chore: v4.2.1 (#6362)

## Summary

Bump to v4.2.1

## Related Issues / Discussions

n/a

## QA Instructions

n/a

## Merge Plan

Do the release after merging.

## Checklist

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

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/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.
 

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/invocations/image.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from pathlib import Path
 from typing import Literal, Optional
 
 import cv2
@@ -504,7 +503,7 @@ 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 +515,12 @@ 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

@@ -586,13 +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:

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/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/frontend/web/package.json

@@ -52,47 +52,48 @@
   },
   "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",
+    "@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",
@@ -104,8 +105,8 @@
     "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",
@@ -116,19 +117,19 @@
   "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",
     "concurrently": "^8.2.2",
@@ -136,20 +137,20 @@
     "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

@@ -142,8 +142,11 @@
         "blue": "Blue",
         "alpha": "Alpha",
         "selected": "Selected",
-        "viewer": "Viewer",
-        "tab": "Tab"
+        "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",
@@ -258,7 +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",
@@ -311,7 +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",
@@ -364,8 +372,7 @@
         "bulkDownloadRequestFailed": "Problem Preparing Download",
         "bulkDownloadFailed": "Download Failed",
         "problemDeletingImages": "Problem Deleting Images",
-        "problemDeletingImagesDesc": "One or more images could not be deleted",
-        "switchTo": "Switch to {{ tab }} (Z)"
+        "problemDeletingImagesDesc": "One or more images could not be deleted"
     },
     "hotkeys": {
         "searchHotkeys": "Search Hotkeys",
@@ -589,13 +596,9 @@
             "desc": "Upscale the current image",
             "title": "Upscale"
         },
-        "backToEditor": {
-            "desc": "Closes the Image Viewer and shows the Editor View (Text to Image tab only)",
-            "title": "Back to Editor"
-        },
-        "openImageViewer": {
-            "desc": "Opens the Image Viewer (Text to Image tab only)",
-            "title": "Open Image Viewer"
+        "toggleViewer": {
+            "desc": "Switches between the Image Viewer and workspace for the current tab.",
+            "title": "Toggle Image Viewer"
         }
     },
     "metadata": {
@@ -936,17 +939,30 @@
             "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",
@@ -1534,7 +1550,7 @@
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "controlLayers": "Control Layers (BETA)",
+        "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
         "toggleVisibility": "Toggle Layer Visibility",
@@ -1548,8 +1564,6 @@
         "addIPAdapter": "Add $t(common.ipAdapter)",
         "regionalGuidance": "Regional Guidance",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "controlNetLayer": "$t(common.controlNet) $t(unifiedCanvas.layer)",
-        "ipAdapterLayer": "$t(common.ipAdapter) $t(unifiedCanvas.layer)",
         "opacity": "Opacity",
         "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
         "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
@@ -1560,7 +1574,9 @@
         "opacityFilter": "Opacity Filter",
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
-        "noLayersAdded": "No Layers Added"
+        "noLayersAdded": "No Layers Added",
+        "layers_one": "Layer",
+        "layers_other": "Layers"
     },
     "ui": {
         "tabs": {

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/src/app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor.ts

@@ -1,60 +1,54 @@
 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,
-  caLayerIsProcessingImageChanged,
   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 { isEqual } from 'lodash-es';
-import { imagesApi } from 'services/api/endpoints/images';
+import { getImageDTO } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
-import type { BatchConfig, ImageDTO } from 'services/api/types';
+import type { BatchConfig } from 'services/api/types';
 import { socketInvocationComplete } from 'services/events/actions';
+import { assert } from 'tsafe';
 
-const matcher = isAnyOf(caLayerImageChanged, caLayerProcessorConfigChanged, caLayerModelChanged);
+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, getOriginalState, cancelActiveListeners, delay, take }) => {
-      const { layerId } = action.payload;
-      const precheckLayerOriginal = getOriginalState()
-        .controlLayers.present.layers.filter(isControlAdapterLayer)
-        .find((l) => l.id === layerId);
-      const precheckLayer = getState()
-        .controlLayers.present.layers.filter(isControlAdapterLayer)
-        .find((l) => l.id === layerId);
-
-      // Conditions to bail
-      const layerDoesNotExist = !precheckLayer;
-      const layerHasNoImage = !precheckLayer?.controlAdapter.image;
-      const layerHasNoProcessorConfig = !precheckLayer?.controlAdapter.processorConfig;
-      const layerIsAlreadyProcessingImage = precheckLayer?.controlAdapter.isProcessingImage;
-      const areImageAndProcessorUnchanged =
-        isEqual(precheckLayer?.controlAdapter.image, precheckLayerOriginal?.controlAdapter.image) &&
-        isEqual(precheckLayer?.controlAdapter.processorConfig, precheckLayerOriginal?.controlAdapter.processorConfig);
-
-      if (
-        layerDoesNotExist ||
-        layerHasNoImage ||
-        layerHasNoProcessorConfig ||
-        areImageAndProcessorUnchanged ||
-        layerIsAlreadyProcessingImage
-      ) {
-        return;
-      }
+    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();
@@ -62,19 +56,31 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
 
       // Delay before starting actual work
       await delay(DEBOUNCE_MS);
-      dispatch(caLayerIsProcessingImageChanged({ layerId, isProcessingImage: true }));
 
       // 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);
-      const image = layer?.controlAdapter.image;
-      const config = layer?.controlAdapter.processorConfig;
 
       // If we have no image or there is no processor config, bail
-      if (!layer || !image || !config) {
+      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 = {
@@ -82,7 +88,11 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
         batch: {
           graph: {
             nodes: {
-              [processorNode.id]: { ...processorNode, is_intermediate: true },
+              [processorNode.id]: {
+                ...processorNode,
+                // Control images are always intermediate - do not save to gallery
+                is_intermediate: true,
+              },
             },
             edges: [],
           },
@@ -90,16 +100,21 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
         },
       };
 
+      // Kick off the processor batch
+      const req = dispatch(
+        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
+          fixedCacheKey: 'enqueueBatch',
+        })
+      );
+
       try {
-        const req = dispatch(
-          queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
-            fixedCacheKey: 'enqueueBatch',
-          })
-        );
         const enqueueResult = await req.unwrap();
-        req.reset();
+        // 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) &&
@@ -108,48 +123,52 @@ export const addControlAdapterPreprocessor = (startAppListening: AppStartListeni
         );
 
         // We still have to check the output type
-        if (isImageOutput(invocationCompleteAction.payload.data.result)) {
-          const { image_name } = invocationCompleteAction.payload.data.result.image;
+        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;
 
-          // Wait for the ImageDTO to be received
-          const [{ payload }] = await take(
-            (action) =>
-              imagesApi.endpoints.getImageDTO.matchFulfilled(action) && action.payload.image_name === image_name
-          );
+        const imageDTO = await getImageDTO(image_name);
+        assert(imageDTO, "Failed to fetch processor output's image DTO");
 
-          const imageDTO = payload as ImageDTO;
-
-          log.debug({ layerId, imageDTO }, 'ControlNet image processed');
-
-          // Update the processed image in the store
-          dispatch(
-            caLayerProcessedImageChanged({
-              layerId,
-              imageDTO,
-            })
-          );
-          dispatch(caLayerIsProcessingImageChanged({ layerId, isProcessingImage: false }));
-        }
+        // 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) {
-        console.log(error);
-        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
-        dispatch(caLayerIsProcessingImageChanged({ layerId, isProcessingImage: false }));
+        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...
+          console.log(error);
+          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;
+          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',
-          })
-        );
+          dispatch(
+            addToast({
+              title: t('queue.graphFailedToQueue'),
+              status: 'error',
+            })
+          );
+        }
+      } finally {
+        req.reset();
       }
     },
   });

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

@@ -1,5 +1,6 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { buildGenerationTabGraph } from 'features/nodes/util/graph/buildGenerationTabGraph';
 import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/buildGenerationTabSDXLGraph';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
@@ -11,6 +12,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       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;
 
@@ -29,7 +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

@@ -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/imageDeleted.ts

@@ -73,25 +73,25 @@ const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, ima
 const deleteControlLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
   state.controlLayers.present.layers.forEach((l) => {
     if (isRegionalGuidanceLayer(l)) {
-      if (l.ipAdapters.some((ipa) => ipa.image?.imageName === imageDTO.image_name)) {
+      if (l.ipAdapters.some((ipa) => ipa.image?.name === imageDTO.image_name)) {
         dispatch(layerDeleted(l.id));
       }
     }
     if (isControlAdapterLayer(l)) {
       if (
-        l.controlAdapter.image?.imageName === imageDTO.image_name ||
-        l.controlAdapter.processedImage?.imageName === imageDTO.image_name
+        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?.imageName === imageDTO.image_name) {
+      if (l.ipAdapter.image?.name === imageDTO.image_name) {
         dispatch(layerDeleted(l.id));
       }
     }
     if (isInitialImageLayer(l)) {
-      if (l.image?.imageName === imageDTO.image_name) {
+      if (l.image?.name === imageDTO.image_name) {
         dispatch(layerDeleted(l.id));
       }
     }

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

@@ -2,7 +2,12 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 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 { isImageOutput } from 'features/nodes/types/common';
 import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
@@ -101,6 +106,7 @@ export const addInvocationCompleteEventListener = (startAppListening: AppStartLi
             }
 
             dispatch(imageSelected(imageDTO));
+            dispatch(isImageViewerOpenChanged(true));
           }
         }
       }

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/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

@@ -6,6 +6,7 @@ import {
 } 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';
@@ -14,9 +15,16 @@ 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 { getConnectedEdges } from 'reactflow';
 
+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 selector = createMemoizedSelector(
   [
     selectControlAdaptersSlice,
@@ -29,21 +37,22 @@ const selector = createMemoizedSelector(
   ],
   (controlAdapters, generation, system, nodes, dynamicPrompts, controlLayers, activeTabName) => {
     const { model } = generation;
+    const { size } = controlLayers.present;
     const { positivePrompt } = controlLayers.present;
 
     const { isConnected } = system;
 
-    const reasons: string[] = [];
+    const reasons: { prefix?: string; content: string }[] = [];
 
     // Cannot generate if not connected
     if (!isConnected) {
-      reasons.push(i18n.t('parameters.invoke.systemDisconnected'));
+      reasons.push({ content: i18n.t('parameters.invoke.systemDisconnected') });
     }
 
     if (activeTabName === 'workflows') {
       if (nodes.shouldValidateGraph) {
         if (!nodes.nodes.length) {
-          reasons.push(i18n.t('parameters.invoke.noNodesInGraph'));
+          reasons.push({ content: i18n.t('parameters.invoke.noNodesInGraph') });
         }
 
         nodes.nodes.forEach((node) => {
@@ -55,7 +64,7 @@ const selector = createMemoizedSelector(
 
           if (!nodeTemplate) {
             // Node type not found
-            reasons.push(i18n.t('parameters.invoke.missingNodeTemplate'));
+            reasons.push({ content: i18n.t('parameters.invoke.missingNodeTemplate') });
             return;
           }
 
@@ -68,17 +77,17 @@ const selector = createMemoizedSelector(
             );
 
             if (!fieldTemplate) {
-              reasons.push(i18n.t('parameters.invoke.missingFieldTemplate'));
+              reasons.push({ content: i18n.t('parameters.invoke.missingFieldTemplate') });
               return;
             }
 
             if (fieldTemplate.required && field.value === undefined && !hasConnection) {
-              reasons.push(
-                i18n.t('parameters.invoke.missingInputForField', {
+              reasons.push({
+                content: i18n.t('parameters.invoke.missingInputForField', {
                   nodeLabel: node.data.label || nodeTemplate.title,
                   fieldLabel: field.label || fieldTemplate.title,
-                })
-              );
+                }),
+              });
               return;
             }
           });
@@ -86,62 +95,94 @@ const selector = createMemoizedSelector(
       }
     } else {
       if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
-        reasons.push(i18n.t('parameters.invoke.noPrompts'));
+        reasons.push({ content: i18n.t('parameters.invoke.noPrompts') });
       }
 
       if (!model) {
-        reasons.push(i18n.t('parameters.invoke.noModelSelected'));
+        reasons.push({ content: i18n.t('parameters.invoke.noModelSelected') });
       }
 
       if (activeTabName === 'generation') {
         // Handling for generation tab
         controlLayers.present.layers
           .filter((l) => l.isEnabled)
-          .flatMap((l) => {
+          .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') {
-              return l.controlAdapter;
-            } else if (l.type === 'ip_adapter_layer') {
-              return l.ipAdapter;
-            } else if (l.type === 'regional_guidance_layer') {
-              return l.ipAdapters;
+              // 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
+              if (l.controlAdapter.type === 't2i_adapter' && (size.width % 64 !== 0 || size.height % 64 !== 0)) {
+                problems.push(i18n.t('parameters.invoke.layer.t2iAdapterIncompatibleDimensions'));
+              }
             }
-            return [];
-          })
-          .forEach((ca, i) => {
-            const hasNoModel = !ca.model;
-            const mismatchedModelBase = ca.model?.base !== model?.base;
-            const hasNoImage = !ca.image;
-            const imageNotProcessed =
-              (ca.type === 'controlnet' || ca.type === 't2i_adapter') && !ca.processedImage && ca.processorConfig;
 
-            if (hasNoModel) {
-              reasons.push(
-                i18n.t('parameters.invoke.noModelForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+            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 (mismatchedModelBase) {
-              // This should never happen, just a sanity check
-              reasons.push(
-                i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+
+            if (l.type === 'initial_image_layer') {
+              // Must have an image
+              if (!l.image) {
+                problems.push(i18n.t('parameters.invoke.layer.initialImageNoImageSelected'));
+              }
             }
-            if (hasNoImage) {
-              reasons.push(
-                i18n.t('parameters.invoke.noControlImageForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+
+            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 (imageNotProcessed) {
-              reasons.push(
-                i18n.t('parameters.invoke.imageNotProcessedForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+
+            if (problems.length) {
+              const content = upperFirst(problems.join(', '));
+              reasons.push({ prefix, content });
             }
           });
       } else {
@@ -154,29 +195,19 @@ const selector = createMemoizedSelector(
             }
 
             if (!ca.model) {
-              reasons.push(
-                i18n.t('parameters.invoke.noModelForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+              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(
-                i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+              reasons.push({
+                content: i18n.t('parameters.invoke.incompatibleBaseModelForControlAdapter', { number: i + 1 }),
+              });
             }
 
             if (
               !ca.controlImage ||
               (isControlNetOrT2IAdapter(ca) && !ca.processedControlImage && ca.processorType !== 'none')
             ) {
-              reasons.push(
-                i18n.t('parameters.invoke.noControlImageForControlAdapter', {
-                  number: i + 1,
-                })
-              );
+              reasons.push({ content: i18n.t('parameters.invoke.noControlImageForControlAdapter', { number: i + 1 }) });
             }
           });
       }
@@ -187,6 +218,6 @@ const selector = createMemoizedSelector(
 );
 
 export const useIsReadyToEnqueue = () => {
-  const { isReady, reasons } = useAppSelector(selector);
-  return { isReady, reasons };
+  const value = useAppSelector(selector);
+  return value;
 };

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

@@ -21,8 +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 { ViewerButton } from 'features/gallery/components/ImageViewer/ViewerButton';
 import { memo, useCallback, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
@@ -217,110 +215,107 @@ 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 w="full" gap={2} alignItems="center">
-      <Flex flex={1} justifyContent="center">
-        <Flex gap={2} marginInlineEnd="auto" />
-      </Flex>
-      <Flex flex={1} gap={2} justifyContent="center">
-        <Tooltip label={`${t('unifiedCanvas.layer')} (Q)`}>
-          <FormControl isDisabled={isStaging} w="5rem">
-            <Combobox value={value} options={LAYER_NAMES_DICT} onChange={handleChangeLayer} />
-          </FormControl>
-        </Tooltip>
+    <Flex alignItems="center" gap={2} flexWrap="wrap">
+      <Tooltip label={`${t('unifiedCanvas.layer')} (Q)`}>
+        <FormControl isDisabled={isStaging} w="5rem">
+          <Combobox value={layerValue} options={layerOptions} onChange={handleChangeLayer} />
+        </FormControl>
+      </Tooltip>
 
-        <IAICanvasMaskOptions />
-        <IAICanvasToolChooserOptions />
+      <IAICanvasMaskOptions />
+      <IAICanvasToolChooserOptions />
 
-        <ButtonGroup>
-          <IconButton
-            aria-label={`${t('unifiedCanvas.move')} (V)`}
-            tooltip={`${t('unifiedCanvas.move')} (V)`}
-            icon={<PiHandGrabbingBold />}
-            isChecked={tool === 'move' || isStaging}
-            onClick={handleSelectMoveTool}
-          />
-          <IconButton
-            aria-label={`${shouldShowBoundingBox ? t('unifiedCanvas.hideBoundingBox') : t('unifiedCanvas.showBoundingBox')} (Shift + H)`}
-            tooltip={`${shouldShowBoundingBox ? t('unifiedCanvas.hideBoundingBox') : t('unifiedCanvas.showBoundingBox')} (Shift + H)`}
-            icon={shouldShowBoundingBox ? <PiEyeBold /> : <PiEyeSlashBold />}
-            onClick={handleSetShouldShowBoundingBox}
-            isDisabled={isStaging}
-          />
-          <IconButton
-            aria-label={`${t('unifiedCanvas.resetView')} (R)`}
-            tooltip={`${t('unifiedCanvas.resetView')} (R)`}
-            icon={<PiCrosshairSimpleBold />}
-            onClick={handleClickResetCanvasView}
-          />
-        </ButtonGroup>
+      <ButtonGroup>
+        <IconButton
+          aria-label={`${t('unifiedCanvas.move')} (V)`}
+          tooltip={`${t('unifiedCanvas.move')} (V)`}
+          icon={<PiHandGrabbingBold />}
+          isChecked={tool === 'move' || isStaging}
+          onClick={handleSelectMoveTool}
+        />
+        <IconButton
+          aria-label={`${shouldShowBoundingBox ? t('unifiedCanvas.hideBoundingBox') : t('unifiedCanvas.showBoundingBox')} (Shift + H)`}
+          tooltip={`${shouldShowBoundingBox ? t('unifiedCanvas.hideBoundingBox') : t('unifiedCanvas.showBoundingBox')} (Shift + H)`}
+          icon={shouldShowBoundingBox ? <PiEyeBold /> : <PiEyeSlashBold />}
+          onClick={handleSetShouldShowBoundingBox}
+          isDisabled={isStaging}
+        />
+        <IconButton
+          aria-label={`${t('unifiedCanvas.resetView')} (R)`}
+          tooltip={`${t('unifiedCanvas.resetView')} (R)`}
+          icon={<PiCrosshairSimpleBold />}
+          onClick={handleClickResetCanvasView}
+        />
+      </ButtonGroup>
 
-        <ButtonGroup>
+      <ButtonGroup>
+        <IconButton
+          aria-label={`${t('unifiedCanvas.mergeVisible')} (Shift+M)`}
+          tooltip={`${t('unifiedCanvas.mergeVisible')} (Shift+M)`}
+          icon={<PiStackBold />}
+          onClick={handleMergeVisible}
+          isDisabled={isStaging}
+        />
+        <IconButton
+          aria-label={`${t('unifiedCanvas.saveToGallery')} (Shift+S)`}
+          tooltip={`${t('unifiedCanvas.saveToGallery')} (Shift+S)`}
+          icon={<PiFloppyDiskBold />}
+          onClick={handleSaveToGallery}
+          isDisabled={isStaging}
+        />
+        {isClipboardAPIAvailable && (
           <IconButton
-            aria-label={`${t('unifiedCanvas.mergeVisible')} (Shift+M)`}
-            tooltip={`${t('unifiedCanvas.mergeVisible')} (Shift+M)`}
-            icon={<PiStackBold />}
-            onClick={handleMergeVisible}
+            aria-label={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
+            tooltip={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
+            icon={<PiCopyBold />}
+            onClick={handleCopyImageToClipboard}
             isDisabled={isStaging}
           />
-          <IconButton
-            aria-label={`${t('unifiedCanvas.saveToGallery')} (Shift+S)`}
-            tooltip={`${t('unifiedCanvas.saveToGallery')} (Shift+S)`}
-            icon={<PiFloppyDiskBold />}
-            onClick={handleSaveToGallery}
-            isDisabled={isStaging}
-          />
-          {isClipboardAPIAvailable && (
-            <IconButton
-              aria-label={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
-              tooltip={`${t('unifiedCanvas.copyToClipboard')} (Cmd/Ctrl+C)`}
-              icon={<PiCopyBold />}
-              onClick={handleCopyImageToClipboard}
-              isDisabled={isStaging}
-            />
-          )}
-          <IconButton
-            aria-label={`${t('unifiedCanvas.downloadAsImage')} (Shift+D)`}
-            tooltip={`${t('unifiedCanvas.downloadAsImage')} (Shift+D)`}
-            icon={<PiDownloadSimpleBold />}
-            onClick={handleDownloadAsImage}
-            isDisabled={isStaging}
-          />
-        </ButtonGroup>
-        <ButtonGroup>
-          <IAICanvasUndoButton />
-          <IAICanvasRedoButton />
-        </ButtonGroup>
+        )}
+        <IconButton
+          aria-label={`${t('unifiedCanvas.downloadAsImage')} (Shift+D)`}
+          tooltip={`${t('unifiedCanvas.downloadAsImage')} (Shift+D)`}
+          icon={<PiDownloadSimpleBold />}
+          onClick={handleDownloadAsImage}
+          isDisabled={isStaging}
+        />
+      </ButtonGroup>
+      <ButtonGroup>
+        <IAICanvasUndoButton />
+        <IAICanvasRedoButton />
+      </ButtonGroup>
 
-        <ButtonGroup>
-          <IconButton
-            aria-label={`${t('common.upload')}`}
-            tooltip={`${t('common.upload')}`}
-            icon={<PiUploadSimpleBold />}
-            isDisabled={isStaging}
-            {...getUploadButtonProps()}
-          />
-          <input {...getUploadInputProps()} />
-          <IconButton
-            aria-label={`${t('unifiedCanvas.clearCanvas')}`}
-            tooltip={`${t('unifiedCanvas.clearCanvas')}`}
-            icon={<PiTrashSimpleBold />}
-            onClick={handleResetCanvas}
-            colorScheme="error"
-            isDisabled={isStaging}
-          />
-        </ButtonGroup>
-        <ButtonGroup>
-          <IAICanvasSettingsButtonPopover />
-        </ButtonGroup>
-      </Flex>
-      <Flex flex={1} justifyContent="center">
-        <Flex gap={2} marginInlineStart="auto">
-          <ViewerButton />
-        </Flex>
-      </Flex>
+      <ButtonGroup>
+        <IconButton
+          aria-label={`${t('common.upload')}`}
+          tooltip={`${t('common.upload')}`}
+          icon={<PiUploadSimpleBold />}
+          isDisabled={isStaging}
+          {...getUploadButtonProps()}
+        />
+        <input {...getUploadInputProps()} />
+        <IconButton
+          aria-label={`${t('unifiedCanvas.clearCanvas')}`}
+          tooltip={`${t('unifiedCanvas.clearCanvas')}`}
+          icon={<PiTrashSimpleBold />}
+          onClick={handleResetCanvas}
+          colorScheme="error"
+          isDisabled={isStaging}
+        />
+      </ButtonGroup>
+      <ButtonGroup>
+        <IAICanvasSettingsButtonPopover />
+      </ButtonGroup>
     </Flex>
   );
 };

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/controlLayers/components/AddLayerButton.tsx

@@ -18,7 +18,12 @@ export const AddLayerButton = memo(() => {
 
   return (
     <Menu>
-      <MenuButton as={Button} leftIcon={<PiPlusBold />} variant="ghost">
+      <MenuButton
+        as={Button}
+        leftIcon={<PiPlusBold />}
+        variant="ghost"
+        data-testid="control-layers-add-layer-menu-button"
+      >
         {t('controlLayers.addLayer')}
       </MenuButton>
       <MenuList>

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

@@ -19,7 +19,6 @@ export const CALayer = memo(({ layerId }: Props) => {
   const dispatch = useAppDispatch();
   const isSelected = useAppSelector((s) => selectCALayerOrThrow(s.controlLayers.present, layerId).isSelected);
   const onClick = useCallback(() => {
-    // Must be capture so that the layer is selected before deleting/resetting/etc
     dispatch(layerSelected(layerId));
   }, [dispatch, layerId]);
   const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });

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

@@ -42,10 +42,10 @@ export const ControlAdapterImagePreview = memo(
     const [isMouseOverImage, setIsMouseOverImage] = useState(false);
 
     const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(
-      controlAdapter.image?.imageName ?? skipToken
+      controlAdapter.image?.name ?? skipToken
     );
     const { currentData: processedControlImage, isError: isErrorProcessedControlImage } = useGetImageDTOQuery(
-      controlAdapter.processedImage?.imageName ?? skipToken
+      controlAdapter.processedImage?.name ?? skipToken
     );
 
     const [changeIsIntermediate] = useChangeImageIsIntermediateMutation();
@@ -124,7 +124,7 @@ export const ControlAdapterImagePreview = memo(
       controlImage &&
       processedControlImage &&
       !isMouseOverImage &&
-      !controlAdapter.isProcessingImage &&
+      !controlAdapter.processorPendingBatchId &&
       controlAdapter.processorConfig !== null;
 
     useEffect(() => {
@@ -190,7 +190,7 @@ export const ControlAdapterImagePreview = memo(
           />
         </>
 
-        {controlAdapter.isProcessingImage && (
+        {controlAdapter.processorPendingBatchId !== null && (
           <Flex
             position="absolute"
             top={0}

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

@@ -42,6 +42,7 @@ export const ControlAdapterModelCombobox = memo(({ modelKey, onChange: onChangeM
     selectedModel,
     getIsDisabled,
     isLoading,
+    groupByType: true,
   });
 
   return (

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

@@ -34,9 +34,7 @@ export const IPAdapterImagePreview = memo(
     const optimalDimension = useAppSelector(selectOptimalDimension);
     const shift = useShiftModifier();
 
-    const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(
-      image?.imageName ?? skipToken
-    );
+    const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(image?.name ?? skipToken);
     const handleResetControlImage = useCallback(() => {
       onChangeImage(null);
     }, [onChangeImage]);

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

@@ -2,14 +2,13 @@ import type { ComboboxOnChange } from '@invoke-ai/ui-library';
 import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import type { ProcessorComponentProps } from 'features/controlLayers/components/ControlAndIPAdapter/processors/types';
 import type { DepthAnythingModelSize, DepthAnythingProcessorConfig } from 'features/controlLayers/util/controlAdapters';
-import { CA_PROCESSOR_DATA, isDepthAnythingModelSize } from 'features/controlLayers/util/controlAdapters';
+import { isDepthAnythingModelSize } from 'features/controlLayers/util/controlAdapters';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 import ProcessorWrapper from './ProcessorWrapper';
 
 type Props = ProcessorComponentProps<DepthAnythingProcessorConfig>;
-const DEFAULTS = CA_PROCESSOR_DATA['depth_anything_image_processor'].buildDefaults();
 
 export const DepthAnythingProcessor = memo(({ onChange, config }: Props) => {
   const { t } = useTranslation();
@@ -38,12 +37,7 @@ export const DepthAnythingProcessor = memo(({ onChange, config }: Props) => {
     <ProcessorWrapper>
       <FormControl>
         <FormLabel m={0}>{t('controlnet.modelSize')}</FormLabel>
-        <Combobox
-          value={value}
-          defaultInputValue={DEFAULTS.model_size}
-          options={options}
-          onChange={handleModelSizeChange}
-        />
+        <Combobox value={value} options={options} onChange={handleModelSizeChange} isSearchable={false} />
       </FormControl>
     </ProcessorWrapper>
   );

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

@@ -32,7 +32,7 @@ export const ControlLayersPanelContent = memo(() => {
       </Flex>
       {layerIdTypePairs.length > 0 && (
         <ScrollableContent>
-          <Flex flexDir="column" gap={2}>
+          <Flex flexDir="column" gap={2} data-testid="control-layers-layer-list">
             {layerIdTypePairs.map(({ id, type }) => (
               <LayerWrapper key={id} id={id} type={type} />
             ))}

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

@@ -1,12 +1,30 @@
-import { Flex, IconButton, Popover, PopoverBody, PopoverContent, PopoverTrigger } from '@invoke-ai/ui-library';
+import {
+  Checkbox,
+  Flex,
+  FormControl,
+  FormLabel,
+  IconButton,
+  Popover,
+  PopoverBody,
+  PopoverContent,
+  PopoverTrigger,
+} from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { setShouldInvertBrushSizeScrollDirection } from 'features/canvas/store/canvasSlice';
 import { GlobalMaskLayerOpacity } from 'features/controlLayers/components/GlobalMaskLayerOpacity';
-import { memo } from 'react';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { RiSettings4Fill } from 'react-icons/ri';
 
 const ControlLayersSettingsPopover = () => {
   const { t } = useTranslation();
-
+  const dispatch = useAppDispatch();
+  const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
+  const handleChangeShouldInvertBrushSizeScrollDirection = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(setShouldInvertBrushSizeScrollDirection(e.target.checked)),
+    [dispatch]
+  );
   return (
     <Popover isLazy>
       <PopoverTrigger>
@@ -16,6 +34,13 @@ const ControlLayersSettingsPopover = () => {
         <PopoverBody>
           <Flex direction="column" gap={2}>
             <GlobalMaskLayerOpacity />
+            <FormControl w="full">
+              <FormLabel flexGrow={1}>{t('unifiedCanvas.invertBrushSizeScrollDirection')}</FormLabel>
+              <Checkbox
+                isChecked={shouldInvertBrushSizeScrollDirection}
+                onChange={handleChangeShouldInvertBrushSizeScrollDirection}
+              />
+            </FormControl>
           </Flex>
         </PopoverBody>
       </PopoverContent>

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

@@ -4,14 +4,17 @@ import { BrushSize } from 'features/controlLayers/components/BrushSize';
 import ControlLayersSettingsPopover from 'features/controlLayers/components/ControlLayersSettingsPopover';
 import { ToolChooser } from 'features/controlLayers/components/ToolChooser';
 import { UndoRedoButtonGroup } from 'features/controlLayers/components/UndoRedoButtonGroup';
-import { ViewerButton } from 'features/gallery/components/ImageViewer/ViewerButton';
+import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
+import { ViewerToggleMenu } from 'features/gallery/components/ImageViewer/ViewerToggleMenu';
 import { memo } from 'react';
 
 export const ControlLayersToolbar = memo(() => {
   return (
     <Flex w="full" gap={2}>
       <Flex flex={1} justifyContent="center">
-        <Flex gap={2} marginInlineEnd="auto" />
+        <Flex gap={2} marginInlineEnd="auto">
+          <ToggleProgressButton />
+        </Flex>
       </Flex>
       <Flex flex={1} gap={2} justifyContent="center">
         <BrushSize />
@@ -21,7 +24,7 @@ export const ControlLayersToolbar = memo(() => {
       </Flex>
       <Flex flex={1} justifyContent="center">
         <Flex gap={2} marginInlineStart="auto">
-          <ViewerButton />
+          <ViewerToggleMenu />
         </Flex>
       </Flex>
     </Flex>

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

@@ -20,6 +20,7 @@ export const DeleteAllLayersButton = memo(() => {
       variant="ghost"
       colorScheme="error"
       isDisabled={isDisabled}
+      data-testid="control-layers-delete-all-layers-button"
     >
       {t('controlLayers.deleteAll')}
     </Button>

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

@@ -8,6 +8,7 @@ import { LayerTitle } from 'features/controlLayers/components/LayerCommon/LayerT
 import { LayerVisibilityToggle } from 'features/controlLayers/components/LayerCommon/LayerVisibilityToggle';
 import { LayerWrapper } from 'features/controlLayers/components/LayerCommon/LayerWrapper';
 import {
+  iiLayerDenoisingStrengthChanged,
   iiLayerImageChanged,
   layerSelected,
   selectIILayerOrThrow,
@@ -36,6 +37,13 @@ export const IILayer = memo(({ layerId }: Props) => {
     [dispatch, layerId]
   );
 
+  const onChangeDenoisingStrength = useCallback(
+    (denoisingStrength: number) => {
+      dispatch(iiLayerDenoisingStrengthChanged({ layerId, denoisingStrength }));
+    },
+    [dispatch, layerId]
+  );
+
   const droppableData = useMemo<IILayerImageDropData>(
     () => ({
       actionType: 'SET_II_LAYER_IMAGE',
@@ -67,7 +75,7 @@ export const IILayer = memo(({ layerId }: Props) => {
       </Flex>
       {isOpen && (
         <Flex flexDir="column" gap={3} px={3} pb={3}>
-          <ImageToImageStrength />
+          <ImageToImageStrength value={layer.denoisingStrength} onChange={onChangeDenoisingStrength} />
           <InitialImagePreview
             image={layer.image}
             onChangeImage={onChangeImage}

invokeai/frontend/web/src/features/controlLayers/components/IILayer/InitialImagePreview.tsx

@@ -32,7 +32,7 @@ export const InitialImagePreview = memo(({ image, onChangeImage, droppableData,
   const optimalDimension = useAppSelector(selectOptimalDimension);
   const shift = useShiftModifier();
 
-  const { currentData: imageDTO, isError: isErrorControlImage } = useGetImageDTOQuery(image?.imageName ?? skipToken);
+  const { currentData: imageDTO, isError: isErrorControlImage } = useGetImageDTOQuery(image?.name ?? skipToken);
 
   const onReset = useCallback(() => {
     onChangeImage(null);

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

@@ -1,19 +1,26 @@
 import { Flex, Spacer, useDisclosure } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { IPALayerIPAdapterWrapper } from 'features/controlLayers/components/IPALayer/IPALayerIPAdapterWrapper';
 import { LayerDeleteButton } from 'features/controlLayers/components/LayerCommon/LayerDeleteButton';
 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 { memo } from 'react';
+import { layerSelected, selectIPALayerOrThrow } from 'features/controlLayers/store/controlLayersSlice';
+import { memo, useCallback } from 'react';
 
 type Props = {
   layerId: string;
 };
 
 export const IPALayer = memo(({ layerId }: Props) => {
+  const dispatch = useAppDispatch();
+  const isSelected = useAppSelector((s) => selectIPALayerOrThrow(s.controlLayers.present, layerId).isSelected);
   const { isOpen, onToggle } = useDisclosure({ defaultIsOpen: true });
+  const onClick = useCallback(() => {
+    dispatch(layerSelected(layerId));
+  }, [dispatch, layerId]);
   return (
-    <LayerWrapper borderColor="base.800">
+    <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="ip_adapter_layer" />

invokeai/frontend/web/src/features/controlLayers/components/LayerCommon/LayerWrapper.tsx

@@ -10,7 +10,16 @@ type Props = PropsWithChildren<{
 
 export const LayerWrapper = memo(({ onClick, borderColor, children }: Props) => {
   return (
-    <Flex gap={2} onClick={onClick} bg={borderColor} px={2} borderRadius="base" py="1px">
+    <Flex
+      gap={2}
+      onClick={onClick}
+      bg={borderColor}
+      px={2}
+      borderRadius="base"
+      py="1px"
+      transitionProperty="all"
+      transitionDuration="0.2s"
+    >
       <Flex flexDir="column" w="full" bg="base.850" borderRadius="base">
         {children}
       </Flex>

invokeai/frontend/web/src/features/controlLayers/components/RGLayer/RGLayerNegativePrompt.tsx

@@ -45,6 +45,7 @@ export const RGLayerNegativePrompt = memo(({ layerId }: Props) => {
           variant="darkFilled"
           paddingRight={30}
           fontSize="sm"
+          spellCheck={false}
         />
         <PromptOverlayButtonWrapper>
           <RGLayerPromptDeleteButton layerId={layerId} polarity="negative" />

invokeai/frontend/web/src/features/controlLayers/components/RGLayer/RGLayerPositivePrompt.tsx

@@ -45,6 +45,7 @@ export const RGLayerPositivePrompt = memo(({ layerId }: Props) => {
           variant="darkFilled"
           paddingRight={30}
           minH={28}
+          spellCheck={false}
         />
         <PromptOverlayButtonWrapper>
           <RGLayerPromptDeleteButton layerId={layerId} polarity="positive" />

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

@@ -6,7 +6,7 @@ import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { useMouseEvents } from 'features/controlLayers/hooks/mouseEventHooks';
 import {
-  $cursorPosition,
+  $lastCursorPos,
   $lastMouseDownPos,
   $tool,
   isRegionalGuidanceLayer,
@@ -48,7 +48,7 @@ const useStageRenderer = (
   const state = useAppSelector((s) => s.controlLayers.present);
   const tool = useStore($tool);
   const mouseEventHandlers = useMouseEvents();
-  const cursorPosition = useStore($cursorPosition);
+  const lastCursorPos = useStore($lastCursorPos);
   const lastMouseDownPos = useStore($lastMouseDownPos);
   const selectedLayerIdColor = useAppSelector(selectSelectedLayerColor);
   const selectedLayerType = useAppSelector(selectSelectedLayerType);
@@ -130,18 +130,18 @@ const useStageRenderer = (
   }, [stage, state.size.width, state.size.height, wrapper]);
 
   useLayoutEffect(() => {
-    log.trace('Rendering tool preview');
     if (asPreview) {
       // Preview should not display tool
       return;
     }
+    log.trace('Rendering tool preview');
     renderers.renderToolPreview(
       stage,
       tool,
       selectedLayerIdColor,
       selectedLayerType,
       state.globalMaskLayerOpacity,
-      cursorPosition,
+      lastCursorPos,
       lastMouseDownPos,
       state.brushSize
     );
@@ -152,7 +152,7 @@ const useStageRenderer = (
     selectedLayerIdColor,
     selectedLayerType,
     state.globalMaskLayerOpacity,
-    cursorPosition,
+    lastCursorPos,
     lastMouseDownPos,
     state.brushSize,
     renderers,
@@ -178,15 +178,24 @@ const useStageRenderer = (
       // Preview should not display bboxes
       return;
     }
-    renderers.renderBbox(stage, state.layers, tool, onBboxChanged);
+    renderers.renderBboxes(stage, state.layers, tool);
   }, [stage, asPreview, state.layers, tool, onBboxChanged, renderers]);
 
   useLayoutEffect(() => {
-    log.trace('Rendering background');
+    if (asPreview) {
+      // Preview should not check for transparency
+      return;
+    }
+    log.trace('Updating bboxes');
+    debouncedRenderers.updateBboxes(stage, state.layers, onBboxChanged);
+  }, [stage, asPreview, state.layers, onBboxChanged]);
+
+  useLayoutEffect(() => {
     if (asPreview) {
       // The preview should not have a background
       return;
     }
+    log.trace('Rendering background');
     renderers.renderBackground(stage, state.size.width, state.size.height);
   }, [stage, asPreview, state.size.width, state.size.height, renderers]);
 
@@ -196,11 +205,11 @@ const useStageRenderer = (
   }, [stage, layerIds, renderers]);
 
   useLayoutEffect(() => {
-    log.trace('Rendering no layers message');
     if (asPreview) {
       // The preview should not display the no layers message
       return;
     }
+    log.trace('Rendering no layers message');
     renderers.renderNoLayersMessage(stage, layerCount, state.size.width, state.size.height);
   }, [stage, layerCount, renderers, asPreview, state.size.width, state.size.height]);
 
@@ -233,7 +242,14 @@ export const StageComponent = memo(({ asPreview = false }: Props) => {
   return (
     <Flex overflow="hidden" w="full" h="full">
       <Flex ref={wrapperRef} w="full" h="full" alignItems="center" justifyContent="center">
-        <Flex ref={containerRef} tabIndex={-1} bg="base.850" borderRadius="base" overflow="hidden" />
+        <Flex
+          ref={containerRef}
+          tabIndex={-1}
+          bg="base.850"
+          borderRadius="base"
+          overflow="hidden"
+          data-testid="control-layers-canvas"
+        />
       </Flex>
     </Flex>
   );

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

@@ -4,9 +4,9 @@ import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import {
   $tool,
+  layerReset,
   selectControlLayersSlice,
   selectedLayerDeleted,
-  selectedLayerReset,
 } from 'features/controlLayers/store/controlLayersSlice';
 import { useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
@@ -22,6 +22,7 @@ export const ToolChooser: React.FC = () => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const isDisabled = useAppSelector(selectIsDisabled);
+  const selectedLayerId = useAppSelector((s) => s.controlLayers.present.selectedLayerId);
   const tool = useStore($tool);
 
   const setToolToBrush = useCallback(() => {
@@ -42,8 +43,11 @@ export const ToolChooser: React.FC = () => {
   useHotkeys('v', setToolToMove, { enabled: !isDisabled }, [isDisabled]);
 
   const resetSelectedLayer = useCallback(() => {
-    dispatch(selectedLayerReset());
-  }, [dispatch]);
+    if (selectedLayerId === null) {
+      return;
+    }
+    dispatch(layerReset(selectedLayerId));
+  }, [dispatch, selectedLayerId]);
   useHotkeys('shift+c', resetSelectedLayer);
 
   const deleteSelectedLayer = useCallback(() => {

invokeai/frontend/web/src/features/controlLayers/hooks/layerStateHooks.ts

@@ -1,4 +1,5 @@
 import { createSelector } from '@reduxjs/toolkit';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import {
   isControlAdapterLayer,
@@ -69,7 +70,7 @@ export const useLayerType = (layerId: string) => {
 export const useLayerOpacity = (layerId: string) => {
   const selectLayer = useMemo(
     () =>
-      createSelector(selectControlLayersSlice, (controlLayers) => {
+      createMemoizedSelector(selectControlLayersSlice, (controlLayers) => {
         const layer = controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
         assert(layer, `Layer ${layerId} not found`);
         return { opacity: Math.round(layer.opacity * 100), isFilterEnabled: layer.isFilterEnabled };

invokeai/frontend/web/src/features/controlLayers/hooks/mouseEventHooks.ts

@@ -3,8 +3,8 @@ import { useStore } from '@nanostores/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { calculateNewBrushSize } from 'features/canvas/hooks/useCanvasZoom';
 import {
-  $cursorPosition,
   $isDrawing,
+  $lastCursorPos,
   $lastMouseDownPos,
   $tool,
   brushSizeChanged,
@@ -15,17 +15,41 @@ import {
 import type Konva from 'konva';
 import type { KonvaEventObject } from 'konva/lib/Node';
 import type { Vector2d } from 'konva/lib/types';
-import { useCallback, useRef } from 'react';
+import { clamp } from 'lodash-es';
+import { useCallback, useMemo, useRef } from 'react';
 
 const getIsFocused = (stage: Konva.Stage) => {
   return stage.container().contains(document.activeElement);
 };
 const getIsMouseDown = (e: KonvaEventObject<MouseEvent>) => e.evt.buttons === 1;
 
+const SNAP_PX = 10;
+
+export const snapPosToStage = (pos: Vector2d, stage: Konva.Stage) => {
+  const snappedPos = { ...pos };
+  // Get the normalized threshold for snapping to the edge of the stage
+  const thresholdX = SNAP_PX / stage.scaleX();
+  const thresholdY = SNAP_PX / stage.scaleY();
+  const stageWidth = stage.width() / stage.scaleX();
+  const stageHeight = stage.height() / stage.scaleY();
+  // Snap to the edge of the stage if within threshold
+  if (pos.x - thresholdX < 0) {
+    snappedPos.x = 0;
+  } else if (pos.x + thresholdX > stageWidth) {
+    snappedPos.x = Math.floor(stageWidth);
+  }
+  if (pos.y - thresholdY < 0) {
+    snappedPos.y = 0;
+  } else if (pos.y + thresholdY > stageHeight) {
+    snappedPos.y = Math.floor(stageHeight);
+  }
+  return snappedPos;
+};
+
 export const getScaledFlooredCursorPosition = (stage: Konva.Stage) => {
   const pointerPosition = stage.getPointerPosition();
   const stageTransform = stage.getAbsoluteTransform().copy();
-  if (!pointerPosition || !stageTransform) {
+  if (!pointerPosition) {
     return;
   }
   const scaledCursorPosition = stageTransform.invert().point(pointerPosition);
@@ -40,11 +64,13 @@ const syncCursorPos = (stage: Konva.Stage): Vector2d | null => {
   if (!pos) {
     return null;
   }
-  $cursorPosition.set(pos);
+  $lastCursorPos.set(pos);
   return pos;
 };
 
-const BRUSH_SPACING = 20;
+const BRUSH_SPACING_PCT = 10;
+const MIN_BRUSH_SPACING_PX = 5;
+const MAX_BRUSH_SPACING_PX = 15;
 
 export const useMouseEvents = () => {
   const dispatch = useAppDispatch();
@@ -60,6 +86,10 @@ export const useMouseEvents = () => {
   const lastCursorPosRef = useRef<[number, number] | null>(null);
   const shouldInvertBrushSizeScrollDirection = useAppSelector((s) => s.canvas.shouldInvertBrushSizeScrollDirection);
   const brushSize = useAppSelector((s) => s.controlLayers.present.brushSize);
+  const brushSpacingPx = useMemo(
+    () => clamp(brushSize / BRUSH_SPACING_PCT, MIN_BRUSH_SPACING_PX, MAX_BRUSH_SPACING_PX),
+    [brushSize]
+  );
 
   const onMouseDown = useCallback(
     (e: KonvaEventObject<MouseEvent>) => {
@@ -71,7 +101,6 @@ export const useMouseEvents = () => {
       if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
         return;
       }
-      $lastMouseDownPos.set(pos);
       if (tool === 'brush' || tool === 'eraser') {
         dispatch(
           rgLayerLineAdded({
@@ -81,6 +110,9 @@ export const useMouseEvents = () => {
           })
         );
         $isDrawing.set(true);
+        $lastMouseDownPos.set(pos);
+      } else if (tool === 'rect') {
+        $lastMouseDownPos.set(snapPosToStage(pos, stage));
       }
     },
     [dispatch, selectedLayerId, selectedLayerType, tool]
@@ -92,21 +124,22 @@ export const useMouseEvents = () => {
       if (!stage) {
         return;
       }
-      const pos = $cursorPosition.get();
+      const pos = $lastCursorPos.get();
       if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
         return;
       }
       const lastPos = $lastMouseDownPos.get();
       const tool = $tool.get();
       if (lastPos && selectedLayerId && tool === 'rect') {
+        const snappedPos = snapPosToStage(pos, stage);
         dispatch(
           rgLayerRectAdded({
             layerId: selectedLayerId,
             rect: {
-              x: Math.min(pos.x, lastPos.x),
-              y: Math.min(pos.y, lastPos.y),
-              width: Math.abs(pos.x - lastPos.x),
-              height: Math.abs(pos.y - lastPos.y),
+              x: Math.min(snappedPos.x, lastPos.x),
+              y: Math.min(snappedPos.y, lastPos.y),
+              width: Math.abs(snappedPos.x - lastPos.x),
+              height: Math.abs(snappedPos.y - lastPos.y),
             },
           })
         );
@@ -132,7 +165,7 @@ export const useMouseEvents = () => {
           // Continue the last line
           if (lastCursorPosRef.current) {
             // Dispatching redux events impacts perf substantially - using brush spacing keeps dispatches to a reasonable number
-            if (Math.hypot(lastCursorPosRef.current[0] - pos.x, lastCursorPosRef.current[1] - pos.y) < BRUSH_SPACING) {
+            if (Math.hypot(lastCursorPosRef.current[0] - pos.x, lastCursorPosRef.current[1] - pos.y) < brushSpacingPx) {
               return;
             }
           }
@@ -145,7 +178,7 @@ export const useMouseEvents = () => {
         $isDrawing.set(true);
       }
     },
-    [dispatch, selectedLayerId, selectedLayerType, tool]
+    [brushSpacingPx, dispatch, selectedLayerId, selectedLayerType, tool]
   );
 
   const onMouseLeave = useCallback(
@@ -155,14 +188,15 @@ export const useMouseEvents = () => {
         return;
       }
       const pos = syncCursorPos(stage);
+      $isDrawing.set(false);
+      $lastCursorPos.set(null);
+      $lastMouseDownPos.set(null);
       if (!pos || !selectedLayerId || selectedLayerType !== 'regional_guidance_layer') {
         return;
       }
       if (getIsFocused(stage) && getIsMouseDown(e) && (tool === 'brush' || tool === 'eraser')) {
         dispatch(rgLayerPointsAdded({ layerId: selectedLayerId, point: [pos.x, pos.y] }));
       }
-      $isDrawing.set(false);
-      $cursorPosition.set(null);
     },
     [selectedLayerId, selectedLayerType, tool, dispatch]
   );
@@ -190,5 +224,10 @@ export const useMouseEvents = () => {
     [selectedLayerType, tool, shouldInvertBrushSizeScrollDirection, dispatch, brushSize]
   );
 
-  return { onMouseDown, onMouseUp, onMouseMove, onMouseLeave, onMouseWheel };
+  const handlers = useMemo(
+    () => ({ onMouseDown, onMouseUp, onMouseMove, onMouseLeave, onMouseWheel }),
+    [onMouseDown, onMouseUp, onMouseMove, onMouseLeave, onMouseWheel]
+  );
+
+  return handlers;
 };

invokeai/frontend/web/src/features/controlLayers/hooks/useControlLayersTitle.ts

@@ -1,51 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
-import {
-  isControlAdapterLayer,
-  isInitialImageLayer,
-  isIPAdapterLayer,
-  isRegionalGuidanceLayer,
-  selectControlLayersSlice,
-} from 'features/controlLayers/store/controlLayersSlice';
-import { useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const selectValidLayerCount = createSelector(selectControlLayersSlice, (controlLayers) => {
-  let count = 0;
-  controlLayers.present.layers.forEach((l) => {
-    if (isRegionalGuidanceLayer(l)) {
-      const hasTextPrompt = Boolean(l.positivePrompt || l.negativePrompt);
-      const hasAtLeastOneImagePrompt = l.ipAdapters.filter((ipa) => Boolean(ipa.image)).length > 0;
-      if (hasTextPrompt || hasAtLeastOneImagePrompt) {
-        count += 1;
-      }
-    }
-    if (isControlAdapterLayer(l)) {
-      if (l.controlAdapter.image || l.controlAdapter.processedImage) {
-        count += 1;
-      }
-    }
-    if (isIPAdapterLayer(l)) {
-      if (l.ipAdapter.image) {
-        count += 1;
-      }
-    }
-    if (isInitialImageLayer(l)) {
-      if (l.image) {
-        count += 1;
-      }
-    }
-  });
-
-  return count;
-});
-
-export const useControlLayersTitle = () => {
-  const { t } = useTranslation();
-  const validLayerCount = useAppSelector(selectValidLayerCount);
-  const title = useMemo(() => {
-    const suffix = validLayerCount > 0 ? ` (${validLayerCount})` : '';
-    return `${t('controlLayers.controlLayers')}${suffix}`;
-  }, [t, validLayerCount]);
-  return title;
-};

invokeai/frontend/web/src/features/controlLayers/store/controlLayersSlice.ts

@@ -27,7 +27,7 @@ import { modelChanged } from 'features/parameters/store/generationSlice';
 import type { ParameterAutoNegative } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import type { IRect, Vector2d } from 'konva/lib/types';
-import { isEqual, partition } from 'lodash-es';
+import { isEqual, partition, unset } from 'lodash-es';
 import { atom } from 'nanostores';
 import type { RgbColor } from 'react-colorful';
 import type { UndoableOptions } from 'redux-undo';
@@ -49,7 +49,7 @@ import type {
 } from './types';
 
 export const initialControlLayersState: ControlLayersState = {
-  _version: 1,
+  _version: 3,
   selectedLayerId: null,
   brushSize: 100,
   layers: [],
@@ -79,16 +79,6 @@ export const isRenderableLayer = (
   layer?.type === 'regional_guidance_layer' ||
   layer?.type === 'control_adapter_layer' ||
   layer?.type === 'initial_image_layer';
-const resetLayer = (layer: Layer) => {
-  if (layer.type === 'regional_guidance_layer') {
-    layer.maskObjects = [];
-    layer.bbox = null;
-    layer.isEnabled = true;
-    layer.needsPixelBbox = false;
-    layer.bboxNeedsUpdate = false;
-    return;
-  }
-};
 
 export const selectCALayerOrThrow = (state: ControlLayersState, layerId: string): ControlAdapterLayer => {
   const layer = state.layers.find((l) => l.id === layerId);
@@ -134,6 +124,12 @@ const getVectorMaskPreviewColor = (state: ControlLayersState): RgbColor => {
   const lastColor = rgLayers[rgLayers.length - 1]?.previewColor;
   return LayerColors.next(lastColor);
 };
+const exclusivelySelectLayer = (state: ControlLayersState, layerId: string) => {
+  for (const layer of state.layers) {
+    layer.isSelected = layer.id === layerId;
+  }
+  state.selectedLayerId = layerId;
+};
 
 export const controlLayersSlice = createSlice({
   name: 'controlLayers',
@@ -141,14 +137,7 @@ export const controlLayersSlice = createSlice({
   reducers: {
     //#region Any Layer Type
     layerSelected: (state, action: PayloadAction<string>) => {
-      for (const layer of state.layers.filter(isRenderableLayer)) {
-        if (layer.id === action.payload) {
-          layer.isSelected = true;
-          state.selectedLayerId = action.payload;
-        } else {
-          layer.isSelected = false;
-        }
-      }
+      exclusivelySelectLayer(state, action.payload);
     },
     layerVisibilityToggled: (state, action: PayloadAction<string>) => {
       const layer = state.layers.find((l) => l.id === action.payload);
@@ -163,6 +152,9 @@ export const controlLayersSlice = createSlice({
         layer.x = x;
         layer.y = y;
       }
+      if (isRegionalGuidanceLayer(layer)) {
+        layer.uploadedMaskImage = null;
+      }
     },
     layerBboxChanged: (state, action: PayloadAction<{ layerId: string; bbox: IRect | null }>) => {
       const { layerId, bbox } = action.payload;
@@ -173,14 +165,19 @@ export const controlLayersSlice = createSlice({
         if (bbox === null && layer.type === 'regional_guidance_layer') {
           // The layer was fully erased, empty its objects to prevent accumulation of invisible objects
           layer.maskObjects = [];
-          layer.needsPixelBbox = false;
+          layer.uploadedMaskImage = null;
         }
       }
     },
     layerReset: (state, action: PayloadAction<string>) => {
       const layer = state.layers.find((l) => l.id === action.payload);
-      if (layer) {
-        resetLayer(layer);
+      // TODO(psyche): Should other layer types also have reset functionality?
+      if (isRegionalGuidanceLayer(layer)) {
+        layer.maskObjects = [];
+        layer.bbox = null;
+        layer.isEnabled = true;
+        layer.bboxNeedsUpdate = false;
+        layer.uploadedMaskImage = null;
       }
     },
     layerDeleted: (state, action: PayloadAction<string>) => {
@@ -213,12 +210,6 @@ export const controlLayersSlice = createSlice({
       moveToFront(renderableLayers, cb);
       state.layers = [...ipAdapterLayers, ...renderableLayers];
     },
-    selectedLayerReset: (state) => {
-      const layer = state.layers.find((l) => l.id === state.selectedLayerId);
-      if (layer) {
-        resetLayer(layer);
-      }
-    },
     selectedLayerDeleted: (state) => {
       state.layers = state.layers.filter((l) => l.id !== state.selectedLayerId);
       state.selectedLayerId = state.layers[0]?.id ?? null;
@@ -250,17 +241,16 @@ export const controlLayersSlice = createSlice({
           controlAdapter,
         };
         state.layers.push(layer);
-        state.selectedLayerId = layer.id;
-        for (const layer of state.layers.filter(isRenderableLayer)) {
-          if (layer.id !== layerId) {
-            layer.isSelected = false;
-          }
-        }
+        exclusivelySelectLayer(state, layer.id);
       },
       prepare: (controlAdapter: ControlNetConfigV2 | T2IAdapterConfigV2) => ({
         payload: { layerId: uuidv4(), controlAdapter },
       }),
     },
+    caLayerRecalled: (state, action: PayloadAction<ControlAdapterLayer>) => {
+      state.layers.push({ ...action.payload, isSelected: true });
+      exclusivelySelectLayer(state, action.payload.id);
+    },
     caLayerImageChanged: (state, action: PayloadAction<{ layerId: string; imageDTO: ImageDTO | null }>) => {
       const { layerId, imageDTO } = action.payload;
       const layer = selectCALayerOrThrow(state, layerId);
@@ -344,19 +334,13 @@ export const controlLayersSlice = createSlice({
       const layer = selectCALayerOrThrow(state, layerId);
       layer.opacity = opacity;
     },
-    caLayerIsProcessingImageChanged: (
+    caLayerProcessorPendingBatchIdChanged: (
       state,
-      action: PayloadAction<{ layerId: string; isProcessingImage: boolean }>
+      action: PayloadAction<{ layerId: string; batchId: string | null }>
     ) => {
-      const { layerId, isProcessingImage } = action.payload;
+      const { layerId, batchId } = action.payload;
       const layer = selectCALayerOrThrow(state, layerId);
-      layer.controlAdapter.isProcessingImage = isProcessingImage;
-    },
-    caLayerControlNetsDeleted: (state) => {
-      state.layers = state.layers.filter((l) => !isControlAdapterLayer(l) || l.controlAdapter.type !== 'controlnet');
-    },
-    caLayerT2IAdaptersDeleted: (state) => {
-      state.layers = state.layers.filter((l) => !isControlAdapterLayer(l) || l.controlAdapter.type !== 't2i_adapter');
+      layer.controlAdapter.processorPendingBatchId = batchId;
     },
     //#endregion
 
@@ -368,12 +352,17 @@ export const controlLayersSlice = createSlice({
           id: getIPALayerId(layerId),
           type: 'ip_adapter_layer',
           isEnabled: true,
+          isSelected: true,
           ipAdapter,
         };
         state.layers.push(layer);
+        exclusivelySelectLayer(state, layer.id);
       },
       prepare: (ipAdapter: IPAdapterConfigV2) => ({ payload: { layerId: uuidv4(), ipAdapter } }),
     },
+    ipaLayerRecalled: (state, action: PayloadAction<IPAdapterLayer>) => {
+      state.layers.push(action.payload);
+    },
     ipaLayerImageChanged: (state, action: PayloadAction<{ layerId: string; imageDTO: ImageDTO | null }>) => {
       const { layerId, imageDTO } = action.payload;
       const layer = selectIPALayerOrThrow(state, layerId);
@@ -407,9 +396,6 @@ export const controlLayersSlice = createSlice({
       const layer = selectIPALayerOrThrow(state, layerId);
       layer.ipAdapter.clipVisionModel = clipVisionModel;
     },
-    ipaLayersDeleted: (state) => {
-      state.layers = state.layers.filter((l) => !isIPAdapterLayer(l));
-    },
     //#endregion
 
     //#region CA or IPA Layers
@@ -451,22 +437,21 @@ export const controlLayersSlice = createSlice({
           x: 0,
           y: 0,
           autoNegative: 'invert',
-          needsPixelBbox: false,
           positivePrompt: '',
           negativePrompt: null,
           ipAdapters: [],
           isSelected: true,
+          uploadedMaskImage: null,
         };
         state.layers.push(layer);
-        state.selectedLayerId = layer.id;
-        for (const layer of state.layers.filter(isRenderableLayer)) {
-          if (layer.id !== layerId) {
-            layer.isSelected = false;
-          }
-        }
+        exclusivelySelectLayer(state, layer.id);
       },
       prepare: () => ({ payload: { layerId: uuidv4() } }),
     },
+    rgLayerRecalled: (state, action: PayloadAction<RegionalGuidanceLayer>) => {
+      state.layers.push({ ...action.payload, isSelected: true });
+      exclusivelySelectLayer(state, action.payload.id);
+    },
     rgLayerPositivePromptChanged: (state, action: PayloadAction<{ layerId: string; prompt: string | null }>) => {
       const { layerId, prompt } = action.payload;
       const layer = selectRGLayerOrThrow(state, layerId);
@@ -505,9 +490,7 @@ export const controlLayersSlice = createSlice({
           strokeWidth: state.brushSize,
         });
         layer.bboxNeedsUpdate = true;
-        if (!layer.needsPixelBbox && tool === 'eraser') {
-          layer.needsPixelBbox = true;
-        }
+        layer.uploadedMaskImage = null;
       },
       prepare: (payload: { layerId: string; points: [number, number, number, number]; tool: DrawingTool }) => ({
         payload: { ...payload, lineUuid: uuidv4() },
@@ -524,6 +507,7 @@ export const controlLayersSlice = createSlice({
       // TODO: Handle this in the event listener
       lastLine.points.push(point[0] - layer.x, point[1] - layer.y);
       layer.bboxNeedsUpdate = true;
+      layer.uploadedMaskImage = null;
     },
     rgLayerRectAdded: {
       reducer: (state, action: PayloadAction<{ layerId: string; rect: IRect; rectUuid: string }>) => {
@@ -543,9 +527,15 @@ export const controlLayersSlice = createSlice({
           height: rect.height,
         });
         layer.bboxNeedsUpdate = true;
+        layer.uploadedMaskImage = null;
       },
       prepare: (payload: { layerId: string; rect: IRect }) => ({ payload: { ...payload, rectUuid: uuidv4() } }),
     },
+    rgLayerMaskImageUploaded: (state, action: PayloadAction<{ layerId: string; imageDTO: ImageDTO }>) => {
+      const { layerId, imageDTO } = action.payload;
+      const layer = selectRGLayerOrThrow(state, layerId);
+      layer.uploadedMaskImage = imageDTOToImageWithDims(imageDTO);
+    },
     rgLayerAutoNegativeChanged: (
       state,
       action: PayloadAction<{ layerId: string; autoNegative: ParameterAutoNegative }>
@@ -639,16 +629,17 @@ export const controlLayersSlice = createSlice({
           isEnabled: true,
           image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
           isSelected: true,
+          denoisingStrength: 0.75,
         };
         state.layers.push(layer);
-        state.selectedLayerId = layer.id;
-        for (const layer of state.layers.filter(isRenderableLayer)) {
-          if (layer.id !== layerId) {
-            layer.isSelected = false;
-          }
-        }
+        exclusivelySelectLayer(state, layer.id);
       },
-      prepare: (imageDTO: ImageDTO | null) => ({ payload: { layerId: 'initial_image_layer', imageDTO } }),
+      prepare: (imageDTO: ImageDTO | null) => ({ payload: { layerId: INITIAL_IMAGE_LAYER_ID, imageDTO } }),
+    },
+    iiLayerRecalled: (state, action: PayloadAction<InitialImageLayer>) => {
+      state.layers = state.layers.filter((l) => (isInitialImageLayer(l) ? false : true));
+      state.layers.push({ ...action.payload, isSelected: true });
+      exclusivelySelectLayer(state, action.payload.id);
     },
     iiLayerImageChanged: (state, action: PayloadAction<{ layerId: string; imageDTO: ImageDTO | null }>) => {
       const { layerId, imageDTO } = action.payload;
@@ -663,6 +654,11 @@ export const controlLayersSlice = createSlice({
       const layer = selectIILayerOrThrow(state, layerId);
       layer.opacity = opacity;
     },
+    iiLayerDenoisingStrengthChanged: (state, action: PayloadAction<{ layerId: string; denoisingStrength: number }>) => {
+      const { layerId, denoisingStrength } = action.payload;
+      const layer = selectIILayerOrThrow(state, layerId);
+      layer.denoisingStrength = denoisingStrength;
+    },
     //#endregion
 
     //#region Globals
@@ -792,11 +788,11 @@ export const {
   layerMovedToFront,
   layerMovedBackward,
   layerMovedToBack,
-  selectedLayerReset,
   selectedLayerDeleted,
   allLayersDeleted,
   // CA Layers
   caLayerAdded,
+  caLayerRecalled,
   caLayerImageChanged,
   caLayerProcessedImageChanged,
   caLayerModelChanged,
@@ -804,27 +800,27 @@ export const {
   caLayerProcessorConfigChanged,
   caLayerIsFilterEnabledChanged,
   caLayerOpacityChanged,
-  caLayerIsProcessingImageChanged,
-  caLayerControlNetsDeleted,
-  caLayerT2IAdaptersDeleted,
+  caLayerProcessorPendingBatchIdChanged,
   // IPA Layers
   ipaLayerAdded,
+  ipaLayerRecalled,
   ipaLayerImageChanged,
   ipaLayerMethodChanged,
   ipaLayerModelChanged,
   ipaLayerCLIPVisionModelChanged,
-  ipaLayersDeleted,
   // CA or IPA Layers
   caOrIPALayerWeightChanged,
   caOrIPALayerBeginEndStepPctChanged,
   // RG Layers
   rgLayerAdded,
+  rgLayerRecalled,
   rgLayerPositivePromptChanged,
   rgLayerNegativePromptChanged,
   rgLayerPreviewColorChanged,
   rgLayerLineAdded,
   rgLayerPointsAdded,
   rgLayerRectAdded,
+  rgLayerMaskImageUploaded,
   rgLayerAutoNegativeChanged,
   rgLayerIPAdapterAdded,
   rgLayerIPAdapterDeleted,
@@ -836,8 +832,10 @@ export const {
   rgLayerIPAdapterCLIPVisionModelChanged,
   // II Layer
   iiLayerAdded,
+  iiLayerRecalled,
   iiLayerImageChanged,
   iiLayerOpacityChanged,
+  iiLayerDenoisingStrengthChanged,
   // Globals
   positivePromptChanged,
   negativePromptChanged,
@@ -857,13 +855,26 @@ export const selectControlLayersSlice = (state: RootState) => state.controlLayer
 
 /* eslint-disable-next-line @typescript-eslint/no-explicit-any */
 const migrateControlLayersState = (state: any): any => {
+  if (state._version === 1) {
+    // Reset state for users on v1 (e.g. beta users), some changes could cause
+    state = deepClone(initialControlLayersState);
+  }
+  if (state._version === 2) {
+    // The CA `isProcessingImage` flag was replaced with a `processorPendingBatchId` property, fix up CA layers
+    for (const layer of (state as ControlLayersState).layers) {
+      if (layer.type === 'control_adapter_layer') {
+        layer.controlAdapter.processorPendingBatchId = null;
+        unset(layer.controlAdapter, 'isProcessingImage');
+      }
+    }
+  }
   return state;
 };
 
 export const $isDrawing = atom(false);
 export const $lastMouseDownPos = atom<Vector2d | null>(null);
 export const $tool = atom<Tool>('brush');
-export const $cursorPosition = atom<Vector2d | null>(null);
+export const $lastCursorPos = atom<Vector2d | null>(null);
 
 // IDs for singleton Konva layers and objects
 export const TOOL_PREVIEW_LAYER_ID = 'tool_preview_layer';
@@ -883,20 +894,22 @@ export const RG_LAYER_NAME = 'regional_guidance_layer';
 export const RG_LAYER_LINE_NAME = 'regional_guidance_layer.line';
 export const RG_LAYER_OBJECT_GROUP_NAME = 'regional_guidance_layer.object_group';
 export const RG_LAYER_RECT_NAME = 'regional_guidance_layer.rect';
+export const INITIAL_IMAGE_LAYER_ID = 'singleton_initial_image_layer';
 export const INITIAL_IMAGE_LAYER_NAME = 'initial_image_layer';
 export const INITIAL_IMAGE_LAYER_IMAGE_NAME = 'initial_image_layer.image';
 export const LAYER_BBOX_NAME = 'layer.bbox';
+export const COMPOSITING_RECT_NAME = 'compositing-rect';
 
 // Getters for non-singleton layer and object IDs
-const getRGLayerId = (layerId: string) => `${RG_LAYER_NAME}_${layerId}`;
+export const getRGLayerId = (layerId: string) => `${RG_LAYER_NAME}_${layerId}`;
 const getRGLayerLineId = (layerId: string, lineId: string) => `${layerId}.line_${lineId}`;
 const getRGLayerRectId = (layerId: string, lineId: string) => `${layerId}.rect_${lineId}`;
 export const getRGLayerObjectGroupId = (layerId: string, groupId: string) => `${layerId}.objectGroup_${groupId}`;
 export const getLayerBboxId = (layerId: string) => `${layerId}.bbox`;
-const getCALayerId = (layerId: string) => `control_adapter_layer_${layerId}`;
+export const getCALayerId = (layerId: string) => `control_adapter_layer_${layerId}`;
 export const getCALayerImageId = (layerId: string, imageName: string) => `${layerId}.image_${imageName}`;
 export const getIILayerImageId = (layerId: string, imageName: string) => `${layerId}.image_${imageName}`;
-const getIPALayerId = (layerId: string) => `ip_adapter_layer_${layerId}`;
+export const getIPALayerId = (layerId: string) => `ip_adapter_layer_${layerId}`;
 
 export const controlLayersPersistConfig: PersistConfig<ControlLayersState> = {
   name: controlLayersSlice.name,

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

@@ -1,89 +1,119 @@
-import type {
-  ControlNetConfigV2,
-  ImageWithDims,
-  IPAdapterConfigV2,
-  T2IAdapterConfigV2,
+import {
+  zControlNetConfigV2,
+  zImageWithDims,
+  zIPAdapterConfigV2,
+  zT2IAdapterConfigV2,
 } from 'features/controlLayers/util/controlAdapters';
 import type { AspectRatioState } from 'features/parameters/components/ImageSize/types';
-import type {
-  ParameterAutoNegative,
-  ParameterHeight,
-  ParameterNegativePrompt,
-  ParameterNegativeStylePromptSDXL,
-  ParameterPositivePrompt,
-  ParameterPositiveStylePromptSDXL,
-  ParameterWidth,
+import {
+  type ParameterHeight,
+  type ParameterNegativePrompt,
+  type ParameterNegativeStylePromptSDXL,
+  type ParameterPositivePrompt,
+  type ParameterPositiveStylePromptSDXL,
+  type ParameterWidth,
+  zAutoNegative,
+  zParameterNegativePrompt,
+  zParameterPositivePrompt,
+  zParameterStrength,
 } from 'features/parameters/types/parameterSchemas';
-import type { IRect } from 'konva/lib/types';
-import type { RgbColor } from 'react-colorful';
+import { z } from 'zod';
 
-export type DrawingTool = 'brush' | 'eraser';
+const zTool = z.enum(['brush', 'eraser', 'move', 'rect']);
+export type Tool = z.infer<typeof zTool>;
+const zDrawingTool = zTool.extract(['brush', 'eraser']);
+export type DrawingTool = z.infer<typeof zDrawingTool>;
 
-export type Tool = DrawingTool | 'move' | 'rect';
+const zPoints = z.array(z.number()).refine((points) => points.length % 2 === 0, {
+  message: 'Must have an even number of points',
+});
+const zVectorMaskLine = z.object({
+  id: z.string(),
+  type: z.literal('vector_mask_line'),
+  tool: zDrawingTool,
+  strokeWidth: z.number().min(1),
+  points: zPoints,
+});
+export type VectorMaskLine = z.infer<typeof zVectorMaskLine>;
 
-export type VectorMaskLine = {
-  id: string;
-  type: 'vector_mask_line';
-  tool: DrawingTool;
-  strokeWidth: number;
-  points: number[];
-};
+const zVectorMaskRect = z.object({
+  id: z.string(),
+  type: z.literal('vector_mask_rect'),
+  x: z.number(),
+  y: z.number(),
+  width: z.number().min(1),
+  height: z.number().min(1),
+});
+export type VectorMaskRect = z.infer<typeof zVectorMaskRect>;
 
-export type VectorMaskRect = {
-  id: string;
-  type: 'vector_mask_rect';
-  x: number;
-  y: number;
-  width: number;
-  height: number;
-};
+const zLayerBase = z.object({
+  id: z.string(),
+  isEnabled: z.boolean().default(true),
+  isSelected: z.boolean().default(true),
+});
 
-type LayerBase = {
-  id: string;
-  isEnabled: boolean;
-};
+const zRect = z.object({
+  x: z.number(),
+  y: z.number(),
+  width: z.number().min(1),
+  height: z.number().min(1),
+});
+const zRenderableLayerBase = zLayerBase.extend({
+  x: z.number(),
+  y: z.number(),
+  bbox: zRect.nullable(),
+  bboxNeedsUpdate: z.boolean(),
+});
 
-type RenderableLayerBase = LayerBase & {
-  x: number;
-  y: number;
-  bbox: IRect | null;
-  bboxNeedsUpdate: boolean;
-  isSelected: boolean;
-};
+const zControlAdapterLayer = zRenderableLayerBase.extend({
+  type: z.literal('control_adapter_layer'),
+  opacity: z.number().gte(0).lte(1),
+  isFilterEnabled: z.boolean(),
+  controlAdapter: z.discriminatedUnion('type', [zControlNetConfigV2, zT2IAdapterConfigV2]),
+});
+export type ControlAdapterLayer = z.infer<typeof zControlAdapterLayer>;
 
-export type ControlAdapterLayer = RenderableLayerBase & {
-  type: 'control_adapter_layer'; // technically, also t2i adapter layer
-  opacity: number;
-  isFilterEnabled: boolean;
-  controlAdapter: ControlNetConfigV2 | T2IAdapterConfigV2;
-};
+const zIPAdapterLayer = zLayerBase.extend({
+  type: z.literal('ip_adapter_layer'),
+  ipAdapter: zIPAdapterConfigV2,
+});
+export type IPAdapterLayer = z.infer<typeof zIPAdapterLayer>;
 
-export type IPAdapterLayer = LayerBase & {
-  type: 'ip_adapter_layer';
-  ipAdapter: IPAdapterConfigV2;
-};
+const zRgbColor = z.object({
+  r: z.number().int().min(0).max(255),
+  g: z.number().int().min(0).max(255),
+  b: z.number().int().min(0).max(255),
+});
+const zRegionalGuidanceLayer = zRenderableLayerBase.extend({
+  type: z.literal('regional_guidance_layer'),
+  maskObjects: z.array(z.discriminatedUnion('type', [zVectorMaskLine, zVectorMaskRect])),
+  positivePrompt: zParameterPositivePrompt.nullable(),
+  negativePrompt: zParameterNegativePrompt.nullable(),
+  ipAdapters: z.array(zIPAdapterConfigV2),
+  previewColor: zRgbColor,
+  autoNegative: zAutoNegative,
+  uploadedMaskImage: zImageWithDims.nullable(),
+});
+export type RegionalGuidanceLayer = z.infer<typeof zRegionalGuidanceLayer>;
 
-export type RegionalGuidanceLayer = RenderableLayerBase & {
-  type: 'regional_guidance_layer';
-  maskObjects: (VectorMaskLine | VectorMaskRect)[];
-  positivePrompt: ParameterPositivePrompt | null;
-  negativePrompt: ParameterNegativePrompt | null; // Up to one text prompt per mask
-  ipAdapters: IPAdapterConfigV2[]; // Any number of image prompts
-  previewColor: RgbColor;
-  autoNegative: ParameterAutoNegative;
-  needsPixelBbox: boolean; // Needs the slower pixel-based bbox calculation - set to true when an there is an eraser object
-};
+const zInitialImageLayer = zRenderableLayerBase.extend({
+  type: z.literal('initial_image_layer'),
+  opacity: z.number().gte(0).lte(1),
+  image: zImageWithDims.nullable(),
+  denoisingStrength: zParameterStrength,
+});
+export type InitialImageLayer = z.infer<typeof zInitialImageLayer>;
 
-export type InitialImageLayer = RenderableLayerBase & {
-  type: 'initial_image_layer';
-  opacity: number;
-  image: ImageWithDims | null;
-};
-
-export type Layer = RegionalGuidanceLayer | ControlAdapterLayer | IPAdapterLayer | InitialImageLayer;
+export const zLayer = z.discriminatedUnion('type', [
+  zRegionalGuidanceLayer,
+  zControlAdapterLayer,
+  zIPAdapterLayer,
+  zInitialImageLayer,
+]);
+export type Layer = z.infer<typeof zLayer>;
 
 export type ControlLayersState = {
-  _version: 1;
+  _version: 3;
   selectedLayerId: string | null;
   layers: Layer[];
   brushSize: number;

invokeai/frontend/web/src/features/controlLayers/util/bbox.ts

@@ -2,7 +2,6 @@ import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
 import { imageDataToDataURL } from 'features/canvas/util/blobToDataURL';
 import { RG_LAYER_OBJECT_GROUP_NAME } from 'features/controlLayers/store/controlLayersSlice';
 import Konva from 'konva';
-import type { Layer as KonvaLayerType } from 'konva/lib/Layer';
 import type { IRect } from 'konva/lib/types';
 import { assert } from 'tsafe';
 
@@ -54,34 +53,30 @@ const getImageDataBbox = (imageData: ImageData): Extents | null => {
 };
 
 /**
- * Get the bounding box of a regional prompt konva layer. This function has special handling for regional prompt layers.
- * @param layer The konva layer to get the bounding box of.
- * @param preview Whether to open a new tab displaying the rendered layer, which is used to calculate the bbox.
+ * Clones a regional guidance konva layer onto an offscreen stage/canvas. This allows the pixel data for a given layer
+ * to be captured, manipulated or analyzed without interference from other layers.
+ * @param layer The konva layer to clone.
+ * @returns The cloned stage and layer.
  */
-export const getLayerBboxPixels = (layer: KonvaLayerType, preview: boolean = false): IRect | null => {
-  // To calculate the layer's bounding box, we must first export it to a pixel array, then do some math.
-  //
-  // Though it is relatively fast, we can't use Konva's `getClientRect`. It programmatically determines the rect
-  // by calculating the extents of individual shapes from their "vector" shape data.
-  //
-  // This doesn't work when some shapes are drawn with composite operations that "erase" pixels, like eraser lines.
-  // These shapes' extents are still calculated as if they were solid, leading to a bounding box that is too large.
+const getIsolatedRGLayerClone = (layer: Konva.Layer): { stageClone: Konva.Stage; layerClone: Konva.Layer } => {
   const stage = layer.getStage();
 
-  // Construct and offscreen canvas on which we will do the bbox calculations.
+  // Construct an offscreen canvas with the same dimensions as the layer's stage.
   const offscreenStageContainer = document.createElement('div');
-  const offscreenStage = new Konva.Stage({
+  const stageClone = new Konva.Stage({
     container: offscreenStageContainer,
+    x: stage.x(),
+    y: stage.y(),
     width: stage.width(),
     height: stage.height(),
   });
 
   // Clone the layer and filter out unwanted children.
   const layerClone = layer.clone();
-  offscreenStage.add(layerClone);
+  stageClone.add(layerClone);
 
   for (const child of layerClone.getChildren()) {
-    if (child.name() === RG_LAYER_OBJECT_GROUP_NAME) {
+    if (child.name() === RG_LAYER_OBJECT_GROUP_NAME && child.hasChildren()) {
       // We need to cache the group to ensure it composites out eraser strokes correctly
       child.opacity(1);
       child.cache();
@@ -91,11 +86,31 @@ export const getLayerBboxPixels = (layer: KonvaLayerType, preview: boolean = fal
     }
   }
 
+  return { stageClone, layerClone };
+};
+
+/**
+ * Get the bounding box of a regional prompt konva layer. This function has special handling for regional prompt layers.
+ * @param layer The konva layer to get the bounding box of.
+ * @param preview Whether to open a new tab displaying the rendered layer, which is used to calculate the bbox.
+ */
+export const getLayerBboxPixels = (layer: Konva.Layer, preview: boolean = false): IRect | null => {
+  // To calculate the layer's bounding box, we must first export it to a pixel array, then do some math.
+  //
+  // Though it is relatively fast, we can't use Konva's `getClientRect`. It programmatically determines the rect
+  // by calculating the extents of individual shapes from their "vector" shape data.
+  //
+  // This doesn't work when some shapes are drawn with composite operations that "erase" pixels, like eraser lines.
+  // These shapes' extents are still calculated as if they were solid, leading to a bounding box that is too large.
+  const { stageClone, layerClone } = getIsolatedRGLayerClone(layer);
+
   // Get a worst-case rect using the relatively fast `getClientRect`.
   const layerRect = layerClone.getClientRect();
-
+  if (layerRect.width === 0 || layerRect.height === 0) {
+    return null;
+  }
   // Capture the image data with the above rect.
-  const layerImageData = offscreenStage
+  const layerImageData = stageClone
     .toCanvas(layerRect)
     .getContext('2d')
     ?.getImageData(0, 0, layerRect.width, layerRect.height);
@@ -114,8 +129,8 @@ export const getLayerBboxPixels = (layer: KonvaLayerType, preview: boolean = fal
 
   // Correct the bounding box to be relative to the layer's position.
   const correctedLayerBbox = {
-    x: layerBbox.minX - Math.floor(stage.x()) + layerRect.x - Math.floor(layer.x()),
-    y: layerBbox.minY - Math.floor(stage.y()) + layerRect.y - Math.floor(layer.y()),
+    x: layerBbox.minX - Math.floor(stageClone.x()) + layerRect.x - Math.floor(layer.x()),
+    y: layerBbox.minY - Math.floor(stageClone.y()) + layerRect.y - Math.floor(layer.y()),
     width: layerBbox.maxX - layerBbox.minX,
     height: layerBbox.maxY - layerBbox.minY,
   };
@@ -123,7 +138,13 @@ export const getLayerBboxPixels = (layer: KonvaLayerType, preview: boolean = fal
   return correctedLayerBbox;
 };
 
-export const getLayerBboxFast = (layer: KonvaLayerType): IRect | null => {
+/**
+ * Get the bounding box of a konva layer. This function is faster than `getLayerBboxPixels` but less accurate. It
+ * should only be used when there are no eraser strokes or shapes in the layer.
+ * @param layer The konva layer to get the bounding box of.
+ * @returns The bounding box of the layer.
+ */
+export const getLayerBboxFast = (layer: Konva.Layer): IRect => {
   const bbox = layer.getClientRect(GET_CLIENT_RECT_CONFIG);
   return {
     x: Math.floor(bbox.x),

invokeai/frontend/web/src/features/controlLayers/util/controlAdapters.test.ts

@@ -4,20 +4,74 @@ import { assert } from 'tsafe';
 import { describe, test } from 'vitest';
 
 import type {
+  _CannyProcessorConfig,
+  _ColorMapProcessorConfig,
+  _ContentShuffleProcessorConfig,
+  _DepthAnythingProcessorConfig,
+  _DWOpenposeProcessorConfig,
+  _HedProcessorConfig,
+  _LineartAnimeProcessorConfig,
+  _LineartProcessorConfig,
+  _MediapipeFaceProcessorConfig,
+  _MidasDepthProcessorConfig,
+  _MlsdProcessorConfig,
+  _NormalbaeProcessorConfig,
+  _PidiProcessorConfig,
+  _ZoeDepthProcessorConfig,
+  CannyProcessorConfig,
   CLIPVisionModelV2,
+  ColorMapProcessorConfig,
+  ContentShuffleProcessorConfig,
   ControlModeV2,
   DepthAnythingModelSize,
+  DepthAnythingProcessorConfig,
+  DWOpenposeProcessorConfig,
+  HedProcessorConfig,
   IPMethodV2,
+  LineartAnimeProcessorConfig,
+  LineartProcessorConfig,
+  MediapipeFaceProcessorConfig,
+  MidasDepthProcessorConfig,
+  MlsdProcessorConfig,
+  NormalbaeProcessorConfig,
+  PidiProcessorConfig,
   ProcessorConfig,
   ProcessorTypeV2,
+  ZoeDepthProcessorConfig,
 } from './controlAdapters';
 
 describe('Control Adapter Types', () => {
-  test('ProcessorType', () => assert<Equals<ProcessorConfig['type'], ProcessorTypeV2>>());
-  test('IP Adapter Method', () => assert<Equals<NonNullable<S['IPAdapterInvocation']['method']>, IPMethodV2>>());
-  test('CLIP Vision Model', () =>
-    assert<Equals<NonNullable<S['IPAdapterInvocation']['clip_vision_model']>, CLIPVisionModelV2>>());
-  test('Control Mode', () => assert<Equals<NonNullable<S['ControlNetInvocation']['control_mode']>, ControlModeV2>>());
-  test('DepthAnything Model Size', () =>
-    assert<Equals<NonNullable<S['DepthAnythingImageProcessorInvocation']['model_size']>, DepthAnythingModelSize>>());
+  test('ProcessorType', () => {
+    assert<Equals<ProcessorConfig['type'], ProcessorTypeV2>>();
+  });
+  test('IP Adapter Method', () => {
+    assert<Equals<NonNullable<S['IPAdapterInvocation']['method']>, IPMethodV2>>();
+  });
+  test('CLIP Vision Model', () => {
+    assert<Equals<NonNullable<S['IPAdapterInvocation']['clip_vision_model']>, CLIPVisionModelV2>>();
+  });
+  test('Control Mode', () => {
+    assert<Equals<NonNullable<S['ControlNetInvocation']['control_mode']>, ControlModeV2>>();
+  });
+  test('DepthAnything Model Size', () => {
+    assert<Equals<NonNullable<S['DepthAnythingImageProcessorInvocation']['model_size']>, DepthAnythingModelSize>>();
+  });
+  test('Processor Configs', () => {
+    // The processor configs are manually modeled zod schemas. This test ensures that the inferred types are correct.
+    // The types prefixed with `_` are types generated from OpenAPI, while the types without the prefix are manually modeled.
+    assert<Equals<_CannyProcessorConfig, CannyProcessorConfig>>();
+    assert<Equals<_ColorMapProcessorConfig, ColorMapProcessorConfig>>();
+    assert<Equals<_ContentShuffleProcessorConfig, ContentShuffleProcessorConfig>>();
+    assert<Equals<_DepthAnythingProcessorConfig, DepthAnythingProcessorConfig>>();
+    assert<Equals<_HedProcessorConfig, HedProcessorConfig>>();
+    assert<Equals<_LineartAnimeProcessorConfig, LineartAnimeProcessorConfig>>();
+    assert<Equals<_LineartProcessorConfig, LineartProcessorConfig>>();
+    assert<Equals<_MediapipeFaceProcessorConfig, MediapipeFaceProcessorConfig>>();
+    assert<Equals<_MidasDepthProcessorConfig, MidasDepthProcessorConfig>>();
+    assert<Equals<_MlsdProcessorConfig, MlsdProcessorConfig>>();
+    assert<Equals<_NormalbaeProcessorConfig, NormalbaeProcessorConfig>>();
+    assert<Equals<_DWOpenposeProcessorConfig, DWOpenposeProcessorConfig>>();
+    assert<Equals<_PidiProcessorConfig, PidiProcessorConfig>>();
+    assert<Equals<_ZoeDepthProcessorConfig, ZoeDepthProcessorConfig>>();
+  });
 });

invokeai/frontend/web/src/features/controlLayers/util/controlAdapters.ts

@@ -1,9 +1,5 @@
 import { deepClone } from 'common/util/deepClone';
-import type {
-  ParameterControlNetModel,
-  ParameterIPAdapterModel,
-  ParameterT2IAdapterModel,
-} from 'features/parameters/types/parameterSchemas';
+import { zModelIdentifierField } from 'features/nodes/types/common';
 import { merge, omit } from 'lodash-es';
 import type {
   BaseModelType,
@@ -28,90 +24,201 @@ import type {
 } from 'services/api/types';
 import { z } from 'zod';
 
+const zId = z.string().min(1);
+
+const zCannyProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('canny_image_processor'),
+  low_threshold: z.number().int().gte(0).lte(255),
+  high_threshold: z.number().int().gte(0).lte(255),
+});
+export type _CannyProcessorConfig = Required<
+  Pick<CannyImageProcessorInvocation, 'id' | 'type' | 'low_threshold' | 'high_threshold'>
+>;
+export type CannyProcessorConfig = z.infer<typeof zCannyProcessorConfig>;
+
+const zColorMapProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('color_map_image_processor'),
+  color_map_tile_size: z.number().int().gte(1),
+});
+export type _ColorMapProcessorConfig = Required<
+  Pick<ColorMapImageProcessorInvocation, 'id' | 'type' | 'color_map_tile_size'>
+>;
+export type ColorMapProcessorConfig = z.infer<typeof zColorMapProcessorConfig>;
+
+const zContentShuffleProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('content_shuffle_image_processor'),
+  w: z.number().int().gte(0),
+  h: z.number().int().gte(0),
+  f: z.number().int().gte(0),
+});
+export type _ContentShuffleProcessorConfig = Required<
+  Pick<ContentShuffleImageProcessorInvocation, 'id' | 'type' | 'w' | 'h' | 'f'>
+>;
+export type ContentShuffleProcessorConfig = z.infer<typeof zContentShuffleProcessorConfig>;
+
 const zDepthAnythingModelSize = z.enum(['large', 'base', 'small']);
 export type DepthAnythingModelSize = z.infer<typeof zDepthAnythingModelSize>;
 export const isDepthAnythingModelSize = (v: unknown): v is DepthAnythingModelSize =>
   zDepthAnythingModelSize.safeParse(v).success;
-
-export type CannyProcessorConfig = Required<
-  Pick<CannyImageProcessorInvocation, 'id' | 'type' | 'low_threshold' | 'high_threshold'>
->;
-export type ColorMapProcessorConfig = Required<
-  Pick<ColorMapImageProcessorInvocation, 'id' | 'type' | 'color_map_tile_size'>
->;
-export type ContentShuffleProcessorConfig = Required<
-  Pick<ContentShuffleImageProcessorInvocation, 'id' | 'type' | 'w' | 'h' | 'f'>
->;
-export type DepthAnythingProcessorConfig = Required<
+const zDepthAnythingProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('depth_anything_image_processor'),
+  model_size: zDepthAnythingModelSize,
+});
+export type _DepthAnythingProcessorConfig = Required<
   Pick<DepthAnythingImageProcessorInvocation, 'id' | 'type' | 'model_size'>
 >;
-export type HedProcessorConfig = Required<Pick<HedImageProcessorInvocation, 'id' | 'type' | 'scribble'>>;
-type LineartAnimeProcessorConfig = Required<Pick<LineartAnimeImageProcessorInvocation, 'id' | 'type'>>;
-export type LineartProcessorConfig = Required<Pick<LineartImageProcessorInvocation, 'id' | 'type' | 'coarse'>>;
-export type MediapipeFaceProcessorConfig = Required<
+export type DepthAnythingProcessorConfig = z.infer<typeof zDepthAnythingProcessorConfig>;
+
+const zHedProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('hed_image_processor'),
+  scribble: z.boolean(),
+});
+export type _HedProcessorConfig = Required<Pick<HedImageProcessorInvocation, 'id' | 'type' | 'scribble'>>;
+export type HedProcessorConfig = z.infer<typeof zHedProcessorConfig>;
+
+const zLineartAnimeProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('lineart_anime_image_processor'),
+});
+export type _LineartAnimeProcessorConfig = Required<Pick<LineartAnimeImageProcessorInvocation, 'id' | 'type'>>;
+export type LineartAnimeProcessorConfig = z.infer<typeof zLineartAnimeProcessorConfig>;
+
+const zLineartProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('lineart_image_processor'),
+  coarse: z.boolean(),
+});
+export type _LineartProcessorConfig = Required<Pick<LineartImageProcessorInvocation, 'id' | 'type' | 'coarse'>>;
+export type LineartProcessorConfig = z.infer<typeof zLineartProcessorConfig>;
+
+const zMediapipeFaceProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('mediapipe_face_processor'),
+  max_faces: z.number().int().gte(1),
+  min_confidence: z.number().gte(0).lte(1),
+});
+export type _MediapipeFaceProcessorConfig = Required<
   Pick<MediapipeFaceProcessorInvocation, 'id' | 'type' | 'max_faces' | 'min_confidence'>
 >;
-export type MidasDepthProcessorConfig = Required<
+export type MediapipeFaceProcessorConfig = z.infer<typeof zMediapipeFaceProcessorConfig>;
+
+const zMidasDepthProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('midas_depth_image_processor'),
+  a_mult: z.number().gte(0),
+  bg_th: z.number().gte(0),
+});
+export type _MidasDepthProcessorConfig = Required<
   Pick<MidasDepthImageProcessorInvocation, 'id' | 'type' | 'a_mult' | 'bg_th'>
 >;
-export type MlsdProcessorConfig = Required<Pick<MlsdImageProcessorInvocation, 'id' | 'type' | 'thr_v' | 'thr_d'>>;
-type NormalbaeProcessorConfig = Required<Pick<NormalbaeImageProcessorInvocation, 'id' | 'type'>>;
-export type DWOpenposeProcessorConfig = Required<
+export type MidasDepthProcessorConfig = z.infer<typeof zMidasDepthProcessorConfig>;
+
+const zMlsdProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('mlsd_image_processor'),
+  thr_v: z.number().gte(0),
+  thr_d: z.number().gte(0),
+});
+export type _MlsdProcessorConfig = Required<Pick<MlsdImageProcessorInvocation, 'id' | 'type' | 'thr_v' | 'thr_d'>>;
+export type MlsdProcessorConfig = z.infer<typeof zMlsdProcessorConfig>;
+
+const zNormalbaeProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('normalbae_image_processor'),
+});
+export type _NormalbaeProcessorConfig = Required<Pick<NormalbaeImageProcessorInvocation, 'id' | 'type'>>;
+export type NormalbaeProcessorConfig = z.infer<typeof zNormalbaeProcessorConfig>;
+
+const zDWOpenposeProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('dw_openpose_image_processor'),
+  draw_body: z.boolean(),
+  draw_face: z.boolean(),
+  draw_hands: z.boolean(),
+});
+export type _DWOpenposeProcessorConfig = Required<
   Pick<DWOpenposeImageProcessorInvocation, 'id' | 'type' | 'draw_body' | 'draw_face' | 'draw_hands'>
 >;
-export type PidiProcessorConfig = Required<Pick<PidiImageProcessorInvocation, 'id' | 'type' | 'safe' | 'scribble'>>;
-type ZoeDepthProcessorConfig = Required<Pick<ZoeDepthImageProcessorInvocation, 'id' | 'type'>>;
+export type DWOpenposeProcessorConfig = z.infer<typeof zDWOpenposeProcessorConfig>;
 
-export type ProcessorConfig =
-  | CannyProcessorConfig
-  | ColorMapProcessorConfig
-  | ContentShuffleProcessorConfig
-  | DepthAnythingProcessorConfig
-  | HedProcessorConfig
-  | LineartAnimeProcessorConfig
-  | LineartProcessorConfig
-  | MediapipeFaceProcessorConfig
-  | MidasDepthProcessorConfig
-  | MlsdProcessorConfig
-  | NormalbaeProcessorConfig
-  | DWOpenposeProcessorConfig
-  | PidiProcessorConfig
-  | ZoeDepthProcessorConfig;
+const zPidiProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('pidi_image_processor'),
+  safe: z.boolean(),
+  scribble: z.boolean(),
+});
+export type _PidiProcessorConfig = Required<Pick<PidiImageProcessorInvocation, 'id' | 'type' | 'safe' | 'scribble'>>;
+export type PidiProcessorConfig = z.infer<typeof zPidiProcessorConfig>;
 
-export type ImageWithDims = {
-  imageName: string;
-  width: number;
-  height: number;
-};
+const zZoeDepthProcessorConfig = z.object({
+  id: zId,
+  type: z.literal('zoe_depth_image_processor'),
+});
+export type _ZoeDepthProcessorConfig = Required<Pick<ZoeDepthImageProcessorInvocation, 'id' | 'type'>>;
+export type ZoeDepthProcessorConfig = z.infer<typeof zZoeDepthProcessorConfig>;
 
-type ControlAdapterBase = {
-  id: string;
-  weight: number;
-  image: ImageWithDims | null;
-  processedImage: ImageWithDims | null;
-  isProcessingImage: boolean;
-  processorConfig: ProcessorConfig | null;
-  beginEndStepPct: [number, number];
-};
+const zProcessorConfig = z.discriminatedUnion('type', [
+  zCannyProcessorConfig,
+  zColorMapProcessorConfig,
+  zContentShuffleProcessorConfig,
+  zDepthAnythingProcessorConfig,
+  zHedProcessorConfig,
+  zLineartAnimeProcessorConfig,
+  zLineartProcessorConfig,
+  zMediapipeFaceProcessorConfig,
+  zMidasDepthProcessorConfig,
+  zMlsdProcessorConfig,
+  zNormalbaeProcessorConfig,
+  zDWOpenposeProcessorConfig,
+  zPidiProcessorConfig,
+  zZoeDepthProcessorConfig,
+]);
+export type ProcessorConfig = z.infer<typeof zProcessorConfig>;
+
+export const zImageWithDims = z.object({
+  name: z.string(),
+  width: z.number().int().positive(),
+  height: z.number().int().positive(),
+});
+export type ImageWithDims = z.infer<typeof zImageWithDims>;
+
+const zBeginEndStepPct = z
+  .tuple([z.number().gte(0).lte(1), z.number().gte(0).lte(1)])
+  .refine(([begin, end]) => begin < end, {
+    message: 'Begin must be less than end',
+  });
+
+const zControlAdapterBase = z.object({
+  id: zId,
+  weight: z.number().gte(0).lte(1),
+  image: zImageWithDims.nullable(),
+  processedImage: zImageWithDims.nullable(),
+  processorConfig: zProcessorConfig.nullable(),
+  processorPendingBatchId: z.string().nullable().default(null),
+  beginEndStepPct: zBeginEndStepPct,
+});
 
 const zControlModeV2 = z.enum(['balanced', 'more_prompt', 'more_control', 'unbalanced']);
 export type ControlModeV2 = z.infer<typeof zControlModeV2>;
 export const isControlModeV2 = (v: unknown): v is ControlModeV2 => zControlModeV2.safeParse(v).success;
 
-export type ControlNetConfigV2 = ControlAdapterBase & {
-  type: 'controlnet';
-  model: ParameterControlNetModel | null;
-  controlMode: ControlModeV2;
-};
-export const isControlNetConfigV2 = (ca: ControlNetConfigV2 | T2IAdapterConfigV2): ca is ControlNetConfigV2 =>
-  ca.type === 'controlnet';
+export const zControlNetConfigV2 = zControlAdapterBase.extend({
+  type: z.literal('controlnet'),
+  model: zModelIdentifierField.nullable(),
+  controlMode: zControlModeV2,
+});
+export type ControlNetConfigV2 = z.infer<typeof zControlNetConfigV2>;
 
-export type T2IAdapterConfigV2 = ControlAdapterBase & {
-  type: 't2i_adapter';
-  model: ParameterT2IAdapterModel | null;
-};
-export const isT2IAdapterConfigV2 = (ca: ControlNetConfigV2 | T2IAdapterConfigV2): ca is T2IAdapterConfigV2 =>
-  ca.type === 't2i_adapter';
+export const zT2IAdapterConfigV2 = zControlAdapterBase.extend({
+  type: z.literal('t2i_adapter'),
+  model: zModelIdentifierField.nullable(),
+});
+export type T2IAdapterConfigV2 = z.infer<typeof zT2IAdapterConfigV2>;
 
 const zCLIPVisionModelV2 = z.enum(['ViT-H', 'ViT-G']);
 export type CLIPVisionModelV2 = z.infer<typeof zCLIPVisionModelV2>;
@@ -121,16 +228,17 @@ const zIPMethodV2 = z.enum(['full', 'style', 'composition']);
 export type IPMethodV2 = z.infer<typeof zIPMethodV2>;
 export const isIPMethodV2 = (v: unknown): v is IPMethodV2 => zIPMethodV2.safeParse(v).success;
 
-export type IPAdapterConfigV2 = {
-  id: string;
-  type: 'ip_adapter';
-  weight: number;
-  method: IPMethodV2;
-  image: ImageWithDims | null;
-  model: ParameterIPAdapterModel | null;
-  clipVisionModel: CLIPVisionModelV2;
-  beginEndStepPct: [number, number];
-};
+export const zIPAdapterConfigV2 = z.object({
+  id: zId,
+  type: z.literal('ip_adapter'),
+  weight: z.number().gte(0).lte(1),
+  method: zIPMethodV2,
+  image: zImageWithDims.nullable(),
+  model: zModelIdentifierField.nullable(),
+  clipVisionModel: zCLIPVisionModelV2,
+  beginEndStepPct: zBeginEndStepPct,
+});
+export type IPAdapterConfigV2 = z.infer<typeof zIPAdapterConfigV2>;
 
 const zProcessorTypeV2 = z.enum([
   'canny_image_processor',
@@ -190,7 +298,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     buildNode: (image, config) => ({
       ...config,
       type: 'canny_image_processor',
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -207,7 +315,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     buildNode: (image, config) => ({
       ...config,
       type: 'color_map_image_processor',
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
     }),
   },
   content_shuffle_image_processor: {
@@ -223,7 +331,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -239,7 +347,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       resolution: minDim(image),
     }),
   },
@@ -254,7 +362,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -269,7 +377,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -285,7 +393,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -302,7 +410,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -319,7 +427,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -336,7 +444,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -351,7 +459,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -369,7 +477,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       image_resolution: minDim(image),
     }),
   },
@@ -385,7 +493,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
       detect_resolution: minDim(image),
       image_resolution: minDim(image),
     }),
@@ -400,7 +508,7 @@ export const CA_PROCESSOR_DATA: CAProcessorsData = {
     }),
     buildNode: (image, config) => ({
       ...config,
-      image: { image_name: image.imageName },
+      image: { image_name: image.name },
     }),
   },
 };
@@ -413,8 +521,8 @@ export const initialControlNetV2: Omit<ControlNetConfigV2, 'id'> = {
   controlMode: 'balanced',
   image: null,
   processedImage: null,
-  isProcessingImage: false,
   processorConfig: CA_PROCESSOR_DATA.canny_image_processor.buildDefaults(),
+  processorPendingBatchId: null,
 };
 
 export const initialT2IAdapterV2: Omit<T2IAdapterConfigV2, 'id'> = {
@@ -424,8 +532,8 @@ export const initialT2IAdapterV2: Omit<T2IAdapterConfigV2, 'id'> = {
   beginEndStepPct: [0, 1],
   image: null,
   processedImage: null,
-  isProcessingImage: false,
   processorConfig: CA_PROCESSOR_DATA.canny_image_processor.buildDefaults(),
+  processorPendingBatchId: null,
 };
 
 export const initialIPAdapterV2: Omit<IPAdapterConfigV2, 'id'> = {
@@ -462,7 +570,7 @@ export const buildControlAdapterProcessorV2 = (
 };
 
 export const imageDTOToImageWithDims = ({ image_name, width, height }: ImageDTO): ImageWithDims => ({
-  imageName: image_name,
+  name: image_name,
   width,
   height,
 });

invokeai/frontend/web/src/features/controlLayers/util/renderers.ts

@@ -1,12 +1,13 @@
 import { getStore } from 'app/store/nanostores/store';
 import { rgbaColorToString, rgbColorToString } from 'features/canvas/util/colorToString';
-import { getScaledFlooredCursorPosition } from 'features/controlLayers/hooks/mouseEventHooks';
+import { getScaledFlooredCursorPosition, snapPosToStage } from 'features/controlLayers/hooks/mouseEventHooks';
 import {
   $tool,
   BACKGROUND_LAYER_ID,
   BACKGROUND_RECT_ID,
   CA_LAYER_IMAGE_NAME,
   CA_LAYER_NAME,
+  COMPOSITING_RECT_NAME,
   getCALayerImageId,
   getIILayerImageId,
   getLayerBboxId,
@@ -211,12 +212,13 @@ const renderToolPreview = (
   }
 
   if (cursorPos && lastMouseDownPos && tool === 'rect') {
+    const snappedPos = snapPosToStage(cursorPos, stage);
     const rectPreview = toolPreviewLayer.findOne<Konva.Rect>(`#${TOOL_PREVIEW_RECT_ID}`);
     rectPreview?.setAttrs({
-      x: Math.min(cursorPos.x, lastMouseDownPos.x),
-      y: Math.min(cursorPos.y, lastMouseDownPos.y),
-      width: Math.abs(cursorPos.x - lastMouseDownPos.x),
-      height: Math.abs(cursorPos.y - lastMouseDownPos.y),
+      x: Math.min(snappedPos.x, lastMouseDownPos.x),
+      y: Math.min(snappedPos.y, lastMouseDownPos.y),
+      width: Math.abs(snappedPos.x - lastMouseDownPos.x),
+      height: Math.abs(snappedPos.y - lastMouseDownPos.y),
     });
     rectPreview?.visible(true);
   } else {
@@ -323,6 +325,12 @@ const createVectorMaskRect = (reduxObject: VectorMaskRect, konvaGroup: Konva.Gro
   return vectorMaskRect;
 };
 
+const createCompositingRect = (konvaLayer: Konva.Layer): Konva.Rect => {
+  const compositingRect = new Konva.Rect({ name: COMPOSITING_RECT_NAME, listening: false });
+  konvaLayer.add(compositingRect);
+  return compositingRect;
+};
+
 /**
  * Renders a vector mask layer.
  * @param stage The konva stage to render on.
@@ -400,15 +408,53 @@ const renderRegionalGuidanceLayer = (
     groupNeedsCache = true;
   }
 
-  if (konvaObjectGroup.children.length === 0) {
+  if (konvaObjectGroup.getChildren().length === 0) {
     // No objects - clear the cache to reset the previous pixel data
     konvaObjectGroup.clearCache();
-  } else if (groupNeedsCache) {
-    konvaObjectGroup.cache();
+    return;
   }
 
-  // Updating group opacity does not require re-caching
-  if (konvaObjectGroup.opacity() !== globalMaskLayerOpacity) {
+  const compositingRect =
+    konvaLayer.findOne<Konva.Rect>(`.${COMPOSITING_RECT_NAME}`) ?? createCompositingRect(konvaLayer);
+
+  /**
+   * When the group is selected, we use a rect of the selected preview color, composited over the shapes. This allows
+   * shapes to render as a "raster" layer with all pixels drawn at the same color and opacity.
+   *
+   * Without this special handling, each shape is drawn individually with the given opacity, atop the other shapes. The
+   * effect is like if you have a Photoshop Group consisting of many shapes, each of which has the given opacity.
+   * Overlapping shapes will have their colors blended together, and the final color is the result of all the shapes.
+   *
+   * Instead, with the special handling, the effect is as if you drew all the shapes at 100% opacity, flattened them to
+   * a single raster image, and _then_ applied the 50% opacity.
+   */
+  if (reduxLayer.isSelected && tool !== 'move') {
+    // We must clear the cache first so Konva will re-draw the group with the new compositing rect
+    if (konvaObjectGroup.isCached()) {
+      konvaObjectGroup.clearCache();
+    }
+    // The user is allowed to reduce mask opacity to 0, but we need the opacity for the compositing rect to work
+    konvaObjectGroup.opacity(1);
+
+    compositingRect.setAttrs({
+      // The rect should be the size of the layer - use the fast method if we don't have a pixel-perfect bbox already
+      ...(!reduxLayer.bboxNeedsUpdate && reduxLayer.bbox ? reduxLayer.bbox : getLayerBboxFast(konvaLayer)),
+      fill: rgbColor,
+      opacity: globalMaskLayerOpacity,
+      // Draw this rect only where there are non-transparent pixels under it (e.g. the mask shapes)
+      globalCompositeOperation: 'source-in',
+      visible: true,
+      // This rect must always be on top of all other shapes
+      zIndex: konvaObjectGroup.getChildren().length,
+    });
+  } else {
+    // The compositing rect should only be shown when the layer is selected.
+    compositingRect.visible(false);
+    // Cache only if needed - or if we are on this code path and _don't_ have a cache
+    if (groupNeedsCache || !konvaObjectGroup.isCached()) {
+      konvaObjectGroup.cache();
+    }
+    // Updating group opacity does not require re-caching
     konvaObjectGroup.opacity(globalMaskLayerOpacity);
   }
 };
@@ -418,6 +464,7 @@ const createInitialImageLayer = (stage: Konva.Stage, reduxLayer: InitialImageLay
     id: reduxLayer.id,
     name: INITIAL_IMAGE_LAYER_NAME,
     imageSmoothingEnabled: true,
+    listening: false,
   });
   stage.add(konvaLayer);
   return konvaLayer;
@@ -437,6 +484,9 @@ const updateInitialImageLayerImageAttrs = (
   konvaImage: Konva.Image,
   reduxLayer: InitialImageLayer
 ) => {
+  // Konva erroneously reports NaN for width and height when the stage is hidden. This causes errors when caching,
+  // but it doesn't seem to break anything.
+  // TODO(psyche): Investigate and report upstream.
   const newWidth = stage.width() / stage.scaleX();
   const newHeight = stage.height() / stage.scaleY();
   if (
@@ -464,7 +514,7 @@ const updateInitialImageLayerImageSource = async (
   reduxLayer: InitialImageLayer
 ) => {
   if (reduxLayer.image) {
-    const { imageName } = reduxLayer.image;
+    const imageName = reduxLayer.image.name;
     const req = getStore().dispatch(imagesApi.endpoints.getImageDTO.initiate(imageName));
     const imageDTO = await req.unwrap();
     req.unsubscribe();
@@ -497,7 +547,7 @@ const renderInitialImageLayer = (stage: Konva.Stage, reduxLayer: InitialImageLay
   let imageSourceNeedsUpdate = false;
   if (canvasImageSource instanceof HTMLImageElement) {
     const image = reduxLayer.image;
-    if (image && canvasImageSource.id !== getCALayerImageId(reduxLayer.id, image.imageName)) {
+    if (image && canvasImageSource.id !== getCALayerImageId(reduxLayer.id, image.name)) {
       imageSourceNeedsUpdate = true;
     } else if (!image) {
       imageSourceNeedsUpdate = true;
@@ -518,6 +568,7 @@ const createControlNetLayer = (stage: Konva.Stage, reduxLayer: ControlAdapterLay
     id: reduxLayer.id,
     name: CA_LAYER_NAME,
     imageSmoothingEnabled: true,
+    listening: false,
   });
   stage.add(konvaLayer);
   return konvaLayer;
@@ -539,7 +590,7 @@ const updateControlNetLayerImageSource = async (
 ) => {
   const image = reduxLayer.controlAdapter.processedImage ?? reduxLayer.controlAdapter.image;
   if (image) {
-    const { imageName } = image;
+    const imageName = image.name;
     const req = getStore().dispatch(imagesApi.endpoints.getImageDTO.initiate(imageName));
     const imageDTO = await req.unwrap();
     req.unsubscribe();
@@ -572,6 +623,9 @@ const updateControlNetLayerImageAttrs = (
   reduxLayer: ControlAdapterLayer
 ) => {
   let needsCache = false;
+  // Konva erroneously reports NaN for width and height when the stage is hidden. This causes errors when caching,
+  // but it doesn't seem to break anything.
+  // TODO(psyche): Investigate and report upstream.
   const newWidth = stage.width() / stage.scaleX();
   const newHeight = stage.height() / stage.scaleY();
   const hasFilter = konvaImage.filters() !== null && konvaImage.filters().length > 0;
@@ -607,7 +661,7 @@ const renderControlNetLayer = (stage: Konva.Stage, reduxLayer: ControlAdapterLay
   let imageSourceNeedsUpdate = false;
   if (canvasImageSource instanceof HTMLImageElement) {
     const image = reduxLayer.controlAdapter.processedImage ?? reduxLayer.controlAdapter.image;
-    if (image && canvasImageSource.id !== getCALayerImageId(reduxLayer.id, image.imageName)) {
+    if (image && canvasImageSource.id !== getCALayerImageId(reduxLayer.id, image.name)) {
       imageSourceNeedsUpdate = true;
     } else if (!image) {
       imageSourceNeedsUpdate = true;
@@ -656,6 +710,7 @@ const renderLayers = (
     if (isInitialImageLayer(reduxLayer)) {
       renderInitialImageLayer(stage, reduxLayer);
     }
+    // IP Adapter layers are not rendered
   }
 };
 
@@ -670,6 +725,7 @@ const createBboxRect = (reduxLayer: Layer, konvaLayer: Konva.Layer) => {
     id: getLayerBboxId(reduxLayer.id),
     name: LAYER_BBOX_NAME,
     strokeWidth: 1,
+    visible: false,
   });
   konvaLayer.add(rect);
   return rect;
@@ -679,18 +735,10 @@ const createBboxRect = (reduxLayer: Layer, konvaLayer: Konva.Layer) => {
  * Renders the bounding boxes for the layers.
  * @param stage The konva stage to render on
  * @param reduxLayers An array of all redux layers to draw bboxes for
- * @param selectedLayerId The selected layer's id
  * @param tool The current tool
- * @param onBboxChanged Callback for when the bbox is changed
- * @param onBboxMouseDown Callback for when the bbox is clicked
  * @returns
  */
-const renderBbox = (
-  stage: Konva.Stage,
-  reduxLayers: Layer[],
-  tool: Tool,
-  onBboxChanged: (layerId: string, bbox: IRect | null) => void
-) => {
+const renderBboxes = (stage: Konva.Stage, reduxLayers: Layer[], tool: Tool) => {
   // Hide all bboxes so they don't interfere with getClientRect
   for (const bboxRect of stage.find<Konva.Rect>(`.${LAYER_BBOX_NAME}`)) {
     bboxRect.visible(false);
@@ -701,36 +749,59 @@ const renderBbox = (
     return;
   }
 
-  for (const reduxLayer of reduxLayers) {
-    if (reduxLayer.type === 'regional_guidance_layer') {
-      const konvaLayer = stage.findOne<Konva.Layer>(`#${reduxLayer.id}`);
-      assert(konvaLayer, `Layer ${reduxLayer.id} not found in stage`);
+  for (const reduxLayer of reduxLayers.filter(isRegionalGuidanceLayer)) {
+    if (!reduxLayer.bbox) {
+      continue;
+    }
+    const konvaLayer = stage.findOne<Konva.Layer>(`#${reduxLayer.id}`);
+    assert(konvaLayer, `Layer ${reduxLayer.id} not found in stage`);
 
-      let bbox = reduxLayer.bbox;
+    const bboxRect = konvaLayer.findOne<Konva.Rect>(`.${LAYER_BBOX_NAME}`) ?? createBboxRect(reduxLayer, konvaLayer);
 
-      // We only need to recalculate the bbox if the layer has changed and it has objects
-      if (reduxLayer.bboxNeedsUpdate && reduxLayer.maskObjects.length) {
-        // We only need to use the pixel-perfect bounding box if the layer has eraser strokes
-        bbox = reduxLayer.needsPixelBbox ? getLayerBboxPixels(konvaLayer) : getLayerBboxFast(konvaLayer);
-        // Update the layer's bbox in the redux store
-        onBboxChanged(reduxLayer.id, bbox);
+    bboxRect.setAttrs({
+      visible: !reduxLayer.bboxNeedsUpdate,
+      listening: reduxLayer.isSelected,
+      x: reduxLayer.bbox.x,
+      y: reduxLayer.bbox.y,
+      width: reduxLayer.bbox.width,
+      height: reduxLayer.bbox.height,
+      stroke: reduxLayer.isSelected ? BBOX_SELECTED_STROKE : '',
+    });
+  }
+};
+
+/**
+ * Calculates the bbox of each regional guidance layer. Only calculates if the mask has changed.
+ * @param stage The konva stage to render on.
+ * @param reduxLayers An array of redux layers to calculate bboxes for
+ * @param onBboxChanged Callback for when the bounding box changes
+ */
+const updateBboxes = (
+  stage: Konva.Stage,
+  reduxLayers: Layer[],
+  onBboxChanged: (layerId: string, bbox: IRect | null) => void
+) => {
+  for (const rgLayer of reduxLayers.filter(isRegionalGuidanceLayer)) {
+    const konvaLayer = stage.findOne<Konva.Layer>(`#${rgLayer.id}`);
+    assert(konvaLayer, `Layer ${rgLayer.id} not found in stage`);
+    // We only need to recalculate the bbox if the layer has changed
+    if (rgLayer.bboxNeedsUpdate) {
+      const bboxRect = konvaLayer.findOne<Konva.Rect>(`.${LAYER_BBOX_NAME}`) ?? createBboxRect(rgLayer, konvaLayer);
+
+      // Hide the bbox while we calculate the new bbox, else the bbox will be included in the calculation
+      const visible = bboxRect.visible();
+      bboxRect.visible(false);
+
+      if (rgLayer.maskObjects.length === 0) {
+        // No objects - no bbox to calculate
+        onBboxChanged(rgLayer.id, null);
+      } else {
+        // Calculate the bbox by rendering the layer and checking its pixels
+        onBboxChanged(rgLayer.id, getLayerBboxPixels(konvaLayer));
       }
 
-      if (!bbox) {
-        continue;
-      }
-
-      const rect = konvaLayer.findOne<Konva.Rect>(`.${LAYER_BBOX_NAME}`) ?? createBboxRect(reduxLayer, konvaLayer);
-
-      rect.setAttrs({
-        visible: true,
-        listening: reduxLayer.isSelected,
-        x: bbox.x,
-        y: bbox.y,
-        width: bbox.width,
-        height: bbox.height,
-        stroke: reduxLayer.isSelected ? BBOX_SELECTED_STROKE : '',
-      });
+      // Restore the visibility of the bbox
+      bboxRect.visible(visible);
     }
   }
 };
@@ -847,10 +918,11 @@ const renderNoLayersMessage = (stage: Konva.Stage, layerCount: number, width: nu
 export const renderers = {
   renderToolPreview,
   renderLayers,
-  renderBbox,
+  renderBboxes,
   renderBackground,
   renderNoLayersMessage,
   arrangeLayers,
+  updateBboxes,
 };
 
 const DEBOUNCE_MS = 300;
@@ -858,10 +930,11 @@ const DEBOUNCE_MS = 300;
 export const debouncedRenderers = {
   renderToolPreview: debounce(renderToolPreview, DEBOUNCE_MS),
   renderLayers: debounce(renderLayers, DEBOUNCE_MS),
-  renderBbox: debounce(renderBbox, DEBOUNCE_MS),
+  renderBboxes: debounce(renderBboxes, DEBOUNCE_MS),
   renderBackground: debounce(renderBackground, DEBOUNCE_MS),
   renderNoLayersMessage: debounce(renderNoLayersMessage, DEBOUNCE_MS),
   arrangeLayers: debounce(arrangeLayers, DEBOUNCE_MS),
+  updateBboxes: debounce(updateBboxes, DEBOUNCE_MS),
 };
 
 /**

invokeai/frontend/web/src/features/deleteImageModal/store/selectors.ts

@@ -46,18 +46,16 @@ export const getImageUsage = (
 
   const isControlLayerImage = controlLayers.layers.some((l) => {
     if (isRegionalGuidanceLayer(l)) {
-      return l.ipAdapters.some((ipa) => ipa.image?.imageName === image_name);
+      return l.ipAdapters.some((ipa) => ipa.image?.name === image_name);
     }
     if (isControlAdapterLayer(l)) {
-      return (
-        l.controlAdapter.image?.imageName === image_name || l.controlAdapter.processedImage?.imageName === image_name
-      );
+      return l.controlAdapter.image?.name === image_name || l.controlAdapter.processedImage?.name === image_name;
     }
     if (isIPAdapterLayer(l)) {
-      return l.ipAdapter.image?.imageName === image_name;
+      return l.ipAdapter.image?.name === image_name;
     }
     if (isInitialImageLayer(l)) {
-      return l.image?.imageName === image_name;
+      return l.image?.name === image_name;
     }
     return false;
   });

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -73,6 +73,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const handleSendToImageToImage = useCallback(() => {
     dispatch(sentImageToImg2Img());
     dispatch(iiLayerAdded(imageDTO));
+    dispatch(setActiveTab('generation'));
   }, [dispatch, imageDTO]);
 
   const handleSendToCanvas = useCallback(() => {

invokeai/frontend/web/src/features/gallery/components/ImageGrid/GalleryImage.tsx

@@ -11,6 +11,7 @@ import type { GallerySelectionDraggableData, ImageDraggableData, TypesafeDraggab
 import { getGalleryImageDataTestId } from 'features/gallery/components/ImageGrid/getGalleryImageDataTestId';
 import { useMultiselect } from 'features/gallery/hooks/useMultiselect';
 import { useScrollIntoView } from 'features/gallery/hooks/useScrollIntoView';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import type { MouseEvent } from 'react';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -102,6 +103,10 @@ const GalleryImage = (props: HoverableImageProps) => {
     setIsHovered(true);
   }, []);
 
+  const onDoubleClick = useCallback(() => {
+    dispatch(isImageViewerOpenChanged(true));
+  }, [dispatch]);
+
   const handleMouseOut = useCallback(() => {
     setIsHovered(false);
   }, []);
@@ -143,6 +148,7 @@ const GalleryImage = (props: HoverableImageProps) => {
       >
         <IAIDndImage
           onClick={handleClick}
+          onDoubleClick={onDoubleClick}
           imageDTO={imageDTO}
           draggableData={draggableData}
           isSelected={isSelected}

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/DataViewer.tsx

@@ -1,5 +1,6 @@
 import { Box, Flex, IconButton, Tooltip, useShiftModifier } from '@invoke-ai/ui-library';
 import { getOverlayScrollbarsParams } from 'common/components/OverlayScrollbars/constants';
+import { Formatter } from 'fracturedjsonjs';
 import { isString } from 'lodash-es';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
 import type { CSSProperties } from 'react';
@@ -7,6 +8,8 @@ import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiCopyBold, PiDownloadSimpleBold } from 'react-icons/pi';
 
+const formatter = new Formatter();
+
 type Props = {
   label: string;
   data: unknown;
@@ -20,7 +23,7 @@ const overlayscrollbarsOptions = getOverlayScrollbarsParams('scroll', 'scroll').
 
 const DataViewer = (props: Props) => {
   const { label, data, fileName, withDownload = true, withCopy = true, extraCopyActions } = props;
-  const dataString = useMemo(() => (isString(data) ? data : JSON.stringify(data, null, 2)), [data]);
+  const dataString = useMemo(() => (isString(data) ? data : formatter.Serialize(data)) ?? '', [data]);
   const shift = useShiftModifier();
   const handleCopy = useCallback(() => {
     navigator.clipboard.writeText(dataString);

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx

@@ -1,12 +1,10 @@
 import { useAppSelector } from 'app/store/storeHooks';
 import { MetadataControlNets } from 'features/metadata/components/MetadataControlNets';
-import { MetadataControlNetsV2 } from 'features/metadata/components/MetadataControlNetsV2';
 import { MetadataIPAdapters } from 'features/metadata/components/MetadataIPAdapters';
-import { MetadataIPAdaptersV2 } from 'features/metadata/components/MetadataIPAdaptersV2';
 import { MetadataItem } from 'features/metadata/components/MetadataItem';
+import { MetadataLayers } from 'features/metadata/components/MetadataLayers';
 import { MetadataLoRAs } from 'features/metadata/components/MetadataLoRAs';
 import { MetadataT2IAdapters } from 'features/metadata/components/MetadataT2IAdapters';
-import { MetadataT2IAdaptersV2 } from 'features/metadata/components/MetadataT2IAdaptersV2';
 import { handlers } from 'features/metadata/util/handlers';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { memo } from 'react';
@@ -39,8 +37,7 @@ const ImageMetadataActions = (props: Props) => {
       <MetadataItem metadata={metadata} handlers={handlers.scheduler} />
       <MetadataItem metadata={metadata} handlers={handlers.cfgScale} />
       <MetadataItem metadata={metadata} handlers={handlers.cfgRescaleMultiplier} />
-      <MetadataItem metadata={metadata} handlers={handlers.initialImage} />
-      <MetadataItem metadata={metadata} handlers={handlers.strength} />
+      {activeTabName !== 'generation' && <MetadataItem metadata={metadata} handlers={handlers.strength} />}
       <MetadataItem metadata={metadata} handlers={handlers.hrfEnabled} />
       <MetadataItem metadata={metadata} handlers={handlers.hrfMethod} />
       <MetadataItem metadata={metadata} handlers={handlers.hrfStrength} />
@@ -52,12 +49,10 @@ const ImageMetadataActions = (props: Props) => {
       <MetadataItem metadata={metadata} handlers={handlers.refinerStart} />
       <MetadataItem metadata={metadata} handlers={handlers.refinerSteps} />
       <MetadataLoRAs metadata={metadata} />
+      {activeTabName === 'generation' && <MetadataLayers metadata={metadata} />}
       {activeTabName !== 'generation' && <MetadataControlNets metadata={metadata} />}
       {activeTabName !== 'generation' && <MetadataT2IAdapters metadata={metadata} />}
       {activeTabName !== 'generation' && <MetadataIPAdapters metadata={metadata} />}
-      {activeTabName === 'generation' && <MetadataControlNetsV2 metadata={metadata} />}
-      {activeTabName === 'generation' && <MetadataT2IAdaptersV2 metadata={metadata} />}
-      {activeTabName === 'generation' && <MetadataIPAdaptersV2 metadata={metadata} />}
     </>
   );
 };

invokeai/frontend/web/src/features/gallery/components/ImageViewer/CurrentImageButtons.tsx

@@ -16,6 +16,7 @@ import ParamUpscalePopover from 'features/parameters/components/Upscale/ParamUps
 import { useIsQueueMutationInProgress } from 'features/queue/hooks/useIsQueueMutationInProgress';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { selectSystemSlice } from 'features/system/store/systemSlice';
+import { setActiveTab } from 'features/ui/store/uiSlice';
 import { useGetAndLoadEmbeddedWorkflow } from 'features/workflowLibrary/hooks/useGetAndLoadEmbeddedWorkflow';
 import { memo, useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
@@ -84,6 +85,7 @@ const CurrentImageButtons = () => {
     }
     dispatch(sentImageToImg2Img());
     dispatch(iiLayerAdded(imageDTO));
+    dispatch(setActiveTab('generation'));
   }, [dispatch, imageDTO]);
 
   useHotkeys('shift+i', handleSendToImageToImage, [imageDTO]);

invokeai/frontend/web/src/features/gallery/components/ImageViewer/CurrentImagePreview.tsx

@@ -22,7 +22,21 @@ const selectLastSelectedImageName = createSelector(
   (lastSelectedImage) => lastSelectedImage?.image_name
 );
 
-const CurrentImagePreview = () => {
+type Props = {
+  isDragDisabled?: boolean;
+  isDropDisabled?: boolean;
+  withNextPrevButtons?: boolean;
+  withMetadata?: boolean;
+  alwaysShowProgress?: boolean;
+};
+
+const CurrentImagePreview = ({
+  isDragDisabled = false,
+  isDropDisabled = false,
+  withNextPrevButtons = true,
+  withMetadata = true,
+  alwaysShowProgress = false,
+}: Props) => {
   const { t } = useTranslation();
   const shouldShowImageDetails = useAppSelector((s) => s.ui.shouldShowImageDetails);
   const imageName = useAppSelector(selectLastSelectedImageName);
@@ -52,30 +66,35 @@ const CurrentImagePreview = () => {
   // Show and hide the next/prev buttons on mouse move
   const [shouldShowNextPrevButtons, setShouldShowNextPrevButtons] = useState<boolean>(false);
   const timeoutId = useRef(0);
-  const onMouseMove = useCallback(() => {
+  const onMouseOver = useCallback(() => {
     setShouldShowNextPrevButtons(true);
     window.clearTimeout(timeoutId.current);
+  }, []);
+  const onMouseOut = useCallback(() => {
     timeoutId.current = window.setTimeout(() => {
       setShouldShowNextPrevButtons(false);
-    }, 1000);
+    }, 500);
   }, []);
 
   return (
     <Flex
-      onMouseMove={onMouseMove}
+      onMouseOver={onMouseOver}
+      onMouseOut={onMouseOut}
       width="full"
       height="full"
       alignItems="center"
       justifyContent="center"
       position="relative"
     >
-      {hasDenoiseProgress && shouldShowProgressInViewer ? (
+      {hasDenoiseProgress && (shouldShowProgressInViewer || alwaysShowProgress) ? (
         <ProgressImage />
       ) : (
         <IAIDndImage
           imageDTO={imageDTO}
           droppableData={droppableData}
           draggableData={draggableData}
+          isDragDisabled={isDragDisabled}
+          isDropDisabled={isDropDisabled}
           isUploadDisabled={true}
           fitContainer
           useThumbailFallback
@@ -84,26 +103,13 @@ const CurrentImagePreview = () => {
           dataTestId="image-preview"
         />
       )}
+      {shouldShowImageDetails && imageDTO && withMetadata && (
+        <Box position="absolute" opacity={0.8} top={0} width="full" height="full" borderRadius="base">
+          <ImageMetadataViewer image={imageDTO} />
+        </Box>
+      )}
       <AnimatePresence>
-        {shouldShowImageDetails && imageDTO && (
-          <Box
-            as={motion.div}
-            key="metadataViewer"
-            initial={initial}
-            animate={animateMetadata}
-            exit={exit}
-            position="absolute"
-            top={0}
-            width="full"
-            height="full"
-            borderRadius="base"
-          >
-            <ImageMetadataViewer image={imageDTO} />
-          </Box>
-        )}
-      </AnimatePresence>
-      <AnimatePresence>
-        {shouldShowNextPrevButtons && imageDTO && (
+        {withNextPrevButtons && shouldShowNextPrevButtons && imageDTO && (
           <Box
             as={motion.div}
             key="nextPrevButtons"
@@ -133,10 +139,6 @@ const animateArrows: AnimationProps['animate'] = {
   opacity: 1,
   transition: { duration: 0.07 },
 };
-const animateMetadata: AnimationProps['animate'] = {
-  opacity: 0.8,
-  transition: { duration: 0.07 },
-};
 const exit: AnimationProps['exit'] = {
   opacity: 0,
   transition: { duration: 0.07 },

invokeai/frontend/web/src/features/gallery/components/ImageViewer/EditorButton.tsx

@@ -1,37 +0,0 @@
-import { Button } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import type { InvokeTabName } from 'features/ui/store/tabMap';
-import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import { useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-
-import { useImageViewer } from './useImageViewer';
-
-const TAB_NAME_TO_TKEY: Record<InvokeTabName, string> = {
-  generation: 'ui.tabs.generationTab',
-  canvas: 'ui.tabs.canvasTab',
-  workflows: 'ui.tabs.workflowsTab',
-  models: 'ui.tabs.modelsTab',
-  queue: 'ui.tabs.queueTab',
-};
-
-const TAB_NAME_TO_TKEY_SHORT: Record<InvokeTabName, string> = {
-  generation: 'ui.tabs.generation',
-  canvas: 'ui.tabs.canvas',
-  workflows: 'ui.tabs.workflows',
-  models: 'ui.tabs.models',
-  queue: 'ui.tabs.queue',
-};
-
-export const EditorButton = () => {
-  const { t } = useTranslation();
-  const { onClose } = useImageViewer();
-  const activeTabName = useAppSelector(activeTabNameSelector);
-  const tooltip = useMemo(() => t('gallery.switchTo', { tab: t(TAB_NAME_TO_TKEY[activeTabName]) }), [t, activeTabName]);
-
-  return (
-    <Button aria-label={tooltip} tooltip={tooltip} onClick={onClose} variant="ghost">
-      {t(TAB_NAME_TO_TKEY_SHORT[activeTabName])}
-    </Button>
-  );
-};

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageViewer.tsx

@@ -5,26 +5,12 @@ import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/To
 import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import type { InvokeTabName } from 'features/ui/store/tabMap';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
-import type { AnimationProps } from 'framer-motion';
-import { AnimatePresence, motion } from 'framer-motion';
 import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 
 import CurrentImageButtons from './CurrentImageButtons';
 import CurrentImagePreview from './CurrentImagePreview';
-import { EditorButton } from './EditorButton';
-
-const initial: AnimationProps['initial'] = {
-  opacity: 0,
-};
-const animate: AnimationProps['animate'] = {
-  opacity: 1,
-  transition: { duration: 0.07 },
-};
-const exit: AnimationProps['exit'] = {
-  opacity: 0,
-  transition: { duration: 0.07 },
-};
+import { ViewerToggleMenu } from './ViewerToggleMenu';
 
 const VIEWER_ENABLED_TABS: InvokeTabName[] = ['canvas', 'generation', 'workflows'];
 
@@ -42,49 +28,44 @@ export const ImageViewer = memo(() => {
   useHotkeys('z', onToggle, { enabled: isViewerEnabled }, [isViewerEnabled, onToggle]);
   useHotkeys('esc', onClose, { enabled: isViewerEnabled }, [isViewerEnabled, onClose]);
 
+  if (!shouldShowViewer) {
+    return null;
+  }
+
   return (
-    <AnimatePresence>
-      {shouldShowViewer && (
-        <Flex
-          key="imageViewer"
-          as={motion.div}
-          initial={initial}
-          animate={animate}
-          exit={exit}
-          layerStyle="first"
-          borderRadius="base"
-          position="absolute"
-          flexDirection="column"
-          top={0}
-          right={0}
-          bottom={0}
-          left={0}
-          p={2}
-          rowGap={4}
-          alignItems="center"
-          justifyContent="center"
-          zIndex={10} // reactflow puts its minimap at 5, so we need to be above that
-        >
-          <Flex w="full" gap={2}>
-            <Flex flex={1} justifyContent="center">
-              <Flex gap={2} marginInlineEnd="auto">
-                <ToggleProgressButton />
-                <ToggleMetadataViewerButton />
-              </Flex>
-            </Flex>
-            <Flex flex={1} gap={2} justifyContent="center">
-              <CurrentImageButtons />
-            </Flex>
-            <Flex flex={1} justifyContent="center">
-              <Flex gap={2} marginInlineStart="auto">
-                <EditorButton />
-              </Flex>
-            </Flex>
+    <Flex
+      layerStyle="first"
+      borderRadius="base"
+      position="absolute"
+      flexDirection="column"
+      top={0}
+      right={0}
+      bottom={0}
+      left={0}
+      p={2}
+      rowGap={4}
+      alignItems="center"
+      justifyContent="center"
+      zIndex={10} // reactflow puts its minimap at 5, so we need to be above that
+    >
+      <Flex w="full" gap={2}>
+        <Flex flex={1} justifyContent="center">
+          <Flex gap={2} marginInlineEnd="auto">
+            <ToggleProgressButton />
+            <ToggleMetadataViewerButton />
           </Flex>
-          <CurrentImagePreview />
         </Flex>
-      )}
-    </AnimatePresence>
+        <Flex flex={1} gap={2} justifyContent="center">
+          <CurrentImageButtons />
+        </Flex>
+        <Flex flex={1} justifyContent="center">
+          <Flex gap={2} marginInlineStart="auto">
+            <ViewerToggleMenu />
+          </Flex>
+        </Flex>
+      </Flex>
+      <CurrentImagePreview />
+    </Flex>
   );
 });
 

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ImageViewerWorkflows.tsx

@@ -0,0 +1,45 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { ToggleMetadataViewerButton } from 'features/gallery/components/ImageViewer/ToggleMetadataViewerButton';
+import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
+import { memo } from 'react';
+
+import CurrentImageButtons from './CurrentImageButtons';
+import CurrentImagePreview from './CurrentImagePreview';
+
+export const ImageViewerWorkflows = memo(() => {
+  return (
+    <Flex
+      layerStyle="first"
+      borderRadius="base"
+      position="absolute"
+      flexDirection="column"
+      top={0}
+      right={0}
+      bottom={0}
+      left={0}
+      p={2}
+      rowGap={4}
+      alignItems="center"
+      justifyContent="center"
+      zIndex={10} // reactflow puts its minimap at 5, so we need to be above that
+    >
+      <Flex w="full" gap={2}>
+        <Flex flex={1} justifyContent="center">
+          <Flex gap={2} marginInlineEnd="auto">
+            <ToggleProgressButton />
+            <ToggleMetadataViewerButton />
+          </Flex>
+        </Flex>
+        <Flex flex={1} gap={2} justifyContent="center">
+          <CurrentImageButtons />
+        </Flex>
+        <Flex flex={1} justifyContent="center">
+          <Flex gap={2} marginInlineStart="auto" />
+        </Flex>
+      </Flex>
+      <CurrentImagePreview />
+    </Flex>
+  );
+});
+
+ImageViewerWorkflows.displayName = 'ImageViewerWorkflows';

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ToggleMetadataViewerButton.tsx

@@ -35,6 +35,7 @@ export const ToggleMetadataViewerButton = memo(() => {
       isDisabled={!imageDTO}
       variant="outline"
       colorScheme={shouldShowImageDetails ? 'invokeBlue' : 'base'}
+      data-testid="toggle-show-metadata-button"
     />
   );
 });

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ToggleProgressButton.tsx

@@ -22,6 +22,7 @@ export const ToggleProgressButton = memo(() => {
       onClick={onClick}
       variant="outline"
       colorScheme={shouldShowProgressInViewer ? 'invokeBlue' : 'base'}
+      data-testid="toggle-show-progress-button"
     />
   );
 });

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ViewerButton.tsx

@@ -1,17 +0,0 @@
-import { Button } from '@invoke-ai/ui-library';
-import { useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-
-import { useImageViewer } from './useImageViewer';
-
-export const ViewerButton = () => {
-  const { t } = useTranslation();
-  const { onOpen } = useImageViewer();
-  const tooltip = useMemo(() => t('gallery.switchTo', { tab: t('common.viewer') }), [t]);
-
-  return (
-    <Button aria-label={tooltip} tooltip={tooltip} onClick={onOpen} variant="ghost" pointerEvents="auto">
-      {t('common.viewer')}
-    </Button>
-  );
-};

invokeai/frontend/web/src/features/gallery/components/ImageViewer/ViewerToggleMenu.tsx

@@ -0,0 +1,67 @@
+import {
+  Button,
+  Flex,
+  Icon,
+  Popover,
+  PopoverArrow,
+  PopoverBody,
+  PopoverContent,
+  PopoverTrigger,
+  Text,
+} from '@invoke-ai/ui-library';
+import { useTranslation } from 'react-i18next';
+import { PiCaretDownBold, PiCheckBold, PiEyeBold, PiPencilBold } from 'react-icons/pi';
+
+import { useImageViewer } from './useImageViewer';
+
+export const ViewerToggleMenu = () => {
+  const { t } = useTranslation();
+  const { isOpen, onClose, onOpen } = useImageViewer();
+
+  return (
+    <Popover isLazy>
+      <PopoverTrigger>
+        <Button variant="outline" data-testid="toggle-viewer-menu-button">
+          <Flex gap={3} w="full" alignItems="center">
+            {isOpen ? <Icon as={PiEyeBold} /> : <Icon as={PiPencilBold} />}
+            <Text fontSize="md">{isOpen ? t('common.viewing') : t('common.editing')}</Text>
+            <Icon as={PiCaretDownBold} />
+          </Flex>
+        </Button>
+      </PopoverTrigger>
+      <PopoverContent p={2}>
+        <PopoverArrow />
+        <PopoverBody>
+          <Flex flexDir="column">
+            <Button onClick={onOpen} variant="ghost" h="auto" w="auto" p={2}>
+              <Flex gap={2} w="full">
+                <Icon as={PiCheckBold} visibility={isOpen ? 'visible' : 'hidden'} />
+                <Flex flexDir="column" gap={2} alignItems="flex-start">
+                  <Text fontWeight="semibold" color="base.100">
+                    {t('common.viewing')}
+                  </Text>
+                  <Text fontWeight="normal" color="base.300">
+                    {t('common.viewingDesc')}
+                  </Text>
+                </Flex>
+              </Flex>
+            </Button>
+            <Button onClick={onClose} variant="ghost" h="auto" w="auto" p={2}>
+              <Flex gap={2} w="full">
+                <Icon as={PiCheckBold} visibility={isOpen ? 'hidden' : 'visible'} />
+                <Flex flexDir="column" gap={2} alignItems="flex-start">
+                  <Text fontWeight="semibold" color="base.100">
+                    {t('common.editing')}
+                  </Text>
+                  <Text fontWeight="normal" color="base.300">
+                    {t('common.editingDesc')}
+                  </Text>
+                </Flex>
+              </Flex>
+            </Button>
+          </Flex>
+        </PopoverBody>
+      </PopoverContent>
+    </Popover>
+  );
+};

invokeai/frontend/web/src/features/gallery/store/gallerySlice.ts

@@ -21,7 +21,7 @@ const initialGalleryState: GalleryState = {
   boardSearchText: '',
   limit: INITIAL_IMAGE_LIMIT,
   offset: 0,
-  isImageViewerOpen: false,
+  isImageViewerOpen: true,
 };
 
 export const gallerySlice = createSlice({
@@ -30,11 +30,9 @@ export const gallerySlice = createSlice({
   reducers: {
     imageSelected: (state, action: PayloadAction<ImageDTO | null>) => {
       state.selection = action.payload ? [action.payload] : [];
-      state.isImageViewerOpen = true;
     },
     selectionChanged: (state, action: PayloadAction<ImageDTO[]>) => {
       state.selection = uniqBy(action.payload, (i) => i.image_name);
-      state.isImageViewerOpen = true;
     },
     shouldAutoSwitchChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldAutoSwitch = action.payload;

invokeai/frontend/web/src/features/metadata/components/MetadataControlNetsV2.tsx

@@ -1,72 +0,0 @@
-import { MetadataItemView } from 'features/metadata/components/MetadataItemView';
-import type { ControlNetConfigV2Metadata, MetadataHandlers } from 'features/metadata/types';
-import { handlers } from 'features/metadata/util/handlers';
-import { useCallback, useEffect, useMemo, useState } from 'react';
-
-type Props = {
-  metadata: unknown;
-};
-
-export const MetadataControlNetsV2 = ({ metadata }: Props) => {
-  const [controlNets, setControlNets] = useState<ControlNetConfigV2Metadata[]>([]);
-
-  useEffect(() => {
-    const parse = async () => {
-      try {
-        const parsed = await handlers.controlNetsV2.parse(metadata);
-        setControlNets(parsed);
-      } catch (e) {
-        setControlNets([]);
-      }
-    };
-    parse();
-  }, [metadata]);
-
-  const label = useMemo(() => handlers.controlNetsV2.getLabel(), []);
-
-  return (
-    <>
-      {controlNets.map((controlNet) => (
-        <MetadataViewControlNet
-          key={controlNet.id}
-          label={label}
-          controlNet={controlNet}
-          handlers={handlers.controlNetsV2}
-        />
-      ))}
-    </>
-  );
-};
-
-const MetadataViewControlNet = ({
-  label,
-  controlNet,
-  handlers,
-}: {
-  label: string;
-  controlNet: ControlNetConfigV2Metadata;
-  handlers: MetadataHandlers<ControlNetConfigV2Metadata[], ControlNetConfigV2Metadata>;
-}) => {
-  const onRecall = useCallback(() => {
-    if (!handlers.recallItem) {
-      return;
-    }
-    handlers.recallItem(controlNet, true);
-  }, [handlers, controlNet]);
-
-  const [renderedValue, setRenderedValue] = useState<React.ReactNode>(null);
-  useEffect(() => {
-    const _renderValue = async () => {
-      if (!handlers.renderItemValue) {
-        setRenderedValue(null);
-        return;
-      }
-      const rendered = await handlers.renderItemValue(controlNet);
-      setRenderedValue(rendered);
-    };
-
-    _renderValue();
-  }, [handlers, controlNet]);
-
-  return <MetadataItemView label={label} isDisabled={false} onRecall={onRecall} renderedValue={renderedValue} />;
-};

invokeai/frontend/web/src/features/metadata/components/MetadataItem.tsx

@@ -3,6 +3,7 @@ import { MetadataItemView } from 'features/metadata/components/MetadataItemView'
 import { useMetadataItem } from 'features/metadata/hooks/useMetadataItem';
 import type { MetadataHandlers } from 'features/metadata/types';
 import { MetadataParseFailedToken } from 'features/metadata/util/parsers';
+import { isSymbol } from 'lodash-es';
 
 type MetadataItemProps<T> = {
   metadata: unknown;
@@ -17,6 +18,10 @@ const _MetadataItem = typedMemo(<T,>({ metadata, handlers, direction = 'row' }:
     return null;
   }
 
+  if (handlers.getIsVisible && !isSymbol(value) && !handlers.getIsVisible(value)) {
+    return null;
+  }
+
   return (
     <MetadataItemView
       label={label}

invokeai/frontend/web/src/features/metadata/components/MetadataLayers.tsx

@@ -1,5 +1,6 @@
+import type { Layer } from 'features/controlLayers/store/types';
 import { MetadataItemView } from 'features/metadata/components/MetadataItemView';
-import type { IPAdapterConfigV2Metadata, MetadataHandlers } from 'features/metadata/types';
+import type { MetadataHandlers } from 'features/metadata/types';
 import { handlers } from 'features/metadata/util/handlers';
 import { useCallback, useEffect, useMemo, useState } from 'react';
 
@@ -7,52 +8,47 @@ type Props = {
   metadata: unknown;
 };
 
-export const MetadataIPAdaptersV2 = ({ metadata }: Props) => {
-  const [ipAdapters, setIPAdapters] = useState<IPAdapterConfigV2Metadata[]>([]);
+export const MetadataLayers = ({ metadata }: Props) => {
+  const [layers, setLayers] = useState<Layer[]>([]);
 
   useEffect(() => {
     const parse = async () => {
       try {
-        const parsed = await handlers.ipAdaptersV2.parse(metadata);
-        setIPAdapters(parsed);
+        const parsed = await handlers.layers.parse(metadata);
+        setLayers(parsed);
       } catch (e) {
-        setIPAdapters([]);
+        setLayers([]);
       }
     };
     parse();
   }, [metadata]);
 
-  const label = useMemo(() => handlers.ipAdaptersV2.getLabel(), []);
+  const label = useMemo(() => handlers.layers.getLabel(), []);
 
   return (
     <>
-      {ipAdapters.map((ipAdapter) => (
-        <MetadataViewIPAdapter
-          key={ipAdapter.id}
-          label={label}
-          ipAdapter={ipAdapter}
-          handlers={handlers.ipAdaptersV2}
-        />
+      {layers.map((layer) => (
+        <MetadataViewLayer key={layer.id} label={label} layer={layer} handlers={handlers.layers} />
       ))}
     </>
   );
 };
 
-const MetadataViewIPAdapter = ({
+const MetadataViewLayer = ({
   label,
-  ipAdapter,
+  layer,
   handlers,
 }: {
   label: string;
-  ipAdapter: IPAdapterConfigV2Metadata;
-  handlers: MetadataHandlers<IPAdapterConfigV2Metadata[], IPAdapterConfigV2Metadata>;
+  layer: Layer;
+  handlers: MetadataHandlers<Layer[], Layer>;
 }) => {
   const onRecall = useCallback(() => {
     if (!handlers.recallItem) {
       return;
     }
-    handlers.recallItem(ipAdapter, true);
-  }, [handlers, ipAdapter]);
+    handlers.recallItem(layer, true);
+  }, [handlers, layer]);
 
   const [renderedValue, setRenderedValue] = useState<React.ReactNode>(null);
   useEffect(() => {
@@ -61,12 +57,12 @@ const MetadataViewIPAdapter = ({
         setRenderedValue(null);
         return;
       }
-      const rendered = await handlers.renderItemValue(ipAdapter);
+      const rendered = await handlers.renderItemValue(layer);
       setRenderedValue(rendered);
     };
 
     _renderValue();
-  }, [handlers, ipAdapter]);
+  }, [handlers, layer]);
 
   return <MetadataItemView label={label} isDisabled={false} onRecall={onRecall} renderedValue={renderedValue} />;
 };

invokeai/frontend/web/src/features/metadata/components/MetadataT2IAdaptersV2.tsx

@@ -1,72 +0,0 @@
-import { MetadataItemView } from 'features/metadata/components/MetadataItemView';
-import type { MetadataHandlers, T2IAdapterConfigV2Metadata } from 'features/metadata/types';
-import { handlers } from 'features/metadata/util/handlers';
-import { useCallback, useEffect, useMemo, useState } from 'react';
-
-type Props = {
-  metadata: unknown;
-};
-
-export const MetadataT2IAdaptersV2 = ({ metadata }: Props) => {
-  const [t2iAdapters, setT2IAdapters] = useState<T2IAdapterConfigV2Metadata[]>([]);
-
-  useEffect(() => {
-    const parse = async () => {
-      try {
-        const parsed = await handlers.t2iAdaptersV2.parse(metadata);
-        setT2IAdapters(parsed);
-      } catch (e) {
-        setT2IAdapters([]);
-      }
-    };
-    parse();
-  }, [metadata]);
-
-  const label = useMemo(() => handlers.t2iAdaptersV2.getLabel(), []);
-
-  return (
-    <>
-      {t2iAdapters.map((t2iAdapter) => (
-        <MetadataViewT2IAdapter
-          key={t2iAdapter.id}
-          label={label}
-          t2iAdapter={t2iAdapter}
-          handlers={handlers.t2iAdaptersV2}
-        />
-      ))}
-    </>
-  );
-};
-
-const MetadataViewT2IAdapter = ({
-  label,
-  t2iAdapter,
-  handlers,
-}: {
-  label: string;
-  t2iAdapter: T2IAdapterConfigV2Metadata;
-  handlers: MetadataHandlers<T2IAdapterConfigV2Metadata[], T2IAdapterConfigV2Metadata>;
-}) => {
-  const onRecall = useCallback(() => {
-    if (!handlers.recallItem) {
-      return;
-    }
-    handlers.recallItem(t2iAdapter, true);
-  }, [handlers, t2iAdapter]);
-
-  const [renderedValue, setRenderedValue] = useState<React.ReactNode>(null);
-  useEffect(() => {
-    const _renderValue = async () => {
-      if (!handlers.renderItemValue) {
-        setRenderedValue(null);
-        return;
-      }
-      const rendered = await handlers.renderItemValue(t2iAdapter);
-      setRenderedValue(rendered);
-    };
-
-    _renderValue();
-  }, [handlers, t2iAdapter]);
-
-  return <MetadataItemView label={label} isDisabled={false} onRecall={onRecall} renderedValue={renderedValue} />;
-};

invokeai/frontend/web/src/features/metadata/types.ts

@@ -1,9 +1,4 @@
 import type { ControlNetConfig, IPAdapterConfig, T2IAdapterConfig } from 'features/controlAdapters/store/types';
-import type {
-  ControlNetConfigV2,
-  IPAdapterConfigV2,
-  T2IAdapterConfigV2,
-} from 'features/controlLayers/util/controlAdapters';
 import type { O } from 'ts-toolbelt';
 
 /**
@@ -50,6 +45,14 @@ export type MetadataParseFunc<T = unknown> = (metadata: unknown) => Promise<T>;
  */
 export type MetadataValidateFunc<T> = (value: T) => Promise<T>;
 
+/**
+ * A function that determines whether a metadata item should be visible.
+ *
+ * @param value The value to check.
+ * @returns True if the item should be visible, false otherwise.
+ */
+type MetadataGetIsVisibleFunc<T> = (value: T) => boolean;
+
 export type MetadataHandlers<TValue = unknown, TItem = unknown> = {
   /**
    * Gets the label of the current metadata item as a string.
@@ -111,6 +114,14 @@ export type MetadataHandlers<TValue = unknown, TItem = unknown> = {
    * @returns The rendered item.
    */
   renderItemValue?: MetadataRenderValueFunc<TItem>;
+  /**
+   * Checks if a parsed metadata value should be visible.
+   * If not provided, the item is always visible.
+   *
+   * @param value The value to check.
+   * @returns True if the item should be visible, false otherwise.
+   */
+  getIsVisible?: MetadataGetIsVisibleFunc<TValue>;
 };
 
 // TODO(psyche): The types for item handlers should be able to be inferred from the type of the value:
@@ -127,6 +138,7 @@ type BuildMetadataHandlersArg<TValue, TItem> = {
   getLabel: MetadataGetLabelFunc;
   renderValue?: MetadataRenderValueFunc<TValue>;
   renderItemValue?: MetadataRenderValueFunc<TItem>;
+  getIsVisible?: MetadataGetIsVisibleFunc<TValue>;
 };
 
 export type BuildMetadataHandlers = <TValue, TItem>(
@@ -140,11 +152,3 @@ export type AnyControlAdapterConfigMetadata =
   | ControlNetConfigMetadata
   | T2IAdapterConfigMetadata
   | IPAdapterConfigMetadata;
-
-export type ControlNetConfigV2Metadata = O.NonNullable<ControlNetConfigV2, 'model'>;
-export type T2IAdapterConfigV2Metadata = O.NonNullable<T2IAdapterConfigV2, 'model'>;
-export type IPAdapterConfigV2Metadata = O.NonNullable<IPAdapterConfigV2, 'model'>;
-export type AnyControlAdapterConfigV2Metadata =
-  | ControlNetConfigV2Metadata
-  | T2IAdapterConfigV2Metadata
-  | IPAdapterConfigV2Metadata;

invokeai/frontend/web/src/features/metadata/util/handlers.ts

@@ -1,9 +1,9 @@
 import { objectKeys } from 'common/util/objectKeys';
 import { toast } from 'common/util/toast';
+import type { Layer } from 'features/controlLayers/store/types';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import type {
   AnyControlAdapterConfigMetadata,
-  AnyControlAdapterConfigV2Metadata,
   BuildMetadataHandlers,
   MetadataGetLabelFunc,
   MetadataHandlers,
@@ -16,6 +16,7 @@ import { fetchModelConfig } from 'features/metadata/util/modelFetchingHelpers';
 import { validators } from 'features/metadata/util/validators';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { t } from 'i18next';
+import { assert } from 'tsafe';
 
 import { parsers } from './parsers';
 import { recallers } from './recallers';
@@ -44,13 +45,48 @@ const renderControlAdapterValue: MetadataRenderValueFunc<AnyControlAdapterConfig
     return `${value.model.key} (${value.model.base.toUpperCase()}) - ${value.weight}`;
   }
 };
-const renderControlAdapterValueV2: MetadataRenderValueFunc<AnyControlAdapterConfigV2Metadata> = async (value) => {
-  try {
-    const modelConfig = await fetchModelConfig(value.model.key ?? 'none');
-    return `${modelConfig.name} (${modelConfig.base.toUpperCase()}) - ${value.weight}`;
-  } catch {
-    return `${value.model.key} (${value.model.base.toUpperCase()}) - ${value.weight}`;
+const renderLayerValue: MetadataRenderValueFunc<Layer> = async (layer) => {
+  if (layer.type === 'initial_image_layer') {
+    let rendered = t('controlLayers.globalInitialImageLayer');
+    if (layer.image) {
+      rendered += ` (${layer.image})`;
+    }
+    return rendered;
   }
+  if (layer.type === 'control_adapter_layer') {
+    let rendered = t('controlLayers.globalControlAdapterLayer');
+    const model = layer.controlAdapter.model;
+    if (model) {
+      rendered += ` (${model.name} - ${model.base.toUpperCase()})`;
+    }
+    return rendered;
+  }
+  if (layer.type === 'ip_adapter_layer') {
+    let rendered = t('controlLayers.globalIPAdapterLayer');
+    const model = layer.ipAdapter.model;
+    if (model) {
+      rendered += ` (${model.name} - ${model.base.toUpperCase()})`;
+    }
+    return rendered;
+  }
+  if (layer.type === 'regional_guidance_layer') {
+    const rendered = t('controlLayers.regionalGuidanceLayer');
+    const items: string[] = [];
+    if (layer.positivePrompt) {
+      items.push(`Positive: ${layer.positivePrompt}`);
+    }
+    if (layer.negativePrompt) {
+      items.push(`Negative: ${layer.negativePrompt}`);
+    }
+    if (layer.ipAdapters.length > 0) {
+      items.push(`${layer.ipAdapters.length} IP Adapters`);
+    }
+    return `${rendered} (${items.join(', ')})`;
+  }
+  assert(false, 'Unknown layer type');
+};
+const renderLayersValue: MetadataRenderValueFunc<Layer[]> = async (layers) => {
+  return `${layers.length} ${t('controlLayers.layers', { count: layers.length })}`;
 };
 
 const parameterSetToast = (parameter: string, description?: string) => {
@@ -73,26 +109,6 @@ const parameterNotSetToast = (parameter: string, description?: string) => {
   });
 };
 
-// const allParameterSetToast = (description?: string) => {
-//   toast({
-//     title: t('toast.parametersSet'),
-//     status: 'info',
-//     description,
-//     duration: 2500,
-//     isClosable: true,
-//   });
-// };
-
-// const allParameterNotSetToast = (description?: string) => {
-//   toast({
-//     title: t('toast.parametersNotSet'),
-//     status: 'warning',
-//     description,
-//     duration: 2500,
-//     isClosable: true,
-//   });
-// };
-
 const buildParse =
   <TValue, TItem>(arg: {
     parser: MetadataParseFunc<TValue>;
@@ -171,6 +187,7 @@ const buildHandlers: BuildMetadataHandlers = ({
   itemValidator,
   renderValue,
   renderItemValue,
+  getIsVisible,
 }) => ({
   parse: buildParse({ parser, getLabel }),
   parseItem: itemParser ? buildParseItem({ itemParser, getLabel }) : undefined,
@@ -179,6 +196,7 @@ const buildHandlers: BuildMetadataHandlers = ({
   getLabel,
   renderValue: renderValue ?? resolveToString,
   renderItemValue: renderItemValue ?? resolveToString,
+  getIsVisible,
 });
 
 export const handlers = {
@@ -198,12 +216,6 @@ export const handlers = {
     recaller: recallers.cfgScale,
   }),
   height: buildHandlers({ getLabel: () => t('metadata.height'), parser: parsers.height, recaller: recallers.height }),
-  initialImage: buildHandlers({
-    getLabel: () => t('metadata.initImage'),
-    parser: parsers.initialImage,
-    recaller: recallers.initialImage,
-    renderValue: async (imageDTO) => imageDTO.image_name,
-  }),
   negativePrompt: buildHandlers({
     getLabel: () => t('metadata.negativePrompt'),
     parser: parsers.negativePrompt,
@@ -350,35 +362,17 @@ export const handlers = {
     itemValidator: validators.t2iAdapter,
     renderItemValue: renderControlAdapterValue,
   }),
-  controlNetsV2: buildHandlers({
-    getLabel: () => t('common.controlNet'),
-    parser: parsers.controlNetsV2,
-    itemParser: parsers.controlNetV2,
-    recaller: recallers.controlNetsV2,
-    itemRecaller: recallers.controlNetV2,
-    validator: validators.controlNetsV2,
-    itemValidator: validators.controlNetV2,
-    renderItemValue: renderControlAdapterValueV2,
-  }),
-  ipAdaptersV2: buildHandlers({
-    getLabel: () => t('common.ipAdapter'),
-    parser: parsers.ipAdaptersV2,
-    itemParser: parsers.ipAdapterV2,
-    recaller: recallers.ipAdaptersV2,
-    itemRecaller: recallers.ipAdapterV2,
-    validator: validators.ipAdaptersV2,
-    itemValidator: validators.ipAdapterV2,
-    renderItemValue: renderControlAdapterValueV2,
-  }),
-  t2iAdaptersV2: buildHandlers({
-    getLabel: () => t('common.t2iAdapter'),
-    parser: parsers.t2iAdaptersV2,
-    itemParser: parsers.t2iAdapterV2,
-    recaller: recallers.t2iAdaptersV2,
-    itemRecaller: recallers.t2iAdapterV2,
-    validator: validators.t2iAdaptersV2,
-    itemValidator: validators.t2iAdapterV2,
-    renderItemValue: renderControlAdapterValueV2,
+  layers: buildHandlers({
+    getLabel: () => t('controlLayers.layers_one'),
+    parser: parsers.layers,
+    itemParser: parsers.layer,
+    recaller: recallers.layers,
+    itemRecaller: recallers.layer,
+    validator: validators.layers,
+    itemValidator: validators.layer,
+    renderItemValue: renderLayerValue,
+    renderValue: renderLayersValue,
+    getIsVisible: (value) => value.length > 0,
   }),
 } as const;
 
@@ -435,9 +429,9 @@ export const parseAndRecallImageDimensions = async (metadata: unknown) => {
 };
 
 // These handlers should be omitted when recalling to control layers
-const TO_CONTROL_LAYERS_SKIP_KEYS: (keyof typeof handlers)[] = ['controlNets', 'ipAdapters', 't2iAdapters'];
+const TO_CONTROL_LAYERS_SKIP_KEYS: (keyof typeof handlers)[] = ['controlNets', 'ipAdapters', 't2iAdapters', 'strength'];
 // These handlers should be omitted when recalling to the rest of the app
-const NOT_TO_CONTROL_LAYERS_SKIP_KEYS: (keyof typeof handlers)[] = ['controlNetsV2', 'ipAdaptersV2', 't2iAdaptersV2'];
+const NOT_TO_CONTROL_LAYERS_SKIP_KEYS: (keyof typeof handlers)[] = ['layers'];
 
 export const parseAndRecallAllMetadata = async (
   metadata: unknown,

invokeai/frontend/web/src/features/metadata/util/modelFetchingHelpers.ts

@@ -1,4 +1,5 @@
 import { getStore } from 'app/store/nanostores/store';
+import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { isModelIdentifier, isModelIdentifierV2 } from 'features/nodes/types/common';
 import { modelsApi } from 'services/api/endpoints/models';
 import type { AnyModelConfig, BaseModelType, ModelType } from 'services/api/types';
@@ -68,6 +69,24 @@ const fetchModelConfigByAttrs = async (name: string, base: BaseModelType, type:
   }
 };
 
+/**
+ * Fetches the model config given an identifier. First attempts to fetch by key, then falls back to fetching by attrs.
+ * @param identifier The model identifier.
+ * @returns A promise that resolves to the model config.
+ * @throws {ModelConfigNotFoundError} If the model config is unable to be fetched.
+ */
+export const fetchModelConfigByIdentifier = async (identifier: ModelIdentifierField): Promise<AnyModelConfig> => {
+  try {
+    return await fetchModelConfig(identifier.key);
+  } catch {
+    try {
+      return await fetchModelConfigByAttrs(identifier.name, identifier.base, identifier.type);
+    } catch {
+      throw new ModelConfigNotFoundError(`Unable to retrieve model config for identifier ${identifier}`);
+    }
+  }
+};
+
 /**
  * Fetches the model config for a given model key and type, and ensures that the model config is of a specific type.
  * @param key The model key.

invokeai/frontend/web/src/features/metadata/util/parsers.ts

@@ -1,10 +1,12 @@
-import { getStore } from 'app/store/nanostores/store';
 import {
   initialControlNet,
   initialIPAdapter,
   initialT2IAdapter,
 } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
+import { getCALayerId, getIPALayerId, INITIAL_IMAGE_LAYER_ID } from 'features/controlLayers/store/controlLayersSlice';
+import type { ControlAdapterLayer, InitialImageLayer, IPAdapterLayer, Layer } from 'features/controlLayers/store/types';
+import { zLayer } from 'features/controlLayers/store/types';
 import {
   CA_PROCESSOR_DATA,
   imageDTOToImageWithDims,
@@ -17,12 +19,9 @@ import type { LoRA } from 'features/lora/store/loraSlice';
 import { defaultLoRAConfig } from 'features/lora/store/loraSlice';
 import type {
   ControlNetConfigMetadata,
-  ControlNetConfigV2Metadata,
   IPAdapterConfigMetadata,
-  IPAdapterConfigV2Metadata,
   MetadataParseFunc,
   T2IAdapterConfigMetadata,
-  T2IAdapterConfigV2Metadata,
 } from 'features/metadata/types';
 import { fetchModelConfigWithTypeGuard, getModelKey } from 'features/metadata/util/modelFetchingHelpers';
 import { zControlField, zIPAdapterField, zModelIdentifierField, zT2IAdapterField } from 'features/nodes/types/common';
@@ -69,8 +68,7 @@ import {
   isParameterWidth,
 } from 'features/parameters/types/parameterSchemas';
 import { get, isArray, isString } from 'lodash-es';
-import { getImageDTO, imagesApi } from 'services/api/endpoints/images';
-import type { ImageDTO } from 'services/api/types';
+import { getImageDTO } from 'services/api/endpoints/images';
 import {
   isControlNetModelConfig,
   isIPAdapterModelConfig,
@@ -80,6 +78,7 @@ import {
   isT2IAdapterModelConfig,
   isVAEModelConfig,
 } from 'services/api/types';
+import { assert } from 'tsafe';
 import { v4 as uuidv4 } from 'uuid';
 
 export const MetadataParsePendingToken = Symbol('pending');
@@ -149,14 +148,6 @@ const parseCFGRescaleMultiplier: MetadataParseFunc<ParameterCFGRescaleMultiplier
 const parseScheduler: MetadataParseFunc<ParameterScheduler> = (metadata) =>
   getProperty(metadata, 'scheduler', isParameterScheduler);
 
-const parseInitialImage: MetadataParseFunc<ImageDTO> = async (metadata) => {
-  const imageName = await getProperty(metadata, 'init_image', isString);
-  const imageDTORequest = getStore().dispatch(imagesApi.endpoints.getImageDTO.initiate(imageName));
-  const imageDTO = await imageDTORequest.unwrap();
-  imageDTORequest.unsubscribe();
-  return imageDTO;
-};
-
 const parseWidth: MetadataParseFunc<ParameterWidth> = (metadata) => getProperty(metadata, 'width', isParameterWidth);
 
 const parseHeight: MetadataParseFunc<ParameterHeight> = (metadata) =>
@@ -309,7 +300,7 @@ const parseControlNet: MetadataParseFunc<ControlNetConfigMetadata> = async (meta
 
 const parseAllControlNets: MetadataParseFunc<ControlNetConfigMetadata[]> = async (metadata) => {
   try {
-    const controlNetsRaw = await getProperty(metadata, 'controlnets', isArray || undefined);
+    const controlNetsRaw = await getProperty(metadata, 'controlnets', isArray);
     const parseResults = await Promise.allSettled(controlNetsRaw.map((cn) => parseControlNet(cn)));
     const controlNets = parseResults
       .filter((result): result is PromiseFulfilledResult<ControlNetConfigMetadata> => result.status === 'fulfilled')
@@ -439,8 +430,103 @@ const parseAllIPAdapters: MetadataParseFunc<IPAdapterConfigMetadata[]> = async (
   }
 };
 
-//#region V2/Control Layers
-const parseControlNetV2: MetadataParseFunc<ControlNetConfigV2Metadata> = async (metadataItem) => {
+//#region Control Layers
+const parseLayer: MetadataParseFunc<Layer> = async (metadataItem) => zLayer.parseAsync(metadataItem);
+
+const parseLayers: MetadataParseFunc<Layer[]> = async (metadata) => {
+  // We need to support recalling pre-Control Layers metadata into Control Layers. A separate set of parsers handles
+  // taking pre-CL metadata and parsing it into layers. It doesn't always map 1-to-1, so this is best-effort. For
+  // example, CL Control Adapters don't support resize mode, so we simply omit that property.
+
+  try {
+    const layers: Layer[] = [];
+
+    try {
+      const control_layers = await getProperty(metadata, 'control_layers');
+      const controlLayersRaw = await getProperty(control_layers, 'layers', isArray);
+      const controlLayersParseResults = await Promise.allSettled(controlLayersRaw.map(parseLayer));
+      const controlLayers = controlLayersParseResults
+        .filter((result): result is PromiseFulfilledResult<Layer> => result.status === 'fulfilled')
+        .map((result) => result.value);
+      layers.push(...controlLayers);
+    } catch {
+      // no-op
+    }
+
+    try {
+      const controlNetsRaw = await getProperty(metadata, 'controlnets', isArray);
+      const controlNetsParseResults = await Promise.allSettled(
+        controlNetsRaw.map(async (cn) => await parseControlNetToControlAdapterLayer(cn))
+      );
+      const controlNetsAsLayers = controlNetsParseResults
+        .filter((result): result is PromiseFulfilledResult<ControlAdapterLayer> => result.status === 'fulfilled')
+        .map((result) => result.value);
+      layers.push(...controlNetsAsLayers);
+    } catch {
+      // no-op
+    }
+
+    try {
+      const t2iAdaptersRaw = await getProperty(metadata, 't2iAdapters', isArray);
+      const t2iAdaptersParseResults = await Promise.allSettled(
+        t2iAdaptersRaw.map(async (cn) => await parseT2IAdapterToControlAdapterLayer(cn))
+      );
+      const t2iAdaptersAsLayers = t2iAdaptersParseResults
+        .filter((result): result is PromiseFulfilledResult<ControlAdapterLayer> => result.status === 'fulfilled')
+        .map((result) => result.value);
+      layers.push(...t2iAdaptersAsLayers);
+    } catch {
+      // no-op
+    }
+
+    try {
+      const ipAdaptersRaw = await getProperty(metadata, 'ipAdapters', isArray);
+      const ipAdaptersParseResults = await Promise.allSettled(
+        ipAdaptersRaw.map(async (cn) => await parseIPAdapterToIPAdapterLayer(cn))
+      );
+      const ipAdaptersAsLayers = ipAdaptersParseResults
+        .filter((result): result is PromiseFulfilledResult<IPAdapterLayer> => result.status === 'fulfilled')
+        .map((result) => result.value);
+      layers.push(...ipAdaptersAsLayers);
+    } catch {
+      // no-op
+    }
+
+    try {
+      const initialImageLayer = await parseInitialImageToInitialImageLayer(metadata);
+      layers.push(initialImageLayer);
+    } catch {
+      // no-op
+    }
+
+    return layers;
+  } catch {
+    return [];
+  }
+};
+
+const parseInitialImageToInitialImageLayer: MetadataParseFunc<InitialImageLayer> = async (metadata) => {
+  const denoisingStrength = await getProperty(metadata, 'strength', isParameterStrength);
+  const imageName = await getProperty(metadata, 'init_image', isString);
+  const imageDTO = await getImageDTO(imageName);
+  assert(imageDTO, 'ImageDTO is null');
+  const layer: InitialImageLayer = {
+    id: INITIAL_IMAGE_LAYER_ID,
+    type: 'initial_image_layer',
+    bbox: null,
+    bboxNeedsUpdate: true,
+    x: 0,
+    y: 0,
+    isEnabled: true,
+    opacity: 1,
+    image: imageDTOToImageWithDims(imageDTO),
+    isSelected: true,
+    denoisingStrength,
+  };
+  return layer;
+};
+
+const parseControlNetToControlAdapterLayer: MetadataParseFunc<ControlAdapterLayer> = async (metadataItem) => {
   const control_model = await getProperty(metadataItem, 'control_model');
   const key = await getModelKey(control_model, 'controlnet');
   const controlNetModel = await fetchModelConfigWithTypeGuard(key, isControlNetModelConfig);
@@ -469,7 +555,6 @@ const parseControlNetV2: MetadataParseFunc<ControlNetConfigV2Metadata> = async (
     .catch(null)
     .parse(await getProperty(metadataItem, 'control_mode'));
 
-  const id = uuidv4();
   const defaultPreprocessor = controlNetModel.default_settings?.preprocessor;
   const processorConfig = isProcessorTypeV2(defaultPreprocessor)
     ? CA_PROCESSOR_DATA[defaultPreprocessor].buildDefaults()
@@ -481,36 +566,35 @@ const parseControlNetV2: MetadataParseFunc<ControlNetConfigV2Metadata> = async (
   const imageDTO = image ? await getImageDTO(image.image_name) : null;
   const processedImageDTO = processedImage ? await getImageDTO(processedImage.image_name) : null;
 
-  const controlNet: ControlNetConfigV2Metadata = {
-    id,
-    type: 'controlnet',
-    model: zModelIdentifierField.parse(controlNetModel),
-    weight: typeof control_weight === 'number' ? control_weight : initialControlNetV2.weight,
-    beginEndStepPct,
-    controlMode: control_mode ?? initialControlNetV2.controlMode,
-    image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
-    processedImage: processedImageDTO ? imageDTOToImageWithDims(processedImageDTO) : null,
-    processorConfig,
-    isProcessingImage: false,
+  const layer: ControlAdapterLayer = {
+    id: getCALayerId(uuidv4()),
+    bbox: null,
+    bboxNeedsUpdate: true,
+    isEnabled: true,
+    isFilterEnabled: true,
+    isSelected: true,
+    opacity: 1,
+    type: 'control_adapter_layer',
+    x: 0,
+    y: 0,
+    controlAdapter: {
+      id: uuidv4(),
+      type: 'controlnet',
+      model: zModelIdentifierField.parse(controlNetModel),
+      weight: typeof control_weight === 'number' ? control_weight : initialControlNetV2.weight,
+      beginEndStepPct,
+      controlMode: control_mode ?? initialControlNetV2.controlMode,
+      image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
+      processedImage: processedImageDTO ? imageDTOToImageWithDims(processedImageDTO) : null,
+      processorConfig,
+      processorPendingBatchId: null,
+    },
   };
 
-  return controlNet;
+  return layer;
 };
 
-const parseAllControlNetsV2: MetadataParseFunc<ControlNetConfigV2Metadata[]> = async (metadata) => {
-  try {
-    const controlNetsRaw = await getProperty(metadata, 'controlnets', isArray || undefined);
-    const parseResults = await Promise.allSettled(controlNetsRaw.map((cn) => parseControlNetV2(cn)));
-    const controlNets = parseResults
-      .filter((result): result is PromiseFulfilledResult<ControlNetConfigV2Metadata> => result.status === 'fulfilled')
-      .map((result) => result.value);
-    return controlNets;
-  } catch {
-    return [];
-  }
-};
-
-const parseT2IAdapterV2: MetadataParseFunc<T2IAdapterConfigV2Metadata> = async (metadataItem) => {
+const parseT2IAdapterToControlAdapterLayer: MetadataParseFunc<ControlAdapterLayer> = async (metadataItem) => {
   const t2i_adapter_model = await getProperty(metadataItem, 't2i_adapter_model');
   const key = await getModelKey(t2i_adapter_model, 't2i_adapter');
   const t2iAdapterModel = await fetchModelConfigWithTypeGuard(key, isT2IAdapterModelConfig);
@@ -536,7 +620,6 @@ const parseT2IAdapterV2: MetadataParseFunc<T2IAdapterConfigV2Metadata> = async (
     .catch(null)
     .parse(await getProperty(metadataItem, 'end_step_percent'));
 
-  const id = uuidv4();
   const defaultPreprocessor = t2iAdapterModel.default_settings?.preprocessor;
   const processorConfig = isProcessorTypeV2(defaultPreprocessor)
     ? CA_PROCESSOR_DATA[defaultPreprocessor].buildDefaults()
@@ -548,35 +631,34 @@ const parseT2IAdapterV2: MetadataParseFunc<T2IAdapterConfigV2Metadata> = async (
   const imageDTO = image ? await getImageDTO(image.image_name) : null;
   const processedImageDTO = processedImage ? await getImageDTO(processedImage.image_name) : null;
 
-  const t2iAdapter: T2IAdapterConfigV2Metadata = {
-    id,
-    type: 't2i_adapter',
-    model: zModelIdentifierField.parse(t2iAdapterModel),
-    weight: typeof weight === 'number' ? weight : initialT2IAdapterV2.weight,
-    beginEndStepPct,
-    image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
-    processedImage: processedImageDTO ? imageDTOToImageWithDims(processedImageDTO) : null,
-    processorConfig,
-    isProcessingImage: false,
+  const layer: ControlAdapterLayer = {
+    id: getCALayerId(uuidv4()),
+    bbox: null,
+    bboxNeedsUpdate: true,
+    isEnabled: true,
+    isFilterEnabled: true,
+    isSelected: true,
+    opacity: 1,
+    type: 'control_adapter_layer',
+    x: 0,
+    y: 0,
+    controlAdapter: {
+      id: uuidv4(),
+      type: 't2i_adapter',
+      model: zModelIdentifierField.parse(t2iAdapterModel),
+      weight: typeof weight === 'number' ? weight : initialT2IAdapterV2.weight,
+      beginEndStepPct,
+      image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
+      processedImage: processedImageDTO ? imageDTOToImageWithDims(processedImageDTO) : null,
+      processorConfig,
+      processorPendingBatchId: null,
+    },
   };
 
-  return t2iAdapter;
+  return layer;
 };
 
-const parseAllT2IAdaptersV2: MetadataParseFunc<T2IAdapterConfigV2Metadata[]> = async (metadata) => {
-  try {
-    const t2iAdaptersRaw = await getProperty(metadata, 't2iAdapters', isArray);
-    const parseResults = await Promise.allSettled(t2iAdaptersRaw.map((t2iAdapter) => parseT2IAdapterV2(t2iAdapter)));
-    const t2iAdapters = parseResults
-      .filter((result): result is PromiseFulfilledResult<T2IAdapterConfigV2Metadata> => result.status === 'fulfilled')
-      .map((result) => result.value);
-    return t2iAdapters;
-  } catch {
-    return [];
-  }
-};
-
-const parseIPAdapterV2: MetadataParseFunc<IPAdapterConfigV2Metadata> = async (metadataItem) => {
+const parseIPAdapterToIPAdapterLayer: MetadataParseFunc<IPAdapterLayer> = async (metadataItem) => {
   const ip_adapter_model = await getProperty(metadataItem, 'ip_adapter_model');
   const key = await getModelKey(ip_adapter_model, 'ip_adapter');
   const ipAdapterModel = await fetchModelConfigWithTypeGuard(key, isIPAdapterModelConfig);
@@ -602,39 +684,32 @@ const parseIPAdapterV2: MetadataParseFunc<IPAdapterConfigV2Metadata> = async (me
     .catch(null)
     .parse(await getProperty(metadataItem, 'end_step_percent'));
 
-  const id = uuidv4();
   const beginEndStepPct: [number, number] = [
     begin_step_percent ?? initialIPAdapterV2.beginEndStepPct[0],
     end_step_percent ?? initialIPAdapterV2.beginEndStepPct[1],
   ];
   const imageDTO = image ? await getImageDTO(image.image_name) : null;
 
-  const ipAdapter: IPAdapterConfigV2Metadata = {
-    id,
-    type: 'ip_adapter',
-    model: zModelIdentifierField.parse(ipAdapterModel),
-    weight: typeof weight === 'number' ? weight : initialIPAdapterV2.weight,
-    beginEndStepPct,
-    image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
-    clipVisionModel: initialIPAdapterV2.clipVisionModel, // TODO: This needs to be added to the zIPAdapterField...
-    method: method ?? initialIPAdapterV2.method,
+  const layer: IPAdapterLayer = {
+    id: getIPALayerId(uuidv4()),
+    type: 'ip_adapter_layer',
+    isEnabled: true,
+    isSelected: true,
+    ipAdapter: {
+      id: uuidv4(),
+      type: 'ip_adapter',
+      model: zModelIdentifierField.parse(ipAdapterModel),
+      weight: typeof weight === 'number' ? weight : initialIPAdapterV2.weight,
+      beginEndStepPct,
+      image: imageDTO ? imageDTOToImageWithDims(imageDTO) : null,
+      clipVisionModel: initialIPAdapterV2.clipVisionModel, // TODO: This needs to be added to the zIPAdapterField...
+      method: method ?? initialIPAdapterV2.method,
+    },
   };
 
-  return ipAdapter;
-};
-
-const parseAllIPAdaptersV2: MetadataParseFunc<IPAdapterConfigV2Metadata[]> = async (metadata) => {
-  try {
-    const ipAdaptersRaw = await getProperty(metadata, 'ipAdapters', isArray);
-    const parseResults = await Promise.allSettled(ipAdaptersRaw.map((ipAdapter) => parseIPAdapterV2(ipAdapter)));
-    const ipAdapters = parseResults
-      .filter((result): result is PromiseFulfilledResult<IPAdapterConfigV2Metadata> => result.status === 'fulfilled')
-      .map((result) => result.value);
-    return ipAdapters;
-  } catch {
-    return [];
-  }
+  return layer;
 };
+//#endregion
 
 export const parsers = {
   createdBy: parseCreatedBy,
@@ -647,7 +722,6 @@ export const parsers = {
   cfgScale: parseCFGScale,
   cfgRescaleMultiplier: parseCFGRescaleMultiplier,
   scheduler: parseScheduler,
-  initialImage: parseInitialImage,
   width: parseWidth,
   height: parseHeight,
   steps: parseSteps,
@@ -672,10 +746,9 @@ export const parsers = {
   t2iAdapters: parseAllT2IAdapters,
   ipAdapter: parseIPAdapter,
   ipAdapters: parseAllIPAdapters,
-  controlNetV2: parseControlNetV2,
-  controlNetsV2: parseAllControlNetsV2,
-  t2iAdapterV2: parseT2IAdapterV2,
-  t2iAdaptersV2: parseAllT2IAdaptersV2,
-  ipAdapterV2: parseIPAdapterV2,
-  ipAdaptersV2: parseAllIPAdaptersV2,
+  controlNetToControlLayer: parseControlNetToControlAdapterLayer,
+  t2iAdapterToControlAdapterLayer: parseT2IAdapterToControlAdapterLayer,
+  ipAdapterToIPAdapterLayer: parseIPAdapterToIPAdapterLayer,
+  layer: parseLayer,
+  layers: parseLayers,
 } as const;

invokeai/frontend/web/src/features/metadata/util/recallers.ts

@@ -1,4 +1,5 @@
 import { getStore } from 'app/store/nanostores/store';
+import { deepClone } from 'common/util/deepClone';
 import {
   controlAdapterRecalled,
   controlNetsReset,
@@ -6,31 +7,32 @@ import {
   t2iAdaptersReset,
 } from 'features/controlAdapters/store/controlAdaptersSlice';
 import {
-  caLayerAdded,
-  caLayerControlNetsDeleted,
-  caLayerT2IAdaptersDeleted,
+  allLayersDeleted,
+  caLayerRecalled,
+  getCALayerId,
+  getIPALayerId,
+  getRGLayerId,
   heightChanged,
-  iiLayerAdded,
-  ipaLayerAdded,
-  ipaLayersDeleted,
+  iiLayerRecalled,
+  ipaLayerRecalled,
   negativePrompt2Changed,
   negativePromptChanged,
   positivePrompt2Changed,
   positivePromptChanged,
+  rgLayerRecalled,
   widthChanged,
 } from 'features/controlLayers/store/controlLayersSlice';
+import type { Layer } from 'features/controlLayers/store/types';
 import { setHrfEnabled, setHrfMethod, setHrfStrength } from 'features/hrf/store/hrfSlice';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import { loraRecalled, lorasReset } from 'features/lora/store/loraSlice';
 import type {
   ControlNetConfigMetadata,
-  ControlNetConfigV2Metadata,
   IPAdapterConfigMetadata,
-  IPAdapterConfigV2Metadata,
   MetadataRecallFunc,
   T2IAdapterConfigMetadata,
-  T2IAdapterConfigV2Metadata,
 } from 'features/metadata/types';
+import { fetchModelConfigByIdentifier } from 'features/metadata/util/modelFetchingHelpers';
 import { modelSelected } from 'features/parameters/store/actions';
 import {
   setCfgRescaleMultiplier,
@@ -72,7 +74,8 @@ import {
   setRefinerStart,
   setRefinerSteps,
 } from 'features/sdxl/store/sdxlSlice';
-import type { ImageDTO } from 'services/api/types';
+import { getImageDTO } from 'services/api/endpoints/images';
+import { v4 as uuidv4 } from 'uuid';
 
 const recallPositivePrompt: MetadataRecallFunc<ParameterPositivePrompt> = (positivePrompt) => {
   getStore().dispatch(positivePromptChanged(positivePrompt));
@@ -106,10 +109,6 @@ const recallScheduler: MetadataRecallFunc<ParameterScheduler> = (scheduler) => {
   getStore().dispatch(setScheduler(scheduler));
 };
 
-const recallInitialImage: MetadataRecallFunc<ImageDTO> = async (imageDTO) => {
-  getStore().dispatch(iiLayerAdded(imageDTO));
-};
-
 const setSizeOptions = { updateAspectRatio: true, clamp: true };
 
 const recallWidth: MetadataRecallFunc<ParameterWidth> = (width) => {
@@ -244,50 +243,96 @@ const recallIPAdapters: MetadataRecallFunc<IPAdapterConfigMetadata[]> = (ipAdapt
   });
 };
 
-//#region V2/Control Layer
-const recallControlNetV2: MetadataRecallFunc<ControlNetConfigV2Metadata> = (controlNet) => {
-  getStore().dispatch(caLayerAdded(controlNet));
-};
-
-const recallControlNetsV2: MetadataRecallFunc<ControlNetConfigV2Metadata[]> = (controlNets) => {
+//#region Control Layers
+const recallLayer: MetadataRecallFunc<Layer> = async (layer) => {
   const { dispatch } = getStore();
-  dispatch(caLayerControlNetsDeleted());
-  if (!controlNets.length) {
+  // We need to check for the existence of all images and models when recalling. If they do not exist, SMITE THEM!
+  // Also, we need fresh IDs for all objects when recalling, to prevent multiple layers with the same ID.
+  if (layer.type === 'control_adapter_layer') {
+    const clone = deepClone(layer);
+    if (clone.controlAdapter.image) {
+      const imageDTO = await getImageDTO(clone.controlAdapter.image.name);
+      if (!imageDTO) {
+        clone.controlAdapter.image = null;
+      }
+    }
+    if (clone.controlAdapter.processedImage) {
+      const imageDTO = await getImageDTO(clone.controlAdapter.processedImage.name);
+      if (!imageDTO) {
+        clone.controlAdapter.processedImage = null;
+      }
+    }
+    if (clone.controlAdapter.model) {
+      try {
+        await fetchModelConfigByIdentifier(clone.controlAdapter.model);
+      } catch {
+        clone.controlAdapter.model = null;
+      }
+    }
+    clone.id = getCALayerId(uuidv4());
+    clone.controlAdapter.id = uuidv4();
+    dispatch(caLayerRecalled(clone));
     return;
   }
-  controlNets.forEach((controlNet) => {
-    dispatch(caLayerAdded(controlNet));
-  });
-};
-
-const recallT2IAdapterV2: MetadataRecallFunc<T2IAdapterConfigV2Metadata> = (t2iAdapter) => {
-  getStore().dispatch(caLayerAdded(t2iAdapter));
-};
-
-const recallT2IAdaptersV2: MetadataRecallFunc<T2IAdapterConfigV2Metadata[]> = (t2iAdapters) => {
-  const { dispatch } = getStore();
-  dispatch(caLayerT2IAdaptersDeleted());
-  if (!t2iAdapters.length) {
+  if (layer.type === 'ip_adapter_layer') {
+    const clone = deepClone(layer);
+    if (clone.ipAdapter.image) {
+      const imageDTO = await getImageDTO(clone.ipAdapter.image.name);
+      if (!imageDTO) {
+        clone.ipAdapter.image = null;
+      }
+    }
+    if (clone.ipAdapter.model) {
+      try {
+        await fetchModelConfigByIdentifier(clone.ipAdapter.model);
+      } catch {
+        clone.ipAdapter.model = null;
+      }
+    }
+    clone.id = getIPALayerId(uuidv4());
+    clone.ipAdapter.id = uuidv4();
+    dispatch(ipaLayerRecalled(clone));
     return;
   }
-  t2iAdapters.forEach((t2iAdapters) => {
-    dispatch(caLayerAdded(t2iAdapters));
-  });
-};
 
-const recallIPAdapterV2: MetadataRecallFunc<IPAdapterConfigV2Metadata> = (ipAdapter) => {
-  getStore().dispatch(ipaLayerAdded(ipAdapter));
-};
+  if (layer.type === 'regional_guidance_layer') {
+    const clone = deepClone(layer);
+    // Strip out the uploaded mask image property - this is an intermediate image
+    clone.uploadedMaskImage = null;
 
-const recallIPAdaptersV2: MetadataRecallFunc<IPAdapterConfigV2Metadata[]> = (ipAdapters) => {
-  const { dispatch } = getStore();
-  dispatch(ipaLayersDeleted());
-  if (!ipAdapters.length) {
+    for (const ipAdapter of clone.ipAdapters) {
+      if (ipAdapter.image) {
+        const imageDTO = await getImageDTO(ipAdapter.image.name);
+        if (!imageDTO) {
+          ipAdapter.image = null;
+        }
+      }
+      if (ipAdapter.model) {
+        try {
+          await fetchModelConfigByIdentifier(ipAdapter.model);
+        } catch {
+          ipAdapter.model = null;
+        }
+      }
+      ipAdapter.id = uuidv4();
+    }
+    clone.id = getRGLayerId(uuidv4());
+    dispatch(rgLayerRecalled(clone));
     return;
   }
-  ipAdapters.forEach((ipAdapter) => {
-    dispatch(ipaLayerAdded(ipAdapter));
-  });
+
+  if (layer.type === 'initial_image_layer') {
+    dispatch(iiLayerRecalled(layer));
+    return;
+  }
+};
+
+const recallLayers: MetadataRecallFunc<Layer[]> = (layers) => {
+  const { dispatch } = getStore();
+  dispatch(allLayersDeleted());
+  for (const l of layers) {
+    recallLayer(l);
+  }
 };
 
 export const recallers = {
@@ -299,7 +344,6 @@ export const recallers = {
   cfgScale: recallCFGScale,
   cfgRescaleMultiplier: recallCFGRescaleMultiplier,
   scheduler: recallScheduler,
-  initialImage: recallInitialImage,
   width: recallWidth,
   height: recallHeight,
   steps: recallSteps,
@@ -324,10 +368,6 @@ export const recallers = {
   t2iAdapter: recallT2IAdapter,
   ipAdapters: recallIPAdapters,
   ipAdapter: recallIPAdapter,
-  controlNetV2: recallControlNetV2,
-  controlNetsV2: recallControlNetsV2,
-  t2iAdapterV2: recallT2IAdapterV2,
-  t2iAdaptersV2: recallT2IAdaptersV2,
-  ipAdapterV2: recallIPAdapterV2,
-  ipAdaptersV2: recallIPAdaptersV2,
+  layer: recallLayer,
+  layers: recallLayers,
 } as const;

invokeai/frontend/web/src/features/metadata/util/validators.ts

@@ -1,17 +1,16 @@
 import { getStore } from 'app/store/nanostores/store';
+import type { Layer } from 'features/controlLayers/store/types';
 import type { LoRA } from 'features/lora/store/loraSlice';
 import type {
   ControlNetConfigMetadata,
-  ControlNetConfigV2Metadata,
   IPAdapterConfigMetadata,
-  IPAdapterConfigV2Metadata,
   MetadataValidateFunc,
   T2IAdapterConfigMetadata,
-  T2IAdapterConfigV2Metadata,
 } from 'features/metadata/types';
 import { InvalidModelConfigError } from 'features/metadata/util/modelFetchingHelpers';
 import type { ParameterSDXLRefinerModel, ParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import type { BaseModelType } from 'services/api/types';
+import { assert } from 'tsafe';
 
 /**
  * Checks the given base model type against the currently-selected model's base type and throws an error if they are
@@ -111,58 +110,39 @@ const validateIPAdapters: MetadataValidateFunc<IPAdapterConfigMetadata[]> = (ipA
   return new Promise((resolve) => resolve(validatedIPAdapters));
 };
 
-const validateControlNetV2: MetadataValidateFunc<ControlNetConfigV2Metadata> = (controlNet) => {
-  validateBaseCompatibility(controlNet.model?.base, 'ControlNet incompatible with currently-selected model');
-  return new Promise((resolve) => resolve(controlNet));
-};
-
-const validateControlNetsV2: MetadataValidateFunc<ControlNetConfigV2Metadata[]> = (controlNets) => {
-  const validatedControlNets: ControlNetConfigV2Metadata[] = [];
-  controlNets.forEach((controlNet) => {
-    try {
-      validateBaseCompatibility(controlNet.model?.base, 'ControlNet incompatible with currently-selected model');
-      validatedControlNets.push(controlNet);
-    } catch {
-      // This is a no-op - we want to continue validating the rest of the ControlNets, and an empty list is valid.
+const validateLayer: MetadataValidateFunc<Layer> = async (layer) => {
+  if (layer.type === 'control_adapter_layer') {
+    const model = layer.controlAdapter.model;
+    assert(model, 'Control Adapter layer missing model');
+    validateBaseCompatibility(model.base, 'Layer incompatible with currently-selected model');
+  }
+  if (layer.type === 'ip_adapter_layer') {
+    const model = layer.ipAdapter.model;
+    assert(model, 'IP Adapter layer missing model');
+    validateBaseCompatibility(model.base, 'Layer incompatible with currently-selected model');
+  }
+  if (layer.type === 'regional_guidance_layer') {
+    for (const ipa of layer.ipAdapters) {
+      const model = ipa.model;
+      assert(model, 'IP Adapter layer missing model');
+      validateBaseCompatibility(model.base, 'Layer incompatible with currently-selected model');
     }
-  });
-  return new Promise((resolve) => resolve(validatedControlNets));
+  }
+
+  return layer;
 };
 
-const validateT2IAdapterV2: MetadataValidateFunc<T2IAdapterConfigV2Metadata> = (t2iAdapter) => {
-  validateBaseCompatibility(t2iAdapter.model?.base, 'T2I Adapter incompatible with currently-selected model');
-  return new Promise((resolve) => resolve(t2iAdapter));
-};
-
-const validateT2IAdaptersV2: MetadataValidateFunc<T2IAdapterConfigV2Metadata[]> = (t2iAdapters) => {
-  const validatedT2IAdapters: T2IAdapterConfigV2Metadata[] = [];
-  t2iAdapters.forEach((t2iAdapter) => {
+const validateLayers: MetadataValidateFunc<Layer[]> = async (layers) => {
+  const validatedLayers: Layer[] = [];
+  for (const l of layers) {
     try {
-      validateBaseCompatibility(t2iAdapter.model?.base, 'T2I Adapter incompatible with currently-selected model');
-      validatedT2IAdapters.push(t2iAdapter);
+      const validated = await validateLayer(l);
+      validatedLayers.push(validated);
     } catch {
-      // This is a no-op - we want to continue validating the rest of the T2I Adapters, and an empty list is valid.
+      // This is a no-op - we want to continue validating the rest of the layers, and an empty list is valid.
     }
-  });
-  return new Promise((resolve) => resolve(validatedT2IAdapters));
-};
-
-const validateIPAdapterV2: MetadataValidateFunc<IPAdapterConfigV2Metadata> = (ipAdapter) => {
-  validateBaseCompatibility(ipAdapter.model?.base, 'IP Adapter incompatible with currently-selected model');
-  return new Promise((resolve) => resolve(ipAdapter));
-};
-
-const validateIPAdaptersV2: MetadataValidateFunc<IPAdapterConfigV2Metadata[]> = (ipAdapters) => {
-  const validatedIPAdapters: IPAdapterConfigV2Metadata[] = [];
-  ipAdapters.forEach((ipAdapter) => {
-    try {
-      validateBaseCompatibility(ipAdapter.model?.base, 'IP Adapter incompatible with currently-selected model');
-      validatedIPAdapters.push(ipAdapter);
-    } catch {
-      // This is a no-op - we want to continue validating the rest of the IP Adapters, and an empty list is valid.
-    }
-  });
-  return new Promise((resolve) => resolve(validatedIPAdapters));
+  }
+  return new Promise((resolve) => resolve(validatedLayers));
 };
 
 export const validators = {
@@ -176,10 +156,6 @@ export const validators = {
   t2iAdapters: validateT2IAdapters,
   ipAdapter: validateIPAdapter,
   ipAdapters: validateIPAdapters,
-  controlNetV2: validateControlNetV2,
-  controlNetsV2: validateControlNetsV2,
-  t2iAdapterV2: validateT2IAdapterV2,
-  t2iAdaptersV2: validateT2IAdaptersV2,
-  ipAdapterV2: validateIPAdapterV2,
-  ipAdaptersV2: validateIPAdaptersV2,
+  layer: validateLayer,
+  layers: validateLayers,
 } as const;

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/TopPanel.tsx

@@ -1,6 +1,5 @@
 import { Flex, Spacer } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { ViewerButton } from 'features/gallery/components/ImageViewer/ViewerButton';
 import AddNodeButton from 'features/nodes/components/flow/panels/TopPanel/AddNodeButton';
 import ClearFlowButton from 'features/nodes/components/flow/panels/TopPanel/ClearFlowButton';
 import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
@@ -23,7 +22,6 @@ const TopCenterPanel = () => {
       <ClearFlowButton />
       <SaveWorkflowButton />
       <WorkflowLibraryMenu />
-      <ViewerButton />
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/util/graph/addControlLayersToGraph.ts

@@ -2,25 +2,28 @@ import { getStore } from 'app/store/nanostores/store';
 import type { RootState } from 'app/store/store';
 import {
   isControlAdapterLayer,
+  isInitialImageLayer,
   isIPAdapterLayer,
   isRegionalGuidanceLayer,
+  rgLayerMaskImageUploaded,
 } from 'features/controlLayers/store/controlLayersSlice';
-import {
-  type ControlNetConfigV2,
-  type ImageWithDims,
-  type IPAdapterConfigV2,
-  isControlNetConfigV2,
-  isT2IAdapterConfigV2,
-  type ProcessorConfig,
-  type T2IAdapterConfigV2,
+import type { InitialImageLayer, Layer, RegionalGuidanceLayer } from 'features/controlLayers/store/types';
+import type {
+  ControlNetConfigV2,
+  ImageWithDims,
+  IPAdapterConfigV2,
+  ProcessorConfig,
+  T2IAdapterConfigV2,
 } from 'features/controlLayers/util/controlAdapters';
 import { getRegionalPromptLayerBlobs } from 'features/controlLayers/util/getLayerBlobs';
 import type { ImageField } from 'features/nodes/types/common';
 import {
   CONTROL_NET_COLLECT,
+  IMAGE_TO_LATENTS,
   IP_ADAPTER_COLLECT,
   NEGATIVE_CONDITIONING,
   NEGATIVE_CONDITIONING_COLLECT,
+  NOISE,
   POSITIVE_CONDITIONING,
   POSITIVE_CONDITIONING_COLLECT,
   PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX,
@@ -28,16 +31,20 @@ import {
   PROMPT_REGION_NEGATIVE_COND_PREFIX,
   PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX,
   PROMPT_REGION_POSITIVE_COND_PREFIX,
+  RESIZE,
   T2I_ADAPTER_COLLECT,
 } from 'features/nodes/util/graph/constants';
 import { upsertMetadata } from 'features/nodes/util/graph/metadata';
 import { size } from 'lodash-es';
-import { imagesApi } from 'services/api/endpoints/images';
+import { getImageDTO, imagesApi } from 'services/api/endpoints/images';
 import type {
+  BaseModelType,
   CollectInvocation,
   ControlNetInvocation,
-  CoreMetadataInvocation,
   Edge,
+  ImageDTO,
+  ImageResizeInvocation,
+  ImageToLatentsInvocation,
   IPAdapterInvocation,
   NonNullableGraph,
   S,
@@ -45,369 +52,33 @@ import type {
 } from 'services/api/types';
 import { assert } from 'tsafe';
 
-const buildControlImage = (
-  image: ImageWithDims | null,
-  processedImage: ImageWithDims | null,
-  processorConfig: ProcessorConfig | null
-): ImageField => {
-  if (processedImage && processorConfig) {
-    // We've processed the image in the app - use it for the control image.
-    return {
-      image_name: processedImage.imageName,
-    };
-  } else if (image) {
-    // No processor selected, and we have an image - the user provided a processed image, use it for the control image.
-    return {
-      image_name: image.imageName,
-    };
-  }
-  assert(false, 'Attempted to add unprocessed control image');
-};
-
-const buildControlNetMetadata = (controlNet: ControlNetConfigV2): S['ControlNetMetadataField'] => {
-  const { beginEndStepPct, controlMode, image, model, processedImage, processorConfig, weight } = controlNet;
-
-  assert(model, 'ControlNet model is required');
-  assert(image, 'ControlNet image is required');
-
-  const processed_image =
-    processedImage && processorConfig
-      ? {
-          image_name: processedImage.imageName,
-        }
-      : null;
-
-  return {
-    control_model: model,
-    control_weight: weight,
-    control_mode: controlMode,
-    begin_step_percent: beginEndStepPct[0],
-    end_step_percent: beginEndStepPct[1],
-    resize_mode: 'just_resize',
-    image: {
-      image_name: image.imageName,
-    },
-    processed_image,
-  };
-};
-
-const addControlNetCollectorSafe = (graph: NonNullableGraph, denoiseNodeId: string) => {
-  if (graph.nodes[CONTROL_NET_COLLECT]) {
-    // You see, we've already got one!
-    return;
-  }
-  // Add the ControlNet collector
-  const controlNetIterateNode: CollectInvocation = {
-    id: CONTROL_NET_COLLECT,
-    type: 'collect',
-    is_intermediate: true,
-  };
-  graph.nodes[CONTROL_NET_COLLECT] = controlNetIterateNode;
-  graph.edges.push({
-    source: { node_id: CONTROL_NET_COLLECT, field: 'collection' },
-    destination: {
-      node_id: denoiseNodeId,
-      field: 'control',
-    },
-  });
-};
-
-const addGlobalControlNetsToGraph = async (
-  controlNets: ControlNetConfigV2[],
+export const addControlLayersToGraph = async (
+  state: RootState,
   graph: NonNullableGraph,
   denoiseNodeId: string
-) => {
-  if (controlNets.length === 0) {
-    return;
-  }
-  const controlNetMetadata: CoreMetadataInvocation['controlnets'] = [];
-  addControlNetCollectorSafe(graph, denoiseNodeId);
-
-  for (const controlNet of controlNets) {
-    if (!controlNet.model) {
-      return;
-    }
-    const { id, beginEndStepPct, controlMode, image, model, processedImage, processorConfig, weight } = controlNet;
-
-    const controlNetNode: ControlNetInvocation = {
-      id: `control_net_${id}`,
-      type: 'controlnet',
-      is_intermediate: true,
-      begin_step_percent: beginEndStepPct[0],
-      end_step_percent: beginEndStepPct[1],
-      control_mode: controlMode,
-      resize_mode: 'just_resize',
-      control_model: model,
-      control_weight: weight,
-      image: buildControlImage(image, processedImage, processorConfig),
-    };
-
-    graph.nodes[controlNetNode.id] = controlNetNode;
-
-    controlNetMetadata.push(buildControlNetMetadata(controlNet));
-
-    graph.edges.push({
-      source: { node_id: controlNetNode.id, field: 'control' },
-      destination: {
-        node_id: CONTROL_NET_COLLECT,
-        field: 'item',
-      },
-    });
-  }
-  upsertMetadata(graph, { controlnets: controlNetMetadata });
-};
-
-const buildT2IAdapterMetadata = (t2iAdapter: T2IAdapterConfigV2): S['T2IAdapterMetadataField'] => {
-  const { beginEndStepPct, image, model, processedImage, processorConfig, weight } = t2iAdapter;
-
-  assert(model, 'T2I Adapter model is required');
-  assert(image, 'T2I Adapter image is required');
-
-  const processed_image =
-    processedImage && processorConfig
-      ? {
-          image_name: processedImage.imageName,
-        }
-      : null;
-
-  return {
-    t2i_adapter_model: model,
-    weight,
-    begin_step_percent: beginEndStepPct[0],
-    end_step_percent: beginEndStepPct[1],
-    resize_mode: 'just_resize',
-    image: {
-      image_name: image.imageName,
-    },
-    processed_image,
-  };
-};
-
-const addT2IAdapterCollectorSafe = (graph: NonNullableGraph, denoiseNodeId: string) => {
-  if (graph.nodes[T2I_ADAPTER_COLLECT]) {
-    // You see, we've already got one!
-    return;
-  }
-  // Even though denoise_latents' t2i adapter input is collection or scalar, keep it simple and always use a collect
-  const t2iAdapterCollectNode: CollectInvocation = {
-    id: T2I_ADAPTER_COLLECT,
-    type: 'collect',
-    is_intermediate: true,
-  };
-  graph.nodes[T2I_ADAPTER_COLLECT] = t2iAdapterCollectNode;
-  graph.edges.push({
-    source: { node_id: T2I_ADAPTER_COLLECT, field: 'collection' },
-    destination: {
-      node_id: denoiseNodeId,
-      field: 't2i_adapter',
-    },
-  });
-};
-
-const addGlobalT2IAdaptersToGraph = async (
-  t2iAdapters: T2IAdapterConfigV2[],
-  graph: NonNullableGraph,
-  denoiseNodeId: string
-) => {
-  if (t2iAdapters.length === 0) {
-    return;
-  }
-  const t2iAdapterMetadata: CoreMetadataInvocation['t2iAdapters'] = [];
-  addT2IAdapterCollectorSafe(graph, denoiseNodeId);
-
-  for (const t2iAdapter of t2iAdapters) {
-    if (!t2iAdapter.model) {
-      return;
-    }
-    const { id, beginEndStepPct, image, model, processedImage, processorConfig, weight } = t2iAdapter;
-
-    const t2iAdapterNode: T2IAdapterInvocation = {
-      id: `t2i_adapter_${id}`,
-      type: 't2i_adapter',
-      is_intermediate: true,
-      begin_step_percent: beginEndStepPct[0],
-      end_step_percent: beginEndStepPct[1],
-      resize_mode: 'just_resize',
-      t2i_adapter_model: model,
-      weight: weight,
-      image: buildControlImage(image, processedImage, processorConfig),
-    };
-
-    graph.nodes[t2iAdapterNode.id] = t2iAdapterNode;
-
-    t2iAdapterMetadata.push(buildT2IAdapterMetadata(t2iAdapter));
-
-    graph.edges.push({
-      source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' },
-      destination: {
-        node_id: T2I_ADAPTER_COLLECT,
-        field: 'item',
-      },
-    });
-  }
-
-  upsertMetadata(graph, { t2iAdapters: t2iAdapterMetadata });
-};
-
-const buildIPAdapterMetadata = (ipAdapter: IPAdapterConfigV2): S['IPAdapterMetadataField'] => {
-  const { weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapter;
-
-  assert(model, 'IP Adapter model is required');
-  assert(image, 'IP Adapter image is required');
-
-  return {
-    ip_adapter_model: model,
-    clip_vision_model: clipVisionModel,
-    weight,
-    method,
-    begin_step_percent: beginEndStepPct[0],
-    end_step_percent: beginEndStepPct[1],
-    image: {
-      image_name: image.imageName,
-    },
-  };
-};
-
-const addIPAdapterCollectorSafe = (graph: NonNullableGraph, denoiseNodeId: string) => {
-  if (graph.nodes[IP_ADAPTER_COLLECT]) {
-    // You see, we've already got one!
-    return;
-  }
-
-  const ipAdapterCollectNode: CollectInvocation = {
-    id: IP_ADAPTER_COLLECT,
-    type: 'collect',
-    is_intermediate: true,
-  };
-  graph.nodes[IP_ADAPTER_COLLECT] = ipAdapterCollectNode;
-  graph.edges.push({
-    source: { node_id: IP_ADAPTER_COLLECT, field: 'collection' },
-    destination: {
-      node_id: denoiseNodeId,
-      field: 'ip_adapter',
-    },
-  });
-};
-
-const addGlobalIPAdaptersToGraph = async (
-  ipAdapters: IPAdapterConfigV2[],
-  graph: NonNullableGraph,
-  denoiseNodeId: string
-) => {
-  if (ipAdapters.length === 0) {
-    return;
-  }
-  const ipAdapterMetdata: CoreMetadataInvocation['ipAdapters'] = [];
-  addIPAdapterCollectorSafe(graph, denoiseNodeId);
-
-  for (const ipAdapter of ipAdapters) {
-    const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapter;
-    assert(image, 'IP Adapter image is required');
-    assert(model, 'IP Adapter model is required');
-
-    const ipAdapterNode: IPAdapterInvocation = {
-      id: `ip_adapter_${id}`,
-      type: 'ip_adapter',
-      is_intermediate: true,
-      weight,
-      method,
-      ip_adapter_model: model,
-      clip_vision_model: clipVisionModel,
-      begin_step_percent: beginEndStepPct[0],
-      end_step_percent: beginEndStepPct[1],
-      image: {
-        image_name: image.imageName,
-      },
-    };
-
-    graph.nodes[ipAdapterNode.id] = ipAdapterNode;
-
-    ipAdapterMetdata.push(buildIPAdapterMetadata(ipAdapter));
-
-    graph.edges.push({
-      source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
-      destination: {
-        node_id: IP_ADAPTER_COLLECT,
-        field: 'item',
-      },
-    });
-  }
-
-  upsertMetadata(graph, { ipAdapters: ipAdapterMetdata });
-};
-
-export const addControlLayersToGraph = async (state: RootState, graph: NonNullableGraph, denoiseNodeId: string) => {
-  const { dispatch } = getStore();
+): Promise<Layer[]> => {
   const mainModel = state.generation.model;
   assert(mainModel, 'Missing main model when building graph');
   const isSDXL = mainModel.base === 'sdxl';
 
-  // Add global control adapters
-  const globalControlNets = state.controlLayers.present.layers
-    // Must be a CA layer
-    .filter(isControlAdapterLayer)
-    // Must be enabled
-    .filter((l) => l.isEnabled)
-    // We want the CAs themselves
-    .map((l) => l.controlAdapter)
-    // Must be a ControlNet
-    .filter(isControlNetConfigV2)
-    .filter((ca) => {
-      const hasModel = Boolean(ca.model);
-      const modelMatchesBase = ca.model?.base === mainModel.base;
-      const hasControlImage = ca.image || (ca.processedImage && ca.processorConfig);
-      return hasModel && modelMatchesBase && hasControlImage;
-    });
-  addGlobalControlNetsToGraph(globalControlNets, graph, denoiseNodeId);
+  // Filter out layers with incompatible base model, missing control image
+  const validLayers = state.controlLayers.present.layers.filter((l) => isValidLayer(l, mainModel.base));
 
-  const globalT2IAdapters = state.controlLayers.present.layers
-    // Must be a CA layer
-    .filter(isControlAdapterLayer)
-    // Must be enabled
-    .filter((l) => l.isEnabled)
-    // We want the CAs themselves
-    .map((l) => l.controlAdapter)
-    // Must have a ControlNet CA
-    .filter(isT2IAdapterConfigV2)
-    .filter((ca) => {
-      const hasModel = Boolean(ca.model);
-      const modelMatchesBase = ca.model?.base === mainModel.base;
-      const hasControlImage = ca.image || (ca.processedImage && ca.processorConfig);
-      return hasModel && modelMatchesBase && hasControlImage;
-    });
-  addGlobalT2IAdaptersToGraph(globalT2IAdapters, graph, denoiseNodeId);
+  const validControlAdapters = validLayers.filter(isControlAdapterLayer).map((l) => l.controlAdapter);
+  for (const ca of validControlAdapters) {
+    addGlobalControlAdapterToGraph(ca, graph, denoiseNodeId);
+  }
 
-  const globalIPAdapters = state.controlLayers.present.layers
-    // Must be an IP Adapter layer
-    .filter(isIPAdapterLayer)
-    // Must be enabled
-    .filter((l) => l.isEnabled)
-    // We want the IP Adapters themselves
-    .map((l) => l.ipAdapter)
-    .filter((ca) => {
-      const hasModel = Boolean(ca.model);
-      const modelMatchesBase = ca.model?.base === mainModel.base;
-      const hasControlImage = Boolean(ca.image);
-      return hasModel && modelMatchesBase && hasControlImage;
-    });
-  addGlobalIPAdaptersToGraph(globalIPAdapters, graph, denoiseNodeId);
-
-  const rgLayers = state.controlLayers.present.layers
-    // Only RG layers are get masks
-    .filter(isRegionalGuidanceLayer)
-    // Only visible layers are rendered on the canvas
-    .filter((l) => l.isEnabled)
-    // Only layers with prompts get added to the graph
-    .filter((l) => {
-      const hasTextPrompt = Boolean(l.positivePrompt || l.negativePrompt);
-      const hasIPAdapter = l.ipAdapters.length !== 0;
-      return hasTextPrompt || hasIPAdapter;
-    });
-
-  const layerIds = rgLayers.map((l) => l.id);
-  const blobs = await getRegionalPromptLayerBlobs(layerIds);
-  assert(size(blobs) === size(layerIds), 'Mismatch between layer IDs and blobs');
+  const validIPAdapters = validLayers.filter(isIPAdapterLayer).map((l) => l.ipAdapter);
+  for (const ipAdapter of validIPAdapters) {
+    addGlobalIPAdapterToGraph(ipAdapter, graph, denoiseNodeId);
+  }
 
+  const initialImageLayers = validLayers.filter(isInitialImageLayer);
+  assert(initialImageLayers.length <= 1, 'Only one initial image layer allowed');
+  if (initialImageLayers[0]) {
+    addInitialImageLayerToGraph(state, graph, denoiseNodeId, initialImageLayers[0]);
+  }
   // TODO: We should probably just use conditioning collectors by default, and skip all this fanagling with re-routing
   // the existing conditioning nodes.
 
@@ -470,22 +141,16 @@ export const addControlLayersToGraph = async (state: RootState, graph: NonNullab
     },
   });
 
-  // Upload the blobs to the backend, add each to graph
-  // TODO: Store the uploaded image names in redux to reuse them, so long as the layer hasn't otherwise changed. This
-  // would be a great perf win - not only would we skip re-uploading the same image, but we'd be able to use the node
-  // cache (currently, when we re-use the same mask data, since it is a different image, the node cache is not used).
-  for (const layer of rgLayers) {
+  const validRGLayers = validLayers.filter(isRegionalGuidanceLayer);
+  const layerIds = validRGLayers.map((l) => l.id);
+  const blobs = await getRegionalPromptLayerBlobs(layerIds);
+  assert(size(blobs) === size(layerIds), 'Mismatch between layer IDs and blobs');
+
+  for (const layer of validRGLayers) {
     const blob = blobs[layer.id];
     assert(blob, `Blob for layer ${layer.id} not found`);
-
-    const file = new File([blob], `${layer.id}_mask.png`, { type: 'image/png' });
-    const req = dispatch(
-      imagesApi.endpoints.uploadImage.initiate({ file, image_category: 'mask', is_intermediate: true })
-    );
-    req.reset();
-
-    // TODO: This will raise on network error
-    const { image_name } = await req.unwrap();
+    // Upload the mask image, or get the cached image if it exists
+    const { image_name } = await getMaskImage(layer, blob);
 
     // The main mask-to-tensor node
     const maskToTensorNode: S['AlphaMaskToTensorInvocation'] = {
@@ -632,15 +297,11 @@ export const addControlLayersToGraph = async (state: RootState, graph: NonNullab
       }
     }
 
-    // TODO(psyche): For some reason, I have to explicitly annotate regionalIPAdapters here. Not sure why.
-    const regionalIPAdapters: IPAdapterConfigV2[] = layer.ipAdapters.filter((ipAdapter) => {
-      const hasModel = Boolean(ipAdapter.model);
-      const modelMatchesBase = ipAdapter.model?.base === mainModel.base;
-      const hasControlImage = Boolean(ipAdapter.image);
-      return hasModel && modelMatchesBase && hasControlImage;
-    });
+    const validRegionalIPAdapters: IPAdapterConfigV2[] = layer.ipAdapters.filter((ipa) =>
+      isValidIPAdapter(ipa, mainModel.base)
+    );
 
-    for (const ipAdapter of regionalIPAdapters) {
+    for (const ipAdapter of validRegionalIPAdapters) {
       addIPAdapterCollectorSafe(graph, denoiseNodeId);
       const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapter;
       assert(model, 'IP Adapter model is required');
@@ -657,7 +318,7 @@ export const addControlLayersToGraph = async (state: RootState, graph: NonNullab
         begin_step_percent: beginEndStepPct[0],
         end_step_percent: beginEndStepPct[1],
         image: {
-          image_name: image.imageName,
+          image_name: image.name,
         },
       };
 
@@ -678,4 +339,369 @@ export const addControlLayersToGraph = async (state: RootState, graph: NonNullab
       });
     }
   }
+
+  upsertMetadata(graph, { control_layers: { layers: validLayers, version: state.controlLayers.present._version } });
+  return validLayers;
+};
+
+const getMaskImage = async (layer: RegionalGuidanceLayer, blob: Blob): Promise<ImageDTO> => {
+  if (layer.uploadedMaskImage) {
+    const imageDTO = await getImageDTO(layer.uploadedMaskImage.name);
+    if (imageDTO) {
+      return imageDTO;
+    }
+  }
+  const { dispatch } = getStore();
+  // No cached mask, or the cached image no longer exists - we need to upload the mask image
+  const file = new File([blob], `${layer.id}_mask.png`, { type: 'image/png' });
+  const req = dispatch(
+    imagesApi.endpoints.uploadImage.initiate({ file, image_category: 'mask', is_intermediate: true })
+  );
+  req.reset();
+
+  const imageDTO = await req.unwrap();
+  dispatch(rgLayerMaskImageUploaded({ layerId: layer.id, imageDTO }));
+  return imageDTO;
+};
+
+const buildControlImage = (
+  image: ImageWithDims | null,
+  processedImage: ImageWithDims | null,
+  processorConfig: ProcessorConfig | null
+): ImageField => {
+  if (processedImage && processorConfig) {
+    // We've processed the image in the app - use it for the control image.
+    return {
+      image_name: processedImage.name,
+    };
+  } else if (image) {
+    // No processor selected, and we have an image - the user provided a processed image, use it for the control image.
+    return {
+      image_name: image.name,
+    };
+  }
+  assert(false, 'Attempted to add unprocessed control image');
+};
+
+const addGlobalControlAdapterToGraph = (
+  controlAdapter: ControlNetConfigV2 | T2IAdapterConfigV2,
+  graph: NonNullableGraph,
+  denoiseNodeId: string
+) => {
+  if (controlAdapter.type === 'controlnet') {
+    addGlobalControlNetToGraph(controlAdapter, graph, denoiseNodeId);
+  }
+  if (controlAdapter.type === 't2i_adapter') {
+    addGlobalT2IAdapterToGraph(controlAdapter, graph, denoiseNodeId);
+  }
+};
+
+const addControlNetCollectorSafe = (graph: NonNullableGraph, denoiseNodeId: string) => {
+  if (graph.nodes[CONTROL_NET_COLLECT]) {
+    // You see, we've already got one!
+    return;
+  }
+  // Add the ControlNet collector
+  const controlNetIterateNode: CollectInvocation = {
+    id: CONTROL_NET_COLLECT,
+    type: 'collect',
+    is_intermediate: true,
+  };
+  graph.nodes[CONTROL_NET_COLLECT] = controlNetIterateNode;
+  graph.edges.push({
+    source: { node_id: CONTROL_NET_COLLECT, field: 'collection' },
+    destination: {
+      node_id: denoiseNodeId,
+      field: 'control',
+    },
+  });
+};
+
+const addGlobalControlNetToGraph = (controlNet: ControlNetConfigV2, graph: NonNullableGraph, denoiseNodeId: string) => {
+  const { id, beginEndStepPct, controlMode, image, model, processedImage, processorConfig, weight } = controlNet;
+  assert(model, 'ControlNet model is required');
+  const controlImage = buildControlImage(image, processedImage, processorConfig);
+  addControlNetCollectorSafe(graph, denoiseNodeId);
+
+  const controlNetNode: ControlNetInvocation = {
+    id: `control_net_${id}`,
+    type: 'controlnet',
+    is_intermediate: true,
+    begin_step_percent: beginEndStepPct[0],
+    end_step_percent: beginEndStepPct[1],
+    control_mode: controlMode,
+    resize_mode: 'just_resize',
+    control_model: model,
+    control_weight: weight,
+    image: controlImage,
+  };
+
+  graph.nodes[controlNetNode.id] = controlNetNode;
+
+  graph.edges.push({
+    source: { node_id: controlNetNode.id, field: 'control' },
+    destination: {
+      node_id: CONTROL_NET_COLLECT,
+      field: 'item',
+    },
+  });
+};
+
+const addT2IAdapterCollectorSafe = (graph: NonNullableGraph, denoiseNodeId: string) => {
+  if (graph.nodes[T2I_ADAPTER_COLLECT]) {
+    // You see, we've already got one!
+    return;
+  }
+  // Even though denoise_latents' t2i adapter input is collection or scalar, keep it simple and always use a collect
+  const t2iAdapterCollectNode: CollectInvocation = {
+    id: T2I_ADAPTER_COLLECT,
+    type: 'collect',
+    is_intermediate: true,
+  };
+  graph.nodes[T2I_ADAPTER_COLLECT] = t2iAdapterCollectNode;
+  graph.edges.push({
+    source: { node_id: T2I_ADAPTER_COLLECT, field: 'collection' },
+    destination: {
+      node_id: denoiseNodeId,
+      field: 't2i_adapter',
+    },
+  });
+};
+
+const addGlobalT2IAdapterToGraph = (t2iAdapter: T2IAdapterConfigV2, graph: NonNullableGraph, denoiseNodeId: string) => {
+  const { id, beginEndStepPct, image, model, processedImage, processorConfig, weight } = t2iAdapter;
+  assert(model, 'T2I Adapter model is required');
+  const controlImage = buildControlImage(image, processedImage, processorConfig);
+  addT2IAdapterCollectorSafe(graph, denoiseNodeId);
+
+  const t2iAdapterNode: T2IAdapterInvocation = {
+    id: `t2i_adapter_${id}`,
+    type: 't2i_adapter',
+    is_intermediate: true,
+    begin_step_percent: beginEndStepPct[0],
+    end_step_percent: beginEndStepPct[1],
+    resize_mode: 'just_resize',
+    t2i_adapter_model: model,
+    weight: weight,
+    image: controlImage,
+  };
+
+  graph.nodes[t2iAdapterNode.id] = t2iAdapterNode;
+
+  graph.edges.push({
+    source: { node_id: t2iAdapterNode.id, field: 't2i_adapter' },
+    destination: {
+      node_id: T2I_ADAPTER_COLLECT,
+      field: 'item',
+    },
+  });
+};
+
+const addIPAdapterCollectorSafe = (graph: NonNullableGraph, denoiseNodeId: string) => {
+  if (graph.nodes[IP_ADAPTER_COLLECT]) {
+    // You see, we've already got one!
+    return;
+  }
+
+  const ipAdapterCollectNode: CollectInvocation = {
+    id: IP_ADAPTER_COLLECT,
+    type: 'collect',
+    is_intermediate: true,
+  };
+  graph.nodes[IP_ADAPTER_COLLECT] = ipAdapterCollectNode;
+  graph.edges.push({
+    source: { node_id: IP_ADAPTER_COLLECT, field: 'collection' },
+    destination: {
+      node_id: denoiseNodeId,
+      field: 'ip_adapter',
+    },
+  });
+};
+
+const addGlobalIPAdapterToGraph = (ipAdapter: IPAdapterConfigV2, graph: NonNullableGraph, denoiseNodeId: string) => {
+  addIPAdapterCollectorSafe(graph, denoiseNodeId);
+  const { id, weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapter;
+  assert(image, 'IP Adapter image is required');
+  assert(model, 'IP Adapter model is required');
+
+  const ipAdapterNode: IPAdapterInvocation = {
+    id: `ip_adapter_${id}`,
+    type: 'ip_adapter',
+    is_intermediate: true,
+    weight,
+    method,
+    ip_adapter_model: model,
+    clip_vision_model: clipVisionModel,
+    begin_step_percent: beginEndStepPct[0],
+    end_step_percent: beginEndStepPct[1],
+    image: {
+      image_name: image.name,
+    },
+  };
+
+  graph.nodes[ipAdapterNode.id] = ipAdapterNode;
+
+  graph.edges.push({
+    source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
+    destination: {
+      node_id: IP_ADAPTER_COLLECT,
+      field: 'item',
+    },
+  });
+};
+
+const addInitialImageLayerToGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  denoiseNodeId: string,
+  layer: InitialImageLayer
+) => {
+  const { vaePrecision, model } = state.generation;
+  const { refinerModel, refinerStart } = state.sdxl;
+  const { width, height } = state.controlLayers.present.size;
+  assert(layer.isEnabled, 'Initial image layer is not enabled');
+  assert(layer.image, 'Initial image layer has no image');
+
+  const isSDXL = model?.base === 'sdxl';
+  const useRefinerStartEnd = isSDXL && Boolean(refinerModel);
+
+  const denoiseNode = graph.nodes[denoiseNodeId];
+  assert(denoiseNode?.type === 'denoise_latents', `Missing denoise node or incorrect type: ${denoiseNode?.type}`);
+
+  const { denoisingStrength } = layer;
+  denoiseNode.denoising_start = useRefinerStartEnd
+    ? Math.min(refinerStart, 1 - denoisingStrength)
+    : 1 - denoisingStrength;
+  denoiseNode.denoising_end = useRefinerStartEnd ? refinerStart : 1;
+
+  const i2lNode: ImageToLatentsInvocation = {
+    type: 'i2l',
+    id: IMAGE_TO_LATENTS,
+    is_intermediate: true,
+    use_cache: true,
+    fp32: vaePrecision === 'fp32',
+  };
+
+  graph.nodes[i2lNode.id] = i2lNode;
+  graph.edges.push({
+    source: {
+      node_id: IMAGE_TO_LATENTS,
+      field: 'latents',
+    },
+    destination: {
+      node_id: denoiseNode.id,
+      field: 'latents',
+    },
+  });
+
+  if (layer.image.width !== width || layer.image.height !== height) {
+    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
+
+    // Create a resize node, explicitly setting its image
+    const resizeNode: ImageResizeInvocation = {
+      id: RESIZE,
+      type: 'img_resize',
+      image: {
+        image_name: layer.image.name,
+      },
+      is_intermediate: true,
+      width,
+      height,
+    };
+
+    graph.nodes[RESIZE] = resizeNode;
+
+    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'image' },
+      destination: {
+        node_id: IMAGE_TO_LATENTS,
+        field: 'image',
+      },
+    });
+
+    // The `RESIZE` node also passes its width and height to `NOISE`
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'width' },
+      destination: {
+        node_id: NOISE,
+        field: 'width',
+      },
+    });
+
+    graph.edges.push({
+      source: { node_id: RESIZE, field: 'height' },
+      destination: {
+        node_id: NOISE,
+        field: 'height',
+      },
+    });
+  } else {
+    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
+    i2lNode.image = {
+      image_name: layer.image.name,
+    };
+
+    // Pass the image's dimensions to the `NOISE` node
+    graph.edges.push({
+      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
+      destination: {
+        node_id: NOISE,
+        field: 'width',
+      },
+    });
+    graph.edges.push({
+      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
+      destination: {
+        node_id: NOISE,
+        field: 'height',
+      },
+    });
+  }
+
+  upsertMetadata(graph, { generation_mode: isSDXL ? 'sdxl_img2img' : 'img2img' });
+};
+
+const isValidControlAdapter = (ca: ControlNetConfigV2 | T2IAdapterConfigV2, base: BaseModelType): boolean => {
+  // Must be have a model that matches the current base and must have a control image
+  const hasModel = Boolean(ca.model);
+  const modelMatchesBase = ca.model?.base === base;
+  const hasControlImage = Boolean(ca.image || (ca.processedImage && ca.processorConfig));
+  return hasModel && modelMatchesBase && hasControlImage;
+};
+
+const isValidIPAdapter = (ipa: IPAdapterConfigV2, base: BaseModelType): boolean => {
+  // Must be have a model that matches the current base and must have a control image
+  const hasModel = Boolean(ipa.model);
+  const modelMatchesBase = ipa.model?.base === base;
+  const hasImage = Boolean(ipa.image);
+  return hasModel && modelMatchesBase && hasImage;
+};
+
+const isValidLayer = (layer: Layer, base: BaseModelType) => {
+  if (!layer.isEnabled) {
+    return false;
+  }
+  if (isControlAdapterLayer(layer)) {
+    return isValidControlAdapter(layer.controlAdapter, base);
+  }
+  if (isIPAdapterLayer(layer)) {
+    return isValidIPAdapter(layer.ipAdapter, base);
+  }
+  if (isInitialImageLayer(layer)) {
+    if (!layer.image) {
+      return false;
+    }
+    return true;
+  }
+  if (isRegionalGuidanceLayer(layer)) {
+    if (layer.maskObjects.length === 0) {
+      // Layer has no mask, meaning any guidance would be applied to an empty region.
+      return false;
+    }
+    const hasTextPrompt = Boolean(layer.positivePrompt) || Boolean(layer.negativePrompt);
+    const hasIPAdapter = layer.ipAdapters.filter((ipa) => isValidIPAdapter(ipa, base)).length > 0;
+    return hasTextPrompt || hasIPAdapter;
+  }
+  return false;
 };

invokeai/frontend/web/src/features/nodes/util/graph/addInitialImageToLinearGraph.ts

@@ -1,125 +0,0 @@
-import type { RootState } from 'app/store/store';
-import { isInitialImageLayer } from 'features/controlLayers/store/controlLayersSlice';
-import { upsertMetadata } from 'features/nodes/util/graph/metadata';
-import type { ImageResizeInvocation, ImageToLatentsInvocation, NonNullableGraph } from 'services/api/types';
-import { assert } from 'tsafe';
-
-import { IMAGE_TO_LATENTS, NOISE, RESIZE } from './constants';
-
-export const addInitialImageToLinearGraph = (
-  state: RootState,
-  graph: NonNullableGraph,
-  denoiseNodeId: string
-): void => {
-  // Remove Existing UNet Connections
-  const { img2imgStrength, vaePrecision, model } = state.generation;
-  const { refinerModel, refinerStart } = state.sdxl;
-  const { width, height } = state.controlLayers.present.size;
-  const initialImageLayer = state.controlLayers.present.layers.find(isInitialImageLayer);
-  const initialImage = initialImageLayer?.isEnabled ? initialImageLayer?.image : null;
-
-  if (!initialImage) {
-    return;
-  }
-
-  const isSDXL = model?.base === 'sdxl';
-  const useRefinerStartEnd = isSDXL && Boolean(refinerModel);
-
-  const denoiseNode = graph.nodes[denoiseNodeId];
-  assert(denoiseNode?.type === 'denoise_latents', `Missing denoise node or incorrect type: ${denoiseNode?.type}`);
-
-  denoiseNode.denoising_start = useRefinerStartEnd ? Math.min(refinerStart, 1 - img2imgStrength) : 1 - img2imgStrength;
-  denoiseNode.denoising_end = useRefinerStartEnd ? refinerStart : 1;
-
-  // We conditionally hook the image in depending on if a resize is needed
-  const i2lNode: ImageToLatentsInvocation = {
-    type: 'i2l',
-    id: IMAGE_TO_LATENTS,
-    is_intermediate: true,
-    use_cache: true,
-    fp32: vaePrecision === 'fp32',
-  };
-
-  graph.nodes[i2lNode.id] = i2lNode;
-  graph.edges.push({
-    source: {
-      node_id: IMAGE_TO_LATENTS,
-      field: 'latents',
-    },
-    destination: {
-      node_id: denoiseNode.id,
-      field: 'latents',
-    },
-  });
-
-  if (initialImage.width !== width || initialImage.height !== height) {
-    // The init image needs to be resized to the specified width and height before being passed to `IMAGE_TO_LATENTS`
-
-    // Create a resize node, explicitly setting its image
-    const resizeNode: ImageResizeInvocation = {
-      id: RESIZE,
-      type: 'img_resize',
-      image: {
-        image_name: initialImage.imageName,
-      },
-      is_intermediate: true,
-      width,
-      height,
-    };
-
-    graph.nodes[RESIZE] = resizeNode;
-
-    // The `RESIZE` node then passes its image to `IMAGE_TO_LATENTS`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'image' },
-      destination: {
-        node_id: IMAGE_TO_LATENTS,
-        field: 'image',
-      },
-    });
-
-    // The `RESIZE` node also passes its width and height to `NOISE`
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'width' },
-      destination: {
-        node_id: NOISE,
-        field: 'width',
-      },
-    });
-
-    graph.edges.push({
-      source: { node_id: RESIZE, field: 'height' },
-      destination: {
-        node_id: NOISE,
-        field: 'height',
-      },
-    });
-  } else {
-    // We are not resizing, so we need to set the image on the `IMAGE_TO_LATENTS` node explicitly
-    i2lNode.image = {
-      image_name: initialImage.imageName,
-    };
-
-    // Pass the image's dimensions to the `NOISE` node
-    graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'width' },
-      destination: {
-        node_id: NOISE,
-        field: 'width',
-      },
-    });
-    graph.edges.push({
-      source: { node_id: IMAGE_TO_LATENTS, field: 'height' },
-      destination: {
-        node_id: NOISE,
-        field: 'height',
-      },
-    });
-  }
-
-  upsertMetadata(graph, {
-    generation_mode: isSDXL ? 'sdxl_img2img' : 'img2img',
-    strength: img2imgStrength,
-    init_image: initialImage.imageName,
-  });
-};

invokeai/frontend/web/src/features/nodes/util/graph/buildAdHocUpscaleGraph.ts

@@ -1,5 +1,5 @@
 import type { RootState } from 'app/store/store';
-import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
+import { getBoardField } from 'features/nodes/util/graph/graphBuilderUtils';
 import type { ESRGANInvocation, Graph, NonNullableGraph } from 'services/api/types';
 
 import { ESRGAN } from './constants';
@@ -18,7 +18,7 @@ export const buildAdHocUpscaleGraph = ({ image_name, state }: Arg): Graph => {
     type: 'esrgan',
     image: { image_name },
     model_name: esrganModelName,
-    is_intermediate: getIsIntermediate(state),
+    is_intermediate: false,
     board: getBoardField(state),
   };
 

invokeai/frontend/web/src/features/nodes/util/graph/buildGenerationTabGraph.ts

@@ -1,8 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
+import { isInitialImageLayer, isRegionalGuidanceLayer } from 'features/controlLayers/store/controlLayersSlice';
 import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { addControlLayersToGraph } from 'features/nodes/util/graph/addControlLayersToGraph';
-import { addInitialImageToLinearGraph } from 'features/nodes/util/graph/addInitialImageToLinearGraph';
 import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
 import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
@@ -232,24 +232,23 @@ export const buildGenerationTabGraph = async (state: RootState): Promise<NonNull
     LATENTS_TO_IMAGE
   );
 
-  addInitialImageToLinearGraph(state, graph, DENOISE_LATENTS);
-
   // Add Seamless To Graph
   if (seamlessXAxis || seamlessYAxis) {
     addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
     modelLoaderNodeId = SEAMLESS;
   }
 
-  // optionally add custom VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
-
   // add LoRA support
   await addLoRAsToGraph(state, graph, DENOISE_LATENTS, modelLoaderNodeId);
 
-  await addControlLayersToGraph(state, graph, DENOISE_LATENTS);
+  const addedLayers = await addControlLayersToGraph(state, graph, DENOISE_LATENTS);
 
+  // optionally add custom VAE
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
+
+  const shouldUseHRF = !addedLayers.some((l) => isInitialImageLayer(l) || isRegionalGuidanceLayer(l));
   // High resolution fix.
-  if (state.hrf.hrfEnabled) {
+  if (state.hrf.hrfEnabled && shouldUseHRF) {
     addHrfToGraph(state, graph);
   }
 

invokeai/frontend/web/src/features/nodes/util/graph/buildGenerationTabSDXLGraph.ts

@@ -2,7 +2,6 @@ import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
 import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
 import { addControlLayersToGraph } from 'features/nodes/util/graph/addControlLayersToGraph';
-import { addInitialImageToLinearGraph } from 'features/nodes/util/graph/addInitialImageToLinearGraph';
 import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
@@ -242,8 +241,6 @@ export const buildGenerationTabSDXLGraph = async (state: RootState): Promise<Non
     LATENTS_TO_IMAGE
   );
 
-  addInitialImageToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
-
   // Add Seamless To Graph
   if (seamlessXAxis || seamlessYAxis) {
     addSeamlessToLinearGraph(state, graph, modelLoaderNodeId);
@@ -258,14 +255,14 @@ export const buildGenerationTabSDXLGraph = async (state: RootState): Promise<Non
     }
   }
 
-  // optionally add custom VAE
-  await addVAEToGraph(state, graph, modelLoaderNodeId);
-
   // add LoRA support
   await addSDXLLoRAsToGraph(state, graph, SDXL_DENOISE_LATENTS, modelLoaderNodeId);
 
   await addControlLayersToGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  // optionally add custom VAE
+  await addVAEToGraph(state, graph, modelLoaderNodeId);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

invokeai/frontend/web/src/features/parameters/components/Canvas/ParamImageToImageStrength.tsx

@@ -0,0 +1,20 @@
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import ImageToImageStrength from 'features/parameters/components/ImageToImage/ImageToImageStrength';
+import { setImg2imgStrength } from 'features/parameters/store/generationSlice';
+import { memo, useCallback } from 'react';
+
+const ParamImageToImageStrength = () => {
+  const img2imgStrength = useAppSelector((s) => s.generation.img2imgStrength);
+  const dispatch = useAppDispatch();
+
+  const onChange = useCallback(
+    (v: number) => {
+      dispatch(setImg2imgStrength(v));
+    },
+    [dispatch]
+  );
+
+  return <ImageToImageStrength value={img2imgStrength} onChange={onChange} />;
+};
+
+export default memo(ParamImageToImageStrength);

invokeai/frontend/web/src/features/parameters/components/Core/ParamNegativePrompt.tsx

@@ -33,12 +33,13 @@ export const ParamNegativePrompt = memo(() => {
           name="negativePrompt"
           ref={textareaRef}
           value={prompt}
-          placeholder={t('parameters.negativePromptPlaceholder')}
+          placeholder={t('parameters.globalNegativePromptPlaceholder')}
           onChange={onChange}
           onKeyDown={onKeyDown}
           fontSize="sm"
           variant="darkFilled"
           paddingRight={30}
+          spellCheck={false}
         />
         <PromptOverlayButtonWrapper>
           <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />

invokeai/frontend/web/src/features/parameters/components/Core/ParamPositivePrompt.tsx

@@ -49,12 +49,13 @@ export const ParamPositivePrompt = memo(() => {
           name="prompt"
           ref={textareaRef}
           value={prompt}
-          placeholder={t('parameters.positivePromptPlaceholder')}
+          placeholder={t('parameters.globalPositivePromptPlaceholder')}
           onChange={onChange}
           minH={28}
           onKeyDown={onKeyDown}
           variant="darkFilled"
           paddingRight={30}
+          spellCheck={false}
         />
         <PromptOverlayButtonWrapper>
           <AddPromptTriggerButton isOpen={isOpen} onOpen={onOpen} />

invokeai/frontend/web/src/features/parameters/components/ImageToImage/ImageToImageStrength.tsx

@@ -1,14 +1,17 @@
 import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
-import { setImg2imgStrength } from 'features/parameters/store/generationSlice';
-import { memo, useCallback } from 'react';
+import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 const marks = [0, 0.5, 1];
 
-const ImageToImageStrength = () => {
-  const img2imgStrength = useAppSelector((s) => s.generation.img2imgStrength);
+type Props = {
+  value: number;
+  onChange: (v: number) => void;
+};
+
+const ImageToImageStrength = ({ value, onChange }: Props) => {
   const initial = useAppSelector((s) => s.config.sd.img2imgStrength.initial);
   const sliderMin = useAppSelector((s) => s.config.sd.img2imgStrength.sliderMin);
   const sliderMax = useAppSelector((s) => s.config.sd.img2imgStrength.sliderMax);
@@ -16,11 +19,8 @@ const ImageToImageStrength = () => {
   const numberInputMax = useAppSelector((s) => s.config.sd.img2imgStrength.numberInputMax);
   const coarseStep = useAppSelector((s) => s.config.sd.img2imgStrength.coarseStep);
   const fineStep = useAppSelector((s) => s.config.sd.img2imgStrength.fineStep);
-  const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
-  const handleChange = useCallback((v: number) => dispatch(setImg2imgStrength(v)), [dispatch]);
-
   return (
     <FormControl>
       <InformationalPopover feature="paramDenoisingStrength">
@@ -31,8 +31,8 @@ const ImageToImageStrength = () => {
         fineStep={fineStep}
         min={sliderMin}
         max={sliderMax}
-        onChange={handleChange}
-        value={img2imgStrength}
+        onChange={onChange}
+        value={value}
         defaultValue={initial}
         marks={marks}
       />
@@ -41,8 +41,8 @@ const ImageToImageStrength = () => {
         fineStep={fineStep}
         min={numberInputMin}
         max={numberInputMax}
-        onChange={handleChange}
-        value={img2imgStrength}
+        onChange={onChange}
+        value={value}
         defaultValue={initial}
       />
     </FormControl>

invokeai/frontend/web/src/features/parameters/hooks/usePreselectedImage.ts

@@ -38,6 +38,7 @@ export const usePreselectedImage = (selectedImage?: {
   const handleSendToImg2Img = useCallback(() => {
     if (selectedImageDto) {
       dispatch(iiLayerAdded(selectedImageDto));
+      dispatch(setActiveTab('generation'));
     }
   }, [dispatch, selectedImageDto]);
 

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -16,14 +16,14 @@ import { z } from 'zod';
  */
 
 // #region Positive prompt
-const zParameterPositivePrompt = z.string();
+export const zParameterPositivePrompt = z.string();
 export type ParameterPositivePrompt = z.infer<typeof zParameterPositivePrompt>;
 export const isParameterPositivePrompt = (val: unknown): val is ParameterPositivePrompt =>
   zParameterPositivePrompt.safeParse(val).success;
 // #endregion
 
 // #region Negative prompt
-const zParameterNegativePrompt = z.string();
+export const zParameterNegativePrompt = z.string();
 export type ParameterNegativePrompt = z.infer<typeof zParameterNegativePrompt>;
 export const isParameterNegativePrompt = (val: unknown): val is ParameterNegativePrompt =>
   zParameterNegativePrompt.safeParse(val).success;
@@ -127,7 +127,7 @@ export type ParameterT2IAdapterModel = z.infer<typeof zParameterT2IAdapterModel>
 // #endregion
 
 // #region Strength (l2l strength)
-const zParameterStrength = z.number().min(0).max(1);
+export const zParameterStrength = z.number().min(0).max(1);
 export type ParameterStrength = z.infer<typeof zParameterStrength>;
 export const isParameterStrength = (val: unknown): val is ParameterStrength =>
   zParameterStrength.safeParse(val).success;
@@ -198,6 +198,6 @@ export const isParameterLoRAWeight = (val: unknown): val is ParameterLoRAWeight
 // #endregion
 
 // #region Regional Prompts AutoNegative
-const zAutoNegative = z.enum(['off', 'invert']);
+export const zAutoNegative = z.enum(['off', 'invert']);
 export type ParameterAutoNegative = z.infer<typeof zAutoNegative>;
 // #endregion

invokeai/frontend/web/src/features/queue/components/InvokeQueueBackButton.tsx

@@ -16,25 +16,26 @@ export const InvokeQueueBackButton = memo(() => {
   return (
     <Flex pos="relative" flexGrow={1} minW="240px">
       <QueueIterationsNumberInput />
-      <Button
-        onClick={queueBack}
-        isLoading={isLoading || isLoadingDynamicPrompts}
-        loadingText={invoke}
-        isDisabled={isDisabled}
-        rightIcon={<RiSparkling2Fill />}
-        tooltip={<QueueButtonTooltip />}
-        variant="solid"
-        zIndex={1}
-        colorScheme="invokeYellow"
-        size="lg"
-        w="calc(100% - 60px)"
-        flexShrink={0}
-        justifyContent="space-between"
-        spinnerPlacement="end"
-      >
-        <span>{invoke}</span>
-        <Spacer />
-      </Button>
+      <QueueButtonTooltip>
+        <Button
+          onClick={queueBack}
+          isLoading={isLoading || isLoadingDynamicPrompts}
+          loadingText={invoke}
+          isDisabled={isDisabled}
+          rightIcon={<RiSparkling2Fill />}
+          variant="solid"
+          zIndex={1}
+          colorScheme="invokeYellow"
+          size="lg"
+          w="calc(100% - 60px)"
+          flexShrink={0}
+          justifyContent="space-between"
+          spinnerPlacement="end"
+        >
+          <span>{invoke}</span>
+          <Spacer />
+        </Button>
+      </QueueButtonTooltip>
     </Flex>
   );
 });

invokeai/frontend/web/src/features/queue/components/QueueButtonTooltip.tsx

@@ -1,10 +1,11 @@
-import { Divider, Flex, ListItem, Text, UnorderedList } from '@invoke-ai/ui-library';
+import { Divider, Flex, ListItem, Text, Tooltip, UnorderedList } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import { useIsReadyToEnqueue } from 'common/hooks/useIsReadyToEnqueue';
 import { selectControlLayersSlice } from 'features/controlLayers/store/controlLayersSlice';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
+import type { PropsWithChildren } from 'react';
 import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useEnqueueBatchMutation } from 'services/api/endpoints/queue';
@@ -21,17 +22,32 @@ type Props = {
   prepend?: boolean;
 };
 
-export const QueueButtonTooltip = memo(({ prepend = false }: Props) => {
+export const QueueButtonTooltip = (props: PropsWithChildren<Props>) => {
+  return (
+    <Tooltip label={<TooltipContent prepend={props.prepend} />} maxW={512}>
+      {props.children}
+    </Tooltip>
+  );
+};
+
+const TooltipContent = memo(({ prepend = false }: Props) => {
   const { t } = useTranslation();
   const { isReady, reasons } = useIsReadyToEnqueue();
   const isLoadingDynamicPrompts = useAppSelector((s) => s.dynamicPrompts.isLoading);
   const promptsCount = useAppSelector(selectPromptsCount);
-  const iterations = useAppSelector((s) => s.generation.iterations);
+  const iterationsCount = useAppSelector((s) => s.generation.iterations);
   const autoAddBoardId = useAppSelector((s) => s.gallery.autoAddBoardId);
   const autoAddBoardName = useBoardName(autoAddBoardId);
   const [_, { isLoading }] = useEnqueueBatchMutation({
     fixedCacheKey: 'enqueueBatch',
   });
+  const queueCountPredictionLabel = useMemo(() => {
+    const generationCount = Math.min(promptsCount * iterationsCount, 10000);
+    const prompts = t('queue.prompts', { count: promptsCount });
+    const iterations = t('queue.iterations', { count: iterationsCount });
+    const generations = t('queue.generations', { count: generationCount });
+    return `${promptsCount} ${prompts} \u00d7 ${iterationsCount} ${iterations} -> ${generationCount} ${generations}`.toLowerCase();
+  }, [iterationsCount, promptsCount, t]);
 
   const label = useMemo(() => {
     if (isLoading) {
@@ -52,20 +68,21 @@ export const QueueButtonTooltip = memo(({ prepend = false }: Props) => {
   return (
     <Flex flexDir="column" gap={1}>
       <Text fontWeight="semibold">{label}</Text>
-      <Text>
-        {t('queue.queueCountPrediction', {
-          promptsCount,
-          iterations,
-          count: Math.min(promptsCount * iterations, 10000),
-        })}
-      </Text>
+      <Text>{queueCountPredictionLabel}</Text>
       {reasons.length > 0 && (
         <>
           <Divider opacity={0.2} borderColor="base.900" />
           <UnorderedList>
             {reasons.map((reason, i) => (
-              <ListItem key={`${reason}.${i}`}>
-                <Text>{reason}</Text>
+              <ListItem key={`${reason.content}.${i}`}>
+                <span>
+                  {reason.prefix && (
+                    <Text as="span" fontWeight="semibold">
+                      {reason.prefix}:{' '}
+                    </Text>
+                  )}
+                  <Text as="span">{reason.content}</Text>
+                </span>
               </ListItem>
             ))}
           </UnorderedList>
@@ -82,4 +99,4 @@ export const QueueButtonTooltip = memo(({ prepend = false }: Props) => {
   );
 });
 
-QueueButtonTooltip.displayName = 'QueueButtonTooltip';
+TooltipContent.displayName = 'QueueButtonTooltipContent';