Create new data structures for captioned images, and a list of captioned images. Create auto_caption_image node which can take a single image or list of images to caption

See https://github.com/invoke-ai/InvokeAI/pull/6342#issuecomment-2109912452 for discussion.

README.md

@@ -12,7 +12,7 @@
 
 Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
 
-[Installation][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
+[Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
 
 <div align="center">
 

docs/features/CONFIGURATION.md

@@ -51,13 +51,11 @@ The settings in this file will override the defaults. You only need
 to change this file if the default for a particular setting doesn't
 work for you.
 
+You'll find an example file next to `invokeai.yaml` that shows the default values.
+
 Some settings, like [Model Marketplace API Keys], require the YAML
 to be formatted correctly. Here is a [basic guide to YAML files].
 
-You can fix a broken `invokeai.yaml` by deleting it and running the
-configuration script again -- option [6] in the launcher, "Re-run the
-configure script".
-
 #### Custom Config File Location
 
 You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.

docs/features/TRAINING.md

@@ -4,278 +4,6 @@ title: Training
 
 # :material-file-document: Training
 
-# Textual Inversion Training
-## **Personalizing Text-to-Image Generation**
+Invoke Training has moved to its own repository, with a dedicated UI for accessing common scripts like Textual Inversion and LoRA training.
 
-You may personalize the generated images to provide your own styles or objects
-by training a new LDM checkpoint and introducing a new vocabulary to the fixed
-model as a (.pt) embeddings file. Alternatively, you may use or train
-HuggingFace Concepts embeddings files (.bin) from
-<https://huggingface.co/sd-concepts-library> and its associated
-notebooks.
-
-## **Hardware and Software Requirements**
-
-You will need a GPU to perform training in a reasonable length of
-time, and at least 12 GB of VRAM. We recommend using the [`xformers`
-library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
-training process further. During training, about ~8 GB is temporarily
-needed in order to store intermediate models, checkpoints and logs.
-
-## **Preparing for Training**
-
-To train, prepare a folder that contains 3-5 images that illustrate
-the object or concept. It is good to provide a variety of examples or
-poses to avoid overtraining the system. Format these images as PNG
-(preferred) or JPG. You do not need to resize or crop the images in
-advance, but for more control you may wish to do so.
-
-Place the training images in a directory on the machine InvokeAI runs
-on. We recommend placing them in a subdirectory of the
-`text-inversion-training-data` folder located in the InvokeAI root
-directory, ordinarily `~/invokeai` (Linux/Mac), or
-`C:\Users\your_name\invokeai` (Windows). For example, to create an
-embedding for the "psychedelic" style, you'd place the training images
-into the directory
-`~invokeai/text-inversion-training-data/psychedelic`.
-
-## **Launching Training Using the Console Front End**
-
-InvokeAI 2.3 and higher comes with a text console-based training front
-end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script,
-start training tool selecting choice (3):
-
-```sh
-1 "Generate images with a browser-based interface"
-2 "Explore InvokeAI nodes using a command-line interface"
-3 "Textual inversion training"
-4 "Merge models (diffusers type only)"
-5 "Download and install models"
-6 "Change InvokeAI startup options"
-7 "Re-run the configure script to fix a broken install or to complete a major upgrade"
-8 "Open the developer console"
-9 "Update InvokeAI"
-```
-
-Alternatively, you can select option (8) or from the command line, with the InvokeAI virtual environment active,
-you can then launch the front end with the command `invokeai-ti --gui`.
-
-This will launch a text-based front end that will look like this:
-
-<figure markdown>
-![ti-frontend](../assets/textual-inversion/ti-frontend.png)
-</figure>
-
-The interface is keyboard-based. Move from field to field using
-control-N (^N) to move to the next field and control-P (^P) to the
-previous one. <Tab> and <shift-TAB> work as well. Once a field is
-active, use the cursor keys. In a checkbox group, use the up and down
-cursor keys to move from choice to choice, and <space> to select a
-choice. In a scrollbar, use the left and right cursor keys to increase
-and decrease the value of the scroll. In textfields, type the desired
-values.
-
-The number of parameters may look intimidating, but in most cases the
-predefined defaults work fine. The red circled fields in the above
-illustration are the ones you will adjust most frequently.
-
-### Model Name
-
-This will list all the diffusers models that are currently
-installed. Select the one you wish to use as the basis for your
-embedding. Be aware that if you use a SD-1.X-based model for your
-training, you will only be able to use this embedding with other
-SD-1.X-based models. Similarly, if you train on SD-2.X, you will only
-be able to use the embeddings with models based on SD-2.X.
-
-### Trigger Term
-
-This is the prompt term you will use to trigger the embedding. Type a
-single word or phrase you wish to use as the trigger, example
-"psychedelic" (without angle brackets). Within InvokeAI, you will then
-be able to activate the trigger using the syntax `<psychedelic>`.
-
-### Initializer
-
-This is a single character that is used internally during the training
-process as a placeholder for the trigger term. It defaults to "*" and
-can usually be left alone.
-
-### Resume from last saved checkpoint
-
-As training proceeds, textual inversion will write a series of
-intermediate files that can be used to resume training from where it
-was left off in the case of an interruption. This checkbox will be
-automatically selected if you provide a previously used trigger term
-and at least one checkpoint file is found on disk.
-
-Note that as of 20 January 2023, resume does not seem to be working
-properly due to an issue with the upstream code.
-
-### Data Training Directory
-
-This is the location of the images to be used for training. When you
-select a trigger term like "my-trigger", the frontend will prepopulate
-this field with `~/invokeai/text-inversion-training-data/my-trigger`,
-but you can change the path to wherever you want.
-
-### Output Destination Directory
-
-This is the location of the logs, checkpoint files, and embedding
-files created during training. When you select a trigger term like
-"my-trigger", the frontend will prepopulate this field with
-`~/invokeai/text-inversion-output/my-trigger`, but you can change the
-path to wherever you want.
-
-### Image resolution
-
-The images in the training directory will be automatically scaled to
-the value you use here. For best results, you will want to use the
-same default resolution of the underlying model (512 pixels for
-SD-1.5, 768 for the larger version of SD-2.1).
-
-### Center crop images
-
-If this is selected, your images will be center cropped to make them
-square before resizing them to the desired resolution. Center cropping
-can indiscriminately cut off the top of subjects' heads for portrait
-aspect images, so if you have images like this, you may wish to use a
-photoeditor to manually crop them to a square aspect ratio.
-
-### Mixed precision
-
-Select the floating point precision for the embedding. "no" will
-result in a full 32-bit precision, "fp16" will provide 16-bit
-precision, and "bf16" will provide mixed precision (only available
-when XFormers is used).
-
-### Max training steps
-
-How many steps the training will take before the model converges. Most
-training sets will converge with 2000-3000 steps.
-
-### Batch size
-
-This adjusts how many training images are processed simultaneously in
-each step. Higher values will cause the training process to run more
-quickly, but use more memory. The default size will run with GPUs with
-as little as 12 GB.
-
-### Learning rate
-
-The rate at which the system adjusts its internal weights during
-training. Higher values risk overtraining (getting the same image each
-time), and lower values will take more steps to train a good
-model. The default of 0.0005 is conservative; you may wish to increase
-it to 0.005 to speed up training.
-
-### Scale learning rate by number of GPUs, steps and batch size
-
-If this is selected (the default) the system will adjust the provided
-learning rate to improve performance.
-
-### Use xformers acceleration
-
-This will activate XFormers memory-efficient attention. You need to
-have XFormers installed for this to have an effect.
-
-### Learning rate scheduler
-
-This adjusts how the learning rate changes over the course of
-training. The default "constant" means to use a constant learning rate
-for the entire training session. The other values scale the learning
-rate according to various formulas.
-
-Only "constant" is supported by the XFormers library.
-
-### Gradient accumulation steps
-
-This is a parameter that allows you to use bigger batch sizes than
-your GPU's VRAM would ordinarily accommodate, at the cost of some
-performance.
-
-### Warmup steps
-
-If "constant_with_warmup" is selected in the learning rate scheduler,
-then this provides the number of warmup steps. Warmup steps have a
-very low learning rate, and are one way of preventing early
-overtraining.
-
-## The training run
-
-Start the training run by advancing to the OK button (bottom right)
-and pressing <enter>. A series of progress messages will be displayed
-as the training process proceeds. This may take an hour or two,
-depending on settings and the speed of your system. Various log and
-checkpoint files will be written into the output directory (ordinarily
-`~/invokeai/text-inversion-output/my-model/`)
-
-At the end of successful training, the system will copy the file
-`learned_embeds.bin` into the InvokeAI root directory's `embeddings`
-directory, using a subdirectory named after the trigger token. For
-example, if the trigger token was `psychedelic`, then look for the
-embeddings file in
-`~/invokeai/embeddings/psychedelic/learned_embeds.bin`
-
-You may now launch InvokeAI and try out a prompt that uses the trigger
-term. For example `a plate of banana sushi in <psychedelic> style`.
-
-## **Training with the Command-Line Script**
-
-Training can also be done using a traditional command-line script. It
-can be launched from within the "developer's console", or from the
-command line after activating InvokeAI's virtual environment.
-
-It accepts a large number of arguments, which can be summarized by
-passing the `--help` argument:
-
-```sh
-invokeai-ti --help
-```
-
-Typical usage is shown here:
-```sh
-invokeai-ti \
-       --model=stable-diffusion-1.5 \
-       --resolution=512 \
-       --learnable_property=style \
-       --initializer_token='*' \
-       --placeholder_token='<psychedelic>' \
-       --train_data_dir=/home/lstein/invokeai/training-data/psychedelic \
-       --output_dir=/home/lstein/invokeai/text-inversion-training/psychedelic \
-       --scale_lr \
-       --train_batch_size=8 \
-       --gradient_accumulation_steps=4 \
-       --max_train_steps=3000 \
-       --learning_rate=0.0005 \
-       --resume_from_checkpoint=latest \
-       --lr_scheduler=constant \
-       --mixed_precision=fp16 \
-       --only_save_embeds
-```
-
-## Troubleshooting
-
-### `Cannot load embedding for <trigger>. It was trained on a model with token dimension 1024, but the current model has token dimension 768`
-
-Messages like this indicate you trained the embedding on a different base model than the currently selected one.
-
-For example, in the error above, the training was done on SD2.1 (768x768) but it was used on SD1.5 (512x512).
-
-## Reading
-
-For more information on textual inversion, please see the following
-resources:
-
-* The [textual inversion repository](https://github.com/rinongal/textual_inversion) and
-  associated paper for details and limitations.
-* [HuggingFace's textual inversion training
-  page](https://huggingface.co/docs/diffusers/training/text_inversion)
-* [HuggingFace example script
-  documentation](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
-  (Note that this script is similar to, but not identical, to
-  `textual_inversion`, but produces embed files that are completely compatible.
-
----
-
-copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team
+You can find more by visiting the repo at https://github.com/invoke-ai/invoke-training

docs/installation/010_INSTALL_AUTOMATED.md

@@ -1,8 +1,10 @@
-# Automatic Install
+# Automatic Install & Updates
 
-The installer is used for both new installs and updates.
+**The same packaged installer file can be used for both new installs and updates.**
+Using the installer for updates will leave everything you've added since installation, and just update the core libraries used to run Invoke.
+Simply use the same path you installed to originally.
 
-Both release and pre-release versions can be installed using it. It also supports install a wheel if needed.
+Both release and pre-release versions can be installed using the installer. It also supports install through a wheel if needed.
 
 Be sure to review the [installation requirements] and ensure your system has everything it needs to install Invoke.
 
@@ -96,7 +98,7 @@ Updating is exactly the same as installing - download the latest installer, choo
 
 If you have installation issues, please review the [FAQ]. You can also [create an issue] or ask for help on [discord].
 
-[installation requirements]: INSTALLATION.md#installation-requirements
+[installation requirements]: INSTALL_REQUIREMENTS.md
 [FAQ]: ../help/FAQ.md
 [install some models]: 050_INSTALLING_MODELS.md
 [configuration docs]: ../features/CONFIGURATION.md

docs/installation/INSTALLATION.md

@@ -1,4 +1,4 @@
-# Installation Overview
+# Installation and Updating Overview
 
 Before installing, review the [installation requirements] to ensure your system is set up properly.
 
@@ -6,14 +6,21 @@ See the [FAQ] for frequently-encountered installation issues.
 
 If you need more help, join our [discord] or [create an issue].
 
-<h2>Automatic Install</h2>
+<h2>Automatic Install & Updates </h2>
 
 ✅ The automatic install is the best way to run InvokeAI. Check out the [installation guide] to get started.
 
+⬆️ The same installer is also the best way to update InvokeAI - Simply rerun it for the same folder you installed to.
+
+The installation process simply manages installation for the core libraries & application dependencies that run Invoke.
+Any models, images, or other assets in the Invoke root folder won't be affected by the installation process.
+
 <h2>Manual Install</h2>
 
 If you are familiar with python and want more control over the packages that are installed, you can [install InvokeAI manually via PyPI].
 
+Updates are managed by reinstalling the latest version through PyPi.
+
 <h2>Developer Install</h2>
 
 If you want to contribute to InvokeAI, consult the [developer install guide].

docs/installation/INSTALL_REQUIREMENTS.md

@@ -37,13 +37,13 @@ Invoke runs best with a dedicated GPU, but will fall back to running on CPU, alb
     === "Nvidia"
 
         ```
-        Any GPU with at least 8GB VRAM. Linux only.
+        Any GPU with at least 8GB VRAM.
         ```
 
     === "AMD"
 
         ```
-        Any GPU with at least 16GB VRAM.
+        Any GPU with at least 16GB VRAM. Linux only.
         ```
 
     === "Mac"

invokeai/app/api/routers/app_info.py

@@ -13,7 +13,6 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
 from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch
-from invokeai.backend.image_util.safety_checker import SafetyChecker
 from invokeai.backend.util.logging import logging
 from invokeai.version import __version__
 
@@ -109,9 +108,7 @@ async def get_config() -> AppConfig:
         upscaling_models.append(str(Path(model).stem))
     upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
 
-    nsfw_methods = []
-    if SafetyChecker.safety_checker_available():
-        nsfw_methods.append("nsfw_checker")
+    nsfw_methods = ["nsfw_checker"]
 
     watermarking_methods = ["invisible_watermark"]
 

invokeai/app/api/routers/model_manager.py

@@ -6,7 +6,7 @@ import pathlib
 import shutil
 import traceback
 from copy import deepcopy
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Type
 
 from fastapi import Body, Path, Query, Response, UploadFile
 from fastapi.responses import FileResponse
@@ -16,6 +16,7 @@ from pydantic import AnyHttpUrl, BaseModel, ConfigDict, Field
 from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
+from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install import ModelInstallJob
 from invokeai.app.services.model_records import (
     DuplicateModelException,
@@ -52,6 +53,13 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
+def add_cover_image_to_model_config(config: AnyModelConfig, dependencies: Type[ApiDependencies]) -> AnyModelConfig:
+    """Add a cover image URL to a model configuration."""
+    cover_image = dependencies.invoker.services.model_images.get_url(config.key)
+    config.cover_image = cover_image
+    return config
+
+
 ##############################################################################
 # These are example inputs and outputs that are used in places where Swagger
 # is unable to generate a correct example.
@@ -118,8 +126,7 @@ async def list_model_records(
             record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
         )
     for model in found_models:
-        cover_image = ApiDependencies.invoker.services.model_images.get_url(model.key)
-        model.cover_image = cover_image
+        model = add_cover_image_to_model_config(model, ApiDependencies)
     return ModelsList(models=found_models)
 
 
@@ -160,12 +167,9 @@ async def get_model_record(
     key: str = Path(description="Key of the model record to fetch."),
 ) -> AnyModelConfig:
     """Get a model record"""
-    record_store = ApiDependencies.invoker.services.model_manager.store
     try:
-        config: AnyModelConfig = record_store.get_model(key)
-        cover_image = ApiDependencies.invoker.services.model_images.get_url(key)
-        config.cover_image = cover_image
-        return config
+        config = ApiDependencies.invoker.services.model_manager.store.get_model(key)
+        return add_cover_image_to_model_config(config, ApiDependencies)
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
 
@@ -294,14 +298,15 @@ async def update_model_record(
     installer = ApiDependencies.invoker.services.model_manager.install
     try:
         record_store.update_model(key, changes=changes)
-        model_response: AnyModelConfig = installer.sync_model_path(key)
+        config = installer.sync_model_path(key)
+        config = add_cover_image_to_model_config(config, ApiDependencies)
         logger.info(f"Updated model: {key}")
     except UnknownModelException as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
-    return model_response
+    return config
 
 
 @model_manager_router.get(
@@ -648,6 +653,14 @@ async def convert_model(
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
 
+    # Update the model image if the model had one
+    try:
+        model_image = ApiDependencies.invoker.services.model_images.get(key)
+        ApiDependencies.invoker.services.model_images.save(model_image, new_key)
+        ApiDependencies.invoker.services.model_images.delete(key)
+    except ModelImageFileNotFoundException:
+        pass
+
     # delete the original safetensors file
     installer.delete(key)
 
@@ -655,7 +668,8 @@ async def convert_model(
     shutil.rmtree(cache_path)
 
     # return the config record for the new diffusers directory
-    new_config: AnyModelConfig = store.get_model(new_key)
+    new_config = store.get_model(new_key)
+    new_config = add_cover_image_to_model_config(new_config, ApiDependencies)
     return new_config
 
 

invokeai/app/api_app.py

@@ -164,6 +164,12 @@ def custom_openapi() -> dict[str, Any]:
     for schema_key, schema_json in additional_schemas[1]["$defs"].items():
         openapi_schema["components"]["schemas"][schema_key] = schema_json
 
+    openapi_schema["components"]["schemas"]["InvocationOutputMap"] = {
+        "type": "object",
+        "properties": {},
+        "required": [],
+    }
+
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
         invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
@@ -172,6 +178,8 @@ def custom_openapi() -> dict[str, Any]:
         invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
         outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
         invoker_schema["output"] = outputs_ref
+        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["properties"][invoker.get_type()] = outputs_ref
+        openapi_schema["components"]["schemas"]["InvocationOutputMap"]["required"].append(invoker.get_type())
         invoker_schema["class"] = "invocation"
 
     # This code no longer seems to be necessary?

invokeai/app/invocations/controlnet_image_processors.py

@@ -35,15 +35,16 @@ from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
 from invokeai.backend.image_util.canny import get_canny_edges
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 from invokeai.backend.image_util.hed import HEDProcessor
 from invokeai.backend.image_util.lineart import LineartProcessor
 from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
+from invokeai.backend.image_util.util import np_to_pil, pil_to_np
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, Classification, invocation, invocation_output
 
 
 class ControlField(BaseModel):
@@ -164,13 +165,13 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.3.2",
+    version="1.3.3",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     low_threshold: int = InputField(
         default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
     )
@@ -198,13 +199,13 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     # safe not supported in controlnet_aux v0.0.3
     # safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
@@ -227,13 +228,13 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     coarse: bool = InputField(default=False, description="Whether to use coarse mode")
 
     def run_processor(self, image: Image.Image) -> Image.Image:
@@ -249,13 +250,13 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image) -> Image.Image:
         processor = LineartAnimeProcessor()
@@ -272,15 +273,15 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.4",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
 
     a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
     bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     # depth_and_normal not supported in controlnet_aux v0.0.3
     # depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
 
@@ -303,13 +304,13 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
@@ -320,13 +321,13 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.2"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.3"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
     thr_d: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_d`")
 
@@ -343,13 +344,13 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.2"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.3"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
     scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
 
@@ -370,13 +371,13 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
     h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
     w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
     f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
@@ -400,7 +401,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -416,15 +417,15 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.4",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
 
     max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
     min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         mediapipe_face_processor = MediapipeFaceDetector()
@@ -443,7 +444,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -451,8 +452,8 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     thr_a: float = InputField(default=0, description="Leres parameter `thr_a`")
     thr_b: float = InputField(default=0, description="Leres parameter `thr_b`")
     boost: bool = InputField(default=False, description="Whether to use boost mode")
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
@@ -472,7 +473,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -512,13 +513,13 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.3",
+    version="1.2.4",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
 
-    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
@@ -559,12 +560,12 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""
 
-    color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
+    color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
 
     def run_processor(self, image: Image.Image):
         np_image = np.array(image, dtype=np.uint8)
@@ -591,7 +592,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
     title="Depth Anything Processor",
     tags=["controlnet", "depth", "depth anything"],
     category="controlnet",
-    version="1.1.1",
+    version="1.1.2",
 )
 class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a depth map based on the Depth Anything algorithm"""
@@ -599,7 +600,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
         default="small", description="The size of the depth model to use"
     )
-    resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
+    resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
@@ -614,7 +615,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     title="DW Openpose Image Processor",
     tags=["controlnet", "dwpose", "openpose"],
     category="controlnet",
-    version="1.1.0",
+    version="1.1.1",
 )
 class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Generates an openpose pose from an image using DWPose"""
@@ -622,7 +623,7 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     draw_body: bool = InputField(default=True)
     draw_face: bool = InputField(default=False)
     draw_hands: bool = InputField(default=False)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image):
         dw_openpose = DWOpenposeDetector()
@@ -634,3 +635,27 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
             resolution=self.image_resolution,
         )
         return processed_image
+
+
+@invocation(
+    "heuristic_resize",
+    title="Heuristic Resize",
+    tags=["image, controlnet"],
+    category="image",
+    version="1.0.1",
+    classification=Classification.Prototype,
+)
+class HeuristicResizeInvocation(BaseInvocation):
+    """Resize an image using a heuristic method. Preserves edge maps."""
+
+    image: ImageField = InputField(description="The image to resize")
+    width: int = InputField(default=512, ge=1, description="The width to resize to (px)")
+    height: int = InputField(default=512, ge=1, description="The height to resize to (px)")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name, "RGB")
+        np_img = pil_to_np(image)
+        np_resized = heuristic_resize(np_img, (self.width, self.height))
+        resized = np_to_pil(np_resized)
+        image_dto = context.images.save(image=resized)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/image.py

@@ -1,11 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from pathlib import Path
-from typing import Literal, Optional
+from typing import Literal, Optional, List, Union
 
 import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
@@ -16,7 +16,7 @@ from invokeai.app.invocations.fields import (
     WithBoard,
     WithMetadata,
 )
-from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.invocations.primitives import ImageOutput, CaptionImageOutputs, CaptionImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
@@ -67,6 +67,56 @@ class BlankImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         return ImageOutput.build(image_dto)
 
 
+@invocation(
+    "auto_caption_image",
+    title="Automatically Caption Image",
+    tags=["image", "caption"],
+    category="image",
+    version="1.2.2",
+)
+class CaptionImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Adds a caption to an image"""
+
+    images: Union[ImageField,List[ImageField]] = InputField(description="The image to caption")
+    prompt: str = InputField(default="Describe this list of images in 20 words or less", description="Describe how you would like the image to be captioned.")
+
+    def invoke(self, context: InvocationContext) -> CaptionImageOutputs:
+
+        model_id = "vikhyatk/moondream2"
+        model_revision = "2024-04-02"
+        tokenizer = AutoTokenizer.from_pretrained(model_id, revision=model_revision)
+        moondream_model = AutoModelForCausalLM.from_pretrained(
+            model_id, trust_remote_code=True, revision=model_revision
+        )
+        output: CaptionImageOutputs = CaptionImageOutputs()
+        try:
+            from PIL.Image import Image
+            images: List[Image] = []
+            image_fields = self.images if isinstance(self.images, list) else [self.images]
+            for image in image_fields:
+                images.append(context.images.get_pil(image.image_name))
+            answers: List[str] = moondream_model.batch_answer(
+                images=images,
+                prompts=[self.prompt] * len(images),
+                tokenizer=tokenizer,
+            )
+            assert isinstance(answers, list)
+            for i, answer in enumerate(answers):
+                output.images.append(CaptionImageOutput(
+                    image=image_fields[i],
+                    width=images[i].width,
+                    height=images[i].height,
+                    caption=answer
+                ))
+        except:
+            raise
+        finally:
+            del moondream_model
+            del tokenizer
+
+        return output
+
+
 @invocation(
     "img_crop",
     title="Crop Image",
@@ -195,7 +245,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
 class MaskFromAlphaInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Extracts the alpha channel of an image as a mask."""
 
-    image: ImageField = InputField(description="The image to create the mask from")
+    image: List[ImageField] = InputField(description="The image to create the mask from")
     invert: bool = InputField(default=False, description="Whether or not to invert the mask")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -504,7 +554,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Blur NSFW Image",
     tags=["image", "nsfw"],
     category="image",
-    version="1.2.2",
+    version="1.2.3",
 )
 class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add blur to NSFW-flagged images"""
@@ -516,23 +566,12 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         logger = context.logger
         logger.debug("Running NSFW checker")
-        if SafetyChecker.has_nsfw_concept(image):
-            logger.info("A potentially NSFW image has been detected. Image will be blurred.")
-            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
-            caution = self._get_caution_img()
-            blurry_image.paste(caution, (0, 0), caution)
-            image = blurry_image
+        image = SafetyChecker.blur_if_nsfw(image)
 
         image_dto = context.images.save(image=image)
 
         return ImageOutput.build(image_dto)
 
-    def _get_caution_img(self) -> Image.Image:
-        import invokeai.app.assets.images as image_assets
-
-        caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
-        return caution.resize((caution.width // 2, caution.height // 2))
-
 
 @invocation(
     "img_watermark",

invokeai/app/invocations/latent.py

@@ -3,7 +3,7 @@ import inspect
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import Any, Iterator, List, Literal, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
 
 import einops
 import numpy as np
@@ -11,7 +11,6 @@ import numpy.typing as npt
 import torch
 import torchvision
 import torchvision.transforms as T
-from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.adapter import T2IAdapter
@@ -21,9 +20,12 @@ from diffusers.models.attention_processor import (
     LoRAXFormersAttnProcessor,
     XFormersAttnProcessor,
 )
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from diffusers.schedulers import DPMSolverSDEScheduler
-from diffusers.schedulers import SchedulerMixin as Scheduler
+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, ImageFilter
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
@@ -341,7 +343,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
     cfg_scale: Union[float, List[float]] = InputField(
-        default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
+        default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale"
     )
     denoising_start: float = InputField(
         default=0.0,
@@ -521,9 +523,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
 
         if is_sdxl:
-            return SDXLConditioningInfo(
-                embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids
-            ), regions
+            return (
+                SDXLConditioningInfo(embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids),
+                regions,
+            )
         return BasicConditioningInfo(embeds=text_embedding), regions
 
     def get_conditioning_data(
@@ -563,6 +566,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
             dtype=unet.dtype,
         )
 
+        if isinstance(self.cfg_scale, list):
+            assert (
+                len(self.cfg_scale) == self.steps
+            ), "cfg_scale (list) must have the same length as the number of steps"
+
         conditioning_data = TextConditioningData(
             uncond_text=uncond_text_embedding,
             cond_text=cond_text_embedding,
@@ -578,13 +586,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         unet: UNet2DConditionModel,
         scheduler: Scheduler,
     ) -> StableDiffusionGeneratorPipeline:
-        # TODO:
-        # configure_model_padding(
-        #    unet,
-        #    self.seamless,
-        #    self.seamless_axes,
-        # )
-
         class FakeVae:
             class FakeVaeConfig:
                 def __init__(self) -> None:
@@ -820,7 +821,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         denoising_start: float,
         denoising_end: float,
         seed: int,
-    ) -> Tuple[int, List[int], int]:
+    ) -> Tuple[int, List[int], int, Dict[str, Any]]:
         assert isinstance(scheduler, ConfigMixin)
         if scheduler.config.get("cpu_only", False):
             scheduler.set_timesteps(steps, device="cpu")
@@ -848,13 +849,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
         timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
         num_inference_steps = len(timesteps) // scheduler.order
 
-        scheduler_step_kwargs = {}
+        scheduler_step_kwargs: Dict[str, Any] = {}
         scheduler_step_signature = inspect.signature(scheduler.step)
         if "generator" in scheduler_step_signature.parameters:
             # At some point, someone decided that schedulers that accept a generator should use the original seed with
             # all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
             # reproducibility.
-            scheduler_step_kwargs = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
+            scheduler_step_kwargs.update({"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)})
+        if isinstance(scheduler, TCDScheduler):
+            scheduler_step_kwargs.update({"eta": 1.0})
 
         return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
 

invokeai/app/invocations/model.py

@@ -190,6 +190,75 @@ class LoRALoaderInvocation(BaseInvocation):
         return output
 
 
+@invocation_output("lora_selector_output")
+class LoRASelectorOutput(BaseInvocationOutput):
+    """Model loader output"""
+
+    lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
+
+
+@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.0")
+class LoRASelectorInvocation(BaseInvocation):
+    """Selects a LoRA model and weight."""
+
+    lora: ModelIdentifierField = InputField(
+        description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA", ui_type=UIType.LoRAModel
+    )
+    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
+
+    def invoke(self, context: InvocationContext) -> LoRASelectorOutput:
+        return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
+
+
+@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.0.0")
+class LoRACollectionLoader(BaseInvocation):
+    """Applies a collection of LoRAs to the provided UNet and CLIP models."""
+
+    loras: LoRAField | list[LoRAField] = InputField(
+        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    )
+    unet: Optional[UNetField] = InputField(
+        default=None,
+        description=FieldDescriptions.unet,
+        input=Input.Connection,
+        title="UNet",
+    )
+    clip: Optional[CLIPField] = InputField(
+        default=None,
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+        title="CLIP",
+    )
+
+    def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
+        output = LoRALoaderOutput()
+        loras = self.loras if isinstance(self.loras, list) else [self.loras]
+        added_loras: list[str] = []
+
+        for lora in loras:
+            if lora.lora.key in added_loras:
+                continue
+
+            if not context.models.exists(lora.lora.key):
+                raise Exception(f"Unknown lora: {lora.lora.key}!")
+
+            assert lora.lora.base in (BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2)
+
+            added_loras.append(lora.lora.key)
+
+            if self.unet is not None:
+                if output.unet is None:
+                    output.unet = self.unet.model_copy(deep=True)
+                output.unet.loras.append(lora)
+
+            if self.clip is not None:
+                if output.clip is None:
+                    output.clip = self.clip.model_copy(deep=True)
+                output.clip.loras.append(lora)
+
+        return output
+
+
 @invocation_output("sdxl_lora_loader_output")
 class SDXLLoRALoaderOutput(BaseInvocationOutput):
     """SDXL LoRA Loader Output"""
@@ -279,6 +348,72 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
         return output
 
 
+@invocation(
+    "sdxl_lora_collection_loader",
+    title="SDXL LoRA Collection Loader",
+    tags=["model"],
+    category="model",
+    version="1.0.0",
+)
+class SDXLLoRACollectionLoader(BaseInvocation):
+    """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
+
+    loras: LoRAField | list[LoRAField] = InputField(
+        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    )
+    unet: Optional[UNetField] = InputField(
+        default=None,
+        description=FieldDescriptions.unet,
+        input=Input.Connection,
+        title="UNet",
+    )
+    clip: Optional[CLIPField] = InputField(
+        default=None,
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+        title="CLIP",
+    )
+    clip2: Optional[CLIPField] = InputField(
+        default=None,
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+        title="CLIP 2",
+    )
+
+    def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
+        output = SDXLLoRALoaderOutput()
+        loras = self.loras if isinstance(self.loras, list) else [self.loras]
+        added_loras: list[str] = []
+
+        for lora in loras:
+            if lora.lora.key in added_loras:
+                continue
+
+            if not context.models.exists(lora.lora.key):
+                raise Exception(f"Unknown lora: {lora.lora.key}!")
+
+            assert lora.lora.base is BaseModelType.StableDiffusionXL
+
+            added_loras.append(lora.lora.key)
+
+            if self.unet is not None:
+                if output.unet is None:
+                    output.unet = self.unet.model_copy(deep=True)
+                output.unet.loras.append(lora)
+
+            if self.clip is not None:
+                if output.clip is None:
+                    output.clip = self.clip.model_copy(deep=True)
+                output.clip.loras.append(lora)
+
+            if self.clip2 is not None:
+                if output.clip2 is None:
+                    output.clip2 = self.clip2.model_copy(deep=True)
+                output.clip2.loras.append(lora)
+
+        return output
+
+
 @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""

invokeai/app/invocations/primitives.py

@@ -1,6 +1,6 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Optional
+from typing import Optional, List
 
 import torch
 
@@ -247,6 +247,17 @@ class ImageOutput(BaseInvocationOutput):
         )
 
 
+@invocation_output("captioned_image_output")
+class CaptionImageOutput(ImageOutput):
+    caption: str = OutputField(description="Caption for given image")
+
+
+
+@invocation_output("captioned_image_outputs")
+class CaptionImageOutputs(BaseInvocationOutput):
+    images: List[CaptionImageOutput] = OutputField(description="List of captioned images", default=[])
+
+
 @invocation_output("image_collection_output")
 class ImageCollectionOutput(BaseInvocationOutput):
     """Base class for nodes that output a collection of images"""

invokeai/app/services/download/download_default.py

@@ -318,10 +318,8 @@ class DownloadQueueService(DownloadQueueServiceBase):
         in_progress_path.rename(job.download_path)
 
     def _validate_filename(self, directory: str, filename: str) -> bool:
-        pc_name_max = os.pathconf(directory, "PC_NAME_MAX") if hasattr(os, "pathconf") else 260  # hardcoded for windows
-        pc_path_max = (
-            os.pathconf(directory, "PC_PATH_MAX") if hasattr(os, "pathconf") else 32767
-        )  # hardcoded for windows with long names enabled
+        pc_name_max = get_pc_name_max(directory)
+        pc_path_max = get_pc_path_max(directory)
         if "/" in filename:
             return False
         if filename.startswith(".."):
@@ -419,6 +417,26 @@ class DownloadQueueService(DownloadQueueServiceBase):
             self._logger.warning(excp)
 
 
+def get_pc_name_max(directory: str) -> int:
+    if hasattr(os, "pathconf"):
+        try:
+            return os.pathconf(directory, "PC_NAME_MAX")
+        except OSError:
+            # macOS w/ external drives raise OSError
+            pass
+    return 260  # hardcoded for windows
+
+
+def get_pc_path_max(directory: str) -> int:
+    if hasattr(os, "pathconf"):
+        try:
+            return os.pathconf(directory, "PC_PATH_MAX")
+        except OSError:
+            # some platforms may not have this value
+            pass
+    return 32767  # hardcoded for windows with long names enabled
+
+
 # Example on_progress event handler to display a TQDM status bar
 # Activate with:
 #   download_service.download(DownloadJob('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().update))

invokeai/backend/image_util/__init__.py

@@ -4,5 +4,4 @@ Initialization file for invokeai.backend.image_util methods.
 
 from .infill_methods.patchmatch import PatchMatch  # noqa: F401
 from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata  # noqa: F401
-from .seamless import configure_model_padding  # noqa: F401
 from .util import InitImageResizer, make_grid  # noqa: F401

invokeai/backend/image_util/safety_checker.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 import numpy as np
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from PIL import Image
+from PIL import Image, ImageFilter
 from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
@@ -16,6 +16,7 @@ from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
+repo_id = "CompVis/stable-diffusion-safety-checker"
 CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
 
 
@@ -24,30 +25,30 @@ class SafetyChecker:
     Wrapper around SafetyChecker model.
     """
 
-    safety_checker = None
     feature_extractor = None
-    tried_load: bool = False
+    safety_checker = None
 
     @classmethod
     def _load_safety_checker(cls):
-        if cls.tried_load:
+        if cls.safety_checker is not None and cls.feature_extractor is not None:
             return
 
         try:
-            cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(get_config().models_path / CHECKER_PATH)
-            cls.feature_extractor = AutoFeatureExtractor.from_pretrained(get_config().models_path / CHECKER_PATH)
+            model_path = get_config().models_path / CHECKER_PATH
+            if model_path.exists():
+                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(model_path)
+                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(model_path)
+            else:
+                model_path.mkdir(parents=True, exist_ok=True)
+                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
+                cls.feature_extractor.save_pretrained(model_path, safe_serialization=True)
+                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(repo_id)
+                cls.safety_checker.save_pretrained(model_path, safe_serialization=True)
         except Exception as e:
             logger.warning(f"Could not load NSFW checker: {str(e)}")
-        cls.tried_load = True
-
-    @classmethod
-    def safety_checker_available(cls) -> bool:
-        return Path(get_config().models_path, CHECKER_PATH).exists()
 
     @classmethod
     def has_nsfw_concept(cls, image: Image.Image) -> bool:
-        if not cls.safety_checker_available() and cls.tried_load:
-            return False
         cls._load_safety_checker()
         if cls.safety_checker is None or cls.feature_extractor is None:
             return False
@@ -60,3 +61,24 @@ class SafetyChecker:
         with SilenceWarnings():
             checked_image, has_nsfw_concept = cls.safety_checker(images=x_image, clip_input=features.pixel_values)
         return has_nsfw_concept[0]
+
+    @classmethod
+    def blur_if_nsfw(cls, image: Image.Image) -> Image.Image:
+        if cls.has_nsfw_concept(image):
+            logger.warning("A potentially NSFW image has been detected. Image will be blurred.")
+            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
+            caution = cls._get_caution_img()
+            # Center the caution image on the blurred image
+            x = (blurry_image.width - caution.width) // 2
+            y = (blurry_image.height - caution.height) // 2
+            blurry_image.paste(caution, (x, y), caution)
+            image = blurry_image
+
+        return image
+
+    @classmethod
+    def _get_caution_img(cls) -> Image.Image:
+        import invokeai.app.assets.images as image_assets
+
+        caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
+        return caution.resize((caution.width // 2, caution.height // 2))

invokeai/backend/image_util/seamless.py

@@ -1,52 +0,0 @@
-import torch.nn as nn
-
-
-def _conv_forward_asymmetric(self, input, weight, bias):
-    """
-    Patch for Conv2d._conv_forward that supports asymmetric padding
-    """
-    working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
-    working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
-    return nn.functional.conv2d(
-        working,
-        weight,
-        bias,
-        self.stride,
-        nn.modules.utils._pair(0),
-        self.dilation,
-        self.groups,
-    )
-
-
-def configure_model_padding(model, seamless, seamless_axes):
-    """
-    Modifies the 2D convolution layers to use a circular padding mode based on
-    the `seamless` and `seamless_axes` options.
-    """
-    # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
-    for m in model.modules():
-        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-            if seamless:
-                m.asymmetric_padding_mode = {}
-                m.asymmetric_padding = {}
-                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-                m.asymmetric_padding["x"] = (
-                    m._reversed_padding_repeated_twice[0],
-                    m._reversed_padding_repeated_twice[1],
-                    0,
-                    0,
-                )
-                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-                m.asymmetric_padding["y"] = (
-                    0,
-                    0,
-                    m._reversed_padding_repeated_twice[2],
-                    m._reversed_padding_repeated_twice[3],
-                )
-                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
-            else:
-                m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
-                if hasattr(m, "asymmetric_padding_mode"):
-                    del m.asymmetric_padding_mode
-                if hasattr(m, "asymmetric_padding"):
-                    del m.asymmetric_padding

invokeai/backend/image_util/util.py

@@ -144,10 +144,8 @@ def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.n
     h = float(input_image.shape[0])
     w = float(input_image.shape[1])
     scaling_factor = float(resolution) / min(h, w)
-    h *= scaling_factor
-    w *= scaling_factor
-    h = int(np.round(h / 64.0)) * 64
-    w = int(np.round(w / 64.0)) * 64
+    h = int(h * scaling_factor)
+    w = int(w * scaling_factor)
     if scaling_factor > 1:
         return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_LANCZOS4)
     else:

invokeai/backend/model_manager/probe.py

@@ -51,6 +51,7 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched
     },
     BaseModelType.StableDiffusionXL: {
         ModelVariantType.Normal: "sd_xl_base.yaml",
+        ModelVariantType.Inpaint: "sd_xl_inpaint.yaml",
     },
     BaseModelType.StableDiffusionXLRefiner: {
         ModelVariantType.Normal: "sd_xl_refiner.yaml",

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -13,6 +13,7 @@ from diffusers import (
     LCMScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
+    TCDScheduler,
     UniPCMultistepScheduler,
 )
 
@@ -40,4 +41,5 @@ SCHEDULER_MAP = {
     "dpmpp_sde_k": (DPMSolverSDEScheduler, {"use_karras_sigmas": True, "noise_sampler_seed": 0}),
     "unipc": (UniPCMultistepScheduler, {"cpu_only": True}),
     "lcm": (LCMScheduler, {}),
+    "tcd": (TCDScheduler, {}),
 }

invokeai/backend/stable_diffusion/seamless.py

@@ -1,89 +1,51 @@
-from __future__ import annotations
-
 from contextlib import contextmanager
-from typing import Callable, List, Union
+from typing import Callable, List, Optional, Tuple, Union
 
+import torch
 import torch.nn as nn
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
+from diffusers.models.lora import LoRACompatibleConv
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 
 
-def _conv_forward_asymmetric(self, input, weight, bias):
-    """
-    Patch for Conv2d._conv_forward that supports asymmetric padding
-    """
-    working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
-    working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
-    return nn.functional.conv2d(
-        working,
-        weight,
-        bias,
-        self.stride,
-        nn.modules.utils._pair(0),
-        self.dilation,
-        self.groups,
-    )
-
-
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
     if not seamless_axes:
         yield
         return
 
-    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
-    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
+    # override conv_forward
+    # https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
+    def _conv_forward_asymmetric(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
+        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
+        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
+        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
+        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
+        return torch.nn.functional.conv2d(
+            working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
+        )
+
+    original_layers: List[Tuple[nn.Conv2d, Callable]] = []
+
     try:
-        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
-        skipped_layers = 1
-        for m_name, m in model.named_modules():
-            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                continue
+        x_mode = "circular" if "x" in seamless_axes else "constant"
+        y_mode = "circular" if "y" in seamless_axes else "constant"
 
-            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
-                # down_blocks.1.resnets.1.conv1
-                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
-                block_num = int(block_num)
-                resnet_num = int(resnet_num)
+        conv_layers: List[torch.nn.Conv2d] = []
 
-                if block_num >= len(model.down_blocks) - skipped_layers:
-                    continue
+        for module in model.modules():
+            if isinstance(module, torch.nn.Conv2d):
+                conv_layers.append(module)
 
-                # Skip the second resnet (could be configurable)
-                if resnet_num > 0:
-                    continue
-
-                # Skip Conv2d layers (could be configurable)
-                if submodule_name == "conv2":
-                    continue
-
-            m.asymmetric_padding_mode = {}
-            m.asymmetric_padding = {}
-            m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-            m.asymmetric_padding["x"] = (
-                m._reversed_padding_repeated_twice[0],
-                m._reversed_padding_repeated_twice[1],
-                0,
-                0,
-            )
-            m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-            m.asymmetric_padding["y"] = (
-                0,
-                0,
-                m._reversed_padding_repeated_twice[2],
-                m._reversed_padding_repeated_twice[3],
-            )
-
-            to_restore.append((m, m._conv_forward))
-            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+        for layer in conv_layers:
+            if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
+                layer.lora_layer = lambda *x: 0
+            original_layers.append((layer, layer._conv_forward))
+            layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)
 
         yield
 
     finally:
-        for module, orig_conv_forward in to_restore:
-            module._conv_forward = orig_conv_forward
-            if hasattr(module, "asymmetric_padding_mode"):
-                del module.asymmetric_padding_mode
-            if hasattr(module, "asymmetric_padding"):
-                del module.asymmetric_padding
+        for layer, orig_conv_forward in original_layers:
+            layer._conv_forward = orig_conv_forward

invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml

@@ -0,0 +1,98 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        weighting_config:
+          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2816
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 9
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
+        context_dim: 2048
+        spatial_transformer_attn_type: softmax-xformers
+        legacy: False
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          # crossattn cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+            params:
+              layer: hidden
+              layer_idx: 11
+          # crossattn and vector cond
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+              legacy: False
+          # vector cond
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+          # vector cond
+          - is_trainable: False
+            input_key: target_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256  # multiplied by two
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

invokeai/frontend/web/.eslintrc.js

@@ -10,6 +10,8 @@ module.exports = {
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
     // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
     'i18next/no-literal-string': 'error',
+    // https://eslint.org/docs/latest/rules/no-console
+    'no-console': 'error',
   },
   overrides: [
     /**

invokeai/frontend/web/.gitignore

@@ -43,4 +43,5 @@ stats.html
 yalc.lock
 
 # vitest
-tsconfig.vitest-temp.json
+tsconfig.vitest-temp.json
+coverage/

invokeai/frontend/web/package.json

@@ -25,7 +25,7 @@
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
     "lint:knip": "knip",
-    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:0 src/main.tsx",
+    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
@@ -35,6 +35,7 @@
     "storybook": "storybook dev -p 6006",
     "build-storybook": "storybook build",
     "test": "vitest",
+    "test:ui": "vitest --coverage --ui",
     "test:no-watch": "vitest --no-watch"
   },
   "madge": {
@@ -52,47 +53,48 @@
   },
   "dependencies": {
     "@chakra-ui/react-use-size": "^2.1.0",
-    "@dagrejs/dagre": "^1.1.1",
-    "@dagrejs/graphlib": "^2.2.1",
+    "@dagrejs/dagre": "^1.1.2",
+    "@dagrejs/graphlib": "^2.2.2",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
-    "@fontsource-variable/inter": "^5.0.17",
-    "@invoke-ai/ui-library": "^0.0.21",
+    "@fontsource-variable/inter": "^5.0.18",
+    "@invoke-ai/ui-library": "^0.0.25",
     "@nanostores/react": "^0.7.2",
-    "@reduxjs/toolkit": "2.2.2",
+    "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
     "chakra-react-select": "^4.7.6",
     "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
-    "framer-motion": "^11.0.22",
-    "i18next": "^23.10.1",
-    "i18next-http-backend": "^2.5.0",
+    "fracturedjsonjs": "^4.0.1",
+    "framer-motion": "^11.1.8",
+    "i18next": "^23.11.3",
+    "i18next-http-backend": "^2.5.1",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.6",
     "lodash-es": "^4.17.21",
-    "nanostores": "^0.10.0",
+    "nanostores": "^0.10.3",
     "new-github-issue-url": "^1.0.0",
-    "overlayscrollbars": "^2.6.1",
-    "overlayscrollbars-react": "^0.5.5",
+    "overlayscrollbars": "^2.7.3",
+    "overlayscrollbars-react": "^0.5.6",
     "query-string": "^9.0.0",
-    "react": "^18.2.0",
+    "react": "^18.3.1",
     "react-colorful": "^5.6.1",
-    "react-dom": "^18.2.0",
+    "react-dom": "^18.3.1",
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.51.2",
+    "react-hook-form": "^7.51.4",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^14.1.0",
-    "react-icons": "^5.0.1",
+    "react-i18next": "^14.1.1",
+    "react-icons": "^5.2.0",
     "react-konva": "^18.2.10",
-    "react-redux": "9.1.0",
-    "react-resizable-panels": "^2.0.16",
+    "react-redux": "9.1.2",
+    "react-resizable-panels": "^2.0.19",
     "react-select": "5.8.0",
     "react-use": "^17.5.0",
-    "react-virtuoso": "^4.7.5",
-    "reactflow": "^11.10.4",
+    "react-virtuoso": "^4.7.10",
+    "reactflow": "^11.11.3",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
     "redux-undo": "^1.1.0",
@@ -101,10 +103,11 @@
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
     "use-debounce": "^10.0.0",
+    "use-device-pixel-ratio": "^1.1.2",
     "use-image": "^1.1.1",
     "uuid": "^9.0.1",
-    "zod": "^3.22.4",
-    "zod-validation-error": "^3.0.3"
+    "zod": "^3.23.6",
+    "zod-validation-error": "^3.2.0"
   },
   "peerDependencies": {
     "@chakra-ui/react": "^2.8.2",
@@ -115,40 +118,42 @@
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.0.4",
-    "@storybook/addon-interactions": "^8.0.4",
-    "@storybook/addon-links": "^8.0.4",
-    "@storybook/addon-storysource": "^8.0.4",
-    "@storybook/manager-api": "^8.0.4",
-    "@storybook/react": "^8.0.4",
-    "@storybook/react-vite": "^8.0.4",
-    "@storybook/theming": "^8.0.4",
+    "@storybook/addon-essentials": "^8.0.10",
+    "@storybook/addon-interactions": "^8.0.10",
+    "@storybook/addon-links": "^8.0.10",
+    "@storybook/addon-storysource": "^8.0.10",
+    "@storybook/manager-api": "^8.0.10",
+    "@storybook/react": "^8.0.10",
+    "@storybook/react-vite": "^8.0.10",
+    "@storybook/theming": "^8.0.10",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.11.30",
-    "@types/react": "^18.2.73",
-    "@types/react-dom": "^18.2.22",
+    "@types/node": "^20.12.10",
+    "@types/react": "^18.3.1",
+    "@types/react-dom": "^18.3.0",
     "@types/uuid": "^9.0.8",
     "@vitejs/plugin-react-swc": "^3.6.0",
+    "@vitest/coverage-v8": "^1.5.0",
+    "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.0",
     "eslint-plugin-i18next": "^6.0.3",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.6.1",
+    "knip": "^5.12.3",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.7.5",
     "prettier": "^3.2.5",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.0.4",
+    "storybook": "^8.0.10",
     "ts-toolbelt": "^9.6.0",
     "tsafe": "^1.6.6",
-    "typescript": "^5.4.3",
-    "vite": "^5.2.6",
-    "vite-plugin-css-injected-by-js": "^3.5.0",
-    "vite-plugin-dts": "^3.8.0",
+    "typescript": "^5.4.5",
+    "vite": "^5.2.11",
+    "vite-plugin-css-injected-by-js": "^3.5.1",
+    "vite-plugin-dts": "^3.9.1",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.3.2",
-    "vitest": "^1.4.0"
+    "vitest": "^1.6.0"
   }
 }

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

@@ -76,7 +76,9 @@
         "aboutHeading": "Nutzen Sie Ihre kreative Energie",
         "toResolve": "Lösen",
         "add": "Hinzufügen",
-        "loglevel": "Protokoll Stufe"
+        "loglevel": "Protokoll Stufe",
+        "selected": "Ausgewählt",
+        "beta": "Beta"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -86,7 +88,7 @@
         "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
         "deleteImage_one": "Lösche Bild",
-        "deleteImage_other": "",
+        "deleteImage_other": "Lösche {{count}} Bilder",
         "copy": "Kopieren",
         "download": "Runterladen",
         "setCurrentImage": "Setze aktuelle Bild",
@@ -397,7 +399,14 @@
         "cancel": "Stornieren",
         "defaultSettingsSaved": "Standardeinstellungen gespeichert",
         "addModels": "Model hinzufügen",
-        "deleteModelImage": "Lösche Model Bild"
+        "deleteModelImage": "Lösche Model Bild",
+        "hfTokenInvalidErrorMessage": "Falscher oder fehlender HuggingFace Schlüssel.",
+        "huggingFaceRepoID": "HuggingFace Repo ID",
+        "hfToken": "HuggingFace Schlüssel",
+        "hfTokenInvalid": "Falscher oder fehlender HF Schlüssel",
+        "huggingFacePlaceholder": "besitzer/model-name",
+        "hfTokenSaved": "HF Schlüssel gespeichert",
+        "hfTokenUnableToVerify": "Konnte den HF Schlüssel nicht validieren"
     },
     "parameters": {
         "images": "Bilder",
@@ -686,7 +695,11 @@
         "hands": "Hände",
         "dwOpenpose": "DW Openpose",
         "dwOpenposeDescription": "Posenschätzung mit DW Openpose",
-        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus"
+        "selectCLIPVisionModel": "Wähle ein CLIP Vision Model aus",
+        "ipAdapterMethod": "Methode",
+        "composition": "Nur Komposition",
+        "full": "Voll",
+        "style": "Nur Style"
     },
     "queue": {
         "status": "Status",
@@ -717,7 +730,6 @@
         "resume": "Wieder aufnehmen",
         "item": "Auftrag",
         "notReady": "Warteschlange noch nicht bereit",
-        "queueCountPrediction": "{{promptsCount}} Prompts × {{iterations}} Iterationen -> {{count}} Generationen",
         "clearQueueAlertDialog": "\"Die Warteschlange leeren\" stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
         "completedIn": "Fertig in",
         "cancelBatchSucceeded": "Stapel abgebrochen",

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

@@ -88,11 +88,13 @@
         "negativePrompt": "Negative Prompt",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
+        "editor": "Editor",
         "error": "Error",
         "file": "File",
         "folder": "Folder",
         "format": "format",
         "githubLabel": "Github",
+        "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
@@ -140,7 +142,11 @@
         "blue": "Blue",
         "alpha": "Alpha",
         "selected": "Selected",
-        "viewer": "Viewer"
+        "tab": "Tab",
+        "viewing": "Viewing",
+        "viewingDesc": "Review images in a large gallery view",
+        "editing": "Editing",
+        "editingDesc": "Edit on the Control Layers canvas"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -156,6 +162,7 @@
         "balanced": "Balanced",
         "base": "Base",
         "beginEndStepPercent": "Begin / End Step Percentage",
+        "beginEndStepPercentShort": "Begin/End %",
         "bgth": "bg_th",
         "canny": "Canny",
         "cannyDescription": "Canny edge detection",
@@ -224,10 +231,11 @@
         "composition": "Composition Only",
         "safe": "Safe",
         "saveControlImage": "Save Control Image",
-        "scribble": "scribble",
+        "scribble": "Scribble",
         "selectModel": "Select a model",
         "selectCLIPVisionModel": "Select a CLIP Vision model",
-        "setControlImageDimensions": "Set Control Image Dimensions To W/H",
+        "setControlImageDimensions": "Copy size to W/H (optimize for model)",
+        "setControlImageDimensionsForce": "Copy size to W/H (ignore model)",
         "showAdvanced": "Show Advanced",
         "small": "Small",
         "toggleControlNet": "Toggle this ControlNet",
@@ -253,7 +261,6 @@
         "queue": "Queue",
         "queueFront": "Add to Front of Queue",
         "queueBack": "Add to Queue",
-        "queueCountPrediction": "{{promptsCount}} prompts \u00d7 {{iterations}} iterations -> {{count}} generations",
         "queueEmpty": "Queue Empty",
         "enqueueing": "Queueing Batch",
         "resume": "Resume",
@@ -306,7 +313,13 @@
         "batchFailedToQueue": "Failed to Queue Batch",
         "graphQueued": "Graph queued",
         "graphFailedToQueue": "Failed to queue graph",
-        "openQueue": "Open Queue"
+        "openQueue": "Open Queue",
+        "prompts_one": "Prompt",
+        "prompts_other": "Prompts",
+        "iterations_one": "Iteration",
+        "iterations_other": "Iterations",
+        "generations_one": "Generation",
+        "generations_other": "Generations"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -582,6 +595,10 @@
         "upscale": {
             "desc": "Upscale the current image",
             "title": "Upscale"
+        },
+        "toggleViewer": {
+            "desc": "Switches between the Image Viewer and workspace for the current tab.",
+            "title": "Toggle Image Viewer"
         }
     },
     "metadata": {
@@ -757,6 +774,7 @@
         "cannotConnectOutputToOutput": "Cannot connect output to output",
         "cannotConnectToSelf": "Cannot connect to self",
         "cannotDuplicateConnection": "Cannot create duplicate connections",
+        "cannotMixAndMatchCollectionItemTypes": "Cannot mix and match collection item types",
         "nodePack": "Node pack",
         "collection": "Collection",
         "collectionFieldType": "{{name}} Collection",
@@ -915,23 +933,37 @@
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} missing input",
             "missingNodeTemplate": "Missing node template",
             "noControlImageForControlAdapter": "Control Adapter #{{number}} has no control image",
+            "imageNotProcessedForControlAdapter": "Control Adapter #{{number}}'s image is not processed",
             "noInitialImageSelected": "No initial image selected",
             "noModelForControlAdapter": "Control Adapter #{{number}} has no model selected.",
             "incompatibleBaseModelForControlAdapter": "Control Adapter #{{number}} model is incompatible with main model.",
             "noModelSelected": "No model selected",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected"
+            "systemDisconnected": "System disconnected",
+            "layer": {
+                "initialImageNoImageSelected": "no initial image selected",
+                "controlAdapterNoModelSelected": "no Control Adapter model selected",
+                "controlAdapterIncompatibleBaseModel": "incompatible Control Adapter base model",
+                "controlAdapterNoImageSelected": "no Control Adapter image selected",
+                "controlAdapterImageNotProcessed": "Control Adapter image not processed",
+                "t2iAdapterIncompatibleDimensions": "T2I Adapter requires image dimension to be multiples of 64",
+                "ipAdapterNoModelSelected": "no IP adapter selected",
+                "ipAdapterIncompatibleBaseModel": "incompatible IP Adapter base model",
+                "ipAdapterNoImageSelected": "no IP Adapter image selected",
+                "rgNoPromptsOrIPAdapters": "no text prompts or IP Adapters",
+                "rgNoRegion": "no region selected"
+            }
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
+        "globalNegativePromptPlaceholder": "Global Negative Prompt",
         "noiseThreshold": "Noise Threshold",
         "patchmatchDownScaleSize": "Downscale",
         "perlinNoise": "Perlin Noise",
         "positivePromptPlaceholder": "Positive Prompt",
+        "globalPositivePromptPlaceholder": "Global Positive Prompt",
         "iterations": "Iterations",
-        "iterationsWithCount_one": "{{count}} Iteration",
-        "iterationsWithCount_other": "{{count}} Iterations",
         "scale": "Scale",
         "scaleBeforeProcessing": "Scale Before Processing",
         "scaledHeight": "Scaled H",
@@ -1511,7 +1543,7 @@
     "app": {
         "storeNotInitialized": "Store is not initialized"
     },
-    "regionalPrompts": {
+    "controlLayers": {
         "deleteAll": "Delete All",
         "addLayer": "Add Layer",
         "moveToFront": "Move to Front",
@@ -1519,8 +1551,7 @@
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "regionalControl": "Regional Control (ALPHA)",
-        "enableRegionalPrompts": "Enable $t(regionalPrompts.regionalPrompts)",
+        "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
         "toggleVisibility": "Toggle Layer Visibility",
@@ -1531,6 +1562,35 @@
         "maskPreviewColor": "Mask Preview Color",
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
-        "addIPAdapter": "Add $t(common.ipAdapter)"
+        "addIPAdapter": "Add $t(common.ipAdapter)",
+        "regionalGuidance": "Regional Guidance",
+        "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
+        "opacity": "Opacity",
+        "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
+        "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
+        "globalIPAdapter": "Global $t(common.ipAdapter)",
+        "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
+        "globalInitialImage": "Global Initial Image",
+        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
+        "opacityFilter": "Opacity Filter",
+        "clearProcessor": "Clear Processor",
+        "resetProcessor": "Reset Processor to Defaults",
+        "noLayersAdded": "No Layers Added",
+        "layers_one": "Layer",
+        "layers_other": "Layers"
+    },
+    "ui": {
+        "tabs": {
+            "generation": "Generation",
+            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
+            "canvas": "Canvas",
+            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
+            "workflows": "Workflows",
+            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
+            "models": "Models",
+            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
+            "queue": "Queue",
+            "queueTab": "$t(ui.tabs.queue) $t(common.tab)"
+        }
     }
 }

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

@@ -25,7 +25,24 @@
         "areYouSure": "¿Estas seguro?",
         "batch": "Administrador de lotes",
         "modelManager": "Administrador de modelos",
-        "communityLabel": "Comunidad"
+        "communityLabel": "Comunidad",
+        "direction": "Dirección",
+        "ai": "Ia",
+        "add": "Añadir",
+        "auto": "Automático",
+        "copyError": "Error $t(gallery.copy)",
+        "details": "Detalles",
+        "or": "o",
+        "checkpoint": "Punto de control",
+        "controlNet": "ControlNet",
+        "aboutHeading": "Sea dueño de su poder creativo",
+        "advanced": "Avanzado",
+        "data": "Fecha",
+        "delete": "Borrar",
+        "copy": "Copiar",
+        "beta": "Beta",
+        "on": "En",
+        "aboutDesc": "¿Utilizas Invoke para trabajar? Mira aquí:"
     },
     "gallery": {
         "galleryImageSize": "Tamaño de la imagen",
@@ -443,7 +460,13 @@
         "previousImage": "Imagen anterior",
         "nextImage": "Siguiente imagen",
         "showOptionsPanel": "Mostrar el panel lateral",
-        "menu": "Menú"
+        "menu": "Menú",
+        "showGalleryPanel": "Mostrar panel de galería",
+        "loadMore": "Cargar más",
+        "about": "Acerca de",
+        "createIssue": "Crear un problema",
+        "resetUI": "Interfaz de usuario $t(accessibility.reset)",
+        "mode": "Modo"
     },
     "nodes": {
         "zoomInNodes": "Acercar",
@@ -456,5 +479,68 @@
         "reloadNodeTemplates": "Recargar las plantillas de nodos",
         "loadWorkflow": "Cargar el flujo de trabajo",
         "downloadWorkflow": "Descargar el flujo de trabajo en un archivo JSON"
+    },
+    "boards": {
+        "autoAddBoard": "Agregar panel automáticamente",
+        "changeBoard": "Cambiar el panel",
+        "clearSearch": "Borrar la búsqueda",
+        "deleteBoard": "Borrar el panel",
+        "selectBoard": "Seleccionar un panel",
+        "uncategorized": "Sin categoría",
+        "cancel": "Cancelar",
+        "addBoard": "Agregar un panel",
+        "movingImagesToBoard_one": "Moviendo {{count}} imagen al panel:",
+        "movingImagesToBoard_many": "Moviendo {{count}} imágenes al panel:",
+        "movingImagesToBoard_other": "Moviendo {{count}} imágenes al panel:",
+        "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
+        "deleteBoardAndImages": "Borrar el panel y las imágenes",
+        "loading": "Cargando...",
+        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar",
+        "move": "Mover",
+        "menuItemAutoAdd": "Agregar automáticamente a este panel",
+        "searchBoard": "Buscando paneles…",
+        "topMessage": "Este panel contiene imágenes utilizadas en las siguientes funciones:",
+        "downloadBoard": "Descargar panel",
+        "deleteBoardOnly": "Borrar solo el panel",
+        "myBoard": "Mi panel",
+        "noMatching": "No hay paneles que coincidan"
+    },
+    "accordions": {
+        "compositing": {
+            "title": "Composición",
+            "infillTab": "Relleno"
+        },
+        "generation": {
+            "title": "Generación"
+        },
+        "image": {
+            "title": "Imagen"
+        },
+        "control": {
+            "title": "Control"
+        },
+        "advanced": {
+            "options": "$t(accordions.advanced.title) opciones",
+            "title": "Avanzado"
+        }
+    },
+    "ui": {
+        "tabs": {
+            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
+            "canvas": "Lienzo",
+            "generation": "Generación",
+            "queue": "Cola",
+            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
+            "workflows": "Flujos de trabajo",
+            "models": "Modelos",
+            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
+            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
+            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)"
+        }
+    },
+    "controlLayers": {
+        "layers_one": "Capa",
+        "layers_many": "Capas",
+        "layers_other": "Capas"
     }
 }

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

@@ -5,7 +5,7 @@
         "reportBugLabel": "Segnala un errore",
         "settingsLabel": "Impostazioni",
         "img2img": "Immagine a Immagine",
-        "unifiedCanvas": "Tela unificata",
+        "unifiedCanvas": "Tela",
         "nodes": "Flussi di lavoro",
         "upload": "Caricamento",
         "load": "Carica",
@@ -74,7 +74,18 @@
         "file": "File",
         "toResolve": "Da risolvere",
         "add": "Aggiungi",
-        "loglevel": "Livello di log"
+        "loglevel": "Livello di log",
+        "beta": "Beta",
+        "positivePrompt": "Prompt positivo",
+        "negativePrompt": "Prompt negativo",
+        "selected": "Selezionato",
+        "goTo": "Vai a",
+        "editor": "Editor",
+        "tab": "Scheda",
+        "viewing": "Visualizza",
+        "viewingDesc": "Rivedi le immagini in un'ampia vista della galleria",
+        "editing": "Modifica",
+        "editingDesc": "Modifica nell'area Livelli di controllo"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -180,8 +191,8 @@
             "desc": "Mostra le informazioni sui metadati dell'immagine corrente"
         },
         "sendToImageToImage": {
-            "title": "Invia a Immagine a Immagine",
-            "desc": "Invia l'immagine corrente a da Immagine a Immagine"
+            "title": "Invia a Generazione da immagine",
+            "desc": "Invia l'immagine corrente a Generazione da immagine"
         },
         "deleteImage": {
             "title": "Elimina immagine",
@@ -334,6 +345,10 @@
         "remixImage": {
             "desc": "Utilizza tutti i parametri tranne il seme dell'immagine corrente",
             "title": "Remixa l'immagine"
+        },
+        "toggleViewer": {
+            "title": "Attiva/disattiva il visualizzatore di immagini",
+            "desc": "Passa dal Visualizzatore immagini all'area di lavoro per la scheda corrente."
         }
     },
     "modelManager": {
@@ -471,8 +486,8 @@
         "scaledHeight": "Altezza ridimensionata",
         "infillMethod": "Metodo di riempimento",
         "tileSize": "Dimensione piastrella",
-        "sendToImg2Img": "Invia a Immagine a Immagine",
-        "sendToUnifiedCanvas": "Invia a Tela Unificata",
+        "sendToImg2Img": "Invia a Generazione da immagine",
+        "sendToUnifiedCanvas": "Invia alla Tela",
         "downloadImage": "Scarica l'immagine",
         "usePrompt": "Usa Prompt",
         "useSeed": "Usa Seme",
@@ -508,13 +523,11 @@
             "incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo #{{number}} non è compatibile con il modello principale.",
             "missingNodeTemplate": "Modello di nodo mancante",
             "missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante",
-            "missingFieldTemplate": "Modello di campo mancante"
+            "missingFieldTemplate": "Modello di campo mancante",
+            "imageNotProcessedForControlAdapter": "L'immagine dell'adattatore di controllo #{{number}} non è stata elaborata"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
-        "iterationsWithCount_one": "{{count}} Iterazione",
-        "iterationsWithCount_many": "{{count}} Iterazioni",
-        "iterationsWithCount_other": "{{count}} Iterazioni",
         "isAllowedToUpscale": {
             "useX2Model": "L'immagine è troppo grande per l'ampliamento con il modello x4, utilizza il modello x2",
             "tooLarge": "L'immagine è troppo grande per l'ampliamento, seleziona un'immagine più piccola"
@@ -534,7 +547,10 @@
         "infillMosaicMinColor": "Colore minimo",
         "infillMosaicMaxColor": "Colore massimo",
         "infillMosaicTileHeight": "Altezza piastrella",
-        "infillColorValue": "Colore di riempimento"
+        "infillColorValue": "Colore di riempimento",
+        "globalSettings": "Impostazioni globali",
+        "globalPositivePromptPlaceholder": "Prompt positivo globale",
+        "globalNegativePromptPlaceholder": "Prompt negativo globale"
     },
     "settings": {
         "models": "Modelli",
@@ -559,7 +575,7 @@
         "intermediatesCleared_one": "Cancellata {{count}} immagine intermedia",
         "intermediatesCleared_many": "Cancellate {{count}} immagini intermedie",
         "intermediatesCleared_other": "Cancellate {{count}} immagini intermedie",
-        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato di Tela Unificata e ControlNet.",
+        "clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato della Tela e degli Adattatori di Controllo.",
         "intermediatesClearedFailed": "Problema con la cancellazione delle immagini intermedie",
         "clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
         "clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
@@ -575,8 +591,8 @@
         "imageCopied": "Immagine copiata",
         "imageNotLoadedDesc": "Impossibile trovare l'immagine",
         "canvasMerged": "Tela unita",
-        "sentToImageToImage": "Inviato a Immagine a Immagine",
-        "sentToUnifiedCanvas": "Inviato a Tela Unificata",
+        "sentToImageToImage": "Inviato a Generazione da immagine",
+        "sentToUnifiedCanvas": "Inviato alla Tela",
         "parametersNotSet": "Parametri non impostati",
         "metadataLoadFailed": "Impossibile caricare i metadati",
         "serverError": "Errore del Server",
@@ -795,7 +811,7 @@
         "float": "In virgola mobile",
         "currentImageDescription": "Visualizza l'immagine corrente nell'editor dei nodi",
         "fieldTypesMustMatch": "I tipi di campo devono corrispondere",
-        "edge": "Bordo",
+        "edge": "Collegamento",
         "currentImage": "Immagine corrente",
         "integer": "Numero Intero",
         "inputMayOnlyHaveOneConnection": "L'ingresso può avere solo una connessione",
@@ -845,7 +861,9 @@
         "resetToDefaultValue": "Ripristina il valore predefinito",
         "noFieldsViewMode": "Questo flusso di lavoro non ha campi selezionati da visualizzare. Visualizza il flusso di lavoro completo per configurare i valori.",
         "edit": "Modifica",
-        "graph": "Grafico"
+        "graph": "Grafico",
+        "showEdgeLabelsHelp": "Mostra etichette sui collegamenti, che indicano i nodi collegati",
+        "showEdgeLabels": "Mostra le etichette del collegamento"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -922,7 +940,7 @@
         "colorMapTileSize": "Dimensione piastrella",
         "mediapipeFaceDescription": "Rilevamento dei volti tramite Mediapipe",
         "hedDescription": "Rilevamento dei bordi nidificati olisticamente",
-        "setControlImageDimensions": "Imposta le dimensioni dell'immagine di controllo su L/A",
+        "setControlImageDimensions": "Copia le dimensioni in L/A (ottimizza per il modello)",
         "maxFaces": "Numero massimo di volti",
         "addT2IAdapter": "Aggiungi $t(common.t2iAdapter)",
         "addControlNet": "Aggiungi $t(common.controlNet)",
@@ -951,12 +969,17 @@
         "mediapipeFace": "Mediapipe Volto",
         "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
         "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
-        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision"
+        "selectCLIPVisionModel": "Seleziona un modello CLIP Vision",
+        "ipAdapterMethod": "Metodo",
+        "full": "Completo",
+        "composition": "Solo la composizione",
+        "style": "Solo lo stile",
+        "beginEndStepPercentShort": "Inizio/Fine %",
+        "setControlImageDimensionsForce": "Copia le dimensioni in L/A (ignora il modello)"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
         "queueBack": "Aggiungi alla coda",
-        "queueCountPrediction": "{{promptsCount}} prompt × {{iterations}} iterazioni -> {{count}} generazioni",
         "queue": "Coda",
         "status": "Stato",
         "pruneSucceeded": "Rimossi {{item_count}} elementi completati dalla coda",
@@ -993,7 +1016,7 @@
         "cancelBatchSucceeded": "Lotto annullato",
         "clearTooltip": "Annulla e cancella tutti gli elementi",
         "current": "Attuale",
-        "pauseTooltip": "Sospende l'elaborazione",
+        "pauseTooltip": "Sospendi l'elaborazione",
         "failed": "Falliti",
         "cancelItem": "Annulla l'elemento",
         "next": "Prossimo",
@@ -1394,6 +1417,12 @@
             "paragraphs": [
                 "La dimensione del bordo del passaggio di coerenza."
             ]
+        },
+        "ipAdapterMethod": {
+            "heading": "Metodo",
+            "paragraphs": [
+                "Metodo con cui applicare l'adattatore IP corrente."
+            ]
         }
     },
     "sdxl": {
@@ -1522,5 +1551,56 @@
         "compatibleEmbeddings": "Incorporamenti compatibili",
         "addPromptTrigger": "Aggiungi Trigger nel prompt",
         "noMatchingTriggers": "Nessun Trigger corrispondente"
+    },
+    "controlLayers": {
+        "opacityFilter": "Filtro opacità",
+        "deleteAll": "Cancella tutto",
+        "addLayer": "Aggiungi Livello",
+        "moveToFront": "Sposta in primo piano",
+        "moveToBack": "Sposta in fondo",
+        "moveForward": "Sposta avanti",
+        "moveBackward": "Sposta indietro",
+        "brushSize": "Dimensioni del pennello",
+        "globalMaskOpacity": "Opacità globale della maschera",
+        "autoNegative": "Auto Negativo",
+        "toggleVisibility": "Attiva/disattiva la visibilità dei livelli",
+        "deletePrompt": "Cancella il prompt",
+        "debugLayers": "Debug dei Livelli",
+        "rectangle": "Rettangolo",
+        "maskPreviewColor": "Colore anteprima maschera",
+        "addPositivePrompt": "Aggiungi $t(common.positivePrompt)",
+        "addNegativePrompt": "Aggiungi $t(common.negativePrompt)",
+        "addIPAdapter": "Aggiungi $t(common.ipAdapter)",
+        "regionalGuidance": "Guida regionale",
+        "regionalGuidanceLayer": "$t(unifiedCanvas.layer) $t(controlLayers.regionalGuidance)",
+        "opacity": "Opacità",
+        "globalControlAdapter": "$t(controlnet.controlAdapter_one) Globale",
+        "globalControlAdapterLayer": "$t(controlnet.controlAdapter_one) - $t(unifiedCanvas.layer) Globale",
+        "globalIPAdapter": "$t(common.ipAdapter) Globale",
+        "globalIPAdapterLayer": "$t(common.ipAdapter) - $t(unifiedCanvas.layer) Globale",
+        "globalInitialImage": "Immagine iniziale",
+        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) - $t(unifiedCanvas.layer) Globale",
+        "clearProcessor": "Cancella processore",
+        "resetProcessor": "Ripristina il processore alle impostazioni predefinite",
+        "noLayersAdded": "Nessun livello aggiunto",
+        "resetRegion": "Reimposta la regione",
+        "controlLayers": "Livelli di controllo",
+        "layers_one": "Livello",
+        "layers_many": "Livelli",
+        "layers_other": "Livelli"
+    },
+    "ui": {
+        "tabs": {
+            "generation": "Generazione",
+            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
+            "canvas": "Tela",
+            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
+            "workflows": "Flussi di lavoro",
+            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
+            "models": "Modelli",
+            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
+            "queue": "Coda",
+            "queueTab": "$t(ui.tabs.queue) $t(common.tab)"
+        }
     }
 }

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

@@ -570,7 +570,6 @@
         "pauseSucceeded": "処理が一時停止されました",
         "queueFront": "キューの先頭へ追加",
         "queueBack": "キューに追加",
-        "queueCountPrediction": "{{promptsCount}} プロンプト × {{iterations}} イテレーション -> {{count}} 枚生成",
         "pause": "一時停止",
         "queue": "キュー",
         "pauseTooltip": "処理を一時停止",

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

@@ -505,7 +505,6 @@
         "completed": "완성된",
         "queueBack": "Queue에 추가",
         "cancelFailed": "항목 취소 중 발생한 문제",
-        "queueCountPrediction": "Queue에 {{predicted}} 추가",
         "batchQueued": "Batch Queued",
         "pauseFailed": "프로세서 중지 중 발생한 문제",
         "clearFailed": "Queue 제거 중 발생한 문제",

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

@@ -383,8 +383,6 @@
         "useCpuNoise": "Gebruik CPU-ruis",
         "imageActions": "Afbeeldingshandeling",
         "iterations": "Iteraties",
-        "iterationsWithCount_one": "{{count}} iteratie",
-        "iterationsWithCount_other": "{{count}} iteraties",
         "coherenceMode": "Modus"
     },
     "settings": {
@@ -940,7 +938,6 @@
         "completed": "Voltooid",
         "queueBack": "Voeg toe aan wachtrij",
         "cancelFailed": "Fout bij annuleren onderdeel",
-        "queueCountPrediction": "Voeg {{predicted}} toe aan wachtrij",
         "batchQueued": "Reeks in wachtrij geplaatst",
         "pauseFailed": "Fout bij onderbreken verwerker",
         "clearFailed": "Fout bij wissen van wachtrij",

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

@@ -76,7 +76,18 @@
         "localSystem": "Локальная система",
         "aboutDesc": "Используя Invoke для работы? Проверьте это:",
         "add": "Добавить",
-        "loglevel": "Уровень логов"
+        "loglevel": "Уровень логов",
+        "beta": "Бета",
+        "selected": "Выбрано",
+        "positivePrompt": "Позитивный запрос",
+        "negativePrompt": "Негативный запрос",
+        "editor": "Редактор",
+        "goTo": "Перейти к",
+        "tab": "Вкладка",
+        "viewing": "Просмотр",
+        "editing": "Редактирование",
+        "viewingDesc": "Просмотр изображений в режиме большой галереи",
+        "editingDesc": "Редактировать на холсте слоёв управления"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -87,8 +98,8 @@
         "deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
         "deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
         "deleteImage_one": "Удалить изображение",
-        "deleteImage_few": "",
-        "deleteImage_many": "",
+        "deleteImage_few": "Удалить {{count}} изображения",
+        "deleteImage_many": "Удалить {{count}} изображений",
         "assets": "Ресурсы",
         "autoAssignBoardOnClick": "Авто-назначение доски по клику",
         "deleteSelection": "Удалить выделенное",
@@ -336,6 +347,10 @@
         "remixImage": {
             "desc": "Используйте все параметры, кроме сида из текущего изображения",
             "title": "Ремикс изображения"
+        },
+        "toggleViewer": {
+            "title": "Переключить просмотр изображений",
+            "desc": "Переключение между средством просмотра изображений и рабочей областью для текущей вкладки."
         }
     },
     "modelManager": {
@@ -512,7 +527,8 @@
             "missingNodeTemplate": "Отсутствует шаблон узла",
             "missingFieldTemplate": "Отсутствует шаблон поля",
             "addingImagesTo": "Добавление изображений в",
-            "invoke": "Создать"
+            "invoke": "Создать",
+            "imageNotProcessedForControlAdapter": "Изображение адаптера контроля №{{number}} не обрабатывается"
         },
         "isAllowedToUpscale": {
             "useX2Model": "Изображение слишком велико для увеличения с помощью модели x4. Используйте модель x2",
@@ -523,9 +539,6 @@
         "useCpuNoise": "Использовать шум CPU",
         "imageActions": "Действия с изображениями",
         "iterations": "Кол-во",
-        "iterationsWithCount_one": "{{count}} Интеграция",
-        "iterationsWithCount_few": "{{count}} Итерации",
-        "iterationsWithCount_many": "{{count}} Итераций",
         "useSize": "Использовать размер",
         "coherenceMode": "Режим",
         "aspect": "Соотношение",
@@ -541,7 +554,10 @@
         "infillMosaicTileHeight": "Высота плиток",
         "infillMosaicMinColor": "Мин цвет",
         "infillMosaicMaxColor": "Макс цвет",
-        "infillColorValue": "Цвет заливки"
+        "infillColorValue": "Цвет заливки",
+        "globalSettings": "Глобальные настройки",
+        "globalNegativePromptPlaceholder": "Глобальный негативный запрос",
+        "globalPositivePromptPlaceholder": "Глобальный запрос"
     },
     "settings": {
         "models": "Модели",
@@ -706,7 +722,9 @@
         "coherenceModeBoxBlur": "коробчатое размытие",
         "discardCurrent": "Отбросить текущее",
         "invertBrushSizeScrollDirection": "Инвертировать прокрутку для размера кисти",
-        "initialFitImageSize": "Подогнать размер изображения при перебросе"
+        "initialFitImageSize": "Подогнать размер изображения при перебросе",
+        "hideBoundingBox": "Скрыть ограничительную рамку",
+        "showBoundingBox": "Показать ограничительную рамку"
     },
     "accessibility": {
         "uploadImage": "Загрузить изображение",
@@ -849,7 +867,10 @@
         "editMode": "Открыть в редакторе узлов",
         "resetToDefaultValue": "Сбросить к стандартному значкнию",
         "edit": "Редактировать",
-        "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений."
+        "noFieldsViewMode": "В этом рабочем процессе нет выбранных полей для отображения. Просмотрите полный рабочий процесс для настройки значений.",
+        "graph": "График",
+        "showEdgeLabels": "Показать метки на ребрах",
+        "showEdgeLabelsHelp": "Показать метки на ребрах, указывающие на соединенные узлы"
     },
     "controlnet": {
         "amult": "a_mult",
@@ -917,8 +938,8 @@
         "lineartAnime": "Контурный рисунок в стиле аниме",
         "mediapipeFaceDescription": "Обнаружение лиц с помощью Mediapipe",
         "hedDescription": "Целостное обнаружение границ",
-        "setControlImageDimensions": "Установите размеры контрольного изображения на Ш/В",
-        "scribble": "каракули",
+        "setControlImageDimensions": "Скопируйте размер в Ш/В (оптимизируйте для модели)",
+        "scribble": "Штрихи",
         "maxFaces": "Макс Лица",
         "mlsdDescription": "Минималистичный детектор отрезков линии",
         "resizeSimple": "Изменить размер (простой)",
@@ -933,7 +954,18 @@
         "small": "Маленький",
         "body": "Тело",
         "hands": "Руки",
-        "selectCLIPVisionModel": "Выбрать модель CLIP Vision"
+        "selectCLIPVisionModel": "Выбрать модель CLIP Vision",
+        "ipAdapterMethod": "Метод",
+        "full": "Всё",
+        "mlsd": "M-LSD",
+        "h": "H",
+        "style": "Только стиль",
+        "dwOpenpose": "DW Openpose",
+        "pidi": "PIDI",
+        "composition": "Только композиция",
+        "hed": "HED",
+        "beginEndStepPercentShort": "Начало/конец %",
+        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1312,6 +1344,12 @@
             "paragraphs": [
                 "Плавно укладывайте изображение вдоль вертикальной оси."
             ]
+        },
+        "ipAdapterMethod": {
+            "heading": "Метод",
+            "paragraphs": [
+                "Метод, с помощью которого применяется текущий IP-адаптер."
+            ]
         }
     },
     "metadata": {
@@ -1359,7 +1397,6 @@
         "completed": "Выполнено",
         "queueBack": "Добавить в очередь",
         "cancelFailed": "Проблема с отменой элемента",
-        "queueCountPrediction": "{{promptsCount}} запросов × {{iterations}} изображений -> {{count}} генераций",
         "batchQueued": "Пакетная очередь",
         "pauseFailed": "Проблема с приостановкой рендеринга",
         "clearFailed": "Проблема с очисткой очереди",
@@ -1475,7 +1512,11 @@
         "projectWorkflows": "Рабочие процессы проекта",
         "defaultWorkflows": "Стандартные рабочие процессы",
         "name": "Имя",
-        "noRecentWorkflows": "Нет последних рабочих процессов"
+        "noRecentWorkflows": "Нет последних рабочих процессов",
+        "loadWorkflow": "Рабочий процесс $t(common.load)",
+        "convertGraph": "Конвертировать график",
+        "loadFromGraph": "Загрузка рабочего процесса из графика",
+        "autoLayout": "Автоматическое расположение"
     },
     "hrf": {
         "enableHrf": "Включить исправление высокого разрешения",
@@ -1528,5 +1569,56 @@
         "addPromptTrigger": "Добавить триггер запроса",
         "compatibleEmbeddings": "Совместимые встраивания",
         "noMatchingTriggers": "Нет соответствующих триггеров"
+    },
+    "controlLayers": {
+        "moveToBack": "На задний план",
+        "moveForward": "Переместить вперёд",
+        "moveBackward": "Переместить назад",
+        "brushSize": "Размер кисти",
+        "controlLayers": "Слои управления",
+        "globalMaskOpacity": "Глобальная непрозрачность маски",
+        "autoNegative": "Авто негатив",
+        "deletePrompt": "Удалить запрос",
+        "resetRegion": "Сбросить регион",
+        "debugLayers": "Слои отладки",
+        "rectangle": "Прямоугольник",
+        "maskPreviewColor": "Цвет предпросмотра маски",
+        "addNegativePrompt": "Добавить $t(common.negativePrompt)",
+        "regionalGuidance": "Региональная точность",
+        "opacity": "Непрозрачность",
+        "globalControlAdapter": "Глобальный $t(controlnet.controlAdapter_one)",
+        "globalControlAdapterLayer": "Глобальный $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
+        "globalIPAdapter": "Глобальный $t(common.ipAdapter)",
+        "globalIPAdapterLayer": "Глобальный $t(common.ipAdapter) $t(unifiedCanvas.layer)",
+        "opacityFilter": "Фильтр непрозрачности",
+        "deleteAll": "Удалить всё",
+        "addLayer": "Добавить слой",
+        "moveToFront": "На передний план",
+        "toggleVisibility": "Переключить видимость слоя",
+        "addPositivePrompt": "Добавить $t(common.positivePrompt)",
+        "addIPAdapter": "Добавить $t(common.ipAdapter)",
+        "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
+        "resetProcessor": "Сброс процессора по умолчанию",
+        "clearProcessor": "Чистый процессор",
+        "globalInitialImage": "Глобальное исходное изображение",
+        "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
+        "noLayersAdded": "Без слоев",
+        "layers_one": "Слой",
+        "layers_few": "Слоя",
+        "layers_many": "Слоев"
+    },
+    "ui": {
+        "tabs": {
+            "generation": "Генерация",
+            "canvas": "Холст",
+            "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
+            "models": "Модели",
+            "generationTab": "$t(ui.tabs.generation) $t(common.tab)",
+            "workflows": "Рабочие процессы",
+            "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
+            "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
+            "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
+            "queue": "Очередь"
+        }
     }
 }

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

@@ -66,7 +66,7 @@
         "saveAs": "保存为",
         "ai": "ai",
         "or": "或",
-        "aboutDesc": "使用 Invoke 工作？查看：",
+        "aboutDesc": "使用 Invoke 工作？来看看：",
         "add": "添加",
         "loglevel": "日志级别",
         "copy": "复制",
@@ -445,7 +445,6 @@
             "useX2Model": "图像太大，无法使用 x4 模型，使用 x2 模型作为替代",
             "tooLarge": "图像太大无法进行放大，请选择更小的图像"
         },
-        "iterationsWithCount_other": "{{count}} 次迭代生成",
         "cfgRescaleMultiplier": "CFG 重缩放倍数",
         "useSize": "使用尺寸",
         "setToOptimalSize": "优化模型大小",
@@ -853,7 +852,6 @@
         "pruneSucceeded": "从队列修剪 {{item_count}} 个已完成的项目",
         "notReady": "无法排队",
         "batchFailedToQueue": "批次加入队列失败",
-        "queueCountPrediction": "{{promptsCount}} 提示词 × {{iterations}} 迭代次数 -> {{count}} 次生成",
         "batchQueued": "加入队列的批次",
         "front": "前",
         "pruneTooltip": "修剪 {{item_count}} 个已完成的项目",

invokeai/frontend/web/scripts/typegen.js

@@ -1,3 +1,4 @@
+/* eslint-disable no-console */
 import fs from 'node:fs';
 
 import openapiTS from 'openapi-typescript';

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

@@ -67,6 +67,8 @@ export const useSocketIO = () => {
 
     if ($isDebugging.get() || import.meta.env.MODE === 'development') {
       window.$socketOptions = $socketOptions;
+      // This is only enabled manually for debugging, console is allowed.
+      /* eslint-disable-next-line no-console */
       console.log('Socket initialized', socket);
     }
 
@@ -75,6 +77,8 @@ export const useSocketIO = () => {
     return () => {
       if ($isDebugging.get() || import.meta.env.MODE === 'development') {
         window.$socketOptions = undefined;
+        // This is only enabled manually for debugging, console is allowed.
+        /* eslint-disable-next-line no-console */
         console.log('Socket teardown', socket);
       }
       socket.disconnect();

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

@@ -20,15 +20,14 @@ export type LoggerNamespace =
   | 'models'
   | 'config'
   | 'canvas'
-  | 'txt2img'
-  | 'img2img'
+  | 'generation'
   | 'nodes'
   | 'system'
   | 'socketio'
   | 'session'
   | 'queue'
   | 'dnd'
-  | 'regionalPrompts';
+  | 'controlLayers';
 
 export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 

invokeai/frontend/web/src/app/store/middleware/debugLoggerMiddleware.ts

@@ -1,3 +1,6 @@
+/* eslint-disable no-console */
+// This is only enabled manually for debugging, console is allowed.
+
 import type { Middleware, MiddlewareAPI } from '@reduxjs/toolkit';
 import { diff } from 'jsondiffpatch';
 

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,7 +1,6 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
 import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
-import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
@@ -25,13 +24,6 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     };
   }
 
-  if (nodeTemplatesBuilt.match(action)) {
-    return {
-      ...action,
-      payload: '<Node templates omitted>',
-    };
-  }
-
   if (socketGeneratorProgress.match(action)) {
     const sanitized = deepClone(action);
     if (sanitized.payload.data.progress_image) {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -16,6 +16,7 @@ import { addCanvasMaskSavedToGalleryListener } from 'app/store/middleware/listen
 import { addCanvasMaskToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet';
 import { addCanvasMergedListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMerged';
 import { addCanvasSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery';
+import { addControlAdapterPreprocessor } from 'app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor';
 import { addControlNetAutoProcessListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess';
 import { addControlNetImageProcessedListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed';
 import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas';
@@ -31,7 +32,6 @@ import { addImagesStarredListener } from 'app/store/middleware/listenerMiddlewar
 import { addImagesUnstarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesUnstarred';
 import { addImageToDeleteSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected';
 import { addImageUploadedFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageUploaded';
-import { addInitialImageSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/initialImageSelected';
 import { addModelSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelSelected';
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
@@ -72,9 +72,6 @@ const startAppListening = listenerMiddleware.startListening as AppStartListening
 // Image uploaded
 addImageUploadedFulfilledListener(startAppListening);
 
-// Image selected
-addInitialImageSelectedListener(startAppListening);
-
 // Image deleted
 addRequestedSingleImageDeletionListener(startAppListening);
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
@@ -157,3 +154,4 @@ addUpscaleRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
+addControlAdapterPreprocessor(startAppListening);

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

@@ -1,9 +1,9 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { allLayersDeleted } from 'features/controlLayers/store/controlLayersSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppStartListening) => {
@@ -14,19 +14,14 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
 
       // Remove all deleted images from the UI
 
-      let wasInitialImageReset = false;
       let wasCanvasReset = false;
       let wasNodeEditorReset = false;
       let wereControlAdaptersReset = false;
+      let wereControlLayersReset = false;
 
-      const { generation, canvas, nodes, controlAdapters } = getState();
+      const { canvas, nodes, controlAdapters, controlLayers } = getState();
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(generation, canvas, nodes, controlAdapters, image_name);
-
-        if (imageUsage.isInitialImage && !wasInitialImageReset) {
-          dispatch(clearInitialImage());
-          wasInitialImageReset = true;
-        }
+        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
 
         if (imageUsage.isCanvasImage && !wasCanvasReset) {
           dispatch(resetCanvas());
@@ -42,6 +37,11 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
           dispatch(controlAdaptersReset());
           wereControlAdaptersReset = true;
         }
+
+        if (imageUsage.isControlLayerImage && !wereControlLayersReset) {
+          dispatch(allLayersDeleted());
+          wereControlLayersReset = true;
+        }
       });
     },
   });

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

@@ -0,0 +1,174 @@
+import { isAnyOf } from '@reduxjs/toolkit';
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppDispatch } from 'app/store/store';
+import { parseify } from 'common/util/serialize';
+import {
+  caLayerImageChanged,
+  caLayerModelChanged,
+  caLayerProcessedImageChanged,
+  caLayerProcessorConfigChanged,
+  caLayerProcessorPendingBatchIdChanged,
+  caLayerRecalled,
+  isControlAdapterLayer,
+} from 'features/controlLayers/store/controlLayersSlice';
+import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
+import { isImageOutput } from 'features/nodes/types/common';
+import { addToast } from 'features/system/store/systemSlice';
+import { t } from 'i18next';
+import { getImageDTO } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { BatchConfig } from 'services/api/types';
+import { socketInvocationComplete } from 'services/events/actions';
+import { assert } from 'tsafe';
+
+const matcher = isAnyOf(caLayerImageChanged, caLayerProcessorConfigChanged, caLayerModelChanged, caLayerRecalled);
+
+const DEBOUNCE_MS = 300;
+const log = logger('session');
+
+/**
+ * Simple helper to cancel a batch and reset the pending batch ID
+ */
+const cancelProcessorBatch = async (dispatch: AppDispatch, layerId: string, batchId: string) => {
+  const req = dispatch(queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: [batchId] }));
+  log.trace({ batchId }, 'Cancelling existing preprocessor batch');
+  try {
+    await req.unwrap();
+  } catch {
+    // no-op
+  } finally {
+    req.reset();
+    // Always reset the pending batch ID - the cancel req could fail if the batch doesn't exist
+    dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
+  }
+};
+
+export const addControlAdapterPreprocessor = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher,
+    effect: async (action, { dispatch, getState, cancelActiveListeners, delay, take, signal }) => {
+      const layerId = caLayerRecalled.match(action) ? action.payload.id : action.payload.layerId;
+
+      // Cancel any in-progress instances of this listener
+      cancelActiveListeners();
+      log.trace('Control Layer CA auto-process triggered');
+
+      // Delay before starting actual work
+      await delay(DEBOUNCE_MS);
+
+      // Double-check that we are still eligible for processing
+      const state = getState();
+      const layer = state.controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
+
+      // If we have no image or there is no processor config, bail
+      if (!layer) {
+        return;
+      }
+
+      const image = layer.controlAdapter.image;
+      const config = layer.controlAdapter.processorConfig;
+
+      if (!image || !config) {
+        // The user has reset the image or config, so we should clear the processed image
+        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
+      }
+
+      // At this point, the user has stopped fiddling with the processor settings and there is a processor selected.
+
+      // If there is a pending processor batch, cancel it.
+      if (layer.controlAdapter.processorPendingBatchId) {
+        cancelProcessorBatch(dispatch, layerId, layer.controlAdapter.processorPendingBatchId);
+      }
+
+      // @ts-expect-error: TS isn't able to narrow the typing of buildNode and `config` will error...
+      const processorNode = CA_PROCESSOR_DATA[config.type].buildNode(image, config);
+      const enqueueBatchArg: BatchConfig = {
+        prepend: true,
+        batch: {
+          graph: {
+            nodes: {
+              [processorNode.id]: {
+                ...processorNode,
+                // Control images are always intermediate - do not save to gallery
+                is_intermediate: true,
+              },
+            },
+            edges: [],
+          },
+          runs: 1,
+        },
+      };
+
+      // Kick off the processor batch
+      const req = dispatch(
+        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
+          fixedCacheKey: 'enqueueBatch',
+        })
+      );
+
+      try {
+        const enqueueResult = await req.unwrap();
+        // TODO(psyche): Update the pydantic models, pretty sure we will _always_ have a batch_id here, but the model says it's optional
+        assert(enqueueResult.batch.batch_id, 'Batch ID not returned from queue');
+        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: enqueueResult.batch.batch_id }));
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
+
+        // Wait for the processor node to complete
+        const [invocationCompleteAction] = await take(
+          (action): action is ReturnType<typeof socketInvocationComplete> =>
+            socketInvocationComplete.match(action) &&
+            action.payload.data.queue_batch_id === enqueueResult.batch.batch_id &&
+            action.payload.data.source_node_id === processorNode.id
+        );
+
+        // We still have to check the output type
+        assert(
+          isImageOutput(invocationCompleteAction.payload.data.result),
+          `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
+        );
+        const { image_name } = invocationCompleteAction.payload.data.result.image;
+
+        const imageDTO = await getImageDTO(image_name);
+        assert(imageDTO, "Failed to fetch processor output's image DTO");
+
+        // Whew! We made it. Update the layer with the processed image
+        log.debug({ layerId, imageDTO }, 'ControlNet image processed');
+        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO }));
+        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
+      } catch (error) {
+        if (signal.aborted) {
+          // The listener was canceled - we need to cancel the pending processor batch, if there is one (could have changed by now).
+          const pendingBatchId = getState()
+            .controlLayers.present.layers.filter(isControlAdapterLayer)
+            .find((l) => l.id === layerId)?.controlAdapter.processorPendingBatchId;
+          if (pendingBatchId) {
+            cancelProcessorBatch(dispatch, layerId, pendingBatchId);
+          }
+          log.trace('Control Adapter preprocessor cancelled');
+        } else {
+          // Some other error condition...
+          log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
+
+          if (error instanceof Object) {
+            if ('data' in error && 'status' in error) {
+              if (error.status === 403) {
+                dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
+                return;
+              }
+            }
+          }
+
+          dispatch(
+            addToast({
+              title: t('queue.graphFailedToQueue'),
+              status: 'error',
+            })
+          );
+        }
+      } finally {
+        req.reset();
+      }
+    },
+  });
+};

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

@@ -8,8 +8,8 @@ import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
 import { getCanvasData } from 'features/canvas/util/getCanvasData';
 import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
 import { canvasGraphBuilt } from 'features/nodes/store/actions';
-import { buildCanvasGraph } from 'features/nodes/util/graph/buildCanvasGraph';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
+import { buildCanvasGraph } from 'features/nodes/util/graph/canvas/buildCanvasGraph';
 import { imagesApi } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
@@ -30,7 +30,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'unifiedCanvas',
+      enqueueRequested.match(action) && action.payload.tabName === 'canvas',
     effect: async (action, { getState, dispatch }) => {
       const log = logger('queue');
       const { prepend } = action.payload;

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

@@ -1,35 +1,27 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildLinearImageToImageGraph } from 'features/nodes/util/graph/buildLinearImageToImageGraph';
-import { buildLinearSDXLImageToImageGraph } from 'features/nodes/util/graph/buildLinearSDXLImageToImageGraph';
-import { buildLinearSDXLTextToImageGraph } from 'features/nodes/util/graph/buildLinearSDXLTextToImageGraph';
-import { buildLinearTextToImageGraph } from 'features/nodes/util/graph/buildLinearTextToImageGraph';
+import { buildGenerationTabGraph } from 'features/nodes/util/graph/generation/buildGenerationTabGraph';
+import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/generation/buildGenerationTabSDXLGraph';
 import { queueApi } from 'services/api/endpoints/queue';
 
 export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && (action.payload.tabName === 'txt2img' || action.payload.tabName === 'img2img'),
+      enqueueRequested.match(action) && action.payload.tabName === 'generation',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
+      const { shouldShowProgressInViewer } = state.ui;
       const model = state.generation.model;
       const { prepend } = action.payload;
 
       let graph;
 
-      if (model && model.base === 'sdxl') {
-        if (action.payload.tabName === 'txt2img') {
-          graph = await buildLinearSDXLTextToImageGraph(state);
-        } else {
-          graph = await buildLinearSDXLImageToImageGraph(state);
-        }
+      if (model?.base === 'sdxl') {
+        graph = await buildGenerationTabSDXLGraph(state);
       } else {
-        if (action.payload.tabName === 'txt2img') {
-          graph = await buildLinearTextToImageGraph(state);
-        } else {
-          graph = await buildLinearImageToImageGraph(state);
-        }
+        graph = await buildGenerationTabGraph(state);
       }
 
       const batchConfig = prepareLinearUIBatch(state, graph, prepend);
@@ -39,7 +31,14 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      req.reset();
+      try {
+        await req.unwrap();
+        if (shouldShowProgressInViewer) {
+          dispatch(isImageViewerOpenChanged(true));
+        }
+      } finally {
+        req.reset();
+      }
     },
   });
 };

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

@@ -8,12 +8,12 @@ import type { BatchConfig } from 'services/api/types';
 export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: (action): action is ReturnType<typeof enqueueRequested> =>
-      enqueueRequested.match(action) && action.payload.tabName === 'nodes',
+      enqueueRequested.match(action) && action.payload.tabName === 'workflows',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const { nodes, edges } = state.nodes;
+      const { nodes, edges } = state.nodes.present;
       const workflow = state.workflow;
-      const graph = buildNodesGraph(state.nodes);
+      const graph = buildNodesGraph(state.nodes.present);
       const builtWorkflow = buildWorkflowWithValidation({
         nodes,
         edges,
@@ -39,7 +39,11 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      req.reset();
+      try {
+        await req.unwrap();
+      } finally {
+        req.reset();
+      }
     },
   });
 };

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

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
-import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
+import { $templates } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
@@ -9,7 +9,7 @@ import { appInfoApi } from 'services/api/endpoints/appInfo';
 export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: (action, { getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
@@ -20,7 +20,7 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
 
       log.debug({ nodeTemplates: parseify(nodeTemplates) }, `Built ${size(nodeTemplates)} node templates`);
 
-      dispatch(nodeTemplatesBuilt(nodeTemplates));
+      $templates.set(nodeTemplates);
     },
   });
 

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

@@ -1,5 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppDispatch, RootState } from 'app/store/store';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import {
   controlAdapterImageChanged,
@@ -7,6 +8,13 @@ import {
   selectControlAdapterAll,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import {
+  isControlAdapterLayer,
+  isInitialImageLayer,
+  isIPAdapterLayer,
+  isRegionalGuidanceLayer,
+  layerDeleted,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
@@ -14,12 +22,82 @@ import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
 import { isImageFieldInputInstance } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
-import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { clamp, forEach } from 'lodash-es';
 import { api } from 'services/api';
 import { imagesApi } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
 import { imagesSelectors } from 'services/api/util';
 
+const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  state.nodes.present.nodes.forEach((node) => {
+    if (!isInvocationNode(node)) {
+      return;
+    }
+
+    forEach(node.data.inputs, (input) => {
+      if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
+        dispatch(
+          fieldImageValueChanged({
+            nodeId: node.data.id,
+            fieldName: input.name,
+            value: undefined,
+          })
+        );
+      }
+    });
+  });
+};
+
+const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  forEach(selectControlAdapterAll(state.controlAdapters), (ca) => {
+    if (
+      ca.controlImage === imageDTO.image_name ||
+      (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+    ) {
+      dispatch(
+        controlAdapterImageChanged({
+          id: ca.id,
+          controlImage: null,
+        })
+      );
+      dispatch(
+        controlAdapterProcessedImageChanged({
+          id: ca.id,
+          processedControlImage: null,
+        })
+      );
+    }
+  });
+};
+
+const deleteControlLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  state.controlLayers.present.layers.forEach((l) => {
+    if (isRegionalGuidanceLayer(l)) {
+      if (l.ipAdapters.some((ipa) => ipa.image?.name === imageDTO.image_name)) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isControlAdapterLayer(l)) {
+      if (
+        l.controlAdapter.image?.name === imageDTO.image_name ||
+        l.controlAdapter.processedImage?.name === imageDTO.image_name
+      ) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isIPAdapterLayer(l)) {
+      if (l.ipAdapter.image?.name === imageDTO.image_name) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isInitialImageLayer(l)) {
+      if (l.image?.name === imageDTO.image_name) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+  });
+};
+
 export const addRequestedSingleImageDeletionListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: imageDeletionConfirmed,
@@ -73,50 +151,9 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
       }
 
       imageDTOs.forEach((imageDTO) => {
-        // reset init image if we deleted it
-        if (getState().generation.initialImage?.imageName === imageDTO.image_name) {
-          dispatch(clearInitialImage());
-        }
-
-        // reset control adapters that use the deleted images
-        forEach(selectControlAdapterAll(getState().controlAdapters), (ca) => {
-          if (
-            ca.controlImage === imageDTO.image_name ||
-            (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
-          ) {
-            dispatch(
-              controlAdapterImageChanged({
-                id: ca.id,
-                controlImage: null,
-              })
-            );
-            dispatch(
-              controlAdapterProcessedImageChanged({
-                id: ca.id,
-                processedControlImage: null,
-              })
-            );
-          }
-        });
-
-        // reset nodes that use the deleted images
-        getState().nodes.nodes.forEach((node) => {
-          if (!isInvocationNode(node)) {
-            return;
-          }
-
-          forEach(node.data.inputs, (input) => {
-            if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
-              dispatch(
-                fieldImageValueChanged({
-                  nodeId: node.data.id,
-                  fieldName: input.name,
-                  value: undefined,
-                })
-              );
-            }
-          });
-        });
+        deleteControlAdapterImages(state, dispatch, imageDTO);
+        deleteNodesImages(state, dispatch, imageDTO);
+        deleteControlLayerImages(state, dispatch, imageDTO);
       });
 
       // Delete from server
@@ -168,50 +205,9 @@ export const addRequestedSingleImageDeletionListener = (startAppListening: AppSt
         }
 
         imageDTOs.forEach((imageDTO) => {
-          // reset init image if we deleted it
-          if (getState().generation.initialImage?.imageName === imageDTO.image_name) {
-            dispatch(clearInitialImage());
-          }
-
-          // reset control adapters that use the deleted images
-          forEach(selectControlAdapterAll(getState().controlAdapters), (ca) => {
-            if (
-              ca.controlImage === imageDTO.image_name ||
-              (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
-            ) {
-              dispatch(
-                controlAdapterImageChanged({
-                  id: ca.id,
-                  controlImage: null,
-                })
-              );
-              dispatch(
-                controlAdapterProcessedImageChanged({
-                  id: ca.id,
-                  processedControlImage: null,
-                })
-              );
-            }
-          });
-
-          // reset nodes that use the deleted images
-          getState().nodes.nodes.forEach((node) => {
-            if (!isInvocationNode(node)) {
-              return;
-            }
-
-            forEach(node.data.inputs, (input) => {
-              if (isImageFieldInputInstance(input) && input.value?.image_name === imageDTO.image_name) {
-                dispatch(
-                  fieldImageValueChanged({
-                    nodeId: node.data.id,
-                    fieldName: input.name,
-                    value: undefined,
-                  })
-                );
-              }
-            });
-          });
+          deleteControlAdapterImages(state, dispatch, imageDTO);
+          deleteNodesImages(state, dispatch, imageDTO);
+          deleteControlLayerImages(state, dispatch, imageDTO);
         });
       } catch {
         // no-op

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

@@ -7,10 +7,16 @@ import {
   controlAdapterImageChanged,
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  caLayerImageChanged,
+  iiLayerImageChanged,
+  ipaLayerImageChanged,
+  rgLayerIPAdapterImageChanged,
+} from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const dndDropped = createAction<{
@@ -47,18 +53,6 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
-      /**
-       * Image dropped on initial image
-       */
-      if (
-        overData.actionType === 'SET_INITIAL_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        dispatch(initialImageChanged(activeData.payload.imageDTO));
-        return;
-      }
-
       /**
        * Image dropped on ControlNet
        */
@@ -83,6 +77,79 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
+      /**
+       * Image dropped on Control Adapter Layer
+       */
+      if (
+        overData.actionType === 'SET_CA_LAYER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { layerId } = overData.context;
+        dispatch(
+          caLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
+        return;
+      }
+
+      /**
+       * Image dropped on IP Adapter Layer
+       */
+      if (
+        overData.actionType === 'SET_IPA_LAYER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { layerId } = overData.context;
+        dispatch(
+          ipaLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
+        return;
+      }
+
+      /**
+       * Image dropped on RG Layer IP Adapter
+       */
+      if (
+        overData.actionType === 'SET_RG_LAYER_IP_ADAPTER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { layerId, ipAdapterId } = overData.context;
+        dispatch(
+          rgLayerIPAdapterImageChanged({
+            layerId,
+            ipAdapterId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
+        return;
+      }
+
+      /**
+       * Image dropped on II Layer Image
+       */
+      if (
+        overData.actionType === 'SET_II_LAYER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { layerId } = overData.context;
+        dispatch(
+          iiLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
+        return;
+      }
+
       /**
        * Image dropped on Canvas
        */

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

@@ -14,7 +14,6 @@ export const addImageToDeleteSelectedListener = (startAppListening: AppStartList
 
       const isImageInUse =
         imagesUsage.some((i) => i.isCanvasImage) ||
-        imagesUsage.some((i) => i.isInitialImage) ||
         imagesUsage.some((i) => i.isControlImage) ||
         imagesUsage.some((i) => i.isNodesImage);
 

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

@@ -6,8 +6,14 @@ import {
   controlAdapterImageChanged,
   controlAdapterIsEnabledChanged,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  caLayerImageChanged,
+  iiLayerImageChanged,
+  ipaLayerImageChanged,
+  rgLayerIPAdapterImageChanged,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { initialImageChanged, selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { addToast } from 'features/system/store/systemSlice';
 import { t } from 'i18next';
 import { omit } from 'lodash-es';
@@ -108,15 +114,48 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
-      if (postUploadAction?.type === 'SET_INITIAL_IMAGE') {
-        dispatch(initialImageChanged(imageDTO));
+      if (postUploadAction?.type === 'SET_CA_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(caLayerImageChanged({ layerId, imageDTO }));
         dispatch(
           addToast({
             ...DEFAULT_UPLOADED_TOAST,
-            description: t('toast.setInitialImage'),
+            description: t('toast.setControlImage'),
+          })
+        );
+      }
+
+      if (postUploadAction?.type === 'SET_IPA_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(ipaLayerImageChanged({ layerId, imageDTO }));
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
+      }
+
+      if (postUploadAction?.type === 'SET_RG_LAYER_IP_ADAPTER_IMAGE') {
+        const { layerId, ipAdapterId } = postUploadAction;
+        dispatch(rgLayerIPAdapterImageChanged({ layerId, ipAdapterId, imageDTO }));
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
+          })
+        );
+      }
+
+      if (postUploadAction?.type === 'SET_II_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(iiLayerImageChanged({ layerId, imageDTO }));
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: t('toast.setControlImage'),
           })
         );
-        return;
       }
 
       if (postUploadAction?.type === 'SET_NODES_IMAGE') {

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

@@ -1,21 +0,0 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { initialImageSelected } from 'features/parameters/store/actions';
-import { initialImageChanged } from 'features/parameters/store/generationSlice';
-import { addToast } from 'features/system/store/systemSlice';
-import { makeToast } from 'features/system/util/makeToast';
-import { t } from 'i18next';
-
-export const addInitialImageSelectedListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: initialImageSelected,
-    effect: (action, { dispatch }) => {
-      if (!action.payload) {
-        dispatch(addToast(makeToast({ title: t('toast.imageNotLoadedDesc'), status: 'error' })));
-        return;
-      }
-
-      dispatch(initialImageChanged(action.payload));
-      dispatch(addToast(makeToast(t('toast.sentToImageToImage'))));
-    },
-  });
-};

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

@@ -6,9 +6,10 @@ import {
   controlAdapterModelCleared,
   selectControlAdapterAll,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { loraRemoved } from 'features/lora/store/loraSlice';
 import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
-import { heightChanged, modelChanged, vaeSelected, widthChanged } from 'features/parameters/store/generationSlice';
+import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice';
@@ -69,16 +70,22 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
       dispatch(modelChanged(defaultModelInList, currentModel));
 
       const optimalDimension = getOptimalDimension(defaultModelInList);
-      if (getIsSizeOptimal(state.generation.width, state.generation.height, optimalDimension)) {
+      if (
+        getIsSizeOptimal(
+          state.controlLayers.present.size.width,
+          state.controlLayers.present.size.height,
+          optimalDimension
+        )
+      ) {
         return;
       }
       const { width, height } = calculateNewSize(
-        state.generation.aspectRatio.value,
+        state.controlLayers.present.size.aspectRatio.value,
         optimalDimension * optimalDimension
       );
 
-      dispatch(widthChanged(width));
-      dispatch(heightChanged(height));
+      dispatch(widthChanged({ width }));
+      dispatch(heightChanged({ height }));
       return;
     }
   }

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

@@ -1,5 +1,6 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { positivePromptChanged } from 'features/controlLayers/store/controlLayersSlice';
 import {
   combinatorialToggled,
   isErrorChanged,
@@ -10,11 +11,16 @@ import {
   promptsChanged,
 } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
-import { setPositivePrompt } from 'features/parameters/store/generationSlice';
 import { utilitiesApi } from 'services/api/endpoints/utilities';
 import { socketConnected } from 'services/events/actions';
 
-const matcher = isAnyOf(setPositivePrompt, combinatorialToggled, maxPromptsChanged, maxPromptsReset, socketConnected);
+const matcher = isAnyOf(
+  positivePromptChanged,
+  combinatorialToggled,
+  maxPromptsChanged,
+  maxPromptsReset,
+  socketConnected
+);
 
 export const addDynamicPromptsListener = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -22,7 +28,7 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
     effect: async (action, { dispatch, getState, cancelActiveListeners, delay }) => {
       cancelActiveListeners();
       const state = getState();
-      const { positivePrompt } = state.generation;
+      const { positivePrompt } = state.controlLayers.present;
       const { maxPrompts } = state.dynamicPrompts;
 
       if (state.config.disabledFeatures.includes('dynamicPrompting')) {
@@ -32,7 +38,7 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
       const cachedPrompts = utilitiesApi.endpoints.dynamicPrompts.select({
         prompt: positivePrompt,
         max_prompts: maxPrompts,
-      })(getState()).data;
+      })(state).data;
 
       if (cachedPrompts) {
         dispatch(promptsChanged(cachedPrompts.prompts));
@@ -40,8 +46,8 @@ export const addDynamicPromptsListener = (startAppListening: AppStartListening)
         return;
       }
 
-      if (!getShouldProcessPrompt(state.generation.positivePrompt)) {
-        dispatch(promptsChanged([state.generation.positivePrompt]));
+      if (!getShouldProcessPrompt(positivePrompt)) {
+        dispatch(promptsChanged([positivePrompt]));
         dispatch(parsingErrorChanged(undefined));
         dispatch(isErrorChanged(false));
         return;

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

@@ -1,14 +1,13 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import { setDefaultSettings } from 'features/parameters/store/actions';
 import {
-  heightRecalled,
   setCfgRescaleMultiplier,
   setCfgScale,
   setScheduler,
   setSteps,
   vaePrecisionChanged,
   vaeSelected,
-  widthRecalled,
 } from 'features/parameters/store/generationSlice';
 import {
   isParameterCFGRescaleMultiplier,
@@ -97,16 +96,16 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
             dispatch(setScheduler(scheduler));
           }
         }
-
+        const setSizeOptions = { updateAspectRatio: true, clamp: true };
         if (width) {
           if (isParameterWidth(width)) {
-            dispatch(widthRecalled(width));
+            dispatch(widthChanged({ width, ...setSizeOptions }));
           }
         }
 
         if (height) {
           if (isParameterHeight(height)) {
-            dispatch(heightRecalled(height));
+            dispatch(heightChanged({ height, ...setSizeOptions }));
           }
         }
 

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

@@ -1,5 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketGeneratorProgress } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -9,6 +12,13 @@ export const addGeneratorProgressEventListener = (startAppListening: AppStartLis
     actionCreator: socketGeneratorProgress,
     effect: (action) => {
       log.trace(action.payload, `Generator progress`);
+      const { source_node_id, step, total_steps, progress_image } = action.payload.data;
+      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.IN_PROGRESS;
+        nes.progress = (step + 1) / total_steps;
+        nes.progressImage = progress_image ?? null;
+      }
     },
   });
 };

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

@@ -1,10 +1,18 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
 import { parseify } from 'common/util/serialize';
 import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
-import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
+import {
+  boardIdSelected,
+  galleryViewChanged,
+  imageSelected,
+  isImageViewerOpenChanged,
+} from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
 import { isImageOutput } from 'features/nodes/types/common';
+import { zNodeStatus } from 'features/nodes/types/invocation';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -23,7 +31,7 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
       const { data } = action.payload;
       log.debug({ data: parseify(data) }, `Invocation complete (${action.payload.data.node.type})`);
 
-      const { result, node, queue_batch_id } = data;
+      const { result, node, queue_batch_id, source_node_id } = data;
       // This complete event has an associated image output
       if (isImageOutput(result) && !nodeTypeDenylist.includes(node.type)) {
         const { image_name } = result.image;
@@ -101,9 +109,20 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
             }
 
             dispatch(imageSelected(imageDTO));
+            dispatch(isImageViewerOpenChanged(true));
           }
         }
       }
+
+      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.COMPLETED;
+        if (nes.progress !== null) {
+          nes.progress = 1;
+        }
+        nes.outputs.push(result);
+        upsertExecutionState(nes.nodeId, nes);
+      }
     },
   });
 };

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

@@ -1,5 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketInvocationError } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -9,6 +12,15 @@ export const addInvocationErrorEventListener = (startAppListening: AppStartListe
     actionCreator: socketInvocationError,
     effect: (action) => {
       log.error(action.payload, `Invocation error (${action.payload.data.node.type})`);
+      const { source_node_id } = action.payload.data;
+      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.FAILED;
+        nes.error = action.payload.data.error;
+        nes.progress = null;
+        nes.progressImage = null;
+        upsertExecutionState(nes.nodeId, nes);
+      }
     },
   });
 };

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

@@ -1,5 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
 import { socketInvocationStarted } from 'services/events/actions';
 
 const log = logger('socketio');
@@ -9,6 +12,12 @@ export const addInvocationStartedEventListener = (startAppListening: AppStartLis
     actionCreator: socketInvocationStarted,
     effect: (action) => {
       log.debug(action.payload, `Invocation started (${action.payload.data.node.type})`);
+      const { source_node_id } = action.payload.data;
+      const nes = deepClone($nodeExecutionStates.get()[source_node_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.IN_PROGRESS;
+        upsertExecutionState(nes.nodeId, nes);
+      }
     },
   });
 };

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

@@ -1,5 +1,9 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
+import { forEach } from 'lodash-es';
 import { queueApi, queueItemsAdapter } from 'services/api/endpoints/queue';
 import { socketQueueItemStatusChanged } from 'services/events/actions';
 
@@ -54,6 +58,21 @@ export const addSocketQueueItemStatusChangedEventListener = (startAppListening:
       dispatch(
         queueApi.util.invalidateTags(['CurrentSessionQueueItem', 'NextSessionQueueItem', 'InvocationCacheStatus'])
       );
+
+      if (['in_progress'].includes(action.payload.data.queue_item.status)) {
+        forEach($nodeExecutionStates.get(), (nes) => {
+          if (!nes) {
+            return;
+          }
+          const clone = deepClone(nes);
+          clone.status = zNodeStatus.enum.PENDING;
+          clone.error = null;
+          clone.progress = null;
+          clone.progressImage = null;
+          clone.outputs = [];
+          $nodeExecutionStates.setKey(clone.nodeId, clone);
+        });
+      }
     },
   });
 };

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

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { updateAllNodesRequested } from 'features/nodes/store/actions';
-import { nodeReplaced } from 'features/nodes/store/nodesSlice';
+import { $templates, nodeReplaced } from 'features/nodes/store/nodesSlice';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
@@ -14,7 +14,8 @@ export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartLi
     actionCreator: updateAllNodesRequested,
     effect: (action, { dispatch, getState }) => {
       const log = logger('nodes');
-      const { nodes, templates } = getState().nodes;
+      const { nodes } = getState().nodes.present;
+      const templates = $templates.get();
 
       let unableToUpdateCount = 0;
 
@@ -24,7 +25,7 @@ export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartLi
           unableToUpdateCount++;
           return;
         }
-        if (!getNeedsUpdate(node, template)) {
+        if (!getNeedsUpdate(node.data, template)) {
           // No need to increment the count here, since we're not actually updating
           return;
         }

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

@@ -2,6 +2,7 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
 import { workflowLoaded, workflowLoadRequested } from 'features/nodes/store/actions';
+import { $templates } from 'features/nodes/store/nodesSlice';
 import { $flow } from 'features/nodes/store/reactFlowInstance';
 import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/types/error';
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
@@ -14,10 +15,10 @@ import { fromZodError } from 'zod-validation-error';
 export const addWorkflowLoadRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: workflowLoadRequested,
-    effect: (action, { dispatch, getState }) => {
+    effect: (action, { dispatch }) => {
       const log = logger('nodes');
       const { workflow, asCopy } = action.payload;
-      const nodeTemplates = getState().nodes.templates;
+      const nodeTemplates = $templates.get();
 
       try {
         const { workflow: validatedWorkflow, warnings } = validateWorkflow(workflow, nodeTemplates);

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

@@ -10,22 +10,23 @@ import {
   controlAdaptersPersistConfig,
   controlAdaptersSlice,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  controlLayersPersistConfig,
+  controlLayersSlice,
+  controlLayersUndoableConfig,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { deleteImageModalSlice } from 'features/deleteImageModal/store/slice';
 import { dynamicPromptsPersistConfig, dynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { galleryPersistConfig, gallerySlice } from 'features/gallery/store/gallerySlice';
 import { hrfPersistConfig, hrfSlice } from 'features/hrf/store/hrfSlice';
 import { loraPersistConfig, loraSlice } from 'features/lora/store/loraSlice';
 import { modelManagerV2PersistConfig, modelManagerV2Slice } from 'features/modelManagerV2/store/modelManagerV2Slice';
-import { nodesPersistConfig, nodesSlice } from 'features/nodes/store/nodesSlice';
+import { nodesPersistConfig, nodesSlice, nodesUndoableConfig } from 'features/nodes/store/nodesSlice';
+import { workflowSettingsPersistConfig, workflowSettingsSlice } from 'features/nodes/store/workflowSettingsSlice';
 import { workflowPersistConfig, workflowSlice } from 'features/nodes/store/workflowSlice';
 import { generationPersistConfig, generationSlice } from 'features/parameters/store/generationSlice';
 import { postprocessingPersistConfig, postprocessingSlice } from 'features/parameters/store/postprocessingSlice';
 import { queueSlice } from 'features/queue/store/queueSlice';
-import {
-  regionalPromptsPersistConfig,
-  regionalPromptsSlice,
-  regionalPromptsUndoableConfig,
-} from 'features/regionalPrompts/store/regionalPromptsSlice';
 import { sdxlPersistConfig, sdxlSlice } from 'features/sdxl/store/sdxlSlice';
 import { configSlice } from 'features/system/store/configSlice';
 import { systemPersistConfig, systemSlice } from 'features/system/store/systemSlice';
@@ -50,7 +51,7 @@ const allReducers = {
   [canvasSlice.name]: canvasSlice.reducer,
   [gallerySlice.name]: gallerySlice.reducer,
   [generationSlice.name]: generationSlice.reducer,
-  [nodesSlice.name]: nodesSlice.reducer,
+  [nodesSlice.name]: undoable(nodesSlice.reducer, nodesUndoableConfig),
   [postprocessingSlice.name]: postprocessingSlice.reducer,
   [systemSlice.name]: systemSlice.reducer,
   [configSlice.name]: configSlice.reducer,
@@ -65,7 +66,8 @@ const allReducers = {
   [queueSlice.name]: queueSlice.reducer,
   [workflowSlice.name]: workflowSlice.reducer,
   [hrfSlice.name]: hrfSlice.reducer,
-  [regionalPromptsSlice.name]: undoable(regionalPromptsSlice.reducer, regionalPromptsUndoableConfig),
+  [controlLayersSlice.name]: undoable(controlLayersSlice.reducer, controlLayersUndoableConfig),
+  [workflowSettingsSlice.name]: workflowSettingsSlice.reducer,
   [api.reducerPath]: api.reducer,
 };
 
@@ -110,7 +112,8 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [loraPersistConfig.name]: loraPersistConfig,
   [modelManagerV2PersistConfig.name]: modelManagerV2PersistConfig,
   [hrfPersistConfig.name]: hrfPersistConfig,
-  [regionalPromptsPersistConfig.name]: regionalPromptsPersistConfig,
+  [controlLayersPersistConfig.name]: controlLayersPersistConfig,
+  [workflowSettingsPersistConfig.name]: workflowSettingsPersistConfig,
 };
 
 const unserialize: UnserializeFunction = (data, key) => {

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

@@ -70,6 +70,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
     onMouseOver,
     onMouseOut,
     dataTestId,
+    ...rest
   } = props;
 
   const [isHovered, setIsHovered] = useState(false);
@@ -138,6 +139,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
           minH={minSize ? minSize : undefined}
           userSelect="none"
           cursor={isDragDisabled || !imageDTO ? 'default' : 'pointer'}
+          {...rest}
         >
           {imageDTO && (
             <Flex

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

@@ -17,14 +17,10 @@ const accept: Accept = {
 const selectPostUploadAction = createMemoizedSelector(activeTabNameSelector, (activeTabName) => {
   let postUploadAction: PostUploadAction = { type: 'TOAST' };
 
-  if (activeTabName === 'unifiedCanvas') {
+  if (activeTabName === 'canvas') {
     postUploadAction = { type: 'SET_CANVAS_INITIAL_IMAGE' };
   }
 
-  if (activeTabName === 'img2img') {
-    postUploadAction = { type: 'SET_INITIAL_IMAGE' };
-  }
-
   return postUploadAction;
 });
 

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

@@ -67,7 +67,7 @@ export const useGlobalHotkeys = () => {
   useHotkeys(
     '1',
     () => {
-      dispatch(setActiveTab('txt2img'));
+      dispatch(setActiveTab('generation'));
     },
     [dispatch]
   );
@@ -75,7 +75,7 @@ export const useGlobalHotkeys = () => {
   useHotkeys(
     '2',
     () => {
-      dispatch(setActiveTab('img2img'));
+      dispatch(setActiveTab('canvas'));
     },
     [dispatch]
   );
@@ -83,31 +83,23 @@ export const useGlobalHotkeys = () => {
   useHotkeys(
     '3',
     () => {
-      dispatch(setActiveTab('unifiedCanvas'));
+      dispatch(setActiveTab('workflows'));
     },
     [dispatch]
   );
 
   useHotkeys(
     '4',
-    () => {
-      dispatch(setActiveTab('nodes'));
-    },
-    [dispatch]
-  );
-
-  useHotkeys(
-    '5',
     () => {
       if (isModelManagerEnabled) {
-        dispatch(setActiveTab('modelManager'));
+        dispatch(setActiveTab('models'));
       }
     },
     [dispatch, isModelManagerEnabled]
   );
 
   useHotkeys(
-    isModelManagerEnabled ? '6' : '5',
+    isModelManagerEnabled ? '5' : '4',
     () => {
       dispatch(setActiveTab('queue'));
     },

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

@@ -13,6 +13,7 @@ type UseGroupedModelComboboxArg<T extends AnyModelConfig> = {
   onChange: (value: T | null) => void;
   getIsDisabled?: (model: T) => boolean;
   isLoading?: boolean;
+  groupByType?: boolean;
 };
 
 type UseGroupedModelComboboxReturn = {
@@ -23,17 +24,21 @@ type UseGroupedModelComboboxReturn = {
   noOptionsMessage: () => string;
 };
 
+const groupByBaseFunc = <T extends AnyModelConfig>(model: T) => model.base.toUpperCase();
+const groupByBaseAndTypeFunc = <T extends AnyModelConfig>(model: T) =>
+  `${model.base.toUpperCase()} / ${model.type.replaceAll('_', ' ').toUpperCase()}`;
+
 export const useGroupedModelCombobox = <T extends AnyModelConfig>(
   arg: UseGroupedModelComboboxArg<T>
 ): UseGroupedModelComboboxReturn => {
   const { t } = useTranslation();
   const base_model = useAppSelector((s) => s.generation.model?.base ?? 'sdxl');
-  const { modelConfigs, selectedModel, getIsDisabled, onChange, isLoading } = arg;
+  const { modelConfigs, selectedModel, getIsDisabled, onChange, isLoading, groupByType = false } = arg;
   const options = useMemo<GroupBase<ComboboxOption>[]>(() => {
     if (!modelConfigs) {
       return [];
     }
-    const groupedModels = groupBy(modelConfigs, 'base');
+    const groupedModels = groupBy(modelConfigs, groupByType ? groupByBaseAndTypeFunc : groupByBaseFunc);
     const _options = reduce(
       groupedModels,
       (acc, val, label) => {
@@ -49,9 +54,9 @@ export const useGroupedModelCombobox = <T extends AnyModelConfig>(
       },
       [] as GroupBase<ComboboxOption>[]
     );
-    _options.sort((a) => (a.label === base_model ? -1 : 1));
+    _options.sort((a) => (a.label?.split('/')[0]?.toLowerCase().includes(base_model) ? -1 : 1));
     return _options;
-  }, [getIsDisabled, modelConfigs, base_model]);
+  }, [modelConfigs, groupByType, getIsDisabled, base_model]);
 
   const value = useMemo(
     () =>

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

@@ -1,3 +1,4 @@
+import { useStore } from '@nanostores/react';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import {
@@ -5,133 +6,231 @@ import {
   selectControlAdaptersSlice,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import { selectControlLayersSlice } from 'features/controlLayers/store/controlLayersSlice';
+import type { Layer } from 'features/controlLayers/store/types';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
-import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
+import { $templates, selectNodesSlice } from 'features/nodes/store/nodesSlice';
+import type { Templates } from 'features/nodes/store/types';
+import { selectWorkflowSettingsSlice } from 'features/nodes/store/workflowSettingsSlice';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { selectGenerationSlice } from 'features/parameters/store/generationSlice';
 import { selectSystemSlice } from 'features/system/store/systemSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import i18n from 'i18next';
-import { forEach } from 'lodash-es';
+import { forEach, upperFirst } from 'lodash-es';
+import { useMemo } from 'react';
 import { getConnectedEdges } from 'reactflow';
 
-const selector = createMemoizedSelector(
-  [
-    selectControlAdaptersSlice,
-    selectGenerationSlice,
-    selectSystemSlice,
-    selectNodesSlice,
-    selectDynamicPromptsSlice,
-    activeTabNameSelector,
-  ],
-  (controlAdapters, generation, system, nodes, dynamicPrompts, activeTabName) => {
-    const { initialImage, model, positivePrompt } = generation;
+const LAYER_TYPE_TO_TKEY: Record<Layer['type'], string> = {
+  initial_image_layer: 'controlLayers.globalInitialImage',
+  control_adapter_layer: 'controlLayers.globalControlAdapter',
+  ip_adapter_layer: 'controlLayers.globalIPAdapter',
+  regional_guidance_layer: 'controlLayers.regionalGuidance',
+};
 
-    const { isConnected } = system;
+const createSelector = (templates: Templates) =>
+  createMemoizedSelector(
+    [
+      selectControlAdaptersSlice,
+      selectGenerationSlice,
+      selectSystemSlice,
+      selectNodesSlice,
+      selectWorkflowSettingsSlice,
+      selectDynamicPromptsSlice,
+      selectControlLayersSlice,
+      activeTabNameSelector,
+    ],
+    (controlAdapters, generation, system, nodes, workflowSettings, dynamicPrompts, controlLayers, activeTabName) => {
+      const { model } = generation;
+      const { size } = controlLayers.present;
+      const { positivePrompt } = controlLayers.present;
 
-    const reasons: string[] = [];
+      const { isConnected } = system;
 
-    // Cannot generate if not connected
-    if (!isConnected) {
-      reasons.push(i18n.t('parameters.invoke.systemDisconnected'));
-    }
+      const reasons: { prefix?: string; content: string }[] = [];
 
-    if (activeTabName === 'img2img' && !initialImage) {
-      reasons.push(i18n.t('parameters.invoke.noInitialImageSelected'));
-    }
+      // Cannot generate if not connected
+      if (!isConnected) {
+        reasons.push({ content: i18n.t('parameters.invoke.systemDisconnected') });
+      }
 
-    if (activeTabName === 'nodes') {
-      if (nodes.shouldValidateGraph) {
-        if (!nodes.nodes.length) {
-          reasons.push(i18n.t('parameters.invoke.noNodesInGraph'));
-        }
-
-        nodes.nodes.forEach((node) => {
-          if (!isInvocationNode(node)) {
-            return;
+      if (activeTabName === 'workflows') {
+        if (workflowSettings.shouldValidateGraph) {
+          if (!nodes.nodes.length) {
+            reasons.push({ content: i18n.t('parameters.invoke.noNodesInGraph') });
           }
 
-          const nodeTemplate = nodes.templates[node.data.type];
-
-          if (!nodeTemplate) {
-            // Node type not found
-            reasons.push(i18n.t('parameters.invoke.missingNodeTemplate'));
-            return;
-          }
-
-          const connectedEdges = getConnectedEdges([node], nodes.edges);
-
-          forEach(node.data.inputs, (field) => {
-            const fieldTemplate = nodeTemplate.inputs[field.name];
-            const hasConnection = connectedEdges.some(
-              (edge) => edge.target === node.id && edge.targetHandle === field.name
-            );
-
-            if (!fieldTemplate) {
-              reasons.push(i18n.t('parameters.invoke.missingFieldTemplate'));
+          nodes.nodes.forEach((node) => {
+            if (!isInvocationNode(node)) {
               return;
             }
 
-            if (fieldTemplate.required && field.value === undefined && !hasConnection) {
-              reasons.push(
-                i18n.t('parameters.invoke.missingInputForField', {
-                  nodeLabel: node.data.label || nodeTemplate.title,
-                  fieldLabel: field.label || fieldTemplate.title,
-                })
+            const nodeTemplate = templates[node.data.type];
+
+            if (!nodeTemplate) {
+              // Node type not found
+              reasons.push({ content: i18n.t('parameters.invoke.missingNodeTemplate') });
+              return;
+            }
+
+            const connectedEdges = getConnectedEdges([node], nodes.edges);
+
+            forEach(node.data.inputs, (field) => {
+              const fieldTemplate = nodeTemplate.inputs[field.name];
+              const hasConnection = connectedEdges.some(
+                (edge) => edge.target === node.id && edge.targetHandle === field.name
               );
-              return;
-            }
+
+              if (!fieldTemplate) {
+                reasons.push({ content: i18n.t('parameters.invoke.missingFieldTemplate') });
+                return;
+              }
+
+              if (fieldTemplate.required && field.value === undefined && !hasConnection) {
+                reasons.push({
+                  content: i18n.t('parameters.invoke.missingInputForField', {
+                    nodeLabel: node.data.label || nodeTemplate.title,
+                    fieldLabel: field.label || fieldTemplate.title,
+                  }),
+                });
+                return;
+              }
+            });
           });
-        });
-      }
-    } else {
-      if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
-        reasons.push(i18n.t('parameters.invoke.noPrompts'));
-      }
-
-      if (!model) {
-        reasons.push(i18n.t('parameters.invoke.noModelSelected'));
-      }
-
-      selectControlAdapterAll(controlAdapters).forEach((ca, i) => {
-        if (!ca.isEnabled) {
-          return;
+        }
+      } else {
+        if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
+          reasons.push({ content: i18n.t('parameters.invoke.noPrompts') });
         }
 
-        if (!ca.model) {
-          reasons.push(
-            i18n.t('parameters.invoke.noModelForControlAdapter', {
-              number: i + 1,
-            })
-          );
-        } else if (ca.model.base !== model?.base) {
-          // This should never happen, just a sanity check
-          reasons.push(
-            i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', {
-              number: i + 1,
-            })
-          );
+        if (!model) {
+          reasons.push({ content: i18n.t('parameters.invoke.noModelSelected') });
         }
 
-        if (
-          !ca.controlImage ||
-          (isControlNetOrT2IAdapter(ca) && !ca.processedControlImage && ca.processorType !== 'none')
-        ) {
-          reasons.push(
-            i18n.t('parameters.invoke.noControlImageForControlAdapter', {
-              number: i + 1,
-            })
-          );
+        if (activeTabName === 'generation') {
+          // Handling for generation tab
+          controlLayers.present.layers
+            .filter((l) => l.isEnabled)
+            .forEach((l, i) => {
+              const layerLiteral = i18n.t('controlLayers.layers_one');
+              const layerNumber = i + 1;
+              const layerType = i18n.t(LAYER_TYPE_TO_TKEY[l.type]);
+              const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
+              const problems: string[] = [];
+              if (l.type === 'control_adapter_layer') {
+                // Must have model
+                if (!l.controlAdapter.model) {
+                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterNoModelSelected'));
+                }
+                // Model base must match
+                if (l.controlAdapter.model?.base !== model?.base) {
+                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterIncompatibleBaseModel'));
+                }
+                // Must have a control image OR, if it has a processor, it must have a processed image
+                if (!l.controlAdapter.image) {
+                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterNoImageSelected'));
+                } else if (l.controlAdapter.processorConfig && !l.controlAdapter.processedImage) {
+                  problems.push(i18n.t('parameters.invoke.layer.controlAdapterImageNotProcessed'));
+                }
+                // T2I Adapters require images have dimensions that are multiples of 64 (SD1.5) or 32 (SDXL)
+                if (l.controlAdapter.type === 't2i_adapter') {
+                  const multiple = model?.base === 'sdxl' ? 32 : 64;
+                  if (size.width % multiple !== 0 || size.height % multiple !== 0) {
+                    problems.push(i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleDimensions'));
+                  }
+                }
+              }
+
+              if (l.type === 'ip_adapter_layer') {
+                // Must have model
+                if (!l.ipAdapter.model) {
+                  problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
+                }
+                // Model base must match
+                if (l.ipAdapter.model?.base !== model?.base) {
+                  problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
+                }
+                // Must have an image
+                if (!l.ipAdapter.image) {
+                  problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
+                }
+              }
+
+              if (l.type === 'initial_image_layer') {
+                // Must have an image
+                if (!l.image) {
+                  problems.push(i18n.t('parameters.invoke.layer.initialImageNoImageSelected'));
+                }
+              }
+
+              if (l.type === 'regional_guidance_layer') {
+                // Must have a region
+                if (l.maskObjects.length === 0) {
+                  problems.push(i18n.t('parameters.invoke.layer.rgNoRegion'));
+                }
+                // Must have at least 1 prompt or IP Adapter
+                if (l.positivePrompt === null && l.negativePrompt === null && l.ipAdapters.length === 0) {
+                  problems.push(i18n.t('parameters.invoke.layer.rgNoPromptsOrIPAdapters'));
+                }
+                l.ipAdapters.forEach((ipAdapter) => {
+                  // Must have model
+                  if (!ipAdapter.model) {
+                    problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
+                  }
+                  // Model base must match
+                  if (ipAdapter.model?.base !== model?.base) {
+                    problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
+                  }
+                  // Must have an image
+                  if (!ipAdapter.image) {
+                    problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
+                  }
+                });
+              }
+
+              if (problems.length) {
+                const content = upperFirst(problems.join(', '));
+                reasons.push({ prefix, content });
+              }
+            });
+        } else {
+          // Handling for all other tabs
+          selectControlAdapterAll(controlAdapters)
+            .filter((ca) => ca.isEnabled)
+            .forEach((ca, i) => {
+              if (!ca.isEnabled) {
+                return;
+              }
+
+              if (!ca.model) {
+                reasons.push({ content: i18n.t('parameters.invoke.noModelForControlAdapter', { number: i + 1 }) });
+              } else if (ca.model.base !== model?.base) {
+                // This should never happen, just a sanity check
+                reasons.push({
+                  content: i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', { number: i + 1 }),
+                });
+              }
+
+              if (
+                !ca.controlImage ||
+                (isControlNetOrT2IAdapter(ca) && !ca.processedControlImage && ca.processorType !== 'none')
+              ) {
+                reasons.push({
+                  content: i18n.t('parameters.invoke.noControlImageForControlAdapter', { number: i + 1 }),
+                });
+              }
+            });
         }
-      });
+      }
+
+      return { isReady: !reasons.length, reasons };
     }
-
-    return { isReady: !reasons.length, reasons };
-  }
-);
+  );
 
 export const useIsReadyToEnqueue = () => {
-  const { isReady, reasons } = useAppSelector(selector);
-  return { isReady, reasons };
+  const templates = useStore($templates);
+  const selector = useMemo(() => createSelector(templates), [templates]);
+  const value = useAppSelector(selector);
+  return value;
 };

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

@@ -0,0 +1,3 @@
+export const stopPropagation = (e: React.MouseEvent) => {
+  e.stopPropagation();
+};

invokeai/frontend/web/src/features/canvas/components/IAICanvasToolbar/IAICanvasToolbar.tsx

@@ -21,7 +21,6 @@ import {
   setShouldShowBoundingBox,
 } from 'features/canvas/store/canvasSlice';
 import type { CanvasLayer } from 'features/canvas/store/canvasTypes';
-import { LAYER_NAMES_DICT } from 'features/canvas/store/canvasTypes';
 import { memo, useCallback, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
@@ -216,13 +215,20 @@ const IAICanvasToolbar = () => {
     [dispatch, isMaskEnabled]
   );
 
-  const value = useMemo(() => LAYER_NAMES_DICT.filter((o) => o.value === layer)[0], [layer]);
+  const layerOptions = useMemo<{ label: string; value: CanvasLayer }[]>(
+    () => [
+      { label: t('unifiedCanvas.base'), value: 'base' },
+      { label: t('unifiedCanvas.mask'), value: 'mask' },
+    ],
+    [t]
+  );
+  const layerValue = useMemo(() => layerOptions.filter((o) => o.value === layer)[0] ?? null, [layer, layerOptions]);
 
   return (
     <Flex alignItems="center" gap={2} flexWrap="wrap">
       <Tooltip label={`${t('unifiedCanvas.layer')} (Q)`}>
         <FormControl isDisabled={isStaging} w="5rem">
-          <Combobox value={value} options={LAYER_NAMES_DICT} onChange={handleChangeLayer} />
+          <Combobox value={layerValue} options={layerOptions} onChange={handleChangeLayer} />
         </FormControl>
       </Tooltip>
 

invokeai/frontend/web/src/features/canvas/hooks/useCanvasHotkeys.ts

@@ -75,7 +75,7 @@ const useInpaintingCanvasHotkeys = () => {
 
   const onKeyDown = useCallback(
     (e: KeyboardEvent) => {
-      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'unifiedCanvas') {
+      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'canvas') {
         return;
       }
       if ($toolStash.get() || $tool.get() === 'move') {
@@ -90,7 +90,7 @@ const useInpaintingCanvasHotkeys = () => {
   );
   const onKeyUp = useCallback(
     (e: KeyboardEvent) => {
-      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'unifiedCanvas') {
+      if (e.repeat || e.key !== ' ' || isInteractiveTarget(e.target) || activeTabName !== 'canvas') {
         return;
       }
       if (!$toolStash.get() || $tool.get() !== 'move') {

invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts

@@ -8,6 +8,7 @@ import calculateScale from 'features/canvas/util/calculateScale';
 import { STAGE_PADDING_PERCENTAGE } from 'features/canvas/util/constants';
 import floorCoordinates from 'features/canvas/util/floorCoordinates';
 import getScaledBoundingBoxDimensions from 'features/canvas/util/getScaledBoundingBoxDimensions';
+import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
 import { initialAspectRatioState } from 'features/parameters/components/ImageSize/constants';
 import type { AspectRatioState } from 'features/parameters/components/ImageSize/types';
 import { modelChanged } from 'features/parameters/store/generationSlice';
@@ -588,8 +589,9 @@ export const canvasSlice = createSlice({
   },
   extraReducers: (builder) => {
     builder.addCase(modelChanged, (state, action) => {
-      if (action.meta.previousModel?.base === action.payload?.base) {
-        // The base model hasn't changed, we don't need to optimize the size
+      const newModel = action.payload;
+      if (!newModel || action.meta.previousModel?.base === newModel.base) {
+        // Model was cleared or the base didn't change
         return;
       }
       const optimalDimension = getOptimalDimension(action.payload);
@@ -597,14 +599,8 @@ export const canvasSlice = createSlice({
       if (getIsSizeOptimal(width, height, optimalDimension)) {
         return;
       }
-      setBoundingBoxDimensionsReducer(
-        state,
-        {
-          width,
-          height,
-        },
-        optimalDimension
-      );
+      const newSize = calculateNewSize(state.aspectRatio.value, optimalDimension * optimalDimension);
+      setBoundingBoxDimensionsReducer(state, newSize, optimalDimension);
     });
 
     builder.addCase(socketQueueItemStatusChanged, (state, action) => {

invokeai/frontend/web/src/features/canvas/store/canvasTypes.ts

@@ -5,11 +5,6 @@ import { z } from 'zod';
 
 export type CanvasLayer = 'base' | 'mask';
 
-export const LAYER_NAMES_DICT: { label: string; value: CanvasLayer }[] = [
-  { label: 'Base', value: 'base' },
-  { label: 'Mask', value: 'mask' },
-];
-
 const zBoundingBoxScaleMethod = z.enum(['none', 'auto', 'manual']);
 export type BoundingBoxScaleMethod = z.infer<typeof zBoundingBoxScaleMethod>;
 export const isBoundingBoxScaleMethod = (v: unknown): v is BoundingBoxScaleMethod =>

invokeai/frontend/web/src/features/controlAdapters/components/ControlAdapterConfig.tsx

@@ -76,7 +76,7 @@ const ControlAdapterConfig = (props: { id: string; number: number }) => {
         <Box minW={0} w="full" transitionProperty="common" transitionDuration="0.1s">
           <ParamControlAdapterModel id={id} />
         </Box>
-        {activeTabName === 'unifiedCanvas' && <ControlNetCanvasImageImports id={id} />}
+        {activeTabName === 'canvas' && <ControlNetCanvasImageImports id={id} />}
         <IconButton
           size="sm"
           tooltip={t('controlnet.duplicate')}

invokeai/frontend/web/src/features/controlAdapters/components/ControlAdapterImagePreview.tsx

@@ -13,9 +13,10 @@ import {
   controlAdapterImageChanged,
   selectControlAdaptersSlice,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
-import { heightChanged, selectOptimalDimension, widthChanged } from 'features/parameters/store/generationSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { memo, useCallback, useEffect, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -92,15 +93,16 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
       return;
     }
 
-    if (activeTabName === 'unifiedCanvas') {
+    if (activeTabName === 'canvas') {
       dispatch(setBoundingBoxDimensions({ width: controlImage.width, height: controlImage.height }, optimalDimension));
     } else {
+      const options = { updateAspectRatio: true, clamp: true };
       const { width, height } = calculateNewSize(
         controlImage.width / controlImage.height,
         optimalDimension * optimalDimension
       );
-      dispatch(widthChanged(width));
-      dispatch(heightChanged(height));
+      dispatch(widthChanged({ width, ...options }));
+      dispatch(heightChanged({ height, ...options }));
     }
   }, [controlImage, activeTabName, dispatch, optimalDimension]);
 

invokeai/frontend/web/src/features/controlAdapters/components/parameters/ParamControlAdapterIPMethod.tsx

@@ -52,7 +52,7 @@ const ParamControlAdapterIPMethod = ({ id }: Props) => {
 
   return (
     <FormControl>
-      <InformationalPopover feature="ipAdapterMethod">
+      <InformationalPopover feature="controlNetResizeMode">
         <FormLabel>{t('controlnet.ipAdapterMethod')}</FormLabel>
       </InformationalPopover>
       <Combobox value={value} options={options} isDisabled={!isEnabled} onChange={handleIPMethodChanged} />

invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts

@@ -1,12 +1,11 @@
 import type { PayloadAction, Update } from '@reduxjs/toolkit';
-import { createEntityAdapter, createSlice, isAnyOf } from '@reduxjs/toolkit';
+import { createEntityAdapter, createSlice } from '@reduxjs/toolkit';
 import { getSelectorsOptions } from 'app/store/createMemoizedSelector';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { deepClone } from 'common/util/deepClone';
 import { buildControlAdapter } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
 import { zModelIdentifierField } from 'features/nodes/types/common';
-import { maskLayerIPAdapterAdded } from 'features/regionalPrompts/store/regionalPromptsSlice';
 import { merge, uniq } from 'lodash-es';
 import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { socketInvocationError } from 'services/events/actions';
@@ -383,10 +382,6 @@ export const controlAdaptersSlice = createSlice({
     builder.addCase(socketInvocationError, (state) => {
       state.pendingControlImages = [];
     });
-
-    builder.addCase(maskLayerIPAdapterAdded, (state, action) => {
-      caAdapter.addOne(state, buildControlAdapter(action.meta.uuid, 'ip_adapter'));
-    });
   },
 });
 
@@ -417,8 +412,6 @@ export const {
   t2iAdaptersReset,
 } = controlAdaptersSlice.actions;
 
-export const isAnyControlAdapterAdded = isAnyOf(controlAdapterAdded, controlAdapterRecalled);
-
 export const selectControlAdaptersSlice = (state: RootState) => state.controlAdapters;
 
 /* eslint-disable-next-line @typescript-eslint/no-explicit-any */

invokeai/frontend/web/src/features/controlAdapters/store/types.ts

@@ -5,22 +5,7 @@ import type {
   ParameterT2IAdapterModel,
 } from 'features/parameters/types/parameterSchemas';
 import type { components } from 'services/api/schema';
-import type {
-  CannyImageProcessorInvocation,
-  ColorMapImageProcessorInvocation,
-  ContentShuffleImageProcessorInvocation,
-  DepthAnythingImageProcessorInvocation,
-  DWOpenposeImageProcessorInvocation,
-  HedImageProcessorInvocation,
-  LineartAnimeImageProcessorInvocation,
-  LineartImageProcessorInvocation,
-  MediapipeFaceProcessorInvocation,
-  MidasDepthImageProcessorInvocation,
-  MlsdImageProcessorInvocation,
-  NormalbaeImageProcessorInvocation,
-  PidiImageProcessorInvocation,
-  ZoeDepthImageProcessorInvocation,
-} from 'services/api/types';
+import type { Invocation } from 'services/api/types';
 import type { O } from 'ts-toolbelt';
 import { z } from 'zod';
 
@@ -28,20 +13,20 @@ import { z } from 'zod';
  * Any ControlNet processor node
  */
 export type ControlAdapterProcessorNode =
-  | CannyImageProcessorInvocation
-  | ColorMapImageProcessorInvocation
-  | ContentShuffleImageProcessorInvocation
-  | DepthAnythingImageProcessorInvocation
-  | HedImageProcessorInvocation
-  | LineartAnimeImageProcessorInvocation
-  | LineartImageProcessorInvocation
-  | MediapipeFaceProcessorInvocation
-  | MidasDepthImageProcessorInvocation
-  | MlsdImageProcessorInvocation
-  | NormalbaeImageProcessorInvocation
-  | DWOpenposeImageProcessorInvocation
-  | PidiImageProcessorInvocation
-  | ZoeDepthImageProcessorInvocation;
+  | Invocation<'canny_image_processor'>
+  | Invocation<'color_map_image_processor'>
+  | Invocation<'content_shuffle_image_processor'>
+  | Invocation<'depth_anything_image_processor'>
+  | Invocation<'hed_image_processor'>
+  | Invocation<'lineart_anime_image_processor'>
+  | Invocation<'lineart_image_processor'>
+  | Invocation<'mediapipe_face_processor'>
+  | Invocation<'midas_depth_image_processor'>
+  | Invocation<'mlsd_image_processor'>
+  | Invocation<'normalbae_image_processor'>
+  | Invocation<'dw_openpose_image_processor'>
+  | Invocation<'pidi_image_processor'>
+  | Invocation<'zoe_depth_image_processor'>;
 
 /**
  * Any ControlNet processor type
@@ -71,7 +56,7 @@ export const isControlAdapterProcessorType = (v: unknown): v is ControlAdapterPr
  * The Canny processor node, with parameters flagged as required
  */
 export type RequiredCannyImageProcessorInvocation = O.Required<
-  CannyImageProcessorInvocation,
+  Invocation<'canny_image_processor'>,
   'type' | 'low_threshold' | 'high_threshold' | 'image_resolution' | 'detect_resolution'
 >;
 
@@ -79,7 +64,7 @@ export type RequiredCannyImageProcessorInvocation = O.Required<
  * The Color Map processor node, with parameters flagged as required
  */
 export type RequiredColorMapImageProcessorInvocation = O.Required<
-  ColorMapImageProcessorInvocation,
+  Invocation<'color_map_image_processor'>,
   'type' | 'color_map_tile_size'
 >;
 
@@ -87,7 +72,7 @@ export type RequiredColorMapImageProcessorInvocation = O.Required<
  * The ContentShuffle processor node, with parameters flagged as required
  */
 export type RequiredContentShuffleImageProcessorInvocation = O.Required<
-  ContentShuffleImageProcessorInvocation,
+  Invocation<'content_shuffle_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution' | 'w' | 'h' | 'f'
 >;
 
@@ -95,7 +80,7 @@ export type RequiredContentShuffleImageProcessorInvocation = O.Required<
  * The DepthAnything processor node, with parameters flagged as required
  */
 export type RequiredDepthAnythingImageProcessorInvocation = O.Required<
-  DepthAnythingImageProcessorInvocation,
+  Invocation<'depth_anything_image_processor'>,
   'type' | 'model_size' | 'resolution' | 'offload'
 >;
 
@@ -108,7 +93,7 @@ export const isDepthAnythingModelSize = (v: unknown): v is DepthAnythingModelSiz
  * The HED processor node, with parameters flagged as required
  */
 export type RequiredHedImageProcessorInvocation = O.Required<
-  HedImageProcessorInvocation,
+  Invocation<'hed_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution' | 'scribble'
 >;
 
@@ -116,7 +101,7 @@ export type RequiredHedImageProcessorInvocation = O.Required<
  * The Lineart Anime processor node, with parameters flagged as required
  */
 export type RequiredLineartAnimeImageProcessorInvocation = O.Required<
-  LineartAnimeImageProcessorInvocation,
+  Invocation<'lineart_anime_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution'
 >;
 
@@ -124,7 +109,7 @@ export type RequiredLineartAnimeImageProcessorInvocation = O.Required<
  * The Lineart processor node, with parameters flagged as required
  */
 export type RequiredLineartImageProcessorInvocation = O.Required<
-  LineartImageProcessorInvocation,
+  Invocation<'lineart_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution' | 'coarse'
 >;
 
@@ -132,7 +117,7 @@ export type RequiredLineartImageProcessorInvocation = O.Required<
  * The MediapipeFace processor node, with parameters flagged as required
  */
 export type RequiredMediapipeFaceProcessorInvocation = O.Required<
-  MediapipeFaceProcessorInvocation,
+  Invocation<'mediapipe_face_processor'>,
   'type' | 'max_faces' | 'min_confidence' | 'image_resolution' | 'detect_resolution'
 >;
 
@@ -140,7 +125,7 @@ export type RequiredMediapipeFaceProcessorInvocation = O.Required<
  * The MidasDepth processor node, with parameters flagged as required
  */
 export type RequiredMidasDepthImageProcessorInvocation = O.Required<
-  MidasDepthImageProcessorInvocation,
+  Invocation<'midas_depth_image_processor'>,
   'type' | 'a_mult' | 'bg_th' | 'image_resolution' | 'detect_resolution'
 >;
 
@@ -148,7 +133,7 @@ export type RequiredMidasDepthImageProcessorInvocation = O.Required<
  * The MLSD processor node, with parameters flagged as required
  */
 export type RequiredMlsdImageProcessorInvocation = O.Required<
-  MlsdImageProcessorInvocation,
+  Invocation<'mlsd_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution' | 'thr_v' | 'thr_d'
 >;
 
@@ -156,7 +141,7 @@ export type RequiredMlsdImageProcessorInvocation = O.Required<
  * The NormalBae processor node, with parameters flagged as required
  */
 export type RequiredNormalbaeImageProcessorInvocation = O.Required<
-  NormalbaeImageProcessorInvocation,
+  Invocation<'normalbae_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution'
 >;
 
@@ -164,7 +149,7 @@ export type RequiredNormalbaeImageProcessorInvocation = O.Required<
  * The DW Openpose processor node, with parameters flagged as required
  */
 export type RequiredDWOpenposeImageProcessorInvocation = O.Required<
-  DWOpenposeImageProcessorInvocation,
+  Invocation<'dw_openpose_image_processor'>,
   'type' | 'image_resolution' | 'draw_body' | 'draw_face' | 'draw_hands'
 >;
 
@@ -172,14 +157,14 @@ export type RequiredDWOpenposeImageProcessorInvocation = O.Required<
  * The Pidi processor node, with parameters flagged as required
  */
 export type RequiredPidiImageProcessorInvocation = O.Required<
-  PidiImageProcessorInvocation,
+  Invocation<'pidi_image_processor'>,
   'type' | 'detect_resolution' | 'image_resolution' | 'safe' | 'scribble'
 >;
 
 /**
  * The ZoeDepth processor node, with parameters flagged as required
  */
-export type RequiredZoeDepthImageProcessorInvocation = O.Required<ZoeDepthImageProcessorInvocation, 'type'>;
+export type RequiredZoeDepthImageProcessorInvocation = O.Required<Invocation<'zoe_depth_image_processor'>, 'type'>;
 
 /**
  * Any ControlNet Processor node, with its parameters flagged as required

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

@@ -0,0 +1,47 @@
+import { Button, Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { useAddCALayer, useAddIILayer, useAddIPALayer } from 'features/controlLayers/hooks/addLayerHooks';
+import { rgLayerAdded } from 'features/controlLayers/store/controlLayersSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiPlusBold } from 'react-icons/pi';
+
+export const AddLayerButton = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const [addCALayer, isAddCALayerDisabled] = useAddCALayer();
+  const [addIPALayer, isAddIPALayerDisabled] = useAddIPALayer();
+  const [addIILayer, isAddIILayerDisabled] = useAddIILayer();
+  const addRGLayer = useCallback(() => {
+    dispatch(rgLayerAdded());
+  }, [dispatch]);
+
+  return (
+    <Menu>
+      <MenuButton
+        as={Button}
+        leftIcon={<PiPlusBold />}
+        variant="ghost"
+        data-testid="control-layers-add-layer-menu-button"
+      >
+        {t('controlLayers.addLayer')}
+      </MenuButton>
+      <MenuList>
+        <MenuItem icon={<PiPlusBold />} onClick={addRGLayer}>
+          {t('controlLayers.regionalGuidanceLayer')}
+        </MenuItem>
+        <MenuItem icon={<PiPlusBold />} onClick={addCALayer} isDisabled={isAddCALayerDisabled}>
+          {t('controlLayers.globalControlAdapterLayer')}
+        </MenuItem>
+        <MenuItem icon={<PiPlusBold />} onClick={addIPALayer} isDisabled={isAddIPALayerDisabled}>
+          {t('controlLayers.globalIPAdapterLayer')}
+        </MenuItem>
+        <MenuItem icon={<PiPlusBold />} onClick={addIILayer} isDisabled={isAddIILayerDisabled}>
+          {t('controlLayers.globalInitialImageLayer')}
+        </MenuItem>
+      </MenuList>
+    </Menu>
+  );
+});
+
+AddLayerButton.displayName = 'AddLayerButton';

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

@@ -1,13 +1,13 @@
 import { Button, Flex } from '@invoke-ai/ui-library';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAddIPAdapterToIPALayer } from 'features/controlLayers/hooks/addLayerHooks';
 import {
-  isVectorMaskLayer,
-  maskLayerIPAdapterAdded,
-  maskLayerNegativePromptChanged,
-  maskLayerPositivePromptChanged,
-  selectRegionalPromptsSlice,
-} from 'features/regionalPrompts/store/regionalPromptsSlice';
+  isRegionalGuidanceLayer,
+  rgLayerNegativePromptChanged,
+  rgLayerPositivePromptChanged,
+  selectControlLayersSlice,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
@@ -19,11 +19,12 @@ type AddPromptButtonProps = {
 export const AddPromptButtons = ({ layerId }: AddPromptButtonProps) => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
+  const [addIPAdapter, isAddIPAdapterDisabled] = useAddIPAdapterToIPALayer(layerId);
   const selectValidActions = useMemo(
     () =>
-      createMemoizedSelector(selectRegionalPromptsSlice, (regionalPrompts) => {
-        const layer = regionalPrompts.present.layers.find((l) => l.id === layerId);
-        assert(isVectorMaskLayer(layer), `Layer ${layerId} not found or not an RP layer`);
+      createMemoizedSelector(selectControlLayersSlice, (controlLayers) => {
+        const layer = controlLayers.present.layers.find((l) => l.id === layerId);
+        assert(isRegionalGuidanceLayer(layer), `Layer ${layerId} not found or not an RP layer`);
         return {
           canAddPositivePrompt: layer.positivePrompt === null,
           canAddNegativePrompt: layer.negativePrompt === null,
@@ -33,13 +34,10 @@ export const AddPromptButtons = ({ layerId }: AddPromptButtonProps) => {
   );
   const validActions = useAppSelector(selectValidActions);
   const addPositivePrompt = useCallback(() => {
-    dispatch(maskLayerPositivePromptChanged({ layerId, prompt: '' }));
+    dispatch(rgLayerPositivePromptChanged({ layerId, prompt: '' }));
   }, [dispatch, layerId]);
   const addNegativePrompt = useCallback(() => {
-    dispatch(maskLayerNegativePromptChanged({ layerId, prompt: '' }));
-  }, [dispatch, layerId]);
-  const addIPAdapter = useCallback(() => {
-    dispatch(maskLayerIPAdapterAdded(layerId));
+    dispatch(rgLayerNegativePromptChanged({ layerId, prompt: '' }));
   }, [dispatch, layerId]);
 
   return (
@@ -62,7 +60,13 @@ export const AddPromptButtons = ({ layerId }: AddPromptButtonProps) => {
       >
         {t('common.negativePrompt')}
       </Button>
-      <Button size="sm" variant="ghost" leftIcon={<PiPlusBold />} onClick={addIPAdapter}>
+      <Button
+        size="sm"
+        variant="ghost"
+        leftIcon={<PiPlusBold />}
+        onClick={addIPAdapter}
+        isDisabled={isAddIPAdapterDisabled}
+      >
         {t('common.ipAdapter')}
       </Button>
     </Flex>

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

@@ -10,7 +10,7 @@ import {
   PopoverTrigger,
 } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { brushSizeChanged, initialRegionalPromptsState } from 'features/regionalPrompts/store/regionalPromptsSlice';
+import { brushSizeChanged, initialControlLayersState } from 'features/controlLayers/store/controlLayersSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -20,22 +20,22 @@ const formatPx = (v: number | string) => `${v} px`;
 export const BrushSize = memo(() => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
-  const brushSize = useAppSelector((s) => s.regionalPrompts.present.brushSize);
+  const brushSize = useAppSelector((s) => s.controlLayers.present.brushSize);
   const onChange = useCallback(
     (v: number) => {
-      dispatch(brushSizeChanged(v));
+      dispatch(brushSizeChanged(Math.round(v)));
     },
     [dispatch]
   );
   return (
     <FormControl w="min-content">
-      <FormLabel m={0}>{t('regionalPrompts.brushSize')}</FormLabel>
+      <FormLabel m={0}>{t('controlLayers.brushSize')}</FormLabel>
       <Popover isLazy>
         <PopoverTrigger>
           <CompositeNumberInput
             min={1}
             max={600}
-            defaultValue={initialRegionalPromptsState.brushSize}
+            defaultValue={initialControlLayersState.brushSize}
             value={brushSize}
             onChange={onChange}
             w={24}
@@ -48,7 +48,7 @@ export const BrushSize = memo(() => {
             <CompositeSlider
               min={1}
               max={300}
-              defaultValue={initialRegionalPromptsState.brushSize}
+              defaultValue={initialControlLayersState.brushSize}
               value={brushSize}
               onChange={onChange}
               marks={marks}

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

@@ -0,0 +1,45 @@
+import { Flex, Spacer, useDisclosure } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { CALayerControlAdapterWrapper } from 'features/controlLayers/components/CALayer/CALayerControlAdapterWrapper';
+import { LayerDeleteButton } from 'features/controlLayers/components/LayerCommon/LayerDeleteButton';
+import { LayerMenu } from 'features/controlLayers/components/LayerCommon/LayerMenu';
+import { LayerTitle } from 'features/controlLayers/components/LayerCommon/LayerTitle';
+import { LayerVisibilityToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
+import { LayerWrapper } from 'features/controlLayers/components/LayerCommon/LayerWrapper';
+import { layerSelected, selectCALayerOrThrow } from 'features/controlLayers/store/controlLayersSlice';
+import { memo, useCallback } from 'react';
+
+import CALayerOpacity from './CALayerOpacity';
+
+type Props = {
+  layerId: string;
+};
+
+export const CALayer = memo(({ layerId }: Props) => {
+  const dispatch = useAppDispatch();
+  const isSelected = useAppSelector((s) => selectCALayerOrThrow(s.controlLayers.present, layerId).isSelected);
+  const onClick = useCallback(() => {
+    dispatch(layerSelected(layerId));
+  }, [dispatch, layerId]);
+  const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });
+
+  return (
+    <LayerWrapper onClick={onClick} borderColor={isSelected ? 'base.400' : 'base.800'}>
+      <Flex gap={3} alignItems="center" p={3} cursor="pointer" onDoubleClick={onToggle}>
+        <LayerVisibilityToggle layerId={layerId} />
+        <LayerTitle type="control_adapter_layer" />
+        <Spacer />
+        <CALayerOpacity layerId={layerId} />
+        <LayerMenu layerId={layerId} />
+        <LayerDeleteButton layerId={layerId} />
+      </Flex>
+      {isOpen && (
+        <Flex flexDir="column" gap={3} px={3} pb={3}>
+          <CALayerControlAdapterWrapper layerId={layerId} />
+        </Flex>
+      )}
+    </LayerWrapper>
+  );
+});
+
+CALayer.displayName = 'CALayer';

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayerControlAdapterWrapper.tsx

@@ -0,0 +1,121 @@
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { ControlAdapter } from 'features/controlLayers/components/ControlAndIPAdapter/ControlAdapter';
+import {
+  caLayerControlModeChanged,
+  caLayerImageChanged,
+  caLayerModelChanged,
+  caLayerProcessorConfigChanged,
+  caOrIPALayerBeginEndStepPctChanged,
+  caOrIPALayerWeightChanged,
+  selectCALayerOrThrow,
+} from 'features/controlLayers/store/controlLayersSlice';
+import type { ControlModeV2, ProcessorConfig } from 'features/controlLayers/util/controlAdapters';
+import type { CALayerImageDropData } from 'features/dnd/types';
+import { memo, useCallback, useMemo } from 'react';
+import type {
+  CALayerImagePostUploadAction,
+  ControlNetModelConfig,
+  ImageDTO,
+  T2IAdapterModelConfig,
+} from 'services/api/types';
+
+type Props = {
+  layerId: string;
+};
+
+export const CALayerControlAdapterWrapper = memo(({ layerId }: Props) => {
+  const dispatch = useAppDispatch();
+  const controlAdapter = useAppSelector((s) => selectCALayerOrThrow(s.controlLayers.present, layerId).controlAdapter);
+
+  const onChangeBeginEndStepPct = useCallback(
+    (beginEndStepPct: [number, number]) => {
+      dispatch(
+        caOrIPALayerBeginEndStepPctChanged({
+          layerId,
+          beginEndStepPct,
+        })
+      );
+    },
+    [dispatch, layerId]
+  );
+
+  const onChangeControlMode = useCallback(
+    (controlMode: ControlModeV2) => {
+      dispatch(
+        caLayerControlModeChanged({
+          layerId,
+          controlMode,
+        })
+      );
+    },
+    [dispatch, layerId]
+  );
+
+  const onChangeWeight = useCallback(
+    (weight: number) => {
+      dispatch(caOrIPALayerWeightChanged({ layerId, weight }));
+    },
+    [dispatch, layerId]
+  );
+
+  const onChangeProcessorConfig = useCallback(
+    (processorConfig: ProcessorConfig | null) => {
+      dispatch(caLayerProcessorConfigChanged({ layerId, processorConfig }));
+    },
+    [dispatch, layerId]
+  );
+
+  const onChangeModel = useCallback(
+    (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig) => {
+      dispatch(
+        caLayerModelChanged({
+          layerId,
+          modelConfig,
+        })
+      );
+    },
+    [dispatch, layerId]
+  );
+
+  const onChangeImage = useCallback(
+    (imageDTO: ImageDTO | null) => {
+      dispatch(caLayerImageChanged({ layerId, imageDTO }));
+    },
+    [dispatch, layerId]
+  );
+
+  const droppableData = useMemo<CALayerImageDropData>(
+    () => ({
+      actionType: 'SET_CA_LAYER_IMAGE',
+      context: {
+        layerId,
+      },
+      id: layerId,
+    }),
+    [layerId]
+  );
+
+  const postUploadAction = useMemo<CALayerImagePostUploadAction>(
+    () => ({
+      layerId,
+      type: 'SET_CA_LAYER_IMAGE',
+    }),
+    [layerId]
+  );
+
+  return (
+    <ControlAdapter
+      controlAdapter={controlAdapter}
+      onChangeBeginEndStepPct={onChangeBeginEndStepPct}
+      onChangeControlMode={onChangeControlMode}
+      onChangeWeight={onChangeWeight}
+      onChangeProcessorConfig={onChangeProcessorConfig}
+      onChangeModel={onChangeModel}
+      onChangeImage={onChangeImage}
+      droppableData={droppableData}
+      postUploadAction={postUploadAction}
+    />
+  );
+});
+
+CALayerControlAdapterWrapper.displayName = 'CALayerControlAdapterWrapper';

invokeai/frontend/web/src/features/controlLayers/components/CALayer/CALayerOpacity.tsx

@@ -0,0 +1,98 @@
+import {
+  CompositeNumberInput,
+  CompositeSlider,
+  Flex,
+  FormControl,
+  FormLabel,
+  IconButton,
+  Popover,
+  PopoverArrow,
+  PopoverBody,
+  PopoverContent,
+  PopoverTrigger,
+  Switch,
+} from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { stopPropagation } from 'common/util/stopPropagation';
+import { useLayerOpacity } from 'features/controlLayers/hooks/layerStateHooks';
+import { caLayerIsFilterEnabledChanged, caLayerOpacityChanged } from 'features/controlLayers/store/controlLayersSlice';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiDropHalfFill } from 'react-icons/pi';
+
+type Props = {
+  layerId: string;
+};
+
+const marks = [0, 25, 50, 75, 100];
+const formatPct = (v: number | string) => `${v} %`;
+
+const CALayerOpacity = ({ layerId }: Props) => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { opacity, isFilterEnabled } = useLayerOpacity(layerId);
+  const onChangeOpacity = useCallback(
+    (v: number) => {
+      dispatch(caLayerOpacityChanged({ layerId, opacity: v / 100 }));
+    },
+    [dispatch, layerId]
+  );
+  const onChangeFilter = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(caLayerIsFilterEnabledChanged({ layerId, isFilterEnabled: e.target.checked }));
+    },
+    [dispatch, layerId]
+  );
+  return (
+    <Popover isLazy>
+      <PopoverTrigger>
+        <IconButton
+          aria-label={t('controlLayers.opacity')}
+          size="sm"
+          icon={<PiDropHalfFill size={16} />}
+          variant="ghost"
+          onDoubleClick={stopPropagation}
+        />
+      </PopoverTrigger>
+      <PopoverContent onDoubleClick={stopPropagation}>
+        <PopoverArrow />
+        <PopoverBody>
+          <Flex direction="column" gap={2}>
+            <FormControl orientation="horizontal" w="full">
+              <FormLabel m={0} flexGrow={1} cursor="pointer">
+                {t('controlLayers.opacityFilter')}
+              </FormLabel>
+              <Switch isChecked={isFilterEnabled} onChange={onChangeFilter} />
+            </FormControl>
+            <FormControl orientation="horizontal">
+              <FormLabel m={0}>{t('controlLayers.opacity')}</FormLabel>
+              <CompositeSlider
+                min={0}
+                max={100}
+                step={1}
+                value={opacity}
+                defaultValue={100}
+                onChange={onChangeOpacity}
+                marks={marks}
+                w={48}
+              />
+              <CompositeNumberInput
+                min={0}
+                max={100}
+                step={1}
+                value={opacity}
+                defaultValue={100}
+                onChange={onChangeOpacity}
+                w={24}
+                format={formatPct}
+              />
+            </FormControl>
+          </Flex>
+        </PopoverBody>
+      </PopoverContent>
+    </Popover>
+  );
+};
+
+export default memo(CALayerOpacity);

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapter.tsx

@@ -0,0 +1,117 @@
+import { Box, Divider, Flex, Icon, IconButton } from '@invoke-ai/ui-library';
+import { ControlAdapterModelCombobox } from 'features/controlLayers/components/ControlAndIPAdapter/ControlAdapterModelCombobox';
+import type {
+  ControlModeV2,
+  ControlNetConfigV2,
+  ProcessorConfig,
+  T2IAdapterConfigV2,
+} from 'features/controlLayers/util/controlAdapters';
+import type { TypesafeDroppableData } from 'features/dnd/types';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiCaretUpBold } from 'react-icons/pi';
+import { useToggle } from 'react-use';
+import type { ControlNetModelConfig, ImageDTO, PostUploadAction, T2IAdapterModelConfig } from 'services/api/types';
+
+import { ControlAdapterBeginEndStepPct } from './ControlAdapterBeginEndStepPct';
+import { ControlAdapterControlModeSelect } from './ControlAdapterControlModeSelect';
+import { ControlAdapterImagePreview } from './ControlAdapterImagePreview';
+import { ControlAdapterProcessorConfig } from './ControlAdapterProcessorConfig';
+import { ControlAdapterProcessorTypeSelect } from './ControlAdapterProcessorTypeSelect';
+import { ControlAdapterWeight } from './ControlAdapterWeight';
+
+type Props = {
+  controlAdapter: ControlNetConfigV2 | T2IAdapterConfigV2;
+  onChangeBeginEndStepPct: (beginEndStepPct: [number, number]) => void;
+  onChangeControlMode: (controlMode: ControlModeV2) => void;
+  onChangeWeight: (weight: number) => void;
+  onChangeProcessorConfig: (processorConfig: ProcessorConfig | null) => void;
+  onChangeModel: (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig) => void;
+  onChangeImage: (imageDTO: ImageDTO | null) => void;
+  droppableData: TypesafeDroppableData;
+  postUploadAction: PostUploadAction;
+};
+
+export const ControlAdapter = memo(
+  ({
+    controlAdapter,
+    onChangeBeginEndStepPct,
+    onChangeControlMode,
+    onChangeWeight,
+    onChangeProcessorConfig,
+    onChangeModel,
+    onChangeImage,
+    droppableData,
+    postUploadAction,
+  }: Props) => {
+    const { t } = useTranslation();
+    const [isExpanded, toggleIsExpanded] = useToggle(false);
+
+    return (
+      <Flex flexDir="column" gap={3} position="relative" w="full">
+        <Flex gap={3} alignItems="center" w="full">
+          <Box minW={0} w="full" transitionProperty="common" transitionDuration="0.1s">
+            <ControlAdapterModelCombobox modelKey={controlAdapter.model?.key ?? null} onChange={onChangeModel} />
+          </Box>
+
+          <IconButton
+            size="sm"
+            tooltip={isExpanded ? t('controlnet.hideAdvanced') : t('controlnet.showAdvanced')}
+            aria-label={isExpanded ? t('controlnet.hideAdvanced') : t('controlnet.showAdvanced')}
+            onClick={toggleIsExpanded}
+            variant="ghost"
+            icon={
+              <Icon
+                boxSize={4}
+                as={PiCaretUpBold}
+                transform={isExpanded ? 'rotate(0deg)' : 'rotate(180deg)'}
+                transitionProperty="common"
+                transitionDuration="normal"
+              />
+            }
+          />
+        </Flex>
+        <Flex gap={3} w="full">
+          <Flex flexDir="column" gap={3} w="full" h="full">
+            {controlAdapter.type === 'controlnet' && (
+              <ControlAdapterControlModeSelect
+                controlMode={controlAdapter.controlMode}
+                onChange={onChangeControlMode}
+              />
+            )}
+            <ControlAdapterWeight weight={controlAdapter.weight} onChange={onChangeWeight} />
+            <ControlAdapterBeginEndStepPct
+              beginEndStepPct={controlAdapter.beginEndStepPct}
+              onChange={onChangeBeginEndStepPct}
+            />
+          </Flex>
+          <Flex alignItems="center" justifyContent="center" h={36} w={36} aspectRatio="1/1">
+            <ControlAdapterImagePreview
+              controlAdapter={controlAdapter}
+              onChangeImage={onChangeImage}
+              droppableData={droppableData}
+              postUploadAction={postUploadAction}
+            />
+          </Flex>
+        </Flex>
+        {isExpanded && (
+          <>
+            <Divider />
+            <Flex flexDir="column" gap={3} w="full">
+              <ControlAdapterProcessorTypeSelect
+                config={controlAdapter.processorConfig}
+                onChange={onChangeProcessorConfig}
+              />
+              <ControlAdapterProcessorConfig
+                config={controlAdapter.processorConfig}
+                onChange={onChangeProcessorConfig}
+              />
+            </Flex>
+          </>
+        )}
+      </Flex>
+    );
+  }
+);
+
+ControlAdapter.displayName = 'ControlAdapter';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterBeginEndStepPct.tsx

@@ -0,0 +1,43 @@
+import { CompositeRangeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+type Props = {
+  beginEndStepPct: [number, number];
+  onChange: (beginEndStepPct: [number, number]) => void;
+};
+
+const formatPct = (v: number) => `${Math.round(v * 100)}%`;
+const ariaLabel = ['Begin Step %', 'End Step %'];
+
+export const ControlAdapterBeginEndStepPct = memo(({ beginEndStepPct, onChange }: Props) => {
+  const { t } = useTranslation();
+  const onReset = useCallback(() => {
+    onChange([0, 1]);
+  }, [onChange]);
+
+  return (
+    <FormControl orientation="horizontal">
+      <InformationalPopover feature="controlNetBeginEnd">
+        <FormLabel m={0}>{t('controlnet.beginEndStepPercentShort')}</FormLabel>
+      </InformationalPopover>
+      <CompositeRangeSlider
+        aria-label={ariaLabel}
+        value={beginEndStepPct}
+        onChange={onChange}
+        onReset={onReset}
+        min={0}
+        max={1}
+        step={0.05}
+        fineStep={0.01}
+        minStepsBetweenThumbs={1}
+        formatValue={formatPct}
+        marks
+        withThumbTooltip
+      />
+    </FormControl>
+  );
+});
+
+ControlAdapterBeginEndStepPct.displayName = 'ControlAdapterBeginEndStepPct';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterControlModeSelect.tsx

@@ -0,0 +1,60 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import type { ControlModeV2 } from 'features/controlLayers/util/controlAdapters';
+import { isControlModeV2 } from 'features/controlLayers/util/controlAdapters';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { assert } from 'tsafe';
+
+type Props = {
+  controlMode: ControlModeV2;
+  onChange: (controlMode: ControlModeV2) => void;
+};
+
+export const ControlAdapterControlModeSelect = memo(({ controlMode, onChange }: Props) => {
+  const { t } = useTranslation();
+  const CONTROL_MODE_DATA = useMemo(
+    () => [
+      { label: t('controlnet.balanced'), value: 'balanced' },
+      { label: t('controlnet.prompt'), value: 'more_prompt' },
+      { label: t('controlnet.control'), value: 'more_control' },
+      { label: t('controlnet.megaControl'), value: 'unbalanced' },
+    ],
+    [t]
+  );
+
+  const handleControlModeChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      assert(isControlModeV2(v?.value));
+      onChange(v.value);
+    },
+    [onChange]
+  );
+
+  const value = useMemo(
+    () => CONTROL_MODE_DATA.filter((o) => o.value === controlMode)[0],
+    [CONTROL_MODE_DATA, controlMode]
+  );
+
+  if (!controlMode) {
+    return null;
+  }
+
+  return (
+    <FormControl>
+      <InformationalPopover feature="controlNetControlMode">
+        <FormLabel m={0}>{t('controlnet.control')}</FormLabel>
+      </InformationalPopover>
+      <Combobox
+        value={value}
+        options={CONTROL_MODE_DATA}
+        onChange={handleControlModeChange}
+        isClearable={false}
+        isSearchable={false}
+      />
+    </FormControl>
+  );
+});
+
+ControlAdapterControlModeSelect.displayName = 'ControlAdapterControlModeSelect';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterImagePreview.tsx

@@ -0,0 +1,217 @@
+import type { SystemStyleObject } from '@invoke-ai/ui-library';
+import { Box, Flex, Spinner, useShiftModifier } from '@invoke-ai/ui-library';
+import { skipToken } from '@reduxjs/toolkit/query';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIDndImage from 'common/components/IAIDndImage';
+import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
+import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
+import type { ControlNetConfigV2, T2IAdapterConfigV2 } from 'features/controlLayers/util/controlAdapters';
+import type { ImageDraggableData, TypesafeDroppableData } from 'features/dnd/types';
+import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import { memo, useCallback, useEffect, useMemo, useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiArrowCounterClockwiseBold, PiFloppyDiskBold, PiRulerBold } from 'react-icons/pi';
+import {
+  useAddImageToBoardMutation,
+  useChangeImageIsIntermediateMutation,
+  useGetImageDTOQuery,
+  useRemoveImageFromBoardMutation,
+} from 'services/api/endpoints/images';
+import type { ImageDTO, PostUploadAction } from 'services/api/types';
+
+type Props = {
+  controlAdapter: ControlNetConfigV2 | T2IAdapterConfigV2;
+  onChangeImage: (imageDTO: ImageDTO | null) => void;
+  droppableData: TypesafeDroppableData;
+  postUploadAction: PostUploadAction;
+};
+
+export const ControlAdapterImagePreview = memo(
+  ({ controlAdapter, onChangeImage, droppableData, postUploadAction }: Props) => {
+    const { t } = useTranslation();
+    const dispatch = useAppDispatch();
+    const autoAddBoardId = useAppSelector((s) => s.gallery.autoAddBoardId);
+    const isConnected = useAppSelector((s) => s.system.isConnected);
+    const activeTabName = useAppSelector(activeTabNameSelector);
+    const optimalDimension = useAppSelector(selectOptimalDimension);
+    const shift = useShiftModifier();
+
+    const [isMouseOverImage, setIsMouseOverImage] = useState(false);
+
+    const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(
+      controlAdapter.image?.name ?? skipToken
+    );
+    const { currentData: processedControlImage, isError: isErrorProcessedControlImage } = useGetImageDTOQuery(
+      controlAdapter.processedImage?.name ?? skipToken
+    );
+
+    const [changeIsIntermediate] = useChangeImageIsIntermediateMutation();
+    const [addToBoard] = useAddImageToBoardMutation();
+    const [removeFromBoard] = useRemoveImageFromBoardMutation();
+    const handleResetControlImage = useCallback(() => {
+      onChangeImage(null);
+    }, [onChangeImage]);
+
+    const handleSaveControlImage = useCallback(async () => {
+      if (!processedControlImage) {
+        return;
+      }
+
+      await changeIsIntermediate({
+        imageDTO: processedControlImage,
+        is_intermediate: false,
+      }).unwrap();
+
+      if (autoAddBoardId !== 'none') {
+        addToBoard({
+          imageDTO: processedControlImage,
+          board_id: autoAddBoardId,
+        });
+      } else {
+        removeFromBoard({ imageDTO: processedControlImage });
+      }
+    }, [processedControlImage, changeIsIntermediate, autoAddBoardId, addToBoard, removeFromBoard]);
+
+    const handleSetControlImageToDimensions = useCallback(() => {
+      if (!controlImage) {
+        return;
+      }
+
+      if (activeTabName === 'canvas') {
+        dispatch(
+          setBoundingBoxDimensions({ width: controlImage.width, height: controlImage.height }, optimalDimension)
+        );
+      } else {
+        const options = { updateAspectRatio: true, clamp: true };
+
+        if (shift) {
+          const { width, height } = controlImage;
+          dispatch(widthChanged({ width, ...options }));
+          dispatch(heightChanged({ height, ...options }));
+        } else {
+          const { width, height } = calculateNewSize(
+            controlImage.width / controlImage.height,
+            optimalDimension * optimalDimension
+          );
+          dispatch(widthChanged({ width, ...options }));
+          dispatch(heightChanged({ height, ...options }));
+        }
+      }
+    }, [controlImage, activeTabName, dispatch, optimalDimension, shift]);
+
+    const handleMouseEnter = useCallback(() => {
+      setIsMouseOverImage(true);
+    }, []);
+
+    const handleMouseLeave = useCallback(() => {
+      setIsMouseOverImage(false);
+    }, []);
+
+    const draggableData = useMemo<ImageDraggableData | undefined>(() => {
+      if (controlImage) {
+        return {
+          id: controlAdapter.id,
+          payloadType: 'IMAGE_DTO',
+          payload: { imageDTO: controlImage },
+        };
+      }
+    }, [controlImage, controlAdapter.id]);
+
+    const shouldShowProcessedImage =
+      controlImage &&
+      processedControlImage &&
+      !isMouseOverImage &&
+      !controlAdapter.processorPendingBatchId &&
+      controlAdapter.processorConfig !== null;
+
+    useEffect(() => {
+      if (isConnected && (isErrorControlImage || isErrorProcessedControlImage)) {
+        handleResetControlImage();
+      }
+    }, [handleResetControlImage, isConnected, isErrorControlImage, isErrorProcessedControlImage]);
+
+    return (
+      <Flex
+        onMouseEnter={handleMouseEnter}
+        onMouseLeave={handleMouseLeave}
+        position="relative"
+        w="full"
+        h={36}
+        alignItems="center"
+        justifyContent="center"
+      >
+        <IAIDndImage
+          draggableData={draggableData}
+          droppableData={droppableData}
+          imageDTO={controlImage}
+          isDropDisabled={shouldShowProcessedImage}
+          postUploadAction={postUploadAction}
+        />
+
+        <Box
+          position="absolute"
+          top={0}
+          insetInlineStart={0}
+          w="full"
+          h="full"
+          opacity={shouldShowProcessedImage ? 1 : 0}
+          transitionProperty="common"
+          transitionDuration="normal"
+          pointerEvents="none"
+        >
+          <IAIDndImage
+            draggableData={draggableData}
+            droppableData={droppableData}
+            imageDTO={processedControlImage}
+            isUploadDisabled={true}
+          />
+        </Box>
+
+        <>
+          <IAIDndImageIcon
+            onClick={handleResetControlImage}
+            icon={controlImage ? <PiArrowCounterClockwiseBold size={16} /> : undefined}
+            tooltip={t('controlnet.resetControlImage')}
+          />
+          <IAIDndImageIcon
+            onClick={handleSaveControlImage}
+            icon={controlImage ? <PiFloppyDiskBold size={16} /> : undefined}
+            tooltip={t('controlnet.saveControlImage')}
+            styleOverrides={saveControlImageStyleOverrides}
+          />
+          <IAIDndImageIcon
+            onClick={handleSetControlImageToDimensions}
+            icon={controlImage ? <PiRulerBold size={16} /> : undefined}
+            tooltip={shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')}
+            styleOverrides={setControlImageDimensionsStyleOverrides}
+          />
+        </>
+
+        {controlAdapter.processorPendingBatchId !== null && (
+          <Flex
+            position="absolute"
+            top={0}
+            insetInlineStart={0}
+            w="full"
+            h="full"
+            alignItems="center"
+            justifyContent="center"
+            opacity={0.8}
+            borderRadius="base"
+            bg="base.900"
+          >
+            <Spinner size="xl" color="base.400" />
+          </Flex>
+        )}
+      </Flex>
+    );
+  }
+);
+
+ControlAdapterImagePreview.displayName = 'ControlAdapterImagePreview';
+
+const saveControlImageStyleOverrides: SystemStyleObject = { mt: 6 };
+const setControlImageDimensionsStyleOverrides: SystemStyleObject = { mt: 12 };

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterModelCombobox.tsx

@@ -0,0 +1,63 @@
+import { Combobox, FormControl, Tooltip } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useControlNetAndT2IAdapterModels } from 'services/api/hooks/modelsByType';
+import type { AnyModelConfig, ControlNetModelConfig, T2IAdapterModelConfig } from 'services/api/types';
+
+type Props = {
+  modelKey: string | null;
+  onChange: (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig) => void;
+};
+
+export const ControlAdapterModelCombobox = memo(({ modelKey, onChange: onChangeModel }: Props) => {
+  const { t } = useTranslation();
+  const currentBaseModel = useAppSelector((s) => s.generation.model?.base);
+  const [modelConfigs, { isLoading }] = useControlNetAndT2IAdapterModels();
+  const selectedModel = useMemo(() => modelConfigs.find((m) => m.key === modelKey), [modelConfigs, modelKey]);
+
+  const _onChange = useCallback(
+    (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig | null) => {
+      if (!modelConfig) {
+        return;
+      }
+      onChangeModel(modelConfig);
+    },
+    [onChangeModel]
+  );
+
+  const getIsDisabled = useCallback(
+    (model: AnyModelConfig): boolean => {
+      const isCompatible = currentBaseModel === model.base;
+      const hasMainModel = Boolean(currentBaseModel);
+      return !hasMainModel || !isCompatible;
+    },
+    [currentBaseModel]
+  );
+
+  const { options, value, onChange, noOptionsMessage } = useGroupedModelCombobox({
+    modelConfigs,
+    onChange: _onChange,
+    selectedModel,
+    getIsDisabled,
+    isLoading,
+    groupByType: true,
+  });
+
+  return (
+    <Tooltip label={selectedModel?.description}>
+      <FormControl isInvalid={!value || currentBaseModel !== selectedModel?.base} w="full">
+        <Combobox
+          options={options}
+          placeholder={t('controlnet.selectModel')}
+          value={value}
+          onChange={onChange}
+          noOptionsMessage={noOptionsMessage}
+        />
+      </FormControl>
+    </Tooltip>
+  );
+});
+
+ControlAdapterModelCombobox.displayName = 'ControlAdapterModelCombobox';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterProcessorConfig.tsx

@@ -0,0 +1,85 @@
+import type { ProcessorConfig } from 'features/controlLayers/util/controlAdapters';
+import { memo } from 'react';
+
+import { CannyProcessor } from './processors/CannyProcessor';
+import { ColorMapProcessor } from './processors/ColorMapProcessor';
+import { ContentShuffleProcessor } from './processors/ContentShuffleProcessor';
+import { DepthAnythingProcessor } from './processors/DepthAnythingProcessor';
+import { DWOpenposeProcessor } from './processors/DWOpenposeProcessor';
+import { HedProcessor } from './processors/HedProcessor';
+import { LineartProcessor } from './processors/LineartProcessor';
+import { MediapipeFaceProcessor } from './processors/MediapipeFaceProcessor';
+import { MidasDepthProcessor } from './processors/MidasDepthProcessor';
+import { MlsdImageProcessor } from './processors/MlsdImageProcessor';
+import { PidiProcessor } from './processors/PidiProcessor';
+
+type Props = {
+  config: ProcessorConfig | null;
+  onChange: (config: ProcessorConfig | null) => void;
+};
+
+export const ControlAdapterProcessorConfig = memo(({ config, onChange }: Props) => {
+  if (!config) {
+    return null;
+  }
+
+  if (config.type === 'canny_image_processor') {
+    return <CannyProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'color_map_image_processor') {
+    return <ColorMapProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'depth_anything_image_processor') {
+    return <DepthAnythingProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'hed_image_processor') {
+    return <HedProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'lineart_image_processor') {
+    return <LineartProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'content_shuffle_image_processor') {
+    return <ContentShuffleProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'lineart_anime_image_processor') {
+    // No configurable options for this processor
+    return null;
+  }
+
+  if (config.type === 'mediapipe_face_processor') {
+    return <MediapipeFaceProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'midas_depth_image_processor') {
+    return <MidasDepthProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'mlsd_image_processor') {
+    return <MlsdImageProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'normalbae_image_processor') {
+    // No configurable options for this processor
+    return null;
+  }
+
+  if (config.type === 'dw_openpose_image_processor') {
+    return <DWOpenposeProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'pidi_image_processor') {
+    return <PidiProcessor onChange={onChange} config={config} />;
+  }
+
+  if (config.type === 'zoe_depth_image_processor') {
+    return null;
+  }
+});
+
+ControlAdapterProcessorConfig.displayName = 'ControlAdapterProcessorConfig';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterProcessorTypeSelect.tsx

@@ -0,0 +1,70 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, Flex, FormControl, FormLabel, IconButton } from '@invoke-ai/ui-library';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import type { ProcessorConfig } from 'features/controlLayers/util/controlAdapters';
+import { CA_PROCESSOR_DATA, isProcessorTypeV2 } from 'features/controlLayers/util/controlAdapters';
+import { configSelector } from 'features/system/store/configSelectors';
+import { includes, map } from 'lodash-es';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiXBold } from 'react-icons/pi';
+import { assert } from 'tsafe';
+
+type Props = {
+  config: ProcessorConfig | null;
+  onChange: (config: ProcessorConfig | null) => void;
+};
+
+const selectDisabledProcessors = createMemoizedSelector(
+  configSelector,
+  (config) => config.sd.disabledControlNetProcessors
+);
+
+export const ControlAdapterProcessorTypeSelect = memo(({ config, onChange }: Props) => {
+  const { t } = useTranslation();
+  const disabledProcessors = useAppSelector(selectDisabledProcessors);
+  const options = useMemo(() => {
+    return map(CA_PROCESSOR_DATA, ({ labelTKey }, type) => ({ value: type, label: t(labelTKey) })).filter(
+      (o) => !includes(disabledProcessors, o.value)
+    );
+  }, [disabledProcessors, t]);
+
+  const _onChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      if (!v) {
+        onChange(null);
+      } else {
+        assert(isProcessorTypeV2(v.value));
+        onChange(CA_PROCESSOR_DATA[v.value].buildDefaults());
+      }
+    },
+    [onChange]
+  );
+  const clearProcessor = useCallback(() => {
+    onChange(null);
+  }, [onChange]);
+  const value = useMemo(() => options.find((o) => o.value === config?.type) ?? null, [options, config?.type]);
+
+  return (
+    <Flex gap={2}>
+      <FormControl>
+        <InformationalPopover feature="controlNetProcessor">
+          <FormLabel m={0}>{t('controlnet.processor')}</FormLabel>
+        </InformationalPopover>
+        <Combobox value={value} options={options} onChange={_onChange} isSearchable={false} isClearable={false} />
+      </FormControl>
+      <IconButton
+        aria-label={t('controlLayers.clearProcessor')}
+        onClick={clearProcessor}
+        isDisabled={!config}
+        icon={<PiXBold />}
+        variant="ghost"
+        size="sm"
+      />
+    </Flex>
+  );
+});
+
+ControlAdapterProcessorTypeSelect.displayName = 'ControlAdapterProcessorTypeSelect';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/ControlAdapterWeight.tsx

@@ -0,0 +1,55 @@
+import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+type Props = {
+  weight: number;
+  onChange: (weight: number) => void;
+};
+
+const formatValue = (v: number) => v.toFixed(2);
+const marks = [0, 1, 2];
+
+export const ControlAdapterWeight = memo(({ weight, onChange }: Props) => {
+  const { t } = useTranslation();
+  const initial = useAppSelector((s) => s.config.sd.ca.weight.initial);
+  const sliderMin = useAppSelector((s) => s.config.sd.ca.weight.sliderMin);
+  const sliderMax = useAppSelector((s) => s.config.sd.ca.weight.sliderMax);
+  const numberInputMin = useAppSelector((s) => s.config.sd.ca.weight.numberInputMin);
+  const numberInputMax = useAppSelector((s) => s.config.sd.ca.weight.numberInputMax);
+  const coarseStep = useAppSelector((s) => s.config.sd.ca.weight.coarseStep);
+  const fineStep = useAppSelector((s) => s.config.sd.ca.weight.fineStep);
+
+  return (
+    <FormControl orientation="horizontal">
+      <InformationalPopover feature="controlNetWeight">
+        <FormLabel m={0}>{t('controlnet.weight')}</FormLabel>
+      </InformationalPopover>
+      <CompositeSlider
+        value={weight}
+        onChange={onChange}
+        defaultValue={initial}
+        min={sliderMin}
+        max={sliderMax}
+        step={coarseStep}
+        fineStep={fineStep}
+        marks={marks}
+        formatValue={formatValue}
+      />
+      <CompositeNumberInput
+        value={weight}
+        onChange={onChange}
+        min={numberInputMin}
+        max={numberInputMax}
+        step={coarseStep}
+        fineStep={fineStep}
+        maxW={20}
+        defaultValue={initial}
+      />
+    </FormControl>
+  );
+});
+
+ControlAdapterWeight.displayName = 'ControlAdapterWeight';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/IPAdapter.tsx

@@ -0,0 +1,72 @@
+import { Box, Flex } from '@invoke-ai/ui-library';
+import { ControlAdapterBeginEndStepPct } from 'features/controlLayers/components/ControlAndIPAdapter/ControlAdapterBeginEndStepPct';
+import { ControlAdapterWeight } from 'features/controlLayers/components/ControlAndIPAdapter/ControlAdapterWeight';
+import { IPAdapterImagePreview } from 'features/controlLayers/components/ControlAndIPAdapter/IPAdapterImagePreview';
+import { IPAdapterMethod } from 'features/controlLayers/components/ControlAndIPAdapter/IPAdapterMethod';
+import { IPAdapterModelSelect } from 'features/controlLayers/components/ControlAndIPAdapter/IPAdapterModelSelect';
+import type { CLIPVisionModelV2, IPAdapterConfigV2, IPMethodV2 } from 'features/controlLayers/util/controlAdapters';
+import type { TypesafeDroppableData } from 'features/dnd/types';
+import { memo } from 'react';
+import type { ImageDTO, IPAdapterModelConfig, PostUploadAction } from 'services/api/types';
+
+type Props = {
+  ipAdapter: IPAdapterConfigV2;
+  onChangeBeginEndStepPct: (beginEndStepPct: [number, number]) => void;
+  onChangeWeight: (weight: number) => void;
+  onChangeIPMethod: (method: IPMethodV2) => void;
+  onChangeModel: (modelConfig: IPAdapterModelConfig) => void;
+  onChangeCLIPVisionModel: (clipVisionModel: CLIPVisionModelV2) => void;
+  onChangeImage: (imageDTO: ImageDTO | null) => void;
+  droppableData: TypesafeDroppableData;
+  postUploadAction: PostUploadAction;
+};
+
+export const IPAdapter = memo(
+  ({
+    ipAdapter,
+    onChangeBeginEndStepPct,
+    onChangeWeight,
+    onChangeIPMethod,
+    onChangeModel,
+    onChangeCLIPVisionModel,
+    onChangeImage,
+    droppableData,
+    postUploadAction,
+  }: Props) => {
+    return (
+      <Flex flexDir="column" gap={4} position="relative" w="full">
+        <Flex gap={3} alignItems="center" w="full">
+          <Box minW={0} w="full" transitionProperty="common" transitionDuration="0.1s">
+            <IPAdapterModelSelect
+              modelKey={ipAdapter.model?.key ?? null}
+              onChangeModel={onChangeModel}
+              clipVisionModel={ipAdapter.clipVisionModel}
+              onChangeCLIPVisionModel={onChangeCLIPVisionModel}
+            />
+          </Box>
+        </Flex>
+        <Flex gap={4} w="full" alignItems="center">
+          <Flex flexDir="column" gap={3} w="full">
+            <IPAdapterMethod method={ipAdapter.method} onChange={onChangeIPMethod} />
+            <ControlAdapterWeight weight={ipAdapter.weight} onChange={onChangeWeight} />
+            <ControlAdapterBeginEndStepPct
+              beginEndStepPct={ipAdapter.beginEndStepPct}
+              onChange={onChangeBeginEndStepPct}
+            />
+          </Flex>
+          <Flex alignItems="center" justifyContent="center" h={36} w={36} aspectRatio="1/1">
+            <IPAdapterImagePreview
+              image={ipAdapter.image}
+              onChangeImage={onChangeImage}
+              ipAdapterId={ipAdapter.id}
+              droppableData={droppableData}
+              postUploadAction={postUploadAction}
+            />
+          </Flex>
+        </Flex>
+      </Flex>
+    );
+  }
+);
+
+IPAdapter.displayName = 'IPAdapter';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/IPAdapterImagePreview.tsx

@@ -0,0 +1,113 @@
+import type { SystemStyleObject } from '@invoke-ai/ui-library';
+import { Flex, useShiftModifier } from '@invoke-ai/ui-library';
+import { skipToken } from '@reduxjs/toolkit/query';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIDndImage from 'common/components/IAIDndImage';
+import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
+import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
+import type { ImageWithDims } from 'features/controlLayers/util/controlAdapters';
+import type { ImageDraggableData, TypesafeDroppableData } from 'features/dnd/types';
+import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
+import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
+import { memo, useCallback, useEffect, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiArrowCounterClockwiseBold, PiRulerBold } from 'react-icons/pi';
+import { useGetImageDTOQuery } from 'services/api/endpoints/images';
+import type { ImageDTO, PostUploadAction } from 'services/api/types';
+
+type Props = {
+  image: ImageWithDims | null;
+  onChangeImage: (imageDTO: ImageDTO | null) => void;
+  ipAdapterId: string; // required for the dnd/upload interactions
+  droppableData: TypesafeDroppableData;
+  postUploadAction: PostUploadAction;
+};
+
+export const IPAdapterImagePreview = memo(
+  ({ image, onChangeImage, ipAdapterId, droppableData, postUploadAction }: Props) => {
+    const { t } = useTranslation();
+    const dispatch = useAppDispatch();
+    const isConnected = useAppSelector((s) => s.system.isConnected);
+    const activeTabName = useAppSelector(activeTabNameSelector);
+    const optimalDimension = useAppSelector(selectOptimalDimension);
+    const shift = useShiftModifier();
+
+    const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(image?.name ?? skipToken);
+    const handleResetControlImage = useCallback(() => {
+      onChangeImage(null);
+    }, [onChangeImage]);
+
+    const handleSetControlImageToDimensions = useCallback(() => {
+      if (!controlImage) {
+        return;
+      }
+
+      if (activeTabName === 'canvas') {
+        dispatch(
+          setBoundingBoxDimensions({ width: controlImage.width, height: controlImage.height }, optimalDimension)
+        );
+      } else {
+        const options = { updateAspectRatio: true, clamp: true };
+        if (shift) {
+          const { width, height } = controlImage;
+          dispatch(widthChanged({ width, ...options }));
+          dispatch(heightChanged({ height, ...options }));
+        } else {
+          const { width, height } = calculateNewSize(
+            controlImage.width / controlImage.height,
+            optimalDimension * optimalDimension
+          );
+          dispatch(widthChanged({ width, ...options }));
+          dispatch(heightChanged({ height, ...options }));
+        }
+      }
+    }, [controlImage, activeTabName, dispatch, optimalDimension, shift]);
+
+    const draggableData = useMemo<ImageDraggableData | undefined>(() => {
+      if (controlImage) {
+        return {
+          id: ipAdapterId,
+          payloadType: 'IMAGE_DTO',
+          payload: { imageDTO: controlImage },
+        };
+      }
+    }, [controlImage, ipAdapterId]);
+
+    useEffect(() => {
+      if (isConnected && isErrorControlImage) {
+        handleResetControlImage();
+      }
+    }, [handleResetControlImage, isConnected, isErrorControlImage]);
+
+    return (
+      <Flex position="relative" w="full" h={36} alignItems="center" justifyContent="center">
+        <IAIDndImage
+          draggableData={draggableData}
+          droppableData={droppableData}
+          imageDTO={controlImage}
+          postUploadAction={postUploadAction}
+        />
+
+        <>
+          <IAIDndImageIcon
+            onClick={handleResetControlImage}
+            icon={controlImage ? <PiArrowCounterClockwiseBold size={16} /> : undefined}
+            tooltip={t('controlnet.resetControlImage')}
+          />
+          <IAIDndImageIcon
+            onClick={handleSetControlImageToDimensions}
+            icon={controlImage ? <PiRulerBold size={16} /> : undefined}
+            tooltip={shift ? t('controlnet.setControlImageDimensionsForce') : t('controlnet.setControlImageDimensions')}
+            styleOverrides={setControlImageDimensionsStyleOverrides}
+          />
+        </>
+      </Flex>
+    );
+  }
+);
+
+IPAdapterImagePreview.displayName = 'IPAdapterImagePreview';
+
+const setControlImageDimensionsStyleOverrides: SystemStyleObject = { mt: 6 };

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/IPAdapterMethod.tsx

@@ -0,0 +1,44 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import type { IPMethodV2 } from 'features/controlLayers/util/controlAdapters';
+import { isIPMethodV2 } from 'features/controlLayers/util/controlAdapters';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { assert } from 'tsafe';
+
+type Props = {
+  method: IPMethodV2;
+  onChange: (method: IPMethodV2) => void;
+};
+
+export const IPAdapterMethod = memo(({ method, onChange }: Props) => {
+  const { t } = useTranslation();
+  const options: { label: string; value: IPMethodV2 }[] = useMemo(
+    () => [
+      { label: t('controlnet.full'), value: 'full' },
+      { label: `${t('controlnet.style')} (${t('common.beta')})`, value: 'style' },
+      { label: `${t('controlnet.composition')} (${t('common.beta')})`, value: 'composition' },
+    ],
+    [t]
+  );
+  const _onChange = useCallback<ComboboxOnChange>(
+    (v) => {
+      assert(isIPMethodV2(v?.value));
+      onChange(v.value);
+    },
+    [onChange]
+  );
+  const value = useMemo(() => options.find((o) => o.value === method), [options, method]);
+
+  return (
+    <FormControl>
+      <InformationalPopover feature="ipAdapterMethod">
+        <FormLabel>{t('controlnet.ipAdapterMethod')}</FormLabel>
+      </InformationalPopover>
+      <Combobox value={value} options={options} onChange={_onChange} />
+    </FormControl>
+  );
+});
+
+IPAdapterMethod.displayName = 'IPAdapterMethod';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/IPAdapterModelSelect.tsx

@@ -0,0 +1,100 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, Flex, FormControl, Tooltip } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
+import type { CLIPVisionModelV2 } from 'features/controlLayers/util/controlAdapters';
+import { isCLIPVisionModelV2 } from 'features/controlLayers/util/controlAdapters';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useIPAdapterModels } from 'services/api/hooks/modelsByType';
+import type { AnyModelConfig, IPAdapterModelConfig } from 'services/api/types';
+import { assert } from 'tsafe';
+
+const CLIP_VISION_OPTIONS = [
+  { label: 'ViT-H', value: 'ViT-H' },
+  { label: 'ViT-G', value: 'ViT-G' },
+];
+
+type Props = {
+  modelKey: string | null;
+  onChangeModel: (modelConfig: IPAdapterModelConfig) => void;
+  clipVisionModel: CLIPVisionModelV2;
+  onChangeCLIPVisionModel: (clipVisionModel: CLIPVisionModelV2) => void;
+};
+
+export const IPAdapterModelSelect = memo(
+  ({ modelKey, onChangeModel, clipVisionModel, onChangeCLIPVisionModel }: Props) => {
+    const { t } = useTranslation();
+    const currentBaseModel = useAppSelector((s) => s.generation.model?.base);
+    const [modelConfigs, { isLoading }] = useIPAdapterModels();
+    const selectedModel = useMemo(() => modelConfigs.find((m) => m.key === modelKey), [modelConfigs, modelKey]);
+
+    const _onChangeModel = useCallback(
+      (modelConfig: IPAdapterModelConfig | null) => {
+        if (!modelConfig) {
+          return;
+        }
+        onChangeModel(modelConfig);
+      },
+      [onChangeModel]
+    );
+
+    const _onChangeCLIPVisionModel = useCallback<ComboboxOnChange>(
+      (v) => {
+        assert(isCLIPVisionModelV2(v?.value));
+        onChangeCLIPVisionModel(v.value);
+      },
+      [onChangeCLIPVisionModel]
+    );
+
+    const getIsDisabled = useCallback(
+      (model: AnyModelConfig): boolean => {
+        const isCompatible = currentBaseModel === model.base;
+        const hasMainModel = Boolean(currentBaseModel);
+        return !hasMainModel || !isCompatible;
+      },
+      [currentBaseModel]
+    );
+
+    const { options, value, onChange, noOptionsMessage } = useGroupedModelCombobox({
+      modelConfigs,
+      onChange: _onChangeModel,
+      selectedModel,
+      getIsDisabled,
+      isLoading,
+    });
+
+    const clipVisionModelValue = useMemo(
+      () => CLIP_VISION_OPTIONS.find((o) => o.value === clipVisionModel),
+      [clipVisionModel]
+    );
+
+    return (
+      <Flex gap={4}>
+        <Tooltip label={selectedModel?.description}>
+          <FormControl isInvalid={!value || currentBaseModel !== selectedModel?.base} w="full">
+            <Combobox
+              options={options}
+              placeholder={t('controlnet.selectModel')}
+              value={value}
+              onChange={onChange}
+              noOptionsMessage={noOptionsMessage}
+            />
+          </FormControl>
+        </Tooltip>
+        {selectedModel?.format === 'checkpoint' && (
+          <FormControl isInvalid={!value || currentBaseModel !== selectedModel?.base} width="max-content" minWidth={28}>
+            <Combobox
+              options={CLIP_VISION_OPTIONS}
+              placeholder={t('controlnet.selectCLIPVisionModel')}
+              value={clipVisionModelValue}
+              onChange={_onChangeCLIPVisionModel}
+            />
+          </FormControl>
+        )}
+      </Flex>
+    );
+  }
+);
+
+IPAdapterModelSelect.displayName = 'IPAdapterModelSelect';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/processors/CannyProcessor.tsx

@@ -0,0 +1,67 @@
+import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import type { ProcessorComponentProps } from 'features/controlLayers/components/ControlAndIPAdapter/processors/types';
+import { CA_PROCESSOR_DATA, type CannyProcessorConfig } from 'features/controlLayers/util/controlAdapters';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import ProcessorWrapper from './ProcessorWrapper';
+
+type Props = ProcessorComponentProps<CannyProcessorConfig>;
+const DEFAULTS = CA_PROCESSOR_DATA['canny_image_processor'].buildDefaults();
+
+export const CannyProcessor = ({ onChange, config }: Props) => {
+  const { t } = useTranslation();
+  const handleLowThresholdChanged = useCallback(
+    (v: number) => {
+      onChange({ ...config, low_threshold: v });
+    },
+    [onChange, config]
+  );
+  const handleHighThresholdChanged = useCallback(
+    (v: number) => {
+      onChange({ ...config, high_threshold: v });
+    },
+    [onChange, config]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <FormControl>
+        <FormLabel m={0}>{t('controlnet.lowThreshold')}</FormLabel>
+        <CompositeSlider
+          value={config.low_threshold}
+          onChange={handleLowThresholdChanged}
+          defaultValue={DEFAULTS.low_threshold}
+          min={0}
+          max={255}
+        />
+        <CompositeNumberInput
+          value={config.low_threshold}
+          onChange={handleLowThresholdChanged}
+          defaultValue={DEFAULTS.low_threshold}
+          min={0}
+          max={255}
+        />
+      </FormControl>
+      <FormControl>
+        <FormLabel m={0}>{t('controlnet.highThreshold')}</FormLabel>
+        <CompositeSlider
+          value={config.high_threshold}
+          onChange={handleHighThresholdChanged}
+          defaultValue={DEFAULTS.high_threshold}
+          min={0}
+          max={255}
+        />
+        <CompositeNumberInput
+          value={config.high_threshold}
+          onChange={handleHighThresholdChanged}
+          defaultValue={DEFAULTS.high_threshold}
+          min={0}
+          max={255}
+        />
+      </FormControl>
+    </ProcessorWrapper>
+  );
+};
+
+CannyProcessor.displayName = 'CannyProcessor';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/processors/ColorMapProcessor.tsx

@@ -0,0 +1,47 @@
+import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import type { ProcessorComponentProps } from 'features/controlLayers/components/ControlAndIPAdapter/processors/types';
+import { CA_PROCESSOR_DATA, type ColorMapProcessorConfig } from 'features/controlLayers/util/controlAdapters';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import ProcessorWrapper from './ProcessorWrapper';
+
+type Props = ProcessorComponentProps<ColorMapProcessorConfig>;
+const DEFAULTS = CA_PROCESSOR_DATA['color_map_image_processor'].buildDefaults();
+
+export const ColorMapProcessor = memo(({ onChange, config }: Props) => {
+  const { t } = useTranslation();
+  const handleColorMapTileSizeChanged = useCallback(
+    (v: number) => {
+      onChange({ ...config, color_map_tile_size: v });
+    },
+    [config, onChange]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <FormControl>
+        <FormLabel m={0}>{t('controlnet.colorMapTileSize')}</FormLabel>
+        <CompositeSlider
+          value={config.color_map_tile_size}
+          defaultValue={DEFAULTS.color_map_tile_size}
+          onChange={handleColorMapTileSizeChanged}
+          min={1}
+          max={256}
+          step={1}
+          marks
+        />
+        <CompositeNumberInput
+          value={config.color_map_tile_size}
+          defaultValue={DEFAULTS.color_map_tile_size}
+          onChange={handleColorMapTileSizeChanged}
+          min={1}
+          max={4096}
+          step={1}
+        />
+      </FormControl>
+    </ProcessorWrapper>
+  );
+});
+
+ColorMapProcessor.displayName = 'ColorMapProcessor';

invokeai/frontend/web/src/features/controlLayers/components/ControlAndIPAdapter/processors/ContentShuffleProcessor.tsx

@@ -0,0 +1,79 @@
+import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import type { ProcessorComponentProps } from 'features/controlLayers/components/ControlAndIPAdapter/processors/types';
+import type { ContentShuffleProcessorConfig } from 'features/controlLayers/util/controlAdapters';
+import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import ProcessorWrapper from './ProcessorWrapper';
+
+type Props = ProcessorComponentProps<ContentShuffleProcessorConfig>;
+const DEFAULTS = CA_PROCESSOR_DATA['content_shuffle_image_processor'].buildDefaults();
+
+export const ContentShuffleProcessor = memo(({ onChange, config }: Props) => {
+  const { t } = useTranslation();
+
+  const handleWChanged = useCallback(
+    (v: number) => {
+      onChange({ ...config, w: v });
+    },
+    [config, onChange]
+  );
+
+  const handleHChanged = useCallback(
+    (v: number) => {
+      onChange({ ...config, h: v });
+    },
+    [config, onChange]
+  );
+
+  const handleFChanged = useCallback(
+    (v: number) => {
+      onChange({ ...config, f: v });
+    },
+    [config, onChange]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <FormControl>
+        <FormLabel m={0}>{t('controlnet.w')}</FormLabel>
+        <CompositeSlider
+          value={config.w}
+          defaultValue={DEFAULTS.w}
+          onChange={handleWChanged}
+          min={0}
+          max={4096}
+          marks
+        />
+        <CompositeNumberInput value={config.w} defaultValue={DEFAULTS.w} onChange={handleWChanged} min={0} max={4096} />
+      </FormControl>
+      <FormControl>
+        <FormLabel m={0}>{t('controlnet.h')}</FormLabel>
+        <CompositeSlider
+          value={config.h}
+          defaultValue={DEFAULTS.h}
+          onChange={handleHChanged}
+          min={0}
+          max={4096}
+          marks
+        />
+        <CompositeNumberInput value={config.h} defaultValue={DEFAULTS.h} onChange={handleHChanged} min={0} max={4096} />
+      </FormControl>
+      <FormControl>
+        <FormLabel m={0}>{t('controlnet.f')}</FormLabel>
+        <CompositeSlider
+          value={config.f}
+          defaultValue={DEFAULTS.f}
+          onChange={handleFChanged}
+          min={0}
+          max={4096}
+          marks
+        />
+        <CompositeNumberInput value={config.f} defaultValue={DEFAULTS.f} onChange={handleFChanged} min={0} max={4096} />
+      </FormControl>
+    </ProcessorWrapper>
+  );
+});
+
+ContentShuffleProcessor.displayName = 'ContentShuffleProcessor';