WIP experimentation with replacing einops with native torch ops for faster execution. More investigation needed.

.github/pull_request_template.md

@@ -19,4 +19,3 @@
 - [ ] _The PR has a short but descriptive title, suitable for a changelog_
 - [ ] _Tests added / updated (if applicable)_
 - [ ] _Documentation added / updated (if applicable)_
-- [ ] _Updated `What's New` copy (if doing a release after this PR)_

SECURITY.md

@@ -1,14 +0,0 @@
-# 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

@@ -38,7 +38,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \
     fi &&\

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 `/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:
+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:
 
 | 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 `/invokeai/frontend/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`.
 
 ## Invoke
 
@@ -70,7 +70,7 @@ The Invoke framework provides the interface to the underlying AI systems and is
 
 ### Invoker
 
-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:
+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:
 - **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 `/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.
+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.
 
 ### Services
 
-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).
+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).
 
 ## AI Core
 
-The AI Core is represented by the rest of the code base (i.e. the code outside of `/invokeai/app/`).
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/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.md) list. If you're not sure how to do that,
-take a look a at our [contributing nodes overview](../nodes/contributingNodes.md).
+[Community Nodes](nodes/communityNodes) list. If you're not sure how to do that,
+take a look a at our [contributing nodes overview](contributingNodes).
 
 ## 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](https://github.com/invoke-ai/InvokeAI/blob/main/pyproject.toml) for the default `pytest` options.)
+We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
 
 ## Fast vs. Slow
 All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
@@ -33,7 +33,7 @@ pytest tests -m ""
 
 ## Test Organization
 
-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`.
+All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
 
 TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
 

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](../frontend/index.md)
+* #### [Frontend Documentation](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web)
 * #### [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

@@ -5,7 +5,7 @@ If you're a new contributor to InvokeAI or Open Source Projects, this is the gui
 ## New Contributor Checklist
 
 - [x] Set up your local development environment & fork of InvokAI by following [the steps outlined here](../dev-environment.md)
-- [x] Set up your local tooling with [this guide](../LOCAL_DEVELOPMENT.md). Feel free to skip this step if you already have tooling you're comfortable with.
+- [x] Set up your local tooling with [this guide](InvokeAI/contributing/LOCAL_DEVELOPMENT/#developing-invokeai-in-vscode). Feel free to skip this step if you already have tooling you're comfortable with.
 - [x] Familiarize yourself with [Git](https://www.atlassian.com/git) & our project structure by reading through the [development documentation](development.md)
 - [x] Join the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord
 - [x] Choose an issue to work on! This can be achieved by asking in the #dev-chat channel, tackling a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) or finding an item on the [roadmap](https://github.com/orgs/invoke-ai/projects/7). If nothing in any of those places catches your eye, feel free to work on something of interest to you!
@@ -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

@@ -17,49 +17,46 @@ If you just want to use Invoke, you should use the [installer][installer link].
 ## Setup
 
 1. Run through the [requirements][requirements link].
-2. [Fork and clone][forking link] the [InvokeAI repo][repo link].
-3. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
-4. Create a python virtual environment inside the directory you just created:
+1. [Fork and clone][forking link] the [InvokeAI repo][repo link].
+1. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
+1. Create a python virtual environment inside the directory you just created:
 
-      ```sh
-      python3 -m venv .venv --prompt InvokeAI-Dev
-      ```
+   ```sh
+   python3 -m venv .venv --prompt InvokeAI-Dev
+   ```
 
-5. Activate the venv (you'll need to do this every time you want to run the app):
+1. 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]:
+1. Install the repo as an [editable install][editable install link]:
 
-      ```sh
-      pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
-      ```
+   ```sh
+   pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+   ```
 
-      Refer to the [manual installation][manual install link] instructions for more determining the correct install options. `xformers` is optional, but `dev` and `test` are not.
+   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:
+1. Install the frontend dev toolchain:
 
    - [`nodejs`](https://nodejs.org/) (recommend v20 LTS)
-   - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
+   - [`pnpm`](https://pnpm.io/installation#installing-a-specific-version) (must be v8 - not v9!)
 
-8. Do a production build of the frontend:
+1. Do a production build of the frontend:
 
-      ```sh
-      cd PATH_TO_INVOKEAI_REPO/invokeai/frontend/web
-      pnpm i
-      pnpm build
-      ```
+   ```sh
+   pnpm build
+   ```
 
-9. Start the application:
+1. Start the application:
 
-      ```sh
-      cd PATH_TO_INVOKEAI_REPO
-      python scripts/invokeai-web.py
-      ```
+   ```sh
+   python scripts/invokeai-web.py
+   ```
 
-10. Access the UI at `localhost:9090`.
+1. Access the UI at `localhost:9090`.
 
 ## Updating the UI
 

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](contributors.md). 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](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). 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](../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](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.
 
 By making a contribution to this project, you certify that:
 

docs/faq.md

@@ -209,7 +209,7 @@ checkpoint models.
 
 To solve this, go to the Model Manager tab (the cube), select the
 checkpoint model that's giving you trouble, and press the "Convert"
-button in the upper right of your browser window. This will convert the
+button in the upper right of your browser window. This will conver the
 checkpoint into a diffusers model, after which loading should be
 faster and less memory-intensive.
 

docs/installation/patchmatch.md

@@ -97,16 +97,16 @@ Prior to installing PyPatchMatch, you need to take the following steps:
    sudo pacman -S --needed base-devel
    ```
 
-2. Install `opencv`, `blas`, and required dependencies:
+2. Install `opencv` and `blas`:
 
    ```sh
-   sudo pacman -S opencv blas fmt glew vtk hdf5
+   sudo pacman -S opencv blas
    ```
 
    or for CUDA support
 
    ```sh
-   sudo pacman -S opencv-cuda blas fmt glew vtk hdf5
+   sudo pacman -S opencv-cuda blas
    ```
 
 3. Fix the naming of the `opencv` package configuration file:

docs/nodes/defaultNodes.md

@@ -99,6 +99,7 @@ their descriptions.
 | Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
 | Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
 | Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
+| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
 | String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
 | String Primitive                                              | A string primitive value                                                                                                                             |
 | Subtract Integers                                             | Subtracts two numbers                                                                                                                                |

installer/lib/installer.py

@@ -245,9 +245,6 @@ class InvokeAiInstance:
 
         pip = local[self.pip]
 
-        # Uninstall xformers if it is present; the correct version of it will be reinstalled if needed
-        _ = pip["uninstall", "-yqq", "xformers"] & FG
-
         pipeline = pip[
             "install",
             "--require-virtualenv",
@@ -410,7 +407,7 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     optional_modules: str | None = None
     if OS == "Linux":
         if device == GpuType.ROCM:
-            url = "https://download.pytorch.org/whl/rocm6.1"
+            url = "https://download.pytorch.org/whl/rocm5.6"
         elif device == GpuType.CPU:
             url = "https://download.pytorch.org/whl/cpu"
         elif device == GpuType.CUDA:

installer/lib/messages.py

@@ -259,7 +259,7 @@ def select_gpu() -> GpuType:
                     [
                         f"Detected the [gold1]{OS}-{ARCH}[/] platform",
                         "",
-                        "See [deep_sky_blue1]https://invoke-ai.github.io/InvokeAI/installation/requirements/[/] to ensure your system meets the minimum requirements.",
+                        "See [deep_sky_blue1]https://invoke-ai.github.io/InvokeAI/#system[/] to ensure your system meets the minimum requirements.",
                         "",
                         "[red3]🠶[/] [b]Your GPU drivers must be correctly installed before using InvokeAI![/] [red3]🠴[/]",
                     ]

installer/templates/invoke.sh.in

@@ -68,7 +68,7 @@ do_line_input() {
     printf "2: Open the developer console\n"
     printf "3: Command-line help\n"
     printf "Q: Quit\n\n"
-    printf "To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest\n\n"
+    printf "To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest.\n\n"
     read -p "Please enter 1-4, Q: [1] " yn
     choice=${yn:='1'}
     do_choice $choice

invokeai/app/api/routers/app_info.py

@@ -40,8 +40,6 @@ class AppVersion(BaseModel):
 
     version: str = Field(description="App version")
 
-    highlights: Optional[list[str]] = Field(default=None, description="Highlights of release")
-
 
 class AppDependencyVersions(BaseModel):
     """App depencency Versions Response"""

invokeai/app/api/routers/model_manager.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2023 Lincoln D. Stein
 """FastAPI route for model configuration records."""
 
-import contextlib
 import io
 import pathlib
 import shutil
@@ -11,7 +10,6 @@ from enum import Enum
 from tempfile import TemporaryDirectory
 from typing import List, Optional, Type
 
-import huggingface_hub
 from fastapi import Body, Path, Query, Response, UploadFile
 from fastapi.responses import FileResponse, HTMLResponse
 from fastapi.routing import APIRouter
@@ -29,7 +27,6 @@ from invokeai.app.services.model_records import (
     ModelRecordChanges,
     UnknownModelException,
 )
-from invokeai.app.util.suppress_output import SuppressOutput
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -811,11 +808,7 @@ def get_is_installed(
     for model in installed_models:
         if model.source == starter_model.source:
             return True
-        if (
-            (model.name == starter_model.name or model.name in starter_model.previous_names)
-            and model.base == starter_model.base
-            and model.type == starter_model.type
-        ):
+        if model.name == starter_model.name and model.base == starter_model.base and model.type == starter_model.type:
             return True
     return False
 
@@ -926,51 +919,3 @@ async def get_stats() -> Optional[CacheStats]:
     """Return performance statistics on the model manager's RAM cache. Will return null if no models have been loaded."""
 
     return ApiDependencies.invoker.services.model_manager.load.ram_cache.stats
-
-
-class HFTokenStatus(str, Enum):
-    VALID = "valid"
-    INVALID = "invalid"
-    UNKNOWN = "unknown"
-
-
-class HFTokenHelper:
-    @classmethod
-    def get_status(cls) -> HFTokenStatus:
-        try:
-            if huggingface_hub.get_token_permission(huggingface_hub.get_token()):
-                # Valid token!
-                return HFTokenStatus.VALID
-            # No token set
-            return HFTokenStatus.INVALID
-        except Exception:
-            return HFTokenStatus.UNKNOWN
-
-    @classmethod
-    def set_token(cls, token: str) -> HFTokenStatus:
-        with SuppressOutput(), contextlib.suppress(Exception):
-            huggingface_hub.login(token=token, add_to_git_credential=False)
-        return cls.get_status()
-
-
-@model_manager_router.get("/hf_login", operation_id="get_hf_login_status", response_model=HFTokenStatus)
-async def get_hf_login_status() -> HFTokenStatus:
-    token_status = HFTokenHelper.get_status()
-
-    if token_status is HFTokenStatus.UNKNOWN:
-        ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
-
-    return token_status
-
-
-@model_manager_router.post("/hf_login", operation_id="do_hf_login", response_model=HFTokenStatus)
-async def do_hf_login(
-    token: str = Body(description="Hugging Face token to use for login", embed=True),
-) -> HFTokenStatus:
-    HFTokenHelper.set_token(token)
-    token_status = HFTokenHelper.get_status()
-
-    if token_status is HFTokenStatus.UNKNOWN:
-        ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
-
-    return token_status

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": CancelByDestinationResult}},
+    responses={200: {"model": CancelByBatchIDsResult}},
 )
 async def cancel_by_destination(
     queue_id: str = Path(description="The queue id to perform this operation on"),

invokeai/app/invocations/baseinvocation.py

@@ -4,7 +4,6 @@ from __future__ import annotations
 
 import inspect
 import re
-import sys
 import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -63,7 +62,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
     - `Prototype`: The invocation is not yet stable and may be removed from the application at any time. Workflows built around this invocation may break, and we are *not* committed to supporting this invocation.
     - `Deprecated`: The invocation is deprecated and may be removed in a future version.
     - `Internal`: The invocation is not intended for use by end-users. It may be changed or removed at any time, but is exposed for users to play with.
-    - `Special`: The invocation is a special case and does not fit into any of the other classifications.
     """
 
     Stable = "stable"
@@ -71,7 +69,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
     Prototype = "prototype"
     Deprecated = "deprecated"
     Internal = "internal"
-    Special = "special"
 
 
 class UIConfigBase(BaseModel):
@@ -195,19 +192,12 @@ class BaseInvocation(ABC, BaseModel):
         """Gets a pydantc TypeAdapter for the union of all invocation types."""
         if not cls._typeadapter or cls._typeadapter_needs_update:
             AnyInvocation = TypeAliasType(
-                "AnyInvocation", Annotated[Union[tuple(cls.get_invocations())], Field(discriminator="type")]
+                "AnyInvocation", Annotated[Union[tuple(cls._invocation_classes)], Field(discriminator="type")]
             )
             cls._typeadapter = TypeAdapter(AnyInvocation)
             cls._typeadapter_needs_update = False
         return cls._typeadapter
 
-    @classmethod
-    def invalidate_typeadapter(cls) -> None:
-        """Invalidates the typeadapter, forcing it to be rebuilt on next access. If the invocation allowlist or
-        denylist is changed, this should be called to ensure the typeadapter is updated and validation respects
-        the updated allowlist and denylist."""
-        cls._typeadapter_needs_update = True
-
     @classmethod
     def get_invocations(cls) -> Iterable[BaseInvocation]:
         """Gets all invocations, respecting the allowlist and denylist."""
@@ -489,26 +479,6 @@ def invocation(
             title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
         )
 
-        # Validate the `invoke()` method is implemented
-        if "invoke" in cls.__abstractmethods__:
-            raise ValueError(f'Invocation "{invocation_type}" must implement the "invoke" method')
-
-        # And validate that `invoke()` returns a subclass of `BaseInvocationOutput
-        invoke_return_annotation = signature(cls.invoke).return_annotation
-
-        try:
-            # TODO(psyche): If `invoke()` is not defined, `return_annotation` ends up as the string "BaseInvocationOutput"
-            # instead of the class `BaseInvocationOutput`. This may be a pydantic bug: https://github.com/pydantic/pydantic/issues/7978
-            if isinstance(invoke_return_annotation, str):
-                invoke_return_annotation = getattr(sys.modules[cls.__module__], invoke_return_annotation)
-
-            assert invoke_return_annotation is not BaseInvocationOutput
-            assert issubclass(invoke_return_annotation, BaseInvocationOutput)
-        except Exception:
-            raise ValueError(
-                f'Invocation "{invocation_type}" must have a return annotation of a subclass of BaseInvocationOutput (got "{invoke_return_annotation}")'
-            )
-
         docstring = cls.__doc__
         cls = create_model(
             cls.__qualname__,

invokeai/app/invocations/blend_latents.py

@@ -1,120 +1,98 @@
-from typing import Optional, Union
+from typing import Any, Union
 
 import numpy as np
+import numpy.typing as npt
 import torch
-import torchvision.transforms as T
-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.fields import FieldDescriptions, ImageField, Input, InputField, LatentsField
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
 
 
-def slerp(
-    t: Union[float, np.ndarray],
-    v0: Union[torch.Tensor, np.ndarray],
-    v1: Union[torch.Tensor, np.ndarray],
-    device: torch.device,
-    DOT_THRESHOLD: float = 0.9995,
-):
-    """
-    Spherical linear interpolation
-    Args:
-        t (float/np.ndarray): Float value between 0.0 and 1.0
-        v0 (np.ndarray): Starting vector
-        v1 (np.ndarray): Final vector
-        DOT_THRESHOLD (float): Threshold for considering the two vectors as
-                            colineal. Not recommended to alter this.
-    Returns:
-        v2 (np.ndarray): Interpolation vector between v0 and v1
-    """
-    inputs_are_torch = False
-    if not isinstance(v0, np.ndarray):
-        inputs_are_torch = True
-        v0 = v0.detach().cpu().numpy()
-    if not isinstance(v1, np.ndarray):
-        inputs_are_torch = True
-        v1 = v1.detach().cpu().numpy()
-
-    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
-    if np.abs(dot) > DOT_THRESHOLD:
-        v2 = (1 - t) * v0 + t * v1
-    else:
-        theta_0 = np.arccos(dot)
-        sin_theta_0 = np.sin(theta_0)
-        theta_t = theta_0 * t
-        sin_theta_t = np.sin(theta_t)
-        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
-        s1 = sin_theta_t / sin_theta_0
-        v2 = s0 * v0 + s1 * v1
-
-    if inputs_are_torch:
-        v2 = torch.from_numpy(v2).to(device)
-
-    return v2
-
-
 @invocation(
     "lblend",
     title="Blend Latents",
-    tags=["latents", "blend", "mask"],
+    tags=["latents", "blend"],
     category="latents",
-    version="1.1.0",
+    version="1.0.3",
 )
 class BlendLatentsInvocation(BaseInvocation):
-    """Blend two latents using a given alpha. If a mask is provided, the second latents will be masked before blending.
-    Latents must have same size. Masking functionality added by @dwringer."""
+    """Blend two latents using a given alpha. Latents must have same size."""
 
-    latents_a: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
-    latents_b: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
-    mask: Optional[ImageField] = InputField(default=None, description="Mask for blending in latents B")
-    alpha: float = InputField(ge=0, default=0.5, description=FieldDescriptions.blend_alpha)
-
-    def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
-        if mask_image.mode != "L":
-            mask_image = mask_image.convert("L")
-        mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
-        if mask_tensor.dim() == 3:
-            mask_tensor = mask_tensor.unsqueeze(0)
-        return mask_tensor
-
-    def replace_tensor_from_masked_tensor(
-        self, tensor: torch.Tensor, other_tensor: torch.Tensor, mask_tensor: torch.Tensor
-    ):
-        output = tensor.clone()
-        mask_tensor = mask_tensor.expand(output.shape)
-        if output.dtype != torch.float16:
-            output = torch.add(output, mask_tensor * torch.sub(other_tensor, tensor))
-        else:
-            output = torch.add(output, mask_tensor.half() * torch.sub(other_tensor, tensor))
-        return output
+    latents_a: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    latents_b: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents_a = context.tensors.load(self.latents_a.latents_name)
         latents_b = context.tensors.load(self.latents_b.latents_name)
-        if self.mask is None:
-            mask_tensor = torch.zeros(latents_a.shape[-2:])
-        else:
-            mask_tensor = self.prep_mask_tensor(context.images.get_pil(self.mask.image_name))
-            mask_tensor = tv_resize(mask_tensor, latents_a.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
-
-        latents_b = self.replace_tensor_from_masked_tensor(latents_b, latents_a, mask_tensor)
 
         if latents_a.shape != latents_b.shape:
-            raise ValueError("Latents to blend must be the same size.")
+            raise Exception("Latents to blend must be the same size.")
 
         device = TorchDevice.choose_torch_device()
 
+        def slerp(
+            t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
+            v0: Union[torch.Tensor, npt.NDArray[Any]],
+            v1: Union[torch.Tensor, npt.NDArray[Any]],
+            DOT_THRESHOLD: float = 0.9995,
+        ) -> Union[torch.Tensor, npt.NDArray[Any]]:
+            """
+            Spherical linear interpolation
+            Args:
+                t (float/np.ndarray): Float value between 0.0 and 1.0
+                v0 (np.ndarray): Starting vector
+                v1 (np.ndarray): Final vector
+                DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                                    colineal. Not recommended to alter this.
+            Returns:
+                v2 (np.ndarray): Interpolation vector between v0 and v1
+            """
+            inputs_are_torch = False
+            if not isinstance(v0, np.ndarray):
+                inputs_are_torch = True
+                v0 = v0.detach().cpu().numpy()
+            if not isinstance(v1, np.ndarray):
+                inputs_are_torch = True
+                v1 = v1.detach().cpu().numpy()
+
+            dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+            if np.abs(dot) > DOT_THRESHOLD:
+                v2 = (1 - t) * v0 + t * v1
+            else:
+                theta_0 = np.arccos(dot)
+                sin_theta_0 = np.sin(theta_0)
+                theta_t = theta_0 * t
+                sin_theta_t = np.sin(theta_t)
+                s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+                s1 = sin_theta_t / sin_theta_0
+                v2 = s0 * v0 + s1 * v1
+
+            if inputs_are_torch:
+                v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
+                return v2_torch
+            else:
+                assert isinstance(v2, np.ndarray)
+                return v2
+
         # blend
-        blended_latents = slerp(self.alpha, latents_a, latents_b, device)
+        bl = slerp(self.alpha, latents_a, latents_b)
+        assert isinstance(bl, torch.Tensor)
+        blended_latents: torch.Tensor = bl  # for type checking convenience
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
-        torch.cuda.empty_cache()
+
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=blended_latents)
-        return LatentsOutput.build(latents_name=name, latents=blended_latents)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)

invokeai/app/invocations/compel.py

@@ -95,7 +95,6 @@ class CompelInvocation(BaseInvocation):
                 ti_manager,
             ),
         ):
-            context.util.signal_progress("Building conditioning")
             assert isinstance(text_encoder, CLIPTextModel)
             assert isinstance(tokenizer, CLIPTokenizer)
             compel = Compel(
@@ -192,7 +191,6 @@ class SDXLPromptInvocationBase:
                 ti_manager,
             ),
         ):
-            context.util.signal_progress("Building conditioning")
             assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
             assert isinstance(tokenizer, CLIPTokenizer)
 

invokeai/app/invocations/create_denoise_mask.py

@@ -65,7 +65,6 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
             img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
             # TODO:
-            context.util.signal_progress("Running VAE encoder")
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
 
             masked_latents_name = context.tensors.save(tensor=masked_latents)

invokeai/app/invocations/create_gradient_mask.py

@@ -131,7 +131,6 @@ class CreateGradientMaskInvocation(BaseInvocation):
                     image_tensor = image_tensor.unsqueeze(0)
                 img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
                 masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
-                context.util.signal_progress("Running VAE encoder")
                 masked_latents = ImageToLatentsInvocation.vae_encode(
                     vae_info, self.fp32, self.tiled, masked_image.clone()
                 )

invokeai/app/invocations/denoise_latents.py

@@ -13,7 +13,6 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
 from diffusers.schedulers.scheduling_tcd import TCDScheduler
 from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
-from PIL import Image
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPVisionModelWithProjection
@@ -511,7 +510,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         t2i_adapters: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
         ext_manager: ExtensionsManager,
-        bgr_mode: bool = False,
     ) -> None:
         if t2i_adapters is None:
             return
@@ -521,10 +519,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             t2i_adapters = [t2i_adapters]
 
         for t2i_adapter_field in t2i_adapters:
-            image = context.images.get_pil(t2i_adapter_field.image.image_name)
-            if bgr_mode:  # SDXL t2i trained on cv2's BGR outputs, but PIL won't convert straight to BGR
-                r, g, b = image.split()
-                image = Image.merge("RGB", (b, g, r))
             ext_manager.add_extension(
                 T2IAdapterExt(
                     node_context=context,
@@ -553,9 +547,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 if not isinstance(single_ipa_image_fields, list):
                     single_ipa_image_fields = [single_ipa_image_fields]
 
-                single_ipa_images = [
-                    context.images.get_pil(image.image_name, mode="RGB") for image in single_ipa_image_fields
-                ]
+                single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_image_fields]
                 with image_encoder_model_info as image_encoder_model:
                     assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                     # Get image embeddings from CLIP and ImageProjModel.
@@ -622,17 +614,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
         for t2i_adapter_field in t2i_adapter:
             t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
             t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
-            image = context.images.get_pil(t2i_adapter_field.image.image_name, mode="RGB")
+            image = context.images.get_pil(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
             if t2i_adapter_model_config.base == BaseModelType.StableDiffusion1:
                 max_unet_downscale = 8
             elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
                 max_unet_downscale = 4
-
-                # SDXL adapters are trained on cv2's BGR outputs
-                r, g, b = image.split()
-                image = Image.merge("RGB", (b, g, r))
             else:
                 raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
 
@@ -640,39 +628,29 @@ class DenoiseLatentsInvocation(BaseInvocation):
             with t2i_adapter_loaded_model as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
 
+                # Resize the T2I-Adapter input image.
+                # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
+                # result will match the latent image's dimensions after max_unet_downscale is applied.
+                t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
+                t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
+
                 # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
                 # a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
                 # T2I-Adapter model.
                 #
                 # Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
                 # of the same requirements (e.g. preserving binary masks during resize).
-
-                # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
-                _, _, latent_height, latent_width = latents_shape
-                control_height_resize = latent_height * LATENT_SCALE_FACTOR
-                control_width_resize = latent_width * LATENT_SCALE_FACTOR
                 t2i_image = prepare_control_image(
                     image=image,
                     do_classifier_free_guidance=False,
-                    width=control_width_resize,
-                    height=control_height_resize,
+                    width=t2i_input_width,
+                    height=t2i_input_height,
                     num_channels=t2i_adapter_model.config["in_channels"],  # mypy treats this as a FrozenDict
                     device=t2i_adapter_model.device,
                     dtype=t2i_adapter_model.dtype,
                     resize_mode=t2i_adapter_field.resize_mode,
                 )
 
-                # Resize the T2I-Adapter input image.
-                # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
-                # result will match the latent image's dimensions after max_unet_downscale is applied.
-                # We crop the image to this size so that the positions match the input image on non-standard resolutions
-                t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
-                t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
-                if t2i_image.shape[2] > t2i_input_height or t2i_image.shape[3] > t2i_input_width:
-                    t2i_image = t2i_image[
-                        :, :, : min(t2i_image.shape[2], t2i_input_height), : min(t2i_image.shape[3], t2i_input_width)
-                    ]
-
                 adapter_state = t2i_adapter_model(t2i_image)
 
             if do_classifier_free_guidance:
@@ -920,8 +898,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             #    ext = extension_field.to_extension(exit_stack, context, ext_manager)
             #    ext_manager.add_extension(ext)
             self.parse_controlnet_field(exit_stack, context, self.control, ext_manager)
-            bgr_mode = self.unet.unet.base == BaseModelType.StableDiffusionXL
-            self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, ext_manager, bgr_mode)
+            self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, ext_manager)
 
             # ext: t2i/ip adapter
             ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)

invokeai/app/invocations/fields.py

@@ -41,7 +41,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
     # region Model Field Types
     MainModel = "MainModelField"
     FluxMainModel = "FluxMainModelField"
-    SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
@@ -53,8 +52,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
     CLIPEmbedModel = "CLIPEmbedModelField"
-    CLIPLEmbedModel = "CLIPLEmbedModelField"
-    CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -134,10 +131,8 @@ class FieldDescriptions:
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
     clip_embed_model = "CLIP Embed loader"
-    clip_g_model = "CLIP-G Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
-    mmditx = "MMDiTX"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
@@ -145,7 +140,6 @@ class FieldDescriptions:
     lora_model = "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"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -250,17 +244,6 @@ 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):
-    """A conditioning tensor primitive value"""
-
-    conditioning_name: str = Field(description="The name of conditioning tensor")
 
 
 class ConditioningField(BaseModel):

invokeai/app/invocations/flux_denoise.py

@@ -1,19 +1,15 @@
 from contextlib import ExitStack
 from typing import Callable, Iterator, Optional, Tuple
 
-import numpy as np
-import numpy.typing as npt
 import torch
 import torchvision.transforms as tv_transforms
 from torchvision.transforms.functional import resize as tv_resize
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
     FluxConditioningField,
-    ImageField,
     Input,
     InputField,
     LatentsField,
@@ -21,7 +17,6 @@ from invokeai.app.invocations.fields import (
     WithMetadata,
 )
 from invokeai.app.invocations.flux_controlnet import FluxControlNetField
-from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.model import TransformerField, VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -30,10 +25,7 @@ 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
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.sampling_utils import (
     clip_timestep_schedule_fractional,
@@ -43,7 +35,6 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
-from invokeai.backend.flux.text_conditioning import FluxTextConditioning
 from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
@@ -58,7 +49,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.2.2",
+    version="3.1.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -83,33 +74,14 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.denoising_start,
     )
     denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    add_noise: bool = InputField(default=True, description="Add noise based on denoising start.")
     transformer: TransformerField = InputField(
         description=FieldDescriptions.flux_model,
         input=Input.Connection,
         title="Transformer",
     )
-    positive_text_conditioning: FluxConditioningField | list[FluxConditioningField] = InputField(
+    positive_text_conditioning: FluxConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
-    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,
-    )
-    cfg_scale: float | list[float] = InputField(default=1.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
-    cfg_scale_start_step: int = InputField(
-        default=0,
-        title="CFG Scale Start Step",
-        description="Index of the first step to apply cfg_scale. Negative indices count backwards from the "
-        + "the last step (e.g. a value of -1 refers to the final step).",
-    )
-    cfg_scale_end_step: int = InputField(
-        default=-1,
-        title="CFG Scale End Step",
-        description="Index of the last step to apply cfg_scale. Negative indices count backwards from the "
-        + "last step (e.g. a value of -1 refers to the final step).",
-    )
     width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
     height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
     num_steps: int = InputField(
@@ -129,10 +101,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
-    ip_adapter: IPAdapterField | list[IPAdapterField] | None = InputField(
-        description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection
-    )
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = self._run_diffusion(context)
@@ -147,6 +115,15 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     ):
         inference_dtype = torch.bfloat16
 
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.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=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+
         # 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:
@@ -161,45 +138,15 @@ 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
 
         # 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=packed_h * packed_w,
+            image_seq_len=image_seq_len,
             shift=not is_schnell,
         )
 
@@ -216,12 +163,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     "to be poor. Consider using a FLUX dev model instead."
                 )
 
-            if self.add_noise:
-                # Noise the orig_latents by the appropriate amount for the first timestep.
-                t_0 = timesteps[0]
-                x = t_0 * noise + (1.0 - t_0) * init_latents
-            else:
-                x = init_latents
+            # Noise the orig_latents by the appropriate amount for the first timestep.
+            t_0 = timesteps[0]
+            x = t_0 * noise + (1.0 - t_0) * init_latents
         else:
             # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
             if self.denoising_start > 1e-5:
@@ -236,17 +180,20 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         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)
 
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, 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
         noise = pack(noise)
         x = pack(x)
 
-        # 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]
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
+        assert image_seq_len == x.shape[1]
 
         # Prepare inpaint extension.
         inpaint_extension: InpaintExtension | None = None
@@ -258,21 +205,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 noise=noise,
             )
 
-        # Compute the IP-Adapter image prompt clip embeddings.
-        # We do this before loading other models to minimize peak memory.
-        # TODO(ryand): We should really do this in a separate invocation to benefit from caching.
-        ip_adapter_fields = self._normalize_ip_adapter_fields()
-        pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds = self._prep_ip_adapter_image_prompt_clip_embeds(
-            ip_adapter_fields, context
-        )
-
-        cfg_scale = self.prep_cfg_scale(
-            cfg_scale=self.cfg_scale,
-            timesteps=timesteps,
-            cfg_scale_start_step=self.cfg_scale_start_step,
-            cfg_scale_end_step=self.cfg_scale_end_step,
-        )
-
         with ExitStack() as exit_stack:
             # Prepare ControlNet extensions.
             # Note: We do this before loading the transformer model to minimize peak memory (see implementation).
@@ -321,121 +253,23 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             else:
                 raise ValueError(f"Unsupported model format: {config.format}")
 
-            # Prepare IP-Adapter extensions.
-            pos_ip_adapter_extensions, neg_ip_adapter_extensions = self._prep_ip_adapter_extensions(
-                pos_image_prompt_clip_embeds=pos_image_prompt_clip_embeds,
-                neg_image_prompt_clip_embeds=neg_image_prompt_clip_embeds,
-                ip_adapter_fields=ip_adapter_fields,
-                context=context,
-                exit_stack=exit_stack,
-                dtype=inference_dtype,
-            )
-
             x = denoise(
                 model=transformer,
                 img=x,
                 img_ids=img_ids,
-                pos_regional_prompting_extension=pos_regional_prompting_extension,
-                neg_regional_prompting_extension=neg_regional_prompting_extension,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
                 timesteps=timesteps,
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
-                cfg_scale=cfg_scale,
                 inpaint_extension=inpaint_extension,
                 controlnet_extensions=controlnet_extensions,
-                pos_ip_adapter_extensions=pos_ip_adapter_extensions,
-                neg_ip_adapter_extensions=neg_ip_adapter_extensions,
             )
 
         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
-    ) -> list[float]:
-        """Prepare the cfg_scale schedule.
-
-        - Clips the cfg_scale schedule based on cfg_scale_start_step and cfg_scale_end_step.
-        - If cfg_scale is a list, then it is assumed to be a schedule and is returned as-is.
-        - If cfg_scale is a scalar, then a linear schedule is created from cfg_scale_start_step to cfg_scale_end_step.
-        """
-        # num_steps is the number of denoising steps, which is one less than the number of timesteps.
-        num_steps = len(timesteps) - 1
-
-        # Normalize cfg_scale to a list if it is a scalar.
-        cfg_scale_list: list[float]
-        if isinstance(cfg_scale, float):
-            cfg_scale_list = [cfg_scale] * num_steps
-        elif isinstance(cfg_scale, list):
-            cfg_scale_list = cfg_scale
-        else:
-            raise ValueError(f"Unsupported cfg_scale type: {type(cfg_scale)}")
-        assert len(cfg_scale_list) == num_steps
-
-        # Handle negative indices for cfg_scale_start_step and cfg_scale_end_step.
-        start_step_index = cfg_scale_start_step
-        if start_step_index < 0:
-            start_step_index = num_steps + start_step_index
-        end_step_index = cfg_scale_end_step
-        if end_step_index < 0:
-            end_step_index = num_steps + end_step_index
-
-        # Validate the start and end step indices.
-        if not (0 <= start_step_index < num_steps):
-            raise ValueError(f"Invalid cfg_scale_start_step. Out of range: {cfg_scale_start_step}.")
-        if not (0 <= end_step_index < num_steps):
-            raise ValueError(f"Invalid cfg_scale_end_step. Out of range: {cfg_scale_end_step}.")
-        if start_step_index > end_step_index:
-            raise ValueError(
-                f"cfg_scale_start_step ({cfg_scale_start_step}) must be before cfg_scale_end_step "
-                + f"({cfg_scale_end_step})."
-            )
-
-        # Set values outside the start and end step indices to 1.0. This is equivalent to disabling cfg_scale for those
-        # steps.
-        clipped_cfg_scale = [1.0] * num_steps
-        clipped_cfg_scale[start_step_index : end_step_index + 1] = cfg_scale_list[start_step_index : end_step_index + 1]
-
-        return clipped_cfg_scale
-
     def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
         """Prepare the inpaint mask.
 
@@ -575,112 +409,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return controlnet_extensions
 
-    def _normalize_ip_adapter_fields(self) -> list[IPAdapterField]:
-        if self.ip_adapter is None:
-            return []
-        elif isinstance(self.ip_adapter, IPAdapterField):
-            return [self.ip_adapter]
-        elif isinstance(self.ip_adapter, list):
-            return self.ip_adapter
-        else:
-            raise ValueError(f"Unsupported IP-Adapter type: {type(self.ip_adapter)}")
-
-    def _prep_ip_adapter_image_prompt_clip_embeds(
-        self,
-        ip_adapter_fields: list[IPAdapterField],
-        context: InvocationContext,
-    ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
-        """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
-        clip_image_processor = CLIPImageProcessor()
-
-        pos_image_prompt_clip_embeds: list[torch.Tensor] = []
-        neg_image_prompt_clip_embeds: list[torch.Tensor] = []
-        for ip_adapter_field in ip_adapter_fields:
-            # `ip_adapter_field.image` could be a list or a single ImageField. Normalize to a list here.
-            ipa_image_fields: list[ImageField]
-            if isinstance(ip_adapter_field.image, ImageField):
-                ipa_image_fields = [ip_adapter_field.image]
-            elif isinstance(ip_adapter_field.image, list):
-                ipa_image_fields = ip_adapter_field.image
-            else:
-                raise ValueError(f"Unsupported IP-Adapter image type: {type(ip_adapter_field.image)}")
-
-            if len(ipa_image_fields) != 1:
-                raise ValueError(
-                    f"FLUX IP-Adapter only supports a single image prompt (received {len(ipa_image_fields)})."
-                )
-
-            ipa_images = [context.images.get_pil(image.image_name, mode="RGB") for image in ipa_image_fields]
-
-            pos_images: list[npt.NDArray[np.uint8]] = []
-            neg_images: list[npt.NDArray[np.uint8]] = []
-            for ipa_image in ipa_images:
-                assert ipa_image.mode == "RGB"
-                pos_image = np.array(ipa_image)
-                # We use a black image as the negative image prompt for parity with
-                # https://github.com/XLabs-AI/x-flux-comfyui/blob/45c834727dd2141aebc505ae4b01f193a8414e38/nodes.py#L592-L593
-                # An alternative scheme would be to apply zeros_like() after calling the clip_image_processor.
-                neg_image = np.zeros_like(pos_image)
-                pos_images.append(pos_image)
-                neg_images.append(neg_image)
-
-            with context.models.load(ip_adapter_field.image_encoder_model) as image_encoder_model:
-                assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
-
-                clip_image: torch.Tensor = clip_image_processor(images=pos_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
-                pos_clip_image_embeds = image_encoder_model(clip_image).image_embeds
-
-                clip_image = clip_image_processor(images=neg_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
-                neg_clip_image_embeds = image_encoder_model(clip_image).image_embeds
-
-            pos_image_prompt_clip_embeds.append(pos_clip_image_embeds)
-            neg_image_prompt_clip_embeds.append(neg_clip_image_embeds)
-
-        return pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds
-
-    def _prep_ip_adapter_extensions(
-        self,
-        ip_adapter_fields: list[IPAdapterField],
-        pos_image_prompt_clip_embeds: list[torch.Tensor],
-        neg_image_prompt_clip_embeds: list[torch.Tensor],
-        context: InvocationContext,
-        exit_stack: ExitStack,
-        dtype: torch.dtype,
-    ) -> tuple[list[XLabsIPAdapterExtension], list[XLabsIPAdapterExtension]]:
-        pos_ip_adapter_extensions: list[XLabsIPAdapterExtension] = []
-        neg_ip_adapter_extensions: list[XLabsIPAdapterExtension] = []
-        for ip_adapter_field, pos_image_prompt_clip_embed, neg_image_prompt_clip_embed in zip(
-            ip_adapter_fields, pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds, strict=True
-        ):
-            ip_adapter_model = exit_stack.enter_context(context.models.load(ip_adapter_field.ip_adapter_model))
-            assert isinstance(ip_adapter_model, XlabsIpAdapterFlux)
-            ip_adapter_model = ip_adapter_model.to(dtype=dtype)
-            if ip_adapter_field.mask is not None:
-                raise ValueError("IP-Adapter masks are not yet supported in Flux.")
-            ip_adapter_extension = XLabsIPAdapterExtension(
-                model=ip_adapter_model,
-                image_prompt_clip_embed=pos_image_prompt_clip_embed,
-                weight=ip_adapter_field.weight,
-                begin_step_percent=ip_adapter_field.begin_step_percent,
-                end_step_percent=ip_adapter_field.end_step_percent,
-            )
-            ip_adapter_extension.run_image_proj(dtype=dtype)
-            pos_ip_adapter_extensions.append(ip_adapter_extension)
-
-            ip_adapter_extension = XLabsIPAdapterExtension(
-                model=ip_adapter_model,
-                image_prompt_clip_embed=neg_image_prompt_clip_embed,
-                weight=ip_adapter_field.weight,
-                begin_step_percent=ip_adapter_field.begin_step_percent,
-                end_step_percent=ip_adapter_field.end_step_percent,
-            )
-            ip_adapter_extension.run_image_proj(dtype=dtype)
-            neg_ip_adapter_extensions.append(ip_adapter_extension)
-
-        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:
             lora_info = context.models.load(lora.lora)

invokeai/app/invocations/flux_ip_adapter.py

@@ -1,89 +0,0 @@
-from builtins import float
-from typing import List, Literal, Union
-
-from pydantic import field_validator, model_validator
-from typing_extensions import Self
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import InputField, UIType
-from invokeai.app.invocations.ip_adapter import (
-    CLIP_VISION_MODEL_MAP,
-    IPAdapterField,
-    IPAdapterInvocation,
-    IPAdapterOutput,
-)
-from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import (
-    IPAdapterCheckpointConfig,
-    IPAdapterInvokeAIConfig,
-)
-
-
-@invocation(
-    "flux_ip_adapter",
-    title="FLUX IP-Adapter",
-    tags=["ip_adapter", "control"],
-    category="ip_adapter",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxIPAdapterInvocation(BaseInvocation):
-    """Collects FLUX IP-Adapter info to pass to other nodes."""
-
-    # FLUXIPAdapterInvocation is based closely on IPAdapterInvocation, but with some unsupported features removed.
-
-    image: ImageField = InputField(description="The IP-Adapter image prompt(s).")
-    ip_adapter_model: ModelIdentifierField = InputField(
-        description="The IP-Adapter model.", title="IP-Adapter Model", ui_type=UIType.IPAdapterModel
-    )
-    # Currently, the only known ViT model used by FLUX IP-Adapters is ViT-L.
-    clip_vision_model: Literal["ViT-L"] = InputField(description="CLIP Vision model to use.", default="ViT-L")
-    weight: Union[float, List[float]] = InputField(
-        default=1, description="The weight given to the IP-Adapter", title="Weight"
-    )
-    begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
-    )
-    end_step_percent: float = InputField(
-        default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
-    )
-
-    @field_validator("weight")
-    @classmethod
-    def validate_ip_adapter_weight(cls, v: float) -> float:
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> Self:
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
-    def invoke(self, context: InvocationContext) -> IPAdapterOutput:
-        # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
-        assert isinstance(ip_adapter_info, (IPAdapterInvokeAIConfig, IPAdapterCheckpointConfig))
-
-        # Note: There is a IPAdapterInvokeAIConfig.image_encoder_model_id field, but it isn't trustworthy.
-        image_encoder_starter_model = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
-        image_encoder_model_id = image_encoder_starter_model.source
-        image_encoder_model_name = image_encoder_starter_model.name
-        image_encoder_model = IPAdapterInvocation.get_clip_image_encoder(
-            context, image_encoder_model_id, image_encoder_model_name
-        )
-
-        return IPAdapterOutput(
-            ip_adapter=IPAdapterField(
-                image=self.image,
-                ip_adapter_model=self.ip_adapter_model,
-                image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
-                weight=self.weight,
-                target_blocks=[],  # target_blocks is currently unused for FLUX IP-Adapters.
-                begin_step_percent=self.begin_step_percent,
-                end_step_percent=self.end_step_percent,
-                mask=None,  # mask is currently unused for FLUX IP-Adapters.
-            ),
-        )

invokeai/app/invocations/flux_model_loader.py

@@ -1,89 +0,0 @@
-from typing import Literal
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.util import max_seq_lengths
-from invokeai.backend.model_manager.config import (
-    CheckpointConfigBase,
-    SubModelType,
-)
-
-
-@invocation_output("flux_model_loader_output")
-class FluxModelLoaderOutput(BaseInvocationOutput):
-    """Flux base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-    max_seq_len: Literal[256, 512] = OutputField(
-        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
-        title="Max Seq Length",
-    )
-
-
-@invocation(
-    "flux_model_loader",
-    title="Flux Main Model",
-    tags=["model", "flux"],
-    category="model",
-    version="1.0.4",
-    classification=Classification.Prototype,
-)
-class FluxModelLoaderInvocation(BaseInvocation):
-    """Loads a flux base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.flux_model,
-        ui_type=UIType.FluxMainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-
-        transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
-            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-            max_seq_len=max_seq_lengths[transformer_config.config_path],
-        )

invokeai/app/invocations/flux_text_encoder.py

@@ -1,18 +1,11 @@
 from contextlib import ExitStack
-from typing import Iterator, Literal, Optional, Tuple
+from typing import Iterator, Literal, 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,
-    FluxConditioningField,
-    Input,
-    InputField,
-    TensorField,
-    UIComponent,
-)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -29,7 +22,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
     title="FLUX Text Encoding",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.1",
+    version="1.1.0",
     classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
@@ -48,10 +41,7 @@ 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.", ui_component=UIComponent.Textarea)
-    mask: Optional[TensorField] = InputField(
-        default=None, description="A mask defining the region that this conditioning prompt applies to."
-    )
+    prompt: str = InputField(description="Text prompt to encode.")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
@@ -64,9 +54,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
         )
 
         conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput(
-            conditioning=FluxConditioningField(conditioning_name=conditioning_name, mask=self.mask)
-        )
+        return FluxConditioningOutput.build(conditioning_name)
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
         t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
@@ -83,7 +71,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
-            context.util.signal_progress("Running T5 encoder")
             prompt_embeds = t5_encoder(prompt)
 
         assert isinstance(prompt_embeds, torch.Tensor)
@@ -124,7 +111,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
 
-            context.util.signal_progress("Running CLIP encoder")
             pooled_prompt_embeds = clip_encoder(prompt)
 
         assert isinstance(pooled_prompt_embeds, torch.Tensor)

invokeai/app/invocations/flux_vae_decode.py

@@ -41,8 +41,7 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
         with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
-            vae_dtype = next(iter(vae.parameters())).dtype
-            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
+            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
             img = vae.decode(latents)
 
         img = img.clamp(-1, 1)
@@ -54,7 +53,6 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
         vae_info = context.models.load(self.vae.vae)
-        context.util.signal_progress("Running VAE")
         image = self._vae_decode(vae_info=vae_info, latents=latents)
 
         TorchDevice.empty_cache()

invokeai/app/invocations/flux_vae_encode.py

@@ -44,8 +44,9 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
         with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
-            vae_dtype = next(iter(vae.parameters())).dtype
-            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
+            image_tensor = image_tensor.to(
+                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
+            )
             latents = vae.encode(image_tensor, sample=True, generator=generator)
             return latents
 
@@ -59,7 +60,6 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        context.util.signal_progress("Running VAE")
         latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")

invokeai/app/invocations/image_panels.py

@@ -1,59 +0,0 @@
-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

@@ -117,7 +117,6 @@ class ImageToLatentsInvocation(BaseInvocation):
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
             vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
         )

invokeai/app/invocations/ip_adapter.py

@@ -9,7 +9,6 @@ from invokeai.app.invocations.fields import FieldDescriptions, InputField, Outpu
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
@@ -18,12 +17,6 @@ from invokeai.backend.model_manager.config import (
     IPAdapterInvokeAIConfig,
     ModelType,
 )
-from invokeai.backend.model_manager.starter_models import (
-    StarterModel,
-    clip_vit_l_image_encoder,
-    ip_adapter_sd_image_encoder,
-    ip_adapter_sdxl_image_encoder,
-)
 
 
 class IPAdapterField(BaseModel):
@@ -62,14 +55,10 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-CLIP_VISION_MODEL_MAP: dict[Literal["ViT-L", "ViT-H", "ViT-G"], StarterModel] = {
-    "ViT-L": clip_vit_l_image_encoder,
-    "ViT-H": ip_adapter_sd_image_encoder,
-    "ViT-G": ip_adapter_sdxl_image_encoder,
-}
+CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.5.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -81,7 +70,7 @@ class IPAdapterInvocation(BaseInvocation):
         ui_order=-1,
         ui_type=UIType.IPAdapterModel,
     )
-    clip_vision_model: Literal["ViT-H", "ViT-G", "ViT-L"] = InputField(
+    clip_vision_model: Literal["ViT-H", "ViT-G"] = InputField(
         description="CLIP Vision model to use. Overrides model settings. Mandatory for checkpoint models.",
         default="ViT-H",
         ui_order=2,
@@ -122,11 +111,9 @@ class IPAdapterInvocation(BaseInvocation):
             image_encoder_model_id = ip_adapter_info.image_encoder_model_id
             image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         else:
-            image_encoder_starter_model = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
-            image_encoder_model_id = image_encoder_starter_model.source
-            image_encoder_model_name = image_encoder_starter_model.name
+            image_encoder_model_name = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
 
-        image_encoder_model = self.get_clip_image_encoder(context, image_encoder_model_id, image_encoder_model_name)
+        image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
 
         if self.method == "style":
             if ip_adapter_info.base == "sd-1":
@@ -160,10 +147,7 @@ class IPAdapterInvocation(BaseInvocation):
             ),
         )
 
-    @classmethod
-    def get_clip_image_encoder(
-        cls, context: InvocationContext, image_encoder_model_id: str, image_encoder_model_name: str
-    ) -> AnyModelConfig:
+    def _get_image_encoder(self, context: InvocationContext, image_encoder_model_name: str) -> AnyModelConfig:
         image_encoder_models = context.models.search_by_attrs(
             name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
         )
@@ -175,11 +159,7 @@ class IPAdapterInvocation(BaseInvocation):
             )
 
             installer = context._services.model_manager.install
-            # Note: We hard-code the type to CLIPVision here because if the model contains both a CLIPVision and a
-            # CLIPText model, the probe may treat it as a CLIPText model.
-            job = installer.heuristic_import(
-                image_encoder_model_id, ModelRecordChanges(name=image_encoder_model_name, type=ModelType.CLIPVision)
-            )
+            job = installer.heuristic_import(f"InvokeAI/{image_encoder_model_name}")
             installer.wait_for_job(job, timeout=600)  # Wait for up to 10 minutes
             image_encoder_models = context.models.search_by_attrs(
                 name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision

invokeai/app/invocations/latents_to_image.py

@@ -60,7 +60,6 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
         with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
-            context.util.signal_progress("Running VAE decoder")
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             latents = latents.to(vae.device)
             if self.fp32:

invokeai/app/invocations/mask.py

@@ -5,7 +5,6 @@ from PIL import Image
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
 from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithBoard, WithMetadata
 from invokeai.app.invocations.primitives import ImageOutput, MaskOutput
-from invokeai.backend.image_util.util import pil_to_np
 
 
 @invocation(
@@ -149,55 +148,3 @@ class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         mask_pil = Image.fromarray(mask_np, mode="L")
         image_dto = context.images.save(image=mask_pil)
         return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "apply_tensor_mask_to_image",
-    title="Apply Tensor Mask to Image",
-    tags=["mask"],
-    category="mask",
-    version="1.0.0",
-)
-class ApplyMaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Applies a tensor mask to an image.
-
-    The image is converted to RGBA and the mask is applied to the alpha channel."""
-
-    mask: TensorField = InputField(description="The mask tensor to apply.")
-    image: ImageField = InputField(description="The image to apply the mask to.")
-    invert: bool = InputField(default=False, description="Whether to invert the mask.")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
-        mask = context.tensors.load(self.mask.tensor_name)
-
-        # Squeeze the channel dimension if it exists.
-        if mask.dim() == 3:
-            mask = mask.squeeze(0)
-
-        # Ensure that the mask is binary.
-        if mask.dtype != torch.bool:
-            mask = mask > 0.5
-        mask_np = (mask.float() * 255).byte().cpu().numpy().astype(np.uint8)
-
-        if self.invert:
-            mask_np = 255 - mask_np
-
-        # Apply the mask only to the alpha channel where the original alpha is non-zero. This preserves the original
-        # image's transparency - else the transparent regions would end up as opaque black.
-
-        # Separate the image into R, G, B, and A channels
-        image_np = pil_to_np(image)
-        r, g, b, a = np.split(image_np, 4, axis=-1)
-
-        # Apply the mask to the alpha channel
-        new_alpha = np.where(a.squeeze() > 0, mask_np, a.squeeze())
-
-        # Stack the RGB channels with the modified alpha
-        masked_image_np = np.dstack([r.squeeze(), g.squeeze(), b.squeeze(), new_alpha])
-
-        # Convert back to an image (RGBA)
-        masked_image = Image.fromarray(masked_image_np.astype(np.uint8), "RGBA")
-        image_dto = context.images.save(image=masked_image)
-
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/metadata.py

@@ -40,7 +40,7 @@ class IPAdapterMetadataField(BaseModel):
 
     image: ImageField = Field(description="The IP-Adapter image prompt.")
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
-    clip_vision_model: Literal["ViT-L", "ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
+    clip_vision_model: Literal["ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
     method: Literal["full", "style", "composition"] = Field(description="Method to apply IP Weights with")
     weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
     begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
@@ -147,10 +147,6 @@ GENERATION_MODES = Literal[
     "flux_img2img",
     "flux_inpaint",
     "flux_outpaint",
-    "sd3_txt2img",
-    "sd3_img2img",
-    "sd3_inpaint",
-    "sd3_outpaint",
 ]
 
 

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Optional
+from typing import List, Literal, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,9 +13,11 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
+from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    CheckpointConfigBase,
     ModelType,
     SubModelType,
 )
@@ -137,6 +139,78 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
+@invocation_output("flux_model_loader_output")
+class FluxModelLoaderOutput(BaseInvocationOutput):
+    """Flux base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    max_seq_len: Literal[256, 512] = OutputField(
+        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
+        title="Max Seq Length",
+    )
+
+
+@invocation(
+    "flux_model_loader",
+    title="Flux Main Model",
+    tags=["model", "flux"],
+    category="model",
+    version="1.0.4",
+    classification=Classification.Prototype,
+)
+class FluxModelLoaderInvocation(BaseInvocation):
+    """Loads a flux base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.flux_model,
+        ui_type=UIType.FluxMainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
+    )
+
+    clip_embed_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPEmbedModel,
+        input=Input.Direct,
+        title="CLIP Embed",
+    )
+
+    vae_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
+        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+
+        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+
+        transformer_config = context.models.get_config(transformer)
+        assert isinstance(transformer_config, CheckpointConfigBase)
+
+        return FluxModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+            max_seq_len=max_seq_lengths[transformer_config.config_path],
+        )
+
+
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/param_easing.py

@@ -1,4 +1,43 @@
+import io
+from typing import Literal, Optional
+
+import matplotlib.pyplot as plt
 import numpy as np
+import PIL.Image
+from easing_functions import (
+    BackEaseIn,
+    BackEaseInOut,
+    BackEaseOut,
+    BounceEaseIn,
+    BounceEaseInOut,
+    BounceEaseOut,
+    CircularEaseIn,
+    CircularEaseInOut,
+    CircularEaseOut,
+    CubicEaseIn,
+    CubicEaseInOut,
+    CubicEaseOut,
+    ElasticEaseIn,
+    ElasticEaseInOut,
+    ElasticEaseOut,
+    ExponentialEaseIn,
+    ExponentialEaseInOut,
+    ExponentialEaseOut,
+    LinearInOut,
+    QuadEaseIn,
+    QuadEaseInOut,
+    QuadEaseOut,
+    QuarticEaseIn,
+    QuarticEaseInOut,
+    QuarticEaseOut,
+    QuinticEaseIn,
+    QuinticEaseInOut,
+    QuinticEaseOut,
+    SineEaseIn,
+    SineEaseInOut,
+    SineEaseOut,
+)
+from matplotlib.ticker import MaxNLocator
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import InputField
@@ -26,3 +65,191 @@ class FloatLinearRangeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         param_list = list(np.linspace(self.start, self.stop, self.steps))
         return FloatCollectionOutput(collection=param_list)
+
+
+EASING_FUNCTIONS_MAP = {
+    "Linear": LinearInOut,
+    "QuadIn": QuadEaseIn,
+    "QuadOut": QuadEaseOut,
+    "QuadInOut": QuadEaseInOut,
+    "CubicIn": CubicEaseIn,
+    "CubicOut": CubicEaseOut,
+    "CubicInOut": CubicEaseInOut,
+    "QuarticIn": QuarticEaseIn,
+    "QuarticOut": QuarticEaseOut,
+    "QuarticInOut": QuarticEaseInOut,
+    "QuinticIn": QuinticEaseIn,
+    "QuinticOut": QuinticEaseOut,
+    "QuinticInOut": QuinticEaseInOut,
+    "SineIn": SineEaseIn,
+    "SineOut": SineEaseOut,
+    "SineInOut": SineEaseInOut,
+    "CircularIn": CircularEaseIn,
+    "CircularOut": CircularEaseOut,
+    "CircularInOut": CircularEaseInOut,
+    "ExponentialIn": ExponentialEaseIn,
+    "ExponentialOut": ExponentialEaseOut,
+    "ExponentialInOut": ExponentialEaseInOut,
+    "ElasticIn": ElasticEaseIn,
+    "ElasticOut": ElasticEaseOut,
+    "ElasticInOut": ElasticEaseInOut,
+    "BackIn": BackEaseIn,
+    "BackOut": BackEaseOut,
+    "BackInOut": BackEaseInOut,
+    "BounceIn": BounceEaseIn,
+    "BounceOut": BounceEaseOut,
+    "BounceInOut": BounceEaseInOut,
+}
+
+EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
+
+
+# actually I think for now could just use CollectionOutput (which is list[Any]
+@invocation(
+    "step_param_easing",
+    title="Step Param Easing",
+    tags=["step", "easing"],
+    category="step",
+    version="1.0.2",
+)
+class StepParamEasingInvocation(BaseInvocation):
+    """Experimental per-step parameter easing for denoising steps"""
+
+    easing: EASING_FUNCTION_KEYS = InputField(default="Linear", description="The easing function to use")
+    num_steps: int = InputField(default=20, description="number of denoising steps")
+    start_value: float = InputField(default=0.0, description="easing starting value")
+    end_value: float = InputField(default=1.0, description="easing ending value")
+    start_step_percent: float = InputField(default=0.0, description="fraction of steps at which to start easing")
+    end_step_percent: float = InputField(default=1.0, description="fraction of steps after which to end easing")
+    # if None, then start_value is used prior to easing start
+    pre_start_value: Optional[float] = InputField(default=None, description="value before easing start")
+    # if None, then end value is used prior to easing end
+    post_end_value: Optional[float] = InputField(default=None, description="value after easing end")
+    mirror: bool = InputField(default=False, description="include mirror of easing function")
+    # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
+    # alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing")
+    show_easing_plot: bool = InputField(default=False, description="show easing plot")
+
+    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
+        log_diagnostics = False
+        # convert from start_step_percent to nearest step <= (steps * start_step_percent)
+        # start_step = int(np.floor(self.num_steps * self.start_step_percent))
+        start_step = int(np.round(self.num_steps * self.start_step_percent))
+        # convert from end_step_percent to nearest step >= (steps * end_step_percent)
+        # end_step = int(np.ceil((self.num_steps - 1) * self.end_step_percent))
+        end_step = int(np.round((self.num_steps - 1) * self.end_step_percent))
+
+        # end_step = int(np.ceil(self.num_steps * self.end_step_percent))
+        num_easing_steps = end_step - start_step + 1
+
+        # num_presteps = max(start_step - 1, 0)
+        num_presteps = start_step
+        num_poststeps = self.num_steps - (num_presteps + num_easing_steps)
+        prelist = list(num_presteps * [self.pre_start_value])
+        postlist = list(num_poststeps * [self.post_end_value])
+
+        if log_diagnostics:
+            context.logger.debug("start_step: " + str(start_step))
+            context.logger.debug("end_step: " + str(end_step))
+            context.logger.debug("num_easing_steps: " + str(num_easing_steps))
+            context.logger.debug("num_presteps: " + str(num_presteps))
+            context.logger.debug("num_poststeps: " + str(num_poststeps))
+            context.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
+            context.logger.debug("prelist: " + str(prelist))
+            context.logger.debug("postlist: " + str(postlist))
+
+        easing_class = EASING_FUNCTIONS_MAP[self.easing]
+        if log_diagnostics:
+            context.logger.debug("easing class: " + str(easing_class))
+        easing_list = []
+        if self.mirror:  # "expected" mirroring
+            # if number of steps is even, squeeze duration down to (number_of_steps)/2
+            # and create reverse copy of list to append
+            # if number of steps is odd, squeeze duration down to ceil(number_of_steps/2)
+            # and create reverse copy of list[1:end-1]
+            # but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
+
+            base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
+            if log_diagnostics:
+                context.logger.debug("base easing duration: " + str(base_easing_duration))
+            even_num_steps = num_easing_steps % 2 == 0  # even number of steps
+            easing_function = easing_class(
+                start=self.start_value,
+                end=self.end_value,
+                duration=base_easing_duration - 1,
+            )
+            base_easing_vals = []
+            for step_index in range(base_easing_duration):
+                easing_val = easing_function.ease(step_index)
+                base_easing_vals.append(easing_val)
+                if log_diagnostics:
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
+            if even_num_steps:
+                mirror_easing_vals = list(reversed(base_easing_vals))
+            else:
+                mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
+            if log_diagnostics:
+                context.logger.debug("base easing vals: " + str(base_easing_vals))
+                context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
+            easing_list = base_easing_vals + mirror_easing_vals
+
+        # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
+        # elif self.alt_mirror:  # function mirroring (unintuitive behavior (at least to me))
+        #     # half_ease_duration = round(num_easing_steps - 1 / 2)
+        #     half_ease_duration = round((num_easing_steps - 1) / 2)
+        #     easing_function = easing_class(start=self.start_value,
+        #                                    end=self.end_value,
+        #                                    duration=half_ease_duration,
+        #                                    )
+        #
+        #     mirror_function = easing_class(start=self.end_value,
+        #                                    end=self.start_value,
+        #                                    duration=half_ease_duration,
+        #                                    )
+        #     for step_index in range(num_easing_steps):
+        #         if step_index <= half_ease_duration:
+        #             step_val = easing_function.ease(step_index)
+        #         else:
+        #             step_val = mirror_function.ease(step_index - half_ease_duration)
+        #         easing_list.append(step_val)
+        #         if log_diagnostics: logger.debug(step_index, step_val)
+        #
+
+        else:  # no mirroring (default)
+            easing_function = easing_class(
+                start=self.start_value,
+                end=self.end_value,
+                duration=num_easing_steps - 1,
+            )
+            for step_index in range(num_easing_steps):
+                step_val = easing_function.ease(step_index)
+                easing_list.append(step_val)
+                if log_diagnostics:
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
+
+        if log_diagnostics:
+            context.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("easing_list size: " + str(len(easing_list)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
+
+        param_list = prelist + easing_list + postlist
+
+        if self.show_easing_plot:
+            plt.figure()
+            plt.xlabel("Step")
+            plt.ylabel("Param Value")
+            plt.title("Per-Step Values Based On Easing: " + self.easing)
+            plt.bar(range(len(param_list)), param_list)
+            # plt.plot(param_list)
+            ax = plt.gca()
+            ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+            buf = io.BytesIO()
+            plt.savefig(buf, format="png")
+            buf.seek(0)
+            im = PIL.Image.open(buf)
+            im.show()
+            buf.close()
+
+        # output array of size steps, each entry list[i] is param value for step i
+        return FloatCollectionOutput(collection=param_list)

invokeai/app/invocations/primitives.py

@@ -4,13 +4,7 @@ from typing import Optional
 
 import torch
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     BoundingBoxField,
@@ -24,7 +18,6 @@ from invokeai.app.invocations.fields import (
     InputField,
     LatentsField,
     OutputField,
-    SD3ConditioningField,
     TensorField,
     UIComponent,
 )
@@ -433,17 +426,6 @@ class FluxConditioningOutput(BaseInvocationOutput):
         return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
 
 
-@invocation_output("sd3_conditioning_output")
-class SD3ConditioningOutput(BaseInvocationOutput):
-    """Base class for nodes that output a single SD3 conditioning tensor"""
-
-    conditioning: SD3ConditioningField = OutputField(description=FieldDescriptions.cond)
-
-    @classmethod
-    def build(cls, conditioning_name: str) -> "SD3ConditioningOutput":
-        return cls(conditioning=SD3ConditioningField(conditioning_name=conditioning_name))
-
-
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""
@@ -539,23 +521,3 @@ class BoundingBoxInvocation(BaseInvocation):
 
 
 # endregion
-
-
-@invocation(
-    "image_batch",
-    title="Image Batch",
-    tags=["primitives", "image", "batch", "internal"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class ImageBatchInvocation(BaseInvocation):
-    """Create a batched generation, where the workflow is executed once for each image in the batch."""
-
-    images: list[ImageField] = InputField(min_length=1, description="The images to batch over", input=Input.Direct)
-
-    def __init__(self):
-        raise NotImplementedError("This class should never be executed or instantiated directly.")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        raise NotImplementedError("This class should never be executed or instantiated directly.")

invokeai/app/invocations/sd3_denoise.py

@@ -1,338 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import torch
-import torchvision.transforms as tv_transforms
-from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
-from torchvision.transforms.functional import resize as tv_resize
-from tqdm import tqdm
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.invocations.fields import (
-    DenoiseMaskField,
-    FieldDescriptions,
-    Input,
-    InputField,
-    LatentsField,
-    SD3ConditioningField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import TransformerField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.invocations.sd3_text_encoder import SD3_T5_MAX_SEQ_LEN
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
-from invokeai.backend.model_manager.config import BaseModelType
-from invokeai.backend.sd3.extensions.inpaint_extension import InpaintExtension
-from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import SD3ConditioningInfo
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "sd3_denoise",
-    title="SD3 Denoise",
-    tags=["image", "sd3"],
-    category="image",
-    version="1.1.0",
-    classification=Classification.Prototype,
-)
-class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Run denoising process with a SD3 model."""
-
-    # If latents is provided, this means we are doing image-to-image.
-    latents: Optional[LatentsField] = InputField(
-        default=None, description=FieldDescriptions.latents, input=Input.Connection
-    )
-    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
-    denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.denoise_mask, input=Input.Connection
-    )
-    denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
-    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    transformer: TransformerField = InputField(
-        description=FieldDescriptions.sd3_model, input=Input.Connection, title="Transformer"
-    )
-    positive_conditioning: SD3ConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
-    )
-    negative_conditioning: SD3ConditioningField = InputField(
-        description=FieldDescriptions.negative_cond, input=Input.Connection
-    )
-    cfg_scale: float | list[float] = InputField(default=3.5, description=FieldDescriptions.cfg_scale, title="CFG Scale")
-    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
-    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
-    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
-    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = self._run_diffusion(context)
-        latents = latents.detach().to("cpu")
-
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
-
-    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
-        """Prepare the inpaint mask.
-        - Loads the mask
-        - Resizes if necessary
-        - Casts to same device/dtype as latents
-
-        Args:
-            context (InvocationContext): The invocation context, for loading the inpaint mask.
-            latents (torch.Tensor): A latent image tensor. Used to determine the target shape, device, and dtype for the
-                inpaint mask.
-
-        Returns:
-            torch.Tensor | None: Inpaint mask. Values of 0.0 represent the regions to be fully denoised, and 1.0
-                represent the regions to be preserved.
-        """
-        if self.denoise_mask is None:
-            return None
-        mask = context.tensors.load(self.denoise_mask.mask_name)
-
-        # The input denoise_mask contains values in [0, 1], where 0.0 represents the regions to be fully denoised, and
-        # 1.0 represents the regions to be preserved.
-        # We invert the mask so that the regions to be preserved are 0.0 and the regions to be denoised are 1.0.
-        mask = 1.0 - mask
-
-        _, _, latent_height, latent_width = latents.shape
-        mask = tv_resize(
-            img=mask,
-            size=[latent_height, latent_width],
-            interpolation=tv_transforms.InterpolationMode.BILINEAR,
-            antialias=False,
-        )
-
-        mask = mask.to(device=latents.device, dtype=latents.dtype)
-        return mask
-
-    def _load_text_conditioning(
-        self,
-        context: InvocationContext,
-        conditioning_name: str,
-        joint_attention_dim: int,
-        dtype: torch.dtype,
-        device: torch.device,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        sd3_conditioning = cond_data.conditionings[0]
-        assert isinstance(sd3_conditioning, SD3ConditioningInfo)
-        sd3_conditioning = sd3_conditioning.to(dtype=dtype, device=device)
-
-        t5_embeds = sd3_conditioning.t5_embeds
-        if t5_embeds is None:
-            t5_embeds = torch.zeros(
-                (1, SD3_T5_MAX_SEQ_LEN, joint_attention_dim),
-                device=device,
-                dtype=dtype,
-            )
-
-        clip_prompt_embeds = torch.cat([sd3_conditioning.clip_l_embeds, sd3_conditioning.clip_g_embeds], dim=-1)
-        clip_prompt_embeds = torch.nn.functional.pad(
-            clip_prompt_embeds, (0, t5_embeds.shape[-1] - clip_prompt_embeds.shape[-1])
-        )
-
-        prompt_embeds = torch.cat([clip_prompt_embeds, t5_embeds], dim=-2)
-        pooled_prompt_embeds = torch.cat(
-            [sd3_conditioning.clip_l_pooled_embeds, sd3_conditioning.clip_g_pooled_embeds], dim=-1
-        )
-
-        return prompt_embeds, pooled_prompt_embeds
-
-    def _get_noise(
-        self,
-        num_samples: int,
-        num_channels_latents: int,
-        height: int,
-        width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-        seed: int,
-    ) -> torch.Tensor:
-        # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
-        rand_device = "cpu"
-        rand_dtype = torch.float16
-
-        return torch.randn(
-            num_samples,
-            num_channels_latents,
-            int(height) // LATENT_SCALE_FACTOR,
-            int(width) // LATENT_SCALE_FACTOR,
-            device=rand_device,
-            dtype=rand_dtype,
-            generator=torch.Generator(device=rand_device).manual_seed(seed),
-        ).to(device=device, dtype=dtype)
-
-    def _prepare_cfg_scale(self, num_timesteps: int) -> list[float]:
-        """Prepare the CFG scale list.
-
-        Args:
-            num_timesteps (int): The number of timesteps in the scheduler. Could be different from num_steps depending
-            on the scheduler used (e.g. higher order schedulers).
-
-        Returns:
-            list[float]: _description_
-        """
-        if isinstance(self.cfg_scale, float):
-            cfg_scale = [self.cfg_scale] * num_timesteps
-        elif isinstance(self.cfg_scale, list):
-            assert len(self.cfg_scale) == num_timesteps
-            cfg_scale = self.cfg_scale
-        else:
-            raise ValueError(f"Invalid CFG scale type: {type(self.cfg_scale)}")
-
-        return cfg_scale
-
-    def _run_diffusion(
-        self,
-        context: InvocationContext,
-    ):
-        inference_dtype = TorchDevice.choose_torch_dtype()
-        device = TorchDevice.choose_torch_device()
-
-        transformer_info = context.models.load(self.transformer.transformer)
-
-        # Load/process the conditioning data.
-        # TODO(ryand): Make CFG optional.
-        do_classifier_free_guidance = True
-        pos_prompt_embeds, pos_pooled_prompt_embeds = self._load_text_conditioning(
-            context=context,
-            conditioning_name=self.positive_conditioning.conditioning_name,
-            joint_attention_dim=transformer_info.model.config.joint_attention_dim,
-            dtype=inference_dtype,
-            device=device,
-        )
-        neg_prompt_embeds, neg_pooled_prompt_embeds = self._load_text_conditioning(
-            context=context,
-            conditioning_name=self.negative_conditioning.conditioning_name,
-            joint_attention_dim=transformer_info.model.config.joint_attention_dim,
-            dtype=inference_dtype,
-            device=device,
-        )
-        # TODO(ryand): Support both sequential and batched CFG inference.
-        prompt_embeds = torch.cat([neg_prompt_embeds, pos_prompt_embeds], dim=0)
-        pooled_prompt_embeds = torch.cat([neg_pooled_prompt_embeds, pos_pooled_prompt_embeds], dim=0)
-
-        # Prepare the timestep schedule.
-        # We add an extra step to the end to account for the final timestep of 0.0.
-        timesteps: list[float] = torch.linspace(1, 0, self.steps + 1).tolist()
-        # Clip the timesteps schedule based on denoising_start and denoising_end.
-        timesteps = clip_timestep_schedule_fractional(timesteps, self.denoising_start, self.denoising_end)
-        total_steps = len(timesteps) - 1
-
-        # Prepare the CFG scale list.
-        cfg_scale = self._prepare_cfg_scale(total_steps)
-
-        # 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:
-            init_latents = init_latents.to(device=device, dtype=inference_dtype)
-
-        # Generate initial latent noise.
-        num_channels_latents = transformer_info.model.config.in_channels
-        assert isinstance(num_channels_latents, int)
-        noise = self._get_noise(
-            num_samples=1,
-            num_channels_latents=num_channels_latents,
-            height=self.height,
-            width=self.width,
-            dtype=inference_dtype,
-            device=device,
-            seed=self.seed,
-        )
-
-        # Prepare input latent image.
-        if init_latents is not None:
-            # Noise the init_latents by the appropriate amount for the first timestep.
-            t_0 = timesteps[0]
-            latents = t_0 * noise + (1.0 - t_0) * init_latents
-        else:
-            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
-            if self.denoising_start > 1e-5:
-                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
-            latents = noise
-
-        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
-        # denoising steps.
-        if len(timesteps) <= 1:
-            return latents
-
-        # Prepare inpaint extension.
-        inpaint_mask = self._prep_inpaint_mask(context, latents)
-        inpaint_extension: InpaintExtension | None = None
-        if inpaint_mask is not None:
-            assert init_latents is not None
-            inpaint_extension = InpaintExtension(
-                init_latents=init_latents,
-                inpaint_mask=inpaint_mask,
-                noise=noise,
-            )
-
-        step_callback = self._build_step_callback(context)
-
-        step_callback(
-            PipelineIntermediateState(
-                step=0,
-                order=1,
-                total_steps=total_steps,
-                timestep=int(timesteps[0]),
-                latents=latents,
-            ),
-        )
-
-        with transformer_info.model_on_device() as (cached_weights, transformer):
-            assert isinstance(transformer, SD3Transformer2DModel)
-
-            # 6. Denoising loop
-            for step_idx, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
-                # Expand the latents if we are doing CFG.
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                # Expand the timestep to match the latent model input.
-                # Multiply by 1000 to match the default FlowMatchEulerDiscreteScheduler num_train_timesteps.
-                timestep = torch.tensor([t_curr * 1000], device=device).expand(latent_model_input.shape[0])
-
-                noise_pred = transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    pooled_projections=pooled_prompt_embeds,
-                    joint_attention_kwargs=None,
-                    return_dict=False,
-                )[0]
-
-                # Apply CFG.
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + cfg_scale[step_idx] * (noise_pred_cond - noise_pred_uncond)
-
-                # Compute the previous noisy sample x_t -> x_t-1.
-                latents_dtype = latents.dtype
-                latents = latents.to(dtype=torch.float32)
-                latents = latents + (t_prev - t_curr) * noise_pred
-                latents = latents.to(dtype=latents_dtype)
-
-                if inpaint_extension is not None:
-                    latents = inpaint_extension.merge_intermediate_latents_with_init_latents(latents, t_prev)
-
-                step_callback(
-                    PipelineIntermediateState(
-                        step=step_idx + 1,
-                        order=1,
-                        total_steps=total_steps,
-                        timestep=int(t_curr),
-                        latents=latents,
-                    ),
-                )
-
-        return latents
-
-    def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
-        def step_callback(state: PipelineIntermediateState) -> None:
-            context.util.sd_step_callback(state, BaseModelType.StableDiffusion3)
-
-        return step_callback

invokeai/app/invocations/sd3_image_to_latents.py

@@ -1,65 +0,0 @@
-import einops
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.load.load_base import LoadedModel
-from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
-
-
-@invocation(
-    "sd3_i2l",
-    title="SD3 Image to Latents",
-    tags=["image", "latents", "vae", "i2l", "sd3"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Generates latents from an image."""
-
-    image: ImageField = InputField(description="The image to encode")
-    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
-
-    @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        with vae_info as vae:
-            assert isinstance(vae, AutoencoderKL)
-
-            vae.disable_tiling()
-
-            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
-            with torch.inference_mode():
-                image_tensor_dist = vae.encode(image_tensor).latent_dist
-                # TODO: Use seed to make sampling reproducible.
-                latents: torch.Tensor = image_tensor_dist.sample().to(dtype=vae.dtype)
-
-            latents = vae.config.scaling_factor * latents
-
-        return latents
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-        if image_tensor.dim() == 3:
-            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
-
-        vae_info = context.models.load(self.vae.vae)
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
-
-        latents = latents.to("cpu")
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/sd3_latents_to_image.py

@@ -1,74 +0,0 @@
-from contextlib import nullcontext
-
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from einops import rearrange
-from PIL import Image
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    Input,
-    InputField,
-    LatentsField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "sd3_l2i",
-    title="SD3 Latents to Image",
-    tags=["latents", "image", "vae", "l2i", "sd3"],
-    category="latents",
-    version="1.3.0",
-)
-class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Generates an image from latents."""
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-
-        vae_info = context.models.load(self.vae.vae)
-        assert isinstance(vae_info.model, (AutoencoderKL))
-        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
-            context.util.signal_progress("Running VAE")
-            assert isinstance(vae, (AutoencoderKL))
-            latents = latents.to(vae.device)
-
-            vae.disable_tiling()
-
-            tiling_context = nullcontext()
-
-            # clear memory as vae decode can request a lot
-            TorchDevice.empty_cache()
-
-            with torch.inference_mode(), tiling_context:
-                # copied from diffusers pipeline
-                latents = latents / vae.config.scaling_factor
-                img = vae.decode(latents, return_dict=False)[0]
-
-            img = img.clamp(-1, 1)
-            img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
-            img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
-
-        TorchDevice.empty_cache()
-
-        image_dto = context.images.save(image=img_pil)
-
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/sd3_model_loader.py

@@ -1,108 +0,0 @@
-from typing import Optional
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import SubModelType
-
-
-@invocation_output("sd3_model_loader_output")
-class Sd3ModelLoaderOutput(BaseInvocationOutput):
-    """SD3 base model loader output."""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip_l: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP L")
-    clip_g: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP G")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-
-
-@invocation(
-    "sd3_model_loader",
-    title="SD3 Main Model",
-    tags=["model", "sd3"],
-    category="model",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class Sd3ModelLoaderInvocation(BaseInvocation):
-    """Loads a SD3 base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.sd3_model,
-        ui_type=UIType.SD3MainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder_model: Optional[ModelIdentifierField] = InputField(
-        description=FieldDescriptions.t5_encoder,
-        ui_type=UIType.T5EncoderModel,
-        input=Input.Direct,
-        title="T5 Encoder",
-        default=None,
-    )
-
-    clip_l_model: Optional[ModelIdentifierField] = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPLEmbedModel,
-        input=Input.Direct,
-        title="CLIP L Encoder",
-        default=None,
-    )
-
-    clip_g_model: Optional[ModelIdentifierField] = InputField(
-        description=FieldDescriptions.clip_g_model,
-        ui_type=UIType.CLIPGEmbedModel,
-        input=Input.Direct,
-        title="CLIP G Encoder",
-        default=None,
-    )
-
-    vae_model: Optional[ModelIdentifierField] = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.VAEModel, title="VAE", default=None
-    )
-
-    def invoke(self, context: InvocationContext) -> Sd3ModelLoaderOutput:
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = (
-            self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-            if self.vae_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.VAE})
-        )
-        tokenizer_l = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder_l = (
-            self.clip_l_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-            if self.clip_l_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-        )
-        tokenizer_g = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        clip_encoder_g = (
-            self.clip_g_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-            if self.clip_g_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-        )
-        tokenizer_t5 = (
-            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
-            if self.t5_encoder_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
-        )
-        t5_encoder = (
-            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
-            if self.t5_encoder_model
-            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
-        )
-
-        return Sd3ModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
-            clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
-            clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-        )

invokeai/app/invocations/sd3_text_encoder.py

@@ -1,201 +0,0 @@
-from contextlib import ExitStack
-from typing import Iterator, Tuple
-
-import torch
-from transformers import (
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    T5EncoderModel,
-    T5Tokenizer,
-    T5TokenizerFast,
-)
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-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.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
-
-# The SD3 T5 Max Sequence Length set based on the default in diffusers.
-SD3_T5_MAX_SEQ_LEN = 256
-
-
-@invocation(
-    "sd3_text_encoder",
-    title="SD3 Text Encoding",
-    tags=["prompt", "conditioning", "sd3"],
-    category="conditioning",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class Sd3TextEncoderInvocation(BaseInvocation):
-    """Encodes and preps a prompt for a SD3 image."""
-
-    clip_l: CLIPField = InputField(
-        title="CLIP L",
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-    )
-    clip_g: CLIPField = InputField(
-        title="CLIP G",
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-    )
-
-    # The SD3 models were trained with text encoder dropout, so the T5 encoder can be omitted to save time/memory.
-    t5_encoder: T5EncoderField | None = InputField(
-        title="T5Encoder",
-        default=None,
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
-    prompt: str = InputField(description="Text prompt to encode.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> SD3ConditioningOutput:
-        # Note: The text encoding model are run in separate functions to ensure that all model references are locally
-        # scoped. This ensures that earlier models can be freed and gc'd before loading later models (if necessary).
-
-        clip_l_embeddings, clip_l_pooled_embeddings = self._clip_encode(context, self.clip_l)
-        clip_g_embeddings, clip_g_pooled_embeddings = self._clip_encode(context, self.clip_g)
-
-        t5_embeddings: torch.Tensor | None = None
-        if self.t5_encoder is not None:
-            t5_embeddings = self._t5_encode(context, SD3_T5_MAX_SEQ_LEN)
-
-        conditioning_data = ConditioningFieldData(
-            conditionings=[
-                SD3ConditioningInfo(
-                    clip_l_embeds=clip_l_embeddings,
-                    clip_l_pooled_embeds=clip_l_pooled_embeddings,
-                    clip_g_embeds=clip_g_embeddings,
-                    clip_g_pooled_embeds=clip_g_pooled_embeddings,
-                    t5_embeds=t5_embeddings,
-                )
-            ]
-        )
-
-        conditioning_name = context.conditioning.save(conditioning_data)
-        return SD3ConditioningOutput.build(conditioning_name)
-
-    def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tensor:
-        assert self.t5_encoder is not None
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-
-        prompt = [self.prompt]
-
-        with (
-            t5_text_encoder_info as t5_text_encoder,
-            t5_tokenizer_info as t5_tokenizer,
-        ):
-            context.util.signal_progress("Running T5 encoder")
-            assert isinstance(t5_text_encoder, T5EncoderModel)
-            assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
-
-            text_inputs = t5_tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=max_seq_len,
-                truncation=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = t5_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-            assert isinstance(text_input_ids, torch.Tensor)
-            assert isinstance(untruncated_ids, torch.Tensor)
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = t5_tokenizer.batch_decode(untruncated_ids[:, max_seq_len - 1 : -1])
-                context.logger.warning(
-                    "The following part of your input was truncated because `max_sequence_length` is set to "
-                    f" {max_seq_len} tokens: {removed_text}"
-                )
-
-            prompt_embeds = t5_text_encoder(text_input_ids.to(t5_text_encoder.device))[0]
-
-        assert isinstance(prompt_embeds, torch.Tensor)
-        return prompt_embeds
-
-    def _clip_encode(
-        self, context: InvocationContext, clip_model: CLIPField, tokenizer_max_length: int = 77
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        clip_tokenizer_info = context.models.load(clip_model.tokenizer)
-        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
-
-        prompt = [self.prompt]
-
-        with (
-            clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
-            clip_tokenizer_info as clip_tokenizer,
-            ExitStack() as exit_stack,
-        ):
-            context.util.signal_progress("Running CLIP encoder")
-            assert isinstance(clip_text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
-            assert isinstance(clip_tokenizer, CLIPTokenizer)
-
-            clip_text_encoder_config = clip_text_encoder_info.config
-            assert clip_text_encoder_config is not None
-
-            # Apply LoRA models to the CLIP encoder.
-            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
-            if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
-                # The model is non-quantized, so we can apply the LoRA weights directly into the model.
-                exit_stack.enter_context(
-                    LoRAPatcher.apply_lora_patches(
-                        model=clip_text_encoder,
-                        patches=self._clip_lora_iterator(context, clip_model),
-                        prefix=FLUX_LORA_CLIP_PREFIX,
-                        cached_weights=cached_weights,
-                    )
-                )
-            else:
-                # There are currently no supported CLIP quantized models. Add support here if needed.
-                raise ValueError(f"Unsupported model format: {clip_text_encoder_config.format}")
-
-            clip_text_encoder = clip_text_encoder.eval().requires_grad_(False)
-
-            text_inputs = clip_tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=tokenizer_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = clip_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-            assert isinstance(text_input_ids, torch.Tensor)
-            assert isinstance(untruncated_ids, torch.Tensor)
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = clip_tokenizer.batch_decode(untruncated_ids[:, tokenizer_max_length - 1 : -1])
-                context.logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {tokenizer_max_length} tokens: {removed_text}"
-                )
-            prompt_embeds = clip_text_encoder(
-                input_ids=text_input_ids.to(clip_text_encoder.device), output_hidden_states=True
-            )
-            pooled_prompt_embeds = prompt_embeds[0]
-            prompt_embeds = prompt_embeds.hidden_states[-2]
-
-            return prompt_embeds, pooled_prompt_embeds
-
-    def _clip_lora_iterator(
-        self, context: InvocationContext, clip_model: CLIPField
-    ) -> Iterator[Tuple[LoRAModelRaw, float]]:
-        for lora in clip_model.loras:
-            lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, LoRAModelRaw)
-            yield (lora_info.model, lora.weight)
-            del lora_info

invokeai/app/invocations/segment_anything.py

@@ -1,11 +1,9 @@
-from enum import Enum
 from pathlib import Path
 from typing import Literal
 
 import numpy as np
 import torch
 from PIL import Image
-from pydantic import BaseModel, Field
 from transformers import AutoModelForMaskGeneration, AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
@@ -25,31 +23,12 @@ SEGMENT_ANYTHING_MODEL_IDS: dict[SegmentAnythingModelKey, str] = {
 }
 
 
-class SAMPointLabel(Enum):
-    negative = -1
-    neutral = 0
-    positive = 1
-
-
-class SAMPoint(BaseModel):
-    x: int = Field(..., description="The x-coordinate of the point")
-    y: int = Field(..., description="The y-coordinate of the point")
-    label: SAMPointLabel = Field(..., description="The label of the point")
-
-
-class SAMPointsField(BaseModel):
-    points: list[SAMPoint] = Field(..., description="The points of the object")
-
-    def to_list(self) -> list[list[int]]:
-        return [[point.x, point.y, point.label.value] for point in self.points]
-
-
 @invocation(
     "segment_anything",
     title="Segment Anything",
     tags=["prompt", "segmentation"],
     category="segmentation",
-    version="1.1.0",
+    version="1.0.0",
 )
 class SegmentAnythingInvocation(BaseInvocation):
     """Runs a Segment Anything Model."""
@@ -61,13 +40,7 @@ class SegmentAnythingInvocation(BaseInvocation):
 
     model: SegmentAnythingModelKey = InputField(description="The Segment Anything model to use.")
     image: ImageField = InputField(description="The image to segment.")
-    bounding_boxes: list[BoundingBoxField] | None = InputField(
-        default=None, description="The bounding boxes to prompt the SAM model with."
-    )
-    point_lists: list[SAMPointsField] | None = InputField(
-        default=None,
-        description="The list of point lists to prompt the SAM model with. Each list of points represents a single object.",
-    )
+    bounding_boxes: list[BoundingBoxField] = InputField(description="The bounding boxes to prompt the SAM model with.")
     apply_polygon_refinement: bool = InputField(
         description="Whether to apply polygon refinement to the masks. This will smooth the edges of the masks slightly and ensure that each mask consists of a single closed polygon (before merging).",
         default=True,
@@ -82,12 +55,7 @@ class SegmentAnythingInvocation(BaseInvocation):
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
-        if self.point_lists is not None and self.bounding_boxes is not None:
-            raise ValueError("Only one of point_lists or bounding_box can be provided.")
-
-        if (not self.bounding_boxes or len(self.bounding_boxes) == 0) and (
-            not self.point_lists or len(self.point_lists) == 0
-        ):
+        if len(self.bounding_boxes) == 0:
             combined_mask = torch.zeros(image_pil.size[::-1], dtype=torch.bool)
         else:
             masks = self._segment(context=context, image=image_pil)
@@ -115,13 +83,14 @@ class SegmentAnythingInvocation(BaseInvocation):
         assert isinstance(sam_processor, SamProcessor)
         return SegmentAnythingPipeline(sam_model=sam_model, sam_processor=sam_processor)
 
-    def _segment(self, context: InvocationContext, image: Image.Image) -> list[torch.Tensor]:
+    def _segment(
+        self,
+        context: InvocationContext,
+        image: Image.Image,
+    ) -> list[torch.Tensor]:
         """Use Segment Anything (SAM) to generate masks given an image + a set of bounding boxes."""
         # Convert the bounding boxes to the SAM input format.
-        sam_bounding_boxes = (
-            [[bb.x_min, bb.y_min, bb.x_max, bb.y_max] for bb in self.bounding_boxes] if self.bounding_boxes else None
-        )
-        sam_points = [p.to_list() for p in self.point_lists] if self.point_lists else None
+        sam_bounding_boxes = [[bb.x_min, bb.y_min, bb.x_max, bb.y_max] for bb in self.bounding_boxes]
 
         with (
             context.models.load_remote_model(
@@ -129,7 +98,7 @@ class SegmentAnythingInvocation(BaseInvocation):
             ) as sam_pipeline,
         ):
             assert isinstance(sam_pipeline, SegmentAnythingPipeline)
-            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes, point_lists=sam_points)
+            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes)
 
         masks = self._process_masks(masks)
         if self.apply_polygon_refinement:
@@ -172,10 +141,9 @@ class SegmentAnythingInvocation(BaseInvocation):
 
         return masks
 
-    def _filter_masks(
-        self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField] | None
-    ) -> list[torch.Tensor]:
+    def _filter_masks(self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField]) -> list[torch.Tensor]:
         """Filter the detected masks based on the specified mask filter."""
+        assert len(masks) == len(bounding_boxes)
 
         if self.mask_filter == "all":
             return masks
@@ -183,10 +151,6 @@ class SegmentAnythingInvocation(BaseInvocation):
             # Find the largest mask.
             return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
-            assert (
-                bounding_boxes is not None
-            ), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
-            assert len(masks) == len(bounding_boxes)
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most
             # cases the scores should all be non-None when using this filtering mode. That being said, -1.0 is a

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 or scan_result.scan_err:
+            if scan_result.infected_files != 0:
                 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/model_records/model_records_base.py

@@ -15,7 +15,6 @@ from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
-    ClipVariantType,
     ControlAdapterDefaultSettings,
     MainModelDefaultSettings,
     ModelFormat,
@@ -86,7 +85,7 @@ class ModelRecordChanges(BaseModelExcludeNull):
 
     # Checkpoint-specific changes
     # TODO(MM2): Should we expose these? Feels footgun-y...
-    variant: Optional[ModelVariantType | ClipVariantType] = Field(description="The variant of the model.", default=None)
+    variant: Optional[ModelVariantType] = Field(description="The variant of the model.", default=None)
     prediction_type: Optional[SchedulerPredictionType] = Field(
         description="The prediction type of the model.", default=None
     )

invokeai/app/services/session_processor/session_processor_default.py

@@ -378,9 +378,6 @@ 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

invokeai/app/services/session_queue/session_queue_common.py

@@ -16,7 +16,6 @@ from pydantic import (
 from pydantic_core import to_jsonable_python
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
-from invokeai.app.invocations.fields import ImageField
 from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError
 from invokeai.app.services.workflow_records.workflow_records_common import (
     WorkflowWithoutID,
@@ -52,7 +51,11 @@ class SessionQueueItemNotFoundError(ValueError):
 
 # region Batch
 
-BatchDataType = Union[StrictStr, float, int, ImageField]
+BatchDataType = Union[
+    StrictStr,
+    float,
+    int,
+]
 
 
 class NodeFieldValue(BaseModel):

invokeai/app/services/shared/invocation_context.py

@@ -1,4 +1,3 @@
-from copy import deepcopy
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Callable, Optional, Union
@@ -160,10 +159,6 @@ class LoggerInterface(InvocationContextInterface):
 
 
 class ImagesInterface(InvocationContextInterface):
-    def __init__(self, services: InvocationServices, data: InvocationContextData, util: "UtilInterface") -> None:
-        super().__init__(services, data)
-        self._util = util
-
     def save(
         self,
         image: Image,
@@ -190,8 +185,6 @@ class ImagesInterface(InvocationContextInterface):
             The saved image DTO.
         """
 
-        self._util.signal_progress("Saving image")
-
         # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
         metadata_ = None
         if metadata:
@@ -228,7 +221,7 @@ class ImagesInterface(InvocationContextInterface):
         )
 
     def get_pil(self, image_name: str, mode: IMAGE_MODES | None = None) -> Image:
-        """Gets an image as a PIL Image object. This method returns a copy of the image.
+        """Gets an image as a PIL Image object.
 
         Args:
             image_name: The name of the image to get.
@@ -240,15 +233,11 @@ class ImagesInterface(InvocationContextInterface):
         image = self._services.images.get_pil_image(image_name)
         if mode and mode != image.mode:
             try:
-                # convert makes a copy!
                 image = image.convert(mode)
             except ValueError:
                 self._services.logger.warning(
                     f"Could not convert image from {image.mode} to {mode}. Using original mode instead."
                 )
-        else:
-            # copy the image to prevent the user from modifying the original
-            image = image.copy()
         return image
 
     def get_metadata(self, image_name: str) -> Optional[MetadataField]:
@@ -301,15 +290,15 @@ class TensorsInterface(InvocationContextInterface):
         return name
 
     def load(self, name: str) -> Tensor:
-        """Loads a tensor by name. This method returns a copy of the tensor.
+        """Loads a tensor by name.
 
         Args:
             name: The name of the tensor to load.
 
         Returns:
-            The tensor.
+            The loaded tensor.
         """
-        return self._services.tensors.load(name).clone()
+        return self._services.tensors.load(name)
 
 
 class ConditioningInterface(InvocationContextInterface):
@@ -327,25 +316,21 @@ class ConditioningInterface(InvocationContextInterface):
         return name
 
     def load(self, name: str) -> ConditioningFieldData:
-        """Loads conditioning data by name. This method returns a copy of the conditioning data.
+        """Loads conditioning data by name.
 
         Args:
             name: The name of the conditioning data to load.
 
         Returns:
-            The conditioning data.
+            The loaded conditioning data.
         """
 
-        return deepcopy(self._services.conditioning.load(name))
+        return self._services.conditioning.load(name)
 
 
 class ModelsInterface(InvocationContextInterface):
     """Common API for loading, downloading and managing models."""
 
-    def __init__(self, services: InvocationServices, data: InvocationContextData, util: "UtilInterface") -> None:
-        super().__init__(services, data)
-        self._util = util
-
     def exists(self, identifier: Union[str, "ModelIdentifierField"]) -> bool:
         """Check if a model exists.
 
@@ -378,15 +363,11 @@ class ModelsInterface(InvocationContextInterface):
 
         if isinstance(identifier, str):
             model = self._services.model_manager.store.get_model(identifier)
+            return self._services.model_manager.load.load_model(model, submodel_type)
         else:
-            submodel_type = submodel_type or identifier.submodel_type
+            _submodel_type = submodel_type or identifier.submodel_type
             model = self._services.model_manager.store.get_model(identifier.key)
-
-        message = f"Loading model {model.name}"
-        if submodel_type:
-            message += f" ({submodel_type.value})"
-        self._util.signal_progress(message)
-        return self._services.model_manager.load.load_model(model, submodel_type)
+            return self._services.model_manager.load.load_model(model, _submodel_type)
 
     def load_by_attrs(
         self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
@@ -411,10 +392,6 @@ class ModelsInterface(InvocationContextInterface):
         if len(configs) > 1:
             raise ValueError(f"More than one model found with name {name}, base {base}, and type {type}")
 
-        message = f"Loading model {name}"
-        if submodel_type:
-            message += f" ({submodel_type.value})"
-        self._util.signal_progress(message)
         return self._services.model_manager.load.load_model(configs[0], submodel_type)
 
     def get_config(self, identifier: Union[str, "ModelIdentifierField"]) -> AnyModelConfig:
@@ -485,7 +462,6 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             Path to the downloaded model
         """
-        self._util.signal_progress(f"Downloading model {source}")
         return self._services.model_manager.install.download_and_cache_model(source=source)
 
     def load_local_model(
@@ -508,8 +484,6 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             A LoadedModelWithoutConfig object.
         """
-
-        self._util.signal_progress(f"Loading model {model_path.name}")
         return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
 
     def load_remote_model(
@@ -535,8 +509,6 @@ class ModelsInterface(InvocationContextInterface):
             A LoadedModelWithoutConfig object.
         """
         model_path = self._services.model_manager.install.download_and_cache_model(source=str(source))
-
-        self._util.signal_progress(f"Loading model {source}")
         return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
 
 
@@ -730,12 +702,12 @@ def build_invocation_context(
     """
 
     logger = LoggerInterface(services=services, data=data)
+    images = ImagesInterface(services=services, data=data)
     tensors = TensorsInterface(services=services, data=data)
+    models = ModelsInterface(services=services, data=data)
     config = ConfigInterface(services=services, data=data)
     util = UtilInterface(services=services, data=data, is_canceled=is_canceled)
     conditioning = ConditioningInterface(services=services, data=data)
-    models = ModelsInterface(services=services, data=data, util=util)
-    images = ImagesInterface(services=services, data=data, util=util)
     boards = BoardsInterface(services=services, data=data)
 
     ctx = InvocationContext(

invokeai/app/services/workflow_records/default_workflows/SD3.5 Text to Image.json

@@ -1,382 +0,0 @@
-{
-    "name": "SD3.5 Text to Image",
-   "author": "InvokeAI",
-    "description": "Sample text to image workflow for Stable Diffusion 3.5",
-    "version": "1.0.0",
-    "contact": "invoke@invoke.ai",
-    "tags": "text2image, SD3.5, default",
-  "notes": "",
-    "exposedFields": [
-      {
-        "nodeId": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "fieldName": "model"
-      },
-      {
-        "nodeId": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "fieldName": "prompt"
-      }
-    ],
-    "meta": {
-      "version": "3.0.0",
-      "category": "default"
-    },
-    "id": "e3a51d6b-8208-4d6d-b187-fcfe8b32934c",
-    "nodes": [
-      {
-        "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "type": "invocation",
-        "data": {
-          "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-          "type": "sd3_model_loader",
-          "version": "1.0.0",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": true,
-          "useCache": true,
-          "nodePack": "invokeai",
-          "inputs": {
-            "model": {
-              "name": "model",
-              "label": "",
-              "value": {
-                "key": "f7b20be9-92a8-4cfb-bca4-6c3b5535c10b",
-                "hash": "placeholder",
-                "name": "stable-diffusion-3.5-medium",
-                "base": "sd-3",
-                "type": "main"
-              }
-            },
-            "t5_encoder_model": {
-              "name": "t5_encoder_model",
-              "label": ""
-            },
-            "clip_l_model": {
-              "name": "clip_l_model",
-              "label": ""
-            },
-            "clip_g_model": {
-              "name": "clip_g_model",
-              "label": ""
-            },
-            "vae_model": {
-              "name": "vae_model",
-              "label": ""
-            }
-          }
-        },
-        "position": {
-          "x": -55.58689609637031,
-          "y": -111.53602444662268
-        }
-      },
-      {
-        "id": "f7e394ac-6394-4096-abcb-de0d346506b3",
-        "type": "invocation",
-        "data": {
-          "id": "f7e394ac-6394-4096-abcb-de0d346506b3",
-          "type": "rand_int",
-          "version": "1.0.1",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": true,
-          "useCache": false,
-          "nodePack": "invokeai",
-          "inputs": {
-            "low": {
-              "name": "low",
-              "label": "",
-              "value": 0
-            },
-            "high": {
-              "name": "high",
-              "label": "",
-              "value": 2147483647
-            }
-          }
-        },
-        "position": {
-          "x": 470.45870147220353,
-          "y": 350.3141781644303
-        }
-      },
-      {
-        "id": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
-        "type": "invocation",
-        "data": {
-          "id": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
-          "type": "sd3_l2i",
-          "version": "1.3.0",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": false,
-          "useCache": true,
-          "nodePack": "invokeai",
-          "inputs": {
-            "board": {
-              "name": "board",
-              "label": ""
-            },
-            "metadata": {
-              "name": "metadata",
-              "label": ""
-            },
-            "latents": {
-              "name": "latents",
-              "label": ""
-            },
-            "vae": {
-              "name": "vae",
-              "label": ""
-            }
-          }
-        },
-        "position": {
-          "x": 1192.3097009334897,
-          "y": -366.0994675072209
-        }
-      },
-      {
-        "id": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
-        "type": "invocation",
-        "data": {
-          "id": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
-          "type": "sd3_text_encoder",
-          "version": "1.0.0",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": true,
-          "useCache": true,
-          "nodePack": "invokeai",
-          "inputs": {
-            "clip_l": {
-              "name": "clip_l",
-              "label": ""
-            },
-            "clip_g": {
-              "name": "clip_g",
-              "label": ""
-            },
-            "t5_encoder": {
-              "name": "t5_encoder",
-              "label": ""
-            },
-            "prompt": {
-              "name": "prompt",
-              "label": "",
-              "value": ""
-            }
-          }
-        },
-        "position": {
-          "x": 408.16054647924784,
-          "y": 65.06415352118786
-        }
-      },
-      {
-        "id": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "type": "invocation",
-        "data": {
-          "id": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-          "type": "sd3_text_encoder",
-          "version": "1.0.0",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": true,
-          "useCache": true,
-          "nodePack": "invokeai",
-          "inputs": {
-            "clip_l": {
-              "name": "clip_l",
-              "label": ""
-            },
-            "clip_g": {
-              "name": "clip_g",
-              "label": ""
-            },
-            "t5_encoder": {
-              "name": "t5_encoder",
-              "label": ""
-            },
-            "prompt": {
-              "name": "prompt",
-              "label": "",
-              "value": ""
-            }
-          }
-        },
-        "position": {
-          "x": 378.9283412440941,
-          "y": -302.65777497352553
-        }
-      },
-      {
-        "id": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-        "type": "invocation",
-        "data": {
-          "id": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-          "type": "sd3_denoise",
-          "version": "1.0.0",
-          "label": "",
-          "notes": "",
-          "isOpen": true,
-          "isIntermediate": true,
-          "useCache": true,
-          "nodePack": "invokeai",
-          "inputs": {
-            "board": {
-              "name": "board",
-              "label": ""
-            },
-            "metadata": {
-              "name": "metadata",
-              "label": ""
-            },
-            "transformer": {
-              "name": "transformer",
-              "label": ""
-            },
-            "positive_conditioning": {
-              "name": "positive_conditioning",
-              "label": ""
-            },
-            "negative_conditioning": {
-              "name": "negative_conditioning",
-              "label": ""
-            },
-            "cfg_scale": {
-              "name": "cfg_scale",
-              "label": "",
-              "value": 3.5
-            },
-            "width": {
-              "name": "width",
-              "label": "",
-              "value": 1024
-            },
-            "height": {
-              "name": "height",
-              "label": "",
-              "value": 1024
-            },
-            "steps": {
-              "name": "steps",
-              "label": "",
-              "value": 30
-            },
-            "seed": {
-              "name": "seed",
-              "label": "",
-              "value": 0
-            }
-          }
-        },
-        "position": {
-          "x": 813.7814762740603,
-          "y": -142.20529727605867
-        }
-      }
-    ],
-    "edges": [
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cvae-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48bvae",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
-        "sourceHandle": "vae",
-        "targetHandle": "vae"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ct5_encoder-3b4f7f27-cfc0-4373-a009-99c5290d0cd6t5_encoder",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
-        "sourceHandle": "t5_encoder",
-        "targetHandle": "t5_encoder"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ct5_encoder-e17d34e7-6ed1-493c-9a85-4fcd291cb084t5_encoder",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "sourceHandle": "t5_encoder",
-        "targetHandle": "t5_encoder"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_g-3b4f7f27-cfc0-4373-a009-99c5290d0cd6clip_g",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
-        "sourceHandle": "clip_g",
-        "targetHandle": "clip_g"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_g-e17d34e7-6ed1-493c-9a85-4fcd291cb084clip_g",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "sourceHandle": "clip_g",
-        "targetHandle": "clip_g"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_l-3b4f7f27-cfc0-4373-a009-99c5290d0cd6clip_l",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
-        "sourceHandle": "clip_l",
-        "targetHandle": "clip_l"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_l-e17d34e7-6ed1-493c-9a85-4fcd291cb084clip_l",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "sourceHandle": "clip_l",
-        "targetHandle": "clip_l"
-      },
-      {
-        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ctransformer-c7539f7b-7ac5-49b9-93eb-87ede611409ftransformer",
-        "type": "default",
-        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
-        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-        "sourceHandle": "transformer",
-        "targetHandle": "transformer"
-      },
-      {
-        "id": "reactflow__edge-f7e394ac-6394-4096-abcb-de0d346506b3value-c7539f7b-7ac5-49b9-93eb-87ede611409fseed",
-        "type": "default",
-        "source": "f7e394ac-6394-4096-abcb-de0d346506b3",
-        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-        "sourceHandle": "value",
-        "targetHandle": "seed"
-      },
-      {
-        "id": "reactflow__edge-c7539f7b-7ac5-49b9-93eb-87ede611409flatents-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48blatents",
-        "type": "default",
-        "source": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
-        "sourceHandle": "latents",
-        "targetHandle": "latents"
-      },
-      {
-        "id": "reactflow__edge-e17d34e7-6ed1-493c-9a85-4fcd291cb084conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fpositive_conditioning",
-        "type": "default",
-        "source": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
-        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-        "sourceHandle": "conditioning",
-        "targetHandle": "positive_conditioning"
-      },
-      {
-        "id": "reactflow__edge-3b4f7f27-cfc0-4373-a009-99c5290d0cd6conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fnegative_conditioning",
-        "type": "default",
-        "source": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
-        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
-        "sourceHandle": "conditioning",
-        "targetHandle": "negative_conditioning"
-      }
-    ]
-  }

invokeai/app/util/step_callback.py

@@ -34,25 +34,6 @@ SD1_5_LATENT_RGB_FACTORS = [
     [-0.1307, -0.1874, -0.7445],  # L4
 ]
 
-SD3_5_LATENT_RGB_FACTORS = [
-    [-0.05240681, 0.03251581, 0.0749016],
-    [-0.0580572, 0.00759826, 0.05729818],
-    [0.16144888, 0.01270368, -0.03768577],
-    [0.14418615, 0.08460266, 0.15941818],
-    [0.04894035, 0.0056485, -0.06686988],
-    [0.05187166, 0.19222395, 0.06261094],
-    [0.1539433, 0.04818359, 0.07103094],
-    [-0.08601796, 0.09013458, 0.10893912],
-    [-0.12398469, -0.06766567, 0.0033688],
-    [-0.0439737, 0.07825329, 0.02258823],
-    [0.03101129, 0.06382551, 0.07753657],
-    [-0.01315361, 0.08554491, -0.08772475],
-    [0.06464487, 0.05914605, 0.13262741],
-    [-0.07863674, -0.02261737, -0.12761454],
-    [-0.09923835, -0.08010759, -0.06264447],
-    [-0.03392309, -0.0804029, -0.06078822],
-]
-
 FLUX_LATENT_RGB_FACTORS = [
     [-0.0412, 0.0149, 0.0521],
     [0.0056, 0.0291, 0.0768],
@@ -129,9 +110,6 @@ def stable_diffusion_step_callback(
         sdxl_latent_rgb_factors = torch.tensor(SDXL_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
         sdxl_smooth_matrix = torch.tensor(SDXL_SMOOTH_MATRIX, dtype=sample.dtype, device=sample.device)
         image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
-    elif base_model == BaseModelType.StableDiffusion3:
-        sd3_latent_rgb_factors = torch.tensor(SD3_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
-        image = sample_to_lowres_estimated_image(sample, sd3_latent_rgb_factors)
     else:
         v1_5_latent_rgb_factors = torch.tensor(SD1_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
         image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)

invokeai/backend/flux/custom_block_processor.py

@@ -1,138 +0,0 @@
-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, SingleStreamBlock
-
-
-class CustomDoubleStreamBlockProcessor:
-    """A class containing a custom implementation of DoubleStreamBlock.forward() with additional features
-    (IP-Adapter, etc.).
-    """
-
-    @staticmethod
-    def _double_stream_block_forward(
-        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.
-        """
-        img_mod1, img_mod2 = block.img_mod(vec)
-        txt_mod1, txt_mod2 = block.txt_mod(vec)
-
-        # prepare image for attention
-        img_modulated = block.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = block.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = einops.rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
-        img_q, img_k = block.img_attn.norm(img_q, img_k, img_v)
-
-        # prepare txt for attention
-        txt_modulated = block.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = block.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = einops.rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
-        txt_q, txt_k = block.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        # run actual attention
-        q = torch.cat((txt_q, img_q), dim=2)
-        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_mask=attn_mask)
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-        # calculate the img bloks
-        img = img + img_mod1.gate * block.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * block.img_mlp((1 + img_mod2.scale) * block.img_norm2(img) + img_mod2.shift)
-
-        # calculate the txt bloks
-        txt = txt + txt_mod1.gate * block.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * block.txt_mlp((1 + txt_mod2.scale) * block.txt_norm2(txt) + txt_mod2.shift)
-        return img, txt, img_q
-
-    @staticmethod
-    def custom_double_block_forward(
-        timestep_index: int,
-        total_num_timesteps: int,
-        block_index: int,
-        block: DoubleStreamBlock,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        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
-        """
-        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:
-            img = ip_adapter_extension.run_ip_adapter(
-                timestep_index=timestep_index,
-                total_num_timesteps=total_num_timesteps,
-                block_index=block_index,
-                block=block,
-                img_q=img_q,
-                img=img,
-            )
-
-        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

@@ -1,4 +1,3 @@
-import math
 from typing import Callable
 
 import torch
@@ -7,9 +6,7 @@ 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
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 
@@ -19,17 +16,15 @@ def denoise(
     # model input
     img: torch.Tensor,
     img_ids: torch.Tensor,
-    pos_regional_prompting_extension: RegionalPromptingExtension,
-    neg_regional_prompting_extension: RegionalPromptingExtension | None,
+    txt: torch.Tensor,
+    txt_ids: torch.Tensor,
+    vec: torch.Tensor,
     # sampling parameters
     timesteps: list[float],
     step_callback: Callable[[PipelineIntermediateState], None],
     guidance: float,
-    cfg_scale: list[float],
     inpaint_extension: InpaintExtension | None,
     controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
-    pos_ip_adapter_extensions: list[XLabsIPAdapterExtension],
-    neg_ip_adapter_extensions: list[XLabsIPAdapterExtension],
 ):
     # step 0 is the initial state
     total_steps = len(timesteps) - 1
@@ -42,9 +37,10 @@ def denoise(
             latents=img,
         ),
     )
+    step = 1
     # guidance_vec is ignored for schnell.
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    for step_index, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
+    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
 
         # Run ControlNet models.
@@ -52,20 +48,20 @@ def denoise(
         for controlnet_extension in controlnet_extensions:
             controlnet_residuals.append(
                 controlnet_extension.run_controlnet(
-                    timestep_index=step_index,
+                    timestep_index=step - 1,
                     total_num_timesteps=total_steps,
                     img=img,
                     img_ids=img_ids,
-                    txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
-                    txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
-                    y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
+                    txt=txt,
+                    txt_ids=txt_ids,
+                    y=vec,
                     timesteps=t_vec,
                     guidance=guidance_vec,
                 )
             )
 
         # Merge the ControlNet residuals from multiple ControlNets.
-        # TODO(ryand): We may want to calculate the sum just-in-time to keep peak memory low. Keep in mind, that the
+        # TODO(ryand): We may want to alculate the sum just-in-time to keep peak memory low. Keep in mind, that the
         # 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)
@@ -73,46 +69,15 @@ def denoise(
         pred = model(
             img=img,
             img_ids=img_ids,
-            txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
-            txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
-            y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
             timesteps=t_vec,
             guidance=guidance_vec,
-            timestep_index=step_index,
-            total_num_timesteps=total_steps,
             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]
-
-        # If step_cfg_scale, is 1.0, then we don't need to run the negative prediction.
-        if not math.isclose(step_cfg_scale, 1.0):
-            # TODO(ryand): Add option to run positive and negative predictions in a single batch for better performance
-            # on systems with sufficient VRAM.
-
-            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_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,
-                total_num_timesteps=total_steps,
-                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)
-
         preview_img = img - t_curr * pred
         img = img + (t_prev - t_curr) * pred
 
@@ -122,12 +87,13 @@ def denoise(
 
         step_callback(
             PipelineIntermediateState(
-                step=step_index + 1,
+                step=step,
                 order=1,
                 total_steps=total_steps,
                 timestep=int(t_curr),
                 latents=preview_img,
             ),
         )
+        step += 1
 
     return img

invokeai/backend/flux/extensions/regional_prompting_extension.py

@@ -1,276 +0,0 @@
-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/extensions/xlabs_ip_adapter_extension.py

@@ -1,89 +0,0 @@
-import math
-from typing import List, Union
-
-import einops
-import torch
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
-
-
-class XLabsIPAdapterExtension:
-    def __init__(
-        self,
-        model: XlabsIpAdapterFlux,
-        image_prompt_clip_embed: torch.Tensor,
-        weight: Union[float, List[float]],
-        begin_step_percent: float,
-        end_step_percent: float,
-    ):
-        self._model = model
-        self._image_prompt_clip_embed = image_prompt_clip_embed
-        self._weight = weight
-        self._begin_step_percent = begin_step_percent
-        self._end_step_percent = end_step_percent
-
-        self._image_proj: torch.Tensor | None = None
-
-    def _get_weight(self, timestep_index: int, total_num_timesteps: int) -> float:
-        first_step = math.floor(self._begin_step_percent * total_num_timesteps)
-        last_step = math.ceil(self._end_step_percent * total_num_timesteps)
-
-        if timestep_index < first_step or timestep_index > last_step:
-            return 0.0
-
-        if isinstance(self._weight, list):
-            return self._weight[timestep_index]
-
-        return self._weight
-
-    @staticmethod
-    def run_clip_image_encoder(
-        pil_image: List[Image.Image], image_encoder: CLIPVisionModelWithProjection
-    ) -> torch.Tensor:
-        clip_image_processor = CLIPImageProcessor()
-        clip_image: torch.Tensor = clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(device=image_encoder.device, dtype=image_encoder.dtype)
-        clip_image_embeds = image_encoder(clip_image).image_embeds
-        return clip_image_embeds
-
-    def run_image_proj(self, dtype: torch.dtype):
-        image_prompt_clip_embed = self._image_prompt_clip_embed.to(dtype=dtype)
-        self._image_proj = self._model.image_proj(image_prompt_clip_embed)
-
-    def run_ip_adapter(
-        self,
-        timestep_index: int,
-        total_num_timesteps: int,
-        block_index: int,
-        block: DoubleStreamBlock,
-        img_q: torch.Tensor,
-        img: torch.Tensor,
-    ) -> torch.Tensor:
-        """The logic in this function is based on:
-        https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/modules/layers.py#L245-L301
-        """
-        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
-        if weight < 1e-6:
-            return img
-
-        ip_adapter_block = self._model.ip_adapter_double_blocks.double_blocks[block_index]
-
-        ip_key = ip_adapter_block.ip_adapter_double_stream_k_proj(self._image_proj)
-        ip_value = ip_adapter_block.ip_adapter_double_stream_v_proj(self._image_proj)
-
-        # Reshape projections for multi-head attention.
-        ip_key = einops.rearrange(ip_key, "B L (H D) -> B H L D", H=block.num_heads)
-        ip_value = einops.rearrange(ip_value, "B L (H D) -> B H L D", H=block.num_heads)
-
-        # Compute attention between IP projections and the latent query.
-        ip_attn = torch.nn.functional.scaled_dot_product_attention(
-            img_q, ip_key, ip_value, dropout_p=0.0, is_causal=False
-        )
-        ip_attn = einops.rearrange(ip_attn, "B H L D -> B L (H D)", H=block.num_heads)
-
-        img = img + weight * ip_attn
-
-        return img

invokeai/backend/flux/ip_adapter/ip_double_stream_block_processor.py

@@ -1,93 +0,0 @@
-# This file is based on:
-# https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/modules/layers.py#L221
-import einops
-import torch
-
-from invokeai.backend.flux.math import attention
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
-
-
-class IPDoubleStreamBlockProcessor(torch.nn.Module):
-    """Attention processor for handling IP-adapter with double stream block."""
-
-    def __init__(self, context_dim: int, hidden_dim: int):
-        super().__init__()
-
-        # Ensure context_dim matches the dimension of image_proj
-        self.context_dim = context_dim
-        self.hidden_dim = hidden_dim
-
-        # Initialize projections for IP-adapter
-        self.ip_adapter_double_stream_k_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-        self.ip_adapter_double_stream_v_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_k_proj.weight)
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_k_proj.bias)
-
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_v_proj.weight)
-        torch.nn.init.zeros_(self.ip_adapter_double_stream_v_proj.bias)
-
-    def __call__(
-        self,
-        attn: DoubleStreamBlock,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        vec: torch.Tensor,
-        pe: torch.Tensor,
-        image_proj: torch.Tensor,
-        ip_scale: float = 1.0,
-    ):
-        # Prepare image for attention
-        img_mod1, img_mod2 = attn.img_mod(vec)
-        txt_mod1, txt_mod2 = attn.txt_mod(vec)
-
-        img_modulated = attn.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = attn.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = einops.rearrange(
-            img_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim
-        )
-        img_q, img_k = attn.img_attn.norm(img_q, img_k, img_v)
-
-        txt_modulated = attn.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = attn.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = einops.rearrange(
-            txt_qkv, "B L (K H D) -> K B H L D", K=3, H=attn.num_heads, D=attn.head_dim
-        )
-        txt_q, txt_k = attn.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        q = torch.cat((txt_q, img_q), dim=2)
-        k = torch.cat((txt_k, img_k), dim=2)
-        v = torch.cat((txt_v, img_v), dim=2)
-
-        attn1 = attention(q, k, v, pe=pe)
-        txt_attn, img_attn = attn1[:, : txt.shape[1]], attn1[:, txt.shape[1] :]
-
-        # print(f"txt_attn shape: {txt_attn.size()}")
-        # print(f"img_attn shape: {img_attn.size()}")
-
-        img = img + img_mod1.gate * attn.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * attn.img_mlp((1 + img_mod2.scale) * attn.img_norm2(img) + img_mod2.shift)
-
-        txt = txt + txt_mod1.gate * attn.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * attn.txt_mlp((1 + txt_mod2.scale) * attn.txt_norm2(txt) + txt_mod2.shift)
-
-        # IP-adapter processing
-        ip_query = img_q  # latent sample query
-        ip_key = self.ip_adapter_double_stream_k_proj(image_proj)
-        ip_value = self.ip_adapter_double_stream_v_proj(image_proj)
-
-        # Reshape projections for multi-head attention
-        ip_key = einops.rearrange(ip_key, "B L (H D) -> B H L D", H=attn.num_heads, D=attn.head_dim)
-        ip_value = einops.rearrange(ip_value, "B L (H D) -> B H L D", H=attn.num_heads, D=attn.head_dim)
-
-        # Compute attention between IP projections and the latent query
-        ip_attention = torch.nn.functional.scaled_dot_product_attention(
-            ip_query, ip_key, ip_value, dropout_p=0.0, is_causal=False
-        )
-        ip_attention = einops.rearrange(ip_attention, "B H L D -> B L (H D)", H=attn.num_heads, D=attn.head_dim)
-
-        img = img + ip_scale * ip_attention
-
-        return img, txt

invokeai/backend/flux/ip_adapter/state_dict_utils.py

@@ -1,52 +0,0 @@
-from typing import Any, Dict
-
-import torch
-
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterParams
-
-
-def is_state_dict_xlabs_ip_adapter(sd: Dict[str, Any]) -> bool:
-    """Is the state dict for an XLabs FLUX IP-Adapter model?
-
-    This is intended to be a reasonably high-precision detector, but it is not guaranteed to have perfect precision.
-    """
-    # If all of the expected keys are present, then this is very likely an XLabs IP-Adapter model.
-    expected_keys = {
-        "double_blocks.0.processor.ip_adapter_double_stream_k_proj.bias",
-        "double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight",
-        "double_blocks.0.processor.ip_adapter_double_stream_v_proj.bias",
-        "double_blocks.0.processor.ip_adapter_double_stream_v_proj.weight",
-        "ip_adapter_proj_model.norm.bias",
-        "ip_adapter_proj_model.norm.weight",
-        "ip_adapter_proj_model.proj.bias",
-        "ip_adapter_proj_model.proj.weight",
-    }
-
-    if expected_keys.issubset(sd.keys()):
-        return True
-    return False
-
-
-def infer_xlabs_ip_adapter_params_from_state_dict(state_dict: dict[str, torch.Tensor]) -> XlabsIpAdapterParams:
-    num_double_blocks = 0
-    context_dim = 0
-    hidden_dim = 0
-
-    # Count the number of double blocks.
-    double_block_index = 0
-    while f"double_blocks.{double_block_index}.processor.ip_adapter_double_stream_k_proj.weight" in state_dict:
-        double_block_index += 1
-    num_double_blocks = double_block_index
-
-    hidden_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[0]
-    context_dim = state_dict["double_blocks.0.processor.ip_adapter_double_stream_k_proj.weight"].shape[1]
-    clip_embeddings_dim = state_dict["ip_adapter_proj_model.proj.weight"].shape[1]
-    clip_extra_context_tokens = state_dict["ip_adapter_proj_model.proj.weight"].shape[0] // context_dim
-
-    return XlabsIpAdapterParams(
-        num_double_blocks=num_double_blocks,
-        context_dim=context_dim,
-        hidden_dim=hidden_dim,
-        clip_embeddings_dim=clip_embeddings_dim,
-        clip_extra_context_tokens=clip_extra_context_tokens,
-    )

invokeai/backend/flux/ip_adapter/xlabs_ip_adapter_flux.py

@@ -1,70 +0,0 @@
-from dataclasses import dataclass
-
-import torch
-
-from invokeai.backend.ip_adapter.ip_adapter import ImageProjModel
-
-
-class IPDoubleStreamBlock(torch.nn.Module):
-    def __init__(self, context_dim: int, hidden_dim: int):
-        super().__init__()
-
-        self.context_dim = context_dim
-        self.hidden_dim = hidden_dim
-
-        self.ip_adapter_double_stream_k_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-        self.ip_adapter_double_stream_v_proj = torch.nn.Linear(context_dim, hidden_dim, bias=True)
-
-
-class IPAdapterDoubleBlocks(torch.nn.Module):
-    def __init__(self, num_double_blocks: int, context_dim: int, hidden_dim: int):
-        super().__init__()
-        self.double_blocks = torch.nn.ModuleList(
-            [IPDoubleStreamBlock(context_dim, hidden_dim) for _ in range(num_double_blocks)]
-        )
-
-
-@dataclass
-class XlabsIpAdapterParams:
-    num_double_blocks: int
-    context_dim: int
-    hidden_dim: int
-
-    clip_embeddings_dim: int
-    clip_extra_context_tokens: int
-
-
-class XlabsIpAdapterFlux(torch.nn.Module):
-    def __init__(self, params: XlabsIpAdapterParams):
-        super().__init__()
-        self.image_proj = ImageProjModel(
-            cross_attention_dim=params.context_dim,
-            clip_embeddings_dim=params.clip_embeddings_dim,
-            clip_extra_context_tokens=params.clip_extra_context_tokens,
-        )
-        self.ip_adapter_double_blocks = IPAdapterDoubleBlocks(
-            num_double_blocks=params.num_double_blocks, context_dim=params.context_dim, hidden_dim=params.hidden_dim
-        )
-
-    def load_xlabs_state_dict(self, state_dict: dict[str, torch.Tensor], assign: bool = False):
-        """We need this custom function to load state dicts rather than using .load_state_dict(...) because the model
-        structure does not match the state_dict structure.
-        """
-        # Split the state_dict into the image projection model and the double blocks.
-        image_proj_sd: dict[str, torch.Tensor] = {}
-        double_blocks_sd: dict[str, torch.Tensor] = {}
-        for k, v in state_dict.items():
-            if k.startswith("ip_adapter_proj_model."):
-                image_proj_sd[k] = v
-            elif k.startswith("double_blocks."):
-                double_blocks_sd[k] = v
-            else:
-                raise ValueError(f"Unexpected key: {k}")
-
-        # Initialize the image projection model.
-        image_proj_sd = {k.replace("ip_adapter_proj_model.", ""): v for k, v in image_proj_sd.items()}
-        self.image_proj.load_state_dict(image_proj_sd, assign=assign)
-
-        # Initialize the double blocks.
-        double_blocks_sd = {k.replace("processor.", ""): v for k, v in double_blocks_sd.items()}
-        self.ip_adapter_double_blocks.load_state_dict(double_blocks_sd, assign=assign)

invokeai/backend/flux/math.py

@@ -5,12 +5,16 @@ from einops import rearrange
 from torch import Tensor
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Tensor | None = None) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
     q, k = apply_rope(q, k, pe)
 
-    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)")
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
 
+    # Replaced original einops.rearrange(...) call with torch.reshape(...) for slightly faster performance.
+    # Original call: x = rearrange(x, "B H L D -> B L (H D)")
+    # x = x.permute(0, 2, 1, 3)  # BHLD -> BLHD
+    # x = x.reshape(x.shape[0], x.shape[1], -1)  # BLHD -> BL(HD)
+    x = rearrange(x, "B H L D -> B L (H D)")
     return x
 
 
@@ -23,13 +27,16 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     omega = 1.0 / (theta**scale)
     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)
+    # Replaced original einops.rearrange(...) call with torch.view(...) for slightly faster performance.
+    # Original call: out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    # out = out.view(*out.shape[:-1], 2, 2)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.to(dtype=pos.dtype, device=pos.device)
+    return out.float()
 
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
-    xq_ = xq.view(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.view(*xk.shape[:-1], -1, 1, 2)
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*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.view(*xq.shape).type_as(xq), xk_out.view(*xk.shape).type_as(xk)
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)

invokeai/backend/flux/model.py

@@ -5,12 +5,6 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 
-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,
     EmbedND,
@@ -94,12 +88,8 @@ class Flux(nn.Module):
         timesteps: Tensor,
         y: Tensor,
         guidance: Tensor | None,
-        timestep_index: int,
-        total_num_timesteps: int,
         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.")
@@ -121,19 +111,7 @@ class Flux(nn.Module):
         if controlnet_double_block_residuals is not None:
             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,
-                block_index=block_index,
-                block=block,
-                img=img,
-                txt=txt,
-                vec=vec,
-                pe=pe,
-                ip_adapter_extensions=ip_adapter_extensions,
-                regional_prompting_extension=regional_prompting_extension,
-            )
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
 
             if controlnet_double_block_residuals is not None:
                 img += controlnet_double_block_residuals[block_index]
@@ -145,17 +123,7 @@ class Flux(nn.Module):
             assert len(controlnet_single_block_residuals) == len(self.single_blocks)
 
         for block_index, block in enumerate(self.single_blocks):
-            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,
-            )
+            img = block(img, vec=vec, pe=pe)
 
             if controlnet_single_block_residuals is not None:
                 img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]

invokeai/backend/flux/modules/layers.py

@@ -4,7 +4,6 @@ import math
 from dataclasses import dataclass
 
 import torch
-from einops import rearrange
 from torch import Tensor, nn
 
 from invokeai.backend.flux.math import attention, rope
@@ -66,7 +65,10 @@ class RMSNorm(torch.nn.Module):
         self.scale = nn.Parameter(torch.ones(dim))
 
     def forward(self, x: Tensor):
-        return torch.nn.functional.rms_norm(x, self.scale.shape, self.scale, eps=1e-6)
+        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
 
 
 class QKNorm(torch.nn.Module):
@@ -91,13 +93,14 @@ class SelfAttention(nn.Module):
         self.norm = QKNorm(head_dim)
         self.proj = nn.Linear(dim, dim)
 
-    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
-        qkv = self.qkv(x)
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-        x = attention(q, k, v, pe=pe)
-        x = self.proj(x)
-        return x
+    # Unused code for reference:
+    # def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+    #     qkv = self.qkv(x)
+    #     q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+    #     q, k = self.norm(q, k, v)
+    #     x = attention(q, k, v, pe=pe)
+    #     x = self.proj(x)
+    #     return x
 
 
 @dataclass
@@ -160,14 +163,22 @@ class DoubleStreamBlock(nn.Module):
         img_modulated = self.img_norm1(img)
         img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
         img_qkv = self.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        # img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(
+            2, 0, 3, 1, 4
+        )
+
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
 
         # prepare txt for attention
         txt_modulated = self.txt_norm1(txt)
         txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
         txt_qkv = self.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        # txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(
+            2, 0, 3, 1, 4
+        )
+
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
 
         # run actual attention
@@ -226,7 +237,8 @@ class SingleStreamBlock(nn.Module):
         x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
         qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         q, k = self.norm(q, k, v)
 
         # compute attention

invokeai/backend/flux/sampling_utils.py

@@ -168,17 +168,8 @@ def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtyp
     Returns:
         torch.Tensor: Image position ids.
     """
-
-    if device.type == "mps":
-        orig_dtype = dtype
-        dtype = torch.float16
-
     img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
     img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
     img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
     img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
-
-    if device.type == "mps":
-        img_ids.to(orig_dtype)
-
     return img_ids

invokeai/backend/flux/text_conditioning.py

@@ -1,36 +0,0 @@
-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/image_util/segment_anything/segment_anything_pipeline.py

@@ -1,4 +1,4 @@
-from typing import Optional, TypeAlias
+from typing import Optional
 
 import torch
 from PIL import Image
@@ -7,14 +7,6 @@ from transformers.models.sam.processing_sam import SamProcessor
 
 from invokeai.backend.raw_model import RawModel
 
-# Type aliases for the inputs to the SAM model.
-ListOfBoundingBoxes: TypeAlias = list[list[int]]
-"""A list of bounding boxes. Each bounding box is in the format [xmin, ymin, xmax, ymax]."""
-ListOfPoints: TypeAlias = list[list[int]]
-"""A list of points. Each point is in the format [x, y]."""
-ListOfPointLabels: TypeAlias = list[int]
-"""A list of SAM point labels. Each label is an integer where -1 is background, 0 is neutral, and 1 is foreground."""
-
 
 class SegmentAnythingPipeline(RawModel):
     """A wrapper class for the transformers SAM model and processor that makes it compatible with the model manager."""
@@ -35,53 +27,20 @@ class SegmentAnythingPipeline(RawModel):
 
         return calc_module_size(self._sam_model)
 
-    def segment(
-        self,
-        image: Image.Image,
-        bounding_boxes: list[list[int]] | None = None,
-        point_lists: list[list[list[int]]] | None = None,
-    ) -> torch.Tensor:
+    def segment(self, image: Image.Image, bounding_boxes: list[list[int]]) -> torch.Tensor:
         """Run the SAM model.
 
-        Either bounding_boxes or point_lists must be provided. If both are provided, bounding_boxes will be used and
-        point_lists will be ignored.
-
         Args:
             image (Image.Image): The image to segment.
             bounding_boxes (list[list[int]]): The bounding box prompts. Each bounding box is in the format
                 [xmin, ymin, xmax, ymax].
-            point_lists (list[list[list[int]]]): The points prompts. Each point is in the format [x, y, label].
-                `label` is an integer where -1 is background, 0 is neutral, and 1 is foreground.
 
         Returns:
             torch.Tensor: The segmentation masks. dtype: torch.bool. shape: [num_masks, channels, height, width].
         """
-
-        # Prep the inputs:
-        # - Create a list of bounding boxes or points and labels.
-        # - Add a batch dimension of 1 to the inputs.
-        if bounding_boxes:
-            input_boxes: list[ListOfBoundingBoxes] | None = [bounding_boxes]
-            input_points: list[ListOfPoints] | None = None
-            input_labels: list[ListOfPointLabels] | None = None
-        elif point_lists:
-            input_boxes: list[ListOfBoundingBoxes] | None = None
-            input_points: list[ListOfPoints] | None = []
-            input_labels: list[ListOfPointLabels] | None = []
-            for point_list in point_lists:
-                input_points.append([[p[0], p[1]] for p in point_list])
-                input_labels.append([p[2] for p in point_list])
-
-        else:
-            raise ValueError("Either bounding_boxes or points and labels must be provided.")
-
-        inputs = self._sam_processor(
-            images=image,
-            input_boxes=input_boxes,
-            input_points=input_points,
-            input_labels=input_labels,
-            return_tensors="pt",
-        ).to(self._sam_model.device)
+        # Add batch dimension of 1 to the bounding boxes.
+        boxes = [bounding_boxes]
+        inputs = self._sam_processor(images=image, input_boxes=boxes, return_tensors="pt").to(self._sam_model.device)
         outputs = self._sam_model(**inputs)
         masks = self._sam_processor.post_process_masks(
             masks=outputs.pred_masks,

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -45,9 +45,8 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
     # Constants for FLUX.1
     num_double_layers = 19
     num_single_layers = 38
-    hidden_size = 3072
-    mlp_ratio = 4.0
-    mlp_hidden_dim = int(hidden_size * mlp_ratio)
+    # inner_dim = 3072
+    # mlp_ratio = 4.0
 
     layers: dict[str, AnyLoRALayer] = {}
 
@@ -63,43 +62,30 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
             layers[dst_key] = LoRALayer.from_state_dict_values(values=value)
             assert len(src_layer_dict) == 0
 
-    def add_qkv_lora_layer_if_present(
-        src_keys: list[str],
-        src_weight_shapes: list[tuple[int, int]],
-        dst_qkv_key: str,
-        allow_missing_keys: bool = False,
-    ) -> None:
+    def add_qkv_lora_layer_if_present(src_keys: list[str], dst_qkv_key: str) -> None:
         """Handle the Q, K, V matrices for a transformer block. We need special handling because the diffusers format
         stores them in separate matrices, whereas the BFL format used internally by InvokeAI concatenates them.
         """
-        # If none of the keys are present, return early.
+        # We expect that either all src keys are present or none of them are. Verify this.
         keys_present = [key in grouped_state_dict for key in src_keys]
+        assert all(keys_present) or not any(keys_present)
+
+        # If none of the keys are present, return early.
         if not any(keys_present):
             return
 
+        src_layer_dicts = [grouped_state_dict.pop(key) for key in src_keys]
         sub_layers: list[LoRALayer] = []
-        for src_key, src_weight_shape in zip(src_keys, src_weight_shapes, strict=True):
-            src_layer_dict = grouped_state_dict.pop(src_key, None)
-            if src_layer_dict is not None:
-                values = {
-                    "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
-                    "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
-                }
-                if alpha is not None:
-                    values["alpha"] = torch.tensor(alpha)
-                assert values["lora_down.weight"].shape[1] == src_weight_shape[1]
-                assert values["lora_up.weight"].shape[0] == src_weight_shape[0]
-                sub_layers.append(LoRALayer.from_state_dict_values(values=values))
-                assert len(src_layer_dict) == 0
-            else:
-                if not allow_missing_keys:
-                    raise ValueError(f"Missing LoRA layer: '{src_key}'.")
-                values = {
-                    "lora_up.weight": torch.zeros((src_weight_shape[0], 1)),
-                    "lora_down.weight": torch.zeros((1, src_weight_shape[1])),
-                }
-                sub_layers.append(LoRALayer.from_state_dict_values(values=values))
-        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers)
+        for src_layer_dict in src_layer_dicts:
+            values = {
+                "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
+                "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
+            }
+            if alpha is not None:
+                values["alpha"] = torch.tensor(alpha)
+            sub_layers.append(LoRALayer.from_state_dict_values(values=values))
+            assert len(src_layer_dict) == 0
+        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers, concat_axis=0)
 
     # time_text_embed.timestep_embedder -> time_in.
     add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_1", "time_in.in_layer")
@@ -132,7 +118,6 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"transformer_blocks.{i}.attn.to_k",
                 f"transformer_blocks.{i}.attn.to_v",
             ],
-            [(hidden_size, hidden_size), (hidden_size, hidden_size), (hidden_size, hidden_size)],
             f"double_blocks.{i}.img_attn.qkv",
         )
         add_qkv_lora_layer_if_present(
@@ -141,7 +126,6 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"transformer_blocks.{i}.attn.add_k_proj",
                 f"transformer_blocks.{i}.attn.add_v_proj",
             ],
-            [(hidden_size, hidden_size), (hidden_size, hidden_size), (hidden_size, hidden_size)],
             f"double_blocks.{i}.txt_attn.qkv",
         )
 
@@ -191,14 +175,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"single_transformer_blocks.{i}.attn.to_v",
                 f"single_transformer_blocks.{i}.proj_mlp",
             ],
-            [
-                (hidden_size, hidden_size),
-                (hidden_size, hidden_size),
-                (hidden_size, hidden_size),
-                (mlp_hidden_dim, hidden_size),
-            ],
             f"single_blocks.{i}.linear1",
-            allow_missing_keys=True,
         )
 
         # Output projections.

invokeai/backend/model_manager/config.py

@@ -53,7 +53,6 @@ class BaseModelType(str, Enum):
     Any = "any"
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
-    StableDiffusion3 = "sd-3"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
@@ -84,10 +83,8 @@ class SubModelType(str, Enum):
     Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
-    TextEncoder3 = "text_encoder_3"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
-    Tokenizer3 = "tokenizer_3"
     VAE = "vae"
     VAEDecoder = "vae_decoder"
     VAEEncoder = "vae_encoder"
@@ -95,13 +92,6 @@ class SubModelType(str, Enum):
     SafetyChecker = "safety_checker"
 
 
-class ClipVariantType(str, Enum):
-    """Variant type."""
-
-    L = "large"
-    G = "gigantic"
-
-
 class ModelVariantType(str, Enum):
     """Variant type."""
 
@@ -157,17 +147,6 @@ class ModelSourceType(str, Enum):
 DEFAULTS_PRECISION = Literal["fp16", "fp32"]
 
 
-AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]
-
-
-class SubmodelDefinition(BaseModel):
-    path_or_prefix: str
-    model_type: ModelType
-    variant: AnyVariant = None
-
-    model_config = ConfigDict(protected_namespaces=())
-
-
 class MainModelDefaultSettings(BaseModel):
     vae: str | None = Field(default=None, description="Default VAE for this model (model key)")
     vae_precision: DEFAULTS_PRECISION | None = Field(default=None, description="Default VAE precision for this model")
@@ -214,9 +193,6 @@ class ModelConfigBase(BaseModel):
         schema["required"].extend(["key", "type", "format"])
 
     model_config = ConfigDict(validate_assignment=True, json_schema_extra=json_schema_extra)
-    submodels: Optional[Dict[SubModelType, SubmodelDefinition]] = Field(
-        description="Loadable submodels in this model", default=None
-    )
 
 
 class CheckpointConfigBase(ModelConfigBase):
@@ -359,7 +335,7 @@ class MainConfigBase(ModelConfigBase):
     default_settings: Optional[MainModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
-    variant: AnyVariant = ModelVariantType.Normal
+    variant: ModelVariantType = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
@@ -418,8 +394,6 @@ class IPAdapterBaseConfig(ModelConfigBase):
 class IPAdapterInvokeAIConfig(IPAdapterBaseConfig):
     """Model config for IP Adapter diffusers format models."""
 
-    # TODO(ryand): Should we deprecate this field? From what I can tell, it hasn't been probed correctly for a long
-    # time. Need to go through the history to make sure I'm understanding this fully.
     image_encoder_model_id: str
     format: Literal[ModelFormat.InvokeAI]
 
@@ -443,33 +417,12 @@ class CLIPEmbedDiffusersConfig(DiffusersConfigBase):
 
     type: Literal[ModelType.CLIPEmbed] = ModelType.CLIPEmbed
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
-    variant: ClipVariantType = ClipVariantType.L
 
     @staticmethod
     def get_tag() -> Tag:
         return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}")
 
 
-class CLIPGEmbedDiffusersConfig(CLIPEmbedDiffusersConfig):
-    """Model config for CLIP-G Embeddings."""
-
-    variant: ClipVariantType = ClipVariantType.G
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}.{ClipVariantType.G}")
-
-
-class CLIPLEmbedDiffusersConfig(CLIPEmbedDiffusersConfig):
-    """Model config for CLIP-L Embeddings."""
-
-    variant: ClipVariantType = ClipVariantType.L
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}.{ClipVariantType.L}")
-
-
 class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
@@ -546,8 +499,6 @@ AnyModelConfig = Annotated[
         Annotated[SpandrelImageToImageConfig, SpandrelImageToImageConfig.get_tag()],
         Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
         Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
-        Annotated[CLIPLEmbedDiffusersConfig, CLIPLEmbedDiffusersConfig.get_tag()],
-        Annotated[CLIPGEmbedDiffusersConfig, CLIPGEmbedDiffusersConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

invokeai/backend/model_manager/load/load_default.py

@@ -35,7 +35,6 @@ class ModelLoader(ModelLoaderBase):
         self._logger = logger
         self._ram_cache = ram_cache
         self._torch_dtype = TorchDevice.choose_torch_dtype()
-        self._torch_device = TorchDevice.choose_torch_device()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """

invokeai/backend/model_manager/load/model_loaders/clip_vision.py

@@ -1,41 +0,0 @@
-from pathlib import Path
-from typing import Optional
-
-from transformers import CLIPVisionModelWithProjection
-
-from invokeai.backend.model_manager.config import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    DiffusersConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
-class ClipVisionLoader(ModelLoader):
-    """Class to load CLIPVision models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, DiffusersConfigBase):
-            raise ValueError("Only DiffusersConfigBase models are currently supported here.")
-
-        if submodel_type is not None:
-            raise Exception("There are no submodels in CLIP Vision models.")
-
-        model_path = Path(config.path)
-
-        model = CLIPVisionModelWithProjection.from_pretrained(
-            model_path, torch_dtype=self._torch_dtype, local_files_only=True
-        )
-        assert isinstance(model, CLIPVisionModelWithProjection)
-
-        return model

invokeai/backend/model_manager/load/model_loaders/flux.py

@@ -19,10 +19,6 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_xlabs_controlnet,
 )
 from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
-from invokeai.backend.flux.ip_adapter.state_dict_utils import infer_xlabs_ip_adapter_params_from_state_dict
-from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import (
-    XlabsIpAdapterFlux,
-)
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.util import ae_params, params
@@ -39,7 +35,6 @@ from invokeai.backend.model_manager.config import (
     CLIPEmbedDiffusersConfig,
     ControlNetCheckpointConfig,
     ControlNetDiffusersConfig,
-    IPAdapterCheckpointConfig,
     MainBnbQuantized4bCheckpointConfig,
     MainCheckpointConfig,
     MainGGUFCheckpointConfig,
@@ -84,15 +79,7 @@ class FluxVAELoader(ModelLoader):
             model = AutoEncoder(ae_params[config.config_path])
             sd = load_file(model_path)
             model.load_state_dict(sd, assign=True)
-            # VAE is broken in float16, which mps defaults to
-            if self._torch_dtype == torch.float16:
-                try:
-                    vae_dtype = torch.tensor([1.0], dtype=torch.bfloat16, device=self._torch_device).dtype
-                except TypeError:
-                    vae_dtype = torch.float32
-            else:
-                vae_dtype = self._torch_dtype
-            model.to(vae_dtype)
+            model.to(dtype=self._torch_dtype)
 
         return model
 
@@ -136,9 +123,9 @@ class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
                 "The bnb modules are not available. Please install bitsandbytes if available on your platform."
             )
         match submodel_type:
-            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
+            case SubModelType.Tokenizer2:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
+            case SubModelType.TextEncoder2:
                 te2_model_path = Path(config.path) / "text_encoder_2"
                 model_config = AutoConfig.from_pretrained(te2_model_path)
                 with accelerate.init_empty_weights():
@@ -180,10 +167,10 @@ class T5EncoderCheckpointModel(ModelLoader):
             raise ValueError("Only T5EncoderConfig models are currently supported here.")
 
         match submodel_type:
-            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
+            case SubModelType.Tokenizer2:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
-                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2", torch_dtype="auto")
+            case SubModelType.TextEncoder2:
+                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2")
 
         raise ValueError(
             f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
@@ -365,26 +352,3 @@ class FluxControlnetModel(ModelLoader):
 
         model.load_state_dict(sd, assign=True)
         return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.IPAdapter, format=ModelFormat.Checkpoint)
-class FluxIpAdapterModel(ModelLoader):
-    """Class to load FLUX IP-Adapter models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, IPAdapterCheckpointConfig):
-            raise ValueError(f"Unexpected model config type: {type(config)}.")
-
-        sd = load_file(Path(config.path))
-
-        params = infer_xlabs_ip_adapter_params_from_state_dict(sd)
-
-        with accelerate.init_empty_weights():
-            model = XlabsIpAdapterFlux(params=params)
-
-        model.load_xlabs_state_dict(sd, assign=True)
-        return model

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -22,6 +22,7 @@ from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)
 class GenericDiffusersLoader(ModelLoader):
     """Class to load simple diffusers models."""

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -42,7 +42,6 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 @ModelLoaderRegistry.register(
     base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
 )
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion3, type=ModelType.Main, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
@@ -52,6 +51,13 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 
+    model_base_to_model_type = {
+        BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
+        BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
+        BaseModelType.StableDiffusionXL: "SDXL",
+        BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
+    }
+
     def _load_model(
         self,
         config: AnyModelConfig,
@@ -111,6 +117,8 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             load_class = load_classes[config.base][config.variant]
         except KeyError as e:
             raise Exception(f"No diffusers pipeline known for base={config.base}, variant={config.variant}") from e
+        prediction_type = config.prediction_type.value
+        upcast_attention = config.upcast_attention
 
         # Without SilenceWarnings we get log messages like this:
         # site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
@@ -121,7 +129,13 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         # ['text_model.embeddings.position_ids']
 
         with SilenceWarnings():
-            pipeline = load_class.from_single_file(config.path, torch_dtype=self._torch_dtype)
+            pipeline = load_class.from_single_file(
+                config.path,
+                torch_dtype=self._torch_dtype,
+                prediction_type=prediction_type,
+                upcast_attention=upcast_attention,
+                load_safety_checker=False,
+            )
 
         if not submodel_type:
             return pipeline

invokeai/backend/model_manager/metadata/fetch/huggingface.py

@@ -20,7 +20,7 @@ from typing import Optional
 
 import requests
 from huggingface_hub import HfApi, configure_http_backend, hf_hub_url
-from huggingface_hub.errors import RepositoryNotFoundError, RevisionNotFoundError
+from huggingface_hub.utils._errors import RepositoryNotFoundError, RevisionNotFoundError
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 

invokeai/backend/model_manager/probe.py

@@ -1,7 +1,7 @@
 import json
 import re
 from pathlib import Path
-from typing import Any, Callable, Dict, Literal, Optional, Union
+from typing import Any, Dict, Literal, Optional, Union
 
 import safetensors.torch
 import spandrel
@@ -14,7 +14,6 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_instantx_controlnet,
     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,
 )
@@ -22,7 +21,6 @@ from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import i
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    AnyVariant,
     BaseModelType,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
@@ -34,15 +32,8 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
-    SubmodelDefinition,
-    SubModelType,
-)
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
-from invokeai.backend.model_manager.util.model_util import (
-    get_clip_variant_type,
-    lora_token_vector_length,
-    read_checkpoint_meta,
 )
+from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
 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
@@ -120,7 +111,6 @@ class ModelProbe(object):
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
-        "StableDiffusion3Pipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.VAE,
         "AutoencoderTiny": ModelType.VAE,
@@ -131,12 +121,8 @@ class ModelProbe(object):
         "CLIPTextModel": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
         "FluxControlNetModel": ModelType.ControlNet,
-        "SD3Transformer2DModel": ModelType.Main,
-        "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
     }
 
-    TYPE2VARIANT: Dict[ModelType, Callable[[str], Optional[AnyVariant]]] = {ModelType.CLIPEmbed: get_clip_variant_type}
-
     @classmethod
     def register_probe(
         cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: type[ProbeBase]
@@ -183,10 +169,7 @@ class ModelProbe(object):
         fields["path"] = model_path.as_posix()
         fields["type"] = fields.get("type") or model_type
         fields["base"] = fields.get("base") or probe.get_base_type()
-        variant_func = cls.TYPE2VARIANT.get(fields["type"], None)
-        fields["variant"] = (
-            fields.get("variant") or (variant_func and variant_func(model_path.as_posix())) or probe.get_variant_type()
-        )
+        fields["variant"] = fields.get("variant") or probe.get_variant_type()
         fields["prediction_type"] = fields.get("prediction_type") or probe.get_scheduler_prediction_type()
         fields["image_encoder_model_id"] = fields.get("image_encoder_model_id") or probe.get_image_encoder_model_id()
         fields["name"] = fields.get("name") or cls.get_model_name(model_path)
@@ -233,10 +216,6 @@ class ModelProbe(object):
                 and fields["prediction_type"] == SchedulerPredictionType.VPrediction
             )
 
-        get_submodels = getattr(probe, "get_submodels", None)
-        if fields["base"] == BaseModelType.StableDiffusion3 and callable(get_submodels):
-            fields["submodels"] = get_submodels()
-
         model_info = ModelConfigFactory.make_config(fields)  # , key=fields.get("key", None))
         return model_info
 
@@ -264,6 +243,8 @@ class ModelProbe(object):
                     "cond_stage_model.",
                     "first_stage_model.",
                     "model.diffusion_model.",
+                    # FLUX models in the official BFL format contain keys with the "double_blocks." prefix.
+                    "double_blocks.",
                     # Some FLUX checkpoint files contain transformer keys prefixed with "model.diffusion_model".
                     # This prefix is typically used to distinguish between multiple models bundled in a single file.
                     "model.diffusion_model.double_blocks.",
@@ -271,10 +252,6 @@ class ModelProbe(object):
             ):
                 # Keys starting with double_blocks are associated with Flux models
                 return ModelType.Main
-            # FLUX models in the official BFL format contain keys with the "double_blocks." prefix, but we must be
-            # careful to avoid false positives on XLabs FLUX IP-Adapter models.
-            elif key.startswith("double_blocks.") and "ip_adapter" not in key:
-                return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE
             elif key.startswith(("lora_te_", "lora_unet_")):
@@ -297,14 +274,7 @@ class ModelProbe(object):
                 )
             ):
                 return ModelType.ControlNet
-            elif key.startswith(
-                (
-                    "image_proj.",
-                    "ip_adapter.",
-                    # XLabs FLUX IP-Adapter models have keys startinh with "ip_adapter_proj_model.".
-                    "ip_adapter_proj_model.",
-                )
-            ):
+            elif key.startswith(("image_proj.", "ip_adapter.")):
                 return ModelType.IPAdapter
             elif key in {"emb_params", "string_to_param"}:
                 return ModelType.TextualInversion
@@ -469,7 +439,7 @@ class ModelProbe(object):
         """
         # scan model
         scan_result = scan_file_path(checkpoint)
-        if scan_result.infected_files != 0 or scan_result.scan_err:
+        if scan_result.infected_files != 0:
             raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
 
 
@@ -482,10 +452,8 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "normal": "normalbae_image_processor",
     "sketch": "pidi_image_processor",
     "scribble": "lineart_image_processor",
-    "lineart anime": "lineart_anime_image_processor",
-    "lineart_anime": "lineart_anime_image_processor",
     "lineart": "lineart_image_processor",
-    "soft": "hed_image_processor",
+    "lineart_anime": "lineart_anime_image_processor",
     "softedge": "hed_image_processor",
     "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",
@@ -704,10 +672,6 @@ class IPAdapterCheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-
-        if is_state_dict_xlabs_ip_adapter(checkpoint):
-            return BaseModelType.Flux
-
         for key in checkpoint.keys():
             if not key.startswith(("image_proj.", "ip_adapter.")):
                 continue
@@ -768,33 +732,18 @@ class FolderProbeBase(ProbeBase):
 
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
-        # Handle pipelines with a UNet (i.e SD 1.x, SD2, SDXL).
-        config_path = self.model_path / "unet" / "config.json"
-        if config_path.exists():
-            with open(config_path) as file:
-                unet_conf = json.load(file)
-            if unet_conf["cross_attention_dim"] == 768:
-                return BaseModelType.StableDiffusion1
-            elif unet_conf["cross_attention_dim"] == 1024:
-                return BaseModelType.StableDiffusion2
-            elif unet_conf["cross_attention_dim"] == 1280:
-                return BaseModelType.StableDiffusionXLRefiner
-            elif unet_conf["cross_attention_dim"] == 2048:
-                return BaseModelType.StableDiffusionXL
-            else:
-                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
-
-        # Handle pipelines with a transformer (i.e. SD3).
-        config_path = self.model_path / "transformer" / "config.json"
-        if config_path.exists():
-            with open(config_path) as file:
-                transformer_conf = json.load(file)
-            if transformer_conf["_class_name"] == "SD3Transformer2DModel":
-                return BaseModelType.StableDiffusion3
-            else:
-                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
-
-        raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+        with open(self.model_path / "unet" / "config.json", "r") as file:
+            unet_conf = json.load(file)
+        if unet_conf["cross_attention_dim"] == 768:
+            return BaseModelType.StableDiffusion1
+        elif unet_conf["cross_attention_dim"] == 1024:
+            return BaseModelType.StableDiffusion2
+        elif unet_conf["cross_attention_dim"] == 1280:
+            return BaseModelType.StableDiffusionXLRefiner
+        elif unet_conf["cross_attention_dim"] == 2048:
+            return BaseModelType.StableDiffusionXL
+        else:
+            raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
     def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
@@ -806,23 +755,6 @@ class PipelineFolderProbe(FolderProbeBase):
         else:
             raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")
 
-    def get_submodels(self) -> Dict[SubModelType, SubmodelDefinition]:
-        config = ConfigLoader.load_config(self.model_path, config_name="model_index.json")
-        submodels: Dict[SubModelType, SubmodelDefinition] = {}
-        for key, value in config.items():
-            if key.startswith("_") or not (isinstance(value, list) and len(value) == 2):
-                continue
-            model_loader = str(value[1])
-            if model_type := ModelProbe.CLASS2TYPE.get(model_loader):
-                variant_func = ModelProbe.TYPE2VARIANT.get(model_type, None)
-                submodels[SubModelType(key)] = SubmodelDefinition(
-                    path_or_prefix=(self.model_path / key).resolve().as_posix(),
-                    model_type=model_type,
-                    variant=variant_func and variant_func((self.model_path / key).as_posix()),
-                )
-
-        return submodels
-
     def get_variant_type(self) -> ModelVariantType:
         # This only works for pipelines! Any kind of
         # exception results in our returning the

invokeai/backend/model_manager/starter_models.py

@@ -13,9 +13,6 @@ class StarterModelWithoutDependencies(BaseModel):
     type: ModelType
     format: Optional[ModelFormat] = None
     is_installed: bool = False
-    # allows us to track what models a user has installed across name changes within starter models
-    # if you update a starter model name, please add the old one to this list for that starter model
-    previous_names: list[str] = []
 
 
 class StarterModel(StarterModelWithoutDependencies):
@@ -28,15 +25,6 @@ class StarterModelBundles(BaseModel):
     models: list[StarterModel]
 
 
-cyberrealistic_negative = StarterModel(
-    name="CyberRealistic Negative v3",
-    base=BaseModelType.StableDiffusion1,
-    source="https://huggingface.co/cyberdelia/CyberRealistic_Negative/resolve/main/CyberRealistic_Negative_v3.pt",
-    description="Negative embedding specifically for use with CyberRealistic.",
-    type=ModelType.TextualInversion,
-)
-
-# region CLIP Image Encoders
 ip_adapter_sd_image_encoder = StarterModel(
     name="IP Adapter SD1.5 Image Encoder",
     base=BaseModelType.StableDiffusion1,
@@ -44,6 +32,7 @@ ip_adapter_sd_image_encoder = StarterModel(
     description="IP Adapter SD Image Encoder",
     type=ModelType.CLIPVision,
 )
+
 ip_adapter_sdxl_image_encoder = StarterModel(
     name="IP Adapter SDXL Image Encoder",
     base=BaseModelType.StableDiffusionXL,
@@ -51,16 +40,14 @@ ip_adapter_sdxl_image_encoder = StarterModel(
     description="IP Adapter SDXL Image Encoder",
     type=ModelType.CLIPVision,
 )
-# Note: This model is installed from the same source as the CLIPEmbed model below. The model contains both the image
-# encoder and the text encoder, but we need separate model entries so that they get loaded correctly.
-clip_vit_l_image_encoder = StarterModel(
-    name="clip-vit-large-patch14",
-    base=BaseModelType.Any,
-    source="InvokeAI/clip-vit-large-patch14",
-    description="CLIP ViT-L Image Encoder",
-    type=ModelType.CLIPVision,
+
+cyberrealistic_negative = StarterModel(
+    name="CyberRealistic Negative v3",
+    base=BaseModelType.StableDiffusion1,
+    source="https://huggingface.co/cyberdelia/CyberRealistic_Negative/resolve/main/CyberRealistic_Negative_v3.pt",
+    description="Negative embedding specifically for use with CyberRealistic.",
+    type=ModelType.TextualInversion,
 )
-# endregion
 
 # region TextEncoders
 t5_base_encoder = StarterModel(
@@ -140,22 +127,6 @@ flux_dev = StarterModel(
     type=ModelType.Main,
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
-sd35_medium = StarterModel(
-    name="SD3.5 Medium",
-    base=BaseModelType.StableDiffusion3,
-    source="stabilityai/stable-diffusion-3.5-medium",
-    description="Medium SD3.5 Model: ~15GB",
-    type=ModelType.Main,
-    dependencies=[],
-)
-sd35_large = StarterModel(
-    name="SD3.5 Large",
-    base=BaseModelType.StableDiffusion3,
-    source="stabilityai/stable-diffusion-3.5-large",
-    description="Large SD3.5 Model: ~19G",
-    type=ModelType.Main,
-    dependencies=[],
-)
 cyberrealistic_sd1 = StarterModel(
     name="CyberRealistic v4.1",
     base=BaseModelType.StableDiffusion1,
@@ -262,48 +233,36 @@ easy_neg_sd1 = StarterModel(
 # endregion
 # region IP Adapter
 ip_adapter_sd1 = StarterModel(
-    name="Standard Reference (IP Adapter)",
+    name="IP Adapter",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
-    description="References images with a more generalized/looser degree of precision.",
+    description="IP-Adapter for SD 1.5 models",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
-    previous_names=["IP Adapter"],
 )
 ip_adapter_plus_sd1 = StarterModel(
-    name="Precise Reference (IP Adapter Plus)",
+    name="IP Adapter Plus",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
-    description="References images with a higher degree of precision.",
+    description="Refined IP-Adapter for SD 1.5 models",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
-    previous_names=["IP Adapter Plus"],
 )
 ip_adapter_plus_face_sd1 = StarterModel(
-    name="Face Reference (IP Adapter Plus Face)",
+    name="IP Adapter Plus Face",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
-    description="References images with a higher degree of precision, adapted for faces",
+    description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
-    previous_names=["IP Adapter Plus Face"],
 )
 ip_adapter_sdxl = StarterModel(
-    name="Standard Reference (IP Adapter ViT-H)",
+    name="IP Adapter SDXL",
     base=BaseModelType.StableDiffusionXL,
     source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
-    description="References images with a higher degree of precision.",
+    description="IP-Adapter for SDXL models",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sdxl_image_encoder],
-    previous_names=["IP Adapter SDXL"],
-)
-ip_adapter_flux = StarterModel(
-    name="Standard Reference (XLabs FLUX IP-Adapter v2)",
-    base=BaseModelType.Flux,
-    source="https://huggingface.co/XLabs-AI/flux-ip-adapter-v2/resolve/main/ip_adapter.safetensors",
-    description="References images with a more generalized/looser degree of precision.",
-    type=ModelType.IPAdapter,
-    dependencies=[clip_vit_l_image_encoder],
 )
 # endregion
 # region ControlNet
@@ -322,162 +281,157 @@ qr_code_cnet_sdxl = StarterModel(
     type=ModelType.ControlNet,
 )
 canny_sd1 = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_canny",
-    description="Uses detected edges in the image to control composition.",
+    description="ControlNet weights trained on sd-1.5 with canny conditioning.",
     type=ModelType.ControlNet,
-    previous_names=["canny"],
 )
 inpaint_cnet_sd1 = StarterModel(
-    name="Inpainting",
+    name="inpaint",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_inpaint",
     description="ControlNet weights trained on sd-1.5 with canny conditioning, inpaint version",
     type=ModelType.ControlNet,
-    previous_names=["inpaint"],
 )
 mlsd_sd1 = StarterModel(
-    name="Line Drawing (mlsd)",
+    name="mlsd",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_mlsd",
-    description="Uses straight line detection for controlling the generation.",
+    description="ControlNet weights trained on sd-1.5 with canny conditioning, MLSD version",
     type=ModelType.ControlNet,
-    previous_names=["mlsd"],
 )
 depth_sd1 = StarterModel(
-    name="Depth Map",
+    name="depth",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11f1p_sd15_depth",
-    description="Uses depth information in the image to control the depth in the generation.",
+    description="ControlNet weights trained on sd-1.5 with depth conditioning",
     type=ModelType.ControlNet,
-    previous_names=["depth"],
 )
 normal_bae_sd1 = StarterModel(
-    name="Lighting Detection (Normals)",
+    name="normal_bae",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_normalbae",
-    description="Uses detected lighting information to guide the lighting of the composition.",
+    description="ControlNet weights trained on sd-1.5 with normalbae image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["normal_bae"],
 )
 seg_sd1 = StarterModel(
-    name="Segmentation Map",
+    name="seg",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_seg",
-    description="Uses segmentation maps to guide the structure of the composition.",
+    description="ControlNet weights trained on sd-1.5 with seg image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["seg"],
 )
 lineart_sd1 = StarterModel(
-    name="Lineart",
+    name="lineart",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_lineart",
-    description="Uses lineart detection to guide the lighting of the composition.",
+    description="ControlNet weights trained on sd-1.5 with lineart image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["lineart"],
 )
 lineart_anime_sd1 = StarterModel(
-    name="Lineart Anime",
+    name="lineart_anime",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15s2_lineart_anime",
-    description="Uses anime lineart detection to guide the lighting of the composition.",
+    description="ControlNet weights trained on sd-1.5 with anime image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["lineart_anime"],
 )
 openpose_sd1 = StarterModel(
-    name="Pose Detection (openpose)",
+    name="openpose",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_openpose",
-    description="Uses pose information to control the pose of human characters in the generation.",
+    description="ControlNet weights trained on sd-1.5 with openpose image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["openpose"],
 )
 scribble_sd1 = StarterModel(
-    name="Contour Detection (scribble)",
+    name="scribble",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_scribble",
-    description="Uses edges, contours, or line art in the image to control composition.",
+    description="ControlNet weights trained on sd-1.5 with scribble image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["scribble"],
 )
 softedge_sd1 = StarterModel(
-    name="Soft Edge Detection (softedge)",
+    name="softedge",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_softedge",
-    description="Uses a soft edge detection map to control composition.",
+    description="ControlNet weights trained on sd-1.5 with soft edge conditioning",
     type=ModelType.ControlNet,
-    previous_names=["softedge"],
 )
 shuffle_sd1 = StarterModel(
-    name="Remix (shuffle)",
+    name="shuffle",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11e_sd15_shuffle",
     description="ControlNet weights trained on sd-1.5 with shuffle image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["shuffle"],
 )
 tile_sd1 = StarterModel(
-    name="Tile",
+    name="tile",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11f1e_sd15_tile",
-    description="Uses image data to replicate exact colors/structure in the resulting generation.",
+    description="ControlNet weights trained on sd-1.5 with tiled image conditioning",
+    type=ModelType.ControlNet,
+)
+ip2p_sd1 = StarterModel(
+    name="ip2p",
+    base=BaseModelType.StableDiffusion1,
+    source="lllyasviel/control_v11e_sd15_ip2p",
+    description="ControlNet weights trained on sd-1.5 with ip2p conditioning.",
     type=ModelType.ControlNet,
-    previous_names=["tile"],
 )
 canny_sdxl = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-canny-sdxl-1.0",
-    description="Uses detected edges in the image to control composition.",
+    description="ControlNet weights trained on sdxl-1.0 with canny conditioning, by Xinsir.",
     type=ModelType.ControlNet,
-    previous_names=["canny-sdxl"],
 )
 depth_sdxl = StarterModel(
-    name="Depth Map",
+    name="depth-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="diffusers/controlNet-depth-sdxl-1.0",
-    description="Uses depth information in the image to control the depth in the generation.",
+    description="ControlNet weights trained on sdxl-1.0 with depth conditioning.",
     type=ModelType.ControlNet,
-    previous_names=["depth-sdxl"],
 )
 softedge_sdxl = StarterModel(
-    name="Soft Edge Detection (softedge)",
+    name="softedge-dexined-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="SargeZT/controlNet-sd-xl-1.0-softedge-dexined",
-    description="Uses a soft edge detection map to control composition.",
+    description="ControlNet weights trained on sdxl-1.0 with dexined soft edge preprocessing.",
+    type=ModelType.ControlNet,
+)
+depth_zoe_16_sdxl = StarterModel(
+    name="depth-16bit-zoe-sdxl",
+    base=BaseModelType.StableDiffusionXL,
+    source="SargeZT/controlNet-sd-xl-1.0-depth-16bit-zoe",
+    description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).",
+    type=ModelType.ControlNet,
+)
+depth_zoe_32_sdxl = StarterModel(
+    name="depth-zoe-sdxl",
+    base=BaseModelType.StableDiffusionXL,
+    source="diffusers/controlNet-zoe-depth-sdxl-1.0",
+    description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
     type=ModelType.ControlNet,
-    previous_names=["softedge-dexined-sdxl"],
 )
 openpose_sdxl = StarterModel(
-    name="Pose Detection (openpose)",
+    name="openpose-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-openpose-sdxl-1.0",
-    description="Uses pose information to control the pose of human characters in the generation.",
+    description="ControlNet weights trained on sdxl-1.0 compatible with the DWPose processor by Xinsir.",
     type=ModelType.ControlNet,
-    previous_names=["openpose-sdxl", "controlnet-openpose-sdxl"],
 )
 scribble_sdxl = StarterModel(
-    name="Contour Detection (scribble)",
+    name="scribble-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-scribble-sdxl-1.0",
-    description="Uses edges, contours, or line art in the image to control composition.",
+    description="ControlNet weights trained on sdxl-1.0 compatible with various lineart processors and black/white sketches by Xinsir.",
     type=ModelType.ControlNet,
-    previous_names=["scribble-sdxl", "controlnet-scribble-sdxl"],
 )
 tile_sdxl = StarterModel(
-    name="Tile",
+    name="tile-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-tile-sdxl-1.0",
-    description="Uses image data to replicate exact colors/structure in the resulting generation.",
-    type=ModelType.ControlNet,
-    previous_names=["tile-sdxl"],
-)
-union_cnet_sdxl = StarterModel(
-    name="Multi-Guidance Detection (Union Pro)",
-    base=BaseModelType.StableDiffusionXL,
-    source="InvokeAI/Xinsir-SDXL_Controlnet_Union",
-    description="A unified ControlNet for SDXL model that supports 10+ control types",
+    description="ControlNet weights trained on sdxl-1.0 with tiled image conditioning",
     type=ModelType.ControlNet,
 )
 union_cnet_flux = StarterModel(
@@ -490,52 +444,60 @@ union_cnet_flux = StarterModel(
 # endregion
 # region T2I Adapter
 t2i_canny_sd1 = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny-sd15",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_canny_sd15v2",
-    description="Uses detected edges in the image to control composition",
+    description="T2I Adapter weights trained on sd-1.5 with canny conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["canny-sd15"],
 )
 t2i_sketch_sd1 = StarterModel(
-    name="Sketch",
+    name="sketch-sd15",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_sketch_sd15v2",
-    description="Uses a sketch to control composition",
+    description="T2I Adapter weights trained on sd-1.5 with sketch conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["sketch-sd15"],
 )
 t2i_depth_sd1 = StarterModel(
-    name="Depth Map",
+    name="depth-sd15",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_depth_sd15v2",
-    description="Uses depth information in the image to control the depth in the generation.",
+    description="T2I Adapter weights trained on sd-1.5 with depth conditioning.",
+    type=ModelType.T2IAdapter,
+)
+t2i_zoe_depth_sd1 = StarterModel(
+    name="zoedepth-sd15",
+    base=BaseModelType.StableDiffusion1,
+    source="TencentARC/t2iadapter_zoedepth_sd15v1",
+    description="T2I Adapter weights trained on sd-1.5 with zoe depth conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["depth-sd15"],
 )
 t2i_canny_sdxl = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-canny-sdxl-1.0",
-    description="Uses detected edges in the image to control composition",
+    description="T2I Adapter weights trained on sdxl-1.0 with canny conditioning.",
+    type=ModelType.T2IAdapter,
+)
+t2i_zoe_depth_sdxl = StarterModel(
+    name="zoedepth-sdxl",
+    base=BaseModelType.StableDiffusionXL,
+    source="TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
+    description="T2I Adapter weights trained on sdxl-1.0 with zoe depth conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["canny-sdxl"],
 )
 t2i_lineart_sdxl = StarterModel(
-    name="Lineart",
+    name="lineart-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-lineart-sdxl-1.0",
-    description="Uses lineart detection to guide the lighting of the composition.",
+    description="T2I Adapter weights trained on sdxl-1.0 with lineart conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["lineart-sdxl"],
 )
 t2i_sketch_sdxl = StarterModel(
-    name="Sketch",
+    name="sketch-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-sketch-sdxl-1.0",
-    description="Uses a sketch to control composition",
+    description="T2I Adapter weights trained on sdxl-1.0 with sketch conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["sketch-sdxl"],
 )
 # endregion
 # region SpandrelImageToImage
@@ -585,8 +547,6 @@ STARTER_MODELS: list[StarterModel] = [
     flux_dev_quantized,
     flux_schnell,
     flux_dev,
-    sd35_medium,
-    sd35_large,
     cyberrealistic_sd1,
     rev_animated_sd1,
     dreamshaper_8_sd1,
@@ -606,7 +566,6 @@ STARTER_MODELS: list[StarterModel] = [
     ip_adapter_plus_sd1,
     ip_adapter_plus_face_sd1,
     ip_adapter_sdxl,
-    ip_adapter_flux,
     qr_code_cnet_sd1,
     qr_code_cnet_sdxl,
     canny_sd1,
@@ -622,18 +581,22 @@ STARTER_MODELS: list[StarterModel] = [
     softedge_sd1,
     shuffle_sd1,
     tile_sd1,
+    ip2p_sd1,
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
+    depth_zoe_16_sdxl,
+    depth_zoe_32_sdxl,
     openpose_sdxl,
     scribble_sdxl,
     tile_sdxl,
-    union_cnet_sdxl,
     union_cnet_flux,
     t2i_canny_sd1,
     t2i_sketch_sd1,
     t2i_depth_sd1,
+    t2i_zoe_depth_sd1,
     t2i_canny_sdxl,
+    t2i_zoe_depth_sdxl,
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
@@ -664,6 +627,7 @@ sd1_bundle: list[StarterModel] = [
     softedge_sd1,
     shuffle_sd1,
     tile_sd1,
+    ip2p_sd1,
     swinir,
 ]
 
@@ -674,6 +638,8 @@ sdxl_bundle: list[StarterModel] = [
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
+    depth_zoe_16_sdxl,
+    depth_zoe_32_sdxl,
     openpose_sdxl,
     scribble_sdxl,
     tile_sdxl,
@@ -687,7 +653,6 @@ flux_bundle: list[StarterModel] = [
     t5_8b_quantized_encoder,
     clip_l_encoder,
     union_cnet_flux,
-    ip_adapter_flux,
 ]
 
 STARTER_BUNDLES: dict[str, list[StarterModel]] = {

invokeai/backend/model_manager/util/model_util.py

@@ -8,7 +8,6 @@ import safetensors
 import torch
 from picklescan.scanner import scan_file_path
 
-from invokeai.backend.model_manager.config import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 
 
@@ -44,7 +43,7 @@ def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
     return checkpoint
 
 
-def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> Dict[str, torch.Tensor]:
+def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str, torch.Tensor]:
     if str(path).endswith(".safetensors"):
         try:
             path_str = path.as_posix() if isinstance(path, Path) else path
@@ -52,15 +51,16 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> 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 or scan_result.scan_err:
+            if scan_result.infected_files != 0:
                 raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
-        checkpoint = torch.load(path, map_location=torch.device("meta"))
+        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"))
     return checkpoint
 
 
@@ -165,25 +165,3 @@ def convert_bundle_to_flux_transformer_checkpoint(
         del transformer_state_dict[k]
 
     return original_state_dict
-
-
-def get_clip_variant_type(location: str) -> Optional[ClipVariantType]:
-    try:
-        path = Path(location)
-        config_path = path / "config.json"
-        if not config_path.exists():
-            config_path = path / "text_encoder" / "config.json"
-        if not config_path.exists():
-            return ClipVariantType.L
-        with open(config_path) as file:
-            clip_conf = json.load(file)
-            hidden_size = clip_conf.get("hidden_size", -1)
-            match hidden_size:
-                case 1280:
-                    return ClipVariantType.G
-                case 768:
-                    return ClipVariantType.L
-                case _:
-                    return ClipVariantType.L
-    except Exception:
-        return ClipVariantType.L

invokeai/backend/model_manager/util/select_hf_files.py

@@ -85,7 +85,6 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
     """Select the proper variant files from a list of HuggingFace repo_id paths."""
     result: set[Path] = set()
     subfolder_weights: dict[Path, list[SubfolderCandidate]] = {}
-    safetensors_detected = False
     for path in files:
         if path.suffix in [".onnx", ".pb", ".onnx_data"]:
             if variant == ModelRepoVariant.ONNX:
@@ -120,27 +119,19 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
             # We prefer safetensors over other file formats and an exact variant match. We'll score each file based on
             # variant and format and select the best one.
 
-            if safetensors_detected and path.suffix == ".bin":
-                continue
-
             parent = path.parent
             score = 0
 
             if path.suffix == ".safetensors":
-                safetensors_detected = True
-                if parent in subfolder_weights:
-                    subfolder_weights[parent] = [sfc for sfc in subfolder_weights[parent] if sfc.path.suffix != ".bin"]
                 score += 1
 
             candidate_variant_label = path.suffixes[0] if len(path.suffixes) == 2 else None
 
             # Some special handling is needed here if there is not an exact match and if we cannot infer the variant
             # from the file name. In this case, we only give this file a point if the requested variant is FP32 or DEFAULT.
-            if (
-                variant is not ModelRepoVariant.Default
-                and candidate_variant_label
-                and candidate_variant_label.startswith(f".{variant.value}")
-            ) or (not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]):
+            if candidate_variant_label == f".{variant}" or (
+                not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]
+            ):
                 score += 1
 
             if parent not in subfolder_weights:
@@ -155,7 +146,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # Check if at least one of the files has the explicit fp16 variant.
         at_least_one_fp16 = False
         for candidate in candidate_list:
-            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0].startswith(".fp16"):
+            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0] == ".fp16":
                 at_least_one_fp16 = True
                 break
 
@@ -171,16 +162,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # candidate.
         highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
         if highest_score_candidate:
-            pattern = r"^(.*?)-\d+-of-\d+(\.\w+)$"
-            match = re.match(pattern, highest_score_candidate.path.as_posix())
-            if match:
-                for candidate in candidate_list:
-                    if candidate.path.as_posix().startswith(match.group(1)) and candidate.path.as_posix().endswith(
-                        match.group(2)
-                    ):
-                        result.add(candidate.path)
-            else:
-                result.add(highest_score_candidate.path)
+            result.add(highest_score_candidate.path)
 
     # If one of the architecture-related variants was specified and no files matched other than
     # config and text files then we return an empty list

invokeai/backend/quantization/gguf/ggml_tensor.py

@@ -54,11 +54,6 @@ GGML_TENSOR_OP_TABLE = {
     torch.ops.aten.mul.Tensor: dequantize_and_run,  # pyright: ignore
 }
 
-if torch.backends.mps.is_available():
-    GGML_TENSOR_OP_TABLE.update(
-        {torch.ops.aten.linear.default: dequantize_and_run}  # pyright: ignore
-    )
-
 
 class GGMLTensor(torch.Tensor):
     """A torch.Tensor sub-class holding a quantized GGML tensor.

invokeai/backend/sd3/extensions/inpaint_extension.py

@@ -1,58 +0,0 @@
-import torch
-
-
-class InpaintExtension:
-    """A class for managing inpainting with SD3."""
-
-    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
-        """Initialize InpaintExtension.
-
-        Args:
-            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0).
-            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
-                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
-                inpainted region with the background.
-            noise (torch.Tensor): The noise tensor used to noise the init_latents.
-        """
-        assert init_latents.dim() == inpaint_mask.dim() == noise.dim() == 4
-        assert init_latents.shape[-2:] == inpaint_mask.shape[-2:] == noise.shape[-2:]
-
-        self._init_latents = init_latents
-        self._inpaint_mask = inpaint_mask
-        self._noise = noise
-
-    def _apply_mask_gradient_adjustment(self, t_prev: float) -> torch.Tensor:
-        """Applies inpaint mask gradient adjustment and returns the inpaint mask to be used at the current timestep."""
-        # As we progress through the denoising process, we promote gradient regions of the mask to have a full weight of
-        # 1.0. This helps to produce more coherent seams around the inpainted region. We experimented with a (small)
-        # number of promotion strategies (e.g. gradual promotion based on timestep), but found that a simple cutoff
-        # threshold worked well.
-        # We use a small epsilon to avoid any potential issues with floating point precision.
-        eps = 1e-4
-        mask_gradient_t_cutoff = 0.5
-        if t_prev > mask_gradient_t_cutoff:
-            # Early in the denoising process, use the inpaint mask as-is.
-            return self._inpaint_mask
-        else:
-            # After the cut-off, promote all non-zero mask values to 1.0.
-            mask = self._inpaint_mask.where(self._inpaint_mask <= (0.0 + eps), 1.0)
-
-        return mask
-
-    def merge_intermediate_latents_with_init_latents(
-        self, intermediate_latents: torch.Tensor, t_prev: float
-    ) -> torch.Tensor:
-        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
-        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
-        trajectory.
-
-        This function should be called after each denoising step.
-        """
-
-        mask = self._apply_mask_gradient_adjustment(t_prev)
-
-        # Noise the init latents for the current timestep.
-        noised_init_latents = self._noise * t_prev + (1.0 - t_prev) * self._init_latents
-
-        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
-        return intermediate_latents * mask + noised_init_latents * (1.0 - mask)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -499,22 +499,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     for idx, value in enumerate(single_t2i_adapter_data.adapter_state):
                         accum_adapter_state[idx] += value * t2i_adapter_weight
 
-            # Hack: force compatibility with irregular resolutions by padding the feature map with zeros
-            for idx, tensor in enumerate(accum_adapter_state):
-                # The tensor size is supposed to be some integer downscale factor of the latents size.
-                # Internally, the unet will pad the latents before downscaling between levels when it is no longer divisible by its downscale factor.
-                # If the latent size does not scale down evenly, we need to pad the tensor so that it matches the the downscaled padded latents later on.
-                scale_factor = latents.size()[-1] // tensor.size()[-1]
-                required_padding_width = math.ceil(latents.size()[-1] / scale_factor) - tensor.size()[-1]
-                required_padding_height = math.ceil(latents.size()[-2] / scale_factor) - tensor.size()[-2]
-                tensor = torch.nn.functional.pad(
-                    tensor,
-                    (0, required_padding_width, 0, required_padding_height, 0, 0, 0, 0),
-                    mode="constant",
-                    value=0,
-                )
-                accum_adapter_state[idx] = tensor
-
             down_intrablock_additional_residuals = accum_adapter_state
 
         # Handle inpainting models.

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -49,32 +49,9 @@ class FLUXConditioningInfo:
         return self
 
 
-@dataclass
-class SD3ConditioningInfo:
-    clip_l_pooled_embeds: torch.Tensor
-    clip_l_embeds: torch.Tensor
-    clip_g_pooled_embeds: torch.Tensor
-    clip_g_embeds: torch.Tensor
-    t5_embeds: torch.Tensor | None
-
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self.clip_l_pooled_embeds = self.clip_l_pooled_embeds.to(device=device, dtype=dtype)
-        self.clip_l_embeds = self.clip_l_embeds.to(device=device, dtype=dtype)
-        self.clip_g_pooled_embeds = self.clip_g_pooled_embeds.to(device=device, dtype=dtype)
-        self.clip_g_embeds = self.clip_g_embeds.to(device=device, dtype=dtype)
-        if self.t5_embeds is not None:
-            self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
-        return self
-
-
 @dataclass
 class ConditioningFieldData:
-    conditionings: (
-        List[BasicConditioningInfo]
-        | List[SDXLConditioningInfo]
-        | List[FLUXConditioningInfo]
-        | List[SD3ConditioningInfo]
-    )
+    conditionings: List[BasicConditioningInfo] | List[SDXLConditioningInfo] | List[FLUXConditioningInfo]
 
 
 @dataclass

invokeai/backend/stable_diffusion/extensions/preview.py

@@ -33,7 +33,7 @@ class PreviewExt(ExtensionBase):
     def initial_preview(self, ctx: DenoiseContext):
         self.callback(
             PipelineIntermediateState(
-                step=0,
+                step=-1,
                 order=ctx.scheduler.order,
                 total_steps=len(ctx.inputs.timesteps),
                 timestep=int(ctx.scheduler.config.num_train_timesteps),  # TODO: is there any code which uses it?

invokeai/backend/util/hotfixes.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import diffusers
 import torch
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders.single_file_model import FromOriginalModelMixin
+from diffusers.loaders import FromOriginalControlNetMixin
 from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
 from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 from diffusers.models.embeddings import (
@@ -32,9 +32,7 @@ from invokeai.backend.util.logging import InvokeAILogger
 logger = InvokeAILogger.get_logger(__name__)
 
 
-# NOTE(ryand): I'm not the origina author of this code, but for future reference, it appears that this class was copied
-# from diffusers in order to add support for the encoder_attention_mask argument.
-class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
     """
     A ControlNet model.
 

invokeai/frontend/web/README.md

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

invokeai/frontend/web/knip.ts

@@ -9,7 +9,6 @@ const config: KnipConfig = {
     'src/services/api/schema.ts',
     'src/features/nodes/types/v1/**',
     'src/features/nodes/types/v2/**',
-    'src/features/parameters/types/parameterSchemas.ts',
     // TODO(psyche): maybe we can clean up these utils after canvas v2 release
     'src/features/controlLayers/konva/util.ts',
     // TODO(psyche): restore HRF functionality?

invokeai/frontend/web/package.json

@@ -52,13 +52,13 @@
     }
   },
   "dependencies": {
-    "@atlaskit/pragmatic-drag-and-drop": "^1.4.0",
-    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^1.4.0",
-    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.0.3",
     "@dagrejs/dagre": "^1.1.4",
     "@dagrejs/graphlib": "^2.2.4",
+    "@dnd-kit/core": "^6.1.0",
+    "@dnd-kit/sortable": "^8.0.0",
+    "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.1.0",
-    "@invoke-ai/ui-library": "^0.0.44",
+    "@invoke-ai/ui-library": "^0.0.42",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
@@ -114,7 +114,8 @@
   },
   "peerDependencies": {
     "react": "^18.2.0",
-    "react-dom": "^18.2.0"
+    "react-dom": "^18.2.0",
+    "ts-toolbelt": "^9.6.0"
   },
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
@@ -148,8 +149,8 @@
     "prettier": "^3.3.3",
     "rollup-plugin-visualizer": "^5.12.0",
     "storybook": "^8.3.4",
+    "ts-toolbelt": "^9.6.0",
     "tsafe": "^1.7.5",
-    "type-fest": "^4.26.1",
     "typescript": "^5.6.2",
     "vite": "^5.4.8",
     "vite-plugin-css-injected-by-js": "^3.5.2",

invokeai/frontend/web/pnpm-lock.yaml

@@ -5,27 +5,27 @@ settings:
   excludeLinksFromLockfile: false
 
 dependencies:
-  '@atlaskit/pragmatic-drag-and-drop':
-    specifier: ^1.4.0
-    version: 1.4.0
-  '@atlaskit/pragmatic-drag-and-drop-auto-scroll':
-    specifier: ^1.4.0
-    version: 1.4.0
-  '@atlaskit/pragmatic-drag-and-drop-hitbox':
-    specifier: ^1.0.3
-    version: 1.0.3
   '@dagrejs/dagre':
     specifier: ^1.1.4
     version: 1.1.4
   '@dagrejs/graphlib':
     specifier: ^2.2.4
     version: 2.2.4
+  '@dnd-kit/core':
+    specifier: ^6.1.0
+    version: 6.1.0(react-dom@18.3.1)(react@18.3.1)
+  '@dnd-kit/sortable':
+    specifier: ^8.0.0
+    version: 8.0.0(@dnd-kit/core@6.1.0)(react@18.3.1)
+  '@dnd-kit/utilities':
+    specifier: ^3.2.2
+    version: 3.2.2(react@18.3.1)
   '@fontsource-variable/inter':
     specifier: ^5.1.0
     version: 5.1.0
   '@invoke-ai/ui-library':
-    specifier: ^0.0.44
-    version: 0.0.44(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.42
+    version: 0.0.42(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.3)(react@18.3.1)
@@ -277,12 +277,12 @@ devDependencies:
   storybook:
     specifier: ^8.3.4
     version: 8.3.4
+  ts-toolbelt:
+    specifier: ^9.6.0
+    version: 9.6.0
   tsafe:
     specifier: ^1.7.5
     version: 1.7.5
-  type-fest:
-    specifier: ^4.26.1
-    version: 4.26.1
   typescript:
     specifier: ^5.6.2
     version: 5.6.2
@@ -319,28 +319,6 @@ packages:
       '@jridgewell/trace-mapping': 0.3.25
     dev: true
 
-  /@atlaskit/pragmatic-drag-and-drop-auto-scroll@1.4.0:
-    resolution: {integrity: sha512-5GoikoTSW13UX76F9TDeWB8x3jbbGlp/Y+3aRkHe1MOBMkrWkwNpJ42MIVhhX/6NSeaZiPumP0KbGJVs2tOWSQ==}
-    dependencies:
-      '@atlaskit/pragmatic-drag-and-drop': 1.4.0
-      '@babel/runtime': 7.25.7
-    dev: false
-
-  /@atlaskit/pragmatic-drag-and-drop-hitbox@1.0.3:
-    resolution: {integrity: sha512-/Sbu/HqN2VGLYBhnsG7SbRNg98XKkbF6L7XDdBi+izRybfaK1FeMfodPpm/xnBHPJzwYMdkE0qtLyv6afhgMUA==}
-    dependencies:
-      '@atlaskit/pragmatic-drag-and-drop': 1.4.0
-      '@babel/runtime': 7.25.7
-    dev: false
-
-  /@atlaskit/pragmatic-drag-and-drop@1.4.0:
-    resolution: {integrity: sha512-qRY3PTJIcxfl/QB8Gwswz+BRvlmgAC5pB+J2hL6dkIxgqAgVwOhAamMUKsrOcFU/axG2Q7RbNs1xfoLKDuhoPg==}
-    dependencies:
-      '@babel/runtime': 7.25.7
-      bind-event-listener: 3.0.0
-      raf-schd: 4.0.3
-    dev: false
-
   /@babel/code-frame@7.25.7:
     resolution: {integrity: sha512-0xZJFNE5XMpENsgfHYTw8FbX4kv53mFLn2i3XPoq69LyhYSCBJtitaHx9QnsVTrsogI4Z3+HtEfZ2/GFPOtf5g==}
     engines: {node: '>=6.9.0'}
@@ -515,8 +493,8 @@ packages:
     resolution: {integrity: sha512-MV6D4VLRIHr4PkW4zMyqfrNS1mPlCTiCXwvYGtDFQYr+xHFfonhAuf9WjsSc0nyp2m0OdkSLnzmVKkZFLo25Tg==}
     dev: false
 
-  /@chakra-ui/anatomy@2.3.5:
-    resolution: {integrity: sha512-3im33cUOxCbISjaBlINE2u8BOwJSCdzpjCX0H+0JxK2xz26UaVA5xeI3NYHUoxDnr/QIrgfrllGxS0szYwOcyg==}
+  /@chakra-ui/anatomy@2.3.4:
+    resolution: {integrity: sha512-fFIYN7L276gw0Q7/ikMMlZxP7mvnjRaWJ7f3Jsf9VtDOi6eAYIBRrhQe6+SZ0PGmoOkRaBc7gSE5oeIbgFFyrw==}
     dev: false
 
   /@chakra-ui/breakpoint-utils@2.0.8:
@@ -573,12 +551,12 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/hooks@2.4.3(react@18.3.1):
-    resolution: {integrity: sha512-Sr2zsoTZw3p7HbrUy4aLpTIkE2XXUelAUgg3NGwMzrmx75bE0qVyiuuTFOuyEzGxYVV2Fe8QtcKKilm6RwzTGg==}
+  /@chakra-ui/hooks@2.4.2(react@18.3.1):
+    resolution: {integrity: sha512-LRKiVE1oA7afT5tbbSKAy7Uas2xFHE6IkrQdbhWCHmkHBUtPvjQQDgwtnd4IRZPmoEfNGwoJ/MQpwOM/NRTTwA==}
     peerDependencies:
       react: '>=18'
     dependencies:
-      '@chakra-ui/utils': 2.2.3(react@18.3.1)
+      '@chakra-ui/utils': 2.2.2(react@18.3.1)
       '@zag-js/element-size': 0.31.1
       copy-to-clipboard: 3.3.3
       framesync: 6.1.2
@@ -596,13 +574,13 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/icons@2.2.4(@chakra-ui/react@2.10.4)(react@18.3.1):
+  /@chakra-ui/icons@2.2.4(@chakra-ui/react@2.10.2)(react@18.3.1):
     resolution: {integrity: sha512-l5QdBgwrAg3Sc2BRqtNkJpfuLw/pWRDwwT58J6c4PqQT6wzXxyNa8Q0PForu1ltB5qEiFb1kxr/F/HO1EwNa6g==}
     peerDependencies:
       '@chakra-ui/react': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/react': 2.10.4(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/react': 2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -825,8 +803,8 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/react@2.10.4(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-XyRWnuZ1Uw7Mlj5pKUGO5/WhnIHP/EOrpy6lGZC1yWlkd0eIfIpYMZ1ALTZx4KPEdbBaes48dgiMT2ROCqLhkA==}
+  /@chakra-ui/react@2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-TfIHTqTlxTHYJZBtpiR5EZasPUrLYKJxdbHkdOJb5G1OQ+2c5kKl5XA7c2pMtsEptzb7KxAAIB62t3hxdfWp1w==}
     peerDependencies:
       '@emotion/react': '>=11'
       '@emotion/styled': '>=11'
@@ -834,10 +812,10 @@ packages:
       react: '>=18'
       react-dom: '>=18'
     dependencies:
-      '@chakra-ui/hooks': 2.4.3(react@18.3.1)
-      '@chakra-ui/styled-system': 2.12.1(react@18.3.1)
-      '@chakra-ui/theme': 3.4.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1)
-      '@chakra-ui/utils': 2.2.3(react@18.3.1)
+      '@chakra-ui/hooks': 2.4.2(react@18.3.1)
+      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
+      '@chakra-ui/theme': 3.4.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
+      '@chakra-ui/utils': 2.2.2(react@18.3.1)
       '@emotion/react': 11.13.3(@types/react@18.3.11)(react@18.3.1)
       '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.11)(react@18.3.1)
       '@popperjs/core': 2.11.8
@@ -868,10 +846,10 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/styled-system@2.12.1(react@18.3.1):
-    resolution: {integrity: sha512-DQph1nDiCPtgze7nDe0a36530ByXb5VpPosKGyWMvKocVeZJcDtYG6XM0+V5a0wKuFBXsViBBRIFUTiUesJAcg==}
+  /@chakra-ui/styled-system@2.11.2(react@18.3.1):
+    resolution: {integrity: sha512-y++z2Uop+hjfZX9mbH88F1ikazPv32asD2er56zMJBemUAzweXnHTpiCQbluEDSUDhqmghVZAdb+5L4XLbsRxA==}
     dependencies:
-      '@chakra-ui/utils': 2.2.3(react@18.3.1)
+      '@chakra-ui/utils': 2.2.2(react@18.3.1)
       csstype: 3.1.3
     transitivePeerDependencies:
       - react
@@ -915,14 +893,14 @@ packages:
       color2k: 2.0.3
     dev: false
 
-  /@chakra-ui/theme-tools@2.2.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1):
-    resolution: {integrity: sha512-K/VJd0QcnKik7m+qZTkggqNLep6+MPUu8IP5TUpHsnSM5R/RVjsJIR7gO8IZVAIMIGLLTIhGshHxeMekqv6LcQ==}
+  /@chakra-ui/theme-tools@2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1):
+    resolution: {integrity: sha512-3UhKPyzKbV3l/bg1iQN9PBvffYp+EBOoYMUaeTUdieQRPFzo2jbYR0lNCxqv8h5aGM/k54nCHU2M/GStyi9F2A==}
     peerDependencies:
       '@chakra-ui/styled-system': '>=2.0.0'
     dependencies:
-      '@chakra-ui/anatomy': 2.3.5
-      '@chakra-ui/styled-system': 2.12.1(react@18.3.1)
-      '@chakra-ui/utils': 2.2.3(react@18.3.1)
+      '@chakra-ui/anatomy': 2.3.4
+      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
+      '@chakra-ui/utils': 2.2.2(react@18.3.1)
       color2k: 2.0.3
     transitivePeerDependencies:
       - react
@@ -948,15 +926,15 @@ packages:
       '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
     dev: false
 
-  /@chakra-ui/theme@3.4.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1):
-    resolution: {integrity: sha512-pfewthgZTFNUYeUwGvhPQO/FTIyf375cFV1AT8N1y0aJiw4KDe7YTGm7p0aFy4AwAjH2ydMgeEx/lua4tx8qyQ==}
+  /@chakra-ui/theme@3.4.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1):
+    resolution: {integrity: sha512-ZwFBLfiMC3URwaO31ONXoKH9k0TX0OW3UjdPF3EQkQpYyrk/fm36GkkzajjtdpWEd7rzDLRsQjPmvwNaSoNDtg==}
     peerDependencies:
       '@chakra-ui/styled-system': '>=2.8.0'
     dependencies:
-      '@chakra-ui/anatomy': 2.3.5
-      '@chakra-ui/styled-system': 2.12.1(react@18.3.1)
-      '@chakra-ui/theme-tools': 2.2.7(@chakra-ui/styled-system@2.12.1)(react@18.3.1)
-      '@chakra-ui/utils': 2.2.3(react@18.3.1)
+      '@chakra-ui/anatomy': 2.3.4
+      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
+      '@chakra-ui/theme-tools': 2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
+      '@chakra-ui/utils': 2.2.2(react@18.3.1)
     transitivePeerDependencies:
       - react
     dev: false
@@ -981,8 +959,8 @@ packages:
       lodash.mergewith: 4.6.2
     dev: false
 
-  /@chakra-ui/utils@2.2.3(react@18.3.1):
-    resolution: {integrity: sha512-cldoCQuexZ6e07/9hWHKD4l1QXXlM1Nax9tuQOBvVf/EgwNZt3nZu8zZRDFlhAOKCTQDkmpLTTu+eXXjChNQOw==}
+  /@chakra-ui/utils@2.2.2(react@18.3.1):
+    resolution: {integrity: sha512-jUPLT0JzRMWxpdzH6c+t0YMJYrvc5CLericgITV3zDSXblkfx3DsYXqU11DJTSGZI9dUKzM1Wd0Wswn4eJwvFQ==}
     peerDependencies:
       react: '>=16.8.0'
     dependencies:
@@ -1002,6 +980,49 @@ packages:
     engines: {node: '>17.0.0'}
     dev: false
 
+  /@dnd-kit/accessibility@3.1.0(react@18.3.1):
+    resolution: {integrity: sha512-ea7IkhKvlJUv9iSHJOnxinBcoOI3ppGnnL+VDJ75O45Nss6HtZd8IdN8touXPDtASfeI2T2LImb8VOZcL47wjQ==}
+    peerDependencies:
+      react: '>=16.8.0'
+    dependencies:
+      react: 18.3.1
+      tslib: 2.7.0
+    dev: false
+
+  /@dnd-kit/core@6.1.0(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-J3cQBClB4TVxwGo3KEjssGEXNJqGVWx17aRTZ1ob0FliR5IjYgTxl5YJbKTzA6IzrtelotH19v6y7uoIRUZPSg==}
+    peerDependencies:
+      react: '>=16.8.0'
+      react-dom: '>=16.8.0'
+    dependencies:
+      '@dnd-kit/accessibility': 3.1.0(react@18.3.1)
+      '@dnd-kit/utilities': 3.2.2(react@18.3.1)
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+      tslib: 2.7.0
+    dev: false
+
+  /@dnd-kit/sortable@8.0.0(@dnd-kit/core@6.1.0)(react@18.3.1):
+    resolution: {integrity: sha512-U3jk5ebVXe1Lr7c2wU7SBZjcWdQP+j7peHJfCspnA81enlu88Mgd7CC8Q+pub9ubP7eKVETzJW+IBAhsqbSu/g==}
+    peerDependencies:
+      '@dnd-kit/core': ^6.1.0
+      react: '>=16.8.0'
+    dependencies:
+      '@dnd-kit/core': 6.1.0(react-dom@18.3.1)(react@18.3.1)
+      '@dnd-kit/utilities': 3.2.2(react@18.3.1)
+      react: 18.3.1
+      tslib: 2.7.0
+    dev: false
+
+  /@dnd-kit/utilities@3.2.2(react@18.3.1):
+    resolution: {integrity: sha512-+MKAJEOfaBe5SmV6t34p80MMKhjvUz0vRrvVJbPT0WElzaOJ/1xs+D+KDv+tD/NE5ujfrChEcshd4fLn0wpiqg==}
+    peerDependencies:
+      react: '>=16.8.0'
+    dependencies:
+      react: 18.3.1
+      tslib: 2.7.0
+    dev: false
+
   /@emotion/babel-plugin@11.12.0:
     resolution: {integrity: sha512-y2WQb+oP8Jqvvclh8Q55gLUyb7UFvgv7eJfsj7td5TToBrIUtPay2kMrZi4xjq9qw2vD0ZR5fSho0yqoFgX7Rw==}
     dependencies:
@@ -1675,18 +1696,18 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.44(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-PDseHmdr8oi8cmrpx3UwIYHn4NduAJX2R0pM0pyM54xrCMPMgYiCbC/eOs8Gt4fBc2ziiPZ9UGoW4evnE3YJsg==}
+  /@invoke-ai/ui-library@0.0.42(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-OuDXRipBO5mu+Nv4qN8cd8MiwiGBdq6h4PirVgPI9/ltbdcIzePgUJ0dJns26lflHSTRWW38I16wl4YTw3mNWA==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
       react-dom: ^18.2.0
     dependencies:
       '@chakra-ui/anatomy': 2.2.2
-      '@chakra-ui/icons': 2.2.4(@chakra-ui/react@2.10.4)(react@18.3.1)
+      '@chakra-ui/icons': 2.2.4(@chakra-ui/react@2.10.2)(react@18.3.1)
       '@chakra-ui/layout': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/react': 2.10.4(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/react': 2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
       '@emotion/react': 11.13.3(@types/react@18.3.11)(react@18.3.1)
@@ -4292,10 +4313,6 @@ packages:
       open: 8.4.2
     dev: true
 
-  /bind-event-listener@3.0.0:
-    resolution: {integrity: sha512-PJvH288AWQhKs2v9zyfYdPzlPqf5bXbGMmhmUIY9x4dAUGIWgomO771oBQNwJnMQSnUIXhKu6sgzpBRXTlvb8Q==}
-    dev: false
-
   /bl@4.1.0:
     resolution: {integrity: sha512-1W07cM9gS6DcLperZfFSj+bWLtaPGSOHWhPiGzXmvVJbRLdG82sH/Kn8EtW1VqWVA54AKf2h5k5BbnIbwF3h6w==}
     dependencies:
@@ -7540,10 +7557,6 @@ packages:
     resolution: {integrity: sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==}
     dev: true
 
-  /raf-schd@4.0.3:
-    resolution: {integrity: sha512-tQkJl2GRWh83ui2DiPTJz9wEiMN20syf+5oKfB03yYP7ioZcJwsIK8FjrtLwH1m7C7e+Tt2yYBlrOpdT+dyeIQ==}
-    dev: false
-
   /raf-throttle@2.0.6:
     resolution: {integrity: sha512-C7W6hy78A+vMmk5a/B6C5szjBHrUzWJkVyakjKCK59Uy2CcA7KhO1JUvvH32IXYFIcyJ3FMKP3ZzCc2/71I6Vg==}
     dev: false
@@ -8817,6 +8830,10 @@ packages:
     resolution: {integrity: sha512-tLJxacIQUM82IR7JO1UUkKlYuUTmoY9HBJAmNWFzheSlDS5SPMcNIepejHJa4BpPQLAcbRhRf3GDJzyj6rbKvA==}
     dev: false
 
+  /ts-toolbelt@9.6.0:
+    resolution: {integrity: sha512-nsZd8ZeNUzukXPlJmTBwUAuABDe/9qtVDelJeT/qW0ow3ZS3BsQJtNkan1802aM9Uf68/Y8ljw86Hu0h5IUW3w==}
+    dev: true
+
   /tsafe@1.7.5:
     resolution: {integrity: sha512-tbNyyBSbwfbilFfiuXkSOj82a6++ovgANwcoqBAcO9/REPoZMEQoE8kWPeO0dy5A2D/2Lajr8Ohue5T0ifIvLQ==}
     dev: true

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

@@ -93,12 +93,7 @@
         "placeholderSelectAModel": "Modell auswählen",
         "reset": "Zurücksetzen",
         "none": "Keine",
-        "new": "Neu",
-        "ok": "OK",
-        "close": "Schließen",
-        "clipboard": "Zwischenablage",
-        "generating": "Generieren",
-        "loadingModel": "Lade Modell"
+        "new": "Neu"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -161,11 +156,7 @@
         "displayBoardSearch": "Board durchsuchen",
         "displaySearch": "Bild suchen",
         "go": "Los",
-        "jump": "Springen",
-        "assetsTab": "Dateien, die Sie zur Verwendung in Ihren Projekten hochgeladen haben.",
-        "imagesTab": "Bilder, die Sie in Invoke erstellt und gespeichert haben.",
-        "boardsSettings": "Ordnereinstellungen",
-        "imagesSettings": "Galeriebildereinstellungen"
+        "jump": "Springen"
     },
     "hotkeys": {
         "noHotkeysFound": "Kein Hotkey gefunden",
@@ -276,18 +267,6 @@
             "applyFilter": {
                 "title": "Filter anwenden",
                 "desc": "Wende den ausstehenden Filter auf die ausgewählte Ebene an."
-            },
-            "cancelFilter": {
-                "title": "Filter abbrechen",
-                "desc": "Den ausstehenden Filter abbrechen."
-            },
-            "applyTransform": {
-                "desc": "Die ausstehende Transformation auf die ausgewählte Ebene anwenden.",
-                "title": "Transformation anwenden"
-            },
-            "cancelTransform": {
-                "title": "Transformation abbrechen",
-                "desc": "Die ausstehende Transformation abbrechen."
             }
         },
         "viewer": {
@@ -538,12 +517,14 @@
         "addModels": "Model hinzufügen",
         "deleteModelImage": "Lösche Model Bild",
         "huggingFaceRepoID": "HuggingFace Repo ID",
+        "hfToken": "HuggingFace Schlüssel",
         "huggingFacePlaceholder": "besitzer/model-name",
         "modelSettings": "Modelleinstellungen",
         "typePhraseHere": "Phrase hier eingeben",
         "spandrelImageToImage": "Bild zu Bild (Spandrel)",
         "starterModels": "Einstiegsmodelle",
         "t5Encoder": "T5-Kodierer",
+        "useDefaultSettings": "Standardeinstellungen verwenden",
         "uploadImage": "Bild hochladen",
         "urlOrLocalPath": "URL oder lokaler Pfad",
         "install": "Installieren",
@@ -582,26 +563,7 @@
         "scanResults": "Ergebnisse des Scans",
         "urlOrLocalPathHelper": "URLs sollten auf eine einzelne Datei deuten. Lokale Pfade können zusätzlich auch auf einen Ordner für ein einzelnes Diffusers-Modell hinweisen.",
         "inplaceInstallDesc": "Installieren Sie Modelle, ohne die Dateien zu kopieren. Wenn Sie das Modell verwenden, wird es direkt von seinem Speicherort geladen. Wenn deaktiviert, werden die Dateien während der Installation in das von Invoke verwaltete Modellverzeichnis kopiert.",
-        "scanFolderHelper": "Der Ordner wird rekursiv nach Modellen durchsucht. Dies kann bei sehr großen Ordnern etwas dauern.",
-        "includesNModels": "Enthält {{n}} Modelle und deren Abhängigkeiten",
-        "starterBundles": "Starterpakete",
-        "installingXModels_one": "{{count}} Modell wird installiert",
-        "installingXModels_other": "{{count}} Modelle werden installiert",
-        "skippingXDuplicates_one": ", überspringe {{count}} Duplikat",
-        "skippingXDuplicates_other": ", überspringe {{count}} Duplikate",
-        "installingModel": "Modell wird installiert",
-        "loraTriggerPhrases": "LoRA-Auslösephrasen",
-        "installingBundle": "Bündel wird installiert",
-        "triggerPhrases": "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"
+        "scanFolderHelper": "Der Ordner wird rekursiv nach Modellen durchsucht. Dies kann bei sehr großen Ordnern etwas dauern."
     },
     "parameters": {
         "images": "Bilder",
@@ -642,6 +604,12 @@
         "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}}",
@@ -681,41 +649,10 @@
     "toast": {
         "uploadFailed": "Hochladen fehlgeschlagen",
         "imageCopied": "Bild kopiert",
-        "parametersNotSet": "Parameter nicht zurückgerufen",
+        "parametersNotSet": "Parameter nicht festgelegt",
         "addedToBoard": "Dem Board hinzugefügt",
         "loadedWithWarnings": "Workflow mit Warnungen geladen",
-        "imageSaved": "Bild gespeichert",
-        "linkCopied": "Link kopiert",
-        "problemCopyingLayer": "Ebene kann nicht kopiert werden",
-        "problemSavingLayer": "Ebene kann nicht gespeichert werden",
-        "parameterSetDesc": "{{parameter}} zurückgerufen",
-        "imageUploaded": "Bild hochgeladen",
-        "problemCopyingImage": "Bild kann nicht kopiert werden",
-        "parameterNotSetDesc": "{{parameter}} kann nicht zurückgerufen werden",
-        "prunedQueue": "Warteschlange bereinigt",
-        "modelAddedSimple": "Modell zur Warteschlange hinzugefügt",
-        "parametersSet": "Parameter zurückgerufen",
-        "imageNotLoadedDesc": "Bild konnte nicht gefunden werden",
-        "setControlImage": "Als Kontrollbild festlegen",
-        "sentToUpscale": "An Vergrößerung gesendet",
-        "parameterNotSetDescWithMessage": "{{parameter}} kann nicht zurückgerufen werden: {{message}}",
-        "unableToLoadImageMetadata": "Bildmetadaten können nicht geladen werden",
-        "unableToLoadImage": "Bild kann nicht geladen werden",
-        "serverError": "Serverfehler",
-        "parameterNotSet": "Parameter nicht zurückgerufen",
-        "sessionRef": "Sitzung: {{sessionId}}",
-        "problemDownloadingImage": "Bild kann nicht heruntergeladen werden",
-        "parameters": "Parameter",
-        "parameterSet": "Parameter zurückgerufen",
-        "importFailed": "Import fehlgeschlagen",
-        "importSuccessful": "Import erfolgreich",
-        "setNodeField": "Als Knotenfeld festlegen",
-        "somethingWentWrong": "Etwas ist schief gelaufen",
-        "workflowLoaded": "Arbeitsablauf geladen",
-        "workflowDeleted": "Arbeitsablauf gelöscht",
-        "errorCopied": "Fehler kopiert",
-        "layerCopiedToClipboard": "Ebene in die Zwischenablage kopiert",
-        "sentToCanvas": "An Leinwand gesendet"
+        "imageSaved": "Bild gespeichert"
     },
     "accessibility": {
         "uploadImage": "Bild hochladen",
@@ -730,8 +667,7 @@
         "about": "Über",
         "submitSupportTicket": "Support-Ticket senden",
         "toggleRightPanel": "Rechtes Bedienfeld umschalten (G)",
-        "toggleLeftPanel": "Linkes Bedienfeld umschalten (T)",
-        "uploadImages": "Bild(er) hochladen"
+        "toggleLeftPanel": "Linkes Bedienfeld umschalten (T)"
     },
     "boards": {
         "autoAddBoard": "Board automatisch erstellen",
@@ -766,14 +702,13 @@
         "shared": "Geteilte Ordner",
         "archiveBoard": "Ordner archivieren",
         "archived": "Archiviert",
-        "noBoards": "Kein {{boardType}} Ordner",
+        "noBoards": "Kein {boardType}} Ordner",
         "hideBoards": "Ordner verstecken",
         "viewBoards": "Ordner ansehen",
         "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt.",
         "assetsWithCount_one": "{{count}} in der Sammlung",
         "assetsWithCount_other": "{{count}} in der Sammlung",
-        "deletedBoardsCannotbeRestored": "Gelöschte Ordner können nicht wiederhergestellt werden. Die Auswahl von \"Nur Ordner löschen\" verschiebt Bilder in einen unkategorisierten Zustand.",
-        "updateBoardError": "Fehler beim Aktualisieren des Ordners"
+        "deletedBoardsCannotbeRestored": "Gelöschte Ordner können nicht wiederhergestellt werden. Die Auswahl von \"Nur Ordner löschen\" verschiebt Bilder in einen unkategorisierten Zustand."
     },
     "queue": {
         "status": "Status",
@@ -845,8 +780,7 @@
         "upscaling": "Hochskalierung",
         "canvas": "Leinwand",
         "prompts_one": "Prompt",
-        "prompts_other": "Prompts",
-        "batchSize": "Stapelgröße"
+        "prompts_other": "Prompts"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -861,6 +795,7 @@
         "width": "Breite",
         "createdBy": "Erstellt von",
         "steps": "Schritte",
+        "seamless": "Nahtlos",
         "positivePrompt": "Positiver Prompt",
         "generationMode": "Generierungsmodus",
         "Threshold": "Rauschen-Schwelle",
@@ -876,10 +811,7 @@
         "parameterSet": "Parameter {{parameter}} setzen",
         "recallParameter": "{{label}} Abrufen",
         "parsingFailed": "Parsing Fehlgeschlagen",
-        "canvasV2Metadata": "Leinwand",
-        "guidance": "Führung",
-        "seamlessXAxis": "Nahtlose X Achse",
-        "seamlessYAxis": "Nahtlose Y Achse"
+        "canvasV2Metadata": "Leinwand"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -1086,21 +1018,6 @@
         },
         "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"
         }
     },
     "invocationCache": {
@@ -1220,21 +1137,7 @@
         "workflowNotes": "Notizen",
         "workflowTags": "Tags",
         "workflowVersion": "Version",
-        "saveToGallery": "In Galerie speichern",
-        "noWorkflows": "Keine Arbeitsabläufe",
-        "noMatchingWorkflows": "Keine passenden Arbeitsabläufe",
-        "unknownErrorValidatingWorkflow": "Unbekannter Fehler beim Validieren des Arbeitsablaufes",
-        "inputFieldTypeParseError": "Typ des Eingabefelds {{node}}.{{field}} kann nicht analysiert werden ({{message}})",
-        "workflowSettings": "Arbeitsablauf Editor Einstellungen",
-        "unableToLoadWorkflow": "Arbeitsablauf kann nicht geladen werden",
-        "viewMode": "In linearen Ansicht verwenden",
-        "unableToValidateWorkflow": "Arbeitsablauf kann nicht validiert werden",
-        "outputFieldTypeParseError": "Typ des Ausgabefelds {{node}}.{{field}} kann nicht analysiert werden ({{message}})",
-        "unableToGetWorkflowVersion": "Version des Arbeitsablaufschemas kann nicht bestimmt werden",
-        "unknownFieldType": "$t(nodes.unknownField) Typ: {{type}}",
-        "unknownField": "Unbekanntes Feld",
-        "unableToUpdateNodes_one": "{{count}} Knoten kann nicht aktualisiert werden",
-        "unableToUpdateNodes_other": "{{count}} Knoten können nicht aktualisiert werden"
+        "saveToGallery": "In Galerie speichern"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",
@@ -1364,7 +1267,15 @@
         "enableLogging": "Protokollierung aktivieren"
     },
     "whatsNew": {
-        "whatsNewInInvoke": "Was gibt's Neues"
+        "whatsNewInInvoke": "Was gibt's Neues",
+        "canvasV2Announcement": {
+            "fluxSupport": "Unterstützung für Flux-Modelle",
+            "newCanvas": "Eine leistungsstarke neue Kontrollfläche",
+            "newLayerTypes": "Neue Ebenentypen für noch mehr Kontrolle",
+            "readReleaseNotes": "Anmerkungen zu dieser Version lesen",
+            "watchReleaseVideo": "Video über diese Version anzeigen",
+            "watchUiUpdatesOverview": "Interface-Updates Übersicht"
+        }
     },
     "stylePresets": {
         "name": "Name",
@@ -1415,13 +1326,7 @@
         "pullBboxIntoLayerOk": "Bbox in die Ebene gezogen",
         "saveBboxToGallery": "Bbox in Galerie speichern",
         "tool": {
-            "bbox": "Bbox",
-            "brush": "Pinsel",
-            "eraser": "Radiergummi",
-            "colorPicker": "Farbwähler",
-            "view": "Ansicht",
-            "rectangle": "Rechteck",
-            "move": "Verschieben"
+            "bbox": "Bbox"
         },
         "transform": {
             "fitToBbox": "An Bbox anpassen",
@@ -1463,6 +1368,7 @@
         "deleteReferenceImage": "Referenzbild löschen",
         "referenceImage": "Referenzbild",
         "opacity": "Opazität",
+        "resetCanvas": "Leinwand zurücksetzen",
         "removeBookmark": "Lesezeichen entfernen",
         "rasterLayer": "Raster-Ebene",
         "rasterLayers_withCount_visible": "Raster-Ebenen ({{count}})",
@@ -1539,30 +1445,7 @@
         "layer_one": "Ebene",
         "layer_other": "Ebenen",
         "layer_withCount_one": "Ebene ({{count}})",
-        "layer_withCount_other": "Ebenen ({{count}})",
-        "fill": {
-            "fillStyle": "Füllstil",
-            "diagonal": "Diagonal",
-            "vertical": "Vertikal",
-            "fillColor": "Füllfarbe",
-            "grid": "Raster",
-            "solid": "Solide",
-            "crosshatch": "Kreuzschraffur",
-            "horizontal": "Horizontal"
-        },
-        "filter": {
-            "apply": "Anwenden",
-            "reset": "Zurücksetzen",
-            "cancel": "Abbrechen",
-            "spandrel_filter": {
-                "label": "Bild-zu-Bild Modell",
-                "description": "Ein Bild-zu-Bild Modell auf der ausgewählten Ebene ausführen.",
-                "model": "Modell"
-            },
-            "filters": "Filter",
-            "filterType": "Filtertyp",
-            "filter": "Filter"
-        }
+        "layer_withCount_other": "Ebenen ({{count}})"
     },
     "upsell": {
         "shareAccess": "Zugang teilen",

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

@@ -94,7 +94,6 @@
         "close": "Close",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
-        "clipboard": "Clipboard",
         "on": "On",
         "off": "Off",
         "or": "or",
@@ -122,7 +121,6 @@
         "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
         "loadingImage": "Loading Image",
-        "loadingModel": "Loading Model",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
@@ -175,9 +173,7 @@
         "placeholderSelectAModel": "Select a model",
         "reset": "Reset",
         "none": "None",
-        "new": "New",
-        "generating": "Generating",
-        "warnings": "Warnings"
+        "new": "New"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -264,8 +260,7 @@
         "iterations_one": "Iteration",
         "iterations_other": "Iterations",
         "generations_one": "Generation",
-        "generations_other": "Generations",
-        "batchSize": "Batch Size"
+        "generations_other": "Generations"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -686,8 +681,7 @@
         "recallParameters": "Recall Parameters",
         "recallParameter": "Recall {{label}}",
         "scheduler": "Scheduler",
-        "seamlessXAxis": "Seamless X Axis",
-        "seamlessYAxis": "Seamless Y Axis",
+        "seamless": "Seamless",
         "seed": "Seed",
         "steps": "Steps",
         "strength": "Image to image strength",
@@ -708,8 +702,6 @@
         "baseModel": "Base Model",
         "cancel": "Cancel",
         "clipEmbed": "CLIP Embed",
-        "clipLEmbed": "CLIP-L Embed",
-        "clipGEmbed": "CLIP-G Embed",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -720,12 +712,8 @@
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
         "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
-        "noDefaultSettings": "No default settings configured for this model. Visit the Model Manager to add default settings.",
         "defaultSettings": "Default Settings",
         "defaultSettingsSaved": "Default Settings Saved",
-        "defaultSettingsOutOfSync": "Some settings do not match the model's defaults:",
-        "restoreDefaultSettings": "Click to use the model's default settings.",
-        "usingDefaultSettings": "Using model's default settings",
         "delete": "Delete",
         "deleteConfig": "Delete Config",
         "deleteModel": "Delete Model",
@@ -739,17 +727,7 @@
         "huggingFacePlaceholder": "owner/model-name",
         "huggingFaceRepoID": "HuggingFace Repo ID",
         "huggingFaceHelper": "If multiple models are found in this repo, you will be prompted to select one to install.",
-        "hfTokenLabel": "HuggingFace Token (Required for some models)",
-        "hfTokenHelperText": "A HF token is required to use some models. Click here to create or get your token.",
-        "hfTokenInvalid": "Invalid or Missing HF Token",
-        "hfForbidden": "You do not have access to this HF model",
-        "hfForbiddenErrorMessage": "We recommend visiting the repo page on HuggingFace.com. The owner may require acceptance of terms in order to download.",
-        "hfTokenInvalidErrorMessage": "Invalid or missing HuggingFace token.",
-        "hfTokenRequired": "You are trying to download a model that requires a valid HuggingFace Token.",
-        "hfTokenInvalidErrorMessage2": "Update it in the ",
-        "hfTokenUnableToVerify": "Unable to Verify HF Token",
-        "hfTokenUnableToVerifyErrorMessage": "Unable to verify HuggingFace token. This is likely due to a network error. Please try again later.",
-        "hfTokenSaved": "HF Token Saved",
+        "hfToken": "HuggingFace Token",
         "imageEncoderModelId": "Image Encoder Model ID",
         "includesNModels": "Includes {{n}} models and their dependencies",
         "installQueue": "Install Queue",
@@ -820,6 +798,7 @@
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
         "urlOrLocalPathHelper": "URLs should point to a single file. Local paths can point to a single file or folder for a single diffusers model.",
+        "useDefaultSettings": "Use Default Settings",
         "vae": "VAE",
         "vaePrecision": "VAE Precision",
         "variant": "Variant",
@@ -979,8 +958,6 @@
         "zoomOutNodes": "Zoom Out",
         "betaDesc": "This invocation is in beta. Until it is stable, it may have breaking changes during app updates. We plan to support this invocation long-term.",
         "prototypeDesc": "This invocation is a prototype. It may have breaking changes during app updates and may be removed at any time.",
-        "internalDesc": "This invocation is used internally by Invoke. It may have breaking changes during app updates and may be removed at any time.",
-        "specialDesc": "This invocation some special handling in the app. For example, Batch nodes are used to queue multiple graphs from a single workflow.",
         "imageAccessError": "Unable to find image {{image_name}}, resetting to default",
         "boardAccessError": "Unable to find board {{board_id}}, resetting to default",
         "modelAccessError": "Unable to find model {{key}}, resetting to default",
@@ -1005,7 +982,6 @@
         "controlNetControlMode": "Control Mode",
         "copyImage": "Copy Image",
         "denoisingStrength": "Denoising Strength",
-        "disabledNoRasterContent": "Disabled (No Raster Content)",
         "downloadImage": "Download Image",
         "general": "General",
         "guidance": "Guidance",
@@ -1019,11 +995,8 @@
             "addingImagesTo": "Adding images to",
             "invoke": "Invoke",
             "missingFieldTemplate": "Missing field template",
-            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}}: missing input",
+            "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} missing input",
             "missingNodeTemplate": "Missing node template",
-            "collectionEmpty": "{{nodeLabel}} -> {{fieldLabel}} empty collection",
-            "collectionTooFewItems": "{{nodeLabel}} -> {{fieldLabel}}: too few items, minimum {{minItems}}",
-            "collectionTooManyItems": "{{nodeLabel}} -> {{fieldLabel}}: too many items, maximum {{maxItems}}",
             "noModelSelected": "No model selected",
             "noT5EncoderModelSelected": "No T5 Encoder model selected for FLUX generation",
             "noFLUXVAEModelSelected": "No VAE model selected for FLUX generation",
@@ -1032,14 +1005,26 @@
             "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}}",
-            "canvasIsFiltering": "Canvas is busy (filtering)",
-            "canvasIsTransforming": "Canvas is busy (transforming)",
-            "canvasIsRasterizing": "Canvas is busy (rasterizing)",
-            "canvasIsCompositing": "Canvas is busy (compositing)",
-            "canvasIsSelectingObject": "Canvas is busy (selecting object)",
+            "canvasIsFiltering": "Canvas is filtering",
+            "canvasIsTransforming": "Canvas is transforming",
+            "canvasIsRasterizing": "Canvas is rasterizing",
+            "canvasIsCompositing": "Canvas is compositing",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected"
+            "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"
+            }
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1047,7 +1032,6 @@
         "patchmatchDownScaleSize": "Downscale",
         "perlinNoise": "Perlin Noise",
         "positivePromptPlaceholder": "Positive Prompt",
-        "recallMetadata": "Recall Metadata",
         "iterations": "Iterations",
         "scale": "Scale",
         "scaleBeforeProcessing": "Scale Before Processing",
@@ -1124,9 +1108,6 @@
         "enableInformationalPopovers": "Enable Informational Popovers",
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
-        "enableModelDescriptions": "Enable Model Descriptions in Dropdowns",
-        "modelDescriptionsDisabled": "Model Descriptions in Dropdowns Disabled",
-        "modelDescriptionsDisabledDesc": "Model descriptions in dropdowns have been disabled. Enable them in Settings.",
         "enableInvisibleWatermark": "Enable Invisible Watermark",
         "enableNSFWChecker": "Enable NSFW Checker",
         "general": "General",
@@ -1136,7 +1117,6 @@
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
-        "showDetailedInvocationProgress": "Show Progress Details",
         "showProgressInViewer": "Show Progress Images in Viewer",
         "ui": "User Interface",
         "clearIntermediatesDisabled": "Queue must be empty to clear intermediates",
@@ -1271,33 +1251,6 @@
             "heading": "Mask Adjustments",
             "paragraphs": ["Adjust the mask."]
         },
-        "inpainting": {
-            "heading": "Inpainting",
-            "paragraphs": ["Controls which area is modified, guided by Denoising Strength."]
-        },
-        "rasterLayer": {
-            "heading": "Raster Layer",
-            "paragraphs": ["Pixel-based content of your canvas, used during image generation."]
-        },
-        "regionalGuidance": {
-            "heading": "Regional Guidance",
-            "paragraphs": ["Brush to guide where elements from global prompts should appear."]
-        },
-        "regionalGuidanceAndReferenceImage": {
-            "heading": "Regional Guidance and Regional Reference Image",
-            "paragraphs": [
-                "For Regional Guidance, brush to guide where elements from global prompts should appear.",
-                "For Regional Reference Image, brush to apply a reference image to specific areas."
-            ]
-        },
-        "globalReferenceImage": {
-            "heading": "Global Reference Image",
-            "paragraphs": ["Applies a reference image to influence the entire generation."]
-        },
-        "regionalReferenceImage": {
-            "heading": "Regional Reference Image",
-            "paragraphs": ["Brush to apply a reference image to specific areas."]
-        },
         "controlNet": {
             "heading": "ControlNet",
             "paragraphs": [
@@ -1307,9 +1260,8 @@
         "controlNetBeginEnd": {
             "heading": "Begin / End Step Percentage",
             "paragraphs": [
-                "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."
+                "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."
             ]
         },
         "controlNetControlMode": {
@@ -1319,7 +1271,7 @@
         "controlNetProcessor": {
             "heading": "Processor",
             "paragraphs": [
-                "Method of processing the input image to guide the generation process. Different processors will provide different effects or styles in your generated images."
+                "Method of processing the input image to guide the generation process. Different processors will providedifferent effects or styles in your generated images."
             ]
         },
         "controlNetResizeMode": {
@@ -1327,15 +1279,13 @@
             "paragraphs": ["Method to fit Control Adapter's input image size to the output generation size."]
         },
         "ipAdapterMethod": {
-            "heading": "Mode",
-            "paragraphs": ["The mode defines how the reference image will guide the generation process."]
+            "heading": "Method",
+            "paragraphs": ["Method by which to apply the current IP Adapter."]
         },
         "controlNetWeight": {
             "heading": "Weight",
             "paragraphs": [
-                "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."
+                "Weight of the Control Adapter. Higher weight will lead to larger impacts on the final image."
             ]
         },
         "dynamicPrompts": {
@@ -1416,9 +1366,8 @@
         "paramDenoisingStrength": {
             "heading": "Denoising Strength",
             "paragraphs": [
-                "Controls how much the generated image varies from the raster layer(s).",
-                "Lower strength stays closer to the combined visible raster layers. Higher strength relies more on the global prompt.",
-                "When there are no raster layers with visible content, this setting is ignored."
+                "How much noise is added to the input image.",
+                "0 will result in an identical image, while 1 will result in a completely new image."
             ]
         },
         "paramHeight": {
@@ -1663,17 +1612,15 @@
         "pullBboxIntoReferenceImageError": "Problem Pulling BBox Into ReferenceImage",
         "regionIsEmpty": "Selected region is empty",
         "mergeVisible": "Merge Visible",
-        "mergeDown": "Merge Down",
-        "mergeVisibleOk": "Merged layers",
-        "mergeVisibleError": "Error merging layers",
-        "mergingLayers": "Merging layers",
+        "mergeVisibleOk": "Merged visible layers",
+        "mergeVisibleError": "Error merging visible layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
-        "newSession": "New Session",
+        "resetCanvas": "Reset Canvas",
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
-        "outputOnlyMaskedRegions": "Output Only Generated Regions",
+        "outputOnlyMaskedRegions": "Output Only Masked Regions",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1701,12 +1648,6 @@
         "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)",
-        "asControlLayerResize": "As $t(controlLayers.controlLayer) (Resize)",
         "referenceImage": "Reference Image",
         "regionalReferenceImage": "Regional Reference Image",
         "globalReferenceImage": "Global Reference Image",
@@ -1747,18 +1688,8 @@
         "layer_other": "Layers",
         "layer_withCount_one": "Layer ({{count}})",
         "layer_withCount_other": "Layers ({{count}})",
-        "convertRasterLayerTo": "Convert $t(controlLayers.rasterLayer) To",
-        "convertControlLayerTo": "Convert $t(controlLayers.controlLayer) To",
-        "convertInpaintMaskTo": "Convert $t(controlLayers.inpaintMask) To",
-        "convertRegionalGuidanceTo": "Convert $t(controlLayers.regionalGuidance) To",
-        "copyRasterLayerTo": "Copy $t(controlLayers.rasterLayer) To",
-        "copyControlLayerTo": "Copy $t(controlLayers.controlLayer) To",
-        "copyInpaintMaskTo": "Copy $t(controlLayers.inpaintMask) To",
-        "copyRegionalGuidanceTo": "Copy $t(controlLayers.regionalGuidance) To",
-        "newRasterLayer": "New $t(controlLayers.rasterLayer)",
-        "newControlLayer": "New $t(controlLayers.controlLayer)",
-        "newInpaintMask": "New $t(controlLayers.inpaintMask)",
-        "newRegionalGuidance": "New $t(controlLayers.regionalGuidance)",
+        "convertToControlLayer": "Convert to Control Layer",
+        "convertToRasterLayer": "Convert to Raster Layer",
         "transparency": "Transparency",
         "enableTransparencyEffect": "Enable Transparency Effect",
         "disableTransparencyEffect": "Disable Transparency Effect",
@@ -1774,7 +1705,6 @@
         "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 %",
@@ -1783,41 +1713,18 @@
         "newGallerySessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be sent to the gallery.",
         "newCanvasSession": "New Canvas Session",
         "newCanvasSessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be staged on the canvas.",
-        "resetCanvasLayers": "Reset Canvas Layers",
-        "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)",
+            "balanced": "Balanced",
             "prompt": "Prompt",
             "control": "Control",
             "megaControl": "Mega Control"
         },
         "ipAdapterMethod": {
-            "ipAdapterMethod": "Mode",
-            "full": "Style and Composition",
-            "fullDesc": "Applies visual style (colors, textures) & composition (layout, structure).",
+            "ipAdapterMethod": "IP Adapter Method",
+            "full": "Full",
             "style": "Style Only",
-            "styleDesc": "Applies visual style (colors, textures) without considering its layout.",
-            "composition": "Composition Only",
-            "compositionDesc": "Replicates layout & structure while ignoring the reference's style."
+            "composition": "Composition Only"
         },
         "fill": {
             "fillColor": "Fill Color",
@@ -1847,9 +1754,6 @@
             "process": "Process",
             "apply": "Apply",
             "cancel": "Cancel",
-            "advanced": "Advanced",
-            "processingLayerWith": "Processing layer with the {{type}} filter.",
-            "forMoreControl": "For more control, click Advanced below.",
             "spandrel_filter": {
                 "label": "Image-to-Image Model",
                 "description": "Run an image-to-image model on the selected layer.",
@@ -1938,25 +1842,6 @@
             "apply": "Apply",
             "cancel": "Cancel"
         },
-        "selectObject": {
-            "selectObject": "Select Object",
-            "pointType": "Point Type",
-            "invertSelection": "Invert Selection",
-            "include": "Include",
-            "exclude": "Exclude",
-            "neutral": "Neutral",
-            "apply": "Apply",
-            "reset": "Reset",
-            "saveAs": "Save As",
-            "cancel": "Cancel",
-            "process": "Process",
-            "help1": "Select a single target object. Add <Bold>Include</Bold> and <Bold>Exclude</Bold> points to indicate which parts of the layer are part of the target object.",
-            "help2": "Start with one <Bold>Include</Bold> point within the target object. Add more points to refine the selection. Fewer points typically produce better results.",
-            "help3": "Invert the selection to select everything except the target object.",
-            "clickToAdd": "Click on the layer to add a point",
-            "dragToMove": "Drag a point to move it",
-            "clickToRemove": "Click on a point to remove it"
-        },
         "settings": {
             "snapToGrid": {
                 "label": "Snap to Grid",
@@ -1967,10 +1852,10 @@
                 "label": "Preserve Masked Region",
                 "alert": "Preserving Masked Region"
             },
-            "isolatedStagingPreview": "Isolated Staging Preview",
             "isolatedPreview": "Isolated Preview",
-            "isolatedLayerPreview": "Isolated Layer Preview",
-            "isolatedLayerPreviewDesc": "Whether to show only this layer when performing operations like filtering or transforming.",
+            "isolatedStagingPreview": "Isolated Staging Preview",
+            "isolatedFilteringPreview": "Isolated Filtering Preview",
+            "isolatedTransformingPreview": "Isolated Transforming Preview",
             "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size",
             "pressureSensitivity": "Pressure Sensitivity"
         },
@@ -1996,8 +1881,6 @@
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
             "newRasterLayer": "New Raster Layer",
-            "newInpaintMask": "New Inpaint Mask",
-            "newRegionalGuidance": "New Regional Guidance",
             "cropCanvasToBbox": "Crop Canvas to Bbox"
         },
         "stagingArea": {
@@ -2027,9 +1910,7 @@
         "upscaleModelDesc": "Upscale (image to image) model",
         "missingUpscaleInitialImage": "Missing initial image for upscaling",
         "missingUpscaleModel": "Missing upscale model",
-        "missingTileControlNetModel": "No valid tile ControlNet models installed",
-        "incompatibleBaseModel": "Unsupported main model architecture for upscaling",
-        "incompatibleBaseModelDesc": "Upscaling is supported for SD1.5 and SDXL architecture models only. Change the main model to enable upscaling."
+        "missingTileControlNetModel": "No valid tile ControlNet models installed"
     },
     "stylePresets": {
         "active": "Active",
@@ -2132,74 +2013,13 @@
     },
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
-        "items": [
-            "<StrongComponent>FLUX Regional Guidance (beta)</StrongComponent>: Our beta release of FLUX Regional Guidance is live for regional prompt control.",
-            "<StrongComponent>Various UX Improvements</StrongComponent>: A number of small UX and Quality of Life improvements throughout the app."
-        ],
-        "readReleaseNotes": "Read Release Notes",
-        "watchRecentReleaseVideos": "Watch Recent Release Videos",
-        "watchUiUpdatesOverview": "Watch UI Updates Overview"
-    },
-    "supportVideos": {
-        "supportVideos": "Support Videos",
-        "gettingStarted": "Getting Started",
-        "controlCanvas": "Control Canvas",
-        "watch": "Watch",
-        "studioSessionsDesc1": "Check out the <StudioSessionsPlaylistLink /> for Invoke deep dives.",
-        "studioSessionsDesc2": "Join our <DiscordLink /> to participate in the live sessions and ask questions. Sessions are uploaded to the playlist the following week.",
-        "videos": {
-            "creatingYourFirstImage": {
-                "title": "Creating Your First Image",
-                "description": "Introduction to creating an image from scratch using Invoke's tools."
-            },
-            "usingControlLayersAndReferenceGuides": {
-                "title": "Using Control Layers and Reference Guides",
-                "description": "Learn how to guide your image creation with control layers and reference images."
-            },
-            "understandingImageToImageAndDenoising": {
-                "title": "Understanding Image-to-Image and Denoising",
-                "description": "Overview of image-to-image transformations and denoising in Invoke."
-            },
-            "exploringAIModelsAndConceptAdapters": {
-                "title": "Exploring AI Models and Concept Adapters",
-                "description": "Dive into AI models and how to use concept adapters for creative control."
-            },
-            "creatingAndComposingOnInvokesControlCanvas": {
-                "title": "Creating and Composing on Invoke's Control Canvas",
-                "description": "Learn to compose images using Invoke's control canvas."
-            },
-            "upscaling": {
-                "title": "Upscaling",
-                "description": "How to upscale images with Invoke's tools to enhance resolution."
-            },
-            "howDoIGenerateAndSaveToTheGallery": {
-                "title": "How Do I Generate and Save to the Gallery?",
-                "description": "Steps to generate and save images to the gallery."
-            },
-            "howDoIEditOnTheCanvas": {
-                "title": "How Do I Edit on the Canvas?",
-                "description": "Guide to editing images directly on the canvas."
-            },
-            "howDoIDoImageToImageTransformation": {
-                "title": "How Do I Do Image-to-Image Transformation?",
-                "description": "Tutorial on performing image-to-image transformations in Invoke."
-            },
-            "howDoIUseControlNetsAndControlLayers": {
-                "title": "How Do I Use Control Nets and Control Layers?",
-                "description": "Learn to apply control layers and controlnets to your images."
-            },
-            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
-                "title": "How Do I Use Global IP Adapters and Reference Images?",
-                "description": "Introduction to adding reference images and global IP adapters."
-            },
-            "howDoIUseInpaintMasks": {
-                "title": "How Do I Use Inpaint Masks?",
-                "description": "How to apply inpaint masks for image correction and variation."
-            },
-            "howDoIOutpaint": {
-                "title": "How Do I Outpaint?",
-                "description": "Guide to outpainting beyond the original image borders."
-            }
+        "canvasV2Announcement": {
+            "newCanvas": "A powerful new control canvas",
+            "newLayerTypes": "New layer types for even more control",
+            "fluxSupport": "Support for the Flux family of models",
+            "readReleaseNotes": "Read Release Notes",
+            "watchReleaseVideo": "Watch Release Video",
+            "watchUiUpdatesOverview": "Watch UI Updates Overview"
         }
     }
 }

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

@@ -13,7 +13,7 @@
         "discordLabel": "Discord",
         "back": "Atrás",
         "loading": "Cargando",
-        "postprocessing": "Postprocesamiento",
+        "postprocessing": "Postprocesado",
         "txt2img": "De texto a imagen",
         "accept": "Aceptar",
         "cancel": "Cancelar",
@@ -64,7 +64,7 @@
         "prevPage": "Página Anterior",
         "red": "Rojo",
         "alpha": "Transparencia",
-        "outputs": "Resultados",
+        "outputs": "Salidas",
         "learnMore": "Aprende más",
         "enabled": "Activado",
         "disabled": "Desactivado",
@@ -73,32 +73,7 @@
         "created": "Creado",
         "save": "Guardar",
         "unknownError": "Error Desconocido",
-        "blue": "Azul",
-        "clipboard": "Portapapeles",
-        "loadingImage": "Cargando la imagen",
-        "inpaint": "inpaint",
-        "ipAdapter": "Adaptador IP",
-        "t2iAdapter": "Adaptador T2I",
-        "apply": "Aplicar",
-        "openInViewer": "Abrir en el visor",
-        "off": "Apagar",
-        "generating": "Generando",
-        "ok": "De acuerdo",
-        "placeholderSelectAModel": "Seleccionar un modelo",
-        "reset": "Restablecer",
-        "none": "Ninguno",
-        "new": "Nuevo",
-        "dontShowMeThese": "No mostrar estos",
-        "loadingModel": "Cargando el modelo",
-        "view": "Ver",
-        "edit": "Editar",
-        "safetensors": "Safetensors",
-        "toResolve": "Para resolver",
-        "localSystem": "Sistema local",
-        "notInstalled": "No $t(common.installed)",
-        "outpaint": "outpaint",
-        "simple": "Sencillo",
-        "close": "Cerrar"
+        "blue": "Azul"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -110,63 +85,7 @@
         "deleteImage_other": "Eliminar {{count}} Imágenes",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",
-        "autoAssignBoardOnClick": "Asignar automática tableros al hacer clic",
-        "gallery": "Galería",
-        "noImageSelected": "Sin imágenes seleccionadas",
-        "bulkDownloadRequestFailed": "Error al preparar la descarga",
-        "oldestFirst": "La más antigua primero",
-        "sideBySide": "conjuntamente",
-        "selectForCompare": "Seleccionar para comparar",
-        "alwaysShowImageSizeBadge": "Mostrar siempre las dimensiones de la imagen",
-        "currentlyInUse": "Esta imagen se utiliza actualmente con las siguientes funciones:",
-        "unableToLoad": "No se puede cargar la galería",
-        "selectAllOnPage": "Seleccionar todo en la página",
-        "selectAnImageToCompare": "Seleccione una imagen para comparar",
-        "bulkDownloadFailed": "Error en la descarga",
-        "compareHelp2": "Presione <Kbd> M </Kbd> para recorrer los modos de comparación.",
-        "move": "Mover",
-        "copy": "Copiar",
-        "drop": "Gota",
-        "displayBoardSearch": "Tablero de búsqueda",
-        "deleteSelection": "Borrar selección",
-        "downloadSelection": "Descargar selección",
-        "openInViewer": "Abrir en el visor",
-        "searchImages": "Búsqueda por metadatos",
-        "swapImages": "Intercambiar imágenes",
-        "sortDirection": "Orden de clasificación",
-        "showStarredImagesFirst": "Mostrar imágenes destacadas primero",
-        "go": "Ir",
-        "bulkDownloadRequested": "Preparando la descarga",
-        "image": "imagen",
-        "compareHelp4": "Presione <Kbd> Z </Kbd> o <Kbd> Esc </Kbd> para salir.",
-        "viewerImage": "Ver imagen",
-        "dropOrUpload": "$t(gallery.drop) o cargar",
-        "displaySearch": "Buscar imagen",
-        "download": "Descargar",
-        "exitBoardSearch": "Finalizar búsqueda",
-        "exitSearch": "Salir de la búsqueda de imágenes",
-        "featuresWillReset": "Si elimina esta imagen, dichas funciones se restablecerán inmediatamente.",
-        "jump": "Omitir",
-        "loading": "Cargando",
-        "newestFirst": "La más nueva primero",
-        "unstarImage": "Dejar de ser favorita",
-        "bulkDownloadRequestedDesc": "Su solicitud de descarga se está preparando. Esto puede tardar unos minutos.",
-        "hover": "Desplazar",
-        "compareHelp1": "Mantenga presionada la tecla <Kbd> Alt </Kbd> mientras hace clic en una imagen de la galería o utiliza las teclas de flecha para cambiar la imagen de comparación.",
-        "stretchToFit": "Estirar para encajar",
-        "exitCompare": "Salir de la comparación",
-        "starImage": "Imágenes favoritas",
-        "dropToUpload": "$t(gallery.drop) para cargar",
-        "slider": "Deslizador",
-        "assetsTab": "Archivos que has cargado para utilizarlos en tus proyectos.",
-        "imagesTab": "Imágenes que ha creado y guardado en Invoke.",
-        "compareImage": "Comparar imagen",
-        "boardsSettings": "Ajustes de los tableros",
-        "imagesSettings": "Configuración de imágenes de la galería",
-        "compareHelp3": "Presione <Kbd> C </Kbd> para intercambiar las imágenes comparadas.",
-        "showArchivedBoards": "Mostrar paneles archivados",
-        "closeViewer": "Cerrar visor",
-        "openViewer": "Abrir visor"
+        "autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
     },
     "modelManager": {
         "modelManager": "Gestor de Modelos",
@@ -212,13 +131,7 @@
         "modelDeleted": "Modelo eliminado",
         "modelDeleteFailed": "Error al borrar el modelo",
         "settings": "Ajustes",
-        "syncModels": "Sincronizar las plantillas",
-        "clipEmbed": "Incrustar CLIP",
-        "addModels": "Añadir modelos",
-        "advanced": "Avanzado",
-        "clipGEmbed": "Incrustar CLIP-G",
-        "cancel": "Cancelar",
-        "clipLEmbed": "Incrustar CLIP-L"
+        "syncModels": "Sincronizar las plantillas"
     },
     "parameters": {
         "images": "Imágenes",
@@ -245,19 +158,19 @@
         "useSeed": "Usar Semilla",
         "useAll": "Usar Todo",
         "info": "Información",
-        "showOptionsPanel": "Mostrar panel lateral (O o T)",
+        "showOptionsPanel": "Mostrar panel de opciones",
         "symmetry": "Simetría",
         "copyImage": "Copiar la imagen",
         "general": "General",
         "denoisingStrength": "Intensidad de la eliminación del ruido",
-        "seamlessXAxis": "Eje X sin juntas",
-        "seamlessYAxis": "Eje Y sin juntas",
+        "seamlessXAxis": "Eje x",
+        "seamlessYAxis": "Eje y",
         "scheduler": "Programador",
         "positivePromptPlaceholder": "Prompt Positivo",
         "negativePromptPlaceholder": "Prompt Negativo",
         "controlNetControlMode": "Modo de control",
         "clipSkip": "Omitir el CLIP",
-        "maskBlur": "Desenfoque de máscara",
+        "maskBlur": "Difuminar",
         "patchmatchDownScaleSize": "Reducir a escala",
         "coherenceMode": "Modo"
     },
@@ -289,19 +202,16 @@
         "serverError": "Error en el servidor",
         "canceled": "Procesando la cancelación",
         "connected": "Conectado al servidor",
-        "uploadFailedInvalidUploadDesc": "Deben ser imágenes PNG o JPEG.",
-        "parameterSet": "Parámetro recuperado",
-        "parameterNotSet": "Parámetro no recuperado",
+        "uploadFailedInvalidUploadDesc": "Debe ser una sola imagen PNG o JPEG",
+        "parameterSet": "Conjunto de parámetros",
+        "parameterNotSet": "Parámetro no configurado",
         "problemCopyingImage": "No se puede copiar la imagen",
         "errorCopied": "Error al copiar",
         "baseModelChanged": "Modelo base cambiado",
-        "addedToBoard": "Se agregó a los activos del panel {{name}}",
+        "addedToBoard": "Añadido al tablero",
         "baseModelChangedCleared_one": "Borrado o desactivado {{count}} submodelo incompatible",
         "baseModelChangedCleared_many": "Borrados o desactivados {{count}} submodelos incompatibles",
-        "baseModelChangedCleared_other": "Borrados o desactivados {{count}} submodelos incompatibles",
-        "addedToUncategorized": "Añadido a los activos del tablero $t(boards.uncategorized)",
-        "imagesWillBeAddedTo": "Las imágenes subidas se añadirán a los activos del panel {{boardName}}.",
-        "layerCopiedToClipboard": "Capa copiada en el portapapeles"
+        "baseModelChangedCleared_other": "Borrados o desactivados {{count}} submodelos incompatibles"
     },
     "accessibility": {
         "invokeProgressBar": "Activar la barra de progreso",
@@ -316,8 +226,7 @@
         "mode": "Modo",
         "submitSupportTicket": "Enviar Ticket de Soporte",
         "toggleRightPanel": "Activar o desactivar el panel derecho (G)",
-        "toggleLeftPanel": "Activar o desactivar el panel izquierdo (T)",
-        "uploadImages": "Cargar imagen(es)"
+        "toggleLeftPanel": "Activar o desactivar el panel izquierdo (T)"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -329,8 +238,7 @@
         "showMinimapnodes": "Mostrar el minimapa",
         "reloadNodeTemplates": "Recargar las plantillas de nodos",
         "loadWorkflow": "Cargar el flujo de trabajo",
-        "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON",
-        "boardAccessError": "No se puede encontrar el panel {{board_id}}, se está restableciendo al valor predeterminado"
+        "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON"
     },
     "boards": {
         "autoAddBoard": "Agregar panel automáticamente",
@@ -347,7 +255,7 @@
         "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
         "deleteBoardAndImages": "Borrar el panel y las imágenes",
         "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar solo el panel' transferirá las imágenes a un estado sin categorizar.",
+        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar Solo el Panel' transferirá las imágenes a un estado sin categorizar.",
         "move": "Mover",
         "menuItemAutoAdd": "Agregar automáticamente a este panel",
         "searchBoard": "Buscando paneles…",
@@ -355,33 +263,29 @@
         "downloadBoard": "Descargar panel",
         "deleteBoardOnly": "Borrar solo el panel",
         "myBoard": "Mi panel",
-        "noMatching": "Sin paneles coincidentes",
+        "noMatching": "No hay paneles que coincidan",
         "imagesWithCount_one": "{{count}} imagen",
         "imagesWithCount_many": "{{count}} imágenes",
         "imagesWithCount_other": "{{count}} imágenes",
         "assetsWithCount_one": "{{count}} activo",
         "assetsWithCount_many": "{{count}} activos",
         "assetsWithCount_other": "{{count}} activos",
-        "hideBoards": "Ocultar paneles",
-        "addPrivateBoard": "Agregar un panel privado",
-        "addSharedBoard": "Añadir panel compartido",
+        "hideBoards": "Ocultar Paneles",
+        "addPrivateBoard": "Agregar un tablero privado",
+        "addSharedBoard": "Agregar Panel Compartido",
         "boards": "Paneles",
-        "archiveBoard": "Archivar panel",
+        "archiveBoard": "Archivar Panel",
         "archived": "Archivado",
         "selectedForAutoAdd": "Seleccionado para agregar automáticamente",
-        "unarchiveBoard": "Desarchivar el panel",
-        "noBoards": "No hay paneles {{boardType}}",
-        "shared": "Paneles compartidos",
-        "deletedPrivateBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al elegir \"Eliminar solo el panel\", las imágenes se colocan en un estado privado y sin categoría para el creador de la imagen.",
-        "viewBoards": "Ver paneles",
-        "private": "Paneles privados",
-        "updateBoardError": "No se pudo actualizar el panel"
+        "unarchiveBoard": "Desarchivar el tablero",
+        "noBoards": "No hay tableros {{boardType}}",
+        "shared": "Carpetas compartidas",
+        "deletedPrivateBoardsCannotbeRestored": "Los tableros eliminados no se pueden restaurar. Al elegir \"Eliminar solo tablero\", las imágenes se colocan en un estado privado y sin categoría para el creador de la imagen."
     },
     "accordions": {
         "compositing": {
             "title": "Composición",
-            "infillTab": "Relleno",
-            "coherenceTab": "Parámetros de la coherencia"
+            "infillTab": "Relleno"
         },
         "generation": {
             "title": "Generación"
@@ -405,10 +309,7 @@
             "workflows": "Flujos de trabajo",
             "models": "Modelos",
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
-            "upscaling": "Upscaling",
-            "gallery": "Galería",
-            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
+            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)"
         }
     },
     "queue": {
@@ -416,81 +317,12 @@
         "front": "Delante",
         "batchQueuedDesc_one": "Se agregó {{count}} sesión a {{direction}} la cola",
         "batchQueuedDesc_many": "Se agregaron {{count}} sesiones a {{direction}} la cola",
-        "batchQueuedDesc_other": "Se agregaron {{count}} sesiones a {{direction}} la cola",
-        "clearQueueAlertDialog": "Al vaciar la cola se cancela inmediatamente cualquier elemento de procesamiento y se vaciará la cola por completo. Los filtros pendientes se cancelarán.",
-        "time": "Tiempo",
-        "clearFailed": "Error al vaciar la cola",
-        "cancelFailed": "Error al cancelar el elemento",
-        "resumeFailed": "Error al reanudar el proceso",
-        "pause": "Pausar",
-        "pauseTooltip": "Pausar el proceso",
-        "cancelBatchSucceeded": "Lote cancelado",
-        "pruneSucceeded": "Se purgaron {{item_count}} elementos completados de la cola",
-        "pruneFailed": "Error al purgar la cola",
-        "cancelBatchFailed": "Error al cancelar los lotes",
-        "pauseFailed": "Error al pausar el proceso",
-        "status": "Estado",
-        "origin": "Origen",
-        "destination": "Destino",
-        "generations_one": "Generación",
-        "generations_many": "Generaciones",
-        "generations_other": "Generaciones",
-        "resume": "Reanudar",
-        "queueEmpty": "Cola vacía",
-        "cancelItem": "Cancelar elemento",
-        "cancelBatch": "Cancelar lote",
-        "openQueue": "Abrir la cola",
-        "completed": "Completado",
-        "enqueueing": "Añadir lotes a la cola",
-        "clear": "Limpiar",
-        "pauseSucceeded": "Proceso pausado",
-        "resumeSucceeded": "Proceso reanudado",
-        "resumeTooltip": "Reanudar proceso",
-        "cancel": "Cancelar",
-        "cancelTooltip": "Cancelar artículo actual",
-        "pruneTooltip": "Purgar {{item_count}} elementos completados",
-        "batchQueued": "Lote en cola",
-        "pending": "Pendiente",
-        "item": "Elemento",
-        "total": "Total",
-        "in_progress": "En proceso",
-        "failed": "Fallido",
-        "completedIn": "Completado en",
-        "upscaling": "Upscaling",
-        "canvas": "Lienzo",
-        "generation": "Generación",
-        "workflows": "Flujo de trabajo",
-        "other": "Otro",
-        "queueFront": "Añadir al principio de la cola",
-        "gallery": "Galería",
-        "batchFieldValues": "Valores de procesamiento por lotes",
-        "session": "Sesión",
-        "notReady": "La cola aún no está lista",
-        "graphQueued": "Gráfico en cola",
-        "clearQueueAlertDialog2": "¿Estás seguro que deseas vaciar la cola?",
-        "next": "Siguiente",
-        "iterations_one": "Interacción",
-        "iterations_many": "Interacciones",
-        "iterations_other": "Interacciones",
-        "current": "Actual",
-        "queue": "Cola",
-        "queueBack": "Añadir a la cola",
-        "cancelSucceeded": "Elemento cancelado",
-        "clearTooltip": "Cancelar y limpiar todos los elementos",
-        "clearSucceeded": "Cola vaciada",
-        "canceled": "Cancelado",
-        "batch": "Lote",
-        "graphFailedToQueue": "Error al poner el gráfico en cola",
-        "batchFailedToQueue": "Error al poner en cola el lote",
-        "prompts_one": "Prompt",
-        "prompts_many": "Prompts",
-        "prompts_other": "Prompts",
-        "prune": "Eliminar"
+        "batchQueuedDesc_other": "Se agregaron {{count}} sesiones a {{direction}} la cola"
     },
     "upsell": {
         "inviteTeammates": "Invitar compañeros de equipo",
         "shareAccess": "Compartir acceso",
-        "professionalUpsell": "Disponible en la edición profesional de Invoke. Haga clic aquí o visite invoke.com/pricing para obtener más detalles."
+        "professionalUpsell": "Disponible en la edición profesional de Invoke. Haz clic aquí o visita invoke.com/pricing para obtener más detalles."
     },
     "controlLayers": {
         "layer_one": "Capa",
@@ -498,415 +330,6 @@
         "layer_other": "Capas",
         "layer_withCount_one": "({{count}}) capa",
         "layer_withCount_many": "({{count}}) capas",
-        "layer_withCount_other": "({{count}}) capas",
-        "copyToClipboard": "Copiar al portapapeles"
-    },
-    "whatsNew": {
-        "readReleaseNotes": "Leer las notas de la versión",
-        "watchRecentReleaseVideos": "Ver videos de versiones recientes",
-        "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."
-        ]
-    },
-    "invocationCache": {
-        "enableFailed": "Error al activar la cache",
-        "cacheSize": "Tamaño de la caché",
-        "hits": "Accesos a la caché",
-        "invocationCache": "Caché",
-        "misses": "Errores de la caché",
-        "clear": "Limpiar",
-        "maxCacheSize": "Tamaño máximo de la caché",
-        "enableSucceeded": "Cache activada",
-        "clearFailed": "Error al borrar la cache",
-        "enable": "Activar",
-        "useCache": "Uso de la caché",
-        "disableSucceeded": "Caché desactivada",
-        "clearSucceeded": "Caché borrada",
-        "disable": "Desactivar",
-        "disableFailed": "Error al desactivar la caché"
-    },
-    "hrf": {
-        "hrf": "Solución de alta resolución",
-        "enableHrf": "Activar corrección de alta resolución",
-        "metadata": {
-            "enabled": "Corrección de alta resolución activada",
-            "strength": "Forzar la corrección de alta resolución",
-            "method": "Método de corrección de alta resolución"
-        },
-        "upscaleMethod": "Método de expansión"
-    },
-    "prompt": {
-        "addPromptTrigger": "Añadir activador de los avisos",
-        "compatibleEmbeddings": "Incrustaciones compatibles",
-        "noMatchingTriggers": "No hay activadores coincidentes"
-    },
-    "hotkeys": {
-        "hotkeys": "Atajo del teclado",
-        "canvas": {
-            "selectViewTool": {
-                "desc": "Selecciona la herramienta de Visualización.",
-                "title": "Visualización"
-            },
-            "cancelFilter": {
-                "title": "Cancelar el filtro",
-                "desc": "Cancelar el filtro pendiente."
-            },
-            "applyTransform": {
-                "title": "Aplicar la transformación",
-                "desc": "Aplicar la transformación pendiente a la capa seleccionada."
-            },
-            "applyFilter": {
-                "desc": "Aplicar el filtro pendiente a la capa seleccionada.",
-                "title": "Aplicar filtro"
-            },
-            "selectBrushTool": {
-                "title": "Pincel",
-                "desc": "Selecciona la herramienta pincel."
-            },
-            "selectBboxTool": {
-                "desc": "Seleccionar la herramienta de selección del marco.",
-                "title": "Selección del marco"
-            },
-            "selectMoveTool": {
-                "desc": "Selecciona la herramienta Mover.",
-                "title": "Mover"
-            },
-            "selectRectTool": {
-                "title": "Rectángulo",
-                "desc": "Selecciona la herramienta Rectángulo."
-            },
-            "decrementToolWidth": {
-                "title": "Reducir el ancho de la herramienta",
-                "desc": "Disminuye la anchura de la herramienta pincel o goma de borrar, según la que esté seleccionada."
-            },
-            "incrementToolWidth": {
-                "title": "Incrementar la anchura de la herramienta",
-                "desc": "Aumenta la anchura de la herramienta pincel o goma de borrar, según la que esté seleccionada."
-            },
-            "fitBboxToCanvas": {
-                "title": "Ajustar bordes al lienzo",
-                "desc": "Escala y posiciona la vista para ajustarla a los bodes."
-            },
-            "fitLayersToCanvas": {
-                "title": "Ajustar capas al lienzo",
-                "desc": "Escala y posiciona la vista para que se ajuste a todas las capas visibles."
-            },
-            "setFillToWhite": {
-                "title": "Establecer color en blanco",
-                "desc": "Establece el color actual de la herramienta en blanco."
-            },
-            "resetSelected": {
-                "title": "Restablecer capa",
-                "desc": "Restablecer la capa seleccionada. Solo se aplica a Máscara de retoque y Guía regional."
-            },
-            "setZoomTo400Percent": {
-                "desc": "Ajuste la aplicación del lienzo al 400%.",
-                "title": "Ampliar al 400%"
-            },
-            "transformSelected": {
-                "desc": "Transformar la capa seleccionada.",
-                "title": "Transformar"
-            },
-            "selectColorPickerTool": {
-                "title": "Selector de color",
-                "desc": "Seleccione la herramienta de selección de color."
-            },
-            "selectEraserTool": {
-                "title": "Borrador",
-                "desc": "Selecciona la herramienta Borrador."
-            },
-            "setZoomTo100Percent": {
-                "title": "Ampliar al 100%",
-                "desc": "Ajuste ampliar el lienzo al 100%."
-            },
-            "undo": {
-                "title": "Deshacer",
-                "desc": "Deshacer la última acción en el lienzo."
-            },
-            "nextEntity": {
-                "desc": "Seleccione la siguiente capa de la lista.",
-                "title": "Capa siguiente"
-            },
-            "redo": {
-                "title": "Rehacer",
-                "desc": "Rehacer la última acción en el lienzo."
-            },
-            "prevEntity": {
-                "title": "Capa anterior",
-                "desc": "Seleccione la capa anterior de la lista."
-            },
-            "title": "Lienzo",
-            "setZoomTo200Percent": {
-                "title": "Ampliar al 200%",
-                "desc": "Ajuste la ampliación del lienzo al 200%."
-            },
-            "setZoomTo800Percent": {
-                "title": "Ampliar al 800%",
-                "desc": "Ajuste la ampliación del lienzo al 800%."
-            },
-            "filterSelected": {
-                "desc": "Filtra la capa seleccionada. Solo se aplica a las capas Ráster y Control.",
-                "title": "Filtrar"
-            },
-            "cancelTransform": {
-                "title": "Cancelar transformación",
-                "desc": "Cancelar la transformación pendiente."
-            },
-            "deleteSelected": {
-                "title": "Borrar la capa",
-                "desc": "Borrar la capa seleccionada."
-            },
-            "quickSwitch": {
-                "desc": "Cambiar entre las dos últimas capas seleccionadas. Si una capa está seleccionada, cambia siempre entre ella y la última capa no seleccionada.",
-                "title": "Cambio rápido de capa"
-            }
-        },
-        "app": {
-            "selectModelsTab": {
-                "title": "Seleccione la pestaña Modelos",
-                "desc": "Selecciona la pestaña Modelos."
-            },
-            "focusPrompt": {
-                "desc": "Mueve el foco del cursor a la indicación positiva.",
-                "title": "Enfoque"
-            },
-            "toggleLeftPanel": {
-                "title": "Alternar panel izquierdo",
-                "desc": "Mostrar u ocultar el panel izquierdo."
-            },
-            "selectQueueTab": {
-                "title": "Seleccione la pestaña Cola",
-                "desc": "Seleccione la pestaña Cola."
-            },
-            "selectCanvasTab": {
-                "title": "Seleccione la pestaña Lienzo",
-                "desc": "Selecciona la pestaña Lienzo."
-            },
-            "clearQueue": {
-                "title": "Vaciar cola",
-                "desc": "Cancelar y variar todos los elementos de la cola."
-            },
-            "selectUpscalingTab": {
-                "title": "Selecciona la pestaña Ampliar",
-                "desc": "Selecciona la pestaña Aumento de escala."
-            },
-            "togglePanels": {
-                "desc": "Muestra u oculta los paneles izquierdo y derecho a la vez.",
-                "title": "Alternar paneles"
-            },
-            "toggleRightPanel": {
-                "title": "Alternar panel derecho",
-                "desc": "Mostrar u ocultar el panel derecho."
-            },
-            "invokeFront": {
-                "desc": "Pone en cola la solicitud de compilación y la agrega al principio de la cola.",
-                "title": "Invocar (frente)"
-            },
-            "cancelQueueItem": {
-                "title": "Cancelar",
-                "desc": "Cancelar el elemento de la cola que se está procesando."
-            },
-            "invoke": {
-                "desc": "Pone en cola la solicitud de compilación y la agrega al final de la cola.",
-                "title": "Invocar"
-            },
-            "title": "Aplicación",
-            "selectWorkflowsTab": {
-                "title": "Seleccione la pestaña Flujos de trabajo",
-                "desc": "Selecciona la pestaña Flujos de trabajo."
-            },
-            "resetPanelLayout": {
-                "title": "Reiniciar la posición del panel",
-                "desc": "Restablece los paneles izquierdo y derecho a su tamaño y disposición por defecto."
-            }
-        },
-        "workflows": {
-            "addNode": {
-                "title": "Añadir nodo",
-                "desc": "Abrir añadir nodo."
-            },
-            "selectAll": {
-                "title": "Seleccionar todo",
-                "desc": "Seleccione todos los nodos y enlaces."
-            },
-            "deleteSelection": {
-                "desc": "Borrar todos los nodos y enlaces seleccionados.",
-                "title": "Borrar"
-            },
-            "undo": {
-                "desc": "Deshaga la última acción.",
-                "title": "Deshacer"
-            },
-            "redo": {
-                "desc": "Rehacer la última acción.",
-                "title": "Rehacer"
-            },
-            "pasteSelection": {
-                "desc": "Pegar nodos y bordes copiados.",
-                "title": "Pegar"
-            },
-            "title": "Flujos de trabajo",
-            "copySelection": {
-                "desc": "Copiar nodos y bordes seleccionados.",
-                "title": "Copiar"
-            },
-            "pasteSelectionWithEdges": {
-                "desc": "Pega los nodos copiados, los enlaces y todos los enlaces conectados a los nodos copiados.",
-                "title": "Pegar con enlaces"
-            }
-        },
-        "viewer": {
-            "useSize": {
-                "title": "Usar dimensiones",
-                "desc": "Utiliza las dimensiones de la imagen actual como el tamaño del borde."
-            },
-            "remix": {
-                "title": "Remezcla",
-                "desc": "Recupera todos los metadatos excepto la semilla de la imagen actual."
-            },
-            "loadWorkflow": {
-                "desc": "Carga el flujo de trabajo guardado de la imagen actual (si tiene uno).",
-                "title": "Cargar flujo de trabajo"
-            },
-            "recallAll": {
-                "desc": "Recupera todos los metadatos de la imagen actual.",
-                "title": "Recuperar todos los metadatos"
-            },
-            "recallPrompts": {
-                "desc": "Recuerde las indicaciones positivas y negativas de la imagen actual.",
-                "title": "Recordatorios"
-            },
-            "recallSeed": {
-                "title": "Recuperar semilla",
-                "desc": "Recupera la semilla de la imagen actual."
-            },
-            "runPostprocessing": {
-                "title": "Ejecutar posprocesamiento",
-                "desc": "Ejecutar el posprocesamiento seleccionado en la imagen actual."
-            },
-            "toggleMetadata": {
-                "title": "Mostrar/ocultar los metadatos",
-                "desc": "Mostrar u ocultar la superposición de metadatos de la imagen actual."
-            },
-            "nextComparisonMode": {
-                "desc": "Desplácese por los modos de comparación.",
-                "title": "Siguiente comparación"
-            },
-            "title": "Visor de imágenes",
-            "toggleViewer": {
-                "title": "Mostrar/Ocultar el visor de imágenes",
-                "desc": "Mostrar u ocultar el visor de imágenes. Solo disponible en la pestaña Lienzo."
-            },
-            "swapImages": {
-                "title": "Intercambiar imágenes en la comparación",
-                "desc": "Intercambia las imágenes que se están comparando."
-            }
-        },
-        "gallery": {
-            "clearSelection": {
-                "title": "Limpiar selección",
-                "desc": "Borrar la selección actual, si hay alguna."
-            },
-            "galleryNavUp": {
-                "title": "Subir",
-                "desc": "Navega hacia arriba en la cuadrícula de la galería y selecciona esa imagen. Si estás en la parte superior de la página, ve a la página anterior."
-            },
-            "galleryNavLeft": {
-                "title": "Izquierda",
-                "desc": "Navegue hacia la izquierda en la rejilla de la galería, seleccionando esa imagen. Si está en la primera imagen de la fila, vaya a la fila anterior. Si está en la primera imagen de la página, vaya a la página anterior."
-            },
-            "galleryNavDown": {
-                "title": "Bajar",
-                "desc": "Navegue hacia abajo en la parrilla de la galería, seleccionando esa imagen. Si se encuentra al final de la página, vaya a la página siguiente."
-            },
-            "galleryNavRight": {
-                "title": "A la derecha",
-                "desc": "Navegue hacia la derecha en la rejilla de la galería, seleccionando esa imagen. Si está en la última imagen de la fila, vaya a la fila siguiente. Si está en la última imagen de la página, vaya a la página siguiente."
-            },
-            "galleryNavUpAlt": {
-                "desc": "Igual que arriba, pero selecciona la imagen de comparación, abriendo el modo de comparación si no está ya abierto.",
-                "title": "Arriba (Comparar imagen)"
-            },
-            "deleteSelection": {
-                "desc": "Borrar todas las imágenes seleccionadas. Por defecto, se le pedirá que confirme la eliminación. Si las imágenes están actualmente en uso en la aplicación, se te avisará.",
-                "title": "Borrar"
-            },
-            "title": "Galería",
-            "selectAllOnPage": {
-                "title": "Seleccionar todo en la página",
-                "desc": "Seleccionar todas las imágenes en la página actual."
-            }
-        },
-        "searchHotkeys": "Buscar teclas de acceso rápido",
-        "noHotkeysFound": "Sin teclas de acceso rápido",
-        "clearSearch": "Limpiar la búsqueda"
-    },
-    "metadata": {
-        "guidance": "Orientación",
-        "createdBy": "Creado por",
-        "noImageDetails": "Sin detalles en la imagen",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
-        "height": "Altura",
-        "imageDimensions": "Dimensiones de la imagen",
-        "seamlessXAxis": "Eje X sin juntas",
-        "seamlessYAxis": "Eje Y sin juntas",
-        "generationMode": "Modo de generación",
-        "scheduler": "Programador",
-        "width": "Ancho",
-        "Threshold": "Umbral de ruido",
-        "canvasV2Metadata": "Lienzo",
-        "metadata": "Metadatos",
-        "model": "Modelo",
-        "allPrompts": "Todas las indicaciones",
-        "cfgScale": "Escala CFG",
-        "imageDetails": "Detalles de la imagen",
-        "negativePrompt": "Indicación negativa",
-        "noMetaData": "Sin metadatos",
-        "parameterSet": "Parámetro {{parameter}} establecido",
-        "vae": "Autocodificador",
-        "workflow": "Flujo de trabajo",
-        "seed": "Semilla",
-        "strength": "Forzar imagen a imagen",
-        "recallParameters": "Parámetros de recuperación",
-        "recallParameter": "Recuperar {{label}}",
-        "steps": "Pasos",
-        "noRecallParameters": "Sin parámetros para recuperar",
-        "parsingFailed": "Error al analizar"
-    },
-    "system": {
-        "logLevel": {
-            "debug": "Depurar",
-            "info": "Información",
-            "warn": "Advertir",
-            "fatal": "Grave",
-            "error": "Error",
-            "trace": "Rastro",
-            "logLevel": "Nivel del registro"
-        },
-        "enableLogging": "Activar registro",
-        "logNamespaces": {
-            "workflows": "Flujos de trabajo",
-            "system": "Sistema",
-            "metadata": "Metadatos",
-            "gallery": "Galería",
-            "logNamespaces": "Espacios para los nombres de registro",
-            "generation": "Generación",
-            "events": "Eventos",
-            "canvas": "Lienzo",
-            "config": "Ajustes",
-            "models": "Modelos",
-            "queue": "Cola"
-        }
-    },
-    "newUserExperience": {
-        "downloadStarterModels": "Descargar modelos de inicio",
-        "toGetStarted": "Para empezar, introduzca un mensaje en el cuadro y haga clic en <StrongComponent>Invocar</StrongComponent> para generar su primera imagen. Seleccione una plantilla para mejorar los resultados. Puede elegir guardar sus imágenes directamente en <StrongComponent>Galería</StrongComponent> o editarlas en <StrongComponent>Lienzo</StrongComponent>.",
-        "importModels": "Importar modelos",
-        "noModelsInstalled": "Parece que no tienes ningún modelo instalado",
-        "gettingStartedSeries": "¿Desea más orientación? Consulte nuestra <LinkComponent>Serie de introducción</LinkComponent> para obtener consejos sobre cómo aprovechar todo el potencial de Invoke Studio.",
-        "toGetStartedLocal": "Para empezar, asegúrate de descargar o importar los modelos necesarios para ejecutar Invoke. A continuación, introduzca un mensaje en el cuadro y haga clic en <StrongComponent>Invocar</StrongComponent> para generar su primera imagen. Seleccione una plantilla para mejorar los resultados. Puede elegir guardar sus imágenes directamente en <StrongComponent>Galería</StrongComponent> o editarlas en el <StrongComponent>Lienzo</StrongComponent>."
+        "layer_withCount_other": "({{count}}) capas"
     }
 }