):
+5. At this point, you should have Invoke installed, a venv set up and activated, and the server running. But you will see a warning in the terminal that no UI was found. If you go to the URL for the server, you won't get a UI.
+
+      This is because the UI build is not distributed with the source code. You need to build it manually. End the running server instance.
+
+      If you only want to edit the docs, you can stop here and skip to the **Documentation** section below.
+
+6. Install the frontend dev toolchain:
+
+      - [`nodejs`](https://nodejs.org/) (v20+)
+
+      - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
+
+7. Do a production build of the frontend:
 
       ```sh
-      source .venv/bin/activate
-      ```
-
-6. Install the repo as an [editable install][editable install link]:
-
-      ```sh
-      pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
-      ```
-
-      Refer to the [manual installation][manual install link] instructions for more determining the correct install options. `xformers` is optional, but `dev` and `test` are not.
-
-7. Install the frontend dev toolchain:
-
-   - [`nodejs`](https://nodejs.org/) (recommend v20 LTS)
-   - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
-
-8. Do a production build of the frontend:
-
-      ```sh
-      cd PATH_TO_INVOKEAI_REPO/invokeai/frontend/web
+      cd <PATH_TO_INVOKEAI_REPO>/invokeai/frontend/web
       pnpm i
       pnpm build
       ```
 
-9. Start the application:
-
-      ```sh
-      cd PATH_TO_INVOKEAI_REPO
-      python scripts/invokeai-web.py
-      ```
-
-10. Access the UI at `localhost:9090`.
+8. Restart the server and navigate to the URL. You should get a UI. After making changes to the python code, restart the server to see those changes.
 
 ## Updating the UI
 
-You'll need to run `pnpm build` every time you pull in new changes. Another option is to skip the build and instead run the app in dev mode:
+You'll need to run `pnpm build` every time you pull in new changes.
+
+Another option is to skip the build and instead run the UI in dev mode:
 
 ```sh
 pnpm dev
 ```
 
-This starts a dev server at `localhost:5173`, which you will use instead of `localhost:9090`.
+This starts a vite dev server for the UI at `127.0.0.1:5173`, which you will use instead of `127.0.0.1:9090`.
 
-The dev mode is substantially slower than the production build but may be more convenient if you just need to test things out.
+The dev mode is substantially slower than the production build but may be more convenient if you just need to test things out. It will hot-reload the UI as you make changes to the frontend code. Sometimes the hot-reload doesn't work, and you need to manually refresh the browser tab.
 
 ## Documentation
 
-The documentation is built with `mkdocs`. To preview it locally, you need a additional set of packages installed.
+The documentation is built with `mkdocs`. It provides a hot-reload dev server for the docs. Start it with `mkdocs serve`.
 
-```sh
-# after activating the venv
-pip install -e ".[docs]"
-```
-
-Then, you can start a live docs dev server, which will auto-refresh when you edit the docs:
-
-```sh
-mkdocs serve
-```
-
-On macOS and Linux, there is a `make` target for this:
-
-```sh
-make docs
-```
-
-[installer link]: ../installation/installer.md
+[launcher link]: ../installation/quick_start.md
 [forking link]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo
 [requirements link]: ../installation/requirements.md
 [repo link]: https://github.com/invoke-ai/InvokeAI

docs/faq.md

@@ -1,26 +1,18 @@
 # FAQ
 
-!!! info "How to Reinstall"
-
-    Many issues can be resolved by re-installing the application. You won't lose any data by re-installing. We suggest downloading the [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest) and using it to re-install the application. Consult the [installer guide](./installation/installer.md) for more information.
-
-    When you run the installer, you'll have an option to select the version to install. If you aren't ready to upgrade, you choose the current version to fix a broken install.
-
 If the troubleshooting steps on this page don't get you up and running, please either [create an issue] or hop on [discord] for help.
 
 ## How to Install
 
-You can download the latest installers [here](https://github.com/invoke-ai/InvokeAI/releases).
-
-Note that any releases marked as _pre-release_ are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the [latest release].
+Follow the [Quick Start guide](./installation/quick_start.md) to install Invoke.
 
 ## Downloading models and using existing models
 
 The Model Manager tab in the UI provides a few ways to install models, including using your already-downloaded models. You'll see a popup directing you there on first startup. For more information, see the [model install docs].
 
-## Missing models after updating to v4
+## Missing models after updating from v3
 
-If you find some models are missing after updating to v4, it's likely they weren't correctly registered before the update and didn't get picked up in the migration.
+If you find some models are missing after updating from v3, it's likely they weren't correctly registered before the update and didn't get picked up in the migration.
 
 You can use the `Scan Folder` tab in the Model Manager UI to fix this. The models will either be in the old, now-unused `autoimport` folder, or your `models` folder.
 
@@ -37,115 +29,27 @@ Follow the same steps to scan and import the missing models.
 ## Slow generation
 
 - Check the [system requirements] to ensure that your system is capable of generating images.
-- Check the `ram` setting in `invokeai.yaml`. This setting tells Invoke how much of your system RAM can be used to cache models. Having this too high or too low can slow things down. That said, it's generally safest to not set this at all and instead let Invoke manage it.
-- Check the `vram` setting in `invokeai.yaml`. This setting tells Invoke how much of your GPU VRAM can be used to cache models. Counter-intuitively, if this setting is too high, Invoke will need to do a lot of shuffling of models as it juggles the VRAM cache and the currently-loaded model. The default value of 0.25 is generally works well for GPUs without 16GB or more VRAM. Even on a 24GB card, the default works well.
-- Check that your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. If your GPU isn't used, re-install to ensure the correct versions of torch get installed.
-- If you are on Windows, you may have exceeded your GPU's VRAM capacity and are using slower [shared GPU memory](#shared-gpu-memory-windows). There's a guide to opt out of this behaviour in the linked FAQ entry.
-
-## Shared GPU Memory (Windows)
-
-!!! tip "Nvidia GPUs with driver 536.40"
-
-    This only applies to current Nvidia cards with driver 536.40 or later, released in June 2023.
-
-When the GPU doesn't have enough VRAM for a task, Windows is able to allocate some of its CPU RAM to the GPU. This is much slower than VRAM, but it does allow the system to generate when it otherwise might no have enough VRAM.
-
-When shared GPU memory is used, generation slows down dramatically - but at least it doesn't crash.
-
-If you'd like to opt out of this behavior and instead get an error when you exceed your GPU's VRAM, follow [this guide from Nvidia](https://nvidia.custhelp.com/app/answers/detail/a_id/5490).
-
-Here's how to get the python path required in the linked guide:
-
-- Run `invoke.bat`.
-- Select option 2 for developer console.
-- At least one python path will be printed. Copy the path that includes your invoke installation directory (typically the first).
-
-## Installer cannot find python (Windows)
-
-Ensure that you checked **Add python.exe to PATH** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, you can re-run the python installer, choose the Modify option and check the box.
+- Follow the [Low-VRAM mode guide](./features/low-vram.md) to optimize performance.
+- Check that your generations are happening on your GPU (if you have one). Invoke will log what is being used for generation upon startup. If your GPU isn't used, re-install to and ensure you select the appropriate GPU option.
+- If you are on Windows with an Nvidia GPU, you may have exceeded your GPU's VRAM capacity and are triggering Nvidia's "sysmem fallback". There's a guide to opt out of this behaviour in the [Low-VRAM mode guide](./features/low-vram.md).
 
 ## Triton error on startup
 
-This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
+This can be safely ignored. Invoke doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
 
-## Updated to 3.4.0 and xformers can’t load C++/CUDA
+## Unable to Copy on Firefox
 
-An issue occurred with your PyTorch update. Follow these steps to fix :
+Firefox does not allow Invoke to directly access the clipboard by default. As a result, you may be unable to use certain copy functions. You can fix this by configuring Firefox to allow access to write to the clipboard:
 
-1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
-2. Run:`pip install ".[xformers]" --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121`
-   - If you run into an error with `typing_extensions`, re-open the developer console and run: `pip install -U typing-extensions`
-
-Note that v3.4.0 is an old, unsupported version. Please upgrade to the [latest release].
-
-## Install failed and says `pip` is out of date
-
-An out of date `pip` typically won't cause an installation to fail. The cause of the error can likely be found above the message that says `pip` is out of date.
-
-If you saw that warning but the install went well, don't worry about it (but you can update `pip` afterwards if you'd like).
+- Go to `about:config` and click the Accept button
+- Search for `dom.events.asyncClipboard.clipboardItem`
+- Set it to `true` by clicking the toggle button
+- Restart Firefox
 
 ## Replicate image found online
 
 Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
 
-## OSErrors on Windows while installing dependencies
-
-During a zip file installation or an update, installation stops with an error like this:
-
-![broken-dependency-screenshot](./assets/troubleshooting/broken-dependency.png){:width="800px"}
-
-To resolve this, re-install the application as described above.
-
-## HuggingFace install failed due to invalid access token
-
-Some HuggingFace models require you to authenticate using an [access token].
-
-Invoke doesn't manage this token for you, but it's easy to set it up:
-
-- Follow the instructions in the link above to create an access token. Copy it.
-- Run the launcher script.
-- Select option 2 (developer console).
-- Paste the following command:
-
-  ```sh
-  python -c "import huggingface_hub; huggingface_hub.login()"
-  ```
-
-- Paste your access token when prompted and press Enter. You won't see anything when you paste it.
-- Type `n` if prompted about git credentials.
-
-If you get an error, try the command again - maybe the token didn't paste correctly.
-
-Once your token is set, start Invoke and try downloading the model again. The installer will automatically use the access token.
-
-If the install still fails, you may not have access to the model.
-
-## Stable Diffusion XL generation fails after trying to load UNet
-
-InvokeAI is working in other respects, but when trying to generate
-images with Stable Diffusion XL you get a "Server Error". The text log
-in the launch window contains this log line above several more lines of
-error messages:
-
-`INFO --> Loading model:D:\LONG\PATH\TO\MODEL, type sdxl:main:unet`
-
-This failure mode occurs when there is a network glitch during
-downloading the very large SDXL model.
-
-To address this, first go to the Model Manager and delete the
-Stable-Diffusion-XL-base-1.X model. Then, click the HuggingFace tab,
-paste the Repo ID stabilityai/stable-diffusion-xl-base-1.0 and install
-the model.
-
-## Package dependency conflicts during installation or update
-
-If you have previously installed InvokeAI or another Stable Diffusion
-package, the installer may occasionally pick up outdated libraries and
-either the installer or `invoke` will fail with complaints about
-library conflicts.
-
-To resolve this, re-install the application as described above.
-
 ## Invalid configuration file
 
 Everything seems to install ok, you get a `ValidationError` when starting up the app.
@@ -154,64 +58,9 @@ This is caused by an invalid setting in the `invokeai.yaml` configuration file.
 
 Check the [configuration docs] for more detail about the settings and how to specify them.
 
-## `ModuleNotFoundError: No module named 'controlnet_aux'`
+## Out of Memory Errors
 
-`controlnet_aux` is a dependency of Invoke and appears to have been packaged or distributed strangely. Sometimes, it doesn't install correctly. This is outside our control.
-
-If you encounter this error, the solution is to remove the package from the `pip` cache and re-run the Invoke installer so a fresh, working version of `controlnet_aux` can be downloaded and installed:
-
-- Run the Invoke launcher
-- Choose the developer console option
-- Run this command: `pip cache remove controlnet_aux`
-- Close the terminal window
-- Download and run the [installer][latest release], selecting your current install location
-
-## Out of Memory Issues
-
-The models are large, VRAM is expensive, and you may find yourself
-faced with Out of Memory errors when generating images. Here are some
-tips to reduce the problem:
-
-!!! info "Optimizing for GPU VRAM"
-
-    === "4GB VRAM GPU"
-
-        This should be adequate for 512x512 pixel images using Stable Diffusion 1.5
-        and derived models, provided that you do not use the NSFW checker. It won't be loaded unless you go into the UI settings and turn it on.
-
-        If you are on a CUDA-enabled GPU, we will automatically use xformers or torch-sdp to reduce VRAM requirements, though you can explicitly configure this. See the [configuration docs].
-
-    === "6GB VRAM GPU"
-
-        This is a border case. Using the SD 1.5 series you should be able to
-        generate images up to 640x640 with the NSFW checker enabled, and up to
-        1024x1024 with it disabled.
-
-        If you run into persistent memory issues there are a series of
-        environment variables that you can set before launching InvokeAI that
-        alter how the PyTorch machine learning library manages memory. See
-        <https://pytorch.org/docs/stable/notes/cuda.html#memory-management> for
-        a list of these tweaks.
-
-    === "12GB VRAM GPU"
-
-        This should be sufficient to generate larger images up to about 1280x1280.
-  
-## Checkpoint Models Load Slowly or Use Too Much RAM
-
-The difference between diffusers models (a folder containing multiple
-subfolders) and checkpoint models (a file ending with .safetensors or
-.ckpt) is that InvokeAI is able to load diffusers models into memory
-incrementally, while checkpoint models must be loaded all at
-once. With very large models, or systems with limited RAM, you may
-experience slowdowns and other memory-related issues when loading
-checkpoint models.
-
-To solve this, go to the Model Manager tab (the cube), select the
-checkpoint model that's giving you trouble, and press the "Convert"
-button in the upper right of your browser window. This will convert the
-checkpoint into a diffusers model, after which loading should be
-faster and less memory-intensive.
+The models are large, VRAM is expensive, and you may find yourself faced with Out of Memory errors when generating images. Follow our [Low-VRAM mode guide](./features/low-vram.md) to configure Invoke to prevent these.
 
 ## Memory Leak (Linux)
 
@@ -253,8 +102,6 @@ Note the differences between memory allocated as chunks in an arena vs. memory a
 
 [model install docs]: ./installation/models.md
 [system requirements]: ./installation/requirements.md
-[latest release]: https://github.com/invoke-ai/InvokeAI/releases/latest
 [create an issue]: https://github.com/invoke-ai/InvokeAI/issues
 [discord]: https://discord.gg/ZmtBAhwWhy
 [configuration docs]: ./configuration.md
-[access token]: https://huggingface.co/docs/hub/security-tokens#how-to-manage-user-access-tokens

docs/features/low-vram.md

@@ -0,0 +1,163 @@
+---
+title: Low-VRAM mode
+---
+
+As of v5.6.0, Invoke has a low-VRAM mode. It works on systems with dedicated GPUs (Nvidia GPUs on Windows/Linux and AMD GPUs on Linux).
+
+This allows you to generate even if your GPU doesn't have enough VRAM to hold full models. Most users should be able to run even the beefiest models - like the ~24GB unquantised FLUX dev model.
+
+## Enabling Low-VRAM mode
+
+To enable Low-VRAM mode, add this line to your `invokeai.yaml` configuration file, then restart Invoke:
+
+```yaml
+enable_partial_loading: true
+```
+
+**Windows users should also [disable the Nvidia sysmem fallback](#disabling-nvidia-sysmem-fallback-windows-only)**.
+
+It is possible to fine-tune the settings for best performance or if you still get out-of-memory errors (OOMs).
+
+!!! tip "How to find `invokeai.yaml`"
+
+    The `invokeai.yaml` configuration file lives in your install directory. To access it, run the **Invoke Community Edition** launcher and click the install location. This will open your install directory in a file explorer window.
+
+    You'll see `invokeai.yaml` there and can edit it with any text editor. After making changes, restart Invoke.
+
+    If you don't see `invokeai.yaml`, launch Invoke once. It will create the file on its first startup.
+
+## Details and fine-tuning
+
+Low-VRAM mode involves 4 features, each of which can be configured or fine-tuned:
+
+- Partial model loading (`enable_partial_loading`)
+- Dynamic RAM and VRAM cache sizes (`max_cache_ram_gb`, `max_cache_vram_gb`)
+- Working memory (`device_working_mem_gb`)
+- Keeping a RAM weight copy (`keep_ram_copy_of_weights`)
+
+Read on to learn about these features and understand how to fine-tune them for your system and use-cases.
+
+### Partial model loading
+
+Invoke's partial model loading works by streaming model "layers" between RAM and VRAM as they are needed.
+
+When an operation needs layers that are not in VRAM, but there isn't enough room to load them, inactive layers are offloaded to RAM to make room.
+
+#### Enabling partial model loading
+
+As described above, you can enable partial model loading by adding this line to `invokeai.yaml`:
+
+```yaml
+enable_partial_loading: true
+```
+
+### Dynamic RAM and VRAM cache sizes
+
+Loading models from disk is slow and can be a major bottleneck for performance. Invoke uses two model caches - RAM and VRAM - to reduce loading from disk to a minimum.
+
+By default, Invoke manages these caches' sizes dynamically for best performance.
+
+#### Fine-tuning cache sizes
+
+Prior to v5.6.0, the cache sizes were static, and for best performance, many users needed to manually fine-tune the `ram` and `vram` settings in `invokeai.yaml`.
+
+As of v5.6.0, the caches are dynamically sized. The `ram` and `vram` settings are no longer used, and new settings are added to configure the cache.
+
+**Most users will not need to fine-tune the cache sizes.**
+
+But, if your GPU has enough VRAM to hold models fully, you might get a perf boost by manually setting the cache sizes in `invokeai.yaml`:
+
+```yaml
+# The default max cache RAM size is logged on InvokeAI startup. It is determined based on your system RAM / VRAM.
+# You can override the default value by setting `max_cache_ram_gb`.
+# Increasing `max_cache_ram_gb` will increase the amount of RAM used to cache inactive models, resulting in faster model
+# reloads for the cached models.
+# As an example, if your system has 32GB of RAM and no other heavy processes, setting the `max_cache_ram_gb` to 28GB
+# might be a good value to achieve aggressive model caching.
+max_cache_ram_gb: 28
+# The default max cache VRAM size is adjusted dynamically based on the amount of available VRAM (taking into
+# consideration the VRAM used by other processes).
+# You can override the default value by setting `max_cache_vram_gb`. Note that this value takes precedence over the
+# `device_working_mem_gb`.
+# It is recommended to set the VRAM cache size to be as large as possible while leaving enough room for the working
+# memory of the tasks you will be doing. For example, on a 24GB GPU that will be running unquantized FLUX without any
+# auxiliary models, 18GB might be a good value.
+max_cache_vram_gb: 18
+```
+
+!!! tip "Max safe value for `max_cache_vram_gb`"
+
+    To determine the max safe value for `max_cache_vram_gb`, subtract `device_working_mem_gb` from your GPU's VRAM. As described below, the default for `device_working_mem_gb` is 3GB.
+
+    For example, if you have a 12GB GPU, the max safe value for `max_cache_vram_gb` is `12GB - 3GB = 9GB`.
+
+    If you had increased `device_working_mem_gb` to 4GB, then the max safe value for `max_cache_vram_gb` is `12GB - 4GB = 8GB`.
+
+### Working memory
+
+Invoke cannot use _all_ of your VRAM for model caching and loading. It requires some VRAM to use as working memory for various operations.
+
+Invoke reserves 3GB VRAM as working memory by default, which is enough for most use-cases. However, it is possible to fine-tune this setting if you still get OOMs.
+
+#### Fine-tuning working memory
+
+You can increase the working memory size in `invokeai.yaml` to prevent OOMs:
+
+```yaml
+# The default is 3GB - bump it up to 4GB to prevent OOMs.
+device_working_mem_gb: 4
+```
+
+!!! tip "Operations may request more working memory"
+
+    For some operations, we can determine VRAM requirements in advance and allocate additional working memory to prevent OOMs.
+
+    VAE decoding is one such operation. This operation converts the generation process's output into an image. For large image outputs, this might use more than the default working memory size of 3GB.
+
+    During this decoding step, Invoke calculates how much VRAM will be required to decode and requests that much VRAM from the model manager. If the amount exceeds the working memory size, the model manager will offload cached model layers from VRAM until there's enough VRAM to decode.
+
+    Once decoding completes, the model manager "reclaims" the extra VRAM allocated as working memory for future model loading operations.
+
+### Keeping a RAM weight copy
+
+Invoke has the option of keeping a RAM copy of all model weights, even when they are loaded onto the GPU. This optimization is _on_ by default, and enables faster model switching and LoRA patching. Disabling this feature will reduce the average RAM load while running Invoke (peak RAM likely won't change), at the cost of slower model switching and LoRA patching. If you have limited RAM, you can disable this optimization:
+
+```yaml
+# Set to false to reduce the average RAM usage at the cost of slower model switching and LoRA patching.
+keep_ram_copy_of_weights: false
+```
+
+### Disabling Nvidia sysmem fallback (Windows only)
+
+On Windows, Nvidia GPUs are able to use system RAM when their VRAM fills up via **sysmem fallback**. While it sounds like a good idea on the surface, in practice it causes massive slowdowns during generation.
+
+It is strongly suggested to disable this feature:
+
+- Open the **NVIDIA Control Panel** app.
+- Expand **3D Settings** on the left panel.
+- Click **Manage 3D Settings** in the left panel.
+- Find **CUDA - Sysmem Fallback Policy** in the right panel and set it to **Prefer No Sysmem Fallback**.
+
+![cuda-sysmem-fallback](./cuda-sysmem-fallback.png)
+
+!!! tip "Invoke does the same thing, but better"
+
+    If the sysmem fallback feature sounds familiar, that's because Invoke's partial model loading strategy is conceptually very similar - use VRAM when there's room, else fall back to RAM.
+
+    Unfortunately, the Nvidia implementation is not optimized for applications like Invoke and does more harm than good.
+
+## Troubleshooting
+
+### Windows page file
+
+Invoke has high virtual memory (a.k.a. 'committed memory') requirements. This can cause issues on Windows if the page file size limits are hit. (See this issue for the technical details on why this happens: https://github.com/invoke-ai/InvokeAI/issues/7563).
+
+If you run out of page file space, InvokeAI may crash. Often, these crashes will happen with one of the following errors:
+
+- InvokeAI exits with Windows error code `3221225477`
+- InvokeAI crashes without an error, but `eventvwr.msc` reveals an error with code `0xc0000005` (the hex equivalent of `3221225477`)
+
+If you are running out of page file space, try the following solutions:
+
+- Make sure that you have sufficient disk space for the page file to grow. Watch your disk usage as Invoke runs. If it climbs near 100% leading up to the crash, then this is very likely the source of the issue. Clear out some disk space to resolve the issue.
+- Make sure that your page file is set to "System managed size" (this is the default) rather than a custom size. Under the "System managed size" policy, the page file will grow dynamically as needed. 

docs/index.md

@@ -50,11 +50,9 @@ title: Invoke
 
 ## Installation
 
-The [installer script](installation/installer.md) is the easiest way to install and update the application.
+The [Invoke Launcher](installation/quick_start.md) is the easiest way to install, update and run Invoke on Windows, macOS and Linux.
 
-You can also install Invoke as python package [via PyPI](installation/manual.md) or [docker](installation/docker.md).
-
-See the [installation section](./installation/index.md) for more information.
+You can also install Invoke as [python package](installation/manual.md) or with [docker](installation/docker.md).
 
 ## Help
 

docs/installation/docker.md

@@ -4,7 +4,7 @@ title: Docker
 
 !!! warning "macOS users"
 
-    Docker can not access the GPU on macOS, so your generation speeds will be slow. Use the [installer](./installer.md) instead.
+    Docker can not access the GPU on macOS, so your generation speeds will be slow. Use the [launcher](./quick_start.md) instead.
 
 !!! tip "Linux and Windows Users"
 

docs/installation/index.md

@@ -1,36 +0,0 @@
-# Installation and Updating Overview
-
-Before installing, review the [installation requirements](./requirements.md) to ensure your system is set up properly.
-
-See the [FAQ](../faq.md) for frequently-encountered installation issues.
-
-If you need more help, join our [discord](https://discord.gg/ZmtBAhwWhy) or [create a GitHub issue](https://github.com/invoke-ai/InvokeAI/issues).
-
-## Automated Installer & Updates
-
-✅ The automated [installer](./installer.md) is the best way to install Invoke.
-
-⬆️ The same installer is also the best way to update Invoke - simply rerun it for the same folder you installed to.
-
-The installation process simply manages installation for the core libraries & application dependencies that run Invoke.
-
-Models, images, or other assets in the Invoke root folder won't be affected by the installation process.
-
-## Manual Install
-
-If you are familiar with python and want more control over the packages that are installed, you can [install Invoke manually via PyPI](./manual.md).
-
-Updates are managed by reinstalling the latest version through PyPi.
-
-## Developer Install
-
-If you want to contribute to InvokeAI, you'll need to set up a [dev environment](../contributing/dev-environment.md).
-
-## Docker
-
-Invoke publishes docker images. See the [docker installation guide](./docker.md) for details.
-
-## Other Installation Guides
-
-- [PyPatchMatch](./patchmatch.md)
-- [Installing Models](./models.md)

docs/installation/legacy_scripts.md

@@ -1,4 +1,10 @@
-# Automatic Install & Updates
+# Legacy Scripts
+
+!!! warning "Legacy Scripts"
+
+    We recommend using the Invoke Launcher to install and update Invoke. It's a desktop application for Windows, macOS and Linux. It takes care of a lot of nitty gritty details for you.
+
+    Follow the [quick start guide](./quick_start.md) to get started.
 
 !!! tip "Use the installer to update"
 

docs/installation/manual.md

@@ -4,11 +4,11 @@
 
     **Python experience is mandatory.**
 
-    If you want to use Invoke locally, you should probably use the [installer](./installer.md).
+    If you want to use Invoke locally, you should probably use the [launcher](./quick_start.md).
 
-    If you want to contribute to Invoke, instead follow the [dev environment](../contributing/dev-environment.md) guide.
+    If you want to contribute to Invoke or run the app on the latest dev branch, instead follow the [dev environment](../contributing/dev-environment.md) guide.
 
-InvokeAI is distributed as a python package on PyPI, installable with `pip`. There are a few things that are handled by the installer and launcher that you'll need to manage manually, described in this guide.
+InvokeAI is distributed as a python package on PyPI, installable with `pip`. There are a few things that are handled by the launcher that you'll need to manage manually, described in this guide.
 
 ## Requirements
 
@@ -16,43 +16,39 @@ Before you start, go through the [installation requirements](./requirements.md).
 
 ## Walkthrough
 
-1. Create a directory to contain your InvokeAI library, configuration files, and models. This is known as the "runtime" or "root" directory, and typically lives in your home directory under the name `invokeai`.
+We'll use [`uv`](https://github.com/astral-sh/uv) to install python and create a virtual environment, then install the `invokeai` package. `uv` is a modern, very fast alternative to `pip`.
+
+The following commands vary depending on the version of Invoke being installed and the system onto which it is being installed.
+
+1. Install `uv` as described in its [docs](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer). We suggest using the standalone installer method.
+
+    Run `uv --version` to confirm that `uv` is installed and working. After installation, you may need to restart your terminal to get access to `uv`.
+
+2. Create a directory for your installation, typically in your home directory (e.g. `~/invokeai` or `$Home/invokeai`):
 
     === "Linux/macOS"
 
         ```bash
         mkdir ~/invokeai
+        cd ~/invokeai
         ```
 
     === "Windows (PowerShell)"
 
         ```bash
         mkdir $Home/invokeai
-        ```
-
-1. Enter the root directory and create a virtual Python environment within it named `.venv`.
-
-    !!! warning "Virtual Environment Location"
-
-        While you may create the virtual environment anywhere in the file system, we recommend that you create it within the root directory as shown here. This allows the application to automatically detect its data directories.
-
-        If you choose a different location for the venv, then you _must_ set the `INVOKEAI_ROOT` environment variable or specify the root directory using the `--root` CLI arg.
-
-    === "Linux/macOS"
-
-        ```bash
-        cd ~/invokeai
-        python3 -m venv .venv --prompt InvokeAI
-        ```
-
-    === "Windows (PowerShell)"
-
-        ```bash
         cd $Home/invokeai
-        python3 -m venv .venv --prompt InvokeAI
         ```
 
-1. Activate the new environment:
+3. Create a virtual environment in that directory:
+
+    ```sh
+    uv venv --relocatable --prompt invoke --python 3.11 --python-preference only-managed .venv
+    ```
+
+    This command creates a portable virtual environment at `.venv` complete with a portable python 3.11. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
+
+4. Activate the virtual environment:
 
     === "Linux/macOS"
 
@@ -60,41 +56,48 @@ Before you start, go through the [installation requirements](./requirements.md).
         source .venv/bin/activate
         ```
 
-    === "Windows"
+    === "Windows (PowerShell)"
 
         ```ps
         .venv\Scripts\activate
         ```
 
-    !!! info "Permissions Error (Windows)"
+5. Choose a version to install. Review the [GitHub releases page](https://github.com/invoke-ai/InvokeAI/releases).
 
-        If you get a permissions error at this point, run this command and try again.
+6. Determine the package package specifier to use when installing. This is a performance optimization.
 
-        `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser`
+    - If you have an Nvidia 20xx series GPU or older, use `invokeai[xformers]`.
+    - If you have an Nvidia 30xx series GPU or newer, or do not have an Nvidia GPU, use `invokeai`.
 
-    The command-line prompt should change to to show `(InvokeAI)`, indicating the venv is active.
+7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
 
-1. Make sure that pip is installed in your virtual environment and up to date:
+    === "Invoke v5 or later"
 
-    ```bash
-    python3 -m pip install --upgrade pip
+        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.1`.
+        - **In all other cases, do not use an index.**
+
+    === "Invoke v4"
+
+        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm5.2`.
+        - **In all other cases, do not use an index.**
+
+8. Install the `invokeai` package. Substitute the package specifier and version.
+
+    ```sh
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --python-preference only-managed --force-reinstall
     ```
 
-1. Install the InvokeAI Package. The base command is `pip install InvokeAI --use-pep517`, but you may need to change this depending on your system and the desired features.
+    If you determined you needed to use a `PyPI` index URL in the previous step, you'll need to add `--index=<INDEX_URL>` like this:
 
-    - You may need to provide an [extra index URL](https://pip.pypa.io/en/stable/cli/pip_install/#cmdoption-extra-index-url). Select your platform configuration using [this tool on the PyTorch website](https://pytorch.org/get-started/locally/). Copy the `--extra-index-url` string from this and append it to your install command.
+    ```sh
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
+    ```
 
-        ```bash
-        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
-        ```
-
-    - If you have a CUDA GPU and want to install with `xformers`, you need to add an option to the package name. Note that `xformers` is not strictly necessary. PyTorch includes an implementation of the SDP attention algorithm with similar performance for most GPUs.
-
-        ```bash
-        pip install "InvokeAI[xformers]" --use-pep517
-        ```
-
-1. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:
+9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:
 
     === "Linux/macOS"
 
@@ -102,17 +105,31 @@ Before you start, go through the [installation requirements](./requirements.md).
         deactivate && source .venv/bin/activate
         ```
 
-    === "Windows"
+    === "Windows (PowerShell)"
 
         ```ps
         deactivate
         .venv\Scripts\activate
         ```
 
-1. Run the application:
+10. Run the application, specifying the directory you created earlier as the root directory:
 
-    Run `invokeai-web` to start the UI. You must activate the virtual environment before running the app.
+    === "Linux/macOS"
 
-    !!! warning
+        ```bash
+        invokeai-web --root ~/invokeai
+        ```
 
-        If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.
+    === "Windows (PowerShell)"
+
+        ```bash
+        invokeai-web --root $Home/invokeai
+        ```
+
+## Headless Install and Launch Scripts
+
+If you run Invoke on a headless server, you might want to install and run Invoke on the command line.
+
+We do not plan to maintain scripts to do this moving forward, instead focusing our dev resources on the GUI [launcher](../installation/quick_start.md).
+
+You can create your own scripts for this by copying the handful of commands in this guide. `uv`'s [`pip` interface docs](https://docs.astral.sh/uv/reference/cli/#uv-pip-install) may be useful.

docs/installation/quick_start.md

@@ -0,0 +1,128 @@
+# Invoke Community Edition Quick Start
+
+Welcome to Invoke! Follow these steps to install, update, and get started creating.
+
+## Step 1: System Requirements
+
+Invoke runs on Windows 10+, macOS 14+ and Linux (Ubuntu 20.04+ is well-tested).
+
+Hardware requirements vary significantly depending on model and image output size. The requirements below are rough guidelines.
+
+- All Apple Silicon (M1, M2, etc) Macs work, but 16GB+ memory is recommended.
+- AMD GPUs are supported on Linux only. The VRAM requirements are the same as Nvidia GPUs.
+
+!!! info "Hardware Requirements (Windows/Linux)"
+
+    === "SD1.5 - 512×512"
+
+        - GPU: Nvidia 10xx series or later, 4GB+ VRAM.
+        - Memory: At least 8GB RAM.
+        - Disk: 10GB for base installation plus 30GB for models.
+
+    === "SDXL - 1024×1024"
+
+        - GPU: Nvidia 20xx series or later, 8GB+ VRAM.
+        - Memory: At least 16GB RAM.
+        - Disk: 10GB for base installation plus 100GB for models.
+
+    === "FLUX - 1024×1024"
+
+        - GPU: Nvidia 20xx series or later, 10GB+ VRAM.
+        - Memory: At least 32GB RAM.
+        - Disk: 10GB for base installation plus 200GB for models.
+
+More detail on system requirements can be found [here](./requirements.md).
+
+## Step 2: Download
+
+Download the most launcher for your operating system:
+
+- [Download for Windows](https://download.invoke.ai/Invoke%20Community%20Edition.exe)
+- [Download for macOS](https://download.invoke.ai/Invoke%20Community%20Edition.dmg)
+- [Download for Linux](https://download.invoke.ai/Invoke%20Community%20Edition.AppImage)
+
+## Step 3: Install or Update
+
+Run the launcher you just downloaded, click **Install** and follow the instructions to get set up.
+
+If you have an existing Invoke installation, you can select it and let the launcher manage the install. You'll be able to update or launch the installation.
+
+!!! warning "Problem running the launcher on macOS"
+
+    macOS may not allow you to run the launcher. We are working to resolve this by signing the launcher executable. Until that is done, you can either use the [legacy scripts](./legacy_scripts.md) to install, or manually flag the launcher as safe:
+
+    - Open the **Invoke-Installer-mac-arm64.dmg** file.
+    - Drag the launcher to **Applications**.
+    - Open a terminal.
+    - Run `xattr -d 'com.apple.quarantine' /Applications/Invoke\ Community\ Edition.app`.
+
+    You should now be able to run the launcher.
+
+## Step 4: Launch
+
+Once installed, click **Finish**, then **Launch** to start Invoke.
+
+The very first run after an installation or update will take a few extra moments to get ready.
+
+!!! tip "Server Mode"
+
+    The launcher runs Invoke as a desktop application. You can enable **Server Mode** in the launcher's settings to disable this and instead access the UI through your web browser.
+
+## Step 5: Install Models
+
+With Invoke started up, you'll need to install some models.
+
+The quickest way to get started is to install a **Starter Model** bundle. If you already have a model collection, Invoke can use it.
+
+!!! info "Install Models"
+
+    === "Install a Starter Model bundle"
+
+        1. Go to the **Models** tab.
+        2. Click **Starter Models** on the right.
+        3. Click one of the bundles to install its models. Refer to the [system requirements](#step-1-confirm-system-requirements) if you're unsure which model architecture will work for your system.
+
+    === "Use my model collection"
+
+        4. Go to the **Models** tab.
+        5. Click **Scan Folder** on the right.
+        6. Paste the path to your models collection and click **Scan Folder**.
+        7. With **In-place install** enabled, Invoke will leave the model files where they are. If you disable this, **Invoke will move the models into its own folders**.
+
+You’re now ready to start creating!
+
+## Step 6: Learn the Basics
+
+We recommend watching our [Getting Started Playlist](https://www.youtube.com/playlist?list=PLvWK1Kc8iXGrQy8r9TYg6QdUuJ5MMx-ZO). It covers essential features and workflows, including:
+
+- Generating your first image.
+- Using control layers and reference guides.
+- Refining images with advanced workflows.
+
+## Troubleshooting
+
+If installation fails, retrying the install in Repair Mode may fix it. There's a checkbox to enable this on the Review step of the install flow.
+
+If that doesn't fix it, [clearing the `uv` cache](https://docs.astral.sh/uv/reference/cli/#uv-cache-clean) might do the trick:
+
+- Open and start the dev console (button at the bottom-left of the launcher).
+- Run `uv cache clean`.
+- Retry the installation. Enable Repair Mode for good measure.
+
+If you are still unable to install, try installing to a different location and see if that works.
+
+If you still have problems, ask for help on the Invoke [discord](https://discord.gg/ZmtBAhwWhy).
+
+## Other Installation Methods
+
+- You can install the Invoke application as a python package. See our [manual install](./manual.md) docs.
+- You can run Invoke with docker. See our [docker install](./docker.md) docs.
+- You can still use our legacy scripts to install and run Invoke. See the [legacy scripts](./legacy_scripts.md) docs.
+
+## Need Help?
+
+- Visit our [Support Portal](https://support.invoke.ai).
+- Watch the [Getting Started Playlist](https://www.youtube.com/playlist?list=PLvWK1Kc8iXGrQy8r9TYg6QdUuJ5MMx-ZO).
+- Join the conversation on [Discord][discord link].
+
+[discord link]: https://discord.gg/ZmtBAhwWhy

docs/installation/requirements.md

@@ -1,90 +1,35 @@
 # Requirements
 
-## GPU
+Invoke runs on Windows 10+, macOS 14+ and Linux (Ubuntu 20.04+ is well-tested).
 
-!!! warning "Problematic Nvidia GPUs"
+## Hardware
 
-    We do not recommend these GPUs. They cannot operate with half precision, but have insufficient VRAM to generate 512x512 images at full precision.
+Hardware requirements vary significantly depending on model and image output size.
 
-    - NVIDIA 10xx series cards such as the 1080 TI
-    - GTX 1650 series cards
-    - GTX 1660 series cards
+The requirements below are rough guidelines for best performance. GPUs with less VRAM typically still work, if a bit slower. Follow the [Low-VRAM mode guide](./features/low-vram.md) to optimize performance.
 
-Invoke runs best with a dedicated GPU, but will fall back to running on CPU, albeit much slower. You'll need a beefier GPU for SDXL.
+- All Apple Silicon (M1, M2, etc) Macs work, but 16GB+ memory is recommended.
+- AMD GPUs are supported on Linux only. The VRAM requirements are the same as Nvidia GPUs.
 
-!!! example "Stable Diffusion 1.5"
+!!! info "Hardware Requirements (Windows/Linux)"
 
-    === "Nvidia"
+    === "SD1.5 - 512×512"
 
-        ```
-        Any GPU with at least 4GB VRAM.
-        ```
+        - GPU: Nvidia 10xx series or later, 4GB+ VRAM.
+        - Memory: At least 8GB RAM.
+        - Disk: 10GB for base installation plus 30GB for models.
 
-    === "AMD"
+    === "SDXL - 1024×1024"
 
-        ```
-        Any GPU with at least 4GB VRAM. Linux only.
-        ```
+        - GPU: Nvidia 20xx series or later, 8GB+ VRAM.
+        - Memory: At least 16GB RAM.
+        - Disk: 10GB for base installation plus 100GB for models.
 
-    === "Mac"
+    === "FLUX - 1024×1024"
 
-        ```
-        Any Apple Silicon Mac with at least 8GB memory.
-        ```
-
-!!! example "Stable Diffusion XL"
-
-    === "Nvidia"
-
-        ```
-        Any GPU with at least 8GB VRAM.
-        ```
-
-    === "AMD"
-
-        ```
-        Any GPU with at least 16GB VRAM. Linux only.
-        ```
-
-    === "Mac"
-
-        ```
-        Any Apple Silicon Mac with at least 16GB memory.
-        ```
-
-## RAM
-
-At least 12GB of RAM.
-
-## Disk
-
-SSDs will, of course, offer the best performance.
-
-The base application disk usage depends on the torch backend.
-
-!!! example "Disk"
-
-    === "Nvidia (CUDA)"
-
-        ```
-        ~6.5GB
-        ```
-
-    === "AMD (ROCm)"
-
-        ```
-        ~12GB
-        ```
-
-    === "Mac (MPS)"
-
-        ```
-        ~3.5GB
-        ```
-
-You'll need to set aside some space for images, depending on how much you generate. A couple GB is enough to get started.
-
-You'll need a good chunk of space for models. Even if you only install the most popular models and the usual support models (ControlNet, IP Adapter ,etc), you will quickly hit 50GB of models.
+        - GPU: Nvidia 20xx series or later, 10GB+ VRAM.
+        - Memory: At least 32GB RAM.
+        - Disk: 10GB for base installation plus 200GB for models.
 
 !!! info "`tmpfs` on Linux"
 
@@ -92,26 +37,32 @@ You'll need a good chunk of space for models. Even if you only install the most
 
 ## Python
 
+!!! tip "The launcher installs python for you"
+
+    You don't need to do this if you are installing with the [Invoke Launcher](./quick_start.md).
+
 Invoke requires python 3.10 or 3.11. If you don't already have one of these versions installed, we suggest installing 3.11, as it will be supported for longer.
 
-Check that your system has an up-to-date Python installed by running `python --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).
+Check that your system has an up-to-date Python installed by running `python3 --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).
 
-<h3>Installing Python (Windows)</h3>
+!!! info "Installing Python"
 
-- Install python 3.11 with [an official installer].
-- The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox.
-- You may need to install [Microsoft Visual C++ Redistributable].
+    === "Windows"
 
-<h3>Installing Python (macOS)</h3>
+        - Install python 3.11 with [an official installer].
+        - The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox.
+        - You may need to install [Microsoft Visual C++ Redistributable].
 
-- Install python 3.11 with [an official installer].
-- If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): `/Applications/Python\ 3.10/Install\ Certificates.command`
-- If you haven't already, you will need to install the XCode CLI Tools by running `xcode-select --install` in a terminal.
+    === "macOS"
 
-<h3>Installing Python (Linux)</h3>
+        - Install python 3.11 with [an official installer].
+        - If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): `/Applications/Python\ 3.10/Install\ Certificates.command`
+        - If you haven't already, you will need to install the XCode CLI Tools by running `xcode-select --install` in a terminal.
 
-- Follow the [linux install instructions], being sure to install python 3.11.
-- You'll need to install `libglib2.0-0` and `libgl1-mesa-glx` for OpenCV to work. For example, on a Debian system: `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
+    === "Linux"
+
+        - Installing python varies depending on your system. On Ubuntu, you can use the [deadsnakes PPA](https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa).
+        - You'll need to install `libglib2.0-0` and `libgl1-mesa-glx` for OpenCV to work. For example, on a Debian system: `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
 
 ## Drivers
 
@@ -175,7 +126,4 @@ An alternative to installing ROCm locally is to use a [ROCm docker container] to
 [ROCm Documentation]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html
 [cuDNN support matrix]: https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html
 [Nvidia Container Runtime]: https://developer.nvidia.com/container-runtime
-[linux install instructions]: https://docs.python-guide.org/starting/install3/linux/
-[Microsoft Visual C++ Redistributable]: https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
-[an official installer]: https://www.python.org/downloads/
 [CUDA Toolkit Downloads]: https://developer.nvidia.com/cuda-downloads

docs/nodes/communityNodes.md

@@ -49,6 +49,7 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [BriaAI Background Remove](#briaai-remove-background)
     + [Remove Background](#remove-background)    
     + [Retroize](#retroize)
+    + [Stereogram](#stereogram-nodes)
     + [Size Stepper Nodes](#size-stepper-nodes)
     + [Simple Skin Detection](#simple-skin-detection)
     + [Text font to Image](#text-font-to-image)
@@ -526,6 +527,16 @@ View:
 
 <img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
 
+--------------------------------
+### Stereogram Nodes
+
+**Description:** A set of custom nodes for InvokeAI to create cross-view or parallel-view stereograms. Stereograms are 2D images that, when viewed properly, reveal a 3D scene. Check out [r/crossview](https://www.reddit.com/r/CrossView/) for tutorials.
+
+**Node Link:** https://github.com/simonfuhrmann/invokeai-stereo
+
+**Example Workflow and Output**
+</br><img src="https://raw.githubusercontent.com/simonfuhrmann/invokeai-stereo/refs/heads/main/docs/example_promo_03.jpg" width="600" />
+
 --------------------------------
 ### Simple Skin Detection
 

invokeai/app/api/routers/boards.py

@@ -31,7 +31,7 @@ class DeleteBoardResult(BaseModel):
     response_model=BoardDTO,
 )
 async def create_board(
-    board_name: str = Query(description="The name of the board to create"),
+    board_name: str = Query(description="The name of the board to create", max_length=300),
     is_private: bool = Query(default=False, description="Whether the board is private"),
 ) -> BoardDTO:
     """Creates a board"""

invokeai/app/api/routers/model_manager.py

@@ -4,7 +4,6 @@
 import contextlib
 import io
 import pathlib
-import shutil
 import traceback
 from copy import deepcopy
 from enum import Enum
@@ -21,7 +20,6 @@ from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.config import get_config
 from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 from invokeai.app.services.model_records import (
@@ -37,7 +35,7 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
@@ -848,74 +846,6 @@ async def get_starter_models() -> StarterModelResponse:
     return StarterModelResponse(starter_models=starter_models, starter_bundles=starter_bundles)
 
 
-@model_manager_router.get(
-    "/model_cache",
-    operation_id="get_cache_size",
-    response_model=float,
-    summary="Get maximum size of model manager RAM or VRAM cache.",
-)
-async def get_cache_size(cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM)) -> float:
-    """Return the current RAM or VRAM cache size setting (in GB)."""
-    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
-    value = 0.0
-    if cache_type == CacheType.RAM:
-        value = cache.max_cache_size
-    elif cache_type == CacheType.VRAM:
-        value = cache.max_vram_cache_size
-    return value
-
-
-@model_manager_router.put(
-    "/model_cache",
-    operation_id="set_cache_size",
-    response_model=float,
-    summary="Set maximum size of model manager RAM or VRAM cache, optionally writing new value out to invokeai.yaml config file.",
-)
-async def set_cache_size(
-    value: float = Query(description="The new value for the maximum cache size"),
-    cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM),
-    persist: bool = Query(description="Write new value out to invokeai.yaml", default=False),
-) -> float:
-    """Set the current RAM or VRAM cache size setting (in GB). ."""
-    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
-    app_config = get_config()
-    # Record initial state.
-    vram_old = app_config.vram
-    ram_old = app_config.ram
-
-    # Prepare target state.
-    vram_new = vram_old
-    ram_new = ram_old
-    if cache_type == CacheType.RAM:
-        ram_new = value
-    elif cache_type == CacheType.VRAM:
-        vram_new = value
-    else:
-        raise ValueError(f"Unexpected {cache_type=}.")
-
-    config_path = app_config.config_file_path
-    new_config_path = config_path.with_suffix(".yaml.new")
-
-    try:
-        # Try to apply the target state.
-        cache.max_vram_cache_size = vram_new
-        cache.max_cache_size = ram_new
-        app_config.ram = ram_new
-        app_config.vram = vram_new
-        if persist:
-            app_config.write_file(new_config_path)
-            shutil.move(new_config_path, config_path)
-    except Exception as e:
-        # If there was a failure, restore the initial state.
-        cache.max_cache_size = ram_old
-        cache.max_vram_cache_size = vram_old
-        app_config.ram = ram_old
-        app_config.vram = vram_old
-
-        raise RuntimeError("Failed to update cache size") from e
-    return value
-
-
 @model_manager_router.get(
     "/stats",
     operation_id="get_stats",
@@ -928,6 +858,18 @@ async def get_stats() -> Optional[CacheStats]:
     return ApiDependencies.invoker.services.model_manager.load.ram_cache.stats
 
 
+@model_manager_router.post(
+    "/empty_model_cache",
+    operation_id="empty_model_cache",
+    status_code=200,
+)
+async def empty_model_cache() -> None:
+    """Drop all models from the model cache to free RAM/VRAM. 'Locked' models that are in active use will not be dropped."""
+    # Request 1000GB of room in order to force the cache to drop all models.
+    ApiDependencies.invoker.services.logger.info("Emptying model cache.")
+    ApiDependencies.invoker.services.model_manager.load.ram_cache.make_room(1000 * 2**30)
+
+
 class HFTokenStatus(str, Enum):
     VALID = "valid"
     INVALID = "invalid"

invokeai/app/api/routers/session_queue.py

@@ -10,6 +10,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
     Batch,
     BatchStatus,
+    CancelAllExceptCurrentResult,
     CancelByBatchIDsResult,
     CancelByDestinationResult,
     ClearResult,
@@ -94,6 +95,18 @@ async def Pause(
     return ApiDependencies.invoker.services.session_processor.pause()
 
 
+@session_queue_router.put(
+    "/{queue_id}/cancel_all_except_current",
+    operation_id="cancel_all_except_current",
+    responses={200: {"model": CancelAllExceptCurrentResult}},
+)
+async def cancel_all_except_current(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> CancelAllExceptCurrentResult:
+    """Immediately cancels all queue items except in-processing items"""
+    return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
+
+
 @session_queue_router.put(
     "/{queue_id}/cancel_by_batch_ids",
     operation_id="cancel_by_batch_ids",

invokeai/app/api/routers/utilities.py

@@ -25,6 +25,7 @@ async def parse_dynamicprompts(
     prompt: str = Body(description="The prompt to parse with dynamicprompts"),
     max_prompts: int = Body(ge=1, le=10000, default=1000, description="The max number of prompts to generate"),
     combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
+    seed: int | None = Body(None, description="The seed to use for random generation. Only used if not combinatorial"),
 ) -> DynamicPromptsResponse:
     """Creates a batch process"""
     max_prompts = min(max_prompts, 10000)
@@ -35,7 +36,7 @@ async def parse_dynamicprompts(
             generator = CombinatorialPromptGenerator()
             prompts = generator.generate(prompt, max_prompts=max_prompts)
         else:
-            generator = RandomPromptGenerator()
+            generator = RandomPromptGenerator(seed=seed)
             prompts = generator.generate(prompt, num_images=max_prompts)
     except ParseException as e:
         prompts = [prompt]

invokeai/app/api_app.py

@@ -59,11 +59,32 @@ logger.info(f"Using torch device: {torch_device_name}")
 
 loop = asyncio.new_event_loop()
 
+# We may change the port if the default is in use, this global variable is used to store the port so that we can log
+# the correct port when the server starts in the lifespan handler.
+port = app_config.port
+
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # Add startup event to load dependencies
     ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, loop=loop, logger=logger)
+
+    # Log the server address when it starts - in case the network log level is not high enough to see the startup log
+    proto = "https" if app_config.ssl_certfile else "http"
+    msg = f"Invoke running on {proto}://{app_config.host}:{port} (Press CTRL+C to quit)"
+
+    # Logging this way ignores the logger's log level and _always_ logs the message
+    record = logger.makeRecord(
+        name=logger.name,
+        level=logging.INFO,
+        fn="",
+        lno=0,
+        msg=msg,
+        args=(),
+        exc_info=None,
+    )
+    logger.handle(record)
+
     yield
     # Shut down threads
     ApiDependencies.shutdown()
@@ -206,6 +227,7 @@ def invoke_api() -> None:
         else:
             jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
 
+    global port
     port = find_port(app_config.port)
     if port != app_config.port:
         logger.warn(f"Port {app_config.port} in use, using port {port}")
@@ -217,18 +239,17 @@ def invoke_api() -> None:
         host=app_config.host,
         port=port,
         loop="asyncio",
-        log_level=app_config.log_level,
+        log_level=app_config.log_level_network,
         ssl_certfile=app_config.ssl_certfile,
         ssl_keyfile=app_config.ssl_keyfile,
     )
     server = uvicorn.Server(config)
 
     # replace uvicorn's loggers with InvokeAI's for consistent appearance
-    for logname in ["uvicorn.access", "uvicorn"]:
-        log = InvokeAILogger.get_logger(logname)
-        log.handlers.clear()
-        for ch in logger.handlers:
-            log.addHandler(ch)
+    uvicorn_logger = InvokeAILogger.get_logger("uvicorn")
+    uvicorn_logger.handlers.clear()
+    for hdlr in logger.handlers:
+        uvicorn_logger.addHandler(hdlr)
 
     loop.run_until_complete(server.serve())
 

invokeai/app/invocations/batch.py

@@ -0,0 +1,237 @@
+from typing import Literal
+
+from pydantic import BaseModel
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    ImageField,
+    Input,
+    InputField,
+    OutputField,
+)
+from invokeai.app.invocations.primitives import (
+    FloatOutput,
+    ImageOutput,
+    IntegerOutput,
+    StringOutput,
+)
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+BATCH_GROUP_IDS = Literal[
+    "None",
+    "Group 1",
+    "Group 2",
+    "Group 3",
+    "Group 4",
+    "Group 5",
+]
+
+
+class NotExecutableNodeError(Exception):
+    def __init__(self, message: str = "This class should never be executed or instantiated directly."):
+        super().__init__(message)
+
+    pass
+
+
+class BaseBatchInvocation(BaseInvocation):
+    batch_group_id: BATCH_GROUP_IDS = InputField(
+        default="None",
+        description="The ID of this batch node's group. If provided, all batch nodes in with the same ID will be 'zipped' before execution, and all nodes' collections must be of the same size.",
+        input=Input.Direct,
+        title="Batch Group",
+    )
+
+    def __init__(self):
+        raise NotExecutableNodeError()
+
+
+@invocation(
+    "image_batch",
+    title="Image Batch",
+    tags=["primitives", "image", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class ImageBatchInvocation(BaseBatchInvocation):
+    """Create a batched generation, where the workflow is executed once for each image in the batch."""
+
+    images: list[ImageField] = InputField(
+        default=[], min_length=1, description="The images to batch over", input=Input.Direct
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        raise NotExecutableNodeError()
+
+
+@invocation(
+    "string_batch",
+    title="String Batch",
+    tags=["primitives", "string", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class StringBatchInvocation(BaseBatchInvocation):
+    """Create a batched generation, where the workflow is executed once for each string in the batch."""
+
+    strings: list[str] = InputField(
+        default=[],
+        min_length=1,
+        description="The strings to batch over",
+    )
+
+    def invoke(self, context: InvocationContext) -> StringOutput:
+        raise NotExecutableNodeError()
+
+
+@invocation_output("string_generator_output")
+class StringGeneratorOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of strings"""
+
+    strings: list[str] = OutputField(description="The generated strings")
+
+
+class StringGeneratorField(BaseModel):
+    pass
+
+
+@invocation(
+    "string_generator",
+    title="String Generator",
+    tags=["primitives", "string", "number", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class StringGenerator(BaseInvocation):
+    """Generated a range of strings for use in a batched generation"""
+
+    generator: StringGeneratorField = InputField(
+        description="The string generator.",
+        input=Input.Direct,
+        title="Generator Type",
+    )
+
+    def __init__(self):
+        raise NotExecutableNodeError()
+
+    def invoke(self, context: InvocationContext) -> StringGeneratorOutput:
+        raise NotExecutableNodeError()
+
+
+@invocation(
+    "integer_batch",
+    title="Integer Batch",
+    tags=["primitives", "integer", "number", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class IntegerBatchInvocation(BaseBatchInvocation):
+    """Create a batched generation, where the workflow is executed once for each integer in the batch."""
+
+    integers: list[int] = InputField(
+        default=[],
+        min_length=1,
+        description="The integers to batch over",
+    )
+
+    def invoke(self, context: InvocationContext) -> IntegerOutput:
+        raise NotExecutableNodeError()
+
+
+@invocation_output("integer_generator_output")
+class IntegerGeneratorOutput(BaseInvocationOutput):
+    integers: list[int] = OutputField(description="The generated integers")
+
+
+class IntegerGeneratorField(BaseModel):
+    pass
+
+
+@invocation(
+    "integer_generator",
+    title="Integer Generator",
+    tags=["primitives", "int", "number", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class IntegerGenerator(BaseInvocation):
+    """Generated a range of integers for use in a batched generation"""
+
+    generator: IntegerGeneratorField = InputField(
+        description="The integer generator.",
+        input=Input.Direct,
+        title="Generator Type",
+    )
+
+    def __init__(self):
+        raise NotExecutableNodeError()
+
+    def invoke(self, context: InvocationContext) -> IntegerGeneratorOutput:
+        raise NotExecutableNodeError()
+
+
+@invocation(
+    "float_batch",
+    title="Float Batch",
+    tags=["primitives", "float", "number", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class FloatBatchInvocation(BaseBatchInvocation):
+    """Create a batched generation, where the workflow is executed once for each float in the batch."""
+
+    floats: list[float] = InputField(
+        default=[],
+        min_length=1,
+        description="The floats to batch over",
+    )
+
+    def invoke(self, context: InvocationContext) -> FloatOutput:
+        raise NotExecutableNodeError()
+
+
+@invocation_output("float_generator_output")
+class FloatGeneratorOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of floats"""
+
+    floats: list[float] = OutputField(description="The generated floats")
+
+
+class FloatGeneratorField(BaseModel):
+    pass
+
+
+@invocation(
+    "float_generator",
+    title="Float Generator",
+    tags=["primitives", "float", "number", "batch", "special"],
+    category="primitives",
+    version="1.0.0",
+    classification=Classification.Special,
+)
+class FloatGenerator(BaseInvocation):
+    """Generated a range of floats for use in a batched generation"""
+
+    generator: FloatGeneratorField = InputField(
+        description="The float generator.",
+        input=Input.Direct,
+        title="Generator Type",
+    )
+
+    def __init__(self):
+        raise NotExecutableNodeError()
+
+    def invoke(self, context: InvocationContext) -> FloatGeneratorOutput:
+        raise NotExecutableNodeError()

invokeai/app/invocations/compel.py

@@ -19,9 +19,9 @@ from invokeai.app.invocations.model import CLIPField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_patcher import ModelPatcher
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ConditioningFieldData,
@@ -63,29 +63,28 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.models.load(self.clip.tokenizer)
-        text_encoder_info = context.models.load(self.clip.text_encoder)
-
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
             for lora in self.clip.loras:
                 lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, LoRAModelRaw)
+                assert isinstance(lora_info.model, ModelPatchRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
             return
 
         # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
+        text_encoder_info = context.models.load(self.clip.text_encoder)
         ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
 
         with (
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
-            tokenizer_info as tokenizer,
-            LoRAPatcher.apply_lora_patches(
+            context.models.load(self.clip.tokenizer) as tokenizer,
+            LayerPatcher.apply_smart_model_patches(
                 model=text_encoder,
                 patches=_lora_loader(),
                 prefix="lora_te_",
+                dtype=text_encoder.dtype,
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -104,6 +103,7 @@ class CompelInvocation(BaseInvocation):
                 textual_inversion_manager=ti_manager,
                 dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
+                device=TorchDevice.choose_torch_device(),
             )
 
             conjunction = Compel.parse_prompt_string(self.prompt)
@@ -138,9 +138,7 @@ class SDXLPromptInvocationBase:
         lora_prefix: str,
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        tokenizer_info = context.models.load(clip_field.tokenizer)
         text_encoder_info = context.models.load(clip_field.text_encoder)
-
         # return zero on empty
         if prompt == "" and zero_on_empty:
             cpu_text_encoder = text_encoder_info.model
@@ -162,11 +160,11 @@ class SDXLPromptInvocationBase:
                 c_pooled = None
             return c, c_pooled
 
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
             for lora in clip_field.loras:
                 lora_info = context.models.load(lora.lora)
                 lora_model = lora_info.model
-                assert isinstance(lora_model, LoRAModelRaw)
+                assert isinstance(lora_model, ModelPatchRaw)
                 yield (lora_model, lora.weight)
                 del lora_info
             return
@@ -178,11 +176,12 @@ class SDXLPromptInvocationBase:
         with (
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
-            tokenizer_info as tokenizer,
-            LoRAPatcher.apply_lora_patches(
-                text_encoder,
+            context.models.load(clip_field.tokenizer) as tokenizer,
+            LayerPatcher.apply_smart_model_patches(
+                model=text_encoder,
                 patches=_lora_loader(),
                 prefix=lora_prefix,
+                dtype=text_encoder.dtype,
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -205,6 +204,7 @@ class SDXLPromptInvocationBase:
                 truncate_long_prompts=False,  # TODO:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,
+                device=TorchDevice.choose_torch_device(),
             )
 
             conjunction = Compel.parse_prompt_string(prompt)
@@ -222,7 +222,6 @@ class SDXLPromptInvocationBase:
 
         del tokenizer
         del text_encoder
-        del tokenizer_info
         del text_encoder_info
 
         c = c.detach().to("cpu")

invokeai/app/invocations/constants.py

@@ -1,7 +1,5 @@
 from typing import Literal
 
-from invokeai.backend.util.devices import TorchDevice
-
 LATENT_SCALE_FACTOR = 8
 """
 HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
@@ -12,5 +10,3 @@ The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
 
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 """A literal type for PIL image modes supported by Invoke"""
-
-DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()

invokeai/app/invocations/create_denoise_mask.py

@@ -6,7 +6,6 @@ from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField
 from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import VAEField
@@ -29,11 +28,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
     image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
     mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
-    fp32: bool = InputField(
-        default=DEFAULT_PRECISION == torch.float32,
-        description=FieldDescriptions.fp32,
-        ui_order=4,
-    )
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32, ui_order=4)
 
     def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
         if mask_image.mode != "L":

invokeai/app/invocations/create_gradient_mask.py

@@ -7,7 +7,6 @@ from PIL import Image, ImageFilter
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-from invokeai.app.invocations.constants import DEFAULT_PRECISION
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
@@ -76,11 +75,7 @@ class CreateGradientMaskInvocation(BaseInvocation):
         ui_order=7,
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
-    fp32: bool = InputField(
-        default=DEFAULT_PRECISION == torch.float32,
-        description=FieldDescriptions.fp32,
-        ui_order=9,
-    )
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32, ui_order=9)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:

invokeai/app/invocations/denoise_latents.py

@@ -10,7 +10,9 @@ import torchvision.transforms as T
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.models.adapter import T2IAdapter
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
+from diffusers.schedulers.scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from diffusers.schedulers.scheduling_tcd import TCDScheduler
 from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
 from PIL import Image
@@ -37,10 +39,11 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager import BaseModelType, ModelVariantType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_patcher import ModelPatcher
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, DenoiseInputs
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
@@ -83,12 +86,14 @@ def get_scheduler(
     scheduler_info: ModelIdentifierField,
     scheduler_name: str,
     seed: int,
+    unet_config: AnyModelConfig,
 ) -> Scheduler:
     """Load a scheduler and apply some scheduler-specific overrides."""
     # TODO(ryand): Silently falling back to ddim seems like a bad idea. Look into why this was added and remove if
     # possible.
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
     orig_scheduler_info = context.models.load(scheduler_info)
+
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -100,10 +105,17 @@ def get_scheduler(
         "_backup": scheduler_config,
     }
 
+    if hasattr(unet_config, "prediction_type"):
+        scheduler_config["prediction_type"] = unet_config.prediction_type
+
     # make dpmpp_sde reproducable(seed can be passed only in initializer)
     if scheduler_class is DPMSolverSDEScheduler:
         scheduler_config["noise_sampler_seed"] = seed
 
+    if scheduler_class is DPMSolverMultistepScheduler or scheduler_class is DPMSolverSinglestepScheduler:
+        if scheduler_config["_class_name"] == "DEISMultistepScheduler" and scheduler_config["algorithm_type"] == "deis":
+            scheduler_config["algorithm_type"] = "dpmsolver++"
+
     scheduler = scheduler_class.from_config(scheduler_config)
 
     # hack copied over from generate.py
@@ -411,6 +423,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         control_input: ControlField | list[ControlField] | None,
         latents_shape: List[int],
+        device: torch.device,
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> list[ControlNetData] | None:
@@ -452,7 +465,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 height=control_height_resize,
                 # batch_size=batch_size * num_images_per_prompt,
                 # num_images_per_prompt=num_images_per_prompt,
-                device=control_model.device,
+                device=device,
                 dtype=control_model.dtype,
                 control_mode=control_info.control_mode,
                 resize_mode=control_info.resize_mode,
@@ -547,7 +560,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         for single_ip_adapter in ip_adapters:
             with context.models.load(single_ip_adapter.ip_adapter_model) as ip_adapter_model:
                 assert isinstance(ip_adapter_model, IPAdapter)
-                image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
                 # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
                 single_ipa_image_fields = single_ip_adapter.image
                 if not isinstance(single_ipa_image_fields, list):
@@ -556,7 +568,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 single_ipa_images = [
                     context.images.get_pil(image.image_name, mode="RGB") for image in single_ipa_image_fields
                 ]
-                with image_encoder_model_info as image_encoder_model:
+                with context.models.load(single_ip_adapter.image_encoder_model) as image_encoder_model:
                     assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                     # Get image embeddings from CLIP and ImageProjModel.
                     image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
@@ -606,6 +618,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
         latents_shape: list[int],
+        device: torch.device,
         do_classifier_free_guidance: bool,
     ) -> Optional[list[T2IAdapterData]]:
         if t2i_adapter is None:
@@ -621,7 +634,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
             t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
-            t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
             image = context.images.get_pil(t2i_adapter_field.image.image_name, mode="RGB")
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
@@ -637,7 +649,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
 
             t2i_adapter_model: T2IAdapter
-            with t2i_adapter_loaded_model as t2i_adapter_model:
+            with context.models.load(t2i_adapter_field.t2i_adapter_model) as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
 
                 # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
@@ -657,7 +669,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     width=control_width_resize,
                     height=control_height_resize,
                     num_channels=t2i_adapter_model.config["in_channels"],  # mypy treats this as a FrozenDict
-                    device=t2i_adapter_model.device,
+                    device=device,
                     dtype=t2i_adapter_model.dtype,
                     resize_mode=t2i_adapter_field.resize_mode,
                 )
@@ -822,6 +834,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
         _, _, latent_height, latent_width = latents.shape
 
+        # get the unet's config so that we can pass the base to sd_step_callback()
+        unet_config = context.models.get_config(self.unet.unet.key)
+
         conditioning_data = self.get_conditioning_data(
             context=context,
             positive_conditioning_field=self.positive_conditioning,
@@ -841,6 +856,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             scheduler_info=self.unet.scheduler,
             scheduler_name=self.scheduler,
             seed=seed,
+            unet_config=unet_config,
         )
 
         timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
@@ -852,9 +868,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             denoising_end=self.denoising_end,
         )
 
-        # get the unet's config so that we can pass the base to sd_step_callback()
-        unet_config = context.models.get_config(self.unet.unet.key)
-
         ### preview
         def step_callback(state: PipelineIntermediateState) -> None:
             context.util.sd_step_callback(state, unet_config.base)
@@ -926,10 +939,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
             # ext: t2i/ip adapter
             ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)
 
-            unet_info = context.models.load(self.unet.unet)
-            assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
-                unet_info.model_on_device() as (cached_weights, unet),
+                context.models.load(self.unet.unet).model_on_device() as (cached_weights, unet),
                 ModelPatcher.patch_unet_attention_processor(unet, denoise_ctx.inputs.attention_processor_cls),
                 # ext: controlnet
                 ext_manager.patch_extensions(denoise_ctx),
@@ -950,6 +961,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     @torch.no_grad()
     @SilenceWarnings()  # This quenches the NSFW nag from diffusers.
     def _old_invoke(self, context: InvocationContext) -> LatentsOutput:
+        device = TorchDevice.choose_torch_device()
         seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
 
         mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
@@ -964,6 +976,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             context,
             self.t2i_adapter,
             latents.shape,
+            device=device,
             do_classifier_free_guidance=True,
         )
 
@@ -987,43 +1000,43 @@ class DenoiseLatentsInvocation(BaseInvocation):
         def step_callback(state: PipelineIntermediateState) -> None:
             context.util.sd_step_callback(state, unet_config.base)
 
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
             for lora in self.unet.loras:
                 lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, LoRAModelRaw)
+                assert isinstance(lora_info.model, ModelPatchRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
             return
 
-        unet_info = context.models.load(self.unet.unet)
-        assert isinstance(unet_info.model, UNet2DConditionModel)
         with (
             ExitStack() as exit_stack,
-            unet_info.model_on_device() as (cached_weights, unet),
+            context.models.load(self.unet.unet).model_on_device() as (cached_weights, unet),
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
             SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
-            LoRAPatcher.apply_lora_patches(
+            LayerPatcher.apply_smart_model_patches(
                 model=unet,
                 patches=_lora_loader(),
                 prefix="lora_unet_",
+                dtype=unet.dtype,
                 cached_weights=cached_weights,
             ),
         ):
             assert isinstance(unet, UNet2DConditionModel)
-            latents = latents.to(device=unet.device, dtype=unet.dtype)
+            latents = latents.to(device=device, dtype=unet.dtype)
             if noise is not None:
-                noise = noise.to(device=unet.device, dtype=unet.dtype)
+                noise = noise.to(device=device, dtype=unet.dtype)
             if mask is not None:
-                mask = mask.to(device=unet.device, dtype=unet.dtype)
+                mask = mask.to(device=device, dtype=unet.dtype)
             if masked_latents is not None:
-                masked_latents = masked_latents.to(device=unet.device, dtype=unet.dtype)
+                masked_latents = masked_latents.to(device=device, dtype=unet.dtype)
 
             scheduler = get_scheduler(
                 context=context,
                 scheduler_info=self.unet.scheduler,
                 scheduler_name=self.scheduler,
                 seed=seed,
+                unet_config=unet_config,
             )
 
             pipeline = self.create_pipeline(unet, scheduler)
@@ -1033,7 +1046,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 context=context,
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
-                device=unet.device,
+                device=device,
                 dtype=unet.dtype,
                 latent_height=latent_height,
                 latent_width=latent_width,
@@ -1046,6 +1059,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 context=context,
                 control_input=self.control,
                 latents_shape=latents.shape,
+                device=device,
                 # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
                 do_classifier_free_guidance=True,
                 exit_stack=exit_stack,
@@ -1063,7 +1077,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
                 scheduler,
-                device=unet.device,
+                device=device,
                 steps=self.steps,
                 denoising_start=self.denoising_start,
                 denoising_end=self.denoising_end,

invokeai/app/invocations/fields.py

@@ -56,6 +56,7 @@ class UIType(str, Enum, metaclass=MetaEnum):
     CLIPLEmbedModel = "CLIPLEmbedModelField"
     CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
+    ControlLoRAModel = "ControlLoRAModelField"
     # endregion
 
     # region Misc Field Types
@@ -143,6 +144,7 @@ class FieldDescriptions:
     controlnet_model = "ControlNet model to load"
     vae_model = "VAE model to load"
     lora_model = "LoRA model to load"
+    control_lora_model = "Control LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
     flux_model = "Flux model (Transformer) to load"
     sd3_model = "SD3 model (MMDiTX) to load"
@@ -298,6 +300,13 @@ class BoundingBoxField(BaseModel):
             raise ValueError(f"y_min ({self.y_min}) is greater than y_max ({self.y_max}).")
         return self
 
+    def tuple(self) -> Tuple[int, int, int, int]:
+        """
+        Returns the bounding box as a tuple suitable for use with PIL's `Image.crop()` method.
+        This method returns a tuple of the form (left, upper, right, lower) == (x_min, y_min, x_max, y_max).
+        """
+        return (self.x_min, self.y_min, self.x_max, self.y_max)
+
 
 class MetadataField(RootModel[dict[str, Any]]):
     """

invokeai/app/invocations/flux_control_lora_loader.py

@@ -0,0 +1,49 @@
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
+from invokeai.app.invocations.model import ControlLoRAField, ModelIdentifierField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("flux_control_lora_loader_output")
+class FluxControlLoRALoaderOutput(BaseInvocationOutput):
+    """Flux Control LoRA Loader Output"""
+
+    control_lora: ControlLoRAField = OutputField(
+        title="Flux Control LoRA", description="Control LoRAs to apply on model loading", default=None
+    )
+
+
+@invocation(
+    "flux_control_lora_loader",
+    title="Flux Control LoRA",
+    tags=["lora", "model", "flux"],
+    category="model",
+    version="1.1.0",
+    classification=Classification.Prototype,
+)
+class FluxControlLoRALoaderInvocation(BaseInvocation):
+    """LoRA model and Image to use with FLUX transformer generation."""
+
+    lora: ModelIdentifierField = InputField(
+        description=FieldDescriptions.control_lora_model, title="Control LoRA", ui_type=UIType.ControlLoRAModel
+    )
+    image: ImageField = InputField(description="The image to encode.")
+    weight: float = InputField(description="The weight of the LoRA.", default=1.0)
+
+    def invoke(self, context: InvocationContext) -> FluxControlLoRALoaderOutput:
+        if not context.models.exists(self.lora.key):
+            raise ValueError(f"Unknown lora: {self.lora.key}!")
+
+        return FluxControlLoRALoaderOutput(
+            control_lora=ControlLoRAField(
+                lora=self.lora,
+                img=self.image,
+                weight=self.weight,
+            )
+        )

invokeai/app/invocations/flux_denoise.py

@@ -1,10 +1,12 @@
 from contextlib import ExitStack
-from typing import Callable, Iterator, Optional, Tuple
+from typing import Callable, Iterator, Optional, Tuple, Union
 
+import einops
 import numpy as np
 import numpy.typing as npt
 import torch
 import torchvision.transforms as tv_transforms
+from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
@@ -21,8 +23,9 @@ from invokeai.app.invocations.fields import (
     WithMetadata,
 )
 from invokeai.app.invocations.flux_controlnet import FluxControlNetField
+from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
 from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.model import TransformerField, VAEField
+from invokeai.app.invocations.model import ControlLoRAField, LoRAField, TransformerField, VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
@@ -44,10 +47,10 @@ from invokeai.backend.flux.sampling_utils import (
     unpack,
 )
 from invokeai.backend.flux.text_conditioning import FluxTextConditioning
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
 from invokeai.backend.util.devices import TorchDevice
@@ -89,6 +92,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
         title="Transformer",
     )
+    control_lora: Optional[ControlLoRAField] = InputField(
+        description=FieldDescriptions.control_lora_model, input=Input.Connection, title="Control LoRA", default=None
+    )
     positive_text_conditioning: FluxConditioningField | list[FluxConditioningField] = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
@@ -193,8 +199,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             else None
         )
 
-        transformer_info = context.models.load(self.transformer.transformer)
-        is_schnell = "schnell" in transformer_info.config.config_path
+        transformer_config = context.models.get_config(self.transformer.transformer)
+        is_schnell = "schnell" in getattr(transformer_config, "config_path", "")
 
         # Calculate the timestep schedule.
         timesteps = get_schedule(
@@ -234,6 +240,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         if len(timesteps) <= 1:
             return x
 
+        if is_schnell and self.control_lora:
+            raise ValueError("Control LoRAs cannot be used with FLUX Schnell")
+
+        # Prepare the extra image conditioning tensor if a FLUX structural control image is provided.
+        img_cond = self._prep_structural_control_img_cond(context)
+
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
         img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
@@ -241,6 +253,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # Pack all latent tensors.
         init_latents = pack(init_latents) if init_latents is not None else None
         inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
+        img_cond = pack(img_cond) if img_cond is not None else None
         noise = pack(noise)
         x = pack(x)
 
@@ -263,7 +276,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # TODO(ryand): We should really do this in a separate invocation to benefit from caching.
         ip_adapter_fields = self._normalize_ip_adapter_fields()
         pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds = self._prep_ip_adapter_image_prompt_clip_embeds(
-            ip_adapter_fields, context
+            ip_adapter_fields, context, device=x.device
         )
 
         cfg_scale = self.prep_cfg_scale(
@@ -286,41 +299,40 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             )
 
             # Load the transformer model.
-            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
+            (cached_weights, transformer) = exit_stack.enter_context(
+                context.models.load(self.transformer.transformer).model_on_device()
+            )
             assert isinstance(transformer, Flux)
-            config = transformer_info.config
+            config = transformer_config
             assert config is not None
 
-            # Apply LoRA models to the transformer.
-            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
+            # Determine if the model is quantized.
+            # If the model is quantized, then we need to apply the LoRA weights as sidecar layers. This results in
+            # slower inference than direct patching, but is agnostic to the quantization format.
             if config.format in [ModelFormat.Checkpoint]:
-                # The model is non-quantized, so we can apply the LoRA weights directly into the model.
-                exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
-                        model=transformer,
-                        patches=self._lora_iterator(context),
-                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
-                        cached_weights=cached_weights,
-                    )
-                )
+                model_is_quantized = False
             elif config.format in [
                 ModelFormat.BnbQuantizedLlmInt8b,
                 ModelFormat.BnbQuantizednf4b,
                 ModelFormat.GGUFQuantized,
             ]:
-                # The model is quantized, so apply the LoRA weights as sidecar layers. This results in slower inference,
-                # than directly patching the weights, but is agnostic to the quantization format.
-                exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_sidecar_patches(
-                        model=transformer,
-                        patches=self._lora_iterator(context),
-                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
-                        dtype=inference_dtype,
-                    )
-                )
+                model_is_quantized = True
             else:
                 raise ValueError(f"Unsupported model format: {config.format}")
 
+            # Apply LoRA models to the transformer.
+            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
+            exit_stack.enter_context(
+                LayerPatcher.apply_smart_model_patches(
+                    model=transformer,
+                    patches=self._lora_iterator(context),
+                    prefix=FLUX_LORA_TRANSFORMER_PREFIX,
+                    dtype=inference_dtype,
+                    cached_weights=cached_weights,
+                    force_sidecar_patching=model_is_quantized,
+                )
+            )
+
             # Prepare IP-Adapter extensions.
             pos_ip_adapter_extensions, neg_ip_adapter_extensions = self._prep_ip_adapter_extensions(
                 pos_image_prompt_clip_embeds=pos_image_prompt_clip_embeds,
@@ -345,6 +357,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 controlnet_extensions=controlnet_extensions,
                 pos_ip_adapter_extensions=pos_ip_adapter_extensions,
                 neg_ip_adapter_extensions=neg_ip_adapter_extensions,
+                img_cond=img_cond,
             )
 
         x = unpack(x.float(), self.height, self.width)
@@ -501,15 +514,18 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # before loading the models. Then make sure that all VAE encoding is done before loading the ControlNets to
         # minimize peak memory.
 
-        # First, load the ControlNet models so that we can determine the ControlNet types.
-        controlnet_models = [context.models.load(controlnet.control_model) for controlnet in controlnets]
-
         # Calculate the controlnet conditioning tensors.
         # We do this before loading the ControlNet models because it may require running the VAE, and we are trying to
         # keep peak memory down.
         controlnet_conds: list[torch.Tensor] = []
-        for controlnet, controlnet_model in zip(controlnets, controlnet_models, strict=True):
+        for controlnet in controlnets:
             image = context.images.get_pil(controlnet.image.image_name)
+
+            # HACK(ryand): We have to load the ControlNet model to determine whether the VAE needs to be run. We really
+            # shouldn't have to load the model here. There's a risk that the model will be dropped from the model cache
+            # before we load it into VRAM and thus we'll have to load it again (context:
+            # https://github.com/invoke-ai/InvokeAI/issues/7513).
+            controlnet_model = context.models.load(controlnet.control_model)
             if isinstance(controlnet_model.model, InstantXControlNetFlux):
                 if self.controlnet_vae is None:
                     raise ValueError("A ControlNet VAE is required when using an InstantX FLUX ControlNet.")
@@ -539,10 +555,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         # Finally, load the ControlNet models and initialize the ControlNet extensions.
         controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension] = []
-        for controlnet, controlnet_cond, controlnet_model in zip(
-            controlnets, controlnet_conds, controlnet_models, strict=True
-        ):
-            model = exit_stack.enter_context(controlnet_model)
+        for controlnet, controlnet_cond in zip(controlnets, controlnet_conds, strict=True):
+            model = exit_stack.enter_context(context.models.load(controlnet.control_model))
 
             if isinstance(model, XLabsControlNetFlux):
                 controlnet_extensions.append(
@@ -575,6 +589,29 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return controlnet_extensions
 
+    def _prep_structural_control_img_cond(self, context: InvocationContext) -> torch.Tensor | None:
+        if self.control_lora is None:
+            return None
+
+        if not self.controlnet_vae:
+            raise ValueError("controlnet_vae must be set when using a FLUX Control LoRA.")
+
+        # Load the conditioning image and resize it to the target image size.
+        cond_img = context.images.get_pil(self.control_lora.img.image_name)
+        cond_img = cond_img.convert("RGB")
+        cond_img = cond_img.resize((self.width, self.height), Image.Resampling.BICUBIC)
+        cond_img = np.array(cond_img)
+
+        # Normalize the conditioning image to the range [-1, 1].
+        # This normalization is based on the original implementations here:
+        # https://github.com/black-forest-labs/flux/blob/805da8571a0b49b6d4043950bd266a65328c243b/src/flux/modules/image_embedders.py#L34
+        # https://github.com/black-forest-labs/flux/blob/805da8571a0b49b6d4043950bd266a65328c243b/src/flux/modules/image_embedders.py#L60
+        img_cond = torch.from_numpy(cond_img).float() / 127.5 - 1.0
+        img_cond = einops.rearrange(img_cond, "h w c -> 1 c h w")
+
+        vae_info = context.models.load(self.controlnet_vae.vae)
+        return FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=img_cond)
+
     def _normalize_ip_adapter_fields(self) -> list[IPAdapterField]:
         if self.ip_adapter is None:
             return []
@@ -589,6 +626,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         self,
         ip_adapter_fields: list[IPAdapterField],
         context: InvocationContext,
+        device: torch.device,
     ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
         """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
         clip_image_processor = CLIPImageProcessor()
@@ -628,11 +666,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
 
                 clip_image: torch.Tensor = clip_image_processor(images=pos_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
+                clip_image = clip_image.to(device=device, dtype=image_encoder_model.dtype)
                 pos_clip_image_embeds = image_encoder_model(clip_image).image_embeds
 
                 clip_image = clip_image_processor(images=neg_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
+                clip_image = clip_image.to(device=device, dtype=image_encoder_model.dtype)
                 neg_clip_image_embeds = image_encoder_model(clip_image).image_embeds
 
             pos_image_prompt_clip_embeds.append(pos_clip_image_embeds)
@@ -681,10 +719,15 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return pos_ip_adapter_extensions, neg_ip_adapter_extensions
 
-    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
-        for lora in self.transformer.loras:
+    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelPatchRaw, float]]:
+        loras: list[Union[LoRAField, ControlLoRAField]] = [*self.transformer.loras]
+        if self.control_lora:
+            # Note: Since FLUX structural control LoRAs modify the shape of some weights, it is important that they are
+            # applied last.
+            loras.append(self.control_lora)
+        for lora in loras:
             lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, LoRAModelRaw)
+            assert isinstance(lora_info.model, ModelPatchRaw)
             yield (lora_info.model, lora.weight)
             del lora_info
 

invokeai/app/invocations/flux_lora_loader.py

@@ -8,7 +8,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import CLIPField, LoRAField, ModelIdentifierField, TransformerField
+from invokeai.app.invocations.model import CLIPField, LoRAField, ModelIdentifierField, T5EncoderField, TransformerField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.config import BaseModelType
 
@@ -21,6 +21,9 @@ class FluxLoRALoaderOutput(BaseInvocationOutput):
         default=None, description=FieldDescriptions.transformer, title="FLUX Transformer"
     )
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    t5_encoder: Optional[T5EncoderField] = OutputField(
+        default=None, description=FieldDescriptions.t5_encoder, title="T5 Encoder"
+    )
 
 
 @invocation(
@@ -28,7 +31,7 @@ class FluxLoRALoaderOutput(BaseInvocationOutput):
     title="FLUX LoRA",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.1.0",
+    version="1.2.0",
     classification=Classification.Prototype,
 )
 class FluxLoRALoaderInvocation(BaseInvocation):
@@ -50,6 +53,12 @@ class FluxLoRALoaderInvocation(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
+    t5_encoder: T5EncoderField | None = InputField(
+        default=None,
+        title="T5 Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
 
     def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
         lora_key = self.lora.key
@@ -62,6 +71,8 @@ class FluxLoRALoaderInvocation(BaseInvocation):
             raise ValueError(f'LoRA "{lora_key}" already applied to transformer.')
         if self.clip and any(lora.lora.key == lora_key for lora in self.clip.loras):
             raise ValueError(f'LoRA "{lora_key}" already applied to CLIP encoder.')
+        if self.t5_encoder and any(lora.lora.key == lora_key for lora in self.t5_encoder.loras):
+            raise ValueError(f'LoRA "{lora_key}" already applied to T5 encoder.')
 
         output = FluxLoRALoaderOutput()
 
@@ -82,6 +93,14 @@ class FluxLoRALoaderInvocation(BaseInvocation):
                     weight=self.weight,
                 )
             )
+        if self.t5_encoder is not None:
+            output.t5_encoder = self.t5_encoder.model_copy(deep=True)
+            output.t5_encoder.loras.append(
+                LoRAField(
+                    lora=self.lora,
+                    weight=self.weight,
+                )
+            )
 
         return output
 
@@ -91,14 +110,14 @@ class FluxLoRALoaderInvocation(BaseInvocation):
     title="FLUX LoRA Collection Loader",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.1.0",
+    version="1.3.0",
     classification=Classification.Prototype,
 )
 class FLUXLoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to a FLUX transformer."""
 
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
 
     transformer: Optional[TransformerField] = InputField(
@@ -113,13 +132,30 @@ class FLUXLoRACollectionLoader(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
+    t5_encoder: T5EncoderField | None = InputField(
+        default=None,
+        title="T5 Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
 
     def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
         output = FluxLoRALoaderOutput()
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
+        if self.transformer is not None:
+            output.transformer = self.transformer.model_copy(deep=True)
+
+        if self.clip is not None:
+            output.clip = self.clip.model_copy(deep=True)
+
+        if self.t5_encoder is not None:
+            output.t5_encoder = self.t5_encoder.model_copy(deep=True)
+
         for lora in loras:
+            if lora is None:
+                continue
             if lora.lora.key in added_loras:
                 continue
 
@@ -130,14 +166,13 @@ class FLUXLoRACollectionLoader(BaseInvocation):
 
             added_loras.append(lora.lora.key)
 
-            if self.transformer is not None:
-                if output.transformer is None:
-                    output.transformer = self.transformer.model_copy(deep=True)
+            if self.transformer is not None and output.transformer is not None:
                 output.transformer.loras.append(lora)
 
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
+            if self.clip is not None and output.clip is not None:
                 output.clip.loras.append(lora)
 
+            if self.t5_encoder is not None and output.t5_encoder is not None:
+                output.t5_encoder.loras.append(lora)
+
         return output

invokeai/app/invocations/flux_model_loader.py

@@ -10,6 +10,10 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.t5_model_identifier import (
+    preprocess_t5_encoder_model_identifier,
+    preprocess_t5_tokenizer_model_identifier,
+)
 from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
@@ -36,7 +40,7 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
     title="Flux Main Model",
     tags=["model", "flux"],
     category="model",
-    version="1.0.4",
+    version="1.0.5",
     classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):
@@ -74,8 +78,8 @@ class FluxModelLoaderInvocation(BaseInvocation):
         tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
         clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
 
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        tokenizer2 = preprocess_t5_tokenizer_model_identifier(self.t5_encoder_model)
+        t5_encoder = preprocess_t5_encoder_model_identifier(self.t5_encoder_model)
 
         transformer_config = context.models.get_config(transformer)
         assert isinstance(transformer_config, CheckpointConfigBase)
@@ -83,7 +87,7 @@ class FluxModelLoaderInvocation(BaseInvocation):
         return FluxModelLoaderOutput(
             transformer=TransformerField(transformer=transformer, loras=[]),
             clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder, loras=[]),
             vae=VAEField(vae=vae),
             max_seq_len=max_seq_lengths[transformer_config.config_path],
         )

invokeai/app/invocations/flux_text_encoder.py

@@ -2,7 +2,7 @@ from contextlib import ExitStack
 from typing import Iterator, Literal, Optional, Tuple
 
 import torch
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import (
@@ -17,10 +17,10 @@ from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_T5_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
 
 
@@ -69,17 +69,46 @@ class FluxTextEncoderInvocation(BaseInvocation):
         )
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-
         prompt = [self.prompt]
 
+        t5_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+        t5_encoder_config = t5_encoder_info.config
+        assert t5_encoder_config is not None
+
         with (
-            t5_text_encoder_info as t5_text_encoder,
-            t5_tokenizer_info as t5_tokenizer,
+            t5_encoder_info.model_on_device() as (cached_weights, t5_text_encoder),
+            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
+            ExitStack() as exit_stack,
         ):
             assert isinstance(t5_text_encoder, T5EncoderModel)
-            assert isinstance(t5_tokenizer, T5Tokenizer)
+            assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
+
+            # Determine if the model is quantized.
+            # If the model is quantized, then we need to apply the LoRA weights as sidecar layers. This results in
+            # slower inference than direct patching, but is agnostic to the quantization format.
+            if t5_encoder_config.format in [ModelFormat.T5Encoder, ModelFormat.Diffusers]:
+                model_is_quantized = False
+            elif t5_encoder_config.format in [
+                ModelFormat.BnbQuantizedLlmInt8b,
+                ModelFormat.BnbQuantizednf4b,
+                ModelFormat.GGUFQuantized,
+            ]:
+                model_is_quantized = True
+            else:
+                raise ValueError(f"Unsupported model format: {t5_encoder_config.format}")
+
+            # Apply LoRA models to the T5 encoder.
+            # Note: We apply the LoRA after the encoder has been moved to its target device for faster patching.
+            exit_stack.enter_context(
+                LayerPatcher.apply_smart_model_patches(
+                    model=t5_text_encoder,
+                    patches=self._t5_lora_iterator(context),
+                    prefix=FLUX_LORA_T5_PREFIX,
+                    dtype=t5_text_encoder.dtype,
+                    cached_weights=cached_weights,
+                    force_sidecar_patching=model_is_quantized,
+                )
+            )
 
             t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
@@ -90,31 +119,30 @@ class FluxTextEncoderInvocation(BaseInvocation):
         return prompt_embeds
 
     def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
-        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
-        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
-
         prompt = [self.prompt]
 
+        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
+        clip_text_encoder_config = clip_text_encoder_info.config
+        assert clip_text_encoder_config is not None
+
         with (
             clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
-            clip_tokenizer_info as clip_tokenizer,
+            context.models.load(self.clip.tokenizer) as clip_tokenizer,
             ExitStack() as exit_stack,
         ):
             assert isinstance(clip_text_encoder, CLIPTextModel)
             assert isinstance(clip_tokenizer, CLIPTokenizer)
 
-            clip_text_encoder_config = clip_text_encoder_info.config
-            assert clip_text_encoder_config is not None
-
             # Apply LoRA models to the CLIP encoder.
             # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
             if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
+                    LayerPatcher.apply_smart_model_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context),
                         prefix=FLUX_LORA_CLIP_PREFIX,
+                        dtype=clip_text_encoder.dtype,
                         cached_weights=cached_weights,
                     )
                 )
@@ -130,9 +158,16 @@ class FluxTextEncoderInvocation(BaseInvocation):
         assert isinstance(pooled_prompt_embeds, torch.Tensor)
         return pooled_prompt_embeds
 
-    def _clip_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
+    def _clip_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelPatchRaw, float]]:
         for lora in self.clip.loras:
             lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, LoRAModelRaw)
+            assert isinstance(lora_info.model, ModelPatchRaw)
+            yield (lora_info.model, lora.weight)
+            del lora_info
+
+    def _t5_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelPatchRaw, float]]:
+        for lora in self.t5_encoder.loras:
+            lora_info = context.models.load(lora.lora)
+            assert isinstance(lora_info.model, ModelPatchRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

invokeai/app/invocations/flux_vae_decode.py

@@ -3,6 +3,7 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -24,7 +25,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Latents to Image",
     tags=["latents", "image", "vae", "l2i", "flux"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -38,8 +39,23 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
+    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoEncoder) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
+        # element size (precision).
+        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 1090  # Determined experimentally.
+        working_memory = out_h * out_w * element_size * scaling_constant
+
+        # We add a 20% buffer to the working memory estimate to be safe.
+        working_memory = working_memory * 1.2
+        return int(working_memory)
+
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
-        with vae_info as vae:
+        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
+        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoEncoder)
             vae_dtype = next(iter(vae.parameters())).dtype
             latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)

invokeai/app/invocations/ideal_size.py

@@ -21,7 +21,7 @@ class IdealSizeOutput(BaseInvocationOutput):
     "ideal_size",
     title="Ideal Size",
     tags=["latents", "math", "ideal_size"],
-    version="1.0.3",
+    version="1.0.4",
 )
 class IdealSizeInvocation(BaseInvocation):
     """Calculates the ideal size for generation to avoid duplication"""
@@ -41,11 +41,16 @@ class IdealSizeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> IdealSizeOutput:
         unet_config = context.models.get_config(self.unet.unet.key)
         aspect = self.width / self.height
-        dimension: float = 512
-        if unet_config.base == BaseModelType.StableDiffusion2:
+
+        if unet_config.base == BaseModelType.StableDiffusion1:
+            dimension = 512
+        elif unet_config.base == BaseModelType.StableDiffusion2:
             dimension = 768
-        elif unet_config.base == BaseModelType.StableDiffusionXL:
+        elif unet_config.base in (BaseModelType.StableDiffusionXL, BaseModelType.Flux, BaseModelType.StableDiffusion3):
             dimension = 1024
+        else:
+            raise ValueError(f"Unsupported model type: {unet_config.base}")
+
         dimension = dimension * self.multiplier
         min_dimension = math.floor(dimension * 0.5)
         model_area = dimension * dimension  # hardcoded for now since all models are trained on square images

invokeai/app/invocations/image.py

@@ -13,6 +13,7 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
+    BoundingBoxField,
     ColorField,
     FieldDescriptions,
     ImageField,
@@ -23,6 +24,7 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.misc import SEED_MAX
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
@@ -161,12 +163,12 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
     crop: bool = InputField(default=False, description="Crop to base image dimensions")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        base_image = context.images.get_pil(self.base_image.image_name)
-        image = context.images.get_pil(self.image.image_name)
+        base_image = context.images.get_pil(self.base_image.image_name, mode="RGBA")
+        image = context.images.get_pil(self.image.image_name, mode="RGBA")
         mask = None
         if self.mask is not None:
-            mask = context.images.get_pil(self.mask.image_name)
-            mask = ImageOps.invert(mask.convert("L"))
+            mask = context.images.get_pil(self.mask.image_name, mode="L")
+            mask = ImageOps.invert(mask)
         # TODO: probably shouldn't invert mask here... should user be required to do it?
 
         min_x = min(0, self.x)
@@ -176,7 +178,11 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         new_image = Image.new(mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0))
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
-        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
+
+        # Create a temporary image to paste the image with transparency
+        temp_image = Image.new("RGBA", new_image.size)
+        temp_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
+        new_image = Image.alpha_composite(new_image, temp_image)
 
         if self.crop:
             base_w, base_h = base_image.size
@@ -301,14 +307,44 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     blur_type: Literal["gaussian", "box"] = InputField(default="gaussian", description="The type of blur")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name, mode="RGBA")
 
+        # Split the image into RGBA channels
+        r, g, b, a = image.split()
+
+        # Premultiply RGB channels by alpha
+        premultiplied_image = ImageChops.multiply(image, a.convert("RGBA"))
+        premultiplied_image.putalpha(a)
+
+        # Apply the blur
         blur = (
             ImageFilter.GaussianBlur(self.radius) if self.blur_type == "gaussian" else ImageFilter.BoxBlur(self.radius)
         )
-        blur_image = image.filter(blur)
+        blurred_image = premultiplied_image.filter(blur)
 
-        image_dto = context.images.save(image=blur_image)
+        # Split the blurred image into RGBA channels
+        r, g, b, a_orig = blurred_image.split()
+
+        # Convert to float using NumPy. float 32/64 division are much faster than float 16
+        r = numpy.array(r, dtype=numpy.float32)
+        g = numpy.array(g, dtype=numpy.float32)
+        b = numpy.array(b, dtype=numpy.float32)
+        a = numpy.array(a_orig, dtype=numpy.float32) / 255.0  # Normalize alpha to [0, 1]
+
+        # Unpremultiply RGB channels by alpha
+        r /= a + 1e-6  # Add a small epsilon to avoid division by zero
+        g /= a + 1e-6
+        b /= a + 1e-6
+
+        # Convert back to PIL images
+        r = Image.fromarray(numpy.uint8(numpy.clip(r, 0, 255)))
+        g = Image.fromarray(numpy.uint8(numpy.clip(g, 0, 255)))
+        b = Image.fromarray(numpy.uint8(numpy.clip(b, 0, 255)))
+
+        # Merge back into a single image
+        result_image = Image.merge("RGBA", (r, g, b, a_orig))
+
+        image_dto = context.images.save(image=result_image)
 
         return ImageOutput.build(image_dto)
 
@@ -807,7 +843,7 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add or subtract a value from a specific color channel of an image."""
@@ -817,18 +853,22 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
     offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        pil_image = context.images.get_pil(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name, "RGBA")
 
         # extract the channel and mode from the input and reference tuple
         mode = CHANNEL_FORMATS[self.channel][0]
         channel_number = CHANNEL_FORMATS[self.channel][1]
 
         # Convert PIL image to new format
-        converted_image = numpy.array(pil_image.convert(mode)).astype(int)
+        converted_image = numpy.array(image.convert(mode)).astype(int)
         image_channel = converted_image[:, :, channel_number]
 
-        # Adjust the value, clipping to 0..255
-        image_channel = numpy.clip(image_channel + self.offset, 0, 255)
+        if self.channel == "Hue (HSV)":
+            # loop around the values because hue is special
+            image_channel = (image_channel + self.offset) % 256
+        else:
+            # Adjust the value, clipping to 0..255
+            image_channel = numpy.clip(image_channel + self.offset, 0, 255)
 
         # Put the channel back into the image
         converted_image[:, :, channel_number] = image_channel
@@ -836,6 +876,10 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
         # Convert back to RGBA format and output
         pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
 
+        # restore the alpha channel
+        if self.channel != "Alpha (RGBA)":
+            pil_image.putalpha(image.getchannel("A"))
+
         image_dto = context.images.save(image=pil_image)
 
         return ImageOutput.build(image_dto)
@@ -863,7 +907,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
         "value",
     ],
     category="image",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Scale a specific color channel of an image."""
@@ -874,14 +918,14 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
     invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        pil_image = context.images.get_pil(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
         # extract the channel and mode from the input and reference tuple
         mode = CHANNEL_FORMATS[self.channel][0]
         channel_number = CHANNEL_FORMATS[self.channel][1]
 
         # Convert PIL image to new format
-        converted_image = numpy.array(pil_image.convert(mode)).astype(float)
+        converted_image = numpy.array(image.convert(mode)).astype(float)
         image_channel = converted_image[:, :, channel_number]
 
         # Adjust the value, clipping to 0..255
@@ -897,6 +941,10 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
         # Convert back to RGBA format and output
         pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
 
+        # restore the alpha channel
+        if self.channel != "Alpha (RGBA)":
+            pil_image.putalpha(image.getchannel("A"))
+
         image_dto = context.images.save(image=pil_image)
 
         return ImageOutput.build(image_dto)
@@ -962,10 +1010,10 @@ class CanvasPasteBackInvocation(BaseInvocation, WithMetadata, WithBoard):
 
 @invocation(
     "mask_from_id",
-    title="Mask from ID",
+    title="Mask from Segmented Image",
     tags=["image", "mask", "id"],
     category="image",
-    version="1.0.0",
+    version="1.0.1",
 )
 class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generate a mask for a particular color in an ID Map"""
@@ -975,40 +1023,24 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
     threshold: int = InputField(default=100, description="Threshold for color detection")
     invert: bool = InputField(default=False, description="Whether or not to invert the mask")
 
-    def rgba_to_hex(self, rgba_color: tuple[int, int, int, int]):
-        r, g, b, a = rgba_color
-        hex_code = "#{:02X}{:02X}{:02X}{:02X}".format(r, g, b, int(a * 255))
-        return hex_code
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name, mode="RGBA")
 
-    def id_to_mask(self, id_mask: Image.Image, color: tuple[int, int, int, int], threshold: int = 100):
-        if id_mask.mode != "RGB":
-            id_mask = id_mask.convert("RGB")
-
-        # Can directly just use the tuple but I'll leave this rgba_to_hex here
-        # incase anyone prefers using hex codes directly instead of the color picker
-        hex_color_str = self.rgba_to_hex(color)
-        rgb_color = numpy.array([int(hex_color_str[i : i + 2], 16) for i in (1, 3, 5)])
+        np_color = numpy.array(self.color.tuple())
 
         # Maybe there's a faster way to calculate this distance but I can't think of any right now.
-        color_distance = numpy.linalg.norm(id_mask - rgb_color, axis=-1)
+        color_distance = numpy.linalg.norm(image - np_color, axis=-1)
 
         # Create a mask based on the threshold and the distance calculated above
-        binary_mask = (color_distance < threshold).astype(numpy.uint8) * 255
+        binary_mask = (color_distance < self.threshold).astype(numpy.uint8) * 255
 
         # Convert the mask back to PIL
         binary_mask_pil = Image.fromarray(binary_mask)
 
-        return binary_mask_pil
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        mask = self.id_to_mask(image, self.color.tuple(), self.threshold)
-
         if self.invert:
-            mask = ImageOps.invert(mask)
+            binary_mask_pil = ImageOps.invert(binary_mask_pil)
 
-        image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
+        image_dto = context.images.save(image=binary_mask_pil, image_category=ImageCategory.MASK)
 
         return ImageOutput.build(image_dto)
 
@@ -1055,3 +1087,123 @@ class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
             image_dto = context.images.save(image=generated_image)
 
         return ImageOutput.build(image_dto)
+
+
+@invocation(
+    "img_noise",
+    title="Add Image Noise",
+    tags=["image", "noise"],
+    category="image",
+    version="1.0.1",
+)
+class ImageNoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Add noise to an image"""
+
+    image: ImageField = InputField(description="The image to add noise to")
+    seed: int = InputField(
+        default=0,
+        ge=0,
+        le=SEED_MAX,
+        description=FieldDescriptions.seed,
+    )
+    noise_type: Literal["gaussian", "salt_and_pepper"] = InputField(
+        default="gaussian",
+        description="The type of noise to add",
+    )
+    amount: float = InputField(default=0.1, ge=0, le=1, description="The amount of noise to add")
+    noise_color: bool = InputField(default=True, description="Whether to add colored noise")
+    size: int = InputField(default=1, ge=1, description="The size of the noise points")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name, mode="RGBA")
+
+        # Save out the alpha channel
+        alpha = image.getchannel("A")
+
+        # Set the seed for numpy random
+        rs = numpy.random.RandomState(numpy.random.MT19937(numpy.random.SeedSequence(self.seed)))
+
+        if self.noise_type == "gaussian":
+            if self.noise_color:
+                noise = rs.normal(0, 1, (image.height // self.size, image.width // self.size, 3)) * 255
+            else:
+                noise = rs.normal(0, 1, (image.height // self.size, image.width // self.size)) * 255
+                noise = numpy.stack([noise] * 3, axis=-1)
+        elif self.noise_type == "salt_and_pepper":
+            if self.noise_color:
+                noise = rs.choice(
+                    [0, 255], (image.height // self.size, image.width // self.size, 3), p=[1 - self.amount, self.amount]
+                )
+            else:
+                noise = rs.choice(
+                    [0, 255], (image.height // self.size, image.width // self.size), p=[1 - self.amount, self.amount]
+                )
+                noise = numpy.stack([noise] * 3, axis=-1)
+
+        noise = Image.fromarray(noise.astype(numpy.uint8), mode="RGB").resize(
+            (image.width, image.height), Image.Resampling.NEAREST
+        )
+        noisy_image = Image.blend(image.convert("RGB"), noise, self.amount).convert("RGBA")
+
+        # Paste back the alpha channel
+        noisy_image.putalpha(alpha)
+
+        image_dto = context.images.save(image=noisy_image)
+
+        return ImageOutput.build(image_dto)
+
+
+@invocation(
+    "crop_image_to_bounding_box",
+    title="Crop Image to Bounding Box",
+    category="image",
+    version="1.0.0",
+    tags=["image", "crop"],
+    classification=Classification.Beta,
+)
+class CropImageToBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Crop an image to the given bounding box. If the bounding box is omitted, the image is cropped to the non-transparent pixels."""
+
+    image: ImageField = InputField(description="The image to crop")
+    bounding_box: BoundingBoxField | None = InputField(
+        default=None, description="The bounding box to crop the image to"
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name)
+
+        bounding_box = self.bounding_box.tuple() if self.bounding_box is not None else image.getbbox()
+
+        cropped_image = image.crop(bounding_box)
+
+        image_dto = context.images.save(image=cropped_image)
+        return ImageOutput.build(image_dto)
+
+
+@invocation(
+    "paste_image_into_bounding_box",
+    title="Paste Image into Bounding Box",
+    category="image",
+    version="1.0.0",
+    tags=["image", "crop"],
+    classification=Classification.Beta,
+)
+class PasteImageIntoBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Paste the source image into the target image at the given bounding box.
+
+    The source image must be the same size as the bounding box, and the bounding box must fit within the target image."""
+
+    source_image: ImageField = InputField(description="The image to paste")
+    target_image: ImageField = InputField(description="The image to paste into")
+    bounding_box: BoundingBoxField = InputField(description="The bounding box to paste the image into")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        source_image = context.images.get_pil(self.source_image.image_name, mode="RGBA")
+        target_image = context.images.get_pil(self.target_image.image_name, mode="RGBA")
+
+        bounding_box = self.bounding_box.tuple()
+
+        target_image.paste(source_image, bounding_box, source_image)
+
+        image_dto = context.images.save(image=target_image)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/image_to_latents.py

@@ -13,7 +13,7 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -26,6 +26,7 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
+from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation(
@@ -49,7 +50,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
     # offer a way to directly set None values.
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
-    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
     @staticmethod
     def vae_encode(
@@ -98,7 +99,7 @@ class ImageToLatentsInvocation(BaseInvocation):
                 )
 
             # non_noised_latents_from_image
-            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
+            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae.dtype)
             with torch.inference_mode(), tiling_context:
                 latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
 

invokeai/app/invocations/latents_to_image.py

@@ -12,7 +12,7 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -34,7 +34,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.3.0",
+    version="1.3.1",
 )
 class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -51,18 +51,60 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
     # offer a way to directly set None values.
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
-    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
+    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
+
+    def _estimate_working_memory(
+        self, latents: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
+    ) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
+        # element size (precision). This estimate is accurate for both SD1 and SDXL.
+        element_size = 4 if self.fp32 else 2
+        scaling_constant = 960  # Determined experimentally.
+
+        if use_tiling:
+            tile_size = self.tile_size
+            if tile_size == 0:
+                tile_size = vae.tile_sample_min_size
+                assert isinstance(tile_size, int)
+            out_h = tile_size
+            out_w = tile_size
+            working_memory = out_h * out_w * element_size * scaling_constant
+
+            # We add 25% to the working memory estimate when tiling is enabled to account for factors like tile overlap
+            # and number of tiles. We could make this more precise in the future, but this should be good enough for
+            # most use cases.
+            working_memory = working_memory * 1.25
+        else:
+            out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+            out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+            working_memory = out_h * out_w * element_size * scaling_constant
+
+        if self.fp32:
+            # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
+            working_memory += 250 * 2**20
+
+        # We add 20% to the working memory estimate to be safe.
+        working_memory = int(working_memory * 1.2)
+        return working_memory
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
+        use_tiling = self.tiled or context.config.get().force_tiled_decode
+
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+
+        estimated_working_memory = self._estimate_working_memory(latents, use_tiling, vae_info.model)
+        with (
+            SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
+            vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),
+        ):
             context.util.signal_progress("Running VAE decoder")
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
-            latents = latents.to(vae.device)
+            latents = latents.to(TorchDevice.choose_torch_device())
             if self.fp32:
                 vae.to(dtype=torch.float32)
 
@@ -88,7 +130,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.config.get().force_tiled_decode:
+            if use_tiling:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()

invokeai/app/invocations/mask.py

@@ -2,9 +2,22 @@ import numpy as np
 import torch
 from PIL import Image
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
-from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithBoard, WithMetadata
-from invokeai.app.invocations.primitives import ImageOutput, MaskOutput
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    Classification,
+    InvocationContext,
+    invocation,
+)
+from invokeai.app.invocations.fields import (
+    BoundingBoxField,
+    ColorField,
+    ImageField,
+    InputField,
+    TensorField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.primitives import BoundingBoxOutput, ImageOutput, MaskOutput
 from invokeai.backend.image_util.util import pil_to_np
 
 
@@ -73,7 +86,7 @@ class AlphaMaskToTensorInvocation(BaseInvocation):
     title="Invert Tensor Mask",
     tags=["conditioning"],
     category="conditioning",
-    version="1.0.0",
+    version="1.1.0",
     classification=Classification.Beta,
 )
 class InvertTensorMaskInvocation(BaseInvocation):
@@ -83,6 +96,15 @@ class InvertTensorMaskInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
         mask = context.tensors.load(self.mask.tensor_name)
+
+        # Verify dtype and shape.
+        assert mask.dtype == torch.bool
+        assert mask.dim() in [2, 3]
+
+        # Unsqueeze the channel dimension if it is missing. The MaskOutput type expects a single channel.
+        if mask.dim() == 2:
+            mask = mask.unsqueeze(0)
+
         inverted = ~mask
 
         return MaskOutput(
@@ -201,3 +223,48 @@ class ApplyMaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_dto = context.images.save(image=masked_image)
 
         return ImageOutput.build(image_dto)
+
+
+WHITE = ColorField(r=255, g=255, b=255, a=255)
+
+
+@invocation(
+    "get_image_mask_bounding_box",
+    title="Get Image Mask Bounding Box",
+    tags=["mask"],
+    category="mask",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
+class GetMaskBoundingBoxInvocation(BaseInvocation):
+    """Gets the bounding box of the given mask image."""
+
+    mask: ImageField = InputField(description="The mask to crop.")
+    margin: int = InputField(default=0, description="Margin to add to the bounding box.")
+    mask_color: ColorField = InputField(default=WHITE, description="Color of the mask in the image.")
+
+    def invoke(self, context: InvocationContext) -> BoundingBoxOutput:
+        mask = context.images.get_pil(self.mask.image_name, mode="RGBA")
+        mask_np = np.array(mask)
+
+        # Convert mask_color to RGBA tuple
+        mask_color_rgb = self.mask_color.tuple()
+
+        # Find the bounding box of the mask color
+        y, x = np.where(np.all(mask_np == mask_color_rgb, axis=-1))
+
+        if len(x) == 0 or len(y) == 0:
+            # No pixels found with the given color
+            return BoundingBoxOutput(bounding_box=BoundingBoxField(x_min=0, y_min=0, x_max=0, y_max=0))
+
+        left, upper, right, lower = x.min(), y.min(), x.max(), y.max()
+
+        # Add the margin
+        left = max(0, left - self.margin)
+        upper = max(0, upper - self.margin)
+        right = min(mask_np.shape[1], right + self.margin)
+        lower = min(mask_np.shape[0], lower + self.margin)
+
+        bounding_box = BoundingBoxField(x_min=left, y_min=upper, x_max=right, y_max=lower)
+
+        return BoundingBoxOutput(bounding_box=bounding_box)

invokeai/app/invocations/model.py

@@ -10,7 +10,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
 from invokeai.backend.model_manager.config import (
@@ -65,14 +65,10 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
-class TransformerField(BaseModel):
-    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
-    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
-
-
 class T5EncoderField(BaseModel):
     tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
     text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
 class VAEField(BaseModel):
@@ -80,6 +76,15 @@ class VAEField(BaseModel):
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
+class ControlLoRAField(LoRAField):
+    img: ImageField = Field(description="Image to use in structural conditioning")
+
+
+class TransformerField(BaseModel):
+    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
+
+
 @invocation_output("unet_output")
 class UNetOutput(BaseInvocationOutput):
     """Base class for invocations that output a UNet field."""
@@ -201,7 +206,7 @@ class LoRALoaderInvocation(BaseInvocation):
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
-            raise Exception(f"Unkown lora: {lora_key}!")
+            raise Exception(f"Unknown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
             raise Exception(f'LoRA "{lora_key}" already applied to unet')
@@ -252,12 +257,12 @@ class LoRASelectorInvocation(BaseInvocation):
         return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
 
 
-@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.0.0")
+@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.1.0")
 class LoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to the provided UNet and CLIP models."""
 
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -277,7 +282,14 @@ class LoRACollectionLoader(BaseInvocation):
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
+        if self.unet is not None:
+            output.unet = self.unet.model_copy(deep=True)
+        if self.clip is not None:
+            output.clip = self.clip.model_copy(deep=True)
+
         for lora in loras:
+            if lora is None:
+                continue
             if lora.lora.key in added_loras:
                 continue
 
@@ -288,14 +300,10 @@ class LoRACollectionLoader(BaseInvocation):
 
             added_loras.append(lora.lora.key)
 
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
+            if self.unet is not None and output.unet is not None:
                 output.unet.loras.append(lora)
 
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
+            if self.clip is not None and output.clip is not None:
                 output.clip.loras.append(lora)
 
         return output
@@ -395,13 +403,13 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
     title="SDXL LoRA Collection Loader",
     tags=["model"],
     category="model",
-    version="1.0.0",
+    version="1.1.0",
 )
 class SDXLLoRACollectionLoader(BaseInvocation):
     """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
 
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -427,7 +435,18 @@ class SDXLLoRACollectionLoader(BaseInvocation):
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
+        if self.unet is not None:
+            output.unet = self.unet.model_copy(deep=True)
+
+        if self.clip is not None:
+            output.clip = self.clip.model_copy(deep=True)
+
+        if self.clip2 is not None:
+            output.clip2 = self.clip2.model_copy(deep=True)
+
         for lora in loras:
+            if lora is None:
+                continue
             if lora.lora.key in added_loras:
                 continue
 
@@ -438,19 +457,13 @@ class SDXLLoRACollectionLoader(BaseInvocation):
 
             added_loras.append(lora.lora.key)
 
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
+            if self.unet is not None and output.unet is not None:
                 output.unet.loras.append(lora)
 
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
+            if self.clip is not None and output.clip is not None:
                 output.clip.loras.append(lora)
 
-            if self.clip2 is not None:
-                if output.clip2 is None:
-                    output.clip2 = self.clip2.model_copy(deep=True)
+            if self.clip2 is not None and output.clip2 is not None:
                 output.clip2.loras.append(lora)
 
         return output
@@ -468,7 +481,7 @@ class VAELoaderInvocation(BaseInvocation):
         key = self.vae_model.key
 
         if not context.models.exists(key):
-            raise Exception(f"Unkown vae: {key}!")
+            raise Exception(f"Unknown vae: {key}!")
 
         return VAEOutput(vae=VAEField(vae=self.vae_model))
 

invokeai/app/invocations/primitives.py

@@ -7,7 +7,6 @@ import torch
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -417,6 +416,7 @@ class ColorInvocation(BaseInvocation):
 class MaskOutput(BaseInvocationOutput):
     """A torch mask tensor."""
 
+    # shape: [1, H, W], dtype: bool
     mask: TensorField = OutputField(description="The mask.")
     width: int = OutputField(description="The width of the mask in pixels.")
     height: int = OutputField(description="The height of the mask in pixels.")
@@ -539,23 +539,3 @@ class BoundingBoxInvocation(BaseInvocation):
 
 
 # endregion
-
-
-@invocation(
-    "image_batch",
-    title="Image Batch",
-    tags=["primitives", "image", "batch", "internal"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class ImageBatchInvocation(BaseInvocation):
-    """Create a batched generation, where the workflow is executed once for each image in the batch."""
-
-    images: list[ImageField] = InputField(min_length=1, description="The images to batch over", input=Input.Direct)
-
-    def __init__(self):
-        raise NotImplementedError("This class should never be executed or instantiated directly.")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        raise NotImplementedError("This class should never be executed or instantiated directly.")

invokeai/app/invocations/sd3_image_to_latents.py

@@ -16,6 +16,7 @@ from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation(
@@ -39,7 +40,7 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
 
             vae.disable_tiling()
 
-            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
+            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae.dtype)
             with torch.inference_mode():
                 image_tensor_dist = vae.encode(image_tensor).latent_dist
                 # TODO: Use seed to make sampling reproducible.

invokeai/app/invocations/sd3_latents_to_image.py

@@ -6,6 +6,7 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -26,7 +27,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="SD3 Latents to Image",
     tags=["latents", "image", "vae", "l2i", "sd3"],
     category="latents",
-    version="1.3.0",
+    version="1.3.1",
 )
 class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -40,16 +41,34 @@ class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
+    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoencoderKL) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
+        # element size (precision).
+        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 1230  # Determined experimentally.
+        working_memory = out_h * out_w * element_size * scaling_constant
+
+        # We add a 20% buffer to the working memory estimate to be safe.
+        working_memory = working_memory * 1.2
+        return int(working_memory)
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL))
-        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
+        with (
+            SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
+            vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),
+        ):
             context.util.signal_progress("Running VAE")
             assert isinstance(vae, (AutoencoderKL))
-            latents = latents.to(vae.device)
+            latents = latents.to(TorchDevice.choose_torch_device())
 
             vae.disable_tiling()
 

invokeai/app/invocations/sd3_model_loader.py

@@ -10,6 +10,10 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.t5_model_identifier import (
+    preprocess_t5_encoder_model_identifier,
+    preprocess_t5_tokenizer_model_identifier,
+)
 from invokeai.backend.model_manager.config import SubModelType
 
 
@@ -88,21 +92,13 @@ class Sd3ModelLoaderInvocation(BaseInvocation):
             if self.clip_g_model
             else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
         )
-        tokenizer_t5 = (
-            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
-            if self.t5_encoder_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
-        )
-        t5_encoder = (
-            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
-            if self.t5_encoder_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
-        )
+        tokenizer_t5 = preprocess_t5_tokenizer_model_identifier(self.t5_encoder_model or self.model)
+        t5_encoder = preprocess_t5_encoder_model_identifier(self.t5_encoder_model or self.model)
 
         return Sd3ModelLoaderOutput(
             transformer=TransformerField(transformer=transformer, loras=[]),
             clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
             clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder, loras=[]),
             vae=VAEField(vae=vae),
         )

invokeai/app/invocations/sd3_text_encoder.py

@@ -16,11 +16,12 @@ from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import SD3ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
 
 # The SD3 T5 Max Sequence Length set based on the default in diffusers.
 SD3_T5_MAX_SEQ_LEN = 256
@@ -86,14 +87,11 @@ class Sd3TextEncoderInvocation(BaseInvocation):
 
     def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tensor:
         assert self.t5_encoder is not None
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-
         prompt = [self.prompt]
 
         with (
-            t5_text_encoder_info as t5_text_encoder,
-            t5_tokenizer_info as t5_tokenizer,
+            context.models.load(self.t5_encoder.text_encoder) as t5_text_encoder,
+            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
         ):
             context.util.signal_progress("Running T5 encoder")
             assert isinstance(t5_text_encoder, T5EncoderModel)
@@ -120,7 +118,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
                     f" {max_seq_len} tokens: {removed_text}"
                 )
 
-            prompt_embeds = t5_text_encoder(text_input_ids.to(t5_text_encoder.device))[0]
+            prompt_embeds = t5_text_encoder(text_input_ids.to(TorchDevice.choose_torch_device()))[0]
 
         assert isinstance(prompt_embeds, torch.Tensor)
         return prompt_embeds
@@ -128,14 +126,12 @@ class Sd3TextEncoderInvocation(BaseInvocation):
     def _clip_encode(
         self, context: InvocationContext, clip_model: CLIPField, tokenizer_max_length: int = 77
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        clip_tokenizer_info = context.models.load(clip_model.tokenizer)
-        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
-
         prompt = [self.prompt]
 
+        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
         with (
             clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
-            clip_tokenizer_info as clip_tokenizer,
+            context.models.load(clip_model.tokenizer) as clip_tokenizer,
             ExitStack() as exit_stack,
         ):
             context.util.signal_progress("Running CLIP encoder")
@@ -150,10 +146,11 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
+                    LayerPatcher.apply_smart_model_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context, clip_model),
                         prefix=FLUX_LORA_CLIP_PREFIX,
+                        dtype=clip_text_encoder.dtype,
                         cached_weights=cached_weights,
                     )
                 )
@@ -184,7 +181,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
                     f" {tokenizer_max_length} tokens: {removed_text}"
                 )
             prompt_embeds = clip_text_encoder(
-                input_ids=text_input_ids.to(clip_text_encoder.device), output_hidden_states=True
+                input_ids=text_input_ids.to(TorchDevice.choose_torch_device()), output_hidden_states=True
             )
             pooled_prompt_embeds = prompt_embeds[0]
             prompt_embeds = prompt_embeds.hidden_states[-2]
@@ -193,9 +190,9 @@ class Sd3TextEncoderInvocation(BaseInvocation):
 
     def _clip_lora_iterator(
         self, context: InvocationContext, clip_model: CLIPField
-    ) -> Iterator[Tuple[LoRAModelRaw, float]]:
+    ) -> Iterator[Tuple[ModelPatchRaw, float]]:
         for lora in clip_model.loras:
             lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, LoRAModelRaw)
+            assert isinstance(lora_info.model, ModelPatchRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

invokeai/app/invocations/segment_anything.py

@@ -49,7 +49,7 @@ class SAMPointsField(BaseModel):
     title="Segment Anything",
     tags=["prompt", "segmentation"],
     category="segmentation",
-    version="1.1.0",
+    version="1.2.0",
 )
 class SegmentAnythingInvocation(BaseInvocation):
     """Runs a Segment Anything Model."""
@@ -96,8 +96,10 @@ class SegmentAnythingInvocation(BaseInvocation):
             # masks contains bool values, so we merge them via max-reduce.
             combined_mask, _ = torch.stack(masks).max(dim=0)
 
+        # Unsqueeze the channel dimension.
+        combined_mask = combined_mask.unsqueeze(0)
         mask_tensor_name = context.tensors.save(combined_mask)
-        height, width = combined_mask.shape
+        _, height, width = combined_mask.shape
         return MaskOutput(mask=TensorField(tensor_name=mask_tensor_name), width=width, height=height)
 
     @staticmethod

invokeai/app/invocations/spandrel_image_to_image.py

@@ -22,6 +22,7 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.tiles.tiles import calc_tiles_min_overlap
 from invokeai.backend.tiles.utils import TBLR, Tile
+from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation("spandrel_image_to_image", title="Image-to-Image", tags=["upscale"], category="upscale", version="1.3.0")
@@ -102,7 +103,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             (height * scale, width * scale, channels), dtype=torch.uint8, device=torch.device("cpu")
         )
 
-        image_tensor = image_tensor.to(device=spandrel_model.device, dtype=spandrel_model.dtype)
+        image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=spandrel_model.dtype)
 
         # Run the model on each tile.
         pbar = tqdm(list(zip(tiles, scaled_tiles, strict=True)), desc="Upscaling Tiles")
@@ -116,9 +117,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 raise CanceledException
 
             # Extract the current tile from the input tensor.
-            input_tile = image_tensor[
-                :, :, tile.coords.top : tile.coords.bottom, tile.coords.left : tile.coords.right
-            ].to(device=spandrel_model.device, dtype=spandrel_model.dtype)
+            input_tile = image_tensor[:, :, tile.coords.top : tile.coords.bottom, tile.coords.left : tile.coords.right]
 
             # Run the model on the tile.
             output_tile = spandrel_model.run(input_tile)
@@ -151,15 +150,12 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return pil_image
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
         # revisit this.
         image = context.images.get_pil(self.image.image_name, mode="RGB")
 
-        # Load the model.
-        spandrel_model_info = context.models.load(self.image_to_image_model)
-
         def step_callback(step: int, total_steps: int) -> None:
             context.util.signal_progress(
                 message=f"Processing tile {step}/{total_steps}",
@@ -167,7 +163,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             )
 
         # Do the upscaling.
-        with spandrel_model_info as spandrel_model:
+        with context.models.load(self.image_to_image_model) as spandrel_model:
             assert isinstance(spandrel_model, SpandrelImageToImageModel)
 
             # Upscale the image
@@ -200,15 +196,12 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
         description="If true, the output image will be resized to the nearest multiple of 8 in both dimensions.",
     )
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
         # revisit this.
         image = context.images.get_pil(self.image.image_name, mode="RGB")
 
-        # Load the model.
-        spandrel_model_info = context.models.load(self.image_to_image_model)
-
         # The target size of the image, determined by the provided scale. We'll run the upscaler until we hit this size.
         # Later, we may mutate this value if the model doesn't upscale the image or if the user requested a multiple of 8.
         target_width = int(image.width * self.scale)
@@ -221,7 +214,7 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
             )
 
         # Do the upscaling.
-        with spandrel_model_info as spandrel_model:
+        with context.models.load(self.image_to_image_model) as spandrel_model:
             assert isinstance(spandrel_model, SpandrelImageToImageModel)
 
             iteration = 1

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -22,8 +22,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.patches.layer_patcher import LayerPatcher
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
@@ -194,30 +194,31 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
             context.util.sd_step_callback(state, unet_config.base)
 
         # Prepare an iterator that yields the UNet's LoRA models and their weights.
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
             for lora in self.unet.loras:
                 lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, LoRAModelRaw)
+                assert isinstance(lora_info.model, ModelPatchRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
 
-        # Load the UNet model.
-        unet_info = context.models.load(self.unet.unet)
-
+        device = TorchDevice.choose_torch_device()
         with (
             ExitStack() as exit_stack,
-            unet_info as unet,
-            LoRAPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
+            context.models.load(self.unet.unet) as unet,
+            LayerPatcher.apply_smart_model_patches(
+                model=unet, patches=_lora_loader(), prefix="lora_unet_", dtype=unet.dtype
+            ),
         ):
             assert isinstance(unet, UNet2DConditionModel)
-            latents = latents.to(device=unet.device, dtype=unet.dtype)
+            latents = latents.to(device=device, dtype=unet.dtype)
             if noise is not None:
-                noise = noise.to(device=unet.device, dtype=unet.dtype)
+                noise = noise.to(device=device, dtype=unet.dtype)
             scheduler = get_scheduler(
                 context=context,
                 scheduler_info=self.unet.scheduler,
                 scheduler_name=self.scheduler,
                 seed=seed,
+                unet_config=unet_config,
             )
             pipeline = self.create_pipeline(unet=unet, scheduler=scheduler)
 
@@ -226,7 +227,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 context=context,
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
-                device=unet.device,
+                device=device,
                 dtype=unet.dtype,
                 latent_height=latent_tile_height,
                 latent_width=latent_tile_width,
@@ -239,6 +240,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 context=context,
                 control_input=self.control,
                 latents_shape=list(latents.shape),
+                device=device,
                 # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
                 do_classifier_free_guidance=True,
                 exit_stack=exit_stack,
@@ -264,7 +266,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
 
             timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
                 scheduler,
-                device=unet.device,
+                device=device,
                 steps=self.steps,
                 denoising_start=self.denoising_start,
                 denoising_end=self.denoising_end,

invokeai/app/services/board_records/board_records_common.py

@@ -57,7 +57,7 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
 
 
 class BoardChanges(BaseModel, extra="forbid"):
-    board_name: Optional[str] = Field(default=None, description="The board's new name.")
+    board_name: Optional[str] = Field(default=None, description="The board's new name.", max_length=300)
     cover_image_name: Optional[str] = Field(default=None, description="The name of the board's new cover image.")
     archived: Optional[bool] = Field(default=None, description="Whether or not the board is archived")
 

invokeai/app/services/config/config_default.py

@@ -13,7 +13,6 @@ from functools import lru_cache
 from pathlib import Path
 from typing import Any, Literal, Optional
 
-import psutil
 import yaml
 from pydantic import BaseModel, Field, PrivateAttr, field_validator
 from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict
@@ -25,8 +24,6 @@ from invokeai.frontend.cli.arg_parser import InvokeAIArgs
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
-DEFAULT_RAM_CACHE = 10.0
-DEFAULT_VRAM_CACHE = 0.25
 DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
 PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
@@ -36,24 +33,6 @@ LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
 CONFIG_SCHEMA_VERSION = "4.0.2"
 
 
-def get_default_ram_cache_size() -> float:
-    """Run a heuristic for the default RAM cache based on installed RAM."""
-
-    # On some machines, psutil.virtual_memory().total gives a value that is slightly less than the actual RAM, so the
-    # limits are set slightly lower than than what we expect the actual RAM to be.
-
-    GB = 1024**3
-    max_ram = psutil.virtual_memory().total / GB
-
-    if max_ram >= 60:
-        return 15.0
-    if max_ram >= 30:
-        return 7.5
-    if max_ram >= 14:
-        return 4.0
-    return 2.1  # 2.1 is just large enough for sd 1.5 ;-)
-
-
 class URLRegexTokenPair(BaseModel):
     url_regex: str = Field(description="Regular expression to match against the URL")
     token: str = Field(description="Token to use when the URL matches the regex")
@@ -97,15 +76,21 @@ class InvokeAIAppConfig(BaseSettings):
         log_format: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.<br>Valid values: `plain`, `color`, `syslog`, `legacy`
         log_level: Emit logging messages at this level or higher.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
         log_sql: Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.
+        log_level_network: Log level for network-related messages. 'info' and 'debug' are very verbose.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
         use_memory_db: Use in-memory database. Useful for development.
         dev_reload: Automatically reload when Python sources are changed. Does not reload node definitions.
         profile_graphs: Enable graph profiling using `cProfile`.
         profile_prefix: An optional prefix for profile output files.
         profiles_dir: Path to profiles output directory.
-        ram: Maximum memory amount used by memory model cache for rapid switching (GB).
-        vram: Amount of VRAM reserved for model storage (GB).
-        lazy_offload: Keep models in VRAM until their space is needed.
+        max_cache_ram_gb: The maximum amount of CPU RAM to use for model caching in GB. If unset, the limit will be configured based on the available RAM. In most cases, it is recommended to leave this unset.
+        max_cache_vram_gb: The amount of VRAM to use for model caching in GB. If unset, the limit will be configured based on the available VRAM and the device_working_mem_gb. In most cases, it is recommended to leave this unset.
         log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
+        device_working_mem_gb: The amount of working memory to keep available on the compute device (in GB). Has no effect if running on CPU. If you are experiencing OOM errors, try increasing this value.
+        enable_partial_loading: Enable partial loading of models. This enables models to run with reduced VRAM requirements (at the cost of slower speed) by streaming the model from RAM to VRAM as its used. In some edge cases, partial loading can cause models to run more slowly if they were previously being fully loaded into VRAM.
+        keep_ram_copy_of_weights: Whether to keep a full RAM copy of a model's weights when the model is loaded in VRAM. Keeping a RAM copy increases average RAM usage, but speeds up model switching and LoRA patching (assuming there is sufficient RAM). Set this to False if RAM pressure is consistently high.
+        ram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_ram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
+        vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
+        lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
         precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
@@ -163,6 +148,7 @@ class InvokeAIAppConfig(BaseSettings):
     log_format:              LOG_FORMAT = Field(default="color",            description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.')
     log_level:                LOG_LEVEL = Field(default="info",             description="Emit logging messages at this level or higher.")
     log_sql:                       bool = Field(default=False,              description="Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.")
+    log_level_network:        LOG_LEVEL = Field(default='warning',          description="Log level for network-related messages. 'info' and 'debug' are very verbose.")
 
     # Development
     use_memory_db:                 bool = Field(default=False,              description="Use in-memory database. Useful for development.")
@@ -172,10 +158,16 @@ class InvokeAIAppConfig(BaseSettings):
     profiles_dir:                  Path = Field(default=Path("profiles"),   description="Path to profiles output directory.")
 
     # CACHE
-    ram:                           float = Field(default_factory=get_default_ram_cache_size, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
-    vram:                          float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB).")
-    lazy_offload:                  bool = Field(default=True,               description="Keep models in VRAM until their space is needed.")
+    max_cache_ram_gb:   Optional[float] = Field(default=None, gt=0,         description="The maximum amount of CPU RAM to use for model caching in GB. If unset, the limit will be configured based on the available RAM. In most cases, it is recommended to leave this unset.")
+    max_cache_vram_gb:  Optional[float] = Field(default=None, ge=0,         description="The amount of VRAM to use for model caching in GB. If unset, the limit will be configured based on the available VRAM and the device_working_mem_gb. In most cases, it is recommended to leave this unset.")
     log_memory_usage:              bool = Field(default=False,              description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.")
+    device_working_mem_gb:        float = Field(default=3,                  description="The amount of working memory to keep available on the compute device (in GB). Has no effect if running on CPU. If you are experiencing OOM errors, try increasing this value.")
+    enable_partial_loading:        bool = Field(default=False,              description="Enable partial loading of models. This enables models to run with reduced VRAM requirements (at the cost of slower speed) by streaming the model from RAM to VRAM as its used. In some edge cases, partial loading can cause models to run more slowly if they were previously being fully loaded into VRAM.")
+    keep_ram_copy_of_weights:      bool = Field(default=True,              description="Whether to keep a full RAM copy of a model's weights when the model is loaded in VRAM. Keeping a RAM copy increases average RAM usage, but speeds up model switching and LoRA patching (assuming there is sufficient RAM). Set this to False if RAM pressure is consistently high.")
+    # Deprecated CACHE configs
+    ram:                Optional[float] = Field(default=None, gt=0,         description="DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_ram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.")
+    vram:               Optional[float] = Field(default=None, ge=0,         description="DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.")
+    lazy_offload:                  bool = Field(default=True,               description="DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.")
 
     # DEVICE
     device:                      DEVICE = Field(default="auto",             description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")

invokeai/app/services/download/download_default.py

@@ -8,7 +8,7 @@ import time
 import traceback
 from pathlib import Path
 from queue import Empty, PriorityQueue
-from typing import Any, Dict, List, Literal, Optional, Set
+from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Set
 
 import requests
 from pydantic.networks import AnyHttpUrl
@@ -28,11 +28,13 @@ from invokeai.app.services.download.download_base import (
     ServiceInactiveException,
     UnknownJobIDException,
 )
-from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.util.misc import get_iso_timestamp
 from invokeai.backend.model_manager.metadata import RemoteModelFile
 from invokeai.backend.util.logging import InvokeAILogger
 
+if TYPE_CHECKING:
+    from invokeai.app.services.events.events_base import EventServiceBase
+
 # Maximum number of bytes to download during each call to requests.iter_content()
 DOWNLOAD_CHUNK_SIZE = 100000
 

invokeai/app/services/events/__init__.py

@@ -1 +0,0 @@
-from .events_base import EventServiceBase  # noqa F401

invokeai/app/services/events/events_common.py

@@ -4,6 +4,7 @@ from fastapi_events.handlers.local import local_handler
 from fastapi_events.registry.payload_schema import registry as payload_schema
 from pydantic import BaseModel, ConfigDict, Field
 
+from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
@@ -18,7 +19,7 @@ from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
 
 if TYPE_CHECKING:
     from invokeai.app.services.download.download_base import DownloadJob
-    from invokeai.app.services.model_install.model_install_common import ModelInstallJob
+    from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
 
 
 class EventBase(BaseModel):
@@ -422,7 +423,7 @@ class ModelInstallDownloadStartedEvent(ModelEventBase):
     __event_name__ = "model_install_download_started"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
     local_path: str = Field(description="Where model is downloading to")
     bytes: int = Field(description="Number of bytes downloaded so far")
     total_bytes: int = Field(description="Total size of download, including all files")
@@ -443,7 +444,7 @@ class ModelInstallDownloadStartedEvent(ModelEventBase):
         ]
         return cls(
             id=job.id,
-            source=str(job.source),
+            source=job.source,
             local_path=job.local_path.as_posix(),
             parts=parts,
             bytes=job.bytes,
@@ -458,7 +459,7 @@ class ModelInstallDownloadProgressEvent(ModelEventBase):
     __event_name__ = "model_install_download_progress"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
     local_path: str = Field(description="Where model is downloading to")
     bytes: int = Field(description="Number of bytes downloaded so far")
     total_bytes: int = Field(description="Total size of download, including all files")
@@ -479,7 +480,7 @@ class ModelInstallDownloadProgressEvent(ModelEventBase):
         ]
         return cls(
             id=job.id,
-            source=str(job.source),
+            source=job.source,
             local_path=job.local_path.as_posix(),
             parts=parts,
             bytes=job.bytes,
@@ -494,11 +495,11 @@ class ModelInstallDownloadsCompleteEvent(ModelEventBase):
     __event_name__ = "model_install_downloads_complete"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
 
     @classmethod
     def build(cls, job: "ModelInstallJob") -> "ModelInstallDownloadsCompleteEvent":
-        return cls(id=job.id, source=str(job.source))
+        return cls(id=job.id, source=job.source)
 
 
 @payload_schema.register
@@ -508,11 +509,11 @@ class ModelInstallStartedEvent(ModelEventBase):
     __event_name__ = "model_install_started"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
 
     @classmethod
     def build(cls, job: "ModelInstallJob") -> "ModelInstallStartedEvent":
-        return cls(id=job.id, source=str(job.source))
+        return cls(id=job.id, source=job.source)
 
 
 @payload_schema.register
@@ -522,14 +523,14 @@ class ModelInstallCompleteEvent(ModelEventBase):
     __event_name__ = "model_install_complete"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
     key: str = Field(description="Model config record key")
     total_bytes: Optional[int] = Field(description="Size of the model (may be None for installation of a local path)")
 
     @classmethod
     def build(cls, job: "ModelInstallJob") -> "ModelInstallCompleteEvent":
         assert job.config_out is not None
-        return cls(id=job.id, source=str(job.source), key=(job.config_out.key), total_bytes=job.total_bytes)
+        return cls(id=job.id, source=job.source, key=(job.config_out.key), total_bytes=job.total_bytes)
 
 
 @payload_schema.register
@@ -539,11 +540,11 @@ class ModelInstallCancelledEvent(ModelEventBase):
     __event_name__ = "model_install_cancelled"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
 
     @classmethod
     def build(cls, job: "ModelInstallJob") -> "ModelInstallCancelledEvent":
-        return cls(id=job.id, source=str(job.source))
+        return cls(id=job.id, source=job.source)
 
 
 @payload_schema.register
@@ -553,7 +554,7 @@ class ModelInstallErrorEvent(ModelEventBase):
     __event_name__ = "model_install_error"
 
     id: int = Field(description="The ID of the install job")
-    source: str = Field(description="Source of the model; local path, repo_id or url")
+    source: ModelSource = Field(description="Source of the model; local path, repo_id or url")
     error_type: str = Field(description="The name of the exception")
     error: str = Field(description="A text description of the exception")
 
@@ -561,7 +562,7 @@ class ModelInstallErrorEvent(ModelEventBase):
     def build(cls, job: "ModelInstallJob") -> "ModelInstallErrorEvent":
         assert job.error_type is not None
         assert job.error is not None
-        return cls(id=job.id, source=str(job.source), error_type=job.error_type, error=job.error)
+        return cls(id=job.id, source=job.source, error_type=job.error_type, error=job.error)
 
 
 class BulkDownloadEventBase(EventBase):

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -20,7 +20,7 @@ from invokeai.app.services.invocation_stats.invocation_stats_common import (
     NodeExecutionStatsSummary,
 )
 from invokeai.app.services.invoker import Invoker
-from invokeai.backend.model_manager.load.model_cache import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 
 # Size of 1GB in bytes.
 GB = 2**30

invokeai/app/services/model_install/model_install_base.py

@@ -3,18 +3,20 @@
 
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import TYPE_CHECKING, List, Optional, Union
 
 from pydantic.networks import AnyHttpUrl
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.download import DownloadQueueServiceBase
-from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob, ModelSource
 from invokeai.app.services.model_records import ModelRecordChanges, ModelRecordServiceBase
 from invokeai.backend.model_manager import AnyModelConfig
 
+if TYPE_CHECKING:
+    from invokeai.app.services.events.events_base import EventServiceBase
+
 
 class ModelInstallServiceBase(ABC):
     """Abstract base class for InvokeAI model installation."""

invokeai/app/services/model_install/model_install_default.py

@@ -9,7 +9,7 @@ from pathlib import Path
 from queue import Empty, Queue
 from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
 
 import torch
 import yaml
@@ -20,7 +20,6 @@ from requests import Session
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.download import DownloadQueueServiceBase, MultiFileDownloadJob
-from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_install.model_install_base import ModelInstallServiceBase
 from invokeai.app.services.model_install.model_install_common import (
@@ -57,6 +56,10 @@ from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.util import slugify
 
+if TYPE_CHECKING:
+    from invokeai.app.services.events.events_base import EventServiceBase
+
+
 TMPDIR_PREFIX = "tmpinstall_"
 
 
@@ -438,9 +441,10 @@ class ModelInstallService(ModelInstallServiceBase):
         variants = "|".join(ModelRepoVariant.__members__.values())
         hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
         source_obj: Optional[StringLikeSource] = None
+        source_stripped = source.strip('"')
 
-        if Path(source).exists():  # A local file or directory
-            source_obj = LocalModelSource(path=Path(source))
+        if Path(source_stripped).exists():  # A local file or directory
+            source_obj = LocalModelSource(path=Path(source_stripped))
         elif match := re.match(hf_repoid_re, source):
             source_obj = HFModelSource(
                 repo_id=match.group(1),

invokeai/app/services/model_load/model_load_base.py

@@ -7,7 +7,7 @@ from typing import Callable, Optional
 
 from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
 class ModelLoadServiceBase(ABC):
@@ -24,7 +24,7 @@ class ModelLoadServiceBase(ABC):
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
 
     @abstractmethod

invokeai/app/services/model_load/model_load_default.py

@@ -18,7 +18,7 @@ from invokeai.backend.model_manager.load import (
     ModelLoaderRegistry,
     ModelLoaderRegistryBase,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
@@ -30,7 +30,7 @@ class ModelLoadService(ModelLoadServiceBase):
     def __init__(
         self,
         app_config: InvokeAIAppConfig,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
         registry: Optional[Type[ModelLoaderRegistryBase]] = ModelLoaderRegistry,
     ):
         """Initialize the model load service."""
@@ -45,7 +45,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self._invoker = invoker
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
@@ -78,9 +78,8 @@ class ModelLoadService(ModelLoadServiceBase):
         self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
     ) -> LoadedModelWithoutConfig:
         cache_key = str(model_path)
-        ram_cache = self.ram_cache
         try:
-            return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+            return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)
         except IndexError:
             pass
 
@@ -109,5 +108,5 @@ class ModelLoadService(ModelLoadServiceBase):
         )
         assert loader is not None
         raw_model = loader(model_path)
-        ram_cache.put(key=cache_key, model=raw_model)
-        return LoadedModelWithoutConfig(_locker=ram_cache.get(key=cache_key))
+        self._ram_cache.put(key=cache_key, model=raw_model)
+        return LoadedModelWithoutConfig(cache_record=self._ram_cache.get(key=cache_key), cache=self._ram_cache)

invokeai/app/services/model_manager/model_manager_default.py

@@ -16,7 +16,8 @@ from invokeai.app.services.model_load.model_load_base import ModelLoadServiceBas
 from invokeai.app.services.model_load.model_load_default import ModelLoadService
 from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordServiceBase
-from invokeai.backend.model_manager.load import ModelCache, ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
@@ -81,11 +82,13 @@ class ModelManagerService(ModelManagerServiceBase):
         logger.setLevel(app_config.log_level.upper())
 
         ram_cache = ModelCache(
-            max_cache_size=app_config.ram,
-            max_vram_cache_size=app_config.vram,
-            lazy_offloading=app_config.lazy_offload,
-            logger=logger,
+            execution_device_working_mem_gb=app_config.device_working_mem_gb,
+            enable_partial_loading=app_config.enable_partial_loading,
+            keep_ram_copy_of_weights=app_config.keep_ram_copy_of_weights,
+            max_ram_cache_size_gb=app_config.max_cache_ram_gb,
+            max_vram_cache_size_gb=app_config.max_cache_vram_gb,
             execution_device=execution_device or TorchDevice.choose_torch_device(),
+            logger=logger,
         )
         loader = ModelLoadService(
             app_config=app_config,

invokeai/app/services/session_processor/session_processor_default.py

@@ -439,7 +439,9 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         poll_now_event.wait(self._polling_interval)
                         continue
 
-                    self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
+                    self._invoker.services.logger.info(
+                        f"Executing queue item {self._queue_item.item_id}, session {self._queue_item.session_id}"
+                    )
                     cancel_event.clear()
 
                     # Run the graph

invokeai/app/services/session_queue/session_queue_base.py

@@ -5,6 +5,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
     Batch,
     BatchStatus,
+    CancelAllExceptCurrentResult,
     CancelByBatchIDsResult,
     CancelByDestinationResult,
     CancelByQueueIDResult,
@@ -112,6 +113,11 @@ class SessionQueueBase(ABC):
         """Cancels all queue items with matching queue ID"""
         pass
 
+    @abstractmethod
+    def cancel_all_except_current(self, queue_id: str) -> CancelAllExceptCurrentResult:
+        """Cancels all queue items except in-progress items"""
+        pass
+
     @abstractmethod
     def list_queue_items(
         self,

invokeai/app/services/session_queue/session_queue_common.py

@@ -108,8 +108,16 @@ class Batch(BaseModel):
             return v
         for batch_data_list in v:
             for datum in batch_data_list:
+                if not datum.items:
+                    continue
+
+                # Special handling for numbers - they can be mixed
+                # TODO(psyche): Update BatchDatum to have a `type` field to specify the type of the items, then we can have strict float and int fields
+                if all(isinstance(item, (int, float)) for item in datum.items):
+                    continue
+
                 # Get the type of the first item in the list
-                first_item_type = type(datum.items[0]) if datum.items else None
+                first_item_type = type(datum.items[0])
                 for item in datum.items:
                     if type(item) is not first_item_type:
                         raise BatchItemsTypeError("All items in a batch must have the same type")
@@ -366,6 +374,12 @@ class CancelByQueueIDResult(CancelByBatchIDsResult):
     pass
 
 
+class CancelAllExceptCurrentResult(CancelByBatchIDsResult):
+    """Result of canceling all except current"""
+
+    pass
+
+
 class IsEmptyResult(BaseModel):
     """Result of checking if the session queue is empty"""
 

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -9,6 +9,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
     Batch,
     BatchStatus,
+    CancelAllExceptCurrentResult,
     CancelByBatchIDsResult,
     CancelByDestinationResult,
     CancelByQueueIDResult,
@@ -510,6 +511,39 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByQueueIDResult(canceled=count)
 
+    def cancel_all_except_current(self, queue_id: str) -> CancelAllExceptCurrentResult:
+        try:
+            where = """--sql
+                WHERE
+                  queue_id == ?
+                  AND status == 'pending'
+                """
+            self.__lock.acquire()
+            self.__cursor.execute(
+                f"""--sql
+                SELECT COUNT(*)
+                FROM session_queue
+                {where};
+                """,
+                (queue_id,),
+            )
+            count = self.__cursor.fetchone()[0]
+            self.__cursor.execute(
+                f"""--sql
+                UPDATE session_queue
+                SET status = 'canceled'
+                {where};
+                """,
+                (queue_id,),
+            )
+            self.__conn.commit()
+        except Exception:
+            self.__conn.rollback()
+            raise
+        finally:
+            self.__lock.release()
+        return CancelAllExceptCurrentResult(canceled=count)
+
     def get_queue_item(self, item_id: int) -> SessionQueueItem:
         try:
             self.__lock.acquire()

invokeai/app/services/shared/graph.py

@@ -51,15 +51,18 @@ class Edge(BaseModel):
     source: EdgeConnection = Field(description="The connection for the edge's from node and field")
     destination: EdgeConnection = Field(description="The connection for the edge's to node and field")
 
+    def __str__(self):
+        return f"{self.source.node_id}.{self.source.field} -> {self.destination.node_id}.{self.destination.field}"
 
-def get_output_field(node: BaseInvocation, field: str) -> Any:
+
+def get_output_field_type(node: BaseInvocation, field: str) -> Any:
     node_type = type(node)
     node_outputs = get_type_hints(node_type.get_output_annotation())
     node_output_field = node_outputs.get(field) or None
     return node_output_field
 
 
-def get_input_field(node: BaseInvocation, field: str) -> Any:
+def get_input_field_type(node: BaseInvocation, field: str) -> Any:
     node_type = type(node)
     node_inputs = get_type_hints(node_type)
     node_input_field = node_inputs.get(field) or None
@@ -93,6 +96,10 @@ def is_list_or_contains_list(t):
     return False
 
 
+def is_any(t: Any) -> bool:
+    return t == Any or Any in get_args(t)
+
+
 def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
     if not from_type:
         return False
@@ -102,13 +109,7 @@ def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
     # TODO: this is pretty forgiving on generic types. Clean that up (need to handle optionals and such)
     if from_type and to_type:
         # Ports are compatible
-        if (
-            from_type == to_type
-            or from_type == Any
-            or to_type == Any
-            or Any in get_args(from_type)
-            or Any in get_args(to_type)
-        ):
+        if from_type == to_type or is_any(from_type) or is_any(to_type):
             return True
 
         if from_type in get_args(to_type):
@@ -140,10 +141,10 @@ def are_connections_compatible(
     """Determines if a connection between fields of two nodes is compatible."""
 
     # TODO: handle iterators and collectors
-    from_node_field = get_output_field(from_node, from_field)
-    to_node_field = get_input_field(to_node, to_field)
+    from_type = get_output_field_type(from_node, from_field)
+    to_type = get_input_field_type(to_node, to_field)
 
-    return are_connection_types_compatible(from_node_field, to_node_field)
+    return are_connection_types_compatible(from_type, to_type)
 
 
 T = TypeVar("T")
@@ -440,17 +441,19 @@ class Graph(BaseModel):
                 self.get_node(edge.destination.node_id),
                 edge.destination.field,
             ):
-                raise InvalidEdgeError(
-                    f"Invalid edge from {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-                )
+                raise InvalidEdgeError(f"Edge source and target types do not match ({edge})")
 
         # Validate all iterators & collectors
         # TODO: may need to validate all iterators & collectors in subgraphs so edge connections in parent graphs will be available
         for node in self.nodes.values():
-            if isinstance(node, IterateInvocation) and not self._is_iterator_connection_valid(node.id):
-                raise InvalidEdgeError(f"Invalid iterator node {node.id}")
-            if isinstance(node, CollectInvocation) and not self._is_collector_connection_valid(node.id):
-                raise InvalidEdgeError(f"Invalid collector node {node.id}")
+            if isinstance(node, IterateInvocation):
+                err = self._is_iterator_connection_valid(node.id)
+                if err is not None:
+                    raise InvalidEdgeError(f"Invalid iterator node ({node.id}): {err}")
+            if isinstance(node, CollectInvocation):
+                err = self._is_collector_connection_valid(node.id)
+                if err is not None:
+                    raise InvalidEdgeError(f"Invalid collector node ({node.id}): {err}")
 
         return None
 
@@ -477,11 +480,11 @@ class Graph(BaseModel):
 
     def _is_destination_field_Any(self, edge: Edge) -> bool:
         """Checks if the destination field for an edge is of type typing.Any"""
-        return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
+        return get_input_field_type(self.get_node(edge.destination.node_id), edge.destination.field) == Any
 
     def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
         """Checks if the destination field for an edge is of type typing.Any"""
-        return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
+        return get_input_field_type(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
 
     def _validate_edge(self, edge: Edge):
         """Validates that a new edge doesn't create a cycle in the graph"""
@@ -491,55 +494,40 @@ class Graph(BaseModel):
             from_node = self.get_node(edge.source.node_id)
             to_node = self.get_node(edge.destination.node_id)
         except NodeNotFoundError:
-            raise InvalidEdgeError("One or both nodes don't exist: {edge.source.node_id} -> {edge.destination.node_id}")
+            raise InvalidEdgeError(f"One or both nodes don't exist ({edge})")
 
         # Validate that an edge to this node+field doesn't already exist
         input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
         if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
-            raise InvalidEdgeError(
-                f"Edge to node {edge.destination.node_id} field {edge.destination.field} already exists"
-            )
+            raise InvalidEdgeError(f"Edge already exists ({edge})")
 
         # Validate that no cycles would be created
         g = self.nx_graph_flat()
         g.add_edge(edge.source.node_id, edge.destination.node_id)
         if not nx.is_directed_acyclic_graph(g):
-            raise InvalidEdgeError(
-                f"Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}"
-            )
+            raise InvalidEdgeError(f"Edge creates a cycle in the graph ({edge})")
 
         # Validate that the field types are compatible
         if not are_connections_compatible(from_node, edge.source.field, to_node, edge.destination.field):
-            raise InvalidEdgeError(
-                f"Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-            )
+            raise InvalidEdgeError(f"Field types are incompatible ({edge})")
 
         # Validate if iterator output type matches iterator input type (if this edge results in both being set)
         if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
-            if not self._is_iterator_connection_valid(edge.destination.node_id, new_input=edge.source):
-                raise InvalidEdgeError(
-                    f"Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-                )
+            err = self._is_iterator_connection_valid(edge.destination.node_id, new_input=edge.source)
+            if err is not None:
+                raise InvalidEdgeError(f"Iterator input type does not match iterator output type ({edge}): {err}")
 
         # Validate if iterator input type matches output type (if this edge results in both being set)
         if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
-            if not self._is_iterator_connection_valid(edge.source.node_id, new_output=edge.destination):
-                raise InvalidEdgeError(
-                    f"Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-                )
+            err = self._is_iterator_connection_valid(edge.source.node_id, new_output=edge.destination)
+            if err is not None:
+                raise InvalidEdgeError(f"Iterator output type does not match iterator input type ({edge}): {err}")
 
         # Validate if collector input type matches output type (if this edge results in both being set)
         if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
-            if not self._is_collector_connection_valid(edge.destination.node_id, new_input=edge.source):
-                raise InvalidEdgeError(
-                    f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-                )
-
-        # Validate that we are not connecting collector to iterator (currently unsupported)
-        if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
-            raise InvalidEdgeError(
-                f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-            )
+            err = self._is_collector_connection_valid(edge.destination.node_id, new_input=edge.source)
+            if err is not None:
+                raise InvalidEdgeError(f"Collector output type does not match collector input type ({edge}): {err}")
 
         # Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
         if (
@@ -548,10 +536,9 @@ class Graph(BaseModel):
             and not self._is_destination_field_list_of_Any(edge)
             and not self._is_destination_field_Any(edge)
         ):
-            if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
-                raise InvalidEdgeError(
-                    f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
-                )
+            err = self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination)
+            if err is not None:
+                raise InvalidEdgeError(f"Collector input type does not match collector output type ({edge}): {err}")
 
     def has_node(self, node_id: str) -> bool:
         """Determines whether or not a node exists in the graph."""
@@ -634,7 +621,7 @@ class Graph(BaseModel):
         node_id: str,
         new_input: Optional[EdgeConnection] = None,
         new_output: Optional[EdgeConnection] = None,
-    ) -> bool:
+    ) -> str | None:
         inputs = [e.source for e in self._get_input_edges(node_id, "collection")]
         outputs = [e.destination for e in self._get_output_edges(node_id, "item")]
 
@@ -645,29 +632,47 @@ class Graph(BaseModel):
 
         # Only one input is allowed for iterators
         if len(inputs) > 1:
-            return False
+            return "Iterator may only have one input edge"
+
+        input_node = self.get_node(inputs[0].node_id)
 
         # Get input and output fields (the fields linked to the iterator's input/output)
-        input_field = get_output_field(self.get_node(inputs[0].node_id), inputs[0].field)
-        output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
+        input_field_type = get_output_field_type(input_node, inputs[0].field)
+        output_field_types = [get_input_field_type(self.get_node(e.node_id), e.field) for e in outputs]
 
         # Input type must be a list
-        if get_origin(input_field) is not list:
-            return False
+        if get_origin(input_field_type) is not list:
+            return "Iterator input must be a collection"
 
         # Validate that all outputs match the input type
-        input_field_item_type = get_args(input_field)[0]
-        if not all((are_connection_types_compatible(input_field_item_type, f) for f in output_fields)):
-            return False
+        input_field_item_type = get_args(input_field_type)[0]
+        if not all((are_connection_types_compatible(input_field_item_type, t) for t in output_field_types)):
+            return "Iterator outputs must connect to an input with a matching type"
 
-        return True
+        # Collector input type must match all iterator output types
+        if isinstance(input_node, CollectInvocation):
+            # Traverse the graph to find the first collector input edge. Collectors validate that their collection
+            # inputs are all of the same type, so we can use the first input edge to determine the collector's type
+            first_collector_input_edge = self._get_input_edges(input_node.id, "item")[0]
+            first_collector_input_type = get_output_field_type(
+                self.get_node(first_collector_input_edge.source.node_id), first_collector_input_edge.source.field
+            )
+            resolved_collector_type = (
+                first_collector_input_type
+                if get_origin(first_collector_input_type) is None
+                else get_args(first_collector_input_type)
+            )
+            if not all((are_connection_types_compatible(resolved_collector_type, t) for t in output_field_types)):
+                return "Iterator collection type must match all iterator output types"
+
+        return None
 
     def _is_collector_connection_valid(
         self,
         node_id: str,
         new_input: Optional[EdgeConnection] = None,
         new_output: Optional[EdgeConnection] = None,
-    ) -> bool:
+    ) -> str | None:
         inputs = [e.source for e in self._get_input_edges(node_id, "item")]
         outputs = [e.destination for e in self._get_output_edges(node_id, "collection")]
 
@@ -677,38 +682,42 @@ class Graph(BaseModel):
             outputs.append(new_output)
 
         # Get input and output fields (the fields linked to the iterator's input/output)
-        input_fields = [get_output_field(self.get_node(e.node_id), e.field) for e in inputs]
-        output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
+        input_field_types = [get_output_field_type(self.get_node(e.node_id), e.field) for e in inputs]
+        output_field_types = [get_input_field_type(self.get_node(e.node_id), e.field) for e in outputs]
 
         # Validate that all inputs are derived from or match a single type
         input_field_types = {
-            t
-            for input_field in input_fields
-            for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
-            if t != NoneType
+            resolved_type
+            for input_field_type in input_field_types
+            for resolved_type in (
+                [input_field_type] if get_origin(input_field_type) is None else get_args(input_field_type)
+            )
+            if resolved_type != NoneType
         }  # Get unique types
         type_tree = nx.DiGraph()
         type_tree.add_nodes_from(input_field_types)
         type_tree.add_edges_from([e for e in itertools.permutations(input_field_types, 2) if issubclass(e[1], e[0])])
         type_degrees = type_tree.in_degree(type_tree.nodes)
         if sum((t[1] == 0 for t in type_degrees)) != 1:  # type: ignore
-            return False  # There is more than one root type
+            return "Collector input collection items must be of a single type"
 
         # Get the input root type
         input_root_type = next(t[0] for t in type_degrees if t[1] == 0)  # type: ignore
 
         # Verify that all outputs are lists
-        if not all(is_list_or_contains_list(f) for f in output_fields):
-            return False
+        if not all(is_list_or_contains_list(t) or is_any(t) for t in output_field_types):
+            return "Collector output must connect to a collection input"
 
         # Verify that all outputs match the input type (are a base class or the same class)
         if not all(
-            is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
-            for f in output_fields
+            is_any(t)
+            or is_union_subtype(input_root_type, get_args(t)[0])
+            or issubclass(input_root_type, get_args(t)[0])
+            for t in output_field_types
         ):
-            return False
+            return "Collector outputs must connect to a collection input with a matching type"
 
-        return True
+        return None
 
     def nx_graph(self) -> nx.DiGraph:
         """Returns a NetworkX DiGraph representing the layout of this graph"""

invokeai/app/util/t5_model_identifier.py

@@ -0,0 +1,26 @@
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.backend.model_manager.config import BaseModelType, SubModelType
+
+
+def preprocess_t5_encoder_model_identifier(model_identifier: ModelIdentifierField) -> ModelIdentifierField:
+    """A helper function to normalize a T5 encoder model identifier so that T5 models associated with FLUX
+    or SD3 models can be used interchangeably.
+    """
+    if model_identifier.base == BaseModelType.Any:
+        return model_identifier.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+    elif model_identifier.base == BaseModelType.StableDiffusion3:
+        return model_identifier.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+    else:
+        raise ValueError(f"Unsupported model base: {model_identifier.base}")
+
+
+def preprocess_t5_tokenizer_model_identifier(model_identifier: ModelIdentifierField) -> ModelIdentifierField:
+    """A helper function to normalize a T5 tokenizer model identifier so that T5 models associated with FLUX
+    or SD3 models can be used interchangeably.
+    """
+    if model_identifier.base == BaseModelType.Any:
+        return model_identifier.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+    elif model_identifier.base == BaseModelType.StableDiffusion3:
+        return model_identifier.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+    else:
+        raise ValueError(f"Unsupported model base: {model_identifier.base}")

invokeai/backend/flux/denoise.py

@@ -30,6 +30,8 @@ def denoise(
     controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
     pos_ip_adapter_extensions: list[XLabsIPAdapterExtension],
     neg_ip_adapter_extensions: list[XLabsIPAdapterExtension],
+    # extra img tokens
+    img_cond: torch.Tensor | None,
 ):
     # step 0 is the initial state
     total_steps = len(timesteps) - 1
@@ -69,9 +71,9 @@ def denoise(
         # controlnet_residuals datastructure is efficient in that it likely contains multiple references to the same
         # tensors. Calculating the sum materializes each tensor into its own instance.
         merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
-
+        pred_img = torch.cat((img, img_cond), dim=-1) if img_cond is not None else img
         pred = model(
-            img=img,
+            img=pred_img,
             img_ids=img_ids,
             txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
             txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,

invokeai/backend/flux/extensions/xlabs_ip_adapter_extension.py

@@ -8,6 +8,7 @@ from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
 from invokeai.backend.flux.modules.layers import DoubleStreamBlock
+from invokeai.backend.util.devices import TorchDevice
 
 
 class XLabsIPAdapterExtension:
@@ -45,7 +46,7 @@ class XLabsIPAdapterExtension:
     ) -> torch.Tensor:
         clip_image_processor = CLIPImageProcessor()
         clip_image: torch.Tensor = clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(device=image_encoder.device, dtype=image_encoder.dtype)
+        clip_image = clip_image.to(device=TorchDevice.choose_torch_device(), dtype=image_encoder.dtype)
         clip_image_embeds = image_encoder(clip_image).image_embeds
         return clip_image_embeds
 

invokeai/backend/flux/model.py

@@ -1,6 +1,7 @@
 # Initially pulled from https://github.com/black-forest-labs/flux
 
 from dataclasses import dataclass
+from typing import Optional
 
 import torch
 from torch import Tensor, nn
@@ -35,6 +36,7 @@ class FluxParams:
     theta: int
     qkv_bias: bool
     guidance_embed: bool
+    out_channels: Optional[int] = None
 
 
 class Flux(nn.Module):
@@ -47,7 +49,7 @@ class Flux(nn.Module):
 
         self.params = params
         self.in_channels = params.in_channels
-        self.out_channels = self.in_channels
+        self.out_channels = params.out_channels or self.in_channels
         if params.hidden_size % params.num_heads != 0:
             raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
         pe_dim = params.hidden_size // params.num_heads

invokeai/backend/flux/modules/conditioner.py

@@ -1,11 +1,19 @@
 # Initially pulled from https://github.com/black-forest-labs/flux
 
 from torch import Tensor, nn
-from transformers import PreTrainedModel, PreTrainedTokenizer
+from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
+
+from invokeai.backend.util.devices import TorchDevice
 
 
 class HFEncoder(nn.Module):
-    def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool, max_length: int):
+    def __init__(
+        self,
+        encoder: PreTrainedModel,
+        tokenizer: PreTrainedTokenizer | PreTrainedTokenizerFast,
+        is_clip: bool,
+        max_length: int,
+    ):
         super().__init__()
         self.max_length = max_length
         self.is_clip = is_clip
@@ -26,7 +34,7 @@ class HFEncoder(nn.Module):
         )
 
         outputs = self.hf_module(
-            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
+            input_ids=batch_encoding["input_ids"].to(TorchDevice.choose_torch_device()),
             attention_mask=None,
             output_hidden_states=False,
         )

invokeai/backend/image_util/hed.py

@@ -18,6 +18,7 @@ from invokeai.backend.image_util.util import (
     resize_image_to_resolution,
     safe_step,
 )
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 
 
 class DoubleConvBlock(torch.nn.Module):
@@ -109,7 +110,7 @@ class HEDProcessor:
         Returns:
             The detected edges.
         """
-        device = next(iter(self.network.parameters())).device
+        device = get_effective_device(self.network)
         np_image = pil_to_np(input_image)
         np_image = normalize_image_channel_count(np_image)
         np_image = resize_image_to_resolution(np_image, detect_resolution)
@@ -183,7 +184,7 @@ class HEDEdgeDetector:
             The detected edges.
         """
 
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
 
         np_image = pil_to_np(image)
 

invokeai/backend/image_util/infill_methods/lama.py

@@ -7,6 +7,7 @@ from PIL import Image
 
 import invokeai.backend.util.logging as logger
 from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 
 
 def norm_img(np_img):
@@ -31,7 +32,7 @@ class LaMA:
         mask = norm_img(mask)
         mask = (mask > 0) * 1
 
-        device = next(self._model.buffers()).device
+        device = get_effective_device(self._model)
         image = torch.from_numpy(image).unsqueeze(0).to(device)
         mask = torch.from_numpy(mask).unsqueeze(0).to(device)
 

invokeai/backend/image_util/lineart.py

@@ -17,6 +17,7 @@ from invokeai.backend.image_util.util import (
     pil_to_np,
     resize_image_to_resolution,
 )
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 
 
 class ResidualBlock(nn.Module):
@@ -130,7 +131,7 @@ class LineartProcessor:
         Returns:
             The detected lineart.
         """
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
 
         np_image = pil_to_np(input_image)
         np_image = normalize_image_channel_count(np_image)
@@ -201,7 +202,7 @@ class LineartEdgeDetector:
         Returns:
             The detected edges.
         """
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
 
         np_image = pil_to_np(image)
 

invokeai/backend/image_util/lineart_anime.py

@@ -19,6 +19,7 @@ from invokeai.backend.image_util.util import (
     pil_to_np,
     resize_image_to_resolution,
 )
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 
 
 class UnetGenerator(nn.Module):
@@ -171,7 +172,7 @@ class LineartAnimeProcessor:
         Returns:
             The detected lineart.
         """
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
         np_image = pil_to_np(input_image)
 
         np_image = normalize_image_channel_count(np_image)
@@ -239,7 +240,7 @@ class LineartAnimeEdgeDetector:
 
     def run(self, image: Image.Image) -> Image.Image:
         """Processes an image and returns the detected edges."""
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
 
         np_image = pil_to_np(image)
 

invokeai/backend/image_util/mlsd/utils.py

@@ -14,6 +14,8 @@ import numpy as np
 import torch
 from torch.nn import functional as F
 
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+
 
 def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5):
     '''
@@ -49,7 +51,7 @@ def pred_lines(image, model,
                dist_thr=20.0):
     h, w, _ = image.shape
 
-    device = next(iter(model.parameters())).device
+    device = get_effective_device(model)
     h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
 
     resized_image = np.concatenate([cv2.resize(image, (input_shape[1], input_shape[0]), interpolation=cv2.INTER_AREA),
@@ -108,7 +110,7 @@ def pred_squares(image,
     '''
     h, w, _ = image.shape
     original_shape = [h, w]
-    device = next(iter(model.parameters())).device
+    device = get_effective_device(model)
 
     resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA),
                                     np.ones([input_shape[0], input_shape[1], 1])], axis=-1)

invokeai/backend/image_util/normal_bae/__init__.py

@@ -13,6 +13,7 @@ from PIL import Image
 
 from invokeai.backend.image_util.normal_bae.nets.NNET import NNET
 from invokeai.backend.image_util.util import np_to_pil, pil_to_np, resize_to_multiple
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 
 
 class NormalMapDetector:
@@ -64,7 +65,7 @@ class NormalMapDetector:
     def run(self, image: Image.Image):
         """Processes an image and returns the detected normal map."""
 
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
         np_image = pil_to_np(image)
 
         height, width, _channels = np_image.shape

invokeai/backend/image_util/pidi/__init__.py

@@ -11,6 +11,7 @@ from PIL import Image
 
 from invokeai.backend.image_util.pidi.model import PiDiNet, pidinet
 from invokeai.backend.image_util.util import nms, normalize_image_channel_count, np_to_pil, pil_to_np, safe_step
+from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
 
 
 class PIDINetDetector:
@@ -45,7 +46,7 @@ class PIDINetDetector:
     ) -> Image.Image:
         """Processes an image and returns the detected edges."""
 
-        device = next(iter(self.model.parameters())).device
+        device = get_effective_device(self.model)
 
         np_img = pil_to_np(image)
         np_img = normalize_image_channel_count(np_img)

invokeai/backend/lora/layers/any_lora_layer.py

@@ -1,11 +0,0 @@
-from typing import Union
-
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-from invokeai.backend.lora.layers.full_layer import FullLayer
-from invokeai.backend.lora.layers.ia3_layer import IA3Layer
-from invokeai.backend.lora.layers.loha_layer import LoHALayer
-from invokeai.backend.lora.layers.lokr_layer import LoKRLayer
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.layers.norm_layer import NormLayer
-
-AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer, ConcatenatedLoRALayer]

invokeai/backend/lora/layers/concatenated_lora_layer.py

@@ -1,55 +0,0 @@
-from typing import Optional, Sequence
-
-import torch
-
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
-
-
-class ConcatenatedLoRALayer(LoRALayerBase):
-    """A LoRA layer that is composed of multiple LoRA layers concatenated along a specified axis.
-
-    This class was created to handle a special case with FLUX LoRA models. In the BFL FLUX model format, the attention
-    Q, K, V matrices are concatenated along the first dimension. In the diffusers LoRA format, the Q, K, V matrices are
-    stored as separate tensors. This class enables diffusers LoRA layers to be used in BFL FLUX models.
-    """
-
-    def __init__(self, lora_layers: Sequence[LoRALayer], concat_axis: int = 0):
-        super().__init__(alpha=None, bias=None)
-
-        self.lora_layers = lora_layers
-        self.concat_axis = concat_axis
-
-    def rank(self) -> int | None:
-        return None
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        # TODO(ryand): Currently, we pass orig_weight=None to the sub-layers. If we want to support sub-layers that
-        # require this value, we will need to implement chunking of the original weight tensor here.
-        # Note that we must apply the sub-layer scales here.
-        layer_weights = [lora_layer.get_weight(None) * lora_layer.scale() for lora_layer in self.lora_layers]  # pyright: ignore[reportArgumentType]
-        return torch.cat(layer_weights, dim=self.concat_axis)
-
-    def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
-        # TODO(ryand): Currently, we pass orig_bias=None to the sub-layers. If we want to support sub-layers that
-        # require this value, we will need to implement chunking of the original bias tensor here.
-        # Note that we must apply the sub-layer scales here.
-        layer_biases: list[torch.Tensor] = []
-        for lora_layer in self.lora_layers:
-            layer_bias = lora_layer.get_bias(None)
-            if layer_bias is not None:
-                layer_biases.append(layer_bias * lora_layer.scale())
-
-        if len(layer_biases) == 0:
-            return None
-
-        assert len(layer_biases) == len(self.lora_layers)
-        return torch.cat(layer_biases, dim=self.concat_axis)
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        super().to(device=device, dtype=dtype)
-        for lora_layer in self.lora_layers:
-            lora_layer.to(device=device, dtype=dtype)
-
-    def calc_size(self) -> int:
-        return super().calc_size() + sum(lora_layer.calc_size() for lora_layer in self.lora_layers)

invokeai/backend/lora/lora_patcher.py

@@ -1,302 +0,0 @@
-from contextlib import contextmanager
-from typing import Dict, Iterable, Optional, Tuple
-
-import torch
-
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.sidecar_layers.concatenated_lora.concatenated_lora_linear_sidecar_layer import (
-    ConcatenatedLoRALinearSidecarLayer,
-)
-from invokeai.backend.lora.sidecar_layers.lora.lora_linear_sidecar_layer import LoRALinearSidecarLayer
-from invokeai.backend.lora.sidecar_layers.lora_sidecar_module import LoRASidecarModule
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
-
-
-class LoRAPatcher:
-    @staticmethod
-    @torch.no_grad()
-    @contextmanager
-    def apply_lora_patches(
-        model: torch.nn.Module,
-        patches: Iterable[Tuple[LoRAModelRaw, float]],
-        prefix: str,
-        cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ):
-        """Apply one or more LoRA patches to a model within a context manager.
-
-        Args:
-            model (torch.nn.Module): The model to patch.
-            patches (Iterable[Tuple[LoRAModelRaw, float]]): An iterator that returns tuples of LoRA patches and
-                associated weights. An iterator is used so that the LoRA patches do not need to be loaded into memory
-                all at once.
-            prefix (str): The keys in the patches will be filtered to only include weights with this prefix.
-            cached_weights (Optional[Dict[str, torch.Tensor]], optional): Read-only copy of the model's state dict in
-                CPU RAM, for efficient unpatching purposes.
-        """
-        original_weights = OriginalWeightsStorage(cached_weights)
-        try:
-            for patch, patch_weight in patches:
-                LoRAPatcher.apply_lora_patch(
-                    model=model,
-                    prefix=prefix,
-                    patch=patch,
-                    patch_weight=patch_weight,
-                    original_weights=original_weights,
-                )
-                del patch
-
-            yield
-        finally:
-            for param_key, weight in original_weights.get_changed_weights():
-                model.get_parameter(param_key).copy_(weight)
-
-    @staticmethod
-    @torch.no_grad()
-    def apply_lora_patch(
-        model: torch.nn.Module,
-        prefix: str,
-        patch: LoRAModelRaw,
-        patch_weight: float,
-        original_weights: OriginalWeightsStorage,
-    ):
-        """Apply a single LoRA patch to a model.
-
-        Args:
-            model (torch.nn.Module): The model to patch.
-            prefix (str): A string prefix that precedes keys used in the LoRAs weight layers.
-            patch (LoRAModelRaw): The LoRA model to patch in.
-            patch_weight (float): The weight of the LoRA patch.
-            original_weights (OriginalWeightsStorage): Storage for the original weights of the model, for unpatching.
-        """
-        if patch_weight == 0:
-            return
-
-        # If the layer keys contain a dot, then they are not flattened, and can be directly used to access model
-        # submodules. If the layer keys do not contain a dot, then they are flattened, meaning that all '.' have been
-        # replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly
-        # without searching, but some legacy code still uses flattened keys.
-        layer_keys_are_flattened = "." not in next(iter(patch.layers.keys()))
-
-        prefix_len = len(prefix)
-
-        for layer_key, layer in patch.layers.items():
-            if not layer_key.startswith(prefix):
-                continue
-
-            module_key, module = LoRAPatcher._get_submodule(
-                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
-            )
-
-            # All of the LoRA weight calculations will be done on the same device as the module weight.
-            # (Performance will be best if this is a CUDA device.)
-            device = module.weight.device
-            dtype = module.weight.dtype
-
-            layer_scale = layer.scale()
-
-            # We intentionally move to the target device first, then cast. Experimentally, this was found to
-            # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
-            # same thing in a single call to '.to(...)'.
-            layer.to(device=device)
-            layer.to(dtype=torch.float32)
-
-            # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
-            # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
-            for param_name, lora_param_weight in layer.get_parameters(module).items():
-                param_key = module_key + "." + param_name
-                module_param = module.get_parameter(param_name)
-
-                # Save original weight
-                original_weights.save(param_key, module_param)
-
-                if module_param.shape != lora_param_weight.shape:
-                    lora_param_weight = lora_param_weight.reshape(module_param.shape)
-
-                lora_param_weight *= patch_weight * layer_scale
-                module_param += lora_param_weight.to(dtype=dtype)
-
-            layer.to(device=TorchDevice.CPU_DEVICE)
-
-    @staticmethod
-    @torch.no_grad()
-    @contextmanager
-    def apply_lora_sidecar_patches(
-        model: torch.nn.Module,
-        patches: Iterable[Tuple[LoRAModelRaw, float]],
-        prefix: str,
-        dtype: torch.dtype,
-    ):
-        """Apply one or more LoRA sidecar patches to a model within a context manager. Sidecar patches incur some
-        overhead compared to normal LoRA patching, but they allow for LoRA layers to applied to base layers in any
-        quantization format.
-
-        Args:
-            model (torch.nn.Module): The model to patch.
-            patches (Iterable[Tuple[LoRAModelRaw, float]]): An iterator that returns tuples of LoRA patches and
-                associated weights. An iterator is used so that the LoRA patches do not need to be loaded into memory
-                all at once.
-            prefix (str): The keys in the patches will be filtered to only include weights with this prefix.
-            dtype (torch.dtype): The compute dtype of the sidecar layers. This cannot easily be inferred from the model,
-                since the sidecar layers are typically applied on top of quantized layers whose weight dtype is
-                different from their compute dtype.
-        """
-        original_modules: dict[str, torch.nn.Module] = {}
-        try:
-            for patch, patch_weight in patches:
-                LoRAPatcher._apply_lora_sidecar_patch(
-                    model=model,
-                    prefix=prefix,
-                    patch=patch,
-                    patch_weight=patch_weight,
-                    original_modules=original_modules,
-                    dtype=dtype,
-                )
-            yield
-        finally:
-            # Restore original modules.
-            # Note: This logic assumes no nested modules in original_modules.
-            for module_key, orig_module in original_modules.items():
-                module_parent_key, module_name = LoRAPatcher._split_parent_key(module_key)
-                parent_module = model.get_submodule(module_parent_key)
-                LoRAPatcher._set_submodule(parent_module, module_name, orig_module)
-
-    @staticmethod
-    def _apply_lora_sidecar_patch(
-        model: torch.nn.Module,
-        patch: LoRAModelRaw,
-        patch_weight: float,
-        prefix: str,
-        original_modules: dict[str, torch.nn.Module],
-        dtype: torch.dtype,
-    ):
-        """Apply a single LoRA sidecar patch to a model."""
-
-        if patch_weight == 0:
-            return
-
-        # If the layer keys contain a dot, then they are not flattened, and can be directly used to access model
-        # submodules. If the layer keys do not contain a dot, then they are flattened, meaning that all '.' have been
-        # replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed directly
-        # without searching, but some legacy code still uses flattened keys.
-        layer_keys_are_flattened = "." not in next(iter(patch.layers.keys()))
-
-        prefix_len = len(prefix)
-
-        for layer_key, layer in patch.layers.items():
-            if not layer_key.startswith(prefix):
-                continue
-
-            module_key, module = LoRAPatcher._get_submodule(
-                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
-            )
-
-            # Initialize the LoRA sidecar layer.
-            lora_sidecar_layer = LoRAPatcher._initialize_lora_sidecar_layer(module, layer, patch_weight)
-
-            # Replace the original module with a LoRASidecarModule if it has not already been done.
-            if module_key in original_modules:
-                # The module has already been patched with a LoRASidecarModule. Append to it.
-                assert isinstance(module, LoRASidecarModule)
-                lora_sidecar_module = module
-            else:
-                # The module has not yet been patched with a LoRASidecarModule. Create one.
-                lora_sidecar_module = LoRASidecarModule(module, [])
-                original_modules[module_key] = module
-                module_parent_key, module_name = LoRAPatcher._split_parent_key(module_key)
-                module_parent = model.get_submodule(module_parent_key)
-                LoRAPatcher._set_submodule(module_parent, module_name, lora_sidecar_module)
-
-            # Move the LoRA sidecar layer to the same device/dtype as the orig module.
-            # TODO(ryand): Experiment with moving to the device first, then casting. This could be faster.
-            lora_sidecar_layer.to(device=lora_sidecar_module.orig_module.weight.device, dtype=dtype)
-
-            # Add the LoRA sidecar layer to the LoRASidecarModule.
-            lora_sidecar_module.add_lora_layer(lora_sidecar_layer)
-
-    @staticmethod
-    def _split_parent_key(module_key: str) -> tuple[str, str]:
-        """Split a module key into its parent key and module name.
-
-        Args:
-            module_key (str): The module key to split.
-
-        Returns:
-            tuple[str, str]: A tuple containing the parent key and module name.
-        """
-        split_key = module_key.rsplit(".", 1)
-        if len(split_key) == 2:
-            return tuple(split_key)
-        elif len(split_key) == 1:
-            return "", split_key[0]
-        else:
-            raise ValueError(f"Invalid module key: {module_key}")
-
-    @staticmethod
-    def _initialize_lora_sidecar_layer(orig_layer: torch.nn.Module, lora_layer: AnyLoRALayer, patch_weight: float):
-        # TODO(ryand): Add support for more original layer types and LoRA layer types.
-        if isinstance(orig_layer, torch.nn.Linear) or (
-            isinstance(orig_layer, LoRASidecarModule) and isinstance(orig_layer.orig_module, torch.nn.Linear)
-        ):
-            if isinstance(lora_layer, LoRALayer):
-                return LoRALinearSidecarLayer(lora_layer=lora_layer, weight=patch_weight)
-            elif isinstance(lora_layer, ConcatenatedLoRALayer):
-                return ConcatenatedLoRALinearSidecarLayer(concatenated_lora_layer=lora_layer, weight=patch_weight)
-            else:
-                raise ValueError(f"Unsupported Linear LoRA layer type: {type(lora_layer)}")
-        else:
-            raise ValueError(f"Unsupported layer type: {type(orig_layer)}")
-
-    @staticmethod
-    def _set_submodule(parent_module: torch.nn.Module, module_name: str, submodule: torch.nn.Module):
-        try:
-            submodule_index = int(module_name)
-            # If the module name is an integer, then we use the __setitem__ method to set the submodule.
-            parent_module[submodule_index] = submodule  # type: ignore
-        except ValueError:
-            # If the module name is not an integer, then we use the setattr method to set the submodule.
-            setattr(parent_module, module_name, submodule)
-
-    @staticmethod
-    def _get_submodule(
-        model: torch.nn.Module, layer_key: str, layer_key_is_flattened: bool
-    ) -> tuple[str, torch.nn.Module]:
-        """Get the submodule corresponding to the given layer key.
-
-        Args:
-            model (torch.nn.Module): The model to search.
-            layer_key (str): The layer key to search for.
-            layer_key_is_flattened (bool): Whether the layer key is flattened. If flattened, then all '.' have been
-                replaced with '_'. Non-flattened keys are preferred, because they allow submodules to be accessed
-                directly without searching, but some legacy code still uses flattened keys.
-
-        Returns:
-            tuple[str, torch.nn.Module]: A tuple containing the module key and the submodule.
-        """
-        if not layer_key_is_flattened:
-            return layer_key, model.get_submodule(layer_key)
-
-        # Handle flattened keys.
-        assert "." not in layer_key
-
-        module = model
-        module_key = ""
-        key_parts = layer_key.split("_")
-
-        submodule_name = key_parts.pop(0)
-
-        while len(key_parts) > 0:
-            try:
-                module = module.get_submodule(submodule_name)
-                module_key += "." + submodule_name
-                submodule_name = key_parts.pop(0)
-            except Exception:
-                submodule_name += "_" + key_parts.pop(0)
-
-        module = module.get_submodule(submodule_name)
-        module_key = (module_key + "." + submodule_name).lstrip(".")
-
-        return module_key, module

invokeai/backend/lora/sidecar_layers/concatenated_lora/concatenated_lora_linear_sidecar_layer.py

@@ -1,34 +0,0 @@
-import torch
-
-from invokeai.backend.lora.layers.concatenated_lora_layer import ConcatenatedLoRALayer
-
-
-class ConcatenatedLoRALinearSidecarLayer(torch.nn.Module):
-    def __init__(
-        self,
-        concatenated_lora_layer: ConcatenatedLoRALayer,
-        weight: float,
-    ):
-        super().__init__()
-
-        self._concatenated_lora_layer = concatenated_lora_layer
-        self._weight = weight
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        x_chunks: list[torch.Tensor] = []
-        for lora_layer in self._concatenated_lora_layer.lora_layers:
-            x_chunk = torch.nn.functional.linear(input, lora_layer.down)
-            if lora_layer.mid is not None:
-                x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.mid)
-            x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.up, bias=lora_layer.bias)
-            x_chunk *= self._weight * lora_layer.scale()
-            x_chunks.append(x_chunk)
-
-        # TODO(ryand): Generalize to support concat_axis != 0.
-        assert self._concatenated_lora_layer.concat_axis == 0
-        x = torch.cat(x_chunks, dim=-1)
-        return x
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self._concatenated_lora_layer.to(device=device, dtype=dtype)
-        return self

invokeai/backend/lora/sidecar_layers/lora/lora_linear_sidecar_layer.py

@@ -1,27 +0,0 @@
-import torch
-
-from invokeai.backend.lora.layers.lora_layer import LoRALayer
-
-
-class LoRALinearSidecarLayer(torch.nn.Module):
-    def __init__(
-        self,
-        lora_layer: LoRALayer,
-        weight: float,
-    ):
-        super().__init__()
-
-        self._lora_layer = lora_layer
-        self._weight = weight
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = torch.nn.functional.linear(x, self._lora_layer.down)
-        if self._lora_layer.mid is not None:
-            x = torch.nn.functional.linear(x, self._lora_layer.mid)
-        x = torch.nn.functional.linear(x, self._lora_layer.up, bias=self._lora_layer.bias)
-        x *= self._weight * self._lora_layer.scale()
-        return x
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self._lora_layer.to(device=device, dtype=dtype)
-        return self

invokeai/backend/lora/sidecar_layers/lora_sidecar_module.py

@@ -1,24 +0,0 @@
-import torch
-
-
-class LoRASidecarModule(torch.nn.Module):
-    """A LoRA sidecar module that wraps an original module and adds LoRA layers to it."""
-
-    def __init__(self, orig_module: torch.nn.Module, lora_layers: list[torch.nn.Module]):
-        super().__init__()
-        self.orig_module = orig_module
-        self._lora_layers = lora_layers
-
-    def add_lora_layer(self, lora_layer: torch.nn.Module):
-        self._lora_layers.append(lora_layer)
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        x = self.orig_module(input)
-        for lora_layer in self._lora_layers:
-            x += lora_layer(input)
-        return x
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self._orig_module.to(device=device, dtype=dtype)
-        for lora_layer in self._lora_layers:
-            lora_layer.to(device=device, dtype=dtype)

invokeai/backend/model_manager/config.py

@@ -67,6 +67,7 @@ class ModelType(str, Enum):
     Main = "main"
     VAE = "vae"
     LoRA = "lora"
+    ControlLoRa = "control_lora"
     ControlNet = "controlnet"  # used by model_probe
     TextualInversion = "embedding"
     IPAdapter = "ip_adapter"
@@ -273,6 +274,36 @@ class LoRALyCORISConfig(LoRAConfigBase):
         return Tag(f"{ModelType.LoRA.value}.{ModelFormat.LyCORIS.value}")
 
 
+class ControlAdapterConfigBase(BaseModel):
+    default_settings: Optional[ControlAdapterDefaultSettings] = Field(
+        description="Default settings for this model", default=None
+    )
+
+
+class ControlLoRALyCORISConfig(ModelConfigBase, ControlAdapterConfigBase):
+    """Model config for Control LoRA models."""
+
+    type: Literal[ModelType.ControlLoRa] = ModelType.ControlLoRa
+    trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
+    format: Literal[ModelFormat.LyCORIS] = ModelFormat.LyCORIS
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.ControlLoRa.value}.{ModelFormat.LyCORIS.value}")
+
+
+class ControlLoRADiffusersConfig(ModelConfigBase, ControlAdapterConfigBase):
+    """Model config for Control LoRA models."""
+
+    type: Literal[ModelType.ControlLoRa] = ModelType.ControlLoRa
+    trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
+    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.ControlLoRa.value}.{ModelFormat.Diffusers.value}")
+
+
 class LoRADiffusersConfig(LoRAConfigBase):
     """Model config for LoRA/Diffusers models."""
 
@@ -304,12 +335,6 @@ class VAEDiffusersConfig(ModelConfigBase):
         return Tag(f"{ModelType.VAE.value}.{ModelFormat.Diffusers.value}")
 
 
-class ControlAdapterConfigBase(BaseModel):
-    default_settings: Optional[ControlAdapterDefaultSettings] = Field(
-        description="Default settings for this model", default=None
-    )
-
-
 class ControlNetDiffusersConfig(DiffusersConfigBase, ControlAdapterConfigBase):
     """Model config for ControlNet models (diffusers version)."""
 
@@ -535,6 +560,8 @@ AnyModelConfig = Annotated[
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
         Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
         Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
+        Annotated[ControlLoRALyCORISConfig, ControlLoRALyCORISConfig.get_tag()],
+        Annotated[ControlLoRADiffusersConfig, ControlLoRADiffusersConfig.get_tag()],
         Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],
         Annotated[T5EncoderConfig, T5EncoderConfig.get_tag()],
         Annotated[T5EncoderBnbQuantizedLlmInt8bConfig, T5EncoderBnbQuantizedLlmInt8bConfig.get_tag()],

invokeai/backend/model_manager/load/__init__.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig, ModelLoaderBase
 from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache_default import ModelCache
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry, ModelLoaderRegistryBase
 
 # This registers the subclasses that implement loaders of specific model types

invokeai/backend/model_manager/load/load_base.py

@@ -5,7 +5,6 @@ Base class for model loading in InvokeAI.
 
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
-from dataclasses import dataclass
 from logging import Logger
 from pathlib import Path
 from typing import Any, Dict, Generator, Optional, Tuple
@@ -18,19 +17,17 @@ from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 
 
-@dataclass
 class LoadedModelWithoutConfig:
-    """
-    Context manager object that mediates transfer from RAM<->VRAM.
+    """Context manager object that mediates transfer from RAM<->VRAM.
 
     This is a context manager object that has two distinct APIs:
 
     1. Older API (deprecated):
-    Use the LoadedModel object directly as a context manager.
-    It will move the model into VRAM (on CUDA devices), and
+    Use the LoadedModel object directly as a context manager.  It will move the model into VRAM (on CUDA devices), and
     return the model in a form suitable for passing to torch.
     Example:
     ```
@@ -40,13 +37,9 @@ class LoadedModelWithoutConfig:
     ```
 
     2. Newer API (recommended):
-    Call the LoadedModel's `model_on_device()` method in a
-    context. It returns a tuple consisting of a copy of
-    the model's state dict in CPU RAM followed by a copy
-    of the model in VRAM. The state dict is provided to allow
-    LoRAs and other model patchers to return the model to
-    its unpatched state without expensive copy and restore
-    operations.
+    Call the LoadedModel's `model_on_device()` method in a context. It returns a tuple consisting of a copy of the
+    model's state dict in CPU RAM followed by a copy of the model in VRAM. The state dict is provided to allow LoRAs and
+    other model patchers to return the model to its unpatched state without expensive copy and restore operations.
 
     Example:
     ```
@@ -55,43 +48,48 @@ class LoadedModelWithoutConfig:
         image = vae.decode(latents)[0]
     ```
 
-    The state_dict should be treated as a read-only object and
-    never modified. Also be aware that some loadable models do
-    not have a state_dict, in which case this value will be None.
+    The state_dict should be treated as a read-only object and never modified. Also be aware that some loadable models
+    do not have a state_dict, in which case this value will be None.
     """
 
-    _locker: ModelLockerBase
+    def __init__(self, cache_record: CacheRecord, cache: ModelCache):
+        self._cache_record = cache_record
+        self._cache = cache
 
     def __enter__(self) -> AnyModel:
-        """Context entry."""
-        self._locker.lock()
+        self._cache.lock(self._cache_record, None)
         return self.model
 
     def __exit__(self, *args: Any, **kwargs: Any) -> None:
-        """Context exit."""
-        self._locker.unlock()
+        self._cache.unlock(self._cache_record)
 
     @contextmanager
-    def model_on_device(self) -> Generator[Tuple[Optional[Dict[str, torch.Tensor]], AnyModel], None, None]:
-        """Return a tuple consisting of the model's state dict (if it exists) and the locked model on execution device."""
-        locked_model = self._locker.lock()
+    def model_on_device(
+        self, working_mem_bytes: Optional[int] = None
+    ) -> Generator[Tuple[Optional[Dict[str, torch.Tensor]], AnyModel], None, None]:
+        """Return a tuple consisting of the model's state dict (if it exists) and the locked model on execution device.
+
+        :param working_mem_bytes: The amount of working memory to keep available on the compute device when loading the
+            model.
+        """
+        self._cache.lock(self._cache_record, working_mem_bytes)
         try:
-            state_dict = self._locker.get_state_dict()
-            yield (state_dict, locked_model)
+            yield (self._cache_record.cached_model.get_cpu_state_dict(), self._cache_record.cached_model.model)
         finally:
-            self._locker.unlock()
+            self._cache.unlock(self._cache_record)
 
     @property
     def model(self) -> AnyModel:
         """Return the model without locking it."""
-        return self._locker.model
+        return self._cache_record.cached_model.model
 
 
-@dataclass
 class LoadedModel(LoadedModelWithoutConfig):
     """Context manager object that mediates transfer from RAM<->VRAM."""
 
-    config: Optional[AnyModelConfig] = None
+    def __init__(self, config: Optional[AnyModelConfig], cache_record: CacheRecord, cache: ModelCache):
+        super().__init__(cache_record=cache_record, cache=cache)
+        self.config = config
 
 
 # TODO(MM2):
@@ -110,7 +108,7 @@ class ModelLoaderBase(ABC):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         pass
@@ -138,6 +136,6 @@ class ModelLoaderBase(ABC):
 
     @property
     @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         pass

invokeai/backend/model_manager/load/load_default.py

@@ -14,7 +14,8 @@ from invokeai.backend.model_manager import (
 )
 from invokeai.backend.model_manager.config import DiffusersConfigBase
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache, get_model_cache_key
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.util.devices import TorchDevice
@@ -28,7 +29,7 @@ class ModelLoader(ModelLoaderBase):
         self,
         app_config: InvokeAIAppConfig,
         logger: Logger,
-        ram_cache: ModelCacheBase[AnyModel],
+        ram_cache: ModelCache,
     ):
         """Initialize the loader."""
         self._app_config = app_config
@@ -54,11 +55,11 @@ class ModelLoader(ModelLoaderBase):
             raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}")
 
         with skip_torch_weight_init():
-            locker = self._load_and_cache(model_config, submodel_type)
-        return LoadedModel(config=model_config, _locker=locker)
+            cache_record = self._load_and_cache(model_config, submodel_type)
+        return LoadedModel(config=model_config, cache_record=cache_record, cache=self._ram_cache)
 
     @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+    def ram_cache(self) -> ModelCache:
         """Return the ram cache associated with this loader."""
         return self._ram_cache
 
@@ -66,10 +67,10 @@ class ModelLoader(ModelLoaderBase):
         model_base = self._app_config.models_path
         return (model_base / config.path).resolve()
 
-    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
+    def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> CacheRecord:
         stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
+            return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
         except IndexError:
             pass
 
@@ -78,16 +79,11 @@ class ModelLoader(ModelLoaderBase):
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
-            config.key,
-            submodel_type=submodel_type,
+            get_model_cache_key(config.key, submodel_type),
             model=loaded_model,
         )
 
-        return self._ram_cache.get(
-            key=config.key,
-            submodel_type=submodel_type,
-            stats_name=stats_name,
-        )
+        return self._ram_cache.get(key=get_model_cache_key(config.key, submodel_type), stats_name=stats_name)
 
     def get_size_fs(
         self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None

invokeai/backend/model_manager/load/model_cache/__init__.py

@@ -1,6 +0,0 @@
-"""Init file for ModelCache."""
-
-from .model_cache_base import ModelCacheBase, CacheStats  # noqa F401
-from .model_cache_default import ModelCache  # noqa F401
-
-_all__ = ["ModelCacheBase", "ModelCache", "CacheStats"]

invokeai/backend/model_manager/load/model_cache/cache_record.py

@@ -0,0 +1,33 @@
+from dataclasses import dataclass
+
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+
+
+@dataclass
+class CacheRecord:
+    """A class that represents a model in the model cache."""
+
+    # Cache key.
+    key: str
+    # Model in memory.
+    cached_model: CachedModelWithPartialLoad | CachedModelOnlyFullLoad
+    _locks: int = 0
+
+    def lock(self) -> None:
+        """Lock this record."""
+        self._locks += 1
+
+    def unlock(self) -> None:
+        """Unlock this record."""
+        self._locks -= 1
+        assert self._locks >= 0
+
+    @property
+    def is_locked(self) -> bool:
+        """Return true if record is locked."""
+        return self._locks > 0

invokeai/backend/model_manager/load/model_cache/cache_stats.py

@@ -0,0 +1,15 @@
+from dataclasses import dataclass, field
+from typing import Dict
+
+
+@dataclass
+class CacheStats(object):
+    """Collect statistics on cache performance."""
+
+    hits: int = 0  # cache hits
+    misses: int = 0  # cache misses
+    high_watermark: int = 0  # amount of cache used
+    in_cache: int = 0  # number of models in cache
+    cleared: int = 0  # number of models cleared to make space
+    cache_size: int = 0  # total size of cache
+    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_only_full_load.py

@@ -0,0 +1,98 @@
+from typing import Any
+
+import torch
+
+
+class CachedModelOnlyFullLoad:
+    """A wrapper around a PyTorch model to handle full loads and unloads between the CPU and the compute device.
+    Note: "VRAM" is used throughout this class to refer to the memory on the compute device. It could be CUDA memory,
+    MPS memory, etc.
+    """
+
+    def __init__(
+        self, model: torch.nn.Module | Any, compute_device: torch.device, total_bytes: int, keep_ram_copy: bool = False
+    ):
+        """Initialize a CachedModelOnlyFullLoad.
+        Args:
+            model (torch.nn.Module | Any): The model to wrap. Should be on the CPU.
+            compute_device (torch.device): The compute device to move the model to.
+            total_bytes (int): The total size (in bytes) of all the weights in the model.
+            keep_ram_copy (bool): Whether to keep a read-only copy of the model's state dict in RAM. Keeping a RAM copy
+                increases RAM usage, but speeds up model offload from VRAM and LoRA patching (assuming there is
+                sufficient RAM).
+        """
+        # model is often a torch.nn.Module, but could be any model type. Throughout this class, we handle both cases.
+        self._model = model
+        self._compute_device = compute_device
+        self._offload_device = torch.device("cpu")
+
+        # A CPU read-only copy of the model's state dict.
+        self._cpu_state_dict: dict[str, torch.Tensor] | None = None
+        if isinstance(model, torch.nn.Module) and keep_ram_copy:
+            self._cpu_state_dict = model.state_dict()
+
+        self._total_bytes = total_bytes
+        self._is_in_vram = False
+
+    @property
+    def model(self) -> torch.nn.Module:
+        return self._model
+
+    def get_cpu_state_dict(self) -> dict[str, torch.Tensor] | None:
+        """Get a read-only copy of the model's state dict in RAM."""
+        # TODO(ryand): Document this better.
+        return self._cpu_state_dict
+
+    def total_bytes(self) -> int:
+        """Get the total size (in bytes) of all the weights in the model."""
+        return self._total_bytes
+
+    def cur_vram_bytes(self) -> int:
+        """Get the size (in bytes) of the weights that are currently in VRAM."""
+        if self._is_in_vram:
+            return self._total_bytes
+        else:
+            return 0
+
+    def is_in_vram(self) -> bool:
+        """Return true if the model is currently in VRAM."""
+        return self._is_in_vram
+
+    def full_load_to_vram(self) -> int:
+        """Load all weights into VRAM (if supported by the model).
+        Returns:
+            The number of bytes loaded into VRAM.
+        """
+        if self._is_in_vram:
+            # Already in VRAM.
+            return 0
+
+        if not hasattr(self._model, "to"):
+            # Model doesn't support moving to a device.
+            return 0
+
+        if self._cpu_state_dict is not None:
+            new_state_dict: dict[str, torch.Tensor] = {}
+            for k, v in self._cpu_state_dict.items():
+                new_state_dict[k] = v.to(self._compute_device, copy=True)
+            self._model.load_state_dict(new_state_dict, assign=True)
+        self._model.to(self._compute_device)
+
+        self._is_in_vram = True
+        return self._total_bytes
+
+    def full_unload_from_vram(self) -> int:
+        """Unload all weights from VRAM.
+        Returns:
+            The number of bytes unloaded from VRAM.
+        """
+        if not self._is_in_vram:
+            # Already in RAM.
+            return 0
+
+        if self._cpu_state_dict is not None:
+            self._model.load_state_dict(self._cpu_state_dict, assign=True)
+        self._model.to(self._offload_device)
+
+        self._is_in_vram = False
+        return self._total_bytes

invokeai/backend/model_manager/load/model_cache/cached_model/cached_model_with_partial_load.py

@@ -0,0 +1,339 @@
+import torch
+
+from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.custom_modules.custom_module_mixin import (
+    CustomModuleMixin,
+)
+from invokeai.backend.util.calc_tensor_size import calc_tensor_size
+from invokeai.backend.util.logging import InvokeAILogger
+
+
+class CachedModelWithPartialLoad:
+    """A wrapper around a PyTorch model to handle partial loads and unloads between the CPU and the compute device.
+
+    Note: "VRAM" is used throughout this class to refer to the memory on the compute device. It could be CUDA memory,
+    MPS memory, etc.
+    """
+
+    def __init__(self, model: torch.nn.Module, compute_device: torch.device, keep_ram_copy: bool = False):
+        self._model = model
+        self._compute_device = compute_device
+
+        model_state_dict = model.state_dict()
+        # A CPU read-only copy of the model's state dict. Used for faster model unloads from VRAM, and to speed up LoRA
+        # patching. Set to `None` if keep_ram_copy is False.
+        self._cpu_state_dict: dict[str, torch.Tensor] | None = model_state_dict if keep_ram_copy else None
+
+        # A dictionary of the size of each tensor in the state dict.
+        # HACK(ryand): We use this dictionary any time we are doing byte tracking calculations. We do this for
+        # consistency in case the application code has modified the model's size (e.g. by casting to a different
+        # precision). Of course, this means that we are making model cache load/unload decisions based on model size
+        # data that may not be fully accurate.
+        self._state_dict_bytes = {k: calc_tensor_size(v) for k, v in model_state_dict.items()}
+
+        self._total_bytes = sum(self._state_dict_bytes.values())
+        self._cur_vram_bytes: int | None = None
+
+        self._modules_that_support_autocast = self._find_modules_that_support_autocast()
+        self._keys_in_modules_that_do_not_support_autocast = self._find_keys_in_modules_that_do_not_support_autocast(
+            model_state_dict
+        )
+        self._state_dict_keys_by_module_prefix = self._group_state_dict_keys_by_module_prefix(model_state_dict)
+
+    def _find_modules_that_support_autocast(self) -> dict[str, torch.nn.Module]:
+        """Find all modules that support autocasting."""
+        return {n: m for n, m in self._model.named_modules() if isinstance(m, CustomModuleMixin)}  # type: ignore
+
+    def _find_keys_in_modules_that_do_not_support_autocast(self, state_dict: dict[str, torch.Tensor]) -> set[str]:
+        keys_in_modules_that_do_not_support_autocast: set[str] = set()
+        for key in state_dict.keys():
+            for module_name in self._modules_that_support_autocast.keys():
+                if key.startswith(module_name):
+                    break
+            else:
+                keys_in_modules_that_do_not_support_autocast.add(key)
+        return keys_in_modules_that_do_not_support_autocast
+
+    def _group_state_dict_keys_by_module_prefix(self, state_dict: dict[str, torch.Tensor]) -> dict[str, list[str]]:
+        """A helper function that groups state dict keys by module prefix.
+
+        Example:
+        ```
+        state_dict = {
+            "weight": ...,
+            "module.submodule.weight": ...,
+            "module.submodule.bias": ...,
+            "module.other_submodule.weight": ...,
+            "module.other_submodule.bias": ...,
+        }
+
+        output = group_state_dict_keys_by_module_prefix(state_dict)
+
+        # The output will be:
+        output = {
+            "": [
+                "weight",
+            ],
+            "module.submodule": [
+                "module.submodule.weight",
+                "module.submodule.bias",
+            ],
+            "module.other_submodule": [
+                "module.other_submodule.weight",
+                "module.other_submodule.bias",
+            ],
+        }
+        ```
+        """
+        state_dict_keys_by_module_prefix: dict[str, list[str]] = {}
+        for key in state_dict.keys():
+            split = key.rsplit(".", 1)
+            # `split` will have length 1 if the root module has parameters.
+            module_name = split[0] if len(split) > 1 else ""
+            if module_name not in state_dict_keys_by_module_prefix:
+                state_dict_keys_by_module_prefix[module_name] = []
+            state_dict_keys_by_module_prefix[module_name].append(key)
+        return state_dict_keys_by_module_prefix
+
+    def _move_non_persistent_buffers_to_device(self, device: torch.device):
+        """Move the non-persistent buffers to the target device. These buffers are not included in the state dict,
+        so we need to move them manually.
+        """
+        # HACK(ryand): Typically, non-persistent buffers are moved when calling module.to(device). We don't move entire
+        # modules, because we manage the devices of individual tensors using the state dict. Since non-persistent
+        # buffers are not included in the state dict, we need to handle them manually. The only way to do this is by
+        # using private torch.nn.Module attributes.
+        for module in self._model.modules():
+            for name, buffer in module.named_buffers():
+                if name in module._non_persistent_buffers_set:
+                    module._buffers[name] = buffer.to(device, copy=True)
+
+    def _set_autocast_enabled_in_all_modules(self, enabled: bool):
+        """Set autocast_enabled flag in all modules that support device autocasting."""
+        for module in self._modules_that_support_autocast.values():
+            module.set_device_autocasting_enabled(enabled)
+
+    @property
+    def model(self) -> torch.nn.Module:
+        return self._model
+
+    def get_cpu_state_dict(self) -> dict[str, torch.Tensor] | None:
+        """Get a read-only copy of the model's state dict in RAM."""
+        # TODO(ryand): Document this better.
+        return self._cpu_state_dict
+
+    def total_bytes(self) -> int:
+        """Get the total size (in bytes) of all the weights in the model."""
+        return self._total_bytes
+
+    def cur_vram_bytes(self) -> int:
+        """Get the size (in bytes) of the weights that are currently in VRAM."""
+        if self._cur_vram_bytes is None:
+            cur_state_dict = self._model.state_dict()
+            self._cur_vram_bytes = sum(
+                self._state_dict_bytes[k]
+                for k, v in cur_state_dict.items()
+                if v.device.type == self._compute_device.type
+            )
+        return self._cur_vram_bytes
+
+    def full_load_to_vram(self) -> int:
+        """Load all weights into VRAM."""
+        return self.partial_load_to_vram(self.total_bytes())
+
+    def full_unload_from_vram(self) -> int:
+        """Unload all weights from VRAM."""
+        return self.partial_unload_from_vram(self.total_bytes())
+
+    def _load_state_dict_with_device_conversion(
+        self, state_dict: dict[str, torch.Tensor], keys_to_convert: set[str], target_device: torch.device
+    ):
+        if self._cpu_state_dict is not None:
+            # Run the fast version.
+            self._load_state_dict_with_fast_device_conversion(
+                state_dict=state_dict,
+                keys_to_convert=keys_to_convert,
+                target_device=target_device,
+                cpu_state_dict=self._cpu_state_dict,
+            )
+        else:
+            # Run the low-virtual-memory version.
+            self._load_state_dict_with_jit_device_conversion(
+                state_dict=state_dict,
+                keys_to_convert=keys_to_convert,
+                target_device=target_device,
+            )
+
+    def _load_state_dict_with_jit_device_conversion(
+        self,
+        state_dict: dict[str, torch.Tensor],
+        keys_to_convert: set[str],
+        target_device: torch.device,
+    ):
+        """A custom state dict loading implementation with good peak memory properties.
+
+        This implementation has the important property that it copies parameters to the target device one module at a time
+        rather than applying all of the device conversions and then calling load_state_dict(). This is done to minimize the
+        peak virtual memory usage. Specifically, we want to avoid a case where we hold references to all of the CPU weights
+        and CUDA weights simultaneously, because Windows will reserve virtual memory for both.
+        """
+        for module_name, module in self._model.named_modules():
+            module_keys = self._state_dict_keys_by_module_prefix.get(module_name, [])
+            # Calculate the length of the module name prefix.
+            prefix_len = len(module_name)
+            if prefix_len > 0:
+                prefix_len += 1
+
+            module_state_dict = {}
+            for key in module_keys:
+                if key in keys_to_convert:
+                    # It is important that we overwrite `state_dict[key]` to avoid keeping two copies of the same
+                    # parameter.
+                    state_dict[key] = state_dict[key].to(target_device)
+                # Note that we keep parameters that have not been moved to a new device in case the module implements
+                # weird custom state dict loading logic that requires all parameters to be present.
+                module_state_dict[key[prefix_len:]] = state_dict[key]
+
+            if len(module_state_dict) > 0:
+                # We set strict=False, because if `module` has both parameters and child modules, then we are loading a
+                # state dict that only contains the parameters of `module` (not its children).
+                # We assume that it is rare for non-leaf modules to have parameters. Calling load_state_dict() on non-leaf
+                # modules will recurse through all of the children, so is a bit wasteful.
+                incompatible_keys = module.load_state_dict(module_state_dict, strict=False, assign=True)
+                # Missing keys are ok, unexpected keys are not.
+                assert len(incompatible_keys.unexpected_keys) == 0
+
+    def _load_state_dict_with_fast_device_conversion(
+        self,
+        state_dict: dict[str, torch.Tensor],
+        keys_to_convert: set[str],
+        target_device: torch.device,
+        cpu_state_dict: dict[str, torch.Tensor],
+    ):
+        """Convert parameters to the target device and load them into the model. Leverages the `cpu_state_dict` to speed
+        up transfers of weights to the CPU.
+        """
+        for key in keys_to_convert:
+            if target_device.type == "cpu":
+                state_dict[key] = cpu_state_dict[key]
+            else:
+                state_dict[key] = state_dict[key].to(target_device)
+
+        self._model.load_state_dict(state_dict, assign=True)
+
+    @torch.no_grad()
+    def partial_load_to_vram(self, vram_bytes_to_load: int) -> int:
+        """Load more weights into VRAM without exceeding vram_bytes_to_load.
+
+        Returns:
+            The number of bytes loaded into VRAM.
+        """
+        # TODO(ryand): Handle the case where an exception is thrown while loading or unloading weights. At the very
+        # least, we should reset self._cur_vram_bytes to None.
+
+        vram_bytes_loaded = 0
+
+        cur_state_dict = self._model.state_dict()
+
+        # Identify the keys that will be loaded into VRAM.
+        keys_to_load: set[str] = set()
+
+        # First, process the keys that *must* be loaded into VRAM.
+        for key in self._keys_in_modules_that_do_not_support_autocast:
+            param = cur_state_dict[key]
+            if param.device.type == self._compute_device.type:
+                continue
+
+            keys_to_load.add(key)
+            param_size = self._state_dict_bytes[key]
+            vram_bytes_loaded += param_size
+
+        if vram_bytes_loaded > vram_bytes_to_load:
+            logger = InvokeAILogger.get_logger()
+            logger.warning(
+                f"Loading {vram_bytes_loaded / 2**20} MB into VRAM, but only {vram_bytes_to_load / 2**20} MB were "
+                "requested. This is the minimum set of weights in VRAM required to run the model."
+            )
+
+        # Next, process the keys that can optionally be loaded into VRAM.
+        fully_loaded = True
+        for key, param in cur_state_dict.items():
+            # Skip the keys that have already been processed above.
+            if key in keys_to_load:
+                continue
+
+            if param.device.type == self._compute_device.type:
+                continue
+
+            param_size = self._state_dict_bytes[key]
+            if vram_bytes_loaded + param_size > vram_bytes_to_load:
+                # TODO(ryand): Should we just break here? If we couldn't fit this parameter into VRAM, is it really
+                # worth continuing to search for a smaller parameter that would fit?
+                fully_loaded = False
+                continue
+
+            keys_to_load.add(key)
+            vram_bytes_loaded += param_size
+
+        if len(keys_to_load) > 0:
+            # We load the entire state dict, not just the parameters that changed, in case there are modules that
+            # override _load_from_state_dict() and do some funky stuff that requires the entire state dict.
+            # Alternatively, in the future, grouping parameters by module could probably solve this problem.
+            self._load_state_dict_with_device_conversion(cur_state_dict, keys_to_load, self._compute_device)
+
+        if self._cur_vram_bytes is not None:
+            self._cur_vram_bytes += vram_bytes_loaded
+
+        if fully_loaded:
+            self._set_autocast_enabled_in_all_modules(False)
+        else:
+            self._set_autocast_enabled_in_all_modules(True)
+
+        # Move all non-persistent buffers to the compute device. These are a weird edge case and do not participate in
+        # the vram_bytes_loaded tracking.
+        self._move_non_persistent_buffers_to_device(self._compute_device)
+
+        return vram_bytes_loaded
+
+    @torch.no_grad()
+    def partial_unload_from_vram(self, vram_bytes_to_free: int, keep_required_weights_in_vram: bool = False) -> int:
+        """Unload weights from VRAM until vram_bytes_to_free bytes are freed. Or the entire model is unloaded.
+
+        :param keep_required_weights_in_vram: If True, any weights that must be kept in VRAM to run the model will be
+            kept in VRAM.
+
+        Returns:
+            The number of bytes unloaded from VRAM.
+        """
+        vram_bytes_freed = 0
+        required_weights_in_vram = 0
+
+        offload_device = "cpu"
+        cur_state_dict = self._model.state_dict()
+
+        # Identify the keys that will be offloaded to CPU.
+        keys_to_offload: set[str] = set()
+
+        for key, param in cur_state_dict.items():
+            if vram_bytes_freed >= vram_bytes_to_free:
+                break
+
+            if param.device.type == offload_device:
+                continue
+
+            if keep_required_weights_in_vram and key in self._keys_in_modules_that_do_not_support_autocast:
+                required_weights_in_vram += self._state_dict_bytes[key]
+                continue
+
+            keys_to_offload.add(key)
+            vram_bytes_freed += self._state_dict_bytes[key]
+
+        if len(keys_to_offload) > 0:
+            self._load_state_dict_with_device_conversion(cur_state_dict, keys_to_offload, torch.device("cpu"))
+
+        if self._cur_vram_bytes is not None:
+            self._cur_vram_bytes -= vram_bytes_freed
+
+        # We may have gone from a fully-loaded model to a partially-loaded model, so we need to reapply the custom
+        # layers.
+        self._set_autocast_enabled_in_all_modules(True)
+        return vram_bytes_freed

invokeai/backend/model_manager/load/model_cache/dev_utils.py

@@ -0,0 +1,33 @@
+from contextlib import contextmanager
+
+import torch
+
+from invokeai.backend.util.logging import InvokeAILogger
+
+
+@contextmanager
+def log_operation_vram_usage(operation_name: str):
+    """A helper function for tuning working memory requirements for memory-intensive ops.
+
+    Sample usage:
+
+    ```python
+    with log_operation_vram_usage("some_operation"):
+        some_operation()
+    ```
+    """
+    torch.cuda.synchronize()
+    torch.cuda.reset_peak_memory_stats()
+    max_allocated_before = torch.cuda.max_memory_allocated()
+    max_reserved_before = torch.cuda.max_memory_reserved()
+    try:
+        yield
+    finally:
+        torch.cuda.synchronize()
+        max_allocated_after = torch.cuda.max_memory_allocated()
+        max_reserved_after = torch.cuda.max_memory_reserved()
+        logger = InvokeAILogger.get_logger()
+        logger.info(
+            f">>>{operation_name} Peak VRAM allocated: {(max_allocated_after - max_allocated_before) / 2**20} MB, "
+            f"Peak VRAM reserved: {(max_reserved_after - max_reserved_before) / 2**20} MB"
+        )

invokeai/backend/model_manager/load/model_cache/model_cache.py

@@ -0,0 +1,659 @@
+import gc
+import logging
+import threading
+import time
+from functools import wraps
+from logging import Logger
+from typing import Any, Callable, Dict, List, Optional
+
+import psutil
+import torch
+
+from invokeai.backend.model_manager import AnyModel, SubModelType
+from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
+from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
+from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_only_full_load import (
+    CachedModelOnlyFullLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.cached_model.cached_model_with_partial_load import (
+    CachedModelWithPartialLoad,
+)
+from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch_module_autocast import (
+    apply_custom_layers_to_model,
+)
+from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.logging import InvokeAILogger
+from invokeai.backend.util.prefix_logger_adapter import PrefixedLoggerAdapter
+
+# Size of a GB in bytes.
+GB = 2**30
+
+# Size of a MB in bytes.
+MB = 2**20
+
+
+# TODO(ryand): Where should this go? The ModelCache shouldn't be concerned with submodels.
+def get_model_cache_key(model_key: str, submodel_type: Optional[SubModelType] = None) -> str:
+    """Get the cache key for a model based on the optional submodel type."""
+    if submodel_type:
+        return f"{model_key}:{submodel_type.value}"
+    else:
+        return model_key
+
+
+def synchronized(method: Callable[..., Any]) -> Callable[..., Any]:
+    """A decorator that applies the class's self._lock to the method."""
+
+    @wraps(method)
+    def wrapper(self, *args, **kwargs):
+        with self._lock:  # Automatically acquire and release the lock
+            return method(self, *args, **kwargs)
+
+    return wrapper
+
+
+class ModelCache:
+    """A cache for managing models in memory.
+
+    The cache is based on two levels of model storage:
+    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
+    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
+
+    The model cache is based on the following assumptions:
+    - storage_device_mem_size > execution_device_mem_size
+    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
+
+    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
+    the execution_device.
+
+    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
+    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
+    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
+
+    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
+    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
+    configuration.
+
+    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
+    the context, and unload outside the context.
+
+    Example usage:
+    ```
+    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
+    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
+        do_something_on_gpu(SD1)
+    ```
+    """
+
+    def __init__(
+        self,
+        execution_device_working_mem_gb: float,
+        enable_partial_loading: bool,
+        keep_ram_copy_of_weights: bool,
+        max_ram_cache_size_gb: float | None = None,
+        max_vram_cache_size_gb: float | None = None,
+        execution_device: torch.device | str = "cuda",
+        storage_device: torch.device | str = "cpu",
+        log_memory_usage: bool = False,
+        logger: Optional[Logger] = None,
+    ):
+        """Initialize the model RAM cache.
+
+        :param execution_device_working_mem_gb: The amount of working memory to keep on the GPU (in GB) i.e. non-model
+            VRAM.
+        :param enable_partial_loading: Whether to enable partial loading of models.
+        :param max_ram_cache_size_gb: The maximum amount of CPU RAM to use for model caching in GB. This parameter is
+            kept to maintain compatibility with previous versions of the model cache, but should be deprecated in the
+            future. If set, this parameter overrides the default cache size logic.
+        :param max_vram_cache_size_gb: The amount of VRAM to use for model caching in GB. This parameter is kept to
+            maintain compatibility with previous versions of the model cache, but should be deprecated in the future.
+            If set, this parameter overrides the default cache size logic.
+        :param execution_device: Torch device to load active model into [torch.device('cuda')]
+        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
+        :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
+            operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
+            snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
+            behaviour.
+        :param logger: InvokeAILogger to use (otherwise creates one)
+        """
+        self._enable_partial_loading = enable_partial_loading
+        self._keep_ram_copy_of_weights = keep_ram_copy_of_weights
+        self._execution_device_working_mem_gb = execution_device_working_mem_gb
+        self._execution_device: torch.device = torch.device(execution_device)
+        self._storage_device: torch.device = torch.device(storage_device)
+
+        self._max_ram_cache_size_gb = max_ram_cache_size_gb
+        self._max_vram_cache_size_gb = max_vram_cache_size_gb
+
+        self._logger = PrefixedLoggerAdapter(
+            logger or InvokeAILogger.get_logger(self.__class__.__name__), "MODEL CACHE"
+        )
+        self._log_memory_usage = log_memory_usage
+        self._stats: Optional[CacheStats] = None
+
+        self._cached_models: Dict[str, CacheRecord] = {}
+        self._cache_stack: List[str] = []
+
+        self._ram_cache_size_bytes = self._calc_ram_available_to_model_cache()
+
+        # A lock applied to all public method calls to make the ModelCache thread-safe.
+        # At the time of writing, the ModelCache should only be accessed from two threads:
+        # - The graph execution thread
+        # - Requests to empty the cache from a separate thread
+        self._lock = threading.RLock()
+
+    @property
+    @synchronized
+    def stats(self) -> Optional[CacheStats]:
+        """Return collected CacheStats object."""
+        return self._stats
+
+    @stats.setter
+    @synchronized
+    def stats(self, stats: CacheStats) -> None:
+        """Set the CacheStats object for collecting cache statistics."""
+        self._stats = stats
+
+    @synchronized
+    def put(self, key: str, model: AnyModel) -> None:
+        """Add a model to the cache."""
+        if key in self._cached_models:
+            self._logger.debug(
+                f"Attempted to add model {key} ({model.__class__.__name__}), but it already exists in the cache. No action necessary."
+            )
+            return
+
+        size = calc_model_size_by_data(self._logger, model)
+        self.make_room(size)
+
+        # Inject custom modules into the model.
+        if isinstance(model, torch.nn.Module):
+            apply_custom_layers_to_model(model)
+
+        # Partial loading only makes sense on CUDA.
+        # - When running on CPU, there is no 'loading' to do.
+        # - When running on MPS, memory is shared with the CPU, so the default OS memory management already handles this
+        #   well.
+        running_with_cuda = self._execution_device.type == "cuda"
+
+        # Wrap model.
+        if isinstance(model, torch.nn.Module) and running_with_cuda and self._enable_partial_loading:
+            wrapped_model = CachedModelWithPartialLoad(
+                model, self._execution_device, keep_ram_copy=self._keep_ram_copy_of_weights
+            )
+        else:
+            wrapped_model = CachedModelOnlyFullLoad(
+                model, self._execution_device, size, keep_ram_copy=self._keep_ram_copy_of_weights
+            )
+
+        cache_record = CacheRecord(key=key, cached_model=wrapped_model)
+        self._cached_models[key] = cache_record
+        self._cache_stack.append(key)
+        self._logger.debug(
+            f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size/MB:.2f}MB)"
+        )
+
+    @synchronized
+    def get(self, key: str, stats_name: Optional[str] = None) -> CacheRecord:
+        """Retrieve a model from the cache.
+
+        :param key: Model key
+        :param stats_name: A human-readable id for the model for the purposes of stats reporting.
+
+        Raises IndexError if the model is not in the cache.
+        """
+        if key in self._cached_models:
+            if self.stats:
+                self.stats.hits += 1
+        else:
+            if self.stats:
+                self.stats.misses += 1
+            self._logger.debug(f"Cache miss: {key}")
+            raise IndexError(f"The model with key {key} is not in the cache.")
+
+        cache_entry = self._cached_models[key]
+
+        # more stats
+        if self.stats:
+            stats_name = stats_name or key
+            self.stats.high_watermark = max(self.stats.high_watermark, self._get_ram_in_use())
+            self.stats.in_cache = len(self._cached_models)
+            self.stats.loaded_model_sizes[stats_name] = max(
+                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.cached_model.total_bytes()
+            )
+
+        # This moves the entry to the top (right end) of the stack.
+        self._cache_stack = [k for k in self._cache_stack if k != key]
+        self._cache_stack.append(key)
+
+        self._logger.debug(f"Cache hit: {key} (Type: {cache_entry.cached_model.model.__class__.__name__})")
+        return cache_entry
+
+    @synchronized
+    def lock(self, cache_entry: CacheRecord, working_mem_bytes: Optional[int]) -> None:
+        """Lock a model for use and move it into VRAM."""
+        if cache_entry.key not in self._cached_models:
+            self._logger.info(
+                f"Locking model cache entry {cache_entry.key} "
+                f"(Type: {cache_entry.cached_model.model.__class__.__name__}), but it has already been dropped from "
+                "the RAM cache. This is a sign that the model loading order is non-optimal in the invocation code "
+                "(See https://github.com/invoke-ai/InvokeAI/issues/7513)."
+            )
+        # cache_entry = self._cached_models[key]
+        cache_entry.lock()
+
+        self._logger.debug(
+            f"Locking model {cache_entry.key} (Type: {cache_entry.cached_model.model.__class__.__name__})"
+        )
+
+        if self._execution_device.type == "cpu":
+            # Models don't need to be loaded into VRAM if we're running on CPU.
+            return
+
+        try:
+            self._load_locked_model(cache_entry, working_mem_bytes)
+            self._logger.debug(
+                f"Finished locking model {cache_entry.key} (Type: {cache_entry.cached_model.model.__class__.__name__})"
+            )
+        except torch.cuda.OutOfMemoryError:
+            self._logger.warning("Insufficient GPU memory to load model. Aborting")
+            cache_entry.unlock()
+            raise
+        except Exception:
+            cache_entry.unlock()
+            raise
+
+        self._log_cache_state()
+
+    @synchronized
+    def unlock(self, cache_entry: CacheRecord) -> None:
+        """Unlock a model."""
+        if cache_entry.key not in self._cached_models:
+            self._logger.info(
+                f"Unlocking model cache entry {cache_entry.key} "
+                f"(Type: {cache_entry.cached_model.model.__class__.__name__}), but it has already been dropped from "
+                "the RAM cache. This is a sign that the model loading order is non-optimal in the invocation code "
+                "(See https://github.com/invoke-ai/InvokeAI/issues/7513)."
+            )
+        # cache_entry = self._cached_models[key]
+        cache_entry.unlock()
+        self._logger.debug(
+            f"Unlocked model {cache_entry.key} (Type: {cache_entry.cached_model.model.__class__.__name__})"
+        )
+
+    def _load_locked_model(self, cache_entry: CacheRecord, working_mem_bytes: Optional[int] = None) -> None:
+        """Helper function for self.lock(). Loads a locked model into VRAM."""
+        start_time = time.time()
+
+        # Calculate model_vram_needed, the amount of additional VRAM that will be used if we fully load the model into
+        # VRAM.
+        model_cur_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+        model_total_bytes = cache_entry.cached_model.total_bytes()
+        model_vram_needed = model_total_bytes - model_cur_vram_bytes
+
+        vram_available = self._get_vram_available(working_mem_bytes)
+        self._logger.debug(
+            f"Before unloading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+        # Make room for the model in VRAM.
+        # 1. If the model can fit entirely in VRAM, then make enough room for it to be loaded fully.
+        # 2. If the model can't fit fully into VRAM, then unload all other models and load as much of the model as
+        #    possible.
+        vram_bytes_freed = self._offload_unlocked_models(model_vram_needed, working_mem_bytes)
+        self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed/MB):.2f}MB")
+
+        # Check the updated vram_available after offloading.
+        vram_available = self._get_vram_available(working_mem_bytes)
+        self._logger.debug(
+            f"After unloading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+        if vram_available < 0:
+            # There is insufficient VRAM available. As a last resort, try to unload the model being locked from VRAM,
+            # as it may still be loaded from a previous use.
+            vram_bytes_freed_from_own_model = self._move_model_to_ram(cache_entry, -vram_available)
+            vram_available = self._get_vram_available(working_mem_bytes)
+            self._logger.debug(
+                f"Unloaded {vram_bytes_freed_from_own_model/MB:.2f}MB from the model being locked ({cache_entry.key})."
+            )
+
+        # Move as much of the model as possible into VRAM.
+        # For testing, only allow 10% of the model to be loaded into VRAM.
+        # vram_available = int(model_vram_needed * 0.1)
+        # We add 1 MB to the available VRAM to account for small errors in memory tracking (e.g. off-by-one). A fully
+        # loaded model is much faster than a 95% loaded model.
+        model_bytes_loaded = self._move_model_to_vram(cache_entry, vram_available + MB)
+
+        model_cur_vram_bytes = cache_entry.cached_model.cur_vram_bytes()
+        vram_available = self._get_vram_available(working_mem_bytes)
+        loaded_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
+        self._logger.info(
+            f"Loaded model '{cache_entry.key}' ({cache_entry.cached_model.model.__class__.__name__}) onto "
+            f"{self._execution_device.type} device in {(time.time() - start_time):.2f}s. "
+            f"Total model size: {model_total_bytes/MB:.2f}MB, "
+            f"VRAM: {model_cur_vram_bytes/MB:.2f}MB ({loaded_percent:.1%})"
+        )
+        self._logger.debug(f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded/MB):.2f}MB, ")
+        self._logger.debug(
+            f"After loading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
+        )
+
+    def _move_model_to_vram(self, cache_entry: CacheRecord, vram_available: int) -> int:
+        try:
+            if isinstance(cache_entry.cached_model, CachedModelWithPartialLoad):
+                return cache_entry.cached_model.partial_load_to_vram(vram_available)
+            elif isinstance(cache_entry.cached_model, CachedModelOnlyFullLoad):  # type: ignore
+                # Partial load is not supported, so we have not choice but to try and fit it all into VRAM.
+                return cache_entry.cached_model.full_load_to_vram()
+            else:
+                raise ValueError(f"Unsupported cached model type: {type(cache_entry.cached_model)}")
+        except Exception as e:
+            if isinstance(e, torch.cuda.OutOfMemoryError):
+                self._logger.warning("Insufficient GPU memory to load model. Aborting")
+            # If an exception occurs, the model could be left in a bad state, so we delete it from the cache entirely.
+            self._delete_cache_entry(cache_entry)
+            raise
+
+    def _move_model_to_ram(self, cache_entry: CacheRecord, vram_bytes_to_free: int) -> int:
+        try:
+            if isinstance(cache_entry.cached_model, CachedModelWithPartialLoad):
+                return cache_entry.cached_model.partial_unload_from_vram(
+                    vram_bytes_to_free, keep_required_weights_in_vram=cache_entry.is_locked
+                )
+            elif isinstance(cache_entry.cached_model, CachedModelOnlyFullLoad):  # type: ignore
+                return cache_entry.cached_model.full_unload_from_vram()
+            else:
+                raise ValueError(f"Unsupported cached model type: {type(cache_entry.cached_model)}")
+        except Exception:
+            # If an exception occurs, the model could be left in a bad state, so we delete it from the cache entirely.
+            self._delete_cache_entry(cache_entry)
+            raise
+
+    def _get_vram_available(self, working_mem_bytes: Optional[int]) -> int:
+        """Calculate the amount of additional VRAM available for the cache to use (takes into account the working
+        memory).
+        """
+        # If self._max_vram_cache_size_gb is set, then it overrides the default logic.
+        if self._max_vram_cache_size_gb is not None:
+            vram_total_available_to_cache = int(self._max_vram_cache_size_gb * GB)
+            return vram_total_available_to_cache - self._get_vram_in_use()
+
+        working_mem_bytes_default = int(self._execution_device_working_mem_gb * GB)
+        working_mem_bytes = max(working_mem_bytes or working_mem_bytes_default, working_mem_bytes_default)
+
+        if self._execution_device.type == "cuda":
+            # TODO(ryand): It is debatable whether we should use memory_reserved() or memory_allocated() here.
+            # memory_reserved() includes memory reserved by the torch CUDA memory allocator that may or may not be
+            # re-used for future allocations. For now, we use memory_allocated() to be conservative.
+            # vram_reserved = torch.cuda.memory_reserved(self._execution_device)
+            vram_allocated = torch.cuda.memory_allocated(self._execution_device)
+            vram_free, _vram_total = torch.cuda.mem_get_info(self._execution_device)
+            vram_available_to_process = vram_free + vram_allocated
+        elif self._execution_device.type == "mps":
+            vram_reserved = torch.mps.driver_allocated_memory()
+            # TODO(ryand): Is it accurate that MPS shares memory with the CPU?
+            vram_free = psutil.virtual_memory().available
+            vram_available_to_process = vram_free + vram_reserved
+        else:
+            raise ValueError(f"Unsupported execution device: {self._execution_device.type}")
+
+        vram_total_available_to_cache = vram_available_to_process - working_mem_bytes
+        vram_cur_available_to_cache = vram_total_available_to_cache - self._get_vram_in_use()
+        return vram_cur_available_to_cache
+
+    def _get_vram_in_use(self) -> int:
+        """Get the amount of VRAM currently in use by the cache."""
+        if self._execution_device.type == "cuda":
+            return torch.cuda.memory_allocated()
+        elif self._execution_device.type == "mps":
+            return torch.mps.current_allocated_memory()
+        else:
+            raise ValueError(f"Unsupported execution device type: {self._execution_device.type}")
+        # Alternative definition of VRAM in use:
+        # return sum(ce.cached_model.cur_vram_bytes() for ce in self._cached_models.values())
+
+    def _calc_ram_available_to_model_cache(self) -> int:
+        """Calculate the amount of RAM available for the cache to use."""
+        # If self._max_ram_cache_size_gb is set, then it overrides the default logic.
+        if self._max_ram_cache_size_gb is not None:
+            self._logger.info(f"Using user-defined RAM cache size: {self._max_ram_cache_size_gb} GB.")
+            return int(self._max_ram_cache_size_gb * GB)
+
+        # Heuristics for dynamically calculating the RAM cache size, **in order of increasing priority**:
+        # 1. As an initial default, use 50% of the total RAM for InvokeAI.
+        #   - Assume a 2GB baseline for InvokeAI's non-model RAM usage, and use the rest of the RAM for the model cache.
+        # 2. On a system with a lot of RAM, users probably don't want InvokeAI to eat up too much RAM.
+        #    There are diminishing returns to storing more and more models. So, we apply an upper bound. (Keep in mind
+        #    that most OSes have some amount of disk caching, which we still benefit from if there is excess memory,
+        #    even if we drop models from the cache.)
+        #    - On systems without a CUDA device, the upper bound is 32GB.
+        #    - On systems with a CUDA device, the upper bound is 1x the amount of VRAM (less the working memory).
+        # 3. Absolute minimum of 4GB.
+
+        # NOTE(ryand): We explored dynamically adjusting the RAM cache size based on memory pressure (using psutil), but
+        # decided against it for now, for the following reasons:
+        # - It was surprisingly difficult to get memory metrics with consistent definitions across OSes. (If you go
+        #   down this path again, don't underestimate the amount of complexity here and be sure to test rigorously on all
+        #   OSes.)
+        # - Making the RAM cache size dynamic opens the door for performance regressions that are hard to diagnose and
+        #   hard for users to understand. It is better for users to see that their RAM is maxed out, and then override
+        #   the default value if desired.
+
+        # Lookup the total VRAM size for the CUDA execution device.
+        total_cuda_vram_bytes: int | None = None
+        if self._execution_device.type == "cuda":
+            _, total_cuda_vram_bytes = torch.cuda.mem_get_info(self._execution_device)
+
+        # Apply heuristic 1.
+        # ------------------
+        heuristics_applied = [1]
+        total_system_ram_bytes = psutil.virtual_memory().total
+        # Assumed baseline RAM used by InvokeAI for non-model stuff.
+        baseline_ram_used_by_invokeai = 2 * GB
+        ram_available_to_model_cache = int(total_system_ram_bytes * 0.5 - baseline_ram_used_by_invokeai)
+
+        # Apply heuristic 2.
+        # ------------------
+        max_ram_cache_size_bytes = 32 * GB
+        if total_cuda_vram_bytes is not None:
+            if self._max_vram_cache_size_gb is not None:
+                max_ram_cache_size_bytes = int(self._max_vram_cache_size_gb * GB)
+            else:
+                max_ram_cache_size_bytes = total_cuda_vram_bytes - int(self._execution_device_working_mem_gb * GB)
+        if ram_available_to_model_cache > max_ram_cache_size_bytes:
+            heuristics_applied.append(2)
+            ram_available_to_model_cache = max_ram_cache_size_bytes
+
+        # Apply heuristic 3.
+        # ------------------
+        if ram_available_to_model_cache < 4 * GB:
+            heuristics_applied.append(3)
+            ram_available_to_model_cache = 4 * GB
+
+        self._logger.info(
+            f"Calculated model RAM cache size: {ram_available_to_model_cache / MB:.2f} MB. Heuristics applied: {heuristics_applied}."
+        )
+        return ram_available_to_model_cache
+
+    def _get_ram_in_use(self) -> int:
+        """Get the amount of RAM currently in use."""
+        return sum(ce.cached_model.total_bytes() for ce in self._cached_models.values())
+
+    def _get_ram_available(self) -> int:
+        """Get the amount of RAM available for the cache to use."""
+        return self._ram_cache_size_bytes - self._get_ram_in_use()
+
+    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
+        if self._log_memory_usage:
+            return MemorySnapshot.capture()
+        return None
+
+    def _get_vram_state_str(self, model_cur_vram_bytes: int, model_total_bytes: int, vram_available: int) -> str:
+        """Helper function for preparing a VRAM state log string."""
+        model_cur_vram_bytes_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
+        return (
+            f"model_total={model_total_bytes/MB:.0f} MB, "
+            + f"model_vram={model_cur_vram_bytes/MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
+            # + f"vram_total={int(self._max_vram_cache_size * GB)/MB:.0f} MB, "
+            + f"vram_available={(vram_available/MB):.0f} MB, "
+        )
+
+    def _offload_unlocked_models(self, vram_bytes_required: int, working_mem_bytes: Optional[int] = None) -> int:
+        """Offload models from the execution_device until vram_bytes_required bytes are available, or all models are
+        offloaded. Of course, locked models are not offloaded.
+
+        Returns:
+            int: The number of bytes freed based on believed model sizes. The actual change in VRAM may be different.
+        """
+        self._logger.debug(
+            f"Offloading unlocked models with goal of making room for {vram_bytes_required/MB:.2f}MB of VRAM."
+        )
+        vram_bytes_freed = 0
+        # TODO(ryand): Give more thought to the offloading policy used here.
+        cache_entries_increasing_size = sorted(self._cached_models.values(), key=lambda x: x.cached_model.total_bytes())
+        for cache_entry in cache_entries_increasing_size:
+            # We do not fully trust the count of bytes freed, so we check again on each iteration.
+            vram_available = self._get_vram_available(working_mem_bytes)
+            vram_bytes_to_free = vram_bytes_required - vram_available
+            if vram_bytes_to_free <= 0:
+                break
+            if cache_entry.is_locked:
+                # TODO(ryand): In the future, we may want to partially unload locked models, but this requires careful
+                # handling of model patches (e.g. LoRA).
+                continue
+            cache_entry_bytes_freed = self._move_model_to_ram(cache_entry, vram_bytes_to_free)
+            if cache_entry_bytes_freed > 0:
+                self._logger.debug(
+                    f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed/MB):.0f} MB."
+                )
+            vram_bytes_freed += cache_entry_bytes_freed
+
+        TorchDevice.empty_cache()
+        return vram_bytes_freed
+
+    def _log_cache_state(self, title: str = "Model cache state:", include_entry_details: bool = True):
+        if self._logger.getEffectiveLevel() > logging.DEBUG:
+            # Short circuit if the logger is not set to debug. Some of the data lookups could take a non-negligible
+            # amount of time.
+            return
+
+        log = f"{title}\n"
+
+        log_format = "  {:<30} Limit: {:>7.1f} MB, Used: {:>7.1f} MB ({:>5.1%}), Available: {:>7.1f} MB ({:>5.1%})\n"
+
+        ram_in_use_bytes = self._get_ram_in_use()
+        ram_available_bytes = self._get_ram_available()
+        ram_size_bytes = ram_in_use_bytes + ram_available_bytes
+        ram_in_use_bytes_percent = ram_in_use_bytes / ram_size_bytes if ram_size_bytes > 0 else 0
+        ram_available_bytes_percent = ram_available_bytes / ram_size_bytes if ram_size_bytes > 0 else 0
+        log += log_format.format(
+            f"Storage Device ({self._storage_device.type})",
+            ram_size_bytes / MB,
+            ram_in_use_bytes / MB,
+            ram_in_use_bytes_percent,
+            ram_available_bytes / MB,
+            ram_available_bytes_percent,
+        )
+
+        if self._execution_device.type != "cpu":
+            vram_in_use_bytes = self._get_vram_in_use()
+            vram_available_bytes = self._get_vram_available(None)
+            vram_size_bytes = vram_in_use_bytes + vram_available_bytes
+            vram_in_use_bytes_percent = vram_in_use_bytes / vram_size_bytes if vram_size_bytes > 0 else 0
+            vram_available_bytes_percent = vram_available_bytes / vram_size_bytes if vram_size_bytes > 0 else 0
+            log += log_format.format(
+                f"Compute Device ({self._execution_device.type})",
+                vram_size_bytes / MB,
+                vram_in_use_bytes / MB,
+                vram_in_use_bytes_percent,
+                vram_available_bytes / MB,
+                vram_available_bytes_percent,
+            )
+
+        if torch.cuda.is_available():
+            log += "  {:<30} {:.1f} MB\n".format("CUDA Memory Allocated:", torch.cuda.memory_allocated() / MB)
+        log += "  {:<30} {}\n".format("Total models:", len(self._cached_models))
+
+        if include_entry_details and len(self._cached_models) > 0:
+            log += "  Models:\n"
+            log_format = (
+                "    {:<80} total={:>7.1f} MB, vram={:>7.1f} MB ({:>5.1%}), ram={:>7.1f} MB ({:>5.1%}), locked={}\n"
+            )
+            for cache_record in self._cached_models.values():
+                total_bytes = cache_record.cached_model.total_bytes()
+                cur_vram_bytes = cache_record.cached_model.cur_vram_bytes()
+                cur_vram_bytes_percent = cur_vram_bytes / total_bytes if total_bytes > 0 else 0
+                cur_ram_bytes = total_bytes - cur_vram_bytes
+                cur_ram_bytes_percent = cur_ram_bytes / total_bytes if total_bytes > 0 else 0
+
+                log += log_format.format(
+                    f"{cache_record.key} ({cache_record.cached_model.model.__class__.__name__}):",
+                    total_bytes / MB,
+                    cur_vram_bytes / MB,
+                    cur_vram_bytes_percent,
+                    cur_ram_bytes / MB,
+                    cur_ram_bytes_percent,
+                    cache_record.is_locked,
+                )
+
+        self._logger.debug(log)
+
+    @synchronized
+    def make_room(self, bytes_needed: int) -> None:
+        """Make enough room in the cache to accommodate a new model of indicated size.
+
+        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
+        external references to the model, there's nothing that the cache can do about it, and those models will not be
+        garbage-collected.
+        """
+        self._logger.debug(f"Making room for {bytes_needed/MB:.2f}MB of RAM.")
+        self._log_cache_state(title="Before dropping models:")
+
+        ram_bytes_available = self._get_ram_available()
+        ram_bytes_to_free = max(0, bytes_needed - ram_bytes_available)
+
+        ram_bytes_freed = 0
+        pos = 0
+        models_cleared = 0
+        while ram_bytes_freed < ram_bytes_to_free and pos < len(self._cache_stack):
+            model_key = self._cache_stack[pos]
+            cache_entry = self._cached_models[model_key]
+
+            if not cache_entry.is_locked:
+                ram_bytes_freed += cache_entry.cached_model.total_bytes()
+                self._logger.debug(
+                    f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes()/MB):.2f}MB."
+                )
+                self._delete_cache_entry(cache_entry)
+                del cache_entry
+                models_cleared += 1
+            else:
+                pos += 1
+
+        if models_cleared > 0:
+            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
+            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
+            # is high even if no garbage gets collected.)
+            #
+            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
+            # - If models had to be cleared, it's a signal that we are close to our memory limit.
+            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
+            #   collected.
+            #
+            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
+            # immediately when their reference count hits 0.
+            if self.stats:
+                self.stats.cleared = models_cleared
+            gc.collect()
+
+        TorchDevice.empty_cache()
+        self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed/MB:.2f}MB of RAM.")
+        self._log_cache_state(title="After dropping models:")
+
+    def _delete_cache_entry(self, cache_entry: CacheRecord) -> None:
+        """Delete cache_entry from the cache if it exists. No exception is thrown if it doesn't exist."""
+        self._cache_stack = [key for key in self._cache_stack if key != cache_entry.key]
+        self._cached_models.pop(cache_entry.key, None)

invokeai/backend/model_manager/load/model_cache/model_cache_base.py

@@ -1,221 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
-# TODO: Add Stalker's proper name to copyright
-"""
-Manage a RAM cache of diffusion/transformer models for fast switching.
-They are moved between GPU VRAM and CPU RAM as necessary. If the cache
-grows larger than a preset maximum, then the least recently used
-model will be cleared and (re)loaded from disk when next needed.
-"""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
-from logging import Logger
-from typing import Dict, Generic, Optional, TypeVar
-
-import torch
-
-from invokeai.backend.model_manager.config import AnyModel, SubModelType
-
-
-class ModelLockerBase(ABC):
-    """Base class for the model locker used by the loader."""
-
-    @abstractmethod
-    def lock(self) -> AnyModel:
-        """Lock the contained model and move it into VRAM."""
-        pass
-
-    @abstractmethod
-    def unlock(self) -> None:
-        """Unlock the contained model, and remove it from VRAM."""
-        pass
-
-    @abstractmethod
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        pass
-
-    @property
-    @abstractmethod
-    def model(self) -> AnyModel:
-        """Return the model."""
-        pass
-
-
-T = TypeVar("T")
-
-
-@dataclass
-class CacheRecord(Generic[T]):
-    """
-    Elements of the cache:
-
-    key: Unique key for each model, same as used in the models database.
-    model: Model in memory.
-    state_dict: A read-only copy of the model's state dict in RAM. It will be
-                used as a template for creating a copy in the VRAM.
-    size: Size of the model
-    loaded: True if the model's state dict is currently in VRAM
-
-    Before a model is executed, the state_dict template is copied into VRAM,
-    and then injected into the model. When the model is finished, the VRAM
-    copy of the state dict is deleted, and the RAM version is reinjected
-    into the model.
-
-    The state_dict should be treated as a read-only attribute. Do not attempt
-    to patch or otherwise modify it. Instead, patch the copy of the state_dict
-    after it is loaded into the execution device (e.g. CUDA) using the `LoadedModel`
-    context manager call `model_on_device()`.
-    """
-
-    key: str
-    model: T
-    device: torch.device
-    state_dict: Optional[Dict[str, torch.Tensor]]
-    size: int
-    loaded: bool = False
-    _locks: int = 0
-
-    def lock(self) -> None:
-        """Lock this record."""
-        self._locks += 1
-
-    def unlock(self) -> None:
-        """Unlock this record."""
-        self._locks -= 1
-        assert self._locks >= 0
-
-    @property
-    def locked(self) -> bool:
-        """Return true if record is locked."""
-        return self._locks > 0
-
-
-@dataclass
-class CacheStats(object):
-    """Collect statistics on cache performance."""
-
-    hits: int = 0  # cache hits
-    misses: int = 0  # cache misses
-    high_watermark: int = 0  # amount of cache used
-    in_cache: int = 0  # number of models in cache
-    cleared: int = 0  # number of models cleared to make space
-    cache_size: int = 0  # total size of cache
-    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)
-
-
-class ModelCacheBase(ABC, Generic[T]):
-    """Virtual base class for RAM model cache."""
-
-    @property
-    @abstractmethod
-    def storage_device(self) -> torch.device:
-        """Return the storage device (e.g. "CPU" for RAM)."""
-        pass
-
-    @property
-    @abstractmethod
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        pass
-
-    @property
-    @abstractmethod
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        pass
-
-    @property
-    @abstractmethod
-    def max_cache_size(self) -> float:
-        """Return the maximum size the RAM cache can grow to."""
-        pass
-
-    @max_cache_size.setter
-    @abstractmethod
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-
-    @property
-    @abstractmethod
-    def max_vram_cache_size(self) -> float:
-        """Return the maximum size the VRAM cache can grow to."""
-        pass
-
-    @max_vram_cache_size.setter
-    @abstractmethod
-    def max_vram_cache_size(self, value: float) -> float:
-        """Set the maximum size the VRAM cache can grow to."""
-        pass
-
-    @abstractmethod
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload from VRAM any models not actively in use."""
-        pass
-
-    @abstractmethod
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device."""
-        pass
-
-    @property
-    @abstractmethod
-    def stats(self) -> Optional[CacheStats]:
-        """Return collected CacheStats object."""
-        pass
-
-    @stats.setter
-    @abstractmethod
-    def stats(self, stats: CacheStats) -> None:
-        """Set the CacheStats object for collectin cache statistics."""
-        pass
-
-    @property
-    @abstractmethod
-    def logger(self) -> Logger:
-        """Return the logger used by the cache."""
-        pass
-
-    @abstractmethod
-    def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size."""
-        pass
-
-    @abstractmethod
-    def put(
-        self,
-        key: str,
-        model: T,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> None:
-        """Store model under key and optional submodel_type."""
-        pass
-
-    @abstractmethod
-    def get(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        stats_name: Optional[str] = None,
-    ) -> ModelLockerBase:
-        """
-        Retrieve model using key and optional submodel_type.
-
-        :param key: Opaque model key
-        :param submodel_type: Type of the submodel to fetch
-        :param stats_name: A human-readable id for the model for the purposes of
-        stats reporting.
-
-        This may raise an IndexError if the model is not in the cache.
-        """
-        pass
-
-    @abstractmethod
-    def cache_size(self) -> int:
-        """Get the total size of the models currently cached."""
-        pass
-
-    @abstractmethod
-    def print_cuda_stats(self) -> None:
-        """Log debugging information on CUDA usage."""
-        pass

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -1,426 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
-# TODO: Add Stalker's proper name to copyright
-""" """
-
-import gc
-import math
-import time
-from contextlib import suppress
-from logging import Logger
-from typing import Dict, List, Optional
-
-import torch
-
-from invokeai.backend.model_manager import AnyModel, SubModelType
-from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
-    CacheRecord,
-    CacheStats,
-    ModelCacheBase,
-    ModelLockerBase,
-)
-from invokeai.backend.model_manager.load.model_cache.model_locker import ModelLocker
-from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.logging import InvokeAILogger
-
-# Size of a GB in bytes.
-GB = 2**30
-
-# Size of a MB in bytes.
-MB = 2**20
-
-
-class ModelCache(ModelCacheBase[AnyModel]):
-    """A cache for managing models in memory.
-
-    The cache is based on two levels of model storage:
-    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
-    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
-
-    The model cache is based on the following assumptions:
-    - storage_device_mem_size > execution_device_mem_size
-    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
-
-    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
-    the execution_device.
-
-    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
-    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
-    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
-
-    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
-    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
-    configuration.
-
-    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
-    the context, and unload outside the context.
-
-    Example usage:
-    ```
-    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
-    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
-        do_something_on_gpu(SD1)
-    ```
-    """
-
-    def __init__(
-        self,
-        max_cache_size: float,
-        max_vram_cache_size: float,
-        execution_device: torch.device = torch.device("cuda"),
-        storage_device: torch.device = torch.device("cpu"),
-        precision: torch.dtype = torch.float16,
-        lazy_offloading: bool = True,
-        log_memory_usage: bool = False,
-        logger: Optional[Logger] = None,
-    ):
-        """
-        Initialize the model RAM cache.
-
-        :param max_cache_size: Maximum size of the storage_device cache in GBs.
-        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
-        :param execution_device: Torch device to load active model into [torch.device('cuda')]
-        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
-        :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
-        :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
-            operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
-            snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
-            behaviour.
-        :param logger: InvokeAILogger to use (otherwise creates one)
-        """
-        # allow lazy offloading only when vram cache enabled
-        self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
-        self._max_cache_size: float = max_cache_size
-        self._max_vram_cache_size: float = max_vram_cache_size
-        self._execution_device: torch.device = execution_device
-        self._storage_device: torch.device = storage_device
-        self._logger = logger or InvokeAILogger.get_logger(self.__class__.__name__)
-        self._log_memory_usage = log_memory_usage
-        self._stats: Optional[CacheStats] = None
-
-        self._cached_models: Dict[str, CacheRecord[AnyModel]] = {}
-        self._cache_stack: List[str] = []
-
-    @property
-    def logger(self) -> Logger:
-        """Return the logger used by the cache."""
-        return self._logger
-
-    @property
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        return self._lazy_offloading
-
-    @property
-    def storage_device(self) -> torch.device:
-        """Return the storage device (e.g. "CPU" for RAM)."""
-        return self._storage_device
-
-    @property
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        return self._execution_device
-
-    @property
-    def max_cache_size(self) -> float:
-        """Return the cap on cache size."""
-        return self._max_cache_size
-
-    @max_cache_size.setter
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on cache size."""
-        self._max_cache_size = value
-
-    @property
-    def max_vram_cache_size(self) -> float:
-        """Return the cap on vram cache size."""
-        return self._max_vram_cache_size
-
-    @max_vram_cache_size.setter
-    def max_vram_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-        self._max_vram_cache_size = value
-
-    @property
-    def stats(self) -> Optional[CacheStats]:
-        """Return collected CacheStats object."""
-        return self._stats
-
-    @stats.setter
-    def stats(self, stats: CacheStats) -> None:
-        """Set the CacheStats object for collectin cache statistics."""
-        self._stats = stats
-
-    def cache_size(self) -> int:
-        """Get the total size of the models currently cached."""
-        total = 0
-        for cache_record in self._cached_models.values():
-            total += cache_record.size
-        return total
-
-    def put(
-        self,
-        key: str,
-        model: AnyModel,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> None:
-        """Store model under key and optional submodel_type."""
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            return
-        size = calc_model_size_by_data(self.logger, model)
-        self.make_room(size)
-
-        running_on_cpu = self.execution_device == torch.device("cpu")
-        state_dict = model.state_dict() if isinstance(model, torch.nn.Module) and not running_on_cpu else None
-        cache_record = CacheRecord(key=key, model=model, device=self.storage_device, state_dict=state_dict, size=size)
-        self._cached_models[key] = cache_record
-        self._cache_stack.append(key)
-
-    def get(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        stats_name: Optional[str] = None,
-    ) -> ModelLockerBase:
-        """
-        Retrieve model using key and optional submodel_type.
-
-        :param key: Opaque model key
-        :param submodel_type: Type of the submodel to fetch
-        :param stats_name: A human-readable id for the model for the purposes of
-        stats reporting.
-
-        This may raise an IndexError if the model is not in the cache.
-        """
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            if self.stats:
-                self.stats.hits += 1
-        else:
-            if self.stats:
-                self.stats.misses += 1
-            raise IndexError(f"The model with key {key} is not in the cache.")
-
-        cache_entry = self._cached_models[key]
-
-        # more stats
-        if self.stats:
-            stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GB)
-            self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
-            self.stats.in_cache = len(self._cached_models)
-            self.stats.loaded_model_sizes[stats_name] = max(
-                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.size
-            )
-
-        # this moves the entry to the top (right end) of the stack
-        with suppress(Exception):
-            self._cache_stack.remove(key)
-        self._cache_stack.append(key)
-        return ModelLocker(
-            cache=self,
-            cache_entry=cache_entry,
-        )
-
-    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
-        if self._log_memory_usage:
-            return MemorySnapshot.capture()
-        return None
-
-    def _make_cache_key(self, model_key: str, submodel_type: Optional[SubModelType] = None) -> str:
-        if submodel_type:
-            return f"{model_key}:{submodel_type.value}"
-        else:
-            return model_key
-
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload models from the execution_device to make room for size_required.
-
-        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
-        """
-        reserved = self._max_vram_cache_size * GB
-        vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
-        for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
-            if vram_in_use <= reserved:
-                break
-            if not cache_entry.loaded:
-                continue
-            if not cache_entry.locked:
-                self.move_model_to_device(cache_entry, self.storage_device)
-                cache_entry.loaded = False
-                vram_in_use = torch.cuda.memory_allocated() + size_required
-                self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
-                )
-
-        TorchDevice.empty_cache()
-
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device.
-
-        :param cache_entry: The CacheRecord for the model
-        :param target_device: The torch.device to move the model into
-
-        May raise a torch.cuda.OutOfMemoryError
-        """
-        self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
-        source_device = cache_entry.device
-
-        # Note: We compare device types only so that 'cuda' == 'cuda:0'.
-        # This would need to be revised to support multi-GPU.
-        if torch.device(source_device).type == torch.device(target_device).type:
-            return
-
-        # Some models don't have a `to` method, in which case they run in RAM/CPU.
-        if not hasattr(cache_entry.model, "to"):
-            return
-
-        # This roundabout method for moving the model around is done to avoid
-        # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
-        # When moving to VRAM, we copy (not move) each element of the state dict from
-        # RAM to a new state dict in VRAM, and then inject it into the model.
-        # This operation is slightly faster than running `to()` on the whole model.
-        #
-        # When the model needs to be removed from VRAM we simply delete the copy
-        # of the state dict in VRAM, and reinject the state dict that is cached
-        # in RAM into the model. So this operation is very fast.
-        start_model_to_time = time.time()
-        snapshot_before = self._capture_memory_snapshot()
-
-        try:
-            if cache_entry.state_dict is not None:
-                assert hasattr(cache_entry.model, "load_state_dict")
-                if target_device == self.storage_device:
-                    cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
-                else:
-                    new_dict: Dict[str, torch.Tensor] = {}
-                    for k, v in cache_entry.state_dict.items():
-                        new_dict[k] = v.to(target_device, copy=True)
-                    cache_entry.model.load_state_dict(new_dict, assign=True)
-            cache_entry.model.to(target_device)
-            cache_entry.device = target_device
-        except Exception as e:  # blow away cache entry
-            self._delete_cache_entry(cache_entry)
-            raise e
-
-        snapshot_after = self._capture_memory_snapshot()
-        end_model_to_time = time.time()
-        self.logger.debug(
-            f"Moved model '{cache_entry.key}' from {source_device} to"
-            f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
-            f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-        )
-
-        if (
-            snapshot_before is not None
-            and snapshot_after is not None
-            and snapshot_before.vram is not None
-            and snapshot_after.vram is not None
-        ):
-            vram_change = abs(snapshot_before.vram - snapshot_after.vram)
-
-            # If the estimated model size does not match the change in VRAM, log a warning.
-            if not math.isclose(
-                vram_change,
-                cache_entry.size,
-                rel_tol=0.1,
-                abs_tol=10 * MB,
-            ):
-                self.logger.debug(
-                    f"Moving model '{cache_entry.key}' from {source_device} to"
-                    f" {target_device} caused an unexpected change in VRAM usage. The model's"
-                    " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GB):.3f} GB.\n"
-                    f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-                )
-
-    def print_cuda_stats(self) -> None:
-        """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
-        ram = "%4.2fG" % (self.cache_size() / GB)
-
-        in_ram_models = 0
-        in_vram_models = 0
-        locked_in_vram_models = 0
-        for cache_record in self._cached_models.values():
-            if hasattr(cache_record.model, "device"):
-                if cache_record.model.device == self.storage_device:
-                    in_ram_models += 1
-                else:
-                    in_vram_models += 1
-                if cache_record.locked:
-                    locked_in_vram_models += 1
-
-                self.logger.debug(
-                    f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
-                    f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
-                )
-
-    def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size.
-
-        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
-        external references to the model, there's nothing that the cache can do about it, and those models will not be
-        garbage-collected.
-        """
-        bytes_needed = size
-        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
-        current_size = self.cache_size()
-
-        if current_size + bytes_needed > maximum_size:
-            self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GB):.2f} GB"
-            )
-
-        self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
-
-        pos = 0
-        models_cleared = 0
-        while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
-            model_key = self._cache_stack[pos]
-            cache_entry = self._cached_models[model_key]
-            device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
-            self.logger.debug(
-                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}"
-            )
-
-            if not cache_entry.locked:
-                self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
-                )
-                current_size -= cache_entry.size
-                models_cleared += 1
-                self._delete_cache_entry(cache_entry)
-                del cache_entry
-
-            else:
-                pos += 1
-
-        if models_cleared > 0:
-            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
-            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
-            # is high even if no garbage gets collected.)
-            #
-            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
-            # - If models had to be cleared, it's a signal that we are close to our memory limit.
-            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
-            #   collected.
-            #
-            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
-            # immediately when their reference count hits 0.
-            if self.stats:
-                self.stats.cleared = models_cleared
-            gc.collect()
-
-        TorchDevice.empty_cache()
-        self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
-
-    def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:
-        self._cache_stack.remove(cache_entry.key)
-        del self._cached_models[cache_entry.key]

invokeai/backend/model_manager/load/model_cache/model_locker.py

@@ -1,64 +0,0 @@
-"""
-Base class and implementation of a class that moves models in and out of VRAM.
-"""
-
-from typing import Dict, Optional
-
-import torch
-
-from invokeai.backend.model_manager import AnyModel
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import (
-    CacheRecord,
-    ModelCacheBase,
-    ModelLockerBase,
-)
-
-
-class ModelLocker(ModelLockerBase):
-    """Internal class that mediates movement in and out of GPU."""
-
-    def __init__(self, cache: ModelCacheBase[AnyModel], cache_entry: CacheRecord[AnyModel]):
-        """
-        Initialize the model locker.
-
-        :param cache: The ModelCache object
-        :param cache_entry: The entry in the model cache
-        """
-        self._cache = cache
-        self._cache_entry = cache_entry
-
-    @property
-    def model(self) -> AnyModel:
-        """Return the model without moving it around."""
-        return self._cache_entry.model
-
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        return self._cache_entry.state_dict
-
-    def lock(self) -> AnyModel:
-        """Move the model into the execution device (GPU) and lock it."""
-        self._cache_entry.lock()
-        try:
-            if self._cache.lazy_offloading:
-                self._cache.offload_unlocked_models(self._cache_entry.size)
-            self._cache.move_model_to_device(self._cache_entry, self._cache.execution_device)
-            self._cache_entry.loaded = True
-            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._cache.execution_device}")
-            self._cache.print_cuda_stats()
-        except torch.cuda.OutOfMemoryError:
-            self._cache.logger.warning("Insufficient GPU memory to load model. Aborting")
-            self._cache_entry.unlock()
-            raise
-        except Exception:
-            self._cache_entry.unlock()
-            raise
-
-        return self.model
-
-    def unlock(self) -> None:
-        """Call upon exit from context."""
-        self._cache_entry.unlock()
-        if not self._cache.lazy_offloading:
-            self._cache.offload_unlocked_models(0)
-            self._cache.print_cuda_stats()