chore: bump version to v5.4.0

## Summary

Nodes to support SD3.5 txt2img generations
* adds SD3.5 to starter models
* adds default workflow for SD3.5 txt2img

## Related Issues / Discussions

<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->

## QA Instructions

<!--WHEN APPLICABLE: Describe how you have tested the changes in this
PR. Provide enough detail that a reviewer can reproduce your tests.-->

## Merge Plan

<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->

## Checklist

- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_

README.md

@@ -105,7 +105,7 @@ Invoke features an organized gallery system for easily storing, accessing, and r
 ### Other features
 
 - Support for both ckpt and diffusers models
-- SD1.5, SD2.0, and SDXL support
+- SD1.5, SD2.0, SDXL, and FLUX support
 - Upscaling Tools
 - Embedding Manager & Support
 - Model Manager & Support

docker/Dockerfile

@@ -38,9 +38,9 @@ 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/rocm5.6"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \
     else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \
     fi &&\
 
     # xformers + triton fails to install on arm64

docker/docker-compose.yml

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
 
 x-invokeai: &invokeai
-    image: "local/invokeai:latest"
+    image: "ghcr.io/invoke-ai/invokeai:latest"
     build:
       context: ..
       dockerfile: docker/Dockerfile

docs/contributing/INVOCATIONS.md

@@ -144,7 +144,7 @@ As you might have noticed, we added two new arguments to the `InputField`
 definition for `width` and `height`, called `gt` and `le`. They stand for
 _greater than or equal to_ and _less than or equal to_.
 
-These impose contraints on those fields, and will raise an exception if the
+These impose constraints on those fields, and will raise an exception if the
 values do not meet the constraints. Field constraints are provided by
 **pydantic**, so anything you see in the **pydantic docs** will work.
 

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -239,7 +239,7 @@ Consult the
 get it set up.
 
 Suggest using VSCode's included settings sync so that your remote dev host has
-all the same app settings and extensions automagically.
+all the same app settings and extensions automatically.
 
 ##### One remote dev gotcha
 

docs/contributing/contribution_guides/development.md

@@ -2,7 +2,7 @@
 
 ## **What do I need to know to help?**
 
-If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
+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**

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](InvokeAI/contributing/LOCAL_DEVELOPMENT/#developing-invokeai-in-vscode). Feel free to skip this step if you already have tooling you're comfortable with.
+- [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] 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!

docs/contributing/contribution_guides/tutorials.md

@@ -1,6 +1,6 @@
 # Tutorials
 
-Tutorials help new & existing users expand their abilty to use InvokeAI to the full extent of our features and services.  
+Tutorials help new & existing users expand their ability to use InvokeAI to the full extent of our features and services.  
 
 Currently, we have a set of tutorials available on our [YouTube channel](https://www.youtube.com/@invokeai), but as InvokeAI continues to evolve with new updates, we want to ensure that we are giving our users the resources they need to succeed. 
 
@@ -8,4 +8,4 @@ Tutorials can be in the form of videos or article walkthroughs on a subject of y
 
 ## Contributing
 
-Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
+Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.

docs/contributing/dev-environment.md

@@ -17,46 +17,49 @@ If you just want to use Invoke, you should use the [installer][installer link].
 ## Setup
 
 1. Run through the [requirements][requirements link].
-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:
+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:
 
-   ```sh
-   python3 -m venv .venv --prompt InvokeAI-Dev
-   ```
+      ```sh
+      python3 -m venv .venv --prompt InvokeAI-Dev
+      ```
 
-1. Activate the venv (you'll need to do this every time you want to run the app):
+5. Activate the venv (you'll need to do this every time you want to run the app):
 
-   ```sh
-   source .venv/bin/activate
-   ```
+        ```sh
+        source .venv/bin/activate
+        ```
 
-1. Install the repo as an [editable install][editable install link]:
+6. Install the repo as an [editable install][editable install link]:
 
-   ```sh
-   pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
-   ```
+      ```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.
 
-1. Install the frontend dev toolchain:
+7. Install the frontend dev toolchain:
 
    - [`nodejs`](https://nodejs.org/) (recommend v20 LTS)
-   - [`pnpm`](https://pnpm.io/installation#installing-a-specific-version) (must be v8 - not v9!)
+   - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
 
-1. Do a production build of the frontend:
+8. Do a production build of the frontend:
 
-   ```sh
-   pnpm build
-   ```
+      ```sh
+      cd PATH_TO_INVOKEAI_REPO/invokeai/frontend/web
+      pnpm i
+      pnpm build
+      ```
 
-1. Start the application:
+9. Start the application:
 
-   ```sh
-   python scripts/invokeai-web.py
-   ```
+      ```sh
+      cd PATH_TO_INVOKEAI_REPO
+      python scripts/invokeai-web.py
+      ```
 
-1. Access the UI at `localhost:9090`.
+10. Access the UI at `localhost:9090`.
 
 ## Updating the UI
 

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 conver the
+button in the upper right of your browser window. This will convert the
 checkpoint into a diffusers model, after which loading should be
 faster and less memory-intensive.
 

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` and `blas`:
+2. Install `opencv`, `blas`, and required dependencies:
 
    ```sh
-   sudo pacman -S opencv blas
+   sudo pacman -S opencv blas fmt glew vtk hdf5
    ```
 
    or for CUDA support
 
    ```sh
-   sudo pacman -S opencv-cuda blas
+   sudo pacman -S opencv-cuda blas fmt glew vtk hdf5
    ```
 
 3. Fix the naming of the `opencv` package configuration file:

docs/nodes/communityNodes.md

@@ -21,6 +21,7 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [Clothing Mask](#clothing-mask)
     + [Contrast Limited Adaptive Histogram Equalization](#contrast-limited-adaptive-histogram-equalization)
     + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
+    + [Enhance Detail](#enhance-detail)
     + [Film Grain](#film-grain)
     + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
@@ -40,6 +41,7 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [Metadata-Linked](#metadata-linked-nodes)
     + [Negative Image](#negative-image)
     + [Nightmare Promptgen](#nightmare-promptgen)
+    + [Ollama](#ollama-node)
     + [One Button Prompt](#one-button-prompt)
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
@@ -80,7 +82,7 @@ Note: These are inherited from the core nodes so any update to the core nodes sh
 
 **Example Usage:**
 </br>
-<img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/blob/main/images/spider-depth.png" width="200" /> -> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-dots.png" width="200" /> <img src="https://github.com/skunkworxdark/autostereogram_nodes/raw/main/images/spider-pattern.png" width="200" />
+<img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider.png" width="200" /> -> <img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider-depth.png" width="200" /> -> <img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider-dots.png" width="200" /> <img src="https://raw.githubusercontent.com/skunkworxdark/autostereogram_nodes/refs/heads/main/images/spider-pattern.png" width="200" />
 
 --------------------------------
 ### Average Images
@@ -141,6 +143,17 @@ To be imported, an .obj must use triangulated meshes, so make sure to enable tha
 **Example Usage:**
 </br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
 
+--------------------------------
+### Enhance Detail
+
+**Description:** A single node that can enhance the detail in an image. Increase or decrease details in an image using a guided filter (as opposed to the typical Gaussian blur used by most sharpening filters.) Based on the `Enhance Detail` ComfyUI node from  https://github.com/spacepxl/ComfyUI-Image-Filters
+
+**Node Link:** https://github.com/skunkworxdark/enhance-detail-node
+
+**Example Usage:**
+</br>
+<img src="https://raw.githubusercontent.com/skunkworxdark/enhance-detail-node/refs/heads/main/images/Comparison.png" />
+
 --------------------------------
 ### Film Grain
 
@@ -307,7 +320,7 @@ View:
 **Node Link:** https://github.com/helix4u/load_video_frame
 
 **Output Example:** 
-<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
+<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/refs/heads/main/_git_assets/dance1736978273.gif" width="500" />
 
 --------------------------------
 ### Make 3D
@@ -348,7 +361,7 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **Output Examples** 
 
-<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
+<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" />
 
 --------------------------------
 ### Metadata Linked Nodes
@@ -390,10 +403,23 @@ View:
 
 **Node Link:** [https://github.com/gogurtenjoyer/nightmare-promptgen](https://github.com/gogurtenjoyer/nightmare-promptgen)
 
+--------------------------------
+### Ollama Node
+
+**Description:** Uses Ollama API to expand text prompts for text-to-image generation using local LLMs. Works great for expanding basic prompts into detailed natural language prompts for Flux. Also provides a toggle to unload the LLM model immediately after expanding, to free up VRAM for Invoke to continue the image generation workflow.
+
+**Node Link:** https://github.com/Jonseed/Ollama-Node
+
+**Example Node Graph:**  https://github.com/Jonseed/Ollama-Node/blob/main/Ollama-Node-Flux-example.json
+
+**View:** 
+
+![ollama node](https://raw.githubusercontent.com/Jonseed/Ollama-Node/a3e7cdc55e394cb89c1ea7ed54e106c212c85e8c/ollama-node-screenshot.png)
+
 --------------------------------
 ### One Button Prompt
 
-<img src="https://github.com/AIrjen/OneButtonPrompt_X_InvokeAI/blob/main/images/background.png" width="800" />
+<img src="https://raw.githubusercontent.com/AIrjen/OneButtonPrompt_X_InvokeAI/refs/heads/main/images/background.png" width="800" />
 
 **Description:** an extensive suite of auto prompt generation and prompt helper nodes based on extensive logic. Get creative with the best prompt generator in the world. 
 
@@ -403,7 +429,7 @@ The main node generates interesting prompts based on a set of parameters. There
 
 **Nodes:**
 
-<img src="https://github.com/AIrjen/OneButtonPrompt_X_InvokeAI/blob/main/images/OBP_nodes_invokeai.png" width="800" />
+<img src="https://raw.githubusercontent.com/AIrjen/OneButtonPrompt_X_InvokeAI/refs/heads/main/images/OBP_nodes_invokeai.png" width="800" />
 
 --------------------------------
 ### Oobabooga
@@ -456,7 +482,7 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
 
 **Workflow Examples** 
 
-<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
+<img src="https://raw.githubusercontent.com/skunkworxdark/prompt-tools/refs/heads/main/images/CSVToIndexStringNode.png"/>
 
 --------------------------------
 ### Remote Image
@@ -594,7 +620,7 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
 
 **Output Examples** 
 
-<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
+<img src="https://raw.githubusercontent.com/skunkworxdark/XYGrid_nodes/refs/heads/main/images/collage.png" />
 
 
 --------------------------------

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1690630721,
-        "narHash": "sha256-Y04onHyBQT4Erfr2fc82dbJTfXGYrf4V0ysLUYnPOP8=",
+        "lastModified": 1727955264,
+        "narHash": "sha256-lrd+7mmb5NauRoMa8+J1jFKYVa+rc8aq2qc9+CxPDKc=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "d2b52322f35597c62abf56de91b0236746b2a03d",
+        "rev": "71cd616696bd199ef18de62524f3df3ffe8b9333",
         "type": "github"
       },
       "original": {

flake.nix

@@ -34,7 +34,7 @@
             cudaPackages.cudnn
             cudaPackages.cuda_nvrtc
             cudatoolkit
-            pkgconfig
+            pkg-config
             libconfig
             cmake
             blas
@@ -66,7 +66,7 @@
             black
 
             # Frontend.
-            yarn
+            pnpm_8
             nodejs
           ];
           LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;

installer/install.sh.in

@@ -12,7 +12,7 @@ MINIMUM_PYTHON_VERSION=3.10.0
 MAXIMUM_PYTHON_VERSION=3.11.100
 PYTHON=""
 for candidate in python3.11 python3.10 python3 python ; do
-    if ppath=`which $candidate`; then
+    if ppath=`which $candidate 2>/dev/null`; then
         # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
         # we check that this found executable can actually run
         if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
@@ -30,10 +30,11 @@ done
 if [ -z "$PYTHON" ]; then
     echo "A suitable Python interpreter could not be found"
     echo "Please install Python $MINIMUM_PYTHON_VERSION or higher (maximum $MAXIMUM_PYTHON_VERSION) before running this script. See instructions at $INSTRUCTIONS for help."
-    echo "For the best user experience we suggest enlarging or maximizing this window now."
     read -p "Press any key to exit"
     exit -1
 fi
 
+echo "For the best user experience we suggest enlarging or maximizing this window now."
+
 exec $PYTHON ./lib/main.py ${@}
 read -p "Press any key to exit"

installer/lib/installer.py

@@ -245,6 +245,9 @@ 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",
@@ -282,12 +285,6 @@ class InvokeAiInstance:
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
 
-    def update(self):
-        pass
-
-    def remove(self):
-        pass
-
 
 ### Utility functions ###
 
@@ -402,7 +399,7 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     :rtype: list
     """
 
-    from messages import select_gpu
+    from messages import GpuType, select_gpu
 
     # device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
     device = select_gpu()
@@ -412,16 +409,22 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     url = None
     optional_modules: str | None = None
     if OS == "Linux":
-        if device.value == "rocm":
-            url = "https://download.pytorch.org/whl/rocm5.6"
-        elif device.value == "cpu":
+        if device == GpuType.ROCM:
+            url = "https://download.pytorch.org/whl/rocm6.1"
+        elif device == GpuType.CPU:
             url = "https://download.pytorch.org/whl/cpu"
-        elif device.value == "cuda":
-            # CUDA uses the default PyPi index
+        elif device == GpuType.CUDA:
+            url = "https://download.pytorch.org/whl/cu124"
+            optional_modules = "[onnx-cuda]"
+        elif device == GpuType.CUDA_WITH_XFORMERS:
+            url = "https://download.pytorch.org/whl/cu124"
             optional_modules = "[xformers,onnx-cuda]"
     elif OS == "Windows":
-        if device.value == "cuda":
-            url = "https://download.pytorch.org/whl/cu121"
+        if device == GpuType.CUDA:
+            url = "https://download.pytorch.org/whl/cu124"
+            optional_modules = "[onnx-cuda]"
+        elif device == GpuType.CUDA_WITH_XFORMERS:
+            url = "https://download.pytorch.org/whl/cu124"
             optional_modules = "[xformers,onnx-cuda]"
         elif device.value == "cpu":
             # CPU  uses the default PyPi index, no optional modules

installer/lib/messages.py

@@ -206,6 +206,7 @@ def dest_path(dest: Optional[str | Path] = None) -> Path | None:
 
 
 class GpuType(Enum):
+    CUDA_WITH_XFORMERS = "xformers"
     CUDA = "cuda"
     ROCM = "rocm"
     CPU = "cpu"
@@ -221,11 +222,15 @@ def select_gpu() -> GpuType:
         return GpuType.CPU
 
     nvidia = (
-        "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
+        "an [gold1 b]NVIDIA[/] RTX 3060 or newer GPU using CUDA",
         GpuType.CUDA,
     )
+    vintage_nvidia = (
+        "an [gold1 b]NVIDIA[/] RTX 20xx or older GPU using CUDA+xFormers",
+        GpuType.CUDA_WITH_XFORMERS,
+    )
     amd = (
-        "an [gold1 b]AMD[/] GPU (using ROCm™)",
+        "an [gold1 b]AMD[/] GPU using ROCm",
         GpuType.ROCM,
     )
     cpu = (
@@ -235,14 +240,13 @@ def select_gpu() -> GpuType:
 
     options = []
     if OS == "Windows":
-        options = [nvidia, cpu]
+        options = [nvidia, vintage_nvidia, cpu]
     if OS == "Linux":
-        options = [nvidia, amd, cpu]
+        options = [nvidia, vintage_nvidia, amd, cpu]
     elif OS == "Darwin":
         options = [cpu]
 
     if len(options) == 1:
-        print(f'Your platform [gold1]{OS}-{ARCH}[/] only supports the "{options[0][1]}" driver. Proceeding with that.')
         return options[0][1]
 
     options = {str(i): opt for i, opt in enumerate(options, 1)}
@@ -255,7 +259,7 @@ def select_gpu() -> GpuType:
                     [
                         f"Detected the [gold1]{OS}-{ARCH}[/] platform",
                         "",
-                        "See [deep_sky_blue1]https://invoke-ai.github.io/InvokeAI/#system[/] to ensure your system meets the minimum requirements.",
+                        "See [deep_sky_blue1]https://invoke-ai.github.io/InvokeAI/installation/requirements/[/] 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/boards.py

@@ -5,9 +5,10 @@ from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
 from invokeai.app.services.boards.boards_common import BoardDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 boards_router = APIRouter(prefix="/v1/boards", tags=["boards"])
 
@@ -115,6 +116,8 @@ async def delete_board(
     response_model=Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]],
 )
 async def list_boards(
+    order_by: BoardRecordOrderBy = Query(default=BoardRecordOrderBy.CreatedAt, description="The attribute to order by"),
+    direction: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The direction to order by"),
     all: Optional[bool] = Query(default=None, description="Whether to list all boards"),
     offset: Optional[int] = Query(default=None, description="The page offset"),
     limit: Optional[int] = Query(default=None, description="The number of boards per page"),
@@ -122,9 +125,9 @@ async def list_boards(
 ) -> Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]]:
     """Gets a list of boards"""
     if all:
-        return ApiDependencies.invoker.services.boards.get_all(include_archived)
+        return ApiDependencies.invoker.services.boards.get_all(order_by, direction, include_archived)
     elif offset is not None and limit is not None:
-        return ApiDependencies.invoker.services.boards.get_many(offset, limit, include_archived)
+        return ApiDependencies.invoker.services.boards.get_many(order_by, direction, offset, limit, include_archived)
     else:
         raise HTTPException(
             status_code=400,

invokeai/app/api/routers/model_manager.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2023 Lincoln D. Stein
 """FastAPI route for model configuration records."""
 
+import contextlib
 import io
 import pathlib
 import shutil
@@ -10,6 +11,7 @@ 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
@@ -27,6 +29,7 @@ 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,
@@ -38,7 +41,12 @@ from invokeai.backend.model_manager.load.model_cache.model_cache_base import Cac
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
-from invokeai.backend.model_manager.starter_models import STARTER_MODELS, StarterModel, StarterModelWithoutDependencies
+from invokeai.backend.model_manager.starter_models import (
+    STARTER_BUNDLES,
+    STARTER_MODELS,
+    StarterModel,
+    StarterModelWithoutDependencies,
+)
 
 model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
 
@@ -792,22 +800,52 @@ async def convert_model(
     return new_config
 
 
-@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=list[StarterModel])
-async def get_starter_models() -> list[StarterModel]:
+class StarterModelResponse(BaseModel):
+    starter_models: list[StarterModel]
+    starter_bundles: dict[str, list[StarterModel]]
+
+
+def get_is_installed(
+    starter_model: StarterModel | StarterModelWithoutDependencies, installed_models: list[AnyModelConfig]
+) -> bool:
+    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
+        ):
+            return True
+    return False
+
+
+@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=StarterModelResponse)
+async def get_starter_models() -> StarterModelResponse:
     installed_models = ApiDependencies.invoker.services.model_manager.store.search_by_attr()
-    installed_model_sources = {m.source for m in installed_models}
     starter_models = deepcopy(STARTER_MODELS)
+    starter_bundles = deepcopy(STARTER_BUNDLES)
     for model in starter_models:
-        if model.source in installed_model_sources:
-            model.is_installed = True
+        model.is_installed = get_is_installed(model, installed_models)
         # Remove already-installed dependencies
         missing_deps: list[StarterModelWithoutDependencies] = []
+
         for dep in model.dependencies or []:
-            if dep.source not in installed_model_sources:
+            if not get_is_installed(dep, installed_models):
                 missing_deps.append(dep)
         model.dependencies = missing_deps
 
-    return starter_models
+    for bundle in starter_bundles.values():
+        for model in bundle:
+            model.is_installed = get_is_installed(model, installed_models)
+            # Remove already-installed dependencies
+            missing_deps: list[StarterModelWithoutDependencies] = []
+            for dep in model.dependencies or []:
+                if not get_is_installed(dep, installed_models):
+                    missing_deps.append(dep)
+            model.dependencies = missing_deps
+
+    return StarterModelResponse(starter_models=starter_models, starter_bundles=starter_bundles)
 
 
 @model_manager_router.get(
@@ -888,3 +926,51 @@ 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/workflows.py

@@ -83,7 +83,7 @@ async def create_workflow(
 )
 async def list_workflows(
     page: int = Query(default=0, description="The page to get"),
-    per_page: int = Query(default=10, description="The number of workflows per page"),
+    per_page: Optional[int] = Query(default=None, description="The number of workflows per page"),
     order_by: WorkflowRecordOrderBy = Query(
         default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
     ),
@@ -93,5 +93,5 @@ async def list_workflows(
 ) -> PaginatedResults[WorkflowRecordListItemDTO]:
     """Gets a page of workflows"""
     return ApiDependencies.invoker.services.workflow_records.get_many(
-        page=page, per_page=per_page, order_by=order_by, direction=direction, query=query, category=category
+        order_by=order_by, direction=direction, page=page, per_page=per_page, query=query, category=category
     )

invokeai/app/invocations/baseinvocation.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import inspect
 import re
+import sys
 import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -192,12 +193,19 @@ 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._invocation_classes)], Field(discriminator="type")]
+                "AnyInvocation", Annotated[Union[tuple(cls.get_invocations())], 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."""
@@ -479,6 +487,26 @@ 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/denoise_latents.py

@@ -13,6 +13,7 @@ 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
@@ -510,6 +511,7 @@ 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
@@ -519,6 +521,10 @@ 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,
@@ -547,7 +553,9 @@ 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) for image in single_ipa_image_fields]
+                single_ipa_images = [
+                    context.images.get_pil(image.image_name, mode="RGB") for image in single_ipa_image_fields
+                ]
                 with image_encoder_model_info as image_encoder_model:
                     assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                     # Get image embeddings from CLIP and ImageProjModel.
@@ -614,13 +622,17 @@ 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)
+            image = context.images.get_pil(t2i_adapter_field.image.image_name, mode="RGB")
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
             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}'.")
 
@@ -628,29 +640,39 @@ 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=t2i_input_width,
-                    height=t2i_input_height,
+                    width=control_width_resize,
+                    height=control_height_resize,
                     num_channels=t2i_adapter_model.config["in_channels"],  # mypy treats this as a FrozenDict
                     device=t2i_adapter_model.device,
                     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:
@@ -898,7 +920,8 @@ 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)
-            self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, 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)
 
             # ext: t2i/ip adapter
             ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)

invokeai/app/invocations/fields.py

@@ -41,6 +41,7 @@ class UIType(str, Enum, metaclass=MetaEnum):
     # region Model Field Types
     MainModel = "MainModelField"
     FluxMainModel = "FluxMainModelField"
+    SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
@@ -52,6 +53,8 @@ class UIType(str, Enum, metaclass=MetaEnum):
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
     CLIPEmbedModel = "CLIPEmbedModelField"
+    CLIPLEmbedModel = "CLIPLEmbedModelField"
+    CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -131,8 +134,10 @@ 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"
@@ -140,6 +145,7 @@ 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"
@@ -192,6 +198,7 @@ class FieldDescriptions:
     freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
     freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
     freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
+    instantx_control_mode = "The control mode for InstantX ControlNet union models. Ignored for other ControlNet models. The standard mapping is: canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6). Negative values will be treated as 'None'."
 
 
 class ImageField(BaseModel):
@@ -245,6 +252,12 @@ class FluxConditioningField(BaseModel):
     conditioning_name: str = Field(description="The name of conditioning tensor")
 
 
+class SD3ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_controlnet.py

@@ -0,0 +1,99 @@
+from pydantic import BaseModel, Field, field_validator, model_validator
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES
+
+
+class FluxControlNetField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
+    control_weight: float | list[float] = Field(default=1, description="The weight given to the ControlNet")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+    instantx_control_mode: int | None = Field(default=-1, description=FieldDescriptions.instantx_control_mode)
+
+    @field_validator("control_weight")
+    @classmethod
+    def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
+        validate_weights(v)
+        return v
+
+    @model_validator(mode="after")
+    def validate_begin_end_step_percent(self):
+        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
+        return self
+
+
+@invocation_output("flux_controlnet_output")
+class FluxControlNetOutput(BaseInvocationOutput):
+    """FLUX ControlNet info"""
+
+    control: FluxControlNetField = OutputField(description=FieldDescriptions.control)
+
+
+@invocation(
+    "flux_controlnet",
+    title="FLUX ControlNet",
+    tags=["controlnet", "flux"],
+    category="controlnet",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxControlNetInvocation(BaseInvocation):
+    """Collect FLUX ControlNet info to pass to other nodes."""
+
+    image: ImageField = InputField(description="The control image")
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
+    )
+    control_weight: float | list[float] = InputField(
+        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+    )
+    begin_step_percent: float = InputField(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
+    # Note: We default to -1 instead of None, because in the workflow editor UI None is not currently supported.
+    instantx_control_mode: int | None = InputField(default=-1, description=FieldDescriptions.instantx_control_mode)
+
+    @field_validator("control_weight")
+    @classmethod
+    def validate_control_weight(cls, v: float | list[float]) -> float | list[float]:
+        validate_weights(v)
+        return v
+
+    @model_validator(mode="after")
+    def validate_begin_end_step_percent(self):
+        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
+        return self
+
+    def invoke(self, context: InvocationContext) -> FluxControlNetOutput:
+        return FluxControlNetOutput(
+            control=FluxControlNetField(
+                image=self.image,
+                control_model=self.control_model,
+                control_weight=self.control_weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+                resize_mode=self.resize_mode,
+                instantx_control_mode=self.instantx_control_mode,
+            ),
+        )

invokeai/app/invocations/flux_denoise.py

@@ -1,26 +1,38 @@
 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,
     WithBoard,
     WithMetadata,
 )
-from invokeai.app.invocations.model import TransformerField
+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
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
+from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
 from invokeai.backend.flux.denoise import denoise
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+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,
@@ -44,7 +56,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.0.0",
+    version="3.2.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -77,6 +89,24 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     positive_text_conditioning: FluxConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
+    negative_text_conditioning: 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(
@@ -87,6 +117,18 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
     )
     seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+    control: FluxControlNetField | list[FluxControlNetField] | None = InputField(
+        default=None, input=Input.Connection, description="ControlNet models."
+    )
+    controlnet_vae: VAEField | None = InputField(
+        default=None,
+        description=FieldDescriptions.vae,
+        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:
@@ -96,6 +138,19 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
+    def _load_text_conditioning(
+        self, context: InvocationContext, conditioning_name: str, dtype: torch.dtype
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+        return t5_embeddings, clip_embeddings
+
     def _run_diffusion(
         self,
         context: InvocationContext,
@@ -103,13 +158,15 @@ 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
+        pos_t5_embeddings, pos_clip_embeddings = self._load_text_conditioning(
+            context, self.positive_text_conditioning.conditioning_name, inference_dtype
+        )
+        neg_t5_embeddings: torch.Tensor | None = None
+        neg_clip_embeddings: torch.Tensor | None = None
+        if self.negative_text_conditioning is not None:
+            neg_t5_embeddings, neg_clip_embeddings = self._load_text_conditioning(
+                context, self.negative_text_conditioning.conditioning_name, inference_dtype
+            )
 
         # Load the input latents, if provided.
         init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
@@ -167,11 +224,19 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
-        b, _c, h, w = x.shape
-        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
+        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())
+        pos_bs, pos_t5_seq_len, _ = pos_t5_embeddings.shape
+        pos_txt_ids = torch.zeros(
+            pos_bs, pos_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
+        )
+        neg_txt_ids: torch.Tensor | None = None
+        if neg_t5_embeddings is not None:
+            neg_bs, neg_t5_seq_len, _ = neg_t5_embeddings.shape
+            neg_txt_ids = torch.zeros(
+                neg_bs, neg_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
+            )
 
         # Pack all latent tensors.
         init_latents = pack(init_latents) if init_latents is not None else None
@@ -192,12 +257,36 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 noise=noise,
             )
 
-        with (
-            transformer_info.model_on_device() as (cached_weights, transformer),
-            ExitStack() as exit_stack,
-        ):
-            assert isinstance(transformer, Flux)
+        # 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).
+            controlnet_extensions = self._prep_controlnet_extensions(
+                context=context,
+                exit_stack=exit_stack,
+                latent_height=latent_h,
+                latent_width=latent_w,
+                dtype=inference_dtype,
+                device=x.device,
+            )
+
+            # Load the transformer model.
+            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
+            assert isinstance(transformer, Flux)
             config = transformer_info.config
             assert config is not None
 
@@ -213,7 +302,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                         cached_weights=cached_weights,
                     )
                 )
-            elif config.format in [ModelFormat.BnbQuantizedLlmInt8b, ModelFormat.BnbQuantizednf4b]:
+            elif config.format in [
+                ModelFormat.BnbQuantizedLlmInt8b,
+                ModelFormat.BnbQuantizednf4b,
+                ModelFormat.GGUFQuantized,
+            ]:
                 # The model is quantized, so apply the LoRA weights as sidecar layers. This results in slower inference,
                 # than directly patching the weights, but is agnostic to the quantization format.
                 exit_stack.enter_context(
@@ -227,22 +320,88 @@ 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,
-                txt=t5_embeddings,
-                txt_ids=txt_ids,
-                vec=clip_embeddings,
+                txt=pos_t5_embeddings,
+                txt_ids=pos_txt_ids,
+                vec=pos_clip_embeddings,
+                neg_txt=neg_t5_embeddings,
+                neg_txt_ids=neg_txt_ids,
+                neg_vec=neg_clip_embeddings,
                 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
 
+    @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.
 
@@ -284,6 +443,210 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # `latents`.
         return mask.expand_as(latents)
 
+    def _prep_controlnet_extensions(
+        self,
+        context: InvocationContext,
+        exit_stack: ExitStack,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> list[XLabsControlNetExtension | InstantXControlNetExtension]:
+        # Normalize the controlnet input to list[ControlField].
+        controlnets: list[FluxControlNetField]
+        if self.control is None:
+            controlnets = []
+        elif isinstance(self.control, FluxControlNetField):
+            controlnets = [self.control]
+        elif isinstance(self.control, list):
+            controlnets = self.control
+        else:
+            raise ValueError(f"Unsupported controlnet type: {type(self.control)}")
+
+        # TODO(ryand): Add a field to the model config so that we can distinguish between XLabs and InstantX ControlNets
+        # before loading the models. Then make sure that all VAE encoding is done before loading the ControlNets to
+        # minimize peak memory.
+
+        # First, load the ControlNet models so that we can determine the ControlNet types.
+        controlnet_models = [context.models.load(controlnet.control_model) for controlnet in controlnets]
+
+        # Calculate the controlnet conditioning tensors.
+        # We do this before loading the ControlNet models because it may require running the VAE, and we are trying to
+        # keep peak memory down.
+        controlnet_conds: list[torch.Tensor] = []
+        for controlnet, controlnet_model in zip(controlnets, controlnet_models, strict=True):
+            image = context.images.get_pil(controlnet.image.image_name)
+            if isinstance(controlnet_model.model, InstantXControlNetFlux):
+                if self.controlnet_vae is None:
+                    raise ValueError("A ControlNet VAE is required when using an InstantX FLUX ControlNet.")
+                vae_info = context.models.load(self.controlnet_vae.vae)
+                controlnet_conds.append(
+                    InstantXControlNetExtension.prepare_controlnet_cond(
+                        controlnet_image=image,
+                        vae_info=vae_info,
+                        latent_height=latent_height,
+                        latent_width=latent_width,
+                        dtype=dtype,
+                        device=device,
+                        resize_mode=controlnet.resize_mode,
+                    )
+                )
+            elif isinstance(controlnet_model.model, XLabsControlNetFlux):
+                controlnet_conds.append(
+                    XLabsControlNetExtension.prepare_controlnet_cond(
+                        controlnet_image=image,
+                        latent_height=latent_height,
+                        latent_width=latent_width,
+                        dtype=dtype,
+                        device=device,
+                        resize_mode=controlnet.resize_mode,
+                    )
+                )
+
+        # Finally, load the ControlNet models and initialize the ControlNet extensions.
+        controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension] = []
+        for controlnet, controlnet_cond, controlnet_model in zip(
+            controlnets, controlnet_conds, controlnet_models, strict=True
+        ):
+            model = exit_stack.enter_context(controlnet_model)
+
+            if isinstance(model, XLabsControlNetFlux):
+                controlnet_extensions.append(
+                    XLabsControlNetExtension(
+                        model=model,
+                        controlnet_cond=controlnet_cond,
+                        weight=controlnet.control_weight,
+                        begin_step_percent=controlnet.begin_step_percent,
+                        end_step_percent=controlnet.end_step_percent,
+                    )
+                )
+            elif isinstance(model, InstantXControlNetFlux):
+                instantx_control_mode: torch.Tensor | None = None
+                if controlnet.instantx_control_mode is not None and controlnet.instantx_control_mode >= 0:
+                    instantx_control_mode = torch.tensor(controlnet.instantx_control_mode, dtype=torch.long)
+                    instantx_control_mode = instantx_control_mode.reshape([-1, 1])
+
+                controlnet_extensions.append(
+                    InstantXControlNetExtension(
+                        model=model,
+                        controlnet_cond=controlnet_cond,
+                        instantx_control_mode=instantx_control_mode,
+                        weight=controlnet.control_weight,
+                        begin_step_percent=controlnet.begin_step_percent,
+                        end_step_percent=controlnet.end_step_percent,
+                    )
+                )
+            else:
+                raise ValueError(f"Unsupported ControlNet model type: {type(model)}")
+
+        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

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

@@ -0,0 +1,89 @@
+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/ip_adapter.py

@@ -9,6 +9,7 @@ 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,
@@ -17,6 +18,12 @@ 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):
@@ -55,10 +62,14 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
+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,
+}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.1")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.5.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -70,7 +81,7 @@ class IPAdapterInvocation(BaseInvocation):
         ui_order=-1,
         ui_type=UIType.IPAdapterModel,
     )
-    clip_vision_model: Literal["ViT-H", "ViT-G"] = InputField(
+    clip_vision_model: Literal["ViT-H", "ViT-G", "ViT-L"] = InputField(
         description="CLIP Vision model to use. Overrides model settings. Mandatory for checkpoint models.",
         default="ViT-H",
         ui_order=2,
@@ -111,9 +122,11 @@ 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_model_name = CLIP_VISION_MODEL_MAP[self.clip_vision_model]
+            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 = self._get_image_encoder(context, image_encoder_model_name)
+        image_encoder_model = self.get_clip_image_encoder(context, image_encoder_model_id, image_encoder_model_name)
 
         if self.method == "style":
             if ip_adapter_info.base == "sd-1":
@@ -147,7 +160,10 @@ class IPAdapterInvocation(BaseInvocation):
             ),
         )
 
-    def _get_image_encoder(self, context: InvocationContext, image_encoder_model_name: str) -> AnyModelConfig:
+    @classmethod
+    def get_clip_image_encoder(
+        cls, context: InvocationContext, image_encoder_model_id: str, 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
         )
@@ -159,7 +175,11 @@ class IPAdapterInvocation(BaseInvocation):
             )
 
             installer = context._services.model_manager.install
-            job = installer.heuristic_import(f"InvokeAI/{image_encoder_model_name}")
+            # 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)
+            )
             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/mask.py

@@ -5,6 +5,7 @@ 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(
@@ -148,3 +149,55 @@ 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-H", "ViT-G"] = Field(description="The CLIP Vision model")
+    clip_vision_model: Literal["ViT-L", "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)")

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional
+from typing import List, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,11 +13,9 @@ 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,
 )
@@ -139,78 +137,6 @@ 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/primitives.py

@@ -18,6 +18,7 @@ from invokeai.app.invocations.fields import (
     InputField,
     LatentsField,
     OutputField,
+    SD3ConditioningField,
     TensorField,
     UIComponent,
 )
@@ -426,6 +427,17 @@ 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"""

invokeai/app/invocations/sd3_denoise.py

@@ -0,0 +1,260 @@
+from typing import Callable, Tuple
+
+import torch
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+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 (
+    FieldDescriptions,
+    Input,
+    InputField,
+    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.model_manager.config import BaseModelType
+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="1.0.0",
+    classification=Classification.Prototype,
+)
+class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Run denoising process with a SD3 model."""
+
+    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 _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 scheduler.
+        scheduler = FlowMatchEulerDiscreteScheduler()
+        scheduler.set_timesteps(num_inference_steps=self.steps, device=device)
+        timesteps = scheduler.timesteps
+        assert isinstance(timesteps, torch.Tensor)
+
+        # Prepare the CFG scale list.
+        cfg_scale = self._prepare_cfg_scale(len(timesteps))
+
+        # 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,
+        )
+        latents: torch.Tensor = noise
+
+        total_steps = len(timesteps)
+        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 in tqdm(list(enumerate(timesteps))):
+                # 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.
+                timestep = t.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 = scheduler.step(model_output=noise_pred, timestep=t, sample=latents, return_dict=False)[0]
+
+                # TODO(ryand): This MPS dtype handling was copied from diffusers, I haven't tested to see if it's
+                # needed.
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                step_callback(
+                    PipelineIntermediateState(
+                        step=step_idx + 1,
+                        order=1,
+                        total_steps=total_steps,
+                        timestep=int(t),
+                        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_latents_to_image.py

@@ -0,0 +1,73 @@
+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:
+            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

@@ -0,0 +1,108 @@
+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

@@ -0,0 +1,199 @@
+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,
+        ):
+            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,
+        ):
+            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

@@ -1,9 +1,11 @@
+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
@@ -23,12 +25,31 @@ 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="1.0.0",
+    version="1.1.0",
 )
 class SegmentAnythingInvocation(BaseInvocation):
     """Runs a Segment Anything Model."""
@@ -40,7 +61,13 @@ 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] = InputField(description="The bounding boxes to prompt the SAM model with.")
+    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.",
+    )
     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,
@@ -55,7 +82,12 @@ class SegmentAnythingInvocation(BaseInvocation):
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
-        if len(self.bounding_boxes) == 0:
+        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
+        ):
             combined_mask = torch.zeros(image_pil.size[::-1], dtype=torch.bool)
         else:
             masks = self._segment(context=context, image=image_pil)
@@ -83,14 +115,13 @@ 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]
+        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
 
         with (
             context.models.load_remote_model(
@@ -98,7 +129,7 @@ class SegmentAnythingInvocation(BaseInvocation):
             ) as sam_pipeline,
         ):
             assert isinstance(sam_pipeline, SegmentAnythingPipeline)
-            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes)
+            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes, point_lists=sam_points)
 
         masks = self._process_masks(masks)
         if self.apply_polygon_refinement:
@@ -141,9 +172,10 @@ class SegmentAnythingInvocation(BaseInvocation):
 
         return masks
 
-    def _filter_masks(self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField]) -> list[torch.Tensor]:
+    def _filter_masks(
+        self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField] | None
+    ) -> 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
@@ -151,6 +183,10 @@ 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/board_records/board_records_base.py

@@ -1,7 +1,8 @@
 from abc import ABC, abstractmethod
 
-from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecord
+from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecord, BoardRecordOrderBy
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class BoardRecordStorageBase(ABC):
@@ -39,12 +40,19 @@ class BoardRecordStorageBase(ABC):
 
     @abstractmethod
     def get_many(
-        self, offset: int = 0, limit: int = 10, include_archived: bool = False
+        self,
+        order_by: BoardRecordOrderBy,
+        direction: SQLiteDirection,
+        offset: int = 0,
+        limit: int = 10,
+        include_archived: bool = False,
     ) -> OffsetPaginatedResults[BoardRecord]:
         """Gets many board records."""
         pass
 
     @abstractmethod
-    def get_all(self, include_archived: bool = False) -> list[BoardRecord]:
+    def get_all(
+        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
+    ) -> list[BoardRecord]:
         """Gets all board records."""
         pass

invokeai/app/services/board_records/board_records_common.py

@@ -1,8 +1,10 @@
 from datetime import datetime
+from enum import Enum
 from typing import Optional, Union
 
 from pydantic import BaseModel, Field
 
+from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import get_iso_timestamp
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
@@ -60,6 +62,13 @@ class BoardChanges(BaseModel, extra="forbid"):
     archived: Optional[bool] = Field(default=None, description="Whether or not the board is archived")
 
 
+class BoardRecordOrderBy(str, Enum, metaclass=MetaEnum):
+    """The order by options for board records"""
+
+    CreatedAt = "created_at"
+    Name = "board_name"
+
+
 class BoardRecordNotFoundException(Exception):
     """Raised when an board record is not found."""
 

invokeai/app/services/board_records/board_records_sqlite.py

@@ -8,10 +8,12 @@ from invokeai.app.services.board_records.board_records_common import (
     BoardRecord,
     BoardRecordDeleteException,
     BoardRecordNotFoundException,
+    BoardRecordOrderBy,
     BoardRecordSaveException,
     deserialize_board_record,
 )
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.app.util.misc import uuid_string
 
@@ -144,7 +146,12 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         return self.get(board_id)
 
     def get_many(
-        self, offset: int = 0, limit: int = 10, include_archived: bool = False
+        self,
+        order_by: BoardRecordOrderBy,
+        direction: SQLiteDirection,
+        offset: int = 0,
+        limit: int = 10,
+        include_archived: bool = False,
     ) -> OffsetPaginatedResults[BoardRecord]:
         try:
             self._lock.acquire()
@@ -154,17 +161,16 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
                 SELECT *
                 FROM boards
                 {archived_filter}
-                ORDER BY created_at DESC
+                ORDER BY {order_by} {direction}
                 LIMIT ? OFFSET ?;
             """
 
             # Determine archived filter condition
-            if include_archived:
-                archived_filter = ""
-            else:
-                archived_filter = "WHERE archived = 0"
+            archived_filter = "" if include_archived else "WHERE archived = 0"
 
-            final_query = base_query.format(archived_filter=archived_filter)
+            final_query = base_query.format(
+                archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
+            )
 
             # Execute query to fetch boards
             self._cursor.execute(final_query, (limit, offset))
@@ -198,23 +204,32 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         finally:
             self._lock.release()
 
-    def get_all(self, include_archived: bool = False) -> list[BoardRecord]:
+    def get_all(
+        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
+    ) -> list[BoardRecord]:
         try:
             self._lock.acquire()
 
-            base_query = """
-                SELECT *
-                FROM boards
-                {archived_filter}
-                ORDER BY created_at DESC
-            """
-
-            if include_archived:
-                archived_filter = ""
+            if order_by == BoardRecordOrderBy.Name:
+                base_query = """
+                    SELECT *
+                    FROM boards
+                    {archived_filter}
+                    ORDER BY LOWER(board_name) {direction}
+                """
             else:
-                archived_filter = "WHERE archived = 0"
+                base_query = """
+                    SELECT *
+                    FROM boards
+                    {archived_filter}
+                    ORDER BY {order_by} {direction}
+                """
 
-            final_query = base_query.format(archived_filter=archived_filter)
+            archived_filter = "" if include_archived else "WHERE archived = 0"
+
+            final_query = base_query.format(
+                archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
+            )
 
             self._cursor.execute(final_query)
 

invokeai/app/services/boards/boards_base.py

@@ -1,8 +1,9 @@
 from abc import ABC, abstractmethod
 
-from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
 from invokeai.app.services.boards.boards_common import BoardDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class BoardServiceABC(ABC):
@@ -43,12 +44,19 @@ class BoardServiceABC(ABC):
 
     @abstractmethod
     def get_many(
-        self, offset: int = 0, limit: int = 10, include_archived: bool = False
+        self,
+        order_by: BoardRecordOrderBy,
+        direction: SQLiteDirection,
+        offset: int = 0,
+        limit: int = 10,
+        include_archived: bool = False,
     ) -> OffsetPaginatedResults[BoardDTO]:
         """Gets many boards."""
         pass
 
     @abstractmethod
-    def get_all(self, include_archived: bool = False) -> list[BoardDTO]:
+    def get_all(
+        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
+    ) -> list[BoardDTO]:
         """Gets all boards."""
         pass

invokeai/app/services/boards/boards_default.py

@@ -1,8 +1,9 @@
-from invokeai.app.services.board_records.board_records_common import BoardChanges
+from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
 from invokeai.app.services.boards.boards_base import BoardServiceABC
 from invokeai.app.services.boards.boards_common import BoardDTO, board_record_to_dto
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
 
 class BoardService(BoardServiceABC):
@@ -47,9 +48,16 @@ class BoardService(BoardServiceABC):
         self.__invoker.services.board_records.delete(board_id)
 
     def get_many(
-        self, offset: int = 0, limit: int = 10, include_archived: bool = False
+        self,
+        order_by: BoardRecordOrderBy,
+        direction: SQLiteDirection,
+        offset: int = 0,
+        limit: int = 10,
+        include_archived: bool = False,
     ) -> OffsetPaginatedResults[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_many(offset, limit, include_archived)
+        board_records = self.__invoker.services.board_records.get_many(
+            order_by, direction, offset, limit, include_archived
+        )
         board_dtos = []
         for r in board_records.items:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
@@ -63,8 +71,10 @@ class BoardService(BoardServiceABC):
 
         return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
-    def get_all(self, include_archived: bool = False) -> list[BoardDTO]:
-        board_records = self.__invoker.services.board_records.get_all(include_archived)
+    def get_all(
+        self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
+    ) -> list[BoardDTO]:
+        board_records = self.__invoker.services.board_records.get_all(order_by, direction, include_archived)
         board_dtos = []
         for r in board_records:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)

invokeai/app/services/config/config_default.py

@@ -250,9 +250,9 @@ class InvokeAIAppConfig(BaseSettings):
             )
 
             if as_example:
-                file.write(
-                    "# This is an example file with default and example settings. Use the values here as a baseline.\n\n"
-                )
+                file.write("# This is an example file with default and example settings.\n")
+                file.write("# You should not copy this whole file into your config.\n")
+                file.write("# Only add the settings you need to change to your config file.\n\n")
             file.write("# Internal metadata - do not edit:\n")
             file.write(yaml.dump(meta_dict, sort_keys=False))
             file.write("\n")

invokeai/app/services/image_files/image_files_disk.py

@@ -110,15 +110,26 @@ class DiskImageFileStorage(ImageFileStorageBase):
         except Exception as e:
             raise ImageFileDeleteException from e
 
-    # TODO: make this a bit more flexible for e.g. cloud storage
     def get_path(self, image_name: str, thumbnail: bool = False) -> Path:
-        path = self.__output_folder / image_name
+        base_folder = self.__thumbnails_folder if thumbnail else self.__output_folder
+        filename = get_thumbnail_name(image_name) if thumbnail else image_name
 
-        if thumbnail:
-            thumbnail_name = get_thumbnail_name(image_name)
-            path = self.__thumbnails_folder / thumbnail_name
+        # Strip any path information from the filename
+        basename = Path(filename).name
 
-        return path
+        if basename != filename:
+            raise ValueError("Invalid image name, potential directory traversal detected")
+
+        image_path = base_folder / basename
+
+        # Ensure the image path is within the base folder to prevent directory traversal
+        resolved_base = base_folder.resolve()
+        resolved_image_path = image_path.resolve()
+
+        if not resolved_image_path.is_relative_to(resolved_base):
+            raise ValueError("Image path outside outputs folder, potential directory traversal detected")
+
+        return resolved_image_path
 
     def validate_path(self, path: Union[str, Path]) -> bool:
         """Validates the path given for an image or thumbnail."""

invokeai/app/services/model_records/model_records_base.py

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

@@ -1,3 +1,4 @@
+from copy import deepcopy
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Callable, Optional, Union
@@ -221,7 +222,7 @@ class ImagesInterface(InvocationContextInterface):
         )
 
     def get_pil(self, image_name: str, mode: IMAGE_MODES | None = None) -> Image:
-        """Gets an image as a PIL Image object.
+        """Gets an image as a PIL Image object. This method returns a copy of the image.
 
         Args:
             image_name: The name of the image to get.
@@ -233,11 +234,15 @@ 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]:
@@ -290,15 +295,15 @@ class TensorsInterface(InvocationContextInterface):
         return name
 
     def load(self, name: str) -> Tensor:
-        """Loads a tensor by name.
+        """Loads a tensor by name. This method returns a copy of the tensor.
 
         Args:
             name: The name of the tensor to load.
 
         Returns:
-            The loaded tensor.
+            The tensor.
         """
-        return self._services.tensors.load(name)
+        return self._services.tensors.load(name).clone()
 
 
 class ConditioningInterface(InvocationContextInterface):
@@ -316,16 +321,16 @@ class ConditioningInterface(InvocationContextInterface):
         return name
 
     def load(self, name: str) -> ConditioningFieldData:
-        """Loads conditioning data by name.
+        """Loads conditioning data by name. This method returns a copy of the conditioning data.
 
         Args:
             name: The name of the conditioning data to load.
 
         Returns:
-            The loaded conditioning data.
+            The conditioning data.
         """
 
-        return self._services.conditioning.load(name)
+        return deepcopy(self._services.conditioning.load(name))
 
 
 class ModelsInterface(InvocationContextInterface):

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

@@ -0,0 +1,382 @@
+{
+    "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/services/workflow_records/workflow_records_base.py

@@ -39,11 +39,11 @@ class WorkflowRecordsStorageBase(ABC):
     @abstractmethod
     def get_many(
         self,
-        page: int,
-        per_page: int,
         order_by: WorkflowRecordOrderBy,
         direction: SQLiteDirection,
         category: WorkflowCategory,
+        page: int,
+        per_page: Optional[int],
         query: Optional[str],
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
         """Gets many workflows."""

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -125,11 +125,11 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
 
     def get_many(
         self,
-        page: int,
-        per_page: int,
         order_by: WorkflowRecordOrderBy,
         direction: SQLiteDirection,
         category: WorkflowCategory,
+        page: int = 0,
+        per_page: Optional[int] = None,
         query: Optional[str] = None,
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
         try:
@@ -153,6 +153,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 """
             main_params: list[int | str] = [category.value]
             count_params: list[int | str] = [category.value]
+
             stripped_query = query.strip() if query else None
             if stripped_query:
                 wildcard_query = "%" + stripped_query + "%"
@@ -161,20 +162,28 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 main_params.extend([wildcard_query, wildcard_query])
                 count_params.extend([wildcard_query, wildcard_query])
 
-            main_query += f" ORDER BY {order_by.value} {direction.value} LIMIT ? OFFSET ?;"
-            main_params.extend([per_page, page * per_page])
+            main_query += f" ORDER BY {order_by.value} {direction.value}"
+
+            if per_page:
+                main_query += " LIMIT ? OFFSET ?"
+                main_params.extend([per_page, page * per_page])
+
             self._cursor.execute(main_query, main_params)
             rows = self._cursor.fetchall()
             workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
 
             self._cursor.execute(count_query, count_params)
             total = self._cursor.fetchone()[0]
-            pages = total // per_page + (total % per_page > 0)
+
+            if per_page:
+                pages = total // per_page + (total % per_page > 0)
+            else:
+                pages = 1  # If no pagination, there is only one page
 
             return PaginatedResults(
                 items=workflows,
                 page=page,
-                per_page=per_page,
+                per_page=per_page if per_page else total,
                 pages=pages,
                 total=total,
             )

invokeai/app/util/step_callback.py

@@ -34,6 +34,25 @@ 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],
@@ -110,6 +129,9 @@ 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/controlnet/controlnet_flux_output.py

@@ -0,0 +1,58 @@
+from dataclasses import dataclass
+
+import torch
+
+
+@dataclass
+class ControlNetFluxOutput:
+    single_block_residuals: list[torch.Tensor] | None
+    double_block_residuals: list[torch.Tensor] | None
+
+    def apply_weight(self, weight: float):
+        if self.single_block_residuals is not None:
+            for i in range(len(self.single_block_residuals)):
+                self.single_block_residuals[i] = self.single_block_residuals[i] * weight
+        if self.double_block_residuals is not None:
+            for i in range(len(self.double_block_residuals)):
+                self.double_block_residuals[i] = self.double_block_residuals[i] * weight
+
+
+def add_tensor_lists_elementwise(
+    list1: list[torch.Tensor] | None, list2: list[torch.Tensor] | None
+) -> list[torch.Tensor] | None:
+    """Add two tensor lists elementwise that could be None."""
+    if list1 is None and list2 is None:
+        return None
+    if list1 is None:
+        return list2
+    if list2 is None:
+        return list1
+
+    new_list: list[torch.Tensor] = []
+    for list1_tensor, list2_tensor in zip(list1, list2, strict=True):
+        new_list.append(list1_tensor + list2_tensor)
+    return new_list
+
+
+def add_controlnet_flux_outputs(
+    controlnet_output_1: ControlNetFluxOutput, controlnet_output_2: ControlNetFluxOutput
+) -> ControlNetFluxOutput:
+    return ControlNetFluxOutput(
+        single_block_residuals=add_tensor_lists_elementwise(
+            controlnet_output_1.single_block_residuals, controlnet_output_2.single_block_residuals
+        ),
+        double_block_residuals=add_tensor_lists_elementwise(
+            controlnet_output_1.double_block_residuals, controlnet_output_2.double_block_residuals
+        ),
+    )
+
+
+def sum_controlnet_flux_outputs(
+    controlnet_outputs: list[ControlNetFluxOutput],
+) -> ControlNetFluxOutput:
+    controlnet_output_sum = ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
+
+    for controlnet_output in controlnet_outputs:
+        controlnet_output_sum = add_controlnet_flux_outputs(controlnet_output_sum, controlnet_output)
+
+    return controlnet_output_sum

invokeai/backend/flux/controlnet/instantx_controlnet_flux.py

@@ -0,0 +1,180 @@
+# This file was initially copied from:
+# https://github.com/huggingface/diffusers/blob/99f608218caa069a2f16dcf9efab46959b15aec0/src/diffusers/models/controlnet_flux.py
+
+
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+
+from invokeai.backend.flux.controlnet.zero_module import zero_module
+from invokeai.backend.flux.model import FluxParams
+from invokeai.backend.flux.modules.layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+
+@dataclass
+class InstantXControlNetFluxOutput:
+    controlnet_block_samples: list[torch.Tensor] | None
+    controlnet_single_block_samples: list[torch.Tensor] | None
+
+
+# NOTE(ryand): Mapping between diffusers FLUX transformer params and BFL FLUX transformer params:
+# - Diffusers: BFL
+# - in_channels: in_channels
+# - num_layers: depth
+# - num_single_layers: depth_single_blocks
+# - attention_head_dim: hidden_size // num_heads
+# - num_attention_heads: num_heads
+# - joint_attention_dim: context_in_dim
+# - pooled_projection_dim: vec_in_dim
+# - guidance_embeds: guidance_embed
+# - axes_dims_rope: axes_dim
+
+
+class InstantXControlNetFlux(torch.nn.Module):
+    def __init__(self, params: FluxParams, num_control_modes: int | None = None):
+        """
+        Args:
+            params (FluxParams): The parameters for the FLUX model.
+            num_control_modes (int | None, optional): The number of controlnet modes. If non-None, then the model is a
+                'union controlnet' model and expects a mode conditioning input at runtime.
+        """
+        super().__init__()
+
+        # The following modules mirror the base FLUX transformer model.
+        # -------------------------------------------------------------
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
+        )
+        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        # The following modules are specific to the ControlNet model.
+        # -----------------------------------------------------------
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.double_blocks)):
+            self.controlnet_blocks.append(zero_module(nn.Linear(self.hidden_size, self.hidden_size)))
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(len(self.single_blocks)):
+            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.hidden_size, self.hidden_size)))
+
+        self.is_union = False
+        if num_control_modes is not None:
+            self.is_union = True
+            self.controlnet_mode_embedder = nn.Embedding(num_control_modes, self.hidden_size)
+
+        self.controlnet_x_embedder = zero_module(torch.nn.Linear(self.in_channels, self.hidden_size))
+
+    def forward(
+        self,
+        controlnet_cond: torch.Tensor,
+        controlnet_mode: torch.Tensor | None,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: torch.Tensor,
+        guidance: torch.Tensor | None = None,
+    ) -> InstantXControlNetFluxOutput:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        img = self.img_in(img)
+
+        # Add controlnet_cond embedding.
+        img = img + self.controlnet_x_embedder(controlnet_cond)
+
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        # If this is a union ControlNet, then concat the control mode embedding to the T5 text embedding.
+        if self.is_union:
+            if controlnet_mode is None:
+                # We allow users to enter 'None' as the controlnet_mode if they don't want to worry about this input.
+                # We've chosen to use a zero-embedding in this case.
+                zero_index = torch.zeros([1, 1], dtype=torch.long, device=txt.device)
+                controlnet_mode_emb = torch.zeros_like(self.controlnet_mode_embedder(zero_index))
+            else:
+                controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
+            txt = torch.cat([controlnet_mode_emb, txt], dim=1)
+            txt_ids = torch.cat([txt_ids[:, :1, :], txt_ids], dim=1)
+        else:
+            assert controlnet_mode is None
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        double_block_samples: list[torch.Tensor] = []
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            double_block_samples.append(img)
+
+        img = torch.cat((txt, img), 1)
+
+        single_block_samples: list[torch.Tensor] = []
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+            single_block_samples.append(img[:, txt.shape[1] :])
+
+        # ControlNet Block
+        controlnet_double_block_samples: list[torch.Tensor] = []
+        for double_block_sample, controlnet_block in zip(double_block_samples, self.controlnet_blocks, strict=True):
+            double_block_sample = controlnet_block(double_block_sample)
+            controlnet_double_block_samples.append(double_block_sample)
+
+        controlnet_single_block_samples: list[torch.Tensor] = []
+        for single_block_sample, controlnet_block in zip(
+            single_block_samples, self.controlnet_single_blocks, strict=True
+        ):
+            single_block_sample = controlnet_block(single_block_sample)
+            controlnet_single_block_samples.append(single_block_sample)
+
+        return InstantXControlNetFluxOutput(
+            controlnet_block_samples=controlnet_double_block_samples or None,
+            controlnet_single_block_samples=controlnet_single_block_samples or None,
+        )

invokeai/backend/flux/controlnet/state_dict_utils.py

@@ -0,0 +1,295 @@
+from typing import Any, Dict
+
+import torch
+
+from invokeai.backend.flux.model import FluxParams
+
+
+def is_state_dict_xlabs_controlnet(sd: Dict[str, Any]) -> bool:
+    """Is the state dict for an XLabs ControlNet 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 ControlNet model.
+    expected_keys = {
+        "controlnet_blocks.0.bias",
+        "controlnet_blocks.0.weight",
+        "input_hint_block.0.bias",
+        "input_hint_block.0.weight",
+        "pos_embed_input.bias",
+        "pos_embed_input.weight",
+    }
+
+    if expected_keys.issubset(sd.keys()):
+        return True
+    return False
+
+
+def is_state_dict_instantx_controlnet(sd: Dict[str, Any]) -> bool:
+    """Is the state dict for an InstantX ControlNet 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 InstantX ControlNet model.
+    expected_keys = {
+        "controlnet_blocks.0.bias",
+        "controlnet_blocks.0.weight",
+        "controlnet_x_embedder.bias",
+        "controlnet_x_embedder.weight",
+    }
+
+    if expected_keys.issubset(sd.keys()):
+        return True
+    return False
+
+
+def _fuse_weights(*t: torch.Tensor) -> torch.Tensor:
+    """Fuse weights along dimension 0.
+
+    Used to fuse q, k, v attention weights into a single qkv tensor when converting from diffusers to BFL format.
+    """
+    # TODO(ryand): Double check dim=0 is correct.
+    return torch.cat(t, dim=0)
+
+
+def _convert_flux_double_block_sd_from_diffusers_to_bfl_format(
+    sd: Dict[str, torch.Tensor], double_block_index: int
+) -> Dict[str, torch.Tensor]:
+    """Convert the state dict for a double block from diffusers format to BFL format."""
+    to_prefix = f"double_blocks.{double_block_index}"
+    from_prefix = f"transformer_blocks.{double_block_index}"
+
+    new_sd: dict[str, torch.Tensor] = {}
+
+    # Check one key to determine if this block exists.
+    if f"{from_prefix}.attn.add_q_proj.bias" not in sd:
+        return new_sd
+
+    # txt_attn.qkv
+    new_sd[f"{to_prefix}.txt_attn.qkv.bias"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.add_q_proj.bias"),
+        sd.pop(f"{from_prefix}.attn.add_k_proj.bias"),
+        sd.pop(f"{from_prefix}.attn.add_v_proj.bias"),
+    )
+    new_sd[f"{to_prefix}.txt_attn.qkv.weight"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.add_q_proj.weight"),
+        sd.pop(f"{from_prefix}.attn.add_k_proj.weight"),
+        sd.pop(f"{from_prefix}.attn.add_v_proj.weight"),
+    )
+
+    # img_attn.qkv
+    new_sd[f"{to_prefix}.img_attn.qkv.bias"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.bias"),
+        sd.pop(f"{from_prefix}.attn.to_k.bias"),
+        sd.pop(f"{from_prefix}.attn.to_v.bias"),
+    )
+    new_sd[f"{to_prefix}.img_attn.qkv.weight"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.weight"),
+        sd.pop(f"{from_prefix}.attn.to_k.weight"),
+        sd.pop(f"{from_prefix}.attn.to_v.weight"),
+    )
+
+    # Handle basic 1-to-1 key conversions.
+    key_map = {
+        # img_attn
+        "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
+        "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
+        "attn.to_out.0.weight": "img_attn.proj.weight",
+        "attn.to_out.0.bias": "img_attn.proj.bias",
+        # img_mlp
+        "ff.net.0.proj.weight": "img_mlp.0.weight",
+        "ff.net.0.proj.bias": "img_mlp.0.bias",
+        "ff.net.2.weight": "img_mlp.2.weight",
+        "ff.net.2.bias": "img_mlp.2.bias",
+        # img_mod
+        "norm1.linear.weight": "img_mod.lin.weight",
+        "norm1.linear.bias": "img_mod.lin.bias",
+        # txt_attn
+        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
+        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
+        "attn.to_add_out.weight": "txt_attn.proj.weight",
+        "attn.to_add_out.bias": "txt_attn.proj.bias",
+        # txt_mlp
+        "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
+        "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
+        "ff_context.net.2.weight": "txt_mlp.2.weight",
+        "ff_context.net.2.bias": "txt_mlp.2.bias",
+        # txt_mod
+        "norm1_context.linear.weight": "txt_mod.lin.weight",
+        "norm1_context.linear.bias": "txt_mod.lin.bias",
+    }
+    for from_key, to_key in key_map.items():
+        new_sd[f"{to_prefix}.{to_key}"] = sd.pop(f"{from_prefix}.{from_key}")
+
+    return new_sd
+
+
+def _convert_flux_single_block_sd_from_diffusers_to_bfl_format(
+    sd: Dict[str, torch.Tensor], single_block_index: int
+) -> Dict[str, torch.Tensor]:
+    """Convert the state dict for a single block from diffusers format to BFL format."""
+    to_prefix = f"single_blocks.{single_block_index}"
+    from_prefix = f"single_transformer_blocks.{single_block_index}"
+
+    new_sd: dict[str, torch.Tensor] = {}
+
+    # Check one key to determine if this block exists.
+    if f"{from_prefix}.attn.to_q.bias" not in sd:
+        return new_sd
+
+    # linear1 (qkv)
+    new_sd[f"{to_prefix}.linear1.bias"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.bias"),
+        sd.pop(f"{from_prefix}.attn.to_k.bias"),
+        sd.pop(f"{from_prefix}.attn.to_v.bias"),
+        sd.pop(f"{from_prefix}.proj_mlp.bias"),
+    )
+    new_sd[f"{to_prefix}.linear1.weight"] = _fuse_weights(
+        sd.pop(f"{from_prefix}.attn.to_q.weight"),
+        sd.pop(f"{from_prefix}.attn.to_k.weight"),
+        sd.pop(f"{from_prefix}.attn.to_v.weight"),
+        sd.pop(f"{from_prefix}.proj_mlp.weight"),
+    )
+
+    # Handle basic 1-to-1 key conversions.
+    key_map = {
+        # linear2
+        "proj_out.weight": "linear2.weight",
+        "proj_out.bias": "linear2.bias",
+        # modulation
+        "norm.linear.weight": "modulation.lin.weight",
+        "norm.linear.bias": "modulation.lin.bias",
+        # norm
+        "attn.norm_k.weight": "norm.key_norm.scale",
+        "attn.norm_q.weight": "norm.query_norm.scale",
+    }
+    for from_key, to_key in key_map.items():
+        new_sd[f"{to_prefix}.{to_key}"] = sd.pop(f"{from_prefix}.{from_key}")
+
+    return new_sd
+
+
+def convert_diffusers_instantx_state_dict_to_bfl_format(sd: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    """Convert an InstantX ControlNet state dict to the format that can be loaded by our internal
+    InstantXControlNetFlux model.
+
+    The original InstantX ControlNet model was developed to be used in diffusers. We have ported the original
+    implementation to InstantXControlNetFlux to make it compatible with BFL-style models. This function converts the
+    original state dict to the format expected by InstantXControlNetFlux.
+    """
+    # Shallow copy sd so that we can pop keys from it without modifying the original.
+    sd = sd.copy()
+
+    new_sd: dict[str, torch.Tensor] = {}
+
+    # Handle basic 1-to-1 key conversions.
+    basic_key_map = {
+        # Base model keys.
+        # ----------------
+        # txt_in keys.
+        "context_embedder.bias": "txt_in.bias",
+        "context_embedder.weight": "txt_in.weight",
+        # guidance_in MLPEmbedder keys.
+        "time_text_embed.guidance_embedder.linear_1.bias": "guidance_in.in_layer.bias",
+        "time_text_embed.guidance_embedder.linear_1.weight": "guidance_in.in_layer.weight",
+        "time_text_embed.guidance_embedder.linear_2.bias": "guidance_in.out_layer.bias",
+        "time_text_embed.guidance_embedder.linear_2.weight": "guidance_in.out_layer.weight",
+        # vector_in MLPEmbedder keys.
+        "time_text_embed.text_embedder.linear_1.bias": "vector_in.in_layer.bias",
+        "time_text_embed.text_embedder.linear_1.weight": "vector_in.in_layer.weight",
+        "time_text_embed.text_embedder.linear_2.bias": "vector_in.out_layer.bias",
+        "time_text_embed.text_embedder.linear_2.weight": "vector_in.out_layer.weight",
+        # time_in MLPEmbedder keys.
+        "time_text_embed.timestep_embedder.linear_1.bias": "time_in.in_layer.bias",
+        "time_text_embed.timestep_embedder.linear_1.weight": "time_in.in_layer.weight",
+        "time_text_embed.timestep_embedder.linear_2.bias": "time_in.out_layer.bias",
+        "time_text_embed.timestep_embedder.linear_2.weight": "time_in.out_layer.weight",
+        # img_in keys.
+        "x_embedder.bias": "img_in.bias",
+        "x_embedder.weight": "img_in.weight",
+    }
+    for old_key, new_key in basic_key_map.items():
+        v = sd.pop(old_key, None)
+        if v is not None:
+            new_sd[new_key] = v
+
+    # Handle the double_blocks.
+    block_index = 0
+    while True:
+        converted_double_block_sd = _convert_flux_double_block_sd_from_diffusers_to_bfl_format(sd, block_index)
+        if len(converted_double_block_sd) == 0:
+            break
+        new_sd.update(converted_double_block_sd)
+        block_index += 1
+
+    # Handle the single_blocks.
+    block_index = 0
+    while True:
+        converted_singe_block_sd = _convert_flux_single_block_sd_from_diffusers_to_bfl_format(sd, block_index)
+        if len(converted_singe_block_sd) == 0:
+            break
+        new_sd.update(converted_singe_block_sd)
+        block_index += 1
+
+    # Transfer controlnet keys as-is.
+    for k in list(sd.keys()):
+        if k.startswith("controlnet_"):
+            new_sd[k] = sd.pop(k)
+
+    # Assert that all keys have been handled.
+    assert len(sd) == 0
+    return new_sd
+
+
+def infer_flux_params_from_state_dict(sd: Dict[str, torch.Tensor]) -> FluxParams:
+    """Infer the FluxParams from the shape of a FLUX state dict. When a model is distributed in diffusers format, this
+    information is all contained in the config.json file that accompanies the model. However, being apple to infer the
+    params from the state dict enables us to load models (e.g. an InstantX ControlNet) from a single weight file.
+    """
+    hidden_size = sd["img_in.weight"].shape[0]
+    mlp_hidden_dim = sd["double_blocks.0.img_mlp.0.weight"].shape[0]
+    # mlp_ratio is a float, but we treat it as an int here to avoid having to think about possible float precision
+    # issues. In practice, mlp_ratio is usually 4.
+    mlp_ratio = mlp_hidden_dim // hidden_size
+
+    head_dim = sd["double_blocks.0.img_attn.norm.query_norm.scale"].shape[0]
+    num_heads = hidden_size // head_dim
+
+    # Count the number of double blocks.
+    double_block_index = 0
+    while f"double_blocks.{double_block_index}.img_attn.qkv.weight" in sd:
+        double_block_index += 1
+
+    # Count the number of single blocks.
+    single_block_index = 0
+    while f"single_blocks.{single_block_index}.linear1.weight" in sd:
+        single_block_index += 1
+
+    return FluxParams(
+        in_channels=sd["img_in.weight"].shape[1],
+        vec_in_dim=sd["vector_in.in_layer.weight"].shape[1],
+        context_in_dim=sd["txt_in.weight"].shape[1],
+        hidden_size=hidden_size,
+        mlp_ratio=mlp_ratio,
+        num_heads=num_heads,
+        depth=double_block_index,
+        depth_single_blocks=single_block_index,
+        # axes_dim cannot be inferred from the state dict. The hard-coded value is correct for dev/schnell models.
+        axes_dim=[16, 56, 56],
+        # theta cannot be inferred from the state dict. The hard-coded value is correct for dev/schnell models.
+        theta=10_000,
+        qkv_bias="double_blocks.0.img_attn.qkv.bias" in sd,
+        guidance_embed="guidance_in.in_layer.weight" in sd,
+    )
+
+
+def infer_instantx_num_control_modes_from_state_dict(sd: Dict[str, torch.Tensor]) -> int | None:
+    """Infer the number of ControlNet Union modes from the shape of a InstantX ControlNet state dict.
+
+    Returns None if the model is not a ControlNet Union model. Otherwise returns the number of modes.
+    """
+    mode_embedder_key = "controlnet_mode_embedder.weight"
+    if mode_embedder_key not in sd:
+        return None
+
+    return sd[mode_embedder_key].shape[0]

invokeai/backend/flux/controlnet/xlabs_controlnet_flux.py

@@ -0,0 +1,130 @@
+# This file was initially based on:
+# https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/controlnet.py
+
+
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+
+from invokeai.backend.flux.controlnet.zero_module import zero_module
+from invokeai.backend.flux.model import FluxParams
+from invokeai.backend.flux.modules.layers import DoubleStreamBlock, EmbedND, MLPEmbedder, timestep_embedding
+
+
+@dataclass
+class XLabsControlNetFluxOutput:
+    controlnet_double_block_residuals: list[torch.Tensor] | None
+
+
+class XLabsControlNetFlux(torch.nn.Module):
+    """A ControlNet model for FLUX.
+
+    The architecture is very similar to the base FLUX model, with the following differences:
+    - A `controlnet_depth` parameter is passed to control the number of double_blocks that the ControlNet is applied to.
+      In order to keep the ControlNet small, this is typically much less than the depth of the base FLUX model.
+    - There is a set of `controlnet_blocks` that are applied to the output of each double_block.
+    """
+
+    def __init__(self, params: FluxParams, controlnet_depth: int = 2):
+        super().__init__()
+
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else torch.nn.Identity()
+        )
+        self.txt_in = torch.nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = torch.nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(controlnet_depth)
+            ]
+        )
+
+        # Add ControlNet blocks.
+        self.controlnet_blocks = torch.nn.ModuleList([])
+        for _ in range(controlnet_depth):
+            controlnet_block = torch.nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = zero_module(controlnet_block)
+            self.controlnet_blocks.append(controlnet_block)
+        self.pos_embed_input = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.input_hint_block = torch.nn.Sequential(
+            torch.nn.Conv2d(3, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            zero_module(torch.nn.Conv2d(16, 16, 3, padding=1)),
+        )
+
+    def forward(
+        self,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: torch.Tensor,
+        guidance: torch.Tensor | None = None,
+    ) -> XLabsControlNetFluxOutput:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        controlnet_cond = self.input_hint_block(controlnet_cond)
+        controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        controlnet_cond = self.pos_embed_input(controlnet_cond)
+        img = img + controlnet_cond
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        block_res_samples: list[torch.Tensor] = []
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            block_res_samples.append(img)
+
+        controlnet_block_res_samples: list[torch.Tensor] = []
+        for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks, strict=True):
+            block_res_sample = controlnet_block(block_res_sample)
+            controlnet_block_res_samples.append(block_res_sample)
+
+        return XLabsControlNetFluxOutput(controlnet_double_block_residuals=controlnet_block_res_samples)

invokeai/backend/flux/controlnet/zero_module.py

@@ -0,0 +1,12 @@
+from typing import TypeVar
+
+import torch
+
+T = TypeVar("T", bound=torch.nn.Module)
+
+
+def zero_module(module: T) -> T:
+    """Initialize the parameters of a module to zero."""
+    for p in module.parameters():
+        torch.nn.init.zeros_(p)
+    return module

invokeai/backend/flux/custom_block_processor.py

@@ -0,0 +1,83 @@
+import einops
+import torch
+
+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
+
+
+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
+    ) -> 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)
+        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],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """A custom implementation of DoubleStreamBlock.forward() with additional features:
+        - IP-Adapter support
+        """
+        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(block, img, txt, vec, pe)
+
+        # 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

invokeai/backend/flux/denoise.py

@@ -1,9 +1,14 @@
+import math
 from typing import Callable
 
 import torch
 from tqdm import tqdm
 
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
+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.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
 
@@ -13,14 +18,23 @@ def denoise(
     # model input
     img: torch.Tensor,
     img_ids: torch.Tensor,
+    # positive text conditioning
     txt: torch.Tensor,
     txt_ids: torch.Tensor,
     vec: torch.Tensor,
+    # negative text conditioning
+    neg_txt: torch.Tensor | None,
+    neg_txt_ids: torch.Tensor | None,
+    neg_vec: torch.Tensor | None,
     # 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
@@ -33,11 +47,34 @@ 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 t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
+    for step_index, (t_curr, t_prev) in tqdm(list(enumerate(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.
+        controlnet_residuals: list[ControlNetFluxOutput] = []
+        for controlnet_extension in controlnet_extensions:
+            controlnet_residuals.append(
+                controlnet_extension.run_controlnet(
+                    timestep_index=step_index,
+                    total_num_timesteps=total_steps,
+                    img=img,
+                    img_ids=img_ids,
+                    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
+        # controlnet_residuals datastructure is efficient in that it likely contains multiple references to the same
+        # tensors. Calculating the sum materializes each tensor into its own instance.
+        merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
+
         pred = model(
             img=img,
             img_ids=img_ids,
@@ -46,8 +83,39 @@ def denoise(
             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,
         )
 
+        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_txt is None or neg_txt_ids is None or neg_vec is None:
+                raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
+
+            neg_pred = model(
+                img=img,
+                img_ids=img_ids,
+                txt=neg_txt,
+                txt_ids=neg_txt_ids,
+                y=neg_vec,
+                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,
+            )
+            pred = neg_pred + step_cfg_scale * (pred - neg_pred)
+
         preview_img = img - t_curr * pred
         img = img + (t_prev - t_curr) * pred
 
@@ -57,13 +125,12 @@ def denoise(
 
         step_callback(
             PipelineIntermediateState(
-                step=step,
+                step=step_index + 1,
                 order=1,
                 total_steps=total_steps,
                 timestep=int(t_curr),
                 latents=preview_img,
             ),
         )
-        step += 1
 
     return img

invokeai/backend/flux/extensions/base_controlnet_extension.py

@@ -0,0 +1,45 @@
+import math
+from abc import ABC, abstractmethod
+from typing import List, Union
+
+import torch
+
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
+
+
+class BaseControlNetExtension(ABC):
+    def __init__(
+        self,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        self._weight = weight
+        self._begin_step_percent = begin_step_percent
+        self._end_step_percent = end_step_percent
+
+    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
+
+    @abstractmethod
+    def run_controlnet(
+        self,
+        timestep_index: int,
+        total_num_timesteps: int,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        y: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance: torch.Tensor | None,
+    ) -> ControlNetFluxOutput: ...

invokeai/backend/flux/extensions/instantx_controlnet_extension.py

@@ -0,0 +1,194 @@
+import math
+from typing import List, Union
+
+import torch
+from PIL.Image import Image
+
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES, prepare_control_image
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import (
+    InstantXControlNetFlux,
+    InstantXControlNetFluxOutput,
+)
+from invokeai.backend.flux.extensions.base_controlnet_extension import BaseControlNetExtension
+from invokeai.backend.flux.sampling_utils import pack
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+
+
+class InstantXControlNetExtension(BaseControlNetExtension):
+    def __init__(
+        self,
+        model: InstantXControlNetFlux,
+        controlnet_cond: torch.Tensor,
+        instantx_control_mode: torch.Tensor | None,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        super().__init__(
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+        self._model = model
+        # The VAE-encoded and 'packed' control image to pass to the ControlNet model.
+        self._controlnet_cond = controlnet_cond
+        # TODO(ryand): Should we define an enum for the instantx_control_mode? Is it likely to change for future models?
+        # The control mode for InstantX ControlNet union models.
+        # See the values defined here: https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union#control-mode
+        # Expected shape: (batch_size, 1), Expected dtype: torch.long
+        # If None, a zero-embedding will be used.
+        self._instantx_control_mode = instantx_control_mode
+
+        # TODO(ryand): Pass in these params if a new base transformer / InstantX ControlNet pair get released.
+        self._flux_transformer_num_double_blocks = 19
+        self._flux_transformer_num_single_blocks = 38
+
+    @classmethod
+    def prepare_controlnet_cond(
+        cls,
+        controlnet_image: Image,
+        vae_info: LoadedModel,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        resized_controlnet_image = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Shift the image from [0, 1] to [-1, 1].
+        resized_controlnet_image = resized_controlnet_image * 2 - 1
+
+        # Run VAE encoder.
+        controlnet_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=resized_controlnet_image)
+        controlnet_cond = pack(controlnet_cond)
+
+        return controlnet_cond
+
+    @classmethod
+    def from_controlnet_image(
+        cls,
+        model: InstantXControlNetFlux,
+        controlnet_image: Image,
+        instantx_control_mode: torch.Tensor | None,
+        vae_info: LoadedModel,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        resized_controlnet_image = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Shift the image from [0, 1] to [-1, 1].
+        resized_controlnet_image = resized_controlnet_image * 2 - 1
+
+        # Run VAE encoder.
+        controlnet_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=resized_controlnet_image)
+        controlnet_cond = pack(controlnet_cond)
+
+        return cls(
+            model=model,
+            controlnet_cond=controlnet_cond,
+            instantx_control_mode=instantx_control_mode,
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+
+    def _instantx_output_to_controlnet_output(
+        self, instantx_output: InstantXControlNetFluxOutput
+    ) -> ControlNetFluxOutput:
+        # The `interval_control` logic here is based on
+        # https://github.com/huggingface/diffusers/blob/31058cdaef63ca660a1a045281d156239fba8192/src/diffusers/models/transformers/transformer_flux.py#L507-L511
+
+        # Handle double block residuals.
+        double_block_residuals: list[torch.Tensor] = []
+        double_block_samples = instantx_output.controlnet_block_samples
+        if double_block_samples:
+            interval_control = self._flux_transformer_num_double_blocks / len(double_block_samples)
+            interval_control = int(math.ceil(interval_control))
+            for i in range(self._flux_transformer_num_double_blocks):
+                double_block_residuals.append(double_block_samples[i // interval_control])
+
+        # Handle single block residuals.
+        single_block_residuals: list[torch.Tensor] = []
+        single_block_samples = instantx_output.controlnet_single_block_samples
+        if single_block_samples:
+            interval_control = self._flux_transformer_num_single_blocks / len(single_block_samples)
+            interval_control = int(math.ceil(interval_control))
+            for i in range(self._flux_transformer_num_single_blocks):
+                single_block_residuals.append(single_block_samples[i // interval_control])
+
+        return ControlNetFluxOutput(
+            double_block_residuals=double_block_residuals or None,
+            single_block_residuals=single_block_residuals or None,
+        )
+
+    def run_controlnet(
+        self,
+        timestep_index: int,
+        total_num_timesteps: int,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        y: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance: torch.Tensor | None,
+    ) -> ControlNetFluxOutput:
+        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
+        if weight < 1e-6:
+            return ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
+
+        # Make sure inputs have correct device and dtype.
+        self._controlnet_cond = self._controlnet_cond.to(device=img.device, dtype=img.dtype)
+        self._instantx_control_mode = (
+            self._instantx_control_mode.to(device=img.device) if self._instantx_control_mode is not None else None
+        )
+
+        instantx_output: InstantXControlNetFluxOutput = self._model(
+            controlnet_cond=self._controlnet_cond,
+            controlnet_mode=self._instantx_control_mode,
+            img=img,
+            img_ids=img_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            timesteps=timesteps,
+            y=y,
+            guidance=guidance,
+        )
+
+        controlnet_output = self._instantx_output_to_controlnet_output(instantx_output)
+        controlnet_output.apply_weight(weight)
+        return controlnet_output

invokeai/backend/flux/extensions/xlabs_controlnet_extension.py

@@ -0,0 +1,150 @@
+from typing import List, Union
+
+import torch
+from PIL.Image import Image
+
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES, prepare_control_image
+from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput
+from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux, XLabsControlNetFluxOutput
+from invokeai.backend.flux.extensions.base_controlnet_extension import BaseControlNetExtension
+
+
+class XLabsControlNetExtension(BaseControlNetExtension):
+    def __init__(
+        self,
+        model: XLabsControlNetFlux,
+        controlnet_cond: torch.Tensor,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        super().__init__(
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+
+        self._model = model
+        # _controlnet_cond is the control image passed to the ControlNet model.
+        # Pixel values are in the range [-1, 1]. Shape: (batch_size, 3, height, width).
+        self._controlnet_cond = controlnet_cond
+
+        # TODO(ryand): Pass in these params if a new base transformer / XLabs ControlNet pair get released.
+        self._flux_transformer_num_double_blocks = 19
+        self._flux_transformer_num_single_blocks = 38
+
+    @classmethod
+    def prepare_controlnet_cond(
+        cls,
+        controlnet_image: Image,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        controlnet_cond = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Map pixel values from [0, 1] to [-1, 1].
+        controlnet_cond = controlnet_cond * 2 - 1
+
+        return controlnet_cond
+
+    @classmethod
+    def from_controlnet_image(
+        cls,
+        model: XLabsControlNetFlux,
+        controlnet_image: Image,
+        latent_height: int,
+        latent_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        resize_mode: CONTROLNET_RESIZE_VALUES,
+        weight: Union[float, List[float]],
+        begin_step_percent: float,
+        end_step_percent: float,
+    ):
+        image_height = latent_height * LATENT_SCALE_FACTOR
+        image_width = latent_width * LATENT_SCALE_FACTOR
+
+        controlnet_cond = prepare_control_image(
+            image=controlnet_image,
+            do_classifier_free_guidance=False,
+            width=image_width,
+            height=image_height,
+            device=device,
+            dtype=dtype,
+            control_mode="balanced",
+            resize_mode=resize_mode,
+        )
+
+        # Map pixel values from [0, 1] to [-1, 1].
+        controlnet_cond = controlnet_cond * 2 - 1
+
+        return cls(
+            model=model,
+            controlnet_cond=controlnet_cond,
+            weight=weight,
+            begin_step_percent=begin_step_percent,
+            end_step_percent=end_step_percent,
+        )
+
+    def _xlabs_output_to_controlnet_output(self, xlabs_output: XLabsControlNetFluxOutput) -> ControlNetFluxOutput:
+        # The modulo index logic used here is based on:
+        # https://github.com/XLabs-AI/x-flux/blob/47495425dbed499be1e8e5a6e52628b07349cba2/src/flux/model.py#L198-L200
+
+        # Handle double block residuals.
+        double_block_residuals: list[torch.Tensor] = []
+        xlabs_double_block_residuals = xlabs_output.controlnet_double_block_residuals
+        if xlabs_double_block_residuals is not None:
+            for i in range(self._flux_transformer_num_double_blocks):
+                double_block_residuals.append(xlabs_double_block_residuals[i % len(xlabs_double_block_residuals)])
+
+        return ControlNetFluxOutput(
+            double_block_residuals=double_block_residuals,
+            single_block_residuals=None,
+        )
+
+    def run_controlnet(
+        self,
+        timestep_index: int,
+        total_num_timesteps: int,
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        y: torch.Tensor,
+        timesteps: torch.Tensor,
+        guidance: torch.Tensor | None,
+    ) -> ControlNetFluxOutput:
+        weight = self._get_weight(timestep_index=timestep_index, total_num_timesteps=total_num_timesteps)
+        if weight < 1e-6:
+            return ControlNetFluxOutput(single_block_residuals=None, double_block_residuals=None)
+
+        xlabs_output: XLabsControlNetFluxOutput = self._model(
+            img=img,
+            img_ids=img_ids,
+            controlnet_cond=self._controlnet_cond,
+            txt=txt,
+            txt_ids=txt_ids,
+            timesteps=timesteps,
+            y=y,
+            guidance=guidance,
+        )
+
+        controlnet_output = self._xlabs_output_to_controlnet_output(xlabs_output)
+        controlnet_output.apply_weight(weight)
+        return controlnet_output

invokeai/backend/flux/extensions/xlabs_ip_adapter_extension.py

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

@@ -0,0 +1,93 @@
+# 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

@@ -0,0 +1,50 @@
+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]
+
+    return XlabsIpAdapterParams(
+        num_double_blocks=num_double_blocks,
+        context_dim=context_dim,
+        hidden_dim=hidden_dim,
+        clip_embeddings_dim=clip_embeddings_dim,
+    )

invokeai/backend/flux/ip_adapter/xlabs_ip_adapter_flux.py

@@ -0,0 +1,67 @@
+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
+
+
+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
+        )
+        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

@@ -16,7 +16,10 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    scale = (
+        torch.arange(0, dim, 2, dtype=torch.float32 if pos.device.type == "mps" else torch.float64, device=pos.device)
+        / dim
+    )
     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)

invokeai/backend/flux/model.py

@@ -5,6 +5,8 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 
+from invokeai.backend.flux.custom_block_processor import CustomDoubleStreamBlockProcessor
+from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.modules.layers import (
     DoubleStreamBlock,
     EmbedND,
@@ -87,7 +89,12 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
-        guidance: Tensor | None = None,
+        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],
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -105,12 +112,39 @@ class Flux(nn.Module):
         ids = torch.cat((txt_ids, img_ids), dim=1)
         pe = self.pe_embedder(ids)
 
-        for block in self.double_blocks:
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+        # Validate double_block_residuals shape.
+        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,
+            )
+
+            if controlnet_double_block_residuals is not None:
+                img += controlnet_double_block_residuals[block_index]
 
         img = torch.cat((txt, img), 1)
-        for block in self.single_blocks:
+
+        # Validate single_block_residuals shape.
+        if controlnet_single_block_residuals is not None:
+            assert len(controlnet_single_block_residuals) == len(self.single_blocks)
+
+        for block_index, block in enumerate(self.single_blocks):
             img = block(img, vec=vec, pe=pe)
+
+            if controlnet_single_block_residuals is not None:
+                img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]
+
         img = img[:, txt.shape[1] :, ...]
 
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)

invokeai/backend/flux/sampling_utils.py

@@ -168,8 +168,17 @@ 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/image_util/segment_anything/segment_anything_pipeline.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, TypeAlias
 
 import torch
 from PIL import Image
@@ -7,6 +7,14 @@ 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."""
@@ -27,20 +35,53 @@ class SegmentAnythingPipeline(RawModel):
 
         return calc_module_size(self._sam_model)
 
-    def segment(self, image: Image.Image, bounding_boxes: list[list[int]]) -> torch.Tensor:
+    def segment(
+        self,
+        image: Image.Image,
+        bounding_boxes: list[list[int]] | None = None,
+        point_lists: list[list[list[int]]] | None = None,
+    ) -> 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].
         """
-        # 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)
+
+        # 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)
         outputs = self._sam_model(**inputs)
         masks = self._sam_processor.post_process_masks(
             masks=outputs.pred_masks,

invokeai/backend/model_manager/config.py

@@ -53,6 +53,7 @@ class BaseModelType(str, Enum):
     Any = "any"
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
+    StableDiffusion3 = "sd-3"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
@@ -83,8 +84,10 @@ 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"
@@ -92,6 +95,13 @@ class SubModelType(str, Enum):
     SafetyChecker = "safety_checker"
 
 
+class ClipVariantType(str, Enum):
+    """Variant type."""
+
+    L = "large"
+    G = "gigantic"
+
+
 class ModelVariantType(str, Enum):
     """Variant type."""
 
@@ -114,6 +124,7 @@ class ModelFormat(str, Enum):
     T5Encoder = "t5_encoder"
     BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
     BnbQuantizednf4b = "bnb_quantized_nf4b"
+    GGUFQuantized = "gguf_quantized"
 
 
 class SchedulerPredictionType(str, Enum):
@@ -146,6 +157,15 @@ 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
+
+
 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")
@@ -192,12 +212,15 @@ 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):
     """Model config for checkpoint-style models."""
 
-    format: Literal[ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b] = Field(
+    format: Literal[ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b, ModelFormat.GGUFQuantized] = Field(
         description="Format of the provided checkpoint model", default=ModelFormat.Checkpoint
     )
     config_path: str = Field(description="path to the checkpoint model config file")
@@ -334,7 +357,7 @@ class MainConfigBase(ModelConfigBase):
     default_settings: Optional[MainModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
-    variant: ModelVariantType = ModelVariantType.Normal
+    variant: AnyVariant = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
@@ -363,6 +386,21 @@ class MainBnbQuantized4bCheckpointConfig(CheckpointConfigBase, MainConfigBase):
         return Tag(f"{ModelType.Main.value}.{ModelFormat.BnbQuantizednf4b.value}")
 
 
+class MainGGUFCheckpointConfig(CheckpointConfigBase, MainConfigBase):
+    """Model config for main checkpoint models."""
+
+    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
+    upcast_attention: bool = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.format = ModelFormat.GGUFQuantized
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.Main.value}.{ModelFormat.GGUFQuantized.value}")
+
+
 class MainDiffusersConfig(DiffusersConfigBase, MainConfigBase):
     """Model config for main diffusers models."""
 
@@ -378,6 +416,8 @@ 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]
 
@@ -401,12 +441,33 @@ 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."""
 
@@ -466,6 +527,7 @@ AnyModelConfig = Annotated[
         Annotated[MainDiffusersConfig, MainDiffusersConfig.get_tag()],
         Annotated[MainCheckpointConfig, MainCheckpointConfig.get_tag()],
         Annotated[MainBnbQuantized4bCheckpointConfig, MainBnbQuantized4bCheckpointConfig.get_tag()],
+        Annotated[MainGGUFCheckpointConfig, MainGGUFCheckpointConfig.get_tag()],
         Annotated[VAEDiffusersConfig, VAEDiffusersConfig.get_tag()],
         Annotated[VAECheckpointConfig, VAECheckpointConfig.get_tag()],
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
@@ -482,6 +544,8 @@ 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/model_loaders/clip_vision.py

@@ -0,0 +1,41 @@
+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/controlnet.py

@@ -8,17 +8,36 @@ from diffusers import ControlNetModel
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
+)
+from invokeai.backend.model_manager.config import (
     BaseModelType,
+    ControlNetCheckpointConfig,
     ModelFormat,
     ModelType,
+    SubModelType,
 )
-from invokeai.backend.model_manager.config import ControlNetCheckpointConfig, SubModelType
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Diffusers
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion1, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Diffusers
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusion2, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Diffusers
+)
+@ModelLoaderRegistry.register(
+    base=BaseModelType.StableDiffusionXL, type=ModelType.ControlNet, format=ModelFormat.Checkpoint
+)
 class ControlNetLoader(GenericDiffusersLoader):
     """Class to load ControlNet models."""
 

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

@@ -10,6 +10,19 @@ from safetensors.torch import load_file
 from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
 from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
+from invokeai.backend.flux.controlnet.state_dict_utils import (
+    convert_diffusers_instantx_state_dict_to_bfl_format,
+    infer_flux_params_from_state_dict,
+    infer_instantx_num_control_modes_from_state_dict,
+    is_state_dict_instantx_controlnet,
+    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
@@ -24,8 +37,12 @@ from invokeai.backend.model_manager import (
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
     CLIPEmbedDiffusersConfig,
+    ControlNetCheckpointConfig,
+    ControlNetDiffusersConfig,
+    IPAdapterCheckpointConfig,
     MainBnbQuantized4bCheckpointConfig,
     MainCheckpointConfig,
+    MainGGUFCheckpointConfig,
     T5EncoderBnbQuantizedLlmInt8bConfig,
     T5EncoderConfig,
     VAECheckpointConfig,
@@ -35,6 +52,8 @@ from invokeai.backend.model_manager.load.model_loader_registry import ModelLoade
 from invokeai.backend.model_manager.util.model_util import (
     convert_bundle_to_flux_transformer_checkpoint,
 )
+from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
+from invokeai.backend.quantization.gguf.utils import TORCH_COMPATIBLE_QTYPES
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 try:
@@ -109,9 +128,9 @@ class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
                 "The bnb modules are not available. Please install bitsandbytes if available on your platform."
             )
         match submodel_type:
-            case SubModelType.Tokenizer2:
+            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
+            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 te2_model_path = Path(config.path) / "text_encoder_2"
                 model_config = AutoConfig.from_pretrained(te2_model_path)
                 with accelerate.init_empty_weights():
@@ -153,10 +172,10 @@ class T5EncoderCheckpointModel(ModelLoader):
             raise ValueError("Only T5EncoderConfig models are currently supported here.")
 
         match submodel_type:
-            case SubModelType.Tokenizer2:
+            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
-                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2")
+            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
+                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2", torch_dtype="auto")
 
         raise ValueError(
             f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
@@ -204,6 +223,52 @@ class FluxCheckpointModel(ModelLoader):
         return model
 
 
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.GGUFQuantized)
+class FluxGGUFCheckpointModel(ModelLoader):
+    """Class to load GGUF main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, CheckpointConfigBase):
+            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
+
+        match submodel_type:
+            case SubModelType.Transformer:
+                return self._load_from_singlefile(config)
+
+        raise ValueError(
+            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
+        )
+
+    def _load_from_singlefile(
+        self,
+        config: AnyModelConfig,
+    ) -> AnyModel:
+        assert isinstance(config, MainGGUFCheckpointConfig)
+        model_path = Path(config.path)
+
+        with SilenceWarnings():
+            model = Flux(params[config.config_path])
+
+            # HACK(ryand): We shouldn't be hard-coding the compute_dtype here.
+            sd = gguf_sd_loader(model_path, compute_dtype=torch.bfloat16)
+
+        # HACK(ryand): There are some broken GGUF models in circulation that have the wrong shape for img_in.weight.
+        # We override the shape here to fix the issue.
+        # Example model with this issue (Q4_K_M): https://civitai.com/models/705823/ggufk-flux-unchained-km-quants
+        img_in_weight = sd.get("img_in.weight", None)
+        if img_in_weight is not None and img_in_weight._ggml_quantization_type in TORCH_COMPATIBLE_QTYPES:
+            expected_img_in_weight_shape = model.img_in.weight.shape
+            img_in_weight.quantized_data = img_in_weight.quantized_data.view(expected_img_in_weight_shape)
+            img_in_weight.tensor_shape = expected_img_in_weight_shape
+
+        model.load_state_dict(sd, assign=True)
+        return model
+
+
 @ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.BnbQuantizednf4b)
 class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
     """Class to load main models."""
@@ -244,3 +309,74 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
                 sd = convert_bundle_to_flux_transformer_checkpoint(sd)
             model.load_state_dict(sd, assign=True)
         return model
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
+class FluxControlnetModel(ModelLoader):
+    """Class to load FLUX ControlNet models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, ControlNetCheckpointConfig):
+            model_path = Path(config.path)
+        elif isinstance(config, ControlNetDiffusersConfig):
+            # If this is a diffusers directory, we simply ignore the config file and load from the weight file.
+            model_path = Path(config.path) / "diffusion_pytorch_model.safetensors"
+        else:
+            raise ValueError(f"Unexpected ControlNet model config type: {type(config)}")
+
+        sd = load_file(model_path)
+
+        # Detect the FLUX ControlNet model type from the state dict.
+        if is_state_dict_xlabs_controlnet(sd):
+            return self._load_xlabs_controlnet(sd)
+        elif is_state_dict_instantx_controlnet(sd):
+            return self._load_instantx_controlnet(sd)
+        else:
+            raise ValueError("Do not recognize the state dict as an XLabs or InstantX ControlNet model.")
+
+    def _load_xlabs_controlnet(self, sd: dict[str, torch.Tensor]) -> AnyModel:
+        with accelerate.init_empty_weights():
+            # HACK(ryand): Is it safe to assume dev here?
+            model = XLabsControlNetFlux(params["flux-dev"])
+
+        model.load_state_dict(sd, assign=True)
+        return model
+
+    def _load_instantx_controlnet(self, sd: dict[str, torch.Tensor]) -> AnyModel:
+        sd = convert_diffusers_instantx_state_dict_to_bfl_format(sd)
+        flux_params = infer_flux_params_from_state_dict(sd)
+        num_control_modes = infer_instantx_num_control_modes_from_state_dict(sd)
+
+        with accelerate.init_empty_weights():
+            model = InstantXControlNetFlux(flux_params, num_control_modes)
+
+        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

@@ -22,7 +22,6 @@ 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

@@ -42,6 +42,7 @@ 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)
@@ -51,13 +52,6 @@ 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,
@@ -117,8 +111,6 @@ 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`.
@@ -129,13 +121,7 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         # ['text_model.embeddings.position_ids']
 
         with SilenceWarnings():
-            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,
-            )
+            pipeline = load_class.from_single_file(config.path, torch_dtype=self._torch_dtype)
 
         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.utils._errors import RepositoryNotFoundError, RevisionNotFoundError
+from huggingface_hub.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, Dict, Literal, Optional, Union
+from typing import Any, Callable, Dict, Literal, Optional, Union
 
 import safetensors.torch
 import spandrel
@@ -10,6 +10,11 @@ from picklescan.scanner import scan_file_path
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.util.misc import uuid_string
+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,
 )
@@ -17,6 +22,7 @@ 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,
@@ -28,8 +34,17 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
+    SubmodelDefinition,
+    SubModelType,
 )
-from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
+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.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
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
@@ -105,6 +120,7 @@ class ModelProbe(object):
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
+        "StableDiffusion3Pipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.VAE,
         "AutoencoderTiny": ModelType.VAE,
@@ -114,8 +130,13 @@ class ModelProbe(object):
         "CLIPModel": ModelType.CLIPEmbed,
         "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]
@@ -162,7 +183,10 @@ 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()
-        fields["variant"] = fields.get("variant") or probe.get_variant_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["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)
@@ -187,6 +211,7 @@ class ModelProbe(object):
         if fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE] and fields["format"] in [
             ModelFormat.Checkpoint,
             ModelFormat.BnbQuantizednf4b,
+            ModelFormat.GGUFQuantized,
         ]:
             ckpt_config_path = cls._get_checkpoint_config_path(
                 model_path,
@@ -208,6 +233,10 @@ 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
 
@@ -220,7 +249,7 @@ class ModelProbe(object):
 
     @classmethod
     def get_model_type_from_checkpoint(cls, model_path: Path, checkpoint: Optional[CkptType] = None) -> ModelType:
-        if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth"):
+        if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth", ".gguf"):
             raise InvalidModelConfigException(f"{model_path}: unrecognized suffix")
 
         if model_path.name == "learned_embeds.bin":
@@ -235,8 +264,6 @@ 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.",
@@ -244,6 +271,10 @@ 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_")):
@@ -252,9 +283,28 @@ class ModelProbe(object):
             # LoRA models, but as of the time of writing, we support Diffusers FLUX PEFT LoRA models.
             elif key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight", "lora_A.weight", "lora_B.weight")):
                 return ModelType.LoRA
-            elif key.startswith(("controlnet", "control_model", "input_blocks")):
+            elif key.startswith(
+                (
+                    "controlnet",
+                    "control_model",
+                    "input_blocks",
+                    # XLabs FLUX ControlNet models have keys starting with "controlnet_blocks."
+                    # For example: https://huggingface.co/XLabs-AI/flux-controlnet-collections/blob/86ab1e915a389d5857135c00e0d350e9e38a9048/flux-canny-controlnet_v2.safetensors
+                    # TODO(ryand): This is very fragile. XLabs FLUX ControlNet models also contain keys starting with
+                    # "double_blocks.", which we check for above. But, I'm afraid to modify this logic because it is so
+                    # delicate.
+                    "controlnet_blocks",
+                )
+            ):
                 return ModelType.ControlNet
-            elif key.startswith(("image_proj.", "ip_adapter.")):
+            elif key.startswith(
+                (
+                    "image_proj.",
+                    "ip_adapter.",
+                    # XLabs FLUX IP-Adapter models have keys startinh with "ip_adapter_proj_model.".
+                    "ip_adapter_proj_model.",
+                )
+            ):
                 return ModelType.IPAdapter
             elif key in {"emb_params", "string_to_param"}:
                 return ModelType.TextualInversion
@@ -278,12 +328,10 @@ class ModelProbe(object):
             return ModelType.SpandrelImageToImage
         except spandrel.UnsupportedModelError:
             pass
-        except RuntimeError as e:
-            if "No such file or directory" in str(e):
-                # This error is expected if the model_path does not exist (which is the case in some unit tests).
-                pass
-            else:
-                raise e
+        except Exception as e:
+            logger.warning(
+                f"Encountered error while probing to determine if {model_path} is a Spandrel model. Ignoring. Error: {e}"
+            )
 
         raise InvalidModelConfigException(f"Unable to determine model type for {model_path}")
 
@@ -408,6 +456,8 @@ class ModelProbe(object):
                 model = torch.load(model_path, map_location="cpu")
                 assert isinstance(model, dict)
                 return model
+            elif model_path.suffix.endswith(".gguf"):
+                return gguf_sd_loader(model_path, compute_dtype=torch.float32)
             else:
                 return safetensors.torch.load_file(model_path)
 
@@ -432,9 +482,11 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "normal": "normalbae_image_processor",
     "sketch": "pidi_image_processor",
     "scribble": "lineart_image_processor",
-    "lineart": "lineart_image_processor",
+    "lineart anime": "lineart_anime_image_processor",
     "lineart_anime": "lineart_anime_image_processor",
+    "lineart": "lineart_image_processor",
     "softedge": "hed_image_processor",
+    "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",
     "pose": "dw_openpose_image_processor",
     "mediapipe": "mediapipe_face_processor",
@@ -446,7 +498,8 @@ MODEL_NAME_TO_PREPROCESSOR = {
 
 def get_default_settings_controlnet_t2i_adapter(model_name: str) -> Optional[ControlAdapterDefaultSettings]:
     for k, v in MODEL_NAME_TO_PREPROCESSOR.items():
-        if k in model_name:
+        model_name_lower = model_name.lower()
+        if k in model_name_lower:
             return ControlAdapterDefaultSettings(preprocessor=v)
     return None
 
@@ -477,6 +530,8 @@ class CheckpointProbeBase(ProbeBase):
             or "model.diffusion_model.double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
         ):
             return ModelFormat.BnbQuantizednf4b
+        elif any(isinstance(v, GGMLTensor) for v in state_dict.values()):
+            return ModelFormat.GGUFQuantized
         return ModelFormat("checkpoint")
 
     def get_variant_type(self) -> ModelVariantType:
@@ -618,6 +673,11 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
+        if is_state_dict_xlabs_controlnet(checkpoint) or is_state_dict_instantx_controlnet(checkpoint):
+            # TODO(ryand): Should I distinguish between XLabs, InstantX and other ControlNet models by implementing
+            # get_format()?
+            return BaseModelType.Flux
+
         for key_name in (
             "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
             "controlnet_mid_block.bias",
@@ -643,6 +703,10 @@ 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
@@ -703,18 +767,33 @@ class FolderProbeBase(ProbeBase):
 
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
-        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}")
+        # 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}")
 
     def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
@@ -726,6 +805,23 @@ 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
@@ -839,22 +935,19 @@ class ControlNetFolderProbe(FolderProbeBase):
             raise InvalidModelConfigException(f"Cannot determine base type for {self.model_path}")
         with open(config_file, "r") as file:
             config = json.load(file)
+
+        if config.get("_class_name", None) == "FluxControlNetModel":
+            return BaseModelType.Flux
+
         # no obvious way to distinguish between sd2-base and sd2-768
         dimension = config["cross_attention_dim"]
-        base_model = (
-            BaseModelType.StableDiffusion1
-            if dimension == 768
-            else (
-                BaseModelType.StableDiffusion2
-                if dimension == 1024
-                else BaseModelType.StableDiffusionXL
-                if dimension == 2048
-                else None
-            )
-        )
-        if not base_model:
-            raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
-        return base_model
+        if dimension == 768:
+            return BaseModelType.StableDiffusion1
+        if dimension == 1024:
+            return BaseModelType.StableDiffusion2
+        if dimension == 2048:
+            return BaseModelType.StableDiffusionXL
+        raise InvalidModelConfigException(f"Unable to determine model base for {self.model_path}")
 
 
 class LoRAFolderProbe(FolderProbeBase):

invokeai/backend/model_manager/search.py

@@ -130,7 +130,7 @@ class ModelSearch:
                 return
 
         for n in file_names:
-            if n.endswith((".ckpt", ".bin", ".pth", ".safetensors", ".pt")):
+            if n.endswith((".ckpt", ".bin", ".pth", ".safetensors", ".pt", ".gguf")):
                 try:
                     self.model_found(absolute_path / n)
                 except KeyboardInterrupt:

invokeai/backend/model_manager/util/model_util.py

@@ -8,6 +8,9 @@ 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
+
 
 def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
     checkpoint = {}
@@ -54,7 +57,11 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False) -> Dict[str
             scan_result = scan_file_path(path)
             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
 
 
@@ -159,3 +166,20 @@ 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]:
+    path = Path(location)
+    config_path = path / "config.json"
+    if not config_path.exists():
+        return None
+    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 None

invokeai/backend/model_manager/util/select_hf_files.py

@@ -129,9 +129,11 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
 
             # 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 candidate_variant_label == f".{variant}" or (
-                not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.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]):
                 score += 1
 
             if parent not in subfolder_weights:
@@ -146,7 +148,7 @@ 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] == ".fp16":
+            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0].startswith(".fp16"):
                 at_least_one_fp16 = True
                 break
 
@@ -162,7 +164,16 @@ 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:
-            result.add(highest_score_candidate.path)
+            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)
 
     # 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

@@ -0,0 +1,157 @@
+from typing import overload
+
+import gguf
+import torch
+
+from invokeai.backend.quantization.gguf.utils import (
+    DEQUANTIZE_FUNCTIONS,
+    TORCH_COMPATIBLE_QTYPES,
+    dequantize,
+)
+
+
+def dequantize_and_run(func, args, kwargs):
+    """A helper function for running math ops on GGMLTensor inputs.
+
+    Dequantizes the inputs, and runs the function.
+    """
+    dequantized_args = [a.get_dequantized_tensor() if hasattr(a, "get_dequantized_tensor") else a for a in args]
+    dequantized_kwargs = {
+        k: v.get_dequantized_tensor() if hasattr(v, "get_dequantized_tensor") else v for k, v in kwargs.items()
+    }
+    return func(*dequantized_args, **dequantized_kwargs)
+
+
+def apply_to_quantized_tensor(func, args, kwargs):
+    """A helper function to apply a function to a quantized GGML tensor, and re-wrap the result in a GGMLTensor.
+
+    Assumes that the first argument is a GGMLTensor.
+    """
+    # We expect the first argument to be a GGMLTensor, and all other arguments to be non-GGMLTensors.
+    ggml_tensor = args[0]
+    assert isinstance(ggml_tensor, GGMLTensor)
+    assert all(not isinstance(a, GGMLTensor) for a in args[1:])
+    assert all(not isinstance(v, GGMLTensor) for v in kwargs.values())
+
+    new_data = func(ggml_tensor.quantized_data, *args[1:], **kwargs)
+
+    if new_data.dtype != ggml_tensor.quantized_data.dtype:
+        # This is intended to catch calls such as `.to(dtype-torch.float32)`, which are not supported on GGMLTensors.
+        raise ValueError("Operation changed the dtype of GGMLTensor unexpectedly.")
+
+    return GGMLTensor(
+        new_data, ggml_tensor._ggml_quantization_type, ggml_tensor.tensor_shape, ggml_tensor.compute_dtype
+    )
+
+
+GGML_TENSOR_OP_TABLE = {
+    # Ops to run on the quantized tensor.
+    torch.ops.aten.detach.default: apply_to_quantized_tensor,  # pyright: ignore
+    torch.ops.aten._to_copy.default: apply_to_quantized_tensor,  # pyright: ignore
+    # Ops to run on dequantized tensors.
+    torch.ops.aten.t.default: dequantize_and_run,  # pyright: ignore
+    torch.ops.aten.addmm.default: dequantize_and_run,  # pyright: ignore
+    torch.ops.aten.mul.Tensor: dequantize_and_run,  # pyright: ignore
+}
+
+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.
+
+    The underlying tensor is quantized, but the GGMLTensor class provides a dequantized view of the tensor on-the-fly
+    when it is used in operations.
+    """
+
+    @staticmethod
+    def __new__(
+        cls,
+        data: torch.Tensor,
+        ggml_quantization_type: gguf.GGMLQuantizationType,
+        tensor_shape: torch.Size,
+        compute_dtype: torch.dtype,
+    ):
+        # Type hinting is not supported for torch.Tensor._make_wrapper_subclass, so we ignore the errors.
+        return torch.Tensor._make_wrapper_subclass(  # pyright: ignore
+            cls,
+            data.shape,
+            dtype=data.dtype,
+            layout=data.layout,
+            device=data.device,
+            strides=data.stride(),
+            storage_offset=data.storage_offset(),
+        )
+
+    def __init__(
+        self,
+        data: torch.Tensor,
+        ggml_quantization_type: gguf.GGMLQuantizationType,
+        tensor_shape: torch.Size,
+        compute_dtype: torch.dtype,
+    ):
+        self.quantized_data = data
+        self._ggml_quantization_type = ggml_quantization_type
+        # The dequantized shape of the tensor.
+        self.tensor_shape = tensor_shape
+        self.compute_dtype = compute_dtype
+
+    def __repr__(self, *, tensor_contents=None):
+        return f"GGMLTensor(type={self._ggml_quantization_type.name}, dequantized_shape=({self.tensor_shape})"
+
+    @overload
+    def size(self, dim: None = None) -> torch.Size: ...
+
+    @overload
+    def size(self, dim: int) -> int: ...
+
+    def size(self, dim: int | None = None):
+        """Return the size of the tensor after dequantization. I.e. the shape that will be used in any math ops."""
+        if dim is not None:
+            return self.tensor_shape[dim]
+        return self.tensor_shape
+
+    @property
+    def shape(self) -> torch.Size:  # pyright: ignore[reportIncompatibleVariableOverride] pyright doesn't understand this for some reason.
+        """The shape of the tensor after dequantization. I.e. the shape that will be used in any math ops."""
+        return self.size()
+
+    @property
+    def quantized_shape(self) -> torch.Size:
+        """The shape of the quantized tensor."""
+        return self.quantized_data.shape
+
+    def requires_grad_(self, mode: bool = True) -> torch.Tensor:
+        """The GGMLTensor class is currently only designed for inference (not training). Setting requires_grad to True
+        is not supported. This method is a no-op.
+        """
+        return self
+
+    def get_dequantized_tensor(self):
+        """Return the dequantized tensor.
+
+        Args:
+            dtype: The dtype of the dequantized tensor.
+        """
+        if self._ggml_quantization_type in TORCH_COMPATIBLE_QTYPES:
+            return self.quantized_data.to(self.compute_dtype)
+        elif self._ggml_quantization_type in DEQUANTIZE_FUNCTIONS:
+            # TODO(ryand): Look into how the dtype param is intended to be used.
+            return dequantize(
+                data=self.quantized_data, qtype=self._ggml_quantization_type, oshape=self.tensor_shape, dtype=None
+            ).to(self.compute_dtype)
+        else:
+            # There is no GPU implementation for this quantization type, so fallback to the numpy implementation.
+            new = gguf.quants.dequantize(self.quantized_data.cpu().numpy(), self._ggml_quantization_type)
+            return torch.from_numpy(new).to(self.quantized_data.device, dtype=self.compute_dtype)
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args, kwargs):
+        # We will likely hit cases here in the future where a new op is encountered that is not yet supported.
+        # The new op simply needs to be added to the GGML_TENSOR_OP_TABLE.
+        if func in GGML_TENSOR_OP_TABLE:
+            return GGML_TENSOR_OP_TABLE[func](func, args, kwargs)
+        return NotImplemented

invokeai/backend/quantization/gguf/loaders.py

@@ -0,0 +1,22 @@
+from pathlib import Path
+
+import gguf
+import torch
+
+from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
+from invokeai.backend.quantization.gguf.utils import TORCH_COMPATIBLE_QTYPES
+
+
+def gguf_sd_loader(path: Path, compute_dtype: torch.dtype) -> dict[str, GGMLTensor]:
+    reader = gguf.GGUFReader(path)
+
+    sd: dict[str, GGMLTensor] = {}
+    for tensor in reader.tensors:
+        torch_tensor = torch.from_numpy(tensor.data)
+        shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))
+        if tensor.tensor_type in TORCH_COMPATIBLE_QTYPES:
+            torch_tensor = torch_tensor.view(*shape)
+        sd[tensor.name] = GGMLTensor(
+            torch_tensor, ggml_quantization_type=tensor.tensor_type, tensor_shape=shape, compute_dtype=compute_dtype
+        )
+    return sd

invokeai/backend/quantization/gguf/utils.py

@@ -0,0 +1,308 @@
+# Largely based on https://github.com/city96/ComfyUI-GGUF
+
+from typing import Callable, Optional, Union
+
+import gguf
+import torch
+
+TORCH_COMPATIBLE_QTYPES = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}
+
+# K Quants #
+QK_K = 256
+K_SCALE_SIZE = 12
+
+
+def get_scale_min(scales: torch.Tensor):
+    n_blocks = scales.shape[0]
+    scales = scales.view(torch.uint8)
+    scales = scales.reshape((n_blocks, 3, 4))
+
+    d, m, m_d = torch.split(scales, scales.shape[-2] // 3, dim=-2)
+
+    sc = torch.cat([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], dim=-1)
+    min = torch.cat([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], dim=-1)
+
+    return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8)))
+
+
+# Legacy Quants #
+def dequantize_blocks_Q8_0(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    d, x = split_block_dims(blocks, 2)
+    d = d.view(torch.float16).to(dtype)
+    x = x.view(torch.int8)
+    return d * x
+
+
+def dequantize_blocks_Q5_1(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    d, m, qh, qs = split_block_dims(blocks, 2, 2, 4)
+    d = d.view(torch.float16).to(dtype)
+    m = m.view(torch.float16).to(dtype)
+    qh = to_uint32(qh)
+
+    qh = qh.reshape((n_blocks, 1)) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
+    ql = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape(1, 1, 2, 1)
+    qh = (qh & 1).to(torch.uint8)
+    ql = (ql & 0x0F).reshape((n_blocks, -1))
+
+    qs = ql | (qh << 4)
+    return (d * qs) + m
+
+
+def dequantize_blocks_Q5_0(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    d, qh, qs = split_block_dims(blocks, 2, 4)
+    d = d.view(torch.float16).to(dtype)
+    qh = to_uint32(qh)
+
+    qh = qh.reshape(n_blocks, 1) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32)
+    ql = qs.reshape(n_blocks, -1, 1, block_size // 2) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape(1, 1, 2, 1)
+
+    qh = (qh & 1).to(torch.uint8)
+    ql = (ql & 0x0F).reshape(n_blocks, -1)
+
+    qs = (ql | (qh << 4)).to(torch.int8) - 16
+    return d * qs
+
+
+def dequantize_blocks_Q4_1(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    d, m, qs = split_block_dims(blocks, 2, 2)
+    d = d.view(torch.float16).to(dtype)
+    m = m.view(torch.float16).to(dtype)
+
+    qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape(1, 1, 2, 1)
+    qs = (qs & 0x0F).reshape(n_blocks, -1)
+
+    return (d * qs) + m
+
+
+def dequantize_blocks_Q4_0(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    d, qs = split_block_dims(blocks, 2)
+    d = d.view(torch.float16).to(dtype)
+
+    qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor(
+        [0, 4], device=d.device, dtype=torch.uint8
+    ).reshape((1, 1, 2, 1))
+    qs = (qs & 0x0F).reshape((n_blocks, -1)).to(torch.int8) - 8
+    return d * qs
+
+
+def dequantize_blocks_BF16(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    return (blocks.view(torch.int16).to(torch.int32) << 16).view(torch.float32)
+
+
+def dequantize_blocks_Q6_K(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    (
+        ql,
+        qh,
+        scales,
+        d,
+    ) = split_block_dims(blocks, QK_K // 2, QK_K // 4, QK_K // 16)
+
+    scales = scales.view(torch.int8).to(dtype)
+    d = d.view(torch.float16).to(dtype)
+    d = (d * scales).reshape((n_blocks, QK_K // 16, 1))
+
+    ql = ql.reshape((n_blocks, -1, 1, 64)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 2, 1)
+    )
+    ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
+    qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 4, 1)
+    )
+    qh = (qh & 0x03).reshape((n_blocks, -1, 32))
+    q = (ql | (qh << 4)).to(torch.int8) - 32
+    q = q.reshape((n_blocks, QK_K // 16, -1))
+
+    return (d * q).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q5_K(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    d, dmin, scales, qh, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE, QK_K // 8)
+
+    d = d.view(torch.float16).to(dtype)
+    dmin = dmin.view(torch.float16).to(dtype)
+
+    sc, m = get_scale_min(scales)
+
+    d = (d * sc).reshape((n_blocks, -1, 1))
+    dm = (dmin * m).reshape((n_blocks, -1, 1))
+
+    ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 2, 1)
+    )
+    qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor(list(range(8)), device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 8, 1)
+    )
+    ql = (ql & 0x0F).reshape((n_blocks, -1, 32))
+    qh = (qh & 0x01).reshape((n_blocks, -1, 32))
+    q = ql | (qh << 4)
+
+    return (d * q - dm).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q4_K(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    d, dmin, scales, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE)
+    d = d.view(torch.float16).to(dtype)
+    dmin = dmin.view(torch.float16).to(dtype)
+
+    sc, m = get_scale_min(scales)
+
+    d = (d * sc).reshape((n_blocks, -1, 1))
+    dm = (dmin * m).reshape((n_blocks, -1, 1))
+
+    qs = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 2, 1)
+    )
+    qs = (qs & 0x0F).reshape((n_blocks, -1, 32))
+
+    return (d * qs - dm).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q3_K(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    hmask, qs, scales, d = split_block_dims(blocks, QK_K // 8, QK_K // 4, 12)
+    d = d.view(torch.float16).to(dtype)
+
+    lscales, hscales = scales[:, :8], scales[:, 8:]
+    lscales = lscales.reshape((n_blocks, 1, 8)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(
+        (1, 2, 1)
+    )
+    lscales = lscales.reshape((n_blocks, 16))
+    hscales = hscales.reshape((n_blocks, 1, 4)) >> torch.tensor(
+        [0, 2, 4, 6], device=d.device, dtype=torch.uint8
+    ).reshape((1, 4, 1))
+    hscales = hscales.reshape((n_blocks, 16))
+    scales = (lscales & 0x0F) | ((hscales & 0x03) << 4)
+    scales = scales.to(torch.int8) - 32
+
+    dl = (d * scales).reshape((n_blocks, 16, 1))
+
+    ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 4, 1)
+    )
+    qh = hmask.reshape(n_blocks, -1, 1, 32) >> torch.tensor(list(range(8)), device=d.device, dtype=torch.uint8).reshape(
+        (1, 1, 8, 1)
+    )
+    ql = ql.reshape((n_blocks, 16, QK_K // 16)) & 3
+    qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & 1) ^ 1
+    q = ql.to(torch.int8) - (qh << 2).to(torch.int8)
+
+    return (dl * q).reshape((n_blocks, QK_K))
+
+
+def dequantize_blocks_Q2_K(
+    blocks: torch.Tensor, block_size: int, type_size: int, dtype: Optional[torch.dtype] = None
+) -> torch.Tensor:
+    n_blocks = blocks.shape[0]
+
+    scales, qs, d, dmin = split_block_dims(blocks, QK_K // 16, QK_K // 4, 2)
+    d = d.view(torch.float16).to(dtype)
+    dmin = dmin.view(torch.float16).to(dtype)
+
+    # (n_blocks, 16, 1)
+    dl = (d * (scales & 0xF)).reshape((n_blocks, QK_K // 16, 1))
+    ml = (dmin * (scales >> 4)).reshape((n_blocks, QK_K // 16, 1))
+
+    shift = torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1))
+
+    qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & 3
+    qs = qs.reshape((n_blocks, QK_K // 16, 16))
+    qs = dl * qs - ml
+
+    return qs.reshape((n_blocks, -1))
+
+
+DEQUANTIZE_FUNCTIONS: dict[
+    gguf.GGMLQuantizationType, Callable[[torch.Tensor, int, int, Optional[torch.dtype]], torch.Tensor]
+] = {
+    gguf.GGMLQuantizationType.BF16: dequantize_blocks_BF16,
+    gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0,
+    gguf.GGMLQuantizationType.Q5_1: dequantize_blocks_Q5_1,
+    gguf.GGMLQuantizationType.Q5_0: dequantize_blocks_Q5_0,
+    gguf.GGMLQuantizationType.Q4_1: dequantize_blocks_Q4_1,
+    gguf.GGMLQuantizationType.Q4_0: dequantize_blocks_Q4_0,
+    gguf.GGMLQuantizationType.Q6_K: dequantize_blocks_Q6_K,
+    gguf.GGMLQuantizationType.Q5_K: dequantize_blocks_Q5_K,
+    gguf.GGMLQuantizationType.Q4_K: dequantize_blocks_Q4_K,
+    gguf.GGMLQuantizationType.Q3_K: dequantize_blocks_Q3_K,
+    gguf.GGMLQuantizationType.Q2_K: dequantize_blocks_Q2_K,
+}
+
+
+def is_torch_compatible(tensor: Optional[torch.Tensor]):
+    return getattr(tensor, "tensor_type", None) in TORCH_COMPATIBLE_QTYPES
+
+
+def is_quantized(tensor: torch.Tensor):
+    return not is_torch_compatible(tensor)
+
+
+def dequantize(
+    data: torch.Tensor, qtype: gguf.GGMLQuantizationType, oshape: torch.Size, dtype: Optional[torch.dtype] = None
+):
+    """
+    Dequantize tensor back to usable shape/dtype
+    """
+    block_size, type_size = gguf.GGML_QUANT_SIZES[qtype]
+    dequantize_blocks = DEQUANTIZE_FUNCTIONS[qtype]
+
+    rows = data.reshape((-1, data.shape[-1])).view(torch.uint8)
+
+    n_blocks = rows.numel() // type_size
+    blocks = rows.reshape((n_blocks, type_size))
+    blocks = dequantize_blocks(blocks, block_size, type_size, dtype)
+    return blocks.reshape(oshape)
+
+
+def to_uint32(x: torch.Tensor) -> torch.Tensor:
+    x = x.view(torch.uint8).to(torch.int32)
+    return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1)
+
+
+def split_block_dims(blocks: torch.Tensor, *args):
+    n_max = blocks.shape[1]
+    dims = list(args) + [n_max - sum(args)]
+    return torch.split(blocks, dims, dim=1)
+
+
+PATCH_TYPES = Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]]

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -171,8 +171,19 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
+
+        # On 30xx and 40xx series GPUs, `torch-sdp` is faster than `xformers`. This corresponds to a CUDA major
+        # version of 8 or higher. So, for major version 7 or below, we prefer `xformers`.
+        # See:
+        # - https://developer.nvidia.com/cuda-gpus
+        # - https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
+        try:
+            prefer_xformers = torch.cuda.is_available() and torch.cuda.get_device_properties("cuda").major <= 7  # type: ignore # Type of "get_device_properties" is partially unknown
+        except Exception:
+            prefer_xformers = False
+
         config = get_config()
-        if config.attention_type == "xformers":
+        if config.attention_type == "xformers" and is_xformers_available() and prefer_xformers:
             self.enable_xformers_memory_efficient_attention()
             return
         elif config.attention_type == "sliced":
@@ -187,20 +198,24 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             self.disable_attention_slicing()
             return
         elif config.attention_type == "torch-sdp":
-            if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
-                return
-            else:
-                raise Exception("torch-sdp attention slicing not available")
+            # torch-sdp is the default in diffusers.
+            return
 
-        # the remainder if this code is called when attention_type=='auto'
+        # See https://github.com/invoke-ai/InvokeAI/issues/7049 for context.
+        # Bumping torch from 2.2.2 to 2.4.1 caused the sliced attention implementation to produce incorrect results.
+        # For now, if a user is on an MPS device and has not explicitly set the attention_type, then we select the
+        # non-sliced torch-sdp implementation. This keeps things working on MPS at the cost of increased peak memory
+        # utilization.
+        if torch.backends.mps.is_available():
+            return
+
+        # The remainder if this code is called when attention_type=='auto'.
         if self.unet.device.type == "cuda":
-            if is_xformers_available():
+            if is_xformers_available() and prefer_xformers:
                 self.enable_xformers_memory_efficient_attention()
                 return
-            elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
-                return
+            # torch-sdp is the default in diffusers.
+            return
 
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free
@@ -484,6 +499,22 @@ 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

@@ -49,9 +49,32 @@ 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]
+    conditionings: (
+        List[BasicConditioningInfo]
+        | List[SDXLConditioningInfo]
+        | List[FLUXConditioningInfo]
+        | List[SD3ConditioningInfo]
+    )
 
 
 @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=-1,
+                step=0,
                 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 import FromOriginalControlNetMixin
+from diffusers.loaders.single_file_model import FromOriginalModelMixin
 from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
 from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 from diffusers.models.embeddings import (
@@ -32,7 +32,9 @@ from invokeai.backend.util.logging import InvokeAILogger
 logger = InvokeAILogger.get_logger(__name__)
 
 
-class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
+# 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):
     """
     A ControlNet model.
 

invokeai/frontend/web/package.json

@@ -52,49 +52,51 @@
     }
   },
   "dependencies": {
-    "@dagrejs/dagre": "^1.1.3",
-    "@dagrejs/graphlib": "^2.2.3",
+    "@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": "^5.0.20",
-    "@invoke-ai/ui-library": "^0.0.37",
+    "@fontsource-variable/inter": "^5.1.0",
+    "@invoke-ai/ui-library": "^0.0.43",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
     "async-mutex": "^0.5.0",
-    "chakra-react-select": "^4.9.1",
+    "chakra-react-select": "^4.9.2",
     "cmdk": "^1.0.0",
     "compare-versions": "^6.1.1",
     "dateformat": "^5.0.3",
     "fracturedjsonjs": "^4.0.2",
-    "framer-motion": "^11.3.24",
-    "i18next": "^23.12.2",
-    "i18next-http-backend": "^2.5.2",
+    "framer-motion": "^11.10.0",
+    "i18next": "^23.15.1",
+    "i18next-http-backend": "^2.6.1",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
-    "konva": "^9.3.14",
+    "konva": "^9.3.15",
     "lodash-es": "^4.17.21",
-    "lru-cache": "^11.0.0",
+    "lru-cache": "^11.0.1",
     "nanoid": "^5.0.7",
-    "nanostores": "^0.11.2",
+    "nanostores": "^0.11.3",
     "new-github-issue-url": "^1.0.0",
     "overlayscrollbars": "^2.10.0",
     "overlayscrollbars-react": "^0.5.6",
+    "perfect-freehand": "^1.2.2",
     "query-string": "^9.1.0",
+    "raf-throttle": "^2.0.6",
     "react": "^18.3.1",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.3.1",
-    "react-dropzone": "^14.2.3",
+    "react-dropzone": "^14.2.9",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.52.2",
+    "react-hook-form": "^7.53.0",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^14.1.3",
-    "react-icons": "^5.2.1",
+    "react-i18next": "^15.0.2",
+    "react-icons": "^5.3.0",
     "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.1.2",
+    "react-resizable-panels": "^2.1.4",
     "react-use": "^17.5.1",
-    "react-virtuoso": "^4.9.0",
+    "react-virtuoso": "^4.10.4",
     "reactflow": "^11.11.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
@@ -102,56 +104,55 @@
     "rfdc": "^1.4.1",
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
-    "socket.io-client": "^4.7.5",
+    "socket.io-client": "^4.8.0",
     "stable-hash": "^0.0.4",
-    "use-debounce": "^10.0.2",
+    "use-debounce": "^10.0.3",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^10.0.0",
     "zod": "^3.23.8",
-    "zod-validation-error": "^3.3.1"
+    "zod-validation-error": "^3.4.0"
   },
   "peerDependencies": {
     "react": "^18.2.0",
-    "react-dom": "^18.2.0",
-    "ts-toolbelt": "^9.6.0"
+    "react-dom": "^18.2.0"
   },
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.2.8",
-    "@storybook/addon-interactions": "^8.2.8",
-    "@storybook/addon-links": "^8.2.8",
-    "@storybook/addon-storysource": "^8.2.8",
-    "@storybook/manager-api": "^8.2.8",
-    "@storybook/react": "^8.2.8",
-    "@storybook/react-vite": "^8.2.8",
-    "@storybook/theming": "^8.2.8",
+    "@storybook/addon-essentials": "^8.3.4",
+    "@storybook/addon-interactions": "^8.3.4",
+    "@storybook/addon-links": "^8.3.4",
+    "@storybook/addon-storysource": "^8.3.4",
+    "@storybook/manager-api": "^8.3.4",
+    "@storybook/react": "^8.3.4",
+    "@storybook/react-vite": "^8.3.4",
+    "@storybook/theming": "^8.3.4",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.14.15",
-    "@types/react": "^18.3.3",
+    "@types/node": "^20.16.10",
+    "@types/react": "^18.3.11",
     "@types/react-dom": "^18.3.0",
     "@types/uuid": "^10.0.0",
-    "@vitejs/plugin-react-swc": "^3.7.0",
-    "@vitest/coverage-v8": "^1.5.0",
-    "@vitest/ui": "^1.5.0",
+    "@vitejs/plugin-react-swc": "^3.7.1",
+    "@vitest/coverage-v8": "^1.6.0",
+    "@vitest/ui": "^1.6.0",
     "concurrently": "^8.2.2",
     "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
-    "eslint": "^8.57.0",
-    "eslint-plugin-i18next": "^6.0.9",
+    "eslint": "^8.57.1",
+    "eslint-plugin-i18next": "^6.1.0",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.27.2",
+    "knip": "^5.31.0",
     "openapi-types": "^12.1.3",
-    "openapi-typescript": "^7.3.0",
+    "openapi-typescript": "^7.4.1",
     "prettier": "^3.3.3",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.2.8",
-    "ts-toolbelt": "^9.6.0",
-    "tsafe": "^1.7.2",
-    "typescript": "^5.5.4",
-    "vite": "^5.4.0",
-    "vite-plugin-css-injected-by-js": "^3.5.1",
+    "storybook": "^8.3.4",
+    "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",
     "vite-plugin-dts": "^3.9.1",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.3.2",

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

@@ -4,7 +4,7 @@
         "reportBugLabel": "Fehler melden",
         "settingsLabel": "Einstellungen",
         "img2img": "Bild zu Bild",
-        "nodes": "Workflows",
+        "nodes": "Arbeitsabläufe",
         "upload": "Hochladen",
         "load": "Laden",
         "statusDisconnected": "Getrennt",
@@ -93,7 +93,10 @@
         "placeholderSelectAModel": "Modell auswählen",
         "reset": "Zurücksetzen",
         "none": "Keine",
-        "new": "Neu"
+        "new": "Neu",
+        "ok": "OK",
+        "close": "Schließen",
+        "clipboard": "Zwischenablage"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -156,7 +159,11 @@
         "displayBoardSearch": "Board durchsuchen",
         "displaySearch": "Bild suchen",
         "go": "Los",
-        "jump": "Springen"
+        "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"
     },
     "hotkeys": {
         "noHotkeysFound": "Kein Hotkey gefunden",
@@ -263,6 +270,22 @@
             "quickSwitch": {
                 "title": "Ebenen schnell umschalten",
                 "desc": "Wechseln Sie zwischen den beiden zuletzt gewählten Ebenen. Wenn eine Ebene mit einem Lesezeichen versehen ist, wird zwischen ihr und der letzten nicht markierten Ebene gewechselt."
+            },
+            "applyFilter": {
+                "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": {
@@ -513,14 +536,12 @@
         "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",
@@ -559,7 +580,18 @@
         "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."
+        "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"
     },
     "parameters": {
         "images": "Bilder",
@@ -645,24 +677,57 @@
     "toast": {
         "uploadFailed": "Hochladen fehlgeschlagen",
         "imageCopied": "Bild kopiert",
-        "parametersNotSet": "Parameter nicht festgelegt",
+        "parametersNotSet": "Parameter nicht zurückgerufen",
         "addedToBoard": "Dem Board hinzugefügt",
-        "loadedWithWarnings": "Workflow mit Warnungen geladen"
+        "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"
     },
     "accessibility": {
         "uploadImage": "Bild hochladen",
         "previousImage": "Vorheriges Bild",
-        "showOptionsPanel": "Seitenpanel anzeigen",
         "reset": "Zurücksetzten",
         "nextImage": "Nächstes Bild",
-        "showGalleryPanel": "Galerie-Panel anzeigen",
         "menu": "Menü",
         "invokeProgressBar": "Invoke Fortschrittsanzeige",
         "mode": "Modus",
         "resetUI": "$t(accessibility.reset) von UI",
         "createIssue": "Ticket erstellen",
         "about": "Über",
-        "submitSupportTicket": "Support-Ticket senden"
+        "submitSupportTicket": "Support-Ticket senden",
+        "toggleRightPanel": "Rechtes Bedienfeld umschalten (G)",
+        "toggleLeftPanel": "Linkes Bedienfeld umschalten (T)",
+        "uploadImages": "Bild(er) hochladen"
     },
     "boards": {
         "autoAddBoard": "Board automatisch erstellen",
@@ -697,12 +762,13 @@
         "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"
+        "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."
     },
     "queue": {
         "status": "Status",
@@ -789,7 +855,6 @@
         "width": "Breite",
         "createdBy": "Erstellt von",
         "steps": "Schritte",
-        "seamless": "Nahtlos",
         "positivePrompt": "Positiver Prompt",
         "generationMode": "Generierungsmodus",
         "Threshold": "Rauschen-Schwelle",
@@ -805,7 +870,8 @@
         "parameterSet": "Parameter {{parameter}} setzen",
         "recallParameter": "{{label}} Abrufen",
         "parsingFailed": "Parsing Fehlgeschlagen",
-        "canvasV2Metadata": "Leinwand"
+        "canvasV2Metadata": "Leinwand",
+        "guidance": "Führung"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -930,7 +996,8 @@
         },
         "paramScheduler": {
             "paragraphs": [
-                "\"Planer\" definiert, wie iterativ Rauschen zu einem Bild hinzugefügt wird, oder wie ein Sample bei der Ausgabe eines Modells aktualisiert wird."
+                "Verwendeter Planer währende des Generierungsprozesses.",
+                "Jeder Planer definiert, wie einem Bild iterativ Rauschen hinzugefügt wird, oder wie ein Sample basierend auf der Ausgabe eines Modells aktualisiert wird."
             ],
             "heading": "Planer"
         },
@@ -956,6 +1023,61 @@
         },
         "ipAdapterMethod": {
             "heading": "Methode"
+        },
+        "refinerScheduler": {
+            "heading": "Planer",
+            "paragraphs": [
+                "Planer, der während der Veredelungsphase des Generierungsprozesses verwendet wird.",
+                "Ähnlich wie der Generierungsplaner."
+            ]
+        },
+        "compositingCoherenceMode": {
+            "paragraphs": [
+                "Verwendete Methode zur Erstellung eines kohärenten Bildes mit dem neu generierten maskierten Bereich."
+            ],
+            "heading": "Modus"
+        },
+        "compositingCoherencePass": {
+            "heading": "Kohärenzdurchlauf"
+        },
+        "controlNet": {
+            "heading": "ControlNet"
+        },
+        "compositingMaskAdjustments": {
+            "paragraphs": [
+                "Die Maske anpassen."
+            ],
+            "heading": "Maskenanpassungen"
+        },
+        "compositingMaskBlur": {
+            "paragraphs": [
+                "Der Unschärferadius der Maske."
+            ],
+            "heading": "Maskenunschärfe"
+        },
+        "compositingBlurMethod": {
+            "paragraphs": [
+                "Die auf den maskierten Bereich angewendete Unschärfemethode."
+            ],
+            "heading": "Unschärfemethode"
+        },
+        "controlNetResizeMode": {
+            "heading": "Größenänderungsmodus"
+        },
+        "paramWidth": {
+            "heading": "Breite",
+            "paragraphs": [
+                "Breite des generierten Bildes. Muss ein Vielfaches von 8 sein."
+            ]
+        },
+        "controlNetControlMode": {
+            "heading": "Kontrollmodus"
+        },
+        "controlNetProcessor": {
+            "heading": "Prozessor"
+        },
+        "patchmatchDownScaleSize": {
+            "heading": "Herunterskalieren"
         }
     },
     "invocationCache": {
@@ -1059,7 +1181,37 @@
         "missingFieldTemplate": "Fehlende Feldvorlage",
         "missingNode": "Fehlender Aufrufknoten",
         "missingInvocationTemplate": "Fehlende Aufrufvorlage",
-        "edit": "Bearbeiten"
+        "edit": "Bearbeiten",
+        "workflowAuthor": "Autor",
+        "graph": "Graph",
+        "workflowDescription": "Kurze Beschreibung",
+        "versionUnknown": " Version unbekannt",
+        "workflow": "Arbeitsablauf",
+        "noGraph": "Kein Graph",
+        "version": "Version",
+        "zoomInNodes": "Hineinzoomen",
+        "zoomOutNodes": "Herauszoomen",
+        "workflowName": "Name",
+        "unknownNode": "Unbekannter Knoten",
+        "workflowContact": "Kontaktdaten",
+        "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"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",
@@ -1127,7 +1279,17 @@
         "openWorkflow": "Arbeitsablauf öffnen",
         "saveWorkflowToProject": "Arbeitsablauf in Projekt speichern",
         "workflowCleared": "Arbeitsablauf gelöscht",
-        "loading": "Lade Arbeitsabläufe"
+        "loading": "Lade Arbeitsabläufe",
+        "name": "Name",
+        "ascending": "Aufsteigend",
+        "defaultWorkflows": "Standard Arbeitsabläufe",
+        "userWorkflows": "Benutzer Arbeitsabläufe",
+        "projectWorkflows": "Projekt Arbeitsabläufe",
+        "opened": "Geöffnet",
+        "loadWorkflow": "Arbeitsablauf $t(common.load)",
+        "updated": "Aktualisiert",
+        "created": "Erstellt",
+        "descending": "Absteigend"
     },
     "sdxl": {
         "concatPromptStyle": "Verknüpfen von Prompt & Stil",
@@ -1179,15 +1341,7 @@
         "enableLogging": "Protokollierung aktivieren"
     },
     "whatsNew": {
-        "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"
-        }
+        "whatsNewInInvoke": "Was gibt's Neues"
     },
     "stylePresets": {
         "name": "Name",
@@ -1219,7 +1373,16 @@
         "searchByName": "Nach Name suchen",
         "promptTemplateCleared": "Promptvorlage gelöscht",
         "preview": "Vorschau",
-        "positivePrompt": "Positiv-Prompt"
+        "positivePrompt": "Positiv-Prompt",
+        "active": "Aktiv",
+        "deleteTemplate2": "Sind Sie sicher, dass Sie diese Vorlage löschen möchten? Dies kann nicht rückgängig gemacht werden.",
+        "deleteTemplate": "Vorlage löschen",
+        "copyTemplate": "Vorlage kopieren",
+        "editTemplate": "Vorlage bearbeiten",
+        "deleteImage": "Bild löschen",
+        "defaultTemplates": "Standardvorlagen",
+        "nameColumn": "'name'",
+        "exportDownloaded": "Export heruntergeladen"
     },
     "newUserExperience": {
         "gettingStartedSeries": "Wünschen Sie weitere Anleitungen? In unserer <LinkComponent>Einführungsserie</LinkComponent> finden Sie Tipps, wie Sie das Potenzial von Invoke Studio voll ausschöpfen können.",
@@ -1232,13 +1395,22 @@
             "bbox": "Bbox"
         },
         "transform": {
-            "fitToBbox": "An Bbox anpassen"
+            "fitToBbox": "An Bbox anpassen",
+            "reset": "Zurücksetzen",
+            "apply": "Anwenden",
+            "cancel": "Abbrechen"
         },
         "pullBboxIntoLayerError": "Problem, Bbox in die Ebene zu ziehen",
         "pullBboxIntoLayer": "Bbox in Ebene ziehen",
         "HUD": {
             "bbox": "Bbox",
-            "scaledBbox": "Skalierte Bbox"
+            "scaledBbox": "Skalierte Bbox",
+            "entityStatus": {
+                "isHidden": "{{title}} ist ausgeblendet",
+                "isDisabled": "{{title}} ist deaktiviert",
+                "isLocked": "{{title}} ist gesperrt",
+                "isEmpty": "{{title}} ist leer"
+            }
         },
         "fitBboxToLayers": "Bbox an Ebenen anpassen",
         "pullBboxIntoReferenceImage": "Bbox ins Referenzbild ziehen",
@@ -1248,13 +1420,108 @@
         "clipToBbox": "Pinselstriche auf Bbox beschränken",
         "canvasContextMenu": {
             "saveBboxToGallery": "Bbox in Galerie speichern",
-            "bboxGroup": "Aus Bbox erstellen"
-        }
+            "bboxGroup": "Aus Bbox erstellen",
+            "canvasGroup": "Leinwand",
+            "newGlobalReferenceImage": "Neues globales Referenzbild",
+            "newRegionalReferenceImage": "Neues regionales Referenzbild",
+            "newControlLayer": "Neue Kontroll-Ebene",
+            "newRasterLayer": "Neue Raster-Ebene"
+        },
+        "rectangle": "Rechteck",
+        "saveCanvasToGallery": "Leinwand in Galerie speichern",
+        "newRasterLayerError": "Problem beim Erstellen einer Raster-Ebene",
+        "saveLayerToAssets": "Ebene in Galerie speichern",
+        "deleteReferenceImage": "Referenzbild löschen",
+        "referenceImage": "Referenzbild",
+        "opacity": "Opazität",
+        "resetCanvas": "Leinwand zurücksetzen",
+        "removeBookmark": "Lesezeichen entfernen",
+        "rasterLayer": "Raster-Ebene",
+        "rasterLayers_withCount_visible": "Raster-Ebenen ({{count}})",
+        "controlLayers_withCount_visible": "Kontroll-Ebenen ({{count}})",
+        "deleteSelected": "Ausgewählte löschen",
+        "newRegionalReferenceImageError": "Problem beim Erstellen eines regionalen Referenzbilds",
+        "newControlLayerOk": "Kontroll-Ebene erstellt",
+        "newControlLayerError": "Problem beim Erstellen einer Kontroll-Ebene",
+        "newRasterLayerOk": "Raster-Layer erstellt",
+        "moveToFront": "Nach vorne bringen",
+        "copyToClipboard": "In die Zwischenablage kopieren",
+        "controlLayers_withCount_hidden": "Kontroll-Ebenen ({{count}} ausgeblendet)",
+        "clearCaches": "Cache leeren",
+        "controlLayer": "Kontroll-Ebene",
+        "rasterLayers_withCount_hidden": "Raster-Ebenen ({{count}} ausgeblendet)",
+        "transparency": "Transparenz",
+        "canvas": "Leinwand",
+        "global": "Global",
+        "regional": "Regional",
+        "newGlobalReferenceImageOk": "Globales Referenzbild erstellt",
+        "savedToGalleryError": "Fehler beim Speichern in der Galerie",
+        "savedToGalleryOk": "In Galerie gespeichert",
+        "newGlobalReferenceImageError": "Problem beim Erstellen eines globalen Referenzbilds",
+        "newRegionalReferenceImageOk": "Regionales Referenzbild erstellt",
+        "duplicate": "Duplizieren",
+        "regionalReferenceImage": "Regionales Referenzbild",
+        "globalReferenceImage": "Globales Referenzbild",
+        "regionIsEmpty": "Ausgewählte Region is leer",
+        "mergeVisible": "Sichtbare vereinen",
+        "mergeVisibleOk": "Sichtbare Ebenen vereinen",
+        "mergeVisibleError": "Fehler beim Vereinen sichtbarer Ebenen",
+        "clearHistory": "Verlauf leeren",
+        "addLayer": "Ebene hinzufügen",
+        "width": "Breite",
+        "weight": "Gewichtung",
+        "addReferenceImage": "$t(controlLayers.referenceImage) hinzufügen",
+        "addInpaintMask": "$t(controlLayers.inpaintMask) hinzufügen",
+        "addGlobalReferenceImage": "$t(controlLayers.globalReferenceImage) hinzufügen",
+        "regionalGuidance": "Regionale Führung",
+        "globalReferenceImages_withCount_visible": "Globale Referenzbilder ({{count}})",
+        "addPositivePrompt": "$t(controlLayers.prompt) hinzufügen",
+        "locked": "Gesperrt",
+        "showHUD": "HUD anzeigen",
+        "addNegativePrompt": "$t(controlLayers.negativePrompt) hinzufügen",
+        "addRasterLayer": "$t(controlLayers.rasterLayer) hinzufügen",
+        "addRegionalGuidance": "$t(controlLayers.regionalGuidance) hinzufügen",
+        "addControlLayer": "$t(controlLayers.controlLayer) hinzufügen",
+        "newCanvasSession": "Neue Leinwand-Sitzung",
+        "replaceLayer": "Ebene ersetzen",
+        "newGallerySession": "Neue Galerie-Sitzung",
+        "unlocked": "Entsperrt",
+        "showProgressOnCanvas": "Fortschritt auf Leinwand anzeigen",
+        "controlMode": {
+            "balanced": "Ausgewogen"
+        },
+        "globalReferenceImages_withCount_hidden": "Globale Referenzbilder ({{count}} ausgeblendet)",
+        "sendToGallery": "An Galerie senden",
+        "stagingArea": {
+            "accept": "Annehmen",
+            "next": "Nächste",
+            "discardAll": "Alle verwerfen",
+            "discard": "Verwerfen",
+            "previous": "Vorherige"
+        },
+        "regionalGuidance_withCount_visible": "Regionale Führung ({{count}})",
+        "regionalGuidance_withCount_hidden": "Regionale Führung ({{count}} ausgeblendet)",
+        "settings": {
+            "snapToGrid": {
+                "on": "Ein",
+                "off": "Aus",
+                "label": "Am Raster ausrichten"
+            }
+        },
+        "layer_one": "Ebene",
+        "layer_other": "Ebenen",
+        "layer_withCount_one": "Ebene ({{count}})",
+        "layer_withCount_other": "Ebenen ({{count}})"
     },
     "upsell": {
         "shareAccess": "Zugang teilen",
         "professional": "Professionell",
         "inviteTeammates": "Teamkollegen einladen",
         "professionalUpsell": "Verfügbar in der Professional Edition von Invoke. Klicken Sie hier oder besuchen Sie invoke.com/pricing für weitere Details."
+    },
+    "upscaling": {
+        "creativity": "Kreativität",
+        "structure": "Struktur",
+        "scale": "Maßstab"
     }
 }

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

@@ -10,9 +10,10 @@
         "previousImage": "Previous Image",
         "reset": "Reset",
         "resetUI": "$t(accessibility.reset) UI",
-        "showGalleryPanel": "Show Gallery Panel",
-        "showOptionsPanel": "Show Side Panel",
-        "uploadImage": "Upload Image"
+        "toggleRightPanel": "Toggle Right Panel (G)",
+        "toggleLeftPanel": "Toggle Left Panel (T)",
+        "uploadImage": "Upload Image",
+        "uploadImages": "Upload Image(s)"
     },
     "boards": {
         "addBoard": "Add Board",
@@ -53,7 +54,8 @@
         "imagesWithCount_one": "{{count}} image",
         "imagesWithCount_other": "{{count}} images",
         "assetsWithCount_one": "{{count}} asset",
-        "assetsWithCount_other": "{{count}} assets"
+        "assetsWithCount_other": "{{count}} assets",
+        "updateBoardError": "Error updating board"
     },
     "accordions": {
         "generation": {
@@ -89,11 +91,14 @@
         "batch": "Batch Manager",
         "beta": "Beta",
         "cancel": "Cancel",
+        "close": "Close",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
+        "clipboard": "Clipboard",
         "on": "On",
         "off": "Off",
         "or": "or",
+        "ok": "Ok",
         "checkpoint": "Checkpoint",
         "communityLabel": "Community",
         "controlNet": "ControlNet",
@@ -279,8 +284,10 @@
         "gallery": "Gallery",
         "alwaysShowImageSizeBadge": "Always Show Image Size Badge",
         "assets": "Assets",
+        "assetsTab": "Files you’ve uploaded for use in your projects.",
         "autoAssignBoardOnClick": "Auto-Assign Board on Click",
         "autoSwitchNewImages": "Auto-Switch to New Images",
+        "boardsSettings": "Boards Settings",
         "copy": "Copy",
         "currentlyInUse": "This image is currently in use in the following features:",
         "drop": "Drop",
@@ -299,6 +306,8 @@
         "gallerySettings": "Gallery Settings",
         "go": "Go",
         "image": "image",
+        "imagesTab": "Images you’ve created and saved within Invoke.",
+        "imagesSettings": "Gallery Images Settings",
         "jump": "Jump",
         "loading": "Loading",
         "newestFirst": "Newest First",
@@ -657,6 +666,7 @@
         "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
         "createdBy": "Created By",
         "generationMode": "Generation Mode",
+        "guidance": "Guidance",
         "height": "Height",
         "imageDetails": "Image Details",
         "imageDimensions": "Image Dimensions",
@@ -672,7 +682,8 @@
         "recallParameters": "Recall Parameters",
         "recallParameter": "Recall {{label}}",
         "scheduler": "Scheduler",
-        "seamless": "Seamless",
+        "seamlessXAxis": "Seamless X Axis",
+        "seamlessYAxis": "Seamless Y Axis",
         "seed": "Seed",
         "steps": "Steps",
         "strength": "Image to image strength",
@@ -697,14 +708,18 @@
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
         "convertToDiffusers": "Convert To Diffusers",
-        "convertToDiffusersHelpText1": "This model will be converted to the \ud83e\udde8 Diffusers format.",
+        "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
         "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
         "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
         "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",
@@ -718,8 +733,19 @@
         "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.",
-        "hfToken": "HuggingFace Token",
+        "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",
         "imageEncoderModelId": "Image Encoder Model ID",
+        "includesNModels": "Includes {{n}} models and their dependencies",
         "installQueue": "Install Queue",
         "inplaceInstall": "In-place install",
         "inplaceInstallDesc": "Install models without copying the files. When using the model, it will be loaded from its this location. If disabled, the model file(s) will be copied into the Invoke-managed models directory during installation.",
@@ -773,6 +799,8 @@
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
         "spandrelImageToImage": "Image to Image (Spandrel)",
+        "starterBundles": "Starter Bundles",
+        "starterBundleHelpText": "Easily install all models needed to get started with a base model, including a main model, controlnets, IP adapters, and more. Selecting a bundle will skip any models that you already have installed.",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
@@ -786,11 +814,16 @@
         "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",
-        "width": "Width"
+        "width": "Width",
+        "installingBundle": "Installing Bundle",
+        "installingModel": "Installing Model",
+        "installingXModels_one": "Installing {{count}} model",
+        "installingXModels_other": "Installing {{count}} models",
+        "skippingXDuplicates_one": ", skipping {{count}} duplicate",
+        "skippingXDuplicates_other": ", skipping {{count}} duplicates"
     },
     "models": {
         "addLora": "Add LoRA",
@@ -852,6 +885,8 @@
         "ipAdapter": "IP-Adapter",
         "loadingNodes": "Loading Nodes...",
         "loadWorkflow": "Load Workflow",
+        "noWorkflows": "No Workflows",
+        "noMatchingWorkflows": "No Matching Workflows",
         "noWorkflow": "No Workflow",
         "mismatchedVersion": "Invalid node: node {{node}} of type {{type}} has mismatched version (try updating?)",
         "missingTemplate": "Invalid node: node {{node}} of type {{type}} missing template (not installed?)",
@@ -868,6 +903,7 @@
         "nodeType": "Node Type",
         "noFieldsLinearview": "No fields added to Linear View",
         "noFieldsViewMode": "This workflow has no selected fields to display. View the full workflow to configure values.",
+        "workflowHelpText": "Need Help? Check out our guide to <LinkComponent>Getting Started with Workflows</LinkComponent>.",
         "noNodeSelected": "No node selected",
         "nodeOpacity": "Node Opacity",
         "nodeVersion": "Node Version",
@@ -1011,6 +1047,7 @@
         "patchmatchDownScaleSize": "Downscale",
         "perlinNoise": "Perlin Noise",
         "positivePromptPlaceholder": "Positive Prompt",
+        "recallMetadata": "Recall Metadata",
         "iterations": "Iterations",
         "scale": "Scale",
         "scaleBeforeProcessing": "Scale Before Processing",
@@ -1081,11 +1118,15 @@
         "antialiasProgressImages": "Antialias Progress Images",
         "beta": "Beta",
         "confirmOnDelete": "Confirm On Delete",
+        "confirmOnNewSession": "Confirm On New Session",
         "developer": "Developer",
         "displayInProgress": "Display Progress Images",
         "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",
@@ -1110,13 +1151,15 @@
         "reloadingIn": "Reloading in"
     },
     "toast": {
-        "addedToBoard": "Added to board",
+        "addedToBoard": "Added to board {{name}}'s assets",
+        "addedToUncategorized": "Added to board $t(boards.uncategorized)'s assets",
         "baseModelChanged": "Base Model Changed",
         "baseModelChangedCleared_one": "Cleared or disabled {{count}} incompatible submodel",
         "baseModelChangedCleared_other": "Cleared or disabled {{count}} incompatible submodels",
         "canceled": "Processing Canceled",
         "connected": "Connected to Server",
         "imageCopied": "Image Copied",
+        "linkCopied": "Link Copied",
         "unableToLoadImage": "Unable to Load Image",
         "unableToLoadImageMetadata": "Unable to Load Image Metadata",
         "unableToLoadStylePreset": "Unable to Load Style Preset",
@@ -1158,7 +1201,10 @@
         "setNodeField": "Set as node field",
         "somethingWentWrong": "Something Went Wrong",
         "uploadFailed": "Upload failed",
-        "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
+        "imagesWillBeAddedTo": "Uploaded images will be added to board {{boardName}}'s assets.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Must be maximum of 1 PNG or JPEG image.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Must be maximum of {{count}} PNG or JPEG images.",
+        "uploadFailedInvalidUploadDesc": "Must be PNG or JPEG images.",
         "workflowLoaded": "Workflow Loaded",
         "problemRetrievingWorkflow": "Problem Retrieving Workflow",
         "workflowDeleted": "Workflow Deleted",
@@ -1224,6 +1270,33 @@
             "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": [
@@ -1513,6 +1586,7 @@
         }
     },
     "workflows": {
+        "chooseWorkflowFromLibrary": "Choose Workflow from Library",
         "defaultWorkflows": "Default Workflows",
         "userWorkflows": "User Workflows",
         "projectWorkflows": "Project Workflows",
@@ -1525,7 +1599,9 @@
         "openWorkflow": "Open Workflow",
         "updated": "Updated",
         "uploadWorkflow": "Load from File",
+        "uploadAndSaveWorkflow": "Upload to Library",
         "deleteWorkflow": "Delete Workflow",
+        "deleteWorkflow2": "Are you sure you want to delete this workflow? This cannot be undone.",
         "unnamedWorkflow": "Unnamed Workflow",
         "downloadWorkflow": "Save to File",
         "saveWorkflow": "Save Workflow",
@@ -1548,9 +1624,13 @@
         "loadFromGraph": "Load Workflow from Graph",
         "convertGraph": "Convert Graph",
         "loadWorkflow": "$t(common.load) Workflow",
-        "autoLayout": "Auto Layout"
+        "autoLayout": "Auto Layout",
+        "edit": "Edit",
+        "download": "Download",
+        "copyShareLink": "Copy Share Link",
+        "copyShareLinkForWorkflow": "Copy Share Link for Workflow",
+        "delete": "Delete"
     },
-    "app": {},
     "controlLayers": {
         "regional": "Regional",
         "global": "Global",
@@ -1561,6 +1641,7 @@
         "saveCanvasToGallery": "Save Canvas to Gallery",
         "saveBboxToGallery": "Save Bbox to Gallery",
         "saveLayerToAssets": "Save Layer to Assets",
+        "cropLayerToBbox": "Crop Layer to Bbox",
         "savedToGalleryOk": "Saved to Gallery",
         "savedToGalleryError": "Error saving to gallery",
         "newGlobalReferenceImageOk": "Created Global Reference Image",
@@ -1571,14 +1652,16 @@
         "newControlLayerError": "Problem Creating Control Layer",
         "newRasterLayerOk": "Created Raster Layer",
         "newRasterLayerError": "Problem Creating Raster Layer",
+        "newFromImage": "New from Image",
         "pullBboxIntoLayerOk": "Bbox Pulled Into Layer",
         "pullBboxIntoLayerError": "Problem Pulling BBox Into Layer",
         "pullBboxIntoReferenceImageOk": "Bbox Pulled Into ReferenceImage",
         "pullBboxIntoReferenceImageError": "Problem Pulling BBox Into ReferenceImage",
         "regionIsEmpty": "Selected region is empty",
         "mergeVisible": "Merge Visible",
-        "mergeVisibleOk": "Merged visible layers",
-        "mergeVisibleError": "Error merging visible layers",
+        "mergeDown": "Merge Down",
+        "mergeVisibleOk": "Merged layers",
+        "mergeVisibleError": "Error merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
         "resetCanvas": "Reset Canvas",
@@ -1613,6 +1696,8 @@
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
         "regionalGuidance": "Regional Guidance",
+        "canvasAsRasterLayer": "$t(controlLayers.canvas) as $t(controlLayers.rasterLayer)",
+        "canvasAsControlLayer": "$t(controlLayers.canvas) as $t(controlLayers.controlLayer)",
         "referenceImage": "Reference Image",
         "regionalReferenceImage": "Regional Reference Image",
         "globalReferenceImage": "Global Reference Image",
@@ -1623,19 +1708,20 @@
         "sendToCanvas": "Send To Canvas",
         "newLayerFromImage": "New Layer from Image",
         "newCanvasFromImage": "New Canvas from Image",
+        "newImg2ImgCanvasFromImage": "New Img2Img from Image",
         "copyToClipboard": "Copy to Clipboard",
         "sendToCanvasDesc": "Pressing Invoke stages your work in progress on the canvas.",
         "viewProgressInViewer": "View progress and outputs in the <Btn>Image Viewer</Btn>.",
         "viewProgressOnCanvas": "View progress and stage outputs on the <Btn>Canvas</Btn>.",
         "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
-        "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
-        "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
-        "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
-        "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
         "rasterLayer_withCount_other": "Raster Layers",
+        "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
         "controlLayer_withCount_other": "Control Layers",
+        "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
         "inpaintMask_withCount_other": "Inpaint Masks",
+        "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
         "regionalGuidance_withCount_other": "Regional Guidance",
+        "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
         "globalReferenceImage_withCount_other": "Global Reference Images",
         "opacity": "Opacity",
         "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
@@ -1648,13 +1734,22 @@
         "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
         "globalReferenceImages_withCount_visible": "Global Reference Images ({{count}})",
         "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
-        "layer": "Layer",
         "layer_one": "Layer",
         "layer_other": "Layers",
         "layer_withCount_one": "Layer ({{count}})",
         "layer_withCount_other": "Layers ({{count}})",
-        "convertToControlLayer": "Convert to Control Layer",
-        "convertToRasterLayer": "Convert to Raster Layer",
+        "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)",
         "transparency": "Transparency",
         "enableTransparencyEffect": "Enable Transparency Effect",
         "disableTransparencyEffect": "Disable Transparency Effect",
@@ -1674,6 +1769,11 @@
         "negativePrompt": "Negative Prompt",
         "beginEndStepPercentShort": "Begin/End %",
         "weight": "Weight",
+        "newGallerySession": "New Gallery Session",
+        "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.",
+        "replaceCurrent": "Replace Current",
         "controlMode": {
             "controlMode": "Control Mode",
             "balanced": "Balanced",
@@ -1727,7 +1827,7 @@
                 "label": "Canny Edge Detection",
                 "description": "Generates an edge map from the selected layer using the Canny edge detection algorithm.",
                 "low_threshold": "Low Threshold",
-                "high_threshold": "Hight Threshold"
+                "high_threshold": "High Threshold"
             },
             "color_map": {
                 "label": "Color Map",
@@ -1795,10 +1895,33 @@
         "transform": {
             "transform": "Transform",
             "fitToBbox": "Fit to Bbox",
+            "fitMode": "Fit Mode",
+            "fitModeContain": "Contain",
+            "fitModeCover": "Cover",
+            "fitModeFill": "Fill",
             "reset": "Reset",
             "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",
@@ -1809,11 +1932,12 @@
                 "label": "Preserve Masked Region",
                 "alert": "Preserving Masked Region"
             },
-            "isolatedPreview": "Isolated Preview",
             "isolatedStagingPreview": "Isolated Staging Preview",
-            "isolatedFilteringPreview": "Isolated Filtering Preview",
-            "isolatedTransformingPreview": "Isolated Transforming Preview",
-            "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size"
+            "isolatedPreview": "Isolated Preview",
+            "isolatedLayerPreview": "Isolated Layer Preview",
+            "isolatedLayerPreviewDesc": "Whether to show only this layer when performing operations like filtering or transforming.",
+            "invertBrushSizeScrollDirection": "Invert Scroll for Brush Size",
+            "pressureSensitivity": "Pressure Sensitivity"
         },
         "HUD": {
             "bbox": "Bbox",
@@ -1828,6 +1952,7 @@
             }
         },
         "canvasContextMenu": {
+            "canvasGroup": "Canvas",
             "saveToGalleryGroup": "Save To Gallery",
             "saveCanvasToGallery": "Save Canvas To Gallery",
             "saveBboxToGallery": "Save Bbox To Gallery",
@@ -1835,7 +1960,10 @@
             "newGlobalReferenceImage": "New Global Reference Image",
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
-            "newRasterLayer": "New Raster Layer"
+            "newRasterLayer": "New Raster Layer",
+            "newInpaintMask": "New Inpaint Mask",
+            "newRegionalGuidance": "New Regional Guidance",
+            "cropCanvasToBbox": "Crop Canvas to Bbox"
         },
         "stagingArea": {
             "accept": "Accept",
@@ -1958,18 +2086,20 @@
         }
     },
     "newUserExperience": {
-        "toGetStarted": "To get started, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
-        "gettingStartedSeries": "Want more guidance? Check out our <LinkComponent>Getting Started Series</LinkComponent> for tips on unlocking the full potential of the Invoke Studio."
+        "toGetStartedLocal": "To get started, make sure to download or import models needed to run Invoke. Then, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. Select a prompt template to improve results. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
+        "toGetStarted": "To get started, enter a prompt in the box and click <StrongComponent>Invoke</StrongComponent> to generate your first image. Select a prompt template to improve results. You can choose to save your images directly to the <StrongComponent>Gallery</StrongComponent> or edit them to the <StrongComponent>Canvas</StrongComponent>.",
+        "gettingStartedSeries": "Want more guidance? Check out our <LinkComponent>Getting Started Series</LinkComponent> for tips on unlocking the full potential of the Invoke Studio.",
+        "downloadStarterModels": "Download Starter Models",
+        "importModels": "Import Models",
+        "noModelsInstalled": "It looks like you don't have any models installed"
     },
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
-        "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"
-        }
+        "line1": "<ItalicComponent>Select Object</ItalicComponent> tool for precise object selection and editing",
+        "line2": "Expanded Flux support, now with Global Reference Images",
+        "line3": "Improved tooltips and context menus",
+        "readReleaseNotes": "Read Release Notes",
+        "watchRecentReleaseVideos": "Watch Recent Release Videos",
+        "watchUiUpdatesOverview": "Watch UI Updates Overview"
     }
 }

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

@@ -219,14 +219,14 @@
         "uploadImage": "Cargar imagen",
         "previousImage": "Imagen anterior",
         "nextImage": "Siguiente imagen",
-        "showOptionsPanel": "Mostrar el panel lateral",
         "menu": "Menú",
-        "showGalleryPanel": "Mostrar panel de galería",
         "about": "Acerca de",
         "createIssue": "Crear un problema",
         "resetUI": "Interfaz de usuario $t(accessibility.reset)",
         "mode": "Modo",
-        "submitSupportTicket": "Enviar Ticket de Soporte"
+        "submitSupportTicket": "Enviar Ticket de Soporte",
+        "toggleRightPanel": "Activar o desactivar el panel derecho (G)",
+        "toggleLeftPanel": "Activar o desactivar el panel izquierdo (T)"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -275,7 +275,12 @@
         "addSharedBoard": "Agregar Panel Compartido",
         "boards": "Paneles",
         "archiveBoard": "Archivar Panel",
-        "archived": "Archivado"
+        "archived": "Archivado",
+        "selectedForAutoAdd": "Seleccionado para agregar automáticamente",
+        "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": {
@@ -318,5 +323,13 @@
         "inviteTeammates": "Invitar compañeros de equipo",
         "shareAccess": "Compartir acceso",
         "professionalUpsell": "Disponible en la edición profesional de Invoke. Haz clic aquí o visita invoke.com/pricing para obtener más detalles."
+    },
+    "controlLayers": {
+        "layer_one": "Capa",
+        "layer_many": "Capas",
+        "layer_other": "Capas",
+        "layer_withCount_one": "({{count}}) capa",
+        "layer_withCount_many": "({{count}}) capas",
+        "layer_withCount_other": "({{count}}) capas"
     }
 }

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

@@ -4,8 +4,7 @@
         "uploadImage": "Lataa kuva",
         "invokeProgressBar": "Invoken edistymispalkki",
         "nextImage": "Seuraava kuva",
-        "previousImage": "Edellinen kuva",
-        "showOptionsPanel": "Näytä asetukset"
+        "previousImage": "Edellinen kuva"
     },
     "common": {
         "languagePickerLabel": "Kielen valinta",

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

@@ -65,7 +65,7 @@
         "blue": "Blu",
         "alpha": "Alfa",
         "copy": "Copia",
-        "on": "Attivato",
+        "on": "Acceso",
         "checkpoint": "Checkpoint",
         "safetensors": "Safetensors",
         "ai": "ia",
@@ -85,13 +85,16 @@
         "openInViewer": "Apri nel visualizzatore",
         "apply": "Applica",
         "loadingImage": "Caricamento immagine",
-        "off": "Disattivato",
+        "off": "Spento",
         "edit": "Modifica",
         "placeholderSelectAModel": "Seleziona un modello",
         "reset": "Reimposta",
         "none": "Niente",
         "new": "Nuovo",
-        "view": "Vista"
+        "view": "Vista",
+        "close": "Chiudi",
+        "clipboard": "Appunti",
+        "ok": "Ok"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -155,7 +158,11 @@
         "move": "Sposta",
         "gallery": "Galleria",
         "openViewer": "Apri visualizzatore",
-        "closeViewer": "Chiudi visualizzatore"
+        "closeViewer": "Chiudi visualizzatore",
+        "imagesTab": "Immagini create e salvate in Invoke.",
+        "assetsTab": "File che hai caricato per usarli nei tuoi progetti.",
+        "boardsSettings": "Impostazioni Bacheche",
+        "imagesSettings": "Impostazioni Immagini Galleria"
     },
     "hotkeys": {
         "searchHotkeys": "Cerca tasti di scelta rapida",
@@ -321,6 +328,22 @@
             "selectViewTool": {
                 "title": "Strumento Visualizza",
                 "desc": "Seleziona lo strumento Visualizza."
+            },
+            "applyFilter": {
+                "title": "Applica filtro",
+                "desc": "Applica il filtro in sospeso al livello selezionato."
+            },
+            "cancelFilter": {
+                "title": "Annulla filtro",
+                "desc": "Annulla il filtro in sospeso."
+            },
+            "cancelTransform": {
+                "desc": "Annulla la trasformazione in sospeso.",
+                "title": "Annulla Trasforma"
+            },
+            "applyTransform": {
+                "title": "Applica trasformazione",
+                "desc": "Applica la trasformazione in sospeso al livello selezionato."
             }
         },
         "workflows": {
@@ -521,7 +544,6 @@
         "defaultSettingsSaved": "Impostazioni predefinite salvate",
         "defaultSettings": "Impostazioni predefinite",
         "metadata": "Metadati",
-        "useDefaultSettings": "Usa le impostazioni predefinite",
         "triggerPhrases": "Frasi Trigger",
         "deleteModelImage": "Elimina l'immagine del modello",
         "localOnly": "solo locale",
@@ -556,7 +578,26 @@
         "noMatchingModels": "Nessun modello corrispondente",
         "starterModelsInModelManager": "I modelli iniziali possono essere trovati in Gestione Modelli",
         "spandrelImageToImage": "Immagine a immagine (Spandrel)",
-        "learnMoreAboutSupportedModels": "Scopri di più sui modelli che supportiamo"
+        "learnMoreAboutSupportedModels": "Scopri di più sui modelli che supportiamo",
+        "starterBundles": "Pacchetti per iniziare",
+        "installingBundle": "Installazione del pacchetto",
+        "skippingXDuplicates_one": ", saltando {{count}} duplicato",
+        "skippingXDuplicates_many": ", saltando {{count}} duplicati",
+        "skippingXDuplicates_other": ", saltando {{count}} duplicati",
+        "installingModel": "Installazione del modello",
+        "installingXModels_one": "Installazione di {{count}} modello",
+        "installingXModels_many": "Installazione di {{count}} modelli",
+        "installingXModels_other": "Installazione di {{count}} modelli",
+        "includesNModels": "Include {{n}} modelli e le loro dipendenze",
+        "starterBundleHelpText": "Installa facilmente tutti i modelli necessari per iniziare con un modello base, tra cui un modello principale, controlnet, adattatori IP e altro. Selezionando un pacchetto salterai tutti i modelli che hai già installato.",
+        "noDefaultSettings": "Nessuna impostazione predefinita configurata per questo modello. Visita Gestione Modelli per aggiungere impostazioni predefinite.",
+        "defaultSettingsOutOfSync": "Alcune impostazioni non corrispondono a quelle predefinite del modello:",
+        "restoreDefaultSettings": "Fare clic per utilizzare le impostazioni predefinite del modello.",
+        "usingDefaultSettings": "Utilizzo delle impostazioni predefinite del modello",
+        "huggingFace": "HuggingFace",
+        "huggingFaceRepoID": "HuggingFace Repository ID",
+        "clipEmbed": "CLIP Embed",
+        "t5Encoder": "T5 Encoder"
     },
     "parameters": {
         "images": "Immagini",
@@ -574,8 +615,8 @@
         "scale": "Scala",
         "imageFit": "Adatta l'immagine iniziale alle dimensioni di output",
         "scaleBeforeProcessing": "Scala prima dell'elaborazione",
-        "scaledWidth": "Larghezza ridimensionata",
-        "scaledHeight": "Altezza ridimensionata",
+        "scaledWidth": "Larghezza scalata",
+        "scaledHeight": "Altezza scalata",
         "infillMethod": "Metodo di riempimento",
         "tileSize": "Dimensione piastrella",
         "downloadImage": "Scarica l'immagine",
@@ -617,7 +658,11 @@
                 "ipAdapterIncompatibleBaseModel": "Il modello base dell'adattatore IP non è compatibile",
                 "ipAdapterNoImageSelected": "Nessuna immagine dell'adattatore IP selezionata",
                 "rgNoPromptsOrIPAdapters": "Nessun prompt o adattatore IP",
-                "rgNoRegion": "Nessuna regione selezionata"
+                "rgNoRegion": "Nessuna regione selezionata",
+                "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza riquadro è {{width}}",
+                "t2iAdapterIncompatibleBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza riquadro è {{height}}",
+                "t2iAdapterIncompatibleScaledBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, larghezza del riquadro scalato {{width}}",
+                "t2iAdapterIncompatibleScaledBboxHeight": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, altezza del riquadro scalato {{height}}"
             },
             "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), altezza riquadro è {{height}}",
             "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), larghezza riquadro è {{width}}",
@@ -625,7 +670,11 @@
             "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), altezza del riquadro scalato è {{height}}",
             "noT5EncoderModelSelected": "Nessun modello di encoder T5 selezionato per la generazione con FLUX",
             "noCLIPEmbedModelSelected": "Nessun modello CLIP Embed selezionato per la generazione con FLUX",
-            "noFLUXVAEModelSelected": "Nessun modello VAE selezionato per la generazione con FLUX"
+            "noFLUXVAEModelSelected": "Nessun modello VAE selezionato per la generazione con FLUX",
+            "canvasIsTransforming": "La tela sta trasformando",
+            "canvasIsRasterizing": "La tela sta rasterizzando",
+            "canvasIsCompositing": "La tela è in fase di composizione",
+            "canvasIsFiltering": "La tela sta filtrando"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -644,7 +693,13 @@
         "processImage": "Elabora Immagine",
         "sendToUpscale": "Invia a Amplia",
         "postProcessing": "Post-elaborazione (Shift + U)",
-        "guidance": "Guida"
+        "guidance": "Guida",
+        "gaussianBlur": "Sfocatura Gaussiana",
+        "boxBlur": "Sfocatura Box",
+        "staged": "Maschera espansa",
+        "optimizedImageToImage": "Immagine-a-immagine ottimizzata",
+        "sendToCanvas": "Invia alla Tela",
+        "coherenceMinDenoise": "Riduzione minima del rumore"
     },
     "settings": {
         "models": "Modelli",
@@ -678,7 +733,11 @@
         "enableInformationalPopovers": "Abilita testo informativo a comparsa",
         "reloadingIn": "Ricaricando in",
         "informationalPopoversDisabled": "Testo informativo a comparsa disabilitato",
-        "informationalPopoversDisabledDesc": "I testi informativi a comparsa sono disabilitati. Attivali nelle impostazioni."
+        "informationalPopoversDisabledDesc": "I testi informativi a comparsa sono disabilitati. Attivali nelle impostazioni.",
+        "confirmOnNewSession": "Conferma su nuova sessione",
+        "enableModelDescriptions": "Abilita le descrizioni dei modelli nei menu a discesa",
+        "modelDescriptionsDisabled": "Descrizioni dei modelli nei menu a discesa disabilitate",
+        "modelDescriptionsDisabledDesc": "Le descrizioni dei modelli nei menu a discesa sono state disabilitate. Abilitale nelle Impostazioni."
     },
     "toast": {
         "uploadFailed": "Caricamento fallito",
@@ -687,7 +746,7 @@
         "serverError": "Errore del Server",
         "connected": "Connesso al server",
         "canceled": "Elaborazione annullata",
-        "uploadFailedInvalidUploadDesc": "Deve essere una singola immagine PNG o JPEG",
+        "uploadFailedInvalidUploadDesc": "Devono essere immagini PNG o JPEG.",
         "parameterSet": "Parametro richiamato",
         "parameterNotSet": "Parametro non richiamato",
         "problemCopyingImage": "Impossibile copiare l'immagine",
@@ -696,7 +755,7 @@
         "baseModelChangedCleared_other": "Cancellati o disabilitati {{count}} sottomodelli incompatibili",
         "loadedWithWarnings": "Flusso di lavoro caricato con avvisi",
         "imageUploaded": "Immagine caricata",
-        "addedToBoard": "Aggiunto alla bacheca",
+        "addedToBoard": "Aggiunto alle risorse della bacheca {{name}}",
         "modelAddedSimple": "Modello aggiunto alla Coda",
         "imageUploadFailed": "Caricamento immagine non riuscito",
         "setControlImage": "Imposta come immagine di controllo",
@@ -721,7 +780,26 @@
         "somethingWentWrong": "Qualcosa è andato storto",
         "outOfMemoryErrorDesc": "Le impostazioni della generazione attuale superano la capacità del sistema. Modifica le impostazioni e riprova.",
         "importFailed": "Importazione non riuscita",
-        "importSuccessful": "Importazione riuscita"
+        "importSuccessful": "Importazione riuscita",
+        "layerSavedToAssets": "Livello salvato nelle risorse",
+        "problemSavingLayer": "Impossibile salvare il livello",
+        "unableToLoadImage": "Impossibile caricare l'immagine",
+        "problemCopyingLayer": "Impossibile copiare il livello",
+        "sentToCanvas": "Inviato alla Tela",
+        "sentToUpscale": "Inviato a Amplia",
+        "unableToLoadStylePreset": "Impossibile caricare lo stile predefinito",
+        "stylePresetLoaded": "Stile predefinito caricato",
+        "unableToLoadImageMetadata": "Impossibile caricare i metadati dell'immagine",
+        "imageSaved": "Immagine salvata",
+        "imageSavingFailed": "Salvataggio dell'immagine non riuscito",
+        "layerCopiedToClipboard": "Livello copiato negli appunti",
+        "imageNotLoadedDesc": "Impossibile trovare l'immagine",
+        "linkCopied": "Collegamento copiato",
+        "addedToUncategorized": "Aggiunto alle risorse della bacheca $t(boards.uncategorized)",
+        "imagesWillBeAddedTo": "Le immagini caricate verranno aggiunte alle risorse della bacheca {{boardName}}.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Devi caricare al massimo 1 immagine PNG o JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_many": "Devi caricare al massimo {{count}} immagini PNG o JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Devi caricare al massimo {{count}} immagini PNG o JPEG."
     },
     "accessibility": {
         "invokeProgressBar": "Barra di avanzamento generazione",
@@ -729,14 +807,15 @@
         "previousImage": "Immagine precedente",
         "nextImage": "Immagine successiva",
         "reset": "Reimposta",
-        "showOptionsPanel": "Mostra il pannello laterale",
         "menu": "Menu",
-        "showGalleryPanel": "Mostra il pannello Galleria",
         "mode": "Modalità",
         "resetUI": "$t(accessibility.reset) l'Interfaccia Utente",
         "createIssue": "Segnala un problema",
         "about": "Informazioni",
-        "submitSupportTicket": "Invia ticket di supporto"
+        "submitSupportTicket": "Invia ticket di supporto",
+        "toggleLeftPanel": "Attiva/disattiva il pannello sinistro (T)",
+        "toggleRightPanel": "Attiva/disattiva il pannello destro (G)",
+        "uploadImages": "Carica immagine(i)"
     },
     "nodes": {
         "zoomOutNodes": "Rimpicciolire",
@@ -856,7 +935,7 @@
         "clearWorkflowDesc": "Cancellare questo flusso di lavoro e avviarne uno nuovo?",
         "clearWorkflow": "Cancella il flusso di lavoro",
         "clearWorkflowDesc2": "Il tuo flusso di lavoro attuale presenta modifiche non salvate.",
-        "viewMode": "Utilizzare nella vista lineare",
+        "viewMode": "Usa la vista lineare",
         "reorderLinearView": "Riordina la vista lineare",
         "editMode": "Modifica nell'editor del flusso di lavoro",
         "resetToDefaultValue": "Ripristina il valore predefinito",
@@ -874,7 +953,10 @@
         "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino ai valori predefiniti",
         "boardAccessError": "Impossibile trovare la bacheca {{board_id}}, ripristino ai valori predefiniti",
         "modelAccessError": "Impossibile trovare il modello {{key}}, ripristino ai valori predefiniti",
-        "saveToGallery": "Salva nella Galleria"
+        "saveToGallery": "Salva nella Galleria",
+        "noMatchingWorkflows": "Nessun flusso di lavoro corrispondente",
+        "noWorkflows": "Nessun flusso di lavoro",
+        "workflowHelpText": "Hai bisogno di aiuto? Consulta la nostra guida <LinkComponent>Introduzione ai flussi di lavoro</LinkComponent>."
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -918,7 +1000,8 @@
         "noBoards": "Nessuna bacheca {{boardType}}",
         "hideBoards": "Nascondi bacheche",
         "viewBoards": "Visualizza bacheche",
-        "deletedPrivateBoardsCannotbeRestored": "Le bacheche cancellate non possono essere ripristinate. Selezionando 'Cancella solo bacheca', le immagini verranno spostate nella bacheca \"Non categorizzato\" privata dell'autore dell'immagine."
+        "deletedPrivateBoardsCannotbeRestored": "Le bacheche cancellate non possono essere ripristinate. Selezionando 'Cancella solo bacheca', le immagini verranno spostate nella bacheca \"Non categorizzato\" privata dell'autore dell'immagine.",
+        "updateBoardError": "Errore durante l'aggiornamento della bacheca"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1006,7 +1089,8 @@
         "noLoRAsInstalled": "Nessun LoRA installato",
         "addLora": "Aggiungi LoRA",
         "defaultVAE": "VAE predefinito",
-        "concepts": "Concetti"
+        "concepts": "Concetti",
+        "lora": "LoRA"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1063,7 +1147,8 @@
             "paragraphs": [
                 "Scegli quanti livelli del modello CLIP saltare.",
                 "Alcuni modelli funzionano meglio con determinate impostazioni di CLIP Skip."
-            ]
+            ],
+            "heading": "CLIP Skip"
         },
         "compositingCoherencePass": {
             "heading": "Passaggio di Coerenza",
@@ -1403,6 +1488,61 @@
             "paragraphs": [
                 "La struttura determina quanto l'immagine finale rispecchierà il layout dell'originale. Una struttura bassa permette cambiamenti significativi, mentre una struttura alta conserva la composizione e il layout originali."
             ]
+        },
+        "fluxDevLicense": {
+            "heading": "Licenza non commerciale",
+            "paragraphs": [
+                "I modelli FLUX.1 [dev] sono concessi in licenza con la licenza non commerciale FLUX [dev]. Per utilizzare questo tipo di modello per scopi commerciali in Invoke, visita il nostro sito Web per saperne di più."
+            ]
+        },
+        "optimizedDenoising": {
+            "heading": "Immagine-a-immagine ottimizzata",
+            "paragraphs": [
+                "Abilita 'Immagine-a-immagine ottimizzata' per una scala di riduzione del rumore più graduale per le trasformazioni da immagine a immagine e di inpainting con modelli Flux. Questa impostazione migliora la capacità di controllare la quantità di modifica applicata a un'immagine, ma può essere disattivata se preferisci usare la scala di riduzione rumore standard. Questa impostazione è ancora in fase di messa a punto ed è in stato beta."
+            ]
+        },
+        "paramGuidance": {
+            "heading": "Guida",
+            "paragraphs": [
+                "Controlla quanto il prompt influenza il processo di generazione.",
+                "Valori di guida elevati possono causare sovrasaturazione e una guida elevata o bassa può causare risultati di generazione distorti. La guida si applica solo ai modelli FLUX DEV."
+            ]
+        },
+        "regionalReferenceImage": {
+            "paragraphs": [
+                "Pennello per applicare un'immagine di riferimento ad aree specifiche."
+            ],
+            "heading": "Immagine di riferimento Regionale"
+        },
+        "rasterLayer": {
+            "paragraphs": [
+                "Contenuto basato sui pixel della tua tela, utilizzato durante la generazione dell'immagine."
+            ],
+            "heading": "Livello Raster"
+        },
+        "regionalGuidance": {
+            "heading": "Guida Regionale",
+            "paragraphs": [
+                "Pennello per guidare la posizione in cui devono apparire gli elementi dei prompt globali."
+            ]
+        },
+        "regionalGuidanceAndReferenceImage": {
+            "heading": "Guida regionale e immagine di riferimento regionale",
+            "paragraphs": [
+                "Per la Guida Regionale, utilizzare il pennello per indicare dove devono apparire gli elementi dei prompt globali.",
+                "Per l'immagine di riferimento regionale, utilizzare il pennello per applicare un'immagine di riferimento ad aree specifiche."
+            ]
+        },
+        "globalReferenceImage": {
+            "heading": "Immagine di riferimento Globale",
+            "paragraphs": [
+                "Applica un'immagine di riferimento per influenzare l'intera generazione."
+            ]
+        },
+        "inpainting": {
+            "paragraphs": [
+                "Controlla quale area viene modificata, in base all'intensità di riduzione del rumore."
+            ]
         }
     },
     "sdxl": {
@@ -1424,7 +1564,6 @@
         "refinerSteps": "Passi Affinamento"
     },
     "metadata": {
-        "seamless": "Senza giunture",
         "positivePrompt": "Prompt positivo",
         "negativePrompt": "Prompt negativo",
         "generationMode": "Modalità generazione",
@@ -1451,7 +1590,11 @@
         "parameterSet": "Parametro {{parameter}} impostato",
         "parsingFailed": "Analisi non riuscita",
         "recallParameter": "Richiama {{label}}",
-        "canvasV2Metadata": "Tela"
+        "canvasV2Metadata": "Tela",
+        "guidance": "Guida",
+        "seamlessXAxis": "Asse X senza giunte",
+        "seamlessYAxis": "Asse Y senza giunte",
+        "vae": "VAE"
     },
     "hrf": {
         "enableHrf": "Abilita Correzione Alta Risoluzione",
@@ -1496,7 +1639,18 @@
         "convertGraph": "Converti grafico",
         "loadWorkflow": "$t(common.load) Flusso di lavoro",
         "autoLayout": "Disposizione automatica",
-        "loadFromGraph": "Carica il flusso di lavoro dal grafico"
+        "loadFromGraph": "Carica il flusso di lavoro dal grafico",
+        "userWorkflows": "Flussi di lavoro utente",
+        "projectWorkflows": "Flussi di lavoro del progetto",
+        "defaultWorkflows": "Flussi di lavoro predefiniti",
+        "uploadAndSaveWorkflow": "Carica nella libreria",
+        "chooseWorkflowFromLibrary": "Scegli il flusso di lavoro dalla libreria",
+        "deleteWorkflow2": "Vuoi davvero eliminare questo flusso di lavoro? Questa operazione non può essere annullata.",
+        "edit": "Modifica",
+        "download": "Scarica",
+        "copyShareLink": "Copia Condividi Link",
+        "copyShareLinkForWorkflow": "Copia Condividi Link del Flusso di lavoro",
+        "delete": "Elimina"
     },
     "accordions": {
         "compositing": {
@@ -1535,7 +1689,343 @@
         "addPositivePrompt": "Aggiungi $t(controlLayers.prompt)",
         "addNegativePrompt": "Aggiungi $t(controlLayers.negativePrompt)",
         "regionalGuidance": "Guida regionale",
-        "opacity": "Opacità"
+        "opacity": "Opacità",
+        "mergeVisible": "Fondi il visibile",
+        "mergeVisibleOk": "Livelli uniti",
+        "deleteReferenceImage": "Elimina l'immagine di riferimento",
+        "referenceImage": "Immagine di riferimento",
+        "fitBboxToLayers": "Adatta il riquadro di delimitazione ai livelli",
+        "mergeVisibleError": "Errore durante l'unione dei livelli",
+        "regionalReferenceImage": "Immagine di riferimento Regionale",
+        "newLayerFromImage": "Nuovo livello da immagine",
+        "newCanvasFromImage": "Nuova tela da immagine",
+        "globalReferenceImage": "Immagine di riferimento Globale",
+        "copyToClipboard": "Copia negli appunti",
+        "sendingToCanvas": "Effettua le generazioni nella Tela",
+        "clearHistory": "Cancella la cronologia",
+        "inpaintMask": "Maschera Inpaint",
+        "sendToGallery": "Invia alla Galleria",
+        "controlLayer": "Livello di Controllo",
+        "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
+        "rasterLayer_withCount_many": "Livelli Raster",
+        "rasterLayer_withCount_other": "Livelli Raster",
+        "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
+        "controlLayer_withCount_many": "Livelli di controllo",
+        "controlLayer_withCount_other": "Livelli di controllo",
+        "clipToBbox": "Ritaglia i tratti al riquadro",
+        "duplicate": "Duplica",
+        "width": "Larghezza",
+        "addControlLayer": "Aggiungi $t(controlLayers.controlLayer)",
+        "addInpaintMask": "Aggiungi $t(controlLayers.inpaintMask)",
+        "addRegionalGuidance": "Aggiungi $t(controlLayers.regionalGuidance)",
+        "sendToCanvasDesc": "Premendo Invoke il lavoro in corso viene visualizzato sulla tela.",
+        "addRasterLayer": "Aggiungi $t(controlLayers.rasterLayer)",
+        "clearCaches": "Svuota le cache",
+        "regionIsEmpty": "La regione selezionata è vuota",
+        "recalculateRects": "Ricalcola rettangoli",
+        "removeBookmark": "Rimuovi segnalibro",
+        "saveCanvasToGallery": "Salva la tela nella Galleria",
+        "regional": "Regionale",
+        "global": "Globale",
+        "canvas": "Tela",
+        "bookmark": "Segnalibro per cambio rapido",
+        "newRegionalReferenceImageOk": "Immagine di riferimento regionale creata",
+        "newRegionalReferenceImageError": "Problema nella creazione dell'immagine di riferimento regionale",
+        "newControlLayerOk": "Livello di controllo creato",
+        "bboxOverlay": "Mostra sovrapposizione riquadro",
+        "resetCanvas": "Reimposta la tela",
+        "outputOnlyMaskedRegions": "Solo regioni mascherate in uscita",
+        "enableAutoNegative": "Abilita Auto Negativo",
+        "disableAutoNegative": "Disabilita Auto Negativo",
+        "showHUD": "Mostra HUD",
+        "maskFill": "Riempimento maschera",
+        "addReferenceImage": "Aggiungi $t(controlLayers.referenceImage)",
+        "addGlobalReferenceImage": "Aggiungi $t(controlLayers.globalReferenceImage)",
+        "sendingToGallery": "Inviare generazioni alla Galleria",
+        "sendToGalleryDesc": "Premendo Invoke viene generata e salvata un'immagine unica nella tua galleria.",
+        "sendToCanvas": "Invia alla Tela",
+        "viewProgressInViewer": "Visualizza i progressi e i risultati nel <Btn>Visualizzatore immagini</Btn>.",
+        "viewProgressOnCanvas": "Visualizza i progressi e i risultati nella <Btn>Tela</Btn>.",
+        "saveBboxToGallery": "Salva il riquadro di delimitazione nella Galleria",
+        "cropLayerToBbox": "Ritaglia il livello al riquadro di delimitazione",
+        "savedToGalleryError": "Errore durante il salvataggio nella galleria",
+        "rasterLayer": "Livello Raster",
+        "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
+        "regionalGuidance_withCount_many": "Guide regionali",
+        "regionalGuidance_withCount_other": "Guide regionali",
+        "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
+        "inpaintMask_withCount_many": "Maschere Inpaint",
+        "inpaintMask_withCount_other": "Maschere Inpaint",
+        "savedToGalleryOk": "Salvato nella Galleria",
+        "newGlobalReferenceImageOk": "Immagine di riferimento globale creata",
+        "newGlobalReferenceImageError": "Problema nella creazione dell'immagine di riferimento globale",
+        "newControlLayerError": "Problema nella creazione del livello di controllo",
+        "newRasterLayerOk": "Livello raster creato",
+        "newRasterLayerError": "Problema nella creazione del livello raster",
+        "saveLayerToAssets": "Salva il livello nelle Risorse",
+        "pullBboxIntoLayerError": "Problema nel caricare il riquadro nel livello",
+        "pullBboxIntoReferenceImageOk": "Contenuto del riquadro inserito nell'immagine di riferimento",
+        "pullBboxIntoLayerOk": "Riquadro caricato nel livello",
+        "pullBboxIntoReferenceImageError": "Problema nell'inserimento del contenuto del riquadro nell'immagine di riferimento",
+        "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
+        "globalReferenceImage_withCount_many": "Immagini di riferimento Globali",
+        "globalReferenceImage_withCount_other": "Immagini di riferimento Globali",
+        "controlMode": {
+            "balanced": "Bilanciato",
+            "controlMode": "Modalità di controllo",
+            "prompt": "Prompt",
+            "control": "Controllo",
+            "megaControl": "Mega Controllo"
+        },
+        "negativePrompt": "Prompt Negativo",
+        "prompt": "Prompt Positivo",
+        "beginEndStepPercentShort": "Inizio/Fine %",
+        "stagingOnCanvas": "Genera immagini nella",
+        "ipAdapterMethod": {
+            "full": "Completo",
+            "style": "Solo Stile",
+            "composition": "Solo Composizione",
+            "ipAdapterMethod": "Metodo Adattatore IP"
+        },
+        "showingType": "Mostra {{type}}",
+        "dynamicGrid": "Griglia dinamica",
+        "tool": {
+            "view": "Muovi",
+            "colorPicker": "Selettore Colore",
+            "rectangle": "Rettangolo",
+            "bbox": "Riquadro di delimitazione",
+            "move": "Sposta",
+            "brush": "Pennello",
+            "eraser": "Cancellino"
+        },
+        "filter": {
+            "apply": "Applica",
+            "reset": "Reimposta",
+            "process": "Elabora",
+            "cancel": "Annulla",
+            "autoProcess": "Processo automatico",
+            "filterType": "Tipo Filtro",
+            "filter": "Filtro",
+            "filters": "Filtri",
+            "mlsd_detection": {
+                "score_threshold": "Soglia di punteggio",
+                "distance_threshold": "Soglia di distanza",
+                "description": "Genera una mappa dei segmenti di linea dal livello selezionato utilizzando il modello di rilevamento dei segmenti di linea MLSD.",
+                "label": "Rilevamento segmenti di linea"
+            },
+            "content_shuffle": {
+                "label": "Mescola contenuto",
+                "scale_factor": "Fattore di scala",
+                "description": "Mescola il contenuto del livello selezionato, in modo simile all'effetto \"liquefa\"."
+            },
+            "mediapipe_face_detection": {
+                "min_confidence": "Confidenza minima",
+                "label": "Rilevamento del volto MediaPipe",
+                "max_faces": "Max volti",
+                "description": "Rileva i volti nel livello selezionato utilizzando il modello di rilevamento dei volti MediaPipe."
+            },
+            "dw_openpose_detection": {
+                "draw_face": "Disegna il volto",
+                "description": "Rileva le pose umane nel livello selezionato utilizzando il modello DW Openpose.",
+                "label": "Rilevamento DW Openpose",
+                "draw_hands": "Disegna le mani",
+                "draw_body": "Disegna il corpo"
+            },
+            "normal_map": {
+                "description": "Genera una mappa delle normali dal livello selezionato.",
+                "label": "Mappa delle normali"
+            },
+            "lineart_edge_detection": {
+                "label": "Rilevamento bordi Lineart",
+                "coarse": "Grossolano",
+                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi Lineart."
+            },
+            "depth_anything_depth_estimation": {
+                "model_size_small": "Piccolo",
+                "model_size_small_v2": "Piccolo v2",
+                "model_size": "Dimensioni modello",
+                "model_size_large": "Grande",
+                "model_size_base": "Base",
+                "description": "Genera una mappa di profondità dal livello selezionato utilizzando un modello Depth Anything."
+            },
+            "color_map": {
+                "label": "Mappa colore",
+                "description": "Crea una mappa dei colori dal livello selezionato.",
+                "tile_size": "Dimens. Piastrella"
+            },
+            "canny_edge_detection": {
+                "high_threshold": "Soglia superiore",
+                "low_threshold": "Soglia inferiore",
+                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando l'algoritmo di rilevamento dei bordi Canny.",
+                "label": "Rilevamento bordi Canny"
+            },
+            "spandrel_filter": {
+                "scale": "Scala di destinazione",
+                "autoScaleDesc": "Il modello selezionato verrà eseguito fino al raggiungimento della scala di destinazione.",
+                "description": "Esegue un modello immagine-a-immagine sul livello selezionato.",
+                "label": "Modello Immagine-a-Immagine",
+                "model": "Modello",
+                "autoScale": "Auto Scala"
+            },
+            "pidi_edge_detection": {
+                "quantize_edges": "Quantizza i bordi",
+                "scribble": "Scarabocchio",
+                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi PiDiNet.",
+                "label": "Rilevamento bordi PiDiNet"
+            },
+            "hed_edge_detection": {
+                "label": "Rilevamento bordi HED",
+                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi HED.",
+                "scribble": "Scarabocchio"
+            },
+            "lineart_anime_edge_detection": {
+                "description": "Genera una mappa dei bordi dal livello selezionato utilizzando il modello di rilevamento dei bordi Lineart Anime.",
+                "label": "Rilevamento bordi Lineart Anime"
+            }
+        },
+        "controlLayers_withCount_hidden": "Livelli di controllo ({{count}} nascosti)",
+        "regionalGuidance_withCount_hidden": "Guida regionale ({{count}} nascosti)",
+        "fill": {
+            "grid": "Griglia",
+            "crosshatch": "Tratteggio incrociato",
+            "fillColor": "Colore di riempimento",
+            "fillStyle": "Stile riempimento",
+            "solid": "Solido",
+            "vertical": "Verticale",
+            "horizontal": "Orizzontale",
+            "diagonal": "Diagonale"
+        },
+        "rasterLayers_withCount_hidden": "Livelli raster ({{count}} nascosti)",
+        "inpaintMasks_withCount_hidden": "Maschere Inpaint ({{count}} nascoste)",
+        "regionalGuidance_withCount_visible": "Guide regionali ({{count}})",
+        "locked": "Bloccato",
+        "hidingType": "Nascondere {{type}}",
+        "logDebugInfo": "Registro Info Debug",
+        "inpaintMasks_withCount_visible": "Maschere Inpaint ({{count}})",
+        "layer_one": "Livello",
+        "layer_many": "Livelli",
+        "layer_other": "Livelli",
+        "disableTransparencyEffect": "Disabilita l'effetto trasparenza",
+        "controlLayers_withCount_visible": "Livelli di controllo ({{count}})",
+        "transparency": "Trasparenza",
+        "newCanvasSessionDesc": "Questo cancellerà la tela e tutte le impostazioni, eccetto la selezione del modello. Le generazioni saranno effettuate sulla tela.",
+        "rasterLayers_withCount_visible": "Livelli raster ({{count}})",
+        "globalReferenceImages_withCount_visible": "Immagini di riferimento Globali ({{count}})",
+        "globalReferenceImages_withCount_hidden": "Immagini di riferimento globali ({{count}} nascoste)",
+        "layer_withCount_one": "Livello ({{count}})",
+        "layer_withCount_many": "Livelli ({{count}})",
+        "layer_withCount_other": "Livelli ({{count}})",
+        "unlocked": "Sbloccato",
+        "enableTransparencyEffect": "Abilita l'effetto trasparenza",
+        "replaceLayer": "Sostituisci livello",
+        "pullBboxIntoLayer": "Carica l'immagine delimitata nel riquadro",
+        "pullBboxIntoReferenceImage": "Carica l'immagine delimitata nel riquadro",
+        "showProgressOnCanvas": "Mostra i progressi sulla Tela",
+        "weight": "Peso",
+        "newGallerySession": "Nuova sessione Galleria",
+        "newGallerySessionDesc": "Questo cancellerà la tela e tutte le impostazioni, eccetto la selezione del modello. Le generazioni saranno inviate alla galleria.",
+        "newCanvasSession": "Nuova sessione Tela",
+        "deleteSelected": "Elimina selezione",
+        "settings": {
+            "isolatedStagingPreview": "Anteprima di generazione isolata",
+            "isolatedPreview": "Anteprima isolata",
+            "invertBrushSizeScrollDirection": "Inverti scorrimento per dimensione pennello",
+            "snapToGrid": {
+                "label": "Aggancia alla griglia",
+                "on": "Acceso",
+                "off": "Spento"
+            },
+            "pressureSensitivity": "Sensibilità alla pressione",
+            "preserveMask": {
+                "alert": "Preservare la regione mascherata",
+                "label": "Preserva la regione mascherata"
+            },
+            "isolatedLayerPreview": "Anteprima livello isolato",
+            "isolatedLayerPreviewDesc": "Se visualizzare solo questo livello quando si eseguono operazioni come il filtraggio o la trasformazione."
+        },
+        "transform": {
+            "reset": "Reimposta",
+            "fitToBbox": "Adatta al Riquadro",
+            "transform": "Trasforma",
+            "apply": "Applica",
+            "cancel": "Annulla",
+            "fitMode": "Adattamento",
+            "fitModeContain": "Contieni",
+            "fitModeFill": "Riempi",
+            "fitModeCover": "Copri"
+        },
+        "stagingArea": {
+            "next": "Successiva",
+            "discard": "Scarta",
+            "discardAll": "Scarta tutto",
+            "accept": "Accetta",
+            "saveToGallery": "Salva nella Galleria",
+            "previous": "Precedente",
+            "showResultsOn": "Risultati visualizzati",
+            "showResultsOff": "Risultati nascosti"
+        },
+        "HUD": {
+            "bbox": "Riquadro di delimitazione",
+            "entityStatus": {
+                "isHidden": "{{title}} è nascosto",
+                "isLocked": "{{title}} è bloccato",
+                "isTransforming": "{{title}} sta trasformando",
+                "isFiltering": "{{title}} sta filtrando",
+                "isEmpty": "{{title}} è vuoto",
+                "isDisabled": "{{title}} è disabilitato"
+            },
+            "scaledBbox": "Riquadro scalato"
+        },
+        "canvasContextMenu": {
+            "newControlLayer": "Nuovo Livello di Controllo",
+            "newRegionalReferenceImage": "Nuova immagine di riferimento Regionale",
+            "newGlobalReferenceImage": "Nuova immagine di riferimento Globale",
+            "bboxGroup": "Crea dal riquadro di delimitazione",
+            "saveBboxToGallery": "Salva il riquadro nella Galleria",
+            "cropCanvasToBbox": "Ritaglia la Tela al riquadro",
+            "canvasGroup": "Tela",
+            "newRasterLayer": "Nuovo Livello Raster",
+            "saveCanvasToGallery": "Salva la Tela nella Galleria",
+            "saveToGalleryGroup": "Salva nella Galleria",
+            "newInpaintMask": "Nuova maschera Inpaint",
+            "newRegionalGuidance": "Nuova Guida Regionale"
+        },
+        "newImg2ImgCanvasFromImage": "Nuova Immagine da immagine",
+        "copyRasterLayerTo": "Copia $t(controlLayers.rasterLayer) in",
+        "copyControlLayerTo": "Copia $t(controlLayers.controlLayer) in",
+        "copyInpaintMaskTo": "Copia $t(controlLayers.inpaintMask) in",
+        "selectObject": {
+            "dragToMove": "Trascina un punto per spostarlo",
+            "clickToAdd": "Fare clic sul livello per aggiungere un punto",
+            "clickToRemove": "Clicca su un punto per rimuoverlo",
+            "help3": "Inverte la selezione per selezionare tutto tranne l'oggetto di destinazione.",
+            "pointType": "Tipo punto",
+            "apply": "Applica",
+            "reset": "Reimposta",
+            "cancel": "Annulla",
+            "selectObject": "Seleziona oggetto",
+            "invertSelection": "Inverti selezione",
+            "exclude": "Escludi",
+            "include": "Includi",
+            "neutral": "Neutro",
+            "saveAs": "Salva come",
+            "process": "Elabora",
+            "help1": "Seleziona un singolo oggetto di destinazione. Aggiungi i punti <Bold>Includi</Bold> e <Bold>Escludi</Bold> per indicare quali parti del livello fanno parte dell'oggetto di destinazione.",
+            "help2": "Inizia con un punto <Bold>Include</Bold> all'interno dell'oggetto di destinazione. Aggiungi altri punti per perfezionare la selezione. Meno punti in genere producono risultati migliori."
+        },
+        "convertControlLayerTo": "Converti $t(controlLayers.controlLayer) in",
+        "newRasterLayer": "Nuovo $t(controlLayers.rasterLayer)",
+        "newRegionalGuidance": "Nuova $t(controlLayers.regionalGuidance)",
+        "canvasAsRasterLayer": "$t(controlLayers.canvas) come $t(controlLayers.rasterLayer)",
+        "canvasAsControlLayer": "$t(controlLayers.canvas) come $t(controlLayers.controlLayer)",
+        "convertInpaintMaskTo": "Converti $t(controlLayers.inpaintMask) in",
+        "copyRegionalGuidanceTo": "Copia $t(controlLayers.regionalGuidance) in",
+        "convertRasterLayerTo": "Converti $t(controlLayers.rasterLayer) in",
+        "convertRegionalGuidanceTo": "Converti $t(controlLayers.regionalGuidance) in",
+        "newControlLayer": "Nuovo $t(controlLayers.controlLayer)",
+        "newInpaintMask": "Nuova $t(controlLayers.inpaintMask)",
+        "replaceCurrent": "Sostituisci corrente",
+        "mergeDown": "Unire in basso"
     },
     "ui": {
         "tabs": {
@@ -1547,7 +2037,8 @@
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "queue": "Coda",
             "upscaling": "Amplia",
-            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
+            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
+            "gallery": "Galleria"
         }
     },
     "upscaling": {
@@ -1617,5 +2108,47 @@
         "noTemplates": "Nessun modello",
         "acceptedColumnsKeys": "Colonne/chiavi accettate:",
         "promptTemplateCleared": "Modello di prompt cancellato"
+    },
+    "newUserExperience": {
+        "gettingStartedSeries": "Desideri maggiori informazioni? Consulta la nostra <LinkComponent>Getting Started Series</LinkComponent> per suggerimenti su come sfruttare appieno il potenziale di Invoke Studio.",
+        "toGetStarted": "Per iniziare, inserisci un prompt nella casella e fai clic su <StrongComponent>Invoke</StrongComponent> per generare la tua prima immagine. Seleziona un modello di prompt per migliorare i risultati. Puoi scegliere di salvare le tue immagini direttamente nella <StrongComponent>Galleria</StrongComponent> o modificarle nella <StrongComponent>Tela</StrongComponent>.",
+        "importModels": "Importa modelli",
+        "downloadStarterModels": "Scarica i modelli per iniziare",
+        "noModelsInstalled": "Sembra che tu non abbia installato alcun modello",
+        "toGetStartedLocal": "Per iniziare, assicurati di scaricare o importare i modelli necessari per eseguire Invoke. Quindi, inserisci un prompt nella casella e fai clic su <StrongComponent>Invoke</StrongComponent> per generare la tua prima immagine. Seleziona un modello di prompt per migliorare i risultati. Puoi scegliere di salvare le tue immagini direttamente nella <StrongComponent>Galleria</StrongComponent> o modificarle nella <StrongComponent>Tela</StrongComponent>."
+    },
+    "whatsNew": {
+        "whatsNewInInvoke": "Novità in Invoke",
+        "line2": "Supporto Flux esteso, ora con immagini di riferimento globali",
+        "line3": "Tooltip e menu contestuali migliorati",
+        "readReleaseNotes": "Leggi le note di rilascio",
+        "watchRecentReleaseVideos": "Guarda i video su questa versione",
+        "line1": "Strumento <ItalicComponent>Seleziona oggetto</ItalicComponent> per la selezione e la modifica precise degli oggetti",
+        "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia"
+    },
+    "system": {
+        "logLevel": {
+            "info": "Info",
+            "warn": "Avviso",
+            "fatal": "Fatale",
+            "error": "Errore",
+            "debug": "Debug",
+            "trace": "Traccia",
+            "logLevel": "Livello di registro"
+        },
+        "logNamespaces": {
+            "workflows": "Flussi di lavoro",
+            "generation": "Generazione",
+            "canvas": "Tela",
+            "config": "Configurazione",
+            "models": "Modelli",
+            "gallery": "Galleria",
+            "queue": "Coda",
+            "events": "Eventi",
+            "system": "Sistema",
+            "metadata": "Metadati",
+            "logNamespaces": "Elementi del registro"
+        },
+        "enableLogging": "Abilita la registrazione"
     }
 }