chore: bump version to v5.4.1rc1

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>

.github/pull_request_template.md

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

docker/Dockerfile

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

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/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:

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",
@@ -407,7 +410,7 @@ def get_torch_source() -> Tuple[str | None, str | None]:
     optional_modules: str | None = None
     if OS == "Linux":
         if device == GpuType.ROCM:
-            url = "https://download.pytorch.org/whl/rocm5.6"
+            url = "https://download.pytorch.org/whl/rocm6.1"
         elif device == GpuType.CPU:
             url = "https://download.pytorch.org/whl/cpu"
         elif device == GpuType.CUDA:

installer/lib/messages.py

@@ -259,7 +259,7 @@ def select_gpu() -> GpuType:
                     [
                         f"Detected the [gold1]{OS}-{ARCH}[/] platform",
                         "",
-                        "See [deep_sky_blue1]https://invoke-ai.github.io/InvokeAI/#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/app_info.py

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

invokeai/app/api/routers/model_manager.py

@@ -1,6 +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,
@@ -808,7 +811,11 @@ def get_is_installed(
     for model in installed_models:
         if model.source == starter_model.source:
             return True
-        if model.name == starter_model.name and model.base == starter_model.base and model.type == starter_model.type:
+        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
 
@@ -919,3 +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/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"
@@ -246,6 +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_denoise.py

@@ -1,15 +1,19 @@
 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,
@@ -17,6 +21,7 @@ from invokeai.app.invocations.fields import (
     WithMetadata,
 )
 from invokeai.app.invocations.flux_controlnet import FluxControlNetField
+from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.model import TransformerField, VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -26,6 +31,8 @@ from invokeai.backend.flux.denoise import denoise
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
 from invokeai.backend.flux.extensions.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,
@@ -49,7 +56,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.1.0",
+    version="3.2.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -82,6 +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(
@@ -96,10 +121,15 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         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:
         latents = self._run_diffusion(context)
@@ -108,6 +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,
@@ -115,13 +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
@@ -182,8 +227,16 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         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
@@ -204,6 +257,21 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 noise=noise,
             )
 
+        # Compute the IP-Adapter image prompt clip embeddings.
+        # We do this before loading other models to minimize peak memory.
+        # TODO(ryand): We should really do this in a separate invocation to benefit from caching.
+        ip_adapter_fields = self._normalize_ip_adapter_fields()
+        pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds = self._prep_ip_adapter_image_prompt_clip_embeds(
+            ip_adapter_fields, context
+        )
+
+        cfg_scale = self.prep_cfg_scale(
+            cfg_scale=self.cfg_scale,
+            timesteps=timesteps,
+            cfg_scale_start_step=self.cfg_scale_start_step,
+            cfg_scale_end_step=self.cfg_scale_end_step,
+        )
+
         with ExitStack() as exit_stack:
             # Prepare ControlNet extensions.
             # Note: We do this before loading the transformer model to minimize peak memory (see implementation).
@@ -252,23 +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.
 
@@ -408,6 +541,112 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return controlnet_extensions
 
+    def _normalize_ip_adapter_fields(self) -> list[IPAdapterField]:
+        if self.ip_adapter is None:
+            return []
+        elif isinstance(self.ip_adapter, IPAdapterField):
+            return [self.ip_adapter]
+        elif isinstance(self.ip_adapter, list):
+            return self.ip_adapter
+        else:
+            raise ValueError(f"Unsupported IP-Adapter type: {type(self.ip_adapter)}")
+
+    def _prep_ip_adapter_image_prompt_clip_embeds(
+        self,
+        ip_adapter_fields: list[IPAdapterField],
+        context: InvocationContext,
+    ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+        """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
+        clip_image_processor = CLIPImageProcessor()
+
+        pos_image_prompt_clip_embeds: list[torch.Tensor] = []
+        neg_image_prompt_clip_embeds: list[torch.Tensor] = []
+        for ip_adapter_field in ip_adapter_fields:
+            # `ip_adapter_field.image` could be a list or a single ImageField. Normalize to a list here.
+            ipa_image_fields: list[ImageField]
+            if isinstance(ip_adapter_field.image, ImageField):
+                ipa_image_fields = [ip_adapter_field.image]
+            elif isinstance(ip_adapter_field.image, list):
+                ipa_image_fields = ip_adapter_field.image
+            else:
+                raise ValueError(f"Unsupported IP-Adapter image type: {type(ip_adapter_field.image)}")
+
+            if len(ipa_image_fields) != 1:
+                raise ValueError(
+                    f"FLUX IP-Adapter only supports a single image prompt (received {len(ipa_image_fields)})."
+                )
+
+            ipa_images = [context.images.get_pil(image.image_name, mode="RGB") for image in ipa_image_fields]
+
+            pos_images: list[npt.NDArray[np.uint8]] = []
+            neg_images: list[npt.NDArray[np.uint8]] = []
+            for ipa_image in ipa_images:
+                assert ipa_image.mode == "RGB"
+                pos_image = np.array(ipa_image)
+                # We use a black image as the negative image prompt for parity with
+                # https://github.com/XLabs-AI/x-flux-comfyui/blob/45c834727dd2141aebc505ae4b01f193a8414e38/nodes.py#L592-L593
+                # An alternative scheme would be to apply zeros_like() after calling the clip_image_processor.
+                neg_image = np.zeros_like(pos_image)
+                pos_images.append(pos_image)
+                neg_images.append(neg_image)
+
+            with context.models.load(ip_adapter_field.image_encoder_model) as image_encoder_model:
+                assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
+
+                clip_image: torch.Tensor = clip_image_processor(images=pos_images, return_tensors="pt").pixel_values
+                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
+                pos_clip_image_embeds = image_encoder_model(clip_image).image_embeds
+
+                clip_image = clip_image_processor(images=neg_images, return_tensors="pt").pixel_values
+                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
+                neg_clip_image_embeds = image_encoder_model(clip_image).image_embeds
+
+            pos_image_prompt_clip_embeds.append(pos_clip_image_embeds)
+            neg_image_prompt_clip_embeds.append(neg_clip_image_embeds)
+
+        return pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds
+
+    def _prep_ip_adapter_extensions(
+        self,
+        ip_adapter_fields: list[IPAdapterField],
+        pos_image_prompt_clip_embeds: list[torch.Tensor],
+        neg_image_prompt_clip_embeds: list[torch.Tensor],
+        context: InvocationContext,
+        exit_stack: ExitStack,
+        dtype: torch.dtype,
+    ) -> tuple[list[XLabsIPAdapterExtension], list[XLabsIPAdapterExtension]]:
+        pos_ip_adapter_extensions: list[XLabsIPAdapterExtension] = []
+        neg_ip_adapter_extensions: list[XLabsIPAdapterExtension] = []
+        for ip_adapter_field, pos_image_prompt_clip_embed, neg_image_prompt_clip_embed in zip(
+            ip_adapter_fields, pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds, strict=True
+        ):
+            ip_adapter_model = exit_stack.enter_context(context.models.load(ip_adapter_field.ip_adapter_model))
+            assert isinstance(ip_adapter_model, XlabsIpAdapterFlux)
+            ip_adapter_model = ip_adapter_model.to(dtype=dtype)
+            if ip_adapter_field.mask is not None:
+                raise ValueError("IP-Adapter masks are not yet supported in Flux.")
+            ip_adapter_extension = XLabsIPAdapterExtension(
+                model=ip_adapter_model,
+                image_prompt_clip_embed=pos_image_prompt_clip_embed,
+                weight=ip_adapter_field.weight,
+                begin_step_percent=ip_adapter_field.begin_step_percent,
+                end_step_percent=ip_adapter_field.end_step_percent,
+            )
+            ip_adapter_extension.run_image_proj(dtype=dtype)
+            pos_ip_adapter_extensions.append(ip_adapter_extension)
+
+            ip_adapter_extension = XLabsIPAdapterExtension(
+                model=ip_adapter_model,
+                image_prompt_clip_embed=neg_image_prompt_clip_embed,
+                weight=ip_adapter_field.weight,
+                begin_step_percent=ip_adapter_field.begin_step_percent,
+                end_step_percent=ip_adapter_field.end_step_percent,
+            )
+            ip_adapter_extension.run_image_proj(dtype=dtype)
+            neg_ip_adapter_extensions.append(ip_adapter_extension)
+
+        return pos_ip_adapter_extensions, neg_ip_adapter_extensions
+
     def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in self.transformer.loras:
             lora_info = context.models.load(lora.lora)

invokeai/app/invocations/flux_ip_adapter.py

@@ -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/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"
+      },
+      {
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+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "clip_l",
+        "targetHandle": "clip_l"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ctransformer-c7539f7b-7ac5-49b9-93eb-87ede611409ftransformer",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "transformer",
+        "targetHandle": "transformer"
+      },
+      {
+        "id": "reactflow__edge-f7e394ac-6394-4096-abcb-de0d346506b3value-c7539f7b-7ac5-49b9-93eb-87ede611409fseed",
+        "type": "default",
+        "source": "f7e394ac-6394-4096-abcb-de0d346506b3",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "value",
+        "targetHandle": "seed"
+      },
+      {
+        "id": "reactflow__edge-c7539f7b-7ac5-49b9-93eb-87ede611409flatents-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48blatents",
+        "type": "default",
+        "source": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "sourceHandle": "latents",
+        "targetHandle": "latents"
+      },
+      {
+        "id": "reactflow__edge-e17d34e7-6ed1-493c-9a85-4fcd291cb084conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fpositive_conditioning",
+        "type": "default",
+        "source": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "conditioning",
+        "targetHandle": "positive_conditioning"
+      },
+      {
+        "id": "reactflow__edge-3b4f7f27-cfc0-4373-a009-99c5290d0cd6conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fnegative_conditioning",
+        "type": "default",
+        "source": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "conditioning",
+        "targetHandle": "negative_conditioning"
+      }
+    ]
+  }

invokeai/app/util/step_callback.py

@@ -34,6 +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/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

@@ -1,3 +1,4 @@
+import math
 from typing import Callable
 
 import torch
@@ -7,6 +8,7 @@ from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFl
 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
 
@@ -16,15 +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
@@ -37,10 +47,9 @@ 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.
@@ -48,7 +57,7 @@ def denoise(
         for controlnet_extension in controlnet_extensions:
             controlnet_residuals.append(
                 controlnet_extension.run_controlnet(
-                    timestep_index=step - 1,
+                    timestep_index=step_index,
                     total_num_timesteps=total_steps,
                     img=img,
                     img_ids=img_ids,
@@ -61,7 +70,7 @@ def denoise(
             )
 
         # Merge the ControlNet residuals from multiple ControlNets.
-        # TODO(ryand): We may want to alculate the sum just-in-time to keep peak memory low. Keep in mind, that the
+        # 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)
@@ -74,10 +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
 
@@ -87,13 +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/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/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,
@@ -88,8 +90,11 @@ class Flux(nn.Module):
         timesteps: Tensor,
         y: Tensor,
         guidance: Tensor | None,
+        timestep_index: int,
+        total_num_timesteps: int,
         controlnet_double_block_residuals: list[Tensor] | None,
         controlnet_single_block_residuals: list[Tensor] | None,
+        ip_adapter_extensions: list[XLabsIPAdapterExtension],
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -111,7 +116,19 @@ class Flux(nn.Module):
         if controlnet_double_block_residuals is not None:
             assert len(controlnet_double_block_residuals) == len(self.double_blocks)
         for block_index, block in enumerate(self.double_blocks):
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            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]

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."""
 
@@ -147,6 +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")
@@ -193,6 +212,9 @@ 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):
@@ -335,7 +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):
@@ -394,6 +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]
 
@@ -417,12 +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."""
 
@@ -499,6 +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/flux.py

@@ -19,6 +19,10 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_xlabs_controlnet,
 )
 from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
+from invokeai.backend.flux.ip_adapter.state_dict_utils import infer_xlabs_ip_adapter_params_from_state_dict
+from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import (
+    XlabsIpAdapterFlux,
+)
 from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.util import ae_params, params
@@ -35,6 +39,7 @@ from invokeai.backend.model_manager.config import (
     CLIPEmbedDiffusersConfig,
     ControlNetCheckpointConfig,
     ControlNetDiffusersConfig,
+    IPAdapterCheckpointConfig,
     MainBnbQuantized4bCheckpointConfig,
     MainCheckpointConfig,
     MainGGUFCheckpointConfig,
@@ -123,9 +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():
@@ -167,10 +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'}"
@@ -352,3 +357,26 @@ class FluxControlnetModel(ModelLoader):
 
         model.load_state_dict(sd, assign=True)
         return model
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.IPAdapter, format=ModelFormat.Checkpoint)
+class FluxIpAdapterModel(ModelLoader):
+    """Class to load FLUX IP-Adapter models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, IPAdapterCheckpointConfig):
+            raise ValueError(f"Unexpected model config type: {type(config)}.")
+
+        sd = load_file(Path(config.path))
+
+        params = infer_xlabs_ip_adapter_params_from_state_dict(sd)
+
+        with accelerate.init_empty_weights():
+            model = XlabsIpAdapterFlux(params=params)
+
+        model.load_xlabs_state_dict(sd, assign=True)
+        return model

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

@@ -22,7 +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
@@ -14,6 +14,7 @@ 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,
 )
@@ -21,6 +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,
@@ -32,8 +34,15 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
+    SubmodelDefinition,
+    SubModelType,
+)
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
+from invokeai.backend.model_manager.util.model_util import (
+    get_clip_variant_type,
+    lora_token_vector_length,
+    read_checkpoint_meta,
 )
-from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
 from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
@@ -111,6 +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,
@@ -121,8 +131,12 @@ class ModelProbe(object):
         "CLIPTextModel": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
         "FluxControlNetModel": ModelType.ControlNet,
+        "SD3Transformer2DModel": ModelType.Main,
+        "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
     }
 
+    TYPE2VARIANT: Dict[ModelType, Callable[[str], Optional[AnyVariant]]] = {ModelType.CLIPEmbed: get_clip_variant_type}
+
     @classmethod
     def register_probe(
         cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: type[ProbeBase]
@@ -169,7 +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)
@@ -216,6 +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
 
@@ -243,8 +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.",
@@ -252,6 +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_")):
@@ -274,7 +297,14 @@ class ModelProbe(object):
                 )
             ):
                 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
@@ -452,8 +482,9 @@ 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",
@@ -672,6 +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
@@ -732,18 +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:
@@ -755,6 +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

invokeai/backend/model_manager/starter_models.py

@@ -13,6 +13,9 @@ class StarterModelWithoutDependencies(BaseModel):
     type: ModelType
     format: Optional[ModelFormat] = None
     is_installed: bool = False
+    # allows us to track what models a user has installed across name changes within starter models
+    # if you update a starter model name, please add the old one to this list for that starter model
+    previous_names: list[str] = []
 
 
 class StarterModel(StarterModelWithoutDependencies):
@@ -25,22 +28,6 @@ class StarterModelBundles(BaseModel):
     models: list[StarterModel]
 
 
-ip_adapter_sd_image_encoder = StarterModel(
-    name="IP Adapter SD1.5 Image Encoder",
-    base=BaseModelType.StableDiffusion1,
-    source="InvokeAI/ip_adapter_sd_image_encoder",
-    description="IP Adapter SD Image Encoder",
-    type=ModelType.CLIPVision,
-)
-
-ip_adapter_sdxl_image_encoder = StarterModel(
-    name="IP Adapter SDXL Image Encoder",
-    base=BaseModelType.StableDiffusionXL,
-    source="InvokeAI/ip_adapter_sdxl_image_encoder",
-    description="IP Adapter SDXL Image Encoder",
-    type=ModelType.CLIPVision,
-)
-
 cyberrealistic_negative = StarterModel(
     name="CyberRealistic Negative v3",
     base=BaseModelType.StableDiffusion1,
@@ -49,6 +36,32 @@ cyberrealistic_negative = StarterModel(
     type=ModelType.TextualInversion,
 )
 
+# region CLIP Image Encoders
+ip_adapter_sd_image_encoder = StarterModel(
+    name="IP Adapter SD1.5 Image Encoder",
+    base=BaseModelType.StableDiffusion1,
+    source="InvokeAI/ip_adapter_sd_image_encoder",
+    description="IP Adapter SD Image Encoder",
+    type=ModelType.CLIPVision,
+)
+ip_adapter_sdxl_image_encoder = StarterModel(
+    name="IP Adapter SDXL Image Encoder",
+    base=BaseModelType.StableDiffusionXL,
+    source="InvokeAI/ip_adapter_sdxl_image_encoder",
+    description="IP Adapter SDXL Image Encoder",
+    type=ModelType.CLIPVision,
+)
+# Note: This model is installed from the same source as the CLIPEmbed model below. The model contains both the image
+# encoder and the text encoder, but we need separate model entries so that they get loaded correctly.
+clip_vit_l_image_encoder = StarterModel(
+    name="clip-vit-large-patch14",
+    base=BaseModelType.Any,
+    source="InvokeAI/clip-vit-large-patch14",
+    description="CLIP ViT-L Image Encoder",
+    type=ModelType.CLIPVision,
+)
+# endregion
+
 # region TextEncoders
 t5_base_encoder = StarterModel(
     name="t5_base_encoder",
@@ -127,6 +140,22 @@ flux_dev = StarterModel(
     type=ModelType.Main,
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
+sd35_medium = StarterModel(
+    name="SD3.5 Medium",
+    base=BaseModelType.StableDiffusion3,
+    source="stabilityai/stable-diffusion-3.5-medium",
+    description="Medium SD3.5 Model: ~15GB",
+    type=ModelType.Main,
+    dependencies=[],
+)
+sd35_large = StarterModel(
+    name="SD3.5 Large",
+    base=BaseModelType.StableDiffusion3,
+    source="stabilityai/stable-diffusion-3.5-large",
+    description="Large SD3.5 Model: ~19G",
+    type=ModelType.Main,
+    dependencies=[],
+)
 cyberrealistic_sd1 = StarterModel(
     name="CyberRealistic v4.1",
     base=BaseModelType.StableDiffusion1,
@@ -186,6 +215,16 @@ dreamshaper_sdxl = StarterModel(
     type=ModelType.Main,
     dependencies=[sdxl_fp16_vae_fix],
 )
+
+archvis_sdxl = StarterModel(
+    name="Architecture (RealVisXL5)",
+    base=BaseModelType.StableDiffusionXL,
+    source="SG161222/RealVisXL_V5.0",
+    description="A photorealistic model, with architecture among its many use cases",
+    type=ModelType.Main,
+    dependencies=[sdxl_fp16_vae_fix],
+)
+
 sdxl_refiner = StarterModel(
     name="SDXL Refiner",
     base=BaseModelType.StableDiffusionXLRefiner,
@@ -223,36 +262,49 @@ easy_neg_sd1 = StarterModel(
 # endregion
 # region IP Adapter
 ip_adapter_sd1 = StarterModel(
-    name="IP Adapter",
+    name="Standard Reference (IP Adapter)",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
-    description="IP-Adapter for SD 1.5 models",
+    description="References images with a more generalized/looser degree of precision.",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
+    previous_names=["IP Adapter"],
 )
 ip_adapter_plus_sd1 = StarterModel(
-    name="IP Adapter Plus",
+    name="Precise Reference (IP Adapter Plus)",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
-    description="Refined IP-Adapter for SD 1.5 models",
+    description="References images with a higher degree of precision.",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
+    previous_names=["IP Adapter Plus"],
 )
 ip_adapter_plus_face_sd1 = StarterModel(
-    name="IP Adapter Plus Face",
+    name="Face Reference (IP Adapter Plus Face)",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
-    description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
+    description="References images with a higher degree of precision, adapted for faces",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
+    previous_names=["IP Adapter Plus Face"],
 )
 ip_adapter_sdxl = StarterModel(
-    name="IP Adapter SDXL",
+    name="Standard Reference (IP Adapter ViT-H)",
     base=BaseModelType.StableDiffusionXL,
     source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
-    description="IP-Adapter for SDXL models",
+    description="References images with a higher degree of precision.",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sdxl_image_encoder],
+    previous_names=["IP Adapter SDXL"],
+)
+ip_adapter_flux = StarterModel(
+    name="Standard Reference (XLabs FLUX IP-Adapter)",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/flux-ip-adapter.safetensors",
+    description="References images with a more generalized/looser degree of precision.",
+    type=ModelType.IPAdapter,
+    dependencies=[clip_vit_l_image_encoder],
+    previous_names=["XLabs FLUX IP-Adapter"],
 )
 # endregion
 # region ControlNet
@@ -271,157 +323,162 @@ qr_code_cnet_sdxl = StarterModel(
     type=ModelType.ControlNet,
 )
 canny_sd1 = StarterModel(
-    name="canny",
+    name="Hard Edge Detection (canny)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_canny",
-    description="ControlNet weights trained on sd-1.5 with canny conditioning.",
+    description="Uses detected edges in the image to control composition.",
     type=ModelType.ControlNet,
+    previous_names=["canny"],
 )
 inpaint_cnet_sd1 = StarterModel(
-    name="inpaint",
+    name="Inpainting",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_inpaint",
     description="ControlNet weights trained on sd-1.5 with canny conditioning, inpaint version",
     type=ModelType.ControlNet,
+    previous_names=["inpaint"],
 )
 mlsd_sd1 = StarterModel(
-    name="mlsd",
+    name="Line Drawing (mlsd)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_mlsd",
-    description="ControlNet weights trained on sd-1.5 with canny conditioning, MLSD version",
+    description="Uses straight line detection for controlling the generation.",
     type=ModelType.ControlNet,
+    previous_names=["mlsd"],
 )
 depth_sd1 = StarterModel(
-    name="depth",
+    name="Depth Map",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11f1p_sd15_depth",
-    description="ControlNet weights trained on sd-1.5 with depth conditioning",
+    description="Uses depth information in the image to control the depth in the generation.",
     type=ModelType.ControlNet,
+    previous_names=["depth"],
 )
 normal_bae_sd1 = StarterModel(
-    name="normal_bae",
+    name="Lighting Detection (Normals)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_normalbae",
-    description="ControlNet weights trained on sd-1.5 with normalbae image conditioning",
+    description="Uses detected lighting information to guide the lighting of the composition.",
     type=ModelType.ControlNet,
+    previous_names=["normal_bae"],
 )
 seg_sd1 = StarterModel(
-    name="seg",
+    name="Segmentation Map",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_seg",
-    description="ControlNet weights trained on sd-1.5 with seg image conditioning",
+    description="Uses segmentation maps to guide the structure of the composition.",
     type=ModelType.ControlNet,
+    previous_names=["seg"],
 )
 lineart_sd1 = StarterModel(
-    name="lineart",
+    name="Lineart",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_lineart",
-    description="ControlNet weights trained on sd-1.5 with lineart image conditioning",
+    description="Uses lineart detection to guide the lighting of the composition.",
     type=ModelType.ControlNet,
+    previous_names=["lineart"],
 )
 lineart_anime_sd1 = StarterModel(
-    name="lineart_anime",
+    name="Lineart Anime",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15s2_lineart_anime",
-    description="ControlNet weights trained on sd-1.5 with anime image conditioning",
+    description="Uses anime lineart detection to guide the lighting of the composition.",
     type=ModelType.ControlNet,
+    previous_names=["lineart_anime"],
 )
 openpose_sd1 = StarterModel(
-    name="openpose",
+    name="Pose Detection (openpose)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_openpose",
-    description="ControlNet weights trained on sd-1.5 with openpose image conditioning",
+    description="Uses pose information to control the pose of human characters in the generation.",
     type=ModelType.ControlNet,
+    previous_names=["openpose"],
 )
 scribble_sd1 = StarterModel(
-    name="scribble",
+    name="Contour Detection (scribble)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_scribble",
-    description="ControlNet weights trained on sd-1.5 with scribble image conditioning",
+    description="Uses edges, contours, or line art in the image to control composition.",
     type=ModelType.ControlNet,
+    previous_names=["scribble"],
 )
 softedge_sd1 = StarterModel(
-    name="softedge",
+    name="Soft Edge Detection (softedge)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_softedge",
-    description="ControlNet weights trained on sd-1.5 with soft edge conditioning",
+    description="Uses a soft edge detection map to control composition.",
     type=ModelType.ControlNet,
+    previous_names=["softedge"],
 )
 shuffle_sd1 = StarterModel(
-    name="shuffle",
+    name="Remix (shuffle)",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11e_sd15_shuffle",
     description="ControlNet weights trained on sd-1.5 with shuffle image conditioning",
     type=ModelType.ControlNet,
+    previous_names=["shuffle"],
 )
 tile_sd1 = StarterModel(
-    name="tile",
+    name="Tile",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11f1e_sd15_tile",
-    description="ControlNet weights trained on sd-1.5 with tiled image conditioning",
-    type=ModelType.ControlNet,
-)
-ip2p_sd1 = StarterModel(
-    name="ip2p",
-    base=BaseModelType.StableDiffusion1,
-    source="lllyasviel/control_v11e_sd15_ip2p",
-    description="ControlNet weights trained on sd-1.5 with ip2p conditioning.",
+    description="Uses image data to replicate exact colors/structure in the resulting generation.",
     type=ModelType.ControlNet,
+    previous_names=["tile"],
 )
 canny_sdxl = StarterModel(
-    name="canny-sdxl",
+    name="Hard Edge Detection (canny)",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-canny-sdxl-1.0",
-    description="ControlNet weights trained on sdxl-1.0 with canny conditioning, by Xinsir.",
+    description="Uses detected edges in the image to control composition.",
     type=ModelType.ControlNet,
+    previous_names=["canny-sdxl"],
 )
 depth_sdxl = StarterModel(
-    name="depth-sdxl",
+    name="Depth Map",
     base=BaseModelType.StableDiffusionXL,
     source="diffusers/controlNet-depth-sdxl-1.0",
-    description="ControlNet weights trained on sdxl-1.0 with depth conditioning.",
+    description="Uses depth information in the image to control the depth in the generation.",
     type=ModelType.ControlNet,
+    previous_names=["depth-sdxl"],
 )
 softedge_sdxl = StarterModel(
-    name="softedge-dexined-sdxl",
+    name="Soft Edge Detection (softedge)",
     base=BaseModelType.StableDiffusionXL,
     source="SargeZT/controlNet-sd-xl-1.0-softedge-dexined",
-    description="ControlNet weights trained on sdxl-1.0 with dexined soft edge preprocessing.",
-    type=ModelType.ControlNet,
-)
-depth_zoe_16_sdxl = StarterModel(
-    name="depth-16bit-zoe-sdxl",
-    base=BaseModelType.StableDiffusionXL,
-    source="SargeZT/controlNet-sd-xl-1.0-depth-16bit-zoe",
-    description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).",
-    type=ModelType.ControlNet,
-)
-depth_zoe_32_sdxl = StarterModel(
-    name="depth-zoe-sdxl",
-    base=BaseModelType.StableDiffusionXL,
-    source="diffusers/controlNet-zoe-depth-sdxl-1.0",
-    description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
+    description="Uses a soft edge detection map to control composition.",
     type=ModelType.ControlNet,
+    previous_names=["softedge-dexined-sdxl"],
 )
 openpose_sdxl = StarterModel(
-    name="openpose-sdxl",
+    name="Pose Detection (openpose)",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-openpose-sdxl-1.0",
-    description="ControlNet weights trained on sdxl-1.0 compatible with the DWPose processor by Xinsir.",
+    description="Uses pose information to control the pose of human characters in the generation.",
     type=ModelType.ControlNet,
+    previous_names=["openpose-sdxl", "controlnet-openpose-sdxl"],
 )
 scribble_sdxl = StarterModel(
-    name="scribble-sdxl",
+    name="Contour Detection (scribble)",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-scribble-sdxl-1.0",
-    description="ControlNet weights trained on sdxl-1.0 compatible with various lineart processors and black/white sketches by Xinsir.",
+    description="Uses edges, contours, or line art in the image to control composition.",
     type=ModelType.ControlNet,
+    previous_names=["scribble-sdxl", "controlnet-scribble-sdxl"],
 )
 tile_sdxl = StarterModel(
-    name="tile-sdxl",
+    name="Tile",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-tile-sdxl-1.0",
-    description="ControlNet weights trained on sdxl-1.0 with tiled image conditioning",
+    description="Uses image data to replicate exact colors/structure in the resulting generation.",
+    type=ModelType.ControlNet,
+    previous_names=["tile-sdxl"],
+)
+union_cnet_sdxl = StarterModel(
+    name="Multi-Guidance Detection (Union Pro)",
+    base=BaseModelType.StableDiffusionXL,
+    source="InvokeAI/Xinsir-SDXL_Controlnet_Union",
+    description="A unified ControlNet for SDXL model that supports 10+ control types",
     type=ModelType.ControlNet,
 )
 union_cnet_flux = StarterModel(
@@ -434,60 +491,52 @@ union_cnet_flux = StarterModel(
 # endregion
 # region T2I Adapter
 t2i_canny_sd1 = StarterModel(
-    name="canny-sd15",
+    name="Hard Edge Detection (canny)",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_canny_sd15v2",
-    description="T2I Adapter weights trained on sd-1.5 with canny conditioning.",
+    description="Uses detected edges in the image to control composition",
     type=ModelType.T2IAdapter,
+    previous_names=["canny-sd15"],
 )
 t2i_sketch_sd1 = StarterModel(
-    name="sketch-sd15",
+    name="Sketch",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_sketch_sd15v2",
-    description="T2I Adapter weights trained on sd-1.5 with sketch conditioning.",
+    description="Uses a sketch to control composition",
     type=ModelType.T2IAdapter,
+    previous_names=["sketch-sd15"],
 )
 t2i_depth_sd1 = StarterModel(
-    name="depth-sd15",
+    name="Depth Map",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_depth_sd15v2",
-    description="T2I Adapter weights trained on sd-1.5 with depth conditioning.",
-    type=ModelType.T2IAdapter,
-)
-t2i_zoe_depth_sd1 = StarterModel(
-    name="zoedepth-sd15",
-    base=BaseModelType.StableDiffusion1,
-    source="TencentARC/t2iadapter_zoedepth_sd15v1",
-    description="T2I Adapter weights trained on sd-1.5 with zoe depth conditioning.",
+    description="Uses depth information in the image to control the depth in the generation.",
     type=ModelType.T2IAdapter,
+    previous_names=["depth-sd15"],
 )
 t2i_canny_sdxl = StarterModel(
-    name="canny-sdxl",
+    name="Hard Edge Detection (canny)",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-canny-sdxl-1.0",
-    description="T2I Adapter weights trained on sdxl-1.0 with canny conditioning.",
-    type=ModelType.T2IAdapter,
-)
-t2i_zoe_depth_sdxl = StarterModel(
-    name="zoedepth-sdxl",
-    base=BaseModelType.StableDiffusionXL,
-    source="TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
-    description="T2I Adapter weights trained on sdxl-1.0 with zoe depth conditioning.",
+    description="Uses detected edges in the image to control composition",
     type=ModelType.T2IAdapter,
+    previous_names=["canny-sdxl"],
 )
 t2i_lineart_sdxl = StarterModel(
-    name="lineart-sdxl",
+    name="Lineart",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-lineart-sdxl-1.0",
-    description="T2I Adapter weights trained on sdxl-1.0 with lineart conditioning.",
+    description="Uses lineart detection to guide the lighting of the composition.",
     type=ModelType.T2IAdapter,
+    previous_names=["lineart-sdxl"],
 )
 t2i_sketch_sdxl = StarterModel(
-    name="sketch-sdxl",
+    name="Sketch",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-sketch-sdxl-1.0",
-    description="T2I Adapter weights trained on sdxl-1.0 with sketch conditioning.",
+    description="Uses a sketch to control composition",
     type=ModelType.T2IAdapter,
+    previous_names=["sketch-sdxl"],
 )
 # endregion
 # region SpandrelImageToImage
@@ -537,6 +586,8 @@ STARTER_MODELS: list[StarterModel] = [
     flux_dev_quantized,
     flux_schnell,
     flux_dev,
+    sd35_medium,
+    sd35_large,
     cyberrealistic_sd1,
     rev_animated_sd1,
     dreamshaper_8_sd1,
@@ -545,6 +596,7 @@ STARTER_MODELS: list[StarterModel] = [
     deliberate_inpainting_sd1,
     juggernaut_sdxl,
     dreamshaper_sdxl,
+    archvis_sdxl,
     sdxl_refiner,
     sdxl_fp16_vae_fix,
     flux_vae,
@@ -555,6 +607,7 @@ STARTER_MODELS: list[StarterModel] = [
     ip_adapter_plus_sd1,
     ip_adapter_plus_face_sd1,
     ip_adapter_sdxl,
+    ip_adapter_flux,
     qr_code_cnet_sd1,
     qr_code_cnet_sdxl,
     canny_sd1,
@@ -570,22 +623,18 @@ STARTER_MODELS: list[StarterModel] = [
     softedge_sd1,
     shuffle_sd1,
     tile_sd1,
-    ip2p_sd1,
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
-    depth_zoe_16_sdxl,
-    depth_zoe_32_sdxl,
     openpose_sdxl,
     scribble_sdxl,
     tile_sdxl,
+    union_cnet_sdxl,
     union_cnet_flux,
     t2i_canny_sd1,
     t2i_sketch_sd1,
     t2i_depth_sd1,
-    t2i_zoe_depth_sd1,
     t2i_canny_sdxl,
-    t2i_zoe_depth_sdxl,
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
@@ -616,7 +665,6 @@ sd1_bundle: list[StarterModel] = [
     softedge_sd1,
     shuffle_sd1,
     tile_sd1,
-    ip2p_sd1,
     swinir,
 ]
 
@@ -627,8 +675,6 @@ sdxl_bundle: list[StarterModel] = [
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
-    depth_zoe_16_sdxl,
-    depth_zoe_32_sdxl,
     openpose_sdxl,
     scribble_sdxl,
     tile_sdxl,
@@ -642,6 +688,7 @@ flux_bundle: list[StarterModel] = [
     t5_8b_quantized_encoder,
     clip_l_encoder,
     union_cnet_flux,
+    ip_adapter_flux,
 ]
 
 STARTER_BUNDLES: dict[str, list[StarterModel]] = {

invokeai/backend/model_manager/util/model_util.py

@@ -8,6 +8,7 @@ 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
 
 
@@ -165,3 +166,23 @@ def convert_bundle_to_flux_transformer_checkpoint(
         del transformer_state_dict[k]
 
     return original_state_dict
+
+
+def get_clip_variant_type(location: str) -> Optional[ClipVariantType]:
+    try:
+        path = Path(location)
+        config_path = path / "config.json"
+        if not config_path.exists():
+            return ClipVariantType.L
+        with open(config_path) as file:
+            clip_conf = json.load(file)
+            hidden_size = clip_conf.get("hidden_size", -1)
+            match hidden_size:
+                case 1280:
+                    return ClipVariantType.G
+                case 768:
+                    return ClipVariantType.L
+                case _:
+                    return ClipVariantType.L
+    except Exception:
+        return ClipVariantType.L

invokeai/backend/model_manager/util/select_hf_files.py

@@ -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

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

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -499,6 +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/knip.ts

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

invokeai/frontend/web/package.json

@@ -58,7 +58,7 @@
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.1.0",
-    "@invoke-ai/ui-library": "^0.0.42",
+    "@invoke-ai/ui-library": "^0.0.43",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
@@ -114,8 +114,7 @@
   },
   "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",
@@ -149,8 +148,8 @@
     "prettier": "^3.3.3",
     "rollup-plugin-visualizer": "^5.12.0",
     "storybook": "^8.3.4",
-    "ts-toolbelt": "^9.6.0",
     "tsafe": "^1.7.5",
+    "type-fest": "^4.26.1",
     "typescript": "^5.6.2",
     "vite": "^5.4.8",
     "vite-plugin-css-injected-by-js": "^3.5.2",

invokeai/frontend/web/pnpm-lock.yaml

@@ -24,8 +24,8 @@ dependencies:
     specifier: ^5.1.0
     version: 5.1.0
   '@invoke-ai/ui-library':
-    specifier: ^0.0.42
-    version: 0.0.42(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.43
+    version: 0.0.43(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.3)(react@18.3.1)
@@ -277,12 +277,12 @@ devDependencies:
   storybook:
     specifier: ^8.3.4
     version: 8.3.4
-  ts-toolbelt:
-    specifier: ^9.6.0
-    version: 9.6.0
   tsafe:
     specifier: ^1.7.5
     version: 1.7.5
+  type-fest:
+    specifier: ^4.26.1
+    version: 4.26.1
   typescript:
     specifier: ^5.6.2
     version: 5.6.2
@@ -1696,20 +1696,20 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.42(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-OuDXRipBO5mu+Nv4qN8cd8MiwiGBdq6h4PirVgPI9/ltbdcIzePgUJ0dJns26lflHSTRWW38I16wl4YTw3mNWA==}
+  /@invoke-ai/ui-library@0.0.43(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-t3fPYyks07ue3dEBPJuTHbeDLnDckDCOrtvc07mMDbLOnlPEZ0StaeiNGH+oO8qLzAuMAlSTdswgHfzTc2MmPw==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
       react-dom: ^18.2.0
     dependencies:
-      '@chakra-ui/anatomy': 2.2.2
+      '@chakra-ui/anatomy': 2.3.4
       '@chakra-ui/icons': 2.2.4(@chakra-ui/react@2.10.2)(react@18.3.1)
       '@chakra-ui/layout': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/react': 2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/styled-system': 2.9.2
-      '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
+      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
+      '@chakra-ui/theme-tools': 2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
       '@emotion/react': 11.13.3(@types/react@18.3.11)(react@18.3.1)
       '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.11)(react@18.3.1)
       '@fontsource-variable/inter': 5.1.0
@@ -8830,10 +8830,6 @@ packages:
     resolution: {integrity: sha512-tLJxacIQUM82IR7JO1UUkKlYuUTmoY9HBJAmNWFzheSlDS5SPMcNIepejHJa4BpPQLAcbRhRf3GDJzyj6rbKvA==}
     dev: false
 
-  /ts-toolbelt@9.6.0:
-    resolution: {integrity: sha512-nsZd8ZeNUzukXPlJmTBwUAuABDe/9qtVDelJeT/qW0ow3ZS3BsQJtNkan1802aM9Uf68/Y8ljw86Hu0h5IUW3w==}
-    dev: true
-
   /tsafe@1.7.5:
     resolution: {integrity: sha512-tbNyyBSbwfbilFfiuXkSOj82a6++ovgANwcoqBAcO9/REPoZMEQoE8kWPeO0dy5A2D/2Lajr8Ohue5T0ifIvLQ==}
     dev: true

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

@@ -93,7 +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",
@@ -267,6 +274,18 @@
             "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": {
@@ -517,14 +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",
@@ -563,7 +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",
@@ -649,10 +677,41 @@
     "toast": {
         "uploadFailed": "Hochladen fehlgeschlagen",
         "imageCopied": "Bild kopiert",
-        "parametersNotSet": "Parameter nicht festgelegt",
+        "parametersNotSet": "Parameter nicht zurückgerufen",
         "addedToBoard": "Dem Board hinzugefügt",
         "loadedWithWarnings": "Workflow mit Warnungen geladen",
-        "imageSaved": "Bild gespeichert"
+        "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",
@@ -667,7 +726,8 @@
         "about": "Über",
         "submitSupportTicket": "Support-Ticket senden",
         "toggleRightPanel": "Rechtes Bedienfeld umschalten (G)",
-        "toggleLeftPanel": "Linkes Bedienfeld umschalten (T)"
+        "toggleLeftPanel": "Linkes Bedienfeld umschalten (T)",
+        "uploadImages": "Bild(er) hochladen"
     },
     "boards": {
         "autoAddBoard": "Board automatisch erstellen",
@@ -702,7 +762,7 @@
         "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.",
@@ -795,7 +855,6 @@
         "width": "Breite",
         "createdBy": "Erstellt von",
         "steps": "Schritte",
-        "seamless": "Nahtlos",
         "positivePrompt": "Positiver Prompt",
         "generationMode": "Generierungsmodus",
         "Threshold": "Rauschen-Schwelle",
@@ -811,7 +870,8 @@
         "parameterSet": "Parameter {{parameter}} setzen",
         "recallParameter": "{{label}} Abrufen",
         "parsingFailed": "Parsing Fehlgeschlagen",
-        "canvasV2Metadata": "Leinwand"
+        "canvasV2Metadata": "Leinwand",
+        "guidance": "Führung"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -1137,7 +1197,21 @@
         "workflowNotes": "Notizen",
         "workflowTags": "Tags",
         "workflowVersion": "Version",
-        "saveToGallery": "In Galerie speichern"
+        "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",
@@ -1267,15 +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",

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

@@ -94,6 +94,7 @@
         "close": "Close",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
+        "clipboard": "Clipboard",
         "on": "On",
         "off": "Off",
         "or": "or",
@@ -681,7 +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",
@@ -712,8 +714,12 @@
         "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",
@@ -727,7 +733,17 @@
         "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",
@@ -798,7 +814,6 @@
         "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",
@@ -982,6 +997,7 @@
         "controlNetControlMode": "Control Mode",
         "copyImage": "Copy Image",
         "denoisingStrength": "Denoising Strength",
+        "noRasterLayers": "No Raster Layers",
         "downloadImage": "Download Image",
         "general": "General",
         "guidance": "Guidance",
@@ -1032,6 +1048,7 @@
         "patchmatchDownScaleSize": "Downscale",
         "perlinNoise": "Perlin Noise",
         "positivePromptPlaceholder": "Positive Prompt",
+        "recallMetadata": "Recall Metadata",
         "iterations": "Iterations",
         "scale": "Scale",
         "scaleBeforeProcessing": "Scale Before Processing",
@@ -1108,6 +1125,9 @@
         "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",
@@ -1251,6 +1271,33 @@
             "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": [
@@ -1366,8 +1413,9 @@
         "paramDenoisingStrength": {
             "heading": "Denoising Strength",
             "paragraphs": [
-                "How much noise is added to the input image.",
-                "0 will result in an identical image, while 1 will result in a completely new image."
+                "Controls how much the generated image varies from the raster layer(s).",
+                "Lower strength stays closer to the combined visible raster layers. Higher strength relies more on the global prompt.",
+                "When there are no raster layers with visible content, this setting is ignored."
             ]
         },
         "paramHeight": {
@@ -1606,14 +1654,17 @@
         "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",
+        "mergingLayers": "Merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
         "resetCanvas": "Reset Canvas",
@@ -1648,6 +1699,8 @@
         "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",
@@ -1688,8 +1741,18 @@
         "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",
@@ -1713,9 +1776,11 @@
         "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",
+        "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or draw on the canvas to get started.",
         "controlMode": {
             "controlMode": "Control Mode",
-            "balanced": "Balanced",
+            "balanced": "Balanced (recommended)",
             "prompt": "Prompt",
             "control": "Control",
             "megaControl": "Mega Control"
@@ -1754,6 +1819,9 @@
             "process": "Process",
             "apply": "Apply",
             "cancel": "Cancel",
+            "advanced": "Advanced",
+            "processingLayerWith": "Processing layer with the {{type}} filter.",
+            "forMoreControl": "For more control, click Advanced below.",
             "spandrel_filter": {
                 "label": "Image-to-Image Model",
                 "description": "Run an image-to-image model on the selected layer.",
@@ -1842,6 +1910,25 @@
             "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",
@@ -1852,10 +1939,10 @@
                 "label": "Preserve Masked Region",
                 "alert": "Preserving Masked Region"
             },
-            "isolatedPreview": "Isolated Preview",
             "isolatedStagingPreview": "Isolated Staging Preview",
-            "isolatedFilteringPreview": "Isolated Filtering Preview",
-            "isolatedTransformingPreview": "Isolated Transforming Preview",
+            "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"
         },
@@ -1881,6 +1968,8 @@
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
             "newRasterLayer": "New Raster Layer",
+            "newInpaintMask": "New Inpaint Mask",
+            "newRegionalGuidance": "New Regional Guidance",
             "cropCanvasToBbox": "Crop Canvas to Bbox"
         },
         "stagingArea": {
@@ -2013,13 +2102,10 @@
     },
     "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": "<StrongComponent>Layer Merging</StrongComponent>: New <StrongComponent>Merge Down</StrongComponent> and improved <StrongComponent>Merge Visible</StrongComponent> for all layers, with special handling for Regional Guidance and Control Layers.",
+        "line2": "<StrongComponent>HF Token Support</StrongComponent>: Upload models that require Hugging Face authentication.",
+        "readReleaseNotes": "Read Release Notes",
+        "watchRecentReleaseVideos": "Watch Recent Release Videos",
+        "watchUiUpdatesOverview": "Watch UI Updates Overview"
     }
 }

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

@@ -5,8 +5,8 @@
         "reportBugLabel": "Signaler un bug",
         "settingsLabel": "Paramètres",
         "img2img": "Image vers Image",
-        "nodes": "Processus",
-        "upload": "Télécharger",
+        "nodes": "Workflows",
+        "upload": "Importer",
         "load": "Charger",
         "back": "Retour",
         "statusDisconnected": "Hors ligne",
@@ -51,7 +51,7 @@
         "green": "Vert",
         "delete": "Supprimer",
         "simple": "Simple",
-        "template": "Modèle",
+        "template": "Template",
         "advanced": "Avancé",
         "copy": "Copier",
         "saveAs": "Enregistrer sous",
@@ -95,7 +95,8 @@
         "positivePrompt": "Prompt Positif",
         "negativePrompt": "Prompt Négatif",
         "ok": "Ok",
-        "close": "Fermer"
+        "close": "Fermer",
+        "clipboard": "Presse-papier"
     },
     "gallery": {
         "galleryImageSize": "Taille de l'image",
@@ -117,8 +118,8 @@
         "bulkDownloadRequestFailed": "Problème lors de la préparation du téléchargement",
         "copy": "Copier",
         "autoAssignBoardOnClick": "Assigner automatiquement une Planche lors du clic",
-        "dropToUpload": "$t(gallery.drop) pour Charger",
-        "dropOrUpload": "$t(gallery.drop) ou Séléctioner",
+        "dropToUpload": "$t(gallery.drop) pour Importer",
+        "dropOrUpload": "$t(gallery.drop) ou Importer",
         "oldestFirst": "Plus Ancien en premier",
         "deleteImagePermanent": "Les Images supprimées ne peuvent pas être restorées.",
         "displaySearch": "Recherche d'Image",
@@ -161,7 +162,7 @@
         "unstarImage": "Retirer le marquage de l'Image",
         "viewerImage": "Visualisation de l'Image",
         "imagesSettings": "Paramètres des images de la galerie",
-        "assetsTab": "Fichiers que vous avez chargé pour vos projets.",
+        "assetsTab": "Fichiers que vous avez importés pour vos projets.",
         "imagesTab": "Images que vous avez créées et enregistrées dans Invoke.",
         "boardsSettings": "Paramètres des planches"
     },
@@ -219,7 +220,6 @@
         "typePhraseHere": "Écrire une phrase ici",
         "cancel": "Annuler",
         "defaultSettingsSaved": "Paramètres par défaut enregistrés",
-        "hfToken": "Token HuggingFace",
         "imageEncoderModelId": "ID du modèle d'encodeur d'image",
         "path": "Chemin sur le disque",
         "repoVariant": "Variante de dépôt",
@@ -243,7 +243,7 @@
         "noModelsInstalled": "Aucun modèle installé",
         "urlOrLocalPath": "URL ou chemin local",
         "prune": "Vider",
-        "uploadImage": "Charger une image",
+        "uploadImage": "Importer une image",
         "addModels": "Ajouter des modèles",
         "install": "Installer",
         "localOnly": "local uniquement",
@@ -254,7 +254,6 @@
         "loraModels": "LoRAs",
         "main": "Principal",
         "urlOrLocalPathHelper": "Les URL doivent pointer vers un seul fichier. Les chemins locaux peuvent pointer vers un seul fichier ou un dossier pour un seul modèle de diffuseurs.",
-        "useDefaultSettings": "Utiliser les paramètres par défaut",
         "modelImageUpdateFailed": "Mise à jour de l'image du modèle échouée",
         "loraTriggerPhrases": "Phrases de déclenchement LoRA",
         "mainModelTriggerPhrases": "Phrases de déclenchement du modèle principal",
@@ -273,24 +272,39 @@
         "spandrelImageToImage": "Image vers Image (Spandrel)",
         "starterModelsInModelManager": "Les modèles de démarrage peuvent être trouvés dans le gestionnaire de modèles",
         "t5Encoder": "Encodeur T5",
-        "learnMoreAboutSupportedModels": "En savoir plus sur les modèles que nous prenons en charge"
+        "learnMoreAboutSupportedModels": "En savoir plus sur les modèles que nous prenons en charge",
+        "includesNModels": "Contient {{n}} modèles et leurs dépendances",
+        "starterBundles": "Packs de démarrages",
+        "starterBundleHelpText": "Installe facilement tous les modèles nécessaire pour démarrer avec un modèle de base, incluant un modèle principal, ControlNets, IP Adapters et plus encore. Choisir un pack igniorera tous les modèles déjà installés.",
+        "installingXModels_one": "En cours d'installation de {{count}} modèle",
+        "installingXModels_many": "En cours d'installation de {{count}} modèles",
+        "installingXModels_other": "En cours d'installation de {{count}} modèles",
+        "skippingXDuplicates_one": ", en ignorant {{count}} doublon",
+        "skippingXDuplicates_many": ", en ignorant {{count}} doublons",
+        "skippingXDuplicates_other": ", en ignorant {{count}} doublons",
+        "installingModel": "Modèle en cours d'installation",
+        "installingBundle": "Pack en cours d'installation",
+        "noDefaultSettings": "Aucun paramètre par défaut configuré pour ce modèle. Visitez le Gestionnaire de Modèles pour ajouter des paramètres par défaut.",
+        "usingDefaultSettings": "Utilisation des paramètres par défaut du modèle",
+        "defaultSettingsOutOfSync": "Certain paramètres ne correspondent pas aux valeurs par défaut du modèle :",
+        "restoreDefaultSettings": "Cliquez pour utiliser les paramètres par défaut du modèle."
     },
     "parameters": {
         "images": "Images",
-        "steps": "Etapes",
-        "cfgScale": "CFG Echelle",
+        "steps": "Étapes",
+        "cfgScale": "Échelle CFG",
         "width": "Largeur",
         "height": "Hauteur",
         "seed": "Graine",
-        "shuffle": "Mélanger la graine",
+        "shuffle": "Nouvelle graine",
         "noiseThreshold": "Seuil de Bruit",
         "perlinNoise": "Bruit de Perlin",
         "type": "Type",
         "strength": "Force",
         "upscaling": "Agrandissement",
-        "scale": "Echelle",
+        "scale": "Échelle",
         "imageFit": "Ajuster Image Initiale à la Taille de Sortie",
-        "scaleBeforeProcessing": "Echelle Avant Traitement",
+        "scaleBeforeProcessing": "Échelle Avant Traitement",
         "scaledWidth": "Larg. Échelle",
         "scaledHeight": "Haut. Échelle",
         "infillMethod": "Méthode de Remplissage",
@@ -411,35 +425,38 @@
         "clearIntermediatesWithCount_other": "Effacé {{count}} Intermédiaires",
         "informationalPopoversDisabled": "Pop-ups d'information désactivés",
         "informationalPopoversDisabledDesc": "Les pop-ups d'information ont été désactivés. Activez-les dans les paramètres.",
-        "confirmOnNewSession": "Confirmer lors d'une nouvelle session"
+        "confirmOnNewSession": "Confirmer lors d'une nouvelle session",
+        "modelDescriptionsDisabledDesc": "Les descriptions des modèles dans les menus déroulants ont été désactivées. Activez-les dans les paramètres.",
+        "enableModelDescriptions": "Activer les descriptions de modèle dans les menus déroulants",
+        "modelDescriptionsDisabled": "Descriptions de modèle dans les menus déroulants désactivés"
     },
     "toast": {
-        "uploadFailed": "Téléchargement échoué",
+        "uploadFailed": "Importation échouée",
         "imageCopied": "Image copiée",
         "parametersNotSet": "Paramètres non rappelés",
         "serverError": "Erreur du serveur",
-        "uploadFailedInvalidUploadDesc": "Doit être une unique image PNG ou JPEG",
+        "uploadFailedInvalidUploadDesc": "Doit être des images au format PNG ou JPEG.",
         "problemCopyingImage": "Impossible de copier l'image",
         "parameterSet": "Paramètre Rappelé",
         "parameterNotSet": "Paramètre non Rappelé",
         "canceled": "Traitement annulé",
-        "addedToBoard": "Ajouté à la planche",
-        "workflowLoaded": "Processus chargé",
+        "addedToBoard": "Ajouté aux ressources de la planche {{name}}",
+        "workflowLoaded": "Workflow chargé",
         "connected": "Connecté au serveur",
         "setNodeField": "Définir comme champ de nœud",
         "imageUploadFailed": "Échec de l'importation de l'image",
-        "loadedWithWarnings": "Processus chargé avec des avertissements",
+        "loadedWithWarnings": "Workflow chargé avec des avertissements",
         "imageUploaded": "Image importée",
         "modelAddedSimple": "Modèle ajouté à la file d'attente",
         "setControlImage": "Définir comme image de contrôle",
-        "workflowDeleted": "Processus supprimé",
+        "workflowDeleted": "Workflow supprimé",
         "baseModelChangedCleared_one": "Effacé ou désactivé {{count}} sous-modèle incompatible",
         "baseModelChangedCleared_many": "Effacé ou désactivé {{count}} sous-modèles incompatibles",
         "baseModelChangedCleared_other": "Effacé ou désactivé {{count}} sous-modèles incompatibles",
-        "invalidUpload": "Téléchargement invalide",
+        "invalidUpload": "Importation invalide",
         "problemDownloadingImage": "Impossible de télécharger l'image",
-        "problemRetrievingWorkflow": "Problème de récupération du processus",
-        "problemDeletingWorkflow": "Problème de suppression du processus",
+        "problemRetrievingWorkflow": "Problème de récupération du Workflow",
+        "problemDeletingWorkflow": "Problème de suppression du Workflow",
         "prunedQueue": "File d'attente vidée",
         "parameters": "Paramètres",
         "modelImportCanceled": "Importation du modèle annulée",
@@ -468,10 +485,15 @@
         "baseModelChanged": "Modèle de base changé",
         "problemSavingLayer": "Impossible d'enregistrer la couche",
         "imageNotLoadedDesc": "Image introuvable",
-        "linkCopied": "Lien copié"
+        "linkCopied": "Lien copié",
+        "imagesWillBeAddedTo": "Les images Importées seront ajoutées au ressources de la Planche {{boardName}}.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Doit être au maximum une image PNG ou JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_many": "Doit être au maximum {{count}} images PNG ou JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Doit être au maximum {{count}} images PNG ou JPEG.",
+        "addedToUncategorized": "Ajouté aux ressources de la planche $t(boards.uncategorized)"
     },
     "accessibility": {
-        "uploadImage": "Charger une image",
+        "uploadImage": "Importer une image",
         "reset": "Réinitialiser",
         "nextImage": "Image suivante",
         "previousImage": "Image précédente",
@@ -483,7 +505,8 @@
         "submitSupportTicket": "Envoyer un ticket de support",
         "resetUI": "$t(accessibility.reset) l'Interface Utilisateur",
         "toggleRightPanel": "Afficher/Masquer le panneau de droite (G)",
-        "toggleLeftPanel": "Afficher/Masquer le panneau de gauche (T)"
+        "toggleLeftPanel": "Afficher/Masquer le panneau de gauche (T)",
+        "uploadImages": "Importer Image(s)"
     },
     "boards": {
         "move": "Déplacer",
@@ -533,7 +556,7 @@
     "accordions": {
         "advanced": {
             "title": "Avancé",
-            "options": "$t(accordions.advanced.title) Options"
+            "options": "Options $t(accordions.advanced.title)"
         },
         "image": {
             "title": "Image"
@@ -614,7 +637,7 @@
         "graphQueued": "Graph ajouté à la file d'attente",
         "other": "Autre",
         "generation": "Génération",
-        "workflows": "Processus",
+        "workflows": "Workflows",
         "batchFailedToQueue": "Impossible d'ajouter le Lot dans à la file d'attente",
         "graphFailedToQueue": "Impossible d'ajouter le graph à la file d'attente",
         "item": "Élément",
@@ -687,8 +710,8 @@
                 "desc": "Rappelle toutes les métadonnées pour l'image actuelle."
             },
             "loadWorkflow": {
-                "title": "Charger le processus",
-                "desc": "Charge le processus enregistré de l'image actuelle (s'il en a un)."
+                "title": "Ouvrir un Workflow",
+                "desc": "Charge le workflow enregistré lié à l'image actuelle (s'il en a un)."
             },
             "recallSeed": {
                 "desc": "Rappelle la graine pour l'image actuelle.",
@@ -739,8 +762,8 @@
                 "desc": "Séléctionne l'onglet Agrandissement."
             },
             "selectWorkflowsTab": {
-                "desc": "Sélectionne l'onglet Processus.",
-                "title": "Sélectionner l'onglet Processus"
+                "desc": "Sélectionne l'onglet Workflows.",
+                "title": "Sélectionner l'onglet Workflows"
             },
             "togglePanels": {
                 "desc": "Affiche ou masque les panneaux gauche et droit en même temps.",
@@ -946,11 +969,11 @@
             },
             "undo": {
                 "title": "Annuler",
-                "desc": "Annule la dernière action de processus."
+                "desc": "Annule la dernière action de workflow."
             },
             "redo": {
                 "title": "Rétablir",
-                "desc": "Rétablit la dernière action de processus."
+                "desc": "Rétablit la dernière action de workflow."
             },
             "addNode": {
                 "desc": "Ouvre le menu d'ajout de nœud.",
@@ -968,7 +991,7 @@
                 "desc": "Colle les nœuds et les connections copiés.",
                 "title": "Coller"
             },
-            "title": "Processus"
+            "title": "Workflows"
         }
     },
     "popovers": {
@@ -1355,6 +1378,43 @@
                 "Des valeurs de guidage élevées peuvent entraîner une saturation excessive, et un guidage élevé ou faible peut entraîner des résultats de génération déformés. Le guidage ne s'applique qu'aux modèles FLUX DEV."
             ],
             "heading": "Guidage"
+        },
+        "globalReferenceImage": {
+            "heading": "Image de Référence Globale",
+            "paragraphs": [
+                "Applique une image de référence pour influencer l'ensemble de la génération."
+            ]
+        },
+        "regionalReferenceImage": {
+            "heading": "Image de Référence Régionale",
+            "paragraphs": [
+                "Pinceau pour appliquer une image de référence à des zones spécifiques."
+            ]
+        },
+        "inpainting": {
+            "heading": "Inpainting",
+            "paragraphs": [
+                "Contrôle la zone qui est modifiée, guidé par la force de débruitage."
+            ]
+        },
+        "regionalGuidance": {
+            "heading": "Guide Régional",
+            "paragraphs": [
+                "Pinceau pour guider l'emplacement des éléments provenant des prompts globaux."
+            ]
+        },
+        "regionalGuidanceAndReferenceImage": {
+            "heading": "Guide régional et image de référence régionale",
+            "paragraphs": [
+                "Pour le Guide Régional, utilisez le pinceau pour indiquer où les éléments des prompts globaux doivent apparaître.",
+                "Pour l'image de référence régionale, pinceau pour appliquer une image de référence à des zones spécifiques."
+            ]
+        },
+        "rasterLayer": {
+            "heading": "Couche Rastérisation",
+            "paragraphs": [
+                "Contenu basé sur les pixels de votre toile, utilisé lors de la génération d'images."
+            ]
         }
     },
     "dynamicPrompts": {
@@ -1375,12 +1435,11 @@
         "positivePrompt": "Prompt Positif",
         "allPrompts": "Tous les Prompts",
         "negativePrompt": "Prompt Négatif",
-        "seamless": "Sans jointure",
         "metadata": "Métadonné",
         "scheduler": "Planificateur",
         "imageDetails": "Détails de l'Image",
         "seed": "Graine",
-        "workflow": "Processus",
+        "workflow": "Workflow",
         "width": "Largeur",
         "Threshold": "Seuil de bruit",
         "noMetaData": "Aucune métadonnée trouvée",
@@ -1400,13 +1459,14 @@
         "parameterSet": "Paramètre {{parameter}} défini",
         "parsingFailed": "L'analyse a échoué",
         "recallParameter": "Rappeler {{label}}",
-        "canvasV2Metadata": "Toile"
+        "canvasV2Metadata": "Toile",
+        "guidance": "Guide"
     },
     "sdxl": {
         "freePromptStyle": "Écriture de Prompt manuelle",
         "concatPromptStyle": "Lier Prompt & Style",
-        "negStylePrompt": "Prompt Négatif",
-        "posStylePrompt": "Prompt Positif",
+        "negStylePrompt": "Style Prompt Négatif",
+        "posStylePrompt": "Style Prompt Positif",
         "refinerStart": "Démarrer le Refiner",
         "denoisingStrength": "Force de débruitage",
         "steps": "Étapes",
@@ -1428,8 +1488,8 @@
         "hideMinimapnodes": "Masquer MiniCarte",
         "zoomOutNodes": "Dézoomer",
         "zoomInNodes": "Zoomer",
-        "downloadWorkflow": "Télécharger processus en JSON",
-        "loadWorkflow": "Charger le processus",
+        "downloadWorkflow": "Exporter le Workflow au format JSON",
+        "loadWorkflow": "Charger un Workflow",
         "reloadNodeTemplates": "Recharger les modèles de nœuds",
         "animatedEdges": "Connexions animées",
         "cannotConnectToSelf": "Impossible de se connecter à soi-même",
@@ -1452,16 +1512,16 @@
         "float": "Flottant",
         "mismatchedVersion": "Nœud invalide : le nœud {{node}} de type {{type}} a une version incompatible (essayez de mettre à jour ?)",
         "missingTemplate": "Nœud invalide : le nœud {{node}} de type {{type}} modèle manquant (non installé ?)",
-        "noWorkflow": "Pas de processus",
+        "noWorkflow": "Pas de Workflow",
         "validateConnectionsHelp": "Prévenir la création de connexions invalides et l'invocation de graphes invalides",
-        "workflowSettings": "Paramètres de l'Éditeur de Processus",
-        "workflowValidation": "Erreur de validation du processus",
+        "workflowSettings": "Paramètres de l'Éditeur de Workflow",
+        "workflowValidation": "Erreur de validation du Workflow",
         "executionStateInProgress": "En cours",
         "node": "Noeud",
         "scheduler": "Planificateur",
         "notes": "Notes",
-        "notesDescription": "Ajouter des notes sur votre processus",
-        "unableToLoadWorkflow": "Impossible de charger le processus",
+        "notesDescription": "Ajouter des notes sur votre workflow",
+        "unableToLoadWorkflow": "Impossible de charger le Workflow",
         "addNode": "Ajouter un nœud",
         "problemSettingTitle": "Problème lors de définition du Titre",
         "connectionWouldCreateCycle": "La connexion créerait un cycle",
@@ -1484,7 +1544,7 @@
         "noOutputRecorded": "Aucun résultat enregistré",
         "removeLinearView": "Retirer de la vue linéaire",
         "snapToGrid": "Aligner sur la grille",
-        "workflow": "Processus",
+        "workflow": "Workflow",
         "updateApp": "Mettre à jour l'application",
         "updateNode": "Mettre à jour le nœud",
         "nodeOutputs": "Sorties de nœud",
@@ -1497,7 +1557,7 @@
         "string": "Chaîne de caractères",
         "workflowName": "Nom",
         "snapToGridHelp": "Aligner les nœuds sur la grille lors du déplacement",
-        "unableToValidateWorkflow": "Impossible de valider le processus",
+        "unableToValidateWorkflow": "Impossible de valider le Workflow",
         "validateConnections": "Valider les connexions et le graphique",
         "unableToUpdateNodes_one": "Impossible de mettre à jour {{count}} nœud",
         "unableToUpdateNodes_many": "Impossible de mettre à jour {{count}} nœuds",
@@ -1510,15 +1570,15 @@
         "nodePack": "Paquet de nœuds",
         "sourceNodeDoesNotExist": "Connexion invalide : le nœud source/de sortie {{node}} n'existe pas",
         "sourceNodeFieldDoesNotExist": "Connexion invalide : {{node}}.{{field}} n'existe pas",
-        "unableToGetWorkflowVersion": "Impossible d'obtenir la version du schéma de processus",
-        "newWorkflowDesc2": "Votre processus actuel comporte des modifications non enregistrées.",
+        "unableToGetWorkflowVersion": "Impossible d'obtenir la version du schéma du Workflow",
+        "newWorkflowDesc2": "Votre workflow actuel comporte des modifications non enregistrées.",
         "deletedInvalidEdge": "Connexion invalide supprimé {{source}} -> {{target}}",
         "targetNodeDoesNotExist": "Connexion invalide : le nœud cible/entrée {{node}} n'existe pas",
         "targetNodeFieldDoesNotExist": "Connexion invalide : le champ {{node}}.{{field}} n'existe pas",
         "nodeVersion": "Version du noeud",
-        "clearWorkflowDesc2": "Votre processus actuel comporte des modifications non enregistrées.",
-        "clearWorkflow": "Effacer le Processus",
-        "clearWorkflowDesc": "Effacer ce processus et en commencer un nouveau ?",
+        "clearWorkflowDesc2": "Votre workflow actuel comporte des modifications non enregistrées.",
+        "clearWorkflow": "Effacer le Workflow",
+        "clearWorkflowDesc": "Effacer ce workflow et en commencer un nouveau ?",
         "unsupportedArrayItemType": "type d'élément de tableau non pris en charge \"{{type}}\"",
         "addLinearView": "Ajouter à la vue linéaire",
         "collectionOrScalarFieldType": "{{name}} (Unique ou Collection)",
@@ -1527,7 +1587,7 @@
         "ipAdapter": "IP-Adapter",
         "viewMode": "Utiliser en vue linéaire",
         "collectionFieldType": "{{name}} (Collection)",
-        "newWorkflow": "Nouveau processus",
+        "newWorkflow": "Nouveau Workflow",
         "reorderLinearView": "Réorganiser la vue linéaire",
         "unknownOutput": "Sortie inconnue : {{name}}",
         "outputFieldTypeParseError": "Impossible d'analyser le type du champ de sortie {{node}}.{{field}} ({{message}})",
@@ -1537,13 +1597,13 @@
         "unknownFieldType": "$t(nodes.unknownField) type : {{type}}",
         "inputFieldTypeParseError": "Impossible d'analyser le type du champ d'entrée {{node}}.{{field}} ({{message}})",
         "unableToExtractSchemaNameFromRef": "impossible d'extraire le nom du schéma à partir de la référence",
-        "editMode": "Modifier dans l'éditeur de processus",
-        "unknownErrorValidatingWorkflow": "Erreur inconnue lors de la validation du processus",
+        "editMode": "Modifier dans l'éditeur de Workflow",
+        "unknownErrorValidatingWorkflow": "Erreur inconnue lors de la validation du Workflow",
         "updateAllNodes": "Mettre à jour les nœuds",
         "allNodesUpdated": "Tous les nœuds mis à jour",
-        "newWorkflowDesc": "Créer un nouveau processus ?",
+        "newWorkflowDesc": "Créer un nouveau workflow ?",
         "edit": "Modifier",
-        "noFieldsViewMode": "Ce processus n'a aucun champ sélectionné à afficher. Consultez le processus complet pour configurer les valeurs.",
+        "noFieldsViewMode": "Ce workflow n'a aucun champ sélectionné à afficher. Consultez le workflow complet pour configurer les valeurs.",
         "graph": "Graph",
         "modelAccessError": "Impossible de trouver le modèle {{key}}, réinitialisation aux paramètres par défaut",
         "showEdgeLabelsHelp": "Afficher le nom sur les connections, indiquant les nœuds connectés",
@@ -1557,9 +1617,9 @@
         "missingInvocationTemplate": "Modèle d'invocation manquant",
         "imageAccessError": "Impossible de trouver l'image {{image_name}}, réinitialisation à la valeur par défaut",
         "boardAccessError": "Impossible de trouver la planche {{board_id}}, réinitialisation à la valeur par défaut",
-        "workflowHelpText": "Besoin d'aide ? Consultez notre guide sur <LinkComponent>Comment commencer avec les Processus</LinkComponent>.",
-        "noWorkflows": "Aucun Processus",
-        "noMatchingWorkflows": "Aucun processus correspondant"
+        "workflowHelpText": "Besoin d'aide ? Consultez notre guide sur <LinkComponent>Comment commencer avec les Workflows</LinkComponent>.",
+        "noWorkflows": "Aucun Workflows",
+        "noMatchingWorkflows": "Aucun Workflows correspondant"
     },
     "models": {
         "noMatchingModels": "Aucun modèle correspondant",
@@ -1576,59 +1636,51 @@
     },
     "workflows": {
         "workflowLibrary": "Bibliothèque",
-        "loading": "Chargement des processus",
-        "searchWorkflows": "Rechercher des processus",
-        "workflowCleared": "Processus effacé",
+        "loading": "Chargement des Workflows",
+        "searchWorkflows": "Chercher des Workflows",
+        "workflowCleared": "Workflow effacé",
         "noDescription": "Aucune description",
-        "deleteWorkflow": "Supprimer le processus",
-        "openWorkflow": "Ouvrir le processus",
-        "uploadWorkflow": "Charger à partir du fichier",
-        "workflowName": "Nom du processus",
-        "unnamedWorkflow": "Processus sans nom",
-        "saveWorkflowAs": "Enregistrer le processus sous",
-        "workflows": "Processus",
-        "savingWorkflow": "Enregistrement du processus...",
-        "saveWorkflowToProject": "Enregistrer le processus dans le projet",
+        "deleteWorkflow": "Supprimer le Workflow",
+        "openWorkflow": "Ouvrir le Workflow",
+        "uploadWorkflow": "Charger à partir d'un fichier",
+        "workflowName": "Nom du Workflow",
+        "unnamedWorkflow": "Workflow sans nom",
+        "saveWorkflowAs": "Enregistrer le Workflow sous",
+        "workflows": "Workflows",
+        "savingWorkflow": "Enregistrement du Workflow...",
+        "saveWorkflowToProject": "Enregistrer le Workflow dans le projet",
         "downloadWorkflow": "Enregistrer dans le fichier",
-        "saveWorkflow": "Enregistrer le processus",
-        "problemSavingWorkflow": "Problème de sauvegarde du processus",
-        "workflowEditorMenu": "Menu de l'Éditeur de Processus",
-        "newWorkflowCreated": "Nouveau processus créé",
-        "clearWorkflowSearchFilter": "Réinitialiser le filtre de recherche de processus",
-        "problemLoading": "Problème de chargement des processus",
-        "workflowSaved": "Processus enregistré",
-        "noWorkflows": "Pas de processus",
+        "saveWorkflow": "Enregistrer le Workflow",
+        "problemSavingWorkflow": "Problème de sauvegarde du Workflow",
+        "workflowEditorMenu": "Menu de l'Éditeur de Workflow",
+        "newWorkflowCreated": "Nouveau Workflow créé",
+        "clearWorkflowSearchFilter": "Réinitialiser le filtre de recherche de Workflow",
+        "problemLoading": "Problème de chargement des Workflows",
+        "workflowSaved": "Workflow enregistré",
+        "noWorkflows": "Pas de Workflows",
         "ascending": "Ascendant",
-        "loadFromGraph": "Charger le processus à partir du graphique",
+        "loadFromGraph": "Charger le Workflow à partir du graphique",
         "descending": "Descendant",
         "created": "Créé",
         "updated": "Mis à jour",
-        "loadWorkflow": "$t(common.load) Processus",
+        "loadWorkflow": "$t(common.load) Workflow",
         "convertGraph": "Convertir le graphique",
         "opened": "Ouvert",
         "name": "Nom",
         "autoLayout": "Mise en page automatique",
-        "defaultWorkflows": "Processus par défaut",
-        "userWorkflows": "Processus utilisateur",
-        "projectWorkflows": "Processus du projet",
+        "defaultWorkflows": "Workflows par défaut",
+        "userWorkflows": "Workflows de l'utilisateur",
+        "projectWorkflows": "Workflows du projet",
         "copyShareLink": "Copier le lien de partage",
-        "chooseWorkflowFromLibrary": "Choisir le Processus dans la Bibliothèque",
-        "uploadAndSaveWorkflow": "Charger dans la bibliothèque",
+        "chooseWorkflowFromLibrary": "Choisir le Workflow dans la Bibliothèque",
+        "uploadAndSaveWorkflow": "Importer dans la bibliothèque",
         "edit": "Modifer",
-        "deleteWorkflow2": "Êtes-vous sûr de vouloir supprimer ce processus ? Ceci ne peut pas être annulé.",
+        "deleteWorkflow2": "Êtes-vous sûr de vouloir supprimer ce Workflow ? Cette action ne peut pas être annulé.",
         "download": "Télécharger",
-        "copyShareLinkForWorkflow": "Copier le lien de partage pour le processus",
+        "copyShareLinkForWorkflow": "Copier le lien de partage pour le Workflow",
         "delete": "Supprimer"
     },
     "whatsNew": {
-        "canvasV2Announcement": {
-            "watchReleaseVideo": "Regarder la vidéo de lancement",
-            "newLayerTypes": "Nouveaux types de couches pour un contrôle encore plus précis",
-            "fluxSupport": "Support pour la famille de modèles Flux",
-            "readReleaseNotes": "Lire les notes de version",
-            "newCanvas": "Une nouvelle Toile de contrôle puissant",
-            "watchUiUpdatesOverview": "Regarder l'aperçu des mises à jour de l'UI"
-        },
         "whatsNewInInvoke": "Quoi de neuf dans Invoke"
     },
     "ui": {
@@ -1639,7 +1691,7 @@
             "gallery": "Galerie",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
             "generation": "Génération",
-            "workflows": "Processus",
+            "workflows": "Workflows",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
             "models": "Modèles",
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)"
@@ -1749,7 +1801,9 @@
             "bboxGroup": "Créer à partir de la bounding box",
             "newRegionalReferenceImage": "Nouvelle image de référence régionale",
             "newGlobalReferenceImage": "Nouvelle image de référence globale",
-            "newControlLayer": "Nouveau couche de contrôle"
+            "newControlLayer": "Nouveau couche de contrôle",
+            "newInpaintMask": "Nouveau Masque Inpaint",
+            "newRegionalGuidance": "Nouveau Guide Régional"
         },
         "bookmark": "Marque-page pour Changement Rapide",
         "saveLayerToAssets": "Enregistrer la couche dans les ressources",
@@ -1762,8 +1816,6 @@
                 "on": "Activé",
                 "label": "Aligner sur la grille"
             },
-            "isolatedFilteringPreview": "Aperçu de filtrage isolé",
-            "isolatedTransformingPreview": "Aperçu de transformation isolée",
             "invertBrushSizeScrollDirection": "Inverser le défilement pour la taille du pinceau",
             "pressureSensitivity": "Sensibilité à la pression",
             "preserveMask": {
@@ -1771,9 +1823,10 @@
                 "alert": "Préserver la zone masquée"
             },
             "isolatedPreview": "Aperçu Isolé",
-            "isolatedStagingPreview": "Aperçu de l'attente isolé"
+            "isolatedStagingPreview": "Aperçu de l'attente isolé",
+            "isolatedLayerPreview": "Aperçu de la couche isolée",
+            "isolatedLayerPreviewDesc": "Pour afficher uniquement cette couche lors de l'exécution d'opérations telles que le filtrage ou la transformation."
         },
-        "convertToRasterLayer": "Convertir en Couche de Rastérisation",
         "transparency": "Transparence",
         "moveBackward": "Reculer",
         "rectangle": "Rectangle",
@@ -1896,7 +1949,6 @@
         "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
         "globalReferenceImage_withCount_many": "Images de référence globales",
         "globalReferenceImage_withCount_other": "Images de référence globales",
-        "convertToControlLayer": "Convertir en Couche de Contrôle",
         "layer_withCount_one": "Couche {{count}}",
         "layer_withCount_many": "Couches {{count}}",
         "layer_withCount_other": "Couches {{count}}",
@@ -1959,7 +2011,41 @@
         "pullBboxIntoReferenceImageOk": "Bounding Box insérée dans l'Image de référence",
         "controlLayer_withCount_one": "$t(controlLayers.controlLayer)",
         "controlLayer_withCount_many": "Controler les couches",
-        "controlLayer_withCount_other": "Controler les couches"
+        "controlLayer_withCount_other": "Controler les couches",
+        "copyInpaintMaskTo": "Copier $t(controlLayers.inpaintMask) vers",
+        "copyRegionalGuidanceTo": "Copier $t(controlLayers.regionalGuidance) vers",
+        "convertRasterLayerTo": "Convertir $t(controlLayers.rasterLayer) vers",
+        "selectObject": {
+            "selectObject": "Sélectionner l'objet",
+            "clickToAdd": "Cliquez sur la couche pour ajouter un point",
+            "apply": "Appliquer",
+            "cancel": "Annuler",
+            "dragToMove": "Faites glisser un point pour le déplacer",
+            "clickToRemove": "Cliquez sur un point pour le supprimer",
+            "include": "Inclure",
+            "invertSelection": "Sélection Inversée",
+            "saveAs": "Enregistrer sous",
+            "neutral": "Neutre",
+            "pointType": "Type de point",
+            "exclude": "Exclure",
+            "process": "Traiter",
+            "reset": "Réinitialiser",
+            "help1": "Sélectionnez un seul objet cible. Ajoutez des points <Bold>Inclure</Bold> et <Bold>Exclure</Bold> pour indiquer quelles parties de la couche font partie de l'objet cible.",
+            "help2": "Commencez par un point <Bold>Inclure</Bold> au sein de l'objet cible. Ajoutez d'autres points pour affiner la sélection. Moins de points produisent généralement de meilleurs résultats.",
+            "help3": "Inversez la sélection pour sélectionner tout sauf l'objet cible."
+        },
+        "canvasAsControlLayer": "$t(controlLayers.canvas) en tant que $t(controlLayers.controlLayer)",
+        "convertRegionalGuidanceTo": "Convertir $t(controlLayers.regionalGuidance) vers",
+        "copyRasterLayerTo": "Copier $t(controlLayers.rasterLayer) vers",
+        "newControlLayer": "Nouveau $t(controlLayers.controlLayer)",
+        "newRegionalGuidance": "Nouveau $t(controlLayers.regionalGuidance)",
+        "replaceCurrent": "Remplacer Actuel",
+        "convertControlLayerTo": "Convertir $t(controlLayers.controlLayer) vers",
+        "convertInpaintMaskTo": "Convertir $t(controlLayers.inpaintMask) vers",
+        "copyControlLayerTo": "Copier $t(controlLayers.controlLayer) vers",
+        "newInpaintMask": "Nouveau $t(controlLayers.inpaintMask)",
+        "newRasterLayer": "Nouveau $t(controlLayers.rasterLayer)",
+        "canvasAsRasterLayer": "$t(controlLayers.canvas) en tant que $t(controlLayers.rasterLayer)"
     },
     "upscaling": {
         "exceedsMaxSizeDetails": "La limite maximale d'agrandissement est de {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixels. Veuillez essayer une image plus petite ou réduire votre sélection d'échelle.",
@@ -1980,57 +2066,57 @@
         "missingTileControlNetModel": "Aucun modèle ControlNet valide installé"
     },
     "stylePresets": {
-        "deleteTemplate": "Supprimer le modèle",
-        "editTemplate": "Modifier le modèle",
+        "deleteTemplate": "Supprimer le template",
+        "editTemplate": "Modifier le template",
         "exportFailed": "Impossible de générer et de télécharger le CSV",
         "name": "Nom",
         "acceptedColumnsKeys": "Colonnes/clés acceptées :",
-        "promptTemplatesDesc1": "Les modèles de prompt ajoutent du texte aux prompts que vous écrivez dans la zone de saisie des prompts.",
+        "promptTemplatesDesc1": "Les templates de prompt ajoutent du texte aux prompts que vous écrivez dans la zone de saisie.",
         "private": "Privé",
         "searchByName": "Rechercher par nom",
-        "viewList": "Afficher la liste des modèles",
-        "noTemplates": "Aucun modèle",
+        "viewList": "Afficher la liste des templates",
+        "noTemplates": "Aucun templates",
         "insertPlaceholder": "Insérer un placeholder",
-        "defaultTemplates": "Modèles par défaut",
+        "defaultTemplates": "Template pré-défini",
         "deleteImage": "Supprimer l'image",
-        "createPromptTemplate": "Créer un modèle de prompt",
+        "createPromptTemplate": "Créer un template de prompt",
         "negativePrompt": "Prompt négatif",
-        "promptTemplatesDesc3": "Si vous omettez le placeholder, le modèle sera ajouté à la fin de votre prompt.",
+        "promptTemplatesDesc3": "Si vous omettez le placeholder, le template sera ajouté à la fin de votre prompt.",
         "positivePrompt": "Prompt positif",
-        "choosePromptTemplate": "Choisir un modèle de prompt",
+        "choosePromptTemplate": "Choisir un template de prompt",
         "toggleViewMode": "Basculer le mode d'affichage",
-        "updatePromptTemplate": "Mettre à jour le modèle de prompt",
-        "flatten": "Intégrer le modèle sélectionné dans le prompt actuel",
-        "myTemplates": "Mes modèles",
+        "updatePromptTemplate": "Mettre à jour le template de prompt",
+        "flatten": "Intégrer le template sélectionné dans le prompt actuel",
+        "myTemplates": "Mes Templates",
         "type": "Type",
         "exportDownloaded": "Exportation téléchargée",
-        "clearTemplateSelection": "Supprimer la sélection de modèle",
-        "promptTemplateCleared": "Modèle de prompt effacé",
-        "templateDeleted": "Modèle de prompt supprimé",
-        "exportPromptTemplates": "Exporter mes modèles de prompt (CSV)",
+        "clearTemplateSelection": "Supprimer la sélection de template",
+        "promptTemplateCleared": "Template de prompt effacé",
+        "templateDeleted": "Template de prompt supprimé",
+        "exportPromptTemplates": "Exporter mes templates de prompt (CSV)",
         "nameColumn": "'nom'",
         "positivePromptColumn": "\"prompt\" ou \"prompt_positif\"",
-        "useForTemplate": "Utiliser pour le modèle de prompt",
-        "uploadImage": "Charger une image",
-        "importTemplates": "Importer des modèles de prompt (CSV/JSON)",
+        "useForTemplate": "Utiliser pour le template de prompt",
+        "uploadImage": "Importer une image",
+        "importTemplates": "Importer des templates de prompt (CSV/JSON)",
         "negativePromptColumn": "'prompt_négatif'",
-        "deleteTemplate2": "Êtes-vous sûr de vouloir supprimer ce modèle ? Cette action ne peut pas être annulée.",
+        "deleteTemplate2": "Êtes-vous sûr de vouloir supprimer ce template ? Cette action ne peut pas être annulée.",
         "preview": "Aperçu",
         "shared": "Partagé",
-        "noMatchingTemplates": "Aucun modèle correspondant",
-        "sharedTemplates": "Modèles partagés",
-        "unableToDeleteTemplate": "Impossible de supprimer le modèle de prompt",
+        "noMatchingTemplates": "Aucun templates correspondant",
+        "sharedTemplates": "Template partagés",
+        "unableToDeleteTemplate": "Impossible de supprimer le template de prompt",
         "active": "Actif",
-        "copyTemplate": "Copier le modèle",
-        "viewModeTooltip": "Voici à quoi ressemblera votre prompt avec le modèle actuellement sélectionné. Pour modifier votre prompt, cliquez n'importe où dans la zone de texte.",
-        "promptTemplatesDesc2": "Utilisez la chaîne de remplacement <Pre>{{placeholder}}</Pre> pour spécifier où votre prompt doit être inclus dans le modèle."
+        "copyTemplate": "Copier le template",
+        "viewModeTooltip": "Voici à quoi ressemblera votre prompt avec le template actuellement sélectionné. Pour modifier votre prompt, cliquez n'importe où dans la zone de texte.",
+        "promptTemplatesDesc2": "Utilisez la chaîne de remplacement <Pre>{{placeholder}}</Pre> pour spécifier où votre prompt doit être inclus dans le template."
     },
     "system": {
         "logNamespaces": {
             "config": "Configuration",
             "canvas": "Toile",
             "generation": "Génération",
-            "workflows": "Processus",
+            "workflows": "Workflows",
             "system": "Système",
             "models": "Modèles",
             "logNamespaces": "Journalisation des espaces de noms",
@@ -2051,8 +2137,12 @@
         "enableLogging": "Activer la journalisation"
     },
     "newUserExperience": {
-        "toGetStarted": "Pour commencer, saisissez un prompt dans la boîte et cliquez sur <StrongComponent>Invoke</StrongComponent> pour générer votre première image. Sélectionnez un modèle de prompt pour améliorer les résultats. Vous pouvez choisir de sauvegarder vos images directement dans la <StrongComponent>Galerie</StrongComponent> ou de les modifier sur la <StrongComponent>Toile</StrongComponent>.",
-        "gettingStartedSeries": "Vous souhaitez plus de conseils ? Consultez notre <LinkComponent>Série de démarrage</LinkComponent> pour des astuces sur l'exploitation du plein potentiel de l'Invoke Studio."
+        "toGetStarted": "Pour commencer, saisissez un prompt dans la boîte et cliquez sur <StrongComponent>Invoke</StrongComponent> pour générer votre première image. Sélectionnez un template de prompt pour améliorer les résultats. Vous pouvez choisir de sauvegarder vos images directement dans la <StrongComponent>Galerie</StrongComponent> ou de les modifier sur la <StrongComponent>Toile</StrongComponent>.",
+        "gettingStartedSeries": "Vous souhaitez plus de conseils ? Consultez notre <LinkComponent>Série de démarrage</LinkComponent> pour des astuces sur l'exploitation du plein potentiel de l'Invoke Studio.",
+        "noModelsInstalled": "Il semble qu'aucun modèle ne soit installé",
+        "downloadStarterModels": "Télécharger les modèles de démarrage",
+        "importModels": "Importer des Modèles",
+        "toGetStartedLocal": "Pour commencer, assurez-vous de télécharger ou d'importer des modèles nécessaires pour exécuter Invoke. Ensuite, saisissez le prompt dans la boîte et cliquez sur <StrongComponent>Invoke</StrongComponent> pour générer votre première image. Sélectionnez un template de prompt pour améliorer les résultats. Vous pouvez choisir de sauvegarder vos images directement sur <StrongComponent>Galerie</StrongComponent> ou les modifier sur la <StrongComponent>Toile</StrongComponent>."
     },
     "upsell": {
         "shareAccess": "Partager l'accès",

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

@@ -92,7 +92,9 @@
         "none": "Niente",
         "new": "Nuovo",
         "view": "Vista",
-        "close": "Chiudi"
+        "close": "Chiudi",
+        "clipboard": "Appunti",
+        "ok": "Ok"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -542,7 +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",
@@ -577,7 +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",
@@ -678,7 +698,8 @@
         "boxBlur": "Sfocatura Box",
         "staged": "Maschera espansa",
         "optimizedImageToImage": "Immagine-a-immagine ottimizzata",
-        "sendToCanvas": "Invia alla Tela"
+        "sendToCanvas": "Invia alla Tela",
+        "coherenceMinDenoise": "Riduzione minima del rumore"
     },
     "settings": {
         "models": "Modelli",
@@ -713,7 +734,10 @@
         "reloadingIn": "Ricaricando in",
         "informationalPopoversDisabled": "Testo informativo a comparsa disabilitato",
         "informationalPopoversDisabledDesc": "I testi informativi a comparsa sono disabilitati. Attivali nelle impostazioni.",
-        "confirmOnNewSession": "Conferma su nuova sessione"
+        "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",
@@ -722,7 +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",
@@ -731,7 +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",
@@ -770,7 +794,12 @@
         "imageSavingFailed": "Salvataggio dell'immagine non riuscito",
         "layerCopiedToClipboard": "Livello copiato negli appunti",
         "imageNotLoadedDesc": "Impossibile trovare l'immagine",
-        "linkCopied": "Collegamento copiato"
+        "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",
@@ -785,7 +814,8 @@
         "about": "Informazioni",
         "submitSupportTicket": "Invia ticket di supporto",
         "toggleLeftPanel": "Attiva/disattiva il pannello sinistro (T)",
-        "toggleRightPanel": "Attiva/disattiva il pannello destro (G)"
+        "toggleRightPanel": "Attiva/disattiva il pannello destro (G)",
+        "uploadImages": "Carica immagine(i)"
     },
     "nodes": {
         "zoomOutNodes": "Rimpicciolire",
@@ -1059,7 +1089,8 @@
         "noLoRAsInstalled": "Nessun LoRA installato",
         "addLora": "Aggiungi LoRA",
         "defaultVAE": "VAE predefinito",
-        "concepts": "Concetti"
+        "concepts": "Concetti",
+        "lora": "LoRA"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1116,7 +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",
@@ -1475,6 +1507,42 @@
                 "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": {
@@ -1496,7 +1564,6 @@
         "refinerSteps": "Passi Affinamento"
     },
     "metadata": {
-        "seamless": "Senza giunture",
         "positivePrompt": "Prompt positivo",
         "negativePrompt": "Prompt negativo",
         "generationMode": "Modalità generazione",
@@ -1524,7 +1591,10 @@
         "parsingFailed": "Analisi non riuscita",
         "recallParameter": "Richiama {{label}}",
         "canvasV2Metadata": "Tela",
-        "guidance": "Guida"
+        "guidance": "Guida",
+        "seamlessXAxis": "Asse X senza giunte",
+        "seamlessYAxis": "Asse Y senza giunte",
+        "vae": "VAE"
     },
     "hrf": {
         "enableHrf": "Abilita Correzione Alta Risoluzione",
@@ -1621,11 +1691,11 @@
         "regionalGuidance": "Guida regionale",
         "opacity": "Opacità",
         "mergeVisible": "Fondi il visibile",
-        "mergeVisibleOk": "Livelli visibili uniti",
+        "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 visibili",
+        "mergeVisibleError": "Errore durante l'unione dei livelli",
         "regionalReferenceImage": "Immagine di riferimento Regionale",
         "newLayerFromImage": "Nuovo livello da immagine",
         "newCanvasFromImage": "Nuova tela da immagine",
@@ -1717,7 +1787,7 @@
             "composition": "Solo Composizione",
             "ipAdapterMethod": "Metodo Adattatore IP"
         },
-        "showingType": "Mostrare {{type}}",
+        "showingType": "Mostra {{type}}",
         "dynamicGrid": "Griglia dinamica",
         "tool": {
             "view": "Muovi",
@@ -1845,8 +1915,6 @@
         "layer_withCount_one": "Livello ({{count}})",
         "layer_withCount_many": "Livelli ({{count}})",
         "layer_withCount_other": "Livelli ({{count}})",
-        "convertToControlLayer": "Converti in livello di controllo",
-        "convertToRasterLayer": "Converti in livello raster",
         "unlocked": "Sbloccato",
         "enableTransparencyEffect": "Abilita l'effetto trasparenza",
         "replaceLayer": "Sostituisci livello",
@@ -1859,9 +1927,7 @@
         "newCanvasSession": "Nuova sessione Tela",
         "deleteSelected": "Elimina selezione",
         "settings": {
-            "isolatedFilteringPreview": "Anteprima del filtraggio isolata",
             "isolatedStagingPreview": "Anteprima di generazione isolata",
-            "isolatedTransformingPreview": "Anteprima di trasformazione isolata",
             "isolatedPreview": "Anteprima isolata",
             "invertBrushSizeScrollDirection": "Inverti scorrimento per dimensione pennello",
             "snapToGrid": {
@@ -1873,7 +1939,9 @@
             "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",
@@ -1918,9 +1986,46 @@
             "canvasGroup": "Tela",
             "newRasterLayer": "Nuovo Livello Raster",
             "saveCanvasToGallery": "Salva la Tela nella Galleria",
-            "saveToGalleryGroup": "Salva nella Galleria"
+            "saveToGalleryGroup": "Salva nella Galleria",
+            "newInpaintMask": "Nuova maschera Inpaint",
+            "newRegionalGuidance": "Nuova Guida Regionale"
         },
-        "newImg2ImgCanvasFromImage": "Nuova Immagine da immagine"
+        "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": {
@@ -2006,18 +2111,20 @@
     },
     "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>."
+        "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": {
-        "canvasV2Announcement": {
-            "readReleaseNotes": "Leggi le Note di Rilascio",
-            "fluxSupport": "Supporto per la famiglia di modelli Flux",
-            "newCanvas": "Una nuova potente tela di controllo",
-            "watchReleaseVideo": "Guarda il video di rilascio",
-            "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
-            "newLayerTypes": "Nuovi tipi di livello per un miglior controllo"
-        },
-        "whatsNewInInvoke": "Novità in Invoke"
+        "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": {

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

@@ -229,7 +229,6 @@
         "submitSupportTicket": "サポート依頼を送信する"
     },
     "metadata": {
-        "seamless": "シームレス",
         "Threshold": "ノイズ閾値",
         "seed": "シード",
         "width": "幅",

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

@@ -155,7 +155,6 @@
         "path": "Pad",
         "triggerPhrases": "Triggerzinnen",
         "typePhraseHere": "Typ zin hier in",
-        "useDefaultSettings": "Gebruik standaardinstellingen",
         "modelImageDeleteFailed": "Fout bij verwijderen modelafbeelding",
         "modelImageUpdated": "Modelafbeelding bijgewerkt",
         "modelImageUpdateFailed": "Fout bij bijwerken modelafbeelding",
@@ -666,7 +665,6 @@
         }
     },
     "metadata": {
-        "seamless": "Naadloos",
         "positivePrompt": "Positieve prompt",
         "negativePrompt": "Negatieve prompt",
         "generationMode": "Genereermodus",

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

@@ -94,7 +94,8 @@
         "reset": "Сброс",
         "none": "Ничего",
         "new": "Новый",
-        "ok": "Ok"
+        "ok": "Ok",
+        "close": "Закрыть"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -160,7 +161,9 @@
         "openViewer": "Открыть просмотрщик",
         "closeViewer": "Закрыть просмотрщик",
         "imagesTab": "Изображения, созданные и сохраненные в Invoke.",
-        "assetsTab": "Файлы, которые вы загрузили для использования в своих проектах."
+        "assetsTab": "Файлы, которые вы загрузили для использования в своих проектах.",
+        "boardsSettings": "Настройки доски",
+        "imagesSettings": "Настройки галереи изображений"
     },
     "hotkeys": {
         "searchHotkeys": "Поиск горячих клавиш",
@@ -541,7 +544,6 @@
         "scanResults": "Результаты сканирования",
         "source": "Источник",
         "triggerPhrases": "Триггерные фразы",
-        "useDefaultSettings": "Использовать стандартные настройки",
         "modelName": "Название модели",
         "modelSettings": "Настройки модели",
         "upcastAttention": "Внимание",
@@ -570,7 +572,6 @@
         "simpleModelPlaceholder": "URL или путь к локальному файлу или папке diffusers",
         "urlOrLocalPath": "URL или локальный путь",
         "urlOrLocalPathHelper": "URL-адреса должны указывать на один файл. Локальные пути могут указывать на один файл или папку для одной модели диффузоров.",
-        "hfToken": "Токен HuggingFace",
         "starterModels": "Стартовые модели",
         "textualInversions": "Текстовые инверсии",
         "loraModels": "LoRAs",
@@ -583,7 +584,18 @@
         "learnMoreAboutSupportedModels": "Подробнее о поддерживаемых моделях",
         "t5Encoder": "T5 энкодер",
         "spandrelImageToImage": "Image to Image (Spandrel)",
-        "clipEmbed": "CLIP Embed"
+        "clipEmbed": "CLIP Embed",
+        "installingXModels_one": "Установка {{count}} модели",
+        "installingXModels_few": "Установка {{count}} моделей",
+        "installingXModels_many": "Установка {{count}} моделей",
+        "installingBundle": "Установка пакета",
+        "installingModel": "Установка модели",
+        "starterBundles": "Стартовые пакеты",
+        "skippingXDuplicates_one": ", пропуская {{count}} дубликат",
+        "skippingXDuplicates_few": ", пропуская {{count}} дубликата",
+        "skippingXDuplicates_many": ", пропуская {{count}} дубликатов",
+        "includesNModels": "Включает в себя {{n}} моделей и их зависимостей",
+        "starterBundleHelpText": "Легко установите все модели, необходимые для начала работы с базовой моделью, включая основную модель, сети управления, IP-адаптеры и многое другое. При выборе комплекта все уже установленные модели будут пропущены."
     },
     "parameters": {
         "images": "Изображения",
@@ -730,7 +742,7 @@
         "serverError": "Ошибка сервера",
         "connected": "Подключено к серверу",
         "canceled": "Обработка отменена",
-        "uploadFailedInvalidUploadDesc": "Должно быть одно изображение в формате PNG или JPEG",
+        "uploadFailedInvalidUploadDesc": "Это должны быть изображения PNG или JPEG.",
         "parameterNotSet": "Параметр не задан",
         "parameterSet": "Параметр задан",
         "problemCopyingImage": "Не удается скопировать изображение",
@@ -742,7 +754,7 @@
         "setNodeField": "Установить как поле узла",
         "invalidUpload": "Неверная загрузка",
         "imageUploaded": "Изображение загружено",
-        "addedToBoard": "Добавлено на доску",
+        "addedToBoard": "Добавлено в активы доски {{name}}",
         "workflowLoaded": "Рабочий процесс загружен",
         "problemDeletingWorkflow": "Проблема с удалением рабочего процесса",
         "modelAddedSimple": "Модель добавлена в очередь",
@@ -777,7 +789,13 @@
         "unableToLoadStylePreset": "Невозможно загрузить предустановку стиля",
         "layerCopiedToClipboard": "Слой скопирован в буфер обмена",
         "sentToUpscale": "Отправить на увеличение",
-        "layerSavedToAssets": "Слой сохранен в активах"
+        "layerSavedToAssets": "Слой сохранен в активах",
+        "linkCopied": "Ссылка скопирована",
+        "addedToUncategorized": "Добавлено в активы доски $t(boards.uncategorized)",
+        "imagesWillBeAddedTo": "Загруженные изображения будут добавлены в активы доски {{boardName}}.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Должно быть не более {{count}} изображения в формате PNG или JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_few": "Должно быть не более {{count}} изображений в формате PNG или JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_many": "Должно быть не более {{count}} изображений в формате PNG или JPEG."
     },
     "accessibility": {
         "uploadImage": "Загрузить изображение",
@@ -792,7 +810,8 @@
         "about": "Об этом",
         "submitSupportTicket": "Отправить тикет в службу поддержки",
         "toggleRightPanel": "Переключить правую панель (G)",
-        "toggleLeftPanel": "Переключить левую панель (T)"
+        "toggleLeftPanel": "Переключить левую панель (T)",
+        "uploadImages": "Загрузить изображения"
     },
     "nodes": {
         "zoomInNodes": "Увеличьте масштаб",
@@ -933,7 +952,7 @@
         "saveToGallery": "Сохранить в галерею",
         "noWorkflows": "Нет рабочих процессов",
         "noMatchingWorkflows": "Нет совпадающих рабочих процессов",
-        "workflowHelpText": "Нужна помощь? Ознакомьтесь с нашим руководством <LinkComponent>Getting Started with Workflows</LinkComponent>"
+        "workflowHelpText": "Нужна помощь? Ознакомьтесь с нашим руководством <LinkComponent>Getting Started with Workflows</LinkComponent>."
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1381,7 +1400,6 @@
         }
     },
     "metadata": {
-        "seamless": "Бесшовность",
         "positivePrompt": "Запрос",
         "negativePrompt": "Негативный запрос",
         "generationMode": "Режим генерации",
@@ -1409,7 +1427,8 @@
         "recallParameter": "Отозвать {{label}}",
         "allPrompts": "Все запросы",
         "imageDimensions": "Размеры изображения",
-        "canvasV2Metadata": "Холст"
+        "canvasV2Metadata": "Холст",
+        "guidance": "Точность"
     },
     "queue": {
         "status": "Статус",
@@ -1561,7 +1580,12 @@
         "defaultWorkflows": "Стандартные рабочие процессы",
         "deleteWorkflow2": "Вы уверены, что хотите удалить этот рабочий процесс? Это нельзя отменить.",
         "chooseWorkflowFromLibrary": "Выбрать рабочий процесс из библиотеки",
-        "uploadAndSaveWorkflow": "Загрузить в библиотеку"
+        "uploadAndSaveWorkflow": "Загрузить в библиотеку",
+        "edit": "Редактировать",
+        "download": "Скачать",
+        "copyShareLink": "Скопировать ссылку на общий доступ",
+        "copyShareLinkForWorkflow": "Скопировать ссылку на общий доступ для рабочего процесса",
+        "delete": "Удалить"
     },
     "hrf": {
         "enableHrf": "Включить исправление высокого разрешения",
@@ -1809,14 +1833,12 @@
         },
         "settings": {
             "isolatedPreview": "Изолированный предпросмотр",
-            "isolatedTransformingPreview": "Изолированный предпросмотр преобразования",
             "invertBrushSizeScrollDirection": "Инвертировать прокрутку для размера кисти",
             "snapToGrid": {
                 "label": "Привязка к сетке",
                 "on": "Вкл",
                 "off": "Выкл"
             },
-            "isolatedFilteringPreview": "Изолированный предпросмотр фильтрации",
             "pressureSensitivity": "Чувствительность к давлению",
             "isolatedStagingPreview": "Изолированный предпросмотр на промежуточной стадии",
             "preserveMask": {
@@ -1838,7 +1860,6 @@
         "enableAutoNegative": "Включить авто негатив",
         "maskFill": "Заполнение маски",
         "viewProgressInViewer": "Просматривайте прогресс и результаты в <Btn>Просмотрщике изображений</Btn>.",
-        "convertToRasterLayer": "Конвертировать в растровый слой",
         "tool": {
             "move": "Двигать",
             "bbox": "Ограничительная рамка",
@@ -1890,7 +1911,10 @@
             "fitToBbox": "Вместить в рамку",
             "reset": "Сбросить",
             "apply": "Применить",
-            "cancel": "Отменить"
+            "cancel": "Отменить",
+            "fitModeContain": "Уместить",
+            "fitMode": "Режим подгонки",
+            "fitModeFill": "Заполнить"
         },
         "disableAutoNegative": "Отключить авто негатив",
         "deleteReferenceImage": "Удалить эталонное изображение",
@@ -1903,7 +1927,6 @@
         "newGallerySession": "Новая сессия галереи",
         "sendToCanvasDesc": "Нажатие кнопки Invoke отображает вашу текущую работу на холсте.",
         "globalReferenceImages_withCount_hidden": "Глобальные эталонные изображения ({{count}} скрыто)",
-        "convertToControlLayer": "Конвертировать в контрольный слой",
         "layer_withCount_one": "Слой ({{count}})",
         "layer_withCount_few": "Слои ({{count}})",
         "layer_withCount_many": "Слои ({{count}})",
@@ -1920,7 +1943,8 @@
         "globalReferenceImage": "Глобальное эталонное изображение",
         "sendToGallery": "Отправить в галерею",
         "referenceImage": "Эталонное изображение",
-        "addGlobalReferenceImage": "Добавить $t(controlLayers.globalReferenceImage)"
+        "addGlobalReferenceImage": "Добавить $t(controlLayers.globalReferenceImage)",
+        "newImg2ImgCanvasFromImage": "Новое img2img из изображения"
     },
     "ui": {
         "tabs": {
@@ -2032,14 +2056,6 @@
         }
     },
     "whatsNew": {
-        "canvasV2Announcement": {
-            "newLayerTypes": "Новые типы слоев для еще большего контроля",
-            "readReleaseNotes": "Прочитать информацию о выпуске",
-            "watchReleaseVideo": "Смотреть видео о выпуске",
-            "fluxSupport": "Поддержка семейства моделей Flux",
-            "newCanvas": "Новый мощный холст управления",
-            "watchUiUpdatesOverview": "Обзор обновлений пользовательского интерфейса"
-        },
         "whatsNewInInvoke": "Что нового в Invoke"
     },
     "newUserExperience": {

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

@@ -82,7 +82,21 @@
         "dontShowMeThese": "请勿显示这些内容",
         "beta": "测试版",
         "toResolve": "解决",
-        "tab": "标签页"
+        "tab": "标签页",
+        "apply": "应用",
+        "edit": "编辑",
+        "off": "关",
+        "loadingImage": "正在加载图片",
+        "ok": "确定",
+        "placeholderSelectAModel": "选择一个模型",
+        "close": "关闭",
+        "reset": "重设",
+        "none": "无",
+        "new": "新建",
+        "view": "视图",
+        "alpha": "透明度通道",
+        "openInViewer": "在查看器中打开",
+        "clipboard": "剪贴板"
     },
     "gallery": {
         "galleryImageSize": "预览大小",
@@ -124,7 +138,7 @@
         "selectAllOnPage": "选择本页全部",
         "swapImages": "交换图像",
         "exitBoardSearch": "退出面板搜索",
-        "exitSearch": "退出搜索",
+        "exitSearch": "退出图像搜索",
         "oldestFirst": "最旧在前",
         "sortDirection": "排序方向",
         "showStarredImagesFirst": "优先显示收藏的图片",
@@ -135,17 +149,333 @@
         "searchImages": "按元数据搜索",
         "jump": "跳过",
         "compareHelp2": "按 <Kbd>M</Kbd> 键切换不同的比较模式。",
-        "displayBoardSearch": "显示面板搜索",
-        "displaySearch": "显示搜索",
+        "displayBoardSearch": "板块搜索",
+        "displaySearch": "图像搜索",
         "stretchToFit": "拉伸以适应",
         "exitCompare": "退出对比",
         "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。",
-        "go": "运行"
+        "go": "运行",
+        "boardsSettings": "画板设置",
+        "imagesSettings": "画廊图片设置",
+        "gallery": "画廊",
+        "move": "移动",
+        "imagesTab": "您在Invoke中创建和保存的图片。",
+        "openViewer": "打开查看器",
+        "closeViewer": "关闭查看器",
+        "assetsTab": "您已上传用于项目的文件。"
     },
     "hotkeys": {
         "searchHotkeys": "检索快捷键",
         "noHotkeysFound": "未找到快捷键",
-        "clearSearch": "清除检索项"
+        "clearSearch": "清除检索项",
+        "app": {
+            "cancelQueueItem": {
+                "title": "取消",
+                "desc": "取消当前正在处理的队列项目。"
+            },
+            "selectQueueTab": {
+                "title": "选择队列标签",
+                "desc": "选择队列标签。"
+            },
+            "toggleLeftPanel": {
+                "desc": "显示或隐藏左侧面板。",
+                "title": "开关左侧面板"
+            },
+            "resetPanelLayout": {
+                "title": "重设面板布局",
+                "desc": "将左侧和右侧面板重置为默认大小和布局。"
+            },
+            "togglePanels": {
+                "title": "开关面板",
+                "desc": "同时显示或隐藏左右两侧的面板。"
+            },
+            "selectWorkflowsTab": {
+                "title": "选择工作流标签",
+                "desc": "选择工作流标签。"
+            },
+            "selectModelsTab": {
+                "title": "选择模型标签",
+                "desc": "选择模型标签。"
+            },
+            "toggleRightPanel": {
+                "title": "开关右侧面板",
+                "desc": "显示或隐藏右侧面板。"
+            },
+            "clearQueue": {
+                "title": "清除队列",
+                "desc": "取消并清除所有队列条目。"
+            },
+            "selectCanvasTab": {
+                "title": "选择画布标签",
+                "desc": "选择画布标签。"
+            },
+            "invokeFront": {
+                "desc": "将生成请求排队，添加到队列的前面。",
+                "title": "调用（前台）"
+            },
+            "selectUpscalingTab": {
+                "title": "选择放大选项卡",
+                "desc": "选择高清放大选项卡。"
+            },
+            "focusPrompt": {
+                "title": "聚焦提示",
+                "desc": "将光标焦点移动到正向提示。"
+            },
+            "title": "应用程序",
+            "invoke": {
+                "title": "调用",
+                "desc": "将生成请求排队，添加到队列的末尾。"
+            }
+        },
+        "canvas": {
+            "selectBrushTool": {
+                "title": "画笔工具",
+                "desc": "选择画笔工具。"
+            },
+            "selectEraserTool": {
+                "title": "橡皮擦工具",
+                "desc": "选择橡皮擦工具。"
+            },
+            "title": "画布",
+            "selectColorPickerTool": {
+                "title": "拾色器工具",
+                "desc": "选择拾色器工具。"
+            },
+            "fitBboxToCanvas": {
+                "title": "使边界框适应画布",
+                "desc": "缩放并调整视图以适应边界框。"
+            },
+            "setZoomTo400Percent": {
+                "title": "缩放到400%",
+                "desc": "将画布的缩放设置为400%。"
+            },
+            "setZoomTo800Percent": {
+                "desc": "将画布的缩放设置为800%。",
+                "title": "缩放到800%"
+            },
+            "redo": {
+                "desc": "重做上一次画布操作。",
+                "title": "重做"
+            },
+            "nextEntity": {
+                "title": "下一层",
+                "desc": "在列表中选择下一层。"
+            },
+            "selectRectTool": {
+                "title": "矩形工具",
+                "desc": "选择矩形工具。"
+            },
+            "selectViewTool": {
+                "title": "视图工具",
+                "desc": "选择视图工具。"
+            },
+            "prevEntity": {
+                "desc": "在列表中选择上一层。",
+                "title": "上一层"
+            },
+            "transformSelected": {
+                "desc": "变换所选图层。",
+                "title": "变换"
+            },
+            "selectBboxTool": {
+                "title": "边界框工具",
+                "desc": "选择边界框工具。"
+            },
+            "setZoomTo200Percent": {
+                "title": "缩放到200%",
+                "desc": "将画布的缩放设置为200%。"
+            },
+            "applyFilter": {
+                "title": "应用过滤器",
+                "desc": "将待处理的过滤器应用于所选图层。"
+            },
+            "filterSelected": {
+                "title": "过滤器",
+                "desc": "对所选图层进行过滤。仅适用于栅格层和控制层。"
+            },
+            "cancelFilter": {
+                "title": "取消过滤器",
+                "desc": "取消待处理的过滤器。"
+            },
+            "incrementToolWidth": {
+                "title": "增加工具宽度",
+                "desc": "增加所选的画笔或橡皮擦工具的宽度。"
+            },
+            "decrementToolWidth": {
+                "desc": "减少所选的画笔或橡皮擦工具的宽度。",
+                "title": "减少工具宽度"
+            },
+            "selectMoveTool": {
+                "title": "移动工具",
+                "desc": "选择移动工具。"
+            },
+            "setFillToWhite": {
+                "title": "将颜色设置为白色",
+                "desc": "将当前工具的颜色设置为白色。"
+            },
+            "cancelTransform": {
+                "desc": "取消待处理的变换。",
+                "title": "取消变换"
+            },
+            "applyTransform": {
+                "title": "应用变换",
+                "desc": "将待处理的变换应用于所选图层。"
+            },
+            "setZoomTo100Percent": {
+                "title": "缩放到100%",
+                "desc": "将画布的缩放设置为100%。"
+            },
+            "resetSelected": {
+                "title": "重置图层",
+                "desc": "重置选定的图层。仅适用于修复蒙版和区域指导。"
+            },
+            "undo": {
+                "title": "撤消",
+                "desc": "撤消上一次画布操作。"
+            },
+            "quickSwitch": {
+                "title": "图层快速切换",
+                "desc": "在最后两个选定的图层之间切换。如果某个图层被书签标记，则始终在该图层和最后一个未标记的图层之间切换。"
+            },
+            "fitLayersToCanvas": {
+                "title": "使图层适应画布",
+                "desc": "缩放并调整视图以适应所有可见图层。"
+            },
+            "deleteSelected": {
+                "title": "删除图层",
+                "desc": "删除选定的图层。"
+            }
+        },
+        "hotkeys": "快捷键",
+        "workflows": {
+            "pasteSelection": {
+                "title": "粘贴",
+                "desc": "粘贴复制的节点和边。"
+            },
+            "title": "工作流",
+            "addNode": {
+                "title": "添加节点",
+                "desc": "打开添加节点菜单。"
+            },
+            "copySelection": {
+                "desc": "复制选定的节点和边。",
+                "title": "复制"
+            },
+            "pasteSelectionWithEdges": {
+                "title": "带边缘的粘贴",
+                "desc": "粘贴复制的节点、边，以及与复制的节点连接的所有边。"
+            },
+            "selectAll": {
+                "title": "全选",
+                "desc": "选择所有节点和边。"
+            },
+            "deleteSelection": {
+                "title": "删除",
+                "desc": "删除选定的节点和边。"
+            },
+            "undo": {
+                "title": "撤销",
+                "desc": "撤销上一个工作流操作。"
+            },
+            "redo": {
+                "desc": "重做上一个工作流操作。",
+                "title": "重做"
+            }
+        },
+        "gallery": {
+            "title": "画廊",
+            "galleryNavUp": {
+                "title": "向上导航",
+                "desc": "在图库网格中向上导航，选择该图像。如果在页面顶部，则转到上一页。"
+            },
+            "galleryNavUpAlt": {
+                "title": "向上导航（比较图像）",
+                "desc": "与向上导航相同，但选择比较图像，如果比较模式尚未打开，则将其打开。"
+            },
+            "selectAllOnPage": {
+                "desc": "选择当前页面上的所有图像。",
+                "title": "选页面上的所有内容"
+            },
+            "galleryNavDownAlt": {
+                "title": "向下导航（比较图像）",
+                "desc": "与向下导航相同，但选择比较图像，如果比较模式尚未打开，则将其打开。"
+            },
+            "galleryNavLeftAlt": {
+                "title": "向左导航（比较图像）",
+                "desc": "与向左导航相同，但选择比较图像，如果比较模式尚未打开，则将其打开。"
+            },
+            "clearSelection": {
+                "title": "清除选择",
+                "desc": "清除当前的选择（如果有的话）。"
+            },
+            "deleteSelection": {
+                "title": "删除",
+                "desc": "删除所有选定的图像。默认情况下，系统会提示您确认删除。如果这些图像当前在应用中使用，系统将发出警告。"
+            },
+            "galleryNavLeft": {
+                "title": "向左导航",
+                "desc": "在图库网格中向左导航，选择该图像。如果处于行的第一张图像，转到上一行。如果处于页面的第一张图像，转到上一页。"
+            },
+            "galleryNavRight": {
+                "title": "向右导航",
+                "desc": "在图库网格中向右导航，选择该图像。如果在行的最后一张图像，转到下一行。如果在页面的最后一张图像，转到下一页。"
+            },
+            "galleryNavDown": {
+                "desc": "在图库网格中向下导航，选择该图像。如果在页面底部，则转到下一页。",
+                "title": "向下导航"
+            },
+            "galleryNavRightAlt": {
+                "title": "向右导航（比较图像）",
+                "desc": "与向右导航相同，但选择比较图像，如果比较模式尚未打开，则将其打开。"
+            }
+        },
+        "viewer": {
+            "toggleMetadata": {
+                "desc": "显示或隐藏当前图像的元数据覆盖。",
+                "title": "显示/隐藏元数据"
+            },
+            "recallPrompts": {
+                "desc": "召回当前图像的正面和负面提示。",
+                "title": "召回提示"
+            },
+            "toggleViewer": {
+                "title": "显示/隐藏图像查看器",
+                "desc": "显示或隐藏图像查看器。仅在画布选项卡上可用。"
+            },
+            "recallAll": {
+                "desc": "召回当前图像的所有元数据。",
+                "title": "召回所有元数据"
+            },
+            "recallSeed": {
+                "title": "召回种子",
+                "desc": "召回当前图像的种子。"
+            },
+            "swapImages": {
+                "title": "交换比较图像",
+                "desc": "交换正在比较的图像。"
+            },
+            "nextComparisonMode": {
+                "title": "下一个比较模式",
+                "desc": "环浏览比较模式。"
+            },
+            "loadWorkflow": {
+                "title": "加载工作流",
+                "desc": "加载当前图像的保存工作流程（如果有的话）。"
+            },
+            "title": "图像查看器",
+            "remix": {
+                "title": "混合",
+                "desc": "召回当前图像的所有元数据，除了种子。"
+            },
+            "useSize": {
+                "title": "使用尺寸",
+                "desc": "使用当前图像的尺寸作为边界框尺寸。"
+            },
+            "runPostprocessing": {
+                "title": "行后处理",
+                "desc": "对当前图像运行所选的后处理。"
+            }
+        }
     },
     "modelManager": {
         "modelManager": "模型管理器",
@@ -210,7 +540,6 @@
         "noModelsInstalled": "无已安装的模型",
         "urlOrLocalPathHelper": "链接应该指向单个文件.本地路径可以指向单个文件,或者对于单个扩散模型(diffusers model),可以指向一个文件夹.",
         "modelSettings": "模型设置",
-        "useDefaultSettings": "使用默认设置",
         "scanPlaceholder": "本地文件夹路径",
         "installRepo": "安装仓库",
         "modelImageDeleted": "模型图像已删除",
@@ -249,7 +578,16 @@
         "loraTriggerPhrases": "LoRA 触发词",
         "ipAdapters": "IP适配器",
         "spandrelImageToImage": "图生图(Spandrel)",
-        "starterModelsInModelManager": "您可以在模型管理器中找到初始模型"
+        "starterModelsInModelManager": "您可以在模型管理器中找到初始模型",
+        "noDefaultSettings": "此模型没有配置默认设置。请访问模型管理器添加默认设置。",
+        "clipEmbed": "CLIP 嵌入",
+        "defaultSettingsOutOfSync": "某些设置与模型的默认值不匹配：",
+        "restoreDefaultSettings": "点击以使用模型的默认设置。",
+        "usingDefaultSettings": "使用模型的默认设置",
+        "huggingFace": "HuggingFace",
+        "hfTokenInvalid": "HF 令牌无效或缺失",
+        "hfTokenLabel": "HuggingFace 令牌（某些模型所需）",
+        "hfTokenHelperText": "使用某些模型需要 HF 令牌。点击这里创建或获取你的令牌。"
     },
     "parameters": {
         "images": "图像",
@@ -367,7 +705,7 @@
         "uploadFailed": "上传失败",
         "imageCopied": "图像已复制",
         "parametersNotSet": "参数未恢复",
-        "uploadFailedInvalidUploadDesc": "必须是单张的 PNG 或 JPEG 图片",
+        "uploadFailedInvalidUploadDesc": "必须是单个 PNG 或 JPEG 图像。",
         "connected": "服务器连接",
         "parameterSet": "参数已恢复",
         "parameterNotSet": "参数未恢复",
@@ -379,7 +717,7 @@
         "setControlImage": "设为控制图像",
         "setNodeField": "设为节点字段",
         "imageUploaded": "图像已上传",
-        "addedToBoard": "已添加到面板",
+        "addedToBoard": "添加到{{name}}的资产中",
         "workflowLoaded": "工作流已加载",
         "imageUploadFailed": "图像上传失败",
         "baseModelChangedCleared_other": "已清除或禁用{{count}}个不兼容的子模型",
@@ -416,7 +754,9 @@
         "createIssue": "创建问题",
         "about": "关于",
         "submitSupportTicket": "提交支持工单",
-        "toggleRightPanel": "切换右侧面板(G)"
+        "toggleRightPanel": "切换右侧面板(G)",
+        "uploadImages": "上传图片",
+        "toggleLeftPanel": "开关左侧面板(T)"
     },
     "nodes": {
         "zoomInNodes": "放大",
@@ -569,7 +909,7 @@
         "cancelSucceeded": "项目已取消",
         "queue": "队列",
         "batch": "批处理",
-        "clearQueueAlertDialog": "清除队列时会立即取消所有处理中的项目并且会完全清除队列。",
+        "clearQueueAlertDialog": "清空队列将立即取消所有正在处理的项目，并完全清空队列。待处理的过滤器将被取消。",
         "pending": "待定",
         "completedIn": "完成于",
         "resumeFailed": "恢复处理器时出现问题",
@@ -610,7 +950,15 @@
         "openQueue": "打开队列",
         "prompts_other": "提示词",
         "iterations_other": "迭代",
-        "generations_other": "生成"
+        "generations_other": "生成",
+        "canvas": "画布",
+        "workflows": "工作流",
+        "generation": "生成",
+        "other": "其他",
+        "gallery": "画廊",
+        "destination": "目标存储",
+        "upscaling": "高清放大",
+        "origin": "来源"
     },
     "sdxl": {
         "refinerStart": "Refiner 开始作用时机",
@@ -649,7 +997,6 @@
         "workflow": "工作流",
         "steps": "步数",
         "scheduler": "调度器",
-        "seamless": "无缝",
         "recallParameters": "召回参数",
         "noRecallParameters": "未找到要召回的参数",
         "vae": "VAE",
@@ -658,7 +1005,11 @@
         "parsingFailed": "解析失败",
         "recallParameter": "调用{{label}}",
         "imageDimensions": "图像尺寸",
-        "parameterSet": "已设置参数{{parameter}}"
+        "parameterSet": "已设置参数{{parameter}}",
+        "guidance": "指导",
+        "seamlessXAxis": "无缝 X 轴",
+        "seamlessYAxis": "无缝 Y 轴",
+        "canvasV2Metadata": "画布"
     },
     "models": {
         "noMatchingModels": "无相匹配的模型",
@@ -709,7 +1060,8 @@
         "shared": "共享面板",
         "archiveBoard": "归档面板",
         "archived": "已归档",
-        "assetsWithCount_other": "{{count}}项资源"
+        "assetsWithCount_other": "{{count}}项资源",
+        "updateBoardError": "更新画板出错"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1175,7 +1527,8 @@
     },
     "prompt": {
         "addPromptTrigger": "添加提示词触发器",
-        "noMatchingTriggers": "没有匹配的触发器"
+        "noMatchingTriggers": "没有匹配的触发器",
+        "compatibleEmbeddings": "兼容的嵌入"
     },
     "controlLayers": {
         "autoNegative": "自动反向",
@@ -1186,8 +1539,8 @@
         "moveToFront": "移动到前面",
         "addLayer": "添加层",
         "deletePrompt": "删除提示词",
-        "addPositivePrompt": "添加 $t(common.positivePrompt)",
-        "addNegativePrompt": "添加 $t(common.negativePrompt)",
+        "addPositivePrompt": "添加 $t(controlLayers.prompt)",
+        "addNegativePrompt": "添加 $t(controlLayers.negativePrompt)",
         "rectangle": "矩形",
         "opacity": "透明度"
     },

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

@@ -58,7 +58,6 @@
         "model": "模型",
         "seed": "種子",
         "vae": "VAE",
-        "seamless": "無縫",
         "metadata": "元數據",
         "width": "寬度",
         "height": "高度"

invokeai/frontend/web/src/app/components/App.tsx

@@ -4,6 +4,7 @@ import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useStudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useSyncQueueStatus } from 'app/hooks/useSyncQueueStatus';
 import { useLogger } from 'app/logging/useLogger';
+import { useSyncLoggingConfig } from 'app/logging/useSyncLoggingConfig';
 import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
@@ -59,6 +60,7 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
   useGlobalModifiersInit();
   useGlobalHotkeys();
   useGetOpenAPISchemaQuery();
+  useSyncLoggingConfig();
 
   const { dropzone, isHandlingUpload, setIsHandlingUpload } = useFullscreenDropzone();
 

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -2,6 +2,8 @@ import 'i18n';
 
 import type { Middleware } from '@reduxjs/toolkit';
 import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
+import type { LoggingOverrides } from 'app/logging/logger';
+import { $loggingOverrides, configureLogging } from 'app/logging/logger';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
 import { $customNavComponent } from 'app/store/nanostores/customNavComponent';
@@ -20,7 +22,7 @@ import Loading from 'common/components/Loading/Loading';
 import AppDndContext from 'features/dnd/components/AppDndContext';
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
 import type { PropsWithChildren, ReactNode } from 'react';
-import React, { lazy, memo, useEffect, useMemo } from 'react';
+import React, { lazy, memo, useEffect, useLayoutEffect, useMemo } from 'react';
 import { Provider } from 'react-redux';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
 import { $socketOptions } from 'services/events/stores';
@@ -46,6 +48,7 @@ interface Props extends PropsWithChildren {
   isDebugging?: boolean;
   logo?: ReactNode;
   workflowCategories?: WorkflowCategory[];
+  loggingOverrides?: LoggingOverrides;
 }
 
 const InvokeAIUI = ({
@@ -65,7 +68,26 @@ const InvokeAIUI = ({
   isDebugging = false,
   logo,
   workflowCategories,
+  loggingOverrides,
 }: Props) => {
+  useLayoutEffect(() => {
+    /*
+     * We need to configure logging before anything else happens - useLayoutEffect ensures we set this at the first
+     * possible opportunity.
+     *
+     * Once redux initializes, we will check the user's settings and update the logging config accordingly. See
+     * `useSyncLoggingConfig`.
+     */
+    $loggingOverrides.set(loggingOverrides);
+
+    // Until we get the user's settings, we will use the overrides OR default values.
+    configureLogging(
+      loggingOverrides?.logIsEnabled ?? true,
+      loggingOverrides?.logLevel ?? 'debug',
+      loggingOverrides?.logNamespaces ?? '*'
+    );
+  }, [loggingOverrides]);
+
   useEffect(() => {
     // configure API client token
     if (token) {

invokeai/frontend/web/src/app/logging/logger.ts

@@ -9,11 +9,10 @@ const serializeMessage: MessageSerializer = (message) => {
 };
 
 ROARR.serializeMessage = serializeMessage;
-ROARR.write = createLogWriter();
 
-export const BASE_CONTEXT = {};
+const BASE_CONTEXT = {};
 
-export const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
+const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
 
 export const zLogNamespace = z.enum([
   'canvas',
@@ -35,8 +34,22 @@ export const zLogLevel = z.enum(['trace', 'debug', 'info', 'warn', 'error', 'fat
 export type LogLevel = z.infer<typeof zLogLevel>;
 export const isLogLevel = (v: unknown): v is LogLevel => zLogLevel.safeParse(v).success;
 
+/**
+ * Override logging settings.
+ * @property logIsEnabled Override the enabled log state. Omit to use the user's settings.
+ * @property logNamespaces Override the enabled log namespaces. Use `"*"` for all namespaces. Omit to use the user's settings.
+ * @property logLevel Override the log level. Omit to use the user's settings.
+ */
+export type LoggingOverrides = {
+  logIsEnabled?: boolean;
+  logNamespaces?: LogNamespace[] | '*';
+  logLevel?: LogLevel;
+};
+
+export const $loggingOverrides = atom<LoggingOverrides | undefined>();
+
 // Translate human-readable log levels to numbers, used for log filtering
-export const LOG_LEVEL_MAP: Record<LogLevel, number> = {
+const LOG_LEVEL_MAP: Record<LogLevel, number> = {
   trace: 10,
   debug: 20,
   info: 30,
@@ -44,3 +57,40 @@ export const LOG_LEVEL_MAP: Record<LogLevel, number> = {
   error: 50,
   fatal: 60,
 };
+
+/**
+ * Configure logging, pushing settings to local storage.
+ *
+ * @param logIsEnabled Whether logging is enabled
+ * @param logLevel The log level
+ * @param logNamespaces A list of log namespaces to enable, or '*' to enable all
+ */
+export const configureLogging = (
+  logIsEnabled: boolean = true,
+  logLevel: LogLevel = 'warn',
+  logNamespaces: LogNamespace[] | '*'
+): void => {
+  if (!logIsEnabled) {
+    // Disable console log output
+    localStorage.setItem('ROARR_LOG', 'false');
+  } else {
+    // Enable console log output
+    localStorage.setItem('ROARR_LOG', 'true');
+
+    // Use a filter to show only logs of the given level
+    let filter = `context.logLevel:>=${LOG_LEVEL_MAP[logLevel]}`;
+
+    const namespaces = logNamespaces === '*' ? zLogNamespace.options : logNamespaces;
+
+    if (namespaces.length > 0) {
+      filter += ` AND (${namespaces.map((ns) => `context.namespace:${ns}`).join(' OR ')})`;
+    } else {
+      // This effectively hides all logs because we use namespaces for all logs
+      filter += ' AND context.namespace:undefined';
+    }
+
+    localStorage.setItem('ROARR_FILTER', filter);
+  }
+
+  ROARR.write = createLogWriter();
+};

invokeai/frontend/web/src/app/logging/useLogger.ts

@@ -1,53 +1,9 @@
-import { createLogWriter } from '@roarr/browser-log-writer';
-import { useAppSelector } from 'app/store/storeHooks';
-import {
-  selectSystemLogIsEnabled,
-  selectSystemLogLevel,
-  selectSystemLogNamespaces,
-} from 'features/system/store/systemSlice';
-import { useEffect, useMemo } from 'react';
-import { ROARR, Roarr } from 'roarr';
+import { useMemo } from 'react';
 
 import type { LogNamespace } from './logger';
-import { $logger, BASE_CONTEXT, LOG_LEVEL_MAP, logger } from './logger';
+import { logger } from './logger';
 
 export const useLogger = (namespace: LogNamespace) => {
-  const logLevel = useAppSelector(selectSystemLogLevel);
-  const logNamespaces = useAppSelector(selectSystemLogNamespaces);
-  const logIsEnabled = useAppSelector(selectSystemLogIsEnabled);
-
-  // The provided Roarr browser log writer uses localStorage to config logging to console
-  useEffect(() => {
-    if (logIsEnabled) {
-      // Enable console log output
-      localStorage.setItem('ROARR_LOG', 'true');
-
-      // Use a filter to show only logs of the given level
-      let filter = `context.logLevel:>=${LOG_LEVEL_MAP[logLevel]}`;
-      if (logNamespaces.length > 0) {
-        filter += ` AND (${logNamespaces.map((ns) => `context.namespace:${ns}`).join(' OR ')})`;
-      } else {
-        filter += ' AND context.namespace:undefined';
-      }
-      localStorage.setItem('ROARR_FILTER', filter);
-    } else {
-      // Disable console log output
-      localStorage.setItem('ROARR_LOG', 'false');
-    }
-    ROARR.write = createLogWriter();
-  }, [logLevel, logIsEnabled, logNamespaces]);
-
-  // Update the module-scoped logger context as needed
-  useEffect(() => {
-    // TODO: type this properly
-    //eslint-disable-next-line @typescript-eslint/no-explicit-any
-    const newContext: Record<string, any> = {
-      ...BASE_CONTEXT,
-    };
-
-    $logger.set(Roarr.child(newContext));
-  }, []);
-
   const log = useMemo(() => logger(namespace), [namespace]);
 
   return log;

invokeai/frontend/web/src/app/logging/useSyncLoggingConfig.ts

@@ -0,0 +1,43 @@
+import { useStore } from '@nanostores/react';
+import { $loggingOverrides, configureLogging } from 'app/logging/logger';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
+import {
+  selectSystemLogIsEnabled,
+  selectSystemLogLevel,
+  selectSystemLogNamespaces,
+} from 'features/system/store/systemSlice';
+import { useLayoutEffect } from 'react';
+
+/**
+ * This hook synchronizes the logging configuration stored in Redux with the logging system, which uses localstorage.
+ *
+ * The sync is one-way: from Redux to localstorage. This means that changes made in the UI will be reflected in the
+ * logging system, but changes made directly to localstorage will not be reflected in the UI.
+ *
+ * See {@link configureLogging}
+ */
+export const useSyncLoggingConfig = () => {
+  useAssertSingleton('useSyncLoggingConfig');
+
+  const loggingOverrides = useStore($loggingOverrides);
+
+  const logLevel = useAppSelector(selectSystemLogLevel);
+  const logNamespaces = useAppSelector(selectSystemLogNamespaces);
+  const logIsEnabled = useAppSelector(selectSystemLogIsEnabled);
+
+  useLayoutEffect(() => {
+    configureLogging(
+      loggingOverrides?.logIsEnabled ?? logIsEnabled,
+      loggingOverrides?.logLevel ?? logLevel,
+      loggingOverrides?.logNamespaces ?? logNamespaces
+    );
+  }, [
+    logIsEnabled,
+    logLevel,
+    logNamespaces,
+    loggingOverrides?.logIsEnabled,
+    loggingOverrides?.logLevel,
+    loggingOverrides?.logNamespaces,
+  ]);
+};

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

@@ -2,12 +2,13 @@ import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { deepClone } from 'common/util/deepClone';
-import { selectDefaultControlAdapter, selectDefaultIPAdapter } from 'features/controlLayers/hooks/addLayerHooks';
+import { selectDefaultIPAdapter } from 'features/controlLayers/hooks/addLayerHooks';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
 import {
   controlLayerAdded,
   entityRasterized,
   entitySelected,
+  inpaintMaskAdded,
   rasterLayerAdded,
   referenceImageAdded,
   referenceImageIPAdapterImageChanged,
@@ -17,11 +18,12 @@ import {
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type {
   CanvasControlLayerState,
+  CanvasInpaintMaskState,
   CanvasRasterLayerState,
   CanvasReferenceImageState,
   CanvasRegionalGuidanceState,
 } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject, imageDTOToImageWithDims } from 'features/controlLayers/store/util';
+import { imageDTOToImageObject, imageDTOToImageWithDims, initialControlNet } from 'features/controlLayers/store/util';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { isValidDrop } from 'features/dnd/util/isValidDrop';
 import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
@@ -110,6 +112,46 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
+      /**
+
+      /**
+       * Image dropped on Inpaint Mask
+       */
+      if (
+        overData.actionType === 'ADD_INPAINT_MASK_FROM_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
+        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
+        const overrides: Partial<CanvasInpaintMaskState> = {
+          objects: [imageObject],
+          position: { x, y },
+        };
+        dispatch(inpaintMaskAdded({ overrides, isSelected: true }));
+        return;
+      }
+
+      /**
+
+      /**
+       * Image dropped on Regional Guidance
+       */
+      if (
+        overData.actionType === 'ADD_REGIONAL_GUIDANCE_FROM_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
+        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
+        const overrides: Partial<CanvasRegionalGuidanceState> = {
+          objects: [imageObject],
+          position: { x, y },
+        };
+        dispatch(rgAdded({ overrides, isSelected: true }));
+        return;
+      }
+
       /**
        * Image dropped on Raster layer
        */
@@ -121,11 +163,10 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         const state = getState();
         const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
         const { x, y } = selectCanvasSlice(state).bbox.rect;
-        const defaultControlAdapter = selectDefaultControlAdapter(state);
         const overrides: Partial<CanvasControlLayerState> = {
           objects: [imageObject],
           position: { x, y },
-          controlAdapter: defaultControlAdapter,
+          controlAdapter: deepClone(initialControlNet),
         };
         dispatch(controlLayerAdded({ overrides, isSelected: true }));
         return;

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

@@ -164,7 +164,7 @@ const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
   // We have a VAE selected, need to check if it is available
 
   // Grab just the VAE models
-  const vaeModels = models.filter(isNonFluxVAEModelConfig);
+  const vaeModels = models.filter((m) => isNonFluxVAEModelConfig(m));
 
   // If the current VAE model is available, we don't need to do anything
   if (vaeModels.some((m) => m.key === selectedVAEModel.key)) {
@@ -297,7 +297,7 @@ const handleUpscaleModel: ModelHandler = (models, state, dispatch, log) => {
 
 const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedT5EncoderModel = state.params.t5EncoderModel;
-  const t5EncoderModels = models.filter(isT5EncoderModelConfig);
+  const t5EncoderModels = models.filter((m) => isT5EncoderModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedT5EncoderModel && t5EncoderModels.some((m) => m.key === selectedT5EncoderModel.key)) {
@@ -325,7 +325,7 @@ const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
 
 const handleCLIPEmbedModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedCLIPEmbedModel = state.params.clipEmbedModel;
-  const CLIPEmbedModels = models.filter(isCLIPEmbedModelConfig);
+  const CLIPEmbedModels = models.filter((m) => isCLIPEmbedModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedCLIPEmbedModel && CLIPEmbedModels.some((m) => m.key === selectedCLIPEmbedModel.key)) {
@@ -353,7 +353,7 @@ const handleCLIPEmbedModels: ModelHandler = (models, state, dispatch, log) => {
 
 const handleFLUXVAEModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedFLUXVAEModel = state.params.fluxVAE;
-  const fluxVAEModels = models.filter(isFluxVAEModelConfig);
+  const fluxVAEModels = models.filter((m) => isFluxVAEModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedFLUXVAEModel && fluxVAEModels.some((m) => m.key === selectedFLUXVAEModel.key)) {

invokeai/frontend/web/src/app/store/nanostores/util.ts

@@ -4,6 +4,8 @@ import { atom } from 'nanostores';
 /**
  * A fallback non-writable atom that always returns `false`, used when a nanostores atom is only conditionally available
  * in a hook or component.
+ *
+ * @knipignore
  */
 export const $false: ReadableAtom<boolean> = atom(false);
 /**

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -1,7 +1,7 @@
 import type { FilterType } from 'features/controlLayers/store/filters';
 import type { ParameterPrecision, ParameterScheduler } from 'features/parameters/types/parameterSchemas';
 import type { TabName } from 'features/ui/store/uiTypes';
-import type { O } from 'ts-toolbelt';
+import type { PartialDeep } from 'type-fest';
 
 /**
  * A disable-able application feature
@@ -119,4 +119,4 @@ export type AppConfig = {
   };
 };
 
-export type PartialAppConfig = O.Partial<AppConfig, 'deep'>;
+export type PartialAppConfig = PartialDeep<AppConfig>;

invokeai/frontend/web/src/common/components/IconMenuItem.tsx

@@ -26,5 +26,9 @@ export const IconMenuItem = ({ tooltip, icon, ...props }: Props) => {
 };
 
 export const IconMenuItemGroup = ({ children }: { children: ReactNode }) => {
-  return <Flex gap={2}>{children}</Flex>;
+  return (
+    <Flex gap={2} justifyContent="space-between">
+      {children}
+    </Flex>
+  );
 };

invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts

@@ -1,5 +1,6 @@
 import type { PopoverProps } from '@invoke-ai/ui-library';
 import commercialLicenseBg from 'public/assets/images/commercial-license-bg.png';
+import denoisingStrength from 'public/assets/images/denoising-strength.png';
 
 export type Feature =
   | 'clipSkip'
@@ -23,8 +24,10 @@ export type Feature =
   | 'dynamicPrompts'
   | 'dynamicPromptsMaxPrompts'
   | 'dynamicPromptsSeedBehaviour'
+  | 'globalReferenceImage'
   | 'imageFit'
   | 'infillMethod'
+  | 'inpainting'
   | 'ipAdapterMethod'
   | 'lora'
   | 'loraWeight'
@@ -46,6 +49,7 @@ export type Feature =
   | 'paramVAEPrecision'
   | 'paramWidth'
   | 'patchmatchDownScaleSize'
+  | 'rasterLayer'
   | 'refinerModel'
   | 'refinerNegativeAestheticScore'
   | 'refinerPositiveAestheticScore'
@@ -53,6 +57,9 @@ export type Feature =
   | 'refinerStart'
   | 'refinerSteps'
   | 'refinerCfgScale'
+  | 'regionalGuidance'
+  | 'regionalGuidanceAndReferenceImage'
+  | 'regionalReferenceImage'
   | 'scaleBeforeProcessing'
   | 'seamlessTilingXAxis'
   | 'seamlessTilingYAxis'
@@ -76,6 +83,24 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   clipSkip: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
   },
+  inpainting: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000096702-inpainting-outpainting-and-bounding-box',
+  },
+  rasterLayer: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000094998-raster-layers-and-initial-images',
+  },
+  regionalGuidance: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000165024-regional-guidance-layers',
+  },
+  regionalGuidanceAndReferenceImage: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000165024-regional-guidance-layers',
+  },
+  globalReferenceImage: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000159340-global-and-regional-reference-images-ip-adapters-',
+  },
+  regionalReferenceImage: {
+    href: 'https://support.invoke.ai/support/solutions/articles/151000159340-global-and-regional-reference-images-ip-adapters-',
+  },
   controlNet: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000105880',
   },
@@ -101,7 +126,7 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
     href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
   },
   infillMethod: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158841-infill-and-scaling',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
   },
   scaleBeforeProcessing: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000158841',
@@ -114,6 +139,7 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   },
   paramDenoisingStrength: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000094998-image-to-image',
+    image: denoisingStrength,
   },
   paramHrf: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000096700-how-can-i-get-larger-images-what-does-upscaling-do-',

invokeai/frontend/web/src/common/components/WavyLine.tsx

@@ -0,0 +1,57 @@
+type Props = {
+  /**
+   * The amplitude of the wave. 0 is a straight line, higher values create more pronounced waves.
+   */
+  amplitude: number;
+  /**
+   * The number of segments in the line. More segments create a smoother wave.
+   */
+  segments?: number;
+  /**
+   * The color of the wave.
+   */
+  stroke: string;
+  /**
+   * The width of the wave.
+   */
+  strokeWidth: number;
+  /**
+   * The width of the SVG.
+   */
+  width: number;
+  /**
+   * The height of the SVG.
+   */
+  height: number;
+};
+
+const WavyLine = ({ amplitude, stroke, strokeWidth, width, height, segments = 5 }: Props) => {
+  // Calculate the path dynamically based on waviness
+  const generatePath = () => {
+    if (amplitude === 0) {
+      // If waviness is 0, return a straight line
+      return `M0,${height / 2} L${width},${height / 2}`;
+    }
+
+    const clampedAmplitude = Math.min(height / 2, amplitude); // Cap amplitude to half the height
+    const segmentWidth = width / segments;
+    let path = `M0,${height / 2}`; // Start in the middle of the left edge
+
+    // Loop through each segment and alternate the y position to create waves
+    for (let i = 1; i <= segments; i++) {
+      const x = i * segmentWidth;
+      const y = height / 2 + (i % 2 === 0 ? clampedAmplitude : -clampedAmplitude);
+      path += ` Q${x - segmentWidth / 2},${y} ${x},${height / 2}`;
+    }
+
+    return path;
+  };
+
+  return (
+    <svg width={width} height={height} viewBox={`0 0 ${width} ${height}`} xmlns="http://www.w3.org/2000/svg">
+      <path d={generatePath()} fill="none" stroke={stroke} strokeWidth={strokeWidth} />
+    </svg>
+  );
+};
+
+export default WavyLine;

invokeai/frontend/web/src/common/hooks/useBoolean.ts

@@ -127,8 +127,6 @@ export const buildUseDisclosure = (defaultIsOpen: boolean): [() => UseDisclosure
  *
  * Hook to manage a boolean state. Use this for a local boolean state.
  * @param defaultIsOpen Initial state of the disclosure
- *
- * @knipignore
  */
 export const useDisclosure = (defaultIsOpen: boolean): UseDisclosure => {
   const [isOpen, set] = useState(defaultIsOpen);

invokeai/frontend/web/src/common/hooks/useGroupedModelCombobox.ts

@@ -4,6 +4,7 @@ import { useAppSelector } from 'app/store/storeHooks';
 import type { GroupBase } from 'chakra-react-select';
 import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
+import { selectSystemShouldEnableModelDescriptions } from 'features/system/store/systemSlice';
 import { groupBy, reduce } from 'lodash-es';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -37,6 +38,7 @@ export const useGroupedModelCombobox = <T extends AnyModelConfig>(
 ): UseGroupedModelComboboxReturn => {
   const { t } = useTranslation();
   const base = useAppSelector(selectBaseWithSDXLFallback);
+  const shouldShowModelDescriptions = useAppSelector(selectSystemShouldEnableModelDescriptions);
   const { modelConfigs, selectedModel, getIsDisabled, onChange, isLoading, groupByType = false } = arg;
   const options = useMemo<GroupBase<ComboboxOption>[]>(() => {
     if (!modelConfigs) {
@@ -51,6 +53,7 @@ export const useGroupedModelCombobox = <T extends AnyModelConfig>(
           options: val.map((model) => ({
             label: model.name,
             value: model.key,
+            description: (shouldShowModelDescriptions && model.description) || undefined,
             isDisabled: getIsDisabled ? getIsDisabled(model) : false,
           })),
         });
@@ -60,7 +63,7 @@ export const useGroupedModelCombobox = <T extends AnyModelConfig>(
     );
     _options.sort((a) => (a.label?.split('/')[0]?.toLowerCase().includes(base) ? -1 : 1));
     return _options;
-  }, [modelConfigs, groupByType, getIsDisabled, base]);
+  }, [modelConfigs, groupByType, getIsDisabled, base, shouldShowModelDescriptions]);
 
   const value = useMemo(
     () =>

invokeai/frontend/web/src/common/hooks/useIsReadyToEnqueue.ts

@@ -202,46 +202,6 @@ const createSelector = (
             if (controlLayer.controlAdapter.model?.base !== model?.base) {
               problems.push(i18n.t('parameters.invoke.layer.controlAdapterIncompatibleBaseModel'));
             }
-            // T2I Adapters require images have dimensions that are multiples of 64 (SD1.5) or 32 (SDXL)
-            if (controlLayer.controlAdapter.type === 't2i_adapter') {
-              const multiple = model?.base === 'sdxl' ? 32 : 64;
-              if (bbox.scaleMethod === 'none') {
-                if (bbox.rect.width % 16 !== 0) {
-                  reasons.push({
-                    content: i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleBboxWidth', {
-                      multiple,
-                      width: bbox.rect.width,
-                    }),
-                  });
-                }
-                if (bbox.rect.height % 16 !== 0) {
-                  reasons.push({
-                    content: i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleBboxHeight', {
-                      multiple,
-                      height: bbox.rect.height,
-                    }),
-                  });
-                }
-              } else {
-                if (bbox.scaledSize.width % 16 !== 0) {
-                  reasons.push({
-                    content: i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleScaledBboxWidth', {
-                      multiple,
-                      width: bbox.scaledSize.width,
-                    }),
-                  });
-                }
-                if (bbox.scaledSize.height % 16 !== 0) {
-                  reasons.push({
-                    content: i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleScaledBboxHeight', {
-                      multiple,
-                      height: bbox.scaledSize.height,
-                    }),
-                  });
-                }
-              }
-            }
-
             if (problems.length) {
               const content = upperFirst(problems.join(', '));
               reasons.push({ prefix, content });

invokeai/frontend/web/src/common/hooks/useModelCombobox.ts

@@ -1,5 +1,7 @@
 import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
+import { selectSystemShouldEnableModelDescriptions } from 'features/system/store/systemSlice';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import type { AnyModelConfig } from 'services/api/types';
@@ -24,13 +26,16 @@ type UseModelComboboxReturn = {
 export const useModelCombobox = <T extends AnyModelConfig>(arg: UseModelComboboxArg<T>): UseModelComboboxReturn => {
   const { t } = useTranslation();
   const { modelConfigs, selectedModel, getIsDisabled, onChange, isLoading, optionsFilter = () => true } = arg;
+  const shouldShowModelDescriptions = useAppSelector(selectSystemShouldEnableModelDescriptions);
+
   const options = useMemo<ComboboxOption[]>(() => {
     return modelConfigs.filter(optionsFilter).map((model) => ({
       label: model.name,
       value: model.key,
+      description: (shouldShowModelDescriptions && model.description) || undefined,
       isDisabled: getIsDisabled ? getIsDisabled(model) : false,
     }));
-  }, [optionsFilter, getIsDisabled, modelConfigs]);
+  }, [optionsFilter, getIsDisabled, modelConfigs, shouldShowModelDescriptions]);
 
   const value = useMemo(
     () => options.find((m) => (selectedModel ? m.value === selectedModel.key : false)),

invokeai/frontend/web/src/common/hooks/useSubMenu.tsx

@@ -0,0 +1,161 @@
+import type { MenuButtonProps, MenuItemProps, MenuListProps, MenuProps } from '@invoke-ai/ui-library';
+import { Box, Flex, Icon, Text } from '@invoke-ai/ui-library';
+import { useDisclosure } from 'common/hooks/useBoolean';
+import type { FocusEventHandler, PointerEvent, RefObject } from 'react';
+import { useCallback, useEffect, useRef } from 'react';
+import { PiCaretRightBold } from 'react-icons/pi';
+import { useDebouncedCallback } from 'use-debounce';
+
+const offset: [number, number] = [0, 8];
+
+type UseSubMenuReturn = {
+  parentMenuItemProps: Partial<MenuItemProps>;
+  menuProps: Partial<MenuProps>;
+  menuButtonProps: Partial<MenuButtonProps>;
+  menuListProps: Partial<MenuListProps> & { ref: RefObject<HTMLDivElement> };
+};
+
+/**
+ * A hook that provides the necessary props to create a sub-menu within a menu.
+ *
+ * The sub-menu should be wrapped inside a parent `MenuItem` component.
+ *
+ * Use SubMenuButtonContent to render a button with a label and a right caret icon.
+ *
+ * TODO(psyche): Add keyboard handling for sub-menu.
+ *
+ * @example
+ * ```tsx
+ * const SubMenuExample = () => {
+ *   const subMenu = useSubMenu();
+ *   return (
+ *     <Menu>
+ *       <MenuButton>Open Parent Menu</MenuButton>
+ *       <MenuList>
+ *         <MenuItem>Parent Item 1</MenuItem>
+ *         <MenuItem>Parent Item 2</MenuItem>
+ *         <MenuItem>Parent Item 3</MenuItem>
+ *         <MenuItem {...subMenu.parentMenuItemProps} icon={<PiImageBold />}>
+ *           <Menu {...subMenu.menuProps}>
+ *             <MenuButton {...subMenu.menuButtonProps}>
+ *               <SubMenuButtonContent label="Open Sub Menu" />
+ *             </MenuButton>
+ *             <MenuList {...subMenu.menuListProps}>
+ *               <MenuItem>Sub Item 1</MenuItem>
+ *               <MenuItem>Sub Item 2</MenuItem>
+ *               <MenuItem>Sub Item 3</MenuItem>
+ *             </MenuList>
+ *           </Menu>
+ *         </MenuItem>
+ *       </MenuList>
+ *     </Menu>
+ *   );
+ * };
+ * ```
+ */
+export const useSubMenu = (): UseSubMenuReturn => {
+  const subMenu = useDisclosure(false);
+  const menuListRef = useRef<HTMLDivElement>(null);
+  const closeDebounced = useDebouncedCallback(subMenu.close, 300);
+  const openAndCancelPendingClose = useCallback(() => {
+    closeDebounced.cancel();
+    subMenu.open();
+  }, [closeDebounced, subMenu]);
+  const toggleAndCancelPendingClose = useCallback(() => {
+    if (subMenu.isOpen) {
+      subMenu.close();
+      return;
+    } else {
+      closeDebounced.cancel();
+      subMenu.toggle();
+    }
+  }, [closeDebounced, subMenu]);
+  const onBlurMenuList = useCallback<FocusEventHandler<HTMLDivElement>>(
+    (e) => {
+      // Don't trigger blur if focus is moving to a child element - e.g. from a sub-menu item to another sub-menu item
+      if (e.currentTarget.contains(e.relatedTarget)) {
+        closeDebounced.cancel();
+        return;
+      }
+      subMenu.close();
+    },
+    [closeDebounced, subMenu]
+  );
+
+  const onParentMenuItemPointerLeave = useCallback(
+    (e: PointerEvent<HTMLButtonElement>) => {
+      /**
+       * The pointerleave event is triggered when the pen or touch device is lifted, which would close the sub-menu.
+       * However, we want to keep the sub-menu open until the pen or touch device pressed some other element. This
+       * will be handled in the useEffect below - just ignore the pointerleave event for pen and touch devices.
+       */
+      if (e.pointerType === 'pen' || e.pointerType === 'touch') {
+        return;
+      }
+      subMenu.close();
+    },
+    [subMenu]
+  );
+
+  /**
+   * When using a mouse, the pointerleave events close the menu. But when using a pen or touch device, we need to close
+   * the sub-menu when the user taps outside of the menu list. So we need to listen for clicks outside of the menu list
+   * and close the menu accordingly.
+   */
+  useEffect(() => {
+    const el = menuListRef.current;
+    if (!el) {
+      return;
+    }
+    const controller = new AbortController();
+    window.addEventListener(
+      'click',
+      (e) => {
+        if (menuListRef.current?.contains(e.target as Node)) {
+          return;
+        }
+        subMenu.close();
+      },
+      { signal: controller.signal }
+    );
+    return () => {
+      controller.abort();
+    };
+  }, [subMenu]);
+
+  return {
+    parentMenuItemProps: {
+      onClick: toggleAndCancelPendingClose,
+      onPointerEnter: openAndCancelPendingClose,
+      onPointerLeave: onParentMenuItemPointerLeave,
+      closeOnSelect: false,
+    },
+    menuProps: {
+      isOpen: subMenu.isOpen,
+      onClose: subMenu.close,
+      placement: 'right',
+      offset: offset,
+      closeOnBlur: false,
+    },
+    menuButtonProps: {
+      as: Box,
+      width: 'full',
+      height: 'full',
+    },
+    menuListProps: {
+      ref: menuListRef,
+      onPointerEnter: openAndCancelPendingClose,
+      onPointerLeave: closeDebounced,
+      onBlur: onBlurMenuList,
+    },
+  };
+};
+
+export const SubMenuButtonContent = ({ label }: { label: string }) => {
+  return (
+    <Flex w="full" h="full" flexDir="row" justifyContent="space-between" alignItems="center">
+      <Text>{label}</Text>
+      <Icon as={PiCaretRightBold} />
+    </Flex>
+  );
+};

invokeai/frontend/web/src/common/types.ts

@@ -1,4 +1,12 @@
-type SerializableValue = string | number | boolean | null | undefined | SerializableValue[] | SerializableObject;
+type SerializableValue =
+  | string
+  | number
+  | boolean
+  | null
+  | undefined
+  | SerializableValue[]
+  | readonly SerializableValue[]
+  | SerializableObject;
 export type SerializableObject = {
   [k: string | number]: SerializableValue;
 };

invokeai/frontend/web/src/common/util/fixTooltipCloseOnScrollStyles.ts

@@ -0,0 +1,15 @@
+import type { CSSProperties } from 'react';
+
+/**
+ * Chakra's Tooltip's method of finding the nearest scroll parent has a problem - it assumes the first parent with
+ * `overflow: hidden` is the scroll parent. In this case, the Collapse component has that style, but isn't scrollable
+ * itself. The result is that the tooltip does not close on scroll, because the scrolling happens higher up in the DOM.
+ *
+ * As a hacky workaround, we can set the overflow to `visible`, which allows the scroll parent search to continue up to
+ * the actual scroll parent (in this case, the OverlayScrollbarsComponent in BoardsListWrapper).
+ *
+ * See: https://github.com/chakra-ui/chakra-ui/issues/7871#issuecomment-2453780958
+ */
+export const fixTooltipCloseOnScrollStyles: CSSProperties = {
+  overflow: 'visible',
+};

invokeai/frontend/web/src/features/controlLayers/components/CanvasAddEntityButtons.tsx

@@ -1,5 +1,6 @@
 import { Button, Flex, Heading } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import {
   useAddControlLayer,
   useAddGlobalReferenceImage,
@@ -28,70 +29,80 @@ export const CanvasAddEntityButtons = memo(() => {
       <Flex position="relative" flexDir="column" gap={4} top="20%">
         <Flex flexDir="column" justifyContent="flex-start" gap={2}>
           <Heading size="xs">{t('controlLayers.global')}</Heading>
-          <Button
-            size="sm"
-            variant="ghost"
-            justifyContent="flex-start"
-            leftIcon={<PiPlusBold />}
-            onClick={addGlobalReferenceImage}
-            isDisabled={isFLUX}
-          >
-            {t('controlLayers.globalReferenceImage')}
-          </Button>
+          <InformationalPopover feature="globalReferenceImage">
+            <Button
+              size="sm"
+              variant="ghost"
+              justifyContent="flex-start"
+              leftIcon={<PiPlusBold />}
+              onClick={addGlobalReferenceImage}
+            >
+              {t('controlLayers.globalReferenceImage')}
+            </Button>
+          </InformationalPopover>
         </Flex>
         <Flex flexDir="column" gap={2}>
           <Heading size="xs">{t('controlLayers.regional')}</Heading>
-          <Button
-            size="sm"
-            variant="ghost"
-            justifyContent="flex-start"
-            leftIcon={<PiPlusBold />}
-            onClick={addInpaintMask}
-          >
-            {t('controlLayers.inpaintMask')}
-          </Button>
-          <Button
-            size="sm"
-            variant="ghost"
-            justifyContent="flex-start"
-            leftIcon={<PiPlusBold />}
-            onClick={addRegionalGuidance}
-            isDisabled={isFLUX}
-          >
-            {t('controlLayers.regionalGuidance')}
-          </Button>
-          <Button
-            size="sm"
-            variant="ghost"
-            justifyContent="flex-start"
-            leftIcon={<PiPlusBold />}
-            onClick={addRegionalReferenceImage}
-            isDisabled={isFLUX}
-          >
-            {t('controlLayers.regionalReferenceImage')}
-          </Button>
+          <InformationalPopover feature="inpainting">
+            <Button
+              size="sm"
+              variant="ghost"
+              justifyContent="flex-start"
+              leftIcon={<PiPlusBold />}
+              onClick={addInpaintMask}
+            >
+              {t('controlLayers.inpaintMask')}
+            </Button>
+          </InformationalPopover>
+          <InformationalPopover feature="regionalGuidance">
+            <Button
+              size="sm"
+              variant="ghost"
+              justifyContent="flex-start"
+              leftIcon={<PiPlusBold />}
+              onClick={addRegionalGuidance}
+              isDisabled={isFLUX}
+            >
+              {t('controlLayers.regionalGuidance')}
+            </Button>
+          </InformationalPopover>
+          <InformationalPopover feature="regionalReferenceImage">
+            <Button
+              size="sm"
+              variant="ghost"
+              justifyContent="flex-start"
+              leftIcon={<PiPlusBold />}
+              onClick={addRegionalReferenceImage}
+              isDisabled={isFLUX}
+            >
+              {t('controlLayers.regionalReferenceImage')}
+            </Button>
+          </InformationalPopover>
         </Flex>
         <Flex flexDir="column" justifyContent="flex-start" gap={2}>
           <Heading size="xs">{t('controlLayers.layer_other')}</Heading>
-
-          <Button
-            size="sm"
-            variant="ghost"
-            justifyContent="flex-start"
-            leftIcon={<PiPlusBold />}
-            onClick={addControlLayer}
-          >
-            {t('controlLayers.controlLayer')}
-          </Button>
-          <Button
-            size="sm"
-            variant="ghost"
-            justifyContent="flex-start"
-            leftIcon={<PiPlusBold />}
-            onClick={addRasterLayer}
-          >
-            {t('controlLayers.rasterLayer')}
-          </Button>
+          <InformationalPopover feature="controlNet">
+            <Button
+              size="sm"
+              variant="ghost"
+              justifyContent="flex-start"
+              leftIcon={<PiPlusBold />}
+              onClick={addControlLayer}
+            >
+              {t('controlLayers.controlLayer')}
+            </Button>
+          </InformationalPopover>
+          <InformationalPopover feature="rasterLayer">
+            <Button
+              size="sm"
+              variant="ghost"
+              justifyContent="flex-start"
+              leftIcon={<PiPlusBold />}
+              onClick={addRasterLayer}
+            >
+              {t('controlLayers.rasterLayer')}
+            </Button>
+          </InformationalPopover>
         </Flex>
       </Flex>
     </Flex>

invokeai/frontend/web/src/features/controlLayers/components/CanvasAlerts/CanvasAlertsPreserveMask.tsx

@@ -13,7 +13,7 @@ export const CanvasAlertsPreserveMask = memo(() => {
   }
 
   return (
-    <Alert status="warning" borderRadius="base" fontSize="sm" shadow="md" w="fit-content" alignSelf="flex-end">
+    <Alert status="warning" borderRadius="base" fontSize="sm" shadow="md" w="fit-content">
       <AlertIcon />
       <AlertTitle>{t('controlLayers.settings.preserveMask.alert')}</AlertTitle>
     </Alert>

invokeai/frontend/web/src/features/controlLayers/components/CanvasAlerts/CanvasAlertsSelectedEntityStatus.tsx

@@ -98,7 +98,7 @@ const CanvasAlertsSelectedEntityStatusContent = memo(({ entityIdentifier, adapte
   }
 
   return (
-    <Alert status={alert.status} borderRadius="base" fontSize="sm" shadow="md" w="fit-content" alignSelf="flex-end">
+    <Alert status={alert.status} borderRadius="base" fontSize="sm" shadow="md" w="fit-content">
       <AlertIcon />
       <AlertTitle>{alert.title}</AlertTitle>
     </Alert>

invokeai/frontend/web/src/features/controlLayers/components/CanvasAlerts/CanvasAlertsSendingTo.tsx

@@ -132,7 +132,6 @@ const AlertWrapper = ({
             fontSize="sm"
             shadow="md"
             w="fit-content"
-            alignSelf="flex-end"
           >
             <Flex w="full" alignItems="center">
               <AlertIcon />

invokeai/frontend/web/src/features/controlLayers/components/CanvasAutoProcessSwitch.tsx

@@ -0,0 +1,24 @@
+import { FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { selectAutoProcess, settingsAutoProcessToggled } from 'features/controlLayers/store/canvasSettingsSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+export const CanvasAutoProcessSwitch = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const autoProcess = useAppSelector(selectAutoProcess);
+
+  const onChange = useCallback(() => {
+    dispatch(settingsAutoProcessToggled());
+  }, [dispatch]);
+
+  return (
+    <FormControl w="min-content">
+      <FormLabel m={0}>{t('controlLayers.filter.autoProcess')}</FormLabel>
+      <Switch size="sm" isChecked={autoProcess} onChange={onChange} />
+    </FormControl>
+  );
+});
+
+CanvasAutoProcessSwitch.displayName = 'CanvasAutoProcessSwitch';

invokeai/frontend/web/src/features/controlLayers/components/CanvasContextMenu/CanvasContextMenuGlobalMenuItems.tsx

@@ -1,4 +1,5 @@
-import { MenuGroup, MenuItem } from '@invoke-ai/ui-library';
+import { Menu, MenuButton, MenuGroup, MenuItem, MenuList } from '@invoke-ai/ui-library';
+import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
 import { CanvasContextMenuItemsCropCanvasToBbox } from 'features/controlLayers/components/CanvasContextMenu/CanvasContextMenuItemsCropCanvasToBbox';
 import { NewLayerIcon } from 'features/controlLayers/components/common/icons';
 import {
@@ -16,6 +17,8 @@ import { PiFloppyDiskBold } from 'react-icons/pi';
 
 export const CanvasContextMenuGlobalMenuItems = memo(() => {
   const { t } = useTranslation();
+  const saveSubMenu = useSubMenu();
+  const newSubMenu = useSubMenu();
   const isBusy = useCanvasIsBusy();
   const saveCanvasToGallery = useSaveCanvasToGallery();
   const saveBboxToGallery = useSaveBboxToGallery();
@@ -28,27 +31,41 @@ export const CanvasContextMenuGlobalMenuItems = memo(() => {
     <>
       <MenuGroup title={t('controlLayers.canvasContextMenu.canvasGroup')}>
         <CanvasContextMenuItemsCropCanvasToBbox />
-      </MenuGroup>
-      <MenuGroup title={t('controlLayers.canvasContextMenu.saveToGalleryGroup')}>
-        <MenuItem icon={<PiFloppyDiskBold />} isDisabled={isBusy} onClick={saveCanvasToGallery}>
-          {t('controlLayers.canvasContextMenu.saveCanvasToGallery')}
+        <MenuItem {...saveSubMenu.parentMenuItemProps} icon={<PiFloppyDiskBold />}>
+          <Menu {...saveSubMenu.menuProps}>
+            <MenuButton {...saveSubMenu.menuButtonProps}>
+              <SubMenuButtonContent label={t('controlLayers.canvasContextMenu.saveToGalleryGroup')} />
+            </MenuButton>
+            <MenuList {...saveSubMenu.menuListProps}>
+              <MenuItem icon={<PiFloppyDiskBold />} isDisabled={isBusy} onClick={saveCanvasToGallery}>
+                {t('controlLayers.canvasContextMenu.saveCanvasToGallery')}
+              </MenuItem>
+              <MenuItem icon={<PiFloppyDiskBold />} isDisabled={isBusy} onClick={saveBboxToGallery}>
+                {t('controlLayers.canvasContextMenu.saveBboxToGallery')}
+              </MenuItem>
+            </MenuList>
+          </Menu>
         </MenuItem>
-        <MenuItem icon={<PiFloppyDiskBold />} isDisabled={isBusy} onClick={saveBboxToGallery}>
-          {t('controlLayers.canvasContextMenu.saveBboxToGallery')}
-        </MenuItem>
-      </MenuGroup>
-      <MenuGroup title={t('controlLayers.canvasContextMenu.bboxGroup')}>
-        <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newGlobalReferenceImageFromBbox}>
-          {t('controlLayers.canvasContextMenu.newGlobalReferenceImage')}
-        </MenuItem>
-        <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newRegionalReferenceImageFromBbox}>
-          {t('controlLayers.canvasContextMenu.newRegionalReferenceImage')}
-        </MenuItem>
-        <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newControlLayerFromBbox}>
-          {t('controlLayers.canvasContextMenu.newControlLayer')}
-        </MenuItem>
-        <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newRasterLayerFromBbox}>
-          {t('controlLayers.canvasContextMenu.newRasterLayer')}
+        <MenuItem {...newSubMenu.parentMenuItemProps} icon={<NewLayerIcon />}>
+          <Menu {...newSubMenu.menuProps}>
+            <MenuButton {...newSubMenu.menuButtonProps}>
+              <SubMenuButtonContent label={t('controlLayers.canvasContextMenu.bboxGroup')} />
+            </MenuButton>
+            <MenuList {...newSubMenu.menuListProps}>
+              <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newGlobalReferenceImageFromBbox}>
+                {t('controlLayers.canvasContextMenu.newGlobalReferenceImage')}
+              </MenuItem>
+              <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newRegionalReferenceImageFromBbox}>
+                {t('controlLayers.canvasContextMenu.newRegionalReferenceImage')}
+              </MenuItem>
+              <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newControlLayerFromBbox}>
+                {t('controlLayers.canvasContextMenu.newControlLayer')}
+              </MenuItem>
+              <MenuItem icon={<NewLayerIcon />} isDisabled={isBusy} onClick={newRasterLayerFromBbox}>
+                {t('controlLayers.canvasContextMenu.newRasterLayer')}
+              </MenuItem>
+            </MenuList>
+          </Menu>
         </MenuItem>
       </MenuGroup>
     </>

invokeai/frontend/web/src/features/controlLayers/components/CanvasContextMenu/CanvasContextMenuSelectedEntityMenuItems.tsx

@@ -1,39 +1,43 @@
 import { MenuGroup } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
-import { CanvasEntityMenuItemsCropToBbox } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCropToBbox';
-import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
-import { CanvasEntityMenuItemsFilter } from 'features/controlLayers/components/common/CanvasEntityMenuItemsFilter';
-import { CanvasEntityMenuItemsSave } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSave';
-import { CanvasEntityMenuItemsTransform } from 'features/controlLayers/components/common/CanvasEntityMenuItemsTransform';
+import { ControlLayerMenuItems } from 'features/controlLayers/components/ControlLayer/ControlLayerMenuItems';
+import { InpaintMaskMenuItems } from 'features/controlLayers/components/InpaintMask/InpaintMaskMenuItems';
+import { IPAdapterMenuItems } from 'features/controlLayers/components/IPAdapter/IPAdapterMenuItems';
+import { RasterLayerMenuItems } from 'features/controlLayers/components/RasterLayer/RasterLayerMenuItems';
+import { RegionalGuidanceMenuItems } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItems';
 import {
   EntityIdentifierContext,
   useEntityIdentifierContext,
 } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useEntityTitle } from 'features/controlLayers/hooks/useEntityTitle';
+import { useEntityTypeString } from 'features/controlLayers/hooks/useEntityTypeString';
 import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import {
-  isFilterableEntityIdentifier,
-  isSaveableEntityIdentifier,
-  isTransformableEntityIdentifier,
-} from 'features/controlLayers/store/types';
+import type { PropsWithChildren } from 'react';
 import { memo } from 'react';
+import type { Equals } from 'tsafe';
+import { assert } from 'tsafe';
 
 const CanvasContextMenuSelectedEntityMenuItemsContent = memo(() => {
   const entityIdentifier = useEntityIdentifierContext();
-  const title = useEntityTitle(entityIdentifier);
 
-  return (
-    <MenuGroup title={title}>
-      {isFilterableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsFilter />}
-      {isTransformableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsTransform />}
-      {isSaveableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsCopyToClipboard />}
-      {isSaveableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsSave />}
-      {isTransformableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsCropToBbox />}
-      <CanvasEntityMenuItemsDelete />
-    </MenuGroup>
-  );
+  if (entityIdentifier.type === 'raster_layer') {
+    return <RasterLayerMenuItems />;
+  }
+  if (entityIdentifier.type === 'control_layer') {
+    return <ControlLayerMenuItems />;
+  }
+  if (entityIdentifier.type === 'inpaint_mask') {
+    return <InpaintMaskMenuItems />;
+  }
+  if (entityIdentifier.type === 'regional_guidance') {
+    return <RegionalGuidanceMenuItems />;
+  }
+  if (entityIdentifier.type === 'reference_image') {
+    return <IPAdapterMenuItems />;
+  }
+
+  assert<Equals<typeof entityIdentifier.type, never>>(false);
 });
+
 CanvasContextMenuSelectedEntityMenuItemsContent.displayName = 'CanvasContextMenuSelectedEntityMenuItemsContent';
 
 export const CanvasContextMenuSelectedEntityMenuItems = memo(() => {
@@ -45,9 +49,20 @@ export const CanvasContextMenuSelectedEntityMenuItems = memo(() => {
 
   return (
     <EntityIdentifierContext.Provider value={selectedEntityIdentifier}>
-      <CanvasContextMenuSelectedEntityMenuItemsContent />
+      <CanvasContextMenuSelectedEntityMenuGroup>
+        <CanvasContextMenuSelectedEntityMenuItemsContent />
+      </CanvasContextMenuSelectedEntityMenuGroup>
     </EntityIdentifierContext.Provider>
   );
 });
 
 CanvasContextMenuSelectedEntityMenuItems.displayName = 'CanvasContextMenuSelectedEntityMenuItems';
+
+const CanvasContextMenuSelectedEntityMenuGroup = memo((props: PropsWithChildren) => {
+  const entityIdentifier = useEntityIdentifierContext();
+  const title = useEntityTypeString(entityIdentifier.type);
+
+  return <MenuGroup title={title}>{props.children}</MenuGroup>;
+});
+
+CanvasContextMenuSelectedEntityMenuGroup.displayName = 'CanvasContextMenuSelectedEntityMenuGroup';

invokeai/frontend/web/src/features/controlLayers/components/CanvasDropArea.tsx

@@ -62,6 +62,7 @@ export const CanvasDropArea = memo(() => {
             data={addControlLayerFromImageDropData}
           />
         </GridItem>
+
         <GridItem position="relative">
           <IAIDroppable
             dropLabel={t('controlLayers.canvasContextMenu.newRegionalReferenceImage')}

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu.tsx

@@ -29,7 +29,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
     <Menu>
       <MenuButton
         as={IconButton}
-        size="sm"
+        minW={8}
         variant="link"
         alignSelf="stretch"
         tooltip={t('controlLayers.addLayer')}
@@ -40,7 +40,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
       />
       <MenuList>
         <MenuGroup title={t('controlLayers.global')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addGlobalReferenceImage} isDisabled={isFLUX}>
+          <MenuItem icon={<PiPlusBold />} onClick={addGlobalReferenceImage}>
             {t('controlLayers.globalReferenceImage')}
           </MenuItem>
         </MenuGroup>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBar.tsx

@@ -4,6 +4,7 @@ import { EntityListSelectedEntityActionBarDuplicateButton } from 'features/contr
 import { EntityListSelectedEntityActionBarFill } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFill';
 import { EntityListSelectedEntityActionBarFilterButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFilterButton';
 import { EntityListSelectedEntityActionBarOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarOpacity';
+import { EntityListSelectedEntityActionBarSelectObjectButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarSelectObjectButton';
 import { EntityListSelectedEntityActionBarTransformButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarTransformButton';
 import { memo } from 'react';
 
@@ -16,6 +17,7 @@ export const EntityListSelectedEntityActionBar = memo(() => {
       <Spacer />
       <EntityListSelectedEntityActionBarFill />
       <Flex h="full">
+        <EntityListSelectedEntityActionBarSelectObjectButton />
         <EntityListSelectedEntityActionBarFilterButton />
         <EntityListSelectedEntityActionBarTransformButton />
         <EntityListSelectedEntityActionBarSaveToAssetsButton />