chore: bump version to v5.5.0

Currently translated at 99.3% (1635 of 1645 strings)

translationBot(ui): update translation (Italian)

Currently translated at 99.3% (1634 of 1645 strings)

Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI

SECURITY.md

@@ -0,0 +1,14 @@
+# Security Policy
+
+## Supported Versions
+
+Only the latest version of Invoke will receive security updates. 
+We do not currently maintain multiple versions of the application with updates.
+
+## Reporting a Vulnerability
+
+To report a vulnerability, contact the Invoke team directly at security@invoke.ai
+
+At this time, we do not maintain a formal bug bounty program. 
+
+You can also share identified security issues with our team on huntr.com

docker/Dockerfile

@@ -2,29 +2,42 @@
 
 ## Builder stage
 
-FROM library/ubuntu:23.04 AS builder
+FROM library/ubuntu:24.04 AS builder
 
 ARG DEBIAN_FRONTEND=noninteractive
 RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
 RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt,sharing=locked \
     apt update && apt-get install -y \
-        git \
-        python3-venv \
-        python3-pip \
-        build-essential
+        build-essential \
+        git
 
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv/invokeai
+# Install `uv` for package management
+COPY --from=ghcr.io/astral-sh/uv:0.5.5 /uv /uvx /bin/
 
+ENV VIRTUAL_ENV=/opt/venv
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV PYTHON_VERSION=3.11
+ENV UV_COMPILE_BYTECODE=1
+ENV UV_LINK_MODE=copy
+
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
 ARG BUILDPLATFORM
 
-WORKDIR ${INVOKEAI_SRC}
+# Switch to the `ubuntu` user to work around dependency issues with uv-installed python
+RUN mkdir -p ${VIRTUAL_ENV} && \
+    mkdir -p ${INVOKEAI_SRC} && \
+    chmod -R a+w /opt
+USER ubuntu
 
+# Install python and create the venv
+RUN uv python install ${PYTHON_VERSION} && \
+    uv venv --relocatable --prompt "invoke" --python ${PYTHON_VERSION} ${VIRTUAL_ENV}
+
+WORKDIR ${INVOKEAI_SRC}
 COPY invokeai ./invokeai
 COPY pyproject.toml ./
 
@@ -32,25 +45,18 @@ COPY pyproject.toml ./
 # the local working copy can be bind-mounted into the image
 # at path defined by ${INVOKEAI_SRC}
 # NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is default
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m venv ${VIRTUAL_ENV} &&\
+# x86_64/CUDA is the default
+RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \
-    fi &&\
+    fi && \
+    uv pip install --python ${PYTHON_VERSION} $extra_index_url_arg -e "."
 
-    # xformers + triton fails to install on arm64
-    if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
-        pip install $extra_index_url_arg -e ".[xformers]"; \
-    else \
-        pip install $extra_index_url_arg -e "."; \
-    fi
-
-# #### Build the Web UI ------------------------------------
+#### Build the Web UI ------------------------------------
 
 FROM node:20-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
@@ -66,7 +72,7 @@ RUN npx vite build
 
 #### Runtime stage ---------------------------------------
 
-FROM library/ubuntu:23.04 AS runtime
+FROM library/ubuntu:24.04 AS runtime
 
 ARG DEBIAN_FRONTEND=noninteractive
 ENV PYTHONUNBUFFERED=1
@@ -83,17 +89,16 @@ RUN apt update && apt install -y --no-install-recommends \
         gosu \
         magic-wormhole \
         libglib2.0-0 \
-        libgl1-mesa-glx \
-        python3-venv \
-        python3-pip \
+        libgl1 \
+        libglx-mesa0 \
         build-essential \
         libopencv-dev \
         libstdc++-10-dev &&\
     apt-get clean && apt-get autoclean
 
-
 ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv/invokeai
+ENV VIRTUAL_ENV=/opt/venv
+ENV PYTHON_VERSION=3.11
 ENV INVOKEAI_ROOT=/invokeai
 ENV INVOKEAI_HOST=0.0.0.0
 ENV INVOKEAI_PORT=9090
@@ -101,6 +106,14 @@ ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
 ENV CONTAINER_UID=${CONTAINER_UID:-1000}
 ENV CONTAINER_GID=${CONTAINER_GID:-1000}
 
+# Install `uv` for package management
+# and install python for the ubuntu user (expected to exist on ubuntu >=24.x)
+# this is too tiny to optimize with multi-stage builds, but maybe we'll come back to it
+COPY --from=ghcr.io/astral-sh/uv:0.5.5 /uv /uvx /bin/
+USER ubuntu
+RUN uv python install ${PYTHON_VERSION}
+USER root
+
 # --link requires buldkit w/ dockerfile syntax 1.4
 COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
 COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
@@ -115,7 +128,7 @@ WORKDIR ${INVOKEAI_SRC}
 
 # build patchmatch
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python3 -c "from patchmatch import patch_match"
+RUN python -c "from patchmatch import patch_match"
 
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 

docker/docker-entrypoint.sh

@@ -16,6 +16,9 @@ set -e -o pipefail
 
 USER_ID=${CONTAINER_UID:-1000}
 USER=ubuntu
+# if the user does not exist, create it. It is expected to be present on ubuntu >=24.x
+_=$(id ${USER} 2>&1) || useradd -u ${USER_ID} ${USER}
+# ensure the UID is correct
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
 ### Set the $PUBLIC_KEY env var to enable SSH access.
@@ -36,6 +39,8 @@ fi
 mkdir -p "${INVOKEAI_ROOT}"
 chown --recursive ${USER} "${INVOKEAI_ROOT}" || true
 cd "${INVOKEAI_ROOT}"
+export HF_HOME=${HF_HOME:-$INVOKEAI_ROOT/.cache/huggingface}
+export MPLCONFIGDIR=${MPLCONFIGDIR:-$INVOKEAI_ROOT/.matplotlib}
 
 # Run the CMD as the Container User (not root).
 exec gosu ${USER} "$@"

docs/contributing/ARCHITECTURE.md

@@ -50,7 +50,7 @@ Applications are built on top of the invoke framework. They should construct `in
 
 ### Web UI
 
-The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
+The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/invokeai/frontend` and the backend code is found in `/invokeai/app/api_app.py` and `/invokeai/app/api/`. The code is further organized as such:
 
 | Component | Description |
 | --- | --- |
@@ -62,7 +62,7 @@ The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.t
 
 ### CLI
 
-The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
+The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/invokeai/frontend/cli`.
 
 ## Invoke
 
@@ -70,7 +70,7 @@ The Invoke framework provides the interface to the underlying AI systems and is
 
 ### Invoker
 
-The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
+The invoker (`/invokeai/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
 - **invocation services**, which are used by invocations to interact with core functionality.
 - **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
 
@@ -82,12 +82,12 @@ The session graph does not support looping. This is left as an application probl
 
 ### Invocations
 
-Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
+Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/invokeai/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
 
 ### Services
 
-Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
+Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/invokeai/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
 
 ## AI Core
 
-The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/invokeai/app/`).

docs/contributing/INVOCATIONS.md

@@ -287,8 +287,8 @@ new Invocation ready to be used.
 
 Once you've created a Node, the next step is to share it with the community! The
 best way to do this is to submit a Pull Request to add the Node to the
-[Community Nodes](nodes/communityNodes) list. If you're not sure how to do that,
-take a look a at our [contributing nodes overview](contributingNodes).
+[Community Nodes](../nodes/communityNodes.md) list. If you're not sure how to do that,
+take a look a at our [contributing nodes overview](../nodes/contributingNodes.md).
 
 ## Advanced
 

docs/contributing/MODEL_MANAGER.md

@@ -9,20 +9,20 @@ model. These are the:
   configuration information. Among other things, the record service
   tracks the type of the model, its provenance, and where it can be
   found on disk.
-  
+
 * _ModelInstallServiceBase_ A service for installing models to
   disk. It uses `DownloadQueueServiceBase` to download models and
   their metadata, and `ModelRecordServiceBase` to store that
   information. It is also responsible for managing the InvokeAI
   `models` directory and its contents.
-  
+
 * _DownloadQueueServiceBase_
   A multithreaded downloader responsible
   for downloading models from a remote source to disk. The download
   queue has special methods for downloading repo_id folders from
   Hugging Face, as well as discriminating among model versions in
   Civitai, but can be used for arbitrary content.
-  
+
   * _ModelLoadServiceBase_
   Responsible for loading a model from disk
   into RAM and VRAM and getting it ready for inference.
@@ -207,9 +207,9 @@ for use in the InvokeAI web server. Its signature is:
 
 ```
 def open(
-       cls, 
-    config: InvokeAIAppConfig, 
-    conn: Optional[sqlite3.Connection] = None, 
+       cls,
+    config: InvokeAIAppConfig,
+    conn: Optional[sqlite3.Connection] = None,
     lock: Optional[threading.Lock] = None
     ) -> Union[ModelRecordServiceSQL, ModelRecordServiceFile]:
 ```
@@ -363,7 +363,7 @@ functionality:
 
 * Registering a model config record for a model already located on the
   local filesystem, without moving it or changing its path.
-  
+
 * Installing a model alreadiy located on the local filesystem, by
   moving it into the InvokeAI root directory under the
   `models` folder (or wherever config parameter `models_dir`
@@ -371,21 +371,21 @@ functionality:
 
 * Probing of models to determine their type, base type and other key
   information.
-  
+
 * Interface with the InvokeAI event bus to provide status updates on
   the download, installation and registration process.
-  
+
 * Downloading a model from an arbitrary URL and installing it in
   `models_dir`.
 
 * Special handling for HuggingFace repo_ids to recursively download
   the contents of the repository, paying attention to alternative
   variants such as fp16.
-  
+
 * Saving tags and other metadata about the model into the invokeai database
   when fetching from a repo that provides that type of information,
   (currently only HuggingFace).
-  
+
 ### Initializing the installer
 
 A default installer is created at InvokeAI api startup time and stored
@@ -461,7 +461,7 @@ revision.
 `config` is an optional dict of values that will override the
 autoprobed values for model type, base, scheduler prediction type, and
 so forth. See [Model configuration and
-probing](#Model-configuration-and-probing) for details.
+probing](#model-configuration-and-probing) for details.
 
 `access_token` is an optional access token for accessing resources
 that need authentication.
@@ -494,7 +494,7 @@ source8 = URLModelSource(url='https://civitai.com/api/download/models/63006', ac
 
 for source in [source1, source2, source3, source4, source5, source6, source7]:
    install_job = installer.install_model(source)
-   
+
 source2job = installer.wait_for_installs(timeout=120)
 for source in sources:
     job = source2job[source]
@@ -504,7 +504,7 @@ for source in sources:
   print(f"{source} installed as {model_key}")
  elif job.errored:
      print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
- 
+
 ```
 
 As shown here, the `import_model()` method accepts a variety of

docs/contributing/TESTS.md

@@ -1,6 +1,6 @@
 # InvokeAI Backend Tests
 
-We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
+We use `pytest` to run the backend python tests. (See [pyproject.toml](https://github.com/invoke-ai/InvokeAI/blob/main/pyproject.toml) for the default `pytest` options.)
 
 ## Fast vs. Slow
 All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
@@ -33,7 +33,7 @@ pytest tests -m ""
 
 ## Test Organization
 
-All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
+All backend tests are in the [`tests/`](https://github.com/invoke-ai/InvokeAI/tree/main/tests) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
 
 TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
 

docs/contributing/contribution_guides/development.md

@@ -2,7 +2,7 @@
 
 ## **What do I need to know to help?**
 
-If you are looking to help with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
+If you are looking to help with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential.
 
 
 ## **Get Started**
@@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
 Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web)
+* #### [Frontend Documentation](../frontend/index.md)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
@@ -20,15 +20,15 @@ Once you're setup, for more information, you can review the documentation specif
 
 If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md), [translation](translation.md) or helping support other users and triage issues as they're reported in GitHub.
 
-There are two paths to making a development contribution: 
+There are two paths to making a development contribution:
 
 1. Choosing an open issue to address. Open issues can be found in the [Issues](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen) section of the InvokeAI repository. These are tagged by the issue type (bug, enhancement, etc.) along with the “good first issues” tag denoting if they are suitable for first time contributors.
-    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help. 
+    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help.
 2. Opening a new issue or feature to add. **Please make sure you have searched through existing issues before creating new ones.**
 
 *Regardless of what you choose, please post in the  [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord before you start development in order to confirm that the issue or feature is aligned with the current direction of the project. We value our contributors time and effort and want to ensure that no one’s time is being misspent.*
 
-## Best Practices: 
+## Best Practices:
 * Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
 * Comments! Commenting your code helps reviewers easily understand your contribution
 * Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
@@ -38,7 +38,7 @@ There are two paths to making a development contribution:
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@psychedelicious** is the best person to reach out to. 
+For frontend related work, **@psychedelicious** is the best person to reach out to.
 
 For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
 

docs/contributing/contribution_guides/newContributorChecklist.md

@@ -22,15 +22,15 @@ Before starting these steps, ensure you have your local environment [configured
 2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
 3. Clone the repository to your local machine using:
 
-   ```bash
-   git clone https://github.com/your-GitHub-username/InvokeAI.git
-   ```
+    ```bash
+    git clone https://github.com/your-GitHub-username/InvokeAI.git
+    ```
 
 If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 4. Create a new branch for your fix using:
 
-    ```bash
-    git checkout -b branch-name-here
-    ```
+  ```bash
+  git checkout -b branch-name-here
+  ```
 
 5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
 6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:

docs/contributing/dev-environment.md

@@ -27,9 +27,9 @@ If you just want to use Invoke, you should use the [installer][installer link].
 
 5. Activate the venv (you'll need to do this every time you want to run the app):
 
-        ```sh
-        source .venv/bin/activate
-        ```
+      ```sh
+      source .venv/bin/activate
+      ```
 
 6. Install the repo as an [editable install][editable install link]:
 
@@ -37,7 +37,7 @@ If you just want to use Invoke, you should use the [installer][installer link].
       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.
+      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:
 

docs/contributing/index.md

@@ -34,11 +34,11 @@ Please reach out to @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy)
 
 ## Contributors
 
-This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
+This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](contributors.md). We thank them for their time, hard work and effort.
 
 ## Code of Conduct
 
-The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
+The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](../CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
 By making a contribution to this project, you certify that:
 

invokeai/app/api/routers/session_queue.py

@@ -110,7 +110,7 @@ async def cancel_by_batch_ids(
 @session_queue_router.put(
     "/{queue_id}/cancel_by_destination",
     operation_id="cancel_by_destination",
-    responses={200: {"model": CancelByBatchIDsResult}},
+    responses={200: {"model": CancelByDestinationResult}},
 )
 async def cancel_by_destination(
     queue_id: str = Path(description="The queue id to perform this operation on"),

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/__init__.py

@@ -15,6 +15,11 @@ custom_nodes_readme_path = str(custom_nodes_path / "README.md")
 shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
 shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
 
+# set the same permissions as the destination directory, in case our source is read-only,
+# so that the files are user-writable
+for p in custom_nodes_path.glob("**/*"):
+    p.chmod(custom_nodes_path.stat().st_mode)
+
 # Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
 spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
 if spec is None or spec.loader is None:

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.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.model_patcher import LayerPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ConditioningFieldData,
@@ -66,10 +66,10 @@ class CompelInvocation(BaseInvocation):
         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
@@ -82,7 +82,7 @@ class CompelInvocation(BaseInvocation):
             # 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(
+            LayerPatcher.apply_model_patches(
                 model=text_encoder,
                 patches=_lora_loader(),
                 prefix="lora_te_",
@@ -162,11 +162,11 @@ 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
@@ -179,7 +179,7 @@ class SDXLPromptInvocationBase:
             # 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(
+            LayerPatcher.apply_model_patches(
                 text_encoder,
                 patches=_lora_loader(),
                 prefix=lora_prefix,

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

@@ -37,10 +37,10 @@ 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_patcher import ModelPatcher
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.model_patcher import LayerPatcher
 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 (
@@ -987,10 +987,10 @@ 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
@@ -1003,7 +1003,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             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_model_patches(
                 model=unet,
                 patches=_lora_loader(),
                 prefix="lora_unet_",

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"
@@ -250,6 +252,11 @@ class FluxConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
     conditioning_name: str = Field(description="The name of conditioning tensor")
+    mask: Optional[TensorField] = Field(
+        default=None,
+        description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
+        "included regions should be set to True.",
+    )
 
 
 class SD3ConditioningField(BaseModel):

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
@@ -30,6 +33,7 @@ from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlN
 from invokeai.backend.flux.denoise import denoise
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
@@ -42,10 +46,11 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
-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.flux.text_conditioning import FluxTextConditioning
 from invokeai.backend.model_manager.config import ModelFormat
+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.patches.model_patcher import LayerPatcher
 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
@@ -56,7 +61,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.2.1",
+    version="3.2.2",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -87,10 +92,13 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
         title="Transformer",
     )
-    positive_text_conditioning: FluxConditioningField = InputField(
+    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
     )
-    negative_text_conditioning: FluxConditioningField | None = InputField(
+    negative_text_conditioning: FluxConditioningField | list[FluxConditioningField] | None = InputField(
         default=None,
         description="Negative conditioning tensor. Can be None if cfg_scale is 1.0.",
         input=Input.Connection,
@@ -139,36 +147,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
-    def _load_text_conditioning(
-        self, context: InvocationContext, conditioning_name: str, dtype: torch.dtype
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-        flux_conditioning = flux_conditioning.to(dtype=dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-        return t5_embeddings, clip_embeddings
-
     def _run_diffusion(
         self,
         context: InvocationContext,
     ):
         inference_dtype = torch.bfloat16
 
-        # Load the conditioning data.
-        pos_t5_embeddings, pos_clip_embeddings = self._load_text_conditioning(
-            context, self.positive_text_conditioning.conditioning_name, inference_dtype
-        )
-        neg_t5_embeddings: torch.Tensor | None = None
-        neg_clip_embeddings: torch.Tensor | None = None
-        if self.negative_text_conditioning is not None:
-            neg_t5_embeddings, neg_clip_embeddings = self._load_text_conditioning(
-                context, self.negative_text_conditioning.conditioning_name, inference_dtype
-            )
-
         # Load the input latents, if provided.
         init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
         if init_latents is not None:
@@ -183,15 +167,45 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             dtype=inference_dtype,
             seed=self.seed,
         )
+        b, _c, latent_h, latent_w = noise.shape
+        packed_h = latent_h // 2
+        packed_w = latent_w // 2
+
+        # Load the conditioning data.
+        pos_text_conditionings = self._load_text_conditioning(
+            context=context,
+            cond_field=self.positive_text_conditioning,
+            packed_height=packed_h,
+            packed_width=packed_w,
+            dtype=inference_dtype,
+            device=TorchDevice.choose_torch_device(),
+        )
+        neg_text_conditionings: list[FluxTextConditioning] | None = None
+        if self.negative_text_conditioning is not None:
+            neg_text_conditionings = self._load_text_conditioning(
+                context=context,
+                cond_field=self.negative_text_conditioning,
+                packed_height=packed_h,
+                packed_width=packed_w,
+                dtype=inference_dtype,
+                device=TorchDevice.choose_torch_device(),
+            )
+        pos_regional_prompting_extension = RegionalPromptingExtension.from_text_conditioning(
+            pos_text_conditionings, img_seq_len=packed_h * packed_w
+        )
+        neg_regional_prompting_extension = (
+            RegionalPromptingExtension.from_text_conditioning(neg_text_conditionings, img_seq_len=packed_h * packed_w)
+            if neg_text_conditionings
+            else None
+        )
 
         transformer_info = context.models.load(self.transformer.transformer)
-        is_schnell = "schnell" in transformer_info.config.config_path
+        is_schnell = "schnell" in getattr(transformer_info.config, "config_path", "")
 
         # Calculate the timestep schedule.
-        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
         timesteps = get_schedule(
             num_steps=self.num_steps,
-            image_seq_len=image_seq_len,
+            image_seq_len=packed_h * packed_w,
             shift=not is_schnell,
         )
 
@@ -226,30 +240,26 @@ 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)
 
-        b, _c, latent_h, latent_w = x.shape
         img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
 
-        pos_bs, pos_t5_seq_len, _ = pos_t5_embeddings.shape
-        pos_txt_ids = torch.zeros(
-            pos_bs, pos_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-        )
-        neg_txt_ids: torch.Tensor | None = None
-        if neg_t5_embeddings is not None:
-            neg_bs, neg_t5_seq_len, _ = neg_t5_embeddings.shape
-            neg_txt_ids = torch.zeros(
-                neg_bs, neg_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-            )
-
         # 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)
 
-        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
-        assert image_seq_len == x.shape[1]
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len, packed_h, and
+        # packed_w correctly.
+        assert packed_h * packed_w == x.shape[1]
 
         # Prepare inpaint extension.
         inpaint_extension: InpaintExtension | None = None
@@ -299,7 +309,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             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(
+                    LayerPatcher.apply_model_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
                         prefix=FLUX_LORA_TRANSFORMER_PREFIX,
@@ -314,7 +324,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 # 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(
+                    LayerPatcher.apply_model_sidecar_patches(
                         model=transformer,
                         patches=self._lora_iterator(context),
                         prefix=FLUX_LORA_TRANSFORMER_PREFIX,
@@ -338,12 +348,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 model=transformer,
                 img=x,
                 img_ids=img_ids,
-                txt=pos_t5_embeddings,
-                txt_ids=pos_txt_ids,
-                vec=pos_clip_embeddings,
-                neg_txt=neg_t5_embeddings,
-                neg_txt_ids=neg_txt_ids,
-                neg_vec=neg_clip_embeddings,
+                pos_regional_prompting_extension=pos_regional_prompting_extension,
+                neg_regional_prompting_extension=neg_regional_prompting_extension,
                 timesteps=timesteps,
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
@@ -352,11 +358,49 @@ 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)
         return x
 
+    def _load_text_conditioning(
+        self,
+        context: InvocationContext,
+        cond_field: FluxConditioningField | list[FluxConditioningField],
+        packed_height: int,
+        packed_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> list[FluxTextConditioning]:
+        """Load text conditioning data from a FluxConditioningField or a list of FluxConditioningFields."""
+        # Normalize to a list of FluxConditioningFields.
+        cond_list = [cond_field] if isinstance(cond_field, FluxConditioningField) else cond_field
+
+        text_conditionings: list[FluxTextConditioning] = []
+        for cond_field in cond_list:
+            # Load the text embeddings.
+            cond_data = context.conditioning.load(cond_field.conditioning_name)
+            assert len(cond_data.conditionings) == 1
+            flux_conditioning = cond_data.conditionings[0]
+            assert isinstance(flux_conditioning, FLUXConditioningInfo)
+            flux_conditioning = flux_conditioning.to(dtype=dtype, device=device)
+            t5_embeddings = flux_conditioning.t5_embeds
+            clip_embeddings = flux_conditioning.clip_embeds
+
+            # Load the mask, if provided.
+            mask: Optional[torch.Tensor] = None
+            if cond_field.mask is not None:
+                mask = context.tensors.load(cond_field.mask.tensor_name)
+                mask = mask.to(device=device)
+                mask = RegionalPromptingExtension.preprocess_regional_prompt_mask(
+                    mask, packed_height, packed_width, dtype, device
+                )
+
+            text_conditionings.append(FluxTextConditioning(t5_embeddings, clip_embeddings, mask))
+
+        return text_conditionings
+
     @classmethod
     def prep_cfg_scale(
         cls, cfg_scale: float | list[float], timesteps: list[float], cfg_scale_start_step: int, cfg_scale_end_step: int
@@ -545,6 +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 []
@@ -651,10 +718,15 @@ 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_text_encoder.py

@@ -1,19 +1,26 @@
 from contextlib import ExitStack
-from typing import Iterator, Literal, Tuple
+from typing import Iterator, Literal, Optional, Tuple
 
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    TensorField,
+    UIComponent,
+)
 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.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.model_patcher import LayerPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
 
 
@@ -22,7 +29,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
     title="FLUX Text Encoding",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.0",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
@@ -41,7 +48,10 @@ class FluxTextEncoderInvocation(BaseInvocation):
     t5_max_seq_len: Literal[256, 512] = InputField(
         description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
     )
-    prompt: str = InputField(description="Text prompt to encode.")
+    prompt: str = InputField(description="Text prompt to encode.", ui_component=UIComponent.Textarea)
+    mask: Optional[TensorField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
@@ -54,7 +64,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
         )
 
         conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput.build(conditioning_name)
+        return FluxConditioningOutput(
+            conditioning=FluxConditioningField(conditioning_name=conditioning_name, mask=self.mask)
+        )
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
         t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
@@ -99,7 +111,7 @@ class FluxTextEncoderInvocation(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_model_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context),
                         prefix=FLUX_LORA_CLIP_PREFIX,
@@ -118,9 +130,9 @@ 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

invokeai/app/invocations/image_panels.py

@@ -0,0 +1,59 @@
+from pydantic import ValidationInfo, field_validator
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import InputField, OutputField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("image_panel_coordinate_output")
+class ImagePanelCoordinateOutput(BaseInvocationOutput):
+    x_left: int = OutputField(description="The left x-coordinate of the panel.")
+    y_top: int = OutputField(description="The top y-coordinate of the panel.")
+    width: int = OutputField(description="The width of the panel.")
+    height: int = OutputField(description="The height of the panel.")
+
+
+@invocation(
+    "image_panel_layout",
+    title="Image Panel Layout",
+    tags=["image", "panel", "layout"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class ImagePanelLayoutInvocation(BaseInvocation):
+    """Get the coordinates of a single panel in a grid. (If the full image shape cannot be divided evenly into panels,
+    then the grid may not cover the entire image.)
+    """
+
+    width: int = InputField(description="The width of the entire grid.")
+    height: int = InputField(description="The height of the entire grid.")
+    num_cols: int = InputField(ge=1, default=1, description="The number of columns in the grid.")
+    num_rows: int = InputField(ge=1, default=1, description="The number of rows in the grid.")
+    panel_col_idx: int = InputField(ge=0, default=0, description="The column index of the panel to be processed.")
+    panel_row_idx: int = InputField(ge=0, default=0, description="The row index of the panel to be processed.")
+
+    @field_validator("panel_col_idx")
+    def validate_panel_col_idx(cls, v: int, info: ValidationInfo) -> int:
+        if v < 0 or v >= info.data["num_cols"]:
+            raise ValueError(f"panel_col_idx must be between 0 and {info.data['num_cols'] - 1}")
+        return v
+
+    @field_validator("panel_row_idx")
+    def validate_panel_row_idx(cls, v: int, info: ValidationInfo) -> int:
+        if v < 0 or v >= info.data["num_rows"]:
+            raise ValueError(f"panel_row_idx must be between 0 and {info.data['num_rows'] - 1}")
+        return v
+
+    def invoke(self, context: InvocationContext) -> ImagePanelCoordinateOutput:
+        x_left = self.panel_col_idx * (self.width // self.num_cols)
+        y_top = self.panel_row_idx * (self.height // self.num_rows)
+        width = self.width // self.num_cols
+        height = self.height // self.num_rows
+        return ImagePanelCoordinateOutput(x_left=x_left, y_top=y_top, width=width, height=height)

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,
@@ -49,7 +49,7 @@ 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(

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,
@@ -51,7 +51,7 @@ 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)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:

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 (
@@ -65,11 +65,6 @@ 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")
@@ -80,6 +75,15 @@ 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."""

invokeai/app/invocations/sd3_text_encoder.py

@@ -16,10 +16,10 @@ 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.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.model_patcher import LayerPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
 
 # The SD3 T5 Max Sequence Length set based on the default in diffusers.
@@ -150,7 +150,7 @@ 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_model_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context, clip_model),
                         prefix=FLUX_LORA_CLIP_PREFIX,
@@ -193,9 +193,9 @@ 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/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.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.model_patcher import LayerPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
@@ -194,10 +194,10 @@ 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
 
@@ -207,7 +207,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         with (
             ExitStack() as exit_stack,
             unet_info as unet,
-            LoRAPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
+            LayerPatcher.apply_model_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
         ):
             assert isinstance(unet, UNet2DConditionModel)
             latents = latents.to(device=unet.device, dtype=unet.dtype)

invokeai/app/services/config/config_default.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import copy
+import filecmp
 import locale
 import os
 import re
@@ -96,6 +97,7 @@ 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`.
@@ -162,6 +164,7 @@ 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.")
@@ -525,9 +528,35 @@ def get_config() -> InvokeAIAppConfig:
     ]
     example_config.write_file(config.config_file_path.with_suffix(".example.yaml"), as_example=True)
 
-    # Copy all legacy configs - We know `__path__[0]` is correct here
+    # Copy all legacy configs only if needed
+    # We know `__path__[0]` is correct here
     configs_src = Path(model_configs.__path__[0])  # pyright: ignore [reportUnknownMemberType, reportUnknownArgumentType, reportAttributeAccessIssue]
-    shutil.copytree(configs_src, config.legacy_conf_path, dirs_exist_ok=True)
+    dest_path = config.legacy_conf_path
+
+    # Create destination (we don't need to check for existence)
+    dest_path.mkdir(parents=True, exist_ok=True)
+
+    # Compare directories recursively
+    comparison = filecmp.dircmp(configs_src, dest_path)
+    need_copy = any(
+        [
+            comparison.left_only,  # Files exist only in source
+            comparison.diff_files,  # Files that differ
+            comparison.common_funny,  # Files that couldn't be compared
+        ]
+    )
+
+    if need_copy:
+        # Get permissions from destination directory
+        dest_mode = dest_path.stat().st_mode
+
+        # Copy directory tree
+        shutil.copytree(configs_src, dest_path, dirs_exist_ok=True)
+
+        # Set permissions on copied files to match destination directory
+        dest_path.chmod(dest_mode)
+        for p in dest_path.glob("**/*"):
+            p.chmod(dest_mode)
 
     if config.config_file_path.exists():
         config_from_file = load_and_migrate_config(config.config_file_path)

invokeai/app/services/model_install/model_install_default.py

@@ -438,9 +438,10 @@ 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_default.py

@@ -86,7 +86,7 @@ class ModelLoadService(ModelLoadServiceBase):
 
         def torch_load_file(checkpoint: Path) -> AnyModel:
             scan_result = scan_file_path(checkpoint)
-            if scan_result.infected_files != 0:
+            if scan_result.infected_files != 0 or scan_result.scan_err:
                 raise Exception("The model at {checkpoint} is potentially infected by malware. Aborting load.")
             result = torch_load(checkpoint, map_location="cpu")
             return result

invokeai/app/services/session_processor/session_processor_default.py

@@ -378,6 +378,9 @@ class DefaultSessionProcessor(SessionProcessorBase):
         self._poll_now()
 
     async def _on_queue_item_status_changed(self, event: FastAPIEvent[QueueItemStatusChangedEvent]) -> None:
+        # Make sure the cancel event is for the currently processing queue item
+        if self._queue_item and self._queue_item.item_id != event[1].item_id:
+            return
         if self._queue_item and event[1].status in ["completed", "failed", "canceled"]:
             # When the queue item is canceled via HTTP, the queue item status is set to `"canceled"` and this event is
             # emitted. We need to respond to this event and stop graph execution. This is done by setting the cancel
@@ -436,7 +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/shared/sqlite_migrator/migrations/migration_11.py

@@ -35,7 +35,7 @@ class Migration11Callback:
 
     def _remove_convert_cache(self) -> None:
         """Rename models/.cache to models/.convert_cache."""
-        self._logger.info("Removing .cache directory. Converted models will now be cached in .convert_cache.")
+        self._logger.info("Removing models/.cache directory. Converted models will now be cached in .convert_cache.")
         legacy_convert_path = self._app_config.root_path / "models" / ".cache"
         shutil.rmtree(legacy_convert_path, ignore_errors=True)
 

invokeai/backend/flux/custom_block_processor.py

@@ -1,9 +1,10 @@
 import einops
 import torch
 
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.math import attention
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
+from invokeai.backend.flux.modules.layers import DoubleStreamBlock, SingleStreamBlock
 
 
 class CustomDoubleStreamBlockProcessor:
@@ -13,7 +14,12 @@ class CustomDoubleStreamBlockProcessor:
 
     @staticmethod
     def _double_stream_block_forward(
-        block: DoubleStreamBlock, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor
+        block: DoubleStreamBlock,
+        img: torch.Tensor,
+        txt: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """This function is a direct copy of DoubleStreamBlock.forward(), but it returns some of the intermediate
         values.
@@ -40,7 +46,7 @@ class CustomDoubleStreamBlockProcessor:
         k = torch.cat((txt_k, img_k), dim=2)
         v = torch.cat((txt_v, img_v), dim=2)
 
-        attn = attention(q, k, v, pe=pe)
+        attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
@@ -63,11 +69,15 @@ class CustomDoubleStreamBlockProcessor:
         vec: torch.Tensor,
         pe: torch.Tensor,
         ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        regional_prompting_extension: RegionalPromptingExtension,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """A custom implementation of DoubleStreamBlock.forward() with additional features:
         - IP-Adapter support
         """
-        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(block, img, txt, vec, pe)
+        attn_mask = regional_prompting_extension.get_double_stream_attn_mask(block_index)
+        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(
+            block, img, txt, vec, pe, attn_mask=attn_mask
+        )
 
         # Apply IP-Adapter conditioning.
         for ip_adapter_extension in ip_adapter_extensions:
@@ -81,3 +91,48 @@ class CustomDoubleStreamBlockProcessor:
             )
 
         return img, txt
+
+
+class CustomSingleStreamBlockProcessor:
+    """A class containing a custom implementation of SingleStreamBlock.forward() with additional features (masking,
+    etc.)
+    """
+
+    @staticmethod
+    def _single_stream_block_forward(
+        block: SingleStreamBlock,
+        x: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """This function is a direct copy of SingleStreamBlock.forward()."""
+        mod, _ = block.modulation(vec)
+        x_mod = (1 + mod.scale) * block.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(block.linear1(x_mod), [3 * block.hidden_size, block.mlp_hidden_dim], dim=-1)
+
+        q, k, v = einops.rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
+        q, k = block.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = block.linear2(torch.cat((attn, block.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+    @staticmethod
+    def custom_single_block_forward(
+        timestep_index: int,
+        total_num_timesteps: int,
+        block_index: int,
+        block: SingleStreamBlock,
+        img: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        regional_prompting_extension: RegionalPromptingExtension,
+    ) -> torch.Tensor:
+        """A custom implementation of SingleStreamBlock.forward() with additional features:
+        - Masking
+        """
+        attn_mask = regional_prompting_extension.get_single_stream_attn_mask(block_index)
+        return CustomSingleStreamBlockProcessor._single_stream_block_forward(block, img, vec, pe, attn_mask=attn_mask)

invokeai/backend/flux/denoise.py

@@ -7,6 +7,7 @@ from tqdm import tqdm
 from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.model import Flux
@@ -18,14 +19,8 @@ def denoise(
     # model input
     img: torch.Tensor,
     img_ids: torch.Tensor,
-    # positive text conditioning
-    txt: torch.Tensor,
-    txt_ids: torch.Tensor,
-    vec: torch.Tensor,
-    # negative text conditioning
-    neg_txt: torch.Tensor | None,
-    neg_txt_ids: torch.Tensor | None,
-    neg_vec: torch.Tensor | None,
+    pos_regional_prompting_extension: RegionalPromptingExtension,
+    neg_regional_prompting_extension: RegionalPromptingExtension | None,
     # sampling parameters
     timesteps: list[float],
     step_callback: Callable[[PipelineIntermediateState], None],
@@ -35,6 +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
@@ -61,9 +58,9 @@ def denoise(
                     total_num_timesteps=total_steps,
                     img=img,
                     img_ids=img_ids,
-                    txt=txt,
-                    txt_ids=txt_ids,
-                    y=vec,
+                    txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                    txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                    y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
                     timesteps=t_vec,
                     guidance=guidance_vec,
                 )
@@ -74,13 +71,13 @@ 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=txt,
-            txt_ids=txt_ids,
-            y=vec,
+            txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+            txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+            y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
             timesteps=t_vec,
             guidance=guidance_vec,
             timestep_index=step_index,
@@ -88,6 +85,7 @@ def denoise(
             controlnet_double_block_residuals=merged_controlnet_residuals.double_block_residuals,
             controlnet_single_block_residuals=merged_controlnet_residuals.single_block_residuals,
             ip_adapter_extensions=pos_ip_adapter_extensions,
+            regional_prompting_extension=pos_regional_prompting_extension,
         )
 
         step_cfg_scale = cfg_scale[step_index]
@@ -97,15 +95,15 @@ def denoise(
             # TODO(ryand): Add option to run positive and negative predictions in a single batch for better performance
             # on systems with sufficient VRAM.
 
-            if neg_txt is None or neg_txt_ids is None or neg_vec is None:
+            if neg_regional_prompting_extension is None:
                 raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
 
             neg_pred = model(
                 img=img,
                 img_ids=img_ids,
-                txt=neg_txt,
-                txt_ids=neg_txt_ids,
-                y=neg_vec,
+                txt=neg_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                txt_ids=neg_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                y=neg_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
                 timesteps=t_vec,
                 guidance=guidance_vec,
                 timestep_index=step_index,
@@ -113,6 +111,7 @@ def denoise(
                 controlnet_double_block_residuals=None,
                 controlnet_single_block_residuals=None,
                 ip_adapter_extensions=neg_ip_adapter_extensions,
+                regional_prompting_extension=neg_regional_prompting_extension,
             )
             pred = neg_pred + step_cfg_scale * (pred - neg_pred)
 

invokeai/backend/flux/extensions/regional_prompting_extension.py

@@ -0,0 +1,276 @@
+from typing import Optional
+
+import torch
+import torchvision
+
+from invokeai.backend.flux.text_conditioning import FluxRegionalTextConditioning, FluxTextConditioning
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.mask import to_standard_float_mask
+
+
+class RegionalPromptingExtension:
+    """A class for managing regional prompting with FLUX.
+
+    This implementation is inspired by https://arxiv.org/pdf/2411.02395 (though there are significant differences).
+    """
+
+    def __init__(
+        self,
+        regional_text_conditioning: FluxRegionalTextConditioning,
+        restricted_attn_mask: torch.Tensor | None = None,
+    ):
+        self.regional_text_conditioning = regional_text_conditioning
+        self.restricted_attn_mask = restricted_attn_mask
+
+    def get_double_stream_attn_mask(self, block_index: int) -> torch.Tensor | None:
+        order = [self.restricted_attn_mask, None]
+        return order[block_index % len(order)]
+
+    def get_single_stream_attn_mask(self, block_index: int) -> torch.Tensor | None:
+        order = [self.restricted_attn_mask, None]
+        return order[block_index % len(order)]
+
+    @classmethod
+    def from_text_conditioning(cls, text_conditioning: list[FluxTextConditioning], img_seq_len: int):
+        """Create a RegionalPromptingExtension from a list of text conditionings.
+
+        Args:
+            text_conditioning (list[FluxTextConditioning]): The text conditionings to use for regional prompting.
+            img_seq_len (int): The image sequence length (i.e. packed_height * packed_width).
+        """
+        regional_text_conditioning = cls._concat_regional_text_conditioning(text_conditioning)
+        attn_mask_with_restricted_img_self_attn = cls._prepare_restricted_attn_mask(
+            regional_text_conditioning, img_seq_len
+        )
+        return cls(
+            regional_text_conditioning=regional_text_conditioning,
+            restricted_attn_mask=attn_mask_with_restricted_img_self_attn,
+        )
+
+    # Keeping _prepare_unrestricted_attn_mask for reference as an alternative masking strategy:
+    #
+    # @classmethod
+    # def _prepare_unrestricted_attn_mask(
+    #     cls,
+    #     regional_text_conditioning: FluxRegionalTextConditioning,
+    #     img_seq_len: int,
+    # ) -> torch.Tensor:
+    #     """Prepare an 'unrestricted' attention mask. In this context, 'unrestricted' means that:
+    #     - img self-attention is not masked.
+    #     - img regions attend to both txt within their own region and to global prompts.
+    #     """
+    #     device = TorchDevice.choose_torch_device()
+
+    #     # Infer txt_seq_len from the t5_embeddings tensor.
+    #     txt_seq_len = regional_text_conditioning.t5_embeddings.shape[1]
+
+    #     # In the attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+    #     # Concatenation happens in the following order: [txt_seq, img_seq].
+    #     # There are 4 portions of the attention mask to consider as we prepare it:
+    #     # 1. txt attends to itself
+    #     # 2. txt attends to corresponding regional img
+    #     # 3. regional img attends to corresponding txt
+    #     # 4. regional img attends to itself
+
+    #     # Initialize empty attention mask.
+    #     regional_attention_mask = torch.zeros(
+    #         (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.float16
+    #     )
+
+    #     for image_mask, t5_embedding_range in zip(
+    #         regional_text_conditioning.image_masks, regional_text_conditioning.t5_embedding_ranges, strict=True
+    #     ):
+    #         # 1. txt attends to itself
+    #         regional_attention_mask[
+    #             t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+    #         ] = 1.0
+
+    #         # 2. txt attends to corresponding regional img
+    #         # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+    #         fill_value = image_mask.view(1, img_seq_len) if image_mask is not None else 1.0
+    #         regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = fill_value
+
+    #         # 3. regional img attends to corresponding txt
+    #         # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+    #         fill_value = image_mask.view(img_seq_len, 1) if image_mask is not None else 1.0
+    #         regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = fill_value
+
+    #     # 4. regional img attends to itself
+    #     # Allow unrestricted img self attention.
+    #     regional_attention_mask[txt_seq_len:, txt_seq_len:] = 1.0
+
+    #     # Convert attention mask to boolean.
+    #     regional_attention_mask = regional_attention_mask > 0.5
+
+    #     return regional_attention_mask
+
+    @classmethod
+    def _prepare_restricted_attn_mask(
+        cls,
+        regional_text_conditioning: FluxRegionalTextConditioning,
+        img_seq_len: int,
+    ) -> torch.Tensor | None:
+        """Prepare a 'restricted' attention mask. In this context, 'restricted' means that:
+        - img self-attention is only allowed within regions.
+        - img regions only attend to txt within their own region, not to global prompts.
+        """
+        # Identify background region. I.e. the region that is not covered by any region masks.
+        background_region_mask: None | torch.Tensor = None
+        for image_mask in regional_text_conditioning.image_masks:
+            if image_mask is not None:
+                if background_region_mask is None:
+                    background_region_mask = torch.ones_like(image_mask)
+                background_region_mask *= 1 - image_mask
+
+        if background_region_mask is None:
+            # There are no region masks, short-circuit and return None.
+            # TODO(ryand): We could restrict txt-txt attention across multiple global prompts, but this would
+            # is a rare use case and would make the logic here significantly more complicated.
+            return None
+
+        device = TorchDevice.choose_torch_device()
+
+        # Infer txt_seq_len from the t5_embeddings tensor.
+        txt_seq_len = regional_text_conditioning.t5_embeddings.shape[1]
+
+        # In the attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+        # Concatenation happens in the following order: [txt_seq, img_seq].
+        # There are 4 portions of the attention mask to consider as we prepare it:
+        # 1. txt attends to itself
+        # 2. txt attends to corresponding regional img
+        # 3. regional img attends to corresponding txt
+        # 4. regional img attends to itself
+
+        # Initialize empty attention mask.
+        regional_attention_mask = torch.zeros(
+            (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.float16
+        )
+
+        for image_mask, t5_embedding_range in zip(
+            regional_text_conditioning.image_masks, regional_text_conditioning.t5_embedding_ranges, strict=True
+        ):
+            # 1. txt attends to itself
+            regional_attention_mask[
+                t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+            ] = 1.0
+
+            if image_mask is not None:
+                # 2. txt attends to corresponding regional img
+                # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+                regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = (
+                    image_mask.view(1, img_seq_len)
+                )
+
+                # 3. regional img attends to corresponding txt
+                # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+                regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = (
+                    image_mask.view(img_seq_len, 1)
+                )
+
+                # 4. regional img attends to itself
+                image_mask = image_mask.view(img_seq_len, 1)
+                regional_attention_mask[txt_seq_len:, txt_seq_len:] += image_mask @ image_mask.T
+            else:
+                # We don't allow attention between non-background image regions and global prompts. This helps to ensure
+                # that regions focus on their local prompts. We do, however, allow attention between background regions
+                # and global prompts. If we didn't do this, then the background regions would not attend to any txt
+                # embeddings, which we found experimentally to cause artifacts.
+
+                # 2. global txt attends to background region
+                # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+                regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = (
+                    background_region_mask.view(1, img_seq_len)
+                )
+
+                # 3. background region attends to global txt
+                # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+                regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = (
+                    background_region_mask.view(img_seq_len, 1)
+                )
+
+        # Allow background regions to attend to themselves.
+        regional_attention_mask[txt_seq_len:, txt_seq_len:] += background_region_mask.view(img_seq_len, 1)
+        regional_attention_mask[txt_seq_len:, txt_seq_len:] += background_region_mask.view(1, img_seq_len)
+
+        # Convert attention mask to boolean.
+        regional_attention_mask = regional_attention_mask > 0.5
+
+        return regional_attention_mask
+
+    @classmethod
+    def _concat_regional_text_conditioning(
+        cls,
+        text_conditionings: list[FluxTextConditioning],
+    ) -> FluxRegionalTextConditioning:
+        """Concatenate regional text conditioning data into a single conditioning tensor (with associated masks)."""
+        concat_t5_embeddings: list[torch.Tensor] = []
+        concat_t5_embedding_ranges: list[Range] = []
+        image_masks: list[torch.Tensor | None] = []
+
+        # Choose global CLIP embedding.
+        # Use the first global prompt's CLIP embedding as the global CLIP embedding. If there is no global prompt, use
+        # the first prompt's CLIP embedding.
+        global_clip_embedding: torch.Tensor = text_conditionings[0].clip_embeddings
+        for text_conditioning in text_conditionings:
+            if text_conditioning.mask is None:
+                global_clip_embedding = text_conditioning.clip_embeddings
+                break
+
+        cur_t5_embedding_len = 0
+        for text_conditioning in text_conditionings:
+            concat_t5_embeddings.append(text_conditioning.t5_embeddings)
+
+            concat_t5_embedding_ranges.append(
+                Range(start=cur_t5_embedding_len, end=cur_t5_embedding_len + text_conditioning.t5_embeddings.shape[1])
+            )
+
+            image_masks.append(text_conditioning.mask)
+
+            cur_t5_embedding_len += text_conditioning.t5_embeddings.shape[1]
+
+        t5_embeddings = torch.cat(concat_t5_embeddings, dim=1)
+
+        # Initialize the txt_ids tensor.
+        pos_bs, pos_t5_seq_len, _ = t5_embeddings.shape
+        t5_txt_ids = torch.zeros(
+            pos_bs, pos_t5_seq_len, 3, dtype=t5_embeddings.dtype, device=TorchDevice.choose_torch_device()
+        )
+
+        return FluxRegionalTextConditioning(
+            t5_embeddings=t5_embeddings,
+            clip_embeddings=global_clip_embedding,
+            t5_txt_ids=t5_txt_ids,
+            image_masks=image_masks,
+            t5_embedding_ranges=concat_t5_embedding_ranges,
+        )
+
+    @staticmethod
+    def preprocess_regional_prompt_mask(
+        mask: Optional[torch.Tensor], packed_height: int, packed_width: int, dtype: torch.dtype, device: torch.device
+    ) -> torch.Tensor:
+        """Preprocess a regional prompt mask to match the target height and width.
+        If mask is None, returns a mask of all ones with the target height and width.
+        If mask is not None, resizes the mask to the target height and width using 'nearest' interpolation.
+
+        packed_height and packed_width are the target height and width of the mask in the 'packed' latent space.
+
+        Returns:
+            torch.Tensor: The processed mask. shape: (1, 1, packed_height * packed_width).
+        """
+
+        if mask is None:
+            return torch.ones((1, 1, packed_height * packed_width), dtype=dtype, device=device)
+
+        mask = to_standard_float_mask(mask, out_dtype=dtype)
+
+        tf = torchvision.transforms.Resize(
+            (packed_height, packed_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
+        )
+
+        # Add a batch dimension to the mask, because torchvision expects shape (batch, channels, h, w).
+        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
+        resized_mask = tf(mask)
+
+        # Flatten the height and width dimensions into a single image_seq_len dimension.
+        return resized_mask.flatten(start_dim=2)

invokeai/backend/flux/math.py

@@ -5,10 +5,10 @@ from einops import rearrange
 from torch import Tensor
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Tensor | None = None) -> Tensor:
     q, k = apply_rope(q, k, pe)
 
-    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
     x = rearrange(x, "B H L D -> B L (H D)")
 
     return x
@@ -24,12 +24,12 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     out = torch.einsum("...n,d->...nd", pos, omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.float()
+    return out.to(dtype=pos.dtype, device=pos.device)
 
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_ = xq.view(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.view(*xk.shape[:-1], -1, 1, 2)
     xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
     xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+    return xq_out.view(*xq.shape).type_as(xq), xk_out.view(*xk.shape).type_as(xk)

invokeai/backend/flux/model.py

@@ -1,11 +1,16 @@
 # 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
 
-from invokeai.backend.flux.custom_block_processor import CustomDoubleStreamBlockProcessor
+from invokeai.backend.flux.custom_block_processor import (
+    CustomDoubleStreamBlockProcessor,
+    CustomSingleStreamBlockProcessor,
+)
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.modules.layers import (
     DoubleStreamBlock,
@@ -31,6 +36,7 @@ class FluxParams:
     theta: int
     qkv_bias: bool
     guidance_embed: bool
+    out_channels: Optional[int] = None
 
 
 class Flux(nn.Module):
@@ -43,7 +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
@@ -95,6 +101,7 @@ class Flux(nn.Module):
         controlnet_double_block_residuals: list[Tensor] | None,
         controlnet_single_block_residuals: list[Tensor] | None,
         ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        regional_prompting_extension: RegionalPromptingExtension,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -117,7 +124,6 @@ class Flux(nn.Module):
             assert len(controlnet_double_block_residuals) == len(self.double_blocks)
         for block_index, block in enumerate(self.double_blocks):
             assert isinstance(block, DoubleStreamBlock)
-
             img, txt = CustomDoubleStreamBlockProcessor.custom_double_block_forward(
                 timestep_index=timestep_index,
                 total_num_timesteps=total_num_timesteps,
@@ -128,6 +134,7 @@ class Flux(nn.Module):
                 vec=vec,
                 pe=pe,
                 ip_adapter_extensions=ip_adapter_extensions,
+                regional_prompting_extension=regional_prompting_extension,
             )
 
             if controlnet_double_block_residuals is not None:
@@ -140,7 +147,17 @@ class Flux(nn.Module):
             assert len(controlnet_single_block_residuals) == len(self.single_blocks)
 
         for block_index, block in enumerate(self.single_blocks):
-            img = block(img, vec=vec, pe=pe)
+            assert isinstance(block, SingleStreamBlock)
+            img = CustomSingleStreamBlockProcessor.custom_single_block_forward(
+                timestep_index=timestep_index,
+                total_num_timesteps=total_num_timesteps,
+                block_index=block_index,
+                block=block,
+                img=img,
+                vec=vec,
+                pe=pe,
+                regional_prompting_extension=regional_prompting_extension,
+            )
 
             if controlnet_single_block_residuals is not None:
                 img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]

invokeai/backend/flux/modules/layers.py

@@ -66,10 +66,7 @@ class RMSNorm(torch.nn.Module):
         self.scale = nn.Parameter(torch.ones(dim))
 
     def forward(self, x: Tensor):
-        x_dtype = x.dtype
-        x = x.float()
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
-        return (x * rrms).to(dtype=x_dtype) * self.scale
+        return torch.nn.functional.rms_norm(x, self.scale.shape, self.scale, eps=1e-6)
 
 
 class QKNorm(torch.nn.Module):

invokeai/backend/flux/text_conditioning.py

@@ -0,0 +1,36 @@
+from dataclasses import dataclass
+
+import torch
+
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
+
+
+@dataclass
+class FluxTextConditioning:
+    t5_embeddings: torch.Tensor
+    clip_embeddings: torch.Tensor
+    # If mask is None, the prompt is a global prompt.
+    mask: torch.Tensor | None
+
+
+@dataclass
+class FluxRegionalTextConditioning:
+    # Concatenated text embeddings.
+    # Shape: (1, concatenated_txt_seq_len, 4096)
+    t5_embeddings: torch.Tensor
+    # Shape: (1, concatenated_txt_seq_len, 3)
+    t5_txt_ids: torch.Tensor
+
+    # Global CLIP embeddings.
+    # Shape: (1, 768)
+    clip_embeddings: torch.Tensor
+
+    # A binary mask indicating the regions of the image that the prompt should be applied to. If None, the prompt is a
+    # global prompt.
+    # image_masks[i] is the mask for the ith prompt.
+    # image_masks[i] has shape (1, image_seq_len) and dtype torch.bool.
+    image_masks: list[torch.Tensor | None]
+
+    # List of ranges that represent the embedding ranges for each mask.
+    # t5_embedding_ranges[i] contains the range of the t5 embeddings that correspond to image_masks[i].
+    t5_embedding_ranges: list[Range]

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/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/model_loaders/lora.py

@@ -9,14 +9,6 @@ import torch
 from safetensors.torch import load_file
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
-    lora_model_from_flux_diffusers_state_dict,
-)
-from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import (
-    lora_model_from_flux_kohya_state_dict,
-)
-from invokeai.backend.lora.conversions.sd_lora_conversion_utils import lora_model_from_sd_state_dict
-from invokeai.backend.lora.conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
@@ -28,10 +20,25 @@ from invokeai.backend.model_manager import (
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
+    is_state_dict_likely_flux_control,
+    lora_model_from_flux_control_state_dict,
+)
+from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import (
+    lora_model_from_flux_diffusers_state_dict,
+)
+from invokeai.backend.patches.lora_conversions.flux_kohya_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_kohya_format,
+    lora_model_from_flux_kohya_state_dict,
+)
+from invokeai.backend.patches.lora_conversions.sd_lora_conversion_utils import lora_model_from_sd_state_dict
+from invokeai.backend.patches.lora_conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlLoRa, format=ModelFormat.LyCORIS)
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlLoRa, format=ModelFormat.Diffusers)
 class LoRALoader(ModelLoader):
     """Class to load LoRA models."""
 
@@ -75,7 +82,10 @@ class LoRALoader(ModelLoader):
                 # https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_flux.py#L1194
                 model = lora_model_from_flux_diffusers_state_dict(state_dict=state_dict, alpha=None)
             elif config.format == ModelFormat.LyCORIS:
-                model = lora_model_from_flux_kohya_state_dict(state_dict=state_dict)
+                if is_state_dict_likely_in_flux_kohya_format(state_dict=state_dict):
+                    model = lora_model_from_flux_kohya_state_dict(state_dict=state_dict)
+                elif is_state_dict_likely_flux_control(state_dict=state_dict):
+                    model = lora_model_from_flux_control_state_dict(state_dict=state_dict)
             else:
                 raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
         elif self._model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:

invokeai/backend/model_manager/load/model_util.py

@@ -15,9 +15,9 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 from invokeai.backend.util.calc_tensor_size import calc_tensor_size
@@ -43,7 +43,7 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         (
             TextualInversionModelRaw,
             IPAdapter,
-            LoRAModelRaw,
+            ModelPatchRaw,
             SpandrelImageToImageModel,
             GroundingDinoPipeline,
             SegmentAnythingPipeline,

invokeai/backend/model_manager/probe.py

@@ -15,10 +15,6 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_xlabs_controlnet,
 )
 from invokeai.backend.flux.ip_adapter.state_dict_utils import is_state_dict_xlabs_ip_adapter
-from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils import (
-    is_state_dict_likely_in_flux_diffusers_format,
-)
-from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import is_state_dict_likely_in_flux_kohya_format
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
@@ -43,6 +39,13 @@ from invokeai.backend.model_manager.util.model_util import (
     lora_token_vector_length,
     read_checkpoint_meta,
 )
+from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import is_state_dict_likely_flux_control
+from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_diffusers_format,
+)
+from invokeai.backend.patches.lora_conversions.flux_kohya_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_kohya_format,
+)
 from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
@@ -199,8 +202,8 @@ class ModelProbe(object):
         fields["default_settings"] = fields.get("default_settings")
 
         if not fields["default_settings"]:
-            if fields["type"] in {ModelType.ControlNet, ModelType.T2IAdapter}:
-                fields["default_settings"] = get_default_settings_controlnet_t2i_adapter(fields["name"])
+            if fields["type"] in {ModelType.ControlNet, ModelType.T2IAdapter, ModelType.ControlLoRa}:
+                fields["default_settings"] = get_default_settings_control_adapters(fields["name"])
             elif fields["type"] is ModelType.Main:
                 fields["default_settings"] = get_default_settings_main(fields["base"])
 
@@ -258,6 +261,9 @@ class ModelProbe(object):
         ckpt = checkpoint if checkpoint else read_checkpoint_meta(model_path, scan=True)
         ckpt = ckpt.get("state_dict", ckpt)
 
+        if isinstance(ckpt, dict) and is_state_dict_likely_flux_control(ckpt):
+            return ModelType.ControlLoRa
+
         for key in [str(k) for k in ckpt.keys()]:
             if key.startswith(
                 (
@@ -469,7 +475,7 @@ class ModelProbe(object):
         """
         # scan model
         scan_result = scan_file_path(checkpoint)
-        if scan_result.infected_files != 0:
+        if scan_result.infected_files != 0 or scan_result.scan_err:
             raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
 
 
@@ -485,6 +491,7 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "lineart anime": "lineart_anime_image_processor",
     "lineart_anime": "lineart_anime_image_processor",
     "lineart": "lineart_image_processor",
+    "soft": "hed_image_processor",
     "softedge": "hed_image_processor",
     "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",
@@ -496,7 +503,7 @@ MODEL_NAME_TO_PREPROCESSOR = {
 }
 
 
-def get_default_settings_controlnet_t2i_adapter(model_name: str) -> Optional[ControlAdapterDefaultSettings]:
+def get_default_settings_control_adapters(model_name: str) -> Optional[ControlAdapterDefaultSettings]:
     for k, v in MODEL_NAME_TO_PREPROCESSOR.items():
         model_name_lower = model_name.lower()
         if k in model_name_lower:
@@ -623,8 +630,10 @@ class LoRACheckpointProbe(CheckpointProbeBase):
         return ModelFormat.LyCORIS
 
     def get_base_type(self) -> BaseModelType:
-        if is_state_dict_likely_in_flux_kohya_format(self.checkpoint) or is_state_dict_likely_in_flux_diffusers_format(
-            self.checkpoint
+        if (
+            is_state_dict_likely_in_flux_kohya_format(self.checkpoint)
+            or is_state_dict_likely_in_flux_diffusers_format(self.checkpoint)
+            or is_state_dict_likely_flux_control(self.checkpoint)
         ):
             return BaseModelType.Flux
 
@@ -1033,6 +1042,7 @@ class T2IAdapterFolderProbe(FolderProbeBase):
 ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.VAE, VaeFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.LoRA, LoRAFolderProbe)
+ModelProbe.register_probe("diffusers", ModelType.ControlLoRa, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.T5Encoder, T5EncoderFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
@@ -1045,6 +1055,7 @@ ModelProbe.register_probe("diffusers", ModelType.SpandrelImageToImage, SpandrelI
 ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.VAE, VaeCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.LoRA, LoRACheckpointProbe)
+ModelProbe.register_probe("checkpoint", ModelType.ControlLoRa, LoRACheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)
 ModelProbe.register_probe("checkpoint", ModelType.IPAdapter, IPAdapterCheckpointProbe)

invokeai/backend/model_manager/starter_models.py

@@ -488,6 +488,22 @@ union_cnet_flux = StarterModel(
     type=ModelType.ControlNet,
 )
 # endregion
+# region Control LoRA
+flux_canny_control_lora = StarterModel(
+    name="Hard Edge Detection (Canny)",
+    base=BaseModelType.Flux,
+    source="black-forest-labs/FLUX.1-Canny-dev-lora::flux1-canny-dev-lora.safetensors",
+    description="Uses detected edges in the image to control composition.",
+    type=ModelType.ControlLoRa,
+)
+flux_depth_control_lora = StarterModel(
+    name="Depth Map",
+    base=BaseModelType.Flux,
+    source="black-forest-labs/FLUX.1-Depth-dev-lora::flux1-depth-dev-lora.safetensors",
+    description="Uses depth information in the image to control the depth in the generation.",
+    type=ModelType.ControlLoRa,
+)
+# endregion
 # region T2I Adapter
 t2i_canny_sd1 = StarterModel(
     name="Hard Edge Detection (canny)",
@@ -630,6 +646,8 @@ STARTER_MODELS: list[StarterModel] = [
     tile_sdxl,
     union_cnet_sdxl,
     union_cnet_flux,
+    flux_canny_control_lora,
+    flux_depth_control_lora,
     t2i_canny_sd1,
     t2i_sketch_sd1,
     t2i_depth_sd1,
@@ -688,6 +706,8 @@ flux_bundle: list[StarterModel] = [
     clip_l_encoder,
     union_cnet_flux,
     ip_adapter_flux,
+    flux_canny_control_lora,
+    flux_depth_control_lora,
 ]
 
 STARTER_BUNDLES: dict[str, list[StarterModel]] = {

invokeai/backend/model_manager/util/model_util.py

@@ -44,7 +44,7 @@ def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
     return checkpoint
 
 
-def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str, torch.Tensor]:
+def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> Dict[str, torch.Tensor]:
     if str(path).endswith(".safetensors"):
         try:
             path_str = path.as_posix() if isinstance(path, Path) else path
@@ -52,16 +52,15 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str
         except Exception:
             # TODO: create issue for support "meta"?
             checkpoint = safetensors.torch.load_file(path, device="cpu")
+    elif str(path).endswith(".gguf"):
+        # The GGUF reader used here uses numpy memmap, so these tensors are not loaded into memory during this function
+        checkpoint = gguf_sd_loader(Path(path), compute_dtype=torch.float32)
     else:
         if scan:
             scan_result = scan_file_path(path)
-            if scan_result.infected_files != 0:
+            if scan_result.infected_files != 0 or scan_result.scan_err:
                 raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
-        if str(path).endswith(".gguf"):
-            # The GGUF reader used here uses numpy memmap, so these tensors are not loaded into memory during this function
-            checkpoint = gguf_sd_loader(Path(path), compute_dtype=torch.float32)
-        else:
-            checkpoint = torch.load(path, map_location=torch.device("meta"))
+        checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint
 
 

invokeai/backend/model_patcher.py

@@ -5,17 +5,14 @@ from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
+from typing import Any, Iterator, List, Optional, Tuple, Type, Union
 
-import numpy as np
 import torch
 from diffusers import UNet2DConditionModel
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
-from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.textual_inversion import TextualInversionManager, TextualInversionModelRaw
 
 
@@ -176,180 +173,3 @@ class ModelPatcher:
             assert hasattr(unet, "disable_freeu")  # mypy doesn't pick up this attribute?
             if did_apply_freeu:
                 unet.disable_freeu()
-
-
-class ONNXModelPatcher:
-    # based on
-    # https://github.com/ssube/onnx-web/blob/ca2e436f0623e18b4cfe8a0363fcfcf10508acf7/api/onnx_web/convert/diffusion/lora.py#L323
-    @classmethod
-    @contextmanager
-    def apply_lora(
-        cls,
-        model: IAIOnnxRuntimeModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-        prefix: str,
-    ) -> None:
-        from invokeai.backend.models.base import IAIOnnxRuntimeModel
-
-        if not isinstance(model, IAIOnnxRuntimeModel):
-            raise Exception("Only IAIOnnxRuntimeModel models supported")
-
-        orig_weights = {}
-
-        try:
-            blended_loras: Dict[str, torch.Tensor] = {}
-
-            for lora, lora_weight in loras:
-                for layer_key, layer in lora.layers.items():
-                    if not layer_key.startswith(prefix):
-                        continue
-
-                    layer.to(dtype=torch.float32)
-                    layer_key = layer_key.replace(prefix, "")
-                    # TODO: rewrite to pass original tensor weight(required by ia3)
-                    layer_weight = layer.get_weight(None).detach().cpu().numpy() * lora_weight
-                    if layer_key in blended_loras:
-                        blended_loras[layer_key] += layer_weight
-                    else:
-                        blended_loras[layer_key] = layer_weight
-
-            node_names = {}
-            for node in model.nodes.values():
-                node_names[node.name.replace("/", "_").replace(".", "_").lstrip("_")] = node.name
-
-            for layer_key, lora_weight in blended_loras.items():
-                conv_key = layer_key + "_Conv"
-                gemm_key = layer_key + "_Gemm"
-                matmul_key = layer_key + "_MatMul"
-
-                if conv_key in node_names or gemm_key in node_names:
-                    if conv_key in node_names:
-                        conv_node = model.nodes[node_names[conv_key]]
-                    else:
-                        conv_node = model.nodes[node_names[gemm_key]]
-
-                    weight_name = [n for n in conv_node.input if ".weight" in n][0]
-                    orig_weight = model.tensors[weight_name]
-
-                    if orig_weight.shape[-2:] == (1, 1):
-                        if lora_weight.shape[-2:] == (1, 1):
-                            new_weight = orig_weight.squeeze((3, 2)) + lora_weight.squeeze((3, 2))
-                        else:
-                            new_weight = orig_weight.squeeze((3, 2)) + lora_weight
-
-                        new_weight = np.expand_dims(new_weight, (2, 3))
-                    else:
-                        if orig_weight.shape != lora_weight.shape:
-                            new_weight = orig_weight + lora_weight.reshape(orig_weight.shape)
-                        else:
-                            new_weight = orig_weight + lora_weight
-
-                    orig_weights[weight_name] = orig_weight
-                    model.tensors[weight_name] = new_weight.astype(orig_weight.dtype)
-
-                elif matmul_key in node_names:
-                    weight_node = model.nodes[node_names[matmul_key]]
-                    matmul_name = [n for n in weight_node.input if "MatMul" in n][0]
-
-                    orig_weight = model.tensors[matmul_name]
-                    new_weight = orig_weight + lora_weight.transpose()
-
-                    orig_weights[matmul_name] = orig_weight
-                    model.tensors[matmul_name] = new_weight.astype(orig_weight.dtype)
-
-                else:
-                    # warn? err?
-                    pass
-
-            yield
-
-        finally:
-            # restore original weights
-            for name, orig_weight in orig_weights.items():
-                model.tensors[name] = orig_weight
-
-    @classmethod
-    @contextmanager
-    def apply_ti(
-        cls,
-        tokenizer: CLIPTokenizer,
-        text_encoder: IAIOnnxRuntimeModel,
-        ti_list: List[Tuple[str, Any]],
-    ) -> Iterator[Tuple[CLIPTokenizer, TextualInversionManager]]:
-        from invokeai.backend.models.base import IAIOnnxRuntimeModel
-
-        if not isinstance(text_encoder, IAIOnnxRuntimeModel):
-            raise Exception("Only IAIOnnxRuntimeModel models supported")
-
-        orig_embeddings = None
-
-        try:
-            # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
-            # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
-            # exiting this `apply_ti(...)` context manager.
-            #
-            # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
-            # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
-            ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
-            ti_manager = TextualInversionManager(ti_tokenizer)
-
-            def _get_trigger(ti_name: str, index: int) -> str:
-                trigger = ti_name
-                if index > 0:
-                    trigger += f"-!pad-{i}"
-                return f"<{trigger}>"
-
-            # modify text_encoder
-            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
-
-            # modify tokenizer
-            new_tokens_added = 0
-            for ti_name, ti in ti_list:
-                if ti.embedding_2 is not None:
-                    ti_embedding = (
-                        ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
-                    )
-                else:
-                    ti_embedding = ti.embedding
-
-                for i in range(ti_embedding.shape[0]):
-                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
-
-            embeddings = np.concatenate(
-                (np.copy(orig_embeddings), np.zeros((new_tokens_added, orig_embeddings.shape[1]))),
-                axis=0,
-            )
-
-            for ti_name, _ in ti_list:
-                ti_tokens = []
-                for i in range(ti_embedding.shape[0]):
-                    embedding = ti_embedding[i].detach().numpy()
-                    trigger = _get_trigger(ti_name, i)
-
-                    token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
-                    if token_id == ti_tokenizer.unk_token_id:
-                        raise RuntimeError(f"Unable to find token id for token '{trigger}'")
-
-                    if embeddings[token_id].shape != embedding.shape:
-                        raise ValueError(
-                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension"
-                            f" {embedding.shape[0]}, but the current model has token dimension"
-                            f" {embeddings[token_id].shape[0]}."
-                        )
-
-                    embeddings[token_id] = embedding
-                    ti_tokens.append(token_id)
-
-                if len(ti_tokens) > 1:
-                    ti_manager.pad_tokens[ti_tokens[0]] = ti_tokens[1:]
-
-            text_encoder.tensors["text_model.embeddings.token_embedding.weight"] = embeddings.astype(
-                orig_embeddings.dtype
-            )
-
-            yield ti_tokenizer, ti_manager
-
-        finally:
-            # restore
-            if orig_embeddings is not None:
-                text_encoder.tensors["text_model.embeddings.token_embedding.weight"] = orig_embeddings

invokeai/backend/patches/layers/base_layer_patch.py

@@ -0,0 +1,22 @@
+from abc import ABC, abstractmethod
+
+import torch
+
+
+class BaseLayerPatch(ABC):
+    @abstractmethod
+    def get_parameters(self, orig_module: torch.nn.Module, weight: float) -> dict[str, torch.Tensor]:
+        """Get the parameter residual updates that should be applied to the original parameters. Parameters omitted
+        from the returned dict are not updated.
+        """
+        ...
+
+    @abstractmethod
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        """Move all internal tensors to the specified device and dtype."""
+        ...
+
+    @abstractmethod
+    def calc_size(self) -> int:
+        """Calculate the total size of all internal tensors in bytes."""
+        ...

invokeai/backend/patches/layers/concatenated_lora_layer.py

@@ -2,8 +2,8 @@ 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
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 
 
 class ConcatenatedLoRALayer(LoRALayerBase):
@@ -20,7 +20,7 @@ class ConcatenatedLoRALayer(LoRALayerBase):
         self.lora_layers = lora_layers
         self.concat_axis = concat_axis
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
         return None
 
     def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:

invokeai/backend/patches/layers/flux_control_lora_layer.py

@@ -0,0 +1,19 @@
+import torch
+
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+
+
+class FluxControlLoRALayer(LoRALayer):
+    """A special case of LoRALayer for use with FLUX Control LoRAs that pads the target parameter with zeros if the
+    shapes don't match.
+    """
+
+    def get_parameters(self, orig_module: torch.nn.Module, weight: float) -> dict[str, torch.Tensor]:
+        """This overrides the base class behavior to skip the reshaping step."""
+        scale = self.scale()
+        params = {"weight": self.get_weight(orig_module.weight) * (weight * scale)}
+        bias = self.get_bias(orig_module.bias)
+        if bias is not None:
+            params["bias"] = bias * (weight * scale)
+
+        return params

invokeai/backend/patches/layers/full_layer.py

@@ -2,7 +2,7 @@ from typing import Dict, Optional
 
 import torch
 
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 from invokeai.backend.util.calc_tensor_size import calc_tensor_size
 
 
@@ -20,7 +20,7 @@ class FullLayer(LoRALayerBase):
         cls.warn_on_unhandled_keys(values=values, handled_keys={"diff", "diff_b"})
         return layer
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
         return None
 
     def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:

invokeai/backend/patches/layers/ia3_layer.py

@@ -2,7 +2,7 @@ from typing import Dict, Optional
 
 import torch
 
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 
 
 class IA3Layer(LoRALayerBase):
@@ -16,7 +16,7 @@ class IA3Layer(LoRALayerBase):
         self.weight = weight
         self.on_input = on_input
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
         return None
 
     @classmethod

invokeai/backend/patches/layers/loha_layer.py

@@ -2,7 +2,7 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 from invokeai.backend.util.calc_tensor_size import calc_tensors_size
 
 
@@ -32,7 +32,7 @@ class LoHALayer(LoRALayerBase):
         self.t2 = t2
         assert (self.t1 is None) == (self.t2 is None)
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
         return self.w1_b.shape[0]
 
     @classmethod

invokeai/backend/patches/layers/lokr_layer.py

@@ -2,7 +2,7 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 from invokeai.backend.util.calc_tensor_size import calc_tensors_size
 
 
@@ -39,7 +39,7 @@ class LoKRLayer(LoRALayerBase):
         assert (self.w2 is None) != (self.w2_a is None)
         assert (self.w2_a is None) == (self.w2_b is None)
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
         if self.w1_b is not None:
             return self.w1_b.shape[0]
         elif self.w2_b is not None:

invokeai/backend/patches/layers/lora_layer.py

@@ -2,7 +2,7 @@ from typing import Dict, Optional
 
 import torch
 
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 from invokeai.backend.util.calc_tensor_size import calc_tensors_size
 
 
@@ -55,7 +55,7 @@ class LoRALayer(LoRALayerBase):
 
         return layer
 
-    def rank(self) -> int:
+    def _rank(self) -> int:
         return self.down.shape[0]
 
     def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:

invokeai/backend/patches/layers/lora_layer_base.py

@@ -1,12 +1,13 @@
-from typing import Dict, Optional, Set
+from typing import Optional
 
 import torch
 
 import invokeai.backend.util.logging as logger
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
 from invokeai.backend.util.calc_tensor_size import calc_tensors_size
 
 
-class LoRALayerBase:
+class LoRALayerBase(BaseLayerPatch):
     """Base class for all LoRA-like patching layers."""
 
     # Note: It is tempting to make this a torch.nn.Module sub-class and make all tensors 'torch.nn.Parameter's. Then we
@@ -23,6 +24,7 @@ class LoRALayerBase:
     def _parse_bias(
         cls, bias_indices: torch.Tensor | None, bias_values: torch.Tensor | None, bias_size: torch.Tensor | None
     ) -> torch.Tensor | None:
+        """Helper function to parse a bias tensor from a state dict in LyCORIS format."""
         assert (bias_indices is None) == (bias_values is None) == (bias_size is None)
 
         bias = None
@@ -37,11 +39,14 @@ class LoRALayerBase:
     ) -> float | None:
         return alpha.item() if alpha is not None else None
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
+        """Return the rank of the LoRA-like layer. Or None if the layer does not have a rank. This value is used to
+        calculate the scale.
+        """
         raise NotImplementedError()
 
     def scale(self) -> float:
-        rank = self.rank()
+        rank = self._rank()
         if self._alpha is None or rank is None:
             return 1.0
         return self._alpha / rank
@@ -52,15 +57,23 @@ class LoRALayerBase:
     def get_bias(self, orig_bias: torch.Tensor) -> Optional[torch.Tensor]:
         return self.bias
 
-    def get_parameters(self, orig_module: torch.nn.Module) -> Dict[str, torch.Tensor]:
-        params = {"weight": self.get_weight(orig_module.weight)}
+    def get_parameters(self, orig_module: torch.nn.Module, weight: float) -> dict[str, torch.Tensor]:
+        scale = self.scale()
+        params = {"weight": self.get_weight(orig_module.weight) * (weight * scale)}
         bias = self.get_bias(orig_module.bias)
         if bias is not None:
-            params["bias"] = bias
+            params["bias"] = bias * (weight * scale)
+
+        # Reshape all params to match the original module's shape.
+        for param_name, param_weight in params.items():
+            orig_param = orig_module.get_parameter(param_name)
+            if param_weight.shape != orig_param.shape:
+                params[param_name] = param_weight.reshape(orig_param.shape)
+
         return params
 
     @classmethod
-    def warn_on_unhandled_keys(cls, values: Dict[str, torch.Tensor], handled_keys: Set[str]):
+    def warn_on_unhandled_keys(cls, values: dict[str, torch.Tensor], handled_keys: set[str]):
         """Log a warning if values contains unhandled keys."""
         unknown_keys = set(values.keys()) - handled_keys
         if unknown_keys:

invokeai/backend/patches/layers/norm_layer.py

@@ -2,7 +2,7 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.layers.lora_layer_base import LoRALayerBase
+from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
 from invokeai.backend.util.calc_tensor_size import calc_tensor_size
 
 
@@ -20,7 +20,7 @@ class NormLayer(LoRALayerBase):
         cls.warn_on_unhandled_keys(values, {"w_norm", "b_norm"})
         return layer
 
-    def rank(self) -> int | None:
+    def _rank(self) -> int | None:
         return None
 
     def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:

invokeai/backend/patches/layers/set_parameter_layer.py

@@ -0,0 +1,27 @@
+import torch
+
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.util.calc_tensor_size import calc_tensor_size
+
+
+class SetParameterLayer(BaseLayerPatch):
+    """A layer that sets a single parameter to a new target value.
+    (The diff between the target value and current value is calculated internally.)
+    """
+
+    def __init__(self, param_name: str, weight: torch.Tensor):
+        super().__init__()
+        self.weight = weight
+        self.param_name = param_name
+
+    def get_parameters(self, orig_module: torch.nn.Module, weight: float) -> dict[str, torch.Tensor]:
+        # Note: We intentionally ignore the weight parameter here. This matches the behavior in the official FLUX
+        # Control LoRA implementation.
+        diff = self.weight - orig_module.get_parameter(self.param_name)
+        return {self.param_name: diff}
+
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        self.weight = self.weight.to(device=device, dtype=dtype)
+
+    def calc_size(self) -> int:
+        return calc_tensor_size(self.weight)

invokeai/backend/patches/layers/utils.py

@@ -2,16 +2,16 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-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
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.full_layer import FullLayer
+from invokeai.backend.patches.layers.ia3_layer import IA3Layer
+from invokeai.backend.patches.layers.loha_layer import LoHALayer
+from invokeai.backend.patches.layers.lokr_layer import LoKRLayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.layers.norm_layer import NormLayer
 
 
-def any_lora_layer_from_state_dict(state_dict: Dict[str, torch.Tensor]) -> AnyLoRALayer:
+def any_lora_layer_from_state_dict(state_dict: Dict[str, torch.Tensor]) -> BaseLayerPatch:
     # Detect layers according to LyCORIS detection logic(`weight_list_det`)
     # https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
 

invokeai/backend/patches/lora_conversions/flux_control_lora_utils.py

@@ -0,0 +1,84 @@
+import re
+from typing import Any, Dict
+
+import torch
+
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.layers.set_parameter_layer import SetParameterLayer
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+
+# A regex pattern that matches all of the keys in the Flux Dev/Canny LoRA format.
+# Example keys:
+#   guidance_in.in_layer.lora_B.bias
+#   single_blocks.0.linear1.lora_A.weight
+#   double_blocks.0.img_attn.norm.key_norm.scale
+FLUX_CONTROL_TRANSFORMER_KEY_REGEX = r"(\w+\.)+(lora_A\.weight|lora_B\.weight|lora_B\.bias|scale)"
+
+
+def is_state_dict_likely_flux_control(state_dict: Dict[str, Any]) -> bool:
+    """Checks if the provided state dict is likely in the FLUX Control LoRA format.
+
+    This is intended to be a high-precision detector, but it is not guaranteed to have perfect precision. (A
+    perfect-precision detector would require checking all keys against a whitelist and verifying tensor shapes.)
+    """
+
+    all_keys_match = all(re.match(FLUX_CONTROL_TRANSFORMER_KEY_REGEX, str(k)) for k in state_dict.keys())
+
+    # Check the shape of the img_in weight, because this layer shape is modified by FLUX control LoRAs.
+    lora_a_weight = state_dict.get("img_in.lora_A.weight", None)
+    lora_b_bias = state_dict.get("img_in.lora_B.bias", None)
+    lora_b_weight = state_dict.get("img_in.lora_B.weight", None)
+
+    return (
+        all_keys_match
+        and lora_a_weight is not None
+        and lora_b_bias is not None
+        and lora_b_weight is not None
+        and lora_a_weight.shape[1] == 128
+        and lora_b_weight.shape[0] == 3072
+        and lora_b_bias.shape[0] == 3072
+    )
+
+
+def lora_model_from_flux_control_state_dict(state_dict: Dict[str, torch.Tensor]) -> ModelPatchRaw:
+    # Group keys by layer.
+    grouped_state_dict: dict[str, dict[str, torch.Tensor]] = {}
+    for key, value in state_dict.items():
+        key_props = key.split(".")
+        layer_prop_size = -2 if any(prop in key for prop in ["lora_B", "lora_A"]) else -1
+        layer_name = ".".join(key_props[:layer_prop_size])
+        param_name = ".".join(key_props[layer_prop_size:])
+        if layer_name not in grouped_state_dict:
+            grouped_state_dict[layer_name] = {}
+        grouped_state_dict[layer_name][param_name] = value
+
+    # Create LoRA layers.
+    layers: dict[str, BaseLayerPatch] = {}
+    for layer_key, layer_state_dict in grouped_state_dict.items():
+        prefixed_key = f"{FLUX_LORA_TRANSFORMER_PREFIX}{layer_key}"
+        if layer_key == "img_in":
+            # img_in is a special case because it changes the shape of the original weight.
+            layers[prefixed_key] = FluxControlLoRALayer(
+                layer_state_dict["lora_B.weight"],
+                None,
+                layer_state_dict["lora_A.weight"],
+                None,
+                layer_state_dict["lora_B.bias"],
+            )
+        elif all(k in layer_state_dict for k in ["lora_A.weight", "lora_B.bias", "lora_B.weight"]):
+            layers[prefixed_key] = LoRALayer(
+                layer_state_dict["lora_B.weight"],
+                None,
+                layer_state_dict["lora_A.weight"],
+                None,
+                layer_state_dict["lora_B.bias"],
+            )
+        elif "scale" in layer_state_dict:
+            layers[prefixed_key] = SetParameterLayer("scale", layer_state_dict["scale"])
+        else:
+            raise ValueError(f"{layer_key} not expected")
+
+    return ModelPatchRaw(layers=layers)

invokeai/backend/patches/lora_conversions/flux_diffusers_lora_conversion_utils.py

@@ -2,11 +2,11 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
-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.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 
 
 def is_state_dict_likely_in_flux_diffusers_format(state_dict: Dict[str, torch.Tensor]) -> bool:
@@ -30,7 +30,9 @@ def is_state_dict_likely_in_flux_diffusers_format(state_dict: Dict[str, torch.Te
     return all_keys_in_peft_format and all_expected_keys_present
 
 
-def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor], alpha: float | None) -> LoRAModelRaw:
+def lora_model_from_flux_diffusers_state_dict(
+    state_dict: Dict[str, torch.Tensor], alpha: float | None
+) -> ModelPatchRaw:
     """Loads a state dict in the Diffusers FLUX LoRA format into a LoRAModelRaw object.
 
     This function is based on:
@@ -49,7 +51,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
     mlp_ratio = 4.0
     mlp_hidden_dim = int(hidden_size * mlp_ratio)
 
-    layers: dict[str, AnyLoRALayer] = {}
+    layers: dict[str, BaseLayerPatch] = {}
 
     def add_lora_layer_if_present(src_key: str, dst_key: str) -> None:
         if src_key in grouped_state_dict:
@@ -215,7 +217,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
 
     layers_with_prefix = {f"{FLUX_LORA_TRANSFORMER_PREFIX}{k}": v for k, v in layers.items()}
 
-    return LoRAModelRaw(layers=layers_with_prefix)
+    return ModelPatchRaw(layers=layers_with_prefix)
 
 
 def _group_by_layer(state_dict: Dict[str, torch.Tensor]) -> dict[str, dict[str, torch.Tensor]]:

invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py

@@ -3,10 +3,13 @@ from typing import Any, Dict, TypeVar
 
 import torch
 
-from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_TRANSFORMER_PREFIX
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
+from invokeai.backend.patches.lora_conversions.flux_lora_constants import (
+    FLUX_LORA_CLIP_PREFIX,
+    FLUX_LORA_TRANSFORMER_PREFIX,
+)
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 
 # A regex pattern that matches all of the transformer keys in the Kohya FLUX LoRA format.
 # Example keys:
@@ -36,7 +39,7 @@ def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> boo
     )
 
 
-def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
+def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -> ModelPatchRaw:
     # Group keys by layer.
     grouped_state_dict: dict[str, dict[str, torch.Tensor]] = {}
     for key, value in state_dict.items():
@@ -61,14 +64,14 @@ def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -
     clip_grouped_sd = _convert_flux_clip_kohya_state_dict_to_invoke_format(clip_grouped_sd)
 
     # Create LoRA layers.
-    layers: dict[str, AnyLoRALayer] = {}
+    layers: dict[str, BaseLayerPatch] = {}
     for layer_key, layer_state_dict in transformer_grouped_sd.items():
         layers[FLUX_LORA_TRANSFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
     for layer_key, layer_state_dict in clip_grouped_sd.items():
         layers[FLUX_LORA_CLIP_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
 
     # Create and return the LoRAModelRaw.
-    return LoRAModelRaw(layers=layers)
+    return ModelPatchRaw(layers=layers)
 
 
 T = TypeVar("T")

invokeai/backend/patches/lora_conversions/sd_lora_conversion_utils.py

@@ -2,19 +2,19 @@ from typing import Dict
 
 import torch
 
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 
 
-def lora_model_from_sd_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAModelRaw:
+def lora_model_from_sd_state_dict(state_dict: Dict[str, torch.Tensor]) -> ModelPatchRaw:
     grouped_state_dict: dict[str, dict[str, torch.Tensor]] = _group_state(state_dict)
 
-    layers: dict[str, AnyLoRALayer] = {}
+    layers: dict[str, BaseLayerPatch] = {}
     for layer_key, values in grouped_state_dict.items():
         layers[layer_key] = any_lora_layer_from_state_dict(values)
 
-    return LoRAModelRaw(layers=layers)
+    return ModelPatchRaw(layers=layers)
 
 
 def _group_state(state_dict: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, torch.Tensor]]:

invokeai/backend/patches/model_patch_raw.py

@@ -3,20 +3,17 @@ from typing import Mapping, Optional
 
 import torch
 
-from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
 from invokeai.backend.raw_model import RawModel
 
 
-class LoRAModelRaw(RawModel):  # (torch.nn.Module):
-    def __init__(self, layers: Mapping[str, AnyLoRALayer]):
+class ModelPatchRaw(RawModel):
+    def __init__(self, layers: Mapping[str, BaseLayerPatch]):
         self.layers = layers
 
     def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        for _key, layer in self.layers.items():
+        for layer in self.layers.values():
             layer.to(device=device, dtype=dtype)
 
     def calc_size(self) -> int:
-        model_size = 0
-        for _, layer in self.layers.items():
-            model_size += layer.calc_size()
-        return model_size
+        return sum(layer.calc_size() for layer in self.layers.values())

invokeai/backend/patches/model_patcher.py

@@ -3,26 +3,23 @@ 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.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.pad_with_zeros import pad_with_zeros
+from invokeai.backend.patches.sidecar_wrappers.base_sidecar_wrapper import BaseSidecarWrapper
+from invokeai.backend.patches.sidecar_wrappers.utils import wrap_module_with_sidecar_wrapper
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
-class LoRAPatcher:
+class LayerPatcher:
     @staticmethod
     @torch.no_grad()
     @contextmanager
-    def apply_lora_patches(
+    def apply_model_patches(
         model: torch.nn.Module,
-        patches: Iterable[Tuple[LoRAModelRaw, float]],
+        patches: Iterable[Tuple[ModelPatchRaw, float]],
         prefix: str,
         cached_weights: Optional[Dict[str, torch.Tensor]] = None,
     ):
@@ -40,7 +37,7 @@ class LoRAPatcher:
         original_weights = OriginalWeightsStorage(cached_weights)
         try:
             for patch, patch_weight in patches:
-                LoRAPatcher.apply_lora_patch(
+                LayerPatcher.apply_model_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -52,14 +49,15 @@ class LoRAPatcher:
             yield
         finally:
             for param_key, weight in original_weights.get_changed_weights():
-                model.get_parameter(param_key).copy_(weight)
+                cur_param = model.get_parameter(param_key)
+                cur_param.data = weight.to(dtype=cur_param.dtype, device=cur_param.device, copy=True)
 
     @staticmethod
     @torch.no_grad()
-    def apply_lora_patch(
+    def apply_model_patch(
         model: torch.nn.Module,
         prefix: str,
-        patch: LoRAModelRaw,
+        patch: ModelPatchRaw,
         patch_weight: float,
         original_weights: OriginalWeightsStorage,
     ):
@@ -87,46 +85,70 @@ class LoRAPatcher:
             if not layer_key.startswith(prefix):
                 continue
 
-            module_key, module = LoRAPatcher._get_submodule(
+            module_key, module = LayerPatcher._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
+            LayerPatcher._apply_model_layer_patch(
+                module_to_patch=module,
+                module_to_patch_key=module_key,
+                patch=layer,
+                patch_weight=patch_weight,
+                original_weights=original_weights,
+            )
 
-            layer_scale = layer.scale()
+    @staticmethod
+    @torch.no_grad()
+    def _apply_model_layer_patch(
+        module_to_patch: torch.nn.Module,
+        module_to_patch_key: str,
+        patch: BaseLayerPatch,
+        patch_weight: float,
+        original_weights: OriginalWeightsStorage,
+    ):
+        # 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.)
+        first_param = next(module_to_patch.parameters())
+        device = first_param.device
+        dtype = first_param.dtype
 
-            # 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)
+        # 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(...)'.
+        patch.to(device=device)
+        patch.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)
+        # 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, param_weight in patch.get_parameters(module_to_patch, weight=patch_weight).items():
+            param_key = module_to_patch_key + "." + param_name
+            module_param = module_to_patch.get_parameter(param_name)
 
-                # Save original weight
-                original_weights.save(param_key, module_param)
+            # 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)
+            # HACK(ryand): This condition is only necessary to handle layers in FLUX control LoRAs that change the
+            # shape of the original layer.
+            if module_param.nelement() != param_weight.nelement():
+                assert isinstance(patch, FluxControlLoRALayer)
+                expanded_weight = pad_with_zeros(module_param, param_weight.shape)
+                setattr(
+                    module_to_patch,
+                    param_name,
+                    torch.nn.Parameter(expanded_weight, requires_grad=module_param.requires_grad),
+                )
+                module_param = expanded_weight
 
-                lora_param_weight *= patch_weight * layer_scale
-                module_param += lora_param_weight.to(dtype=dtype)
+            module_param += param_weight.to(dtype=dtype)
 
-            layer.to(device=TorchDevice.CPU_DEVICE)
+        patch.to(device=TorchDevice.CPU_DEVICE)
 
     @staticmethod
     @torch.no_grad()
     @contextmanager
-    def apply_lora_sidecar_patches(
+    def apply_model_sidecar_patches(
         model: torch.nn.Module,
-        patches: Iterable[Tuple[LoRAModelRaw, float]],
+        patches: Iterable[Tuple[ModelPatchRaw, float]],
         prefix: str,
         dtype: torch.dtype,
     ):
@@ -147,7 +169,7 @@ class LoRAPatcher:
         original_modules: dict[str, torch.nn.Module] = {}
         try:
             for patch, patch_weight in patches:
-                LoRAPatcher._apply_lora_sidecar_patch(
+                LayerPatcher._apply_model_sidecar_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -160,14 +182,14 @@ class LoRAPatcher:
             # 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)
+                module_parent_key, module_name = LayerPatcher._split_parent_key(module_key)
                 parent_module = model.get_submodule(module_parent_key)
-                LoRAPatcher._set_submodule(parent_module, module_name, orig_module)
+                LayerPatcher._set_submodule(parent_module, module_name, orig_module)
 
     @staticmethod
-    def _apply_lora_sidecar_patch(
+    def _apply_model_sidecar_patch(
         model: torch.nn.Module,
-        patch: LoRAModelRaw,
+        patch: ModelPatchRaw,
         patch_weight: float,
         prefix: str,
         original_modules: dict[str, torch.nn.Module],
@@ -190,32 +212,50 @@ class LoRAPatcher:
             if not layer_key.startswith(prefix):
                 continue
 
-            module_key, module = LoRAPatcher._get_submodule(
+            module_key, module = LayerPatcher._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)
+            LayerPatcher._apply_model_layer_wrapper_patch(
+                model=model,
+                module_to_patch=module,
+                module_to_patch_key=module_key,
+                patch=layer,
+                patch_weight=patch_weight,
+                original_modules=original_modules,
+                dtype=dtype,
+            )
 
-            # 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)
+    @staticmethod
+    @torch.no_grad()
+    def _apply_model_layer_wrapper_patch(
+        model: torch.nn.Module,
+        module_to_patch: torch.nn.Module,
+        module_to_patch_key: str,
+        patch: BaseLayerPatch,
+        patch_weight: float,
+        original_modules: dict[str, torch.nn.Module],
+        dtype: torch.dtype,
+    ):
+        """Apply a single LoRA wrapper patch to a model."""
+        # Replace the original module with a BaseSidecarWrapper if it has not already been done.
+        if not isinstance(module_to_patch, BaseSidecarWrapper):
+            wrapped_module = wrap_module_with_sidecar_wrapper(orig_module=module_to_patch)
+            original_modules[module_to_patch_key] = module_to_patch
+            module_parent_key, module_name = LayerPatcher._split_parent_key(module_to_patch_key)
+            module_parent = model.get_submodule(module_parent_key)
+            LayerPatcher._set_submodule(module_parent, module_name, wrapped_module)
+        else:
+            assert module_to_patch_key in original_modules
+            wrapped_module = module_to_patch
 
-            # 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)
+        # Move the LoRA layer to the same device/dtype as the orig module.
+        first_param = next(module_to_patch.parameters())
+        device = first_param.device
+        patch.to(device=device, dtype=dtype)
 
-            # Add the LoRA sidecar layer to the LoRASidecarModule.
-            lora_sidecar_module.add_lora_layer(lora_sidecar_layer)
+        # Add the patch to the sidecar wrapper.
+        wrapped_module.add_patch(patch, patch_weight)
 
     @staticmethod
     def _split_parent_key(module_key: str) -> tuple[str, str]:
@@ -235,21 +275,6 @@ class LoRAPatcher:
         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:

invokeai/backend/patches/pad_with_zeros.py

@@ -0,0 +1,9 @@
+import torch
+
+
+def pad_with_zeros(orig_weight: torch.Tensor, target_shape: torch.Size) -> torch.Tensor:
+    """Pad a weight tensor with zeros to match the target shape."""
+    expanded_weight = torch.zeros(target_shape, dtype=orig_weight.dtype, device=orig_weight.device)
+    slices = tuple(slice(0, dim) for dim in orig_weight.shape)
+    expanded_weight[slices] = orig_weight
+    return expanded_weight

invokeai/backend/patches/sidecar_wrappers/base_sidecar_wrapper.py

@@ -0,0 +1,54 @@
+import torch
+
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+
+
+class BaseSidecarWrapper(torch.nn.Module):
+    """A base class for sidecar wrappers.
+
+    A sidecar wrapper is a wrapper for an existing torch.nn.Module that applies a
+    list of patches as 'sidecar' patches. I.e. it applies the sidecar patches during forward inference without modifying
+    the original module.
+
+    Sidecar wrappers are typically used over regular patches when:
+    - The original module is quantized and so the weights can't be patched in the usual way.
+    - The original module is on the CPU and modifying the weights would require backing up the original weights and
+      doubling the CPU memory usage.
+    """
+
+    def __init__(
+        self, orig_module: torch.nn.Module, patches_and_weights: list[tuple[BaseLayerPatch, float]] | None = None
+    ):
+        super().__init__()
+        self._orig_module = orig_module
+        self._patches_and_weights = [] if patches_and_weights is None else patches_and_weights
+
+    @property
+    def orig_module(self) -> torch.nn.Module:
+        return self._orig_module
+
+    def add_patch(self, patch: BaseLayerPatch, patch_weight: float):
+        """Add a patch to the sidecar wrapper."""
+        self._patches_and_weights.append((patch, patch_weight))
+
+    def _aggregate_patch_parameters(
+        self, patches_and_weights: list[tuple[BaseLayerPatch, float]]
+    ) -> dict[str, torch.Tensor]:
+        """Helper function that aggregates the parameters from all patches into a single dict."""
+        params: dict[str, torch.Tensor] = {}
+
+        for patch, patch_weight in patches_and_weights:
+            # TODO(ryand): self._orig_module could be quantized. Depending on what the patch is doing with the original
+            # module, this might fail or return incorrect results.
+            layer_params = patch.get_parameters(self._orig_module, weight=patch_weight)
+
+            for param_name, param_weight in layer_params.items():
+                if param_name not in params:
+                    params[param_name] = param_weight
+                else:
+                    params[param_name] += param_weight
+
+        return params
+
+    def forward(self, *args, **kwargs):  # type: ignore
+        raise NotImplementedError()

invokeai/backend/patches/sidecar_wrappers/conv1d_sidecar_wrapper.py

@@ -0,0 +1,11 @@
+import torch
+
+from invokeai.backend.patches.sidecar_wrappers.base_sidecar_wrapper import BaseSidecarWrapper
+
+
+class Conv1dSidecarWrapper(BaseSidecarWrapper):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        aggregated_param_residuals = self._aggregate_patch_parameters(self._patches_and_weights)
+        return self.orig_module(input) + torch.nn.functional.conv1d(
+            input, aggregated_param_residuals["weight"], aggregated_param_residuals.get("bias", None)
+        )

invokeai/backend/patches/sidecar_wrappers/conv2d_sidecar_wrapper.py

@@ -0,0 +1,11 @@
+import torch
+
+from invokeai.backend.patches.sidecar_wrappers.base_sidecar_wrapper import BaseSidecarWrapper
+
+
+class Conv2dSidecarWrapper(BaseSidecarWrapper):
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        aggregated_param_residuals = self._aggregate_patch_parameters(self._patches_and_weights)
+        return self.orig_module(input) + torch.nn.functional.conv1d(
+            input, aggregated_param_residuals["weight"], aggregated_param_residuals.get("bias", None)
+        )

invokeai/backend/patches/sidecar_wrappers/flux_rms_norm_sidecar_wrapper.py

@@ -0,0 +1,24 @@
+import torch
+
+from invokeai.backend.patches.layers.set_parameter_layer import SetParameterLayer
+from invokeai.backend.patches.sidecar_wrappers.base_sidecar_wrapper import BaseSidecarWrapper
+
+
+class FluxRMSNormSidecarWrapper(BaseSidecarWrapper):
+    """A sidecar wrapper for a FLUX RMSNorm layer.
+
+    This wrapper is a special case. It is added specifically to enable FLUX structural control LoRAs, which overwrite
+    the RMSNorm scale parameters.
+    """
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # Given the narrow focus of this wrapper, we only support a very particular patch configuration:
+        assert len(self._patches_and_weights) == 1
+        patch, _patch_weight = self._patches_and_weights[0]
+        assert isinstance(patch, SetParameterLayer)
+        assert patch.param_name == "scale"
+
+        # Apply the patch.
+        # NOTE(ryand): Currently, we ignore the patch weight when running as a sidecar. It's not clear how this should
+        # be handled.
+        return torch.nn.functional.rms_norm(input, patch.weight.shape, patch.weight, eps=1e-6)

invokeai/backend/patches/sidecar_wrappers/linear_sidecar_wrapper.py

@@ -0,0 +1,66 @@
+import torch
+
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.sidecar_wrappers.base_sidecar_wrapper import BaseSidecarWrapper
+
+
+class LinearSidecarWrapper(BaseSidecarWrapper):
+    def _lora_forward(self, input: torch.Tensor, lora_layer: LoRALayer, lora_weight: float) -> torch.Tensor:
+        """An optimized implementation of the residual calculation for a Linear LoRALayer."""
+        x = torch.nn.functional.linear(input, lora_layer.down)
+        if lora_layer.mid is not None:
+            x = torch.nn.functional.linear(x, lora_layer.mid)
+        x = torch.nn.functional.linear(x, lora_layer.up, bias=lora_layer.bias)
+        x *= lora_weight * lora_layer.scale()
+        return x
+
+    def _concatenated_lora_forward(
+        self, input: torch.Tensor, concatenated_lora_layer: ConcatenatedLoRALayer, lora_weight: float
+    ) -> torch.Tensor:
+        """An optimized implementation of the residual calculation for a Linear ConcatenatedLoRALayer."""
+        x_chunks: list[torch.Tensor] = []
+        for lora_layer in 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 *= lora_weight * lora_layer.scale()
+            x_chunks.append(x_chunk)
+
+        # TODO(ryand): Generalize to support concat_axis != 0.
+        assert concatenated_lora_layer.concat_axis == 0
+        x = torch.cat(x_chunks, dim=-1)
+        return x
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # First, apply the original linear layer.
+        # NOTE: We slice the input to match the original weight shape in order to work with FluxControlLoRAs, which
+        # change the linear layer's in_features.
+        orig_input = input
+        input = orig_input[..., : self.orig_module.in_features]
+        output = self.orig_module(input)
+
+        # Then, apply layers for which we have optimized implementations.
+        unprocessed_patches_and_weights: list[tuple[BaseLayerPatch, float]] = []
+        for patch, patch_weight in self._patches_and_weights:
+            if isinstance(patch, FluxControlLoRALayer):
+                # Note that we use the original input here, not the sliced input.
+                output += self._lora_forward(orig_input, patch, patch_weight)
+            elif isinstance(patch, LoRALayer):
+                output += self._lora_forward(input, patch, patch_weight)
+            elif isinstance(patch, ConcatenatedLoRALayer):
+                output += self._concatenated_lora_forward(input, patch, patch_weight)
+            else:
+                unprocessed_patches_and_weights.append((patch, patch_weight))
+
+        # Finally, apply any remaining patches.
+        if len(unprocessed_patches_and_weights) > 0:
+            aggregated_param_residuals = self._aggregate_patch_parameters(unprocessed_patches_and_weights)
+            output += torch.nn.functional.linear(
+                input, aggregated_param_residuals["weight"], aggregated_param_residuals.get("bias", None)
+            )
+
+        return output

invokeai/backend/patches/sidecar_wrappers/utils.py

@@ -0,0 +1,20 @@
+import torch
+
+from invokeai.backend.flux.modules.layers import RMSNorm
+from invokeai.backend.patches.sidecar_wrappers.conv1d_sidecar_wrapper import Conv1dSidecarWrapper
+from invokeai.backend.patches.sidecar_wrappers.conv2d_sidecar_wrapper import Conv2dSidecarWrapper
+from invokeai.backend.patches.sidecar_wrappers.flux_rms_norm_sidecar_wrapper import FluxRMSNormSidecarWrapper
+from invokeai.backend.patches.sidecar_wrappers.linear_sidecar_wrapper import LinearSidecarWrapper
+
+
+def wrap_module_with_sidecar_wrapper(orig_module: torch.nn.Module) -> torch.nn.Module:
+    if isinstance(orig_module, torch.nn.Linear):
+        return LinearSidecarWrapper(orig_module)
+    elif isinstance(orig_module, torch.nn.Conv1d):
+        return Conv1dSidecarWrapper(orig_module)
+    elif isinstance(orig_module, torch.nn.Conv2d):
+        return Conv2dSidecarWrapper(orig_module)
+    elif isinstance(orig_module, RMSNorm):
+        return FluxRMSNormSidecarWrapper(orig_module)
+    else:
+        raise ValueError(f"No sidecar wrapper found for module type: {type(orig_module)}")

invokeai/backend/quantization/gguf/ggml_tensor.py

@@ -52,6 +52,7 @@ GGML_TENSOR_OP_TABLE = {
     torch.ops.aten.t.default: dequantize_and_run,  # pyright: ignore
     torch.ops.aten.addmm.default: dequantize_and_run,  # pyright: ignore
     torch.ops.aten.mul.Tensor: dequantize_and_run,  # pyright: ignore
+    torch.ops.aten.add.Tensor: dequantize_and_run,  # pyright: ignore
 }
 
 if torch.backends.mps.is_available():

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -5,8 +5,8 @@ from typing import TYPE_CHECKING
 
 from diffusers import UNet2DConditionModel
 
-from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.patches.model_patcher import LayerPatcher
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
 
 if TYPE_CHECKING:
@@ -30,8 +30,8 @@ class LoRAExt(ExtensionBase):
     @contextmanager
     def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         lora_model = self._node_context.models.load(self._model_id).model
-        assert isinstance(lora_model, LoRAModelRaw)
-        LoRAPatcher.apply_lora_patch(
+        assert isinstance(lora_model, ModelPatchRaw)
+        LayerPatcher.apply_model_patch(
             model=unet,
             prefix="lora_unet_",
             patch=lora_model,

invokeai/frontend/web/README.md

@@ -1,3 +1,3 @@
 # Invoke UI
 
-<https://invoke-ai.github.io/InvokeAI/contributing/frontend/OVERVIEW/>
+<https://invoke-ai.github.io/InvokeAI/contributing/frontend/>

invokeai/frontend/web/public/locales/de.json

@@ -96,7 +96,10 @@
         "new": "Neu",
         "ok": "OK",
         "close": "Schließen",
-        "clipboard": "Zwischenablage"
+        "clipboard": "Zwischenablage",
+        "generating": "Generieren",
+        "loadingModel": "Lade Modell",
+        "warnings": "Warnungen"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -268,7 +271,7 @@
                 "title": "Ansichts-Tool"
             },
             "quickSwitch": {
-                "title": "Ebenen schnell umschalten",
+                "title": "Ebenen Schnell-Umschalten",
                 "desc": "Wechseln Sie zwischen den beiden zuletzt gewählten Ebenen. Wenn eine Ebene mit einem Lesezeichen versehen ist, wird zwischen ihr und der letzten nicht markierten Ebene gewechselt."
             },
             "applyFilter": {
@@ -591,7 +594,17 @@
         "loraTriggerPhrases": "LoRA-Auslösephrasen",
         "installingBundle": "Bündel wird installiert",
         "triggerPhrases": "Auslösephrasen",
-        "mainModelTriggerPhrases": "Hauptmodell-Auslösephrasen"
+        "mainModelTriggerPhrases": "Hauptmodell-Auslösephrasen",
+        "noDefaultSettings": "Für dieses Modell sind keine Standardeinstellungen konfiguriert. Besuchen Sie den Modell-Manager, um Standardeinstellungen hinzuzufügen.",
+        "defaultSettingsOutOfSync": "Einige Einstellungen stimmen nicht mit den Standardeinstellungen des Modells überein:",
+        "clipLEmbed": "CLIP-L einbetten",
+        "clipGEmbed": "CLIP-G einbetten",
+        "hfTokenLabel": "HuggingFace-Token (für einige Modelle erforderlich)",
+        "hfTokenHelperText": "Für die Nutzung einiger Modelle ist ein HF-Token erforderlich. Klicken Sie hier, um Ihr Token zu erstellen oder zu erhalten.",
+        "hfForbidden": "Sie haben keinen Zugriff auf dieses HF-Modell",
+        "hfTokenInvalid": "Ungültiges oder fehlendes HF-Token",
+        "restoreDefaultSettings": "Klicken, um die Standardeinstellungen des Modells zu verwenden.",
+        "usingDefaultSettings": "Die Standardeinstellungen des Modells werden verwendet"
     },
     "parameters": {
         "images": "Bilder",
@@ -632,17 +645,22 @@
         "remixImage": "Remix des Bilds erstellen",
         "imageActions": "Weitere Bildaktionen",
         "invoke": {
-            "layer": {
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, Bbox-Breite ist {{width}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, Skalierte Bbox-Breite ist {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, Skalierte Bbox-Höhe ist {{height}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, Bbox-Höhe ist {{height}}"
-            },
             "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Skalierte Bbox-Breite ist {{width}}",
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Skalierte Bbox-Höhe ist {{height}}",
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Bbox-Breite ist {{width}}",
-            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Bbox-Höhe ist {{height}}"
-        }
+            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Bbox-Höhe ist {{height}}",
+            "noNodesInGraph": "Keine Knoten im Graphen",
+            "canvasIsTransforming": "Leinwand ist beschäftigt (wird transformiert)",
+            "canvasIsRasterizing": "Leinwand ist beschäftigt (wird gerastert)",
+            "canvasIsCompositing": "Leinwand ist beschäftigt (wird zusammengesetzt)",
+            "canvasIsFiltering": "Leinwand ist beschäftigt (wird gefiltert)",
+            "canvasIsSelectingObject": "Leinwand ist beschäftigt (wird Objekt ausgewählt)",
+            "noPrompts": "Keine Eingabeaufforderungen generiert"
+        },
+        "seed": "Seed",
+        "patchmatchDownScaleSize": "Herunterskalieren",
+        "seamlessXAxis": "Nahtlose X Achse",
+        "seamlessYAxis": "Nahtlose Y Achse"
     },
     "settings": {
         "displayInProgress": "Zwischenbilder anzeigen",
@@ -841,7 +859,8 @@
         "upscaling": "Hochskalierung",
         "canvas": "Leinwand",
         "prompts_one": "Prompt",
-        "prompts_other": "Prompts"
+        "prompts_other": "Prompts",
+        "batchSize": "Stapelgröße"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -1081,6 +1100,33 @@
         },
         "patchmatchDownScaleSize": {
             "heading": "Herunterskalieren"
+        },
+        "paramHeight": {
+            "heading": "Höhe",
+            "paragraphs": [
+                "Höhe des generierten Bildes. Muss ein Vielfaches von 8 sein."
+            ]
+        },
+        "paramUpscaleMethod": {
+            "heading": "Vergrößerungsmethode",
+            "paragraphs": [
+                "Methode zum Hochskalieren des Bildes für High Resolution Fix."
+            ]
+        },
+        "paramHrf": {
+            "heading": "High Resolution Fix aktivieren"
+        },
+        "seamlessTilingYAxis": {
+            "heading": "Nahtlose Kachelung Y Achse",
+            "paragraphs": [
+                "Nahtloses Kacheln eines Bildes entlang der vertikalen Achse."
+            ]
+        },
+        "seamlessTilingXAxis": {
+            "paragraphs": [
+                "Nahtloses Kacheln eines Bildes entlang der horizontalen Achse."
+            ],
+            "heading": "Nahtlose Kachelung X Achse"
         }
     },
     "invocationCache": {
@@ -1292,7 +1338,15 @@
         "loadWorkflow": "Arbeitsablauf $t(common.load)",
         "updated": "Aktualisiert",
         "created": "Erstellt",
-        "descending": "Absteigend"
+        "descending": "Absteigend",
+        "edit": "Bearbeiten",
+        "loadFromGraph": "Arbeitsablauf aus dem Graph laden",
+        "delete": "Löschen",
+        "copyShareLinkForWorkflow": "Teilen-Link für Arbeitsablauf kopieren",
+        "autoLayout": "Auto Layout",
+        "copyShareLink": "Teilen-Link kopieren",
+        "download": "Herunterladen",
+        "convertGraph": "Graph konvertieren"
     },
     "sdxl": {
         "concatPromptStyle": "Verknüpfen von Prompt & Stil",
@@ -1542,7 +1596,8 @@
             "filters": "Filter",
             "filterType": "Filtertyp",
             "filter": "Filter"
-        }
+        },
+        "bookmark": "Lesezeichen für Schnell-Umschalten"
     },
     "upsell": {
         "shareAccess": "Zugang teilen",

invokeai/frontend/web/public/locales/en.json

@@ -176,7 +176,8 @@
         "reset": "Reset",
         "none": "None",
         "new": "New",
-        "generating": "Generating"
+        "generating": "Generating",
+        "warnings": "Warnings"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -808,6 +809,7 @@
         "starterBundleHelpText": "Easily install all models needed to get started with a base model, including a main model, controlnets, IP adapters, and more. Selecting a bundle will skip any models that you already have installed.",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
+        "controlLora": "Control LoRA",
         "syncModels": "Sync Models",
         "textualInversions": "Textual Inversions",
         "triggerPhrases": "Trigger Phrases",
@@ -1031,6 +1033,7 @@
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), bbox height is {{height}}",
             "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), scaled bbox width is {{width}}",
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), scaled bbox height is {{height}}",
+            "fluxModelMultipleControlLoRAs": "Can only use 1 Control LoRA at a time",
             "canvasIsFiltering": "Canvas is busy (filtering)",
             "canvasIsTransforming": "Canvas is busy (transforming)",
             "canvasIsRasterizing": "Canvas is busy (rasterizing)",
@@ -1038,20 +1041,7 @@
             "canvasIsSelectingObject": "Canvas is busy (selecting object)",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected",
-            "layer": {
-                "controlAdapterNoModelSelected": "no Control Adapter model selected",
-                "controlAdapterIncompatibleBaseModel": "incompatible Control Adapter base model",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, bbox width is {{width}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, bbox height is {{height}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, scaled bbox width is {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, scaled bbox height is {{height}}",
-                "ipAdapterNoModelSelected": "no IP adapter selected",
-                "ipAdapterIncompatibleBaseModel": "incompatible IP Adapter base model",
-                "ipAdapterNoImageSelected": "no IP Adapter image selected",
-                "rgNoPromptsOrIPAdapters": "no text prompts or IP Adapters",
-                "rgNoRegion": "no region selected"
-            }
+            "systemDisconnected": "System disconnected"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1319,8 +1309,9 @@
         "controlNetBeginEnd": {
             "heading": "Begin / End Step Percentage",
             "paragraphs": [
-                "The part of the of the denoising process that will have the Control Adapter applied.",
-                "Generally, Control Adapters applied at the start of the process guide composition, and Control Adapters applied at the end guide details."
+                "This setting determines which portion of the denoising (generation) process incorporates the guidance from this layer.",
+                "• Start Step (%): Specifies when to begin applying the guidance from this layer during the generation process.",
+                "• End Step (%): Specifies when to stop applying this layer's guidance and revert general guidance from the model and other settings."
             ]
         },
         "controlNetControlMode": {
@@ -1338,13 +1329,15 @@
             "paragraphs": ["Method to fit Control Adapter's input image size to the output generation size."]
         },
         "ipAdapterMethod": {
-            "heading": "Method",
-            "paragraphs": ["Method by which to apply the current IP Adapter."]
+            "heading": "Mode",
+            "paragraphs": ["The mode defines how the reference image will guide the generation process."]
         },
         "controlNetWeight": {
             "heading": "Weight",
             "paragraphs": [
-                "Weight of the Control Adapter. Higher weight will lead to larger impacts on the final image."
+                "Adjusts how strongly the layer influences the generation process",
+                "• Higher Weight (.75-2): Creates a more significant impact on the final result.",
+                "• Lower Weight (0-.75): Creates a smaller impact on the final result."
             ]
         },
         "dynamicPrompts": {
@@ -1710,6 +1703,8 @@
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
         "regionalGuidance": "Regional Guidance",
+        "referenceImageRegional": "Reference Image (Regional)",
+        "referenceImageGlobal": "Reference Image (Global)",
         "asRasterLayer": "As $t(controlLayers.rasterLayer)",
         "asRasterLayerResize": "As $t(controlLayers.rasterLayer) (Resize)",
         "asControlLayer": "As $t(controlLayers.controlLayer)",
@@ -1781,6 +1776,7 @@
         "pullBboxIntoLayer": "Pull Bbox into Layer",
         "pullBboxIntoReferenceImage": "Pull Bbox into Reference Image",
         "showProgressOnCanvas": "Show Progress on Canvas",
+        "useImage": "Use Image",
         "prompt": "Prompt",
         "negativePrompt": "Negative Prompt",
         "beginEndStepPercentShort": "Begin/End %",
@@ -1793,6 +1789,22 @@
         "resetGenerationSettings": "Reset Generation Settings",
         "replaceCurrent": "Replace Current",
         "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or draw on the canvas to get started.",
+        "referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton> or drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer to get started.",
+        "warnings": {
+            "problemsFound": "Problems found",
+            "unsupportedModel": "layer not supported for selected base model",
+            "controlAdapterNoModelSelected": "no Control Layer model selected",
+            "controlAdapterIncompatibleBaseModel": "incompatible Control Layer base model",
+            "controlAdapterNoControl": "no control selected/drawn",
+            "ipAdapterNoModelSelected": "no Reference Image model selected",
+            "ipAdapterIncompatibleBaseModel": "incompatible Reference Image base model",
+            "ipAdapterNoImageSelected": "no Reference Image image selected",
+            "rgNoPromptsOrIPAdapters": "no text prompts or Reference Images",
+            "rgNegativePromptNotSupported": "Negative Prompt not supported for selected base model",
+            "rgReferenceImagesNotSupported": "regional Reference Images not supported for selected base model",
+            "rgAutoNegativeNotSupported": "Auto-Negative not supported for selected base model",
+            "rgNoRegion": "no region drawn"
+        },
         "controlMode": {
             "controlMode": "Control Mode",
             "balanced": "Balanced (recommended)",
@@ -1801,10 +1813,13 @@
             "megaControl": "Mega Control"
         },
         "ipAdapterMethod": {
-            "ipAdapterMethod": "IP Adapter Method",
+            "ipAdapterMethod": "Mode",
             "full": "Style and Composition",
+            "fullDesc": "Applies visual style (colors, textures) & composition (layout, structure).",
             "style": "Style Only",
-            "composition": "Composition Only"
+            "styleDesc": "Applies visual style (colors, textures) without considering its layout.",
+            "composition": "Composition Only",
+            "compositionDesc": "Replicates layout & structure while ignoring the reference's style."
         },
         "fill": {
             "fillColor": "Fill Color",
@@ -2097,6 +2112,7 @@
         },
         "logNamespaces": {
             "logNamespaces": "Log Namespaces",
+            "dnd": "Drag and Drop",
             "gallery": "Gallery",
             "models": "Models",
             "config": "Config",
@@ -2120,8 +2136,7 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "<StrongComponent>Workflows</StrongComponent>: Run a workflow for a collection of images using the new <StrongComponent>Image Batch</StrongComponent> node.",
-            "<StrongComponent>FLUX</StrongComponent>: Support for XLabs IP Adapter v2."
+            "<StrongComponent>Flux Control Layers</StrongComponent>: New control models for edge detection and depth mapping are now supported for Flux dev models."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

invokeai/frontend/web/public/locales/es.json

@@ -507,8 +507,7 @@
         "watchUiUpdatesOverview": "Descripción general de las actualizaciones de la interfaz de usuario de Watch",
         "whatsNewInInvoke": "Novedades en Invoke",
         "items": [
-            "<StrongComponent>SD 3.5</StrongComponent>: compatibilidad con SD 3.5 Medium y Large.",
-            "<StrongComponent>Lienzo</StrongComponent>: Se ha simplificado el procesamiento de la capa de control y se ha mejorado la configuración predeterminada del control."
+            "<StrongComponent>SD 3.5</StrongComponent>: compatibilidad con SD 3.5 Medium y Large."
         ]
     },
     "invocationCache": {

invokeai/frontend/web/public/locales/fr.json

@@ -96,7 +96,9 @@
         "negativePrompt": "Prompt Négatif",
         "ok": "Ok",
         "close": "Fermer",
-        "clipboard": "Presse-papier"
+        "clipboard": "Presse-papier",
+        "loadingModel": "Chargement du modèle",
+        "generating": "En Génération"
     },
     "gallery": {
         "galleryImageSize": "Taille de l'image",
@@ -287,7 +289,20 @@
         "noDefaultSettings": "Aucun paramètre par défaut configuré pour ce modèle. Visitez le Gestionnaire de Modèles pour ajouter des paramètres par défaut.",
         "usingDefaultSettings": "Utilisation des paramètres par défaut du modèle",
         "defaultSettingsOutOfSync": "Certain paramètres ne correspondent pas aux valeurs par défaut du modèle :",
-        "restoreDefaultSettings": "Cliquez pour utiliser les paramètres par défaut du modèle."
+        "restoreDefaultSettings": "Cliquez pour utiliser les paramètres par défaut du modèle.",
+        "hfForbiddenErrorMessage": "Nous vous recommandons de visiter la page du modèle sur HuggingFace.com. Le propriétaire peut exiger l'acceptation des conditions pour pouvoir télécharger.",
+        "hfTokenRequired": "Vous essayez de télécharger un modèle qui nécessite un token HuggingFace valide.",
+        "clipLEmbed": "CLIP-L Embed",
+        "hfTokenSaved": "Token HF enregistré",
+        "hfTokenUnableToVerifyErrorMessage": "Impossible de vérifier le token HuggingFace. Cela est probablement dû à une erreur réseau. Veuillez réessayer plus tard.",
+        "clipGEmbed": "CLIP-G Embed",
+        "hfTokenUnableToVerify": "Impossible de vérifier le token HF",
+        "hfTokenInvalidErrorMessage": "Token HuggingFace invalide ou manquant.",
+        "hfTokenLabel": "Token HuggingFace (Requis pour certains modèles)",
+        "hfTokenHelperText": "Un token HF est requis pour utiliser certains modèles. Cliquez ici pour créer ou obtenir votre token.",
+        "hfTokenInvalid": "Token HF invalide ou manquant",
+        "hfForbidden": "Vous n'avez pas accès à ce modèle HF.",
+        "hfTokenInvalidErrorMessage2": "Mettre à jour dans le "
     },
     "parameters": {
         "images": "Images",
@@ -317,19 +332,6 @@
         "info": "Info",
         "showOptionsPanel": "Afficher le panneau latéral (O ou T)",
         "invoke": {
-            "layer": {
-                "rgNoPromptsOrIPAdapters": "aucun prompts ou IP Adapters",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, la largeur de la bounding box mise à l'échelle est {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, la hauteur de la bounding box mise à l'échelle est {{height}}",
-                "ipAdapterNoModelSelected": "aucun IP adapter sélectionné",
-                "ipAdapterNoImageSelected": "aucune image d'IP adapter sélectionnée",
-                "controlAdapterIncompatibleBaseModel": "modèle de base de Control Adapter incompatible",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, la hauteur de la bounding box est {{height}}",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, la largeur de la bounding box est {{width}}",
-                "ipAdapterIncompatibleBaseModel": "modèle de base d'IP adapter incompatible",
-                "rgNoRegion": "aucune zone sélectionnée",
-                "controlAdapterNoModelSelected": "aucun modèle de Control Adapter sélectionné"
-            },
             "noPrompts": "Aucun prompts généré",
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} entrée manquante",
             "missingFieldTemplate": "Modèle de champ manquant",
@@ -349,7 +351,11 @@
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), la largeur de la bounding box est {{width}}",
             "noT5EncoderModelSelected": "Aucun modèle T5 Encoder sélectionné pour la génération FLUX",
             "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), la largeur de la bounding box mise à l'échelle est {{width}}",
-            "canvasIsCompositing": "La toile est en train de composer"
+            "canvasIsCompositing": "La toile est en train de composer",
+            "collectionEmpty": "{{nodeLabel}} -> {{fieldLabel}} collection vide",
+            "collectionTooFewItems": "{{nodeLabel}} -> {{fieldLabel}} : trop peu d'éléments, minimum {{minItems}}",
+            "collectionTooManyItems": "{{nodeLabel}} -> {{fieldLabel}} : trop d'éléments, maximum {{maxItems}}",
+            "canvasIsSelectingObject": "La toile est occupée (sélection d'objet)"
         },
         "negativePromptPlaceholder": "Prompt Négatif",
         "positivePromptPlaceholder": "Prompt Positif",
@@ -390,7 +396,9 @@
         "sendToUpscale": "Envoyer à Agrandir",
         "guidance": "Guidage",
         "postProcessing": "Post-traitement (Maj + U)",
-        "processImage": "Traiter l'image"
+        "processImage": "Traiter l'image",
+        "disabledNoRasterContent": "Désactivé (Aucun contenu raster)",
+        "recallMetadata": "Rappeler les métadonnées"
     },
     "settings": {
         "models": "Modèles",
@@ -428,7 +436,8 @@
         "confirmOnNewSession": "Confirmer lors d'une nouvelle session",
         "modelDescriptionsDisabledDesc": "Les descriptions des modèles dans les menus déroulants ont été désactivées. Activez-les dans les paramètres.",
         "enableModelDescriptions": "Activer les descriptions de modèle dans les menus déroulants",
-        "modelDescriptionsDisabled": "Descriptions de modèle dans les menus déroulants désactivés"
+        "modelDescriptionsDisabled": "Descriptions de modèle dans les menus déroulants désactivés",
+        "showDetailedInvocationProgress": "Afficher les détails de progression"
     },
     "toast": {
         "uploadFailed": "Importation échouée",
@@ -647,7 +656,8 @@
         "iterations_one": "Itération",
         "iterations_many": "Itérations",
         "iterations_other": "Itérations",
-        "back": "fin"
+        "back": "fin",
+        "batchSize": "Taille de lot"
     },
     "prompt": {
         "noMatchingTriggers": "Pas de déclancheurs correspondants",
@@ -1165,7 +1175,8 @@
             "heading": "Force de débruitage",
             "paragraphs": [
                 "Intensité du bruit ajouté à l'image d'entrée.",
-                "0 produira une image identique, tandis que 1 produira une image complètement différente."
+                "0 produira une image identique, tandis que 1 produira une image complètement différente.",
+                "Lorsque aucune couche raster avec du contenu visible n'est présente, ce paramètre est ignoré."
             ]
         },
         "lora": {
@@ -1460,7 +1471,9 @@
         "parsingFailed": "L'analyse a échoué",
         "recallParameter": "Rappeler {{label}}",
         "canvasV2Metadata": "Toile",
-        "guidance": "Guide"
+        "guidance": "Guide",
+        "seamlessXAxis": "Axe X sans bords",
+        "seamlessYAxis": "Axe Y sans bords"
     },
     "sdxl": {
         "freePromptStyle": "Écriture de Prompt manuelle",
@@ -1681,7 +1694,13 @@
         "delete": "Supprimer"
     },
     "whatsNew": {
-        "whatsNewInInvoke": "Quoi de neuf dans Invoke"
+        "whatsNewInInvoke": "Quoi de neuf dans Invoke",
+        "watchRecentReleaseVideos": "Regarder les vidéos des dernières versions",
+        "items": [
+            "<StrongComponent>FLUX Guidage Régional (bêta)</StrongComponent> : Notre version bêta de FLUX Guidage Régional est en ligne pour le contrôle des prompt régionaux."
+        ],
+        "readReleaseNotes": "Notes de version",
+        "watchUiUpdatesOverview": "Aperçu des mises à jour de l'interface utilisateur"
     },
     "ui": {
         "tabs": {
@@ -1789,7 +1808,10 @@
             },
             "process": "Traiter",
             "apply": "Appliquer",
-            "cancel": "Annuler"
+            "cancel": "Annuler",
+            "advanced": "Avancé",
+            "processingLayerWith": "Calque de traitement avec le filtre {{type}}.",
+            "forMoreControl": "Pour plus de contrôle, cliquez sur Avancé ci-dessous."
         },
         "canvasContextMenu": {
             "saveToGalleryGroup": "Enregistrer dans la galerie",
@@ -2042,7 +2064,17 @@
         "convertInpaintMaskTo": "Convertir $t(controlLayers.inpaintMask) vers",
         "copyControlLayerTo": "Copier $t(controlLayers.controlLayer) vers",
         "newInpaintMask": "Nouveau $t(controlLayers.inpaintMask)",
-        "newRasterLayer": "Nouveau $t(controlLayers.rasterLayer)"
+        "newRasterLayer": "Nouveau $t(controlLayers.rasterLayer)",
+        "mergingLayers": "Fusionner les couches",
+        "resetCanvasLayers": "Réinitialiser les couches de la toile",
+        "resetGenerationSettings": "Réinitialiser les paramètres de génération",
+        "mergeDown": "Fusionner",
+        "controlLayerEmptyState": "<UploadButton>Télécharger une image</UploadButton>, faites glisser une image depuis la <GalleryButton>galerie</GalleryButton> sur ce calque, ou dessinez sur la toile pour commencer.",
+        "asRasterLayer": "En tant que $t(controlLayers.rasterLayer)",
+        "asRasterLayerResize": "En tant que $t(controlLayers.rasterLayer) (Redimensionner)",
+        "asControlLayer": "En tant que $t(controlLayers.controlLayer)",
+        "asControlLayerResize": "En $t(controlLayers.controlLayer) (Redimensionner)",
+        "newSession": "Nouvelle session"
     },
     "upscaling": {
         "exceedsMaxSizeDetails": "La limite maximale d'agrandissement est de {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixels. Veuillez essayer une image plus petite ou réduire votre sélection d'échelle.",
@@ -2060,7 +2092,9 @@
         "postProcessingModel": "Modèle de post-traitement",
         "missingUpscaleModel": "Modèle d'agrandissement manquant",
         "missingUpscaleInitialImage": "Image initiale manquante pour l'agrandissement",
-        "missingTileControlNetModel": "Aucun modèle ControlNet valide installé"
+        "missingTileControlNetModel": "Aucun modèle ControlNet valide installé",
+        "incompatibleBaseModelDesc": "L'upscaling est pris en charge uniquement pour les modèles d'architecture SD1.5 et SDXL. Changez le modèle principal pour activer l'upscaling.",
+        "incompatibleBaseModel": "Modèle principal non pris en charge pour l'upscaling"
     },
     "stylePresets": {
         "deleteTemplate": "Supprimer le template",
@@ -2146,5 +2180,62 @@
         "inviteTeammates": "Inviter des collègues",
         "professionalUpsell": "Disponible dans l'édition professionnelle d'Invoke. Cliquez ici ou visitez invoke.com/pricing pour plus de détails.",
         "professional": "Professionnel"
+    },
+    "supportVideos": {
+        "watch": "Regarder",
+        "videos": {
+            "upscaling": {
+                "description": "Comment améliorer la résolution des images avec les outils d'Invoke pour les agrandir.",
+                "title": "Upscaling"
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "description": "Étapes pour générer et enregistrer des images dans la galerie.",
+                "title": "Comment générer et enregistrer dans la galerie ?"
+            },
+            "usingControlLayersAndReferenceGuides": {
+                "title": "Utilisation des couche de contrôle et des guides de référence",
+                "description": "Apprenez à guider la création de vos images avec des couche de contrôle et des images de référence."
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "description": "Plongez dans les modèles d'IA et découvrez comment utiliser les adaptateurs de concepts pour un contrôle créatif.",
+                "title": "Exploration des modèles d'IA et des adaptateurs de concepts"
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "Comment utiliser les réseaux de contrôle et les couches de contrôle ?",
+                "description": "Apprenez à appliquer des couches de contrôle et des ControlNets à vos images."
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "description": "Apprenez à composer des images en utilisant le canvas de contrôle d'Invoke.",
+                "title": "Créer et composer sur le canvas de contrôle d'Invoke"
+            },
+            "howDoIEditOnTheCanvas": {
+                "title": "Comment puis-je modifier sur la toile ?",
+                "description": "Guide pour éditer des images directement sur la toile."
+            },
+            "howDoIDoImageToImageTransformation": {
+                "title": "Comment effectuer une transformation d'image à image ?",
+                "description": "Tutoriel sur la réalisation de transformations d'image à image dans Invoke."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "title": "Comment utiliser les IP Adapters globaux et les images de référence ?",
+                "description": "Introduction à l'ajout d'images de référence et IP Adapters globaux."
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "Comment utiliser les masques d'inpainting ?"
+            },
+            "creatingYourFirstImage": {
+                "title": "Créer votre première image",
+                "description": "Introduction à la création d'une image à partir de zéro en utilisant les outils d'Invoke."
+            },
+            "understandingImageToImageAndDenoising": {
+                "title": "Comprendre l'Image-à-Image et le Débruitage",
+                "description": "Aperçu des transformations d'image à image et du débruitage dans Invoke."
+            }
+        },
+        "gettingStarted": "Commencer",
+        "studioSessionsDesc1": "Consultez le <StudioSessionsPlaylistLink /> pour des approfondissements sur Invoke.",
+        "studioSessionsDesc2": "Rejoignez notre <DiscordLink /> pour participer aux sessions en direct et poser vos questions. Les sessions sont ajoutée dans la playlist la semaine suivante.",
+        "supportVideos": "Vidéos d'assistance",
+        "controlCanvas": "Contrôler la toile"
     }
 }

invokeai/frontend/web/public/locales/it.json

@@ -96,7 +96,8 @@
         "clipboard": "Appunti",
         "ok": "Ok",
         "generating": "Generazione",
-        "loadingModel": "Caricamento del modello"
+        "loadingModel": "Caricamento del modello",
+        "warnings": "Avvisi"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -610,7 +611,8 @@
         "hfForbidden": "Non hai accesso a questo modello HF",
         "hfTokenLabel": "Gettone HuggingFace (richiesto per alcuni modelli)",
         "hfForbiddenErrorMessage": "Consigliamo di visitare la pagina del repository su HuggingFace.com. Il proprietario potrebbe richiedere l'accettazione dei termini per poter effettuare il download.",
-        "hfTokenInvalidErrorMessage2": "Aggiornalo in "
+        "hfTokenInvalidErrorMessage2": "Aggiornalo in ",
+        "controlLora": "Controllo LoRA"
     },
     "parameters": {
         "images": "Immagini",
@@ -662,21 +664,8 @@
             "addingImagesTo": "Aggiungi immagini a",
             "systemDisconnected": "Sistema disconnesso",
             "missingNodeTemplate": "Modello di nodo mancante",
-            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante",
+            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}}: ingresso mancante",
             "missingFieldTemplate": "Modello di campo mancante",
-            "layer": {
-                "controlAdapterNoModelSelected": "Nessun modello di adattatore di controllo selezionato",
-                "controlAdapterIncompatibleBaseModel": "Il modello base dell'adattatore di controllo non è compatibile",
-                "ipAdapterNoModelSelected": "Nessun adattatore IP selezionato",
-                "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
-                "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
-                "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
-                "rgNoRegion": "Nessuna regione selezionata",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza riquadro è {{width}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza riquadro è {{height}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza del riquadro scalato {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza del riquadro scalato {{height}}"
-            },
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), altezza riquadro è {{height}}",
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), larghezza riquadro è {{width}}",
             "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), larghezza del riquadro scalato è {{width}}",
@@ -684,10 +673,15 @@
             "noT5EncoderModelSelected": "Nessun modello di encoder T5 selezionato per la generazione con FLUX",
             "noCLIPEmbedModelSelected": "Nessun modello CLIP Embed selezionato per la generazione con FLUX",
             "noFLUXVAEModelSelected": "Nessun modello VAE selezionato per la generazione con FLUX",
-            "canvasIsTransforming": "La tela sta trasformando",
-            "canvasIsRasterizing": "La tela sta rasterizzando",
-            "canvasIsCompositing": "La tela è in fase di composizione",
-            "canvasIsFiltering": "La tela sta filtrando"
+            "canvasIsTransforming": "La tela è occupata (sta trasformando)",
+            "canvasIsRasterizing": "La tela è occupata (sta rasterizzando)",
+            "canvasIsCompositing": "La tela è occupata (in composizione)",
+            "canvasIsFiltering": "La tela è occupata (sta filtrando)",
+            "collectionTooManyItems": "{{nodeLabel}} -> {{fieldLabel}}: troppi elementi, massimo {{maxItems}}",
+            "canvasIsSelectingObject": "La tela è occupata (selezione dell'oggetto)",
+            "collectionTooFewItems": "{{nodeLabel}} -> {{fieldLabel}}: troppi pochi elementi, minimo {{minItems}}",
+            "collectionEmpty": "{{nodeLabel}} -> {{fieldLabel}} raccolta vuota",
+            "fluxModelMultipleControlLoRAs": "È possibile utilizzare solo 1 Controllo LoRA alla volta"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -972,7 +966,9 @@
         "saveToGallery": "Salva nella Galleria",
         "noMatchingWorkflows": "Nessun flusso di lavoro corrispondente",
         "noWorkflows": "Nessun flusso di lavoro",
-        "workflowHelpText": "Hai bisogno di aiuto? Consulta la nostra guida <LinkComponent>Introduzione ai flussi di lavoro</LinkComponent>."
+        "workflowHelpText": "Hai bisogno di aiuto? Consulta la nostra guida <LinkComponent>Introduzione ai flussi di lavoro</LinkComponent>.",
+        "specialDesc": "Questa invocazione comporta una gestione speciale nell'applicazione. Ad esempio, i nodi Lotto vengono utilizzati per mettere in coda più grafici da un singolo flusso di lavoro.",
+        "internalDesc": "Questa invocazione è utilizzata internamente da Invoke. Potrebbe subire modifiche significative durante gli aggiornamenti dell'app e potrebbe essere rimossa in qualsiasi momento."
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1093,7 +1089,8 @@
         "workflows": "Flussi di lavoro",
         "generation": "Generazione",
         "other": "Altro",
-        "gallery": "Galleria"
+        "gallery": "Galleria",
+        "batchSize": "Dimensione del lotto"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1195,8 +1192,9 @@
         "controlNetBeginEnd": {
             "heading": "Percentuale passi Inizio / Fine",
             "paragraphs": [
-                "La parte del processo di rimozione del rumore in cui verrà applicato l'adattatore di controllo.",
-                "In genere, gli adattatori di controllo applicati all'inizio del processo guidano la composizione, mentre quelli applicati alla fine guidano i dettagli."
+                "Questa impostazione determina quale parte del processo di rimozione del rumore (generazione) incorpora la guida da questo livello.",
+                "• Passo iniziale (%): specifica quando iniziare ad applicare la guida da questo livello durante il processo di generazione.",
+                "• Passo finale (%): specifica quando interrompere l'applicazione della guida di questo livello e ripristinare la guida generale dal modello e altre impostazioni."
             ]
         },
         "noiseUseCPU": {
@@ -1300,7 +1298,9 @@
         "controlNetWeight": {
             "heading": "Peso",
             "paragraphs": [
-                "Peso dell'adattatore di controllo. Un peso maggiore porterà a impatti maggiori sull'immagine finale."
+                "Regola la forza con cui il livello influenza il processo di generazione",
+                "• Peso maggiore (0.75-2): crea un impatto più significativo sul risultato finale.",
+                "• Peso inferiore (0-0.75): crea un impatto minore sul risultato finale."
             ]
         },
         "paramCFGScale": {
@@ -1477,9 +1477,9 @@
             ]
         },
         "ipAdapterMethod": {
-            "heading": "Metodo",
+            "heading": "Modalità",
             "paragraphs": [
-                "Metodo con cui applicare l'adattatore IP corrente."
+                "La modalità definisce il modo in cui l'immagine di riferimento guiderà il processo di generazione."
             ]
         },
         "scale": {
@@ -1801,7 +1801,10 @@
             "full": "Stile e Composizione",
             "style": "Solo Stile",
             "composition": "Solo Composizione",
-            "ipAdapterMethod": "Metodo Adattatore IP"
+            "ipAdapterMethod": "Modalità",
+            "fullDesc": "Applica lo stile visivo (colori, texture) e la composizione (disposizione, struttura).",
+            "styleDesc": "Applica lo stile visivo (colori, texture) senza considerare la disposizione.",
+            "compositionDesc": "Replica disposizione e struttura ignorando lo stile di riferimento."
         },
         "showingType": "Mostra {{type}}",
         "dynamicGrid": "Griglia dinamica",
@@ -2044,7 +2047,33 @@
         "replaceCurrent": "Sostituisci corrente",
         "mergeDown": "Unire in basso",
         "mergingLayers": "Unione dei livelli",
-        "controlLayerEmptyState": "<UploadButton>Carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello oppure disegna sulla tela per iniziare."
+        "controlLayerEmptyState": "<UploadButton>Carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello oppure disegna sulla tela per iniziare.",
+        "useImage": "Usa immagine",
+        "resetGenerationSettings": "Ripristina impostazioni di generazione",
+        "referenceImageEmptyState": "Per iniziare, <UploadButton>carica un'immagine</UploadButton> oppure trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello.",
+        "asRasterLayer": "Come $t(controlLayers.rasterLayer)",
+        "asRasterLayerResize": "Come $t(controlLayers.rasterLayer) (Ridimensiona)",
+        "asControlLayer": "Come $t(controlLayers.controlLayer)",
+        "asControlLayerResize": "Come $t(controlLayers.controlLayer) (Ridimensiona)",
+        "newSession": "Nuova sessione",
+        "resetCanvasLayers": "Ripristina livelli Tela",
+        "referenceImageRegional": "Immagine di riferimento (regionale)",
+        "referenceImageGlobal": "Immagine di riferimento (globale)",
+        "warnings": {
+            "controlAdapterNoModelSelected": "nessun modello selezionato per il livello di controllo",
+            "controlAdapterNoControl": "nessun controllo selezionato/disegnato",
+            "ipAdapterNoModelSelected": "nessun modello di immagine di riferimento selezionato",
+            "rgNoPromptsOrIPAdapters": "nessun prompt testuale o immagini di riferimento",
+            "rgReferenceImagesNotSupported": "Immagini di riferimento regionali non supportate per il modello base selezionato",
+            "rgNoRegion": "nessuna regione disegnata",
+            "problemsFound": "Problemi riscontrati",
+            "unsupportedModel": "livello non supportato per il modello base selezionato",
+            "controlAdapterIncompatibleBaseModel": "modello di base del livello di controllo incompatibile",
+            "rgNegativePromptNotSupported": "Prompt negativo non supportato per il modello base selezionato",
+            "ipAdapterIncompatibleBaseModel": "modello base dell'immagine di riferimento incompatibile",
+            "ipAdapterNoImageSelected": "nessuna immagine di riferimento selezionata",
+            "rgAutoNegativeNotSupported": "Auto-Negativo non supportato per il modello base selezionato"
+        }
     },
     "ui": {
         "tabs": {
@@ -2144,8 +2173,7 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "<StrongComponent>SD 3.5</StrongComponent>: supporto per SD 3.5 Medium e Large.",
-            "<StrongComponent>Tela</StrongComponent>: elaborazione semplificata del livello di controllo e impostazioni di controllo predefinite migliorate."
+            "<StrongComponent>Livelli di controllo Flux</StrongComponent>: nuovi modelli di controllo per il rilevamento dei bordi e la mappatura della profondità sono ora supportati per i modelli di Flux dev."
         ]
     },
     "system": {
@@ -2169,8 +2197,71 @@
             "events": "Eventi",
             "system": "Sistema",
             "metadata": "Metadati",
-            "logNamespaces": "Elementi del registro"
+            "logNamespaces": "Elementi del registro",
+            "dnd": "Trascina e rilascia"
         },
         "enableLogging": "Abilita la registrazione"
+    },
+    "supportVideos": {
+        "gettingStarted": "Iniziare",
+        "supportVideos": "Video di supporto",
+        "videos": {
+            "usingControlLayersAndReferenceGuides": {
+                "title": "Utilizzo di livelli di controllo e guide di riferimento",
+                "description": "Scopri come guidare la creazione delle tue immagini con livelli di controllo e immagini di riferimento."
+            },
+            "creatingYourFirstImage": {
+                "description": "Introduzione alla creazione di un'immagine da zero utilizzando gli strumenti di Invoke.",
+                "title": "Creazione della tua prima immagine"
+            },
+            "understandingImageToImageAndDenoising": {
+                "description": "Panoramica delle trasformazioni immagine-a-immagine e della riduzione del rumore in Invoke.",
+                "title": "Comprendere immagine-a-immagine e riduzione del rumore"
+            },
+            "howDoIDoImageToImageTransformation": {
+                "description": "Tutorial su come eseguire trasformazioni da immagine a immagine in Invoke.",
+                "title": "Come si esegue la trasformazione da immagine-a-immagine?"
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "Come si usano le maschere Inpaint?",
+                "description": "Come applicare maschere inpaint per la correzione e la variazione delle immagini."
+            },
+            "howDoIOutpaint": {
+                "description": "Guida all'outpainting oltre i confini dell'immagine originale.",
+                "title": "Come posso eseguire l'outpainting?"
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "description": "Approfondisci i modelli di intelligenza artificiale e scopri come utilizzare gli adattatori concettuali per il controllo creativo.",
+                "title": "Esplorazione dei modelli di IA e degli adattatori concettuali"
+            },
+            "upscaling": {
+                "title": "Ampliamento",
+                "description": "Come ampliare le immagini con gli strumenti di Invoke per migliorarne la risoluzione."
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "description": "Impara a comporre immagini utilizzando la tela di controllo di Invoke.",
+                "title": "Creare e comporre sulla tela di controllo di Invoke"
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "description": "Passaggi per generare e salvare le immagini nella galleria.",
+                "title": "Come posso generare e salvare nella Galleria?"
+            },
+            "howDoIEditOnTheCanvas": {
+                "title": "Come posso apportare modifiche sulla tela?",
+                "description": "Guida alla modifica delle immagini direttamente sulla tela."
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "Come posso utilizzare le Reti di Controllo e i Livelli di Controllo?",
+                "description": "Impara ad applicare livelli di controllo e reti di controllo alle tue immagini."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "title": "Come si utilizzano gli adattatori IP globali e le immagini di riferimento?",
+                "description": "Introduzione all'aggiunta di immagini di riferimento e adattatori IP globali."
+            }
+        },
+        "controlCanvas": "Tela di Controllo",
+        "watch": "Guarda",
+        "studioSessionsDesc1": "Dai un'occhiata a <StudioSessionsPlaylistLink /> per approfondimenti su Invoke.",
+        "studioSessionsDesc2": "Unisciti al nostro <DiscordLink /> per partecipare alle sessioni live e fare domande. Le sessioni vengono caricate sulla playlist la settimana successiva."
     }
 }

invokeai/frontend/web/public/locales/nl.json

@@ -230,16 +230,7 @@
             "systemDisconnected": "Systeem is niet verbonden",
             "missingNodeTemplate": "Knooppuntsjabloon ontbreekt",
             "missingFieldTemplate": "Veldsjabloon ontbreekt",
-            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan",
-            "layer": {
-                "controlAdapterNoModelSelected": "geen controle-adaptermodel geselecteerd",
-                "controlAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor controle-adapter",
-                "ipAdapterIncompatibleBaseModel": "niet-compatibele basismodel voor IP-adapter",
-                "ipAdapterNoImageSelected": "geen afbeelding voor IP-adapter geselecteerd",
-                "rgNoRegion": "geen gebied geselecteerd",
-                "rgNoPromptsOrIPAdapters": "geen tekstprompts of IP-adapters",
-                "ipAdapterNoModelSelected": "geen IP-adapter geselecteerd"
-            }
+            "addingImagesTo": "Bezig met toevoegen van afbeeldingen aan"
         },
         "patchmatchDownScaleSize": "Verklein",
         "useCpuNoise": "Gebruik CPU-ruis",

invokeai/frontend/web/public/locales/pl.json

@@ -10,7 +10,24 @@
         "load": "Załaduj",
         "statusDisconnected": "Odłączono od serwera",
         "githubLabel": "GitHub",
-        "discordLabel": "Discord"
+        "discordLabel": "Discord",
+        "clipboard": "Schowek",
+        "aboutDesc": "Wykorzystujesz Invoke do pracy? Sprawdź:",
+        "ai": "SI",
+        "areYouSure": "Czy jesteś pewien?",
+        "copyError": "$t(gallery.copy) Błąd",
+        "apply": "Zastosuj",
+        "copy": "Kopiuj",
+        "or": "albo",
+        "add": "Dodaj",
+        "off": "Wyłączony",
+        "accept": "Zaakceptuj",
+        "cancel": "Anuluj",
+        "advanced": "Zawansowane",
+        "back": "Do tyłu",
+        "auto": "Automatyczny",
+        "beta": "Beta",
+        "close": "Wyjdź"
     },
     "gallery": {
         "galleryImageSize": "Rozmiar obrazów",
@@ -65,6 +82,42 @@
         "uploadImage": "Wgrywanie obrazu",
         "previousImage": "Poprzedni obraz",
         "nextImage": "Następny obraz",
-        "menu": "Menu"
+        "menu": "Menu",
+        "mode": "Tryb"
+    },
+    "boards": {
+        "cancel": "Anuluj",
+        "noBoards": "Brak tablic typu {{boardType}}",
+        "imagesWithCount_one": "{{count}} zdjęcie",
+        "imagesWithCount_few": "{{count}} zdjęcia",
+        "imagesWithCount_many": "{{count}} zdjęcia",
+        "private": "Prywatne tablice",
+        "updateBoardError": "Błąd aktualizacji tablicy",
+        "uncategorized": "Nieskategoryzowane",
+        "selectBoard": "Wybierz tablicę",
+        "downloadBoard": "Pobierz tablice",
+        "loading": "Ładowanie...",
+        "move": "Przenieś",
+        "noMatching": "Brak pasujących tablic"
+    },
+    "accordions": {
+        "compositing": {
+            "title": "Kompozycja",
+            "infillTab": "Inskrypcja",
+            "coherenceTab": "Przebieg Koherencji"
+        },
+        "generation": {
+            "title": "Generowanie"
+        },
+        "image": {
+            "title": "Zdjęcie"
+        },
+        "advanced": {
+            "options": "$t(accordions.advanced.title) Opcje",
+            "title": "Zaawansowane"
+        },
+        "control": {
+            "title": "Kontrola"
+        }
     }
 }

invokeai/frontend/web/public/locales/ru.json

@@ -648,19 +648,6 @@
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
             "invoke": "Создать",
-            "layer": {
-                "ipAdapterNoModelSelected": "IP адаптер не выбран",
-                "controlAdapterNoModelSelected": "не выбрана модель адаптера контроля",
-                "controlAdapterIncompatibleBaseModel": "несовместимая базовая модель адаптера контроля",
-                "rgNoRegion": "регион не выбран",
-                "rgNoPromptsOrIPAdapters": "нет текстовых запросов или IP-адаптеров",
-                "ipAdapterIncompatibleBaseModel": "несовместимая базовая модель IP-адаптера",
-                "ipAdapterNoImageSelected": "изображение IP-адаптера не выбрано",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, масштабированная ширина рамки {{width}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, высота рамки {{height}}",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, ширина рамки {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, масштабированная высота рамки {{height}}"
-            },
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), ширина рамки {{width}}",
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), высота рамки {{height}}",
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), масштабированная высота рамки {{height}}",

invokeai/frontend/web/public/locales/vi.json

@@ -46,7 +46,7 @@
         "menuItemAutoAdd": "Tự động thêm cho Bảng này",
         "move": "Di Chuyển",
         "topMessage": "Bảng này chứa ảnh được dùng với những tính năng sau:",
-        "uncategorized": "Chưa Phân Loại",
+        "uncategorized": "Chưa Sắp Xếp",
         "archived": "Được Lưu Trữ",
         "loading": "Đang Tải...",
         "selectBoard": "Chọn Bảng",
@@ -91,7 +91,7 @@
         "sideBySide": "Cạnh Nhau",
         "alwaysShowImageSizeBadge": "Luôn Hiển Thị Kích Thước Ảnh",
         "autoAssignBoardOnClick": "Tự Động Gán Vào Bảng Khi Nhấp Chuột",
-        "jump": "Nhảy Vào",
+        "jump": "Nhảy Đến",
         "go": "Đi",
         "autoSwitchNewImages": "Tự Động Đổi Sang Hình Ảnh Mới",
         "featuresWillReset": "Nếu bạn xoá hình ảnh này, những tính năng đó sẽ lập tức được khởi động lại.",
@@ -133,7 +133,7 @@
         "alpha": "Alpha",
         "edit": "Sửa",
         "nodes": "Workflow",
-        "format": "định dạng",
+        "format": "Định Dạng",
         "delete": "Xoá",
         "details": "Chi Tiết",
         "imageFailedToLoad": "Không Thể Tải Hình Ảnh",
@@ -164,7 +164,7 @@
         "discordLabel": "Discord",
         "back": "Trở Về",
         "advanced": "Nâng Cao",
-        "batch": "Quản Lý Hàng Loạt",
+        "batch": "Quản Lý Lô",
         "modelManager": "Quản Lý Model",
         "dontShowMeThese": "Không hiển thị thứ này",
         "ok": "OK",
@@ -196,7 +196,7 @@
         "areYouSure": "Bạn chắc chứ?",
         "ai": "ai",
         "aboutDesc": "Sử dụng Invoke cho công việc? Xem thử:",
-        "aboutHeading": "Sở Hữu Khả Năng Sáng Tạo Cho Riêng Mình",
+        "aboutHeading": "Quyền Năng Sáng Tạo Của Riêng",
         "enabled": "Đã Bật",
         "close": "Đóng",
         "data": "Dữ Liệu",
@@ -217,7 +217,10 @@
         "direction": "Phương Hướng",
         "unknownError": "Lỗi Không Rõ",
         "selected": "Đã chọn",
-        "tab": "Tab"
+        "tab": "Tab",
+        "loadingModel": "Đang Tải Model",
+        "generating": "Đang Tạo Sinh",
+        "warnings": "Cảnh Báo"
     },
     "prompt": {
         "addPromptTrigger": "Thêm Prompt Trigger",
@@ -229,24 +232,24 @@
         "enqueueing": "Xếp Vào Hàng Hàng Loạt",
         "prompts_other": "Lệnh",
         "iterations_other": "Lặp Lại",
-        "total": "Tổng Cộng",
+        "total": "Tổng",
         "pruneFailed": "Có Vấn Đề Khi Cắt Bớt Mục Khỏi Hàng",
         "clearSucceeded": "Hàng Đã Được Dọn Sạch",
         "cancel": "Huỷ Bỏ",
         "clearQueueAlertDialog2": "Bạn chắc chắn muốn dọn sạch hàng không?",
         "queueEmpty": "Hàng Trống",
         "queueBack": "Thêm Vào Hàng",
-        "batchFieldValues": "Giá Trị Vùng Hàng Loạt",
+        "batchFieldValues": "Giá Trị Vùng Theo Lô",
         "openQueue": "Mở Queue",
         "pause": "Dừng Lại",
         "pauseFailed": "Có Vấn Đề Khi Dừng Lại Bộ Xử Lý",
-        "batchQueued": "Hàng Loạt Đã Vào hàng",
-        "batchFailedToQueue": "Lỗi Khi Xếp Hàng Loạt Vào Hàng",
+        "batchQueued": "Lô Đã Vào Hàng",
+        "batchFailedToQueue": "Lỗi Khi Xếp Lô Vào Hàng",
         "next": "Tiếp Theo",
-        "in_progress": "Đang Tiến Hành",
+        "in_progress": "Đang Chạy",
         "failed": "Thất Bại",
         "canceled": "Bị Huỷ",
-        "cancelBatchFailed": "Có Vấn Đề Khi Huỷ Bỏ Hàng Loạt",
+        "cancelBatchFailed": "Có Vấn Đề Khi Huỷ Bỏ Lô",
         "workflows": "Workflow (Luồng làm việc)",
         "canvas": "Canvas (Vùng ảnh)",
         "upscaling": "Upscale (Nâng Cấp Chất Lượng Hình Ảnh)",
@@ -269,19 +272,19 @@
         "resumeTooltip": "Tiếp Tục Bộ Xử Lý",
         "clearFailed": "Có Vấn Đề Khi Dọn Dẹp Hàng",
         "generations_other": "Máy Tạo Sinh",
-        "cancelBatch": "Huỷ Bỏ Hàng Loạt",
+        "cancelBatch": "Huỷ Bỏ Lô",
         "status": "Trạng Thái",
         "pending": "Đang Chờ",
         "gallery": "Thư Viện",
         "front": "trước",
-        "batch": "Hàng Loạt",
+        "batch": "Lô",
         "origin": "Nguồn Gốc",
         "destination": "Điểm Đến",
         "other": "Khác",
         "graphFailedToQueue": "Lỗi Khi Xếp Đồ Thị Vào Hàng",
         "notReady": "Không Thể Xếp Hàng",
         "cancelItem": "Huỷ Bỏ Mục",
-        "cancelBatchSucceeded": "Mục Hàng Loạt Đã Huỷ Bỏ",
+        "cancelBatchSucceeded": "Lô Đã Huỷ Bỏ",
         "current": "Hiện Tại",
         "time": "Thời Gian",
         "completed": "Hoàn Tất",
@@ -290,7 +293,8 @@
         "cancelSucceeded": "Mục Đã Huỷ Bỏ",
         "completedIn": "Hoàn tất trong",
         "graphQueued": "Đồ Thị Đã Vào Hàng",
-        "batchQueuedDesc_other": "Thêm {{count}} phiên vào {{direction}} của hàng"
+        "batchQueuedDesc_other": "Thêm {{count}} phiên vào {{direction}} của hàng",
+        "batchSize": "Kích Thước Lô"
     },
     "hotkeys": {
         "canvas": {
@@ -453,8 +457,8 @@
                 "title": "Gợi Lại Tất Cả Metadata"
             },
             "recallSeed": {
-                "title": "Gợi Lại Tham Số Hạt Giống",
-                "desc": "Gợi lại tham số hạt giống của ảnh hiện tại."
+                "title": "Gợi Lại Hạt Giống",
+                "desc": "Gợi lại hạt giống của ảnh hiện tại."
             },
             "useSize": {
                 "title": "Dùng Kích Thước",
@@ -486,7 +490,7 @@
                 "title": "Đổi Ảnh So Sánh"
             },
             "remix": {
-                "desc": "Gợi lại tất cả metadata cho tham số hạt giống của ảnh hiện tại.",
+                "desc": "Gợi lại tất cả metadata cho hạt giống của ảnh hiện tại.",
                 "title": "Phối Lại"
             },
             "runPostprocessing": {
@@ -629,7 +633,7 @@
         "modelManager": "Quản Lý Model",
         "name": "Tên",
         "noModelSelected": "Không Có Model Được Chọn",
-        "installQueue": "Tải Xuống Danh Sách Đợi",
+        "installQueue": "Danh Sách Tải Xuống",
         "modelDeleteFailed": "Xoá model thất bại",
         "inplaceInstallDesc": "Tải xuống model mà không sao chép toàn bộ tài nguyên. Khi sử dụng model, nó được sẽ tải từ vị trí được đặt. Nếu bị tắt, toàn bộ tài nguyên của model sẽ được sao chép vào thư mục quản lý model của Invoke trong quá trình tải xuống.",
         "modelType": "Loại Model",
@@ -701,7 +705,7 @@
         "spandrelImageToImage": "Hình Ảnh Sang Hình Ảnh (Spandrel)",
         "starterBundles": "Quà Tân Thủ",
         "vae": "VAE",
-        "urlOrLocalPath": "URL Hoặc Đường Dẫn Trên Máy Chủ",
+        "urlOrLocalPath": "URL / Đường Dẫn",
         "triggerPhrases": "Từ Ngữ Kích Hoạt",
         "variant": "Biến Thể",
         "urlOrLocalPathHelper": "Url cần chỉ vào một tệp duy nhất. Còn đường dẫn trên máy chủ có thể chỉ vào một tệp hoặc một thư mục cho chỉ một model diffusers.",
@@ -719,7 +723,7 @@
         "starterModels": "Model Khởi Đầu",
         "typePhraseHere": "Thêm từ ngữ ở đây",
         "upcastAttention": "Upcast Attention",
-        "vaePrecision": "VAE Precision",
+        "vaePrecision": "Độ Chuẩn VAE",
         "installingBundle": "Đang Tải Nguyên Bộ",
         "installingModel": "Đang Tải Model",
         "installingXModels_other": "Đang tải {{count}} model",
@@ -733,7 +737,9 @@
         "textualInversions": "Bộ Đảo Ngược Văn Bản",
         "loraTriggerPhrases": "Từ Ngữ Kích Hoạt Cho LoRA",
         "width": "Chiều Rộng",
-        "starterModelsInModelManager": "Model khởi đầu có thể tìm thấy ở Trình Quản Lý Model"
+        "starterModelsInModelManager": "Model khởi đầu có thể tìm thấy ở Trình Quản Lý Model",
+        "clipLEmbed": "CLIP-L Embed",
+        "clipGEmbed": "CLIP-G Embed"
     },
     "metadata": {
         "guidance": "Hướng Dẫn",
@@ -745,18 +751,18 @@
         "parameterSet": "Dữ liệu tham số {{parameter}}",
         "positivePrompt": "Lệnh Tích Cực",
         "recallParameter": "Gợi Nhớ {{label}}",
-        "seed": "Tham Số Hạt Giống",
+        "seed": "Hạt Giống",
         "negativePrompt": "Lệnh Tiêu Cực",
         "noImageDetails": "Không tìm thấy chí tiết ảnh",
         "strength": "Mức độ mạnh từ ảnh sang ảnh",
         "Threshold": "Ngưỡng Nhiễu",
         "width": "Chiều Rộng",
-        "steps": "Tham Số Bước",
+        "steps": "Số Bước",
         "vae": "VAE",
         "workflow": "Workflow",
         "seamlessXAxis": "Trục X Liền Mạch",
         "seamlessYAxis": "Trục Y Liền Mạch",
-        "cfgScale": "Thước Đo CFG",
+        "cfgScale": "Thang CFG",
         "allPrompts": "Tất Cả Lệnh",
         "generationMode": "Chế Độ Tạo Sinh",
         "height": "Chiều Dài",
@@ -770,7 +776,7 @@
     },
     "accordions": {
         "generation": {
-            "title": "Generation (Máy Tạo Sinh)"
+            "title": "Generation (Tạo Sinh)"
         },
         "image": {
             "title": "Hình Ảnh"
@@ -780,7 +786,7 @@
             "options": "Lựa Chọn $t(accordions.advanced.title)"
         },
         "compositing": {
-            "coherenceTab": "Coherence Pass (Lớp Kết Hợp)",
+            "coherenceTab": "Coherence Pass (Liên Kết)",
             "title": "Kết Hợp",
             "infillTab": "Infill (Lấp Đầy)"
         },
@@ -789,21 +795,21 @@
         }
     },
     "invocationCache": {
-        "disableSucceeded": "Bộ Nhớ Đệm Kích Hoạt Đã Tắt",
-        "disableFailed": "Có Vấn Đề Khi Tắt Bộ Nhớ Đệm Kích Hoạt",
-        "hits": "Truy Cập Bộ Nhớ Đệm",
-        "maxCacheSize": "Kích Thước Tối Đa Bộ Nhớ Đệm",
-        "cacheSize": "Kích Thước Bộ Nhớ Đệm",
-        "enableFailed": "Có Vấn Đề Khi Bật Bộ Nhớ Đệm Kích Hoạt",
+        "disableSucceeded": "Bộ Nhớ Đệm Đã Tắt",
+        "disableFailed": "Có Vấn Đề Khi Tắt Bộ Nhớ Đệm",
+        "hits": "Số Lần Trúng",
+        "maxCacheSize": "Tối Đa",
+        "cacheSize": "Tổng Cache",
+        "enableFailed": "Có Vấn Đề Khi Bật Bộ Nhớ Đệm",
         "disable": "Tắt",
-        "invocationCache": "Bộ Nhớ Đệm Kích Hoạt",
-        "clearSucceeded": "Bộ Nhớ Đệm Kích Hoạt Đã Được Dọn",
-        "enableSucceeded": "Bộ Nhớ Đệm Kích Hoạt Đã Bật",
+        "invocationCache": "Bộ Nhớ Đệm",
+        "clearSucceeded": "Bộ Nhớ Đệm Đã Được Dọn",
+        "enableSucceeded": "Bộ Nhớ Đệm Đã Bật",
         "useCache": "Dùng Bộ Nhớ Đệm",
         "enable": "Bật",
-        "misses": "Không Truy Cập Bộ Nhớ Đệm",
+        "misses": "Số Lần Trật",
         "clear": "Dọn Dẹp",
-        "clearFailed": "Có Vấn Đề Khi Dọn Dẹp Bộ Nhớ Đệm Kích Hoạt"
+        "clearFailed": "Có Vấn Đề Khi Dọn Dẹp Bộ Nhớ Đệm"
     },
     "hrf": {
         "metadata": {
@@ -905,7 +911,7 @@
         "unknownNode": "Node Không Rõ",
         "unknownNodeType": "Loại Node Không Rõ",
         "unknownTemplate": "Mẫu Trình Bày Không Rõ",
-        "cannotConnectOutputToOutput": "Không thế kết nối đầu ra với đầu vào",
+        "cannotConnectOutputToOutput": "Không thế kết nối đầu ra với đầu ra",
         "cannotConnectToSelf": "Không thể kết nối với chính nó",
         "workflow": "Workflow",
         "addNodeToolTip": "Thêm Node (Shift+A, Space)",
@@ -952,13 +958,15 @@
         "executionStateInProgress": "Đang Xử Lý",
         "showLegendNodes": "Hiển Thị Vùng Nhập",
         "outputFieldTypeParseError": "Không thể phân tích loại dữ liệu đầu ra của {{node}}.{{field}} ({{message}})",
-        "modelAccessError": "Không thể tìm thấy model {{key}}, chuyển về mặc định"
+        "modelAccessError": "Không thể tìm thấy model {{key}}, chuyển về mặc định",
+        "internalDesc": "Trình kích hoạt này được dùng bên trong bởi Invoke. Nó có thể phá hỏng thay đổi trong khi cập nhật ứng dụng và có thể bị xoá bất cứ lúc nào.",
+        "specialDesc": "Trình kích hoạt này có một số xử lý đặc biệt trong ứng dụng. Ví dụ, Node Hàng Loạt được dùng để xếp vào nhiều đồ thị từ một workflow."
     },
     "popovers": {
         "paramCFGRescaleMultiplier": {
-            "heading": "CFG Rescale Multiplier",
+            "heading": "Hệ Số Nhân Thang CFG",
             "paragraphs": [
-                "Hệ số nhân điều chỉnh cho hướng dẫn CFG, dùng cho model được huấn luyện bằng zero-terminal SNR (ztsnr).",
+                "Hệ số nhân điều chỉnh để hướng dẫn cho CFG, dùng cho model được huấn luyện bằng zero-terminal SNR (ztsnr).",
                 "Giá trị khuyến cáo là 0.7 cho những model này."
             ]
         },
@@ -970,10 +978,10 @@
             ]
         },
         "paramCFGScale": {
-            "heading": "Thước Đo CFG",
+            "heading": "Thang CFG",
             "paragraphs": [
                 "Điều khiển mức độ lệnh tác động lên quá trình tạo sinh.",
-                "Giá trị của Thước đo CFG quá cao có thể tạo độ bão hoà quá mức và khiến ảnh tạo sinh bị méo mó. "
+                "Giá trị của Thang CFG quá cao có thể tạo độ bão hoà quá mức và khiến ảnh tạo sinh bị méo mó. "
             ]
         },
         "paramScheduler": {
@@ -984,13 +992,13 @@
             ]
         },
         "compositingCoherencePass": {
-            "heading": "Coherence Pass (Lớp Kết Hợp)",
+            "heading": "Coherence Pass (Liên Kết)",
             "paragraphs": [
                 "Bước thứ hai trong quá trình khử nhiễu để hợp nhất với ảnh inpaint/outpaint."
             ]
         },
         "refinerNegativeAestheticScore": {
-            "heading": "Điểm Tiêu Cực Cho Tiêu Chuẩn",
+            "heading": "Điểm Khác Tiêu Chuẩn",
             "paragraphs": [
                 "Trọng lượng để tạo sinh ảnh giống với ảnh có điểm tiêu chuẩn thấp, dựa vào dữ liệu huấn luyện."
             ]
@@ -998,26 +1006,26 @@
         "refinerCfgScale": {
             "paragraphs": [
                 "Điều khiển mức độ lệnh tác động lên quá trình tạo sinh.",
-                "Giống với thước đo CFG để tạo sinh."
+                "Giống với thang CFG để tạo sinh."
             ],
-            "heading": "Thước Đo CFG"
+            "heading": "Thang CFG"
         },
         "refinerSteps": {
-            "heading": "Tham Số Bước",
+            "heading": "Số Bước",
             "paragraphs": [
                 "Số bước diễn ra trong khi tinh chế các phần nhỏ của quá trình tạo sinh.",
-                "Giống với tham số bước để tạo sinh."
+                "Giống với số bước để tạo sinh."
             ]
         },
         "paramSteps": {
-            "heading": "Tham Số Bước",
+            "heading": "Số Bước",
             "paragraphs": [
                 "Số bước dùng để biểu diễn trong mỗi lần tạo sinh.",
                 "Số bước càng cao thường sẽ tạo ra ảnh tốt hơn nhưng ngốn nhiều thời gian hơn."
             ]
         },
         "paramWidth": {
-            "heading": "Chiều Rộng",
+            "heading": "Rộng",
             "paragraphs": [
                 "Chiều rộng của ảnh tạo sinh. Phải là bội số của 8."
             ]
@@ -1044,14 +1052,14 @@
             "paragraphs": [
                 "Trọng lượng để tạo sinh ảnh giống với ảnh có điểm tiêu chuẩn cao, dựa vào dữ liệu huấn luyện."
             ],
-            "heading": "Điểm Tích Cực Cho Tiêu Chuẩn"
+            "heading": "Điểm Giống Tiêu Chuẩn"
         },
         "paramVAEPrecision": {
             "paragraphs": [
                 "Độ chính xác dùng trong khi mã hoá và giải mã VAE.",
                 "Chính xác một nửa/Fp16 sẽ hiệu quả hơn, đổi lại cho những thay đổi nhỏ với ảnh."
             ],
-            "heading": "VAE Precision"
+            "heading": "Độ Chuẩn VAE"
         },
         "fluxDevLicense": {
             "heading": "Giấy Phép Phi Thương Mại",
@@ -1070,14 +1078,14 @@
             "paragraphs": [
                 "Chiều dài của ảnh tạo sinh. Phải là bội số của 8."
             ],
-            "heading": "Chiều Dài"
+            "heading": "Dài"
         },
         "paramRatio": {
             "paragraphs": [
                 "Tỉ lệ khung hình của kích thước của ảnh được tạo ra.",
                 "Kích thước ảnh (theo số lượng pixel) tương đương với 512x512 được khuyến nghị cho model SD1.5 và kích thước tương đương với 1024x1024 được khuyến nghị cho model SDXL."
             ],
-            "heading": "Tỉ Lệ Khung Hình"
+            "heading": "Tỉ Lệ"
         },
         "seamlessTilingYAxis": {
             "paragraphs": [
@@ -1105,7 +1113,9 @@
         },
         "controlNetWeight": {
             "paragraphs": [
-                "Trọng lượng của Control Adapter. Trọng lượng càng cao sẽ dẫn đến tác động càng lớn lên ảnh cuối cùng."
+                "Điều chỉnh mức độ layer ảnh hưởng đến quá trình xử lý tạo sinh.",
+                "• Trọng Lượng Lớn Hơn (.75-2): Gây ra ảnh hưởng lớn hơn lên kết quả cuối cùng.",
+                "• Trọng Lượng Nhỏ Hơn (0-.75): Gây ra ảnh hưởng nhỏ hơn lên kết quả cuối cùng."
             ],
             "heading": "Trọng Lượng"
         },
@@ -1130,26 +1140,26 @@
         },
         "compositingCoherenceMinDenoise": {
             "paragraphs": [
-                "Sức mạnh khử nhiễu nhỏ nhất cho chế độ kết hợp",
-                "Sức mạnh khử nhiễu nhỏ nhất cho vùng kết hợp khi inpaint/outpaint"
+                "Độ khử nhiễu nhỏ nhất cho chế độ liên kết",
+                "Sức mạnh khử nhiễu nhỏ nhất cho vùng liên kết khi inpaint/outpaint"
             ],
-            "heading": "Độ Khử Nhiễu Tối Thiểu"
+            "heading": "Min Khử Nhiễu"
         },
         "compositingCoherenceEdgeSize": {
             "paragraphs": [
-                "Kích thước cạnh cho lớp kết hợp."
+                "Kích cỡ cạnh dùng cho coherence pass."
             ],
-            "heading": "Kích Thước Cạnh"
+            "heading": "Kích Cỡ Cạnh"
         },
         "compositingMaskBlur": {
-            "heading": "Mask Blur",
+            "heading": "Độ Mờ Vùng",
             "paragraphs": [
                 "Độ mờ của phần được phủ."
             ]
         },
         "ipAdapterMethod": {
             "paragraphs": [
-                "Cách thức dùng để áp dụng IP Adapter hiện tại."
+                "Phương thức định nghĩa cách ảnh mẫu sẽ chỉ dẫn quá trình xử lý tạo sinh."
             ],
             "heading": "Cách Thức"
         },
@@ -1170,7 +1180,7 @@
         "noiseUseCPU": {
             "paragraphs": [
                 "Điều chỉnh độ nhiễu được tạo ra trên CPU hay GPU.",
-                "Với Độ nhiễu CPU được bật, một tham số hạt giống cụ thể sẽ tạo ra hình ảnh giống nhau trên mọi máy.",
+                "Với Độ nhiễu CPU được bật, một hạt giống cụ thể sẽ tạo ra hình ảnh giống nhau trên mọi máy.",
                 "Không có tác động nào đến hiệu suất khi bật Độ nhiễu CPU."
             ],
             "heading": "Dùng Độ Nhiễu CPU"
@@ -1196,10 +1206,11 @@
         },
         "controlNetBeginEnd": {
             "paragraphs": [
-                "Một phần trong quá trình xử lý khử nhiễu mà sẽ được Control Adapter áp dụng.",
-                "Nói chung, Control Adapter áp dụng vào lúc bắt đầu của quá trình hướng dẫn thành phần, và cũng áp dụng vào lúc kết thúc hướng dẫn chi tiết."
+                "Cài đặt này xác định phần xử lý khử nhiễu (trong khi tạo sinh) kết hợp với chỉ dẫn từ layer này.",
+                "• Bước Bắt Đầu (%): Chỉ định lúc bắt đầu áp dụng chỉ dẫn từ layer này trong quá trình tạo sinh.",
+                "• Bước Kết Thúc (%): Chỉ định lúc dừng áp dụng chỉ dẫn của layer này và trở về chỉ dẫn chung từ model và các thiết lập khác."
             ],
-            "heading": "Phần Trăm Tham Số Bước Khi Bắt Đầu/Kết Thúc"
+            "heading": "Phần Trăm Số Bước Khi Bắt Đầu/Kết Thúc"
         },
         "scale": {
             "heading": "Tỉ Lệ",
@@ -1221,12 +1232,12 @@
         },
         "dynamicPromptsSeedBehaviour": {
             "paragraphs": [
-                "Điều khiển cách tham số hạt giống được dùng khi tạo sinh từ lệnh.",
-                "Cứ mỗi lần lặp, một tham số hạt giống mới sẽ được dùng. Dùng nó để khám phá những biến thể từ lệnh trên mỗi tham số hạt giống.",
-                "Ví dụ, nếu bạn có 5 lệnh, mỗi ảnh sẽ dùng cùng tham số hạt giống.",
-                "Một tham số hạt giống mới sẽ được dùng cho từng ảnh. Nó tạo ra nhiều biến thể."
+                "Điều khiển cách hạt giống được dùng khi tạo sinh từ lệnh.",
+                "Cứ mỗi lần lặp, một hạt giống mới sẽ được dùng. Dùng nó để khám phá những biến thể từ lệnh trên mỗi hạt giống.",
+                "Ví dụ, nếu bạn có 5 lệnh, mỗi ảnh sẽ dùng cùng hạt giống.",
+                "Một hạt giống mới sẽ được dùng cho từng ảnh. Nó tạo ra nhiều biến thể."
             ],
-            "heading": "Hành Động Cho Tham Số Hạt Giống"
+            "heading": "Hành Vi Của Hạt Giống"
         },
         "paramGuidance": {
             "heading": "Hướng Dẫn",
@@ -1255,10 +1266,10 @@
         },
         "paramAspect": {
             "paragraphs": [
-                "Tỉ lệ khung hành của ảnh tạo sinh. Điều chỉnh tỉ lệ se cập nhật Chiều Rộng và Chiều Dài tương ứng.",
-                "\"Tối ưu hoá\" sẽ đặt Chiều Rộng và Chiều Dài vào kích thước tối ưu cho model được chọn."
+                "Tỉ lệ khung hành của ảnh tạo sinh. Điều chỉnh tỉ lệ sẽ cập nhật chiều rộng và chiều dài tương ứng.",
+                "\"Tối ưu hoá\" sẽ đặt chiều rộng và chiều dài vào kích thước tối ưu cho model được chọn."
             ],
-            "heading": "Khung Hình"
+            "heading": "Tỉ Lệ"
         },
         "paramNegativeConditioning": {
             "heading": "Lệnh Tiêu Cực",
@@ -1278,7 +1289,7 @@
                 "Nơi trong quá trình xử lý tạo sinh mà refiner bắt đầu được dùng.",
                 "0 nghĩa là bộ refiner sẽ được dùng trong toàn bộ quá trình tạo sinh , 0.8 nghĩa là refiner sẽ được dùng trong 20% cuối cùng quá trình tạo sinh."
             ],
-            "heading": "Nơi Bắt Đầu Refiner"
+            "heading": "Bắt Đầu Refiner"
         },
         "paramUpscaleMethod": {
             "paragraphs": [
@@ -1299,9 +1310,9 @@
             "heading": "Độ Cấu Trúc"
         },
         "infillMethod": {
-            "heading": "Cách Thức Infill",
+            "heading": "Cách Infill",
             "paragraphs": [
-                "Cách thức làm infill trong quá trình inpaint/outpaint."
+                "Cách thức infill trong quá trình inpaint/outpaint."
             ]
         },
         "paramDenoisingStrength": {
@@ -1330,7 +1341,7 @@
                 "Điều khiển độ nhiễu ban đầu được dùng để tạo sinh.",
                 "Tắt lựa chọn \"Ngẫu Nhiên\" để tạo ra kết quá y hệt nhau với cùng một thiết lập tạo sinh."
             ],
-            "heading": "Tham Số Hạt Giống"
+            "heading": "Hạt Giống"
         },
         "clipSkip": {
             "heading": "CLIP Skip",
@@ -1355,7 +1366,7 @@
         "compositingCoherenceMode": {
             "heading": "Chế Độ",
             "paragraphs": [
-                "Cách thức được dùng để kết hợp ảnh với vùng bao phủ vừa được tạo sinh."
+                "Cách thức được dùng để liên kết ảnh với vùng bao phủ vừa được tạo sinh."
             ]
         },
         "paramModel": {
@@ -1380,7 +1391,7 @@
     },
     "models": {
         "addLora": "Thêm LoRA",
-        "concepts": "Khái Niệm",
+        "concepts": "LoRA",
         "loading": "đang tải",
         "lora": "LoRA",
         "noMatchingLoRAs": "Không có LoRA phù hợp",
@@ -1395,30 +1406,17 @@
         "postProcessing": "Xử Lý Hậu Kỳ (Shift + U)",
         "symmetry": "Tính Đối Xứng",
         "type": "Loại",
-        "seed": "Tham Số Hạt Giống",
+        "seed": "Hạt Giống",
         "processImage": "Xử Lý Hình Ảnh",
         "useSize": "Dùng Kích Thước",
         "invoke": {
-            "layer": {
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, tỉ lệ chiều dài hộp giới hạn là {{height}}",
-                "rgNoRegion": "không có vùng được chọn",
-                "ipAdapterNoModelSelected": "không có IP Adapter được lựa chọn",
-                "ipAdapterNoImageSelected": "không có ảnh IP Adapter được lựa chọn",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, chiều dài hộp giới hạn là {{height}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, tỉ lệ chiều rộng hộp giới hạn là {{width}}",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, chiều rộng hộp giới hạn là {{width}}",
-                "rgNoPromptsOrIPAdapters": "không có lệnh chữ hoặc IP Adapter",
-                "controlAdapterIncompatibleBaseModel": "model cơ sở của Control Adapter không tương thích",
-                "ipAdapterIncompatibleBaseModel": "dạng model cơ sở của IP Adapter không tương thích",
-                "controlAdapterNoModelSelected": "không có model Control Adapter được chọn"
-            },
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), chiều rộng hộp giới hạn là {{width}}",
             "noModelSelected": "Không có model được lựa chọn",
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), tỉ lệ chiều dài hộp giới hạn là {{height}}",
-            "canvasIsFiltering": "Canvas đang được lọc",
-            "canvasIsRasterizing": "Canvas đang được raster hoá",
-            "canvasIsTransforming": "Canvas đang được biến đổi",
-            "canvasIsCompositing": "Canvas đang được kết hợp",
+            "canvasIsFiltering": "Canvas đang bận (đang lọc)",
+            "canvasIsRasterizing": "Canvas đang bận (đang raster hoá)",
+            "canvasIsTransforming": "Canvas đang bận (đang biến đổi)",
+            "canvasIsCompositing": "Canvas đang bận (đang kết hợp)",
             "noPrompts": "Không có lệnh được tạo",
             "noNodesInGraph": "Không có node trong đồ thị",
             "addingImagesTo": "Thêm ảnh vào",
@@ -1430,22 +1428,26 @@
             "missingNodeTemplate": "Thiếu mẫu trình bày node",
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), chiều dài hộp giới hạn là {{height}}",
             "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), tỉ lệ chiều rộng hộp giới hạn là {{width}}",
-            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} thiếu đầu ra",
-            "missingFieldTemplate": "Thiếu vùng mẫu trình bày"
+            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}}: thiếu đầu vào",
+            "missingFieldTemplate": "Thiếu vùng mẫu trình bày",
+            "collectionEmpty": "{{nodeLabel}} -> {{fieldLabel}} tài nguyên trống",
+            "collectionTooFewItems": "{{nodeLabel}} -> {{fieldLabel}}: quá ít mục, tối thiểu {{minItems}}",
+            "collectionTooManyItems": "{{nodeLabel}} -> {{fieldLabel}}: quá nhiều mục, tối đa {{maxItems}}",
+            "canvasIsSelectingObject": "Canvas đang bận (đang chọn đồ vật)"
         },
-        "cfgScale": "Thước Đo CFG",
-        "useSeed": "Dùng Tham Số Hạt Giống",
+        "cfgScale": "Thang CFG",
+        "useSeed": "Dùng Hạt Giống",
         "imageActions": "Hành Động Với Hình Ảnh",
-        "steps": "Tham Số Bước",
-        "aspect": "Khung Hình",
+        "steps": "Số Bước",
+        "aspect": "Tỉ Lệ",
         "coherenceMode": "Chế Độ",
-        "coherenceEdgeSize": "Kích Thước Cạnh",
-        "coherenceMinDenoise": "Tham Số Khử Nhiễu Nhỏ Nhất",
+        "coherenceEdgeSize": "Kích Cỡ Cạnh",
+        "coherenceMinDenoise": "Min Khử Nhiễu",
         "denoisingStrength": "Sức Mạnh Khử Nhiễu",
-        "infillMethod": "Cách Thức Infill",
+        "infillMethod": "Cách Infill",
         "setToOptimalSize": "Tối ưu hoá kích cỡ cho model",
-        "maskBlur": "Mask Blur",
-        "width": "Chiều Rộng",
+        "maskBlur": "Độ Mờ Vùng",
+        "width": "Rộng",
         "scale": "Tỉ Lệ",
         "recallMetadata": "Gợi Lại Metadata",
         "clipSkip": "CLIP Skip",
@@ -1453,7 +1455,7 @@
         "boxBlur": "Box Blur",
         "gaussianBlur": "Gaussian Blur",
         "staged": "Staged (Tăng khử nhiễu có hệ thống)",
-        "scaledHeight": "Tỉ Lệ Chiều Dài",
+        "scaledHeight": "Tỉ Lệ Dài",
         "cancel": {
             "cancel": "Huỷ"
         },
@@ -1461,12 +1463,12 @@
         "optimizedImageToImage": "Tối Ưu Hoá Hình Ảnh Sang Hình Ảnh",
         "sendToCanvas": "Gửi Vào Canvas",
         "sendToUpscale": "Gửi Vào Upscale",
-        "scaledWidth": "Tỉ Lệ Chiều Rộng",
+        "scaledWidth": "Tỉ Lệ Rộng",
         "scheduler": "Scheduler",
         "seamlessXAxis": "Trục X Liền Mạch",
         "seamlessYAxis": "Trục Y Liền Mạch",
         "guidance": "Hướng Dẫn",
-        "height": "Chiều Cao",
+        "height": "Dài",
         "noiseThreshold": "Ngưỡng Nhiễu",
         "negativePromptPlaceholder": "Lệnh Tiêu Cực",
         "iterations": "Lặp Lại",
@@ -1479,13 +1481,13 @@
         "useCpuNoise": "Dùng Độ Nhiễu CPU",
         "remixImage": "Phối Lại Hình Ảnh",
         "showOptionsPanel": "Hiển Thị Bảng Bên Cạnh (O hoặc T)",
-        "shuffle": "Xáo Trộn Tham Số Hạt Giống",
+        "shuffle": "Xáo Trộn",
         "setToOptimalSizeTooLarge": "$t(parameters.setToOptimalSize) (lớn quá)",
-        "cfgRescaleMultiplier": "CFG Rescale Multiplier",
+        "cfgRescaleMultiplier": "Hệ Số Nhân Thang CFG",
         "setToOptimalSizeTooSmall": "$t(parameters.setToOptimalSize) (nhỏ quá)",
         "images": "Ảnh Ban Đầu",
         "controlNetControlMode": "Chế Độ Điều Khiển",
-        "lockAspectRatio": "Khoá Tỉ Lệ Khung Hình",
+        "lockAspectRatio": "Khoá Tỉ Lệ",
         "swapDimensions": "Hoán Đổi Kích Thước",
         "copyImage": "Sao Chép Hình Ảnh",
         "downloadImage": "Tải Xuống Hình Ảnh",
@@ -1498,11 +1500,11 @@
     },
     "dynamicPrompts": {
         "seedBehaviour": {
-            "perIterationDesc": "Sử dụng tham số hạt giống khác nhau cho mỗi lần lặp lại",
-            "perPromptDesc": "Sử dụng tham số hạt giống khác nhau cho mỗi hình ảnh",
-            "label": "Hành Động Cho Tham Số Hạt Giống",
-            "perPromptLabel": "Tham Số Hạt Giống Mỗi Hình Ảnh",
-            "perIterationLabel": "Tham Số Hạt Giống Mỗi Lần Lặp Lại"
+            "perIterationDesc": "Sử dụng hạt giống khác nhau cho mỗi lần lặp lại",
+            "perPromptDesc": "Sử dụng hạt giống khác nhau cho mỗi hình ảnh",
+            "label": "Hành Động Cho Hạt Giống",
+            "perPromptLabel": "Một Hạt Giống Mỗi Ảnh",
+            "perIterationLabel": "Hạt Giống Mỗi Lần Lặp Lại"
         },
         "loading": "Tạo Sinh Dùng Dynamic Prompt...",
         "showDynamicPrompts": "HIện Dynamic Prompt",
@@ -1513,9 +1515,9 @@
     "settings": {
         "beta": "Beta",
         "general": "Cài Đặt Chung",
-        "confirmOnDelete": "Chắp Nhận Xoá",
+        "confirmOnDelete": "Xác Nhận Khi Xoá",
         "developer": "Nhà Phát Triển",
-        "confirmOnNewSession": "Chắp Nhận Mở Phiên Mới",
+        "confirmOnNewSession": "Xác Nhận Khi Mở Phiên Mới",
         "antialiasProgressImages": "Xử Lý Khử Răng Cưa Hình Ảnh",
         "models": "Models",
         "informationalPopoversDisabledDesc": "Hộp thoại hỗ trợ thông tin đã tắt. Bật lại trong Cài đặt.",
@@ -1542,24 +1544,25 @@
         "resetWebUIDesc2": "Nếu ảnh không được xuất hiện trong thư viện hoặc điều gì đó không ổn đang diễn ra, hãy thử khởi động lại trước khi báo lỗi trên Github.",
         "displayInProgress": "Hiển Thị Hình Ảnh Đang Xử Lý",
         "intermediatesClearedFailed": "Có Vấn Đề Khi Dọn Sạch Sản Phẩm Trung Gian",
-        "enableInvisibleWatermark": "Bật Chế Độ Ẩn Watermark"
+        "enableInvisibleWatermark": "Bật Chế Độ Ẩn Watermark",
+        "showDetailedInvocationProgress": "Hiện Dữ Liệu Xử Lý"
     },
     "sdxl": {
         "loading": "Đang Tải...",
-        "posAestheticScore": "Điểm Tích Cực Cho Tiêu Chuẩn",
-        "steps": "Tham Số Bước",
-        "refinerSteps": "Tham Số Bước Refiner",
+        "posAestheticScore": "Điểm Giống Tiêu Chuẩn",
+        "steps": "Số Bước",
+        "refinerSteps": "Số Bước Refiner",
         "refinermodel": "Model Refiner",
-        "refinerStart": "Nơi Bắt Đầu Refiner",
+        "refinerStart": "Bắt Đầu Refiner",
         "denoisingStrength": "Sức Mạnh Khử Nhiễu",
         "posStylePrompt": "Điểm Tích Cực Cho Lệnh Phong Cách",
         "scheduler": "Scheduler",
         "refiner": "Refiner",
-        "cfgScale": "Thước Đo CFG",
+        "cfgScale": "Thang CFG",
         "concatPromptStyle": "Liên Kết Lệnh & Phong Cách",
         "freePromptStyle": "Viết Lệnh Thủ Công Cho Phong Cách",
         "negStylePrompt": "Điểm Tiêu Cực Cho Lệnh Phong Cách",
-        "negAestheticScore": "Điểm Tiêu Cực Cho Tiêu Chuẩn",
+        "negAestheticScore": "Điểm Khác Tiêu Chuẩn",
         "noModelsAvailable": "Không có sẵn model"
     },
     "controlLayers": {
@@ -1594,7 +1597,7 @@
         "pullBboxIntoLayerError": "Có Vấn Đề Khi Chuyển Hộp Giới Hạn Thành Layer",
         "pullBboxIntoReferenceImageOk": "Chuyển Hộp Giới Hạn Thành Ảnh Mẫu",
         "clearCaches": "Xoá Bộ Nhớ Đệm",
-        "outputOnlyMaskedRegions": "Chỉ Xuất Đầu Ra Ở Vùng Phủ",
+        "outputOnlyMaskedRegions": "Chỉ Xuất Đầu Ra Ở Vùng Tạo Sinh",
         "addLayer": "Thêm Layer",
         "regional": "Khu Vực",
         "regionIsEmpty": "Vùng được chọn trống",
@@ -1608,10 +1611,13 @@
         "moveForward": "Chuyển Lên Đầu",
         "fitBboxToLayers": "Xếp Vừa Hộp Giới Hạn Vào Layer",
         "ipAdapterMethod": {
-            "full": "Đầy Đủ",
+            "full": "Phong Cách Và Thành Phần",
             "style": "Chỉ Lấy Phong Cách",
             "composition": "Chỉ Lấy Thành Phần",
-            "ipAdapterMethod": "Cách Thức IP Adapter"
+            "ipAdapterMethod": "Cách Thức",
+            "compositionDesc": "Áp dụng cách trình bày và bỏ qua phong cách mẫu.",
+            "fullDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) & thành phần (cách trình bày).",
+            "styleDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) và bỏ qua cách trình bày."
         },
         "deletePrompt": "Xoá Lệnh",
         "rasterLayer": "Layer Dạng Raster",
@@ -1899,7 +1905,33 @@
             "colorPicker": "Chọn Màu"
         },
         "mergingLayers": "Đang gộp layer",
-        "controlLayerEmptyState": "<UploadButton>Tải lên ảnh</UploadButton>, kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này, hoặc vẽ trên canvas để bắt đầu."
+        "controlLayerEmptyState": "<UploadButton>Tải lên ảnh</UploadButton>, kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này, hoặc vẽ trên canvas để bắt đầu.",
+        "referenceImageEmptyState": "<UploadButton>Tải lên ảnh</UploadButton> hoặc kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này để bắt đầu.",
+        "useImage": "Dùng Hình Ảnh",
+        "resetCanvasLayers": "Khởi Động Lại Layer Canvas",
+        "asRasterLayer": "Như $t(controlLayers.rasterLayer)",
+        "asRasterLayerResize": "Như $t(controlLayers.rasterLayer) (Thay Đổi Kích Thước)",
+        "asControlLayer": "Như $t(controlLayers.controlLayer)",
+        "asControlLayerResize": "Như $t(controlLayers.controlLayer) (Thay Đổi Kích Thước)",
+        "newSession": "Phiên Làm Việc Mới",
+        "resetGenerationSettings": "Khởi Động Lại Cài Đặt Tạo Sinh",
+        "referenceImageRegional": "Ảnh Mẫu (Khu Vực)",
+        "referenceImageGlobal": "Ảnh Mẫu (Toàn Vùng)",
+        "warnings": {
+            "problemsFound": "Phát hiện vấn đề",
+            "unsupportedModel": "layer không được hỗ trợ cho model cơ sở này",
+            "controlAdapterNoModelSelected": "không có model được chọn cho Layer Chỉnh Sửa Được",
+            "controlAdapterNoControl": "chưa chọn/vẽ điều khiển",
+            "ipAdapterIncompatibleBaseModel": "model cơ sở cho Ảnh Mẫu không tương thích",
+            "ipAdapterNoImageSelected": "chưa chọn Ảnh Mẫu",
+            "controlAdapterIncompatibleBaseModel": "model cơ sở cho Layer Chỉnh Sửa Được không tương thích",
+            "ipAdapterNoModelSelected": "không có model được chọn cho Ảnh Mẫu",
+            "rgNoPromptsOrIPAdapters": "không có lệnh hoặc Ảnh Mẫu",
+            "rgNegativePromptNotSupported": "Lệnh Tiêu Cực không được hỗ trợ cho model cơ sở được chọn",
+            "rgReferenceImagesNotSupported": "Ảnh Mẫu Khu Vực không được hỗ trợ cho model cơ sở được chọn",
+            "rgAutoNegativeNotSupported": "Tự Động Đảo Chiều không được hỗ trợ cho model cơ sở được chọn",
+            "rgNoRegion": "không có khu vực được vẽ"
+        }
     },
     "stylePresets": {
         "negativePrompt": "Lệnh Tiêu Cực",
@@ -1960,7 +1992,8 @@
             "generation": "Generation",
             "system": "Hệ Thống",
             "canvas": "Canvas",
-            "logNamespaces": "Nơi Được Log"
+            "logNamespaces": "Vùng Ghi Log",
+            "dnd": "Kéo Thả"
         },
         "logLevel": {
             "logLevel": "Cấp Độ Log",
@@ -2124,8 +2157,7 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "<StrongComponent>SD 3.5</StrongComponent>: Hỗ trợ cho Từ ngữ Sang Hình Ảnh trong Workflow với phiên bản SD 3.5 Medium hoặc Large.",
-            "<StrongComponent>Canvas</StrongComponent>: Hợp lý hoá cách xử lý Layer Điều Khiển Được và cải thiện thiết lập điều khiển mặc định."
+            "<StrongComponent>Hướng Dẫn Khu Vực FLUX (beta)</StrongComponent>: Bản beta của Hướng Dẫn Khu Vực FLUX của chúng ta đã có mắt tại bảng điều khiển lệnh khu vực."
         ]
     },
     "upsell": {
@@ -2133,5 +2165,67 @@
         "inviteTeammates": "Thêm Đồng Đội",
         "shareAccess": "Chia Sẻ Quyền Truy Cập",
         "professionalUpsell": "Không có sẵn Phiên Bản Chuyên Nghiệp cho Invoke. Bấm vào đây hoặc đến invoke.com/pricing để thêm chi tiết."
+    },
+    "supportVideos": {
+        "supportVideos": "Video Hỗ Trợ",
+        "gettingStarted": "Bắt Đầu Làm Quen",
+        "studioSessionsDesc1": "Xem thử <StudioSessionsPlaylistLink /> để hiểu rõ Invoke hơn.",
+        "studioSessionsDesc2": "Đến <DiscordLink /> để tham gia vào phiên trực tiếp và hỏi câu hỏi. Các phiên được tải lên danh sách phát vào các tuần.",
+        "videos": {
+            "howDoIDoImageToImageTransformation": {
+                "title": "Làm Sao Để Tôi Dùng Trình Biến Đổi Hình Ảnh Sang Hình Ảnh?",
+                "description": "Hướng dẫn cách thực hiện biến đổi ảnh sang ảnh trong Invoke."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "description": "Giới thiệu về ảnh mẫu và IP adapter toàn vùng.",
+                "title": "Làm Sao Để Tôi Dùng IP Adapter Toàn Vùng Và Ảnh Mẫu?"
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "description": "Học cách sáng tạo ảnh bằng trình điều khiển canvas của Invoke.",
+                "title": "Sáng Tạo Trong Trình Kiểm Soát Canvas Của Invoke"
+            },
+            "upscaling": {
+                "description": "Cách upscale ảnh bằng bộ công cụ của Invoke để nâng cấp độ phân giải.",
+                "title": "Upscale (Nâng Cấp Chất Lượng Hình Ảnh)"
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "title": "Làm Sao Để Tôi Tạo Sinh Và Lưu Vào Thư Viện?",
+                "description": "Các bước để tạo sinh và lưu ảnh vào thư viện."
+            },
+            "howDoIEditOnTheCanvas": {
+                "description": "Hướng dẫn chỉnh sửa ảnh trực tiếp trên canvas.",
+                "title": "Làm Sao Để Tôi Chỉnh Sửa Trên Canvas?"
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "Làm Sao Để Tôi Dùng ControlNet và Layer Điều Khiển Được?",
+                "description": "Học cách áp dụng layer điều khiển được và controlnet vào ảnh của bạn."
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "Làm Sao Để Tôi Dùng Lớp Phủ Inpaint?",
+                "description": "Cách áp dụng lớp phủ inpaint vào chỉnh sửa và thay đổi ảnh."
+            },
+            "howDoIOutpaint": {
+                "title": "Làm Sao Để Tôi Outpaint?",
+                "description": "Hướng dẫn outpaint bên ngoài viền ảnh gốc."
+            },
+            "creatingYourFirstImage": {
+                "description": "Giới thiệu về cách tạo ảnh từ ban đầu bằng công cụ Invoke.",
+                "title": "Tạo Hình Ảnh Đầu Tiên Của Bạn"
+            },
+            "usingControlLayersAndReferenceGuides": {
+                "description": "Học cách chỉ dẫn ảnh được tạo ra bằng layer điều khiển được và ảnh mẫu.",
+                "title": "Dùng Layer Điều Khiển Được và Chỉ Dẫn Mẫu"
+            },
+            "understandingImageToImageAndDenoising": {
+                "title": "Hiểu Rõ Trình Hình Ảnh Sang Hình Ảnh Và Trình Khử Nhiễu",
+                "description": "Tổng quan về trình biến đổi ảnh sang ảnh và trình khử nhiễu trong Invoke."
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "title": "Khám Phá Model AI Và Khái Niệm Về Adapter",
+                "description": "Đào sâu vào model AI và cách dùng những adapter để điều khiển một cách sáng tạo."
+            }
+        },
+        "controlCanvas": "Điều Khiển Canvas",
+        "watch": "Xem"
     }
 }

invokeai/frontend/web/public/locales/zh_CN.json

@@ -661,19 +661,6 @@
             "missingFieldTemplate": "缺失模板",
             "addingImagesTo": "添加图像到",
             "noPrompts": "没有已生成的提示词",
-            "layer": {
-                "ipAdapterNoModelSelected": "未选择IP adapter",
-                "controlAdapterNoModelSelected": "未选择Control Adapter模型",
-                "rgNoPromptsOrIPAdapters": "无文本提示或IP Adapters",
-                "controlAdapterIncompatibleBaseModel": "Control Adapter的基础模型不兼容",
-                "ipAdapterIncompatibleBaseModel": "IP Adapter的基础模型不兼容",
-                "ipAdapterNoImageSelected": "未选择IP Adapter图像",
-                "rgNoRegion": "未选择区域",
-                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，边界框宽度为 {{width}}",
-                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，缩放后的边界框高度为 {{height}}",
-                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，边界框高度为 {{height}}",
-                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}，缩放后的边界框宽度为 {{width}}"
-            },
             "canvasIsFiltering": "画布正在过滤",
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16)，缩放后的边界框高度为 {{height}}",
             "noCLIPEmbedModelSelected": "未为FLUX生成选择CLIP嵌入模型",

invokeai/frontend/web/src/app/hooks/useStudioInitAction.ts

@@ -1,3 +1,4 @@
+import { useStore } from '@nanostores/react';
 import { useAppStore } from 'app/store/storeHooks';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
 import { withResultAsync } from 'common/util/result';
@@ -9,6 +10,7 @@ import { imageDTOToImageObject } from 'features/controlLayers/store/util';
 import { $imageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { sentImageToCanvas } from 'features/gallery/store/actions';
 import { parseAndRecallAllMetadata } from 'features/metadata/util/handlers';
+import { $hasTemplates } from 'features/nodes/store/nodesSlice';
 import { $isWorkflowListMenuIsOpen } from 'features/nodes/store/workflowListMenu';
 import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { toast } from 'features/toast/toast';
@@ -51,6 +53,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   const { t } = useTranslation();
   // Use a ref to ensure that we only perform the action once
   const didInit = useRef(false);
+  const didParseOpenAPISchema = useStore($hasTemplates);
   const store = useAppStore();
   const { getAndLoadWorkflow } = useGetAndLoadLibraryWorkflow();
 
@@ -174,7 +177,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   );
 
   useEffect(() => {
-    if (didInit.current || !action) {
+    if (didInit.current || !action || !didParseOpenAPISchema) {
       return;
     }
 
@@ -187,22 +190,29 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
       case 'selectStylePreset':
         handleSelectStylePreset(action.data.stylePresetId);
         break;
+
       case 'sendToCanvas':
         handleSendToCanvas(action.data.imageName);
         break;
+
       case 'useAllParameters':
         handleUseAllMetadata(action.data.imageName);
         break;
+
       case 'goToDestination':
         handleGoToDestination(action.data.destination);
         break;
+
+      default:
+        break;
     }
   }, [
     handleSendToCanvas,
     handleUseAllMetadata,
     action,
-    handleLoadWorkflow,
     handleSelectStylePreset,
     handleGoToDestination,
+    handleLoadWorkflow,
+    didParseOpenAPISchema,
   ]);
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear.ts

@@ -2,7 +2,6 @@ import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { extractMessageFromAssertionError } from 'common/util/extractMessageFromAssertionError';
-import type { Result } from 'common/util/result';
 import { withResult, withResultAsync } from 'common/util/result';
 import { $canvasManager } from 'features/controlLayers/store/ephemeral';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
@@ -10,11 +9,9 @@ import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGr
 import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
 import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph';
 import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
-import type { Graph } from 'features/nodes/util/graph/generation/Graph';
 import { toast } from 'features/toast/toast';
 import { serializeError } from 'serialize-error';
 import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
-import type { Invocation } from 'services/api/types';
 import { assert, AssertionError } from 'tsafe';
 import type { JsonObject } from 'type-fest';
 
@@ -25,42 +22,32 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
       enqueueRequested.match(action) && action.payload.tabName === 'canvas',
     effect: async (action, { getState, dispatch }) => {
+      log.debug('Enqueue requested');
       const state = getState();
-      const model = state.params.model;
       const { prepend } = action.payload;
 
       const manager = $canvasManager.get();
-      assert(manager, 'No model found in state');
-
-      let buildGraphResult: Result<
-        {
-          g: Graph;
-          noise: Invocation<'noise' | 'flux_denoise' | 'sd3_denoise'>;
-          posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder' | 'sd3_text_encoder'>;
-        },
-        Error
-      >;
+      assert(manager, 'No canvas manager');
 
+      const model = state.params.model;
       assert(model, 'No model found in state');
       const base = model.base;
 
-      switch (base) {
-        case 'sdxl':
-          buildGraphResult = await withResultAsync(() => buildSDXLGraph(state, manager));
-          break;
-        case 'sd-1':
-        case `sd-2`:
-          buildGraphResult = await withResultAsync(() => buildSD1Graph(state, manager));
-          break;
-        case `sd-3`:
-          buildGraphResult = await withResultAsync(() => buildSD3Graph(state, manager));
-          break;
-        case `flux`:
-          buildGraphResult = await withResultAsync(() => buildFLUXGraph(state, manager));
-          break;
-        default:
-          assert(false, `No graph builders for base ${base}`);
-      }
+      const buildGraphResult = await withResultAsync(async () => {
+        switch (base) {
+          case 'sdxl':
+            return await buildSDXLGraph(state, manager);
+          case 'sd-1':
+          case `sd-2`:
+            return await buildSD1Graph(state, manager);
+          case `sd-3`:
+            return await buildSD3Graph(state, manager);
+          case `flux`:
+            return await buildFLUXGraph(state, manager);
+          default:
+            assert(false, `No graph builders for base ${base}`);
+        }
+      });
 
       if (buildGraphResult.isErr()) {
         let description: string | null = null;